title: Artificial Bee Colony algorithm for optimization of truss structures

text: The main goal of the structural optimization is to minimize the weight of structures while satisfying all design requirements imposed by design codes. In this paper, the Artificial Bee Colony algorithm with an adaptive penalty function approach (ABC-AP) is proposed to minimize the weight of truss structures. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. Five truss examples with fixed-geometry and up to 200 elements were studied to verify that the ABC algorithm is an effective optimization algorithm in the creation of an optimal design for truss structures. The results of the ABC-AP compared with results of other optimization algorithms from the literature show that this algorithm is a powerful search and optimization technique for structural design. © 2010 Elsevier B.V. All rights reserved.


title: Kqueue - A Generic and Scalable Event Notification Facility

text: ApplicationsrunningonaUNIX platformneedto benotified whensomeactivity occurson asocketor otherdescriptor, andthis is traditionallydonewith theselect()or poll() systemcalls. However, it hasbeenshown that the performanceof thesecallsdoesnotscalewell with anincreasingnumberof descriptors.Theseinterfacesarealso limited in therespecthatthey areunableto handleother potentiallyinterestingactivitiesthatanapplicationmight be interestedin, thesemight includesignals,file system changes,and AIO completions. This paperpresentsa genericevent delivery mechanism,which allows an application to selectfrom a wide rangeof event sources, andbenotifiedof activity on thesesourcesin a scalable andefficient manner . The mechanismmay be extended to coverfutureeventsourceswithoutchangingtheapplicationinterface.


title: Multiscale Rotation-Invariant Convolutional Neural Networks for Lung Texture Classification

text: We propose a new multiscale rotation-invariant convolutional neural network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography. MRCNN employs Gabor-local binary pattern that introduces a good property in image analysis—invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches. Experimental results on a public interstitial lung disease database show a superior performance of the proposed method to state of the art.


title: Learning force control policies for compliant manipulation

text: Developing robots capable of fine manipulation skills is of major importance in order to build truly assistive robots. These robots need to be compliant in their actuation and control in order to operate safely in human environments. Manipulation tasks imply complex contact interactions with the external world, and involve reasoning about the forces and torques to be applied. Planning under contact conditions is usually impractical due to computational complexity, and a lack of precise dynamics models of the environment. We present an approach to acquiring manipulation skills on compliant robots through reinforcement learning. The initial position control policy for manipulation is initialized through kinesthetic demonstration. We augment this policy with a force/torque profile to be controlled in combination with the position trajectories. We use the Policy Improvement with Path Integrals (PI2) algorithm to learn these force/torque profiles by optimizing a cost function that measures task success. We demonstrate our approach on the Barrett WAM robot arm equipped with a 6-DOF force/torque sensor on two different manipulation tasks: opening a door with a lever door handle, and picking up a pen off the table. We show that the learnt force control policies allow successful, robust execution of the tasks.


title: A generalized back-door criterion

text: We generalize Pearl’s back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables. We also give easily checkable necessary and sufficient graphical criteria for the existence of a set of variables that satisfies our generalized back-door criterion, when considering a single intervention and a single outcome variable. Moreover, if such a set exists, we provide an explicit set that fulfills the criterion. We illustrate the results in several examples. R-code is available in the R-package pcalg.


title: Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

text: We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient (Liu & Wang, 2016) that maximumly decreases the KL divergence with the target distribution. Our method works for any target distribution specified by their unnormalized density function, and can train any black-box architectures that are differentiable in terms of the parameters we want to adapt. As an application of our method, we propose an amortized MLE algorithm for training deep energy model, where a neural sampler is adaptively trained to approximate the likelihood function. Our method mimics an adversarial game between the deep energy model and the neural sampler, and obtains realisticlooking images competitive with the state-of-the-art results.


title: Modeling and Signal Processing of Acoustic Gunshot Recordings

text: Audio recordings of gunshots can provide information about the gun location with respect to the microphone(s), the speed and trajectory of the projectile, and in some cases the type of firearm and ammunition. Recordings obtained under carefully controlled conditions can be well-modeled by geometrical acoustics. Special acoustic processing systems for real time gunshot detection and localization are used by the military and law enforcement agencies for sniper detection. Forensic analysis of audio recordings is also used to provide evidence in criminal and civil cases. This paper reviews the distinctive features and limitations of acoustic gunshot analysis using DSP techniques


title: Toward an Ecological Theory of Adaptation and Aging

text: 1.3 The environmental docility hypothesis suggests that environmental stimuli ("press", in Murray's terms) have a greater demand quality as the competence of the individual decreases. The dynamics of ecological transactions are considered as a function of personal competence, strength of environmental press, the dual characteristics of the individual's response (affective quality and adaptiveness of behavior), adaptation level, and the optimization function. Behavioral homeostasis is maintained by the individual as both respondent and initiator in interaction with his environment. Hypotheses are suggested to account for striving vs. relaxation and for changes in the individual's level of personal competence. Four transaction-al types discussed are environmental engineering, rehabilitation and therapy, individual growth, and active change of the environment. Recent work in the psychology of stimulation (1) has led to theoretical advances in the area of social ecology. We propose an elaboration in this area that is middle-range, in the sense of attempting to account for a limited aspect of human behavior. This contribution to the theory of man-environment relationships deals with the aspects of human responses that can be viewed in evaluative terms, that is, behavior that can be rated on the continuum of adaptiveness, and inner states that can be rated on the continuum of positive to negative. This is, perhaps, a limited view of the human response repertory, but it stems from the traditional concern of the psychologist with mental health and mental illness. Similarly, our view of environment for this purpose is limited to the "demand quality" of the environment, an abstraction that represents only one of many ways of dimensionalizing the environment. We shall use our knowledge from the area of gerontology to provide content for the theoretical structure, but suggest that the constructs are more generally applicable to any area involving the understanding of mental or social pathology [see Lawton and Nahemow, (2) for a more complete discussion]. One way to begin is to look at the old ecological equation B = f (P, E) to acknowledge its veracity and familiarity, but linger on a few of its implications: 1. All behavior is transactional, that is, not explainable solely on the basis of knowledge about either the person behaving or the environment in which it occurs.


title: Adaptive fuzzy PID temperature control system based on OPC and modbus/TCP protocol

text: In this paper, an adaptive fuzzy PID controller is designed to improve the control performance of the resistance furnace, and then is applied to a remote temperature control system based on modbus/TCP industrial Ethernet via OPC communication. The adaptive PID controller is developed by the fuzzy toolbox in Matlab. The data acquisition and devices driving are realized by PCAuto, a SCADA configuration software which is widely used in industry. The data exchange between Matlab and PCAuto is realized by OPC technology. The control process and results verify that the performance of resistance furnace temperature control system is satisfactory and the performance of OPC communication is very efficient and reliable.


title: Spotlight SAR data focusing based on a two-step processing approach

text: We present a new spotlight SAR data-focusing algorithm based on a two-step processing strategy that combines the advantages of two commonly adopted processing approaches: the efficiency of SPECAN algorithms and the precision of stripmap focusing techniques. The first step of the proposed algorithm implements a linear and space-invariant azimuth filtering that is carried out via a deramping-based technique representing a simplified version of the SPECAN approach. This operation allows us to perform a bulk azimuth raw data compression and to achieve a pixel spacing smaller than (or equal to) the expected azimuth resolution of the fully focused image. Thus, the azimuth spectral folding phenomenon, typically affecting the spotlight data, is overcome, and the space-variant characteristics of the stripmap system transfer function are preserved. Accordingly, the residual and precise focusing of the SAR data is achieved by applying a conventional stripmap processing procedure requiring a minor modification and implemented in the frequency domain. The extension of the proposed technique to the case of high bandwidth transmitted chirp signals is also discussed. Experiments carried out on real and simulated data confirm the validity of the presented approach, which is mainly focused on spaceborne systems.


title: Cooperative Control Strategy of Energy Storage System and Microsources for Stabilizing the Microgrid during Islanded Operation

text: In this paper, the cooperative control strategy of microsources and the energy storage system (ESS) during islanded operation is presented and evaluated by a simulation and experiment. The ESS handles the frequency and the voltage as a primary control. And then, the secondary control in microgrid management system returns the current power output of the ESS into zero. The test results show that the proposed cooperative control strategy can regulate the frequency and the voltage, and the secondary control action can contribute to improve the control capability.


