Named entity recognition on Indonesian Twitter posts using long short-term memory networks

The task of Named-Entity Recognition (NER) can support the higher-level tasks such as question answering, text summarization, and information retrieval. This work views NER on Indonesian Twitter posts as a sequence labeling problem using supervised machine learning approach. The architecture used is Long Short-Term Memory Networks (LSTMs), with word embedding and POS tag as the model features. As the result, our model can give a performance with an F1 score of 77.08%.


Scrutinizing WPA2 Password Generating Algorithms in Wireless Routers

A wireless router is a networking device that enables a user to set up a wireless connection to the Internet. A router can offer a secure channel by cryptographic means which provides authenticity and confidentiality. Nowadays, almost all routers use a secure channel by default that is based on Wi-Fi Protected Access II (WPA2). This is a security protocol which is believed not to be susceptible to practical key recovery attacks. However, the passwords should have sufficient entropy to avert bruteforce attacks. In this paper, we compose a strategy on how to reverse-engineer embedded routers. Furthermore, we describe a procedure that can instantly gather a complete wireless authentication trace which enables an offline password recovery attack. Finally, we present a number of use cases where we identify extremely weak password generating algorithms in various routers which are massively deployed in The Netherlands. The algorithms are used to generate the default WPA2 password. Such a password is loaded during device initialization and hardware reset. Users that did not explicitly change their wireless password are most likely vulnerable to practical attacks which can recover their password within minutes. A stolen password allows an adversary to abuse someone else’s internet connection, for instance compromising the firewall, making a fraudulent transaction or performing other criminal activities. Together with the Dutch National Cyber Security Centre we have initiated a responsible disclosure procedure. However, since these routers are also used by many other companies in various countries, our findings seem to relate an international industry wide security issue.


A Simplification-Translation-Restoration Framework for Cross-Domain SMT Applications

Integration of domain specific knowledge into a general purpose statistical machine translation (SMT) system poses challenges due to insufficient bilingual corpora. In this paper we propose a simplification-translation-restoration (STR) framework for domain adaptation in SMT by simplifying domain specific segments of a text. For an in-domain text, we identify the critical segments and modify them to alleviate the data sparseness problem in the out-domain SMT system. After we receive the translation result, these critical segments are then restored according to the provided in-domain knowledge. We conduct experiments on an English-toChinese translation task in the medical domain and evaluate each step of the STR framework. The translation results show significant improvement of our approach over the out-domain and the naïve in-domain SMT systems. 用於跨領域統計式機器翻譯系統之簡化-翻譯-還原架構


A column approximate minimum degree ordering algorithm

Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero pattern of A, that limits the worst-case number of nonzeros in the factorization. The fill-in also depends on P, but Q is selected to reduce an upper bound on the fill-in for any subsequent choice of P. The choice of Q can have a dramatic impact on the number of nonzeros in L and U. One scheme for determining a good column ordering for A is to compute a symmetric ordering that reduces fill-in in the Cholesky factorization of ATA. A conventional minimum degree ordering algorithm would require the sparsity structure of ATA to be computed, which can be expensive both in terms of space and time since ATA may be much denser than A. An alternative is to compute Q directly from the sparsity structure of A; this strategy is used by MATLAB's COLMMD preordering algorithm. A new ordering algorithm, COLAMD, is presented. It is based on the same strategy but uses a better ordering heuristic. COLAMD is faster and computes better orderings, with fewer nonzeros in the factors of the matrix.


Simultaneous map building and localization for an autonomous mobile robot

In this paper, we discuss a significant open problem in mobile robotics: simultaneous map building and localization, which we define as long-term globally referenced position estimation without a priori information. This problem is difficult because of the following paradox: to move precisely, a mobile robot must have an accurate environment map; however, to build an accurate map, the mobile robot’s sensing locations must be known precisely. In this way, simultaneous map building and localization can be seen to present a question of “which came first, the chicken or the egg?” (The map or the motion?) When using ultrasonic sensing, to overcome this issue we equip the vehicle with multiple servo-mounted sonar sensors, to provide a means in which a subset of environment features can be precisely learned from the robot’s initial location and subsequently tracked to provide precise positioning.


Stiffness analysis for a 3-PUU parallel kinematic machine

This paper presents the stiffness characteristics of a three-prismatic-universal–universal (3-PUU) translational parallel kinematic machine (PKM). The stiffness matrix is derived intuitively based upon an alternative approach considering actuations and constraints, and the compliances subject to both actuators and legs are involved in the stiffness model. The stiffness performance of the manipulator is evaluated by utilizing the extremum stiffness values, and the influences of design parameters on the stiffness properties are presented, which will be valuable for the architecture design of a 3-PUU PKM. Moreover, the stiffness behavior of the PKM is investigated via the eigenscrew decomposition of the stiffness matrix, which provides a physical interpretation of the PKM stiffness and allows the identification of the stiffness center and compliant axis. 2007 Elsevier Ltd. All rights reserved.


Controllable Text Generation

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns highly interpretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.


Large-scale visual sentiment ontology and detectors using adjective noun pairs

We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.


A DNA nanorobot functions as a cancer therapeutic in response to a molecular trigger in vivo

Nanoscale robots have potential as intelligent drug delivery systems that respond to molecular triggers. Using DNA origami we constructed an autonomous DNA robot programmed to transport payloads and present them specifically in tumors. Our nanorobot is functionalized on the outside with a DNA aptamer that binds nucleolin, a protein specifically expressed on tumor-associated endothelial cells, and the blood coagulation protease thrombin within its inner cavity. The nucleolin-targeting aptamer serves both as a targeting domain and as a molecular trigger for the mechanical opening of the DNA nanorobot. The thrombin inside is thus exposed and activates coagulation at the tumor site. Using tumor-bearing mouse models, we demonstrate that intravenously injected DNA nanorobots deliver thrombin specifically to tumor-associated blood vessels and induce intravascular thrombosis, resulting in tumor necrosis and inhibition of tumor growth. The nanorobot proved safe and immunologically inert in mice and Bama miniature pigs. Our data show that DNA nanorobots represent a promising strategy for precise drug delivery in cancer therapy.


60-GHz Millimeter-Wave Life Detection System (MLDS) for Noncontact Human Vital-Signal Monitoring

A first reported experimental study of a 60 GHz millimeter-wave life detection system (MLDS) for noncontact human vital-signal monitoring is presented. This detection system is constructed by using V-band millimeter-wave waveguide components. A clutter canceller is implemented in the system with an adjustable attenuator and phase shifter. It performs clutter cancellation for the transmitting power leakage from the circulator and background reflection to enhance the detecting sensitivity of weak vital signals. The noncontact vital signal measurements have been conducted on a human subject in four different physical orientations from distances of 1 and 2 m. The time-domain and spectrum waveforms of the measured breathing and heartbeat are presented. This prototype system will be useful for the development of the 60-GHz CMOS MLDS detector chip design.


Working-memory capacity protects model-based learning from stress.

Accounts of decision-making have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental advances suggest that this classic distinction between habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning, called model-free and model-based learning. Popular neurocomputational accounts of reward processing emphasize the involvement of the dopaminergic system in model-free learning and prefrontal, central executive-dependent control systems in model-based choice. Here we hypothesized that the hypothalamic-pituitary-adrenal (HPA) axis stress response--believed to have detrimental effects on prefrontal cortex function--should selectively attenuate model-based contributions to behavior. To test this, we paired an acute stressor with a sequential decision-making task that affords distinguishing the relative contributions of the two learning strategies. We assessed baseline working-memory (WM) capacity and used salivary cortisol levels to measure HPA axis stress response. We found that stress response attenuates the contribution of model-based, but not model-free, contributions to behavior. Moreover, stress-induced behavioral changes were modulated by individual WM capacity, such that low-WM-capacity individuals were more susceptible to detrimental stress effects than high-WM-capacity individuals. These results enrich existing accounts of the interplay between acute stress, working memory, and prefrontal function and suggest that executive function may be protective against the deleterious effects of acute stress.


A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests

We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.


Temporally Grounding Natural Sentence in Video

We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN)1 is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frameby-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the stateof-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.


