title: Mitigating the Effects of Gun Violence on Children and Youth

abstract: Countless children and youth are exposed to gun violence each year--at home, at school, in their communities, or through the media. Gun violence can leave lasting emotional scars on these children. This article reviews research regarding the psychological effects of gun violence on children and youth, and offers suggestions for how parents, school administrators, and mental health workers can mitigate these negative effects. Children exposed to gun violence may experience negative short- and long-term psychological effects, including anger, withdrawal, posttraumatic stress, and desensitization to violence. All of these outcomes can feed into a continuing cycle of violence. Certain children may be at higher risk for negative outcomes if they are exposed to gun violence. Groups at risk include children injured in gun violence, those who witness violent acts at close proximity, those exposed to high levels of violence in their communities or schools, and those exposed to violent media. Parents, school administrators, and mental health workers all can play key roles in protecting children from gun violence and helping them overcome the effects of gun-related trauma. The authors recommend a number of strategies that adults can adopt to help children cope with gun violence, such as increasing parental monitoring, targeting services to youth at risk of violent activity, and developing therapeutic interventions to help traumatized young people.


title: A novel hybrid quicksort algorithm vectorized using AVX-512 on Intel Skylake

abstract: The modern CPU’s design, which is composed of hierarchical memory and SIMD/vectorization capability, governs the potential for algorithms to be transformed into efficient implementations. The release of the AVX-512 changed things radically, and motivated us to search for an efficient sorting algorithm that can take advantage of it. In this paper, we describe the best strategy we have found, which is a novel two parts hybrid sort, based on the well-known Quicksort algorithm. The central partitioning operation is performed by a new algorithm, and small partitions/arrays are sorted using a branch-free Bitonicbased sort. This study is also an illustration of how classical algorithms can be adapted and enhanced by the AVX-512 extension. We evaluate the performance of our approach on a modern Intel Xeon Skylake and assess the different layers of our implementation by sorting/partitioning integers, double floatingpoint numbers, and key/value pairs of integers. Our results demonstrate that our approach is faster than two libraries of reference: the GNU C++ sort algorithm by a speedup factor of 4, and the Intel IPP library by a speedup factor of 1.4.


title: A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications

abstract: Detecting faces and heads appearing in video feeds are challenging tasks in real-world video surveillance applications due to variations in appearance, occlusions and complex backgrounds. Recently, several CNN architectures have been proposed to increase the accuracy of detectors, although their computational complexity can be an issue, especially for real-time applications, where faces and heads must be detected live using high-resolution cameras. This paper compares the accuracy and complexity of state-of-the-art CNN architectures that are suitable for face and head detection. Single pass and region-based architectures are reviewed and compared empirically to baseline techniques according to accuracy and to time and memory complexity on images from several challenging datasets. The viability of these architectures is analyzed with real-time video surveillance applications in mind. Results suggest that, although CNN architectures can achieve a very high level of accuracy compared to traditional detectors, their computational cost can represent a limitation for many practical real-time applications.


Machine Learning methods for E-mail Classification


title: Automatic Generation of Crossword Puzzles

abstract: Crossword puzzles are used everyday by millions of people for entertainment, but have applications also in educational and rehabilitation contexts. Unfortunately, the generation of ad-hoc puzzles, especially on specific subjects, typically requires a great deal of human expert work. This paper presents the architecture of WebCrow-generation, a system that is able to generate crosswords with no human intervention, including clue generation and crossword compilation. In particular, the proposed system crawls information sources on the Web, extracts definitions from the downloaded pages using state-of-the-art natural language processing techniques and, finally, compiles the crossword schema with the extracted definitions by constraint satisfaction programming. The system has been tested on the creation of Italian crosswords, but the extensive use of machine learning makes the system easily portable to other languages.


title: A survey on privacy issues in digital forensics

abstract: Privacy issues have always been a major concern in ::: computer forensics and security and in case of any ::: investigation whether it is pertaining to computer or ::: not always privacy issues appear. To enable ::: privacy’s protection in the physical world we need ::: the law that should be legislated, but in a digital ::: world by rapidly growing of technology and using ::: the digital devices more and more that generate a ::: huge amount of private data it is impossible to ::: provide fully protected space in cyber world ::: during the transfer, store and collect data. Since its ::: introduction to the field, forensics investigators, ::: and developers have faced challenges in finding the ::: balance between retrieving key evidences and ::: infringing user privacy. This paper looks into ::: developmental trends in computer forensics and ::: security in various aspects in achieving such a ::: balance. In addition, the paper analyses each ::: scenario to determine the trend of solutions in these ::: aspects and evaluate their effectiveness in resolving ::: the aforementioned issues.


title: Mathematical modeling approaches in the study of glaucoma disparities among people of African and European descents.

abstract: Open angle glaucoma (OAG) is a severe ocular disease characterized by progressive and irreversible vision loss. While elevated intraocular pressure (IOP) is a well-established risk factor for OAG, the progression of OAG in many cases, despite IOP treatment, suggests that other risk factors must play significant roles in the development of the disease. For example, various structural properties of the eye, ocular blood flow properties, and systemic conditions have been identified as risk factors for OAG. Ethnicity has also been indicated as a relevant factor that affects the incidence and prevalence of OAG; in fact, OAG is the leading cause of blindness among people of African descent. Numerous clinical studies have been designed to examine the possible correlation and causation between OAG and these factors; however, these studies are met with the challenge of isolating the individual role of multiple interconnected factors. Over the last decade, various mathematical modeling approaches have been implemented in combination with clinical studies in order to provide a mechanical and hemodynamical description of the eye in relation to the entire human body and to assess the contribution of single risk factors to the development of OAG. This review provides a summary of the clinical evidence of ocular structural differences, ocular vascular differences and systemic vascular differences among people of African and European descent, describes the mathematical approaches that have been proposed to study ocular mechanics and hemodynamics while discussing how they could be used to investigate the relevance to OAG of racial disparities, and outlines possible new directions of research.


title: Sharing Knowledge and Expertise: The CSCW View of Knowledge Management

abstract: Knowledge Management (KM) is a diffuse and controversial term, which has been used by a large number of research disciplines. CSCW, over the last 20 years, has taken a critical stance towards most of these approaches, and instead, CSCW shifted the focus towards a practice-based perspective. This paper surveys CSCW researchers' viewpoints on what has become called `knowledge sharing' and `expertise sharing'. These are based in an understanding of the social contexts of knowledge work and practices, as well as in an emphasis on communication among knowledgeable humans. The paper provides a summary and overview of the two strands of knowledge and expertise sharing in CSCW, which, from an analytical standpoint, roughly represent `generations' of research: an `object-centric' and a `people-centric' view. We also survey the challenges and opportunities ahead.


title: A Technique for Representing Coaxial Cables for FDTD-Based Surge Simulations

abstract: The finite-difference time-domain (FDTD) method is a very effective tool for predicting surge phenomena in 3-D structures such as buildings and transmission line towers and in grounding systems such as grounding grids. In this paper, we propose a new technique for representing a coaxial cable for FDTD-based surge simulations. In this technique, the metal sheath of the cable and the electromagnetic field outside the metal sheath are simulated in the FDTD method, while the surge phenomena inside the metal sheath are solved on the basis of transmission line theory. Therefore, this technique can take into account the effect of the metal sheath of the coaxial cable, the traveling wave inside the cable, and wires connected to the internal conductor of the cable at the same time. Using the proposed technique, we predicted the surge phenomena in a coaxial cable and compared them with measured results for the purpose of validation.


39 3 D mesh compression : survey , comparisons and emerging trends


title: A probabilistic evaluation framework for preference aggregation reflecting group homogeneity

abstract: Groups differ in the homogeneity of their members’ preferences. Reflecting this, we propose a probabilistic criterion for evaluating and comparing the adequateness of preference aggregation procedures that takes into account information on the considered group’s homogeneity structure. Further, we discuss two approaches for approximating our criterion if information is only imperfectly given and show how to estimate these approximations from data. As a preparation, we elaborate some general minimal requirements for measuring homogeneity and discuss a specific proposal for a homogeneity measure. Finally, we investigate our framework by comparing aggregation rules in a simulation study.