title: Modeling, Analysis and Testing of Autonomous Operation of an Inverter-Based Microgrid

text: The analysis of the small-signal stability of conventional power systems is well established, but for inverter based microgrids there is a need to establish how circuit and control features give rise to particular oscillatory modes and which of these have poor damping. This paper develops the modeling and analysis of autonomous operation of inverter-based microgrids. Each sub-module is modeled in state-space form and all are combined together on a common reference frame. The model captures the detail of the control loops of the inverter but not the switching action. Some inverter modes are found at relatively high frequency and so a full dynamic model of the network (rather than an algebraic impedance model) is used. The complete model is linearized around an operating point and the resulting system matrix is used to derive the eigenvalues. The eigenvalues (termed "modes") indicate the frequency and damping of oscillatory components in the transient response. A sensitivity analysis is also presented which helps identifying the origin of each of the modes and identify possible feedback signals for design of controllers to improve the system stability. With experience it is possible to simplify the model (reduce the order) if particular modes are not of interest as is the case with synchronous machine models. Experimental results from a microgrid of three 10-kW inverters are used to verify the results obtained from the model


title: Making microgrids work

text: Distributed energy resources including distributed generation and distributed storage are sources of energy located near local loads and can provide a variety of benefits including improved reliability if they are properly operated in the electrical distribution system. Microgrids are systems that have at least one distributed energy resource and associated loads and can form intentional islands in the electrical distribution systems. This paper gives an overview of the microgrid operation. Microgrid testing experiences from different counties was also provided.


title: Smart home research

text: This paper is a survey for smart home research, from definition to current research status. First we give a definition to smart home, and then describe the smart home elements, typical research projects, smart home networks research status, smart home appliances and challenges at last.


title: Machine Learning under Attack: Vulnerability Exploitation and Security Measures

text:
Learning to discriminate between secure and hostile patterns is a crucial problem for species to survive in nature. Mimetism and camouflage are well-known examples of evolving weapons and defenses in the arms race between predators and preys. It is thus clear that all of the information acquired by our senses should not be considered necessarily secure or reliable. In machine learning and pattern recognition systems, however, we have started investigating these issues only recently. This phenomenon has been especially observed in the context of adversarial settings like malware detection and spam filtering, in which data can be purposely manipulated by humans to undermine the outcome of an automatic analysis. As current pattern recognition methods are not natively designed to deal with the intrinsic, adversarial nature of these problems, they exhibit specific vulnerabilities that an attacker may exploit either to mislead learning or to evade detection. Identifying these vulnerabilities and analyzing the impact of the corresponding attacks on learning algorithms has thus been one of the main open issues in the novel research field of adversarial machine learning, along with the design of more secure learning algorithms.
 In the first part of this talk, I introduce a general framework that encompasses and unifies previous work in the field, allowing one to systematically evaluate classifier security against different, potential attacks. As an example of application of this framework, in the second part of the talk, I discuss evasion attacks, where malicious samples are manipulated at test time to evade detection. I then show how carefully-designed poisoning attacks can mislead some learning algorithms by manipulating only a small fraction of their training data. In addition, I discuss some defense mechanisms against both attacks in the context of real-world applications, including biometric identity recognition and computer security. Finally, I briefly discuss our ongoing work on attacks against clustering algorithms, and sketch some promising future research directions.


title: Profile hidden Markov models

text: The recent literature on profile hidden Markov model (profile HMM) methods and software is reviewed. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system suitable for searching databases for remotely homologous sequences. Profile HMM analyses complement standard pairwise comparison methods for large-scale sequence analysis. Several software implementations and two large libraries of profile HMMs of common protein domains are available. HMM methods performed comparably to threading methods in the CASP2 structure prediction exercise.


title: Scaling Agile Development in Mechatronic Organizations - A Comparative Case Study

text: Agile software development principles enable companies to successfully and quickly deliver software by meeting their customers' expectations while focusing on high quality. Many companies working with pure software systems have adopted these principles, but implementing them in companies dealing with non-pure software products is challenging. We identified a set of goals and practices to support large-scale agile development in companies that develop software-intense mechatronic systems. We used an inductive approach based on empirical data collected during a longitudinal study with six companies in the Nordic region. The data collection took place over two years through focus group workshops, individual on-site interviews, and complementary surveys. The primary benefit of large-scale agile development is improved quality, enabled by practices that support regular or continuous integration between teams delivering software, hardware, and mechanics. In this regard, the most beneficial integration cycle for deliveries is every four weeks, while continuous integration on a daily basis would favor software teams, other disciplines does not seem to benefit from faster integration cycles. We identified 108 goals and development practices supporting agile principles among the companies, most of them concerned with integration, therefrom, 26 agile practices are unique to the mechatronics domain to support adopting agile beyond pure software development teams. 16 of these practices are considered as key enablers, confirmed by our control cases.


title: Induced pluripotent stem cell-based modeling of neurodegenerative diseases: a focus on autophagy

text: The advent of cell reprogramming has enabled the generation of induced pluripotent stem cells (iPSCs) from patient skin fibroblasts or blood cells and their subsequent differentiation into tissue-specific cells, including neurons and glia. This approach can be used to recapitulate disease-specific phenotypes in classical cell culture paradigms and thus represents an invaluable asset for disease modeling and drug validation in the framework of personalized medicine. The autophagy pathway is a ubiquitous eukaryotic degradation and recycling system, which relies on lysosomal degradation of unwanted and potentially cytotoxic components. The relevance of autophagy in the pathogenesis of neurodegenerative diseases is underlined by the observation that disease-linked genetic variants of susceptibility factors frequently result in dysregulation of autophagic-lysosomal pathways. In particular, disrupted autophagy is implied in the accumulation of potentially neurotoxic products such as protein aggregates and their precursors and defective turnover of dysfunctional mitochondria. Here, we review the current state of iPSC-based assessment of autophagic dysfunction in the context of neurodegenerative disease modeling. The collected data show that iPSC technology is capable to reveal even subtle alterations in subcellular homeostatic processes, which form the molecular basis for disease manifestation.


title: Semantic Granularity in Ontology-Driven Geographic Information Systems

text: The integration of information of different kinds, such as spatial and alphanumeric at different levels of detail, is a challenge. While a solution is not reached, it is widely recognized that the need to integrate information is so pressing that it does not matter if detail is lost, as long as integration is achieved. This paper shows the potential for information retrieval at different levels of granularity inside the framework of information systems based on ontologies. Ontologies are theories that use a specific vocabulary to describe entities, classes, properties and functions related to a certain view of the world. The use of an ontology, translated into an active information system component, leads to ontology-driven information systems and, in the specific case of GIS, leads to what we call ontology-driven geographic information systems.


This paper proposes a new dc-dc static power converter, designated Bidirectional Current-Fed Flyback-Push-Pull DC-DC Converter. Circuit operation, analysis, simulation, design example and experimental results are included in the paper. What distinguishes the proposed converter from the previous circuits is the existence of input and output inductors, which provides a significant reduction of both the power source and the load side current ripple. The proposed converter is suitable for the renewable electric power systems, such as those having fuel cells as the DC-source power supply. It is also a good candidate for electric vehicle power systems, where bidirectional power flow related with battery charge and discharge is necessary.


title: Semantic Linking of Learning Object Repositories to DBpedia

text: Large-sized repositories of learning objects (LOs) are difficult to create and also to maintain. In this paper we propose a way to reduce this drawback by improving the classification mechanisms of the LO repositories. Specifically, we present a solution to automate the LO classification of the Universia repository, a collection of more than 15 million of LOs represented according to the IEEE LOM standard. Although a small part of these LOs is correctly classified, most are unclassified and therefore searching and accessing information is difficult. Our solution makes use of the categories provided by DBpedia, a linked data repository, to automatically improve its classification through a graph-based filtering algorithm, which selects the most suitable categories for describing a LO. Once selected, these categories will classify the LO, linking its classification metadata with a set of DBpedia categories.


title: Digital color imaging

text: This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented using vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided.


title: Finding an emotional face in a crowd: emotional and perceptual stimulus factors influence visual search efficiency.

