title: Dynamic Dopamine Modulation in the Basal Ganglia: A Neurocomputational Account of Cognitive Deficits in Medicated and Nonmedicated Parkinsonism

abstract: Dopamine (DA) depletion in the basal ganglia (BG) of Parkinson's patients gives rise to both frontal-like and implicit learning impairments. Dopaminergic medication alleviates some cognitive deficits but impairs those that depend on intact areas of the BG, apparently due to DA "overdose." These findings are difficult to accommodate with verbal theories of BG/DA function, owing to complexity of system dynamics: DA dynamically modulates function in the BG, which is itself a modulatory system. This article presents a neural network model that instantiates key biological properties and provides insight into the underlying role of DA in the BG during learning and execution of cognitive tasks. Specifically, the BG modulates the execution of "actions" (e.g., motor responses and working memory updating) being considered in different parts of the frontal cortex. Phasic changes in DA, which occur during error feedback, dynamically modulate the BG threshold for facilitating/suppressing a cortical command in response to particular stimuli. Reduced dynamic range of DA explains Parkinson and DA overdose deficits with a single underlying dysfunction, despite overall differences in raw DA levels. Simulated Parkinsonism and medication effects provide a theoretical basis for behavioral data in probabilistic classification and reversal tasks. The model also provides novel testable predictions for neuropsychological and pharmacological studies, and motivates further investigation of BG/DA interactions with the prefrontal cortex in working memory.


title: Risks and complications of orthodontic miniscrews

abstract: The risks associated with miniscrew placement should be clearly understood by both the clinician and the patient. Complications can arise during miniscrew placement and after orthodontic loading that affect stability and patient safety. A thorough understanding of proper placement technique, bone density and landscape, peri-implant soft-tissue, regional anatomic structures, and patient home care are imperative for optimal patient safety and miniscrew success. The purpose of this article was to review the potential risks and complications of orthodontic miniscrews in regard to insertion, orthodontic loading, peri-implant soft-tissue health, and removal.


title: Reappraisal Reconsidered: A Closer Look at the Costs of an Acclaimed Emotion-Regulation Strategy:

abstract: Cognitive reappraisal is a common form of emotion regulation that often centers on reframing how one thinks about an emotional situation so that one feels better. Given its demonstrated widespread benefits, two conclusions have been drawn about reappraisal: People can use it easily, and people should use it frequently. We critically examine these conclusions and highlight two fundamental drawbacks of reappraisal: First, people are often unable to use reappraisal successfully, and second, even when successful, using reappraisal to feel better is not always functional. To synthesize current research and inspire future research, we present a conceptual framework that systematically considers these drawbacks and how they may be influenced by individual-centered factors (e.g., the individual’s skill) and situation-centered factors (e.g., a stressor’s intensity) to shape outcomes across time. We then summarize the current literature and highlight the importance of considering reappraisal’s costs and benefits in...


title: Semi-Supervised Domain Adaptation with Non-Parametric Copulas

abstract: A new framework based on the theory of copulas is proposed to address semi-supervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate copula functions. Therefore, changes in each of these factors can be detected and corrected to adapt a density model accross different learning domains. Importantly, we introduce a novel vine copula model, which allows for this factorization in a non-parametric manner. Experimental results on regression problems with real-world data illustrate the efficacy of the proposed approach when compared to state-of-the-art techniques.


Detecting Concept Drift with Support Vector Machines


title: How conscious experience and working memory interact

abstract: Active components of classical working memory are conscious, but traditional theory does not account for this fact. Global Workspace theory suggests that consciousness is needed to recruit unconscious specialized networks that carry out detailed working memory functions. The IDA model provides a fine-grained analysis of this process, specifically of two classical working-memory tasks, verbal rehearsal and the utilization of a visual image. In the process, new light is shed on the interactions between conscious and unconscious aspects of working memory.


title: A New Technique to Manage Big Bioinformatics Data Using Genetic Algorithms

abstract: The continuous growth of data, mainly the medical data at laboratories becomes very complex to use and to manage by using traditional ways. So, the researchers start studying genetic information field which increased in the past thirty years in bioinformatics domain (the computer science field, genetic biology field, and DNA). This growth of data becomes known as big bioinformatics data. Thus, efficient algorithms such as Genetic Algorithms are needed to deal with this big and vast amount of bioinformatics data in genetic laboratories. So the researchers proposed two models to manage the big bioinformatics data in addition to the traditional model. The first model by applying Genetic Algorithms before MapReduce, the second model by applying Genetic Algorithms after the MapReduce, and the original or the traditional model by applying only MapReduce without using Genetic Algorithms. The three models were implemented and evaluated using big bioinformatics data collected from the Duchenne Muscular Dystrophy (DMD) disorder. The researchers conclude that the second model is the best one among the three models in reducing the size of the data, in execution time, and in addition to the ability to manage and summarize big bioinformatics data. Finally by comparing the percentage errors of the second model with the first model and the traditional model, the researchers obtained the following results 1.136%, 10.227%, and 11.363% respectively. So the second model is the most accurate model with the less percentage error.


title: Computational Sociolinguistics: A Survey

abstract: Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.


title: Load-Balanced Sensor Grouping for IEEE 802.11ah Networks

abstract: The traditional IEEE 802.11 network is designed for the use of small scale local wireless networks such as home or campus WLANs, where one access point serves a reasonably few number of devices like smartphones, laptops, tablets, and so on. However, the emergence of the Internet of Things (IoT) have changed the scene of wireless communications, while the number of devices rapidly increases and becomes far larger than before. Thus, recently, the IEEE task group ah (TGah) is dedicated to the standardization of a new protocol, called IEEE 802.11ah, which is customized for this type of large-scale networks. IEEE 802.11ah adopts the grouping-based MAC protocol to reduce the contention overhead of each group of devices. However, most existing designs simply randomly assign devices to groups, and less attention has been paid to the problem of forming efficient groups. Therefore, in this paper, we argue that the performance of grouping is closely related to the heterogeneous traffic demands of devices, and propose a load-balanced grouping algorithm to improve channel utilization of each group. Our evaluation shows that the proposed load-balanced grouping outperforms simple random grouping, especially when the network is almost saturated.


title: Outlier Detection for Sensor Systems (ODSS): A MATLAB Macro for Evaluating Microphone Sensor Data Quality

abstract: Microphone sensor systems provide information that may be used for a variety of applications. Such systems generate large amounts of data. One concern is with microphone failure and unusual values that may be generated as part of the information collection process. This paper describes methods and a MATLAB graphical interface that provides rapid evaluation of microphone performance and identifies irregularities. The approach and interface are described. An application to a microphone array used in a wind tunnel is used to illustrate the methodology.


title: Protection of Privacy in Biometric Data

abstract: Biometrics is commonly used in many automated verification systems offering several advantages over traditional verification methods. Since biometric features are associated with individuals, their leakage will violate individuals’ privacy, which can cause serious and continued problems as the biometric data from a person are irreplaceable. To protect the biometric data containing privacy information, a number of privacy-preserving biometric schemes (PPBSs) have been developed over the last decade, but they have various drawbacks. The aim of this paper is to provide a comprehensive overview of the existing PPBSs and give guidance for future privacy-preserving biometric research. In particular, we explain the functional mechanisms of popular PPBSs and present the state-of-the-art privacy-preserving biometric methods based on these mechanisms. Furthermore, we discuss the drawbacks of the existing PPBSs and point out the challenges and future research directions in PPBSs.


title: Atlantic Basin Hurricanes: Indices of Climatic Changes

abstract: Accurate records of basinwide Atlantic and U.S. landfalling hurricanes extend back to the mid 1940s and the turn of the century, respectively, as a result of aircraft reconnaissance and instrumented weather stations along the U.S. coasts. Such long-term records are not exceeded elsewhere in the tropics. The Atlantic hurricanes, U.S. landfalling hurricanes and U.S. normalized damage time series are examined for interannual trends and multidecadal variability. It is found that only weak linear trends can be ascribed to the hurricane activity and that multidecadal variability is more characteristic of the region. Various environmental factors including Caribbean sea level pressures and 200mb zonal winds, the stratospheric Quasi-Biennial Oscillation, the El Nino-Southern Oscillation, African West Sahel rainfall and Atlantic sea surface temperatures, are analyzed for interannual links to the Atlantic hurricane activity. All show significant, concurrent relationships to the frequency, intensity and duration of Atlantic hurricanes. Additionally, variations in the El Nino-Southern Oscillation are significantly linked to changes in U.S. tropical cyclone-caused damages. Finally, much of the multidecadal hurricane activity can be linked to the Atlantic Multidecadal Mode - an empirical orthogonal function pattern derived from a global sea surface temperature record. Such linkages may allow for prediction of Atlantic hurricane activity on a multidecadal basis. These results are placed into the context of climate change and natural hazards policy.


title: Bleu: A Method For Automatic Evaluation Of Machine Translation

abstract: Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.