Thin Structures in Image Based Rendering


title: An IPM-PMASR motor for home appliance washing machines

abstract: The paper presents an innovative electric drive for home appliance washing machines based on a PMASR (Permanent Magnet Assisted Synchronous Reluctance) motor. The motor magnetic model and the power vs. speed characteristic are presented. The PMASR motor is when experimentally compared with a three phase induction motor, that is, nowadays, the state of the art. The laboratory measurements confirm the higher efficiency of the PMASR drive for washing operation and comparable behavior for spinning.


title: Detection of Mass Panic using Internet of Things and Machine Learning

abstract: The increase of emergency situations that cause mass panic in mass gatherings, such as terrorist attacks, random shooting, stampede, and fires, sheds light on the fact that advancements in technology should contribute in timely detecting and reporting serious crowd abnormal behaviour. The new paradigm of the ‘Internet of Things’ (IoT) can contribute to that. In this study, a method for real-time detection of abnormal crowd behaviour in mass gatherings is proposed. This system is based on advanced wireless connections, wearable sensors and machine learning technologies. It is a new crowdsourcing approach that considers humans themselves as the surveillance devices that exist everywhere. A sufficient number of the event’s attendees are supposed to wear an electronic wristband which contains a heart rate sensor, motion sensors and an assisted-GPS, and has a wireless connection. It detects the abnormal behaviour by detecting heart rate increase and abnormal motion. Due to the unavailability of public bio-dataset on mass panic, dataset of this study was collected from 89 subjects wearing the above-mentioned wristband and generating 1054 data samples. Two types of data collected were: firstly, the data of normal daily activities and secondly, the data of abnormal activities resembling the behaviour of escape panic. Moreover, another abnormal dataset was synthetically generated to simulate panic with limited motion. In our proposed approach, two-phases of data analysis are done. Phase-I is a deep machine learning model that was used to analyze the sensors’ collected readings of the wristband and detect if the person has indeed panicked in order to send alerting signals. While phase-II data analysis takes place in the monitoring server that receives the alerting signals to conclude if it is a mass panic incident or a false positive case. Our experiments demonstrate that the proposed system can offer a reliable, accurate, and fast solution for panic detection. This experiment uses the Hajj pilgrimage as a case study.


title: Design and implementation of Web service honeypot

abstract: Web services are increasingly becoming an integral part of next-generation web applications. A Web service is defined as a software system designed to support interoperable machine-to-machine interaction over a network based on a set of XML standards. This new architecture and set of protocols brings new vulnerabilities that can be exploited by attackers. To prevent and detect such attacks, several security techniques are available like authentication and encryption mechanisms, firewalls and intrusion detection systems (IDS). Nevertheless these security methods encounter some problems, especially when dealing with new attacks. Relying on additional security principles seems to be important to well protect Web services. In this paper, we propose using honeypots to detect and study attacks against Web services. Honeypots are used to learn new techniques, tools and motivations of hackers to better protect the production systems against attacks. Our solution (WS Honeypot) is to deploy a honeypot as a web service application. This honeypot captures all request messages and analyses them by using machine learning techniques in order to detect and study attacks.


title: Neural Feedback Text Clustering With BiLSTM-CNN-Kmeans

abstract: Text clustering is a very important technique in the field of data mining. It is widely used in information retrieval and text integration. Most existing studies focus on the optimization of feature extraction methods or clustering algorithms separately. In this paper, we propose a neural feedback clustering algorithm combining bidirectional long short-term memory and convolutional neural network with kmeans method. Unlike previous papers, the proposed algorithm treats feature extraction and clustering as a united process, where clustering results can be used as feedback information to dynamically optimize the parameters of networks. Experimental results show that the proposed algorithm achieves significant improvements compared with other existing text clustering algorithms and has a certain degree of noise robustness.


title: Map-reduce extensions and recursive queries

abstract: We survey the recent wave of extensions to the popular map-reduce systems, including those that have begun to address the implementation of recursive queries using the same computing environment as map-reduce. A central problem is that recursive tasks cannot deliver their output only at the end, which makes recovery from failures much more complicated than in map-reduce and its nonrecursive extensions. We propose several algorithmic ideas for efficient implementation of recursions in the map-reduce environment and discuss several alternatives for supporting recovery from failures without restarting the entire job.


title: General and Familiar Trust in Websites

abstract: When people rely on the web to gather and distribute information, they can build a sense of trust in the websites with which they interact. Understanding the correlates of trust in most websites (general website trust) and trust in websites that one frequently visits (familiar website trust) is crucial for constructing better models of risk perception and online behavior. We conducted an online survey of active Internet users and examined the associations between the two types of web trust and several independent factors: information technology competence, adverse online events, and general dispositions to be trusting or cautious of others. Using a series of nested ordered logistic regression models, we find positive associations between general trust, general caution, and the two types of web trust. The positive effect of information technology competence erases the effect of general caution for general website trust but not for familiar website trust, providing evidence that general trust and self-reported competence are stronger associates of general website trust than broad attitudes about prudence. Finally, the experience of an adverse online event has a strong, negative association with general website trust, but not with familiar website trust. We discuss several implications for online behavior and suggest website policies that can help users make informed decisions about interacting with potentially risky websites.


title: Generalized Karnaugh Map Method for Boolean Functions of Many Variables

abstract: ABSTRACTKarnaugh map method is a popular method for minimization of Boolean functions. It is widely used for four variables. But it is hardly used beyond six variables. In this paper, a tree of Karnaugh maps called generalized Karnaugh map is proposed. This map can be constructed for any number of variables and analysed systematically to generate all prime implicants by going through only one map of 16 squares at a time. A concept called ‘K–don't cares’ is used for this purpose. K–don't cares can be simplified if we do not insist on all prime implicants. Interestingly the new method requires no more than 16 minterms at a time. A seven-variable example is solved to illustrate the method.


title: Reducing primary and secondary traumatic stress symptoms among educators by training them to deliver a resiliency program (ERASE-Stress) following the Christchurch earthquake in New Zealand.

abstract: The current investigation evaluated the impact of a universal school-based resiliency intervention (ERASE-Stress) on educators who were working with elementary schoolchildren exposed to the Canterbury earthquake in New Zealand. In the context of major disasters, educators may suffer from "dual trauma"; they can experience symptoms of both primary trauma (as a result of the disaster itself) and secondary trauma (as a result of working with traumatized students). Sixty-three educators were randomly assigned to either the ERASE-Stress intervention or an alternative Managing Emergencies and Traumatic Incidents (METI) program which served as a control group. Efficacy of the program was evaluated at the end of the training as well as at 8 months follow-up. Compared with educators in the control group, those in the ERASE-Stress intervention significantly reduced their posttraumatic distress and secondary traumatization symptoms, improved their perceived level of professional self-efficacy as a helper of earthquake survivors, developed an optimistic outlook regarding their personal future and enhanced their sense of hope, and honed some of their positive coping strategies and reduced the utilization of some maladaptive coping methods. These beneficial consequences of the ERASE-Stress training make it a potentially useful tool for educators working with traumatized students in the context of major disasters. (PsycINFO Database Record(c) 2016 APA, all rights reserved). Language: en


title: Voltage oscillation elimination in output rectifier diodes in zero-voltage-switching PWM full-bridge converter: An overview

abstract: In the basic zero-voltage-switching (ZVS) full-bridge (FB) converter, the output rectifier diodes suffer from hard-switching, results in severe voltage spike and oscillation across the output rectifier diodes. In this paper, a fundamental approach is proposed to tackle this issue, indicating that clamping the voltage across the winding of the power transformer could eliminate the voltage oscillation in output rectifier diodes. According to the proposed approach, two basic clamping circuits are put forward, based on which, different clamping schemes in the previous publications can be derived and the relevance among them with respect to the structure of the circuit topology are revealed. Finally, a brief review and comparison is conducted on ZVS PWM FB converters with these different clamping schemes, which helps to further demonstrate the similarities and differences among these FB converters.


title: Predicting the popularity of instagram posts for a lifestyle magazine using deep learning

abstract: In this paper we use a Deep Neural Network (DNN) trained on data collected from the visual media-sharing social platform Instagram account of a popular Indian lifestyle magazine to predict the popularity of future posts. This predicted popularity of the post can be used to decide advertising rates and measure performance metrics important for publishing strategy decisions. The DNN primarily uses growth rate in subscriber base, tags associated with the post, time of day when the post was made, day of the week, color descriptors of the image, time between current and previous post, popularity of previous post as features for prediction. This covers majority of the causes of variation in popularity. Mini-batch gradient descend method is used to learn the weights in DNN and the objective function is cross-entropy. The network performs acceptable for real world applications and tolerances are within acceptable limits for the application.


title: A Recommender System Combining Social Networks for Tourist Attractions

abstract: The fast development of Web technologies has introduced a world of big data. How efficiently and effectively to retrieve the information from the ocean of data that the users really want is an important topic. Recommendation systems have become a popular approach to personalized information retrieval. On the other hand, social media have quickly entered into your life. The information from social networks can be an effective indicator for recommender systems. In this paper we present a recommendation mechanism which calculates similarity among users and users' trustability and analyzes information collected from social networks. To validate our method an information system for tourist attractions built on this recommender system has been presented. We further evaluate our system by experiments. The results show our method is feasible and effective.


title: A fast online learning algorithm for distributed mining of BigData

abstract: BigData analytics require that distributed mining of numerous data streams is performed in real-time. Unique challenges associated with designing such distributed mining systems are: online adaptation to incoming data characteristics, online processing of large amounts of heterogeneous data, limited data access and communication capabilities between distributed learners, etc. We propose a general framework for distributed data mining and develop an efficient online learning algorithm based on this. Our framework consists of an ensemble learner and multiple local learners, which can only access different parts of the incoming data. By exploiting the correlations of the learning models among local learners, our proposed learning algorithms can optimize the prediction accuracy while requiring significantly less information exchange and computational complexity than existing state-of-the-art learning solutions.