Coping through emotional approach: scale construction and validation.

Four studies demonstrate the psychometric adequacy and validity of scales designed to assess coping through emotional approach. In separate undergraduate samples, exploratory and confirmatory factor analyses of dispositional (Study 1) and situational (Study 3) coping item sets yielded 2 distinct emotional approach coping factors: emotional processing (i.e., active attempts to acknowledge and understand emotions) and emotional expression. The 2 scales yielded high internal consistency and test-retest reliability, as well as convergent and discriminant validity. A study (Study 2) of young adults and their parents established the scales' interjudge reliabilities. Longitudinal (Study 3) and experimental (Study 4) research supported the predictive validity of the emotional approach coping scales with regard to adjustment to stressful encounters. Findings highlight the utility of functionalist theories of emotion as applied to coping theory.


Lévy flights and random searches

In this work we discuss some recent contributions to the random search problem. Our analysis includes superdiffusive Lévy processes and correlated random walks in several regimes of target site density, mobility and revisitability. We present results in the context of mean-field-like and closed-form average calculations, as well as numerical simulations. We then consider random searches performed in regular lattices and lattices with defects, and we discuss a necessary criterion for distinguishing true superdiffusion from correlated random walk processes. We invoke energy considerations in relation to critical survival states on the edge of extinction, and we analyze the emergence of Lévy behavior in deterministic search walks. Finally, we comment on the random search problem in the context of biological foraging. PACS numbers: 05.50.Fb, 05.40.−a


Jerk-bounded manipulator trajectory planning: design for real-time applications

An online method for obtaining smooth, jerk-bounded trajectories has been developed and implemented. Jerk limitation is important in industrial robot applications, since it results in improved path tracking and reduced wear on the robot. The method described herein uses a concatenation of fifth-order polynomials to provide a smooth trajectory between two way points. The trajectory approximates a linear segment with parabolic blends trajectory. A sine wave template is used to calculate the end conditions (control points) for ramps from zero acceleration to nonzero acceleration. Joining these control points with quintic polynomials results in a controlled quintic trajectory that does not oscillate, and is near time optimal for the jerk and acceleration limits specified. The method requires only the computation of the quintic control points, up to a maximum of eight points per trajectory way point. This provides hard bounds for online motion algorithm computation time. A method for blending these straight-line trajectories over a series of way points is also discussed. Simulations and experimental results on an industrial robot are presented.


Residual Reconstruction for Block-Based Compressed Sensing of Video

A simple block-based compressed-sensing reconstruction for still images is adapted to video. Incorporating reconstruction from a residual arising from motion estimation and compensation, the proposed technique alternatively reconstructs frames of the video sequence and their corresponding motion fields in an iterative fashion. Experimental results reveal that the proposed technique achieves significantly higher quality than a straightforward reconstruction that applies a still-image reconstruction independently frame by frame, a 3D reconstruction that exploits temporal correlation between frames merely in the form of a motion-agnostic 3D transform, and a similar, yet non-iterative, motion-compensated residual reconstruction.


Nonclassical Computation - A Dynamical Systems Perspective

In this chapter we investigate computation from a dynamical systems perspective. A dynamical system is described in terms of its abstract state space, the system’s current state within its state space, and a rule that determines its motion through its state space. In a classical computational system, that rule is given explicitly by the computer program; in a physical system, that rule is the underlying physical law governing the behaviour of the system. So a dynamical systems approach to computation allows us to take a unified view of computation in classical discrete systems and in systems performing non-classical computation. In particular, it gives a route to a computational interpretation of physical embodied systems exploiting the natural dynamics of their material substrates. We start with autonomous (closed) dynamical systems: those whose dynamics is not an explicit function of time, in particular, those with no inputs from an external environment. We begin with computationally conventional discrete systems, examining their computational abilities from a dynamical systems perspective. The aim here is both to introduce the necessary dynamical systems concepts, and to demonstrate how classical computation can be viewed from this perspective. We then move on to continuous dynamical systems, such as those inherent in the complex dynamics of matter, and show how these too can be interpreted computationally, and see how the material embodiment can give such computation “for free”, without the need to explicitly implement the dynamics. We next broaden the outlook to open (non-autonomous) dynamical systems, where the dynamics is a function of time, in the form of inputs from an external environment, and which may be in a closely coupled feedback loop with that environment. We finally look at constructive, or developmental, dynamical systems, where the structure of the state space is changing during the computation. This includes vari-


Energy-aware wireless microsensor networks

Self-configuring wireless sensor networks can be invaluable in many civil and military applications for collecting, processing, and disseminating wide ranges of complex environmental data. Because of this, they have attracted considerable research attention in the last few years. The WINS [1] and SmartDust [2] projects, for instance, aim to integrate sensing, computing, and wireless communication capabilities into a small form factor to enable low-cost production of these tiny nodes in large numbers. Several other groups are investigating efficient hardware/software system architectures, signal processing algorithms, and network protocols for wireless sensor networks [3]-[5]. Sensor nodes are battery driven and hence operate on an extremely frugal energy budget. Further, they must have a lifetime on the order of months to years, since battery replacement is not an option for networks with thousands of physically embedded nodes. In some cases, these networks may be required to operate solely on energy scavenged from the environment through seismic, photovoltaic, or thermal conversion. This transforms energy consumption into the most important factor that determines sensor node lifetime. Conventional low-power design techniques [6] and hardware architectures only provide point solutions which are insufficient for these highly energy-constrained systems. Energy optimization, in the case of sensor networks, is much more complex, since it involves not only reducing the energy consumption of a single sensor node but also maximizing the lifetime of an entire network. The network lifetime can be maximized


Differences in procrastination and motivation between undergraduate and graduate students

Procrastination became increasingly prevalent among students in recent years. However, little research was found that directly compares academic procrastination across different academic grade levels.
  The
  present study used a self-regulated learning perspective to compare procrastination types and associated motivation between undergraduate and graduate students. Sixty-six undergraduate and sixty-eight graduate students responded to a packet of questionnaires concerning their experience in an educational psychology class. The results show that students’ beliefs about the usefulness of procrastination were a better predictor of academic procrastination than self-efficacy beliefs and achievement goal orientations. Student age was related to procrastination types. Among the undergraduate procrastinators, the younger students were more likely to engage in active procrastination while the older students tended to engage in passive procrastination. Implications and future research directions are discussed.


OpenPiton: An Open Source Manycore Research Framework

Industry is building larger, more complex, manycore processors on the back of strong institutional knowledge, but academic projects face difficulties in replicating that scale. To alleviate these difficulties and to develop and share knowledge, the community needs open architecture frameworks for simulation, synthesis, and software exploration which support extensibility, scalability, and configurability, alongside an established base of verification tools and supported software. In this paper we present OpenPiton, an open source framework for building scalable architecture research prototypes from 1 core to 500 million cores. OpenPiton is the world's first open source, general-purpose, multithreaded manycore processor and framework. OpenPiton leverages the industry hardened OpenSPARC T1 core with modifications and builds upon it with a scratch-built, scalable uncore creating a flexible, modern manycore design. In addition, OpenPiton provides synthesis and backend scripts for ASIC and FPGA to enable other researchers to bring their designs to implementation. OpenPiton provides a complete verification infrastructure of over 8000 tests, is supported by mature software tools, runs full-stack multiuser Debian Linux, and is written in industry standard Verilog. Multiple implementations of OpenPiton have been created including a taped-out 25-core implementation in IBM's 32nm process and multiple Xilinx FPGA prototypes.


