title: Computational approaches for emotion detection in text

abstract: Emotions are part and parcel of human life and among other things, highly influence decision making. Computers have been used for decision making for quite some time now but have traditionally relied on factual information. Recently, interest has been growing among researchers to find ways of detecting subjective information used in blogs and other online social media. This paper presents emotion theories that provide a basis for emotion models. It shows how these models have been used by discussing computational approaches to emotion detection. We propose a hybrid based architecture for emotion detection. The SVM algorithm is used for validating the proposed architecture and achieves a prediction accuracy of 96.43% on web blog data.


Measuring requirements testing:: experience report


title: Design and Implementation of 4-Bit Arithmetic and Logic Unit Chip with the Constraint of Power Consumption

abstract: By increasing the demand of enhancing the ability of processors to handle the more complex and challenging processors has resulted in the integration of a number of processor cores into one chip. Still the load of on the processor is not less in generic system. An Arithmetic Logic Unit (ALU) is the heart of all microprocessors. It is a combinational logic unit that performs its logical or arithmetic operations. ALU is getting smaller and more complex nowadays to enable the development of a more powerful but smaller computer. In this proposed paper a 4 bit ALU chip has been design to the benefits of all the computations are done in parallel and available simultaneously, so no clock resources are wasted. The MUX is then simply used to select the required output. Index Terms: ALU, CMOS, VLSI and TTL


title: A Nonuniform Weighted Loss Function for Imbalanced Image Classification

abstract: In the field of deep image classification, it is a challenging task to learn the classifier from imbalanced dataset. Existing deep neural networks usually equally punish the training loss of each sample. To minimize the average loss, the deep classifier inevitably tends to sacrifice the classification accuracy of the minority when the sizes of training samples are imbalanced across different categories. To address this issue, this paper proposes an nonuniform weighted loss function which aims to compensate the bias of training loss for the minority categories. The recent popular datasets are balanced in terms of the sample size across different classes. So we build a new large scale imbalanced dataset to verify the proposed method. Our method start from deriving the gradient formulation of the proposed loss function, and a more complete explanation is given to demonstrate its effectiveness.


title: Monte Carlo methods for massively parallel computers

abstract: Applications that require substantial computational resources today cannot avoid the use of heavily parallel machines. Embracing the opportunities of parallel computing and especially the possibilities provided by a new generation of massively parallel accelerator devices such as GPUs, Intel's Xeon Phi or even FPGAs enables applications and studies that are inaccessible to serial programs. Here we outline the opportunities and challenges of massively parallel computing for Monte Carlo simulations in statistical physics, with a focus on the simulation of systems exhibiting phase transitions and critical phenomena. This covers a range of canonical ensemble Markov chain techniques as well as generalized ensembles such as multicanonical simulations and population annealing. While the examples discussed are for simulations of spin systems, many of the methods are more general and moderate modifications allow them to be applied to other lattice and off-lattice problems including polymers and particle systems. We discuss important algorithmic requirements for such highly parallel simulations, such as the challenges of random-number generation for such cases, and outline a number of general design principles for parallel Monte Carlo codes to perform well.


title: Punjabi Stop Words: A Gurmukhi, Shahmukhi and Roman Scripted Chronicle

abstract: With advent of Unicode encoding, Punjabi language content, written using gurmukhi script as well as in shahmukhi script, is increasing day by day on internet. Processing textual information involves passing it to various pre-processing phases. Stop-word elimination is one such sub phase. 256 Gurmukhi stop words had been identified from poetry, stories and online material and passed to Punjabi stemmer. After stemming, 184 stemmed stop words were generated and these stemmed stop words were passed to transliteration phase. This led to generation of stop words in shahmukhi script. For the first time in scientific community dealing with computational linguistics and literature processing using NLP techniques, the list of 184 stop words of Punjabi language is released for public usage and further NLP applications. The presented list consists of stop words of Punjabi language with their Gurmukhi, Shahmukhi as well as Roman scripted forms.


title: Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies

abstract: We propose Strong Emergent Policy (STEP) approximation, a scalable approach to learn strong decentralized policies for cooperative MAS with a distributed variant of policy iteration. For that, we use function approximation to learn from action recommendations of a decentralized multi-agent planning algorithm. STEP combines decentralized multi-agent planning with centralized learning, only requiring a generative model for distributed black box optimization. We experimentally evaluate STEP in two challenging and stochastic domains with large state and joint action spaces and show that STEP is able to learn stronger policies than standard multi-agent reinforcement learning algorithms, when combining multi-agent open-loop planning with centralized function approximation. The learned policies can be reintegrated into the multi-agent planning process to further improve performance.


title: FaceForensics++: Learning to Detect Manipulated Facial Images

abstract: The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society. At best, this leads to a loss of trust in digital content, but could potentially cause further harm by spreading false information or fake news. This paper examines the realism of state-of-the-art image manipulations, and how difficult it is to detect them, either automatically or by humans. To standardize the evaluation of detection methods, we propose an automated benchmark for facial manipulation detection. In particular, the benchmark is based on DeepFakes, Face2Face, FaceSwap and NeuralTextures as prominent representatives for facial manipulations at random compression level and size. The benchmark is publicly available and contains a hidden test set as well as a database of over 1.8 million manipulated images. This dataset is over an order of magnitude larger than comparable, publicly available, forgery datasets. Based on this data, we performed a thorough analysis of data-driven forgery detectors. We show that the use of additional domainspecific knowledge improves forgery detection to unprecedented accuracy, even in the presence of strong compression, and clearly outperforms human observers.


title: An efficient method of license plate location

abstract: License plate location is an important stage in vehicle license plate recognition for automated transport system. This paper presents a real time and robust method of license plate location. License plate area contains rich edge and texture information. We first extract out the vertical edges of the car image using image enhancement and Sobel operator, then remove most of the background and noise edges by an effective algorithm, and finally search the plate region by a rectangle window in the residual edge image and segment the plate out from the original car image. Experimental results demonstrate the great robustness and efficiency of our method.


title: Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention

abstract: Recognizing implicit discourse relations is a challenging but important task in the field of Natural Language Processing. For such a complex text processing task, different from previous studies, we argue that it is necessary to repeatedly read the arguments and dynamically exploit the efficient features useful for recognizing discourse relations. To mimic the repeated reading strategy, we propose the neural networks with multi-level attention (NNMA), combining the attention mechanism and external memories to gradually fix the attention on some specific words helpful to judging the discourse relations. Experiments on the PDTB dataset show that our proposed method achieves the state-of-art results. The visualization of the attention weights also illustrates the progress that our model observes the arguments on each level and progressively locates the important words.


title: Flux barrier and skew design optimisation of reluctance synchronous machines

abstract: In this paper an investigation into an alternative reluctance synchronous machine rotor flux barrier topology creation technique is presented. The technique investigated implements a high number of flux barrier variables with an alternative asymmetric rotor structure topology in combination with optimisation and finite element analysis. The investigation focuses on maximising average torque and minimising torque ripple in order to study the possibility of achieving acceptable torque ripple machine parameters without implementing rotor skew as torque ripple reduction technique. A further investigation into the effect of rotor skew on the proposed topology is conducted followed by the manufacturing of the proposed flux barrier prototype and testing.


title: Real time face recognition engine using compact features for electronics key

abstract: In this research, a design and implementation of real-time face recognition engine using compact features vector extracted by linear binary pattern (LBP) and zoned discrete cosine transforms (DCT) analysis is proposed for electronics key. The function of LBP is to normalize the lighting variations of the input face image and the function of zoned DCT is to define the local descriptors of the face image. In order to get more compact features vector, the predictive LDA is employed for dimensional reduction. The aims of this research is to develop fast and strong face recognition that can be implemented for electronic key which will be implemented for substituting current security system (PIN and password). In addition, the recognition engine is also designed for real time face recognition which can work on hardware having limited resources such as Intel atom computer, raspberry pi, and android smart phone. The experimental results show that the proposed engine provide high enough recognition rate, and small false rejection rate (FRR) and false acceptance rate (FAR). In addition, the engine needs short processing time.


title: Solar photovoltaic array fed Luo converter based BLDC motor driven water pumping system

abstract: This paper deals with the solar photovoltaic (SPV) array fed water-pumping system using a Luo converter as an intermediate DC-DC converter and a permanent magnet brushless DC (BLDC) motor to drive a centrifugal water pump. Among the different types of DC-DC converters, an elementary Luo converter is selected in order to extract the maximum power available from the SPV array and for safe starting of BLDC motor. The elementary Luo converter with reduced components and single semiconductor switch has inherent features of reducing the ripples in its output current and possessing a boundless region for maximum power point tracking (MPPT). The electronically commutated BLDC motor is used with a voltage source inverter (VSI) operated at fundamental frequency switching thus avoiding the high frequency switching losses resulting in a high efficiency of the system. The SPV array is designed such that the power at rated DC voltage is supplied to the BLDC motor-pump under standard test condition and maximum switch utilization of Luo converter is achieved which results in efficiency improvement of the converter. Performances at various operating conditions such as starting, dynamic and steady state behavior are analyzed and suitability of the proposed system is demonstrated using MATLAB/Simulink based simulation results.


title: Dominance relationships among one-male units in a provisioned free-ranging band of the Sichuan snub-nosed monkeys (Rhinopithecus roxellana) in the Qinling Mountains, China.

abstract: We studied the dominance relationships among one-male units (OMUs) in a provisioned free-ranging band of the Sichuan snub-nosed monkeys (Rhinopithecus roxellana) in the Qinling Mountains of central China from 2001 to 2005. The band was composed of 6-8 OMUs that stayed in the band for several years. Linear dominance orders could be detected using displacement interactions with directional asymmetry among OMUs in 82.3+/-5% of interactions, and ambiguous and reversed interactions in 17.7+/-5%. The dominance rank of OMUs was positively related with the duration of their stay in the band, and this may be attributed to the association of the resident male with adult females, rather than the fighting ability of resident males, as males do not fight seriously with each other. Subordinate units were observed to merge with dominant units resulting in an elevation of their rank order. The linear dominance relationship among OMUs in the Sichuan snub-nosed monkeys may have evolved as a result of competition for preferred food trees.