title: The Role of Medial Prefrontal Cortex in Memory and Decision Making

abstract: Some have claimed that the medial prefrontal cortex (mPFC) mediates decision making. Others suggest mPFC is selectively involved in the retrieval of remote long-term memory. Yet others suggests mPFC supports memory and consolidation on time scales ranging from seconds to days. How can all these roles be reconciled? We propose that the function of the mPFC is to learn associations between context, locations, events, and corresponding adaptive responses, particularly emotional responses. Thus, the ubiquitous involvement of mPFC in both memory and decision making may be due to the fact that almost all such tasks entail the ability to recall the best action or emotional response to specific events in a particular place and time. An interaction between multiple memory systems may explain the changing importance of mPFC to different types of memories over time. In particular, mPFC likely relies on the hippocampus to support rapid learning and memory consolidation.


title: Protection of Low-Voltage DC Microgrids

abstract: In this paper, a low-voltage (LV) DC microgrid protection system design is proposed. The LV DC microgrid is used to interconnect distributed resources and sensitive electronic loads. When designing an LV DC microgrid protection system, knowledge from existing DC power systems can be used. However, in most cases, these systems use grid-connected rectifiers with current-limiting capability during DC faults. In contrast, an LV DC microgrid must be connected to an AC grid through converters with bidirectional power flow and, therefore, a different protection-system design is needed. In this paper, the operating principles and technical data of LV DC protection devices, both available and in the research stage, are presented. Furthermore, different fault-detection and grounding methods are discussed. The influence of the selected protection devices and grounding method on an LV DC microgrid is studied through simulations. The results show that it is possible to use available devices to protect such a system. Problems may arise with high-impedance ground faults which can be difficult to detect.


title: Developing Pre-Service Teachers' Technology Integration Expertise Through the Tpack-Developing Instructional Model

abstract: This study describes the TPACK-Developing Instructional Model which prescribes an instructional process for developing pre-service teachers' Technological Pedagogical Content Knowledge (TPACK) duri...


title: Clustered-dot color halftone watermarks using spatial frequency and color separability

abstract: A framework for clustered-dot color halftone watermarking is proposed. Watermark patterns are embedded in ::: the color halftone on per-separation basis. For typical CMYK printing systems, common desktop RGB color ::: scanners are unable to provide the individual colorant halftone separations, which confounds per-separation ::: detection methods. Not only does the K colorant consistently appear in the scanner channels as it absorbs ::: uniformly across the spectrum, but cross-couplings between CMY separations are also observed in the scanner ::: color channels due to unwanted absorptions. We demonstrate that by exploiting spatial frequency and color ::: separability of clustered-dot color halftones, estimates of the individual colorant halftone separations can be ::: obtained from scanned RGB images. These estimates, though not perfect, allow per-separation detection to ::: operate efficiently. The efficacy of this methodology is demonstrated using continuous phase modulation for the ::: embedding of per-separation watermarks.


title: Domingos Arouca: um percurso de militância nacionalista em Moçambique

abstract: This paper offers a reinterpretation of disputes over history and memory in Mozambique from the analysis of Domingos Arouca's militant path. It emphasizes the political path of Arouca, a nationalist and political prisoner until the early 1970s. the study is based on archival research carried out both in Mozambique and Portugal, analysis of media, as well as on interviews carried out with other nationalists. It proposes a more dense and complex interpretation of the political history and social memories of a lesser known period of Mozambique history the end of colonialism and the transition to independence (1962-1975. This paper aims to be a contribution towards a more sophisticated perspective over the processes of identity reconstruction in contemporary Mozambique.


title: TDL: a transformation description language from feature model to use case for automated use case derivation

abstract: Software product line engineering (SPLE) is a widely adopted approach to systematic software reuse. One basic research issue in SPLE is the product derivation problem, which focuses on how to derive software products from reusable software assets efficiently. In this paper, we focus on a sub-problem of product derivation: the problem of automated use case derivation, i.e. deriving the use cases of a software product in an automated way. We take a feature-oriented approach to this problem, an approach involving two components: a feature model, and the transformation information from the feature model to a set of use cases. In particular, we propose a transformation description language (TDL) to specify the transformation information from a feature model to a set of related use cases, and to support automated derivation of use cases corresponding to a valid feature model configuration. In addition, we also propose a set of criteria to check the validity of a TDL program. Three case studies have been conducted to demonstrate the usability of TDL and the feasibility of the automated use case derivation process based on TDL programs.


title: Towards a resolution of the personality triad: Persons, situations, and behaviors

abstract: The issue of the variant vs. invariant in personality often arises in diVerent forms of the “person– situation” debate, which is based on a false dichotomy between the personal and situational determination of behavior. Previously reported data are summarized that demonstrate how behavior can vary as a function of subtle situational changes while individual consistency is maintained. Further discussion considers the personal source of behavioral invariance, the situational source of behavioral variation, the person–situation interaction, the nature of behavior, and the “personality triad” of persons, situations, and behaviors, in which each element is understood and predicted in terms of the other two. An important goal for future research is further development of theories and methods for conceptualizing and measuring the functional aspects of situations and of behaviors. One reason for the persistence of the person situation debate may be that it serves as a proxy for a deeper, implicit debate over values such as equality vs. individuality, determinism vs. free will, and Xexibility vs. consistency. However, these value dichotomies may be as false as the person–situation debate that they implicitly drive.


title: Symmetry Breaking in Constraint Programming

abstract: We describe a method for symmetry breaking during search (SBDS) in constraint programming. It has the great advantage of not interfering with heuristic choices. It guarantees to return a unique solution from each set of symmetrically equivalent ones, which is the one found first by the variable and value ordering heuristics. We describe an implementation of SBDS in ILOG Solver, and applications to low autocorrelation binary sequences and the n-queens problem. We discuss how SBDS can be applied when there are too many symmetries to reason with individually, and give applications in graph colouring and Ramsey theory.


title: Online human gesture recognition from motion data streams

abstract: Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the stat-of-the-art approaches.


title: Neural computation and the computational theory of cognition. Cognitive science

abstract: We begin by distinguishing computationalism from a number of other theses that are sometimes conflated with it. We also distinguish between several important kinds of computation: computation in a generic sense, digital computation, and analog computation. Then, we defend a weak version of computationalism—neural processes are computations in the generic sense. After that, we reject on empirical grounds the common assimilation of neural computation to either analog or digital computation, concluding that neural computation is sui generis. Analog computation requires continuous signals; digital computation requires strings of digits. But current neuroscientific evidence indicates that typical neural signals, such as spike trains, are graded like continuous signals but are constituted by discrete functional elements (spikes); thus, typical neural signals are neither continuous signals nor strings of digits. It follows that neural computation is sui generis. Finally, we highlight three important consequences of a proper understanding of neural computation for the theory of cognition. First, understanding neural computation requires a specially designed mathematical theory (or theories) rather than the mathematical theories of analog or digital computation. Second, several popular views about neural computation turn out to be incorrect. Third, computational theories of cognition that rely on non-neural notions of computation ought to be replaced or reinterpreted in terms of neural computation.


title: Close partner as sculptor of the ideal self: Behavioral affirmation and the Michelangelo phenomenon

abstract: This work incorporates concepts from the behavioral confirmation tradition, self tradition, and interdependence tradition to identify an interpersonal process termed the Michelangelo phenomenon. The Michelangelo phenomenon describes the means by which the self is shaped by a close partner's perceptions and behavior. Specifically, self movement toward the ideal self is described as a product of partner affirmation, or the degree to which a partner's perceptions of the self and behavior toward the self are congruent with the self's ideal. The results of 4 studies revealed strong associations between perceived partner affirmation and self movement toward the ideal self, using a variety of participant populations and measurement methods. In addition, perceived partner affirmation--particularly perceived partner behavioral affirmation--was strongly associated with quality of couple functioning and stability in ongoing relationships.


title: DeepRank: A New Deep Architecture for Relevance Ranking in Information Retrieval

abstract: This paper concerns a deep learning approach to relevance ranking in information retrieval (IR). Existing deep IR models such as DSSM and CDSSM directly apply neural networks to generate ranking scores, without explicit understandings of the relevance. According to the human judgement process, a relevance label is generated by the following three steps: 1) relevant locations are detected; 2) local relevances are determined; 3) local relevances are aggregated to output the relevance label. In this paper we propose a new deep learning architecture, namely DeepRank, to simulate the above human judgment process. Firstly, a detection strategy is designed to extract the relevant contexts. Then, a measure network is applied to determine the local relevances by utilizing a convolutional neural network (CNN) or two-dimensional gated recurrent units (2D-GRU). Finally, an aggregation network with sequential integration and term gating mechanism is used to produce a global relevance score. DeepRank well captures important IR characteristics, including exact/semantic matching signals, proximity heuristics, query term importance, and diverse relevance requirement. Experiments on both benchmark LETOR dataset and a large scale clickthrough data show that DeepRank can significantly outperform learning to ranking methods, and existing deep learning methods.