text: In this article, we examine how emotional and perceptual stimulus factors influence visual search efficiency. In an initial task, we run a visual search task, using a large number of target/distractor emotion combinations. In two subsequent tasks, we then assess measures of perceptual (rated and computational distances) and emotional (rated valence, arousal and potency) stimulus properties. In a series of regression analyses, we then explore the degree to which target salience (the size of target/distractor dissimilarities) on these emotional and perceptual measures predict the outcome on search efficiency measures (response times and accuracy) from the visual search task. The results show that both emotional and perceptual stimulus salience contribute to visual search efficiency. The results show that among the emotional measures, salience on arousal measures was more influential than valence salience. The importance of the arousal factor may be a contributing factor to contradictory history of results within this field.


title: Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding

text: Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce “deep compression”, a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35× to 49× without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9× to 13×; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35×, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49× from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3× to 4× layerwise speedup and 3× to 7× better energy efficiency.


title: Swarm intelligence and robotics

text: Purpose – The aim of this paper is to provide a review of recent developments in the application of swarm intelligence to robotics. Design/methodology/approach – This paper initially considers swarm intelligence and then discusses its application to robotics through reference to a number of recent research programmes. Findings – Based on the principles of swarm intelligence, which is derived from the swarming behaviour of biological entities, swarm robotics research is widespread but still at an early stage. Much aims to gain an understanding of biological swarming and apply it to autonomous, mobile multi-robot systems. European activities are particularly strong and several large, collaborative projects are underway. Research in the USA has a military bias and much is funded by defence agencies. Originality/value – The paper provides an up-to-date insight into swarm robot research and development.


title: A Span-Extraction Dataset for Chinese Machine Reading Comprehension

text: Machine Reading Comprehension (MRC) has become enormously popular recently and has attracted a lot of attention. However, the existing reading comprehension datasets are mostly in English. In this paper, we introduce a SpanExtraction dataset for Chinese Machine Reading Comprehension to add language diversities in this area. The dataset is composed by near 20,000 real questions annotated by human on Wikipedia paragraphs. We also annotated a challenge set which contains the questions that need comprehensive understanding and multi-sentence inference throughout the context. With the release of the dataset, we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We hope the release of the dataset could further accelerate the machine reading comprehension research in Chinese language.1


title: VNF-FG design and VNF placement for 5G mobile networks

text: Network function virtualization (NFV) is envisioned as one of the critical technologies in 5th-Generation (5G) mobile networks. This paper investigates the virtual network function forwarding graph (VNF-FG) design and virtual network function (VNF) placement for 5G mobile networks. We first propose a two-step method composed of flow designing and flow combining for generating VNF-FGs according to network service requests. For mapping VNFs in the generated VNF-FG to physical resources, we then modify the hybrid NFV environment with introducing more types of physical nodes and mapping modes for the sake of completeness and practicality, and formulate the VNF placement optimization problem for achieving lower bandwidth consumption and lower maximum link utilization simultaneously. To resolve this problem, four genetic algorithms are proposed on the basis of the frameworks of two existing algorithms (multiple objective genetic algorithm and improved non-dominated sorting genetic algorithm). Simulation results show that Greedy-NSGA-II achieves the best performance among our four algorithms. Compared with three non-genetic algorithms (random, backtracking mapping and service chains deployment with affiliation-aware), Greedy-NSGA-II reduces 97.04%, 87.76% and 88.42% of the average total bandwidth consumption, respectively, and achieves only 13.81%, 25.04% and 25.41% of the average maximum link utilization, respectively. Moreover, using our VNF-FG design method and Greedy-NSGA-II together can also reduce the total bandwidth consumption remarkably.


title: A Survey on Semi-Supervised Learning Techniques

text: Semi-supervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semi–supervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semi-supervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semi-supervised learning is a noble indication to shrink human labor and improve accuracy. There has been a large spectrum of ideas on semi-supervised learning. In this paper we bring out some of the key approaches for semi-supervised learning.


title: Explicit reference modeling methodology in parametric CAD system

text: Today parametric associative CAD systems must help companies to create more efficient virtual development processes. While dealing with complex parts (e.g. the number of surfaces of the solid) no CAD modeling methodology is existing. Based on the analysis of industrial designers’ practices as well as student practices on CAD, we identified key factors that lead to better performance. Our objective in this article is to propose a practical method for complex parts modeling in parametric CAD system. An illustration of the performances and the results obtained by this method are presented comparing the traditional method with the proposed one while using an academic case and then an industrial case. 2013 Elsevier B.V. All rights reserved.


title: Memory-Size-Assisted Buffer Overflow Detection

text: -Since the first buffer overflow problem occurred, many detection techniques have been presented. These techniques are effective in detecting most attacks, but some attacks still remain undetected. In order to be more effective, a memory-size-assisted buffer overflow detection(MBOD) is presented. The key feature of buffer overflow is that the size of the source memory is bigger than the size of the destination memory when memory copying operation occurs. By capturing memory copying operation and comparing memory size at run time, MBOD detects buffer overflow. MBOD collects the information of memory size in both dynamic way and static way. An implementation shows that the technique is feasible.


title: Discussions on control loop design in average current mode control [PWM DC/DC power convertors]

text: Large control output ripple in ACMC (average current mode control) PWM DC/DC converters exposes challenges in accurate modeling and design. In this paper, superposition models are utilized to perform control design analysis for ACMC PWM DC/DC converters. The ACMC DC/DC PWM converter system is converted to a cascade control structure and a step-by-step control design algorithm is then proposed. The control design algorithms differ from existing, conventional algorithms because they carefully compensate for newly discovered feed forward influence in the model.


title: A Reinforcement Learning Approach to job-shop Scheduling

text: We apply reinforce merit learning methods to learn domain-specific heuristics for job shop scheduling A repair-based scheduler starts with a critical-path schedule and incrementally repairs constraint violations with the goal of finding a short conflict-free schedule The tem poral difference algorithm is applied to tram a neural network to learn a heuristic evaluation function over s ta tes This evaluation function is used by a one-step lookahead search procedure to find good solutions to new scheduling problems We evaluate this approach on synthetic problems and on problems from a NASA space shuttle pay load processing task The evaluation function is trained on problems involving a small number of jobs and then tested on larger proble ms The TD sclied uler performs better than the best known existing algorithm for this task—Zwehen s iterative repair method based on simulated annealing The results suggest that reinforcement l<arn mg can provide a new method for constructing high-performance scheduling systems


title: The influence of urgency on decision time

text: A fruitful quantitative approach to understanding how the brain makes decisions has been to look at the time needed to make a decision, and how it is affected by factors such as the supply of information, or an individual's expectations. This approach has led to a model of decision-making, consistent with recent neurophysiological data, that explains the observed variability of reaction times and correctly predicts the effects of altered expectations. Can it also predict what happens when the urgency of making the response changes? We asked subjects to make eye movements to low-visibility targets either as fast or as accurately as possible, and found that the model does indeed predict the timing of their responses: the degree of urgency seems to influence the criterion level at which a decision signal triggers a response.


title: Optimal design methodology for LLC resonant converter

text: Although LLC resonant converter can achieve wide operation range with high efficiency, lack of design methodology makes it difficult to be implemented. In this paper, based on the theoretical analysis on the operation principles during normal condition and holdup time, the relationship between converter efficiency and operation range with different circuit parameters has be revealed. An optimal design methodology has been developed based on the revealed relationship. A 1MHz, 1kW LLC converter is designed to verify the proposed method.


title: LLC series resonant converter based LED lamp driver with ZVS

text: This paper proposes an LLC resonant converter based LED (Light Emitting Diode) lamp driver with high power factor. The proposed circuit uses a boost converter for PFC (Power Factor Correction) which operates in continuous conduction mode (CCM) and a quasi half bridge resonant converter to drive the LED lamp load. The LLC converter is designed such that solid state switches of quasi half bridge are working under zero voltage switching (ZVS) to reduce switching losses. The analysis, design, modeling and simulation of 50 W LED driver are carried out using MATLAB/Simulink tool for universal AC mains. The power quality indices are calculated such as total harmonic distortion of AC mains current (THDi), power factor (PF) and crest factor (CF) to evaluate the performance of proposed LED lamp driver.


