title: Deep residual coalesced convolutional network for efficient semantic road segmentation

text: This paper proposes a deep learning-based efficient and compact solution for road scene segmentation problem, named deep residual coalesced convolutional network (RCC-Net). Initially, the RCC-Net performs dimensionality reduction to compress and extract relevant features, from which it is subsequently delivered to the encoder. The encoder adopts the residual network style for efficient model size. In the core of each residual network, three different convolutional layers are simultaneously coalesced for obtaining broader information. The decoder is then altered to upsample the encoder for pixel-wise mapping from the input images to the segmented output. Experimental results reveal the efficacy of the proposed network over the state-of-the-art methods and its capability to be deployed in an average system.


title: Text-Independent Writer Identification via CNN Features and Joint Bayesian

text: This paper proposes a novel method for offline text-independent writer identification by using convolutional neural network (CNN) and joint Bayesian, which consists of two stages, i.e. feature extraction and writer identification. In the stage of feature extraction, since a large number of data is essential to train an effective CNN model with high generalizability and the amount of handwriting is limited in writer identification, a data augmentation technique is first developed to generate thousands of handwriting images for each writer. Then a deep CNN network is designed to extract discriminative features to represent the properties of different writing styles, which is trained by using the generated handwriting images. In the stage of writer identification, the training dataset is used to train the CNN model for feature extraction and the joint Bayesian technique is employed to accomplish the task of writer identification based on the extracted CNN features. The proposed method is tested on two standard benchmark datasets, i.e. ICDAR2013 and CVL dataset. Experimental results demonstrate that the proposed method gets the best performance compared to the state-of-the-art approaches.


title: Mental health of hospital consultants: the effects of stress and satisfaction at work

text:
BACKGROUND
Burnout and psychiatric morbidity among gastroenterologists, surgeons, radiologists, and oncologists in the UK have been estimated by means of a questionnaire-based survey. The relationship between consultants' mental health and their job stress and satisfaction, as well as their job and demographic characteristics, were also examined.

METHODS
Psychiatric morbidity was estimated using the 12-item General Health Questionnaire. The three components of burnout-emotional exhaustion, depersonalization, and low personal accomplishment-were assessed using the Maslach Burnout Inventory. Job stress and satisfaction were measured using study-specific questions.

FINDINGS
Of 1133 consultants, 882 (78%) returned questionnaires. The estimated prevalence of psychiatric morbidity was 27%, with no significant differences between the four specialist groups. Radiologists reported the highest level of burnout in terms of low personal accomplishment. Job satisfaction significantly protected consultants' mental health against job stress. Three sources of stress were associated with both burnout and psychiatric morbidity; feeling overloaded, and its effect on home life; feeling poorly managed and resourced; and dealing with patients' suffering. Burnout was also associated with low satisfaction in three domains: relationships with patients, relatives and staff; professional status/esteem; intellectual stimulation. In addition, being aged 55 years or less and being single were independent risk factors for burnout. Burnout was also more prevalent among consultants who felt insufficiently trained in communication and management skills.

INTERPRETATION
Consultants' mental health is likely to be protected against the high demands of medical practice by maintaining or enhancing job satisfaction, and by providing training in communication and management skills.


title: Real-time traffic sign recognition from video by class-specific discriminative features

text: Article history: Received 1 August 2007 Received in revised form 22 May 2009 Accepted 26 May 2009


title: Progressive skyline computation in database systems

text: The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing algorithms, however, have some serious shortcomings which limit their applicability in practice. In this article we develop branch-and-bound skyline (BBS), an algorithm based on nearest-neighbor search, which is I/O optimal, that is, it performs a single access only to those nodes that may contain skyline points. BBS is simple to implement and supports all types of progressive processing (e.g., user preferences, arbitrary dimensionality, etc). Furthermore, we propose several interesting variations of skyline computation, and show how BBS can be applied for their efficient processing.


title: The Skyline Operator

text: We propose to extend database systems by a Skyline operation. This operation $filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SQL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.


title: PEDOT:PSS "Wires" Printed on Textile for Wearable Electronics.

text: Herein, the fabrication of all-organic conductive wires is demonstrated by utilizing patterning techniques such as inkjet printing and sponge stencil to apply poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS) onto nonwoven polyethylene terephthalate (PET) fabric. The coating of the conducting polymer is only present on the surface of the substrate (penetration depth ∼ 200 μm) to retain the functionality and wearability of the textile. The wires fabricated by different patterning techniques provide a wide range of resistance, i.e., tens of kΩ/□ to less than 2 Ω/□ that allows the resistance to be tailored to a specific application. The sheet resistance is measured to be as low as 1.6 Ω/□, and the breakdown current is as high as 0.37 A for a 1 mm wide line. The specific breakdown current exceeds the previously reported values of macroscopic carbon nanotube based materials. Simple circuits composed of the printed wires are demonstrated, and resistance of the circuit from the measurement agrees with the calculated value based on Kirchhoff's rules. Additionally, the printed PEDOT:PSS wires show less than 6.2% change in sheet resistance after three washing and drying cycles using detergent.


title: A neurocognitive approach to understanding the neurobiology of addiction

text: Recent concepts of addiction to drugs (e.g. cocaine) and non-drugs (e.g. gambling) have proposed that these behaviors are the product of an imbalance between three separate, but interacting, neural systems: an impulsive, largely amygdala-striatum dependent, neural system that promotes automatic, habitual and salient behaviors; a reflective, mainly prefrontal cortex dependent, neural system for decision-making, forecasting the future consequences of a behavior, and inhibitory control; and the insula that integrates interoception states into conscious feelings and into decision-making processes that are involved in uncertain risk and reward. These systems account for poor decision-making (i.e. prioritizing short-term consequences of a decisional option) leading to more elevated addiction risk and relapse. This article provides neural evidence for this three-systems neural model of addiction.


title: Analysis of Word Embeddings and Sequence Features for Clinical Information Extraction

text: This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.


title: Fog computing: Security issues, solutions and robust practices

text: Fog Computing (FC) has extended the services of cloud computing to the edge of the network. It inherits some of the characteristics from cloud computing but FC also have some distinguished features such as geo-distribution, location awareness and low latency. Along with the inherited characteristics, it also inherits the issues and problems of cloud computing like energy efficiency, resource management and security issues. This paper presents the critical analysis of the fog architecture with respect to security. The state of the art work done since 2012 is critical analyzed on the bases of security techniques and security threats. We grouped the existing security techniques on the bases of security goals achieved by each. It will provide a clear and comprehensive distinction between the security areas explored and those, which still need researchers' attention.


title: Specification Inference from Demonstrations

text: Learning from expert demonstrations has received a lot of attention in artificial intelligence and machine learning. The goal is to infer the underlying reward function that an agent is optimizing given a set of observations of the agent’s behavior over time in a variety of circumstances, the system state trajectories, and a plant model specifying the evolution of the system state for different agent’s actions. The system is often modeled as a Markov decision process (Puterman 2014), that is, the next state depends only on the current state and agent’s action, and the the agent’s choice of action depends only on the current state. While the former is a Markovian assumption on the evolution of system state, the later assumes that the target reward function is itself Markovian. In this work, we explore learning a class of non-Markovian reward functions, known in the formal methods literature as specifications. These specifications offer better composition, transferability, and interpretability. We then show that inferring the specification can be done efficiently without unrolling the transition system. We demonstrate on a 2-d grid world example.


title: Comparative study of trust and reputation systems for wireless sensor networks

text: Wireless sensor networks (WSNs) are emerging as useful technology for information extraction from the surrounding environment by using numerous small-sized sensor nodes that are mostly deployed in sensitive, unattended, and (sometimes) hostile territories. Traditional cryptographic approaches are widely used to provide security in WSN. However, because of unattended and insecure deployment, a sensor node may be physically captured by an adversary who may acquire the underlying secret keys, or a subset thereof, to access the critical data and/or other nodes present in the network. Moreover, a node may not properly operate because of insufficient resources or problems in the network link. In recent years, the basic ideas of trust and reputation have been applied to WSNs to monitor the changing behaviors of nodes in a network. Several trust and reputation monitoring (TRM) systems have been proposed, to integrate the concepts of trust in networks as an additional security measure, and various surveys are conducted on the aforementioned system. However, the existing surveys lack a comprehensive discussion on trust application specific to the WSNs. This survey attempts to provide a thorough understanding of trust and reputation as well as their applications in the context of WSNs. The survey discusses the components required to build a TRM and the trust computation phases explained with a study of various security attacks. The study investigates the recent advances in TRMs and includes a concise comparison of various TRMs. Finally, a discussion on open issues and challenges in the implementation of trust-based systems is also presented. Copyright © 2012 John Wiley & Sons, Ltd.