title: A compact octagonal shaped fractal UWB MIMO antenna with 5.5 GHz band-notch characteristics

abstract: In this paper, a compact octagonal shaped fractal ultrawideband (UWB) multiple-input-multiple-output (MEMO) antenna is presented. The application of Minkowski fractal geometry in antenna design provides desired miniaturization and wideband phenomena. To achieve the good isolation both the antenna are placed orthogonal to each other and an L-shape ground stub is enhanced from the ground plane. The C-shape slot is used for WLAN band notch. The proposed antenna has compact size of 40 mm × 25 mm.


title: Adaptive fuzzy systems for backing up a truck-and-trailer

abstract: Fuzzy control systems and neural-network control systems for backing up a simulated truck, and truck-and-trailer, to a loading dock in a parking lot are presented. The supervised backpropagation learning algorithm trained the neural network systems. The robustness of the neural systems was tested by removing random subsets of training data in learning sequences. The neural systems performed well but required extensive computation for training. The fuzzy systems performed well until over 50% of their fuzzy-associative-memory (FAM) rules were removed. They also performed well when the key FAM equilibration rule was replaced with destructive, or 'sabotage', rules. Unsupervised differential competitive learning (DCL) and product-space clustering adaptively generated FAM rules from training data. The original fuzzy control systems and neural control systems generated trajectory data. The DCL system rapidly recovered the underlying FAM rules. Product-space clustering converted the neural truck systems into structured sets of FAM rules that approximated the neural system's behavior. >


title: ICDAR2015 competition on smartphone document capture and OCR (SmartDoc)

abstract: Smartphones are enabling new ways of capture, hence arises the need for seamless and reliable acquisition and digitization of documents, in order to convert them to editable, searchable and a more human-readable format. Current state-of-the-art works lack databases and baseline benchmarks for digitizing mobile captured documents. We have organized a competition for mobile document capture and OCR in order to address this issue. The competition is structured into two independent challenges: smartphone document capture, and smartphone OCR. This report describes the datasets for both challenges ::: along with their ground truth, details the performance evaluation protocols which we used, and presents the final results of the participating methods.


title: Attribute-Based Secure Data Sharing with Efficient Revocation in Fog Computing

abstract: Fog computing is a concept that extends the paradigm of cloud computing to the network edge. The goal of fog computing is to situate resources in the vicinity of end users. As with cloud computing, fog computing provides storage services. The data owners can store their confidential data in many fog nodes, which could cause more challenges for data sharing security. In this paper, we present a novel architecture for data sharing in a fog environment. We explore the benefits of fog computing in addressing one-to-many data sharing applications. This architecture sought to outperform the cloud-based architecture and to ensure further enhancements to system performance, especially from the perspective of security. We will address the security challenges of data sharing, such as fine-grained access control, data confidentiality, collusion resistance, scalability, and the issue of user revocation. Keeping these issues in mind, we will secure data sharing in fog computing by combining attributebased encryption and proxy re-encryption techniques. Findings of this study indicate that our system has the response and processing time faster than classical cloud systems. Further, experimental results show that our system has an efficient user revocation mechanism, and that it provides high scalability and sharing of data in real time with low latency.


title: SpaceNet: A Remote Sensing Dataset and Challenge Series

abstract: Foundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical. Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet. The SpaceNet partners also launched a series of public prize competitions to encourage improvement of remote sensing machine learning algorithms. The first two of these competitions focused on automated building footprint extraction, and the most recent challenge focused on road network extraction. In this paper we discuss the SpaceNet imagery, labels, evaluation metrics, prize challenge results to date, and future plans for the SpaceNet challenge series.


title: Giving up and giving in: The costs and benefits of daily sacrifice in intimate relationships.

abstract: This research provided the first empirical investigation of how approach and avoidance motives for sacrifice in intimate relationships are associated with personal well-being and relationship quality. In Study 1, the nature of everyday sacrifices made by dating partners was examined, and a measure of approach and avoidance motives for sacrifice was developed. In Study 2, which was a 2-week daily experience study of college students in dating relationships, specific predictions from the theoretical model were tested and both longitudinal and dyadic components were included. Whereas approach motives for sacrifice were positively associated with personal well-being and relationship quality, avoidance motives for sacrifice were negatively associated with personal well-being and relationship quality. Sacrificing for avoidance motives was particularly detrimental to the maintenance of relationships over time. Perceptions of a partner's motives for sacrifice were also associated with well-being and relationship quality. Implications for the conceptualization of relationship maintenance processes along these 2 dimensions are discussed.


title: Performance of an Electrically Small Aperture, Axially Displaced Ellipse Reflector Antenna

abstract: This work reports the performance of an axially displaced ellipse (ADE) antenna with an electrically small aperture. The diameter of the antenna is about 20?0, which is considerably smaller than the ADE antennas already reported. This antenna provides a measured peak gain of 33.3 dBi at 8.484 GHz, which corresponds to an overall efficiency of 55%.


title: Digital Watermarking: Algorithms and Applications

abstract: Digital watermarking of multimedia content has become a very active research area over the last several years. A general framework for watermark embedding and detection/decoding is presented here along with a review of some of the algorithms for different media types described in the literature. We highlight some of the differences based on application such as copyright protection, authentication, tamper detection, and data hiding as well as differences in technology and system requirements for different media types such as digital images, video, audio and text.


title: A revolution in optical manipulation

abstract: Optical tweezers use the forces exerted by a strongly focused beam of light to trap and move objects ranging in size from tens of nanometres to tens of micrometres. Since their introduction in 1986, the optical tweezer has become an important tool for research in the fields of biology, physical chemistry and soft condensed matter physics. Recent advances promise to take optical tweezers out of the laboratory and into the mainstream of manufacturing and diagnostics; they may even become consumer products. The next generation of single-beam optical traps offers revolutionary new opportunities for fundamental and applied research.


title: Genetic algorithms and engineering optimization

abstract: Foundations of Genetic Algorithms. Combinatorial Optimization Problems. Multiobjective Optimization Problems. Fuzzy Optimization Problems. Reliability Design Problems. Scheduling Problems. Advanced Transportation Problems. Network Design and Routing. Manufacturing Cell Design. References. Index.


title: Throughput Analysis of Synchronous Data Flow Graphs

abstract: Synchronous Data Flow Graphs (SDFGs) are a useful tool for modeling and analyzing embedded data flow applications, both in a single processor and a multiprocessing context or for application mapping on platforms. Throughput analysis of these SDFGs is an important step for verifying throughput requirements of concurrent real-time applications, for instance within design-space exploration activities. Analysis of SDFGs can be hard, since the worst-case complexity of analysis algorithms is often high. This is also true for throughput analysis. In particular, many algorithms involve a conversion to another kind of data flow graph, the size of which can be exponentially larger than the size of the original graph. In this paper, we present a method for throughput analysis of SDFGs, based on explicit state-space exploration and we show that the method, despite its worst-case complexity, works well in practice, while existing methods often fail. We demonstrate this by comparing the method with state-ofthe- art cycle mean computation algorithms. Moreover, since the state-space exploration method is essentially the same as simulation of the graph, the results of this paper can be easily obtained as a byproduct in existing simulation tools.


COMPARATIVE PERFORMANCE ANALYSIS OF SYMBOL TIMING RECOVERY FOR DVB-S2 RECEIVERS


title: One-Way Quantum Computer Simulation

abstract: In one-way quantum computation (1WQC) model, universal quantum computations are performed using measurements to designated qubits in a highly entangled state. The choices of bases for these measurements as well as the structure of the entanglements specify a quantum algorithm. As scalable and reliable quantum computers have not been implemented yet, quantum computation simulators are the only widely available tools to design and test quantum algorithms. However, simulating the quantum computations on a standard classical computer in most cases requires exponential memory and time. In this paper, a general direct simulator for 1WQC, called OWQS, is presented. Some techniques such as qubit elimination, pattern reordering and implicit simulation of actions are used to considerably reduce the time and memory needed for the simulations. Moreover, our simulator is adjusted to simulate the measurement patterns with a generalized flow without calculating the measurement probabilities which is called extended one-way quantum computation simulator (EOWQS). Experimental results validate the feasibility of the proposed simulators and that OWQS and EOWQS are faster as compared with the well-known quantum circuit simulators, i.e., QuIDDPro and libquantum for simulating 1WQC model. (This paper is an extended version of the paper presented at Euromicro DSD conference (Nikahd et al., 2012) 1].)


title: Pondering the Concept of Abstraction in (Illustrative) Visualization

abstract: We explore the concept of abstraction as it is used in visualization, with the ultimate goal of understanding and formally defining it. Researchers so far have used the concept of abstraction largely by intuition without a precise meaning. This lack of specificity left questions on the characteristics of abstraction, its variants, its control, or its ultimate potential for visualization and, in particular, illustrative visualization mostly unanswered. In this paper we thus provide a first formalization of the abstraction concept and discuss how this formalization affects the application of abstraction in a variety of visualization scenarios. Based on this discussion, we derive a number of open questions still waiting to be answered, thus formulating a research agenda for the use of abstraction for the visual representation and exploration of data. This paper, therefore, is intended to provide a contribution to the discussion of the theoretical foundations of our field, rather than attempting to provide a completed and final theory.


