title: Internet Gaming Disorder Among Slovenian Primary Schoolchildren: Findings From a Nationally Representative Sample of Adolescents

abstract: Background and aimsSince the inclusion of Internet Gaming Disorder (IGD) in the latest (fifth) edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as a tentative disorder, a few psychometric screening instruments have been developed to assess IGD, including the 9-item Internet Gaming Disorder Scale – Short-Form (IGDS9-SF) – a short, valid, and reliable instrument.MethodsDue to the lack of research on IGD in Slovenia, this study aimed to examine the psychometric properties of the IGDS9-SF in addition to investigating the prevalence rates of IGD in a nationally representative sample of eighth graders from Slovenia (N = 1,071).ResultsThe IGDS9-SF underwent rigorous psychometric scrutiny in terms of validity and reliability. Construct validation was investigated with confirmatory factor analysis to examine the factorial structure of the IGDS9-SF and a unidimensional structure appeared to fit the data well. Concurrent and criterion validation were also investigated by examining the ass...


title: Evaluation of early stimulation programs for enhancing brain development

abstract: UNLABELLED ::: The term 'early intervention' designates educational and neuroprotection strategies aimed at enhancing brain development. Early educational strategies seek to take advantage of cerebral plasticity. Neuroprotection, a term initially used to characterize substances capable of preventing cell death, now encompasses all interventions that promote normal development and prevent disabilities, including organisational, therapeutic and environment-modifying measures, such as early stimulation programs. Early stimulation programs were first devised in the United States for vulnerable children in low-income families; positive effects were recorded regarding school failure rates and social problems. Programs have also been implemented in several countries for premature infants and low-birth-weight infants, who are at high risk for neurodevelopmental abnormalities. The programs target the child, the parents or both. The best evaluated programs are the NIDCAP (Newborn Individualized Developmental Care and Assessment Program) in Sweden for babies<1500 g in neonatal intensive care units and the longitudinal multisite program IHDP (Infant Health and Development Program) created in the United States for infants<37 weeks or <2500 g. ::: ::: ::: CONCLUSION ::: Although the NIDCAP and the IHDP targeted different populations, they produced similar effects in several regards: efficacy was greatest with programs involving both the parents and the child; long-term stimulation improved cognitive outcomes and child-parent interactions; cognition showed greater improvements than motor skills and larger benefits were obtained in families that combined several risk factors including low education attainment by the mothers.


title: Does gamification matter?: a systematic mapping about the evaluation of gamification in educational environments

abstract: Gamification is a technique that reuses game design and elements in others contexts, such as in e-commerces systems and virtual learning environments. When applied in the educational area, the gamification purpose is to promote a better user experience by improving students' motivation and engagement. There are already several studies applying gamification in the educational systems, and many ways to evaluate its effects on students. Thus, the aim of this work is to investigate how these studies evaluate gamification and compare its usage in educational environments through a systematic mapping. From the 832 studies returned by the search engines, only 20 of them met the defined selection criteria. As a result, these studies compared and evaluated gamification through students' interaction, performance, and user experience, based on their activities and answers in satisfaction surveys and tests.


title: Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media

abstract: Surrogate strategies are used widely for uncertainty quantification of groundwater models in order to improve computational efficiency. However, their application to dynamic multiphase flow problems is hindered by the curse of dimensionality, the saturation discontinuity due to capillarity effects, and the time-dependence of the multi-output responses. In this paper, we propose a deep convolutional encoder-decoder neural network methodology to tackle these issues. The surrogate modeling task is transformed to an image-to-image regression strategy. This approach extracts high-level coarse features from the high-dimensional input permeability images using an encoder, and then refines the coarse features to provide the output pressure/saturation images through a decoder. A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field. To characterize the high-dimensional time-dependent outputs of the dynamic system, time is treated as an additional input to the network that is trained using pairs of input realizations and of the corresponding system outputs at a limited number of time instances. The proposed method is evaluated using a geological carbon storage process-based multiphase flow model with a 2500-dimensional stochastic permeability field. With a relatively small number of training data, the surrogate model is capable of accurately characterizing the spatio-temporal evolution of the pressure and discontinuous CO2 saturation fields and can be used efficiently to compute the statistics of the system responses.


title: Context Data Categories and Privacy Model for Mobile Data Collection Apps

abstract: Context-aware applications stemming from diverse fields like mobile health, recommender systems, and mobile commerce potentially benefit from knowing aspects of the user’s personality. As filling out personality questionnaires is tedious, we propose the prediction of the user’s personality from smartphone sensor and usage data. In order to collect data for researching the relationship between smartphone data and personality, we developed the Android app TYDR (Track Your Daily Routine) which tracks smart-phone data and utilizes psychometric personality questionnaires. With TYDR, we track a larger variety of smartphone data than similar existing apps, including metadata on notifications, photos taken, and music played back by the user. For the development of TYDR, we introduce a general context data model consisting of four categories that focus on the user’s different types of interactions with the smartphone: physical conditions and activity, device status and usage, core functions usage, and app usage. On top of this, we develop the privacy model PM-MoDaC specifically for apps related to the collection of mobile data, consisting of nine proposed privacy measures. We present the implementation of all of those measures in TYDR. Although the utilization of the user’s personality based on the usage of his or her smartphone is a challenging endeavor, it seems to be a promising approach for various types of context-aware mobile applications.


title: A Comparison of Retrieval Models for Open Domain Story Generation

abstract: In this paper we describe the architecture of an interactive story generation system where a human and computer each take turns writing sentences of an emerging narrative. Each turn begins with the user adding a sentence to the story, where the computer responds with a sentence of its own that continues what has been written so far. Rather than generating the next sentence from scratch, the computer selects the next sentence from a corpus of tens of millions of narrative sentences extracted from Internet weblogs. We compare five different retrieval methods for selecting the most appropriate sentence, and present the results of a user study to determine which of these models produces stories with the highest coherence and overall value.


title: Resveratrol improves health and survival of mice on a high-calorie diet

abstract: Resveratrol (3,5,49-trihydroxystilbene) extends the lifespan of diverse species including Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. In these organisms, lifespan extension is dependent on Sir2, a conserved deacetylase proposed to underlie the beneficial effects of caloric restriction. Here we show that resveratrol shifts the physiology of middle-aged mice on a high-calorie diet towards that of mice on a standard diet and significantly increases their survival. Resveratrol produces changes associated with longer lifespan, including increased insulin sensitivity, reduced insulin-like growth factor-1 (IGF-I) levels, increased AMP-activated protein kinase (AMPK) and peroxisome proliferator-activated receptor-c coactivator 1a (PGC-1a) activity, increased mitochondrial number, and improved motor function. Parametric analysis of gene set enrichment revealed that resveratrol opposed the effects of the high-calorie diet in 144 out of 153 significantly altered pathways. These data show that improving general health in mammals using small molecules is an attainable goal, and point to new approaches for treating obesity-related disorders and diseases of ageing.


title: Movement Control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

abstract: In this paper, a series of control algorithms have been designed for the climbing cloth robot, “Clothbot” to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equipped with semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.


title: Algorithm 849: A concise sparse Cholesky factorization package

abstract: The LDL software package is a set of short, concise routines for factorizing symmetric positive-definite sparse matrices, with some applicability to symmetric indefinite matrices. Its primary purpose is to illustrate much of the basic theory of sparse matrix algorithms in as concise a code as possible, including an elegant method of sparse symmetric factorization that computes the factorization row-by-row but stores it column-by-column. The entire symbolic and numeric factorization consists of less than 50 executable lines of code. The package is written in C, and includes a MATLAB interface.


title: Deep CNN Ensemble with Data Augmentation for Object Detection

abstract: We report on the methods used in our recent DeepEnsembleCoco submission to the PASCAL VOC 2012 challenge, which achieves state-of-the-art performance on the object detection task. Our method is a variant of the R-CNN model proposed Girshick:CVPR14 with two key improvements to training and evaluation. First, our method constructs an ensemble of deep CNN models with different architectures that are complementary to each other. Second, we augment the PASCAL VOC training set with images from the Microsoft COCO dataset to significantly enlarge the amount training data. Importantly, we select a subset of the Microsoft COCO images to be consistent with the PASCAL VOC task. Results on the PASCAL VOC evaluation server show that our proposed method outperform all previous methods on the PASCAL VOC 2012 detection task at time of submission.


title: On Monte Carlo Tree Search and Reinforcement Learning

abstract: Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved wide-spread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL techniques within tree search has not been thoroughly studied yet. In this paper we re-examine in depth this close relation between the two fields; our goal is to improve the cross-awareness between the two communities. We show that a straightforward adaptation of RL semantics within tree search can lead to a wealth of new algorithms, for which the traditional MCTS is only one of the variants. We confirm that planning methods inspired by RL in conjunction with online search demonstrate encouraging results on several classic board games and in arcade video game competitions, where our algorithm recently ranked first. Our study promotes a unified view of learning, planning, and search.


