title: A Millimeter-Wave Tunable Hybrid-Transformer-Based Circular Polarization Duplexer With Sequentially-Rotated Antennas

abstract: This paper presents a millimeter-wave tunable hybrid-transformer-based duplexer concept with dual-antenna configuration. By using orthogonal sequentially-rotated linearly-polarized antennas and the hybrid-transformer-based duplexer, the transmitter and receiver duplexes the two antennas with orthogonal circular-polarized signals. An on-chip tuning technique using magnetic coupling is introduced to improve the isolation of the duplexer in case of impedance imbalances. An alternative duplexer with a similar principle using an on-board rat-race coupler is also proposed and designed for comparison. In order to demonstrate the isolation with practical antenna connections, prototypes have been developed to integrate the on-chip duplexer and the rat-race coupler with on-board aperture-coupled microstrip antennas with sequential rotation technique. Measurement results demonstrate the orthogonal circular polarizations for the receiver and transmitter modes on both prototypes. The achieved isolation by the on-chip tunable duplexer is better than 50 dB between 30.2 and 31.2 GHz, while the achieved isolation by the rat-race version is about 20 dB in the same bandwidth.


title: DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android

abstract: The increasing popularity of Android apps makes them the target of malware authors. To defend against this severe increase of Android malwares and help users make a better evaluation of apps at install time, several approaches have been proposed. However, most of these solutions suffer from some shortcomings; computationally expensive, not general or not robust enough. In this paper, we aim to mitigate Android malware installation through providing robust and lightweight classifiers. We have conducted a thorough analysis to extract relevant features to malware behavior captured at API level, and evaluated different classifiers using the generated feature set. Our results show that we are able to achieve an accuracy as high as 99% and a false positive rate as low as 2.2% using KNN classifier.


title: Hough-transform and extended RANSAC algorithms for automatic detection of 3D building roof planes from Lidar data

abstract: Airborne laser scanner technique is broadly the most appropriate way to acquire rapidly and with high density 3D data over a city. Once the 3D Lidar data are available, the next task is the automatic data processing, with major aim to construct 3D building models. Among the numerous automatic reconstruction methods, the techniques allowing the detection of 3D building roof planes are of crucial importance. Three main methods arise from the literature: region growing, Hough-transform and Random Sample Consensus (RANSAC) paradigm. Since region growing algorithms are sometimes not very transparent and not homogenously applied, this paper focuses only on the Hough-transform and the RANSAC algorithm. Their principles, their pseudocode - rarely detailed in the related literature - as well as their complete analyses are presented in this paper. An analytic comparison of both algorithms, in terms of processing time and sensitivity to cloud characteristics, shows that despite the limitation encountered in both methods, RANSAC algorithm is still more efficient than the first one. Under other advantages, its processing time is negligible even when the input data size is very large. On the other hand, Hough-transform is very sensitive to the segmentation parameters values. Therefore, RANSAC algorithm has been chosen and extended to exceed its limitations. Its major limitation is that it searches to detect the best mathematical plane among 3D building point cloud even if this plane does not always represent a roof plane. So the proposed extension allows harmonizing the mathematical aspect of the algorithm with the geometry of a roof. At last, it is shown that the extended approach provides very satisfying results, even in the case of very weak point density and for different levels of building complexity. Therefore, once the roof planes are successfully detected, the automatic building modelling can be carried out.


title: Vision-based road detection using road models

abstract: Vision-based road detection is very challenging since the road is in an outdoor scenario imaged from a mobile platform.


title: Competing risk analysis using R: an easy guide for clinicians

abstract: In the last decade with widespread use of quantitative analyses in medical research, close co-operation between statisticians and physicians has become essential from the experimental design through all phases of complex statistical analysis. On the other hand, easy-to-use statistical packages allow clinicians to perform basic statistical analyses themselves. Since the software they most commonly use does not perform in depth competing risk analysis, we recommend an add-on package for the R statistical software. We provide all the instructions for downloading it from internet and illustrate how to use it for analysis of a sample dataset of patients who underwent haematopoietic stem cell transplantation for acute leukaemia.


title: Similarity measures for OLAP sessions

abstract: OLAP queries are not normally formulated in isolation, but in the form of sequences called OLAP sessions. Recognizing that two OLAP sessions are similar would be useful for different applications, such as query recommendation and personalization; however, the problem of measuring OLAP session similarity has not been studied so far. In this paper, we aim at filling this gap. First, we propose a set of similarity criteria derived from a user study conducted with a set of OLAP practitioners and researchers. Then, we propose a function for estimating the similarity between OLAP queries based on three components: the query group-by set, its selection predicate, and the measures required in output. To assess the similarity of OLAP sessions, we investigate the feasibility of extending four popular methods for measuring similarity, namely the Levenshtein distance, the Dice coefficient, the tf–idf weight, and the Smith–Waterman algorithm. Finally, we experimentally compare these four extensions to show that the Smith–Waterman extension is the one that best captures the users’ criteria for session similarity.


title: From throw-away traffic to bots: detecting the rise of dga-based malware

abstract: Many botnet detection systems employ a blacklist of known command and control (C&C) domains to detect bots and block their traffic. Similar to signature-based virus detection, such a botnet detection approach is static because the blacklist is updated only after running an external (and often manual) process of domain discovery. As a response, botmasters have begun employing domain generation algorithms (DGAs) to dynamically produce a large number of random domain names and select a small subset for actual C&C use. That is, a C&C domain is randomly generated and used for a very short period of time, thus rendering detection approaches that rely on static domain lists ineffective. Naturally, if we know how a domain generation algorithm works, we can generate the domains ahead of time and still identify and block bot-net C&C traffic. The existing solutions are largely based on reverse engineering of the bot malware executables, which is not always feasible. ::: ::: In this paper we present a new technique to detect randomly generated domains without reversing. Our insight is that most of the DGA-generated (random) domains that a bot queries would result in Non-Existent Domain (NXDomain) responses, and that bots from the same bot-net (with the same DGA algorithm) would generate similar NXDomain traffic. Our approach uses a combination of clustering and classification algorithms. The clustering algorithm clusters domains based on the similarity in the make-ups of domain names as well as the groups of machines that queried these domains. The classification algorithm is used to assign the generated clusters to models of known DGAs. If a cluster cannot be assigned to a known model, then a new model is produced, indicating a new DGA variant or family. We implemented a prototype system and evaluated it on real-world DNS traffic obtained from large ISPs in North America. We report the discovery of twelve DGAs. Half of them are variants of known (botnet) DGAs, and the other half are brand new DGAs that have never been reported before.


title: Fast approximate spectral clustering

abstract: Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-scale problems due to its computational complexity of O(n3) in general, with n the number of data points. We extend the range of spectral clustering by developing a general framework for fast approximate spectral clustering in which a distortion-minimizing local transformation is first applied to the data. This framework is based on a theoretical analysis that provides a statistical characterization of the effect of local distortion on the mis-clustering rate. We develop two concrete instances of our general framework, one based on local k-means clustering (KASP) and one based on random projection trees (RASP). Extensive experiments show that these algorithms can achieve significant speedups with little degradation in clustering accuracy. Specifically, our algorithms outperform k-means by a large margin in terms of accuracy, and run several times faster than approximate spectral clustering based on the Nystrom method, with comparable accuracy and significantly smaller memory footprint. Remarkably, our algorithms make it possible for a single machine to spectral cluster data sets with a million observations within several minutes.


title: Sdn Security: A Survey

abstract: The pull of Software-Defined Networking (SDN) is magnetic. There are few in the networking community who have escaped its impact. As the benefits of network visibility and network device programmability are discussed, the question could be asked as to who exactly will benefit? Will it be the network operator or will it, in fact, be the network intruder? As SDN devices and systems hit the market, security in SDN must be raised on the agenda. This paper presents a comprehensive survey of the research relating to security in software-defined networking that has been carried out to date. Both the security enhancements to be derived from using the SDN framework and the security challenges introduced by the framework are discussed. By categorizing the existing work, a set of conclusions and proposals for future research directions are presented.


title: Attentive Interaction Model: Modeling Changes in View in Argumentation

abstract: We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument, with the goal of predicting if the argument successfully changes the OH's view. The model has two components: (1) vulnerable region detection, an attention model that identifies parts of the OH's reasoning that are amenable to change, and (2) interaction encoding, which identifies the relationship between the content of the OH's reasoning and that of the challenger's argument. Based on evaluation on discussions from the Change My View forum on Reddit, the two components work together to predict an OH's change in view, outperforming several baselines. A posthoc analysis suggests that sentences picked out by the attention model are addressed more frequently by successful arguments than by unsuccessful ones.


title: Wheel Defect Detection With Machine Learning

abstract: Wheel defects on railway wagons have been identified as an important source of damage to the railway infrastructure and rolling stock. They also cause noise and vibration emissions that are costly to mitigate. We propose two machine learning methods to automatically detect these wheel defects, based on the wheel vertical force measured by a permanently installed sensor system on the railway network. Our methods automatically learn different types of wheel defects and predict during normal operation if a wheel has a defect or not. The first method is based on novel features for classifying time series data and it is used for classification with a support vector machine. To evaluate the performance of our method we construct multiple data sets for the following defect types: flat spot, shelling, and non-roundness. We outperform classical defect detection methods for flat spots and demonstrate prediction for the other two defect types for the first time. Motivated by the recent success of artificial neural networks for image classification, we train custom artificial neural networks with convolutional layers on 2-D representations of the measurement time series. The neural network approach improves the performance on wheels with flat spots and non-roundness by explicitly modeling the multi sensor structure of the measurement system through multiple instance learning and shift invariant networks.


title: A C-Ku band, 8 channel T/R module for EW systems

abstract: Aim of this contribution is to illustrate Elettronica's recent development in microwave Transmit/Receive Module technology and present the performance of a C-Ku Band, 8 Channel TX/RX Module for Electronic Warfare Systems. The so-called Blade Module is based on custom Microwave Monolithic Integrated Circuits fabricated in GaAs and GaN technology, designed to perform RF signal conditioning (phase and amplitude control) and front-end functionality (low noise reception, high power transmission, TX/RX commutation). Each channel can provide more than 30dBm saturated output power and can be directly plugged onto an antenna array.