title: Virtual Reality-Guided Motor Imagery Increases Corticomotor Excitability in Healthy Volunteers and Stroke Patients

abstract: OBJECTIVE ::: To investigate the effects of using motor imagery (MI) in combination with a virtual reality (VR) program on healthy volunteers and stroke patients. In addition, this study investigated whether task variability within the VR-guided MI programs would influence corticomotor excitability. ::: ::: ::: METHODS ::: The present study included 15 stroke patients and 15 healthy right-handed volunteers who were presented with four different conditions in a random order: rest, MI alone, VR-guided MI, and VR-guided MI with task variability. The corticomotor excitability of each participant was assessed before, during, and after each condition by measuring changes in the various parameters of motor-evoked potentials (MEPs) of the extensor carpi radials (ECR). Changes in intracortical inhibition (ICI) and intracortical facilitation (ICF) were calculated after each condition as percentages of inhibition (%INH) and facilitation (%FAC) at rest. ::: ::: ::: RESULTS ::: In both groups, the increases in MEP amplitudes were greater during the two VR-guided MI conditions than during MI alone. Additionally, the reductions in ECR %INH in both groups were greater under the condition involving VR-guided MI with task variability than under that involving VR-guided MI with regular interval. ::: ::: ::: CONCLUSION ::: The corticomotor excitability elicited by MI using a VR avatar representation was greater than that elicited by MI with real body observations. Furthermore, the use of task variability in a VR program may enhance neural regeneration after stroke by reducing ICI. The present findings support the use of various VR programs as well as the concept of combining MI with VR programs for neurorehabilitation.


title: Influence of Black Cumin Seeds (Nigella sativa) and Turmeric (Curcuma longa Linn.) Mixture on Performance and Serum Biochemistry of Asian Sea Bass, Lates calcarifer

abstract: A total of 270 fingerlings of Asian sea bass (Lates calcarifer) were allotted into three equal treatments with three equal replicates for each to investigate the effect of dietary supplementation of two levels of Black cumin seeds (Nigella sativa) and Turmeric (Curcuma longa) mixture (BTM) on growth performance and blood chemistry throughout the experimental period (98 days). Fish of the first treatment fed on basal diet and served as a control while fish of the second and third treatments kept on BTM (1:1 w w ) constitute either 5g kg or 1 1


title: Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection

abstract: Deep Convolutional Neural Networks (DCNNs) commonly use generic 'max-pooling' (MP) layers to extract deformation-invariant features, but we argue in favor of a more refined treatment. First, we introduce epitomic con-volution as a building block alternative to the common convolution-MP cascade of DCNNs; while having identical complexity to MP, Epitomic Convolution allows for pa-rameter sharing across different filters, resulting in faster convergence and better generalization. Second, we introduce a Multiple Instance Learning approach to explicitly accommodate global translation and scaling when training a DCNN exclusively with class labels. For this we rely on a 'patchwork' data structure that efficiently lays out all image scales and positions as candidates to a DCNN. Factoring global and local deformations allows a DCNN to 'focus its resources' on the treatment of non-rigid defor-mations and yields a substantial classification accuracy improvement. Third, further pursuing this idea, we develop an efficient DCNN sliding window object detector that employs explicit search over position, scale, and aspect ratio. We provide competitive image classification and localization results on the ImageNet dataset and object detection results on the Pascal VOC 2007 benchmark.


title: Effects of pulsed magnetic stimulation on tumor development and immune functions in mice

abstract: We investigated the effects of pulsed magnetic stimulation on tumor development processes and immune functions in mice. A circular coil (inner diameter = 15 mm, outer diameter = 75 mm) was used in the experiments. Stimulus conditions were pulse width = 238 micros, peak magnetic field = 0.25 T (at the center of the coil), frequency = 25 pulses/s, 1,000 pulses/sample/day and magnetically induced eddy currents in mice = 0.79-1.54 A/m(2). In an animal study, B16-BL6 melanoma model mice were exposed to the pulsed magnetic stimulation for 16 days from the day of injection of cancer cells. A tumor growth study revealed a significant tumor weight decrease in the stimulated group (54% of the sham group). In a cellular study, B16-BL6 cells were also exposed to the magnetic field (1,000 pulses/sample, and eddy currents at the bottom of the dish = 2.36-2.90 A/m(2)); however, the magnetically induced eddy currents had no effect on cell viabilities. Cytokine production in mouse spleens was measured to analyze the immunomodulatory effect after the pulsed magnetic stimulation. tumor necrosis factor (TNF-alpha) production in mouse spleens was significantly activated after the exposure of the stimulus condition described above. These results showed the first evidence of the anti-tumor effect and immunomodulatory effects brought about by the application of repetitive magnetic stimulation and also suggested the possible relationship between anti-tumor effects and the increase of TNF-alpha levels caused by pulsed magnetic stimulation.


title: A 1.8-V /spl Delta//spl Sigma/ modulator interface for an electret microphone with on-chip reference

abstract: The design of a delta-sigma (/spl Delta//spl Sigma/) analog-to-digital converter (ADC) for direct voltage readout of an electret microphone is presented. The ADC is integrated on the same chip with a bandgap voltage reference and is designed to be packaged together with an electret microphone. Having a power consumption of 1.7 mW from a supply voltage of 1.8 V, the circuit is well suited for use in mobile applications. The single-loop, single-bit, fourth-order /spl Delta//spl Sigma/ ADC operates at 64 times oversampling for a signal bandwidth of 11 kHz. The measured dynamic range is 80 dB and the peak signal-to-(noise+distortion) ratio is 62 dB. The harmonic distortion is minimized by using an integrator with an instrumentation amplifier-like input which directly integrates the 125-mV peak single-ended voltage generated by the microphone. A combined continuous-time/switched-capacitor design is used to minimize power consumption.


title: Ironless Axial Flux PM Machine With Active Mechanical Flux Weakening For Automotive Applications

abstract: Because of their high compactness and lightness, together with high efficiency, Axial-Flux PM (AFPM) machines are among the most suitable candidates for several automotive applications. As a drawback, they are limited in speed range because voltage rises linearly with speed. For this reason, they are generally considered not well-suited for applications such as starter/alternators. As a solution, in this paper an AFPM ironless machine prototype with extended flux weakening capabilities is proposed. Flux weakening, and therefore output voltage regulation, is achieved mechanically through a small-sized actuator that displaces each other the two adjacent windings that compose the stator.


title: Codes for distributed PIR with low storage overhead

abstract: Private information retrieval (PIR) protocols allow a user to retrieve a data item from a database without revealing any information about the identity of the item being retrieved. Specifically, in information-theoretic k-server PIR, the database is replicated among k non-communicating servers, and each server learns nothing about the item retrieved by the user. The cost of PIR protocols is usually measured in terms of their communication complexity, which is the total number of bits exchanged between the user and the servers. However, another important cost parameter is the storage overhead, which is the ratio between the total number of bits stored on all the servers and the number of bits in the database. Since single-server information-theoretic PIR is impossible, the storage overhead of all existing PIR protocols is at least 2 (or k, in the case of k-server PIR). In this work, we show that information-theoretic PIR can be achieved with storage overhead arbitrarily close to the optimal value of 1, without sacrificing the communication complexity. Specifically, we prove that all known k-server PIR protocols can be efficiently emulated, while preserving both privacy and communication complexity but significantly reducing the storage overhead. To this end, we distribute the n bits of the database among s + r servers, each storing n/s coded bits (rather than replicas). Notably, our coding scheme remains the same, regardless of the specific k-server PIR protocol being emulated. For every fixed k, the resulting storage overhead (s +r)/s approaches 1 as s grows; explicitly we have equation. Moreover, in the special case k = 2, the storage overhead is only 1 + 1/s. In order to achieve these results, we introduce and study a new kind of binary linear codes, called here k-server PIR codes. Finally, we show how such codes can be constructed from multidimensional cubic, from Steiner systems, and from one-step majority-logic decodable codes.


title: Hierarchical multi-label classification over ticket data using contextual loss

abstract: Maximal automation of routine IT maintenance procedures is an ultimate goal of IT service management. System monitoring, an effective and reliable means for IT problem detection, generates monitoring tickets to be processed by system administrators. IT problems are naturally organized in a hierarchy by specialization. The problem hierarchy is used to help triage tickets to the processing team for problem resolving. In this paper, a hierarchical multi-label classification method is proposed to classify the monitoring tickets by utilizing the problem hierarchy. In order to find the most effective classification, a novel contextual hierarchy (CH) loss is introduced in accordance with the problem hierarchy. Consequently, an arising optimization problem is solved by a new greedy algorithm. An extensive empirical study over ticket data was conducted to validate the effectiveness and efficiency of our method.