Inertial properties of the human trunk of males determined from magnetic resonance imaging

The purpose of this study was to evaluate the segmental parameters of the human trunk of malesin vivo using magnetic resonance imaging (MRI). In addition, the efficacy of volumetric estimation and existing prediction formulas to produce segmental properties similar to those produced by MRI was evaluated. As opposed to finding one representative normal value for these parameters, a range of normal values was defined. For instance, the average trunk mass was 42.2%±3.5% (x±SD) of body mass, but values ranged from 35.8% to 48.0%. To account for segment parameters more accurately, specific anthropometric measures need to be considered in addition to overall measures of body height and mass. These specific measures included segment length, circumference, width, and depth. Studies reporting general percentages based on height and/or mass were found to be inadequate predictors of segmental parameters of the trunk compared with MRI estimates. Volumebased estimates, which assume a uniform density distribution within a segment, were found to correspond closely to MRI values except for the thorax. However, the use of density values reflective of the livingin vivo state would likely alleviate this disparity, thus indicating that the volumetric technique may be effective for deriving segmental parameters for large segments of the trunk. Future research should adopt noninvasive techniques such as MRI and/or volumetric estimation to enhance the predictability of segmental parameters of the body for specific population groups characterized by gender, developmental age, body type, and fitness level. Further efforts should be made to establish standardized boundary definitions for trunk segments to avoid unnecessary confusion, from which substantial errors may be introduced into biomechanical linked-segment analyses of human movement.


A New Dataset for Fine-Grained Citation Field Extraction

Citation field extraction entails segmenting a citation string into its constituent parts, such as title, authors, publisher and year. Despite the importance of this task, there is a lack of well-annotated citation data. This paper presents a new labeled dataset for citation extraction that, in comparison to the previous standard dataset, exceeds four-times more data, supplies detailed nested labels rather than coarse-grained flat labels, and is derived from four different academic fields rather than one. We describe our new dataset in detail, and provide baseline experimental results from a state-of-the-art extraction method.


Learning Feature Pyramids for Human Pose Estimation

Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multibranch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.


Treating patellar tendinopathy with Fascial Manipulation.

According to Fascial Manipulation theory, patellar tendon pain is often due to uncoordinated quadriceps contraction caused by anomalous fascial tension in the thigh. Therefore, the focus of treatment is not the patellar tendon itself, but involves localizing the cause of this incoordination, considered to be within the muscular fascia of the thigh region. Eighteen patients suffering from patellar tendon pain were treated with the Fascial Manipulation technique. Pain was assessed (in VAS) before (VAS 67.8/100) and after (VAS 26.5/100) treatment, plus a follow-up evaluation at 1 month (VAS 17.2/100). Results showed a substantial decrease in pain immediately after treatment (p<0.0001) and remained unchanged or improved in the short term. The results show that the patellar tendon may be only the zone of perceived pain and that interesting results can be obtained by treating the muscular fascia of the quadriceps muscle, whose alteration may cause motor incoordination and subsequent pathology.


Automatic question answering using the web: Beyond the Factoid

In this paper we describe and evaluate a Question Answering (QA) system that goes beyond answering factoid questions. Our approach to QA assumes no restrictions on the type of questions that are handled, and no assumption that the answers to be provided are factoids. We present an unsupervised approach for collecting question and answer pairs from FAQ pages, which we use to collect a corpus of 1 million question/answer pairs from FAQ pages available on the Web. This corpus is used to train various statistical models employed by our QA system: a statistical chunker used to transform a natural language-posed question into a phrase-based query to be submitted for exact match to an off-the-shelf search engine; an answer/question translation model, used to assess the likelihood that a proposed answer is indeed an answer to the posed question; and an answer language model, used to assess the likelihood that a proposed answer is a well-formed answer. We evaluate our QA system in a modular fashion, by comparing the performance of baseline algorithms against our proposed algorithms for various modules in our QA system. The evaluation shows that our system achieves reasonable performance in terms of answer accuracy for a large variety of complex, non-factoid questions.


A simulation-based software design framework for network-centric and parallel systems

In this paper we discuss a software design framework that is capable of realizing network-centricity and the rising multicore technology. Instead of producing static design documents in the form of UML diagrams, we propose automatic generation of a visual simulation model, which represents the target system design. We discuss a design environment that is responsible for the generation and execution of the simulation model.


Computational systems biology

To understand complex biological systems requires the integration of experimental and computational research — in other words a systems biology approach. Computational biology, through pragmatic modelling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on. The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation. Computational systems biology addresses questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and engineering.


A GPU implementation of inclusion-based points-to analysis

Graphics Processing Units (GPUs) have emerged as powerful accelerators for many regular algorithms that operate on dense arrays and matrices. In contrast, we know relatively little about using GPUs to accelerate highly irregular algorithms that operate on pointer-based data structures such as graphs. For the most part, research has focused on GPU implementations of graph analysis algorithms that do not modify the structure of the graph, such as algorithms for breadth-first search and strongly-connected components.
 In this paper, we describe a high-performance GPU implementation of an important graph algorithm used in compilers such as gcc and LLVM: Andersen-style inclusion-based points-to analysis. This algorithm is challenging to parallelize effectively on GPUs because it makes extensive modifications to the structure of the underlying graph and performs relatively little computation. In spite of this, our program, when executed on a 14 Streaming Multiprocessor GPU, achieves an average speedup of 7x compared to a sequential CPU implementation and outperforms a parallel implementation of the same algorithm running on 16 CPU cores.
 Our implementation provides general insights into how to produce high-performance GPU implementations of graph algorithms, and it highlights key differences between optimizing parallel programs for multicore CPUs and for GPUs.


KD-tree acceleration structures for a GPU raytracer

Modern graphics hardware architectures excel at compute-intensive tasks such as ray-triangle intersection, making them attractive target platforms for raytracing. To date, most GPU-based raytracers have relied upon uniform grid acceleration structures. In contrast, the kd-tree has gained widespread use in CPU-based raytracers and is regarded as the best general-purpose acceleration structure. We demonstrate two kd-tree traversal algorithms suitable for GPU implementation and integrate them into a streaming raytracer. We show that for scenes with many objects at different scales, our kd-tree algorithms are up to 8 times faster than a uniform grid. In addition, we identify load balancing and input data recirculation as two fundamental sources of inefficiency when raytracing on current graphics hardware.


Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today's multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.


Guaranteed-Quality Mesh Generation for Curved Surfaces

For several commonly-used solution techniques for partial differential equations, the first step is to divide the problem region into simply-shaped elements, creating a mesh. We present a technique for creating high-quality triangular meshes for regions on curved surfaces. This technique is an extension of previous methods we developed for regions in the plane. For both flat and curved surfaces, the resulting meshes are guaranteed to exhibit the following properties: (1) internal and external boundaries are respected, (2) element shapes are guaranteed—all elements are triangles with angles between 30 and 120 degrees (with the exception of badly shaped elements that may be required by the specified boundary), and (3) element density can be controlled, producing small elements in “interesting” areas and large elements elsewhere. An additional contribution of this paper is the development of a practical generalization of Delaunay triangulation to curved surfaces.


Bluetooth Based Home Automation and Security System Using ARM 9

-Today we are living in 21 century where automation is playing important role in human life. Home automation allows us to control household appliances like light, door, fan, AC etc. It also provides home security and emergency system to be activated. Home automation not only refers to reduce human efforts but also energy saving and time efficiency. The main objective of home automation and security is to help handicapped and old aged people who will enable them to control home appliances and alert them in critical situations. This paper put forwards the design of home automation and security system using ARM7 LPC2148 board. The design is based on a standalone embedded system board ARM7 LPC2148 at home. Home appliances are connected to the ARM7 and communication is established between the ARM7 and ARM9 with Bluetooth device. The home appliances are connected to the input / output ports of the embedded system board and their status is passed to the ARM7. We would develop an authentication to the system for authorized person to access home appliances. The device with low cost and scalable to less modification to the core is much important. It presents the design and implementation of automation system that can monitor and control home appliances via ARM9 S3C2440A board.