Attacking Poverty with Microcredit


title: Evaluating and Improving Resource Allocation for Navy Recruiting

abstract: The recruitment of enlisted men and officers under the All-Volunteer Force concept costs approximately $620 million per year; of this amount approximately $100 million are in advertising expenditures. However, recently Congress and the General Accounting Office have expressed considerable concern over the lack of justification for such expenditures. The issue is further complicated by the recent recruiting shortfalls by all the services and the debate surrounding registration and a possible return to the draft. Finally, results of most profit-oriented sales research are difficult to apply to improving military recruiting efficiency due to the difference in the levels of rapport and credibility necessary to convince a potential recruit to invest several years of his life. ::: ::: This paper reports on modeling and implementation experience geared towards determining the proper allocation of resources between advertising and recruiters. This mix is especially crucial since Congress does not permit funds appropriated for advertising to be used for manpower or vice versa. The study consists of two parts: generation of response functions to estimate the number and mix of enlistments over time and geographically as a function of the various demographics, advertising expenditures and the number of recruiters; a nonlinear programming resource allocation model to improve the budget generation and budget execution capabilities in meeting and assessing the impact of the various multi-faceted quantity and quality goals. The basic model, with updating of the response function parameters, is being used by the Navy to aid in the generation and defense of its annual budget appropriations. Numerical results are given which are compared to the past allocations of the Navy and to results observed in industrial product marketing.


title: Learning a Hierarchical Embedding Model for Personalized Product Search

abstract: Product search is an important part of online shopping. In contrast to many search tasks, the objectives of product search are not confined to retrieving relevant products. Instead, it focuses on finding items that satisfy the needs of individuals and lead to a user purchase. The unique characteristics of product search make search personalization essential for both customers and e-shopping companies. Purchase behavior is highly personal in online shopping and users often provide rich feedback about their decisions (e.g. product reviews). However, the severe mismatch found in the language of queries, products and users make traditional retrieval models based on bag-of-words assumptions less suitable for personalization in product search. In this paper, we propose a hierarchical embedding model to learn semantic representations for entities (i.e. words, products, users and queries) from different levels with their associated language data. Our contributions are three-fold: (1) our work is one of the initial studies on personalized product search; (2) our hierarchical embedding model is the first latent space model that jointly learns distributed representations for queries, products and users with a deep neural network; (3) each component of our network is designed as a generative model so that the whole structure is explainable and extendable. Following the methodology of previous studies, we constructed personalized product search benchmarks with Amazon product data. Experiments show that our hierarchical embedding model significantly outperforms existing product search baselines on multiple benchmark datasets.


title: The Nature and Organization of Individual Differences in Executive Functions: Four General Conclusions

abstract: Executive functions (EFs)—a set of general-purpose control processes that regulate one’s thoughts and behaviors—have become a popular research topic lately and have been studied in many subdisciplines of psychological science. This article summarizes the EF research that our group has conducted to understand the nature of individual differences in EFs and their cognitive and biological underpinnings. In the context of a new theoretical framework that we have been developing (the unity/diversity framework), we describe four general conclusions that have emerged. Specifically, we argue that individual differences in EFs, as measured with simple laboratory tasks, (a) show both unity and diversity (different EFs are correlated yet separable), (b) reflect substantial genetic contributions, (c) are related to various clinically and societally important phenomena, and (d) show some developmental stability.


title: Design comparison of NdFeB and ferrite radial flux magnetic gears

abstract: Magnetic gears promise the benefits of mechanical gears with added advantages from contactless power transfer. Although most literature focuses on minimizing the size of magnetic gears, their material costs must also be reduced to achieve economic feasibility. This work compares the active material costs of NdFeB and ferrite radial flux magnetic gears with surface permanent magnets through a parametric 2D and 3D finite element analysis (FEA) study. Differences in optimal design trends such as pole count and magnet thicknesses are illustrated for the two materials. The results demonstrate that, for most historical price combination scenarios, NdFeB gear designs are capable of achieving lower active material costs than ferrite gear designs, and they are always capable of achieving much higher torque densities. Based on the selected design constraints, relative to a nominal ferrite cost of $10/kg, NdFeB must cost more than $90/kg before ferrite is cost competitive. Additionally, contour plots are provided to show the impact of material price rate variation on the cost break points.


COMPUTATION OF THE HAUSDORFF DISTANCE BETWEEN PLANE VECTOR POLYLINES


title: Constructing sexuality and identity in an online teen chat room

abstract: In this article, we propose that adolescents’ online interactions are both a literal and a metaphoric screen for representing major adolescent developmental issues, such as sexuality and identity. Because of the public nature of Internet chat rooms, they provide an open window into the expression of adolescent concerns. Our study utilizes this window to explore how issues of sexuality and identity are constructed in a teen chat room. We adapt qualitative discourse methodology to microanalyze a half-hour transcript from a monitored teen chat room, comparing it, where relevant, to a second transcript, used in a prior study [Greenfield, P.M., Subrahmanyam, K., 2003. Online discourse in a teen chat room: New codes and new modes of coherence in a visual medium. Journal of Applied Developmental Psychology 24, 713–738.]. Our microanalysis reveals that participants use the online space of teen chat to air adolescent concerns about sexuality and to develop creative strategies to exchange identity information with their peers. This exchange is critical to the activity of bpairing offQ, an important teenage expression of emerging sexuality. Developmental issues from adolescents’ offline lives are reconstructed online with some important differences. The virtual world of teen chat may offer a safer environment for exploring emerging sexuality than the real world. Through the verbally explicit exchange of identity information, participants are able to bpair offQ with partners of their choice, despite the disembodied nature of chat participants.


title: Energy-aware data-centric routing in microsensor networks

abstract: Large-scale wireless sensors are expected to play an increasingly important role in future civilian and military settings where collaborative microsensors could be very effective in monitoring their operations. However, low power and in-network data processing make data-centric routing in wireless sensor networks a challenging problem.In this paper, we propose a novel and efficient energy-aware distributed heuristic, which we refer to as EAD, to build a special rooted broadcast tree with many leaves that is used to facilitate data-centric routing in wireless microsensor networks. Our EAD algorithm makes no assumption on local network topology, and is based on residual power. It makes use of a neighboring broadcast scheduling and distributed competition among neighboring nodess. We discuss the implementation of our scheme, and present an extensive simulation experiments to study the its performance. Our experimental results indicate clearly that our EAD scheme outperforms previous schemes.


title: Creating autonomous agents for playing Super Mario Bros game by means of evolutionary finite state machines

abstract: This paper shows the design and improvement of an autonomous agent based in using evolutionary methods to improve behavioural models (finite state machines), which are part of the individuals to evolve. This leads to the obtention of a so-called bot that follows the Gameplay track rules of the international Mario AI Championship and is able to autonomously complete different scenarios on a simulator of Super Mario Bros. game. Mono- and multi-seed approaches (evaluation in one play or multiple plays respectively) have been analysed, in order to compare respectively the performance of an approach focused in solving a specific scenario, and another more general, devoted to obtain an agent which can play successfully in different scenarios. The analysis considers the machine resources consumption, which turns in a bottleneck in some experiments. However, the methods yield agents which can finish several stages of different difficulty levels, and playing much better than an expert human player, since they can deal with very difficult situations (several enemies surrounding Mario, for instance) in real time. According to the results and considering the competition’s restrictions (time limitations) and objectives (complete scenarios up to difficulty level 3), these agents have enough performance to participate in this competition track.


title: Cloud incident handling and forensic-by-design: cloud storage as a case study

abstract: Summary ::: Information security incident handling strategies or models are important to ensure the security of organisations, particularly in cloud and big data environments. However, existing strategies or models may not adequate as cloud data are generally virtualised, geographically distributed and ephemeral, presenting both technical and jurisdictional challenges. We present an integrated cloud incident handling and forensic-by-design model. We then seek to validate the model using a set of controlled experiments on a cloud-related incident. Three popular cloud storage applications were deployed namely, Dropbox, Google Drive, and OneDrive. This study demonstrates the utility of the model for organisational cloud users to undertake incident investigations (e.g. collect and analyse residual data from cloud storage applications). Copyright © 2016 John Wiley & Sons, Ltd.


title: GeckoGripper: A soft, inflatable robotic gripper using gecko-inspired elastomer micro-fiber adhesives

abstract: This paper proposes GeckoGripper, a novel soft, inflatable gripper based on the controllable adhesion mechanism of gecko-inspired micro-fiber adhesives, to pick-and-place complex and fragile non-planar or planar parts serially or in parallel. Unlike previous fibrillar structures that use peel angle to control the manipulation of parts, we developed an elastomer micro-fiber adhesive that is fabricated on a soft, flexible membrane, increasing the adaptability to non-planar three-dimensional (3D) geometries and controllability in adhesion. The adhesive switching ratio (the ratio between the maximum and minimum adhesive forces) of the developed gripper was measured to be around 204, which is superior to previous works based on peel angle-based release control methods. Adhesion control mechanism based on the stretch of the membrane and superior adaptability to non-planar 3D geometries enable the micro-fibers to pick-and-place various 3D parts as shown in demonstrations.