title: VOS-GAN: Adversarial Learning of Visual-Temporal Dynamics for Unsupervised Dense Prediction in Videos

abstract: Recent GAN-based video generation approaches model videos as the combination of a time-independent scene component and a time-varying motion component, thus factorizing the generation problem into generating background and foreground separately. One of the main limitations of current approaches is that both factors are learned by mapping one source latent space to videos, which complicates the generation task as a single data point must be informative of both background and foreground content. In this paper we propose a GAN framework for video generation that, instead, employs two latent spaces in order to structure the generative process in a more natural way: 1) a latent space to generate the static visual content of a scene (background), which remains the same for the whole video, and 2) a latent space where motion is encoded as a trajectory between sampled points and whose dynamics are modeled through an RNN encoder (jointly trained with the generator and the discriminator) and then mapped by the generator to visual objects' motion. Additionally, we extend current video discrimination approaches by incorporating in the learning procedure motion estimation and, leveraging the peculiarity of the generation process, unsupervised pixel-wise dense predictions. Extensive performance evaluation showed that our approach is able to a) synthesize more realistic videos than state-of-the-art methods, b) learn effectively both local and global video dynamics, as demonstrated by the results achieved on a video action recognition task over the UCF-101 dataset, and c) accurately perform unsupervised video object segmentation on standard video benchmarks, such as DAVIS, SegTrack and F4K-Fish.


title: Harnessing voltage margins for energy efficiency in multicore CPUs

abstract: In this paper, we present the first automated system-level analysis of multicore CPUs based on ARMv8 64-bit architecture (8-core, 28nm X-Gene 2 micro-server by AppliedMicro) when pushed to operate in scaled voltage conditions. We report detailed system-level effects including SDCs, corrected/uncorrected errors and application/system crashes. Our study reveals large voltage margins (that can be harnessed for energy savings) and also large $V_{min}$ variation among the 8 cores of the CPU chip, among 3 different chips (a nominal rated and two sigma chips), and among different benchmarks.Apart from the $V_{min}$ analysis we propose a new composite metric (severity) that aggregates the behavior of cores when undervolted and can support system operation and design protection decisions. Our undervolting characterization findings are the first reported analysis for an enterprise class 64-bit ARMv8 platform and we highlight key differences with previous studies on x86 platforms. We utilize the results of the system characterization along with performance counters information to measure the accuracy of prediction models for the behavior of benchmarks running in particular cores. Finally, we discuss how the detailed characterization and the prediction results can be effectively used to support design and system software decisions to harness voltage margins for energy efficiency while preserving operation correctness. Our findings show that, on average, 19.4% energy saving can be achieved without compromising the performance, while with 25% performance reduction, the energy saving raises to 38.8%.CCS CONCEPTS• Hardware → Power and energy → Power estimation and optimization; • Hardware → Robustness → Hardware reliability → Process, voltage and temperature variations


title: Transforming XML documents to OWL ontologies: A survey

abstract: The aims of XML data conversion to ontologies are the indexing, integration and enrichment of existing ontologies with knowledge acquired from these sources. The contribution of this paper consists in providing a classification of the approaches used for the conversion of XML documents into OWL ontologies. This classification underlines the usage profile of each conversion method, providing a clear description of the advantages and drawbacks belonging to each method. Hence, this paper focuses on two main processes, which are ontology enrichment and ontology population using XML data. Ontology enrichment is related to the schema of the ontology TBox, and ontology population is related to an individual Abox. In addition, the ontologies described in these methods are based on formal languages of the Semantic Web such as OWL Ontology Web Language or RDF Resource Description Framework. These languages are formal because the semantics are formally defined and take advantage of the Description Logics. In contrast, XML data sources are without formal semantics. The XML language is used to store, export and share data between processes able to process the specific data structure. However, even if the semantics is not explicitly expressed, data structure contains the universe of discourse by using a qualified vocabulary regarding a consensual agreement. In order to formalize this semantics, the OWL language provides rich logical constraints. Therefore, these logical constraints are evolved in the transformation of XML documents into OWL documents, allowing the enrichment and the population of the target ontology. To design such a transformation, the current research field establishes connections between OWL constructs classes, predicates, simple or complex data types, etc. and XML constructs elements, attributes, element lists, etc.. Two different approaches for the transformation process are exposed. The instance approaches are based on XML documents without any schema associated. The validation approaches are based on the XML schema and document validated by the associated schema. The second approaches benefit from the schema definition to provide automated transformations with logic constraints. Both approaches are discussed in the text.


title: SLAC: A Sparsely Labeled Dataset for Action Classification and Localization

abstract: This paper describes a procedure for the creation of large-scale video datasets for action classification and localization from unconstrained, realistic web data. The scalability of the proposed procedure is demonstrated by building a novel video benchmark, named SLAC (Sparsely Labeled ACtions), consisting of over 520K untrimmed videos and 1.75M clip annotations spanning 200 action categories. Using our proposed framework, annotating a clip takes merely 8.8 seconds on average. This represents a saving in labeling time of over 95% compared to the traditional procedure of manual trimming and localization of actions. Our approach dramatically reduces the amount of human labeling by automatically identifying hard clips, i.e., clips that contain coherent actions but lead to prediction disagreement between action classifiers. A human annotator can disambiguate whether such a clip truly contains the hypothesized action in a handful of seconds, thus generating labels for highly informative samples at little cost. We show that our large-scale dataset can be used to effectively pre-train action recognition models, significantly improving final metrics on smaller-scale benchmarks after fine-tuning. On Kinetics, UCF-101 and HMDB-51, models pre-trained on SLAC outperform baselines trained from scratch, by 2.0%, 20.1% and 35.4% in top-1 accuracy, respectively when RGB input is used. Furthermore, we introduce a simple procedure that leverages the sparse labels in SLAC to pre-train action localization models. On THUMOS14 and ActivityNet-v1.3, our localization model improves the mAP of baseline model by 8.6% and 2.5%, respectively.


title: Splat Feature Classification With Application to Retinal Hemorrhage Detection in Fundus Images

abstract: A novel splat feature classification method is presented with application to retinal hemorrhage detection in fundus images. Reliable detection of retinal hemorrhages is important in the development of automated screening systems which can be translated into practice. Under our supervised approach, retinal color images are partitioned into nonoverlapping segments covering the entire image. Each segment, i.e., splat, contains pixels with similar color and spatial location. A set of features is extracted from each splat to describe its characteristics relative to its surroundings, employing responses from a variety of filter bank, interactions with neighboring splats, and shape and texture information. An optimal subset of splat features is selected by a filter approach followed by a wrapper approach. A classifier is trained with splat-based expert annotations and evaluated on the publicly available Messidor dataset. An area under the receiver operating characteristic curve of 0.96 is achieved at the splat level and 0.87 at the image level. While we are focused on retinal hemorrhage detection, our approach has potential to be applied to other object detection tasks.


title: Sparse Matrix-Vector Multiplication on GPGPUs

abstract: The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific computing applications: it is the essential kernel for the solution of sparse linear systems and sparse eigenvalue problems by iterative methods. The efficient implementation of the sparse matrix-vector multiplication is therefore crucial and has been the subject of an immense amount of research, with interest renewed with every major new trend in high-performance computing architectures. The introduction of General-Purpose Graphics Processing Units (GPGPUs) is no exception, and many articles have been devoted to this problem. With this article, we provide a review of the techniques for implementing the SpMV kernel on GPGPUs that have appeared in the literature of the last few years. We discuss the issues and tradeoffs that have been encountered by the various researchers, and a list of solutions, organized in categories according to common features. We also provide a performance comparison across different GPGPU models and on a set of test matrices coming from various application domains.


title: Learning from less for better: semi-supervised activity recognition via shared structure discovery

abstract: Despite the active research into, and the development of, human activity recognition over the decades, existing techniques still have several limitations, in particular, poor performance due to insufficient ground-truth data and little support of intra-class variability of activities (i.e., the same activity may be performed in different ways by different individuals, or even by the same individuals with different time frames). Aiming to tackle these two issues, in this paper, we present a robust activity recognition approach by extracting the intrinsic shared structures from activities to handle intra-class variability, and the approach is embedded into a semi-supervised learning framework by utilizing the learned correlations from both labeled and easily-obtained unlabeled data simultaneously. We use l2,1 minimization on both loss function and regularizations to effectively resist outliers in noisy sensor data and improve recognition accuracy by discerning underlying commonalities from activities. Extensive experimental evaluations on four community-contributed public datasets indicate that with little training samples, our proposed approach outperforms a set of classical supervised learning methods as well as those recently proposed semi-supervised approaches.


