title: MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning

abstract: Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes hard to learn the minority class samples. Synthetic oversampling methods address this problem by generating the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. This paper identifies that most of the existing oversampling methods may generate the wrong synthetic minority samples in some scenarios and make learning tasks harder. To this end, a new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems. MWMOTE first identifies the hard-to-learn informative minority class samples and assigns them weights according to their euclidean distance from the nearest majority class samples. It then generates the synthetic samples from the weighted informative minority class samples using a clustering approach. This is done in such a way that all the generated samples lie inside some minority class cluster. MWMOTE has been evaluated extensively on four artificial and 20 real-world data sets. The simulation results show that our method is better than or comparable with some other existing methods in terms of various assessment metrics, such as geometric mean (G-mean) and area under the receiver operating curve (ROC), usually known as area under curve (AUC).


title: A hybrid reverse engineering approach combining metrics and program visualisation

abstract: Surprising as it may seem, many of the early adopters of the object-oriented paradigm already face a number of problems typically encountered in large-scale legacy systems. Consequently, reverse engineering techniques are relevant in an object-oriented context as well. This paper investigates a hybrid approach, combining the immediate appeal of visualisations with the scalability of metrics. We validate such a hybrid approach by showing how CodeCrawler - the experimental platform we built-allowed us to understand the program structure of and identify potential design anomalies in a public-domain software system.


title: A Singular Value Thresholding Algorithm for Matrix Completion

abstract: This paper introduces a novel algorithm to approximate the matrix with minimum nuclear norm among all matrices obeying a set of convex constraints. This problem may be understood as the convex relaxation of a rank minimization problem, and arises in many important applications as in the task of recovering a large matrix from a small subset of its entries (the famous Netflix problem). Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries. This paper develops a simple first-order and easy-to-implement algorithm that is extremely efficient at addressing problems in which the optimal solution has low rank. The algorithm is iterative and produces a sequence of matrices (X^k, Y^k) and at each step, mainly performs a soft-thresholding operation on the singular values of the matrix Y^k. There are two remarkable features making this attractive for low-rank matrix completion problems. The first is that the soft-thresholding operation is applied to a sparse matrix; the second is that the rank of the iterates X^k is empirically nondecreasing. Both these facts allow the algorithm to make use of very minimal storage space and keep the computational cost of each iteration low. We provide numerical examples in which 1,000 by 1,000 matrices are recovered in less than a minute on a modest desktop computer. We also demonstrate that our approach is amenable to very large scale problems by recovering matrices of rank about 10 with nearly a billion unknowns from just about 0.4% of their sampled entries. Our methods are connected with linearized Bregman iterations for l1 minimization, and we develop a framework in which one can understand these algorithms in terms of well-known Lagrange multiplier algorithms.


title: Data mining in software engineering

abstract: The increased availability of data created as part of the software development process allows us to apply novel analysis techniques on the data and use the results to guide the process's optimization. In this paper we describe various data sources and discuss the principles and techniques of data mining as applied on software engineering data. Data that can be mined is generated by most parts of the development process: requirements elicitation, development analysis, testing, debugging, and maintenance. Based on this classification we survey the mining approaches that have been used and categorize them according to the corresponding parts of the development process and the task they assist. Thus the survey provides researchers with a concise overview of data mining techniques applied to software engineering data, and aids practitioners on the selection of appropriate data mining techniques for their work.


Multi-label Learning


title: Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition

abstract: Freehand sketching is a dynamic process where points are sequentially sampled and grouped as strokes for sketch acquisition on electronic devices. To recognize a sketched object, most existing methods discard such important temporal ordering and grouping information from human and simply rasterize sketches into binary images for classification. In this paper, we propose a novel single-branch attentive network architecture RNN-Rasterization-CNN (Sketch-R2CNN for short) to fully leverage the dynamics in sketches for recognition. Sketch-R2CNN takes as input only a vector sketch with grouped sequences of points, and uses an RNN for stroke attention estimation in the vector space and a CNN for 2D feature extraction in the pixel space respectively. To bridge the gap between these two spaces in neural networks, we propose a neural line rasterization module to convert the vector sketch along with the attention estimated by RNN into a bitmap image, which is subsequently consumed by CNN. The neural line rasterization module is designed in a differentiable way to yield a unified pipeline for end-to-end learning. We perform experiments on existing large-scale sketch recognition benchmarks and show that by exploiting the sketch dynamics with the attention mechanism, our method is more robust and achieves better performance than the state-of-the-art methods.


title: Beyond the three-component model of organizational commitment

abstract: This article offers a conceptual critique of the three-component model (TCM) of organizational commitment (Allen & Meyer, 1990) and proposes a reconceptualization based on standard attitude theory. The authors use the attitude–behavior model by Eagly and Chaiken (1993) to demonstrate that the TCM combines fundamentally different attitudinal phenomena. They argue that general organizational commitment can best be understood as an attitude regarding the organization, while normative and continuance commitment are attitudes regarding specific forms of behavior (i.e., staying or leaving). The conceptual analysis shows that the TCM fails to qualify as general model of organizational commitment but instead represents a specific model for predicting turnover. The authors suggest that the use of the TCM be restricted to this purpose and that Eagly and Chaiken’s model be adopted as a generic commitment model template from which a range of models for predicting specific organizational behaviors can be extracted. Finally, they discuss the definition and measurement of the organizational commitment attitude. Covering the affective, cognitive, and behavioral facets of this attitude helps to enhance construct validity and to differentiate the construct from other constructs.


title: Haru: Hardware Design of an Experimental Tabletop Robot Assistant

abstract: This paper discusses the design and development of an experimental tabletop robot called "Haru" based on design thinking methodology. Right from the very beginning of the design process, we have brought an interdisciplinary team that includes animators, performers and sketch artists to help create the first iteration of a distinctive anthropomorphic robot design based on a concept that leverages form factor with functionality. Its unassuming physical affordance is intended to keep human expectation grounded while its actual interactive potential stokes human interest. The meticulous combination of both subtle and pronounced mechanical movements together with its stunning visual displays, highlight its affective affordance. As a result, we have developed the first iteration of our tabletop robot rich in affective potential for use in different research fields involving long-term human-robot interaction.


title: Modeling avalanche breakdown for ESD diodes in integrated circuits

abstract: Usually device compact models do not include breakdown mechanisms which are fundamental for ESD protection devices. This work proposes a novel spice-compatible modeling of breakdown phenomena for ESD diodes. The developed physics based approach includes minority carriers propagation and can be embedded in the simulation of parasitic substrate noise of power devices. The model implemented in VerilogA has been validated with device simulations for a simple structure at different temperatures showing good agreement and robust convergence.


title: Scene labeling with LSTM recurrent neural networks

abstract: This paper addresses the problem of pixel-level segmentation and classification of scene images with an entirely learning-based approach using Long Short Term Memory (LSTM) recurrent neural networks, which are commonly used for sequence classification. We investigate two-dimensional (2D) LSTM networks for natural scene images taking into account the complex spatial dependencies of labels. Prior methods generally have required separate classification and image segmentation stages and/or pre- and post-processing. In our approach, classification, segmentation, and context integration are all carried out by 2D LSTM networks, allowing texture and spatial model parameters to be learned within a single model. The networks efficiently capture local and global contextual information over raw RGB values and adapt well for complex scene images. Our approach, which has a much lower computational complexity than prior methods, achieved state-of-the-art performance over the Stanford Background and the SIFT Flow datasets. In fact, if no pre- or post-processing is applied, LSTM networks outperform other state-of-the-art approaches. Hence, only with a single-core Central Processing Unit (CPU), the running time of our approach is equivalent or better than the compared state-of-the-art approaches which use a Graphics Processing Unit (GPU). Finally, our networks' ability to visualize feature maps from each layer supports the hypothesis that LSTM networks are overall suited for image processing tasks.


title: PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization

abstract: Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.


title: Automatic Chatbot Knowledge Acquisition from Online Forum via Rough Set and Ensemble Learning

abstract: Existing chatbot knowledge bases are mostly hand-constructed, which is time consuming and difficult to adapt to new domains. Automatic chatbot knowledge acquisition method from online forums is presented in this paper. It includes a classification model based on rough set, and the theory of ensemble learning is combined to make a decision. Given a forum, multiple rough set classifiers are constructed and trained first. Then all replies are classified with these classifiers. The final recognition results are drawn by voting to the output of these classifiers. Finally, the related replies are selected as chatbot knowledge. Relevant experiments on a child-care forum prove that the method based on rough set has high recognition efficiency to related replies and the combination of ensemble learning improves the results.


title: Multi-Target Tracking using a 3D-Lidar sensor for autonomous vehicles

abstract: Environmental perception is a prerequisite for autonomous driving and also a challenging task particularly in cluttered dynamic environments such as complex urban situations. In this paper, we present a robust algorithm for Multi-Target Tracking (MTT) using a Velodyne 3D HDL-64 Lidar sensor. The main contribution of this paper is a practical framework for selecting and representing useful information from the sensor raw data. Since the sensor produces a huge amount of data, a perception algorithm cannot be carried out in real-time without simplifying the sensor information. Unlike prior works, we introduce hybrid ground classification and the Region of Interest (ROI) identification method in order to filter out the amount of unwanted raw data for the actual tracking. And the environment is also abstracted based on an occupancy grid map. Moreover, we introduce feature based object geometry for precise estimation of the system state. In contrast to prior approaches, which use object geometry for the classification, we use it in order to compensate the unintended dynamics caused by shape change or occlusion. Our proposed MTT algorithm is able to run in real-time with an average processing time of 20ms. We evaluate it using our experimental vehicle “Leonie” in complex urban scenarios.


