title: Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks

abstract: This paper presents our system design for the Audio-Visual Emotion Challenge ($AV^{+}EC$ 2015). Besides the baseline features, we extract from audio the functionals on low-level descriptors (LLDs) obtained via the YAAFE toolbox, and from video the Local Phase Quantization from Three Orthogonal Planes (LPQ-TOP) features. From the physiological signals, we extract 52 electro-cardiogram (ECG) features and 22 electro-dermal activity (EDA) features from various analysis domains. The extracted features along with the $AV^{+}EC$ 2015 baseline features of audio, ECG or EDA are concatenated for a further feature selection step, in which the concordance correlation coefficient (CCC), instead of the usual Pearson correlation coefficient (CC), has been used as objective function. In addition, offsets between the features and the arousal/valence labels are considered in both feature selection and modeling of the affective dimensions. For the fusion of multimodal features, we propose a Deep Bidirectional Long Short-Term Memory Recurrent Neural Network (DBLSTM-RNN) based multimodal affect prediction framework, in which the initial predictions from the single modalities via the DBLSTM-RNNs are firstly smoothed with Gaussian smoothing, then input into a second layer of DBLSTM-RNN for the final prediction of affective state. Experimental results show that our proposed features and the DBLSTM-RNN based fusion framework obtain very promising results. On the development set, the obtained CCC is up to 0.824 for arousal and 0.688 for valence, and on the test set, the CCC is 0.747 for arousal and 0.609 for valence.


Failures of Deep Learning.


title: Practical lineage tracing in data warehouses

abstract: We consider the view data lineage problem in a warehousing environment: for a given data item in a materialized warehouse view, we want to identify the set of source data items that produced the view item. We formalize the problem and we present a lineage tracing algorithm for relational views with aggregation. Based on our tracing algorithm, we propose a number of schemes for storing auxiliary views that enable consistent and efficient lineage tracing in a multi-source data warehouse. We report on a performance study of the various schemes, identifying which schemes perform best in which settings. Based on our results, we have implemented a lineage tracing package in the WHIPS data warehousing system prototype at Stanford. With this package, users can select view tuples of interest, then efficiently "drill through" to examine the exact source tuples that produced the view tuples of interest.


title: Real-Time Illegal Parking Detection in Outdoor Environments Using 1-D Transformation

abstract: With decreasing costs of high-quality surveillance systems, human activity detection and tracking has become increasingly practical. Accordingly, automated systems have been designed for numerous detection tasks, but the task of detecting illegally parked vehicles has been left largely to the human operators of surveillance systems. We propose a methodology for detecting this event in real time by applying a novel image projection that reduces the dimensionality of the data and, thus, reduces the computational complexity of the segmentation and tracking processes. After event detection, we invert the transformation to recover the original appearance of the vehicle and to allow for further processing that may require 2-D data. We evaluate the performance of our algorithm using the i-LIDS vehicle detection challenge datasets as well as videos we have taken ourselves. These videos test the algorithm in a variety of outdoor conditions, including nighttime video and instances of sudden changes in weather.


title: Exploring the factors affecting MOOC retention

abstract: Massive Open Online Courses (MOOCs) hold the potential to open up educational opportunities to a global audience. However, evidence suggests that only a small proportion of MOOC participants go on to complete their courses and relatively little is understood about the MOOC design and implementation factors that influence retention. This paper reports a survey study of 379 participants enrolled at university in Cairo who were encouraged to take a MOOC of their own choice as part of their development. 122 participants (32.2%) went onto to complete an entire course. There were no significant differences in completion rates by gender, level of study (undergraduate or postgraduate) or MOOC platform. A post-MOOC survey of students' perceptions found that MOOC Course Content was a significant predictor of MOOC retention, with the relationship mediated by the effect of content on the Perceived Effectiveness of the course. Interaction with the instructor of the MOOC was also found to be significant predictor of MOOC retention. Overall these constructs explained 79% of the variance in MOOC retention. We present a model explaining 79% of variance in MOOC learner retention.Course content affects MOOC learner retention via Perceived Effectiveness.Interaction with instructor affects MOOC learner retention directly.Those who pass the mid-point of a MOOC are likely to complete.


title: Automatic Library Generation for Modular Polynomial Multiplication

abstract: Polynomial multiplication is a key algorithm underlying computer algebra systems (CAS) and its efficient implementation is crucial for the performance of CAS. In this paper we design and implement algorithms for polynomial multiplication using approaches based the fast Fourier transform (FFT) and the truncated Fourier transform (TFT). We improve on the state-of-the-art in both theoretical and practical performance. The {\SPIRAL} library generation system is extended and used to automatically generate and tune the performance of a polynomial multiplication library that is optimized for memory hierarchy, vectorization and multi-threading, using new and existing algorithms. The performance tuning has been aided by the use of automation where many code choices are generated and intelligent search is utilized to find the"best"implementation on a given architecture. The performance of autotuned implementations is comparable to, and in some cases better than, the best hand-tuned code.


title: Using psychophysiological techniques to measure user experience with entertainment technologies

abstract: Emerging technologies offer exciting new ways of using entertainment technology to create fantastic play experiences and foster interactions between players. Evaluating entertainment technology is challenging because success isn't defined in terms of productivity and performance, but in terms of enjoyment and interaction. Current subjective methods of evaluating entertainment technology aren't sufficiently robust. This paper describes two experiments designed to test the efficacy of physiological measures as evaluators of user experience with entertainment technologies. We found evidence that there is a different physiological response in the body when playing against a computer versus playing against a friend. These physiological results are mirrored in the subjective reports provided by the participants. In addition, we provide guidelines for collecting physiological data for user experience analysis, which were informed by our empirical investigations. This research provides an initial step towards usi...


title: Intelligence: Genetics, Genes, and Genomics.