Player Profiling with Fallout 3

In previous research we concluded that a personality profile, based on the Five Factor Model, can be constructed from observations of a player’s behavior in a module that we designed for Neverwinter Nights (Lankveld et al. 2011a). In the present research, we investigate whether we can do the same thing in an actual modern commercial video game, in this case the game Fallout 3. We stored automatic observations on 36 participants who played the introductory stages of Fallout 3. We then correlated these observations with the participants’ personality profiles, expressed by values for five personality traits as measured by the standard NEO-FFI questionnaire. Our analysis shows correlations between all five personality traits and the game observations. These results validate and generalize the results from our previous research (Lankveld et al. 2011a). We may conclude that Fallout 3, and by extension other modern video games, allows players to express their personality, and can therefore be used to create person-


Future trends of electrical propulsion and implications to ship design

Over the last 10 years the electrical propulsion fleet grew three times faster than the world fleet. This paper provides an overlook of the main drivers supporting this impressive growth and explores supporting drivers for future growth. The paper also explores two emerging technologies in electrical propulsion and distribution and how these influence ship design: DC distribution and energy storage. with a considerable level of accuracy which led to development of Dynamic Positioning (DP). Since electric propulsion is the most suitable solution to cope with the power fluctuation and redundancy requirements from DP systems, the development of offshore segment was the biggest driver for the development of electrical propulsion, reaching more than 50% of the units in 2013. Figure 2 Distribution of electrical propulsion fleet Dredgers – Dredging equipment requires a considerable amount of power and consequently the auxiliary power tends to be quite high, very often higher than the propulsion power. Therefore, the merit of electric propulsion is to reduce the overall installed power on board. Cruise – The main driver for electrical propulsion is space saving and space optimization. By shifting into electrical propulsion it is possible to reduce the space occupied by main propulsion systems and above all to place the different equipment in a way that the spaces reserved for passengers are maximized in terms of volume and quality. Nevertheless, the redundancy levels offered by electrical propulsion and its ability to meet the regulations on this segment have also contributed significantly for the adoption of this technology. Survey vessels – These vessels are often used to tow underwater survey devices for different applications, e.g. hydrographic, seismic, etc. Therefore, the lower underwater noise offered by electrical propulsion is very attractive. In addition, the load requirements of the different operation modes can vary significantly which also favor electric propulsion. LNG carriers – When laden, LNG carriers use their own cargo as fuel which is released by the cargo containment system in a process known as boil-off. Therefore, the propulsion system is selected so that the boil-off gas can be used as fuel. Initially, most vessels were fitted with steam turbines connected to the propulsion shaft through a gearbox. However, the introduction of dual fuel engines led to the adoption of electric propulsion (Hansen et al. 2007). Icebreakers – Although traditional icebreakers use their own weight to break sheets of ice, it is quite often to have blocks of ice reaching the propellers. This causes a sudden drop of the propeller speed and consequently a sudden increase of torque which could easily stall the propulsion engine with mechanical drive. Electrical drives are able to vary the speed and the torque independently and therefore maintain the torque within the engine’s acceptable limits (Ådnanes, 2003).


Fast and Scalable Learning of Neuro-Symbolic Representations of Biomedical Knowledge

In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. Based on a recently published comprehensive biological knowledge graph (Alshahrani, 2017) that was used for demonstrating neurosymbolic representation learning, we show how to train fast (under 1 minute) log-linear neural embeddings of the entities. We utilize these representations as inputs for machine learning classifiers to enable important tasks such as biological link prediction. Classifiers are trained by concatenating learned entity embeddings to represent entity relations, and training classifiers on the concatenated embeddings to discern true relations from automatically generated negative examples. Our simple embedding methodology greatly improves on classification error compared to previously published state-of-the-art results, yielding a maximum increase of +0.28 F-measure and +0.22 ROC AUC scores for the most difficult biological link prediction problem. Finally, our embedding approach is orders of magnitude faster to train (≤ 1 minute vs. hours), much more economical in terms of embedding dimensions (d = 50 vs. d = 512), and naturally encodes the directionality of the asymmetric biological relations, that can be controlled by the order with which we concatenate the embeddings.


TensorLog: Deep Learning Meets Probabilistic DBs

We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neuralnetwork infrastructure such as Tensorflow or Theano. This leads to a close integration of probabilistic logical reasoning with deep-learning infrastructure: in particular, it enables high-performance deep learning frameworks to be used for tuning the parameters of a probabilistic logic. Experimental results show that TensorLog scales to problems involving hundreds of thousands of knowledge-base triples and tens of thousands of examples.


A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media

Named entity recognition (NER) in Chinese social media is important but difficult because of its informality and strong noise. Previous methods only focus on in-domain supervised learning which is limited by the rare annotated data. However, there are enough corpora in formal domains and massive in-domain unannotated texts which can be used to improve the task. We propose a unified model which can learn from out-of-domain corpora and in-domain unannotated texts. The unified model contains two major functions. One is for cross-domain learning and another for semi-supervised learning. Cross-domain learning function can learn out-of-domain information based on domain similarity. Semi-Supervised learning function can learn in-domain unannotated information by self-training. Both learning functions outperform existing methods for NER in Chinese social media. Finally, our unified model yields nearly 11% absolute improvement over previously published results.


Longitudinal relations between children's exposure to TV violence and their aggressive and violent behavior in young adulthood: 1977-1992.

Although the relation between TV-violence viewing and aggression in childhood has been clearly demonstrated, only a few studies have examined this relation from childhood to adulthood, and these studies of children growing up in the 1960s reported significant relations only for boys. The current study examines the longitudinal relations between TV-violence viewing at ages 6 to 10 and adult aggressive behavior about 15 years later for a sample growing up in the 1970s and 1980s. Follow-up archival data (N = 450) and interview data (N = 329) reveal that childhood exposure to media violence predicts young adult aggressive behavior for both males and females. Identification with aggressive TV characters and perceived realism of TV violence also predict later aggression. These relations persist even when the effects of socioeconomic status, intellectual ability, and a variety of parenting factors are controlled.


Boot Attestation: Secure Remote Reporting with Off-The-Shelf IoT Sensors

A major challenge in computer security is about establishing the trustworthiness of remote platforms. Remote attestation is the most common approach to this challenge. It allows a remote platform to measure and report its system state in a secure way to a third party. Unfortunately, existing attestation solutions either provide low security, as they rely on unrealistic assumptions, or are not applicable to commodity low-cost and resource-constrained devices, as they require custom secure hardware extensions that are difficult to adopt across IoT vendors. In this work, we propose a novel remote attestation scheme, named Boot Attestation, that is particularly optimized for low-cost and resource-constrained embedded devices. In Boot Attestation, software integrity measurements are immediately committed to during boot, thus relaxing the traditional requirement for secure storage and reporting. Our scheme is very light on cryptographic requirements and storage, allowing efficient implementations, even on the most low-end IoT platforms available today. We also describe extensions for more flexible management of ownership and third party (public-key) attestation that may be desired in fully Internet-enabled devices. Our scheme is supported by many existing off-the-shelf devices. To this end, we review the hardware protection capabilities for a number of popular device types and present implementation results for two such commercially available platforms.


Implementation of Sha-256 Algorithm in Fpga Based Processor Soundarya

Hash functions play a significant role in today's cryptographic applications. SHA (Secure Hash Algorithm) is famous message compress standard used in computer cryptography, it can compress a long message to become a short message abstract. In this paper, SHA256 hash algorithm has been implemented using Verilog HDL (Hardware Description Language). The SHA-256 source code is divided into three modules, namely Data path, Memory and Top module. The Verilog code is synthesized using Xilinx software tool. The test vectors have been applied to verify the correctness of the SHA-256 functionality. A comparison between the proposed SHA-256 hash function implementation with other related works shows that it achieves the introduced system can working on reuse data, minimize critical paths and reduce the memory access by using cache memory, reducing clock cycles and needs less silicon area resources. The achieved performance in the term of throughput of the proposed system/architecture is much higher than the other hardware implementations. The proposed system could be used for the implementation of integrity units, and in many other sensitive cryptographic applications, such as, digital signatures, message authentication codes and random number generators.


PESTO: Parameter EStimation TOolbox

Summary
PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB.

Availability and implementation
PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/.

Contact
jan.hasenauer@helmholtz-muenchen.de.

Supplementary information
Supplementary data are available at Bioinformatics online.