title: Single-stage Flyback converter for constant current output LED driver with power factor correction

text: This paper presents a LED driver circuit based on single-stage Flyback converter with power factor correction. Voltage following control and Nonlinear carrier control are applied to constant current mode Flyback converter which is combined with constant current driver technique. By designing the outer loop to stabilize the output voltage and the inner loop to shape input current waveform, that makes the system achieve unity power factor. The circuit doesn't need an expensive multiplier as classical multiplier control. To verify the performance, the proposed circuit was confirmed by simulation and experiment. From the results, the proposed system shows high power factor with 0.96 and constant current output.


title: Approximate analysis of the resonant LCL DC-DC converter

text: An approximate analysis of LCL converters operating above and below resonance is presented. The study, based on replacing the rectifier and load by an equivalent parallel resistance, applies the fundamental harmonics method and the assumption that the current through the diodes of the output rectifier has a sinusoidal waveform. Equations obtained for the conduction angle of these diodes (less than /spl pi/ in discontinuous and equal to /spl pi/ in continuous conduction modes) are used for developing the output-to-input voltage ratio as a function of three independent parameters. The theoretical and experimental results are in good agreement.


title: A Survey on Offline Handwriting Recognition Systems

text: Handwriting recognition is one of the most challenging and fascinating areas in the field of image processing and machine learning. Its various practical applications include digitizing documents, reading handwritten notes and addresses on postcards, reading the amounts on checks and so on. This paper provides an overview of the different techniques used in the different phases of offline handwriting recognition.


title: Online Tracker Optimization for Multi-Pedestrian Tracking Using a Moving Vehicle Camera

text: Multi-pedestrian tracking (MPT) on the road is closely related to a reduction in the possibility of pedestrian–vehicle collisions when using advanced driver assistance systems. Therefore, this paper focuses on MPT on real roads using a moving camera. Although convolutional neural network (CNN)-based single object tracking methods have recently been proposed, the online learning of CNN for MPT is a significant burden in terms of real-time processing. However, because the features extracted from CNN are a good representation and achieve a high generalization capability, to reduce the learning time, a shallow pre-trained CNN was applied in the present study as a feature extractor instead of an object tracker As an online tracker, this study uses random ferns (RF) with pre-trained CNN output feature, which can make the problem computationally tractable and robustness. However, a large RF requires significant amount of memory and computational complexity, and is still a burden in terms of online learning in cases of multi-pedestrian tracking. Therefore, we introduce a teacher–student model compression algorithm for selecting a few optimal ferns for each tracker, thereby, reducing the online learning time. The online learning of a student-RF tracker has an advantage in that it can adaptively update the tracker to achieve robustness against various changes, such as in pose and illumination, and partial occlusions within a limited period of time. The proposed algorithm was successfully applied to benchmark video sequences captured from a moving camera that include multiple pedestrians in various poses. Specifically, the proposed algorithm yields a more accurate tracking performance than the other state-of-the-art methods.


title: SELF-SERV: A Platform for Rapid Composition of Web Services in a Peer-to-Peer Environment

text: The automation of Web services interoperation is gaining a considerable momentum as a paradigm for effective Business-to-Business collaboration [2]. Established enterprises are continuously discovering new opportunities to form alliances with other enterprises, by offering value-added integrated services. However, the technology to compose Web services in appropriate time-frames has not kept pace with the rapid growth and volatility of available opportunities. Indeed, the development of integrated Web services is often ad-hoc and requires a considerable effort of lowlevel programming. This approach is inadequate given the size and the volatility of the Web. Furthermore, the number of services to be integrated may be large, so that approaches where the development of an integrated service requires the understanding of each of the underlying services are inappropriate. In addition, Web services may need to be composed as part of a short term partnership, and disbanded when the partnership is no longer profitable. Hence, the integration of a large number of Web services requires scalable and flexible techniques, such as those based on declarative languages. Also, the execution of an integrated service in existing approaches is usually centralised, whereas the underlying services are distributed and autonomous. This calls for the investigation of distributed execution paradigms (e.g., peer-to-peer models), that do not suffer of the scalability and availability problems of centralised coordination [3]. Motivated by these concerns, we have developed the SELF-SERV platform for rapid composition of Web


title: Metal nanogrids , nanowires , and nanofi bers for transparent electrodes

text: VOLUME 36 • OCTOBER 2011 • www.mrs.org/bulletin © 2011 Materials Research Society Introduction Transparent electrodes are ubiquitously used in optoelectronic devices where light must pass through a section, and current or voltage needs to be applied. These devices include thin-fi lm solar cells (organic, inorganic, hybrid), displays (liquid-crystal display, plasma display, organic light-emitting diode [OLED]), touch screens (resistive, capacitive), electronic books, smart windows, fl exible lighting, and passive devices (such as transparent heating, transparent speakers). 1 The properties of transparent electrodes are extremely important for device performance, and the specifi cations vary based on types of devices. For example, while source roughness and work function are insignifi cant for resistive touch screens, they are crucial for OLEDs and organic solar cells. 1 Moreover, the optical haze of particular transparent electrodes is benefi cial for high-effi ciency solar cells but problematic for certain types of displays. The mechanical properties of the electrode are crucial for fl exible devices but not important for devices on rigid substrates. Across all devices, however, the optical transparency ( T ) and electrical sheet resistance ( Rs ) are the two most important parameters. Unfortunately, these two parameters are in constant competition with one another. Since Rs values are often reported at different transparencies, it is hard to compare two transparent electrodes directly, and hence a fi gure of merit ( σ dc / σ op , where σ dc is the dc conductivity, and σ op is the optical conductivity) Metal nanogrids, nanowires, and nanofi bers for transparent electrodes


title: Microwave-Based Noninvasive Concentration Measurements for Biomedical Applications

text: A Debye relaxation model for the permittivity of aqueous glucose solution is given up to 40 GHz. Measurements of aqueous solutions are compared with invasive measurements on over 40 blood samples. Reflection- and transmission-based techniques are explained, concerning the application in biomedical concentration measurements. Wideband measurements on different sensing structures are introduced. Reflection- and transmission-based sensors are applied to a six-port reflectometer, as well as a homodyne vector network analyzer.


title: Patient and nurse preferences for nurse handover-using preferences to inform policy: a discrete choice experiment protocol.

text:
INTRODUCTION
Nursing bedside handover in hospital has been identified as an opportunity to involve patients and promote patient-centred care. It is important to consider the preferences of both patients and nurses when implementing bedside handover to maximise the successful uptake of this policy. We outline a study which aims to (1) identify, compare and contrast the preferences for various aspects of handover common to nurses and patients while accounting for other factors, such as the time constraints of nurses that may influence these preferences.; (2) identify opportunities for nurses to better involve patients in bedside handover and (3) identify patient and nurse preferences that may challenge the full implementation of bedside handover in the acute medical setting.

METHODS AND ANALYSIS
We outline the protocol for a discrete choice experiment (DCE) which uses a survey design common to both patients and nurses. We describe the qualitative and pilot work undertaken to design the DCE. We use a D-efficient design which is informed by prior coefficients collected during the pilot phase. We also discuss the face-to-face administration of this survey in a population of acutely unwell, hospitalised patients and describe how data collection challenges have been informed by our pilot phase. Mixed multinomial logit regression analysis will be used to estimate the final results.