A Survey of Modern Authorship Attribution Methods


title: Adaptive enhancement and noise reduction in very low light-level video

abstract: A general methodology for noise reduction and contrast enhancement in very noisy image data with low dynamic range is presented. Video footage recorded in very dim light is especially targeted. Smoothing kernels that automatically adapt to the local spatio-temporal intensity structure in the image sequences are constructed in order to preserve and enhance fine spatial detail and prevent motion blur. In color image data, the chromaticity is restored and demosaicing of raw RGB input data is performed simultaneously with the noise reduction. The method is very general, contains few user-defined parameters and has been developed for efficient parallel computation using a GPU. The technique has been applied to image sequences with various degrees of darkness and noise levels, and results from some of these tests, and comparisons to other methods, are presented. The present work has been inspired by research on vision in nocturnal animals, particularly the spatial and temporal visual summation that allows these animals to see in dim light.


title: PolyBot and PolyKinetic TM System: A Modular Robotic Platform for Education

abstract: Modular robotics has been an active area of research for the last decade. In this paper, we argue that it also provides an excellent platform for education. PolyBot is a type of modular reconfigurable robot developed at PARC. The PolyKinetic System provides a robotic scripting language and programming environment for controlling this type of robot. Based on experience with mentoring high school students, and running a tutorial for robotic researchers at IROS’03, we show that, PolyBot and the PolyKineticSystem is effective for educational activities at multiple levels. These include playing, exploring, building, programming, competing, and most of all, learning while having fun.


title: A fully configurable and scalable neural coprocessor IP for SoC implementations of machine learning applications

abstract: This paper presents a fully configurable and programmable coprocessor IP core to efficiently compute Artificial Neural Networks (ANNs) in heterogeneous System-on-Chips (SoC). There is an increasing interest in moving applications involving streamed data such as those arising in machine-learning systems (machine-vision, speech-recognition, etc.) to highly-integrated low-power embedded devices. In this context efficient memory utilization, which is critical to both performance and energy consumption, requires handling multiple memory resources and different address spaces. The proposed IP core cooperates with a companion interconnect IP that acts as an abstraction layer to allow seamless integration of such heterogeneous resources. Dynamic (re)allocation is supported, so that embedded memory resources (Block RAMs) and external blocks (DDR memory) can be transparently used. The coprocessor itself is a configurable vector-matrix product computer with optional non-linear filtering, thus any single layer and multilayer ANN model can be easily implemented. The applicability of the IPs is demonstrated in a Hyperspectral Image (HSI) real-time classification problem.


A Review on Road Accident in Traffic System using Data Mining Techniques


title: Working with UML: A Software Design Process Based on Inspections for the Unified Modeling Language

abstract: Abstract This text describes a simple and effective object oriented software design process template having UML as the modeling language and extensively using inspections to support the construction and maintenance of software products. This software design process uses a sequential organization, based on the waterfall approach, for two reasons: to simplify the explanation of design activities in the context of this text and to make available a standard process that can be continuously improved by developers. The component phases of this design process are described in a way that gives developers the freedom to reorganize the overall process based on their own needs and environment. In addition, a survey is provided of specific literature regarding important aspects of UML and the object oriented paradigm.


title: Management control systems and strategy: A critical review☆

abstract: Abstract This paper reviews research that studies the relationship between management control systems (MCS) and business strategy. Empirical research studies that use contingency approaches and case study applications are examined focusing on specific aspects of MCS and their relationship with strategy. These aspects include cost control orientation, performance evaluation and reward systems, the effect of resource sharing, the role of MCS in influencing strategic change and the choice of interactive and diagnostic controls. More contemporary approaches to the relationship between performance measurement systems and strategy are also considered. It is concluded that our knowledge of the relationship between MCS and strategy is limited, providing considerable scope for further research. A series of future research questions is presented.


title: Planar antenna arrays at 60 GHz realized on a new thermoplastic polymer substrate

abstract: Electromagnetic waves are severely attenuated at the 60 GHz band and therefore high gain antennas are mandatory to assure medium as well as long distance communication. Efficient antennas like dish and waveguide antennas might be used for outdoor applications, however, their big size and/or high cost is an obstacle for cost-efficient and low profile indoor systems. The investigation of highly directional microstrip patch antennas at the mm-wave is done employing the new low loss thermoplastic polymer substrate material ER182. The antennas are designed and solved using a frequency domain solver. Then, the results are numerically verified by re-solving the designed antennas with a time domain solver that yields a good agreement. In addition, a prototype set of 7 different antennas, starting with a single patch antenna and up to a 32×32 planar antenna array, are fabricated and measured. The measurements show a gain of up to 23 dBi, an impedance bandwidth that exceeds 3 GHz, and a realizable HPBW of 2°. The use of ER182 substrate material enhances the antenna efficiency, compared to other well known materials, however the realization of high gain antennas is still very limited compared to waveguide antennas.


title: Homography estimation in omnidirectional vision under the L∞-norm

abstract: Solving the vision problem using convex optimization theory is now a focus in computer vision and robot communities. Second Order Cone Programming (SOCP) is especially effective in these methods. This paper discusses homography estimation in omnidirectional vision under the L∞-norm, which provides a theoretical guarantee of global optimality and a wide field of view. We give three different kinds of frameworks in this paper. This approach provides a theoretical guarantee of global optimality. A robot with this algorithm, which provides global optimality and a wide field of view demonstrated by good performance in experiments for synthetic and real data, has a more exact location and 3D reconstruction ability, which cannot be provided by traditional homography estimate method under traditional vision system.


title: Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition

abstract: Recently, deep learning techniques have been successfully applied to automatic speech recognition tasks -first to phonetic recognition with context-independent deep belief network (DBN) hidden Markov models (HMMs) and later to large vocabulary continuous speech recognition using context-dependent (CD) DBN-HMMs. In this paper, we report our most recent experiments designed to understand the roles of the two main phases of the DBN learning -pre-training and fine tuning -in the recognition performance of a CD-DBN-HMM based large-vocabulary speech recognizer. As expected, we show that pre-training can initialize weights to a point in the space where fine-tuning can be effective and thus is crucial in training deep structured models. However, a moderate increase of the amount of unlabeled pre-training data has an insignificant effect on the final recognition results as long as the original training size is sufficiently large to initialize the DBN weights. On the other hand, with additional labeled training data, the fine-tuning phase of DBN training can significantly improve the recognition accuracy.


title: Noisy audio speech enhancement using wiener filters derived from visual speech

abstract: The aim of this paper is to use visual speech information to create Wiener filters for audio speech enhancement. Wiener filters require estimates of both clean speech statistics and noisy speech statistics. Noisy speech statistics are obtained from the noisy input audio while obtaining clean speech statistics is more difficult and is a major problem in the creation of Wiener filters for speech enhancement. In this work the clean speech statistics are estimated from frames of visual speech that are extracted in synchrony with the audio. The estimation procedure begins by modelling the joint density of clean audio and visual speech features using a Gaussian mixture model (GMM). Using the GMM and an input visual speech vector a maximum a posterior (MAP) estimate of the audio feature is made. The effectiveness of speech enhancement using the visually-derived Wiener filter has been compared to a conventional audio-based Wiener filter implementation using a perceptual evaluation of speech quality (PESQ) analysis. PESQ scores in train noise at different signal-to-noise ratios (SNRs) show that the visuallyderived Wiener filter significantly outperforms the audioWiener filter at lower SNRs.


title: Systematic reviews in requirements engineering: A tertiary study

abstract: There has been an increasing interest in conducting Systematic Literature Reviews (SLR) among Requirements Engineering (RE) researchers in recent years. However, so far there have been no tertiary studies conducted to provide a comprehensive overview of these published SLR in RE. In this paper we present a tertiary study of SLR that focus solely on RE related topics by following the guidelines of Evidence Based Software Engineering. We have conducted both automated search of major online sources and manual search of the RE and SLR related conferences and journals. Our tertiary study has identified 53 distinct systematic reviews published from 2006 to 2014 and reported in 64 publications. We have assessed the resulting SLR for their quality, and coverage of specific RE related topics thus identifying some gaps. We have observed that the quality of SLR in RE has been decreasing over the recent years. There is a strong need to replicate some of these SLR to increase the reliability of their results for future RE research.