CRM ADOPTION IN A HIGHER EDUCATION INSTITUTION

More and more organisations, from private to public sectors, are pursuing higher levels of customer satisfaction, loyalty and retention. With this intent, higher education institutions (HEI) have adopted CRM – Customer Relationship Management. In order to analyse some of the interesting aspects of this phenomenon n, we conducted an action research in a European Institute. The main research question we answered is “how to adopt a CRM strategy in a Higher Education Institution?” Some of the main findings of this study are (1) even though HEI’s main customer is the student, there are others stakeholders that a CRM project must consider; (2) universities can use their internal resources to implement a CRM project successfully; and (3) using Agile software methodology is an effective way to define clearer, more objective and more assertive technical requirements which result in a CRM software that meet send user’s expectations and organizational strategic goals. These findings can help other HEIs 46 Rigo, G. E., Pedron, C. D., Caldeira, M., Araújo, C. C. S., JISTEM, Brazil Vol. 13, No. 1, Jan/Abr., 2016 pp. 45-60 www.jistem.fea.usp.br planning to adopt CRM as a strategic tool to improve their relationship with the stakeholders ́ community and expand their student body.


Air filter particulate loading detection using smartphone audio and optimized ensemble classification

Automotive engine intake filters ensure clean air delivery to the engine, though over time these filters load with contaminants hindering free airflow. Today’s open-loop approach to air filter maintenance has drivers replace elements at predetermined service intervals, causing costly and potentially harmful overand under-replacement. The result is that many vehicles consistently operate with reduced power, increased fuel consumption, or excessive particulate-related wear which may harm the catalyst or damage machined engine surfaces. We present a method of detecting filter contaminant loading from audio data collected by a smartphone and a stand microphone. Our machine learning approach to filter supervision uses Mel-Cepstrum, Fourier and Wavelet features as input into a classification model and applies feature ranking to select the best-differentiating features. We demonstrate the robustness of our technique by showing its efficacy for two vehicle types and different microphones, finding a best result of 79.7% accuracy when classifying a filter into three loading states. Refinements to this technique will help drivers supervise their filters and aid in optimally timing their replacement. This will result in an improvement in vehicle performance, efficiency, and reliability, while reducing the cost of maintenance to vehicle owners. © 2017 Elsevier Ltd. All rights reserved.


Appropriate agile measurement: using metrics and diagnostics to deliver business value

Agile software development continually measures both our product and the process used to create it, to allow improvement. With increased popularity, more risk-averse groups are being drawn to agile, bringing with them modes of evaluation incompatible with agile values and principles. These outmoded metrics drive dysfunctional behaviors which threaten the integrity of an emerging agile culture. This paper collects some of the current thinking on appropriate agile metrics, and proposes simple tools for use by teams or organizations. The intention of these tools is to foster dialogue about the appropriateness of metrics in a given context, and thereby to encourage measurements more congruent with the objectives of agile teamwork


A comparison of methods for differential expression analysis of RNA-seq data

Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.


Compact triple C shaped microstrip patch antenna for WLAN, WiMAX & Wi-Fi application at 2.5 GHz

In the rapid progress of commercial communication applications, the development of compact antenna has an important role. This paper presents the analysis on the performance of single band compact triple C shaped microstrip patch antenna for WLAN, WiMAX and Wi-Fi applications with center frequency at 2.5GHz. The microstrip antenna has a planar geometry and consists of a ground plane, a substrate, a compact patch as radiator. Different specifications of the proposed antenna are measured through computer simulation in free space. The antenna has an impedance bandwidth of 2500 MHz (2487MHz to 2512 MHz). The proposed antenna provides excellent SWR of 1.04 and return loss -32.80 dB while excited by a 50 O microstrip feed line. All simulation results are performed using the CST Microwave Studio.


Unsupervised models for morpheme segmentation and morphology learning


Estimation of variable-speed-drive power consumption from harmonic content

Nonintrusive load monitoring can be used to identify the operating schedule of individual loads strictly from measurements of an aggregate power signal. Unfortunately, certain classes of loads present a continuously varying power demand. The power demand of these loads can be difficult to separate from an aggregate measurement. Variable-speed drives (VSDs) are industrially important variable-demand loads that are difficult to track non-intrusively. This paper proposes a VSD power estimation method based on observed correlations between fundamental and higher harmonic spectral content in current. The technique can be generalized to any load with signature correlations in harmonic content, including many power electronic and electromechanical loads. The approach presented here expands the applicability and field reliability of nonintrusive load monitoring.


"In vivo" spam filtering: a challenge problem for KDD

Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by treating it as a static text classification problem. True in vivo spam filtering has characteristics that make it a rich and challenging domain for data mining. Indeed, real-world datasets with these characteristics are typically difficult to acquire and to share. This paper demonstrates some of these characteristics and argues that researchers should pursue in vivo spam filtering as an accessible domain for investigating them.


Exploiting Heterogeneous Human Mobility Patterns for Intelligent Bus Routing

Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Compared to private transportation, public transportation uses road space more efficiently and produces fewer accidents and emissions. In this paper, we focus on the identification and optimization of flawed bus routes to improve utilization efficiency of public transportation services, according to people's real demand for public transportation. To this end, we first provide an integrated mobility pattern analysis between the location traces of taxicabs and the mobility records in bus transactions. Based on mobility patterns, we propose a localized transportation mode choice model, with which we can accurately predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. We also leverage the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips.


A PID Compensator Control for the Synchronous Rectifier Buck DC-DC Converter

This paper aims to design a PID compensator for the control of Synchronous Rectifier (SR) Buck Converter to improve its conversion efficiency under different load conditions. Since the diode rectifier is replaced by a high frequency MOSFET switch, the SR control method itself will be sufficient under heavy load condition, to attain better normal mode performance. However, this technique does not hold well in light load condition, due to increased switching losses. A new control technique accompanied with PID compensator is introduced in the paper will enable synchronous buck converter to realize ZVS, while feeding light loads. This is also least cost and highly efficient easy technique without use of extra auxiliary switches and RLC components. This control technique also proved to be efficient under input voltage variations. Simulation is done in MATLAB Simulink for proving stabilization provided by the PID compensator for synchronous rectifier (SR) buck converter. Keywords—Synchronous Rectifier (SR), Continuous Condu-ction Mode (CCM), Discontinuous Conduction Mode (DCM), Zero Voltage Switching (ZVS), Proportional-IntegralDerivative (PID).


200V Super Junction MOSFET Fabricated by High Aspect Ratio Trench Filling

In this paper, a new filling process using an anisotropic epitaxial growth was proposed as the fabrication method of a super junction (SJ) MOSFET. The anisotropic growth controlled with silicon (Si) and chlorine (Cl) source gases was applied to filling of the high-aspect-ratio trenches without voids. Using the process we succeeded in filling trenches with aspect ratio of 18, which is the highest aspect ratio works previously reported, and we fabricated a 200V SJ-MOSFET with a 2.7mum pitch trench gate structure. Consequently, the MOSFET showed electrical characteristics of the lowest on-resistance of 1.5 m-ohm-cm 2 and a breakdown voltage of 225V


A survey on IoT communication and computation frameworks: An industrial perspective

This paper surveys fog computing and embedded systems platforms as the building blocks of Internet of Things (IoT). Many concepts around IoT architectures, with various examples, are also discussed. This paper reviews a high-level conceptual layered architecture for IoT from a computational perspective. The architecture incorporates fog computing to address several issues associated with cloud computing; however, it is never a binary decision between fog and cloud. Many of the world's physical objects are being embedded with sensors and actuators, tied by communication infrastructures, and managed by computational algorithms. IoT sensor networks and embedded systems connecting smart objects are revolutionizing how we approach our daily lives, health care, energy, and transportation. Such computational needs are addressed with an array of various models and frameworks. In an attempt to consolidate the use of these models, this paper reviews the state-of-the-art research in IoT, cloud computing, and fog computing.