Are Porter ’ s Five Competitive Forces still Applicable ? A Critical Examination concerning the Relevance for Today ’ s Business


title: Demo Abstract: Cyber-Physical Fingerprinting for Internet of Things Authentication

abstract: Current security and privacy solution to fail to meet the IoT requirements due to computational restrictions and portable nature of IoT objects. In this demo, a novel authentication framework is proposed that exploits the device-specific information to authenticate each object in the IoT. The framework is shown to effectively track the physical environment effects on objects. The experiment shows a sample IoT environment consisting of multiple Raspberry PI units operating as IoT objects. The proposed framework monitors the changes of the physical environment surrounding objects by examining the changes in the device-specific information of each IoT object. The scenario of an emulation attacker is presented in the demo as a case study. The attacker is capable of replicating all the transmitted messages and security keys of one of the IoT objects. Our proposed framework is able to effectively detect such an attack and improve the authentication accuracy for all IoT objects. -Demo Abstract.


title: Probabilistic Model Checking for Complex Cognitive Tasks -- A case study in human-robot interaction

abstract: This paper proposes to use probabilistic model checking to synthesize optimal robot policies in multi-tasking autonomous systems that are subject to human-robot interaction. Given the convincing empirical evidence that human behavior can be related to reinforcement models, we take as input a well-studied Q-table model of the human behavior for flexible scenarios. We first describe an automated procedure to distill a Markov decision process (MDP) for the human in an arbitrary but fixed scenario. The distinctive issue is that -- in contrast to existing models -- under-specification of the human behavior is included. Probabilistic model checking is used to predict the human's behavior. Finally, the MDP model is extended with a robot model. Optimal robot policies are synthesized by analyzing the resulting two-player stochastic game. Experimental results with a prototypical implementation using PRISM show promising results.


title: Intelligent parking lot application using wireless sensor networks

abstract: Wireless sensor networks offer an attractive choice for low- cost and easy-to-deploy solutions for intelligent traffic guidance systems and parking lot applications. In this paper, we propose the use of a combination of magnetic and ultrasonic sensors for accurate and reliable detection of vehicles in a parking lot. We describe a modified version of the min-max algorithm for detection of vehicles using magnetometers, and also an algorithm for ultrasonic sensors. Through extensive real world experiments conducted in a multi-storied university parking space we compare the pros and cons of using different sensing modalities, and show that ultrasonic sensors along with magnetometers is an excellent choice for accurate vehicle detections. We demonstrate the efficacy of our proposed approach by conducting an elaborate car counting experiment lasting over a day, and show promising results using these two sensing modalities.


title: Volumetric parameterization and trivariate b-spline fitting using harmonic functions

abstract: We present a methodology based on discrete volumetric harmonic functions to parameterize a volumetric model in a way that it can be used to fit a single trivariate B-spline to data so that simulation attributes can also be modeled. The resulting model representation is suitable for isogeometric analysis [Hughes T. J. 2005], Imput data consists of both a closed triangle mesh representing the exterior geometric shape of the object and interior triangle meshes that can represent material attributes or other interior features. The trivariate B-spline geometric and attribute representations are generated from the resulting parameterization, creating trivariate B-spline material property representations over the same parameterization in a way that is related to [Martin and Cohen 2001] but is suitable for application to a much larger family of shapes and attributes. The technique constructs a B-spline representation with guaranteed quality of approximation to the original data. Then we focus attention on a model of simulation interest, a femur, consisting of hard outer cortical bone and inner trabecular bone. The femur is a reasonably complex object to model with a single trivariate B-spline since the shape overhangs make it impossible to model by sweeping planar slices. The representation is used in an elastostatic isogeometric analysis, demonstrating its ability to suitably represent objects for isogeometric analysis.


title: Mental health of hospital consultants: the effects of stress and satisfaction at work

abstract: BACKGROUND ::: Burnout and psychiatric morbidity among gastroenterologists, surgeons, radiologists, and oncologists in the UK have been estimated by means of a questionnaire-based survey. The relationship between consultants' mental health and their job stress and satisfaction, as well as their job and demographic characteristics, were also examined. ::: ::: ::: METHODS ::: Psychiatric morbidity was estimated using the 12-item General Health Questionnaire. The three components of burnout-emotional exhaustion, depersonalization, and low personal accomplishment-were assessed using the Maslach Burnout Inventory. Job stress and satisfaction were measured using study-specific questions. ::: ::: ::: FINDINGS ::: Of 1133 consultants, 882 (78%) returned questionnaires. The estimated prevalence of psychiatric morbidity was 27%, with no significant differences between the four specialist groups. Radiologists reported the highest level of burnout in terms of low personal accomplishment. Job satisfaction significantly protected consultants' mental health against job stress. Three sources of stress were associated with both burnout and psychiatric morbidity; feeling overloaded, and its effect on home life; feeling poorly managed and resourced; and dealing with patients' suffering. Burnout was also associated with low satisfaction in three domains: relationships with patients, relatives and staff; professional status/esteem; intellectual stimulation. In addition, being aged 55 years or less and being single were independent risk factors for burnout. Burnout was also more prevalent among consultants who felt insufficiently trained in communication and management skills. ::: ::: ::: INTERPRETATION ::: Consultants' mental health is likely to be protected against the high demands of medical practice by maintaining or enhancing job satisfaction, and by providing training in communication and management skills.


title: Corporate Intranet Implementation: Managing Emergent Technologies and Organizational Practices

abstract: This paper examines the adoption and development of intranets in large business organizations. The authors demonstrate that intranet technology introduces a host of new managerial and technical challenges and requires new approaches to IS development. Evidence from two European corporations indicates that the traditional division of labor and definition of work roles in IS development breaks down. The distinction between developers and users becomes increasingly blurred and new organizational roles and structures associated with intranets are emerging. However, ready-made organizational models for implementing and managing intranets do not exist and the two organizations in this study have followed two different approaches. One organization favors a “planned change” approach, emphasizing management control and careful planning. The other organization prefers an “improvisational” approach, emphasizing experimentation, innovation and local initiative.


title: A methodology for power characterization of associative memories

abstract: Content Addressable Memories (CAM) have become increasingly more important in applications requiring high speed memory search due to their inherent massively parallel processing architecture. We present a complete power analysis methodology for CAM systems to aid the exploration of their power-performance trade-offs in future systems. Our proposed methodology uses detailed transistor level circuit simulation of power behavior and a handful of input data types to simulate full chip power consumption. Furthermore, we applied our power analysis methodology on a custom designed associative memory test chip. This chip was developed by Fermilab for the purpose of developing high performance real-time pattern recognition on high volume data produced by a future large-scale scientific experiment. We applied our methodology to configure a power model for this test chip. Our model is capable of predicting the total average power within 4% of actual power measurements. Our power analysis methodology can be generalized and applied to other CAM-like memory systems and accurately characterize their power behavior.


title: Heart rate variability biofeedback as a method for assessing baroreflex function: A preliminary study of resonance in the cardiovascular system

abstract: This study describes the use of a biofeedback method for the noninvasive study of baroreflex mechanisms. Five previously untrained healthy male participants learned to control oscillations in heart rate using biofeedback training to modify their heart rate variability at specific frequencies. They were instructed to match computer-generated sinusoidal oscillations with oscillations in heart rate at seven frequencies within the range of 0.01–0.14 Hz. All participants successfully produced high-amplitude target-frequency oscillations in both heart rate and blood pressure. Stable and predictable transfer functions between heart rate and blood pressure were obtained in all participants. The highest oscillation amplitudes were produced in the range of 0.055–0.11 Hz for heart rate and 0.02–0.055 Hz for blood pressure. Transfer functions were calculated among sinusoidal oscillations in the target stimuli, heart rate, blood pressure, and respiration for frequencies at which subjects received training. High and low target-frequency oscillation amplitudes at specific frequencies could be explained by resonance among various oscillatory processes in the cardiovascular system. The exact resonant frequencies differed among individuals. Changes in heart rate oscillations could not be completely explained by changes in breathing. The biofeedback method also allowed us to quantity characteristics of inertia, delay, and speed sensitivity in baroreflex system. We discuss the implications of these findings for using heart rate variability biofeedback as an aid in diagnosing various autonomic and cardiovascular system disorders and as a method for treating these disorders.


title: Distributed Event Detection in Wireless Sensor Networks for Disaster Management

abstract: Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and become one of the enabling technologies for disaster early-warning systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity.