title: Tree-based batch mode reinforcement learning

abstract: Reinforcement learning aims to determine an optimal control policy from interaction with a system or from observations gathered from a system. In batch mode, it can be achieved by approximating the so-called Q-function based on a set of four-tuples (xt, ut , rt, xt+1) where xt denotes the system state at time t, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the control policy from this Q-function. The Q-function approximation may be obtained from the limit of a sequence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) and two newly proposed ensemble algorithms, namely extremely and totally randomized trees. We study their performances on several examples and find that the ensemble methods based on regression trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accuracy results are provided by the extremely randomized trees.


title: Structured Generative Models of Natural Source Code

abstract: We study the problem of building generative models of natural source code (NSC); that is, source code written and understood by humans. Our primary contribution is to describe a family of generative models for NSC that have three key properties: First, they incorporate both sequential and hierarchical structure. Second, we learn a distributed representation of source code elements. Finally, they integrate closely with a compiler, which allows leveraging compiler logic and abstractions when building structure into the model. We also develop an extension that includes more complex structure, refining how the model generates identifier tokens based on what variables are currently in scope. Our models can be learned efficiently, and we show empirically that including appropriate structure greatly improves the models, measured by the probability of generating test programs.


title: Continuous hindi speech recognition model based on Kaldi ASR toolkit

abstract: In this paper, continuous Hindi speech recognition model using Kaldi toolkit is presented. For recognition, MFCC and PLP features are extracted from 1000 phonetically balanced Hindi sentence from AMUAV corpus. Acoustic modeling was performed using GMM-HMM and decoding is performed on so called HCLG which is construted from Weight Finite State Transducers (WFSTs). Performance of both monophone and triphone model using N-gram language model is reported which is computed in term of word error rate (WER). A significant reduction in word error rate (WER) was observed using the triphone model. Further, it was found that MFCC feature provide higher recognition accuracy than PLP feature. Goal is to show the performance of Hindi language using present state-of-the-art (Kaldi) system.


title: Fourth-Degree Perineal Laceration in Nonconsenual Fisting: A Case Report, Brief Review of the Literature, and Medicolegal Issues

abstract: We present the case of an 18-year-old girl who was sexually assaulted with transanal forearm penetration resulting in a fourth-degree perineal laceration. Fisting is an uncommon sexual practice consisting in the penetration of the vagina, anus, or both of them with fingers, hand (fist) or forearm.Perineal lacerations are a relatively common finding in both consensual and nonconsensual fisting victims. Even though cases of fisting with first-, second-, and third-degree lacerations have been reported in literature, only one previous case of fourth-degree perineal laceration has been described in a woman, who died after anal fisting.The clinical and morphological findings related to this sexual practice have been presented. No previous reports concerning nonfatal cases of fourth-degree perineal laceration, due to anal fisting, exists in literature. We report a case in which the forensic evaluation and the identification of the sexual assault were delayed because of both the atypical and uncommon pattern of injury and the unconsciousness of the patient. The information gathered thanks to this article would support forensic pathologists and sexual assault forensic examiners in recognizing doubtful cases, and this is particularly important when the suspect of sexual assault arise.


title: Dynamic Ultrasonic Hybrid Localization System for Indoor Mobile Robots

abstract: An accurate dynamic ultrasonic hybrid localization system is presented for autonomous navigation of indoor mobile robots using multiple ultrasonic distance measurements and an extended Kalman filter (EKF). The ultrasonic sensor subsystem is composed of several ultrasonic transmitters (Txs) attached to the ceiling at known positions and several ultrasonic receivers equilaterally located on the top of the mobile robot, which has a moving speed that is not negligible. An EKF-based algorithm with a state/observation vector composed of the robot pose (or the position and the orientation) is presented using odometric and ultrasonic distance measurements. A dynamic distance estimation method is proposed to track the estimates of ultrasonic distance information from available Txs of interest using both odometric information from the robot and actual ultrasonic distance measurements. This continuous dynamic distance estimation allows persistent use of the hybrid self-localization algorithm to accurately determine the pose of the robot. The experimental results with various trajectories clearly show that the proposed method is much more accurate than only the hybrid self-localization algorithm (without the dynamic distance estimation method).


title: RESTful Web service composition with BPEL for REST

abstract: Current Web service technology is evolving towards a simpler approach to define Web service APIs that challenges the assumptions made by existing languages for Web service composition. RESTful Web services introduce a new kind of abstraction, the resource, which does not fit well with the message-oriented paradigm of the Web service description language (WSDL). RESTful Web services are thus hard to compose using the Business Process Execution Language (WS-BPEL), due to its tight coupling to WSDL. The goal of the BPEL for REST extensions presented in this paper is twofold. First, we aim to enable the composition of both RESTful Web services and traditional Web services from within the same process-oriented service composition language. Second, we show how to publish a BPEL process as a RESTful Web service, by exposing selected parts of its execution state using the REST interaction primitives. We include a detailed example on how BPEL for REST can be applied to orchestrate a RESTful e-Commerce scenario and discuss how the proposed extensions affect the architecture of a process execution engine.


title: Deep Convolutional Features for Image Based Retrieval and Scene Categorization

abstract: Several recent approaches showed how the representations learned by Convolutional Neural Networks can be repurposed for novel tasks. Most commonly it has been shown that the activation features of the last fully connected layers (fc7 or fc6) of the network, followed by a linear classifier outperform the state-of-the-art on several recognition challenge datasets. Instead of recognition, this paper focuses on the image retrieval problem and proposes a examines alternative pooling strategies derived for CNN features. The presented scheme uses the features maps from an earlier layer 5 of the CNN architecture, which has been shown to preserve coarse spatial information and is semantically meaningful. We examine several pooling strategies and demonstrate superior performance on the image retrieval task (INRIA Holidays) at the fraction of the computational cost, while using a relatively small memory requirements. In addition to retrieval, we see similar efficiency gains on the SUN397 scene categorization dataset, demonstrating wide applicability of this simple strategy. We also introduce and evaluate a novel GeoPlaces5K dataset from different geographical locations in the world for image retrieval that stresses more dramatic changes in appearance and viewpoint.


Multi-Objective Sentiment Analysis Using Evolutionary Algorithm for Mining Positive & Negative Association Rules


title: The Journal of Positive Psychology, January 2006; 1(1): 3–16 Positive psychology: Past, present, and (possible) future

abstract: What is positive psychology? Where has it come from? Where is it going? These are the questions we address in this article. In defining positive psychology, we distinguish between the meta-psychological level, where the aim of positive psychology is to redress the imbalance in psychology research and practice, and the pragmatic level, which is concerned with what positive psychologists do, in terms of their research, practice, and areas of interest. These distinctions in how we understand positive psychology are then used to shape conceptions of possible futures for positive psychology. In conclusion, we identify several pertinent issues for the consideration of positive psychology as it moves forward. These include the need to synthesize the positive and negative, build on its historical antecedents, integrate across levels of analysis, build constituency with powerful stakeholders, and be aware of the implications of description versus prescription.


title: Road Network Detection Using Probabilistic and Graph Theoretical Methods

abstract: Road network detection from very high resolution satellite and aerial images has diverse and important usage areas such as map generation and updating. Although an expert can label road pixels in a given image, this operation is prone to errors and quite time consuming. Therefore, an automated system is needed to detect the road network in a given satellite or aerial image in a robust manner. In this paper, we propose such a novel system. Our system has three main modules: probabilistic road center detection, road shape extraction, and graph-theory-based road network formation. These modules may be used sequentially or interchangeably depending on the application at hand. To show the strengths and weaknesses of our system, we tested it on several very high resolution satellite (Geoeye, Ikonos, and QuickBird) and aerial image sets. We compared our system with the ones existing in the literature. We also tested the sensitivity of our system to different parameter values. Obtained results indicate that our system can be used in detecting the road network on such images in a reliable and fast manner.


title: Tweet Sentiment: From Classification to Quantification

abstract: Sentiment classification has become a ubiquitous enabling technology in the Twittersphere, since classifying tweets according to the sentiment they convey towards a given entity (be it a product, a person, a political party, or a policy) has many applications in political science, social science, market research, and many others. In this paper we contend that most previous studies dealing with tweet sentiment classification (TSC) use a suboptimal approach. The reason is that the final goal of most such studies is not estimating the class label (e.g., Positive, Negative, or Neutral) of individual tweets, but estimating the relative frequency (a.k.a. "prevalence") of the different classes in the dataset. The latter task is called quantification, and recent research has convincingly shown that it should be tackled as a task of its own, using learning algorithms and evaluation measures different from those used for classification. In this paper we show, on a multiplicity of TSC datasets, that using a quantification-specific algorithm produces substantially better class frequency estimates than a state-of-the-art classification-oriented algorithm routinely used in TSC. We thus argue that researchers interested in tweet sentiment prevalence should switch to quantification-specific (instead of classification-specific) learning algorithms and evaluation measures.