abstract: More is known about the genetics of intelligence than about any other trait, behavioral or biological, which is selectively reviewed in this article. Two of the most interesting genetic findings are that heritability of intelligence increases throughout the life span and that the same genes affect diverse cognitive abilities. The most exciting direction for genetic research on intelligence is to harness the power of the Human Genome Project to identify some of the specific genes responsible for the heritability of intelligence. The next research direction will be functional genomics—for example, understanding the brain pathways between genes and intelligence. Deoxyribonucleic acid (DNA) will integrate life sciences research on intelligence; bottom-up molecular biological research will meet top-down psychological research in the brain. As indicated in the preface to this special section on intelligence, the centenary of Spearman’s seminal article on intelligence (Spearman, 1904) is an appropriate moment to take stock of what we know about this oldest of personological constructs at diverse levels of analysis— genes, physiology, psychology, and sociology—throughout the life span. More is known about the genetics of individual differences in intelligence than any other behavioral trait. This research is reviewed briefly in the first section of the present article—Genetics. The second section—Genes— describes current attempts to harness the power of the Human Genome Project in order to identify some of the presumably many genes responsible for the heritability of intelligence. The third section— Genomics— discusses the next step, functional genomics, which attempts to chart pathways between genes and intelligence. To be able to address these issues, this article needs to assume basic understanding of the psychometric construct of intelligence, which is described in the preface and in other articles in this special section (see also books by Bock, Goode, & Webb, 2000; Brody, 1992; Deary, 2000). The problem with the word intelligence is that it means different things to different people. The present article uses the psychometric definition of intelligence as general cognitive ability—Spearman’s g, which was discovered at the same time that Mendel’s laws of inheritance were rediscovered (Spearman, 1904). That is, the word intelligence will be used to refer to the substantial covariation among diverse measures of cognitive ability as indexed by an unrotated first principalcomponent score, which typically accounts for about 40% of the total variance of diverse cognitive tests, or by a total score across diverse tests as is done in intelligence tests (Jensen, 1998). We also forgo a discussion of the relationship between intelligence and personality, which is discussed in the preface to this special section. In terms of genetics, reviews have been written about traditional personality traits such as the five-factor model (Loehlin, 1992; Plomin, DeFries, McClearn, & McGuffin, 2001); intelligence is especially related to Openness to Experience (McCrae & Costa, 1997). Personality and its relationship to psychopathology has also increasingly become a target for molecular genetic research aimed at identifying specific genes responsible for the ubiquitous heritability of personality traits (Benjamin, Ebstein, & Belmaker, 2002).


title: Designing the spectator experience

abstract: Interaction is increasingly a public affair, taking place in our theatres, galleries, museums, exhibitions and on the city streets. This raises a new design challenge for HCI - how should spectators experience a performer's interaction with a computer? We classify public interfaces (including examples from art, performance and exhibition design) according to the extent to which a performer's manipulations of an interface and their resulting effects are hidden, partially revealed, fully revealed or even amplified for spectators. Our taxonomy uncovers four broad design strategies: 'secretive,' where manipulations and effects are largely hidden; 'expressive,' where they tend to be revealed enabling the spectator to fully appreciate the performer's interaction; 'magical,' where effects are revealed but the manipulations that caused them are hidden; and finally 'suspenseful,' where manipulations are apparent but effects are only revealed as the spectator takes their turn.


title: Low Profile Harmonic Radar Transponder for Tracking Small Endangered Species

abstract: Fragmentation of threatened species' habitat attributable to anthropogenic activity is a major concern. Understanding these animals' dispersal ecology and range utilization can greatly aid in designing preserves for their conservation, but this data is extremely difficult to obtain for populations of small animals. Presented here is a study miniature passive harmonic radar tags which allow the tracking of an endangered snail species without adversely affecting the tagged subjects. Whip antennas were found to be problematic through snagging and interference with snail activity. Alternative low-profile planar antennas were also tested, with bow-tie related antenna forms providing reasonable (up to 7 ft) detection range.


title: WordNet: An on-line lexical database

abstract: Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought.


title: Real time emotion detection from speech using Raspberry Pi 3

abstract: In the past decade, a lot of research has gone into Speech Emotion Recognition. The primary objective of Speech Emotion Recognition is to improve man-machine interface. It can also be used to monitor the psycho physiological state of a person in lie detectors. In recent time, speech emotion recognition also finds its applications in medicine and forensics. It can also be used to recognize different emotions like sad, happy, angry, fear, etc. Basically, it can be used to detect positive and negative emotions. This project mainly focuses on using a DSP processor (Raspberry Pi 3) to recognize the emotions through speech. It can also be implemented using complete software that is MATLAB. DSP has many advantages and features and is basically used by programming it in Python. This project mainly focuses on comparing the implementation through hardware and software. The easiest way to compare is through efficiency and many other features.


title: Malware at its worst: death and destruction

abstract: Purpose ::: ::: ::: ::: ::: Most people are probably aware of malware, but they may not be aware of malware in what may be its most dangerous form, i.e. causing physical harm, even death, to individuals. This paper aims to document how software can cause malicious harm to individuals by attacking modern systems that appear to be neglected and under-researched. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: This paper will review some of the most significant areas of concern with respect to end of days malware, i.e. malware that has a dangerous intent. The areas included are automobiles, medical devices and air traffic control systems. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The potential harmful effects of malware are often not well known by consumers and businesses around the world. These issues are not limited to just financial harm. Lives can actually be in danger. Underestimating the importance of cybersecurity and understanding the dangers that are associated with advancing technology are global issues that will continue unless there is enough awareness to force businesses and governments to address these issues. It is critical that safeguards are established. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: While many papers have been written about malware and the implications of having malicious software infect a computer or a network, little attention has been paid to “end of days” malware. With advancing technology, malware now has the ability to cause serious injury or death to individuals who have minimal or no knowledge of the potential consequences of, for example, driving in an automobile, wearing or having an internal medical device or flying on an airplane. It is up to businesses and governments to address these issues.


title: Whole-brain functional imaging at cellular resolution using light-sheet microscopy

abstract: Brain function relies on communication between large populations of neurons across multiple brain areas, a full understanding of which would require knowledge of the time-varying activity of all neurons in the central nervous system. Here we use light-sheet microscopy to record activity, reported through the genetically encoded calcium indicator GCaMP5G, from the entire volume of the brain of the larval zebrafish in vivo at 0.8 Hz, capturing more than 80% of all neurons at single-cell resolution. Demonstrating how this technique can be used to reveal functionally defined circuits across the brain, we identify two populations of neurons with correlated activity patterns. One circuit consists of hindbrain neurons functionally coupled to spinal cord neuropil. The other consists of an anatomically symmetric population in the anterior hindbrain, with activity in the left and right halves oscillating in antiphase, on a timescale of 20 s, and coupled to equally slow oscillations in the inferior olive.