On the Separation of Concerns in Program Families


title: Online Document Management System for Academic Institutes

abstract: Nowadays, electronic systems enable the rapid creation and distribution of documents. Therefore, people replace paper documents with electronic documents, in academic area electronic documents as information sources have increased in number and retrieving the information among huge number of documents become a problem. A recent study shows positive feedback from students towards establishing a document management system in local universities new innovative tools are necessary to access relevant information, in faculty of university student are very interested to have a system to manage, retrieve and sharing documents. Document management system in faculty can improve efficiency and effectiveness of research and also can improve knowledge sharing among students and get new idea from other researches. students need a well-organized knowledge repository in order to reuse information throughout the research, is creating new opportunities for collaboration, coordination, and information exchange among students that work on a construction project. and consequently reduce costs and response times. The entire service is delivered through INTERNET browse so in any location can access documents.


title: From captions to visual concepts and back

abstract: This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image captions. We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The word detector outputs serve as conditional inputs to a maximum-entropy language model. The language model learns from a set of over 400,000 image descriptions to capture the statistics of word usage. We capture global semantics by re-ranking caption candidates using sentence-level features and a deep multimodal similarity model. Our system is state-of-the-art on the official Microsoft COCO benchmark, producing a BLEU-4 score of 29.1%. When human judges compare the system captions to ones written by other people on our held-out test set, the system captions have equal or better quality 34% of the time.


title: Accelerating 3D Digital Differential Analyzer Ray Tracing Algorithm on the GPU Using CUDA

abstract: We present a novel optimized 3DDDA ray tracing algorithm on GPUs with CUDA architecture. The grid data is built on the host and then copied to the device. The whole traversing procedure is done on the GPU device. So it just needs the data transformation between the host and the device once. The results show that the CUDA implementation parallel code using different block size on GPU achieves 2.9x to 5.9x faster speed compared with the CPU implementation. And the performance is significantly influenced by the size of the CUDA block. 3DDDA algorithm on CUDA architecture can really get the performance accelerated with a reasonable block size, but it is difficult to play high-end GPU performance. The main reason is that 3DDDA algorithm for memory access is discontinuous, so it is difficult to play high-frequency performance of RAM DDR5.


title: LETTERS Current saturation in zero-bandgap, topgated graphene field-effect transistors

abstract: The novel electronic properties of graphene, including a linear energy dispersion relation and purely two-dimensional structure, have led to intense research into possible applications of this material in nanoscale devices. Here we report the first observation of saturating transistor characteristics in a graphene field-effect transistor. The saturation velocity depends on the charge-carrier concentration and we attribute this to scattering by interfacial phonons in the SiO2 layer supporting the graphene channels. Unusual features in the current-voltage characteristic are explained by a field-effect model and diffusive carrier transport in the presence of a singular point in the density of states. The electrostatic modulation of the channel through an efficiently coupled top gate yields transconductances as high as 150 microS microm-1 despite low on-off current ratios. These results demonstrate the feasibility of two-dimensional graphene devices for analogue and radio-frequency circuit applications without the need for bandgap engineering.


title: Participatory Air Pollution Monitoring Using Smartphones

abstract: Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.


title: Joint Physical-Layer and System-Level Power Management for Delay-Sensitive Wireless Communications

abstract: We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g., multimedia data) over a fading channel. Existing research on this topic utilizes either physical-layer centric solutions, namely power-control and adaptive modulation and coding (AMC), or system-level solutions based on dynamic power management (DPM); however, there is currently no rigorous and unified framework for simultaneously utilizing both physical-layer centric and system-level techniques to achieve the minimum possible energy consumption, under delay constraints, in the presence of stochastic and a priori unknown traffic and channel conditions. In this paper, we propose such a framework. We formulate the stochastic optimization problem as a Markov decision process (MDP) and solve it online using reinforcement learning (RL). The advantages of the proposed online method are that 1) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal power-control, AMC, and DPM policies; 2) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and 3) it obviates the need for action exploration, which severely limits the adaptation speed and runtime performance of conventional reinforcement learning algorithms. Our results show that the proposed learning algorithms can converge up to two orders of magnitude faster than a state-of-the-art learning algorithm for physical layer power-control and up to three orders of magnitude faster than conventional reinforcement learning algorithms.


title: Comparison of anatomic and non‐anatomic hepatic resection for hepatocellular carcinoma

abstract: Background ::: The aim of the present study was to compare the prognostic impact of anatomic resection (AR) versus non-anatomic resection (NAR) on patient survival after resection of a single hepatocellular carcinoma (HCC). ::: ::: Methods ::: To control for confounding variable distributions, a 1-to-1 propensity score match was applied to compare the outcomes of AR and NAR. Among 710 patients with a primary, solitary HCC of <5.0 cm in diameter that was resectable by either AR or NAR from 2003 to 2007 in Japan and Korea, 355 patients underwent NAR and 355 underwent AR of at least one section with complete removal of the portal territory containing the tumor. ::: ::: Results ::: Overall survival (OS) was better in the AR than NAR group (hazard ratio 1.67, 95% confidence interval 1.28–2.19, P < 0.001) while disease-free survival showed no significant difference. Significantly fewer patients in the AR than NAR group developed intrahepatic HCC recurrence and multiple intrahepatic recurrences. Patients with poorly differentiated HCC who underwent AR had improved disease-free survival and OS. ::: ::: Conclusions ::: Anatomic resection decreases the risk of tumor recurrence and improves OS in patients with a primary, solitary HCC of <5.0 cm in diameter.


title: Faster algorithms for string matching with k mismatches

abstract: The string matching with mismatches problem is that of finding the number of mismatches between a pattern P of length m and every length m substring of the text T. Currently, the fastest algorithms for this problem are the following. The Galil-Giancarlo algorithm finds all locations where the pattern has at most k errors (where k is part of the input) in time O(nk). The Abrahamson algorithm finds the number of mismatches at every location in time O(n√ m log m). We present an algorithm that is faster than both. Our algorithm finds all locations where the pattern has at most k errors in time O(n√k log k). We also show an algorithm that solves the above problem in time O((n + (nk3)/m) log k).


title: Automatic Topic Labeling Using Ontology-Based Topic Models

abstract: Topic models, which frequently represent topics as multinomial distributions over words, have been extensively used for discovering latent topics in text corpora. Topic labeling, which aims to assign meaningful labels for discovered topics, has recently gained significant attention. In this paper, we argue that the quality of topic labeling can be improved by considering ontology concepts rather than words alone, in contrast to previous works in this area, which usually represent topics via groups of words selected from topics. We have created: (1) a topic model that integrates ontological concepts with topic models in a single framework, where each topic and each concept are represented as a multinomial distribution over concepts and over words, respectively, and (2) a topic labeling method based on the ontological meaning of the concepts included in the discovered topics. In selecting the best topic labels, we rely on the semantic relatedness of the concepts and their ontological classifications. The results of our experiments conducted on two different data sets show that introducing concepts as additional, richer features between topics and words and describing topics in terms of concepts offers an effective method for generating meaningful labels for the discovered topics.


title: Towards a Unified Natural Language Inference Framework to Evaluate Sentence Representations

abstract: We present a large scale unified natural language inference (NLI) dataset for providing insight into how well sentence representations capture distinct types of reasoning. We generate a large-scale NLI dataset by recasting 11 existing datasets from 7 different semantic tasks. We use our dataset of approximately half a million context-hypothesis pairs to test how well sentence encoders capture distinct semantic phenomena that are necessary for general language understanding. Some phenomena that we consider are event factuality, named entity recognition, figurative language, gendered anaphora resolution, and sentiment analysis, extending prior work that included semantic roles and frame semantic parsing. Our dataset will be available at this https URL, to grow over time as additional resources are recast.