ETHICS AND DISSEMINATION
This study has been approved by a university ethics committee as well as two participating hospital ethics committees. Results will be used within a knowledge translation framework to inform any strategies that can be used by nursing staff to improve the uptake of bedside handover. Results will also be disseminated via peer-reviewed journal articles and will be presented at national and international conferences.


title: Passivity of a class of sampled-data systems: Application to haptic interfaces

text: Passivity of systems comprising a continuous time plant and discrete time controller is considered. This topic is motivated by stability considerations arising in the control of robots and force-reflecting human interfaces (“haptic interfaces”). A necessary and sufficient condition for the passivity of a class of sampled-data systems is derived. An example — implementation of a “virtual wall” via a one degree-of-freedom haptic interface — is presented.


title: Mapping high dimensional features onto Hilbert curve: Applying to fast image retrieval

text: The use of high-dimension features is unavoidable in many applications of image retrieval and techniques of dimension reductions are not always efficient. The space-filling curve reduces the number of dimensions to one while preserving the neighborhood relation. In this paper, Hilbert curve, the most neighborhood preserving space-filling curve, is used in shape retrieval. The retrieving is accelerated by ordering the shapes in 1-D dynamical data structure, which enables rapid insertions of new images without changing existing data. A proposal of fast mapping facilitate the computing of 1-D Hilbert indexes from high dimensional features.


title: An Efficient Finger-Knuckle-Print Based Recognition System Fusing SIFT and SURF Matching Scores

text: This paper presents a novel combination of local-local information for an efficient finger-knuckle-print (FKP) based recognition system which is robust to scale and rotation. The non-uniform brightness of the FKP due to relatively curvature surface is corrected and texture is enhanced. The local features of the enhanced FKP are extracted using the scale invariant feature transform (SIFT) and the speeded up robust features (SURF). Corresponding features of the enrolled and the query FKPs are matched using nearest-neighbour-ratio method and then the derived SIFT and SURF matching scores are fused using weighted sum rule. The proposed system is evaluated using PolyU FKP database of 7920 images for both identification mode and verification mode. It is observed that the system performs with CRR of 100% and EER of 0.215%. Further, it is evaluated against various scales and rotations of the query image and is found to be robust for query images downscaled upto 60% and for any orientation of query image.


title: An arousal-based neural model of infant attachment

text: We develop an arousal-based neural model of infant attachment using a deep learning architecture. We show how our model can differentiate between attachment classifications during strange situation-like separation and reunion episodes, in terms of both signalling behaviour and patterns of autonomic arousal, according to the sensitivity of previous interaction.


title: Twitter Polarity Classification with Label Propagation over Lexical Links and the Follower Graph

text: There is high demand for automated tools that assign polarity to microblog content such as tweets (Twitter posts), but this is challenging due to the terseness and informality of tweets in addition to the wide variety and rapid evolution of language in Twitter. It is thus impractical to use standard supervised machine learning techniques dependent on annotated training examples. We do without such annotations by using label propagation to incorporate labels from a maximum entropy classifier trained on noisy labels and knowledge about word types encoded in a lexicon, in combination with the Twitter follower graph. Results on polarity classification for several datasets show that our label propagation approach rivals a model supervised with in-domain annotated tweets, and it outperforms the noisily supervised classifier it exploits as well as a lexicon-based polarity ratio classifier.


title: Learning Visual Similarity Measures for Comparing Never Seen Objects

text: In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled "same" or "different". This is far less informative than the commonly used individual image labels (e.g., "car model X"), but it is cheaper to obtain. The proposed algorithm learns the characteristic differences between local descriptors sampled from pairs of "same" and "different" images. These differences are vector quantized by an ensemble of extremely randomized binary trees, and the similarity measure is computed from the quantized differences. The extremely randomized trees are fast to learn, robust due to the redundant information they carry and they have been proved to be very good clusterers. Furthermore, the trees efficiently combine different feature types (SIFT and geometry). We evaluate our innovative similarity measure on four very different datasets and consistently outperform the state-of-the-art competitive approaches.


title: Ecological momentary assessment.

text: Assessment in clinical psychology typically relies on global retrospective self-reports collected at research or clinic visits, which are limited by recall bias and are not well suited to address how behavior changes over time and across contexts. Ecological momentary assessment (EMA) involves repeated sampling of subjects' current behaviors and experiences in real time, in subjects' natural environments. EMA aims to minimize recall bias, maximize ecological validity, and allow study of microprocesses that influence behavior in real-world contexts. EMA studies assess particular events in subjects' lives or assess subjects at periodic intervals, often by random time sampling, using technologies ranging from written diaries and telephones to electronic diaries and physiological sensors. We discuss the rationale for EMA, EMA designs, methodological and practical issues, and comparisons of EMA and recall data. EMA holds unique promise to advance the science and practice of clinical psychology by shedding light on the dynamics of behavior in real-world settings.


title: Social media for the emergency manager in disaster planning and response

text: This practitioner paper outlines some of the benefits for the use of social media, from the perspective of a locallevel or county-level emergency manager (EM). As compared to state and national level emergency management, because local level EMs have limited manpower and resources, social media can positively or negatively impact the effectiveness of communication before, during and after disaster strikes. Outlined in this paper are six key points where local EMs have specific needs that could be addressed by the effective use of social media and, in the opinion of the authors, represent the top issues that EMs face when considering how to leverage Twitter, Facebook, YouTube, Instagram and other social media platforms. The six needs addressed in this paper include: 1) Best practices for general social media use by EMs, 2) Social media use for internal command and control within the EM group, 3) Developing situation awareness by monitoring social media, especially prior to predicable events, 4) Communicating disaster preparedness messages through social media, 5) Using social media for gathering damage assessment information during, or immediately following a crisis, and 6) Leveraging social media volunteer groups. This short paper picks up where the Federal Emergency Management Agency’s social media training leaves off and attempts to represent these six needs as use cases for researchers and developers to address in future publications and products.


title: The Use of Alternative Social Networking Sites in Higher Educational Settings: A Case Study of the E-Learning Benefits of Ning in Education

text: Distance education as a primary means of instruction is expanding significantly at the college and university level. Simultaneously, the growth of social networking sites (SNS) including Facebook, LinkedIn, and MySpace is also rising among today’s college students. An increasing number of higher education instructors are beginning to combine distance education delivery with SNSs. However, there is currently little research detailing the educational benefits associated with the use of SNSs. Non-commercial, education-based SNSs, such as Ning in Education, have been recently shown to build communities of practice and facilitate social presence for students enrolled in distance education courses. In order to evaluate the largely unexplored educational benefits of SNSs, we surveyed graduate students enrolled in distance education courses using Ning in Education, an education-based SNS, based on their attitudes toward SNSs as productive online tools for teaching and learning. The results of our study suggest that education-based SNSs can be used most effectively in distance education courses as a technological tool for improved online communications among students in higher distance education courses. Introduction The use of distance education courses as a primary instructional delivery option, especially in the higher education community, is expanding at an unprecedented rate. The 9.7% growth rate in the number of college and university students enrolled in at least one online class reported by Allen and Seaman (2007) significantly exceeded the 1.5% growth rate in the overall higher education student population during the same period. Simultaneously, the emergence and growth of commercial social networking sites (SNSs) such as Facebook, Friendster, LinkedIn, LiveJournal, and MySpace has been extensive and widespread (Boyd & Ellison, 2007). Facebook, for example, is currently the fastest growing commercial SNS in the world, with more than 300 million active user profiles (Facebook, 2009). Given the rising popularity of both distance education and SNSs, it seems logical to merge these popular two technologies with the goal of improving online teaching and learning (National School Boards Association [NSBA], 2007; University of Minnesota, 2008). Research has shown that distance education courses are often more successful when they develop communities of practice (Barab & Duffy, 2000; Journal of Interactive Online Learning Brady, Holcomb, and Smith 152 DeSchryver, Mishra, Koehler, & Francis, 2009) as well as encourage high levels of online social presence among students (Anderson, 2005). Fostering a sense of community is critically important, especially in an online environment where students often do not get the opportunity to meet face-to-face with other students or the instructor in the course. Since they facilitate the sharing of information—personal and otherwise—the technologies used in SNSs aid discussion and create intimacy among online students, as they have they ability to connect and build community in a socially and educationally constructed network (Educause Learning Initiative [ELI], 2007). In contrast to SNS, course management systems (CMS), such as Blackboard and Moodle, tend to be very focused and lack the personal touch and networking capacity that SNSs offer. For example, instructors using CMS may pose a question in an online discussion board and each student posts a response. However, these student posts are really not interactions at all, but merely question and answer sessions. Using an SNS that is user centered, rather than class centered, such as a CMS, has the potential to increase student engagement. SNSs can actively encourage online community building, extending learning beyond the boundaries of the classroom (Smith, 2009). A comparison of typical SNS and a traditional CMS appears in Table 1.


title: On battery State of Charge estimation: A new mixed algorithm

text: A new SoC estimation algorithm is presented. The estimation is performed mixing the Coulomb-Counting and the Model-Based estimation approaches. The algorithm was tested on a brand new lithium-ion cell which use a nanoscale technology able to grant a more powerful and safer lithium-ion battery than the batteries currently on the market. The cell, called A123-M1, was first characterized and then identified. The cell was identified with a equivalent electrical circuits model which is usually called 2th Randle model: the used test-bench and the identification process are described. Then the SoC estimation problem is described. A briefly review of the state of art of SoC estimation is given and the drawbacks of the methods which are usually used are presented. Finally the new mixed estimation algorithm is described: the algorithm scheme and the results are shown.