The Obstetric Consequences of Female Genital Mutilation/Cutting: A Systematic Review and Meta-Analysis

Various forms of female genital mutilation/cutting (FGM/C) have been performed for millennia and continue to be prevalent in parts of Africa. Although the health consequences following FGM/C have been broadly investigated, divergent study results have called into question whether FGM/C is associated with obstetric consequences. To clarify the present state of empirical research, we conducted a systematic review of the scientific literature and quantitative meta-analyses of the obstetric consequences of FGM/C. We included 44 primary studies, of which 28 were comparative, involving almost 3 million participants. The methodological study quality was generally low, but several studies reported the same outcome and were sufficiently similar to warrant pooling of effect sizes in meta-analyses. The meta-analyses results showed that prolonged labor, obstetric lacerations, instrumental delivery, obstetric hemorrhage, and difficult delivery are markedly associated with FGM/C, indicating that FGM/C is a factor in their occurrence and significantly increases the risk of delivery complications. There was no significant difference in risk with respect to cesarean section and episiotomy. These results can make up the background documentation for health promotion and health care decisions that inform work to reduce the prevalence of FGM/C and improve the quality of services related to the consequences of FGM/C.


A fully integrated 77GHz FMCW radar system in 65nm CMOS

Millimeter-wave anti-collision radars have been widely investigated in advanced CMOS technologies recently. This paper presents a fully integrated 77GHz FMCW radar system in 65nm CMOS. The FMCW radar transmits a continuous wave which is triangularly modulated in frequency and receives the wave reflected from objects. As can be illustrated in Fig. 11.2.1, for a moving target, the received frequency would be shifted (i.e. Doppler shift), resulting in two different offset frequencies f + and f− for the falling and rising ramps. Denoting the modulation range and period as B and Tm, respectively, we can derive the distance R and the relative velocity VR as listed in Fig. 11.2.1, where fc represents the center frequency and c the speed of light.


An exploration of crowdsourcing citation screening for systematic reviews

Systematic reviews are increasingly used to inform health care decisions, but are expensive to produce. We explore the use of crowdsourcing (distributing tasks to untrained workers via the web) to reduce the cost of screening citations. We used Amazon Mechanical Turk as our platform and 4 previously conducted systematic reviews as examples. For each citation, workers answered 4 or 5 questions that were equivalent to the eligibility criteria. We aggregated responses from multiple workers into an overall decision to include or exclude the citation using 1 of 9 algorithms and compared the performance of these algorithms to the corresponding decisions of trained experts. The most inclusive algorithm (designating a citation as relevant if any worker did) identified 95% to 99% of the citations that were ultimately included in the reviews while excluding 68% to 82% of irrelevant citations. Other algorithms increased the fraction of irrelevant articles excluded at some cost to the inclusion of relevant studies. Crowdworkers completed screening in 4 to 17 days, costing $460 to $2220, a cost reduction of up to 88% compared to trained experts. Crowdsourcing may represent a useful approach to reducing the cost of identifying literature for systematic reviews.


Neural Program Meta-Induction

Most recently proposed methods for Neural Program Induction work under the assumption of having a large set of input/output (I/O) examples for learning any underlying input-output mapping. This paper aims to address the problem of data and computation efficiency of program induction by leveraging information from related tasks. Specifically, we propose two approaches for cross-task knowledge transfer to improve program induction in limited-data scenarios. In our first proposal, portfolio adaptation, a set of induction models is pretrained on a set of related tasks, and the best model is adapted towards the new task using transfer learning. In our second approach, meta program induction, a k-shot learning approach is used to make a model generalize to new tasks without additional training. To test the efficacy of our methods, we constructed a new benchmark of programs written in the Karel programming language [17]. Using an extensive experimental evaluation on the Karel benchmark, we demonstrate that our proposals dramatically outperform the baseline induction method that does not use knowledge transfer. We also analyze the relative performance of the two approaches and study conditions in which they perform best. In particular, meta induction outperforms all existing approaches under extreme data sparsity (when a very small number of examples are available), i.e., fewer than ten. As the number of available I/O examples increase (i.e. a thousand or more), portfolio adapted program induction becomes the best approach. For intermediate data sizes, we demonstrate that the combined method of adapted meta program induction has the strongest performance.


Neural Programmer-Interpreters

We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances. By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-tosequence LSTMs. The program memory allows efficient learning of additional tasks by building on existing programs. NPI can also harness the environment (e.g. a scratch pad with read-write pointers) to cache intermediate results of computation, lessening the long-term memory burden on recurrent hidden units. In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input. Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples. We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms.


Making Neural Programming Architectures Generalize via Recursion

Empirically, neural networks that attempt to learn programs from data have exhibited poor generalizability. Moreover, it has traditionally been difficult to reason about the behavior of these models beyond a certain level of input complexity. In order to address these issues, we propose augmenting neural architectures with a key abstraction: recursion. As an application, we implement recursion in the Neural Programmer-Interpreter framework on four tasks: grade-school addition, bubble sort, topological sort, and quicksort. We demonstrate superior generalizability and interpretability with small amounts of training data. Recursion divides the problem into smaller pieces and drastically reduces the domain of each neural network component, making it tractable to prove guarantees about the overall system’s behavior. Our experience suggests that in order for neural architectures to robustly learn program semantics, it is necessary to incorporate a concept like recursion.


Synthesizing Programs for Images using Reinforced Adversarial Learning

Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level details and represent images as high-level programs. Current methods that combine deep learning and renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets. To mitigate these issues, we present SPIRAL, an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. The goal of this agent is to fool a discriminator network that distinguishes between real and rendered data, trained with a distributed reinforcement learning setup without any supervision. A surprising finding is that using the discriminator’s output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering. To the best of our knowledge, this is the first demonstration of an end-to-end, unsupervised and adversarial inverse graphics agent on challenging real world (MNIST, OMNIGLOT, CELEBA) and synthetic 3D datasets. A video of the agent can be found at https://youtu.be/iSyvwAwa7vk.


On the Cache Behavior of SPLASH-2 Benchmarks on ARM and ALPHA Processors in Gem5 Full System Simulator

Today cache size and hierarchy level of caches play an important role in improving computer performance. By using full system simulations of gem5, the variation in memory bandwidth, system bus throughput, L1 and L2 cache size misses are measured by running SPLASH-2 Benchmarks on ARM and ALPHA Processors. In this work we calculate cache misses, memory bandwidth and system bus throughput by running SPLASH2 benchmarks on gem5 Full System Mode. Our results show that L1 cache misses decrease as L1 cache size is varied from 16KB to 64KB. L1 cache misses are independent of L2 cache size after the program data resides in L2 cache. The memory bandwidth and system bus throughput decreases as L1 and L2 cache size increases.


Multi-connectivity in 5G mmWave cellular networks

The millimeter wave (mmWave) frequencies offer the potential of orders of magnitude increases in capacity for next-generation cellular wireless systems. However, links in mmWave networks are highly susceptible to blocking and may suffer from rapid variations in quality. Connectivity to multiple cells - both in the mmWave and in the traditional lower frequencies - is thus considered essential for robust connectivity. However, one of the challenges in supporting multi-connectivity in the mmWave space is the requirement for the network to track the direction of each link in addition to its power and timing. With highly directional beams and fast varying channels, this directional tracking may be the main bottleneck in realizing robust mmWave networks. To address this challenge, this paper proposes a novel measurement system based on (i) the UE transmitting sounding signals in directions that sweep the angular space, (ii) the mmWave cells measuring the instantaneous received signal strength along with its variance to better capture the dynamics and, consequently, the reliability of a channel/direction and, finally, (iii) a centralized controller making handover and scheduling decisions based on the mmWave cell reports and transmitting the decisions either via a mmWave cell or conventional microwave cell (when control signaling paths are not available). We argue that the proposed scheme enables efficient and highly adaptive cell selection in the presence of the channel variability expected at mmWave frequencies.