title: Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.

text: Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals' brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.


title: InverseRenderNet: Learning single image inverse rendering

text: We show how to train a fully convolutional neural network to perform inverse rendering from a single, uncontrolled image. The network takes an RGB image as input, regresses albedo and normal maps from which we compute lighting coefficients. Our network is trained using large uncontrolled image collections without ground truth. By incorporating a differentiable renderer, our network can learn from self-supervision. Since the problem is ill-posed we introduce additional supervision: 1. We learn a statistical natural illumination prior, 2. Our key insight is to perform offline multiview stereo (MVS) on images containing rich illumination variation. From the MVS pose and depth maps, we can cross project between overlapping views such that Siamese training can be used to ensure consistent estimation of photometric invariants. MVS depth also provides direct coarse supervision for normal map estimation. We believe this is the first attempt to use MVS supervision for learning inverse rendering.


title: Design of ultra high-speed CMOS CML buffers and latches

text: A comprehensive study of ultra high-speed current-mode logic (CML) buffers and regenerative CML latches will be illustrated. A new design procedure to systematically design a chain of tapered CML buffers is proposed. Next, a new 20GHz regenerative latch circuit will be introduced. Experimental results show a higher performance for the new latch architecture compared to a conventional CML latch circuit at ultra high-frequencies. It is also shown, both through the experiments and by using efficient analytical models, why CML buffers are better than CMOS inverters in high-speed low-voltage applications.


title: Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images

text: Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.


title: Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization

text: Can one parallelize complex exploration– exploitation tradeoffs? As an example, consider the problem of optimal highthroughput experimental design, where we wish to sequentially design batches of experiments in order to simultaneously learn a surrogate function mapping stimulus to response and identify the maximum of the function. We formalize the task as a multiarmed bandit problem, where the unknown payoff function is sampled from a Gaussian process (GP), and instead of a single arm, in each round we pull a batch of several arms in parallel. We develop GP-BUCB, a principled algorithm for choosing batches, based on the GP-UCB algorithm for sequential GP optimization. We prove a surprising result; as compared to the sequential approach, the cumulative regret of the parallel algorithm only increases by a constant factor independent of the batch size B. Our results provide rigorous theoretical support for exploiting parallelism in Bayesian global optimization. We demonstrate the effectiveness of our approach on two real-world applications.


title: Using Computer Programming to Enhance Science Learning for 5th Graders in Taipei

text: The purpose of this study is to evaluate the effectiveness of using Scratch Programming in science learning for 5th graders in Taipei. A quasi-experimental design with a single group was used in this study. 96 fifth graders at an elementary school in Taipei participated in 15 weeks of Scratch Programming activities to enrich computer learning during their science learning. Three research instruments were used in this study, including a logical thinking test, a problem solving test, and a learning questionnaire. The results were subjected to t-test and frequency analysis. The findings of this study include (1) the outcomes of the logical thinking test and the problem solving test indicate that students had better performance in logical thinking and problem solving, (2) feedback from the study questionnaire indicates that more than 71.4% of students favored using Scratch Programming in their computer learning, and more than 58.4% of students favored designing Scratch Programming in their science learning, (3) more than 54.7% of students reported that they are willing to do more similar computer programming projects for other science units in the future. In view of the above findings, this study concludes that Scratch Programming is effective for 5th graders in Taipei.


title: New E-Commerce User Interest Patterns

text: The number of online purchases is increasing constantly. Companies have recognized the related opportunities and they are using online channels progressively. In order to acquire potential customers, companies often try to gain a better understanding through the use of web analytics. One of the most useful sources are web log files. Basically, these provide an abundance of important information about the user behavior on a website, such as the path or access time. Mining this so-called clickstream data in the most comprehensive way has become an important task in order to predict the behavior of online customers, optimize webpages, and give personalized recommendations. As the number of customers constantly rises, the volume of the generated data log files also increases, both in terms of size and quantity. Thus, for certain companies, the currently used technologies are no longer sufficient. In this work, a comprehensive workflow will be proposed using a clustering algorithm in a Hadoop ecosystem to investigate user interest patterns. The complete workflow will be demonstrated on an application scenario of one of the largest business-to-business (B2B) electronic commerce websites in Germany. Furthermore, an experimental evaluation method will be applied to verify the applicability and efficiency of the used algorithm, along with the associated framework.


title: S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

text: Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs), called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “ ||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.


title: A requirements monitoring framework for enterprise systems

text: Requirements compliant software is becoming a necessity. Fewer and fewer organizations will run their critical transactions on software that has no visible relationship to its requirements. Businesses wish to see their software being consistent with their policies. Moreover, partnership agreements are pressuring less mature organizations to improve their systems. Businesses that rely on web services, for example, are vulnerable to the problems of their web service providers. While electronic commerce has increased the speed of on-line transactions, the technology for monitoring requirements compliance—especially for transactions—has lagged behind. To address the requirements monitoring problem for enterprise information systems, we integrate techniques for requirements analysis and software execution monitoring. Our framework assists analysts in the development of requirements monitors for enterprise services. The deployed system raises alerts when services succeed or fail to satisfy their specified requirements, thereby making software requirements visible. The framework usage is demonstrated with an analysis of ebXML marketplace specifications. An analyst applies goal analysis to discover potential service obstacles, and then derives requirements monitors and a distributed monitoring system. Once deployed, the monitoring system provides alerts when obstacles occur. A summary of the framework implementation is presented, along with analysis of two monitor component implementations. We conclude that the approach implemented in the framework, called ReqMon, provides real-time feedback on requirements satisfaction, and thereby provides visibility into requirements compliance of enterprise information systems.


title: Differential Dataflow

text: Existing computational models for processing continuously changing input data are unable to efficiently support iterative queries except in limited special cases. This makes it difficult to perform complex tasks, such as social-graph analysis on changing data at interactive timescales, which would greatly benefit those analyzing the behavior of services like Twitter. In this paper we introduce a new model called differential computation, which extends traditional incremental computation to allow arbitrarily nested iteration, and explain—with reference to a publicly available prototype system called Naiad—how differential computation can be efficiently implemented in the context of a declarative dataparallel dataflow language. The resulting system makes it easy to program previously intractable algorithms such as incrementally updated strongly connected components, and integrate them with data transformation operations to obtain practically relevant insights from real data streams.


title: ioCane: A Smart-Phone and Sensor-Augmented Mobility Aid for the Blind

text: We present the design, implementation, and early results of ioCane, a mobility aid for blind cane users that uses detachable cane-mounted ultrasonic sensors connected to a circuit board to send contextual data wirelessly to an Android phone application. The system uses the built-in mobile phone modalities of vibrations and chimes to alert the user to object height and proximity. We believe this plug-and-play solution for visually impaired users has the potential to enhance user mobility and object avoidance with a minimal learning curve. A pilot study testing the performance of the ioCane with blind cane users showed a 47.3% improvement in obstacle avoidance. To our knowledge, the ioCane is the first sensor-based mobility assistance system to integrate natively with a mobile phone without any modifications to the phone or the system, as well as the first system of its kind to be evaluated by actual visually-impaired cane users. ACM Classification


title: "Soft" Exoskeletons for Upper and Lower Body Rehabilitation - Design, Control and Testing

text: The basic concepts for exoskeletal systems have been suggested for some time with applications ranging from construction, manufacturing and mining to rescue and emergency services. In recent years, research has been driven by possible uses in medical/rehabilitation and military applications. Yet there are still significant barriers to the effective use and exploitation of this technology. Among the most pertinent of these factors is the power and actuation system and its impact of control, strength, speed and, perhaps most critically, safety. This work describes the design, construction and testing of an ultra low-mass, full-body exoskeleton system having seven degrees of freedom (DOFs) for the upper limbs and five degrees of freedom (DOFs) for each of the lower limbs. This low mass is primarily due to the use of a new range of pneumatic muscle actuators as the power source for the system. The work presented will show how the system takes advantage of the inherent controllable compliance to produce a unit that is powerful, providing a wide range of functionality (motion and forces over an extended range) in a manner that has high safety integrity for the user. The general layout of both the upper and the lower body exoskeleton is presented together with results from preliminary experiments to demonstrate the potential of the device in limb retraining, rehabilitation and power assist (augmentation) operations.