title: The Internet of Things, Fog and Cloud Continuum: Integration and Challenges

abstract: The Internet of Things needs for computing power and storage are expected to remain on the rise in the next decade. Consequently, the amount of data generated by devices at the edge of the network will also grow. While cloud computing has been an established and effective way of acquiring computation and storage as a service to many applications, it may not be suitable to handle the myriad of data from IoT devices and fulfill largely heterogeneous application requirements. Fog computing has been developed to lie between IoT and the cloud, providing a hierarchy of computing power that can collect, aggregate, and process data from/to IoT devices. Combining fog and cloud may reduce data transfers and communication bottlenecks to the cloud and also contribute to reduced latencies, as fog computing resources exist closer to the edge. This paper examines this IoT-Fog-Cloud ecosystem and provides a literature review from different facets of it: how it can be organized, how management is being addressed, and how applications can benefit from it. Lastly, we present challenging issues yet to be addressed in IoT-Fog-Cloud infrastructures.


title: On the Meaningfulness of “Big Data Quality” (Invited Paper)

abstract: In this paper, we discuss the application of concept of data quality to big data by highlighting how much complex is to define it in a general way. Already data quality is a multidimensional concept, difficult to characterize in precise definitions even in the case of well-structured data. Big data add two further dimensions of complexity: (i) being “very” source specific, and for this we adopt the interesting UNECE classification, and (ii) being highly unstructured and schema-less, often without golden standards to refer to or very difficult to access. After providing a tutorial on data quality in traditional contexts, we analyze big data by providing insights into the UNECE classification, and then, for each type of data source, we choose a specific instance of such a type (notably deep Web data, sensor-generated data, and Twitters/short texts) and discuss how quality dimensions can be defined in these cases. The overall aim of the paper is therefore to identify further research directions in the area of big data quality, by providing at the same time an up-to-date state of the art on data quality.


title: User Survey on Phone Security and Usage

abstract: Mobile phones are widely used nowadays and during the last years developed from simple phones to small computers with an increasing number of features. These result in a wide variety of data stored on the devices which could be a high security risk in case of unauthorized access. A comprehensive user survey was conducted to get information about what data is really stored on the mobile devices, how it is currently protected and if biometric authentication methods could improve the current state. This paper states the results from about 550 users of mobile devices. The analysis revealed a very low securtiy level of the devices. This is partly due to a low security awareness of their owners and partly due to the low acceptance of the offered authentication method based on PIN. Further results like the experiences with mobile thefts and the willingness to use biometric authentication methods as alternative to PIN authentication are also stated.


title: Novel density-based clustering algorithms for uncertain data

abstract: Density-based techniques seem promising for handling data uncertainty in uncertain data clustering. Nevertheless, some issues have not been addressed well in existing algorithms. In this paper, we firstly propose a novel density-based uncertain data clustering algorithm, which improves upon existing algorithms from the following two aspects: (1) it employs an exact method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in previous work; (2) it introduces new definitions of core object probability and direct reachability probability, thus reducing the complexity and avoiding sampling. We then further improve the algorithm by using a novel assignment strategy to ensure that every object will be assigned to the most appropriate cluster. Experimental results show the superiority of our proposed algorithms over existing ones.


title: A Review of Analytics and Clinical Informatics in Health Care

abstract: Federal investment in health information technology has incentivized the adoption of electronic health record systems by physicians and health care organizations; the result has been a massive rise in the collection of patient data in electronic form (i.e. "Big Data"). Health care systems have leveraged Big Data for quality and performance improvements using analytics--the systematic use of data combined with quantitative as well as qualitative analysis to make decisions. Analytics have been utilized in various aspects of health care including predictive risk assessment, clinical decision support, home health monitoring, finance, and resource allocation. Visual analytics is one example of an analytics technique with an array of health care and research applications that are well described in the literature. The proliferation of Big Data and analytics in health care has spawned a growing demand for clinical informatics professionals who can bridge the gap between the medical and information sciences.


title: pSCAN: Fast and exact structural graph clustering

abstract: In this paper, we study the problem of structural graph clustering, a fundamental problem in managing and analyzing graph data. Given a large graph G = (V, E), structural graph clustering is to assign vertices in V to clusters and to identify the sets of hub vertices and outlier vertices as well, such that vertices in the same cluster are densely connected to each other while vertices in different clusters are loosely connected to each other. Firstly, we prove that the existing SCAN approach is worst-case optimal. Nevertheless, it is still not scalable to large graphs due to exhaustively computing structural similarity for every pair of adjacent vertices. Secondly, we make three observations about structural graph clustering, which present opportunities for further optimization. Based on these observations, in this paper we develop a new two-step paradigm for scalable structural graph clustering. Thirdly, following this paradigm, we present a new approach aiming to reduce the number of structural similarity computations. Moreover, we propose optimization techniques to speed up checking whether two vertices are structure-similar to each other. Finally, we conduct extensive performance studies on large real and synthetic graphs, which demonstrate that our new approach outperforms the state-of-the-art approaches by over one order of magnitude. Noticeably, for the twitter graph with 1 billion edges, our approach takes 25 minutes while the state-of-the-art approach cannot finish even after 24 hours.


title: Deep Multi-task Learning for Large-Scale Image Classification

abstract: To achieve more effective solution for large-scale image classification (i.e., classifying millions of images into thousands or even tens of thousands of object classes or categories), a deep multi-task learning algorithm is developed by seamlessly integrating deep CNNs with multi-task learning over the concept ontology, where the concept ontology is used to organize large numbers of object classes or categories hierarchically and determine the inter-related learning tasks automatically. Our deep multi-task learning algorithm can integrate the deep CNNs to learn more discriminative high-level features for image representation, and it can also leverage multi-task learning and inter-level relationship constraint to train more discriminative tree classifier over the concept ontology and control the inter-level error propagation effectively. In our deep multi-task learning algorithm, we can use back propagation to simultaneously refine both the relevant node classifiers (at different levels of the concept ontology) and the deep CNNs according to a joint objective function. The experimental results have demonstrated that our deep multi-task learning algorithm can achieve very competitive results on both the accuracy and the cost of feature extraction for large-scale image classification.