Complete characterization of security notions for probabilistic private-key encryption


title: NASA-Task Load Index (NASA-TLX); 20 years later

abstract: NASA-TLX is a multi-dimensional scale designed to obtain workload estimates from one or more operators while they are performing a task or immediately afterwards. The years of research that precede...


title: Learning the Structure of Deep Convolutional Networks

abstract: In this work, we develop a novel method for automatically learning aspects of the structure of a deep model, in order to improve its performance, especially when labeled training data are scarce. We propose a new convolutional neural network model with the Indian Buffet Process (IBP) prior, termed ibpCNN. The ibpCNN automatically adapts its structure to provided training data, achieves an optimal balance among model complexity, data fidelity and training loss, and thus offers better generalization performance. The proposed ibpCNN captures complicated data distribution in an unsupervised generative way. Therefore, ibpCNN can exploit unlabeled data -- which can be collected at low cost -- to learn its structure. After determining the structure, ibpCNN further learns its parameters according to specified tasks, in an end-to-end fashion, and produces discriminative yet compact representations. We evaluate the performance of ibpCNN, on fully-and semi-supervised image classification tasks, ibpCNN surpasses standard CNN models on benchmark datasets, with much smaller size and higher efficiency.


title: Architecture of a message-driven processor

abstract: We propose a machine architecture for a high-performance processing node for a message-passing, MIMD concurrent computer. The principal mechanisms for attaining this goal are the direct execution and buffering of messages and a memory-based architecture that permits very fast context switches. Our architecture also includes a novel memory organization that permits both indexed and associative accesses and that incorporates an instruction buffer and message queue. Simulation results suggest that this architecture reduces message reception overhead by more than an order of magnitude.


title: Intel® Software Guard Extensions (Intel® SGX) Software Support for Dynamic Memory Allocation inside an Enclave

abstract: Intel® Software Guard Extensions (Intel® SGX) SGX2 extends the Intel® Software Guard Extensions (SGX) instruction set and enables software developers to dynamically manage memory within the SGX environment. This paper reviews the current SGX Software RunTime Environment and proposes additions to the framework which will allow developers to benefit from features enabled by SGX2 such as dynamic heap management, stack expansion, and thread context creation.


title: Secure Data Deduplication with Dynamic Ownership Management in Cloud Storage

abstract: In cloud services, deduplication technology is commonly used to reduce the space and bandwidth requirements of services by eliminating redundant data and storing only a single copy. Deduplication is most effective when multiple users outsource the same data to the cloud storage, but it raises issues relating to security and ownership. Proof-ofownership schemes allow any owner of the same data to prove to the cloud storage server that he owns the data in a robust way. However, if encrypted data is outsourced into the cloud storage and the ownership changes dynamically, deduplication would be hampered. Thus, we propose a secure deduplication scheme that supports dynamic ownership management based on randomized convergent encryption [3] in this study.


title: DCM forward-flyback converter integrated with a 5-order cockcroft-walton voltage multiplier: A steady-state and resonant current analysis

abstract: This paper presents the steady state and resonant current analysis of a Forward-Flyback Voltage Multiplier operating in discontinuous conduction mode. The operation stages are approached, taking into account the resonant current. The static gain and the conditions for the operations modes are obtained. A sinusoidal approximation is proposed to represent the resonant current with respect to the number of stages, voltage multiplier capacitors and leakage inductance. The characteristics of voltage sharing among capacitors, that allows the use of small voltage components, and the high-gain with reduced turns of the transformer are verified. Bench tests were made for a 200 V/2000 V prototype, from one to five stages, highlighting the overall circuit effects for a 200 W output power. Obtained waveforms for different number of voltage multiplier stages validate the theoretical and simulated results, making this converter an alternative to isolated DC/DC systems where the output voltage must be higher than the input voltage.


title: Can Facebook Aid Sustainability? An Investigation of Empathy Expression within the Humans of New York Blog

abstract: This qualitative study offers a novel exploration of the links between social media, virtual intergroup contact, and empathy by examining how empathy is expressed through interactions on a popular social media blog. Global leaders are encouraging individuals to engage in behaviors and support policies that provide basic social foundations. It is difficult to motivate people to undertake such actions. However, research shows that empathy intensifies motivation to help others. It can cause individuals to see the world from the perspective of stigmatized group members and increase positive feelings. Social media offers a new pathway for virtual intergroup contact, providing opportunities to increase conversation about disadvantaged others and empathy. We examined expressions of empathy within a popular blog, Humans of New York (HONY), and engaged in purposeful case selection by focusing on (1) events where specific prosocial action was taken corresponding to interactions on the HONY blog and (2) presentation of people in countries other than the United States. Nine overarching themes; (1) perspective taking, (2) fantasy, (3) empathic concern, (4) personal distress, (5) relatability, (6) prosocial action, (7) community appreciation, (8) anti-empathy, and (9) rejection of anti-empathy, exemplify how the HONY community expresses and shares empathic thoughts and feelings.


title: A Study on the Vulnerabilities of Mobiles Apps associated with Software Modules

abstract: This paper reports a large-scale study that aims to understand how mobile application (app) vulnerabilities are associated with software libraries. We analyze both free and paid apps. Studying paid apps was quite meaningful because it helped us understand how differences in app development/maintenance affect the vulnerabilities associated with libraries. We analyzed 30k free and paid apps collected from the official Android marketplace. Our extensive analyses revealed that approximately 70%/50% of vulnerabilities of free/paid apps stem from software libraries, particularly from third-party libraries. Somewhat paradoxically, we found that more expensive/popular paid apps tend to have more vulnerabilities. This comes from the fact that more expensive/popular paid apps tend to have more functionality, i.e., more code and libraries, which increases the probability of vulnerabilities. Based on our findings, we provide suggestions to stakeholders of mobile app distribution ecosystems.


title: The stochastic vehicle routing problem, a literature review, part I: models

abstract: Building on the work of Gendreau et al. (Eur J Oper Res 88(1):3–12; 1996), we review the past 20 years of scientific literature on stochastic vehicle routing problems. The numerous variants of the problem that have been studied in the literature are described and categorized.


title: Entertainment capture through heart rate activity in physical interactive playgrounds

abstract: An approach for capturing and modeling individual entertainment ("fun") preferences is applied to users of the innovative Playware playground, an interactive physical playground inspired by computer games, in this study. The goal is to construct, using representative statistics computed from children's physiological signals, an estimator of the degree to which games provided by the playground engage the players. For this purpose children's heart rate (HR) signals, and their expressed preferences of how much "fun" particular game variants are, are obtained from experiments using games implemented on the Playware playground. A comprehensive statistical analysis shows that children's reported entertainment preferences correlate well with specific features of the HR signal. Neuro-evolution techniques combined with feature set selection methods permit the construction of user models that predict reported entertainment preferences given HR features. These models are expressed as artificial neural networks and are demonstrated and evaluated on two Playware games and two control tasks requiring physical activity. The best network is able to correctly match expressed preferences in 64% of cases on previously unseen data (p?value 6 · 10?5). The generality of the methodology, its limitations, its usability as a real-time feedback mechanism for entertainment augmentation and as a validation tool are discussed.


title: Interuser Interference in Adjacent Wireless Body Area Networks

abstract: The interuser interference between wireless body area networks worn by two moving persons in an indoor environment at 60 and 2.45 GHz is experimentally investigated. Both omnidirectional antennas (monopoles) and directional antennas (horns) were used in the measurements. The interference power-level variation and carrier-to-interference ratio (CIR) were measured and characterized. Median interference power-level reduction of nearly 20 dB was achieved in all measured channels by adopting 60-GHz radio transmissions compared to 2.45 GHz, both with omnidirectional on-body antennas. A further 20 dB of interference-level reduction was achieved at 60 GHz by adopting directional antennas confining the radiated wave along the body surface. Level crossing rates for interference power variation using omnidirectional antennas range from 2.7 to 7.2 $\text{s}^{-1}$ at 2.45 GHz and 32–64 $\text{s}^{-1}$ at 60 GHz for static to progressively more dynamic links, whereas the corresponding range using 60-GHz directional antennas is reduced to 39–54 $\text{s}^{-1}$ . The median improvement in the instantaneous CIR for the chest-head channel between 60 and 2.45 GHz was approximately 30 dB. The measured interference power level and CIR, in dB, were found to satisfactorily fit the normal distribution according to the normalized root-mean-square error-based fit metric.


title: The Spike-Triggered Average of the Integrate-and-Fire Cell Driven by Gaussian White Noise

abstract: We compute the exact spike-triggered average (STA) of the voltage for the nonleaky integrate-and-fire (IF) cell in continuous time, driven by gaussian white noise. The computation is based on techniques from the theory of renewal processes and continuous-time hidden Markov processes (e.g., the backward and forward Fokker-Planck partial differential equations associated with first-passage time densities). From the STA voltage, it is straightforward to derive the STA input current. The theory also gives an explicit asymptotic approximation for the STA of the leaky IF cell, valid in the low-noise regime σ → 0. We consider both the STA and the conditional average voltage given an observed spike “doublet” event, that is, two spikes separated by some fixed period of silence. In each case, we find that the STA as a function of time-preceding-spike, τ, has a square root singularity as τ approaches zero from below and scales linearly with the scale of injected noise current. We close by briefly examining the discrete-time case, where similar phenomena are observed.


title: ITEM2VEC: Neural item embedding for collaborative filtering

abstract: Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item-based CF that produces embedding for items in a latent space. The method is capable of inferring item-item relations even when user information is not available. We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.