title: Understanding the demographics of twitter users

abstract: Every second, the thoughts and feelings of millions of people across the world are recorded in the form of 140-character tweets using Twitter. However, despite the enormous potential presented by this remarkable data source, we still do not have an understanding of the Twitter population itself: Who are the Twitter users? How representative of the overall population are they? In this paper, we take the first steps towards answering these questions by analyzing data on a set of Twitter users representing over 1% of the U.S. population. We develop techniques that allow us to compare the Twitter population to the U.S. population along three axes (geography, gender, and race/ethnicity), and find that the Twitter population is a highly non-uniform sample of the population.


title: Two-stage control charts for high yield processes

abstract: In this paper, a two-stage control chart for monitoring the defective rate of high-yield processes is proposed and studied. The Cumulative Count of Conforming control chart is generalized by using the number of items inspected until two defective items are observed. As this will increase the time to alarm, a two-stage approach combining both schemes is proposed. The occurrence of a defective within n1 items inspected in the first stage indicates that the process is out of control. If no defective occurs within n1 items inspected, the occurrence of two defectives within the next n2 - n1 in the second stage also indicates that the process is out of control. The probability of making a false alarm at the first and second stages are equal to α1 and α2, respectively. This procedure improves the sensitivity of the control chart in detecting shifting of the process defective rate p when p is at the parts-per-million order of magnitude.


title: Video games and spatial cognition.

abstract: Video game enthusiasts spend many hours at play, and this intense activity has the potential to alter both brain and behavior. We review studies that investigate the ability of video games to modify processes in spatial cognition. We outline the initial stages of research into the underlying mechanisms of learning, and we also consider possible applications of this new knowledge. Several experiments have shown that playing action games induces changes in a number of sensory, perceptual, and attentional abilities that are important for many tasks in spatial cognition. These basic capacities include contrast sensitivity, spatial resolution, the attentional visual field, enumeration, multiple object tracking, and visuomotor coordination and speed. In addition to altering performance on basic tasks, playing action video games has a beneficial effect on more complex spatial tasks such as mental rotation, thus demonstrating that learning generalizes far beyond the training activities in the game. Far transfer of this sort is generally elusive in learning, and we discuss some early attempts to elucidate the brain functions that are responsible. Finally, we suggest that studying video games may contribute not only to an improved understanding of the mechanisms of learning but may also offer new approaches to teaching spatial skills.


title: Stationary dynamic texture synthesis using convolutional neural networks

abstract: In this paper, we utilize deep networks to study the problem of modeling and synthesizing stationary dynamic textures that exhibit both spatial and temporal regularity. Our work is a generalization of the generative static texture model based on convolutional neural networks (CNNs). We extend the static texture synthesis method using CNNs to the dynamical setting by adding the temporal dimension, where the multiple layers of spatial-temporal filters come from off-the-shelf deep 3-dimensional convolutional networks (3D ConvNets) trained in a discriminative fashion. Dynamic textures generation is formulated as an iterative optimization procedure which imposes the correlation statistics derived from 3D ConvNets of the input exemplar on the synthesized one. Synthesis experiments on various video texture exemplars show that such generative method can synthesize realistic dynamic textures of good visual quality.


Advanced Data Mining Approach for Wind Turbines Fault Prediction


title: The Marketing Mix Revisited: Towards the 21st Century Marketing

abstract: The paper assesses the current standing of the 4Ps Marketing Mix framework as the dominant marketing management paradigm and identifies market developments, environmental changes, and trends, as well as changing academic attitudes likely to affect the future of the Mix as theoretical concept and also the favourite management tool of marketing practitioners. It reviews the criticism on the 4Ps emanating from five traditional marketing areas - Consumer Marketing, Relationship Marketing, Services Marketing, Retail Marketing, Industrial Marketing -and the emerging field of Electronic Marketing. The paper identifies two main limitations of the Marketing Mix as management tool, common in all examined domains, namely the model's internal orientation and lack of personalisation. It also identifies several area-specific limitations and underlines the need for further research on the issue. The weaknesses identified in the study seem to support the frequently expressed suggestion that marketing scholars should focus their efforts in formulating the conceptual foundations and marketing methodologies that better address the needs of today's and tomorrow's marketer


title: Personal information management

abstract: Personal Information Management (PIM) is the management of information (e.g. files, emails, and bookmarks) by an individual in support of his/her roles and tasks. Although PIM is practiced daily by millions of people, a research community has never been established. This SIG aims to provide an opportunity for researchers, students and designers who share an interest in the field to meet and discuss key issues. We hope the SIG will lay the foundation for an ongoing PIM research community.


title: Learning to branch in Mixed Integer Programming

abstract: The design of strategies for branching in Mixed Integer Programming (MIP) is guided by cycles of parameter tuning and offline experimentation on an extremely heterogeneous testbed, using the average performance. Once devised, these strategies (and their parameter settings) are essentially input-agnostic. To address these issues, we propose a machine learning (ML) framework for variable branching in MIP. Our method observes the decisions made by Strong Branching (SB), a time-consuming strategy that produces small search trees, collecting features that characterize the candidate branching variables at each node of the tree. Based on the collected data, we learn an easy-to-evaluate surrogate function that mimics the SB strategy, by means of solving a learning-to-rank problem, common in ML. The learned ranking function is then used for branching. The learning is instance-specific, and is performed on-the-fly while executing a branch-and-bound search to solve the instance. Experiments on benchmark instances indicate that our method produces significantly smaller search trees than existing heuristics, and is competitive with a state-of-the-art commercial solver.


title: A 100V gate driver with sub-nanosecond-delay capacitive-coupled level shifting and dynamic timing control for ZVS-based synchronous power converters

abstract: A high-voltage high-speed gate driver to enable synchronous rectifiers with zero-voltage-switching (ZVS) operation is presented in this paper. A capacitive-coupled level-shifter (CCLS) is developed to achieve negligible propagation delay and static current consumption. With only 1 off-chip capacitor, the proposed gate driver possesses strong driving capability and requires no external floating supply for the high-side driving. A dynamic timing control is also proposed not only to enable ZVS operation in the converter for minimizing the capacitive switching loss, but also to eliminate the converter short-circuit power loss. Implemented in a 0.5μm HV CMOS process, the proposed CCLS of the gate driver can shift up a 5V signal to the 100V DC rail with sub-nanosecond delay, improving the FoM by at least 29 times compared with that of state-of-the-art counterparts. The dynamic dead-time control properly enables ZVS operation in a synchronous buck converter under different input voltages (30V to 100V). The power losses of the high-voltage buck converter are thus greatly reduced under different load currents, achieving a maximum power efficiency improvement of 11.5%.


BioReact : Visualization of Systems Biology Network


title: Color fidelity and visibility enhancement of underwater image de-hazing by enhanced fuzzy intensification operator

abstract: This paper presents an optimization based algorithm for underwater image de-hazing problem. Underwater image de-hazing is the most prominent area in research. Underwater images are corrupted due to absorption and scattering. With the effect of that, underwater images have the limitation of low visibility, low color and poor natural appearance. To avoid the mentioned problems, Enhanced fuzzy intensification method is proposed. For each color channel, enhanced fuzzy membership function is derived. Second, the correction of fuzzy based pixel intensification is carried out for each channel to remove haze and to enhance visibility and color. The post processing of fuzzy histogram equalization is implemented for red channel alone when the captured image is having highest value of red channel pixel values. The proposed method provides better results in terms maximum entropy and PSNR with minimum MSE with very minimum computational time compared to existing methodologies.


title: Understanding Alpha Decay

abstract: In this paper, I argue that the ways asset pricing tests account for alpha decay are inadequate. Whenever an anomaly is traded away, its portfolio’s market value increases, leading to outsized positive realized returns. The anomaly will thus appear robust while it is in fact going extinct. A separate issue is that most asset pricing tests make a stationarity assumption tantamount to a constant population alpha. I show that as a result, average returns may be truly significant in sample and yet fail to continue in the future. Furthermore, average returns are biased upward when alpha decays. Using results in the literature, I show that the bias is about 1.4% per year for a typical anomaly. I provide a simple formula to correct for this effect and show how to incorporate alpha decay tests into the asset pricing toolkit.


title: Fast Weighted Total Variation Regularization Algorithm for Blur Identification and Image Restoration

abstract: Images obtained from unconstrained environments may be blurred by unknown kernels and affected due to noise. This paper presents a new total variation minimization-based method for blindly deblurring such images. Unlike the alternating optimization-based algorithms, the proposed algorithm adopts a joint estimation strategy to estimate the unknown blurring kernel and the unknown image in an iterative manner, where each iteration performs two separate image denoising subproblems that admit fast implementation. Experiments are performed on multiple synthetic, grayscale, and color images, and the results demonstrate that the proposed method is effective in blind deblurring.


title: A context-aware authentication framework for smart homes

abstract: There has been a recent rapid increase in the number of Internet of Things (IoT) devices, providing a wide range of services for smart homes such as surveillance cameras, smart lighting, and door locks that can be remotely accessed and controlled. User mobility makes static security mechanisms, such as usernames and passwords, tedious to use. In this paper, we introduce a context-aware authentication framework for smart homes that utilizes contextual information such as the user's location, profile, calendar, request time and access behavior patterns to enable access to home devices. Such contextual information enables our framework to make more informed decisions about whether to accept or deny access requests. The implementation and evaluation demonstrate that the proposed framework provides security in a flexible manner without requiring any additional user intervention.