Adding Value to the Library Catalog by Implementing a Recommendation System


title: Building Multi-Platform User Interfaces with UIML

abstract: There has been a widespread emergence of computing devices in the past few years that go beyond the capabilities of traditional desktop computers. However , users want to use the same kinds of applications and access the same data and information on these appliances that they can access on their desktop computers. The user interfaces (UI) for these platforms go beyond the traditional interaction metaphors. It is a challenge to build UIs for these devices of differing capabilities that allow the end users to perform the same kinds of tasks. The User Interface Markup Language (UIML) is an XML-based language that allows the canonical description of UIs for different platforms. We describe the key aspects of our approach that makes UIML successful in building multi-platform UIs, namely the division in the representation of a UI, the use of a generic vocabulary, a process of transformations and an integrated development environment specifically designed for transformation-based UI development. Finally we describe the initial details of a multi-step usability engineering process for building multi-platform UI using UIML.


title: Exploiting Multi-grain Ranking Constraints for Precisely Searching Visually-similar Vehicles

abstract: Precise search of visually-similar vehicles poses a great challenge in computer vision, which needs to find exactly the same vehicle among a massive vehicles with visually similar appearances for a given query image. In this paper, we model the relationship of vehicle images as multiple grains. Following this, we propose two approaches to alleviate the precise vehicle search problem by exploiting multi-grain ranking constraints. One is Generalized Pairwise Ranking, which generalizes the conventional pairwise from considering only binary similar/dissimilar relations to multiple relations. The other is Multi-Grain based List Ranking, which introduces permutation probability to score a permutation of a multi-grain list, and further optimizes the ranking by the likelihood loss function. We implement the two approaches with multi-attribute classification in a multi-task deep learning framework. To further facilitate the research on precise vehicle search, we also contribute two high-quality and well-annotated vehicle datasets, named VD1 and VD2, which are collected from two different cities with diverse annotated attributes. As two of the largest publicly available precise vehicle search datasets, they contain 1,097,649 and 807,260 vehicle images respectively. Experimental results show that our approaches achieve the state-of-the-art performance on both datasets.


title: Design and simulation of a battery powered electric vehicle implementing two different propulsion system configurations

abstract: This paper presents a design and simulation of a powertrain system for battery powered electric vehicle (EV). Two different configurations are used for the EV propulsion system. The rear wheels of EV are driven by one traction motor through a differential system, and the front wheels are driven by two other traction motors coupled directly in-wheels. Permanent magnet synchronous motors (PMSM) are adopted as the traction motors. Rule-based power management and control (PMC) strategy is presented to manage and control the power flow between rear and front drive systems. Simulation of the whole EV model is established using AVL CRUISE. Vehicle performance under the new European driving cycle (NEDC) is calculated and presented in the simulation results to specify the potential of the proposed system.


FORWARD AND INVERSE KINEMATICS STUDY OF INDUSTRIAL ROBOTS TAKING INTO ACCOUNT CONSTRUCTIVE AND FUNCTIONAL PARAMETER ' S MODELING


title: Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor

abstract: Teaching is inherently a social interaction between teacher and student. Despite this knowledge, many educational tools, such as vocabulary training programs, still model the interaction in a tutoring scenario as unidirectional knowledge transfer rather than a social dialog. Therefore, ongoing research aims to develop virtual agents as more appropriate media in education. Virtual agents can induce the perception of a life-like social interaction partner that communicates through natural modalities such as speech, gestures and emotional expressions. This effect can be additionally enhanced with a physical robotic embodiment. This paper presents the development of social supportive behaviors for a robotic tutor to be used in a language learning application. The effect of these behaviors on the learning performance of students was evaluated. The results support that employing social supportive behavior increases learning efficiency of students.


title: What is Twitter, a social network or a news media?

abstract: Twitter, a microblogging service less than three years old, commands more than 41 million users as of July 2009 and is growing fast. Twitter users tweet about any topic within the 140-character limit and follow others to receive their tweets. The goal of this paper is to study the topological characteristics of Twitter and its power as a new medium of information sharing. We have crawled the entire Twitter site and obtained 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets. In its follower-following topology analysis we have found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks [28]. In order to identify influentials on Twitter, we have ranked users by the number of followers and by PageRank and found two rankings to be similar. Ranking by retweets differs from the previous two rankings, indicating a gap in influence inferred from the number of followers and that from the popularity of one's tweets. We have analyzed the tweets of top trending topics and reported on their temporal behavior and user participation. We have classified the trending topics based on the active period and the tweets and show that the majority (over 85%) of topics are headline news or persistent news in nature. A closer look at retweets reveals that any retweeted tweet is to reach an average of 1,000 users no matter what the number of followers is of the original tweet. Once retweeted, a tweet gets retweeted almost instantly on next hops, signifying fast diffusion of information after the 1st retweet. To the best of our knowledge this work is the first quantitative study on the entire Twittersphere and information diffusion on it.


title: Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling

abstract: In this paper, we apply a context-sensitive technique for multimodal emotion recognition based on feature-level fusion of acoustic and visual cues. We use bidirectional Long ShortTerm Memory (BLSTM) networks which, unlike most other emotion recognition approaches, exploit long-range contextual information for modeling the evolution of emotion within a conversation. We focus on recognizing dimensional emotional labels, which enables us to classify both prototypical and nonprototypical emotional expressions contained in a large audiovisual database. Subject-independent experiments on various classification tasks reveal that the BLSTM network approach generally prevails over standard classification techniques such as Hidden Markov Models or Support Vector Machines, and achieves F1-measures of the order of 72 %, 65 %, and 55 % for the discrimination of three clusters in emotional space and the distinction between three levels of valence and activation, respectively.


title: UDC 004.492 Common Web Application Attack Types and Security Using ASP.NET

abstract: Web applications security is one of the most daunting tasks today, because of security shift from lower levels of ISO OSI model to application level, and because of current situation in IT environment. ASP.NET offers powerful mechanisms to render these attacks futile, but it requires some knowledge of implementing Web application security. This paper focuses on attacks against Web applications, either to gain direct benefit by collecting private information or to disable target sites. It describes the two most common Web application attacks: SQL Injection and Cross Site Scripting, and is based on author’s perennial experience in Web application security. It explains how to use ASP.NET to provide Web applications security. There are some principles of strong Web application security which make up the part of defense mechanisms presented: executing with least privileged account, securing sensitive data (connection string) and proper exception handling (where the new approach is presented using ASP.NET mechanisms for centralized exception logging and presentation). These principles help raise the bar that attacker has to cross and consequently contribute to better security.


title: Comparing face recognition algorithms to humans on challenging tasks

abstract: We compared face identification by humans and machines using images taken under a variety of uncontrolled illumination conditions in both indoor and outdoor settings. Natural variations in a person's day-to-day appearance (e.g., hair style, facial expression, hats, glasses, etc.) contributed to the difficulty of the task. Both humans and machines matched the identity of people (same or different) in pairs of frontal view face images. The degree of difficulty introduced by photometric and appearance-based variability was estimated using a face recognition algorithm created by fusing three top-performing algorithms from a recent international competition. The algorithm computed similarity scores for a constant set of same-identity and different-identity pairings from multiple images. Image pairs were assigned to good, moderate, and poor accuracy groups by ranking the similarity scores for each identity pairing, and dividing these rankings into three strata. This procedure isolated the role of photometric variables from the effects of the distinctiveness of particular identities. Algorithm performance for these constant identity pairings varied dramatically across the groups. In a series of experiments, humans matched image pairs from the good, moderate, and poor conditions, rating the likelihood that the images were of the same person (1: sure same - 5: sure different). Algorithms were more accurate than humans in the good and moderate conditions, but were comparable to humans in the poor accuracy condition. To date, these are the most variable illumination- and appearance-based recognition conditions on which humans and machines have been compared. The finding that machines were never less accurate than humans on these challenging frontal images suggests that face recognition systems may be ready for applications with comparable difficulty. We speculate that the superiority of algorithms over humans in the less challenging conditions may be due to the algorithms' use of detailed, view-specific identity information. Humans may consider this information less important due to its limited potential for robust generalization in suboptimal viewing conditions.