title: Ethical Issues in Plastic and Reconstructive Surgery

abstract: Plastic, reconstructive, and cosmetic surgery refers to a variety of operations performed in order to repair or restore body parts to look normal or to enhance a certain structure or anatomy that is already normal. Several ethical considerations such as a patient's right for autonomy, informed consent, beneficence, and nonmalfeasance need to be given careful consideration. The principal objective of the medical profession is to render services to humanity with full respect for human dignity. Plastic surgeons should merit the confidence of patients entrusted to their care, rendering to each a full measure of service and devotion. They require an extensive amount of education and training. The increases in demand for aesthetic plastic surgery and the advocacy of practice in the media have raised concerns about the circumstances under which cosmetic surgery is ethical and permissible. Innovative research, and new technologies derived from such research, almost always raises ethical and policy concerns. Medical ethics regulate what is, and what is not, correct in promoting plastic surgery to the public. It is essential to create an educated and informed public about the ethical issues in the plastic and reconstructive surgery field. Plastic surgeons need to carefully evaluate the degree of deformity, physical and emotional maturity, and desired outcome of patients who request plastic surgery procedures. Science is a powerful force for change in modern society and plastic surgeons have a responsibility to shepherd that change with thoughtful advocacy and careful ethical scrutiny of their own behavior.


title: Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach

abstract: We demonstrate the effectiveness of using machine learning for model-free prediction of spatiotemporally chaotic systems of arbitrarily large spatial extent and attractor dimension purely from observations of the system's past evolution. We present a parallel scheme with an example implementation based on the reservoir computing paradigm and demonstrate the scalability of our scheme using the Kuramoto-Sivashinsky equation as an example of a spatiotemporally chaotic system.


title: Tactile Sensors for Friction Estimation and Incipient Slip Detection – Towards Dexterous Robotic Manipulation: A Review

abstract: Humans can handle and manipulate objects with ease; however, human dexterity has yet to be matched by artificial systems. Receptors in our fingers and hands provide essential tactile information to the motor control system during dexterous manipulation such that the grip force is scaled to the tangential forces according to the coefficient of friction. Likewise, tactile sensing will become essential for robotic and prosthetic gripping performance as applications move toward unstructured environments. However, most existing research ignores the need to sense the frictional properties of the sensor-object interface, which (along with contact forces and torques) is essential for finding the minimum grip force required to securely grasp an object. Here, we review this problem by surveying the field of tactile sensing from the perspective that sensors should: 1) detect gross slip (to adjust the grip force); 2) detect incipient slip (dependent on the frictional properties of the sensor-object interface and the geometries and mechanics of the sensor and the object) as an indication of grip security; or 3) measure friction on contact with an object and/or following a gross or incipient slip event while manipulating an object. Recommendations are made to help focus future sensor design efforts toward a generalizable and practical solution to sense, and hence control grip security. Specifically, we propose that the sensor mechanics should encourage incipient slip, by allowing parts of the sensor to slip while other parts remain stuck, and that instrumentation should measure displacement and deformation to complement conventional force, pressure, and vibration tactile sensing.


title: Mining sensor data in cyber-physical systems

abstract: A Cyber-Physical System (CPS) integrates physical devices (i.e., sensors) with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. Such a system has wide applications in the scenarios of traffic control, battlefield surveillance, environmental monitoring, and so on. A core element of CPS is the collection and assessment of information from noisy, dynamic, and uncertain physical environments integrated with many types of cyber-space resources. The potential of this integration is unbounded. To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time. Therefore, CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber. The various properties of the physical world must be addressed in information management and knowledge discovery. This paper discusses the problems of mining sensor data in CPS: With a large number of wireless sensors deployed in a designated area, the task is real time detection of intruders that enter the area based on noisy sensor data. The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data. IntruMine first analyzes the trustworthiness of sensor data, then detects the intruders' locations, and verifies the detections based on a graph model of the relationships between sensors and intruders.


title: Il divenire della bildung in Nietzsche e in Spinoza

abstract: The subject of this work is the issue of the Bildung in Nietzsche and in Spinoza. ::: The thesis that sustains the whole investigation is that the two authors debated in this research have expressed a philosophy the heart of it precisely consists in the subject of the education of the man as a way towards his liberation. ::: It is a thesis that has unavoidable and not marginal theoretical consequences because it contrasts with a very constraining and narrow, but popular, conception of Nietzsche as “philosopher of the hammer”, philosopher of the suspicion, that destroys nihilistically all the values and does not built – therefore does not educate – and with a conception of Spinoza as determinist philosopher that denying the freedom can not point out a way of education to freedom. ::: The reading of the philosophy put to use in a elevation of the man is on the other hand acquisition deeply shared by both authors and removes to Nietzsche and Spinoza that aura of perfect adhesion to the necessary that, with different variations, has been ascribed to them and that is expressed in the amor fati in the former and in the geometric order in the the latter. ::: In a non-superficial reading emerges that to both thinkers is dear the paidetic question par excellence: “what can we become?” ::: Our study wants to show, through a textual investigation conducted with historic-philologic method, how the encounter with both thinkers involves the acquisition of many elements that makes anybody is interested in educational practice to think about. ::: Emerges a picture of a Bildung proposing a reinforcement of the individual, respectful of his singularity, in which the form is not fixed, but becomes, it is multiple and constitutes itself in the relationship (conflicting in Nietzsche, communal in Spinoza). ::: In both the paidetic procedure put as central the role of the body, place of the receptiveness par excellance, of the integration of the knowledge and affectivity, essential components of every Bildung. ::: Both authors stresses the importance of a work on memory for an actual education. ::: Nietzsche, caustic detractor of the mass culture, thinks in an antagonistic way the relationship amorphous mass/creative individual where the former serves as working force stock to protect and preserve the creative emergency of the genius whom every educational care must be directed. ::: Otherwise, it is possible to define the character of the spinozistic Bildung “transindividual” because of the importance in it of the relationship, that line joining and dividing at the same time, in the education of the individual. ::: For Spinoza improvement of the others, of the greatest number of men possible, it is not desirable because it is “moral” in itself, but becuse it improves us if it is true that only in the interactions, in the encounters with the best men (more active, less afflicted by pain, converging in the love of God) we increases our activity and we limits the pains that weakens us too. ::: To reinforce the clinamen that already because of our nature of modes pushes us towards the others, to have care of the relationship and to seek the converging of the bodies and the minds starting with those of the master and disciple and then to extend it to the all individuals involved in the educative process, it is not practices to wish for only because simplify transmission of the knowledge – moreover a true thing. ::: It is through these ways that we can get to that be-in-common of the minds and the bodies in which the power of every individual involved in the paidetic process reinforce itself making the Bildung the true area of building of an heterogeneus – politic – body that becomes fundamental ethos for the virtuous-becoming of each one.


Cloud Computing : Empirical Studies in Higher Education


title: Efficient design of a novel wide-illumination-angle Ku/Ka-band feed for reflector antennas

abstract: A novel wide-illumination-angle feed for satellite communication which could operate simultaneously in Ku/Ka uplink and downlink bands is proposed in this paper, based on a highly efficient design routine. Excellent radiation performances and high reflection loss are achieved by employing multi scalar rings, hybrid mode launcher and coaxial structure. Cylindrical modal matching method is employed for generalized scattering matrix calculation inside the feed, while spherical mode expansion is adopted for the feed aperture discontinuity. Key specific parameters are finally compared with the full wave simulation results. Good consistency confirms the validity of our numerical approach and the proposed antenna.


title: Chemokine Signaling Pathway Involved in CCL2 Expression in Patients with Rheumatoid Arthritis

abstract: Purpose ::: Rheumatoid arthritis (RA) is an inflammatory joint disorder, the progression of which leads to the destruction of cartilage and bone. Chemokines are involved in RA pathogenesis. In this study, we investigated the chemokine signaling pathway associated with CCL2 in peripheral blood (PB) and synovial tissues (ST) of RA patients based on our previous work about chemokine signaling pathway involved in the activation of CCL2 production in collagen-induced arthritis rat ST.


title: Interactive and Deterministic Data Cleaning: A Tossed Stone Raises a Thousand Ripples

abstract: We present Falcon, an interactive, deterministic, and declarative data cleaning system, which uses SQL update queries as the language to repair data. Falcon does not rely on the existence of a set of pre-defined data quality rules. On the contrary, it encourages users to explore the data, identify possible problems, and make updates to fix them. Bootstrapped by one user update, Falcon guesses a set of possible sql update queries that can be used to repair the data. The main technical challenge addressed in this paper consists in finding a set of sql update queries that is minimal in size and at the same time fixes the largest number of errors in the data. We formalize this problem as a search in a lattice-shaped space. To guarantee that the chosen updates are semantically correct, Falcon navigates the lattice by interacting with users to gradually validate the set of sql update queries. Besides using traditional one-hop based traverse algorithms (e.g., BFS or DFS), we describe novel multi-hop search algorithms such that Falcon can dive over the lattice and conduct the search efficiently. Our novel search strategy is coupled with a number of optimization techniques to further prune the search space and efficiently maintain the lattice. We have conducted extensive experiments using both real-world and synthetic datasets to show that Falcon can effectively communicate with users in data repairing.