title: Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose Estimation

text: In this paper we present our winning entry at the 2018 ECCV PoseTrack Challenge on 3D human pose estimation. Using a fully-convolutional backbone architecture, we obtain volumetric heatmaps per body joint, which we convert to coordinates using soft-argmax. Absolute person center depth is estimated by a 1D heatmap prediction head. The coordinates are back-projected to 3D camera space, where we minimize the L1 loss. Key to our good results is the training data augmentation with randomly placed occluders from the Pascal VOC dataset. In addition to reaching first place in the Challenge, our method also surpasses the state-of-the-art on the full Human3.6M benchmark when considering methods that use no extra pose datasets in training. Code for applying synthetic occlusions is availabe at https://github.com/ isarandi/synthetic-occlusion.


title: The effect of site quality on repurchase intention in Internet shopping through mediating variables: The case of university students in South Korea

text: We performed a study to determine the influence that site quality has on repurchase intention of Internet shopping through customer satisfaction, customer trust, and customer commitment. Appropriate measures were developed and tested on 230 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The results of the empirical analysis confirmed that site quality can be conceptualized as a composite of six dimensions of shopping convenience, site design, information usefulness, transaction security, payment system, and customer communication. Second, site quality positively affected customer satisfaction and customer trust, but did not affect customer commitment and repurchase intention. Third, site quality can affect repurchase intention by enhancing or attenuating customer satisfaction, customer trust, and customer commitment in online transaction situation. The mediating effect of customer satisfaction, customer trust, and customer commitment between site quality and repurchase intention is identified. Fourth, site quality indirectly affected customer commitment through customer satisfaction. Customer satisfaction indirectly affected repurchase intention through customer trust and customer commitment. Thus, it is found that site quality can be a very important factor to enhance repurchase intention in the customer perspective. © 2013 Elsevier Ltd. All rights reserved.


title: DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging

text: Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec—two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple k-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.


title: QUASY: Quantitative Synthesis Tool

text: We present the tool Q UASY, a quantitative synthesis tool. Q UASY takes qualitative and quantitative specifications and automatic ally onstructs a system that satisfies the qualitative specification and optimizes the qu antitative specification, if such a system exists. The user can choose between a system that satisfies and optimi zes the specifications (a) under all possible environment behaviors or (b) under th most-likely environment behaviors given as a probability distribution on the po ssible input sequences. QUASY solves these two quantitative synthesis problems by reduct ion to instances of 2-player games and Markov Decision Processes (MDPs) with qua ntitative winning objectives. QUASY can also be seen as a game solver for quantitative games. Most notable, it can solve lexicographic mean-payoff games with 2 players, MDPs with meanpayoff objectives, and ergodic MDPs with mean-payoff parit y objectives.


title: Gist: A Solver for Probabilistic Games

text: Gist is a tool that (a) solves the qualitative analysis problem of turn-based probabilistic games with ω-regular objectives; and (b) synthesizes reasonable environment assumptions for synthesis of unrealizable specifications. Our tool provides the first and efficient implementations of several reduction-based techniques to solve turn-based probabilistic games, and uses the analysis of turn-based probabilistic games for synthesizing environment assumptions for unrealizable specifications.


title: Playing Stochastic Games Precisely

text: We study stochastic two-player games where the goal of one player is to achieve precisely a given expected value of the objective function, while the goal of the opponent is the opposite. Potential applications for such games include controller synthesis problems where the optimisation objective is to maximise or minimise a given payoff function while respecting a strict upper or lower bound, respectively. We consider a number of objective functions including reachability, ω-regular, discounted reward, and total reward. We show that precise value games are not determined, and compare the memory requirements for winning strategies. For stopping games we establish necessary and sufficient conditions for the existence of a winning strategy of the controller for a large class of functions, as well as provide the constructions of compact strategies for the studied objectives.


title: PRISM 4.0: Verification of Probabilistic Real-Time Systems

text: This paper describes a major new release of the PRISM probabilistic model checker, adding, in particular, quantitative verification of (priced) probabilistic timed automata. These model systems exhibiting probabilistic, nondeterministic and real-time characteristics. In many application domains, all three aspects are essential; this includes, for example, embedded controllers in automotive or avionic systems, wireless communication protocols such as Bluetooth or Zigbee, and randomised security protocols. PRISM, which is open-source, also contains several new components that are of independent use. These include: an extensible toolkit for building, verifying and refining abstractions of probabilistic models; an explicit-state probabilistic model checking library; a discrete-event simulation engine for statistical model checking; support for generation of optimal adversaries/strategies; and a benchmark suite.


title: The Ins and Outs of the Probabilistic Model Checker MRMC

text: The Markov Reward Model Checker (MRMC) is a software tool for verifying properties over probabilistic models. It supports PCTL and CSL model checking, and their reward extensions. Distinguishing features of MRMC are its support for computing timeand reward-bounded reachability probabilities, (property-driven) bisimulation minimization, and precise on-the-fly steady-state detection. Recent tool features include time-bounded reachability analysis for uniform CTMDPs and CSL model checking by discrete-event simulation. This paper presents the tool’s current status and its implementation details.


title: Connectivity in a citation network: The development of DNA theory

text: The study of citation networks for both articles and journals is routine. In general, these analyses proceed by considering the similarity of articles or journals and submitting the set of similarity measures to some clustering or scaling procedure. Two common methods are found in bibliomettic coupling, where two citing articles are similar to the extent they cite the same literature, and co-citation analysis where cited articles are similar to the extent they are cited by the same citing articles. Methods based on structural and regular equivalence also seek to partition the article based on their positional location. Such methods have in common focus on the articles and partitions of them. We propose a quite different approach where the connective threads through a network are preserved and the focus is on the links in the network rather than on the nodes. Variants of the depth first search algorithm are used to detect and represent the mainstream of the literature of a clearly delineated area of scientific research. The specific citation network is one that consists of ties among the key events and papers that lead to the discovery and modeling of DNA together with the final experimental confirmation of its representation.


title: Semantic similarity based evaluation for C programs through the use of symbolic execution

text: Automatic grading of programs has existed in various fields for many years ago. Within this paper, we propose a method for evaluating C programs. Two approaches are distinguished in this context: static and dynamic analysis methods. Unlike the dynamic analysis that requires an executable program to be evaluated, static analysis could evaluate a program even if it is not totally correct. The proposed method is based on static analysis of programs. It consists of comparing the evaluated program with the evaluator-provided program through their Control Flow Graphs. Here, the great challenge is to deal with the multiplicity of solutions that exists for the same programming problem. As a solution to this weakness, we propose an innovative similarity measure that compares two programs according to their semantic executions. In fact, the evaluated program is compared to the evaluator-provided program called model program by using the symbolic execution technique. The experimentations presented in this work are performed by using a basic implementation of the proposed method. The obtained results reveal a promising realization in the field of automated evaluation of programs. They also show that the proposed method guarantees a considerable approximation to the human program evaluation.


title: Topic Models for Image Annotation and Text Illustration

text: Image annotation, the task of automatically generating description words for a picture, is a key component in various image search and retrieval applications. Creating image databases for model development is, however, costly and time consuming, since the keywords must be hand-coded and the process repeated for new collections. In this work we exploit the vast resource of images and documents available on the web for developing image annotation models without any human involvement. We describe a probabilistic model based on the assumption that images and their co-occurring textual data are generated by mixtures of latent topics. We show that this model outperforms previously proposed approaches when applied to image annotation and the related task of text illustration despite the noisy nature of our dataset.


title: Home telehealth - Current state and future trends

text:
OBJECTIVE
The purpose of this paper is to give an overview about the state of the art in research on home telehealth in an international perspective.

METHOD
The study is based on a review of the scientific literature published between 1990 and 2003 and retrieved via Medline in January/February 2004. All together, the abstracts of 578 publications have been analyzed.