title: A Gate Driver Circuit for Series-Connected IGBTs Based on Quasi-Active Gate Control

abstract: Growth in high-voltage power-conversion applications in recent years shows the importance and requirement of high-voltage/high-power converters in power electronic applications. The major limitation in this industry is the maximum voltage blocking capability of semiconductor switches. To overcome this restriction, the power switches are connected in series to build a high-voltage compact switch. This paper proposes a modified circuit based on quasi-active gate control, which provides the capability of connecting a desired number of insulated gate bipolar transistors (IGBTs) in series. In addition, overcoming the limitation of balancing capacitors’ sizing, alleviation of low-frequency oscillations, and better turn-OFF characteristics are some improvements obtained by the proposed circuit. Simulation results show an excellent voltage balancing between four and eight series-connected IGBTs. Also, experimental results are presented to verify the performance of the proposed circuit.


title: Fully Convolutional Networks for Surface Defect Inspection in Industrial Environment

abstract: In this paper, we propose a reusable and high-efficiency two-stage deep learning based method for surface defect inspection in industrial environment. Aiming to achieve trade-offs between efficiency and accuracy simultaneously, our method makes a novel combination of a segmentation stage (stage1) and a detection stage (stage2), which are consisted of two fully convolutional networks (FCN) separately. In the segmentation stage we use a lightweight FCN to make a spatially dense pixel-wise prediction to inference the area of defect coarsely and quickly. Those predicted defect areas act as the initialization of stage2, guiding the process of detection to refine the segmentation results. We also use an unusual training strategy: training with the patches cropped from the images. Such strategy has greatly utility in industrial inspection where training data may be scarce. We will validate our findings by analyzing the performance obtained on the dataset of DAGM 2007.


title: An Adaptive Genetic Algorithm for Solving N-Queens Problem

abstract: In this paper a Metaheuristic approach for solving the N-Queens Problem is introduced to find the best possible solution in a reasonable amount of time. Genetic Algorithm is used with a novel fitness function as the Metaheuristic. The aim of N-Queens Problem is to place N queens on an N x N chessboard, in a way so that no queen is in conflict with the others. Chromosome representation and genetic operations like Mutation and Crossover are described in detail. Results show that this approach yields promising and satisfactory results in less time compared to that obtained from the previous approaches for several large values of N.


title: Image steganography with Artificial Immune System

abstract: Image steganography is a branch of steganography that hide secret data in host image. In image steganography, capacity and quality of host image are important factors and there is a tradeoff between quality of stego image and efficiency of steganography algorithm. In this paper, we present an efficient algorithm for image steganography based on Artificial Immune System(AIS) and host image partitioning. Our proposed method selects a block of the host image and then employs AIS for finding the best template for embedding message bits in the host image pixels. Consequently, our method finds the best template for embedding rapidly and there is no need to investigate whole the image for finding a template of embedding. Our proposed algorithm has more efficiency in term of embedding capacity and the time of the embedding process compared to other methods.


title: Dreaming and Waking Cognition

abstract: Dreams are often believed to be “symbolic” and thus categorically distinct from the “ordinary” thoughts of waking cognition. But to the contrary, emerging evidence suggests that dreams and waking cognition share a common origin at the neurobiological level, which is reflected in similarity of form, content, and function at the phenomenological level. In both dreams and daydreams, memories of the past form the basis of novel imaginary scenarios. Neural networks that support remembering the past, imagining the future, and creating fictitious scenes remain active across conscious states of wake and sleep. Taken together, this evidence suggests that dreaming is a natural extension of waking conscious experience. This empirically supported conception of dreaming has important clinical applications concerning the “interpretability” of dreams in the therapeutic setting.


title: Online Advertisement Campaign Optimization

abstract: Like any marketing campaigns, online advertisement campaigns need to be monitored, analyzed and optimized. It is more so for online campaigns because online advertisements are usually sold in auction style. Prices can change very dynamically; the creatives, the landing pages and the targeting profiles can all be changed frequently to improve the effectiveness of a campaign. Also, the effectiveness can be measured almost instantly, usually in terms of click through rate and/or the acquisition/conversion rate. It raises many challenging problems in forecasting, data mining and optimization and entails an optimization system for any serious advertisers, publishers or ad networks.


title: Analysis of AIS Intermittency and Vessel Characterization using a Hidden Markov Model.

abstract: In this report we perform a statistical analysis of the Automated Identification System (AIS) communication channel. We base our study on a Hidden Markov Model (HMM). We model the transition between different states of the channel as a Markov Chain (MC); the probability that a message sent by a AIS transmitter will be lost is associated to each state. The MC is not directly observed, but the received time stamps of the AIS reports provide some statistical information about the current state of the channel as well as some information about the parameters of the model. Additionally, the statistic characteristics of the AIS channel are used in a batch anomaly detection algorithm that characterizes vessel as anomalous if their (hidden) transponder state is estimated to be in the off state for too high a fraction of the surveillance time.


On the Effectiveness of Minimal Context Selection for Robust Question Answering


title: Erythrocyte Features for Malaria Parasite Detection in Microscopic Images of Thin Blood Smear: A Review

abstract: Microscopic image analysis of blood smear plays a very important role in characterization of erythrocytes in screening of malaria parasites. The characteristics feature of erythrocyte changes due to malaria parasite infection. The microscopic features of the erythrocyte include morphology, intensity and texture. In this paper, the different features used to differentiate the non- infected and malaria infected erythrocyte have been reviewed.


title: Controversy and Sentiment in Online News

abstract: How do news sources tackle controversial issues? In this work, we take a data-driven approach to understand how controversy interplays with emotional expression and biased language in the news. We begin by introducing a new dataset of controversial and noncontroversial terms collected using crowdsourcing. Then, focusing on 15 major U.S. news outlets, we compare millions of articles discussing controversial and non-controversial issues over a span of 7 months. We find that in general, when it comes to controversial issues, the use of negative affect and biased language is prevalent, while the use of strong emotion is tempered. We also observe many differences across news sources. Using these findings, we show that we can indicate to what extent an issue is controversial, by comparing it with other issues in terms of how they are portrayed across different media.


title: For the Grid and Through the Grid: The Role of Power Line Communications in the Smart Grid

abstract: Are Power Line Communications (PLC) a good candidate for Smart Grid applications? The objective of this paper is to address this important question. To do so, we provide an overview of what PLC can deliver today by surveying its history and describing the most recent technological advances in the area. We then address Smart Grid applications as instances of sensor networking and network control problems and discuss the main conclusions one can draw from the literature on these subjects. The application scenario of PLC within the Smart Grid is then analyzed in detail. Because a necessary ingredient of network planning is modeling, we also discuss two aspects of engineering modeling that relate to our question. The first aspect is modeling the PLC channel through fading models. The second aspect we review is the Smart Grid control and traffic modeling problem which allows us to achieve a better understanding of the communications requirements. Finally, this paper reports recent studies on the electrical and topological properties of a sample power distribution network. Power grid topological studies are very important for PLC networking as the power grid is not only the information source but also the information delivery system-a unique feature when PLC is used for the Smart Grid.