title: Social interactions in multiscale CVEs

abstract: A multiscale Collaborative Virtual Environment (mCVE) is a virtual world in which multiple users can independently resize themselves to work together on different sized aspects of very large and complicated structures. Interactions among users in an mCVE differ in many ways from those in traditional collaborative virtual environments. In this paper we explore collaboration-related issues affected by multiscale, such as social presence, perception of proximity, and cross-scale information sharing. We also report results of an experiment with our mCVE prototype system, which show the impact of multiscale capabilities on social interactions.


title: Schatzker Type IV Medial Tibial Plateau Fractures: A Computed Tomography–based Morphological Subclassification

abstract: Full article available online at Healio.com/Orthopedics Schatzker type IV medial tibial plateau fractures have an unfavorable prognosis, likely due to the mechanism of injury (fracture-dislocation/subluxation type) and possibly due to the involvement of the posterolateral plateau, which is different from previously thought. The aim of this study was to propose a new subclassification of Schatzker type IV fracture pat - terns based on 2-dimensional (2-D) computed tomography and three-dimensional (3-D) reconstruction. The authors defined Schatzker type IV medial tibial plateau fractures as AO/OTA 41 type B fractures (partial articular), with partial or total medial plateau involve- ment, leaving at least the anterolateral quadrant intact. The images of 42 fractures (42 patients) were evaluated. The fractures were further anatomically divided into 2 groups: Group 1 were classic medial unicondylar fractures and Group 2 were more complicated variants involving both condyles, characterized by medial condyle fractures with lateral plateau extension, usually with articular impaction of the centroposterior lateral plateau. Twelve (29%) cases involved only the medial condyle, and 30 (71%) involved both the medial and lateral condyles. Twenty-nine (69%) cases demonstrated posterior coronal fractures. The most common patterns were bicondylar posteromedial plateau fractures with posterolateral quadrant depression (bicondylar posterior fractures: 14 cases, 33%) and total/subtotal medial condyle fractures with posterolateral quadrant depression (13 cases, 31%). The isolated unicondylar posteromedial split fracture was uncommon (2 cases, 5%). Computed tomography-based reconstruction enhances the understanding of fracture anatomy and the relationships between fracture fragments. In Schatzker type IV medial tibial plateau fractures, the involvement of posterolateral quadrants is common.


title: Physiotherapy for persistent postnatal stress urinary incontinence: a randomized controlled trial.

abstract: OBJECTIVE ::: The aim of this study was to compare the effectiveness of multimodal supervised physiotherapy programs with the absence of treatment among women with persistent postnatal stress urinary incontinence. ::: ::: ::: METHODS ::: This was a single-blind randomized controlled trial. Sixty-four women with stress urinary incontinence were randomly assigned to 8 weeks of either multimodal pelvic floor rehabilitation (n = 21), multimodal pelvic floor rehabilitation with abdominal muscle training (n = 23), or control non-pelvic floor rehabilitation (n = 20). The primary outcome measure consisted of a modified 20-minute pad test. The secondary outcome measures included a Visual Analog Scale describing the perceived burden of incontinence, the Urogenital Distress Inventory, the Incontinence Impact Questionnaire, and pelvic floor muscle function measurements. ::: ::: ::: RESULTS ::: Two patients dropped out, leaving 62 for analysis. At follow-up, more than 70% of the women in the treatment groups (14/20 in the pelvic floor and 17/23 in the pelvic floor plus abdominal group) were continent on pad testing compared with 0% of women in the control group. Scores on the pad test, Visual Analog Scale, Urogenital Distress Inventory, and Incontinence Impact Questionnaire improved significantly in both treatment groups (all P <.002), whereas no changes were observed in the control group. Pelvic floor muscle function, however, did not improve significantly in either active group. ::: ::: ::: CONCLUSION ::: Multimodal supervised pelvic floor physiotherapy is an effective treatment for persistent postnatal stress urinary incontinence.


title: Analysis of Credit Card Fraud Detection Techniques: based on Certain Design Criteria

abstract: fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This paper presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.


title: Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension

abstract: Reading comprehension is a challenging task in natural language processing and requires a set of skills to be solved. While current approaches focus on solving the task as a whole, in this paper, we propose to use a neural network `skill' transfer approach. We transfer knowledge from several lower-level language tasks (skills) including textual entailment, named entity recognition, paraphrase detection and question type classification into the reading comprehension model. ::: We conduct an empirical evaluation and show that transferring language skill knowledge leads to significant improvements for the task with much fewer steps compared to the baseline model. We also show that the skill transfer approach is effective even with small amounts of training data. Another finding of this work is that using token-wise deep label supervision for text classification improves the performance of transfer learning.


Advances in Clickstream Data Analysis in Marketing


title: A Query-oriented Approach for Relevance in Citation Networks

abstract: Finding a relevant set of publications for a given topic of interest is a challenging problem. We propose a two-stage query-dependent approach for retrieving relevant papers given a keyword-based query. In the first stage, we utilize content similarity to select an initial seed set of publications; we then augment them by citation links weighted with information such as citation context relevance and age-based attenuation. In the second stage, we construct a multi-layer graph that expands the publications subgraph by including links to the authors, venues, and keywords. This allows us to return recommendations that are both highly authoritative, and also textually related to the query. We show that our staged approach gives superior results on three different benchmark query sets.


title: Passivity and Stability of Human–Robot Interaction Control for Upper-Limb Rehabilitation Robots

abstract: Each year, stroke and traumatic brain injury leave millions of survivors with motion control loss, which results in great demand for recovery training. The great labor intensity in traditional human-based therapies has recently boosted the research on rehabilitation robotics. Existing controllers for rehabilitative robotics cannot solve the closed-loop system stability with uncertain nonlinear dynamics and conflicting human–robot interactions. This paper presents a theoretical framework that establishes the passivity of the closed-loop upper-limb rehabilitative robotic systems and allows rigorous stability analysis of human–robot interaction. Position-dependent stiffness and position-dependent desired trajectory are employed to resolve the possible conflicts in motions between patient and robot. The proposed method also realizes the “assist-as-needed” strategy. In addition, it handles human–robot interactions in such a way that correct movements are encouraged and incorrect ones are suppressed to make the training process more effective. While guaranteeing these properties, the proposed controller allows parameter adjustment to provide flexibility for therapists to adjust and fine tune depending on the conditions of the patients and the progress of their recovery. Simulation and experiment results are presented to illustrate the performance of the method.


title: Fundamental Concepts of Cyber Resilience: Introduction and Overview

abstract: Society is increasingly reliant upon complex and interconnected cyber systems to conduct daily life activities. From personal finance to managing defense capabilities to controlling a vast web of aircraft traffic, digitized information systems and software packages have become integrated at virtually all levels of individual and collective activity. While such integration has been met with immense increases in efficiency of service delivery, it has also been subject to a diverse body of threats from nefarious hackers, groups, and even state government bodies. Such cyber threats have shifted over time to affect various cyber functionalities, such as with direct denial of service (DDoS), data theft, changes to data code, infection via computer virus, and many others.


title: Big data: Issues, challenges, tools and Good practices

abstract: Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. This paper introduces the Big data technology along with its importance in the modern world and existing projects which are effective and important in changing the concept of science into big science and society too. The various challenges and issues in adapting and accepting Big data technology, its tools (Hadoop) are also discussed in detail along with the problems Hadoop is facing. The paper concludes with the Good Big data practices to be followed.


title: Extending the soar cognitive architecture

abstract: One approach in pursuit of general intelligent agents has been to concentrate on the underlying cognitive architecture, of which Soar is a prime example. In the past, Soar has relied on a minimal number of architectural modules together with purely symbolic representations of knowledge. This paper presents the cognitive architecture approach to general intelligence and the traditional, symbolic Soar architecture. This is followed by major additions to Soar: non-symbolic representations, new learning mechanisms, and long-term memories.


title: Multiparametric linear programming: Support set and optimal partition invariancy

abstract: Traditional sensitivity and parametric analysis in linear optimization was based on preserving optimal basis. Interior point methods, however, do not converge to a basic solution (vertex) in general. Recently, there appeared new techniques in sensitivity analysis, which consist in preserving so called support set invariancy and optimal partition invariancy. This paper reflects the renascence of sensitivity and parametric analysis and extends single-parametric results to the case when there are multiple parameters in the objective function and in the right-hand side of equations. Multiparametric approach enables us to study more complex perturbation occurring in linear programs than the simpler sensitivity analysis does. We present a description of the set of admissible parameters under the mentioned invariances, and compare them with the classical optimal basis concept.