RESULTS
The majority of publications (44%) comes from the United States, followed by UK and Japan. Most publications deal with vital sign parameter (VSP) measurement and audio/video consultations ("virtual visits"). Publications about IT tools for improved information access and communication as well as decision support for staff, patients and relatives are relatively sparse. Clinical application domains are mainly chronic diseases, the elderly population and paediatrics.

CONCLUSIONS
Internationally, we observe a trend towards tools and services not only for professionals but also for patients and citizens. However, their impact on the patient-provider relationship and their design for special user groups, such as elderly and/or disabled needs to be further explored. In general, evaluation studies are rare and further research is critical to determine the impacts and benefits, and limitations, of potential solutions and to overcome a number of hinders and restrictions, such as - the lack of standards to combine incompatible information systems; - the lack of an evaluation framework considering legal, ethical, organisational, clinical, usability and technical aspects; - the lack of proper guidelines for practical implementation of home telehealth solutions.


title: Intelligent stock data prediction using predictive data mining techniques

text: Cloud computing is the one of the admired paradigms of current era, which facilitates the users with on demand services and pay as you use services. It has tremendous applications in almost every sphere such as education, gaming, social networking, transportation, medical, business, stock market, pattern matching, etc. Stock market is such an industry where lots of data is generated and benefits are reaped on the basis of accurate prediction. So prediction is a vital part of stock market. Therefore, an attempt is being made to predict the stock market based on the given data set of stock market along with some features; using the techniques available for predictive data mining. Machine learning is one of the upcoming trends of data mining; hence few machine learning algorithms have been used such as Decision tree, Linear model, Random forest and further their results have been compared using the classification evaluation parameters such as H, AUC, ROC, TPR, FPR, etc. Random forest have been consider as the most effective model as it yield the highest accuracy of 54.12% whereas decision tree and linear model gives the accuracy of 51.87% and 52.83% respectively.


title: New VF-power system architecture and evaluation for future aircraft

text: Conventional aircraft power system is a constant-frequency (CF) supply based on mechanical-regulated constant-speed mechanism that has relatively low efficiency. Replacing the CF system with variable-frequency (VF) power improves overall efficiency, and reduce the system's weight and volume. However, this creates a new tier of requirements and design challenges. Novel VF-power architecture is developed with minimization of the power losses throughout the stages of power conversions. Optimal partitioning and grouping of onboard AC loads has been discussed with specific system data. New VF-input multi-functional power converters are also briefly discussed.


title: A State-of-the-Art of Semantic Change Computation

text: This paper reviews state-of-the-art of one emerging field in computational linguistics — semantic change computation, proposing a framework that summarizes the literature by identifying and expounding five essential components in the field: diachronic corpus, diachronic word sense characterization, change modelling, evaluation data and data visualization. Despite the potential of the field, the review shows that current studies are mainly focused on testifying hypotheses proposed in theoretical linguistics and that several core issues remain to be solved: the need for diachronic corpora of languages other than English, the need for comprehensive evaluation data for evaluation, the comparison and construction of approaches to diachronic word sense characterization and change modelling, and further exploration of data visualization techniques for hypothesis


title: A DC-90 GHz 4-Vpp differential linear driver in a 0.13 μm SiGe:C BiCMOS technology for optical modulators

text: In this paper, a linear driver for optical modulators in a 0.13 μm SiGe:C BiCMOS technology with fT/fmax of 300/500 GHz is presented. The driver is implemented following a distributed amplifier topology in a differential manner. In a 50-Ω environment, the circuit delivers a maximum differential output amplitude of 4 Vpp, featuring a small-signal gain of 13 dB and 3-dB bandwidth of 90 GHz. Time-domain measurements using OOK (up to 56 Gb/s) and PAM-4 (at 30Gbaud) are performed, demonstrating the maximum output swing and linearity of the driver. The output power to power dissipation ratio is 3.6%. To the best knowledge of the authors, this is the first time a linear driver for optical modulators demonstrates such bandwidth.


title: FENNEL: streaming graph partitioning for massive scale graphs

text:
Balanced graph partitioning in the streaming setting is a key problem to enable scalable and efficient computations on massive graph data such as web graphs, knowledge graphs, and graphs arising in the context of online social networks. Two families of heuristics for graph partitioning in the streaming setting are in wide use: place the newly arrived vertex in the cluster with the largest number of neighbors or in the cluster with the least number of non-neighbors.
 In this work, we introduce a framework which unifies the two seemingly orthogonal heuristics and allows us to quantify the interpolation between them. More generally, the framework enables a well principled design of scalable, streaming graph partitioning algorithms that are amenable to distributed implementations. We derive a novel one-pass, streaming graph partitioning algorithm and show that it yields significant performance improvements over previous approaches using an extensive set of real-world and synthetic graphs.
 Surprisingly, despite the fact that our algorithm is a one-pass streaming algorithm, we found its performance to be in many cases comparable to the de-facto standard offline software METIS and in some cases even superiror. For instance, for the Twitter graph with more than 1.4 billion of edges, our method partitions the graph in about 40 minutes achieving a balanced partition that cuts as few as 6.8% of edges, whereas it took more than 81/2 hours by METIS to produce a balanced partition that cuts 11.98% of edges. We also demonstrate the performance gains by using our graph partitioner while solving standard PageRank computation in a graph processing platform with respect to the communication cost and runtime.


title: A Language Modeling Approach to Predicting Reading Difficulty

text: We demonstrate a new research approach to the problem of predicting the reading difficulty of a text passage, by recasting readability in terms of statistical language modeling. We derive a measure based on an extension of multinomial naïve Bayes classification that combines multiple language models to estimate the most likely grade level for a given passage. The resulting classifier is not specific to any particular subject and can be trained with relatively little labeled data. We perform predictions for individual Web pages in English and compare our performance to widely-used semantic variables from traditional readability measures. We show that with minimal changes, the classifier may be retrained for use with French Web documents. For both English and French, the classifier maintains consistently good correlation with labeled grade level (0.63 to 0.79) across all test sets. Some traditional semantic variables such as type-token ratio gave the best performance on commercial calibrated test passages, while our language modeling approach gave better accuracy for Web documents and very short passages (less than 10 words).


title: Dynamic projection mapping onto a deformable object with occlusion based on high-speed tracking of dot marker array

text: In recent years, projection mapping has attracted much attention in a variety of fields. Generally, however, the objects in projection mapping are limited to rigid and static or quasistatic objects. Dynamic projection mapping onto a deformable object could remarkably expand the possibilities. In order to achieve such a projection mapping, it is necessary to recognize the deformation of the object even when it is occluded. However, it is still a challenging problem to achieve this task in real-time with low latency. In this paper, we propose an efficient, high-speed tracking method utilizing high-frame-rate imaging. Our method is able to track an array of dot markers arranged on a deformable object even when there is external occlusion caused by the user interaction and self-occlusion caused by the deformation of the object itself. Additionally, our method can be applied to a stretchable object. Dynamic projection mapping with our method showed robust and consistent display onto a sheet of paper and cloth with a tracking performance of about 0.2 ms per frame, with the result that the projected pattern appeared to be printed on the deformable object.


title: Novel Online Methods for Time Series Segmentation

text: To efficiently and effectively mine massive amounts of data in the time series, approximate representation of the data is one of the most commonly used strategies. Piecewise linear approximation is such an approach, which represents a time series by dividing it into segments and approximating each segment with a straight line. In this paper, we first propose a new segmentation criterion that improves computing efficiency. Based on this criterion, two novel online piecewise linear segmentation methods are developed, the feasible space window method and the stepwise feasible space window method. The former usually produces much fewer segments and is faster and more reliable in the running time than other methods. The latter can reduce the representation error with fewer segments. It achieves the best overall performance on the segmentation results compared with other methods. Extensive experiments on a variety of real-world time series have been conducted to demonstrate the advantages of our methods.