The Dynamic Effects of Bundling as a Product Strategy


title: Paragon: QoS-aware scheduling for heterogeneous datacenters

abstract: Large-scale datacenters (DCs) host tens of thousands of diverse applications each day. However, interference between colocated workloads and the difficulty to match applications to one of the many hardware platforms available can degrade performance, violating the quality of service (QoS) guarantees that many cloud workloads require. While previous work has identified the impact of heterogeneity and interference, existing solutions are computationally intensive, cannot be applied online and do not scale beyond few applications. We present Paragon, an online and scalable DC scheduler that is heterogeneity and interference-aware. Paragon is derived from robust analytical methods and instead of profiling each application in detail, it leverages information the system already has about applications it has previously seen. It uses collaborative filtering techniques to quickly and accurately classify an unknown, incoming workload with respect to heterogeneity and interference in multiple shared resources, by identifying similarities to previously scheduled applications. The classification allows Paragon to greedily schedule applications in a manner that minimizes interference and maximizes server utilization. Paragon scales to tens of thousands of servers with marginal scheduling overheads in terms of time or state. We evaluate Paragon with a wide range of workload scenarios, on both small and large-scale systems, including 1,000 servers on EC2. For a 2,500-workload scenario, Paragon enforces performance guarantees for 91% of applications, while significantly improving utilization. In comparison, heterogeneity-oblivious, interference-oblivious and least-loaded schedulers only provide similar guarantees for 14%, 11% and 3% of workloads. The differences are more striking in oversubscribed scenarios where resource efficiency is more critical.


title: Improving legal information retrieval using the Wikipedia knowledge base, legal ontology and the Eurovoc Thesaurus

abstract: The ability to find relevant legal information is a fundamental component of a legal experts and AI. We propose a model for legal information retrieval that exploits the capabilities of a legal ontology enriching it with knowledge extracted from Wikipedia


Variational Structured Stochastic Network


title: CORRECT: Code Reviewer Recommendation in GitHub Based on Cross-Project and Technology Experience

abstract: Peer code review locates common coding rule violations and simple logical errors in the early phases of software development, and thus reduces overall cost. However, in GitHub, identifying an appropriate code reviewer for a pull request is a non-trivial task given that reliable information for reviewer identification is often not readily available. In this paper, we propose a code reviewer recommendation technique that considers not only the relevant cross-project work history (e.g., external library experience) but also the experience of a developer in certain specialized technologies associated with a pull request for determining her expertise as a potential code reviewer. We first motivate our technique using an exploratory study with 10 commercial projects and 10 associated libraries external to those projects. Experiments using 17,115 pull requests from 10 commercial projects and six open source projects show that our technique provides 85%-- 92% recommendation accuracy, about 86% precision and 79%--81% recall in code reviewer recommendation, which are highly promising. Comparison with the state-of-the-art technique also validates the empirical findings and the superiority of our recommendation technique.


title: Applications of Average Geodesic Distance in Manifold learning

abstract: Manifold learning has become a hot issue in the research fields of machine learning and data mining. Current manifold learning algorithms assume that the observed data set has the high density. But, how to evaluate the denseness of the high dimensional observed data set? This paper proposes an algorithm based on the average geodesic distance as the preprocessing step of manifold learning. Moreover, for a high dense data set evaluated, we further utilize the average geodesic distance to quantitatively analyze the mapping relationship between the high-dimensional manifold and the corresponding intrinsic low-dimensional manifold in the known ISOMAP algorithm. Finally, experimental results on two synthetic Swiss-roll data sets show that our method is feasible.


title: Boundary Negotiating Artifacts: Unbinding the Routine of Boundary Objects and Embracing Chaos in Collaborative Work

abstract: Empirical studies of material artifacts in practice continue to be a rich source of theoretical concepts for CSCW. This paper explores the foundational concept of boundary objects and questions the conception that all objects that move between communities of practice are boundary objects. This research presents the results of a year-long ethnographic study of collaborative work, specifically the multidisciplinary collaborative design of a museum exhibition. I suggest that artifacts can serve to establish and destabilize protocols themselves and that artifacts can be used to push boundaries rather than merely sailing across them. Artifacts used for collaboration do not necessarily exist within a web of standardized processes and disorderly processes should not be treated as "special cases".


title: Detecting Malicious Data Injections in Wireless Sensor Networks: A Survey

abstract: Wireless Sensor Networks are widely advocated to monitor environmental parameters, structural integrity of the built environment and use of urban spaces, services and utilities. However, embedded sensors are vulnerable to compromise by external actors through malware but also through their wireless and physical interfaces. Compromised sensors can be made to report false measurements with the aim to produce inappropriate and potentially dangerous responses. Such malicious data injections can be particularly difficult to detect if multiple sensors have been compromised as they could emulate plausible sensor behaviour such as failures or detection of events where none occur. This survey reviews the related work on malicious data injection in wireless sensor networks, derives general principles and a classification of approaches within this domain, compares related studies and identifies areas that require further investigation.


KIDNEY DISEASE PREDICTION USING SVM AND ANN ALGORITHMS


title: The Gamer Motivation Profile: What We Learned From 250,000 Gamers

abstract: Gamers are not a monolithic group; gaming preferences and motivations vary among gamers in important ways. An empirical model of gaming motivations allows developers and researchers to create more effective and engaging experiences for entertainment and serious games. We developed an online app and used an iterative process to create the Gamer Motivation Profile. Using survey data from over 250,000 gamers worldwide, we used factor analysis to develop an empirical framework of gaming motivations and a validated tool to measure those motivations. We identified 6 clusters of gaming motivations: Action (Excitement & Destruction), Social (Collaboration & Competition), Mastery (Strategy & Challenge), Achievement (Power & Completion), Creativity (Design & Discovery), and Immersion (Story & Fantasy). In this talk, we'll first describe the motivations we identified, present findings on the higher-order relationships among these motivations, how they vary by gender and age, and how they are related to the Big 5 personality traits. Respondents to the Gamer Motivation Profile also listed specific games they've enjoyed playing. In the second part of the talk, we'll present case studies of how we have applied this game audience data within the game industry to provide actionable insights for game marketing and production.


title: Large-Margin Multi-Modal Deep Learning for RGB-D Object Recognition

abstract: Most existing feature learning-based methods for RGB-D object recognition either combine RGB and depth data in an undifferentiated manner from the outset, or learn features from color and depth separately, which do not adequately exploit different characteristics of the two modalities or utilize the shared relationship between the modalities. In this paper, we propose a general CNN-based multi-modal learning framework for RGB-D object recognition. We first construct deep CNN layers for color and depth separately, which are then connected with a carefully designed multi-modal layer. This layer is designed to not only discover the most discriminative features for each modality, but is also able to harness the complementary relationship between the two modalities. The results of the multi-modal layer are back-propagated to update parameters of the CNN layers, and the multi-modal feature learning and the back-propagation are iteratively performed until convergence. Experimental results on two widely used RGB-D object datasets show that our method for general multi-modal learning achieves comparable performance to state-of-the-art methods specifically designed for RGB-D data.


Lighting Quality and Energy-Efficiency Effects on Task Performance, Mood, Health, Satisfaction, and Comfort


title: Vehicle parking system implementation using CPLD

abstract: The vehicle parking system is designed to prevent usual problems associated with the parking. This system is designed to solve the problem of locating empty parking slot, congestion and indiscriminate parking. The system has been designed using VHDL and successfully implemented on CPLD (family-MAX V,Device-5M1270ZT144C5, Board-Krypton v1.2). A complete design and layout was drawn out and suitably implemented. The different modules were separately tested and then combined together as a working model of intelligent vehicle parking system. The results discussed in this paper shows that very less hardware is utilized (21%) on CPLD board, thus proving the system to be cost effective.