title: Language Models for Information Retrieval

abstract: Because of the world wide web, information retrieval systems are now used by millions of untrained users all over the world. The search engines that perform the information retrieval tasks, often retrieve thousands of potentially interesting documents to a query. The documents should be ranked in decreasing order of relevance in order to be useful to the user. This book describes a mathematical model of information retrieval based on the use of statistical language models. The approach uses simple document-based unigram models to compute for each document the probability that it generates the query. This probability is used to rank the documents. The study makes the following research contributions. ::: ::: * The development of a model that integrates term weighting, relevance feedback and structured queries. ::: * The development of a model that supports multiple representations of a request or information need by integrating a statistical translation model. ::: * The development of a model that supports multiple representations of a document, for instance by allowing proximity searches or searches for terms from a particular record field (e.g. a search for terms from the title). ::: * A mathematical interpretation of stop word removal and stemming. ::: * A mathematical interpretation of operators for mandatory terms, wildcards and synonyms. ::: * A practical comparison of a language model-based retrieval system with similar systems that are based on well-established models and term weighting algorithms in a controlled experiment. ::: * The application of the model to cross-language information retrieval and adaptive information filtering, and the evaluation of two prototype systems in a controlled experiment. ::: ::: Experimental results on three standard tasks show that the language model-based algorithms work as well as, or better than, today's top-performing retrieval algorithms. The standard tasks investigated are ad-hoc retrieval (when there are no previously retrieved documents to guide the search), retrospective relevance weighting (find the optimum model for a given set of relevant documents), and ad-hoc retrieval using manually formulated Boolean queries. The application to cross-language retrieval and adaptive filtering shows the practical use of respectively structured queries, and relevance feedback.


title: Detecting Impersonation Attack in WiFi Networks Using Deep Learning Approach

abstract: WiFi network traffics will be expected to increase sharply in the coming years, since WiFi network is commonly used for local area connectivity. Unfortunately, there are difficulties in WiFi network research beforehand, since there is no common dataset between researchers on this area. Recently, AWID dataset was published as a comprehensive WiFi network dataset, which derived from real WiFi traces. The previous work on this AWID dataset was unable to classify Impersonation Attack sufficiently. Hence, we focus on optimizing the Impersonation Attack detection. Feature selection can overcome this problem by selecting the most important features for detecting an arbitrary class. We leverage Artificial Neural Network (ANN) for the feature selection and apply Stacked Auto Encoder (SAE), a deep learning algorithm as a classifier for AWID Dataset. Our experiments show that the reduced input features have significantly improved to detect the Impersonation Attack.


title: Design of low inertia manipulator with high stiffness and strength using tension amplifying mechanisms

abstract: This paper presents a novel manipulator for human-robot interaction that has low mass and inertia without losing stiffness and payload performance. A lightweight tension amplifying mechanism that increases the joint stiffness in quadratic order is proposed. High stiffness is essential for precise and rapid manipulation, and low mass and inertia are important factors for safety due to low stored kinetic energy. The proposed tension amplifying mechanism was applied to a 1-DOF elbow joint and then extended to a 3-DOF wrist joint. The developed manipulator was analyzed in terms of inertia, stiffness, and strength properties. Its moving part weighs 3.37 kg, and its inertia is 0.57 kg·m2, which is similar to that of a human arm. The stiffness of the developed elbow joint is 1440Nm/rad, which is comparable to that of the joints with rigid components in industrial manipulators. A detailed description of the design is provided, and thorough analysis verifies the performance of the proposed mechanism.


title: Color harmony for image indexing

abstract: A predictive model for estimating the perceived harmony of ordinary multi-colored images is proposed and evaluated. The model is based on earlier research concerning two-color harmonies. Color regions of images are extracted using mean shift segmentation. Global and local harmony scores are derived for two-color combinations included in different subsets of all segmented regions. Statistical measurements of the obtained harmony scores are used for predicting the perceived overall harmony. The model is validated in a psychophysical experiment, where human observers are judging images on a harmony scale. The findings show that humans do perceive harmony in multi-colored images in similar ways, and that the proposed model results in useful predictions of harmony. The model can be applied in automatic labeling or classification of images.


title: Using quantitative data in mixed-design grounded theory studies: an enhanced path to formal grounded theory in information systems

abstract: This article discusses a key development in the use of grounded theory (GT) in information systems (IS) – the use of quantitative data in mixed studies to build on and elaborate theories. We examine the design of one of our research projects and describe how this mixed-design GT project helped elaborate emerging theories using slices of qualitative and quantitative data. Our contributions are threefold: (i) we show that the use of mixed data and techniques can be leveraged to help build credible theories in IS because it allows researchers to build theories of greater abstraction and scope: it helps sense-making in the drive from substantive to parsimonious formal theories; (ii) in line with classic GT, we propose a mixed typological design to help build a pathway to formal grounded theories in rupture with existing literature; and (iii) we highlight GT as a meta-theory of research design and revisit some of its main principles in a mixed-design perspective.


title: Acquiring Hyponymy Relations From Web Documents

abstract: This paper describes an automatic method for acquiring hyponymy relations from HTML documents on the WWW. Hyponymy relations can play a crucial role in various natural language processing systems. Most existing acquisition methods for hyponymy relations rely on particular linguistic patterns, such as “NP such as NP”. Our method, however, does not use such linguistic patterns, and we expect that our procedure can be applied to a wide range of expressions for which existing methods cannot be used. Our acquisition algorithm uses clues such as itemization or listing in HTML documents and statistical measures such as document frequencies and verb-noun co-occurrences.


What is e-health ?


title: Schema Extraction and Structural Outlier Detection for JSON-based NoSQL Data Stores.

abstract: Although most NoSQL Data Stores are schema-less, information on the structural properties of the persisted data is nevertheless essential during application development. Otherwise, accessing the data becomes simply impractical. In this paper, we introduce an algorithm for schema extraction that is operating outside of the NoSQL data store. Our method is specifically targeted at semi-structured data persisted in NoSQL stores, e.g., in JSON format. Rather than designing the schema up front, extracting a schema in hindsight can be seen as a reverse-engineering step. Based on the extracted schema information, we propose set of similarity measures that capture the degree of heterogeneity of JSON data and which reveal structural outliers in the data. We evaluate our implementation on two real-life datasets: a database from the Wendelstein 7-X project and Web Performance Data.


title: Web Page Clustering Using a Self-Organizing Map

abstract: The continuous growth in the size and use of the Internet is creating difficulties in the search for information. A sophisticated method to organize the layout of the information and assist user navigation is therefore particularly important. In this paper, we evaluate the feasibility of using a self-organizing map (SOM) to mine web log data and provide a visual tool to assist user navigation. We have developed LOGSOM, a system that utilizes Kohonen's self-organizing map to organize web pages into a two-dimensional map. The organization of the web pages is based solely on the users' navigation behavior, rather than the content of the web pages. The resulting map not only provides a meaningful navigation tool (for web users) that is easily incorporated with web browsers, but also serves as a visual analysis tool for webmasters to better understand the characteristics and navigation behaviors of web users visiting their pages.


title: An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP

abstract: Adaptive (video) streaming over HTTP is gradually being adopted, as it offers significant advantages in terms of both user-perceived quality and resource utilization for content and network service providers. In this paper, we focus on the rate-adaptation mechanisms of adaptive streaming and experimentally evaluate two major commercial players (Smooth Streaming, Netflix) and one open source player (OSMF). Our experiments cover three important operating conditions. First, how does an adaptive video player react to either persistent or short-term changes in the underlying network available bandwidth. Can the player quickly converge to the maximum sustainable bitrate? Second, what happens when two adaptive video players compete for available bandwidth in the bottleneck link? Can they share the resources in a stable and fair manner? And third, how does adaptive streaming perform with live content? Is the player able to sustain a short playback delay? We identify major differences between the three players, and significant inefficiencies in each of them.


title: Knowledge transfer between and within alliance partners : Private versus collective benefits of social capital ☆

abstract: This article examines the process through which multilevel network structures translate into knowledge acquisition from alliance partners. The degree of knowledge transfer a multidivisional company achieves from its network of alliance partners is determined not only by the organization's external network structure, but also by the structure of relationships among its business units. By distinguishing two perspectives on the distribution of social capital's benefits – private versus collective – this article's approach reconciles the competing views on what types of network structures create social capital, that is, the brokerage and closure views of the social network literature. Private benefits of brokerage and centrality are more beneficial in interfirm networks, whereas collective benefits provided by network closure and low levels of centralization are more beneficial in intrafirm networks.


title: Kernel Methods for Pattern Analysis

abstract: Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.


title: Interactive Data Visualization: Foundations, Techniques, and Applications

abstract: This book provides the theory, practical details, and tools necessary for building visualizations or systems involving the visualization of data. The authors cover the spectrum of data visualizations, including mathematical and statistical graphs, cartography for displaying geographic information, two- and three-dimensional scientific displays, integrated analysis and visualization tools, and general information visualization techniques. Practitioners, developers, teachers and students as well as those interested in gaining some exposure to the field will get an in-depth understanding of visualization techniques and are provided with sufficient information, often with full source code, to complete an implementation; those with more modest aspirations can focus on the concepts, theory and high-level algorithm details.