Rapid and accurate spoken term detection.


title: A SIFT-SVM method for detecting cars in UAV images

abstract: In the last years, the advent of unmanned aerial vehicles (UAVs) for civilian remote sensing purposes has generated a lot of interest because of the various new applications they can offer. One of them is represented by the automatic detection and counting of cars. In this paper, we propose a novel car detection method. It starts with a feature extraction process based on scalar invariant feature transform (SIFT) thanks to which a set of keypoints is identified in the considered image and opportunely described. Successively, the process discriminates between keypoints assigned to cars and those associated with all remaining objects by means of a support vector machine (SVM) classifier. Experimental results have been conducted on a real UAV scene. They show how the proposed method allows providing interesting detection performances.


title: Beyond simple pessimism: Effects of sadness and anger on social perception

abstract: In keeping with cognitive appraisal models of emotion, it was hypothesized that sadness and anger would exert different influences on causal judgments. Two experiments provided initial support for this hypothesis. Sad Ss perceived situationally caused events as more likely (Experiment 1) and situational forces more responsible for an ambiguous event (Experiment 2) than angry Ss, who, in contrast, perceived events caused by humans as more likely and other people as more responsible. Experiments 3, 4, and 5 showed that the experience of these emotions, rather than their cognitive constituents, mediates these effects. The nonemotional exposure to situational or human agency information did not influence causal judgments (Experiment 3), whereas the induction of sadness and anger without explicit agency information did (Experiments 4 and 5). Discussion is focused on the influence of emotion on social judgment.


title: A review of antennas and propagation for MIMO wireless communications

abstract: Multiple-input-multiple-output (MIMO) wireless systems use multiple antenna elements at transmit and receive to offer improved capacity over single antenna topologies in multipath channels. In such systems, the antenna properties as well as the multipath channel characteristics play a key role in determining communication performance. This paper reviews recent research findings concerning antennas and propagation in MIMO systems. Issues considered include channel capacity computation, channel measurement and modeling approaches, and the impact of antenna element properties and array configuration on system performance. Throughout the discussion, outstanding research questions in these areas are highlighted.


title: User Modeling via Stereotypes

abstract: This paper addresses the problems that must be considered if computers are going to treat their users as individuals with distinct personalities, goals, and so forth. It first outlines the issues, and then proposes stereotypes as a useful mechanism for building models of individual users on the basis of a small amount of information about them. In order to build user models quickly, a large amount of uncertain knowledge must be incorporated into the models. The issue of how to resolve the conflicts that will arise among such inferences is discussed. A system, Grundy, is described that builds models of its users, with the aid of stereotypes, and then exploits those models to guide it in its task, suggesting novels that people may find interesting. If stereotypes are to be useful to Grundy, they must accurately characterize the users of the system. Some techniques to modify stereotypes on the basis of experience are discussed. An analysis of Grundy's performance shows that its user models are effective in guiding its performance.


title: Low Power Mobility System for Micro Planetary Rover "Micro5"

abstract: In this paper, we propose a newly developed suspension system called “Pentad Grade Assist Suspension (PEGASUS)”. PEGASUS has high mobility as well as rocker-bogie suspension, with very simple mechanism called only-one-joint architecture. PEGASUS is able to successfully climb up on the stepalike terrain with low energy consumption by scale model test. The prototype of micro planetary rover Micro5 which has been developed by the rover R&D group of ISAS, Meiji university, and Chuo university, adopts PEGASUS as its mobility system. The Micro5 is under testing on the variety types of land environment.


title: Time perception, depression and sadness

abstract: Abstract This study examined changes in time perception as a function of depressive symptoms, assessed for each participant with the Beck Depression Inventory (BDI). The participants performed a temporal bisection task in which they had to categorize a signal duration of between 400 and 1600 ms as either as short or long. The data showed that the bisection function was shifted toward the right, and that the point of subjective equality was higher in the depressive than in the non-depressive participants. Furthermore, the higher the depression score was, the shorter the signal duration was judged to be. In contrast, the sensitivity to time was similar in these two groups of participants. These results thus indicate that the probe durations were underestimated by the depressive participants. The sadness scores assessed by the Brief Mood Inventory Scale (BMIS) also suggest that the emotional state of sadness in the depressive participants goes some way to explaining their temporal performance. Statistical analyses and modeling of data support the idea according to which these results may be explained by a slowing down of the internal clock in the depressive participants.


title: Potentials and Challenges of Mobile Media in Museums

abstract: In this article we discuss potential scenarios of use for mobile media in museums and the challenges they pose. We examine how the characteristics of mobile technology suit the specific characteristics of a museum setting. Based on these considerations different ways to support visitors with mobile devices are put forward: attentional focus and guidance; satisfaction of situational interest; information adaptation to a specific visitor and to a specific location; information elaboration through facilitation of knowledge exchange and documentation of the visit, as well as assistance in exhibition evaluation. Implications for the development of mobile applications in museums are derived.


title: Microwave filters with improved stopband based on sub-wavelength resonators

abstract: The main aim of this paper is to demonstrate the potentiality of sub-wavelength resonators, namely, split-ring resonators, complementary split-ring resonators, and related structures to the suppression of undesired spurious bands in microwave filters, a key aspect to improve their rejection bandwidths. The main relevant characteristics of the cited resonators are their dimensions (which can be much smaller than signal wavelength at resonance) and their high-Q factor. This allows us to design stopband structures with significant rejection levels, few stages, and small dimensions, which can be integrated within the filter active region. By this means, no extra area is added to the device, while the passband of interest is virtually unaltered. A wide variety of bandpass filters, implemented in both coplanar-waveguide and microstrip technologies, have been designed and fabricated by the authors. The characterization of these devices points out the efficiency of the proposed approach to improve filter responses with harmonic rejection levels near 40 dB in some cases. It is also important to highlight that the conventional design methodology for the filters holds. For certain configurations, the presence of the resonators slightly lowers the phase velocity at the frequencies of interest with the added advantage of some level of reduction in device dimensions.


title: An internetworked self-driving car system-of-systems

abstract: This paper presents an overview of the architecture of an internetworked self-driving car system-of-systems. The architecture considers both the car as a system-of-systems as well as multiple cars participation in a larger multi-vehicle system-of-systems. Each relevant aspect of the architecture is reviewed.


title: A Framework to Estimate the Nutritional Value of Food in Real Time Using Deep Learning Techniques

abstract: There has been a rapid increase in dietary ailments during the last few decades, caused by unhealthy food routine. Mobile-based dietary assessment systems that can record real-time images of the meal and analyze it for nutritional content can be very handy and improve the dietary habits and, therefore, result in a healthy life. This paper proposes a novel system to automatically estimate food attributes such as ingredients and nutritional value by classifying the input image of food. Our method employs different deep learning models for accurate food identification. In addition to image analysis, attributes and ingredients are estimated by extracting semantically related words from a huge corpus of text, collected over the Internet. We performed experiments with a dataset comprising 100 classes, averaging 1000 images for each class to acquire top 1 classification rate of up to 85%. An extension of a benchmark dataset Food-101 is also created to include sub-continental foods. Results show that our proposed system is equally efficient on the basic Food-101 dataset and its extension for sub-continental foods. The proposed system is implemented as a mobile app that has its application in the healthcare sector.


title: Exploiting Google's Edge Network for Massively Multiplayer Online Games

abstract: Massively multiplayer online game servers are challenging to build and maintain; they require always-on availability, low-latency, and high predictability. In many ways, these systems could benefit from pushing application logic to the connected clients. However, historically peer-to-peer networks have not worked when applied to this domain. Pushing game logic to the clients complicates development since client end hosts cannot trust their peers and they have unreliably persistent connections, increasing the possibility of cheating or network failure. Google's Edge Network changes everything we have concluded about peer-to-peer networks over the past decade. Having access to thousands of servers all over the world solves problems of availability, data access, link saturation, security, and control. This new system allows for the inclusion of trusted peers in otherwise untrusted node clusters. Developers can explore peer-to-peer or decentralized algorithms while allowing complete control over their data, code, and players, in their developed virtual worlds. They can offload application game logic to a low-latency scalable system, which provides a cost-effective solution residing closer to the connected clients. In this paper, we investigate the gains game developers may obtain by exploring emerging edge cloud technology. We demonstrate how massively multiplayer online games benefit from this new system though simulation. We compare area-of-interest latency, which is the latency between client interactions within the virtual world for known World of Warcraft and Google data centers locations. We show the benefits to area-of-interest latency gained from using Google's Edge Network, while minimally affecting client to server latency. Finally, we present a novel approach to maximize latency reductions for area-of- interest latency by moving players to optimal peering edge servers, thus reducing distance between clients.