title: A flexible, subjective logic-based framework for misbehavior detection in V2V networks

abstract: Vehicle to Vehicle (V2V) and Vehicle to Infrastruc- ture (V2I) communication aims to increase safety, efficiency, and comfort of drivers. Vehicles periodically broadcast their current status, such as position, velocity, and other information. Received information is stored in a local knowledge base, often called world model, and used for application decisions. Because of the potential impact, V2V communication is an interesting target for malicious attackers. Message integrity protection using cryptographic signatures only protects against outsider attackers. In addition to signatures, misbehavior detection mechanisms comparable to intrusion detection systems (IDS) are needed to detect insider attackers. Given the complexity and large number of foreseen V2V and V2I applications, misbehavior detection cannot be a one-size-fits-all solution. In this paper, we present a flexible framework that can combine a range of different misbehavior detection mechanisms by modeling their outputs using subjective logic. We demonstrate the feasibility of our framework by using a combination of existing detection mechanisms to increase their misbehavior detection results.


title: Modulation of thermal pain-related brain activity with virtual reality: evidence from fMRI

abstract: This study investigated the neural correlates of virtual reality analgesia. Virtual reality significantly reduced subjective pain ratings (i.e. analgesia). Using fMRI, pain-related brain activity was measured for each participant during conditions of no virtual reality and during virtual reality (order randomized). As predicted, virtual reality significantly reduced pain-related brain activity in all five regions of interest; the anterior cingulate cortex, primary and secondary somatosensory cortex, insula, and thalamus (p<0.002, corrected). Results showed direct modulation of human brain pain responses by virtual reality distraction.


title: Emergence of Context-Dependent Variability across a Basal Ganglia Network

abstract: Context dependence is a key feature of cortical-basal ganglia circuit activity, and in songbirds the cortical outflow of a basal ganglia circuit specialized for song, LMAN, shows striking increases in trial-by-trial variability and bursting when birds sing alone rather than to females. To reveal where this variability and its social regulation emerge, we recorded stepwise from corticostriatal (HVC) neurons and their target spiny and pallidal neurons in Area X. We find that corticostriatal and spiny neurons both show precise singing-related firing across both social settings. Pallidal neurons, in contrast, exhibit markedly increased trial-by-trial variation when birds sing alone, created by highly variable pauses in firing. This variability persists even when recurrent inputs from LMAN are ablated. These data indicate that variability and its context sensitivity emerge within the basal ganglia network, suggest a network mechanism for this emergence, and highlight variability generation and regulation as basal ganglia functions.


title: A theory of modularity for automated software development (keynote)

abstract: Automated Software Development (ASD) are technologies for developing customized programs automatically and compositionally from modules. The foundations of ASD are domain-specific algebras, where each program in the target domain maps to a unique expression. Algebraic identities are used to optimize programs automatically. In this keynote, I trace the history of ASD and present a general theory of modularity for ASD that follows from its tenets.


title: Nivolumab in Previously Untreated Melanoma without BRAF Mutation

abstract: BACKGROUND ::: Nivolumab was associated with higher rates of objective response than chemotherapy in a phase 3 study involving patients with ipilimumab-refractory metastatic melanoma. The use of nivolumab in previously untreated patients with advanced melanoma has not been tested in a phase 3 controlled study. ::: ::: ::: METHODS ::: We randomly assigned 418 previously untreated patients who had metastatic melanoma without a BRAF mutation to receive nivolumab (at a dose of 3 mg per kilogram of body weight every 2 weeks and dacarbazine-matched placebo every 3 weeks) or dacarbazine (at a dose of 1000 mg per square meter of body-surface area every 3 weeks and nivolumab-matched placebo every 2 weeks). The primary end point was overall survival. ::: ::: ::: RESULTS ::: At 1 year, the overall rate of survival was 72.9% (95% confidence interval [CI], 65.5 to 78.9) in the nivolumab group, as compared with 42.1% (95% CI, 33.0 to 50.9) in the dacarbazine group (hazard ratio for death, 0.42; 99.79% CI, 0.25 to 0.73; P<0.001). The median progression-free survival was 5.1 months in the nivolumab group versus 2.2 months in the dacarbazine group (hazard ratio for death or progression of disease, 0.43; 95% CI, 0.34 to 0.56; P<0.001). The objective response rate was 40.0% (95% CI, 33.3 to 47.0) in the nivolumab group versus 13.9% (95% CI, 9.5 to 19.4) in the dacarbazine group (odds ratio, 4.06; P<0.001). The survival benefit with nivolumab versus dacarbazine was observed across prespecified subgroups, including subgroups defined by status regarding the programmed death ligand 1 (PD-L1). Common adverse events associated with nivolumab included fatigue, pruritus, and nausea. Drug-related adverse events of grade 3 or 4 occurred in 11.7% of the patients treated with nivolumab and 17.6% of those treated with dacarbazine. ::: ::: ::: CONCLUSIONS ::: Nivolumab was associated with significant improvements in overall survival and progression-free survival, as compared with dacarbazine, among previously untreated patients who had metastatic melanoma without a BRAF mutation. (Funded by Bristol-Myers Squibb; CheckMate 066 ClinicalTrials.gov number, NCT01721772.).


title: Creativity and the brain: Uncovering the neural signature of conceptual expansion

abstract: Neurophysiological studies of creativity thus far have not allowed for clear conclusions to be made regarding the specific neural underpinnings of such complex cognition due to overgeneralizations concerning the creativity construct, heterogeneity in the type of creativity tasks used, and the questionable efficacy of the employed comparison tasks. A novel experimental design was developed in the present fMRI study which rendered it possible to investigate a critical facet of creative cognition – that of conceptual expansion – as distinct from general divergent thinking, working memory, or cognitive load. Brain regions involved in the retention, retrieval and integration of conceptual knowledge such as the anterior inferior frontal gyrus, the temporal poles and the lateral frontopolar cortex were found to be selectively involved during conceptual expansion. The findings go against generic ideas that argue for the dominance of the right hemisphere during creative thinking and indicate the necessity to reconsider the functions of regions such as the anterior cingulate cortex to include more abstract facets of cognitive control. This study represents a new direction in the investigation of creativity in that it highlights the necessity to adopt a process based perspective in which the multifaceted nature of creativity can be truly grasped.


title: Autonomous Robot Dancing Driven by Beats and Emotions of Music ABSTRACT

abstract: Many robot dances are preprogrammed by choreographers for a particular piece of music so that the motions can be smoothly executed and synchronized to the music. We are interested in automating the task of robot dance choreography to allow robots to dance without detailed human planning. Robot dance movements are synchronized to the beats and reflect the emotion of any music. Our work is made up of two parts: (1) The first algorithm plans a sequence of dance movements that is driven by the beats and the emotions detected through the preprocessing of selected dance music. (2) We also contribute a real-time synchronizing algorithm to minimize the error between the execution of the motions and the plan. Our work builds on previous research to extract beats and emotions from music audio. We created a library of parameterized motion primitives, whereby each motion primitive is composed of a set of keyframes and durations and generate the sequence of dance movements from this library. We demonstrate the feasibility of our algorithms on the NAO humanoid robot to show that the robot is capable of using the mappings defined to autonomously dance to any music. Although we present our work using a humanoid robot, our algorithm is applicable to other robots.


title: Review of Maximum Power Point Tracking approaches suitable for PV systems under Partial Shading Conditions

abstract: Maximum Power Point Tracking (MPPT) is an important consideration in Photovoltaic (PV) Systems, particularly as these systems increase in size and are exposed to non-uniform environmental conditions. Conventional MPPT techniques are designed to track the Maximum Power Point under uniform conditions where there is a single maximum power. Under non-uniform environmental conditions, with bypass diodes integrated into the circuit, the Power-Voltage characteristics become more complex resulting in multiple local maxima. Under such conditions, conventional MPPT techniques cannot track the global maxima and may result in considerable energy losses. This review paper explores the approaches designed to mitigate the complexities arising from non-uniform environmental conditions in PV system operation.


title: Structured features in naive Bayes classification

abstract: We propose the structured naive Bayes (SNB) classifier, which augments the ubiquitous naive Bayes classifier with structured features. SNB classifiers facilitate the use of complex features, such as combinatorial objects (e.g., graphs, paths and orders) in a general but systematic way. Underlying the SNB classifier is the recently proposed Probabilistic Sentential Decision Diagram (PSDD), which is a tractable representation of probability distributions over structured spaces. We illustrate the utility and generality of the SNB classifier via case studies. First, we show how we can distinguish players of simple games in terms of play style and skill level based purely on observing the games they play. Second, we show how we can detect anomalous paths taken on graphs based purely on observing the paths themselves.


title: Antenna placement and wave propagation for Car-to-Car communication

abstract: The investigation of various antenna placements for Car-to-Car (C2C) communication has turned out to be crucial to achieve an omnidirectional coverage in the horizontal plane of the antenna. The complex shape and huge electrical size of a car at 5.9 GHz yield a high challenge for the numerical modeling. In this contribution, a new way of handling the analysis of near and far-field propagation for C2C antennas is proposed and applied in examples, which enables to investigate the whole car chassis by asymptotic methods, with a significantly reduced computational effort compared with a full wave analysis. The effect on radiation pattern of an antenna placed on the roof, side mirrors and bumpers is discussed. Dispensable parts of the car have been identified to speed up the simulation without losing accuracy. The influence of a glass roof on the radiation pattern is characterized using full wave methods in order to account for wave guidance, diffractions and creeping waves. Measurements are in good agreement with simulations.