title: Broadcast Encryption with Traitor Tracing

text: In this thesis, we look at definitions and black-box constructions with efficient instantiations for broadcast encryption and traitor tracing. We begin by looking at the security notions for broadcast encryption found in the literature. Since there is no easy way to compare these existing notions, we propose a framework of security notions for which we establish relationships. We then show where existing notions fit within this framework. Second, we present a black-box construction of a decentralized dynamic broadcast encryption scheme. This scheme does not rely on any trusted authorities, and new users can join at any time. It achieves the strongest security notion based on the security of its components and has an efficient instantiation that is fully secure under the DDH assumption in the standard model. Finally, we give a black-box construction of a message-based traitor tracing scheme, which allows tracing not only based on pirate decoders but also based on watermarks contained in a message. Our scheme is the first one to obtain the optimal ciphertext rate of 1 asymptotically. We then show that at today’s data rates, the scheme is already practical for standard choices of values.


title: Cross-domain security of cyber-physical systems

text: The interaction between the cyber domain and the physical domain components and processes can be leveraged to enhance the security of the cyber-physical system. In order to do so, we must first analyze various cyber domain and physical domain information flows, and characterize the relation between them using model functions. In this paper, we present a notion of cross-domain security of cyber-physical systems, whereby we present a security analysis framework that can be used for generating novel cross-domain attack models, attack detection methods, etc. We demonstrate how information flows such as discrete domain signal flows and continuous domain energy flows in the cyber and physical domain can be used to generate model functions using data-driven estimation, and use this model functions for performing various cross-domain security analysis. We also demonstrate the practical applicability of the cross-domain security analysis framework using the cyber-physical manufacturing system as a case study.


title: Facial reconstruction: soft tissue thickness values for South African black females.

text: In forensic science, investigators frequently have to deal with unidentified skeletonised remains. When conventional methods of identification are unsuccessful, forensic facial reconstruction (FFR) may be used, often as a last resort, to assist the process. FFR relies on the relationships between the facial features, subcutaneous soft tissues and underlying bony structure of the skull. The aim of this study was to develop soft tissue thickness (STT) values for South African black females for application to FFR, to compare these values to existing literature or databases and to add these values to existing population data. Computerised tomography scanning was used to determine average population-specific STT values at 28 facial landmarks of 154 black females. Descriptive statistics are provided for these STT values, which were also compared to those reported in three other comparable databases. Many of these STT values are significantly different from those reported for comparable groups, suggesting that individuals from different geographical areas have unique facial features thus requiring population-specific STT values. Repeatability tests indicated that most measurements could be recorded with a high degree of reliability.


title: Fine-Grained Visual Comparisons with Local Learning

text: Given two images, we want to predict which exhibits a particular visual attribute more than the other-even when the two images are quite similar. Existing relative attribute methods rely on global ranking functions; yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering. To address these issues, we propose a local learning approach for fine-grained visual comparisons. Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons. We show how to identify these analogous pairs using learned metrics. With results on three challenging datasets-including a large newly curated dataset for fine-grained comparisons-our method outperforms stateof-the-art methods for relative attribute prediction.


title: Quantity versus quality: A new approach to examine the relationship between technology use and student outcomes

text: The author argues that to examine the relationship between technology use and student outcomes, the quality of technology use—how, and what, technology is used—is a more significant factor than the quantity of technology use—how much technology is used. This argument was exemplified by an empirical study that used both angles to examine the association between technology use and student outcomes. When only the quantity of technology use was examined, no significant association was observed. However, when the quality of technology was examined by investigating the specific types of technology uses, a significant association was identified between technology use and all student outcomes. Furthermore, different types of technology use showed different influences on specific student outcomes. General technology uses were positively associated with student technology proficiency, while subject-specific technology uses were negatively associated with student technology proficiency. Social-communication technology uses were significantly positively associated with developmental outcomes such as self-esteem and positive attitude towards school. Entertainment/exploration technology use showed significant positive association with student learning habits. None of these technology uses had significant influence on student academic outcome. Specific suggestions for integrating technology into schools and future research were provided. Introduction In the last two decades, generous investments have been made in educational technology around the world. For example, the US had invested more than $66 billion in school technology in just 10 years (Quality Education Data, 2004). By 2004, China had spent 100 billion Yuan (about $13.2 billion) on educational technology (Zhao, 2005), and British Journal of Educational Technology Vol 41 No 3 2010 455–472 doi:10.1111/j.1467-8535.2009.00961.x © 2009 The Author. Journal compilation © 2009 Becta. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. the annual expense on educational technology was projected to reach 35.5 billion Yuan in 2007 (Okokok Report, 2004). Ireland’s second national educational technology plan proposed to invest 107.92 million pounds in educational technology (Ireland Ministry of Education and Science, 2001). The generous investments were supported by the strongly held premise that technology can help students learn more efficiently and effectively, and as a result increase student academic achievement. This belief in the connection between technology and student achievement is a theme commonly emphasised in mission statements of educational technology projects and arguments to support educational technology investment (Zhao & Conway, 2001). For example, the first US national educational technology plan claims, ‘Properly used, technology increases students’ learning opportunities, motivation, and achievement’ (U.S. Department of Education, 1996, p. 10). The second plan assures that technology would ‘enhance learning and improve student achievement for all students’ (U.S. Department of Education, 2000, p. 4). The third plan further states that with new technologies, ‘10 years from now we could be looking at the greatest leap forward in achievement in the history of education. By any measure, the improvements will be dramatic’ (U.S. Department of Education, p. 11). However, this premise on the crucial role of technology in student achievement has not been substantially supported by empirical evidence. In fact, findings from different empirical studies focusing on the effect of technology on learning have been inconsistent and contradictory. On the one hand, some studies have identified significant positive impact of technology use on student outcomes in academic areas such as literacy development (Blasewitz & Taylor, 1999; Tracey & Young, 2006), reading comprehension and vocabulary (Scrase, 1998; Stone, 1996; Woehler, 1994), writing (Nix, 1998), mathematics (Elliott & Hall, 1997; Mac Iver, Balfanz & Plank, 1999) and science (Harmer & Cates, 2007; Lazarowitz & Huppert, 1993; Liu, Hsieh, Cho & Schallert, 2006; Reid-Griffin, 2003). For example, Tienken and Wilson (2007) compared seventhgrade students whose teachers used mathematics websites and presentation software in their classrooms with students whose teachers did not teach with these technology tools. They found that the use of these technology tools had a positive effect on students’ learning of basic mathematic skills. In addition, positive impacts have been identified in student developmental areas, including attitude towards learning and self-esteem (Nguyen, Hsieh & Allen, 2006; Sivin-Kachala & Bialo, 2000), motivation, attendance and discipline (eg, Matthew, 1997). For example, using a mixed-method design, Wighting (2006) reported that using computers in the classroom positively affected students’ sense of learning in a community. Similarly, in the UK, a series of research studies have been conducted to examine the effect of the large-scale Tablet PC programmes, and findings reveal that the use of technology has improved student access to curriculum, communication and motivation (eg, Sheehy et al, 2005; Twining et al, 2006). On the other hand, several other researchers have come to very different conclusions. Some argue that technology use may not have any positive impact on student outcomes. For example, The Organization for Economic Co-operation and Development’s (OECD) Programme for International Student Assessment 2003 study found that stu456 British Journal of Educational Technology Vol 41 No 3 2010 © 2009 The Author. Journal compilation © 2009 Becta. dents using computers most frequently at school did not necessarily perform better than students using technology less frequently, and the impact of technology use on student math achievement varied by countries (OECD, 2005). In March 2007, the Institute of Education Sciences released an influential report titled Effectiveness of Reading and Mathematics Software Products: Findings from the First Student Cohort. This study, intended to assess the effects of 16 computer software products designed to teach firstand fourthgrade reading and sixth-grade math, using a rigorous random assignment design, found that ‘test scores in treatment classrooms that were randomly assigned to use products did not differ from test scores in control classrooms by statistically significant margins’ (Dynarski et al, 2007, p. xiii). Furthermore, some studies suggest that technology use might even harm children and their learning (eg, Healy, 1998; Stoll, 1999). For example, Waight and Abd-El-Khalick (2007) found that the use of computer technology restricted rather than promoted ‘inquiry’ in a sixth-grade science classroom. Mixed findings have also emerged from large-scale international studies. A study of the Trends in International Mathematics and Science Study (TIMSS) reported that technology use was negatively related to science achievement among eighth graders in Turkey (Aypay, Erdogan & Sozer, 2007). Another TIMSS study found that while medium use of computer technology was related to higher science scores, extensive use was related to lower science scores (Antonijevic, 2007). Similarly, based on data collected from 175 000 15-year-old students in 31 countries, researchers at the University of Munich announced that performance in math and reading had suffered significantly among students who had more than one computer at home (MacDonald, 2004). Schacter (1999) also identified some negative impacts on student achievement through the review of five large-scale studies that employed diverse research methods to examine the impact of educational technology. Discouraged by these findings, some people have come to the conclusion that putting computers in classrooms has been wasteful and pointless (Oppenheimer, 2003). Existing research on the relationship technology on student learning presents a mixed message (Andrews et al, 2007; O’Dwyer, Russell, Bebell & Tucker-Seeley, 2005; Torgerson & Zhu, 2003). Such mixed and often conflicting findings make it difficult to draw conclusions about the effects of technology, to provide meaningful advice to those who make decisions about technology investment in education and to make practical suggestions for integrating technology into schools. There are at least two problems contributing to the controversy over the relationship between technology use and student outcomes. The first is that technology is often examined at a very general level (Zhao, 2003). Many studies ‘treat technology as an undifferentiated characteristic of schools and classrooms. No distinction is made between different types of technology programs’ (Wenglinsky, 1998, p. 3). We know that technology is a very broad term that includes many kinds of hardware and software. These technologies may have different impacts on student outcomes. Even the same technology can be used differently in various contexts to solve all kinds of problems (Zhao) and thus have ‘different meanings in different settings’ (Peyton & Bruce, Quantity versus quality on technology use 457 © 2009 The Author. Journal compilation © 2009 Becta. 1993, p. 10). Treating technology as if it is a single thing obscures the unique characteristics of different technologies and their uses. The second problem is the focus of the studies. Most studies focus on the impact of the quantity of technology use, in other words, how much or how frequently technology is used, but ignore the quality of technology use, that is, how technology is used. For example, many studies examine the relationship between how much time students spend on using computers or how often they use computers and their achievement (eg, Du, Havard, Yu & Adams, 2004; Mann, Shakeshaft, Becker & Kottkamp, 1999). However, research suggests that the quality of technology use is more critical