title: Current Controversy: Spikes, Bursts, and Synchrony in Generalized Absence Epilepsy: Unresolved Questions Regarding Thalamocortical Synchrony in Absence Epilepsy

abstract: Absence epilepsy is a disorder of thalamocortical networks. Animal models have provided detailed information regarding the core cellular, synaptic, and network features that contribute to the electroencephalogram spike and wave discharge characteristic of typical absence epilepsy. Understanding of seizure networks and dynamics is a critical step toward improving treatments, yet competing conceptual models have evolved to explain seizure initiation and propagation. Recent studies have questioned 2 key model concepts: (1) T-type Ca2+ channel-dependent burst firing in thalamic relay neurons may not be essential for seizure generation, bringing into question the proposed mechanism for the antiepileptic drug ethosuximide in reducing thalamic bursting and (2) widespread synchronized neural activity may not be a core feature of the seizures, indicating that reductions in synchrony would not be a productive therapeutic goal. In this review, I will discuss these current findings, highlight the innovative approaches that have enabled these insights, and provide a unified framework that incorporates these sometimes-conflicting ideas. Finally, I lay out future work that will be necessary to finally resolve the remaining issues.


title: A low-light image enhancement method for both denoising and contrast enlarging

abstract: In this paper, a novel united low-light image enhancement framework for both contrast enhancement and denoising is proposed. First, the low-light image is segmented into superpixels, and the ratio between the local standard deviation and the local gradients is utilized to estimate the noise-texture level of each superpixel. Then the image is inverted to be processed in the following steps. Based on the noise-texture level, a smooth base layer is adaptively extracted by the BM3D filter, and another detail layer is extracted by the first order differential of the inverted image and smoothed with the structural filter. These two layers are adaptively combined to get a noise-free and detail-preserved image. At last, an adaptive enhancement parameter is adopt into the dark channel prior dehazing process to enlarge contrast and prevent over/under enhancement. Experimental results demonstrate that our proposed method outperforms traditional methods in both subjective and objective assessments.


title: Framework for Measuring Perceived Quality in Technical Documentation

abstract: Customer product information (CPI) provides essential information to a user about how to use a product. Given the importance of such technical documentation to enable a more effective use of technology, there is very little research conducted in the domain of improving document quality. This study intends to fill this gap by developing a tentative framework for how to measure user perceived quality (PQ) of technical documentation. Data collection is based on a literature review and interviews with practitioners. The advantages and disadvantages of the approach are evaluated and suggestions for future studies outlined. The implication of this research is that it allows companies that produce technical documentation to measure and thus improve document quality more effectively.


title: Efficient Multi-View 3D Video Multicast with Mobile Edge Computing

abstract: With the emergence of multi-view 3D videos, network operators now face a new challenge to resolve the dramatic increase of the network bandwidth required to support all subscribed views (typically 16 or 32) of a video. Recently, Depth-Image-Based Rendering (DIBR) in Computer Vision has been demonstrated as a promising way for efficient multi-view 3D video multicast, because many views can be synthesized in mobile devices and no longer need to be transmitted. Nevertheless, DIBR is computationally intensive and incurs additional power consumption in mobile devices, and unsubscribed views need to be transmitted to mobile users for DIBR. In this paper, therefore, we aim to leverage Mobile Edge Computing (MEC) for DIBR to foster effective and efficient multi-view video multicast. We formulate a new problem, named Multi-view Multicast Synthesis and Delivery (MMSD) and prove the NP-Hardness. We design an approximation algorithm, named Merge Search Forest Algorithm (MSFA), to choose the view to be synthesized and build a multicast topology including a low-cost forest for supporting each subscribed view. Simulation results manifest that MSFA outperforms the existing approaches by 30% to 50% of the total communication and computation cost.


title: Extracting PICO sentences from clinical trial reports using supervised distant supervision

abstract: Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process. We seek to expedite evidence synthesis by developing machine learning models to automatically extract sentences from articles relevant to PICO elements. Collecting a large corpus of training data for this task would be prohibitively expensive. Therefore, we derive distant supervision (DS) with which to train models using previously conducted reviews. DS entails heuristically deriving 'soft' labels from an available structured resource. However, we have access only to unstructured, free-text summaries of PICO elements for corresponding articles; we must derive from these the desired sentence-level annotations. To this end, we propose a novel method - supervised distant supervision (SDS) - that uses a small amount of direct supervision to better exploit a large corpus of distantly labeled instances by learning to pseudo-annotate articles using the available DS. We show that this approach tends to outperform existing methods with respect to automated PICO extraction.


NLP-Cube: End-to-End Raw Text Processing With Neural Networks


title: Accurate, Efficient and Scalable Graph Embedding

abstract: The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge is to reduce the complexity of layered GCNs and make them parallelizable and scalable on very large graphs — state-of the art techniques are unable to achieve scalability without losing accuracy and efficiency. In this paper, we propose novel parallelization techniques for graph sampling-based GCNs that achieve superior scalable performance on very large graphs without compromising accuracy. Specifically, our GCN guarantees work-efficient training and produces order of magnitude savings in computation and communication. To scale GCN training on tightly-coupled shared memory systems, we develop parallelization strategies for the key steps in training: For the graph sampling step, we exploit parallelism within and across multiple sampling instances, and devise an efficient data structure for concurrent accesses that provides theoretical guarantee of near-linear speedup with number of processing units. For the feature propagation step within the sampled graph, we improve cache utilization and reduce DRAM communication by data partitioning. We prove that our partitioning strategy is a 2-approximation for minimizing the communication time compared to the optimal strategy. We demonstrate that our parallel graph embedding outperforms state-of-the-art methods in scalability (with respect to number of processors, graph size and GCN model size), efficiency and accuracy on several large datasets. On a 40-core Xeon platform, our parallel training achieves 64x speedup (with AVX) in the sampling step and 25x speedup in the feature propagation step, compared to the serial implementation, resulting in a net speedup of 21x. Our scalable algorithm enables deeper GCN, as demonstrated by 1306x speedup on a 3-layer GCN compared to Tensorflow implementation of state-of-the-art.


title: A Distributed Algorithm for Localization Error Detection-Correction, Use in In-Network Faulty Reading Detection: Applicability in Long-Thin Wireless Sensor Networks

abstract: In near future, wireless sensors networks (WSN) are expected to be deployed for a vast variety of applications. For many of these applications, sensed data individually have no meaning without proper knowledge of position where the data was sensed. Although there have been proposed algorithms for localization, many of them fall short with respect to complexity, cost, security and accuracy. Some issues that still hinder the efficiency of available localization techniques are multi-path propagation, security attack (internal and external attack by faulty or malicious nodes), and sometimes specific network topologies. For example, the Long-Thin (LT) topology of WSN is highly prone to localization error due to its special distribution pattern. In many real world WSN applications, the localization is also needed to be Global Positioning Systems (GPS) free. Apart from efficient localization, another important operation in WSN application is the need to have in-network detection of faulty sensor readings, without compromising detection of important events. Looking at these critically important operations of WSN, our first contribution is proposing a self-organized distributed GPS free localization error detection and correction algorithm. Our next contribution is utilizing reliability gained in the proposed localization to construct an improved algorithm for in-network detection of faulty readings. We have also proposed a fault tolerant structure for Long-Thin (LT) WSN topology, that can well utilize both of the proposed algorithms.


title: Fast J-linkage algorithm for camera orientation applications

abstract: A computationally efficient approach to obtain vanishing points in images for camera orientation applications is presented in this paper. Modifications are made to the conventional J-linkage algorithm, named Fast J-linkage, so that the detection of vanishing points is achieved in a computationally efficient manner. This fast algorithm is then utilized to recover camera orientation or rotation matrices from video sequences. Both the computational efficiency and performance aspect of the Fast J-linkage algorithm are compared with the conventional J-linkage algorithm by examining the York Urban image database. In addition, the results of an actual camera rotation implementation is provided and validated by using inertial sensors data. The results obtained indicate the higher computational efficiency and accuracy aspects of the developed vanishing point detection.


title: Enriching EL-Concepts with Greatest Fixpoints

abstract: We investigate the expressive power and computational complexity of ELν, the extension of the lightweight description logic EL with concept constructors for greatest fixpoints. It is shown that ELν has the same expressive power as EL extended with simulation quantifiers and that it can be characterized as a largest fragment of monadic second-order logic that is preserved under simulations and has finite minimal models. As in basic EL, all standard reasoning problems for general TBoxes can be solved in polynomial time. ELν has a range of very desirable properties that EL itself is lacking. Firstly, least common subsumers w.r.t. general TBoxes as well as most specific concepts always exist and can be computed in polynomial time. Secondly, ELν shares with EL the Craig interpolation property and the Beth definability property, but in contrast to EL allows the computation of interpolants and explicit concept definitions in polynomial time.


title: First molecular detection and identification of a potyvirus associated with severe mosaic disease of amaryllis (Hippeastrum hybridum Hort.) in India

abstract: A potyvirus associated with mosaic disease of amaryllis was detected and identified by reverse-transcription and polymerase chain reaction using degenerate primers specific to the core region of coat protein gene of potyviruses followed by cloning and sequencing. The sequence shared highest (98–95%) amino acid identities with Amaryllis potyvirus that is not yet recognised by the International Committee on Taxonomy of Viruses, Amazon lily mosaic virus (tentative potyvirus) and Hippeastrum mosaic virus (recognised potyvirus). Screening bulbs of elite amaryllis cultivars for potyvirus infection would be useful to establish virus-free propagating material for the floriculture industry in India.