title: Analysis and Design of an Input-Series Two-Transistor Forward Converter For High-Input Voltage Multiple-Output Applications

abstract: In this paper, an input-series two-transistor forward converter is proposed and investigated, which is aimed at high-input voltage multiple-output applications. In this converter, all of the switches are operating synchronously, and the input voltage sharing (IVS) of each series-module is achieved automatically by the coupling of primary windings of the common forward integrated transformer. The active IVS processes are analyzed based on the model of the forward integrated transformer. Through the influence analysis when the mismatches in various series-modules are considered, design principles of the key parameters in each series-module are discussed to suppress the input voltage difference. Finally, a 96-W laboratory-made prototype composed of two forward series-modules is built, and the feasibility of the proposed method and the theoretical analysis are verified by the experimental results.


title: Specializing Word Embeddings for Similarity or Relatedness

abstract: We demonstrate the advantage of specializing semantic word embeddings for either similarity or relatedness. We compare two variants of retrofitting and a joint-learning approach, and find that all three yield specialized semantic spaces that capture human intuitions regarding similarity and relatedness better than unspecialized spaces. We also show that using specialized spaces in NLP tasks and applications leads to clear improvements, for document classification and synonym selection, which rely on either similarity or relatedness but not both.


title: Increasing diversity in k-12 computer science: strategies from the field

abstract: In this paper, I describe features of computer science programs which have successfully attracted underrepresented students to study CS. Drawing from my own teaching experiences, research, and teacher education work; I provide strategies which have diversified K-12 CS courses. The paper also points out how many of these features rely on committed partnerships between schools, school districts, universities, and the CS industry.


title: Online payment system using steganography and visual cryptography

abstract: A rapid growth in E-Commerce market is seen in recent time throughout the world. With ever increasing popularity of online shopping, Debit or Credit card fraud and personal information security are major concerns for customers, merchants and banks specifically in the case of CNP (Card Not Present). This paper presents a new approach for providing limited information only that is necessary for fund transfer during online shopping thereby safeguarding customer data and increasing customer confidence and preventing identity theft. The method uses combined application of steganography and visual cryptography for this purpose.


title: Optimal Compression of Propositional Horn Knowledge Bases: Complexity and Approximation

abstract: The color of freeze dried coffee can be darkened by carefully controlling the pressure in the drying chamber during the first 1 to 5 hours of the drying cycle. By maintaining the pressure at between 350 to 650 microns of mercury for the specified time, a surface darkening is achieved without allowing the product to actually melt or puff. Thus, a high quality freeze dried coffee is obtained with a more desirable appearance.


title: The REM Sleep-Memory Consolidation Hypothesis

abstract: It has been hypothesized that REM (rapid eye movement) sleep has an important role in memory consolidation. The evidence for this hypothesis is reviewed and found to be weak and contradictory. Animal studies correlating changes in REM sleep parameters with learning have produced inconsistent results and are confounded by stress effects. Humans with pharmacological and brain lesion-induced suppression of REM sleep do not show memory deficits, and other human sleep-learning studies have not produced consistent results. The time spent in REM sleep is not correlated with learning ability across humans, nor is there a positive relation between REM sleep time or intensity and encephalization across species. Although sleep is clearly important for optimum acquisition and performance of learned tasks, a major role in memory consolidation is unproven.


title: Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance

abstract: The chapter introduces the latest developments and results of Itera- tive Single Data Algorithm (ISDA) for solving large-scale support vector machines (SVMs) problems. First, the equality of a Kernel AdaTron (KA) method (originating from a gradient ascent learning approach) and the Sequential Minimal Optimiza- tion (SMO) learning algorithm (based on an analytic quadratic programming step for a model without bias term b) in designing SVMs with positive definite kernels is shown for both the nonlinear classification and the nonlinear regression tasks. The chapter also introduces the classic Gauss-Seidel procedure and its derivative known as the successive over-relaxation algorithm as viable (and usually faster) training al- gorithms. The convergence theorem for these related iterative algorithms is proven. The second part of the chapter presents the effects and the methods of incorporating explicit bias term b into the ISDA. The algorithms shown here implement the single training data based iteration routine (a.k.a. per-pattern learning). This makes the proposed ISDAs remarkably quick. The final solution in a dual domain is not an approximate one, but it is the optimal set of dual variables which would have been obtained by using any of existing and proven QP problem solvers if they only could deal with huge data sets.


title: Double resonance of broadband microstrip-to-waveguide transition in millimeter-wave band

abstract: Broadband microstrip-to-waveguide transitions are developed in the millimeter-wave band. Two types of novel transitions are developed first. One is a quite broadband transition with an air back-short waveguide in a metal block. In order to avoid the use of an additional part of the metal block, we developed a transition in the multi-layer substrate. Double resonance is a key feature for the broadband operation. It is due to the double stubs not only of the back-short waveguide but also of the parallel-plate waveguide in the substrate. Consequently, bandwidth of the transition is extended by optimization of the stub length in the substrate. Furthermore, equivalent performance is obtained even when via holes are replaced by choke structure. The performances of these transitions are summarized in this paper.


title: Fast Non-Convex Hull Computation

abstract: 3D surface reconstruction usually begins with a point cloud and aims to build a representation of the object producing that point cloud. There are several algorithms to solve this problem, each with different priors over the point cloud, such as the type of object represented, or the method by which it was obtained. In this work, we focus on an algorithm called Non-Convex Hull (NCH), which reconstructs surfaces through a concept similar to the Medial Axis Transform. A new algorithm called Shrinking Planes is proposed to compute the NCH, based on the Shrinking Ball method with a few improvements. We prove that the new method can approximate surfaces to arbitrarily small error, and evaluate its performance on the surface reconstruction task. The new method maintains the same reconstruction quality as the Naive Non-Convex Hull method, while achieving a large performance improvement.


title: Facial attractiveness prediction using psychologically inspired convolutional neural network (PI-CNN)

abstract: This paper proposes a psychologically inspired convolutional neural network (PI-CNN) to achieve automatic facial beauty prediction. Different from the previous methods, the PI-CNN is a hierarchical model that facilitates both the facial beauty representation learning and predictor training. Inspired by the recent psychological studies, significant appearance features of facial detail, lighting and color were used to optimize the PI-CNN facial beauty predictor using a new cascaded fine-tuning method. Experiments indicate that the cascaded fine-tuned PI-CNN predictor is robust to facial appearance variances, and obtains the highest correlation of 0.87 in the SCUT-FBP benchmark database, which is superior to the related hand-designed feature and related deep learning methods.


title: Multi-sensor system for driver's hand-gesture recognition

abstract: We propose a novel multi-sensor system for accurate and power-efficient dynamic car-driver hand-gesture recognition, using a short-range radar, a color camera, and a depth camera, which together make the system robust against variable lighting conditions. We present a procedure to jointly calibrate the radar and depth sensors. We employ convolutional deep neural networks to fuse data from multiple sensors and to classify the gestures. Our algorithm accurately recognizes 10 different gestures acquired indoors and outdoors in a car during the day and at night. It consumes significantly less power than purely vision-based systems.