title: Spatial Correlation and Mobility-Aware Traffic Modeling for Wireless Sensor Networks

text: Recently, there has been a great deal of research on using mobility in wireless sensor networks (WSNs) to facilitate surveillance and reconnaissance in a wide deployment area. Besides providing an extended sensing coverage, node mobility along with spatial correlation introduces new network dynamics, which could lead to the traffic patterns fundamentally different from the traditional (Markovian) models. In this paper, a novel traffic modeling scheme for capturing these dynamics is proposed that takes into account the statistical patterns of node mobility and spatial correlation. The contributions made in this paper are twofold. First, it is shown that the joint effects of mobility and spatial correlation can lead to bursty traffic. More specifically, a high mobility variance and small spatial correlation can give rise to pseudo-long-range-dependent (LRD) traffic (high bursty traffic), whose autocorrelation function decays slowly and hyperbolically up to a certain cutoff time lag. Second, due to the ad hoc nature of WSNs, certain relay nodes may have several routes passing through them, necessitating local traffic aggregations. At these relay nodes, our model predicts that the aggregated traffic also exhibits the bursty behavior characterized by a scaled power-law decayed autocovariance function. According to these findings, a novel traffic shaping protocol using movement coordination is proposed to facilitate effective and efficient resource provisioning strategy. Finally, simulation results reveal a close agreement between the traffic pattern predicted by our theoretical model and the simulated transmissions from multiple independent sources, under specific bounds of the observation intervals.


title: A Review of Facebook Research in the Social Sciences.

text: With over 800 million active users, Facebook is changing the way hundreds of millions of people relate to one another and share information. A rapidly growing body of research has accompanied the meteoric rise of Facebook as social scientists assess the impact of Facebook on social life. In addition, researchers have recognized the utility of Facebook as a novel tool to observe behavior in a naturalistic setting, test hypotheses, and recruit participants. However, research on Facebook emanates from a wide variety of disciplines, with results being published in a broad range of journals and conference proceedings, making it difficult to keep track of various findings. And because Facebook is a relatively recent phenomenon, uncertainty still exists about the most effective ways to do Facebook research. To address these issues, the authors conducted a comprehensive literature search, identifying 412 relevant articles, which were sorted into 5 categories: descriptive analysis of users, motivations for using Facebook, identity presentation, the role of Facebook in social interactions, and privacy and information disclosure. The literature review serves as the foundation from which to assess current findings and offer recommendations to the field for future research on Facebook and online social networks more broadly.


title: Information Disclosure and Control on Facebook: Are They Two Sides of the Same Coin or Two Different Processes?

text: Facebook, the popular social network site, is changing the nature of privacy and the consequences of information disclosure. Despite recent media reports regarding the negative consequences of disclosing information on social network sites such as Facebook, students are generally thought to be unconcerned about the potential costs of this disclosure. The current study explored undergraduate students' information disclosure and information control on Facebook and the personality factors that influence levels of disclosure and control. Participants in this online survey were 343 undergraduate students who were current users of Facebook. Results indicated that participants perceived that they disclosed more information about themselves on Facebook than in general, but participants also reported that information control and privacy were important to them. Participants were very likely to have posted information such as their birthday and e-mail address, and almost all had joined an online network. They were also very likely to post pictures such as a profile picture, pictures with friends, and even pictures at parties and drinking with friends. Contrary to expectations, information disclosure and information control were not significantly negatively correlated, and multiple regression analyses revealed that while disclosure was significantly predicted by the need for popularity, levels of trust and self-esteem predicted information control. Therefore, disclosure and control on Facebook are not as closely related as expected but rather are different processes that are affected by different aspects of personality. Implications of these findings and suggestions for future research are discussed.


title: On the evolution of user interaction in Facebook

text: Online social networks have become extremely popular; numerous sites allow users to interact and share content using social links. Users of these networks often establish hundreds to even thousands of social links with other users. Recently, researchers have suggested examining the activity network - a network that is based on the actual interaction between users, rather than mere friendship - to distinguish between strong and weak links. While initial studies have led to insights on how an activity network is structurally different from the social network itself, a natural and important aspect of the activity network has been disregarded: the fact that over time social links can grow stronger or weaker. In this paper, we study the evolution of activity between users in the Facebook social network to capture this notion. We find that links in the activity network tend to come and go rapidly over time, and the strength of ties exhibits a general decreasing trend of activity as the social network link ages. For example, only 30% of Facebook user pairs interact consistently from one month to the next. Interestingly, we also find that even though the links of the activity network change rapidly over time, many graph-theoretic properties of the activity network remain unchanged.


title: Spectral collaborative filtering

text: Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the cold-start problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the spectral domain, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the spectral domain, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the cold-start problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the spectral domains of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly out-performs state-of-the-art models. Code and data are available at https://github.com/lzheng21/SpectralCF.


title: Eco-Routing Navigation System Based on Multisource Historical and Real-Time Traffic Information

text: Due to increased public awareness on global climate change and other energy and environmental problems, a variety of strategies are being developed and used to reduce the energy consumption and environmental impact of roadway travel. In advanced traveler information systems, recent efforts have been made in developing a new navigation concept called “eco-routing,” which finds a route that requires the least amount of fuel and/or produces the least amount of emissions. This paper presents an eco-routing navigation system that determines the most eco-friendly route between a trip origin and a destination. It consists of the following four components: 1) a Dynamic Roadway Network database, which is a digital map of a roadway network that integrates historical and real-time traffic information from multiple data sources through an embedded data fusion algorithm; 2) an energy/emissions operational parameter set, which is a compilation of energy/emission factors for a variety of vehicle types under various roadway characteristics and traffic conditions; 3) a routing engine, which contains shortest path algorithms used for optimal route calculation; and 4) user interfaces that receive origin-destination inputs from users and display route maps to the users. Each of the system components and the system architecture are described. Example results are also presented to prove the validity of the eco-routing concept and to demonstrate the operability of the developed eco-routing navigation system. In addition, current limitations of the system and areas for future improvements are discussed.