title: A modified two-section UWB Wilkinson power divider

text: A novel Wilkinson power divider with modification of the traditional two-section Wilkinson power divider by adding one open stub on each branch is proposed. This design method is validated by building a divider working in the UWB band (3.1 GHz - 10.6 GHz). It is better in performance, but is about 10% smaller than the traditional three-section power divider and spares one resistor. The simulation and measurement results of the modified divider show equal power split, a good insertion loss of less than 1.1 dB, a good return loss of all ports of less than - 10 dB, and a good isolation is less than -11 dB over the UWB band.


title: Online EM for Unsupervised Models

text: The (batch) EM algorithm plays an important role in unsupervised induction, but it sometimes suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find better solutions than those found by batch EM. We support these findings on four unsupervised tasks: part-of-speech tagging, document classification, word segmentation, and word alignment.


title: Semantic annotation in historical documents

text: This paper presents an approach to proceed with semantic annotation in historical documents from the 19th century that discuss the constitution of the mother language, the Portuguese Language in Brazil. The objective is to generate a group of semantically annotated documents in agreement with a domain ontology. To provide this domain ontology, the Linguistic Instrument Ontology was employed, and it supported the process of automatic semantic annotation. The results obtained with the annotation were analyzed in comparison with the Gold Standard and they presented an elevated level of coincidence, between 0.86 and 1.00 for the F1-score measure. Besides that, it was possible to locate new documents about the discussed domain in a sample of the Revistas Brazileiras. These results prove the efficacy of the approach of automatic semantic annotation.


title: Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task

text: This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).


title: Active current balancing for parallel-connected silicon carbide MOSFETs

text: In high power applications of silicon carbide (SiC) MOSFETs where parallelism is employed, current unbalance can occur and affect the performance and reliability of the power devices. In this paper, factors which cause current unbalance in these devices are analyzed. Among them, the threshold voltage mismatch is identified as a major factor for dynamic current unbalance. The threshold distribution of SiC MOSFETs is investigated, and its effect on current balance is studied in experiments. Based on these analyses, an active current balancing scheme is proposed. It is able to sense the unbalanced current and eliminate it by actively controlling the gate drive signal to each device. The features of fine time resolution and low complexity make this scheme attractive to a wide variety of wide-band-gap device applications. Experimental and simulation results verify the feasibility and effectiveness of the proposed scheme.


title: Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

text: Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this paper, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, CURB, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of fake news and misinformation.


title: The wisdom of the few: a collaborative filtering approach based on expert opinions from the web

text: Nearest-neighbor collaborative filtering provides a successful means of generating recommendations for web users. However, this approach suffers from several shortcomings, including data sparsity and noise, the cold-start problem, and scalability. In this work, we present a novel method for recommending items to users based on expert opinions. Our method is a variation of traditional collaborative filtering: rather than applying a nearest neighbor algorithm to the user-rating data, predictions are computed using a set of expert neighbors from an independent dataset, whose opinions are weighted according to their similarity to the user. This method promises to address some of the weaknesses in traditional collaborative filtering, while maintaining comparable accuracy. We validate our approach by predicting a subset of the Netflix data set. We use ratings crawled from a web portal of expert reviews, measuring results both in terms of prediction accuracy and recommendation list precision. Finally, we explore the ability of our method to generate useful recommendations, by reporting the results of a user-study where users prefer the recommendations generated by our approach.


title: A Survey of Collaborative Filtering Techniques

text: As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, modelbased, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.


title: Features for Measuring Credibility on Facebook Information

text: Abstract—Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.


title: Neural Kinematic Networks for Unsupervised Motion Retargetting

text: We propose a recurrent neural network architecture with a Forward Kinematics layer and cycle consistency based adversarial training objective for unsupervised motion retargetting. Our network captures the high-level properties of an input motion by the forward kinematics layer, and adapts them to a target character with different skeleton bone lengths (e.g., shorter, longer arms etc.). Collecting paired motion training sequences from different characters is expensive. Instead, our network utilizes cycle consistency to learn to solve the Inverse Kinematics problem in an unsupervised manner. Our method works online, i.e., it adapts the motion sequence on-the-fly as new frames are received. In our experiments, we use the Mixamo animation data1 to test our method for a variety of motions and characters and achieve state-of-the-art results. We also demonstrate motion retargetting from monocular human videos to 3D characters using an off-the-shelf 3D pose estimator.


title: A Supervised Learning Framework for Arbitrary Lagrangian-Eulerian Simulations

text: The Arbitrary Lagrangian-Eulerian (ALE) method is used in a variety of engineering and scientific applications for enabling multi-physics simulations. Unfortunately, the ALE method can suffer from simulation failures that require users to adjust parameters iteratively in order to complete a simulation. In this paper, we present a supervised learning framework for predicting conditions leading to simulation failures. To our knowledge, this is the first time machine learning has been applied to ALE simulations. We propose a novel learning representation for mapping the ALE domain onto a supervised learning formulation. We analyze the predictability of these failures and evaluate our framework using well-known test problems.


title: A Compact Multipath Mitigating Ground Plane for Multiband GNSS Antennas

text: This paper presents the design of a novel multipath mitigating ground plane for global navigation satellite system (GNSS) antennas. First, the concept of a compact low multipath cross-plate reflector ground plane (CPRGP) is presented. In comparison with the choke ring and electromagnetic band gap (EBG) ground planes, the proposed CPRGP has compact size, low mass, wide operational bandwidth, and simple configuration. The proposed CPRGP is then integrated with a circularly polarized dual-band GNSS antenna in order to assess the multipath mitigating performance over two frequency bands. Measurement results of the proposed CPRGP with GNSS antenna achieves a front-to-back ratio (FBR) over 25 dB at L1 (1.575 GHz) and L2 (1.227 GHz) bands and maximum backward cross-polarization levels below -23 dB at both bands. Antenna phase center variation remains less than 2 mm across both L1 and L2 bands. Furthermore, the performance comparison of the proposed CPRGP with the commercially available pinwheel antenna and the shallow corrugated ground plane is presented, showing the advantages of CPRGP for high precision GNSS applications.


title: Emotion regulation through listening to music in everyday situations.

text: Music is a stimulus capable of triggering an array of basic and complex emotions. We investigated whether and how individuals employ music to induce specific emotional states in everyday situations for the purpose of emotion regulation. Furthermore, we wanted to examine whether specific emotion-regulation styles influence music selection in specific situations. Participants indicated how likely it would be that they would want to listen to various pieces of music (which are known to elicit specific emotions) in various emotional situations. Data analyses by means of non-metric multidimensional scaling revealed a clear preference for pieces of music that were emotionally congruent with an emotional situation. In addition, we found that specific emotion-regulation styles might influence the selection of pieces of music characterised by specific emotions. Our findings demonstrate emotion-congruent music selection and highlight the important role of specific emotion-regulation styles in the selection of music in everyday situations.


title: Moving beyond the Turing Test with the Allen AI Science Challenge

text: Answering questions correctly from standardized eighth-grade science tests is itself a test of machine intelligence.


title: Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.

text: Although advances in information technology in the past decade have come in quantum leaps in nearly every aspect of our lives, they seem to be coming at a slower pace in the field of medicine. However, the implementation of electronic health records (EHR) in hospitals is increasing rapidly, accelerated by the meaningful use initiatives associated with the Center for Medicare & Medicaid Services EHR Incentive Programs. The transition to electronic medical records and availability of patient data has been associated with increases in the volume and complexity of patient information, as well as an increase in medical alerts, with resulting "alert fatigue" and increased expectations for rapid and accurate diagnosis and treatment. Unfortunately, these increased demands on health care providers create greater risk for diagnostic and therapeutic errors. In the near future, artificial intelligence (AI)/machine learning will likely assist physicians with differential diagnosis of disease, treatment options suggestions, and recommendations, and, in the case of medical imaging, with cues in image interpretation. Mining and advanced analysis of "big data" in health care provide the potential not only to perform "in silico" research but also to provide "real time" diagnostic and (potentially) therapeutic recommendations based on empirical data. "On demand" access to high-performance computing and large health care databases will support and sustain our ability to achieve personalized medicine. The IBM Jeopardy! Challenge, which pitted the best all-time human players against the Watson computer, captured the imagination of millions of people across the world and demonstrated the potential to apply AI approaches to a wide variety of subject matter, including medicine. The combination of AI, big data, and massively parallel computing offers the potential to create a revolutionary way of practicing evidence-based, personalized medicine.


title: Arabic language sentiment analysis on health services

text: The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naïve Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.


title: Neck lift my way: an update.

text:
BACKGROUND
The author updates prior descriptions of an approach to the surgical neck lift that aims for a maximum degree of control over the size, shape, and position of every anatomical feature of the neck that is negatively affecting its appearance.