Value creation process in the fast fashion industry . Towards a networking approach < Value co-creation and the changing role of suppliers and customers >

Purpose – Quick response, short product life cycles, customer-centric businesses, agile supply chains and reduction of lead times are considered to be the core business strategies in the sector of fast fashion. The work is an attempt to identify the most revealing management and organizational tools that support the final value creation and service delivery processes in the international industry of fast fashion Design/Methodology/approach – In order to fulfill such a purpose, the paper detects the sector through the recent developments from Service-Dominant (S-D) Logic and Network Theory scientific proposals Findings – Value is a co-created performance, requiring several actors participation. In the fast fashion, such actors are represented by textile businesses, providers, retailers, stores, customers. They all interact within a supply chain becoming a value chain in which stakeholders interact with each other contributing to the final value generation. At the light of this research, it seems to be restrictive to identify in the lead time reduction the success factor of the fast fashion businesses, but it should be pursued in politics of integration, interaction, co-creation and sharing throughout the chain. Fast fashion is an example of totally integrated global chain working and performing as a whole business in which every single component co-creates the final mutual benefit Practical implications – Textile organizations taking part in the network represented by fast fashion are called to re-organize their activities in function of a highly developed integration level and so to move towards a networking approach, fostering the mutual exchange of resources in order to co-create a common value Originality/value – The paper adds value to the study of the fast fashion industry, valorizing the importance of the co-creating perspective within the supply chain management


Efficient Road Detection and Tracking for Unmanned Aerial Vehicle

An unmanned aerial vehicle (UAV) has many applications in a variety of fields. Detection and tracking of a specific road in UAV videos play an important role in automatic UAV navigation, traffic monitoring, and ground-vehicle tracking, and also is very helpful for constructing road networks for modeling and simulation. In this paper, an efficient road detection and tracking framework in UAV videos is proposed. In particular, a graph-cut-based detection approach is given to accurately extract a specified road region during the initialization stage and in the middle of tracking process, and a fast homography-based road-tracking scheme is developed to automatically track road areas. The high efficiency of our framework is attributed to two aspects: the road detection is performed only when it is necessary and most work in locating the road is rapidly done via very fast homography-based tracking. Experiments are conducted on UAV videos of real road scenes we captured and downloaded from the Internet. The promising results indicate the effectiveness of our proposed framework, with the precision of 98.4% and processing 34 frames per second for 1046 × 595 videos on average.


Inferring new indications for approved drugs via random walk on drug-disease heterogenous networks

Since traditional drug research and development is often time-consuming and high-risk, there is an increasing interest in establishing new medical indications for approved drugs, referred to as drug repositioning, which provides a relatively low-cost and high-efficiency approach for drug discovery. With the explosive growth of large-scale biochemical and phenotypic data, drug repositioning holds great potential for precision medicine in the post-genomic era. It is urgent to develop rational and systematic approaches to predict new indications for approved drugs on a large scale. In this paper, we propose the two-pass random walks with restart on a heterogenous network, TP-NRWRH for short, to predict new indications for approved drugs. Rather than random walk on bipartite network, we integrated the drug-drug similarity network, disease-disease similarity network and known drug-disease association network into one heterogenous network, on which the two-pass random walks with restart is implemented. We have conducted performance evaluation on two datasets of drug-disease associations, and the results show that our method has higher performance than six existing methods. A case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or investigational for neurodegenerative diseases. The experimental results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs. We proposed a two-pass random walk with restart on the drug-disease heterogeneous network, referred to as TP-NRWRH, to predict new indications for approved drugs. Performance evaluation on two independent datasets showed that TP-NRWRH achieved higher performance than six existing methods on 10-fold cross validations. The case study on the Alzheimer’s disease showed that nine of top 10 predicted drugs have been approved or are investigational for neurodegenerative diseases. The results show that our method achieves state-of-the-art performance in predicting new indications for approved drugs.


Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning

Zero-Shot Learning (ZSL) is generally achieved via aligning the semantic relationships between the visual features and the corresponding class semantic descriptions. However, using the global features to represent fine-grained images may lead to sub-optimal results since they neglect the discriminative differences of local regions. Besides, different regions contain distinct discriminative information. The important regions should contribute more to the prediction. To this end, we propose a novel stacked semantics-guided attention (SGA) model to obtain semantic relevant features by using individual class semantic features to progressively guide the visual features to generate an attention map for weighting the importance of different local regions. Feeding both the integrated visual features and the class semantic features into a multi-class classification architecture, the proposed framework can be trained end-to-end. Extensive experimental results on CUB and NABird datasets show that the proposed approach has a consistent improvement on both fine-grained zero-shot classification and retrieval tasks.


Monitoring of gait performance using dynamic time warping on IMU-sensor data

In this paper, a novel method for monitoring the changes in gait joint angle trajectories recorded using the low-cost and wearable Inertial Measurement Units (IMU) is presented. The introduced method is based on Dynamic Time Warping (DTW), an algorithm commonly used for evaluating the similarity of two time series which may vary in time and speed. DTW is employed as the measure of distance between two gait trajectories taken in different time instances, which could be used as an intuitive and effective measure for the evaluation of gait performances. The experimental results presented in the paper demonstrate that the proposed method is applicable for clinically relevant applications and is consequently adaptable to patients with diseases characterized with gait disorders and to different walking scenarios. The proposed method was firstly validated by applying the DTW-based measure on gait trajectories of five healthy subjects recorded while simulating different levels of walking disabilities. Then proposed measure was applied to estimate the distance between the “healthy” gait trajectories and gait trajectories of three patients with Parkinson's disease (PD) while performing single-task and dual-task overground walking. Also, the proposed measure was demonstrated as an effective measure for monitoring the changes in gait patterns of a PD patient before and after medication-based treatment. This result indicates potential use of proposed method for effective pharmacological management of PD.


Evolved Machines Shed Light on Robustness and Resilience

In biomimetic engineering, we may take inspiration from the products of biological evolution: we may instantiate biologically realistic neural architectures and algorithms in robots, or we may construct robots with morphologies that are found in nature. Alternatively, we may take inspiration from the process of evolution: we may evolve populations of robots in simulation and then manufacture physical versions of the most interesting or more capable robots that evolve. If we follow this latter approach and evolve both the neural and morphological subsystems of machines, we can perform controlled experiments that provide unique insight into how bodies and brains can work together to produce adaptive behavior, regardless of whether such bodies and brains are instantiated in a biological or technological substrate. In this paper, we review selected projects that use such methods to investigate the synergies and tradeoffs between neural architecture, morphology, action, and adaptive behavior.


An Introduction to Locally Linear Embedding

Many problemsin informationprocessinginvolvesomeform of dimensionality reduction.Herewe describelocally linearembedding(LLE), anunsupervisedlearningalgorithmthat computeslow dimensional,neighborhood preservingembeddingsof high dimensionaldata.LLE attemptsto discover nonlinearstructurein high dimensionaldataby exploiting thelocal symmetries of linear reconstructions.Notably, LLE mapsits inputs into a single global coordinatesystemof lower dimensionality, and its optimizations— thoughcapableof generatinghighly nonlinearembeddings—donot involve localminima.We illustratethemethodon imagesof lips usedin audiovisual speechsynthesis.


RECENT DEVELOPMENTS IN PAPER CURRENCY RECOGNITION SYSTEM

Currency denomination recognition is one the active research topics at present. And this wide interest is particularly due to the various potential applications it has. Monetary transaction is an integral part of our day to day activities. However, blind people particularly suffer in monetary transactions. They are not able to effectively distinguish between various denominations and are often deceived by other people. Also, a reliable currency recognition system could be used in any sector wherever monetary transaction is of concern. Thus, there is an ardent need to design a system that is helpful in recognition of paper currency notes correctly. Currency denomination detection is a vast area of research and significant progress had been achieved over the years. This paper presents an extensive survey of research on various developments in recent years in identification of currency denomination. A number of techniques applied by various researchers are discussed briefly in order to assess the state of art.


The effect of substrate noise on VCO performance

This study characterizes the effect of substrate noise on a standard component of the RF front end: the voltage controlled oscillator (VCO), as well as evaluating the effect of VCO bias current and guard rings on noise performance. Frequency effects of substrate noise are also examined through the study of VCOs at three different center frequencies: 900 MHz, 2.4 GHz, and 5.2 GHz. Substrate noise is a serious problem that continues to plague mixed-signal designs. Components of the RF frontend are particularly sensitive to substrate noise as the effectiveness of standard isolation techniques degrades at higher frequencies. This study has shown that the phase noise of a VCO is adversely affected by substrate noise. In the extreme, the VCO can lock to the substrate noise. Guard rings can effectively attenuate substrate noise at lower frequencies. For example, at 900 MHz, as much as 25 dB of isolation is observed. At 5.2 GHz, the isolation reduces to 10 dB. Furthermore, the use of guard rings can improve the response of the VCO to injection locking.