title: Popularity prediction of images and videos on Instagram

text: We live in a world surrounded by numerous social media platforms, applications and websites which produce various texts, images and videos (posts) daily. People share their moments with their friends and families via these tools to keep in touch. This extensiveness of social media has led to an expansion of information in various forms. It is difficult to imagine someone totally unfamiliar with these concepts and not having posted any content on a platform. All users, ranging from individuals to large companies, want to get the most of their audiences' attention. Nevertheless, the problem is that not all these posts are admired and noticed by their audience. Therefore, it would be important to know what characteristics a post should have to become the most popular. Studying this enormous data will develop a knowledge from which we can understand the best way to publish our posts. To this end, we gathered images and videos from Instagram accounts and we used some image/video context features to predict the number of likes a post obtains as a meaning of popularity through some regression and classification methods. By the experiments with 10-fold cross-validation, we get the results of Popularity Score prediction with 0.002 in RMSE and Popularity Class prediction with 90.77% accuracy. As we know, this study is the first exploring of Iranian Instagram users for popularity prediction.


title: Optimization Using Simulation of Traffic Light Signal Timings

text: Traffic congestion has become a great challenge and a large burden on both the governments and the citizens in vastly populated cities. The main problem, originally initiated by several factors, continues to threaten the stability of modern cities and the livelihood of its habitants. Hence, improving the control strategies that govern the traffic operations is a powerful solution that can solve the congestion problem. These improvements can be achieved by enhancing the traffic control performance through adjusting the traffic signal timings. This paper focuses on finding various solutions for the addressed problem through the optimization using simulation of traffic signal timings under oversaturated conditions. The system under study is an actual road network in Alexandria, Egypt; where, numerous data have been collected in different time intervals. A series of computer simulation models to represent the actual system as well as proposed solutions have been developed using the ExtendSim simulation environment. Furthermore, an evolutionary optimizer is utilized to attain a set of optimum/near-optimum signal timings to minimize the total time in system of the vehicles, resulting in an improved performance of the road network. Analysis of experimentation results shows that the adopted methodology optimizes the vehicular flow in a multiple-junction urban traffic network.


title: Emergence of neural encoding of auditory objects while listening to competing speakers.

text: A visual scene is perceived in terms of visual objects. Similar ideas have been proposed for the analogous case of auditory scene analysis, although their hypothesized neural underpinnings have not yet been established. Here, we address this question by recording from subjects selectively listening to one of two competing speakers, either of different or the same sex, using magnetoencephalography. Individual neural representations are seen for the speech of the two speakers, with each being selectively phase locked to the rhythm of the corresponding speech stream and from which can be exclusively reconstructed the temporal envelope of that speech stream. The neural representation of the attended speech dominates responses (with latency near 100 ms) in posterior auditory cortex. Furthermore, when the intensity of the attended and background speakers is separately varied over an 8-dB range, the neural representation of the attended speech adapts only to the intensity of that speaker but not to the intensity of the background speaker, suggesting an object-level intensity gain control. In summary, these results indicate that concurrent auditory objects, even if spectrotemporally overlapping and not resolvable at the auditory periphery, are neurally encoded individually in auditory cortex and emerge as fundamental representational units for top-down attentional modulation and bottom-up neural adaptation.


title: The NASD Securities Observation , News Analysis & Regulation System ( SONAR )

text: The Securities Observation, News Analysis, and Regulation (SONAR) system was developed by NASD to monitor the Nasdaq, Over the Counter (OTC), and Nasdaq-Liffe (futures) stock markets for potential insider trading and fraud through misrepresentation. SONAR has been in operational use at NASD since December 2001, processing approximately 10,000 news wires stories and SEC filings, evaluating price/volume models for 25,000 securities, and generating 50-60 alerts (or “breaks”) per day for review by several groups of regulatory analysts and investigators. In addition, SONAR has greatly expanded surveillance coverage to new areas of the market and increased accuracy significantly over an earlier break detection system. SONAR makes use of several AI and statistical techniques, including NLP text mining, statistical regression, rule-based inference, uncertainty, and fuzzy matching. Sonar combines these enabling technologies in a system designed to deliver a steady stream of high-quality breaks to the analysts for further investigation. Additional components including visualization, text search agents, and flexible displays add to the system’s utility. Finally, SONAR is designed as a knowledge-based system. Domain knowledge is maintained by knowledge engineers working closely with the regulatory analysts. In this way, SONAR is adaptable to new market conditions, data sources, and regulatory concerns.


Mood Effects on Cognition : Affective Influences on the Content and Process of Information Processing and Behavior


title: In Defense of Single-column Networks for Crowd Counting

text: Crowd counting usually addressed by density estimation becomes an increasingly important topic in computer vision due to its widespread applications in video surveillance, urban planning, and intelligence gathering. However, it is essentially a challenging task because of the greatly varied sizes of objects, coupled with severe occlusions and vague appearance of extremely small individuals. Existing methods heavily rely on multi-column learning architectures to extract multi-scale features, which however suffer from heavy computational cost, especially undesired for crowd counting. In this paper, we propose the single-column counting network (SCNet) for efficient crowd counting without relying on multi-column networks. SCNet consists of residual fusion modules (RFMs) for multi-scale feature extraction, a pyramid pooling module (PPM) for information fusion, and a sub-pixel convolutional module (SPCM) followed by a bilinear upsampling layer for resolution recovery. Those proposed modules enable our SCNet to fully capture multi-scale features in a compact single-column architecture and estimate high-resolution density map in an efficient way. In addition, we provide a principled paradigm for density map generation and data augmentation for training, which shows further improved performance. Extensive experiments on three benchmark datasets show that our SCNet delivers new state-of-the-art performance and surpasses previous methods by large margins, which demonstrates the great effectiveness of SCNet as a singlecolumn network for crowd counting. c © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms. *Equal contribution 2 WANG et al.: IN DEFENSE OF SINGLE-COLUMN NETWORKS FOR CROWD COUNTING


title: What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries

text:
We analyze the question queries submitted to a large commercial web search engine to get insights about what people ask, and to better tailor the search results to the users' needs. Based on a dataset of about one billion question queries submitted during the year 2012, we investigate askers' querying behavior with the support of automatic query categorization. While the importance of question queries is likely to increase, at present they only make up 3-4% of the total search traffic.
 Since questions are such a small part of the query stream, and are more likely to be unique than shorter queries, click-through information is typically rather sparse. Thus, query categorization methods based on the categories of clicked web documents do not work well for questions. As an alternative, we propose a robust question query classification method that uses the labeled questions from a large community question answering platform (CQA) as a training set. The resulting classifier is then transferred to the web search questions. Even though questions on CQA platforms tend to be different to web search questions, our categorization method proves competitive with strong baselines with respect to classification accuracy.
 To show the scalability of our proposed method we apply the classifiers to about one billion question queries and discuss the trade-offs between performance and accuracy that different classification models offer.


title: Cache-Conscious Data Placement

text: As the gap between memory and processor speeds continues to widen, cache eficiency is an increasingly important component of processor performance. Compiler techniques have been used to improve instruction cache pet$ormance by mapping code with temporal locality to different cache blocks in the virtual address space eliminating cache conflicts. These code placement techniques can be applied directly to the problem of placing data for improved data cache pedormance.In this paper we present a general framework for Cache Conscious Data Placement. This is a compiler directed approach that creates an address placement for the stack (local variables), global variables, heap objects, and constants in order to reduce data cache misses. The placement of data objects is guided by a temporal relationship graph between objects generated via profiling. Our results show that profile driven data placement significantly reduces the data miss rate by 24% on average.


title: Approaches to Interpreter Composition

text: In this paper, we compose six different Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs.


title: A framework for efficient network anomaly intrusion detection with features selection

text: An intrusion Detection System (IDS) provides alarts against intrusion attacks where a traditional firewall fails. Machine learning algorithms aim to detect anomalies using supervised and unsupervised approaches. Features selection techniques identify important features and remove irrelevant and redundant attributes to reduce the dimensionality of feature space. This paper presents a features selection framework for efficient network anomaly detection using different machine learning classifiers. The framework applies different strategies by using filter and wrapper features selection methodologies. The aim of this framework is to select the minimum number of features that achieve the highest accuracy. UNSW-NB15 dataset is used in the experimental results to evaluate the proposed framework. The results show that by using 18 features from one of the filter ranking methods and applying J48 as a classifier, an accuracy of 88% is achieved.