title: Spectral Imaging for Remote Sensing

abstract: ■ Spectral imaging for remote sensing of terrestrial features and objects arose as an alternative to high-spatial-resolution, large-aperture satellite imaging systems. Early applications of spectral imaging were oriented toward ground-cover classification, mineral exploration, and agricultural assessment, employing a small number of carefully chosen spectral bands spread across the visible and infrared regions of the electromagnetic spectrum. Improved versions of these early multispectral imaging sensors continue in use today. A new class of sensor, the hyperspectral imager, has also emerged, employing hundreds of contiguous bands to detect and identify a variety of natural and man-made materials. This overview article introduces the fundamental elements of spectral imaging and discusses the historical evolution of both the sensors and the target detection and classification applications.


title: RePEconstruct: reconstructing binaries with self-modifying code and import address table destruction

abstract: Reverse engineering packed binaries remain a tedious challenge as code packing is continuously being used by malware to hinder detection and analysis. The problem of automatically unpacking binaries has previously been investigated. However, current generic unpackers either do not offer any dump of the unpacked binary at all or produces a set of memory dumps that each lack several structures that make them well-suited for further analysis. In this paper, we present RePEconstruct, a tool that unpacks packed binaries and reconstructs them in a manner well suited for further analysis. RePEconstruct deploys a model of self-modifying code similar to previous work but goes the step further by also utilizing a novel, aggressive, approach to rebuilding the import address table. Our approach relies on both static and dynamic analysis. We build RePEconstruct as a DynamoRIO client and successfully evaluate it against a set of packed applications.


title: A WIDEBAND TWIN-DIAMOND-SHAPED CIRCULARLY POLARIZED PATCH ANTENNA WITH GAP-COUPLED FEED

abstract: A novel twin-diamond-shaped antenna is presented for wideband circularly polarized (CP) radiation. By introducing a twin-diamond-shaped patch and a gap-coupled feed structure, a left- handed circularly polarized (LHCP) antenna in bore-sight direction is obtained. The gap-coupled feed structure consists of a horizontal microstrip arm of approximately 0.285‚0 at the centre frequency and two small diamonds, which greatly contributes to wideband characteristics. The experimental bandwidths of 10-dB return loss and 3-dB axial ratio (AR) for the antenna prototype are about 20% and 9.2%, respectively. The measured gain is more than 6.8dBi over the entire bandwidth.


title: A new method in modeling of rotor bar Skew effect in Induction Motor based on 2D-Modified Winding function Method

abstract: A new method to model the skew effect of rotor bars in induction motors is presented based on a 2D-winding function method (WFM). In the proposed method a new turn function for rotor bar is introduced. Simulation results and experimental tests prove the validity of proposed method. Proposed method is applied for rotor bar fault detection based on Park's vectors approach.


title: Text Localization in Natural Scene Images Based on Conditional Random Field

abstract: This paper proposes a novel hybrid method to robustly and accurately localize texts in natural scene images. A text region detector is designed to generate a text confidence map, based on which text components can be segmented by local binarization approach. A Conditional Random Field (CRF) model, considering the unary component property as well as binary neighboring component relationship, is then presented to label components as "text" or "non-text". Last, text components are grouped into text lines with an energy minimization approach. Experimental results show that the proposed method gives promising performance comparing with the existing methods on ICDAR 2003 competition dataset.


title: Dynamical properties of a compartmental model for traffic networks

abstract: In this paper, we propose a traffic network flow model particularly suitable for qualitative analysis as a dynamical system. Flows at a junction are determined by downstream supply of capacity (lack of congestion) as well as upstream demand of traffic wishing to flow through the junction. This approach is rooted in the celebrated Cell Transmission Model for freeway traffic flow, and we analyze resulting equilibrium flows and convergence properties.


title: Assessing children's inference generation: What do tests of reading comprehension measure?

abstract: Background. Previous research suggests that children with specific comprehension difficulties have problems with the generation of inferences. This raises important questions as to whether poor comprehenders have poor comprehension skills generally, or whether their problems are confined to specific inference types. ::: ::: Aims. The main aims of the study were (a) using two commonly used tests of reading comprehension to classify the questions requiring the generation of inferences, and (b) to investigate the relative performance of skilled and less-skilled comprehenders on questions tapping different inference types. ::: ::: Sample. The performance of 10 poor comprehenders (mean age 110.06 months) was compared with the performance of 10 normal readers (mean age 112.78 months) on two tests of reading comprehension. ::: ::: Method. A qualitative analysis of the NARA II (form 1) and the WORD comprehension subtest was carried out. Participants were then administered the NARA II, WORD comprehension subtest and a test of non-word reading. ::: ::: Results. The NARA II was heavily reliant on the generation of knowledge-based inferences, while the WORD comprehension subtest was biased towards the retention of literal information. Children identified by the NARA II as having comprehension difficulties performed in the normal range on the WORD comprehension subtests. Further, children with comprehension difficulties performed poorly on questions requiring the generation of knowledge-based and elaborative inferences. However, they were able to answer questions requiring attention to literal information or use of cohesive devices at a level comparable to normal readers. ::: ::: Conclusions. Different reading tests tap different types of inferencing skills. Less-skilled comprehenders have particular difficulty applying real-world knowledge to a text during reading, and this has implications for the formulation of effective intervention strategies.