title: Multi-scale Patch based Collaborative Representation for Face Recognition with Margin Distribution Optimization

abstract: Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collaborative representation based classification (CRC) shows very effective face recognition performance with low computational cost. However, the recognition rate of CRC will drop dramatically when the available training samples per subject are very limited. One intuitive solution to this problem is operating CRC on patches and combining the recognition outputs of all patches. Nonetheless, the setting of patch size is a non-trivial task. Considering the fact that patches on different scales can have complementary information for classification, we propose a multi-scale patch based CRC method, while the ensemble of multi-scale outputs is achieved by regularized margin distribution optimization. Our extensive experiments validated that the proposed method outperforms many state-of-the-art patch based face recognition algorithms.


title: High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks

abstract: We present a deep learning approach for high resolution face completion with multiple controllable attributes (e.g., male and smiling) under arbitrary masks. Face completion entails understanding both structural meaningfulness and appearance consistency locally and globally to fill in"holes"whose content do not appear elsewhere in an input image. It is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of"holes"and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial networks (GANs) progressively from low resolution to high resolution with conditional vectors encoding controllable attributes. We design novel network architectures to exploit information across multiple scales effectively and efficiently. We introduce new loss functions encouraging sharp completion. We show that our system can complete faces with large structural and appearance variations using a single feed-forward pass of computation with mean inference time of 0.007 seconds for images at 1024 x 1024 resolution. We also perform a pilot human study that shows our approach outperforms state-of-the-art face completion methods in terms of rank analysis. The code will be released upon publication.


title: LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation

abstract: Pixel-wise semantic segmentation for visual scene understanding not only needs to be accurate, but also efficient in order to find any use in real-time application. Existing algorithms even though are accurate but they do not focus on utilizing the parameters of neural network efficiently. As a result they are huge in terms of parameters and number of operations; hence slow too. In this paper, we propose a novel deep neural network architecture which allows it to learn without any significant increase in number of parameters. Our network uses only 11.5 million parameters and 21.2 GFLOPs for processing an image of resolution 3 × 640 × 360. It gives state-of-the-art performance on CamVid and comparable results on Cityscapes dataset. We also compare our networks processing time on NVIDIA GPU and embedded system device with existing state-of-the-art architectures for different image resolutions.


title: Empirical evaluation of a Q-Learning Algorithm for Model-free Autonomous Soaring

abstract: Autonomous unpowered flight is a challenge for control and guidance systems: all the energy the aircraft might use during flight has to be harvested directly from the atmosphere. We investigate the design of an algorithm that optimizes the closed-loop control of a glider's bank and sideslip angles, while flying in the lower convective layer of the atmosphere in order to increase its mission endurance. Using a Reinforcement Learning approach, we demonstrate the possibility for real-time adaptation of the glider's behaviour to the time-varying and noisy conditions associated with thermal soaring flight. Our approach is online, data-based and model-free, hence avoids the pitfalls of aerological and aircraft modelling and allow us to deal with uncertainties and non-stationarity. Additionally, we put a particular emphasis on keeping low computational requirements in order to make on-board execution feasible. This article presents the stochastic, time-dependent aerological model used for simulation, together with a standard aircraft model. Then we introduce an adaptation of a Q-learning algorithm and demonstrate its ability to control the aircraft and improve its endurance by exploiting updrafts in non-stationary scenarios.


title: Poseidon: Mitigating Interest Flooding DDoS Attacks in Named Data Networking

abstract: Content-Centric Networking (CCN) is an emerging networking paradigm being considered as a possible replacement for the current IP-based host-centric Internet infrastructure. CCN focuses on content distribution, which is arguably not well served by IP. Named-Data Networking (NDN) is an example of CCN. NDN is also an active research project under the NSF Future Internet Architectures (FIA) program. FIA emphasizes security and privacy from the outset and by design. To be a viable Internet architecture, NDN must be resilient against current and emerging threats. This paper focuses on distributed denial-of-service (DDoS) attacks; in particular we address interest flooding, an attack that exploits key architectural features of NDN. We show that an adversary with limited resources can implement such attack, having a significant impact on network performance. We then introduce Poseidon: a framework for detecting and mitigating interest flooding attacks. Finally, we report on results of extensive simulations assessing proposed countermeasure.


title: AutoRM: An effective approach for automatic Web data record mining

abstract: Abstract A Web database typically responds to a query with a Web page, which encodes the query results into semi-structured data objects using HTML tags. We call such data objects Web data records or data records . Mining Web data records is very important for many applications, e.g., meta search, comparative shopping, etc. This paper proposes a new effective approach called AutoRM, which mines data records from single Web page automatically. AutoRM involves three major steps: (1) constructing the DOM tree of the given Web page; (2) mining all sets of adjacent similar C-Records ( C andidate data Records ) from the constructed DOM tree; (3) mining actual data records from C-Records. In many Web pages, similar data records are distributed in bigger and adjacent similar objects. Existing approaches typically identify such objects as data records. Conversely, AutoRM views such objects as C-Records, and mines actual data records from them. One key issue for mining similar data records is the boundary detection of each data record. Existing approaches typically make some brittle assumptions for handling this issue. By making more robust assumptions, AutoRM tends to detect data record boundaries more accurately. Experimental results show that AutoRM is highly effective, and outperforms state-of-the-art approaches.


title: LeakDAF: An Automated Tool for Detecting Leaked Activities and Fragments of Android Applications

abstract: Memory leak, one of the most common problems threatening android apps, might drain the limited memory of mobile devices, cause unexpected delays, no-responses or even crashes to apps. Activity/Fragment Leak is one of the most common and serious causes of memory leaks and has a vast influence on the Android app market. Existing work to identify leaked activities/fragments either depend highly on the experience of developers, or require app's source code and manual interactions. In this paper, we propose an automatic tool named LeakDAF for detecting leaked activities/fragments automatically without manual intervention. LeakDAF makes use of UI testing technique to execute automatically the app under test, and applies memory analysis technique to inspect dumped heap files to identify leaked activities and fragments based on Android's mechanisms for managing them. To evaluate the effectiveness of LeakDAF, we successfully applied it to 35 open source and 64 commercial apps, and we detected at least one leaked activity or fragment for 10 open source apps and 35 commercial apps.


title: Single-Phase AC–AC Converter Based on Quasi-Z-Source Topology

abstract: This paper deals with a new family of quasi-Z-source converters applying to AC/AC power conversion called single-phase quasi-Z-source AC/AC converter (qZSAC). The proposed qZSAC inherits all the advantages of the traditional single-phase Z-source AC/AC converter (ZSAC), which can realize buck-boost, reversing or maintaining phase angle. In addition, the proposed qZSAC has the unique features; namely that the input voltage and output voltage is sharing the same ground; the operation is in continuous current mode (CCM). Compared to the conventional ZSAC, the proposed qZSAC has a lower harmonic distortion input current and a higher efficiency. The proposed qZSAC can control to shape the input current to be sinusoidal and in phase with the input voltage. The operating principles of the proposed qZSAC are described, and a circuit analysis is provided. Simulation results are shown in comparison to that of the conventional SZAC. Experimentation is implemented to verify the operational concept.


title: mHealth Technologies to Influence Physical Activity and Sedentary Behaviors: Behavior Change Techniques, Systematic Review and Meta-Analysis of Randomized Controlled Trials

abstract: Background ::: mHealth programs offer potential for practical and cost-effective delivery of interventions capable of reaching many individuals.


Classification of Textures Based on Features Extracted from Preprocessing Images on Random Windows


title: Rank-biased precision for measurement of retrieval effectiveness

abstract: A range of methods for measuring the effectiveness of information retrieval systems has been proposed. These are typically intended to provide a quantitative single-value summary of a document ranking relative to a query. However, many of these measures have failings. For example, recall is not well founded as a measure of satisfaction, since the user of an actual system cannot judge recall. Average precision is derived from recall, and suffers from the same problem. In addition, average precision lacks key stability properties that are needed for robust experiments. In this article, we introduce a new effectiveness metric, rank-biased precision, that avoids these problems. Rank-biased pre-cision is derived from a simple model of user behavior, is robust if answer rankings are extended to greater depths, and allows accurate quantification of experimental uncertainty, even when only partial relevance judgments are available.


title: Overcoming Catastrophic Forgetting by Incremental Moment Matching

abstract: Catastrophic forgetting is a problem of neural networks that loses the information of the first task after training the second task. Here, we propose a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM incrementally matches the moment of the posterior distribution of the neural network which is trained on the first and the second task, respectively. To make the search space of posterior parameter smooth, the IMM procedure is complemented by various transfer learning techniques including weight transfer, L2-norm of the old and the new parameter, and a variant of dropout with the old parameter. We analyze our approach on a variety of datasets including the MNIST, CIFAR-10, Caltech-UCSD-Birds, and Lifelog datasets. The experimental results show that IMM achieves state-of-the-art performance by balancing the information between an old and a new network.


title: Physics comes to life

abstract: Exploring the strange world of swimming micro-organisms is having an impact on biology, biotechnology and fundamental physics.