The Science of Art A Neurological Theory of Aesthetic Experience


title: User loyalty and online communities: why members of online communities are not faithful

abstract: Online communities are getting increasingly important for several different user groups; at the same time, community members seem to lack loyalty, as they often change from one community to another or use their community less over time. To survive and thrive, online communities must meet members' needs. By using qualitative data are from an extensive online survey of online community users and a representative sample of Internet users, 200 responses to an open quesion regarding community-loyalty was analyzed. Results show that there are 9 main reasons why community-users decrease in their participation over time or, in simple terms, stop using their online community: 1) Lack of interesting people/friends attending, 2) Low quality content, 3) Low usability, 4) Harassment and bullying 5) Time-consuming/isolating, 6) Low trust, 7) Over-commercialized, 8) Dissatisfaction with moderators and 9) Unspecified boring. The results, design implications and future research are discussed.


title: NOMA: An Information Theoretic Perspective

abstract: Non-orthogonal multiple access (NOMA) is a potential enabler for the development of 5G and beyond wireless networks. By allowing multiple users to share the same resource (time/frequency/code/space), NOMA can scale up the number of served users, increase the spectral efficiency, and improve user-fairness compared to existing orthogonal multiple access (OMA) techniques. The basic premise behind NOMA in a single-cell network is to reap the benefits promised by information theory for the downlink and uplink transmission of wireless systems, modeled respectively by the broadcast channel (BC) and multiple access channel (MAC). The capacity regions of the BC and MAC have been established several decades ago, and concurrent non-orthogonal transmission is the optimal transmission strategy in both cases. Unlike the single-cell setting, the capacity region of multi-cell wireless networks, commonly modeled by the interference channel (IC), is in general unknown. However, it is known that OMA is suboptimal. This chapter reviews what information theory promises and proposes for NOMA in single- and multi-cell networks with both single- and multi-antenna nodes. Furthermore, relevant physical layer security channel models are proposed and discussed.


title: Grasping known objects with humanoid robots: A box-based approach

abstract: Autonomous grasping of household objects is one of the major skills that an intelligent service robot necessarily has to provide in order to interact with the environment. In this paper, we propose a grasping strategy for known objects, comprising an off-line, box-based grasp generation technique on 3D shape representations. The complete system is able to robustly detect an object and estimate its pose, flexibly generate grasp hypotheses from the assigned model and perform such hypotheses using visual servoing. We will present experiments implemented on the humanoid platform ARMAR-III.


title: Get another label? improving data quality and data mining using multiple, noisy labelers

abstract: This paper addresses the repeated acquisition of labels for data items when the labeling is imperfect. We examine the improvement (or lack thereof) in data quality via repeated labeling, and focus especially on the improvement of training labels for supervised induction. With the outsourcing of small tasks becoming easier, for example via Rent-A-Coder or Amazon's Mechanical Turk, it often is possible to obtain less-than-expert labeling at low cost. With low-cost labeling, preparing the unlabeled part of the data can become considerably more expensive than labeling. We present repeated-labeling strategies of increasing complexity, and show several main results. (i) Repeated-labeling can improve label quality and model quality, but not always. (ii) When labels are noisy, repeated labeling can be preferable to single labeling even in the traditional setting where labels are not particularly cheap. (iii) As soon as the cost of processing the unlabeled data is not free, even the simple strategy of labeling everything multiple times can give considerable advantage. (iv) Repeatedly labeling a carefully chosen set of points is generally preferable, and we present a robust technique that combines different notions of uncertainty to select data points for which quality should be improved. The bottom line: the results show clearly that when labeling is not perfect, selective acquisition of multiple labels is a strategy that data miners should have in their repertoire; for certain label-quality/cost regimes, the benefit is substantial.


title: Geocompare: a comparison of public and commercial geolocation databases

abstract: We attempt a systematic quantitative comparison of currently available geolocation service providers. We add depth to previous contributions by analyzing inconsistencies across databases for different geographic (RIR) regions and organization (Autonomous Systems) types. We compare results on a country granularity, using a methodology that compares each database against the majority vote across all databases with answers for a given IP address. On a finer granularity than country, rigorous formal comparison gets trickier. Unlike the discrete country labels, coordinates can have nominally different values yet still represent approximately the same location, We compare the databases at a lat-long granularity using an 80 km threshold for two lat-longs coordinates to be in the same geographic region. We describe our process for selecting this threshold, and our centroid-based algorithm for comparing database lat-long results against a majority of responses from the set of databases we evaluated. While not a foolproof methodology ‐ the databases could all be converging to the same wrong answers over time ‐ it assumes that database providers successfully work toward improving the accuracy of their databases over time. In the absence of substantial ground truth, our method offers a systematic way to study the geolocation databases to reveal insights, summarized at the end of the paper. We intend to re-run the comparison experiment using additional databases later in 2011; we welcome constructive feedback on the methodology so we can further improve our next experiment.


title: Dissecting Video Server Selection Strategies in the YouTube CDN

abstract: In this paper, we conduct a detailed study of the YouTube CDN with a view to understanding the mechanisms and policies used to determine which data centers users download video from. Our analysis is conducted using week-long datasets simultaneously collected from the edge of five networks - two university campuses and three ISP networks - located in three different countries. We employ state-of-the-art delay-based geolocation techniques to find the geographical location of YouTube servers. A unique aspect of our work is that we perform our analysis on groups of related YouTube flows. This enables us to infer key aspects of the system design that would be difficult to glean by considering individual flows in isolation. Our results reveal that while the RTT between users and data centers plays a role in the video server selection process, a variety of other factors may influence this selection including load-balancing, diurnal effects, variations across DNS servers within a network, limited availability of rarely accessed video, and the need to alleviate hot-spots that may arise due to popular video content.


title: CN-DBpedia: A Never-Ending Chinese Knowledge Extraction System

abstract: Great efforts have been dedicated to harvesting knowledge bases from online encyclopedias. These knowledge bases play important roles in enabling machines to understand texts. However, most current knowledge bases are in English and non-English knowledge bases, especially Chinese ones, are still very rare. Many previous systems that extract knowledge from online encyclopedias, although are applicable for building a Chinese knowledge base, still suffer from two challenges. The first is that it requires great human efforts to construct an ontology and build a supervised knowledge extraction model. The second is that the update frequency of knowledge bases is very slow. To solve these challenges, we propose a never-ending Chinese Knowledge extraction system, CN-DBpedia, which can automatically generate a knowledge base that is of ever-increasing in size and constantly updated. Specially, we reduce the human costs by reusing the ontology of existing knowledge bases and building an end-to-end facts extraction model. We further propose a smart active update strategy to keep the freshness of our knowledge base with little human costs. The 164 million API calls of the published services justify the success of our system.


title: Inducing Decision Trees with an Ant Colony Optimization Algorithm

abstract: Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel ACO algorithm to induce decision trees, combining commonly used strategies from both traditional decision tree induction algorithms and ACO. The proposed algorithm is compared against three decision tree induction algorithms, namely C4.5, CART and cACDT, in 22 publicly available data sets. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of both C4.5 and CART, which are well-known conventional algorithms for decision tree induction, and the accuracy of the ACO-based cACDT decision tree algorithm.


title: Deterministic parallel graph coloring with hashing

abstract: In this paper we propose a new deterministic parallel graph coloring algorithm. Parallelism is achieved by distribution of vertices to processors by hashing. The hashing is based on markers assigned to each conflict prone vertex.


title: Text classification based on deep belief network and softmax regression

abstract: In this paper, we propose a novel hybrid text classification model based on deep belief network and softmax regression. To solve the sparse high-dimensional matrix computation problem of texts data, a deep belief network is introduced. After the feature extraction with DBN, softmax regression is employed to classify the text in the learned feature space. In pre-training procedures, the deep belief network and softmax regression are first trained, respectively. Then, in the fine-tuning stage, they are transformed into a coherent whole and the system parameters are optimized with Limited-memory Broyden---Fletcher---Goldfarb---Shanno algorithm. The experimental results on Reuters-21,578 and 20-Newsgroup corpus show that the proposed model can converge at fine-tuning stage and perform significantly better than the classical algorithms, such as SVM and KNN.


title: Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school

abstract: General intelligence and personality traits from the Five-Factor model were studied as predictors of academic achievement in a large sample of Estonian schoolchildren from elementary to secondary school. A total of 3618 students (1746 boys and 1872 girls) from all over Estonia attending Grades 2, 3, 4, 6, 8, 10, and 12 participated in this study. Intelligence, as measured by the Raven’s Standard Progressive Matrices, was found to be the best predictor of students’ grade point average (GPA) in all grades. Among personality traits (measured by self-reports on the Estonian Big Five Questionnaire for Children in Grades 2 to 4 and by the NEO Five Factor Inventory in Grades 6 to 12), Openness, Agreeableness, and Conscientiousness correlated positively and Neuroticism correlated negatively with GPA in almost every grade. When all measured variables were entered together into a regression model, intelligence was still the strongest predictor of GPA, being followed by Agreeableness in Grades 2 to 4 and Conscientiousness in Grades 6 to 12. Interactions between predictor variables and age accounted for only a small percentage of variance in GPA, suggesting that academic achievement relies basically on the same mechanisms through the school years.


title: Computation of octanol-water partition coefficients by guiding an additive model with knowledge

abstract: We have developed a new method, i.e., XLOGP3, for logP computation. XLOGP3 predicts the logP value of a query compound by using the known logP value of a reference compound as a starting point. The difference in the logP values of the query compound and the reference compound is then estimated by an additive model. The additive model implemented in XLOGP3 uses a total of 87 atom/group types and two correction factors as descriptors. It is calibrated on a training set of 8199 organic compounds with reliable logP data through a multivariate linear regression analysis. For a given query compound, the compound showing the highest structural similarity in the training set will be selected as the reference compound. Structural similarity is quantified based on topological torsion descriptors. XLOGP3 has been tested along with its predecessor, i.e., XLOGP2, as well as several popular logP methods on two independent test sets: one contains 406 small-molecule drugs approved by the FDA and the other contains 219 o...


Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review


title: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

abstract: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.


title: How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

abstract: Thanks to the digital preservation of cultural heritage materials, multimedia tools (e.g., based on automatic visual processing) considerably ease the work of scholars in the humanities and help them to perform quantitative analysis of their data. In this context, this article assesses three different Convolutional Neural Network (CNN) architectures along with three learning approaches to train them for hieroglyph classification, which is a very challenging task due to the limited availability of segmented ancient Maya glyphs. More precisely, the first approach, the baseline, relies on pretrained networks as feature extractor. The second one investigates a transfer learning method by fine-tuning a pretrained network for our glyph classification task. The third approach considers directly training networks from scratch with our glyph data. The merits of three different network architectures are compared: a generic sequential model (i.e., LeNet), a sketch-specific sequential network (i.e., Sketch-a-Net), and the recent Residual Networks. The sketch-specific model trained from scratch outperforms other models and training strategies. Even for a challenging 150-class classification task, this model achieves 70.3% average accuracy and proves itself promising in case of a small amount of cultural heritage shape data. Furthermore, we visualize the discriminative parts of glyphs with the recent Grad-CAM method, and demonstrate that the discriminative parts learned by the model agree, in general, with the expert annotation of the glyph specificity (diagnostic features). Finally, as a step toward systematic evaluation of these visualizations, we conduct a perceptual crowdsourcing study. Specifically, we analyze the interpretability of the representations from Sketch-a-Net and ResNet-50. Overall, our article takes two important steps toward providing tools to scholars in the digital humanities: increased performance for automation and improved interpretability of algorithms.


title: A novel feature descriptor based on the shearlet transform

abstract: Problems such as image classification, object detection and recognition rely on low-level feature descriptors to represent visual information. Several feature extraction methods have been proposed, including the Histograms of Oriented Gradients (HOG), which captures edge information by analyzing the distribution of intensity gradients and their directions. In addition to directions, the analysis of edge at different scales provides valuable information. Shearlet transforms provide a general framework for analyzing and representing data with anisotropic information at multiple scales. As a consequence, signal singularities, such as edges, can be precisely detected and located in images. Based on the idea of employing histograms to estimate the distribution of edge orientations and on the accurate multi-scale analysis provided by shearlet transforms, we propose a feature descriptor called Histograms of Shearlet Coefficients (HSC). Experimental results comparing HOG with HSC show that HSC provides significantly better results for the problems of texture classification and face identification.


The Coreference Annotation of the CSTNews Corpus


title: Hybrid followee recommendation in microblogging systems

abstract: Followee recommendation plays an important role in information sharing over microblogging plat-forms. Existing followee recommendation schemes adopt either content relevance or social information for followee ranking, suffering poor performance. Based on the observation that microblogging systems have dual roles of social network and news media platform, we propose a novel followee recommendation scheme that takes into account the information sources of both tweet contents and the social structures. We set up a linear weighted model to combine the two factors and further design a simulated annealing algorithm to automatically assign the weights of both factors in order to achieve an optimized combination of them. We conduct comprehensive experiments on real-world datasets collected from Sina Weibo, the largest microblogging system in China. The results demonstrate that our scheme provides a much more accurate followee recommendation for a user compared to existing schemes.


Molecular orbital calculations for complexes of heavier transition elements. II. Hexachloro complexes of tetravalent rhenium, osmium, iridium, and platinum


title: REDUCING THE PEAK TO AVERAGE POWER RATIO OF MIMO-OFDM SYSTEMS

abstract: In this paper, we proposed a particle swarm optimization (PSO) based partial transmit sequence (PTS) technique in order to achieve the lowest Peak-to-Average Power Ratio(PAPR) in Multiple Input Multiple Output- Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems. Our approach consist of applying the PSO based PTS on each antenna of the system in order to find the optimal phase factors, which is a straightforward method to get the minimum PAPR in such a system. The simulation results demonstrate that the PSO based PTS algorithm when applied to MIMO-OFDM systems with a wide range of phase factors, tends to give a high performance. In addition, there is no need to increase the number of particles of the PSO algorithm to enhance the performance of the system. As a result of this, the complexity of finding the minimum PAPR is kept at a reasonable level.


title: Demographic Factors Improve Classification Performance

abstract: Extra-linguistic factors influence language use, and are accounted for by speakers and listeners. Most natural language processing (NLP) tasks to date, however, treat language as uniform. This assumption can harm performance. We investigate the effect of including demographic information on performance in a variety of text-classification tasks. We find that by including age or gender information, we consistently and significantly improve performance over demographic-agnostic models. These results hold across three text-classification tasks in five languages.


title: Towards a more effective method for analyzing mobile eye-tracking data: integrating gaze data with object recognition algorithms

abstract: In this paper we present the outlines of a new project that aims at developing and implementing effective new methods for analyzing gaze data collected with mobile eye-tracking devices. More specifically, we argue for the integration of object recognition algorithms from vision engineering, such as invariant region matching techniques, in gaze analysis software. We present a series of arguments why an object-based approach may provide a significant surplus, in terms of analytical precision, flexibility, additional application areas and cost efficiency, to the existing systems that use predefined areas of analysis. In order to test the actual analytical power of object recognition algorithms for the analysis of gaze data recorded in the wild, we develop a series of test cases in different real world situations, including shopping behavior, navigation, handling and usability of mobile systems. By setting up these case studies in close collaboration with key players in the relevant fields (retailers, signage consultants, market and user-experience research, and developers of eye-tracking hard- and software), we will be able to sketch an accurate picture of the pros and cons of the proposed method in comparison to current analytical practice.


title: A kernel density estimation method for networks, its computational method and a GIS‐based tool

abstract: We develop a kernel density estimation method for estimating the density of points on a network and implement the method in the GIS environment. This method could be applied to, for instance, finding 'hot spots' of traffic accidents, street crimes or leakages in gas and oil pipe lines. We first show that the application of the ordinary two-dimensional kernel method to density estimation on a network produces biased estimates. Second, we formulate a 'natural' extension of the univariate kernel method to density estimation on a network, and prove that its estimator is biased; in particular, it overestimates the densities around nodes. Third, we formulate an unbiased discontinuous kernel function on a network. Fourth, we formulate an unbiased continuous kernel function on a network. Fifth, we develop computational methods for these kernels and derive their computational complexity; and we also develop a plug-in tool for operating these methods in the GIS environment. Sixth, an application of the proposed methods to the density estimation of traffic accidents on streets is illustrated. Lastly, we summarize the major results and describe some suggestions for the practical use of the proposed methods.


title: Pride and Prejudice in Progressive Web Apps: Abusing Native App-like Features in Web Applications

abstract: Progressive Web App (PWA) is a new generation of Web application designed to provide native app-like browsing experiences even when a browser is offline. PWAs make full use of new HTML5 features which include push notification, cache, and service worker to provide short-latency and rich Web browsing experiences. We conduct the first systematic study of the security and privacy aspects unique to PWAs. We identify security flaws in main browsers as well as design flaws in popular third-party push services, that exacerbate the phishing risk. We introduce a new side-channel attack that infers the victim's history of visited PWAs. The proposed attack exploits the offline browsing feature of PWAs using a cache. We demonstrate a cryptocurrency mining attack which abuses service workers. Defenses and recommendations to mitigate the identified security and privacy risks are suggested with in-depth understanding.


title: Effects of physical activity on children's executive function: Contributions of experimental research on aerobic exercise

abstract: Executive function refers to the cognitive processes necessary for goal-directed cognition and behavior, which develop across childhood and adolescence. Recent experimental research indicates that both acute and chronic aerobic exercise promote children’s executive function. Furthermore, there is tentative evidence that not all forms of aerobic exercise benefit executive function equally: Cognitively-engaging exercise appears to have a stronger effect than non-engaging exercise on children’s executive function. This review discusses this evidence as well as the mechanisms that may underlie the association between exercise and executive function. Research from a variety of disciplines is covered, including developmental psychology, kinesiology, cognitive neuroscience, and biopsychology. Finally, these experimental findings are placed within the larger context of known links between action and cognition in infancy and early childhood, and the clinical and practical implications of this research are discussed.


title: Urine Drug Screening: Practical Guide for Clinicians

abstract: Drug testing, commonly used in health care, workplace, and criminal settings, has become widespread during the past decade. Urine drug screens have been the most common method for analysis because of ease of sampling. The simplicity of use and access to rapid results have increased demand for and use of immunoassays; however, these assays are not perfect. Falsepositive results of immunoassays can lead to serious medical or social consequences if results are not confirmed by secondary analysis, such as gas chromatography‐mass spectrometry. The Department of Health and Human Services’ guidelines for the workplace require testing for the following 5 substances: amphetamines, cannabinoids, cocaine, opiates, and phencyclidine. This article discusses potential false-positive results and false-negative results that occur with immunoassays of these substances and with alcohol, benzodiazepines, and tricyclic antidepressants. Other pitfalls, such as adulteration, substitution, and dilution of urine samples, are discussed. Pragmatic concepts summarized in this article should minimize the potential risks of misinterpreting urine drug screens.