Delay-Constrained Hybrid Computation Offloading With Cloud and Fog Computing

To satisfy the delay constraint, the computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single type of offloading destinations, in this paper, we study the hybrid computation offloading problem considering diverse computation and communication capabilities of two types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and computation while completing the computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-computation scheduling problem for the computation offloading to different destinations. To solve the computation offloading problem, we first define a new concept named computation energy efficiency and divide the problem into four subproblems according to the computation energy efficiency of different types of computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form computation offloading solution with the analysis of communication-computation scheduling under the delay constraint. The numerical results show that the proposed hybrid computation offloading solution achieves lower energy consumption than the conventional single-type computation offloading under the delay constraint.


Information Security: End User Behavior and Corporate Culture

Information is the life blood of all modern organizations yet the news media continue to report stories of critical information loss. The purpose of information security is to protect valuable assets, such as information, hardware, software and people. The majority of information security specialists believe that promoting good end user behavior and constraining bad end user behavior is an important component of an effective Information Security Management System (ISMS). Implementing effective information security involves understanding security-related risk, then developing and implementing appropriate controls. In general the better employees are at applying the controls the more secure the organization will be, because even the best designed technical controls and procedures will be of limited value if the staff involved do not understand why they have been implemented and what they are accomplishing. Achieving the required level of understanding usually requires more than an annual awareness training initiative and represents a major challenge for most organizations. In fact, for many organizations it will involve a cultural change to ensure the integration of information security concepts into the organizational culture.


Robust visual inertial odometry using a direct EKF-based approach

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse-distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).


IoT-based continuous glucose monitoring system: A feasibility study

Health monitoring systems based on Internet-of-things (IoT) have been recently introduced to improve the quality of health care services. However, the number of advanced IoT-based continuous glucose monitoring systems is small and the existing systems have several limitations. In this paper we study feasibility of invasive and continuous glucose monitoring (CGM) system utilizing IoT based approach. We designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time glucose, body temperature and contextual data (i.e. environmental temperature) in graphical and human-readable forms to end-users such as patients and doctors. In addition, nRF communication protocol is customized for suiting to the glucose monitoring system and achieving a high level of energy efficiency. Furthermore, we investigate energy consumption of the sensor device and design energy harvesting units for the device. Finally, the work provides many advanced services at a gateway level such as a push notification service for notifying patient and doctors in case of abnormal situations (i.e. too low or too high glucose level). The results show that our system is able to achieve continuous glucose monitoring remotely in real-time. In addition, the results reveal that a high level of energy efficiency can be achieved by applying the customized nRF component, the power management unit and the energy harvesting unit altogether in the sensor device. c © 2017 The Authors. Published by E sevier B.V. i ilit f t f re ce r ra hairs.


Predicting Categorical Emotions by Jointly Learning Primary and Secondary Emotions through Multitask Learning

Detection of human emotions is an essential part of affect-aware human-computer interaction (HCI). In daily conversations, the preferred way of describing affects is by using categorical emotion labels (e.g., sad, anger, surprise). In categorical emotion classification, multiple descriptors (with different degrees of relevance) can be assigned to a sample. Perceptual evaluations have relied on primary and secondary emotions to capture the ambiguous nature of spontaneous recordings. Primary emotion is the most relevant category felt by the evaluator. Secondary emotions capture other emotional cues also conveyed in the stimulus. In most cases, the labels collected from the secondary emotions are discarded, since assigning a single class label to a sample is preferred from an application perspective. In this work, we take advantage of both types of annotations to improve the performance of emotion classification. We collect the labels from all the annotations available for a sample and generate primary and secondary emotion labels. A classifier is then trained using multitask learning with both primary and secondary emotions. We experimentally show that considering secondary emotion labels during the learning process leads to relative improvements of 7.9% in F1-score for an 8-class emotion classification task.


A New Concept Varistor With Epoxy/Microvaristor Composite

A new concept composite varistor is proposed in this paper. This composite varistor has the chains of microvaristors, which are formed by applying an electric field during the curing process and work as current paths. This composite varistor shows superior nonlinear voltage-current characteristics despite small microvaristor content and has a good response against a steep voltage surge of several tens of nanoseconds in front time. In comparison with ceramic varistors where shapes are usually formed at their sintering temperatures of around 1000°C, the composite varistor has an advantage in flexible shape because its shape is formed by curing the base polymer at a low temperature.


On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Model selection strategies for machine learning algorithm s typically involve the numerical optimisation of an appropriate model selection criterion, ofte n based on an estimator of generalisation performance, such as k-fold cross-validation. The error of such an estimator can b e broken down into bias and variance components. While unbiasedness is oft en cited as a beneficial quality of a model selection criterion, we demonstrate that a low varian ce is at least as important, as a nonnegligible variance introduces the potential for over-fitt ing in model selection as well as in training the model. While this observation is in hindsight perhaps rat her obvious, the degradation in performance due to over-fitting the model selection criterion can b e surprisingly large, an observation that appears to have received little attention in the machine lea rning literature to date. In this paper, we show that the effects of this form of over-fitting are often of c mparable magnitude to differences in performance between learning algorithms, and thus canno t be ignored in empirical evaluation. Furthermore, we show that some common performance evaluati on practices are susceptible to a form of selection bias as a result of this form of over-fitting and hence are unreliable. We discuss methods to avoid over-fitting in model selection and sub sequent selection bias in performance evaluation, which we hope will be incorporated into best pra ctice. While this study concentrates on cross-validation based model selection, the findings are quit general and apply to any model selection practice involving the optimisation of a model se lection criterion evaluated over a finite sample of data, including maximisation of the Bayesian evid nce and optimisation of performance bounds.


Design of a 1.8V-input 6.5V-output digital level shifter for trimming application

A 1.8V input, 6.5V output step-up digital level shifter is proposed. It is based on contention mitigated digital level shifter (CMLS) but it uses single supply only instead of two. It uses a complete, stand-alone, and unregulated voltage charge pump circuit that generates a 6.5V output voltage from a 1.8V input. Charge pump output is then used as high-side supply (VDDH) of the step-up level shifter, while the low-side supply (VDDl) is externally-supplied 1.8V. Compared to the conventional level shifter where two separate supply voltages (VDDl and VDDH) are required, the proposed design uses a single supply to shifted-up a digital signals. This technique simplifies the user-end power supply requirement of the system, thus the proposed level shifter design is applicable in many low-power and power-constraint applications such as in mobile and portable devices application. The proposed design is implemented using 0.35-μm TSMC CMOS technology. The schematic and the nestlist have been designed and extracted using LTSPICE tool; while the simulation has been carried out using the HSPICE circuit simulator.


Lazy Theta*: Any-Angle Path Planning and Path Length Analysis in 3D

Grids with blocked and unblocked cells are often used to represent continuous 2D and 3D environments in robotics and video games. The shortest paths formed by the edges of 8neighbor 2D grids can be up to ≈ 8% longer than the shortest paths in the continuous environment. Theta* typically finds much shorter paths than that by propagating information along graph edges (to achieve short runtimes) without constraining paths to be formed by graph edges (to find short “any-angle” paths). We show in this paper that the shortest paths formed by the edges of 26-neighbor 3D grids can be ≈ 13% longer than the shortest paths in the continuous environment, which highlights the need for smart path planning algorithms in 3D. Theta* can be applied to 3D grids in a straight-forward manner, but it performs a line-of-sight check for each unexpanded visible neighbor of each expanded vertex and thus it performs many more line-of-sight checks per expanded vertex on a 26-neighbor 3D grid than on an 8-neighbor 2D grid. We therefore introduce Lazy Theta*, a variant of Theta* which uses lazy evaluation to perform only one line-of-sight check per expanded vertex (but with slightly more expanded vertices). We show experimentally that Lazy Theta* finds paths faster than Theta* on 26-neighbor 3D grids, with one order of magnitude fewer line-of-sight checks and without an increase in path length.