title: Attention-Based Multimodal Fusion for Video Description

abstract: Current methods for video description are based on encoder-decoder sentence generation using recurrent neural networks (RNNs). Recent work has demonstrated the advantages of integrating temporal attention mechanisms into these models, in which the decoder network predicts each word in the description by selectively giving more weight to encoded features from specific time frames. Such methods typically use two different types of features: image features (from an object classification model), and motion features (from an action recognition model), combined by naive concatenation in the model input. Because different feature modalities may carry task-relevant information at different times, fusing them by naive concatenation may limit the model's ability to dynamically determine the relevance of each type of feature to different parts of the description. In this paper, we incorporate audio features in addition to the image and motion features. To fuse these three modalities, we introduce a multimodal attention model that can selectively utilize features from different modalities for each word in the output description. Combining our new multimodal attention model with standard temporal attention outperforms state-of-the-art methods on two standard datasets: YouTube2Text and MSR-VTT.


title: Nested Concept Graphs: Applications for Databases and Mathematical Foundations

abstract: While the basic idea of using Conceptual Graphs as query interface to relational databases has already been stated very early in [Sow84], no approach so far has covered the full expressiveness of modern database query languages. Especially negation and the so-called aggregating functions have not been treated. In this paper, we present Nested Concept Graphs with Cuts which extend the syntactical Concept Graph (which mathematize Conceptual Graphs) to treat simultaneously negation and nesting. With these extensions they have the expressiveness of database query languages (e. g. SQL), which will be exemplified by selected queries.


title: Pedestrian recognition using second-order HOG feature

abstract: Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian recognition which describes object appearance as local histograms of gradient orientation. However, it is incapable of describing higher-order properties of object appearance. In this paper we present a second-order HOG feature which attempts to capture second-order properties of object appearance by estimating the pairwise relationships among spatially neighbor components of HOG feature. In our preliminary experiments, we found that using harmonic-mean or min function to measure pairwise relationship gives satisfactory results. We demonstrate that the proposed second-order HOG feature can significantly improve the HOG feature on several pedestrian datasets, and it is also competitive to other second-order features including GLAC and CoHOG.


title: Cell cluster graph for prediction of biochemical recurrence in prostate cancer patients from tissue microarrays

abstract: Prostate cancer (CaP) is evidenced by profound changes in the spatial distribution of cells. Spatial arrangement and architectural organization of nuclei, especially clustering of the cells, within CaP histopathology is known to be predictive of disease aggressiveness and potentially patient outcome. Quantitative histomorphometry is a relatively new field which attempt to develop and apply novel advanced computerized image analysis and feature extraction methods for the quantitative characterization of tumor morphology on digitized pathology slides. Recently, graph theory has been used to characterize the spatial arrangement of these cells by constructing a graph with cell/nuclei as the node. One disadvantage of several extant graph based algorithms (Voronoi, Delaunay, Minimum Spanning Tree) is that they do not allow for extraction of local spatial attributes from complex networks such as those that emerges from large histopathology images with potentially thousands of nuclei. In this paper, we define a cluster of cells as a node and construct a novel graph called Cell Cluster Graph (CCG) to characterize local spatial architecture. CCG is constructed by first identifying the cell clusters to use as nodes for the construction of the graph. Pairwise spatial relationship between nodes is translated into edges of the CCG, each of which are assigned certain probability, i.e. each edge between any pair of a nodes has a certain probability to exist. Spatial constraints are employed to deconstruct the entire graph into subgraphs and we then extract global and local graph based features from the CCG. We evaluated the ability of the CCG to predict 5 year biochemical failures in men with CaP and who had previously undergone radical prostatectomy. Extracted features from CCG constructed using nuclei as nodal centers on tissue microarray (TMA) images obtained from the surgical specimens of 80 patients allowed us to train a support vector machine classifier via a 3 fold randomized cross validation procedure which yielded a classification accuracy of 83:1±1:2%. By contrast the Voronoi, Delaunay, and Minimum spanning tree based graph classifiers yielded corresponding classification accuracies of 67:1±1:8% and 60:7±0:9% respectively.


title: A Review on Performance Appraisal System: An Ineffective and Destructive Practice?

abstract: 2 Abstract: This paper discusses the issues pertaining to the implementation of performance appraisal exercise in the organizations. This exercise is supposed to improve employees' competency and productivity. However, poor execution of performance appraisal exercise can be detrimental especially to the organizational performance. Performance appraisal also has been noticed to be one of the most problematic activities of human resource management and is considered as rather an unnecessary bureaucratic practice or and even a destructive effect on the relationship of employees and managers. Two main reasons of this problem are employee satisfaction and perceive fairness on the performance appraisal system.


title: Tracker-assisted rate adaptation for MPEG DASH live streaming

abstract: MPEG DASH is a widely used standard for adaptive video streaming over HTTP. The conceptual architecture for DASH includes a web server and clients, which download media segments from the server. Clients select the resolution of video segments by using an Adaptive Bit-Rate (ABR) strategy; in particular, a throughput-based ABR is used in the case of live video applications. However, recent papers show that these strategies may suffer from the presence of proxies/caches in the network, which are instrumental in streaming video on a large scale. To face this issue, we propose to extend the MPEG DASH architecture with a Tracker functionality, enabling client-to-client sharing of control information. This extension paves the way to a novel family of Tracker-assisted strategies that allow a greater design flexibility, while solving the specific issue caused by proxies/caches; in addition, its utility goes beyond the problem at hand, as it can be used by other applications as well, e.g. for peer-to-peer streaming.


title: Composing Semantic Collage for Image Retargeting

abstract: Image retargeting has been applied to display images of any size via devices with various resolutions (e.g., cell phone and TV monitors). To fit an image with the target resolution, certain unimportant regions need to be deleted or distorted, and the key problem is to determine the importance of each pixel. Existing methods predict pixel-wise importance in a bottom-up manner via eye fixation estimation or saliency detection. In contrast, the proposed algorithm estimates the pixel-wise importance based on a top-down criterion where the target image maintains the semantic meaning of the original image. To this end, several semantic components corresponding to foreground objects, action contexts, and background regions are extracted. The semantic component maps are integrated by a classification guided fusion network. Specifically, the deep network classifies the original image as object or scene oriented, and fuses the semantic component maps according to classification results. The network output, referred to as the semantic collage with the same size as the original image, is then fed into any existing optimization method to generate the target image. Extensive experiments are carried out on the RetargetMe data set and S-Retarget database developed in this paper. Experimental results demonstrate the merits of the proposed algorithm over the state-of-the-art image retargeting methods.


title: ECG analysis: a new approach in human identification

abstract: A new approach in human identification is investigated. For this purpose, a standard 12-lead electrocardiogram (ECG) recorded during rest is used. Selected features extracted from the ECG are used to identify a person in a predetermined group. Multivariate analysis is used for the identification task. Experiments show that it is possible to identify a person by features extracted from one lead only. Hence, only three electrodes have to be attached on the person to be identified. This makes the method applicable without too much effort.


title: Speaker recognition based on idiolectal differences between speakers.

abstract: “Familiar” speaker information is explored using non-acoustic features in NIST’s new “extended data” speaker detection task.[1] Word unigrams and bigrams, used in a traditional target/background likelihood ratio framework, are shown to give surprisingly good performance. Performance continues to improve with additional training and/or test data. Bigram performance is also found to be a function of target/model sex and age difference. These initial experiments strongly suggest that further exploration of “familiar” speaker characteristics will likely be an extremely interesting and valuable research direction for recognition of speakers in conversational speech.


3 C-GAN : A N CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY


title: IMM object tracking for high dynamic driving maneuvers

abstract: Classical object tracking approaches use a Kalman-filter with a single dynamic model which is therefore optimised to a single driving maneuver. In contrast the interacting multiple model (IMM) filter allows for several parallel models which are combined to a weighted estimate. Choosing models for different driving modes, such as constant speed, acceleration and strong acceleration changes, the object state estimation can be optimised for highly dynamic driving maneuvers. The paper describes the analysis of Stop&Go situations and the systematic parametrisation of the IMM method based on these statistics. The evaluation of the IMM approach is presented based on real sensor measurements of laser scanners, a radar and a video image processing unit.


title: Single-Crystalline Si STacked ARray (STAR) NAND Flash Memory

abstract: In this paper, a 3-D NAND Flash memory array having multiple single-crystal Si nanowires is investigated. Device structure and fabrication process are described including the electrical isolation of stacked nanowires. Numerical simulation results focused on NAND Flash memory operation are delivered. Devices and array with stacked bit lines are fabricated, and memory characteristics such as program/erase select gate operation are measured. Array scheme is also discussed for the high-density bit-cost scalable 3-D stacked bit-line NAND Flash memory application.


title: Three IQs of AI Systems and their Testing Methods

abstract: The rapid development of artificial intelligence has brought the artificial intelligence threat theory as well as the problem about how to evaluate the intelligence level of intelligent products. Both need to find a quantitative method to evaluate the intelligence level of intelligence systems, including human intelligence. Based on the standard intelligence system and the extended Von Neumann architecture, this paper proposes General IQ, Service IQ and Value IQ evaluation methods for intelligence systems, depending on different evaluation purposes. Among them, the General IQ of intelligence systems is to answer the question of whether the artificial intelligence can surpass the human intelligence, which is reflected in putting the intelligence systems on an equal status and conducting the unified evaluation. The Service IQ and Value IQ of intelligence systems are used to answer the question of how the intelligent products can better serve the human, reflecting the intelligence and required cost of each intelligence system as a product in the process of serving human.