Qualitative Research & Evaluation Methods: Integrating Theory and Practice


title: Image Steganography: A Review

abstract: Steganography can be defined as the study of invisible communication that usually deals with the ways of hiding the existence of the communicated message. If it is achieved successfully, the message does not attract attention from eavesdroppers and attackers. The main objectives of steganography are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data. These are the main factors which make it different from other techniques watermarking and cryptography. This paper includes the important steganography methods and the main focus is on the review of steganography in digital images.


title: Mechanical Design and Analysis of the Novel 6-DOF Variable Stiffness Robot Arm Based on Antagonistic Driven Joints

abstract: This paper proposes four types of conceptual models of the 6-DOF variable stiffness robot arms based on the antagonistic driven joints (ADJs). For convenience of control, the equivalent quadratic torsion spring (EQTS) is selected as the elastic element of the ADJ. The relationship between the output stiffness and the angular displacement of the EQTS is fairly linear. The elastic actuating torque of the ADJ is related to the initial amount of deformation of the EQTS and the angular deflection of the ADJ. The output stiffness of the ADJ is a linear function of the initial amount of deformation of the EQTS. The convenience control of the torque and stiffness of the ADJ will be beneficial to reduce the complexity of the control strategy, and this feature is beneficial for real-time control. In the mechanical solutions, nine types of conceptual models of the EQTSs are presented, and nine types of conceptual models of the ADJs are demonstrated. The cam parameters and the spring parameters of the EQTSs are given. The cam profiles and the pressure angles of the cam-roller mechanisms are illustrated. The elastic actuating torque and output stiffness of the EQTSs and the ADJs are shown. The structure features and actuation characteristics of the EQTSs and the ADJs are compared and analyzed. Since the actuation requirements of the joints of the robot arm differ significantly, four types of conceptual models of the 6-DOF robot arms are assembled based on the different ADJs.


title: Solving Verbal Crypto-Arithmetic Problem by Parallel Genetic Algorithm (PGA)

abstract:  Abstract— Cryptarithmetic puzzles are quite old and their inventor is not known. An example in The American Agriculturist of 1864 disproves the popular notion that it was invented by Sam Loyd. The name cryptarithmetic was coined by puzzlist Minos (pseudonym of Maurice Vatriquant) in the May 1931 issue of Sphinx, a Belgian magazine of recreational mathematics. In the 1955, J. A. H. Hunter introduced the word "alphabetic" to designate cryptarithms, such as Dudeney's, whose letters from meaningful words or phrases. Solving a cryptarithm by hand usually involves a mix of deductions and exhaustive tests of possibilities. Cryptarithmetic is a puzzle consisting of an arithmetic problem in which the digits have been replaced by letters of the alphabet. The goal is to decipher the letters (i.e. Map them back onto the digits) using the constraints provided by arithmetic and the additional constraint that no two letters can have the same numerical value. Cryptarithmetic is a class of constraint satisfaction problems which includes making mathematical relations between meaningful words using simple arithmetic operators like 'plus' in a way that the result is conceptually true, and assigning digits to the letters of these words and generating numbers in order to make correct arithmetic operations as well


title: A smart traffic solution for High Priority Vehicles

abstract: Traffic lights are designed to ensure smooth traffic in the cities. The current traffic system is running same over the past few years. As number of vehicles on the roads is increasing constantly, this system is failing to serve traffic congestion problems especially on the intersections. Due to traffic congestion High Priority Vehicles (HPV) also get stuck in traffic which results delay in their services. HPV like ambulances, fire brigade etc. have to serve various causalities. It is very important for HPV to reach on time. There is a need of system that aims to provide path to HPV to reach as quickly as possible. The proposed work provides priority based approach. It aims at building a user interactive system for HPV in which an HPV driver can send request to the system to which the system responds smartly. Priority of Road Segments (RS) at an intersection is calculated and traffic light turns green for the RS with highest priority. The algorithm is tested on SUMO (Simulation of Urban Mobility) and it has shown positive results by saving more than 50% time in various traffic intensities (low, moderate, high).


Annual GDN Regional Research Competition Project title: The impact of Foreign Direct Investments on employment: The case of Macedonian manufacturing sector Thematic area III. Labour Markets including Retirement, Social Assistance and Health Economics Principal researcher:


title: Technology as small group face-to-face Collaborative Scaffolding

abstract: There is a wealth of evidence that collaboration between learners can enhance the outcomes for all concerned. This supports the theorization of learning as a socio-cultural practice, framed by Vygotsky and developed by other researchers such as Rogoff, Lave and Wenger. However, there is also evidence that working collaboratively may not be a spontaneous response to working in a group, and that teaching learners how to collaborate, and in particular how to work together to negotiate meaning, is a necessary part of the process of learning collaboratively which can enhance outcomes further. A question for the computer supported collaborative learning community then arises as to whether learning to collaborate can be scaffolded through the use of digital tools, and what such tools might look like. This paper reports on the design of a digital system that aims to support the practice of face-to-face collaboration on open-ended tasks. Findings from trials of the system in classrooms in the UK and Chile show that the model is welcomed both by teachers and pupils, and met its objectives of ensuring greater interaction between class members who did not normally work together, and involvement of all individuals in discussion based activities.


title: Learning 3D Segment Descriptors for Place Recognition

abstract: In the absence of global positioning information, place recognition is a key capability for enabling localization, mapping and navigation in any environment. Most place recognition methods rely on images, point clouds, or a combination of both. In this work we leverage a segment extraction and matching approach to achieve place recognition in Light Detection and Ranging (LiDAR) based 3D point cloud maps. One challenge related to this approach is the recognition of segments despite changes in point of view or occlusion. We propose using a learning based method in order to reach a higher recall accuracy then previously proposed methods. Using Convolutional Neural Networks (CNNs), which are state-of-the-art classifiers, we propose a new approach to segment recognition based on learned descriptors. In this paper we compare the effectiveness of three different structures and training methods for CNNs. We demonstrate through several experiments on real-world data collected in an urban driving scenario that the proposed learning based methods outperform hand-crafted descriptors.


title: Fa: A System for Automating Failure Diagnosis

abstract: Failures of Internet services and enterprise systems lead to user dissatisfaction and considerable loss of revenue. Since manual diagnosis is often laborious and slow, there is considerable interest in tools that can diagnose the cause of failures quickly and automatically from system-monitoring data. This paper identifies two key data-mining problems arising in a platform for automated diagnosis called {\em Fa}. Fa uses monitoring data to construct a database of{\em failure signatures} against which data from undiagnosed failures can be matched. Two novel challenges we address are to make signatures robust to the noisy monitoring data in production systems, and to generate reliable confidence estimates for matches. Fa uses a new technique called {\em anomaly-based clustering} when the signature database has no high-confidence match for an undiagnosed failure. This technique clusters monitoring data based on how it differs from the failure data, and pinpoints attributes linked to the failure. We show the effectiveness of Fa through a comprehensive experimental evaluation based on failures from a production setting, a variety of failures injected in a testbed, and synthetic data.


title: Naive Bayes for Text Classification with Unbalanced Classes

abstract: Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1,2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.


title: ARTICLE Doing the right thing: Measuring wellbeing for public policy

abstract: Many experts now recognize that income is not a measure that alone captures the wellbeing of individuals, and governments around the world are starting to rethink the ways in which they measure the welfare of their citizens. Wellbeing is best understood as a multifaceted phenomenon that can be assessed by measuring a wide array of subjective and objective constructs. This review summarizes the state of research on the various domains of wellbeing measured by psychologists and social scientists, and provides an overview of the main theoretical perspectives that integrate these domains. Among these theoretical perspectives, we highlight Well-being Theory, which decomposes the wellbeing construct into five domains: Positive emotion, Engagement, Relationships, Meaning, and Accomplishment (PERMA). We conclude by formulating recommendations for future research on the measurement of wellbeing. These recommendations include the need to combine both objective and subjective indicators, and the use of a dashboard approach to measurement. This approach conveys the multifaceted nature of wellbeing and will help policy-makers and citizens understand which domains of wellbeing should constitute priorities for public policy.


title: Spur-Free MASH Delta-Sigma Modulation

abstract: For multistage noise-shaping (MASH) delta-sigma modulation, this paper presents a new structure that is free of spurs for all input values. The proposed MASH structure cascades several first-order delta-sigma modulators (DSMs) like the traditional MASH structure but has an additional feedforward connection between two adjacent stages. The proposed MASH structure can increase the sequence length and thus reduce spurs. The reason why the proposed MASH structure has a long sequence length for the full input range is mathematically proved, and simulations are performed to verify the effect of the long sequence length. Simulation results show that the performance of the proposed MASH structure is close to that of the ideal DSM. In addition, the proposed MASH structure requires almost the same hardware cost as the traditional MASH structure.


title: Search Personalization with Embeddings

abstract: Recent research has shown that the performance of search personalization depends on the richness of user profiles which normally represent the user’s topical interests. In this paper, we propose a new embedding approach to learning user profiles, where users are embedded on a topical interest space. We then directly utilize the user profiles for search personalization. Experiments on query logs from a major commercial web search engine demonstrate that our embedding approach improves the performance of the search engine and also achieves better search performance than other strong baselines.