title: การศึัาคของเปลื และ ปากใบของผลหม่ Study on Peel Morphology and Stomata of Mulberry (Morus spp.) Fruit

abstract: Mulberry (Morus spp.) can be grown well in temperate and tropical regions. The mulberry fruit contains 0.21% calcium, vitamin B 6 930 mg./kg, and folic acid 6.87 mg/kg. The fruit is in high demand for the world market for fresh consumption and for producing wine. However, after harvest, mulberry fruit loses water quickly and disease occurs. It has a short storage life of only 2-3 days at room temperature (30 o C). We studied peel morphology and stomata of fresh mulberry fruit by using the Scanning Electron Microscope (SEM). Fresh fruits of mulberry cvs. Bureerum 60 and Cheingmai at the ripe stage, 46 days after anthesis and when fully ripe at 53 days after anthesis were used. There were no differences between layers of epicuticular wax and parenchyma of either cultivars at either stage. However, fully ripened fruit of Bureerum 60 had the largest stomata (19.7 μm) that were similar in size to those at the ripe stage (18.9 μm). However, they were larger than those on ripe (17.8 μm) and fully ripened fruit of Cheingmai (17.7 μm). In addition, fully ripened Bureerum 60 fruit had the largest stomatal pore opening (13.5 μm), which was similar to that of ripened fruit of Bureerum 60 (11.4 μm). However, they were larger than those on ripe (11.0 μm) and fully ripened fruit of Cheingmai (9.5 μm). This finding tells us that stomata and stomatal pore opening vary in size between Bureerum 60 and Cheingmai. The rate of water loss is, therefore, different between these two cultivars.


title: Software Architecture Quality Analysis Methods

abstract: The purpose of the architecture evaluation of a software system is to analyze the architecture to identify potential risks and to verify that the quality requirements have been addressed in the design. This survey shows the state of the research at this moment, in this domain, by presenting and discussing eight of the most representative architecture analysis methods. The selection of the studied methods tries to cover as many particular views of objective reflections as possible to be derived from the general goal. The role of the discussion is to offer guidelines related to the use of the most suitable method for an architecture assessment process. We will concentrate on discovering similarities and differences between these eight available methods by making classifications, comparisons and appropriateness studies.


title: Coverage and Rate Analysis for Millimeter-Wave Cellular Networks

abstract: Millimeter wave (mmWave) holds promise as a carrier frequency for fifth generation cellular networks. Because mmWave signals are sensitive to blockage, prior models for cellular networks operated in the ultra high frequency (UHF) band do not apply to analyze mmWave cellular networks directly. Leveraging concepts from stochastic geometry, this paper proposes a general framework to evaluate the coverage and rate performance in mmWave cellular networks. Using a distance-dependent line-of-site (LOS) probability function, the locations of the LOS and non-LOS base stations are modeled as two independent non-homogeneous Poisson point processes, to which different path loss laws are applied. Based on the proposed framework, expressions for the signal-to-noise-and-interference ratio (SINR) and rate coverage probability are derived. The mmWave coverage and rate performance are examined as a function of the antenna geometry and base station density. The case of dense networks is further analyzed by applying a simplified system model, in which the LOS region of a user is approximated as a fixed LOS ball. The results show that dense mmWave networks can achieve comparable coverage and much higher data rates than conventional UHF cellular systems, despite the presence of blockages. The results suggest that the cell size to achieve the optimal SINR scales with the average size of the area that is LOS to a user.


title: Inference by eye: Confidence intervals and how to read pictures of data

abstract: Wider use in psychology of confidence intervals (CIs), especially as error bars in figures, is a desirable development. However, psychologists seldom use CIs and may not understand them well. The authors discuss the interpretation of figures with error bars and analyze the relationship between CIs and statistical significance testing. They propose 7 rules of eye to guide the inferential use of figures with error bars. These include general principles: Seek bars that relate directly to effects of interest, be sensitive to experimental design, and interpret the intervals. They also include guidelines for inferential interpretation of the overlap of CIs on independent group means. Wider use of interval estimation in psychology has the potential to improve research communication substantially.


title: Vehicular Cloud Networks: Architecture, Applications and Security Issues

abstract: Vehicular Ad Hoc Networks (VANET) are the largest real life application of ad-hoc networks where nodes are represented via fast moving vehicles. This paper introduces the future emerging technology, i.e., Vehicular Cloud Networking (VCN) where vehicles and adjacent infrastructure merge with traditional internet clouds to offer different applications ranging from low sized applications to very complex applications. VCN is composed of three types of clouds: Vehicular cloud, Infrastructure cloud and traditional Back-End (IT) cloud. We introduced these clouds via a three tier architecture along with their operations and characteristics. We have proposed use cases of each cloud tier that explain how it is practically created and utilised while taking the vehicular mobility in consideration. Moreover, it is critical to ensure security, privacy and trust of VCN network and its assets. Therefore, to describe the security of VCN, we have provided an in-depth analysis of different threats related to each tier of VCN. The threats related to vehicular cloud and infrastructure cloud are categorized according to their assets, i.e., vehicles, adjacent infrastructure, wireless communication, vehicular messages, and vehicular cloud threats. Similarly, the Back-End cloud threats are categorized into data and network threats. The possible implications of these threats and their effects on various components of VCN are also explained in detail.


title: Very Fast Solution to the PnP Problem with Algebraic Outlier Rejection

abstract: We propose a real-time, robust to outliers and accurate solution to the Perspective-n-Point (PnP) problem. The main advantages of our solution are twofold: first, it in- tegrates the outlier rejection within the pose estimation pipeline with a negligible computational overhead, and sec- ond, its scalability to arbitrarily large number of correspon- dences. Given a set of 3D-to-2D matches, we formulate pose estimation problem as a low-rank homogeneous sys- tem where the solution lies on its 1D null space. Outlier correspondences are those rows of the linear system which perturb the null space and are progressively detected by projecting them on an iteratively estimated solution of the null space. Since our outlier removal process is based on an algebraic criterion which does not require computing the full-pose and reprojecting back all 3D points on the image plane at each step, we achieve speed gains of more than 100× compared to RANSAC strategies. An extensive exper- imental evaluation will show that our solution yields accu- rate results in situations with up to 50% of outliers, and can process more than 1000 correspondences in less than 5ms.


title: Critical Analysis of the Challenges of Solid Waste Management Initiatives in Keko Machungwa Informal Settlement, Dar es Salaam

abstract: Solid waste is inevitable because by nature every human activity generates a certain amount of solid waste. The rate of solid waste generated tends to increase with the increase in population. Despite existence of various efforts on solid waste collection, still the quantity of solid waste collected is small compared to the solid waste generated. The situation is even worse in unplanned settlements of developing countries, where, ineffective solid waste collection is contributed to by haphazard solid waste disposal and hence environmental pollution. Therefore, having an effective and efficient solid waste management system is a major challenge in cities of developing countries, and thus more consulted efforts are needed. This paper presents finding based on the study carried out in Keko Machungwa informal settlement in Dar es Salaam, Tanzania. The study explored challenges facing solid waste management initiatives in Keko Machungwa and recommended measures for improvement. Data for this study was collected through documentary review, household interviews, focus group discussions, physical observations and official interviews. The analysis reveals that solid waste generated in Keko Machungwa is 16 tones per day, among those, only 4.56 (less than 50%) are collected. About 11.44 tones of uncollected waste are illegally dumped into a seasonal stream and in different vacant lots hence causing blockage of the seasonal stream. Different groups engaged in solid waste collection are deterred by many challenges. The challenges include: lack of cooperation among the existing solid waste collection organs, inaccessibility in some places, low public participation, financial constraints and unwillingness of local community to pay for solid waste collection fees. The paper recommends four options on how solid waste management, particularly collection in Keko Machungwa can be enhanced. 1) Alliance among the existing organ, 2) placing dust-bins in different points within the settlement, 3) linking the fees of solid waste collection to other public services and 4) education and awareness creation campaigns accompanied by community stakeholder consultations.


title: Executive functioning in chronic alcoholism and Korsakoff syndrome.

abstract: Korsakoff syndrome (KS) is characterized by dense anterograde and retrograde amnesia. There is often a temporal gradient to the retrograde amnesia, with earlier memories more readily recalled than recent memories. Executive functioning has also been found to be impaired in KS. However, research comparing executive functioning between chronic alcoholics (AL) and patients with KS has been relatively sparse to date. In a group comparison design, executive functioning in 15 KS patients and 16 chronic alcoholic patients was assessed using the Behavioural Assessment of the Dysexecutive Syndrome test (BADS) and other secondary measures. The KS group was found to be significantly more impaired than the AL group on overall performance on the BADS (p < .05). Korsakoff patients are significantly more impaired in executive functioning than non-Korsakoff chronic alcoholics. We thank the participants of the study and also acknowledge the support of the University of Nottingham, particularly Nadina Lincoln, and the Nort...