title: Using Confidence Bounds for Exploitation-Exploration Trade-offs

abstract: We show how a standard tool from statistics --- namely confidence bounds --- can be used to elegantly deal with situations which exhibit an exploitation-exploration trade-off. Our technique for designing and analyzing algorithms for such situations is general and can be applied when an algorithm has to make exploitation-versus-exploration decisions based on uncertain information provided by a random process. We apply our technique to two models with such an exploitation-exploration trade-off. For the adversarial bandit problem with shifting our new algorithm suffers only O((ST)1/2) regret with high probability over T trials with S shifts. Such a regret bound was previously known only in expectation. The second model we consider is associative reinforcement learning with linear value functions. For this model our technique improves the regret from O(T3/4) to O(T1/2).


title: Geometry constrained sparse coding for single image super-resolution

abstract: The choice of the over-complete dictionary that sparsely represents data is of prime importance for sparse coding-based image super-resolution. Sparse coding is a typical unsupervised learning method to generate an over-complete dictionary. However, most of the sparse coding methods for image super-resolution fail to simultaneously consider the geometrical structure of the dictionary and corresponding coefficients, which may result in noticeable super-resolution reconstruction artifacts. In this paper, a novel sparse coding method is proposed to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. Moreover, the proposed method can preserve the incoherence of dictionary entries, which is critical for sparse representation. Inspired by the development on non-local self-similarity and manifold learning, the proposed sparse coding method can provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. Extensive experimental results on image super-resolution have demonstrated the effectiveness of the proposed method.


title: Systematic literature review of ensemble effort estimation

abstract: Systematic review of 24 selected studies on Ensemble Effort Estimation.Studies analyzing based on six review questions.EEE techniques usually yield acceptable estimation accuracy than single models.There is no EEE technique performing better than others in all situations. The need to overcome the weaknesses of single estimation techniques for prediction tasks has given rise to ensemble methods in software development effort estimation (SDEE). An ensemble effort estimation (EEE) technique combines several of the single/classical models found in the SDEE literature. However, to the best of our knowledge, no systematic review has yet been performed with a focus on the use of EEE techniques in SDEE. The purpose of this review is to analyze EEE techniques from six viewpoints: single models used to construct ensembles, ensemble estimation accuracy, rules used to combine single estimates, accuracy comparison of EEE techniques with single models, accuracy comparison between EEE techniques and methodologies used to construct ensemble methods. We performed a systematic review of EEE studies published between 2000 and 2016, and we selected 24 of them to address the questions raised in this review. We found that EEE techniques may be separated into two types: homogeneous and heterogeneous, and that the machine learning single models are the most frequently employed in constructing EEE techniques. We also found that EEE techniques usually yield acceptable estimation accuracy, and in fact are more accurate than single models.


Insider Threat Detection in PRODIGAL


title: Phonological skills and learning to read

abstract: Phonological Awareness and Reading. How Children Read Words. Spelling and Phonological Awareness. How Children Read and Write New Words. Comparisons with Backward Readers and Spellers. Correlations and Longitudinal Predictions. Teaching Children About Sound. Do Children Read and Fail to Learn to Read in Different Ways from Each Other. Theories About Learning to Read.


title: Residual Networks Behave Like Ensembles of Relatively Shallow Networks

abstract: In this work we propose a novel interpretation of residual networks showing that they can be seen as a collection of many paths of differing length. Moreover, residual networks seem to enable very deep networks by leveraging only the short paths during training. To support this observation, we rewrite residual networks as an explicit collection of paths. Unlike traditional models, paths through residual networks vary in length. Further, a lesion study reveals that these paths show ensemble-like behavior in the sense that they do not strongly depend on each other. Finally, and most surprising, most paths are shorter than one might expect, and only the short paths are needed during training, as longer paths do not contribute any gradient. For example, most of the gradient in a residual network with 110 layers comes from paths that are only 10-34 layers deep. Our results reveal one of the key characteristics that seem to enable the training of very deep networks: Residual networks avoid the vanishing gradient problem by introducing short paths which can carry gradient throughout the extent of very deep networks.


title: Forest Fire Modeling and Early Detection using Wireless Sensor Networks

abstract: Early detection of forest fires is the primary way of minimizing their damages. We present the design of a wireless sensor network for early detection of forest fires. We first present the key aspects in modeling forest fires according to the Fire Weather Index (FWI) System which is one of the most comprehensive forest fire danger rating systems in North America. Then, we model the forest fire detection problem as a node k-coverage problem (k ≥ 1) in wireless sensor networks. We propose approximation algorithms for the node k-coverage problem which is shown to be NP-hard. We present a constant-factor centralized algorithm, and a fully distributed version which does not require sensors know their locations. Our simulation study demonstrates that our algorithms: activate near-optimal number of sensors, converge much faster than other algorithms, significantly prolong (almost double) the network lifetime, and can achieve unequal monitoring of different zones in the forest.


title: Bijective parameterization with free boundaries

abstract: We present a fully automatic method for generating guaranteed bijective surface parameterizations from triangulated 3D surfaces partitioned into charts. We do so by using a distortion metric that prevents local folds of triangles in the parameterization and a barrier function that prevents intersection of the chart boundaries. In addition, we show how to modify the line search of an interior point method to directly compute the singularities of the distortion metric and barrier functions to maintain a bijective map. By using an isometric metric that is efficient to compute and a spatial hash to accelerate the evaluation and gradient of the barrier function for the boundary, we achieve fast optimization times. Unlike previous methods, we do not require the boundary be constrained by the user to a non-intersecting shape to guarantee a bijection, and the boundary of the parameterization is free to change shape during the optimization to minimize distortion.


title: Driver Behavior Analysis for Safe Driving: A Survey

abstract: Driver drowsiness and distraction are two main reasons for traffic accidents and the related financial losses. Therefore, researchers have been working for more than a decade on designing driver inattention monitoring systems. As a result, several detection techniques for the detection of both drowsiness and distraction have been proposed in the literature. Some of these techniques were successfully adopted and implemented by the leading car companies. This paper discusses and provides a comprehensive insight into the well-established techniques for driver inattention monitoring and introduces the use of most recent and futuristic solutions exploiting mobile technologies such as smartphones and wearable devices. Then, a proposal is made for the active of such systems into car-to-car communication to support vehicular ad hoc network's (VANET's) primary aim of safe driving. We call this approach the dissemination of driver behavior via C2C communication. Throughout this paper, the most remarkable studies of the last five years were examined thoroughly in order to reveal the recent driver monitoring techniques and demonstrate the basic pros and cons. In addition, the studies were categorized into two groups: driver drowsiness and distraction. Then, research on the driver drowsiness was further divided into two main subgroups based on the exploitation of either visual features or nonvisual features. A comprehensive compilation, including used features, classification methods, accuracy rates, system parameters, and environmental details, was represented as tables to highlight the (dis)advantages and/or limitations of the aforementioned categories. A similar approach was also taken for the methods used for the detection of driver distraction.


Survey on Vanishing Point Detection Method for General Road Region Identification


title: Approaches to Interpreter Composition

abstract: In this paper, we compose six di erent Python and Prolog VMs into 4 pairwise compositions: one using C interpreters; one running on the JVM; one using meta-tracing interpreters; and one using a C interpreter and a meta-tracing interpreter. We show that programs that cross the language barrier frequently execute faster in a meta-tracing composition, and that meta-tracing imposes a significantly lower overhead on composed programs relative to mono-language programs. 1. Overview Programming language composition aims to allow the mixing of programming languages in a fine-grained manner. This vision brings many challenging problems, from the interaction of language semantics to performance. In this paper, we investigate the runtime performance of composed programs in high-level languages. We start from the assumption that execution of such programs is most likely to be through composing language implementations that use interpreters and VMs, rather than traditional compilers. This raises the question: how do di erent styles of composition a ect performance? Clearly, such a question cannot have a single answer but, to the best of our knowledge, this issue has not been explored in the past. This paper's hypothesis is that meta-tracing - a relatively new technique used to produce JIT (Just-In-Time) compilers from interpreters (1) - will lead to faster interpreter composition than traditional approaches. To test this hypothesis, we present a Python and Prolog composition which allows Python programs to embed and call Prolog programs. We then implement the composition in four di erent ways, comparing the absolute times and the relative cross-language costs of each. In addition to the 'traditional' approaches to composing interpreters (in C and upon the JVM), we also investigate the application of meta-tracing to interpreter composition. The experiments we then carry out confirm our initial hypothesis. There is a long tradition of composing Prolog with other languages (with e.g. Icon (2), Lisp (3), and Smalltalk (4)) because one can express certain types of programs far easier in Prolog than in other languages. We have two additional reasons for choosing this pairing. First, these languages represent very di erent points in the language design space: Prolog's inherently


title: Framework for Sentiment Analysis of Arabic Text

abstract: This paper analyses challenges, and provides a model and a framework for mining Arabic tweets to measure customer satisfaction toward telecom companies in Saudi Arabia, to predict the ratio of customer churn and overcome the specific challenges with the semantic sentiment analysis of Arabic text.