title: An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method

abstract: This work presents a method for identifying plant leaf disease and an approach for careful detection of diseases. The goal of proposed work is to diagnose the disease of brinjal leaf using image processing and artificial neural techniques. The diseases on the brinjal are critical issue which makes the sharp decrease in the production of brinjal. The study of interest is the leaf rather than whole brinjal plant because about 85–95 % of diseases occurred on the brinjal leaf like, Bacterial Wilt, Cercospora Leaf Spot, Tobacco mosaic virus (TMV). The methodology to detect brinjal leaf disease in this work includes K-means clustering algorithm for segmentation and Neural-network for classification. The proposed detection model based artiifical neural networks are very effective in recognizing leaf diseases.


title: TinySLAM improvements for indoor navigation

abstract: TinySLAM [1] is one of the most simple SLAM methods but the original implementation [2] is based on the specific robot model and provided as the ad-hoc application. Its key feature is simplicity of implementation and configuration at cost of accuracy (as our tests shown). Some changes were made in the original algorithm in order to minimize an error of estimated trajectories. The introduced model of cell leads to an error decrease on almost all tested indoor sequences. The proposed dynamic probability estimator improves usage of coarse-grained maps when memory efficiency is more desirable than accuracy. Obtained quantitative measurements justify the changes made in tinySLAM in case the method is used in a relatively small environment.


title: Learning hierarchical similarity metrics

abstract: Categories in multi-class data are often part of an underlying semantic taxonomy. Recent work in object classification has found interesting ways to use this taxonomy structure to develop better recognition algorithms. Here we propose a novel framework to learn similarity metrics using the class taxonomy. We show that a nearest neighbor classifier using the learned metrics gets improved performance over the best discriminative methods. Moreover, by incorporating the taxonomy, our learned metrics can also help in some taxonomy specific applications. We show that the metrics can help determine the correct placement of a new category that was not part of the original taxonomy, and can provide effective classification amongst categories local to specific subtrees of the taxonomy.


Reinforcement Learning with Decision Trees.


Combining classifiers in text categorization


' s personal copy Integrating face and voice in person perception


title: Behavioral effects of D3 receptor inhibition and 5-HT4 receptor activation on animals undergoing chronic cannabinoid exposure during adolescence

abstract: Chronic exposure to cannabinoids during adolescence results in long-lasting behavioral deficits that match some symptomatologic aspects of schizophrenia. The aim of this study was to investigate the reversibility of the emotional and the cognitive effects of chronic exposure to cannabinoids during adolescence, via subsequent modulation of the serotoninergic 5-HT4 and dopaminergic D3 receptors. RS67333 as a 5-HT4 agonist and U-99194A as a D3 antagonist were administered separately at 1 mg/kg and 20 mg/kg, and in combination at 0.5 mg/kg and 10 mg/kg to adult animals undergoing chronic treatment with the synthetic cannabinoid receptor agonist WIN55,212-2 (1 mg/kg) during adolescence. Animals were tested for anxiety-like behavior and episodic-like memory in the open field and novel object recognition tests respectively 30 minutes after the last drug administration. Chronic WIN55,212-2 treated animals exhibited a lasting disruption of episodic memory and increased anxiety levels. The effect on episodic-like memory were partially restored by acute administration of RS67333 and U-99194A and completely by administration of both drugs in combination at lower doses. However, only RS67333 (20 mg/kg) improved the anxiogenic-like effect of WIN55,212-2. These findings give further support that chronic exposure to cannabinoids during adolescence may be used as an animal model for schizophrenia, and highlight D3 and 5-HT4 receptors as potential targets for an enhanced treatment of the cognitive aspect of this disease.


title: Gaze estimation method based on an aspherical model of the cornea: surface of revolution about the optical axis of the eye

abstract: A novel gaze estimation method based on a novel aspherical model of the cornea is proposed in this paper. The model is a surface of revolution about the optical axis of the eye. The calculation method is explained on the basis of the model. A prototype system for estimating the point of gaze (POG) has been developed using this method. The proposed method has been found to be more accurate than the gaze estimation method based on a spherical model of the cornea.


Movie Rating Prediction


SUCCESS FACTORS IN INFORMATION SECURITY IMPLEMENTATION IN ORGANIZATIONS


title: GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams

abstract: The real-time discovery of local events (e.g., protests, crimes, disasters) is of great importance to various applications, such as crime monitoring, disaster alarming, and activity recommendation. While this task was nearly impossible years ago due to the lack of timely and reliable data sources, the recent explosive growth in geo-tagged tweet data brings new opportunities to it. That said, how to extract quality local events from geo-tagged tweet streams in real time remains largely unsolved so far. We propose GeoBurst, a method that enables effective and real-time local event detection from geo-tagged tweet streams. With a novel authority measure that captures the geo-topic correlations among tweets, GeoBurst first identifies several pivots in the query window. Such pivots serve as representative tweets for potential local events and naturally attract similar tweets to form candidate events. To select truly interesting local events from the candidate list, GeoBurst further summarizes continuous tweet streams and compares the candidates against historical activities to obtain spatiotemporally bursty ones. Finally, GeoBurst also features an updating module that finds new pivots with little time cost when the query window shifts. As such, GeoBurst is capable of monitoring continuous streams in real time. We used crowdsourcing to evaluate GeoBurst on two real-life data sets that contain millions of geo-tagged tweets. The results demonstrate that GeoBurst significantly outperforms state-of-the-art methods in precision, and is orders of magnitude faster.


title: Capability maturity model, version 1.1

abstract: The capability maturity model (CMM), developed to present sets of recommended practices in a number of key process areas that have been shown to enhance software-development and maintenance capability, is discussed. The CMM was designed to help developers select process-improvement strategies by determining their current process maturity and identifying the issues most critical to improving their software quality and process. The initial release of the CMM, version 1.0, was reviewed and used by the software community during 1991 and 1992. A workshop on CMM 1.0, held in April 1992, was attended by about 200 software professionals. The current version of the CMM is the result of the feedback from that workshop and ongoing feedback from the software community. The technical report that describes version 1.1. is summarised. >


Context Aware Citation Recommendation System


title: A wrapper-based approach to image segmentation and classification

abstract: The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications.


title: Circuit Network Model of Stator Transposition Bar in Large Generators and Calculation of Circulating Current

abstract: Calculation of circulating current in transposition bars is important to the transposition-type design of stator windings in large generators. The circuit network model of the stator transposition bar, including only resistance and reactance parameters, is proposed in this paper, and the calculation method for parameters is given. The analytical algorithm of circulating current in stator transposition bars is proposed based on the circuit network model. Contrary to the existing calculation method for circulating current, the presented analytical algorithm is a general and fast method for different transposition types, which can avoid establishing complex 3-D models. In order to validate the proposed analytical algorithm, the strand current and circulating current of less than 540 $^{\circ} $ with a void transposition bar in a water-cooled turbogenerator are calculated by analytical algorithm and 3-D finite-element method.


title: The Behavioral Economics of Health and Health Care

abstract: People often make decisions in health care that are not in their best interest, ranging from failing to enroll in health insurance to which they are entitled, to engaging in extremely harmful behaviors. Traditional economic theory provides a limited tool kit for improving behavior because it assumes that people make decisions in a rational way, have the mental capacity to deal with huge amounts of information and choice, and have tastes endemic to them and not open to manipulation. Melding economics with psychology, behavioral economics acknowledges that people often do not act rationally in the economic sense. It therefore offers a potentially richer set of tools than provided by traditional economic theory to understand and influence behaviors. Only recently, however, has it been applied to health care. This article provides an overview of behavioral economics, reviews some of its contributions, and shows how it can be used in health care to improve people's decisions and health.


title: PRODUCTIVE STRATEGIES AND MANUFACTURING REORGANIZATION IN MONTREAL'S CENTRAL DISTRICT, 1850-1900

abstract: Discussion of the development of the central districts of 19th-century North American cities has centered around the economic forces shaping and the cultural significance of the transformation from a commercial to a corporate city. This paper builds upon this literature by exploring the impact that different manufacturing trajectories had upon the particular configuration of Montreal’s central district-Old Montreal-between 1850 and 1900. It is shown that a critical feature of the dramatic growth and the reorganization of the district’s manufacturing base was the multiplicity of organizational forms and the flexibility of its built environment. The paper begins with a description of the changing industrial structure of Old Montreal, the multitude of productive strategies created by the district’s firms, and the remodeling of the core’s built environment. This is followed by a detailed examination of the clothing and printing industries based upon municipal assessment rolls, government reports, industrial j...


title: Multiple Resources and Mental Workload

abstract: Objective: The objective is to lay out the rationale for multiple resource theory and the particular 4-D multiple resource model, as well as to show how the model is useful both as a design tool and as a means of predicting multitask workload overload. Background: I describe the discoveries and developments regarding multiple resource theory that have emerged over the past 50 years that contribute to performance and workload prediction. Method: The article presents a history of the multiple resource concept, a computational version of the multiple resource model applied to multitask driving simulation data, and the relation of multiple resources to workload. Results: Research revealed the importance of the four dimensions in accounting for task interference and the association of resources with brain structure. Multiple resource models yielded high correlations between model predictions and data. Lower correlations also identified the existence of additional resources. Conclusion: The model was shown to b...