title: Application-specific resource provisioning for wide-area distributed computing

text: Some modern distributed applications require cooperation among multiple geographically separated computing facilities to perform intensive computing at the end sites and large-scale data transfers in the wide area network. It has been widely recognized that WDM networks are cost-effective means to support data transfers in this type of data-intensive applications. However, neither the traditional approaches to establishing lightpaths between given source destination pairs nor the existing application-level approaches that only consider computing resources but take the underlying connectivity for granted are sufficient. In this article we identify key limitations and issues in existing systems, and focus on joint resource allocation of both computing resources and network resources in federated computing and network systems. A variety of resource allocation schemes that provide modern distributed computing applications with performance and reliability guarantees are presented.


title: The Multidimensional Work Motivation Scale : Validation evidence in seven languages and nine countries

text: The Multidimensional Work Motivation Scale: Validation evidence in seven languages and nine countries Marylène Gagné, Jacques Forest, Maarten Vansteenkiste, Laurence Crevier-Braud, Anja Van den Broeck, Ann Kristin Aspeli, Jenny Bellerose, Charles Benabou, Emanuela Chemolli, Stefan Tomas Güntert, Hallgeir Halvari, Devani Laksmi Indiyastuti, Peter A. Johnson, Marianne Hauan Molstad, Mathias Naudin, Assane Ndao, Anja Hagen Olafsen, Patrice Roussel, Zheni Wang & Cathrine Westbye a School of Psychology, University of Western Australia, Perth, Australia b Department of Organization and Human Resource Management, UQAM School of Management Sciences, Université du Québec à Montréal, Montréal, Canada c Department of Developmental, Personality and Social Psychology, University of Gent, Gent, Belgium d Department of Psychology, Université du Québec à Montréal, Montréal, Canada e Department of Economy and Management, Hogeschool Universiteit Brussel, Brussels, and KU Leuven, Leuven, Belgium f Faculty of Economics and Social Science, Høgskolen i Buskerud og Vestfold, Kongsberg, Norway g Department of Psychology, Université de Montréal, Montréal, Canada h Psychology of Work in Organization and Society Research Group, Swiss Federal Institute of Technology, Zurich, Switzerland i Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta, Indonesia j Institute of Work Psychology, University of Sheffield, Sheffield, United Kingdom k International Institute of Commerce and Development, Université Panthéon Assas — Paris II, Paris, France l Faculty of Economics and Management Sciences, Université Cheikh Anta DIOP, Dakkar, Sénégal m Department of Management and Human Resources, Université Toulouse 1, Toulouse, France Published online: 05 Feb 2014.


title: Beyond budgeting and Agile Software Development: a Conceptual Framework for the Performance Management of Agile Software Development Teams

text: Around the same time as the emergence of agile methods as a formalized concept, the management accounting literature introduced the concept of Beyond Budgeting as a performance management model for changing business environments. Both concepts share many similarities with both having a distinctly agile or adaptive perspective. The Beyond Budgeting model promises to enable companies to keep pace with changing business environments, quickly create and adapt strategy and empower people throughout the organization to make effective changes. This research in progress paper attempts to develop the Beyond Budgeting model within the context of agile software development teams. The twelve Beyond Budgeting principles are discussed and a research framework is presented. This framework is being used in two case studies to investigate the organizational issues and challenges that affect the performance of agile software


title: Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding

text: Semantic slot filling is one of the most challenging problems in spoken language understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for this task, and present several novel architectures designed to efficiently model past and future temporal dependencies. Specifically, we implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants. To facilitate reproducibility, we implemented these networks with the publicly available Theano neural network toolkit and completed experiments on the well-known airline travel information system (ATIS) benchmark. In addition, we compared the approaches on two custom SLU data sets from the entertainment and movies domains. Our results show that the RNN-based models outperform the conditional random field (CRF) baseline by 2% in absolute error reduction on the ATIS benchmark. We improve the state-of-the-art by 0.5% in the Entertainment domain, and 6.7% for the movies domain.


title: MPNET: An End-to-End Deep Neural Network for Object Detection in Surveillance Video

text: Object detection is one of the most important topics in computer vision task and has obtained impressive performance thanks to the use of deep convolutional neural network. For object detection, especially in still image, it has achieved excellent performance during past two years, such as the series of R-CNN which plays a vital role in improving performance. However, with the number of surveillance videos increasing, the current methods may not meet the growing demand. In this paper, we propose a new framework named moving-object proposals generation and prediction framework (MPGP) to reduce the searching space and generate some accurate proposals which can reduce computational cost. In addition, we explore the relation of moving regions in feature map of different layers and predict candidates according to the results of previous frames. Last but not least, we utilize spatial-temporal information to strengthen the detection score and further adjust the location of the bounding boxes. Our MPGP framework can be applied to different region-based networks. Experiments on CUHK data set, XJTU data set, and AVSS data set, show that our approach outperforms the state-of-the-art approaches.


title: Lifetime Portfolio Selection By Dynamic Stochastic Programming

text: Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.


title: Advocating an ethical memory model for artificial companions from a human-centred perspective

text: This paper considers the ethical implications of applying three major ethical theories to the memory structure of an artificial companion that might have different embodiments such as a physical robot or a graphical character on a hand-held device. We start by proposing an ethical memory model and then make use of an action-centric framework to evaluate its ethical implications. The case that we discuss is that of digital artefacts that autonomously record and store user data, where this data are used as a resource for future interaction with users.


title: Efficiently exploring architectural design spaces via predictive modeling

text: Architects use cycle-by-cycle simulation to evaluate design choices and understand tradeoffs and interactions among design parameters. Efficiently exploring exponential-size design spaces with many interacting parameters remains an open problem: the sheer number of experiments renders detailed simulation intractable. We attack this problem via an automated approach that builds accurate, confident predictive design-space models. We simulate sampled points, using the results to teach our models the function describing relationships among design parameters. The models produce highly accurate performance estimates for other points in the space, can be queried to predict performance impacts of architectural changes, and are very fast compared to simulation, enabling efficient discovery of tradeoffs among parameters in different regions. We validate our approach via sensitivity studies on memory hierarchy and CPU design spaces: our models generally predict IPC with only 1-2% error and reduce required simulation by two orders of magnitude. We also show the efficacy of our technique for exploring chip multiprocessor (CMP) design spaces: when trained on a 1% sample drawn from a CMP design space with 250K points and up to 55x performance swings among different system configurations, our models predict performance with only 4-5% error on average. Our approach combines with techniques to reduce time per simulation, achieving net time savings of three-four orders of magnitude.


title: Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor

text: In this paper, a novel controller for brushless DC (BLDC) motor has been presented. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and the rigorous analysis through simulation is performed using simulink tool box in MATLAB environment. The performance of the motor with proposed ANFIS controller is analyzed and compared with classical Proportional Integral (PI) controller, Fuzzy Tuned PID controller and Fuzzy Variable Structure controller. The dynamic characteristics of the brushless DC motor is observed and analyzed using the developed MATLAB/simulink model. Control system response parameters such as overshoot, undershoot, rise time, recovery time and steady state error are measured and compared for the above controllers. In order to validate the performance of the proposed controller under realistic working environment, simulation result has been obtained and analyzed for varying load and varying set speed conditions. & 2014 Elsevier B.V. All rights reserved.


title: High Performance Rogowski Current Transducers

text: A Rogowski current transducer is an invaluable tool for semiconductor and power electronic circuit development since it is non-intrusive and does not saturate at high currents. This paper reviews the operating principles, performance limitations and development of this measurement technology and outlines improvements to the integrator design that enables bandwidths of 10MHz to be achieved.


title: A preamplifier-latch comparator for high accuracy high speed switched-capacitors pipelined ADC

text: In this paper, a gain-boosting preamplifier-latch comparator with minimum input referred offset voltage is proposed. The preamplifier utilizes telescope structure with cross-coupled active loading, which enhances the gain of preamplifier. In order to minimize the delay time of comparator, the input DC range and the ratio of PMOS/NMOS are selected carefully. The circuit is fabricated in TSMC 0.18um with 1.8V power supply.


title: Least-Squares Fitting of Two 3-D Point Sets

text: Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.