title: Bidirectional Non-Filamentary RRAM as an Analog Neuromorphic Synapse, Part I: Al/Mo/Pr0.7Ca0.3MnO3 Material Improvements and Device Measurements

abstract: We report on material improvements to non-filamentary RRAM devices based on Pr0.7Ca0.3MnO3 by introducing an MoOx buffer layer together with a reactive Al electrode, and on device measurements designed to help gauge the performance of these devices as bidirectional analog synapses for on-chip acceleration of the backpropagation algorithm. Previous Al/PCMO devices exhibited degraded LRS retention due to the low activation energy for oxidation of the Al electrode, and Mo/PCMO devices showed low conductance contrast. To control the redox reaction at the metal/PCMO interface, we introduce a 4-nm interfacial layer of conducting MoOx as an oxygen buffer layer. Due to the controlled redox reaction within this Al/Mo/PCMO device, we observed improvements in both retention and conductance on/off ratio. We confirm bidirectional analog synapse characteristics and measure “jump-tables” suitable for large scale neural network simulations that attempt to capture complex and stochastic device behavior [see companion paper]. Finally, switching energy measurements are shown, illustrating a path for future device research toward smaller devices, shorter pulses and lower programming voltages.


title: Floral diagrams and inflorescences: interactive flower modeling using botanical structural constraints

abstract: We present a system for modeling flowers in three dimensions quickly and easily while preserving correct botanical structures. We use floral diagrams and inflorescences, which were developed by botanists to concisely describe structural information of flowers. Floral diagrams represent the layout of floral components on a single flower, while inflorescences are arrangements of multiple flowers. Based on these notions, we created a simple user interface that is specially tailored to flower editing, while retaining a maximum variety of generable models. We also provide sketching interfaces to define the geometries of floral components. Separation of structural editing and editing of geometry makes the authoring process more flexible and efficient. We found that even novice users could easily design various flower models using our technique. Our system is an example of application-customized sketching, illustrating the potential power of a sketching interface that is carefully designed for a specific application.


Dialogue Act Semantic Representation and Classification Using Recurrent Neural Networks


title: A cost effective tomato maturity grading system using image processing for farmers

abstract: Maturity grading or in other words classifying the ripeness of a fruit, based on its color or texture, forms a very important process to be carried out by agriculturists and the food processing industry worldwide. Current techniques mainly involve manual inspection, which leads to erroneous classification, which in turn would cause economical losses due to inferior produce entering the market chain. A loss of yield during storage may also occur with this type of classification, since it would lead to wrong expiry date predictions as well. Several methodologies to automate this process exist but involve highly expensive setups and complicated procedures which are not a viable solution, especially for the agriculturists of a developing nation. In this paper we discuss a cost effective maturity grading system for one of the most popular fruit in the world - the tomato. A novel setup utilizing inexpensive material and image processing algorithms to identify the six important stages of tomato ripening have been presented. All algorithms were designed and developed using Simulink, a part of MATLAB 2011b on a 2.5 GHz CPU. An overall 98% accuracy was achieved with respect to maturity grade detection and an execution speed of greater than 7.6 times was obtained in comparison with two other popular methodologies.


Investigating Redundancy in Emoji Use: Study on a Twitter Based Corpus


Smart-retailing : How SIST are revolutionizing the physical store in an omni-channel context ?


title: A work-efficient parallel breadth-first search algorithm (or how to cope with the nondeterminism of reducers)

abstract: We have developed a multithreaded implementation of breadth-first search (BFS) of a sparse graph using the Cilk++ extensions to C++. Our PBFS program on a single processor runs as quickly as a standar. C++ breadth-first search implementation. PBFS achieves high work-efficiency by using a novel implementation of a multiset data structure, called a "bag," in place of the FIFO queue usually employed in serial breadth-first search algorithms. For a variety of benchmark input graphs whose diameters are significantly smaller than the number of vertices -- a condition met by many real-world graphs -- PBFS demonstrates good speedup with the number of processing cores. Since PBFS employs a nonconstant-time "reducer" -- "hyperobject" feature of Cilk++ -- the work inherent in a PBFS execution depends nondeterministically on how the underlying work-stealing scheduler load-balances the computation. We provide a general method for analyzing nondeterministic programs that use reducers. PBFS also is nondeterministic in that it contains benign races which affect its performance but not its correctness. Fixing these races with mutual-exclusion locks slows down PBFS empirically, but it makes the algorithm amenable to analysis. In particular, we show that for a graph G=(V,E) with diameter D and bounded out-degree, this data-race-free version of PBFS algorithm runs it time O((V+E)/P + Dlg3(V/D)) on P processors, which means that it attains near-perfect linear speedup if P


title: Visualization of trends in subscriber attributes of communities on mobile telecommunications networks

abstract: Churn, the decision for a subscriber to leave a provider, is frequently of interest in the telecommunications industry. Previous research provides evidence that social influence can be a factor in mobile telecommunications churn. In our work, presented at ASONAM, we presented a system, called ChurnVis, to visualize the evolution of mobile telecommunications churn and subscriber actions over time. First, we infer a social network from call detail records. Then, we compute components based on an overlay of this social network and churn activity. We compute summaries of the attributes associated with the subscribers and finally, we visualize the components in a privacy preserving way. The system is able to present summaries of thousands of churn components in graphs of hundreds of millions of edges. One of the drawbacks of the original approach was that churn components were sometimes very large, leading to over-aggregation in the summary data. In this extension of the ASONAM paper, we adapt the ChurnVis approach to operate on the output of a community finding algorithm and present new results based on this adaptation.


title: Object Recognition in X-ray Testing Using Adaptive Sparse Representations

abstract: In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, test patches of the test image are extracted, and for each test patch a dictionary is built concatenating the ‘best’ representative dictionary of each object. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification methodology. Finally, the test image is classified by patch voting. Thus, our approach is able to deal with less constrained conditions including some contrast variability, pose, intra-class variability, size of the image and focal distance. We tested the effectiveness of our method for the detection of four different objects. In our experiments, the recognition rate was more than 97 % in each class, and more than 94 % if the object is occluded less than 15 %. Results show that XASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature.


title: FinBrain: when finance meets AI 2.0

abstract: Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation. As one of the new intelligent needs in the AI 2.0 era, financial intelligence has elicited much attention from the academia and industry. In our current dynamic capital market, financial intelligence demonstrates a fast and accurate machine learning capability to handle complex data and has gradually acquired the potential to become a “financial brain.” In this paper, we survey existing studies on financial intelligence. First, we describe the concept of financial intelligence and elaborate on its position in the financial technology field. Second, we introduce the development of financial intelligence and review state-of-the-art techniques in wealth management, risk management, financial security, financial consulting, and blockchain. Finally, we propose a research framework called FinBrain and summarize four open issues, namely, explainable financial agents and causality, perception and prediction under uncertainty, risk-sensitive and robust decision-making, and multi-agent game and mechanism design. We believe that these research directions can lay the foundation for the development of AI 2.0 in the finance field.