Chapter 3 Trends in Spatial Data Mining


title: Auditory midbrain laminar structure appears adapted to f0 extraction: further evidence and implications of the double critical bandwidth

abstract: The psychoacoustic `critical bandwidth' (CB), e.g. ∼2.6 semitones (=0.22 octave) at 1.5–3 kHz, is known from many spectral integration phenomena. Cat data suggest that it is represented in the inferior colliculus (IC) (Ehret and Merzenich, 1985, Science 227, 1245–1247), where it is consistently related to the fibrodendritic laminae (Schreiner and Langner, 1997, Nature 388, 383–386). The recent discovery of the CB and the double CB (2CB) in the statistics of frequency spacing of spontaneous otoacoustic emissions (Braun, 1997, Hear. Res. 114, 197–203) has initiated further investigations of the novel phenomenon of 2CB. Meta-analysis of psychoacoustic valuation studies of pure-tone intervals again revealed the effects of CB and 2CB. Valuations showed a significant stepwise change with interval size: 2CB indifferent. Scrutiny of cat and human data indicated that for both species, at least in the midspectrum (1–3 kHz in humans), the tonotopic ranges within single IC laminae and the tonotopic distances between neighboring laminae may equal 1 CB (distances to second next laminae being 2CB). This unique architecture would provide the most economical neural convergence of period information from pairs of adjacent harmonic partials 3–6 of complex sound. The resulting summed postsynaptic potentials would thus contain a beat frequency equaling f0 of sound input and being detectable by the known neural behavior of characteristic periodicity response.


title: Behavioural Experiments for Assessing the Abstract Argumentation Semantics of Reinstatement

abstract: Argumentation is a very fertile area of research in Artificial Intelligence, and various semantics have been developed to predict when an argument can be accepted, depending on the abstract structure of its defeaters and defenders. When these semantics make conflicting predictions, theoretical arbitration typically relies on ad hoc examples and normative intuition about what prediction ought to be the correct one. We advocate a complementary, descriptive-experimental method, based on the collection of behavioral data about the way human reasoners handle these critical cases. We report two studies applying this method to the case of reinstatement (both in its simple and floating forms). Results speak for the cognitive plausibility of reinstatement and yet show that it does not yield the full expected recovery of the attacked argument. Furthermore, results show that floating reinstatement yields comparable effects to that of simple reinstatement, thus arguing in favor of preferred argumentation semantics, rather than grounded argumentation semantics. Besides their theoretical value for validating and inspiring argumentation semantics, these results have applied value for developing artificial agents meant to argue with human users.


title: Automatic Music Transcription: Breaking the Glass Ceiling

abstract: Automatic music transcription is considered by many to be the Holy Grail in the field of music signal analysis. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. In order to overcome the limited performance of transcription systems, algorithms have to be tailored to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information across different methods and musical aspects.


title: Virtual reality (VR) technologies in education of industrial design

abstract: Design is an art and art is a design. Today, all industrial products are the result of a design process. Industrial design is a multi-disciplinary field of study, which has a goal to create and produce new objects and it focuses on designing of products by using knowledge related with applied science as well as applied arts and various engineering disciplines. Academic programs related to industrial design focus on achieving the proper balance between practicality and aesthetic pleasure. Courses may include graphic and industrial design basics, manufacturing, modelling and visualization, environmental and human interaction in design. Computer aided design software are strongly emphasized. Students constantly observe, model and test their creations. They investigate the optimal ways to design virtually any type of products, including computer interfaces, appliances, furniture, transportation and recreational items. The developments of new interactive technologies have inevitably affected to education of design and art in recent years. VR is an interdisciplinary emerging high technology. VR interfaces, interaction techniques, and devices have been improved greatly in order to provide more natural and obvious modes of interaction and motivational elements and it is an integrated technology combining; 3D graphics, human-computer interaction, sensor, simulation, display, artificial intelligence and network parallel processing. This study presents notable VR systems have been developed for education and the methods of design, such as modelling and visualization. Keywords: industrial design, interactive technologies, modelling and visualization, environmental and human interaction, virtual reality


title: Vision-based unmanned aerial vehicle navigation using geo-referenced information

abstract: This paper investigates the possibility of augmenting an Unmanned Aerial Vehicle (UAV) navigation system with a passive video camera in order to cope with long-term GPS outages. The paper proposes a vision-based navigation architecture which combines inertial sensors, visual odometry, and registration of the on-board video to a geo-referenced aerial image. The vision-aided navigation system developed is capable of providing high-rate and drift-free state estimation for UAV autonomous navigation without the GPS system. Due to the use of image-to-map registration for absolute position calculation, drift-free position performance depends on the structural characteristics of the terrain. Experimental evaluation of the approach based on offline flight data is provided. In addition the architecture proposed has been implemented on-board an experimental UAV helicopter platform and tested during vision-based autonomous flights.


title: Unifying Low-Level and High-Level Music Similarity Measures

abstract: Measuring music similarity is essential for multimedia retrieval. For music items, this task can be regarded as obtaining a suitable distance measurement between songs defined on a certain feature space. In this paper, we propose three of such distance measures based on the audio content: first, a low-level measure based on tempo-related description; second, a high-level semantic measure based on the inference of different musical dimensions by support vector machines. These dimensions include genre, culture, moods, instruments, rhythm, and tempo annotations. Third, a hybrid measure which combines the above-mentioned distance measures with two existing low-level measures: a Euclidean distance based on principal component analysis of timbral, temporal, and tonal descriptors, and a timbral distance based on single Gaussian Mel-frequency cepstral coefficient (MFCC) modeling. We evaluate our proposed measures against a number of baseline measures. We do this objectively based on a comprehensive set of music collections, and subjectively based on listeners' ratings. Results show that the proposed methods achieve accuracies comparable to the baseline approaches in the case of the tempo and classifier-based measures. The highest accuracies are obtained by the hybrid distance. Furthermore, the proposed classifier-based approach opens up the possibility to explore distance measures that are based on semantic notions.


title: Ultra-wideband, shortpulse ground-penetrating radar: Simulation and measurement

abstract: Ultra-wideband (UWB), short-pulse (SP) radar is investigated theoretically and experimentally for the detection and identification of targets buried in and placed atop soil. The calculations are performed using a rigorous, three-dimensional (3D) method of moments algorithm for perfectly conducting bodies of revolution. Particular targets investigated theoretically include anti-personnel mines, anti-tank mines, and a 55-gallon drum, for which the authors model the time-domain scattered fields and the buried-target late-time resonant frequencies. With regard to the latter, the computed resonant frequencies are utilized to assess the feasibility of resonance-based buried-target identification for this class of targets. The measurements are performed using a novel UWB, SP synthetic aperture radar (SAR) implemented on a mobile boom. Experimental and theoretical results are compared.


title: Unsupervised Learning of Sentence Representations using Convolutional Neural Networks

abstract: We propose a new encoder-decoder approach to learn distributed sentence representations from unlabeled sentences. The word-to-vector representation is used, and convolutional neural networks are employed as sentence encoders, mapping an input sentence into a fixed-length vector. This representation is decoded using long short-term memory recurrent neural networks, considering several tasks, such as reconstructing the input sentence, or predicting the future sentence. We further describe a hierarchical encoder-decoder model to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.


title: Global-Local Face Upsampling Network

abstract: Face hallucination, which is the task of generating a high-resolution face image from a low-resolution input image, is a well-studied problem that is useful in widespread application areas. Face hallucination is particularly challenging when the input face resolution is very low (e.g., 10 x 12 pixels) and/or the image is captured in an uncontrolled setting with large pose and illumination variations. In this paper, we revisit the algorithm introduced in [1] and present a deep interpretation of this framework that achieves state-of-the-art under such challenging scenarios. In our deep network architecture the global and local constraints that define a face can be efficiently modeled and learned end-to-end using training data. Conceptually our network design can be partitioned into two sub-networks: the first one implements the holistic face reconstruction according to global constraints, and the second one enhances face-specific details and enforces local patch statistics. We optimize the deep network using a new loss function for super-resolution that combines reconstruction error with a learned face quality measure in adversarial setting, producing improved visual results. We conduct extensive experiments in both controlled and uncontrolled setups and show that our algorithm improves the state of the art both numerically and visually.


title: Optical bench performance of AcrySof® IQ ReSTOR®, AT LISA® tri, and FineVision® intraocular lenses

abstract: Disclosure ::: ::: DC, XH, and MK are employees of Alcon Research, Ltd., Fort Worth, TX. WH has no proprietary interest in any of the products listed; he has consulted for Alcon Laboratories, Inc. The authors report no other conflicts of interest in this work.


title: A Probabilistic Approach for Web Service Discovery

abstract: Web service discovery is a vital problem in service computing with the increasing number of services. Existing service discovery approaches merely focus on WSDL-based keyword search, semantic matching based on domain knowledge or ontologies, or QoS-based recommendations. The keyword search omits the underlying correlations and semantic knowledge or QoS information is not always available. In this paper, we propose a probabilistic service discovery approach to help web service users to retrieve related services and to improve the search performance. Specifically, we apply a probabilistic model to characterize the latten topics between services and queries, and then propose a matching method based on the topic relevance. Experiments on services from a real service repository confirm the feasibility and efficiency of this proposed method.