title: Patterned Fabric Defect Detection using a Motif-Based Approach

abstract: This paper proposed a patterned fabric defect detection method for sixteen out of seventeen wallpaper groups using a motif-based approach. From the symmetry properties of motifs, the energy of moving subtraction and its variance among motifs are mapped onto an energy-variance space. By learning the distribution of defect-free and defective patterns in this space, boundaries conditions can be determined for defect detection purpose. The proposed method is evaluated on four wallpaper categories, from which all 16 wallpaper groups can be generalized. Altogether, 160 defect-free lattices samples are used for learning the decision boundaries; and 200 other defect-free and 138 other defective samples are used for testing. An overall detection accuracy has reached 93.61%, which outperforms previous approaches.


title: Remembering what could have happened: Neural correlates of episodic counterfactual thinking

abstract: Recent evidence suggests that our capacities to remember the past and to imagine what might happen in the future largely depend on the same core brain network that includes the middle temporal lobe, the posterior cingulate/retrosplenial cortex, the inferior parietal lobe, the medial prefrontal cortex, and the lateral temporal cortex. However, the extent to which regions of this core brain network are also responsible for our capacity to think about what could have happened in our past, yet did not occur (i.e., episodic counterfactual thinking), is still unknown. The present study examined this issue. Using a variation of the experimental recombination paradigm (Addis, Pan, Vu, Laiser, & Schacter, 2009. Neuropsychologia. 47: 2222–2238), participants were asked both to remember personal past events and to envision alternative outcomes to such events while undergoing functional magnetic resonance imaging. Three sets of analyses were performed on the imaging data in order to investigate two related issues. First, a mean-centered spatiotemporal partial least square (PLS) analysis identified a pattern of brain activity across regions of the core network that was common to episodic memory and episodic counterfactual thinking. Second, a non-rotated PLS analysis identified two different patterns of brain activity for likely and unlikely episodic counterfactual thoughts, with the former showing significant overlap with the set of regions engaged during episodic recollection. Finally, a parametric modulation was conducted to explore the differential engagement of brain regions during counterfactual thinking, revealing that areas such as the parahippocampal gyrus and the right hippocampus were modulated by the subjective likelihood of counterfactual simulations. These results suggest that episodic counterfactual thinking engages regions that form the core brain network, and also that the subjective likelihood of our counterfactual thoughts modulates the engagement of different areas within this set of regions.


title: Application of Cluster-Based Local Outlier Factor Algorithm in Anti-Money Laundering

abstract: Financial institutions’ capability in recognizing suspicious money laundering transactional behavioral patterns (SMLTBPs) is critical to anti-money laundering. Combining distance-based unsupervised clustering and local outlier detection, this paper designs a new cluster-based local outlier factor (CBLOF) algorithm to identify SMLTBPs and use authentic and synthetic data experimentally to test its applicability and effectiveness. Keywords-clustering; outlier detection; local outlier factor (LOF); suspicious money laundering transactional behavioral patterns (SMLTBPs); anti-money laundering (AML)


Modelling and compensation of nonlinear loudspeaker


title: Productivity, business profitability, and consumer surplus: three different measures of information technology value

abstract: The business value of information technology (IT) has been debated for a number of years. While some authors have attributed large productivity improvements and substantial consumer benefits to IT, others report that IT has not had any bottom line impact on business profitability. This paper focuses on the fact that while productivity, consumer value, and bush ness profitability are related, they are ultimate1 Allen Lee was the accepting senior editor for this paper. 2 An earlier version of this paper appears in the


title: Connectivity Stability in Autonomous Multi-level UAV Swarms for Wide Area Monitoring

abstract: Many different types of unmanned aerial vehicles (UAVs) have been developed to address a variety of applications ranging from searching and mapping to surveillance. However, for complex wide-area surveillance scenarios, where fleets of autonomous UAVs must be deployed to work collectively on a common goal, multiple types of UAVs should be incorporated forming a heterogeneous UAV system. Indeed, the interconnection of two levels of UAVs---one with high altitude fixed-wing UAVs and one with low altitude rotary-wing UAVs---can provide applicability for scenarios which cannot be addressed by either UAV type. This work considers a bi-level flying ad hoc networks (FANETs), in which each UAV is equipped with ad hoc communication capabilities, in which the higher level fixed-wing swarm serves mainly as a communication bridge for the lower level UAV fleets, which conduct precise information sensing. The interconnection of multiple UAV types poses a significant challenge, since each UAV level moves according to its own mobility pattern, which is constrained by the UAV physical properties. Another important challenge is to form network clusters at the lower level, whereby the intra-level links must provide a certain degree of stability to allow a reliable communication within the UAV system. This article proposes a novel mobility model for the low-level UAVs that combines a pheromone-based model with a multi-hop clustering algorithm. The pheromones permit to focus on the least explored areas with the goal to optimize the coverage while the multi-hop clustering algorithm aims at keeping a stable and connected network. The proposed model works online and is fully distributed. The connection stability is evaluated against different measurements such as stability coefficient and volatility. The performance of the proposed model is compared to other state-of-the-art contributions using simulations. Experimental results demonstrate the ability of the proposed mobility model to significantly improve the network stability while having a limited impact on the wide-area coverage.


title: PLANT LEAF CLASSIFICATION USING PROBABILISTIC INTEGRATION OF SHAPE, TEXTURE AND MARGIN FEATURES

abstract: Plant species classification using leaf samples is a challenging and important problem to solve. This paper introduces a new data set of sixteen samples each of one-hundred plant species; and describes a method designed to work in conditions of small training set size and possibly incomplete extraction of features. This motivates a separate processing of three feature types: shape, texture, and margin; combined using a probabilistic framework. The texture and margin features use histogram accumulation, while a normalised description of contour is used for the shape. Two previously published methods are used to generate separate posterior probability vectors for each feature, using data associated with the k-Nearest Neighbour apparatus. The combined posterior estimates produce the final classification (where missing features could be omitted). We show that both density estimators achieved a 96% mean accuracy of classification when combining the three features in this way (training on 15 samples with unseen cross validation). In addition, the framework can provide an upper bound on the Bayes Risk of the classification problem, and thereby assess the accuracy of the density estimators. Lastly, the high performance of the method is demonstrated for small training set sizes: 91% accuracy is observed with only four training samples.


title: Myocardial damage in dogs affected by heartworm disease (Dirofilaria immitis): Immunohistochemical study of cardiac myoglobin and troponin I in naturally infected dogs

abstract: Abstract It has recently been reported that dogs affected by canine heartworm disease ( Dirofilaria immitis ) can show an increase in plasma levels of myoglobin and cardiac troponin I, two markers of muscle/myocardial injury. In order to determine if this increase is due to myocardial damage, the right ventricle of 24 naturally infected dogs was examined by routine histology and immunohistochemistry with anti-myoglobin and anti-cardiac troponin I antibodies. Microscopic lesions included necrosis and myocyte vacuolization, and were associated with loss of staining for one or both proteins. Results confirm that increased levels of myoglobin and cardiac troponin I are indicative of myocardial damage in dogs affected by heartworm disease.


title: Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing

abstract: We propose an on-chip optical architecture to support massive parallel communication among high-performance spiking laser neurons. Designs for a network protocol, computational element, and waveguide medium are described, and novel methods are considered in relation to prior research in optical on-chip networking, neural networking, and computing. Broadcast-and-weight is a new approach for combining neuromorphic processing and optoelectronic physics, a pairing that is found to yield a variety of advantageous features. We discuss properties and design considerations for architectures for scalable wavelength reuse and biologically relevant organizational capabilities, in addition to aspects of practical feasibility. Given recent developments commercial photonic systems integration and neuromorphic computing, we suggest that a novel approach to photonic spike processing represents a promising opportunity in unconventional computing.


title: Asymptotic Properties of the Fisher Kernel

abstract: This letter analyzes the Fisher kernel from a statistical point of view. The Fisher kernel is a particularly interesting method for constructing a model of the posterior probability that makes intelligent use of unlabeled data (i.e., of the underlying data density). It is important to analyze and ultimately understand the statistical properties of the Fisher kernel. To this end, we first establish sufficient conditions that the constructed posterior model is realizable (i.e., it contains the true distribution). Realizability immediately leads to consistency results. Subsequently, we focus on an asymptotic analysis of the generalization error, which elucidates the learning curves of the Fisher kernel and how unlabeled data contribute to learning. We also point out that the squared or log loss is theoretically preferable-because both yield consistent estimators-to other losses such as the exponential loss, when a linear classifier is used together with the Fisher kernel. Therefore, this letter underlines that the Fisher kernel should be viewed not as a heuristics but as a powerful statistical tool with well-controlled statistical properties.