title: Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals

abstract: Abstract Background and objective According to the World Health Organization (WHO) epilepsy affects approximately 45–50 million people. Electroencephalogram (EEG) records the neurological activity in the brain and it is used to identify epilepsy. Visual inspection of EEG signals is a time-consuming process and it may lead to human error. Feature extraction and classification are two main steps that are required to build an automated epilepsy detection framework. Feature extraction reduces the dimensions of the input signal by retaining informative features and the classifier assigns a proper class label to the extracted feature vector. Our aim is to present effective feature extraction techniques for automated epileptic EEG signal classification. Methods In this study, two effective feature extraction techniques (Local Neighbor Descriptive Pattern [LNDP] and One-dimensional Local Gradient Pattern [1D-LGP]) have been introduced to classify epileptic EEG signals. The classification between epileptic seizure and non-seizure signals is performed using different machine learning classifiers. The benchmark epilepsy EEG dataset provided by the University of Bonn is used in this research. The classification performance is evaluated using 10-fold cross validation. The classifiers used are the Nearest Neighbor (NN), Support Vector Machine (SVM), Decision Tree (DT) and Artificial Neural Network (ANN). The experiments have been repeated for 50 times. Results LNDP and 1D-LGP feature extraction techniques with ANN classifier achieved the average classification accuracy of 99.82% and 99.80%, respectively, for the classification between normal and epileptic EEG signals. Eight different experimental cases were tested. The classification results were better than those of some existing methods. Conclusions This study suggests that LNDP and 1D-LGP could be effective feature extraction techniques for the classification of epileptic EEG signals.


title: Equilibration versus (pre) thermalization in the Bose and Fermi Hubbard models

abstract: We study the Bose and Fermi Hubbard model in the (formal) limit of large coordination numbers $Z\gg1$. Via an expansion into powers of $1/Z$, we establish a hierarchy of correlations which facilitates an approximate analytical derivation of the time-evolution of the reduced density matrices for one and two sites etc. With this method, we study the quantum dynamics (starting in the ground state) after a quantum quench, i.e., after suddenly switching the tunneling rate $J$ from zero to a finite value, which is still in the Mott regime. We find that the reduced density matrices approach a (quasi) equilibrium state after some time. For one lattice site, this state can be described by a thermal state (within the accuracy of our approximation). However, the (quasi) equilibrium state of the reduced density matrices for two sites including the correlations cannot be described by a thermal state. Thus, real thermalization (if it occurs) should take much longer time. This behavior has already been observed in other scenarios and is sometimes called ``pre-thermalization.'' Finally, we compare our results to numerical simulations for finite lattices in one and two dimensions and find qualitative agreement.


title: EchoFlex: Hand Gesture Recognition using Ultrasound Imaging

abstract: Recent improvements in ultrasound imaging enable new opportunities for hand pose detection using wearable devices. Ultrasound imaging has remained under-explored in the HCI community despite being non-invasive, harmless and capable of imaging internal body parts, with applications including smart-watch interaction, prosthesis control and instrument tuition. In this paper, we compare the performance of different forearm mounting positions for a wearable ultrasonographic device. Location plays a fundamental role in ergonomics and performance since the anatomical features differ among positions. We also investigate the performance decrease due to cross-session position shifts and develop a technique to compensate for this misalignment. Our gesture recognition algorithm combines image processing and neural networks to classify the flexion and extension of 10 discrete hand gestures with an accuracy above 98%. Furthermore, this approach can continuously track individual digit flexion with less than 5% NRMSE, and also differentiate between digit flexion at different joints.


title: Using quality models in software package selection

abstract: The growing importance of commercial off-the-shelf software packages requires adapting some software engineering practices, such as requirements elicitation and testing, to this emergent framework. Also, some specific new activities arise, among which selection of software packages plays a prominent role. All the methodologies that have been proposed recently for choosing software packages compare user requirements with the packages' capabilities. There are different types of requirements, such as managerial, political, and, of course, quality requirements. Quality requirements are often difficult to check. This is partly due to their nature, but there is another reason that can be mitigated, namely the lack of structured and widespread descriptions of package domains (that is, categories of software packages such as ERP systems, graphical or data structure libraries, and so on). This absence hampers the accurate description of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process. Our methodology for building structured quality models helps solve this drawback.


title: Automatic Feeding Control for Dense Aquaculture Fish Tanks

abstract: This paper introduces an efficient visual signal processing system to continuously control the feeding process of fish in aquaculture tanks. The aim is to improve the production profit in fish farms by controlling the amount of feed at an optimal rate. The automatic feeding control includes two components: 1) a continuous decision on whether the fish are actively consuming feed, and 2) automatic detection of the number of excess feed populated on the water surface of the tank using a two-stage approach. The amount of feed is initially detected using the correlation filer applied to an optimum local region within the video frame, and then followed by a SVM-based refinement classifier to suppress the falsely detected feed. Having both measures allows us to accurately control the feeding process in an automated manner. Experimental results show that our system can accurately and efficiently estimate both measures.


title: Naive Bayes Super-Resolution Forest

abstract: This paper presents a fast, high-performance method for super resolution with external learning. The first contribution leading to the excellent performance is a bimodal tree for clustering, which successfully exploits the antipodal invariance of the coarse-to-high-res mapping of natural image patches and provides scalability to finer partitions of the underlying coarse patch space. During training an ensemble of such bimodal trees is computed, providing different linearizations of the mapping. The second and main contribution is a fast inference algorithm, which selects the most suitable mapping function within the tree ensemble for each patch by adopting a Local Naive Bayes formulation. The experimental validation shows promising scalability properties that reflect the suitability of the proposed model, which may also be generalized to other tasks. The resulting method is beyond one order of magnitude faster and performs objectively and subjectively better than the current state of the art.


title: A maintenance prediction system using data mining techniques

abstract: In the last years we have assisted to several and deep changes in industrial companies, mainly due to market dynamics and the need to converge with a globalized and impatient world. These changes are transversal to the entire company also impacting on company maintenance function. In an attempt to eliminate faults and keep systems running without interruption, companies incorporated tools into their Information and Communication Technologies (ICT) systems. The benefits are clear in terms of resulting quality and in costs reduction, particularly those related with the data processing time and accuracy of the resulting knowledge. In their daily routine, companies produce and store endless and complex quantities of data of different nature, increasing the difficulty of use in real time. In this sense, considering the relevance of data collected on industrial plants, namely in its maintenance activities, it is intended with this paper to present a functional architecture of a predictive maintenance system, using data mining techniques on data gathered from manufacturing units globally dispersed. Data Mining will identify behavior patterns, allowing a more accurate early detection of faults in machines. The remote data collection is based on an intricate system of distributed agents, which, given its nature, will be responsible for remote data collection through the functional architecture.


title: A Novel Serial Multimodal Biometrics Framework Based on Semisupervised Learning Techniques

abstract: We propose in this paper a novel framework for serial multimodal biometric systems based on semisupervised learning techniques. The proposed framework addresses the inherent issues of user inconvenience and system inefficiency in parallel multimodal biometric systems. Further, it advances the serial multimodal biometric systems by promoting the discriminating power of the weaker but more user convenient trait(s) and saving the use of the stronger but less user convenient trait(s) whenever possible. This is in contrast to other existing serial multimodal biometric systems that suggest optimized orderings of the traits deployed and parameterizations of the corresponding matchers but ignore the most important requirements of common applications. In terms of methodology, we propose to use semisupervised learning techniques to strengthen the matcher(s) on the weaker trait(s), utilizing the coupling relationship between the weaker and the stronger traits. A dimensionality reduction method for the weaker trait(s) based on dependence maximization is proposed to achieve this purpose. Experiments on two prototype systems clearly demonstrate the advantages of the proposed framework and methodology.


title: Do Highlights Affect Comprehension?: Lessons from a User Study

abstract: A largely unquestioned assumption in social reading is that publicly shared annotations improve reading outcomes. In this study, we explore the specific assumption that relevant and irrelevant passive highlighting affects comprehension. Participants were divided by cognitive style based on their degree of Field Dependence-Independence [20]. We found that irrelevant highlights had significant negative effects on reading comprehension for Field Independents (FIs), but not Field Dependents (FDs). This is a surprising result because FDs typically rely on external cues to structure and help process information, whereas FIs use internal cues. This suggests that highlighting cues information but does not structure it.


title: Model Reference Adaptive Control of a Permanent Magnet Brushless DC Motor for UAV Electric Propulsion System

abstract: In a previous work, a relationship between aircraft motion equations and model equations for a permanent magnet brushless DC motor was established to study the influence of machine dynamic performance on aircraft motion path from takeoff to steady level flight. The speed control scheme primarily made up of standard proportional integral controllers presented loss of altitude and exacerbative thrust when system was operated in speed control mode. In this paper a MRAC scheme is proposed for the speed control. It is shown that the application of the MRAC provides superior motor performance as well as an improvement in flight performance of the UAV system. The robustness of the control scheme to parameter changes in moment of inertia, viscous damping and load torque is shown.


title: Perspectives on forecasting research in accounting and finance

abstract: This paper offers some perspectives on forecasting research in accounting and finance. It is maintained that many common areas of forecasting research exist. Yet, most research has focused upon a particular (Box-Jenkins) technique and a particular (reported earnings) variable, virtually neglecting numerous other relevant forecasting research topics. This symposium issue includes papers which address several of these neglected research topics. The eight papers constituting the issue are classified into three categories: (1) univariate time-series modelling; (2) multivariate time-series modelling; and (3) comparison of experts' forecasts with those of statistical models. Following a summary of the papers, some suggestions for future research are offered.