Malware Image Classification using Machine Learning with Local Binary Pattern


The Pre-entry Program at UTMSH: Effect on Academic Performance of First-Year Medical Students


title: Non-rigid registration of 3D surfaces by deformable 2D triangular meshes

abstract: Non-rigid surface registration, particularly registration of human faces, finds a wide variety of applications in computer vision and graphics. We present a new automatic surface registration method which utilizes both attraction forces originating from geometrical and textural similarities, and stresses due to non-linear elasticity of the surfaces. Reference and target surfaces are first mapped onto their feature image planes, then these images are registered by subjecting them to local deformations, and finally 3D correspondences are established. Surfaces are assumed to be elastic sheets and are represented by triangular meshes. The internal elastic forces act as a regularizer in this ill-posed problem. Furthermore, the non-linear elasticity model allows us to handle large deformations, which can be essential, for instance, for facial expressions. The method has been tested successfully on 3D scanned human faces, with and without expressions. The algorithm runs quite efficiently using a multiresolution approach.


title: Corrupt Organizations or Organizations of Corrupt Individuals? Two Types of Organization-Level Corruption

abstract: We focus on two fundamental dimensions of corruption in organizations: (1) whether the individual or the organization is the beneficiary of the corrupt activity and (2) whether the corrupt behavior is undertaken by an individual actor or by two or more actors. We use these dimensions to define a new conceptualization of corruption at the organization level: the organization of corrupt individuals. We contrast this conceptualization with the prevailing concept of organizational corruption and develop propositions that highlight their differences.


title: Energy-Efficient UAV Communication With Trajectory Optimization

abstract: Wireless communication with unmanned aerial vehicles (UAVs) is a promising technology for future communication systems. In this paper, we study energy-efficient UAV communication with a ground terminal via optimizing the UAV's trajectory, a new design paradigm that jointly considers both the communication throughput and the UAV's energy consumption. To this end, we first derive a theoretical model on the propulsion energy consumption of fixed-wing UAVs as a function of the UAV's flying speed, direction and acceleration, based on which the energy efficiency of UAV communication is defined. Then, for the case of unconstrained trajectory optimization, we show that both the rate-maximization and energy-minimization designs lead to vanishing energy efficiency and thus are energy-inefficient in general. Next, we introduce a practical circular UAV trajectory, under which the UAV's flight radius and speed are optimized to maximize the energy efficiency for communication. Furthermore, an efficient design is proposed for maximizing the UAV's energy efficiency with general constraints on its trajectory, including its initial/final locations and velocities, as well as maximum speed and acceleration. Numerical results show that the proposed designs achieve significantly higher energy efficiency for UAV communication as compared with other benchmark schemes.


Synthesis of Pyrrolostatin and Its Analogues


title: Design Study Methodology: Reflections from the Trenches and the Stacks

abstract: Design studies are an increasingly popular form of problem-driven visualization research, yet there is little guidance available about how to do them effectively. In this paper we reflect on our combined experience of conducting twenty-one design studies, as well as reading and reviewing many more, and on an extensive literature review of other field work methods and methodologies. Based on this foundation we provide definitions, propose a methodological framework, and provide practical guidance for conducting design studies. We define a design study as a project in which visualization researchers analyze a specific real-world problem faced by domain experts, design a visualization system that supports solving this problem, validate the design, and reflect about lessons learned in order to refine visualization design guidelines. We characterize two axes - a task clarity axis from fuzzy to crisp and an information location axis from the domain expert's head to the computer - and use these axes to reason about design study contributions, their suitability, and uniqueness from other approaches. The proposed methodological framework consists of 9 stages: learn, winnow, cast, discover, design, implement, deploy, reflect, and write. For each stage we provide practical guidance and outline potential pitfalls. We also conducted an extensive literature survey of related methodological approaches that involve a significant amount of qualitative field work, and compare design study methodology to that of ethnography, grounded theory, and action research.


title: Deep Representation Learning for Trajectory Similarity Computation

abstract: Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to low data quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-the-art methods for k-nearest trajectory search.


title: iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning

abstract: To achieve automatic recommendation of privacy settings for image sharing, a new tool called iPrivacy (image privacy) is developed for releasing the burden from users on setting the privacy preferences when they share their images for special moments. Specifically, this paper consists of the following contributions: 1) massive social images and their privacy settings are leveraged to learn the object-privacy relatedness effectively and identify a set of privacy-sensitive object classes automatically; 2) a deep multi-task learning algorithm is developed to jointly learn more representative deep convolutional neural networks and more discriminative tree classifier, so that we can achieve fast and accurate detection of large numbers of privacy-sensitive object classes; 3) automatic recommendation of privacy settings for image sharing can be achieved by detecting the underlying privacy-sensitive objects from the images being shared, recognizing their classes, and identifying their privacy settings according to the object-privacy relatedness; and 4) one simple solution for image privacy protection is provided by blurring the privacy-sensitive objects automatically. We have conducted extensive experimental studies on real-world images and the results have demonstrated both the efficiency and effectiveness of our proposed approach.


title: A 1-10 Gbps PAM2, PAM4, PAM2 partial response receiver analog front end with dynamic sampler swapping capability for backplane serial communications

abstract: A 1-10 Gbps receiver analog front end in 0.13 /spl mu/m CMOS enables a SERDES cell for backplane serial communications using differential PAM2, PAM4, or PAM2 partial response signaling with adaptive equalization. Dynamic sampler swapping and various built-in diagnostic capabilities enable receiver calibration and self-characterization with accuracy of < 0.4% UI in timing and < 2mV in voltage while receiving live data. Self-characterization results motivate modifications enabling communications at a BER of 10/sup -15/ with receiver sensitivity of +/-15mV.