title: Acupuncture modulates resting state connectivity in default and sensorimotor brain networks

abstract: Previous studies have defined low-frequency, spatially consistent networks in resting fMRI data which may reflect functional connectivity. We sought to explore how a complex somatosensory stimulation, acupuncture, influences intrinsic connectivity in two of these networks: the default mode network (DMN) and sensorimotor network (SMN). We analyzed resting fMRI data taken before and after verum and sham acupuncture. Electrocardiography data were used to infer autonomic modulation through measures of heart rate variability (HRV). Probabilistic independent component analysis was used to separate resting fMRI data into DMN and SMN components. Following verum, but not sham, acupuncture there was increased DMN connectivity with pain (anterior cingulate cortex (ACC), periaqueductal gray), affective (amygdala, ACC), and memory (hippocampal formation, middle temporal gyrus) related brain regions. Furthermore, increased DMN connectivity with the hippocampal formation, a region known to support memory and interconnected with autonomic brain regions, was negatively correlated with acupuncture-induced increase in a sympathetic related HRV metric (LFu), and positively correlated with a parasympathetic related metric (HFu). Following verum, but not sham, acupuncture there was also increased SMN connectivity with pain-related brain regions (ACC, cerebellum). We attribute differences between verum and sham acupuncture to more varied and stronger sensations evoked by verum acupuncture. Our results demonstrate for the first time that acupuncture can enhance the post-stimulation spatial extent of resting brain networks to include anti-nociceptive, memory, and affective brain regions. This modulation and sympathovagal response may relate to acupuncture analgesia and other potential therapeutic effects.


title: Bullying prevention in schools by targeting cognitions, emotions, and behavior: evaluating the effectiveness of the REBE-ViSC Program

abstract: The effectiveness of a class-based antibullying prevention program on cognitions, emotions, and behaviors was investigated. The program consists of a cognitive-behavioral (Rational Emotive Behavioral Education; REBE) and a behavioral (Viennese Social Competence; ViSC) component. The REBE program is based on rational emotive behavioral theory and contains 9 student lessons. The ViSC program is based on social learning theory and comprises 10 student lessons. The order of the programs was experimentally manipulated. The REBE-ViSC program was implemented in 5 schools (14 classes), the ViSC-REBE program was implemented in 3 schools (9 classes), and 3 schools (11 classes) served as an untreated control group. Data were collected during 1 school year at pretest, midpoint, and posttest. Emotions (overt and internalizing anger), cognitions (learning and entitlement), and behaviors (bullying perpetration and bullying victimization) were measured with self-assessments. To examine the effectiveness of the REBE-ViSC/ViSC-REBE program, multilevel growth models were applied (time points at Level 1, individuals at Level 2, and classes at Level 3). The analyses revealed that the program effects differed depending on the order of the programs. The REBE-ViSC condition was more effective in changing negative emotions than the ViSC-REBE condition; both experimental conditions were effective in reducing dysfunctional cognitions, whereas no behavioral change was found in the 2 experimental groups when compared with the control group. To improve program effectiveness regarding behavioral changes, a multilevel whole-school approach including a teacher component is recommended. (PsycINFO Database Record Language: en


title: "Herd Immunity": A Rough Guide

abstract: The term "herd immunity" is widely used but carries a variety of meanings. Some authors use it to describe the proportion immune among individuals in a population. Others use it with reference to a particular threshold proportion of immune individuals that should lead to a decline in incidence of infection. Still others use it to refer to a pattern of immunity that should protect a population from invasion of a new infection. A common implication of the term is that the risk of infection among susceptible individuals in a population is reduced by the presence and proximity of immune individuals (this is sometimes referred to as "indirect protection" or a "herd effect"). We provide brief historical, epidemiologic, theoretical, and pragmatic public health perspectives on this concept.


title: Automated Metadata Construction to Support Portable Building Applications

abstract: Commercial buildings consume nearly 19\% of delivered energy in the U.S, nearly half (42%) of which is consumed in buildings with digital control systems comprised of wired sensor networks. These sensors have scant metadata, and are represented by ``tags'' which are obscure, building-specific and not machine parseable. We develop a human-in-the-loop synthesis technique which uses syntactic and data-driven steps to parse these sensor tags into a common namespace, which can enable portable building applications. We show that our technique allows an expert to fully parse a large fraction (~70%) of the tags with 24, 15 and 43 examples for three large commercial buildings comprising 1586, 2522 and 1865 sensors respectively, and deploy three portable applications on two buildings with less than 30 examples.


SemEval-2016 Task 6: Detecting Stance in Tweets


title: GamiCAD: a gamified tutorial system for first time autocad users

abstract: We present GamiCAD, a gamified in-product, interactive tutorial system for first time AutoCAD users. We introduce a software event driven finite state machine to model a user's progress through a tutorial, which allows the system to provide real-time feedback and recognize success and failures. GamiCAD provides extensive real-time visual and audio feedback that has not been explored before in the context of software tutorials. We perform an empirical evaluation of GamiCAD, comparing it to an equivalent in-product tutorial system without the gamified components. In an evaluation, users using the gamified system reported higher subjective engagement levels and performed a set of testing tasks faster with a higher completion ratio.


title: Indexing Query Graphs to Speedup Graph Query Processing.

abstract: Subgraph/supergraph queries although central to graph an- ::: alytics, are costly as they entail the NP-Complete problem ::: of subgraph isomorphism. We present a fresh solution, the ::: novel principle of which is to acquire and utilize knowledge ::: from the results of previously executed queries. Our ap- ::: proach, iGQ, encompasses two component subindexes to ::: identify if a new query is a subgraph/supergraph of pre- ::: viously executed queries and stores related key informa- ::: tion. iGQ comes with novel query processing and index ::: space management algorithms, including graph replacement ::: policies. The end result is a system that leads to signifi- ::: cant reduction in the number of required subgraph isomor- ::: phism tests and speedups in query processing time. iGQ can ::: be incorporated into any sub/supergraph query processing ::: method and help improve performance. In fact, it is the only ::: contribution that can speedup significantly both subgraph ::: and supergraph query processing. We establish the princi- ::: ples of iGQ and formally prove its correctness. We have im- ::: plemented iGQ and have incorporated it within three popu- ::: lar recent state of the art index-based graph query process- ::: ing solutions. We evaluated its performance using real-world ::: and synthetic graph datasets with different characteristics, ::: and a number of query workloads, showcasing its benefits.


title: Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

abstract: This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.


title: The DLR Robot Motion Simulator Part II: Optimization based path-planning

abstract: In Part I of this paper, a novel motion simulator platform is presented, the DLR Robot Motion Simulator with 7 degrees of freedom (DOF). In this Part II, a path-planning algorithm for mentioned platform will be discussed. By replacing the widely used hexapod kinematics by an antropomorhic, industrial robot arm mounted on a standard linear axis, a comparably larger workspace at lower hardware costs can be achieved. But the serial, redundant kinematics of the industrial robot system also introduces challenges for the path-planning as singularities in the workspace, varying movability of the system and the handling of robot system's kinematical redundancy. By solving an optimization problem with constraints in every sampling step, a feasible trajectory can be generated, fulfilling the task of motion cueing, while respecting the robot's dynamic constraints.


title: The Variational Gaussian Process

abstract: Abstract: Representations offered by deep generative models are fundamentally tied to their inference method from data. Variational inference methods require a rich family of approximating distributions. We construct the variational Gaussian process (VGP), a Bayesian nonparametric model which adapts its shape to match complex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity. We prove a universal approximation theorem for the VGP, demonstrating its representative power for learning any model. For inference we present a variational objective inspired by autoencoders and perform black box inference over a wide class of models. The VGP achieves new state-of-the-art results for unsupervised learning, inferring models such as the deep latent Gaussian model and the recently proposed DRAW.


title: Experience with EMERALD to Date

abstract: After summarizing the EMERALD architecture and the evolutionary process from which EMERALD has evolved, this paper focuses on our experience to date in designing, implementing, and applying EMERALD to various types of anomalies and misuse. The discussion addresses the fundamental importance of good software engineering practice and the importance of the system architecture - in attaining detectability, interoperability, general applicability, and future evolvability. It also considers the importance of correlation among distributed and hierarchical instances of EMERALD, and needs for additional detection and analysis components.


title: Pathological Video Game Use Among Youths: A Two-Year Longitudinal Study

abstract: OBJECTIVES: We aimed to measure the prevalence and length of the problem of pathological video gaming or Internet use, to identify risk and protective factors, to determine whether pathological gaming is a primary or secondary problem, and to identify outcomes for individuals who become or stop being pathological gamers. METHODS: A 2-year, longitudinal, panel study was performed with a general elementary and secondary school population in Singapore, including 3034 children in grades 3 ( N = 743), 4 ( N = 711), 7 ( N = 916), and 8 ( N = 664). Several hypothesized risk and protective factors for developing or overcoming pathological gaming were measured, including weekly amount of game play, impulsivity, social competence, depression, social phobia, anxiety, and school performance. RESULTS: The prevalence of pathological gaming was similar to that in other countries (∼9%). Greater amounts of gaming, lower social competence, and greater impulsivity seemed to act as risk factors for becoming pathological gamers, whereas depression, anxiety, social phobias, and lower school performance seemed to act as outcomes of pathological gaming. CONCLUSION: This study adds important information to the discussion about whether video game “addiction” is similar to other addictive behaviors, demonstrating that it can last for years and is not solely a symptom of comorbid disorders.