METHODS
A 38-year clinical experience guided the development of the operative tactics that define the strategy. Data collected from a records review of 522 consecutive neck lifts performed during the 10-year period 2004 through 2013 further inform the report. The approach has eight features: (1) nearly routine use of open submental access to all tissue layers of the central neck, including a regimen that curbed the problems that may attend an extensive tissue dissection; (2) management of lax neck skin by lateral excision using a specific postauricular incision, or by using the nonexcisional method of redistribution; (3) open lipectomy for precise removal of excess subcutaneous neck and jawline fat; (4) individualized modifications to subplatysmal fat, perihyoid fascia, and anterior digastric muscles; (5) treatment of large, ptotic, or malpositioned submandibular salivary glands by partial excision using a transcutaneous traction suture; (6) the current version of the corset platysmaplasty, which is used to treat static paramedian platysma muscle bands, and to avoid contour imperfections following subplatysmal maneuvers; (7) an approach that facilitates an isolated neck lift; and (8) durable results.

RESULTS
Case examples demonstrate outcomes.

CONCLUSION
Although the updated approach remains relatively complex and invasive, the author believes that the ends justify the means.


Synthetic Biology for Space Exploration : Promises and Societal Implications


title: Emulation of Software Defined Networks Using Mininet in Different Simulation Environments

text: In this paper an evaluation of an SDN emulation tool called Mininet is conducted. Tests were conducted to study Mininnet limitations related to the simulation environment, resource capabilities. To evaluate the later, the scalability of Mininet in term of creating many topologies is tested with varying number of nodes and two different environment scenarios. Results show that the simulation environment has a remarkable effect on the required time for building a topology, for instance, the powerful resources scenario needed only 0.19 sec, whereas, 5.611 sec were needed when the resources were less. However, the required time were increased in both scenarios when the number of nodes was increased into 242.842 and 3718.117 sec for the powerful and less capabilities resources respectively.


title: Identifying Women's Experiences With and Strategies for Mitigating Negative Effects of Online Harassment

text: The popularity, availability, and ubiquity of information and communication technologies create new opportunities for online harassment. The present study evaluates factors associated with young adult women's online harassment experiences through a multi-factor measure accounting for the frequency and severity of negative events. Findings from a survey of 659 undergraduate and graduate students highlight the relationship between harassment, well-being, and engagement in strategies to manage one's online identity. We further identify differences in harassment experiences across three popular social media platforms: Facebook, Twitter, and Instagram. We conclude by discussing this study's contribution to feminist theory and describing five potential design interventions derived from our data that may minimize these negative experiences, mitigate the psychological harm they cause, and provide women with more proactive ways to regain agency when using communication technologies.


title: Silhouette Smoothing for Real-Time Rendering of Mesh Surfaces

text: Coarse piecewise linear approximation of surfaces causes the undesirable polygonal appearance of silhouettes. We present an efficient method for smoothing the silhouettes of coarse triangle meshes using efficient 3D curve reconstruction and simple local remeshing. It does not assume the availability of a fine mesh and generates only a moderate amount of additional data at runtime. Furthermore, polygonal feature edges are also smoothed in a unified framework. Our method is based on a novel interpolation scheme over silhouette triangles, and this ensures that smooth silhouettes are faithfully reconstructed and always change continuously with respect to the continuous movement of the viewpoint or objects. We speed up computation with GPU assistance to achieve real-time rendering of coarse meshes with the smoothed silhouettes. Experiments show that this method outperforms previous methods for silhouette smoothing.


title: Robustness Analysis of Artificial Neural Networks and Support Vector Machine in Making Prediction

text: This This study aims to investigate the robustness of prediction model by comparing artificial neural networks (ANNs), and support vector machine (SVMs) model. The study employs ten years monthly data of six types of macroeconomic variables as independent variables and the average rate of return of one-month time deposit of Indonesian Islamic banks (RR) as dependent variable. Finally, the performance is evaluated through graph analysis, statistical parameters and accuracy rate measurement. This research found that ANNs outperforms SVMs empirically resulted from the training process and overall data prediction. This is indicating that ANNs model is better in the context of capturing all data pattern and explaining the volatility of RR.


title: Biofeedback-based behavioral treatment for chronic tinnitus: results of a randomized controlled trial.

text: Many tinnitus sufferers believe that their tinnitus has an organic basis and thus seek medical rather than psychological treatments. Tinnitus has been found to be associated with negative appraisal, dysfunctional attention shift, and heightened psychophysiological arousal, so cognitive-behavioral interventions and biofeedback are commonly suggested as treatments. This study developed and investigated the efficacy of a biofeedback-based cognitive-behavioral treatment for tinnitus. In total, 130 tinnitus patients were randomly assigned to an intervention or a wait-list control group. Treatment consisted of 12 sessions of a biofeedback-based behavioral intervention over a 3-month period. Patients in the wait-list group participated in the treatment after the intervention group had completed the treatment. Results showed clear improvements regarding tinnitus annoyance, diary ratings of loudness, and feelings of controllability. Furthermore, changes in coping cognitions as well as changes in depressive symptoms were found. Improvements were maintained over a 6-month follow-up period in which medium-to-large effect sizes were observed. The treatment developed and investigated in this study is well accepted and leads to clear and stable improvements. Through demonstration of psychophysiological interrelationships, the treatment enables patients to change their somatic illness perceptions to a more psychosomatic point of view.


title: Lung nodule detection in CT images using deep convolutional neural networks

text: Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor. Here, we present a CADe system for the detection of lung nodules in thoracic CT images. Our system is based on (1) the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and (2) a deep Convolutional Neural Network (CNN), which is trained, using the back-propagation algorithm, to extract valuable volumetric features from the input data and detect lung nodules in sub-volumes of CT images. Considering only those test nodules that have been annotated by four radiologists, our CADe system achieves a sensitivity (true positive rate) of 78.9% with 20 false positives (FPs) per scan, or a sensitivity of 71.2% with 10 FPs per scan. This is achieved without using any segmentation or additional FP reduction procedures, both of which are commonly used in other CADe systems. Furthermore, our CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe system more challenging.


title: MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission

text: 8 1094-7167/04/$20.00 © 2004 IEEE IEEE INTELLIGENT SYSTEMS to elucidate the planet’s past climate, water activity, and habitability. Science is MER’s primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (Mixed Initiative Activity Plan Generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool1 from the Jet Propulsion Laboratory and the Europa planning and scheduling system2 from NASA Ames Research Center. This article discusses the issues arising from combining these tools in this mission’s context.