title: "Preventative" vs. "Reactive": How Parental Mediation Influences Teens' Social Media Privacy Behaviors

abstract: Through an empirical, secondary analysis of 588 teens (ages 12 - 17) and one of their parents living in the USA, we present useful insights into how parental privacy concerns for their teens and different parental mediation strategies (direct intervention versus active mediation) influence teen privacy concerns and privacy risk-taking and risk-coping privacy behaviors in social media. Our results suggest that the use of direct intervention by itself may have a suppressive effect on teens, reducing their exposure to online risks but also their ability to engage with others online and to learn how to effectively cope with online risks. Therefore, it may be beneficial for parents to combine active mediation with direct intervention so that they can protect their teens from severe online risks while empowering teens to engage with others online and learn to make good online privacy choices.


title: Asymmetric Folded Dipole Antenna With High Front-to-Back Ratio for LTE Base Stations

abstract: In this letter, a broadband asymmetric folded dipole antenna with high front-to-back ratio (FBR) for LTE base stations is proposed. The proposed antenna consists of asymmetric folded dipoles, a planar hook-shaped microstripline, and a square metal reflector. By loading an appropriate circular aperture on the reflector, the back radiation of the antenna is effectively reduced, thereby improving the FBR. At S 11 = -15 dB, the simulated and measured impedance bandwidth of the proposed antenna is 58.2% (1.62-2.95 GHz) and 52.1% (1.66-2.83 GHz), respectively. The antenna exhibits desired unidirectional radiation pattern with half-power beamwidth greater than 65 ° at both principal planes and FBR from 18.6 to 25 dB in the frequency range of interest, 1.7-2.7 GHz. Also, it has an average gain of 7.5 dBi.


title: Modulation of corticospinal activity by strong emotions evoked by pictures and classical music: a transcranial magnetic stimulation study

abstract: Using transcranial magnetic stimulation and skin conductance responses, we sought to clarify if, and to what extent, emotional experiences of different valences and intensity activate the hand–motor system and the associated corticospinal tract. For that purpose, we applied a newly developed method to evoke strong emotional experiences by the simultaneous presentation of musical and pictorial stimuli of congruent emotional valence. We uncovered enhanced motor-evoked potentials, irrespective of valence, during the simultaneous presentation of emotional music and picture stimuli (Combined conditions) compared with the single presentation of the two modalities (Picture/Music conditions). In contrast, vegetative arousal was enhanced during both the Combined and Music conditions, compared with the Picture conditions, again irrespective of emotional valence. These findings strongly indicate that arousal is a necessary, but not sufficient, prerequisite for triggering the motor system of the brain. We offer a potential explanation for this discrepant, but intriguing, finding in the paper.


title: Using tri-axial acceleration data to identify behavioral modes of free-ranging animals: general concepts and tools illustrated for griffon vultures.

abstract: Integrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures (Gyps fulvus, Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector machines, classification and regression trees, random forest methods and artificial neural networks and compare them with linear discriminant analysis (LDA) as a baseline for classifying behavioral modes. Using a dataset of 1035 ground-truthed ACC segments, we found that all methods can accurately classify behavior (80-90%) and, as expected, all nonlinear methods outperformed LDA. We also illustrate how ACC-identified behavioral modes provide the means to examine how vulture flight is affected by environmental factors, hence facilitating the integration of behavioral, biomechanical and ecological data. Our analysis of just over three-quarters of a million GPS and ACC measurements obtained from 43 free-ranging vultures across 9783 vulture-days suggests that their annual breeding schedule might be selected primarily in response to seasonal conditions favoring rising-air columns (thermals) and that rare long-range forays of up to 1750 km from the home range are performed despite potentially heavy energetic costs and a low rate of food intake, presumably to explore new breeding, social and long-term resource location opportunities.


title: Cogging Torque Minimization Using Skewed and Separated Magnet Geometries

abstract: In the study, analytical design, analysis and optimization of a 2.5 kW 14-pole, 84-slot permanent magnet synchronous generator (PMSG) have been performed. The performance characteristics of this PMSG such as efficiency, torque, cogging torque and magnetic flux density are assessed. Then, 3D model of the respective generator is acquired to examine the effect of magnet geometry on the cogging torque produced. In that context, the effects of splitted and skewed magnet structures are examined. In the first design, the magnet is modelled with one piece and the rms value of the cogging torque is found as 436.75 mNm. In the second case, a certain skewed slit is made alongside the magnet and that yields a slightly reduced cogging torque of 434.58 mNm. In the other design, the magnet of the first design is divided into two sub-parts, which are then combined together in a skewed fashion. Thus, the value of cogging torque is found as 159.60 mNm. Eventually, by making two certain slits on the last model, cogging torque is further depressed down to 89.95mNm. It is concluded from the obtained results that the last design contributes an improvement in the value of cogging torque up to 80% compared to the initial design .


title: Beyond QWERTY: augmenting touch screen keyboards with multi-touch gestures for non-alphanumeric input

abstract: Although many techniques have been proposed to improve text input on touch screens, the vast majority of this research ignores non-alphanumeric input (i.e., punctuation, symbols, and modifiers). To support this input, widely adopted commercial touch-screen interfaces require mode switches to alternate keyboard layouts for most punctuation and symbols. Our approach is to augment existing ten-finger QWERTY keyboards with multi-touch gestural input that can exist as a complement to the moded-keyboard approach. To inform our design, we conducted a study to elicit user-defined gestures from 20 participants. The final gesture set includes both multi-touch and single-touch gestures for commonly used non-alphanumeric text input. We implemented and conducted a preliminary evaluation of a touch-screen keyboard augmented with this technique. Findings show that using gestures for non-alphanumeric input is no slower than using keys, and that users strongly prefer gestures to a moded-keyboard interface.


title: MAOA, maltreatment, and gene–environment interaction predicting children's mental health: new evidence and a meta-analysis

abstract: MAOA , maltreatment, and gene–environment interaction predicting children's mental health: new evidence and a meta-analysis


title: Half Mode Substrate Integrated Waveguide (HMSIW) Bandpass Filter

abstract: This letter presents the design and experiment of the half mode substrate integrated waveguide (HMSIW) bandpass filters. Three-pole and five-pole HMSIW filters are simulated by using CST software and fabricated with a single layer standard printed circuit board process. Different external-coupling approaches are adopted in the design of the two filters. The measured results are in good agreement with the simulated results. Low insertion loss and good selectivity are achieved


title: Feature selection for time series prediction – A combined filter and wrapper approach for neural networks

abstract: Modelling artificial neural networks for accurate time series prediction poses multiple challenges, in particular specifying the network architecture in accordance with the underlying structure of the time series. The data generating processes may exhibit a variety of stochastic or deterministic time series patterns of single or multiple seasonality, trends and cycles, overlaid with pulses, level shifts and structural breaks, all depending on the discrete time frequency in which it is observed. For heterogeneous datasets of time series, such as the 2008 ESTSP competition, a universal methodology is required for automatic network specification across varying data patterns and time frequencies. We propose a fully data driven forecasting methodology that combines filter and wrapper approaches for feature selection, including automatic feature evaluation, construction and transformation. The methodology identifies time series patterns, creates and transforms explanatory variables and specifies multilayer perceptrons for heterogeneous sets of time series without expert intervention. Examples of the valid and reliable performance in comparison to established benchmark methods are shown for a set of synthetic time series and for the ESTSP'08 competition dataset, where the proposed methodology obtained second place.


title: Stakeholder Excellence ? Framing the evolution and complexity of a stakeholder perspective of the firm

abstract: Analysing and dealing with the needs and demands of stakeholders is a major concern of modern business. This paper attempts to frame, systematize and conceptualize a number of the underlying issues related to the phenomenon. As a result, a vast array of unaddressed and unanswered questions emerges. What unites these is the implicit acceptance of an emerging (contemporary) stakeholder theory of the firm. This largely normative theory provides some understanding of what constitutes a stakeholder, what stakes they seek to protect and the way in which transactions between the stakeholder and the organizations are handled. While the brevity of the contribution necessitates a somewhat superficial treatment of the issues, the paper leads inevitably to the conclusion that the demonstrated complexity of really engaging with stakeholders means that both theory-development and practice still have a long way to go. Copyright © 2002 John Wiley & Sons, Ltd. and ERP Environment.