title: Is there a consensus for CBCT use in Orthodontics?

abstract: This article aims to discuss current evidence and recommendations for cone-beam computed tomography (CBCT) in Orthodontics. In comparison to conventional radiograph, CBCT has higher radiation doses and, for this reason, is not a standard method of diagnosis in Orthodontics. Routine use of CBCT in substitution to conventional radiograph is considered an unaccepted practice. CBCT should be indicated with criteria only after clinical examination has been performed and when the benefits for diagnosis and treatment planning exceed the risks of a greater radiation dose. It should be requested only when there is a potential to provide new information not demonstrated by conventional scans, when it modifies treatment plan or favors treatment execution. The most frequent indication of CBCT in Orthodontics, with some evidence on its clinical efficacy, includes retained/impacted permanent teeth; severe craniofacial anomalies; severe facial discrepancies with indication of orthodontic-surgical treatment; and bone irregularities or malformation of TMJ accompanied by signs and symptoms. In exceptional cases of adult patients when critical tooth movement are planned in regions with deficient buccolingual thickness of the alveolar ridge, CBCT can be indicated provided that there is a perspective of changes in orthodontic treatment planning.


INTEGRATING CONVENTIONAL ERP SYSTEM WITH CLOUD SERVICES : FROM THE PERSPECTIVE OF CLOUD SERVICE TYPE


title: Antibody-modified T cells: CARs take the front seat for hematologic malignancies

abstract: T cells redirected to specific antigen targets with engineered chimeric antigen receptors (CARs) are emerging as powerful therapies in hematologic malignancies. Various CAR designs, manufacturing processes, and study populations, among other variables, have been tested and reported in over 10 clinical trials. Here, we review and compare the results of the reported clinical trials and discuss the progress and key emerging factors that may play a role in effecting tumor responses. We also discuss the outlook for CAR T-cell therapies, including managing toxicities and expanding the availability of personalized cell therapy as a promising approach to all hematologic malignancies. Many questions remain in the field of CAR T cells directed to hematologic malignancies, but the encouraging response rates pave a wide road for future investigation.


AREA AND THE INTENSITY-TIME RELATION IN THE PERIPHERAL RETINA


title: Genetic Algorithms for Predicting the Egyptian Stock Market

abstract: The purpose of this paper is to discover a semi-optimal set of trading rules and to investigate its effectiveness as applied to Egyptian Stocks. The aim is to mix different categories of technical trading rules and let an automatic evolution process decide which rules are to be used for particular time series. This difficult task can be achieved by using genetic algorithms (GA's), they permit the creation of artificial experts taking their decisions from an optimal subset of the a given set of trading rules. The GA's based on the survival of the fittest, do not guarantee a global optimum but they are known to constitute an effective approach in optimizing non-linear functions. Selected liquid stocks are tested and GA trading rules were compared with other conventional and well known technical analysis rules. The Proposed GA system showed clear better average profit and in the same high sharpe ratio, which indicates not only good profitability but also better risk-reward trade-off


title: DarT: The embryo test with the Zebrafish Danio rerio--a general model in ecotoxicology and toxicology.

abstract: The acute fish test is an animal test whose ecotoxicological relevance is worthy of discussion. The primary aim of protection in ecotoxicology is the population and not the individual. Furthermore the concentration of pollutants in the environment is normally not in the lethal range. Therefore the acute fish test covers solely the situation after chemical spills. Nevertheless, acute fish toxicity data still belong to the base set used for the assessment of chemicals. The embryo test with the zebrafish Danio rerio (DarT) is recommended as a substitute for the acute fish test. For validation an international laboratory comparison test was carried out. A summary of the results is presented in this paper. Based on the promising results of testing chemicals and waste water the test design was validated by the DIN-working group "7.6 Fischei-Test". A normed test guideline for testing waste water with fish is available. The test duration is short (48 h) and within the test different toxicological endpoints can be examined. Endpoints from the embryo test are suitable for QSAR-studies. Besides the use in ecotoxicology the introduction as a toxicological model was investigated. Disturbance of pigmentation and effects on the frequency of heart-beat were examined. A further important application is testing of teratogenic chemicals. Based on the results DarT could be a screening test within preclinical studies.


Abstract ‘Modest AdaBoost ’ – Teaching AdaBoost to Generalize Better


Automatic Vehicle Trajectory Extraction from Aerial Video Data


Automating Feature Engineering


title: Monolingual sentence matching for text simplification

abstract: This work improves monolingual sentence alignment for text simplification, specifically for text in standard and simple Wikipedia. We introduce a convolutional neural network structure to model similarity between two sentences. Due to the limitation of available parallel corpora, the model is trained in a semi-supervised way, by using the output of a knowledge-based high performance aligning system. We apply the resulting similarity score to rescore the knowledge-based output, and adapt the model by a small hand-aligned dataset. Experiments show that both rescoring and adaptation improve the performance of knowledge-based method.


title: A Machine Learning-Based Topic Extraction and Categorization of State Universities and Colleges (SUC) Customer Feedbacks

abstract: Academic institutions collect an increasing amount of data through surveys. Aside from the usual numeric ratings obtained from the survey, the hierarchical concerns and sentiments can also be identified through the text-based customer feedbacks. These feedbacks contain text about customer experiences with the products offered and services delivered by an institution. A challenge in analyzing unstructured customer feedback is in making sense of the topics that are expressed in words used to describe these experiences. This study develops a model for text analysis of the customer feedbacks that exploits machine learning algorithms such as topic modeling. This further described the text mining process steps undergone in extracting useful information from the customer survey feedbacks of one of the SUCs in the Philippines, the Bicol State College of Applied Sciences and Technology (BISCAST). Moreover, the Latent Dirichlet Allocation (LDA), a topic modeling method, was used for automatic text summarization and topic extraction from these text-based data. The topmost concerns extracted from the feedbacks were identified. This information provides useful insights for management analysis as well as inputs for policy making.


title: Action of cannabidiol on the anxiety and other effects produced by delta 9-THC in normal subjects.

abstract: The object of the experiment was to verify whether cannabidiol (CBD) reduces the anxiety provoked by delta 9-THC in normal volunteers, and whether this effect occurs by a general block of the action of delta 9-THC or by a specific anxiolytic effect. Appropriate measurements and scales were utilized and the eight volunteers received, the following treatments in a double-blind procedure: 0.5 mg/kg delta 9-THC, 1 mg/kg CBD, a mixture containing 0.5 mg/kg delta 9-THC and 1 mg/kg CBD and placebo and diazepam (10 mg) as controls. Each volunteer received the treatments in a different sequence. It was verified that CBD blocks the anxiety provoked by delta 9-THC, however this effect also extended to marihuana-like effects and to other subjective alterations induced by delta 9-THC. This antagonism does not appear to be caused by a general block of delta 9-THC effects, since no change was detected in the pulse-rate measurements. Several further effects were observed typical of CBD and of an opposite nature to those of delta 9-THC. These results suggest that the effects of CBD, as opposed to those of delta 9-THC, might be involved in the antagonism of effects between the two cannabinoids.


title: A flexible method for criticality assessment in driver assistance systems

abstract: This paper presents an efficient algorithm to compute time-to-x (TTX) criticality measures (e.g. time-to-collision, time-to-brake, time-to-steer). Such measures can be used to trigger warnings and emergency maneuvers in driver assistance systems. Our numerical scheme finds a discrete time approximation of TTX values in real time using a modified binary search algorithm. It computes TTX values with high accuracy by incorporating realistic vehicle dynamics and using realistic emergency maneuver models. It is capable of handling complex object behavior models (e.g. motion prediction based on DGPS maps). Unlike most other methods presented in the literature, our approach enables decisions in scenarios with multiple static and dynamic objects in the scene. The flexibility of our method is demonstrated on two exemplary applications: intersection assistance for left-turn-across-path scenarios and pedestrian protection by automatic steering.


title: Near Data Processing: Impact and Optimization of 3D Memory System Architecture on the Uncore

abstract: A promising recent development that can provide continued scaling of performance is the ability to stack multiple DRAM layers on a multi-core processor die. This paper analyzes the interaction between the interconnection network and the memory hierarchy in such systems, and its impact on system performance. We explore the design considerations of a 3D system with DRAM-on-processor stacking and note that full advantages of 3D can only be achieved by configuring the memory with high number of channels. This significantly increases memory level parallelism which results in decreasing the traffic per DRAM bank, reducing their queuing delays, but increasing it on the interconnection network, making remote accesses expensive. To reduce the latency and traffic on the network, we propose restructuring the memory hierarchy to a memory-side cache organization and also explore the effects of various address translations and OS page allocation strategies. Our results indicate that a carefully designed 3D memory system can already improve performance by 25-35% without looking towards new sophisticated techniques.