title: Motion Control of Ball and Plate System Using Supervisory Fuzzy Controller

abstract: Ball and plate system is a typical under-actuated system with inherent nonlinearity. Motion control of the system included the set-point problem and the tracking problem along desired trajectory. A supervisory fuzzy controller was proposed for studying these problems, which was composed of two layers. The low layer defined a SIRM fuzzy rule and an importance degree for each input item, and the high layer performed supervisory task for ball's velocity and acceleration. When the ball was close to halt, or at high velocity, the supervisory fuzzy controller can overcome disturbance. We implemented this controller to ball and plate system setup in author's laboratory. The experiment results showed little steady-state error and good transient performance than conventional fuzzy controller. Therefore, the control scheme is proposed feasible, and acquired conclusion can be used to investigate similar nonlinear under- actuated dynamics system.


title: Job characteristics and job satisfaction: understanding the role of enterprise resource

abstract: Little research has examined the impacts of enterprise resource planning (ERP) systems implementation on job satisfaction. Based on a 12-month study of 2,794 employees in a telecommunications firm, we found that ERP system implementation moderated the relationships between three job characteristics (skill variety, autonomy, and feedback) and job satisfaction. Our findings highlight the key role that ERP system implementation can have in altering well-established relationships in the context of technology-enabled organizational change situations. This work also extends research on technology diffusion by moving beyond a focus on technology-centric outcomes, such as system use, to understanding broader job outcomes.


title: Depression: Perspectives from Affective Neuroscience

abstract: ▪ Abstract Depression is a disorder of the representation and regulation of mood and emotion. The circuitry underlying the representation and regulation of normal emotion and mood is reviewed, including studies at the animal level, human lesion studies, and human brain imaging studies. This corpus of data is used to construct a model of the ways in which affect can become disordered in depression. Research on the prefrontal cortex, anterior cingulate, hippocampus, and amygdala is reviewed and abnormalities in the structure and function of these different regions in depression is considered. The review concludes with proposals for the specific types of processing abnormalities that result from dysfunctions in different parts of this circuitry and offers suggestions for the major themes upon which future research in this area should be focused.


title: Efficient Optimization of Performance Measures by Classifier Adaptation

abstract: In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing specific performance measure directly, we circumvent this problem by proposing a novel two-step approach called CAPO, namely, to first train nonlinear auxiliary classifiers with existing learning methods and then to adapt auxiliary classifiers for specific performance measures. In the first step, auxiliary classifiers can be obtained efficiently by taking off-the-shelf learning algorithms. For the second step, we show that the classifier adaptation problem can be reduced to a quadratic program problem, which is similar to linear $({\rm SVM}^{\rm perf})$ and can be efficiently solved. By exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear classifier which optimizes a large variety of performance measures, including all the performance measures based on the contingency table and AUC, while keeping high computational efficiency. Empirical studies show that CAPO is effective and of high computational efficiency, and it is even more efficient than linear $({\rm SVM}^{\rm perf})$.


title: Maximum Entropy Markov Models for Information Extraction and Segmentation

abstract: Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelihood of the observations. This paper presents a new Markovian sequence model, closely related to HMMs, that allows observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences. It does this by using the maximum entropy framework to fit a set of exponential models that represent the probability of a state given an observation and the previous state. We present positive experimental results on the segmentation of FAQ’s.


title: Gesture recognition using kinect for sign language translation

abstract: Sign Language is a widely used method of communication among the community of deaf-mute people. It contains some series of body gestures, which enables a person to interact without the need of spoken words. Although the use of sign language is very popular among the deaf-mute people but the other communities don't even try to learn it, this creates a gulf of communication and hence becomes a cause of the isolation of physically impaired people. The problem creates a requirement of a system which can facilitate a way of communication between these two communities. This paper presents a novel method for identification of an isolated sign language gesture using Microsoft Kinect. This paper presents the way of extracting some highly robust features from the depth image provided by Kinect and to use them in creating a robust and accurate gesture recognition system, for the purpose of sign language translation. Apart from translation, the proposed system also opens the doors of endless applications in the field of Human Computer Interaction. The proposed algorithm helps in translating a sign language gesture performed by a user, which in-turn can be used as an input for different types of applications.


VTrust: Regaining Trust on Virtual Calls


title: Code switch language modeling with Functional Head Constraint

abstract: In this paper, we propose for the first time to incorporate the linguistically well-known Functional Head Constraint into a code switch language model for speech recognition. Under this constraint, code switch cannot occur between the functional head and its complements. The constrained code switching language model is obtained by first expanding the search network with a translation model; and then using lattice-based parsing to restrict paths to those permissible under the Functional Head Constraint. We tested our system on two tasks of code switch speech recognition, namely lecture speech recognition and lunch conversation recognition. Our system reduces word error rates (WER) from a baseline mixed language model by 3.72% relative in the first task; and by 5.85% in the second task. It reduces WER from an interpolated language model by 2.51% in the first task; and by 4.57% in the second task. All results are statistically significant. In addition, our method reduces WER for both the matrix language and the embedded language.


title: A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS

abstract: VOS is a new mapping technique that can serve as an alternative to the well-known technique of multidimensional scaling. We present an extensive comparison between the use of multidimensional scaling and the use of VOS for constructing bibliometric maps. In our theoretical analysis, we show the mathematical relation between the two techniques. In our experimental analysis, we use the techniques for constructing maps of authors, journals, and keywords. Two commonly used approaches to bibliometric mapping, both based on multidimensional scaling, turn out to produce maps that suffer from artifacts. Maps constructed using VOS turn out not to have this problem. We conclude that in general maps constructed using VOS provide a more satisfactory representation of a data set than maps constructed using well-known multidimensional scaling approaches.


Comparative Study of Five Text Classification Algorithms with their Improvements


title: Automatic Lymphocyte Detection in H&E Images with Deep Neural Networks

abstract: Automatic detection of lymphocyte in H&E images is a necessary first step in lots of tissue image analysis algorithms. An accurate and robust automated lymphocyte detection approach is of great importance in both computer science and clinical studies. Most of the existing approaches for lymphocyte detection are based on traditional image processing algorithms and/or classic machine learning methods. In the recent years, deep learning techniques have fundamentally transformed the way that a computer interprets images and have become a matchless solution in various pattern recognition problems. In this work, we design a new deep neural network model which extends the fully convolutional network by combining the ideas in several recent techniques, such as shortcut links. Also, we design a new training scheme taking the prior knowledge about lymphocytes into consideration. The training scheme not only efficiently exploits the limited amount of free-form annotations from pathologists, but also naturally supports efficient fine-tuning. As a consequence, our model has the potential of self-improvement by leveraging the errors collected during real applications. Our experiments show that our deep neural network model achieves good performance in the images of different staining conditions or different types of tissues.


title: Adaptive Sampling of RF Fingerprints for Fine-grained Indoor Localization

abstract: Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promising approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a fingerprint database a priori for localization. To address this problem, we visualize the 3-D RF fingerprint data as a function of locations (x-y) and indices of access points (fingerprint), as a tensor and use tensor algebraic methods for an adaptive tubal-sampling of this fingerprint space. In particular, using a recently proposed tensor algebraic framework in [1] , we capture the complexity of the fingerprint space as a low-dimensional tensor-column space. In this formulation, the proposed scheme exploits adaptivity to identify reference points which are highly informative for learning this low-dimensional space. Further, under certain incoherency conditions, we prove that the proposed scheme achieves bounded recovery error and near-optimal sampling complexity. In contrast to several existing work that rely on random sampling, this paper shows that adaptivity in sampling can lead to significant improvements in localization accuracy. The approach is validated on both data generated by the ray-tracing indoor model which accounts for the floor plan and the impact of walls and the real world data. Simulation results show that, while maintaining the same localization accuracy of existing approaches, the amount of samples can be cut down by $71$ percent for the high SNR case and $55$ percent for the low SNR case.


title: Competitor identification and competitor analysis: a broad-based managerial approach

abstract: Managerial myopia in identifying competitive threats is a well-recognized phenomenon (Levitt, 1960; Zajac and Bazerman, 1991). Identifying such threats is particularly problematic, since they may arise from substitutability on the supply side as well as on the demand side. Managers who focus only on the product market arena in scanning their competitive environment may fail to notice threats that are developing due to the resources and latent capabilities of indirect or potential competitors. This paper brings together insights from the fields of strategic management and marketing to develop a simple but powerful set of tools for helping managers overcome this common problem. We present a two-stage framework for competitor identification and analysis that brings into consideration a broad range of competitors, including potential competitors, substitutors, and indirect competitors. Specifically we draw from Peteraf and Bergen's (2001) framework for competitor identification to develop a hierarchy of competitor awareness. That is used, in combination with resource equivalence, to generate hypotheses on competitive analysis. This framework not only extends the ken of managers, but also facilitates an assessment of the strategic opportunities and threats that various competitors represent and allows managers to assess their significance in relative terms. Copyright © 2002 John Wiley & Sons, Ltd.