title: BRIEF REPORT Working Memory, Attention Control, and the N-Back Task: A Question of Construct Validity

abstract: The n-back task requires participants to decide whether each stimulus in a sequence matches the one that appeared n items ago. Although n-back has become a standard “executive” working memory (WM) measure in cognitive neuroscience, it has been subjected to few behavioral tests of construct validity. A combined experimental–correlational study tested the attention-control demands of verbal 2- and 3-back tasks by presenting n 1 “lure” foils. Lures elicited more false alarms than control foils in both 2- and 3-back tasks, and lures caused more misses to targets that immediately followed them compared with control targets, but only in 3-back tasks. N-back thus challenges control over familiarity-based responding. Participants also completed a verbal WM span task (operation span task) and a marker test of general fluid intelligence (Gf; Ravens Advanced Progressive Matrices Test; J. C. Raven, J. E. Raven, & J. H. Court, 1998). N-back and WM span correlated weakly, suggesting they do not reflect primarily a single construct; moreover, both accounted for independent variance in Gf. N-back has face validity as a WM task, but it does not demonstrate convergent validity with at least 1 established WM measure.


title: Preliminary System Dynamics Maps of the Insider Cyber-threat Problem

abstract: Twenty five researchers from eight institutions and a variety of disciplines, viz. computer science, information security, knowledge management, law enforcement, psychology, organization science and system dynamics, found each other February 2004 in the “System Dynamics Modelling for Information Security: An Invitational Group Modeling Workshop” at Software Engineering Institute, Carnegie Mellon University. The exercise produced preliminary system dynamics models of insider and outsider cyber attacks that motivated five institutions, viz. Syracuse University, TECNUN at University of Navarra, CERT/CC at Carnegie Mellon University, University at Albany and Agder University College, to launch an interdisciplinary research proposal (Improving Organizational Security and Survivability by Suppression of Dynamic Triggers). This paper discusses the preliminary system dynamic maps of the insider cyber-threat and describes the main ideas behind the research proposal.


title: GaterNet: Dynamic Filter Selection in Convolutional Neural Network via a Dedicated Global Gating Network.

abstract: The concept of conditional computation for deep nets has been proposed previously to improve model performance by selectively using only parts of the model conditioned on the sample it is processing. In this paper, we investigate input-dependent dynamic filter selection in deep convolutional neural networks (CNNs). The problem is interesting because the idea of forcing different parts of the model to learn from different types of samples may help us acquire better filters in CNNs, improve the model generalization performance and potentially increase the interpretability of model behavior. We propose a novel yet simple framework called GaterNet, which involves a backbone and a gater network. The backbone network is a regular CNN that performs the major computation needed for making a prediction, while a global gater network is introduced to generate binary gates for selectively activating filters in the backbone network based on each input. Extensive experiments on CIFAR and ImageNet datasets show that our models consistently outperform the original models with a large margin. On CIFAR-10, our model also improves upon state-of-the-art results.


title: Electrodes for high-definition transcutaneous DC stimulation for applications in drug delivery and electrotherapy, including tDCS

abstract: Transcutaneous electrical stimulation is applied in a range of biomedical applications including transcranial direct current stimulation (tDCS). tDCS is a non-invasive procedure where a weak direct current (<2 mA) is applied across the scalp to modulate brain function. High-definition tDCS (HD-tDCS) is a technique used to increase the spatial focality of tDCS by passing current across the scalp using <12 mm diameter electrodes. The purpose of this study was to design and optimize "high-definition" electrode-gel parameters for electrode durability, skin safety and subjective pain. Anode and cathode electrode potential, temperature, pH and subjective sensation over time were assessed during application of 2 mA direct current, for up to 22 min on agar gel or subject forearms. A selection of five types of solid-conductors (Ag pellet, Ag/AgCl pellet, rubber pellet, Ag/AgCl ring and Ag/AgCl disc) and seven conductive gels (Signa, Spectra, Tensive, Redux, BioGel, Lectron and CCNY-4) were investigated. The Ag/AgCl ring in combination with CCNY-4 gel resulted in the most favorable outcomes. Under anode stimulations, electrode potential and temperature rises were generally observed in all electrode-gel combinations except for Ag/AgCl ring and disc electrodes. pH remained constant for all solid-conductors except for both Ag and rubber pellet electrodes with Signa and CCNY-4 gels. Sensation ratings were independent of stimulation polarity. Ag/AgCl ring electrodes were found to be the most comfortable followed by Ag, rubber and Ag/AgCl pellet electrodes across all gels.


title: The leadership of emergence: A complex systems leadership theory of emergence at successive organizational levels

abstract: Complexity science reframes leadership by focusing on the dynamic interactions between all individuals, explaining how those interactions can, under certain conditions, produce emergent outcomes. We develop a Leadership of Emergence using this approach, through an analysis of three empirical studies which document emergence in distinct contexts. Each of these studies identifies the same four “conditions” for emergence: the presence of a Dis-equilibrium state, Amplifying actions, Recombination/“Self-organization”, and Stabilizing feedback. From these studies we also show how these conditions can be generated through nine specific behaviors which leaders can enact, including: Disrupt existing patterns through embracing uncertainty and creating controversy, Encourage novelty by allowing experiments and supporting collective action, Provide sensemaking and sensegiving through the artful use of language and symbols, and Stabilize the system by Integrating local constraints. Finally, we suggest ways for advancing a meso-model of leadership, and show how our findings can improve complexity science applications in management.


title: Role of Dopamine Neurons in Reward and Aversion: A Synaptic Plasticity Perspective

abstract: The brain is wired to predict future outcomes. Experience-dependent plasticity at excitatory synapses within dopamine neurons of the ventral tegmental area, a key region for a broad range of motivated behaviors, is thought to be a fundamental cellular mechanism that enables adaptation to a dynamic environment. Thus, depending on the circumstances, dopamine neurons are capable of processing both positive and negative reinforcement learning strategies. In this review, we will discuss how changes in synaptic plasticity of dopamine neurons may affect dopamine release, as well as behavioral adaptations to different environmental conditions falling at opposite ends of a saliency spectrum ranging from reward to aversion.


title: Beyond intelligent interfaces: Exploring, analyzing, and creating success models of cooperative problem solving

abstract: Cooperative problem-solving systems are computer-based systems that augment a person's ability to create, reflect, design, decide, and reason. Our work focuses on supporting cooperative problem solving in the context of high-functionality computer systems. We show how the conceptual framework behind a given system determines crucial aspects of the system's behavior. Several systems are described that attempted to address specific shortcomings of prevailing assumptions, resulting in a new conceptual framework. To further test this resulting framework, we conducted an empirical study of a success model of cooperative problem solving between people in a large hardware store. The conceptual framework is instantiated in a number of new system-building efforts, which are described and discussed.


title: Benchmarking database systems for social network applications

abstract: Graphs have become an indispensable tool for the analysis of linked data. As with any data representation, the need for using database management systems appears when they grow in size and complexity. Associated to those needs, benchmarks appear to assess the performance of such systems in specific scenarios, representative of real use cases. In this paper we propose a microbenchmark based on social networks. This includes a data generator that synthetically creates social graphs, and a set of low level atomic queries that model parts of the behavior of social network users. In order to understand how different data management paradigms are stressed, we execute the benchmark over five different database systems representing graph (Dex and Neo4j), RDF (RDF-3X) and relational (Virtuoso and PostgreSQL) data management. We conclude that reachability queries are those that put all the database systems into more difficulties, justifying themselves, and making them good candidates for more complex benchmarks.


title: Adolescent storm and stress, reconsidered.

abstract: G. S. Hall's (1904) view that adolescence is a period of heightened "storm and stress" is reconsidered in light of contemporary research. The author provides a brief history of the storm-and-stress view and examines 3 key aspects of this view: conflict with parents, mood disruptions, and risk behavior. In all 3 areas, evidence supports a modified storm-and-stress view that takes into account individual differences and cultural variations. Not all adolescents experience storm and stress, but storm and stress is more likely during adolescence than at other ages. Adolescent storm and stress tends to be lower in traditional cultures than in the West but may increase as globalization increases individualism. Similar issues apply to minority cultures in American society. Finally, although the general public is sometimes portrayed by scholars as having a stereotypical view of adolescent storm and stress, both scholars and the general public appear to support a modified storm-and-stress view.


title: Wireless Network Virtualization: A Survey, Some Research Issues and Challenges

abstract: Since wireless network virtualization enables abstraction and sharing of infrastructure and radio spectrum resources, the overall expenses of wireless network deployment and operation can be reduced significantly. Moreover, wireless network virtualization can provide easier migration to newer products or technologies by isolating part of the network. Despite the potential vision of wireless network virtualization, several significant research challenges remain to be addressed before widespread deployment of wireless network virtualization, including isolation, control signaling, resource discovery and allocation, mobility management, network management and operation, and security as well as non-technical issues such as governance regulations, etc. In this paper, we provide a brief survey on some of the works that have already been done to achieve wireless network virtualization, and discuss some research issues and challenges. We identify several important aspects of wireless network virtualization: overview, motivations, framework, performance metrics, enabling technologies, and challenges. Finally, we explore some broader perspectives in realizing wireless network virtualization.