title: Three Phase Three-Level PWM Switched Voltage Source Inverter With Zero Neutral Point Potential

abstract: A new three phase three-level Pulse Width Modulation (PWM) Switched Voltage Source (SVS) inverter with zero neutral point potential is proposed. It consists of three single-phase inverter modules. Each module is composed of a switched voltage source and inverter switches. The major advantage is that the peak value of the phase output voltage is twice as high as that of a conventional neutral-point-clamped (NPC) PWM inverter. Thus, the proposed inverter is suitable for applications with low voltage sources such as batteries, fuel cells, or solar cells. Furthermore, three-level waveforms of the proposed inverter can be achieved without the switch voltage imbalance problem. Since the average neutral point potential of the proposed inverter is zero, a common ground between the input stage and the output stage is possible. Therefore, it can be applied to a transformer-less Power Conditioning System (PCS). The proposed inverter is verified by a PSpice simulation and experimental results based on a laboratory prototype.


title: Discrete Optimization Complete and robust no-fit polygon generation for the irregular stock cutting problem

abstract: The no-fit polygon is a construct that can be used between pairs of shapes for fast and efficient handling of geometry within irregular two-dimensional stock cutting problems. Previously, the no-fit polygon (NFP) has not been widely applied because of the perception that it is difficult to implement and because of the lack of generic approaches that can cope with all problem cases without specific case-by-case handling. This paper introduces a robust orbital method for the creation of no-fit polygons which does not suffer from the typical problem cases found in the other approaches from the literature. Furthermore, the algorithm only involves two simple geometric stages so it is easily understood and implemented. We demonstrate how the approach handles known degenerate cases such as holes, interlocking concavities and jigsaw type pieces and we give generation times for 32 irregular packing benchmark problems from the literature, including real world datasets, to allow further comparison with existing and future approaches. � 2006 Elsevier B.V. All rights reserved.


title: C-Oracle: Predictive thermal management for data centers

abstract: Designing thermal management policies for todaypsilas power-dense server clusters is currently a challenge, since it is difficult to predict the exact temperature and performance that would result from trying to react to a thermal emergency. To address this challenge, in this paper we propose C-Oracle, a software infrastructure for Internet services that dynamically predicts the temperature and performance impact of different thermal management reactions into the future, allowing the thermal management policy to select the best reaction at each point in time. We experimentally evaluate C-Oracle for thermal management policies based on load redistribution and dynamic voltage/frequency scaling in both single-tier and multi-tier services. Our results show that, regardless of management policy or service organization, C-Oracle enables non-trivial decisions that effectively manage thermal emergencies, while avoiding unnecessary performance degradation.


title: Hedonic adaptation and the role of decision and experience utility in public policy

abstract: Many economists are becoming supportive of 'soft' paternalistic interventions that help people to avoid common decision errors without curtailing individual autonomy. To identify when such interventions could be beneficial, and to assess their success, requires a welfare criterion. However, traditional preference or choice-based criteria cannot serve this function because they assume that whatever people choose makes them better off. An alternative criterion that bases welfare on happiness rather than choice avoids this problem but has several of its own drawbacks. Most notably, people often adapt to serious chronic health conditions, and exhibit high levels of happiness, even though both those with and those without the condition agree that it is much preferable to be healthy. After reviewing different lines of research that shed light on the pros and cons of these alternative welfare criteria, we argue that no simple criterion based on either concept can surmount these problems. Instead, evaluations of welfare will inevitably have to be informed by a combination of both approaches, patched together in a fashion that depends on the specific context.


title: Boosting item keyword search with spreading activation

abstract: Most keyword search engines return directly matching keyword phrases. However, publishers cannot anticipate all possible ways in which users would search for the items in their documents. In fact, many times, there may be no direct keyword match between a keyword search phrase and descriptions of relevant items that are perfect matches for the search. We present an automated, high precision-based information retrieval solution to boost item find-ability by bridging the semantic gap between item information and popular keyword search phrases. Our solution achieves an average of 80% F-measure for various boosted matches for keyword search phrases in various categories.


title: Cumulative fatigue damage and life prediction theories: a survey of the state of the art for homogeneous materials

abstract: Abstract Fatigue damage increases with applied load cycles in a cumulative manner. Cumulative fatigue damage analysis plays a key role in life prediction of components and structures subjected to field load histories. Since the introduction of damage accumulation concept by Palmgren about 70 years ago and ‘linear damage rule’ by Miner about 50 years ago, the treatment of cumulative fatigue damage has received increasingly more attention. As a result, many damage models have been developed. Even though early theories on cumulative fatigue damage have been reviewed by several researchers, no comprehensive report has appeared recently to review the considerable efforts made since the late 1970s. This article provides a comprehensive review of cumulative fatigue damage theories for metals and their alloys, emphasizing the approaches developed between the early 1970s to the early 1990s. These theories are grouped into six categories: linear damage rules; nonlinear damage curve and two-stage linearization approaches; life curve modification methods; approaches based on crack growth concepts; continuum damage mechanics models; and energy-based theories.


title: Dynamic Multimodal Measurement of Depression Severity Using Deep Autoencoding

abstract: Depression is one of the most common psychiatric disorders worldwide, with over 350 million people affected. Current methods to screen for and assess depression depend almost entirely on clinical interviews and self-report scales. While useful, such measures lack objective, systematic, and efficient ways of incorporating behavioral observations that are strong indicators of depression presence and severity. Using dynamics of facial and head movement and vocalization, we trained classifiers to detect three levels of depression severity. Participants were a community sample diagnosed with major depressive disorder. They were recorded in clinical interviews (Hamilton Rating Scale for Depression, HRSD) at seven-week intervals over a period of 21 weeks. At each interview, they were scored by the HRSD as moderately to severely depressed, mildly depressed, or remitted. Logistic regression classifiers using leave-one-participant-out validation were compared for facial movement, head movement, and vocal prosody individually and in combination. Accuracy of depression severity measurement from facial movement dynamics was higher than that for head movement dynamics, and each was substantially higher than that for vocal prosody. Accuracy using all three modalities combined only marginally exceeded that of face and head combined. These findings suggest that automatic detection of depression severity from behavioral indicators in patients is feasible and that multimodal measures afford the most powerful detection.


title: Pseudo differential operational transconductance amplifier using common mode feed forward and HD3 feed forward

abstract: In this study a low supply voltage CMOS pseudo differential operational trans conductance amplifier (OTA) with CMFF and HD3-FF to improve CMRR and THD is proposed. Common mode rejection ratio (CMRR) is improved by using the common mode feed forward (CMFF) technology and the THD is simulated result of improved by using a HD3 feed forward (HD3-FF). The proposed OTA shown the CMRR is 68.6dB and the THD is lower than −66dB under the input voltage range of 0.1V with 1.2V supply voltage. The chip has been manufactured in a 0.18 μm-CMOS technology and the chip area is 0.69×0.38 mm2.


title: Comparison of the Effects of Quetiapine XR and Lithium Monotherapy on Actigraphy-Measured Circadian Parameters in Patients With Bipolar II Depression

abstract: The aim of this study was to evaluate the effects of quetiapine XR and lithium on actigraphy-measured circadian parameters in patients with bipolar II depression. ::: This was an 8-week, open-label, prospective, randomized comparative study. The assessments included the 17-item Hamilton Depression Rating Scale score and actigraphic measures concerning the previous 7 days, collected at each visit (weeks 0 [baseline], 1, 2, 4, 6, and 8); the actigraphic data were analyzed with a cosinor analysis. ::: Medication, time, and the interaction between medication and time were significantly associated with acrophase for the entire group (Ps = 0.003, 0.020, and 0.042, respectively). More specifically, acrophase was significantly delayed at weeks 1 and 6 (Ps = 0.004 and 0.039, respectively) in the quetiapine XR group. The F statistics significantly increased over time for the entire group (P < 0.001), and there was a significant increase in F statistics on weeks 4 and 6 in the quetiapine XR group (Ps = 0.016 and 0.020, respectively) and on weeks 4 and 8 in the lithium group (Ps = 0.001 and 0.016, respectively). In addition, scores on the 17-item Hamilton Depression Rating Scale were significantly associated with the F statistics during 8 weeks for the entire group (P = 0.008). ::: Both quetiapine XR and lithium affected several circadian parameters, including peak activity time and robustness of circadian rhythm, but exerted different effects on acrophase in patients with bipolar II depression. In particular, clinical depressive symptoms were associated with robustness of circadian rhythm during the course of the 8-week treatment.


title: Recursive neural networks for object detection

abstract: In this paper, a new recursive neural network model, able to process directed acyclic graphs with labeled edges, is introduced, in order to address the problem of object detection in images. In fact, the detection is a preliminary step in any object recognition system. The proposed method assumes a graph-based representation of images, that combines both spatial and visual features. In particular, after segmentation, an edge between two nodes stands for the adjacency relationship of two homogeneous regions, the edge label collects information on their relative positions, whereas node labels contain visual and geometric information on each region (area, color, texture, etc.). Such graphs are then processed by the recursive model in order to determine the eventual presence and the position of objects inside the image. Some experiments on face detection, carried out on scenes acquired by an indoor camera, are reported, showing very promising results. The proposed technique is general and can be applied in different object detection systems, since it does not include any a priori knowledge on the particular problem.