Face recognition using HOG-EBGM


title: Robot control design using virtual path from android smartphone

text: Progress robot development very varied almost all fields seize the attention of the world's scientists and researchers. For some cases, robots are generally used to help humans in relieving job. The robot can replace human mobilization for move so that the efficiency of wasted time will be less. Many things need to be note what kind of robot control reliable to replace human work in mobilization. Virtual world as in android smartphone greatly benefit humans to creating control tools easy to use anywhere and could represent a desire humans. By making control robot using virtual paths on the Android smartphone which have an accuracy of more than 70% to a scale of 1:50 with the actual track in an environment that is not wide enough, the advantage can be used to control the robot move easily and efficiently compared to the usual manual control robot that will take more time to control The robot moves its place as desired.


title: 3D Human Pose Estimation in RGBD Images for Robotic Task Learning

text: We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.


title: The Levels of Conceptual Interoperability Model

text: Interoperability of systems is not a cookie-cutter-function. There are various levels of interoperability between two systems ranging from no interoperability to full interoperability. In the technical domain, various models for levels of interoperability already exist and are used successfully to determine the degree of interoperability between information technology systems. However, such models are not yet established in the domain of conceptual modeling. This paper introduces a general model dealing with various levels of conceptual interoperability that goes beyond the technical reference models for interoperable solutions. The model is intended to become a bridge between the conceptual design and the technical design for implementation, integration, or federation. It should also contribute to the standardization of V&V procedures as well as to the documentation of systems that are designed to be federated. It is furthermore a framework to determine in the early stages of the federation development process whether meaningful interoperability between systems is possible. To this end, the scope of the model goes beyond the implementation level of actual standards, which focus on the exchange of data using standardized formats and interfaces. Another practical application of the model is, that it enhances the only recently published DoD Net-Centric Data Strategy for the Global Information Grid (GIG) and is directly applicable to derive necessary metadata to reach the DoD Data Goal to “enable Data to be understandable.”


title: Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology

text: Many EDI researchers and practitioners have recognized the importance of high penetration levels for the success of EDI. Unfortunately, such penetration is partly impeded by the resistance of small companies to become EDI capable. To investigate this issue, three major factors are identified that influence the EDI adoption practices of small firms. These factors are: organizational readiness (because of the low levels of IT sophistication and resource availability of small firms), external pressures to adopt (because of the weak market positions of small firms and the network nature of the technology), and perceived benefits (because of the limited impact that IT has on small firms due to under-utilization and lack of integration). By combining the anticipated effects of these factom, we developed a framework of EDI adoption by small businesses. The applicability of this framework is empirically demonstrated using the results of seven case studies. Finally, recommendations are made for the development of successful EDI partner expansion plans. These include the development of a long-term EDI partner expansion plan from the very beginning, the individual assessment of each partner’s EDI preparedness level, and the selection of appropriate influence tactics to expedite adoption by small partners. Specifically, it is suggested that EDI initiators pursue promotional efforts to improve partners’ perceptions of EDI benefits, provide financial and technological assistance to partners with low organizational readiness, and carefully select and enact influence strategies to reduce resistance.


title: The Modeling Process for Stage Models

text: The purpose of this paper is to present and test a modeling procedure because researchers have struggled for decades to develop stages-of-growth models that are both theoretically founded and empirically validated. This article presents the concept and hypothesis of stages, the history of stage models, and a procedure that may serve as a useful tool in modeling stages of growth. Based on previous research and lessons learned from case study experience of the government sector in Norway, a procedure for the stages of growth modeling process is suggested and demonstrated. The procedure is used for developing a stage model for e-government interoperability. This article provides new insight into issues and challenges faced when engaging in stages of growth research. The paper proposes a new approach to stages of growth modeling. The utility of the suggested procedure is to improve theory building and empirical validation. The contribution to academia is the modeling process that can be applied in future developments of stages of growth. The contribution to practice lies in the stage hypothesis of organizational development over time.


title: The Case Research Strategy in Studies of Information Systems

text: This article defines and discusses one of these qualitative methods--the case research strategy. Suggestions are provided for researchers who wish to undertake research employing this approach. Criteria for the evaluation of case research are established and several characteristics useful for categorizing the studies are identified. A sample of papers drawn from information systems journals is reviewed. The paper concludes with examples of research areas that are particularly wellsuited to investigation using the case research approach. ACM categories: H.O., J.O.


title: Does a Water Protocol Improve the Hydration and Health Status of Individuals with Thin Liquid Aspiration Following Stroke? A Randomized Controlled Trial

text: The benefit of water protocols for individuals with thin liquid aspiration remains controversial, with mixed findings from a small number of randomized controlled trials (RCTs). This study aimed to contribute to the evidence of the effectiveness of water protocols with a particular emphasis on health outcomes, especially hydration. An RCT was conducted with patients with known thin liquid aspiration post stroke randomized to receiving thickened liquids only or a water protocol. For the 14 participants in rehabilitation facilities whose data proceeded to analysis, there was no difference in the total amount of beverages consumed between the water protocol group (mean = 1103 ml per day, SD = 215 ml) and the thickened liquids only group (mean = 1103 ml, SD = 247 ml). Participants in the water protocol group drank on average 299 ml (SD 274) of water but offset this by drinking less of the thickened liquids. Their hydration improved over time compared with participants in the thickened liquids only group, but differences between groups were not significant. Twenty-one percent of the total sample was diagnosed with dehydration, and no participants in either group were diagnosed with pneumonia. There were significantly more diagnoses of urinary tract infection in the thickened liquids only group compared to the water protocol group (χ 2 = 5.091, p = 0.024), but no differences between groups with regard to diagnoses of dehydration (χ 2 = 0.884, p = 0.347) or constipation (χ 2 = 0.117, p = 0.733). The findings reinforce evidence about the relative safety of water protocols for patients in rehabilitation post stroke and provide impetus for future research into the potential benefits for hydration status and minimizing adverse health outcomes.


title: A comparative study between RadViz and Star Coordinates

text: RadViz and star coordinates are two of the most popular projection-based multivariate visualization techniques that arrange variables in radial layouts. Formally, the main difference between them consists of a nonlinear normalization step inherent in RadViz. In this paper we show that, although RadViz can be useful when analyzing sparse data, in general this design choice limits its applicability and introduces several drawbacks for exploratory data analysis. In particular, we observe that the normalization step introduces nonlinear distortions, can encumber outlier detection, prevents associating the plots with useful linear mappings, and impedes estimating original data attributes accurately. In addition, users have greater flexibility when choosing different layouts and views of the data in star coordinates. Therefore, we suggest that analysts and researchers should carefully consider whether RadViz's normalization step is beneficial regarding the data sets' characteristics and analysis tasks.


title: The Role of Chatbots in Formal Education

text: Chatbots appeared in large numbers at the beginning of the current decade. Interactive technology, often combined with artificial intelligence, has rapidly invaded and occupied the world of online chat. Chatbots are not just elements of virtual assistants, but are used by organizations and governments on websites, in applications, and instant messaging platforms to promote products, ideas or services. In this paper, the authors firstly present a theoretical and historical background, then discuss the issues of using chatbots as educational assistants, and finally describe the basic steps and challenges of programming a bot.


title: with Emphasis to the Computer and Internet

text: This article discusses the Roles of ICT in education. Information communication technologies (ICT) at present are influencing every aspect of human life. They are playing salient roles in work places, business, education, and entertainment. Moreover, many people recognize ICTs as catalysts for change; change in working conditions, handling and exchanging information, teaching methods, learning approaches, scientific research, and in accessing information. Therefore, this review article discusses the roles of ICTs, the promises, limitations and key challenges of integration to education systems. The review attempts in answering the following questions: (1) What are the benefits of ICTs in education? (2) What are the existing promises of ICT use in education systems of some developing countries? (3) What are the limitations and key challenges of ICTs integration to education systems? The review concludes that regardless of all the limitations characterizing it, ICT benefits education systems to provide quality education in alignment with constructivism, which is a contemporary paradigm of learning.


title: Adversarial Deep Structural Networks for Mammographic Mass Segmentation

text: Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model a potential function, followed by a CRF to perform structured learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with a position priori. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INBreast and DDSM-BCRP, demonstrate that our end-to-end network achieves the state-of-the-art results. 1


title: A new vector evaluated PBIL algorithm for reinsurance analytics

text: The purpose of this paper is to evaluate the performance of a new multiobjective algorithm called Vector Evaluated Population Based Incremental Learning (VEPBIL). The new algorithm was applied in solving a real world application named Reinsurance Contract Optimization (RCO), which is a multiobjective problem consisting of maximizing two conflicting functions: expected return and risk. The VEPBIL was tested on two instances of the problem composed by 7 and 15 layers of real anonymized data. In order to evaluate the algorithm, metrics such as hyper volume, number of solutions and coverage were used. A comparisons against Vector Evaluated Differential evolution (VEDE) is also carried out. The comparison has shown that VEPBIL can dominate about 70% and 50% of solutions from VEDE using 7 and 15 layers respectively, whereas VEDE dominates about 10% and 30% of solutions in the way around.


title: Design of a new counter-balancing stackable mechanism

text: This paper describes design and application of a novel counter-balancing stackable parallel mechanism. The stackable mechanism is designed by combining a five-bar and a couple of parallelograms. This design allows a compact design and wide workspace as compared to prior parallel type counter-balancing mechanisms. A linear geometry of this mechanism guarantees static balancing in the whole workspace. This mechanism was successfully applied to otologic.