title: Metrics to study symptoms of bad software designs

abstract: Design of a software product largely influences its quality. Good design is one of the pre-requisites of a high quality product. Me-trics are usually used to assess the quality of software designs. The metrics for object oriented design focus on design characteristics, such as abstraction, coupling, cohesion, inheritance, polymorphism and encapsulation and are applied at attribute, method, class, pack-age, file and systems levels. Design metrics help the software de-signers to understand the problem areas in a design and to develop prediction models. A number of studies have modeled relation-ships between object oriented metrics and reusability, defects and faults, maintainability, and effort, and cost savings. So design me-trics can give an early indication of goodness of design and thus of the software product developed using that design. If designers know symptoms of bad design then it is helpful for them to avoid the bad design. In this paper, we have explored some of the symp-toms of bad design and studied metric relationships which high-light these symptoms.


title: Mathematical modeling of infectious disease dynamics

abstract: Over the last years, an intensive worldwide effort is speeding up the developments in the establishment of a global surveillance network for combating pandemics of emergent and re-emergent infectious diseases. Scientists from different fields extending from medicine and molecular biology to computer science and applied mathematics have teamed up for rapid assessment of potentially urgent situations. Toward this aim mathematical modeling plays an important role in efforts that focus on predicting, assessing, and controlling potential outbreaks. To better understand and model the contagious dynamics the impact of numerous variables ranging from the micro host–pathogen level to host-to-host interactions, as well as prevailing ecological, social, economic, and demographic factors across the globe have to be analyzed and thoroughly studied. Here, we present and discuss the main approaches that are used for the surveillance and modeling of infectious disease dynamics. We present the basic concepts underpinning ...


title: DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts

abstract: This paper introduces a publicly available historical manuscript database DIVA-HisDB for the evaluation of several Document Image Analysis (DIA) tasks. The database consists of 150 annotated pages of three different medieval manuscripts with challenging layouts. Furthermore, we provide a layout analysis ground-truth which has been iterated on, reviewed, and refined by an expert in medieval studies. DIVA-HisDB and the ground truth can be used for training and evaluating DIA tasks, such as layout analysis, text line segmentation, binarization and writer identification. Layout analysis results of several representative baseline technologies are also presented in order to help researchers evaluate their methods and advance the frontiers of complex historical manuscripts analysis. An optimized state-of-the-art Convolutional Auto-Encoder (CAE) performs with around 95% accuracy, demonstrating that for this challenging layout there is much room for improvement. Finally, we show that existing text line segmentation methods fail due to interlinear and marginal text elements.


title: Flexible capacitive sensors for high resolution pressure measurement

abstract: Thin, flexible, robust capacitive pressure sensors have been the subject of research in many fields where axial strain sensing with high spatial resolution and pressure resolution is desirable for small loads, such as tactile robotics and biomechanics. Simple capacitive pressure sensors have been designed and implemented on flexible substrates in general agreement with performance predicted by an analytical model. Two designs are demonstrated for comparison. The first design uses standard flex circuit technology, and the second design uses photolithography techniques to fabricate capacitive sensors with higher spatial and higher pressure resolution. Sensor arrays of varying sensor size and spacing are tested with applied loads from 0 to 1 MPa. Pressure resolution and linearity of the sensors are significantly improved with the miniaturized, custom fabricated sensor array compared to standard flexible circuit technology.


title: Removing the shadow of suspicion 1 REMOVING THE SHADOW OF SUSPICION: THE EFFECTS OF APOLOGY VERSUS DENIAL FOR REPAIRING COMPETENCE- VERSUS INTEGRITY-BASED TRUST VIOLATIONS

abstract: Two studies were conducted to examine the implications of an apology versus a denial for repairing trust after an alleged violation. Results reveal that trust was repaired more successfully when mistrusted parties: 1) apologized for violations concerning matters of competence but denied culpability for violations concerning matters of integrity, and 2) had apologized for violations when there was subsequent evidence of guilt, but had denied culpability for violations when there was subsequent evidence of innocence. Supplementary analyses also reveal that the interactive effects of violation-type and violation-response on participants' trusting intentions were mediated by their trusting beliefs. Combined, these findings provide needed insight and supporting evidence concerning how trust might be repaired in the aftermath of a perceived violation.


title: Density measurement of rice husk ash filler particles in polypropylene composites

abstract: Abstract A simple and accurate method for determination of filler material density is described. Rice husk ash (RHA) which consists predominantly of silica serves as a filler in polypropylene. The filler density was determined from first principles using a pyknometer. The filler was compounded in polypropylene and the densities of the composites were measured using an electronic densimeter. The measured composite densities agree very closely with theoretical values calculated based on the measured density of the filler material.


title: Reflective Informatics: Conceptual Dimensions for Designing Technologies of Reflection

abstract: Despite demonstrated interest in designing for reflection, relatively little work provides a detailed explication of what exactly is meant by reflection or how to design around it. This paper fills that gap by reviewing and engaging with conceptual and theoretical models of reflection, organized by the disciplinary and epistemological perspectives each embodies. Synthesizing across this theoretical background, the paper identifies three dimensions of reflection: breakdown, inquiry, and transformation. Together, these dimensions serve as the foundation for reflective informatics, a conceptual approach that helps bring clarity and guidance to the discussion of designing for reflection. The paper distinguishes reflective informatics by demonstrating how it both differs from and complements existing related work. Finally, the paper provides a critically reflexive consideration of its own latent assumptions, especially about the value of reflection, and how they might impact work on designing for reflection.


title: Tree Skeleton Extraction from a Single Range Image

abstract: Tree skeleton computation is of significance in the ge- ometric modeling of botanic trees and in the application of forestry. This paper describes an approach to extract branch skeletons from a single range image of a tree. A ba- sis of this approach is that the trunk and branches are mod- eled by generalized circular cylinders, and the tree skeleton is defined as the connected curve-axes of these cylinders. In the proposed system, the range image is partitioned into patches at first based on the discontinuity of depth and axis direction, where each patch only contains points from the same branch. Then each patch is fitted with a series of cir- cular cylinders. Finally the tree skeleton is generated by se- quentially connecting the skeleton points of fitted cylinders. This work shows that cylinder fitting can be used to han- dle the incompleteness of input data, and generate accurate skeleton points and corresponding radii. The main contri- bution of this paper is that we proposed a new definition and computation of tree skeletons and introduced an effi- cient and robust cylinder fitting method. Experiment shows the effectiveness of this approach. (Resume d'auteur)


title: Exploring customer specific KPI selection strategies for an adaptive time critical user interface

abstract: Rapid growth in the number of measures available to describe customer-organization relationships is presenting a serious challenge for Business Intelligence (BI) interface developers as they attempt to provide business users with key customer information without requiring users to painstakingly sift through many interface windows and layers. In this paper we introduce a prototype Intelligent User Interface that we have deployed to partially address this issue. The interface builds on machine learning techniques to construct a ranking model of Key Performance Indicators (KPIs) that are used to select and present the most important customer metrics that can be made available to business users in time critical environments. We provide an overview of the prototype application, the underlying models used for KPI selection, and a comparative evaluation of machine learning and closed form solutions to the ranking and selection problems. Results show that the machine learning based method outperformed the closed form solution with a 66.5% accuracy rate on multi-label attribution in comparison to 54.1% for the closed form solution.


title: A view of the origins and development of Prolog

abstract: Dealing with failure is easy: Work hard to improve. Success is also easy to handle: You've solved the wrong problem. Work hard to improve.


Link Prediction in Heterogeneous Networks: Influence and Time Matters


title: Boosting nearest neighbor classifiers for multiclass recognition

abstract: Nearest neighbor classifiers are a popular method for multiclass recognition in a wide range of computer vision and pattern recognition domains. At the same time, the accuracy of nearest neighbor classi?ers is sensitive to the choice of distance measure. This paper introduces an algorithm that uses boosting to learn a distance measure for multiclass k-nearest neighbor classification. Given a family of distance measures as input, AdaBoost is used to learn a weighted distance measure, that is a linear combination of the input measures. The proposed method can be seen both as a novel way to learn a distance measure from data, and as a novel way to apply boosting to multiclass recognition problems that does not require output codes. In our approach, multiclass recognition of objects is reduced to a single binary recognition task, defined on triples of objects. Preliminary experiments with eight UCI datasets yield no clear winner among our method, boosting using output codes, and k-nn classification using an unoptimized distance measure. Our algorithm did achieve lower error rates in some of the datasets, which indicates that it is a method worth considering for nearest neighbor recognition in various pattern recognition domains.


title: High resolution population estimates from telecommunications data

abstract: Spatial variations in the distribution and composition of populations inform urban development, health-risk analyses, disaster relief, and more. Despite the broad relevance and importance of such data, acquiring local census estimates in a timely and accurate manner is challenging because population counts can change rapidly, are often politically charged, and suffer from logistical and administrative challenges. These limitations necessitate the development of alternative or complementary approaches to population mapping. In this paper we develop an explicit connection between telecommunications data and the underlying population distribution of Milan, Italy. We go on to test the scale invariance of this connection and use telecommunications data in conjunction with high-resolution census data to create easily updated and potentially real time population estimates in time and space.