title: A hierarchical loss and its problems when classifying non-hierarchically

abstract: Failing to distinguish between a sheepdog and a skyscraper should be worse and penalized more than failing to distinguish between a sheepdog and a poodle; after all, sheepdogs and poodles are both breeds of dogs. However, existing metrics of failure (so-called "loss" or "win") used in textual or visual classification/recognition via neural networks seldom leverage a-priori information, such as a sheepdog being more similar to a poodle than to a skyscraper. We define a metric that, inter alia, can penalize failure to distinguish between a sheepdog and a skyscraper more than failure to distinguish between a sheepdog and a poodle. Unlike previously employed possibilities, this metric is based on an ultrametric tree associated with any given tree organization into a semantically meaningful hierarchy of a classifier's classes. An ultrametric tree is a tree with a so-called ultrametric distance metric such that all leaves are at the same distance from the root. Unfortunately, extensive numerical experiments indicate that the standard practice of training neural networks via stochastic gradient descent with random starting points often drives down the hierarchical loss nearly as much when minimizing the standard cross-entropy loss as when trying to minimize the hierarchical loss directly. Thus, this hierarchical loss is unreliable as an objective for plain, randomly started stochastic gradient descent to minimize; the main value of the hierarchical loss may be merely as a meaningful metric of success of a classifier.


title: Path Planning with Kinematic Constraints for Robot Groups

abstract: Path planning for multiple robots is well studied in the AI and robotics communities. For a given discretized environment, robots need to find collision-free paths to a set of specified goal locations. Robots can be fully anonymous, non-anonymous, or organized in groups. Although powerful solvers for this abstract problem exist, they make simplifying assumptions by ignoring kinematic constraints, making it difficult to use the resulting plans on actual robots. In this paper, we present a solution which takes kinematic constraints, such as maximum velocities, into account, while guaranteeing a user-specified minimum safety distance between robots. We demonstrate our approach in simulation and on real robots in 2D and 3D environments.


title: Automatic sense prediction for implicit discourse relations in text

abstract: We present a series of experiments on automatically identifying the sense of implicit discourse relations, i.e. relations that are not marked with a discourse connective such as "but" or "because". We work with a corpus of implicit relations present in newspaper text and report results on a test set that is representative of the naturally occurring distribution of senses. We use several linguistically informed features, including polarity tags, Levin verb classes, length of verb phrases, modality, context, and lexical features. In addition, we revisit past approaches using lexical pairs from unannotated text as features, explain some of their shortcomings and propose modifications. Our best combination of features outperforms the baseline from data intensive approaches by 4% for comparison and 16% for contingency.


title: Driver-Activity Recognition in the Context of Conditionally Autonomous Driving

abstract: This paper presents a novel approach to automated recognition of the driver's activity, which is a crucial factor for determining the take-over readiness in conditionally autonomous driving scenarios. Therefore, an architecture based on head-and eye-tracking data is introduced in this study and several features are analyzed. The proposed approach is evaluated on data recorded during a driving simulator study with 73 subjects performing different secondary tasks while driving in an autonomous setting. The proposed architecture shows promising results towards in-vehicle driver-activity recognition. Furthermore, a significant improvement in the classification performance is demonstrated due to the consideration of novel features derived especially for the autonomous driving context.


title: Symbolic and parametric model checking of discrete-time markov Chains

abstract: We present a language-theoretic approach to symbolic model checking of PCTL over discrete-time Markov chains. The probability with which a path formula is satisfied is represented by a regular expression. A recursive evaluation of the regular expression yields an exact rational value when transition probabilities are rational, and rational functions when some probabilities are left unspecified as parameters of the system. This allows for parametric model checking by evaluating the regular expression for different parameter values, for instance, to study the influence of a lossy channel in the overall reliability of a randomized protocol.


title: Introducing the RECOLA multimodal corpus of remote collaborative and affective interactions

abstract: We present in this paper a new multimodal corpus of spontaneous collaborative and affective interactions in French: RECOLA, which is being made available to the research community. Participants were recorded in dyads during a video conference while completing a task requiring collaboration. Different multimodal data, i.e., audio, video, ECG and EDA, were recorded continuously and synchronously. In total, 46 participants took part in the test, for which the first 5 minutes of interaction were kept to ease annotation. In addition to these recordings, 6 annotators measured emotion continuously on two dimensions: arousal and valence, as well as social behavior labels on live dimensions. The corpus allowed us to take self-report measures of users during task completion. Methodologies and issues related to affective corpus construction are briefly reviewed in this paper. We further detail how the corpus was constructed, i.e., participants, procedure and task, the multimodal recording setup, the annotation of data and some analysis of the quality of these annotations.


title: Orthogonal Least Squares Algorithm for Training Cascade Neural Networks

abstract: This paper proposes a novel constructive training algorithm for cascade neural networks. By reformulating the cascade neural network as a linear-in-the-parameters model, we use the orthogonal least squares (OLS) method to derive a novel objective function for training new hidden units. With this objective function, the sum of squared errors (SSE) of the network can be maximally reduced after each new hidden unit is added, thus leading to a network with less hidden units and better generalization performance. Furthermore, the proposed algorithm considers both the input weights training and output weights training in an integrated framework, which greatly simplifies the training of output weights. The effectiveness of the proposed algorithm is demonstrated by simulation results.


title: Sequentially Rotated Circular Antenna Array With Curved PIFA and Series Feed Network

abstract: This paper describes the design of a broadside circularly polarized uniform circular array based on curved planar inverted F-antenna elements. Circular polarization (CP) is obtained by exploiting the sequential rotation technique and implementing it with a series feed network. The proposed structure is first introduced, and some geometrical considerations are derived. Second, the array radiation body is designed taking into account the mutual coupling among antenna elements. Third, the series feed network usually employed for four-antenna element arrays is analyzed and extended to three and more than four antennas exploiting the special case of equal power distribution. The array is designed with three-, four-, five-, and six-antenna elements, and dimensions, impedance bandwidth (defined for $S_{11}\leq -10$ dB), axial ratio (AR) bandwidth ( $\text {AR}\leq 3$ dB), gain, beamwidth, front-to-back ratio, and cross-polarization level are compared. Arrays with three and five elements are also prototyped to benchmark the numerical analysis results, finding good correspondence.


DAG-Based Distributed Ledgers


title: Multiscale conditional random fields for image labeling

abstract: We propose an approach to include contextual features for labeling images, in which each pixel is assigned to one of a finite set of labels. The features are incorporated into a probabilistic framework, which combines the outputs of several components. Components differ in the information they encode. Some focus on the image-label mapping, while others focus solely on patterns within the label field. Components also differ in their scale, as some focus on fine-resolution patterns while others on coarser, more global structure. A supervised version of the contrastive divergence algorithm is applied to learn these features from labeled image data. We demonstrate performance on two real-world image databases and compare it to a classifier and a Markov random field.


title: Optimal exact string matching based on suffix arrays

abstract: Using the suffix tree of a string S, decision queries of the type Is P a substring of S? can be answered in O(|P|) time and enumeration queries of the type Where are all z occurrences of P in S? can be answered in O(|P|+z) time, totally independent of the size of S. However, in large scale applications as genome analysis, the space requirements of the suffix tree are a severe drawback. The suffix array is a more space economical index structure. Using it and an additional table, Manber and Myers (1993) showed that decision queries and enumeration queries can be answered in O(|P|+log|S|) and O(|P|+log|S|+z) time, respectively, but no optimal time algorithms are known. In this paper, we show how to achieve the optimal O(|P|) and O(|P| + z) time bounds for the suffix array. Our approach is not confined to exact pattern matching. In fact, it can be used to efficiently solve all problems that are usually solved by a top-down traversal of the suffix tree. Experiments show that our method is not only of theoretical interest but also of practical relevance.


title: Recognition of unfamiliar faces

abstract: Abstract People are excellent at identifying faces familiar to them, even from very low quality images, but are bad at recognizing, or even matching, unfamiliar faces. In this review we shall consider some of the factors that affect our abilities to match unfamiliar faces. Major differences in orientation (e.g. inversion) or greyscale information (e.g. negation) affect face processing dramatically, and such effects suggest that representations derived from unfamiliar faces are based on relatively low-level image descriptions. Consistent with this, even relatively minor differences in lighting and viewpoint create problems for human face matching, leading to potentially important problems for the use of images from security videos. The relationships between different parts of the face (its ‘configuration') are as important to the impression created of an upright face as the local features themselves, suggesting further constraints on the representations derived from faces. We go on to consider the contribution of computer face-recognition systems to the understanding of the theory and the practical problems of face identification. Finally, we look to the future of research in this area that will incorporate motion and 3-D shape information.


title: A Case Study: CRM Adoption Success Factor Analysis and Six Sigma DMAIC Application

abstract: With today's increasingly competitive economy, many organizations have initiated customer relationship management (CRM) projects to improve customer satisfaction, revenue growth and employee productivity gains. However, only a few successful CRM implementations have successfully completed. In order to enhance the CRM implementation process and increase the success rate, in this paper, first we present the most significant success factors for CRM implementation identified by the results of literature reviews and a survey we conducted. Then we propose a strategy to integrate Six Sigma DMAIC methodology with the CRM implementation process addressing five critical success factors (CSF). Finally, we provide a case study to show how the proposed approach can be applied in the real CRM implementation projects. We conclude that by considering the critical success factors, the proposed approach can emphasize the critical part of implementation process and provide high possibility of CRM adoption success.