title: A 600-dpi Capacitive Fingerprint Sensor Chip and Image-Synthesis Technique

abstract: This paper examines the possibility of a low-cost, high-resolution fingerprint sensor chip. The test chip is composed of 64/spl times/256 sensing cells (chip size: 2.7/spl times/10.8 mm/sup 2/). A new detection circuit of charge sharing is proposed, which eliminates the influences of internal parasitic capacitances. Thus, the reduced sensing-capacitor size enables a high resolution of 600 dpi, even using a conventional 0.6 /spl mu/m CMOS process. The partial fingerprint images captured are synthesized into a full fingerprint image with an image-synthesis algorithm. The problems and possibilities of this image-synthesis technique are also analyzed and discussed.


title: On the usability of gesture interfaces in virtual reality environments

abstract: This paper discusses several usability issues related to the use of gestures as an input mode in multimodal interfaces. The use of gestures has been suggested before as a natural solution for applications that require hands-free and notouch interaction with computers, such as in virtual reality (VR) environments. We introduce a simple but robust 2D computer vision based gesture recognition system that was successfully used for interaction in VR environments such as CAVEs and Powerwalls. This interface was tested under 3 different scenarios, as a regular pointing device in a GUI interface, as a navigation tool, and as a visualization tool. Our experiments show that the time to completion of simple pointing tasks is considerably slower when compared to a mouse and that its use during even short periods of time causes fatigue. Despite, these drawbacks, the use of gestures as an alternative mode in multimodal interfaces offers several advantages, such as quick access to computing resources that might be embedded in the environment, using a natural and intuitive way, and that scales nicely to group and collaborative applications, where gestures can be used sporadically.


title: Calculating Feature Weights in Naive Bayes with Kullback-Leibler Measure

abstract: Naive Bayesian learning has been popular in data mining applications. However, the performance of naive Bayesian learning is sometimes poor due to the unrealistic assumption that all features are equally important and independent given the class value. Therefore, it is widely known that the performance of naive Bayesian learning can be improved by mitigating this assumption, and many enhancements to the basic naive Bayesian learning have been proposed to resolve this problem including feature selection and feature weighting. In this paper, we propose a new method for calculating the weights of features in naive Bayesian learning using Kullback-Leibler measure. Empirical results are presented comparing this new feature weighting method with some other methods for a number of datasets.


title: Real-time on-campus public transportation monitoring system

abstract: Real-time update is very important in Advanced Public Transportation System (APTS). However, due to the lack of information on the bus's estimated time of arrival (ETA), current location and occupancy (number of seats available) commuters are reluctant to use public transport. The study in this paper focuses on developing a bus tracking system that can track, display the location of the bus in real time and estimate the bus arrival time to the next bus stop. Monitoring system that consists of tracking device and web-based application for monitoring purposes are developed. GPS-based tracking device is used as location tracking mechanism and the location is reported for every 1 second to cloud database. Web application is developed as the graphical user interface (GUI) to assist user in monitoring real-time location of the bus and plan their journey based on information provided such as bus routes, bus stop number and bus arrival time. The updated location of the bus is plotted into Google Maps. Thus, based on result it was found that the proposed method is able to track and monitor the real-time location of the bus and able to estimate the bus's arrival time to the next bus stop.


title: Enhancing Human-Computer Interaction with Input from Active and Passive Brain-Computer Interfaces

abstract: This chapter introduces a formal categorization of BCIs, according to their key characteristics within HCI scenarios. This comprises classical approaches, which we group into active and reactive BCIs, and the new group of passive BCIs. Passive BCIs provide easily applicable and yet efficient interaction channels carrying information on covert aspects of user state, while adding little further usage cost. All of these systems can also be set up as hybrid BCIs, by incorporating information from outside the brain to make predictions, allowing for enhanced robustness over conventional approaches. With these properties, passive and hybrid BCIs are particularly useful in HCI. When any BCI is transferred from the laboratory to real-world situations, one faces new types of problems resulting from uncontrolled environmental factors—mostly leading to artifacts contaminating data and results. The handling of these situations is treated in a brief review of training and calibration strategies. The presented theory is then underpinned by two concrete examples. First, a combination of Event Related Desynchronization (ERD)-based active BCI with gaze control, defining a hybrid BCI as solution for the midas touch problem. And second, a passive BCI based on human error processing, leading to new forms of automated adaptation in HCI. This is in line with the results from other recent studies of passive BCI technology and shows the broad potential of this approach.


title: Optical Fiber Sensors in Physical Intrusion Detection Systems: A Review

abstract: Fiber optic sensors have become a mainstream sensing technology within a large array of applications due to their inherent benefits. They are now used significantly in structural health monitoring, and are an essential solution for monitoring harsh environments. Since their first development over 30 years ago, they have also found promise in security applications. This paper reviews all of the optical fiber-based techniques used in physical intrusion detection systems. It details the different approaches used for sensing, interrogation, and networking, by research groups, attempting to secure both commercial and residential premises from physical security breaches. The advantages and the disadvantages of the systems are discussed, and each of the different perimeter protection methods is outlined, namely, in-ground, perimeter fence, and window and door protection. This paper reviews the progress in optical fiber-based intrusion detection techniques from the past through to the current state-of-the-art systems and identifies areas, which may provide opportunities for improvement, as well as proposing future directions in this field.


title: A Model of a Localized Cross-Border E-Commerce

abstract: By the explosive growth of B2B e-commerce transactions in international supply chains and the rapid increase of business documents in Iran’s cross-border trading, effective management of trade processes over borders is vital in B2B e-commerce systems. Structure of the localized model in this paper is based on three major layers of a B2B e-commerce infrastructure, which are messaging layer, business process layer and content layer. For each of these layers proper standards and solutions are chosen due to Iran’s e-commerce requirements. As it is needed to move smoothly towards electronic documents in Iran, UNedocs standard is suggested to support the contents of both paper and electronic documents. The verification of the suggested model is done by presenting a four phase scenario through case study method. The localized model in this paper tries to make a strategic view of business documents exchange in trade processes, and getting closer to the key target of regional single windows establishment in global trade e-supply chains.


title: Automatic Rumor Detection on Microblogs: A Survey

abstract: The ever-increasing amount of multimedia content on modern social media platforms are valuable in many applications. While the openness and convenience features of social media also foster many rumors online. Without verification, these rumors would reach thousands of users immediately and cause serious damages. Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques. Most rumor detection methods can be categorized in three paradigms: the hand-crafted features based classification approaches, the propagation-based approaches and the neural networks approaches. In this survey, we introduce a formal definition of rumor in comparison with other definitions used in literatures. We summary the studies of automatic rumor detection so far and present details in three paradigms of rumor detection. We also give an introduction on existing datasets for rumor detection which would benefit following researches in this area. We give our suggestions for future rumors detection on microblogs as a conclusion.


title: Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

abstract: Stochastic neurons and hard non-linearities can be useful for a number of reasons in deep learning models, but in many cases they pose a challenging problem: how to estimate the gradient of a loss function with respect to the input of such stochastic or non-smooth neurons? I.e., can we"back-propagate"through these stochastic neurons? We examine this question, existing approaches, and compare four families of solutions, applicable in different settings. One of them is the minimum variance unbiased gradient estimator for stochatic binary neurons (a special case of the REINFORCE algorithm). A second approach, introduced here, decomposes the operation of a binary stochastic neuron into a stochastic binary part and a smooth differentiable part, which approximates the expected effect of the pure stochatic binary neuron to first order. A third approach involves the injection of additive or multiplicative noise in a computational graph that is otherwise differentiable. A fourth approach heuristically copies the gradient with respect to the stochastic output directly as an estimator of the gradient with respect to the sigmoid argument (we call this the straight-through estimator). To explore a context where these estimators are useful, we consider a small-scale version of {\em conditional computation}, where sparse stochastic units form a distributed representation of gaters that can turn off in combinatorially many ways large chunks of the computation performed in the rest of the neural network. In this case, it is important that the gating units produce an actual 0 most of the time. The resulting sparsity can be potentially be exploited to greatly reduce the computational cost of large deep networks for which conditional computation would be useful.


title: An intelligent SDN framework for 5G heterogeneous networks

abstract: In fifth-generation (5G) mobile networks, a major challenge is to effectively improve system capacity and meet dynamic service demands. One promising technology to solve this problem is heterogeneous networks (HetNets), which involve a large number of densified low power nodes (LPNs). This article proposes a software defined network (SDN) based intelligent model that can efficiently manage the heterogeneous infrastructure and resources. In particular, we first review the latest SDN standards and discuss the possible extensions. We then discuss the advantages of SDN in meeting the dynamic nature of services and requirements in 5G HetNets. Finally, we develop a variety of schemes to improve traffic control, subscriber management, and resource allocation. Performance analysis shows that our proposed system is reliable, scalable, and implementable.