A Threads-Only MPI Implementation for the Development of Parallel Programs


title: RF Energy Harvesting for Embedded Systems: A Survey of Tradeoffs and Methodology

abstract: This paper presents an overview of passive Radio Frequency (RF) energy reception and power harvesting circuits for isolated communications and computing systems lacking access to primary power sources. A unified understanding of the energy harvesting alternatives is provided, followed by an elaborate study of RF energy harvesting within the context of embedded systems. A detailed discussion of RF technologies ranging from the directed communications signal reception to dispersed ambient power harvesting is provided. A comparative focus on design tradeoffs and process alterations is provided to represent the diversity in the applications requiring wireless RF harvesting units. Also included is an analysis of system combinations, and how wake up units, active storage, and duty cycling play roles in the consumption and harvesting of RF energy.


title: Very high resolution optical satellites for DEM generation: a review

abstract: The advent of very high resolution (VHR) optical satellites capable of producing stereo images led to a new era in extracting digital elevation model which commenced with the launch of IKONOS. The special specifications of VHR optical satellites besides, the significant economic profit stimulated other countries and companies to have their constellations such as EROS-A1 and EROS-B1 as the cooperation between Israel and ImageSat. QuickBird, WorldView-1 and WorldVew-2 were launched by DigitalGlobe. ALOS and GeoEye-1 were offered by Japan and GeoEye Respectively. In addition to aforementioned satellites, Indian and South Korea initiated their own constellation by launching CartoSat-1 and KOPOSAT-2 respectively.The availability of all so-called satellites make a huge market of stereo images for extracting of digital elevation model and other correspondent applications such as, producing orthorectifcatin images and updating maps. Therefore, there is a need for a comprehensive comparison for scientific and commercial clients to choose appropriate satellite images and methods of generating digital elevation model to obtain optimum results. This paper will thus give a review about the specifications of VHR optical satellites. Then it will discuss the automatic elaborating of digital elevation model. Finally an overview of studies and corresponding results is reported.


The 2004 presidential election in the Dominican Republic


title: Metalorganic Chemical Vapor Deposition of Copper Using (Hexafluoroacetylacetonate)Cu(I)(3,3-dimethyl-1-butene) with a Liquid Delivery System.

abstract: From variable temperature (VT) 1H-nuclear magnetic resonance (NMR) and a heating test, it was found that (hexafluoroacetylacetonate)Cu(I)(3,3-dimethyl-1-butene) [(hfac)Cu(I)(DMB)] was stable up to 65°C. The effects of various process conditions such as substrate temperature, liquid precursor flow rate and hydrogen carrier gas flow rate on the deposition rate, texture, microhardness, surface roughness and uniformity were studied using a direct liquid injection 200 mm metalorganic chemical vapor deposition (MOCVD) reactor with hollow-cathode magnetron (HCM) sputter-deposited Cu substrate on silicon wafer. The MOCVD Cu process with (hfac)Cu(I)(DMB) showed good conformality, continuous film morphology and low resistivity at a substrate temperature of 190°C, vaporizer temperature of 55°C, total pressure of 2.5 Torr and precursor flow rate of 0.5 cm3/min. X-ray diffraction (XRD) analyses demonstrated a strong (111) texture of the copper film. The higher (111) peak intensity and the narrower width at half maximum were obtained when the source feed rate was low. Also the higher (111) peak intensity was observed at higher substrate temperature. At temperature below about 200°C, the microhardness was increased with increasing substrate temperature. In the high temperature regime (>200°C), the hardness was decreased.


title: Real-time eSports Match Result Prediction

abstract: In this paper, we try to predict the winning team of a match in the multiplayer eSports game Dota 2. To address the weaknesses of previous work, we consider more aspects of prior (pre-match) features from individual players' match history, as well as real-time (during-match) features at each minute as the match progresses. We use logistic regression, the proposed Attribute Sequence Model, and their combinations as the prediction models. In a dataset of 78362 matches where 20631 matches contain replay data, our experiments show that adding more aspects of prior features improves accuracy from 58.69% to 71.49%, and introducing real-time features achieves up to 93.73% accuracy when predicting at the 40th minute.


title: The Impact of Unit Leadership and Empowerment on Nurses ’ Organizational Commitment

abstract: Objective: The aim of this study was to test a multilevel model linking unit-level leader-member exchange quality and structural empowerment to nurses' psychological empowerment and organizational commitment at the individual level of analysis. Background: Few studies have examined the contextual effects of unit leadership on individual nurse outcomes. Workplace empowerment has been related to retention outcomes such as organizational commitment in several studies, but few have studied the impact of specific unit characteristics within which nurses work on these outcomes. Methods: We surveyed 3,156 nurses in 217 hospital units to test the multilevel model. Results: A multilevel path analysis revealed significant individual and contextual effects on nurses' organizational commitment. Both unit-level leader-member exchange quality and structural empowerment had significant direct effects on individual-level psychological empowerment and organizational commitment. Psychological empowerment mediated the relationship between core self-evaluations and organizational commitment at the individual level of analysis.


title: Model and simulation of the Mitsubishi RV-M1 robot using MATLAB

abstract: In this paper, the forward and inverse kinematics of a robot manipulator named RV-M1 are analyzed. The forward kinematic model is based on the DH parameters, and the inverse one is derived in closed form. Then, the interactive simulation system is established using MATLAB software which is one of the most used platforms for the modeling and simulation of robot systems. Finally, the simulation results verify the effectiveness of the kinematics equations.


title: A high-gain grid array antenna for 60-GHz antenna-in-package applications

abstract: This paper presents a high-gain grid array antenna (GAA) on FERRO A6M low temperature co-fired ceramic (LTCC) for 60-GHz antenna-in-package (AiP) applications. The high-gain GAA includes four sub-antennas. Dimensions of the sub-antenna are 15 × 15 × 1 mm3, which is composed of four subarrays. Measured results show that the sub-antenna features impedance bandwidth (BW) 50 - 65.6 GHz, 3-dB gain BW 58.5 - 67 GHz with the maximum gain 15 dBi at 62 GHz, and vertical beams in the broadside direction at 60 GHz. The high-gain GAA has dimensions of 30 × 30 × 1 mm3. Simulated results show that it has impedance BW 55.8 - 63.5 GHz, 3-dB gain BW 56.8 - 64 GHz with the maximum gain of 21.4 dBi at 61 GHz, and vertical beams in the broadside direction at 60 GHz. It is a capable candidate for 60-GHz AiP applications.


A Reappraisal of Intentional Cranial Modification Among the Indigenous Inhabitants of Suriname in Prehistoric and Historic Times


Geometric deep learning


Generating Look-alike Names via Distributed Representations


title: Virtopsy versus digital autopsy: virtuous autopsy

abstract: Multislice computed tomography (MSCT) and magnetic resonance imaging (MRI) are being increasingly implemented in forensic pathology. These methods may serve as an adjuvant to classic forensic autopsies. Imaging of the interior of corpses is performed using MSCT and/or MRI. MRI, in addition, is also well suited to the examination of surviving victims of assault, especially choking, and helps visualise internal injuries sometimes not seen on external examination of the victim. Various postprocessing techniques can provide strong forensic evidence for use in legal proceedings. The documentation and analysis of postmortem findings with MSCT and MRI and postprocessing techniques (virtopsy) is investigator independent, objective and noninvasive and will lead to qualitative improvements in forensic pathologic investigation. Apart from the accuracy and three dimensionality that conventional documentations lack, these techniques allow for the re-examination of the corpse and the crime scene even decades later, after burial of the corpse and liberation of the crime scene. We believe that this virtual, noninvasive or minimally invasive approach will improve forensic medicine in the near future.


title: Absolute Photometric Calibration of the Infrared Camera (IRC) aboard AKARI

abstract: The absolute photometric calibration of imaging observations with the Infrared Camera (IRC) aboard the AKARI satellite was performed by monitoring the same stars regularly and by observing a set of standard stars. By our monitoring observations, we confirmed that all channels of the IRC were stable to within 4% and that their sensitivities were constant until the liquid helium was exhausted. Using the data of these repeated observations, we evaluated the intrinsic errors as a function of the brightnesses of objects and found that the errors increase rapidly toward fainter objects. We also checked the consistency between short and long exposure times, and confirmed that the data sampling had been executed as designed. Finally, by comparing the estimated in-band flux densities and the observed data values of standard stars, we obtained conversion factors to the absolute flux densities of all the band/exposure configurations. Their absolute uncertainties are estimated to be less than 6%.