An algorithm for drawing general undirected graphs


title: Millimeter Wave Multi-Beam-Switching Antenna

abstract: The paper introduces a high gain end-fire printed antenna operating at 14 GHz with a bandwidth of 2 GHz. The 3x3 antenna sub-array formed of this element is used to present its beam steering and multi-beam-switching capabilities for millimeter wave Massive MIMO antenna arrays of 5G networks. The co-existence of these two techniques in a Massive MIMO sub-array, aided by the use of a simple two vector excitation matrix technique, is shown in this paper. The high gain and small form factor of the antenna is useful in applications, like 5G arrays, where space is premium and the inherent layered structure also aids to the better cooling of the antenna front end.


title: Direct predictive control of five-level dual flying capacitor active neutral point clamped converters with extended horizons

abstract: Five-level dual-flying-capacitor active-neutral-point-clamped converter (5L-DFC-ANPC) exhibits attractive properties, e.g., soft switching possibilities of line frequency switches, elimination of the transient voltage balancing snubbers, more even loss distribution, etc. Therefore, it has become a very attractive topology to electrical drives, e-mobility, grid-tied photovoltaic applications, etc. However, the existing great amount of switch combinations and capacitors render the modulator design tedious and complicated. Direct control without modulator can be a viable alternative, among which direct model predictive control (DMPC) shows straightforward concept meanwhile excellent control dynamics. In this work, we proposed a DMPC solution for the control of a PMSM drive system with 5L-DFC-ANPC converter. The proposed solution is simple in realization but good in transient control performances. To improve the steady state control performances, we further extended the prediction horizon, resulting in lower current distortions.


title: Design of a compact ultra wideband 3 dB microstrip-slot coupler with high return losses and isolation

abstract: The paper presents the configuration and design method of a compact ultra wideband 3 dB directional coupler with high return loss and isolation. The proposed device is formed by two elliptically shaped microstrip lines, which are broadside coupled through an elliptically shaped slot. In order to improve this coupler's return loss and isolation characteristics a small conducting disk of elliptical shape is included between the two microstrip lines. Results of the full electromagnetic simulations show that the coupler exhibits a coupling of 3.4 dB ± 1.2 dB accompanied by the isolation and return loss of more than 28 dB across the frequency band of 3.0 to 12.0 GHz.


title: Research Article Computationally Efficient Power Allocation Algorithm in Multicarrier-Based Cognitive Radio Networks: OFDM and FBMC Systems

abstract: Cognitive Radio (CR) systems have been proposed to increase the spectrum utilization by opportunistically access the unused spectrum. Multicarrier communication systems are promising candidates for CR systems. Due to its high spectral efficiency, filter bank multicarrier (FBMC) can be considered as an alternative to conventional orthogonal frequency division multiplexing (OFDM) for transmission over the CR networks. This paper addresses the problem of resource allocation in multicarrier-based CR networks. The objective is to maximize the downlink capacity of the network under both total power and interference introduced to the primary users (PUs) constraints. The optimal solution has high computational complexity which makes it unsuitable for practical applications and hence a low complexity suboptimal solution is proposed. The proposed algorithm utilizes the spectrum holes in PUs bands as well as active PU bands. The performance of the proposed algorithm is investigated for OFDM and FBMC based CR systems. Simulation results illustrate that the proposed resource allocation algorithm with low computational complexity achieves near optimal performance and proves the efficiency of using FBMC in CR context.


title: Use of Sequence-Independent, Single-Primer-Amplification (SISPA) for rapid detection, identification, and characterization of avian RNA viruses

abstract: Current technologies with next generation sequencing have revolutionized metagenomics analysis of clinical samples. To achieve the non-selective amplification and recovery of low abundance genetic sequences, a simplified Sequence-Independent, Single-Primer Amplification (SISPA) technique in combination with MiSeq platform was applied to target negative- and positive-sense single-stranded RNA viral sequences. This method allowed successful sequence assembly of full or near full length avian influenza virus (AIV), infectious bronchitis virus (IBV), and Newcastle disease virus (NDV) viral genome. Moreover, SISPA analysis applied to unknown clinical cases of mixed viral infections produced genome assemblies comprising 98% NDV and 99% of IBV genomes. Complete or near complete virus genome sequence was obtained with titers at or above 104.5 EID50/ml (50% embryo infectious dose), and virus identification could be detected with titers at or above 103 EID50/ml. Taken together, these studies demonstrate a simple template enrichment protocol for rapid detection and accurate characterization of avian RNA viruses.


title: Modeling Laser Intensities For Simultaneous Localization and Mapping

abstract: The objective of this letter is to develop a data-driven model of laser intensities and investigate its usage for simultaneous localization and mapping (SLAM) in the field of robotics. In contrast to the standard usage of laser scanners in SLAM to generate geometric models of the environment, the research work on the applications of laser intensities is rather limited. A typical laser scanner measures the distance to an object as well as quantifies the received optical power after reflection which is termed as intensity . The most interesting and relevant aspect of intensities in context of this letter is that they are dependent on an intrinsic surface property i.e., reflectivity as well as extrinsic parameters such as the distance to the surface and angle of incidence with respect to the surface normal. This letter focuses on modeling the influence of extrinsic parameters on intensities to acquire a pose-invariant measure of surface reflectivity. This measure is used in an extension of Hector SLAM in which a robot simultaneously acquires a geometric model augmented with surface reflectivity characteristics. An extensive experimental evaluation is carried out in an indoor environment to highlight the advantages and characteristics of the data-driven model and the proposed Hector SLAM extension.


title: Endocannabinoid System and Psychiatry: In Search of a Neurobiological Basis for Detrimental and Potential Therapeutic Effects

abstract: Public concern on mental health has noticeably increased given the high prevalence of neuropsychiatric disorders. Cognition and emotionality are the most affected functions in neuropsychiatric disorders, i.e., anxiety disorders, depression, and schizophrenia. In this review, most relevant literature on the role of the endocannabinoid (eCB) system in neuropsychiatric disorders will be presented. Evidence from clinical and animal studies is provided for the participation of CB1 and CB2 receptors (CB1R and CB2R) in the above mentioned neuropsychiatric disorders. CBRs are crucial in some of the emotional and cognitive impairments reported, although more research is required to understand the specific role of the eCB system in neuropsychiatric disorders. Cannabidiol (CBD), the main non-psychotropic component of the Cannabis sativa plant, has shown therapeutic potential in several neuropsychiatric disorders. Although further studies are needed, recent studies indicate that CBD therapeutic effects may partially depend on facilitation of eCB-mediated neurotransmission. Last but not least, this review includes recent findings on the role of the eCB system in eating disorders. A deregulation of the eCB system has been proposed to be in the bases of several neuropsychiatric disorders, including eating disorders. Cannabis consumption has been related to the appearance of psychotic symptoms and schizophrenia. In contrast, the pharmacological manipulation of this eCB system has been proposed as a potential strategy for the treatment of anxiety disorders, depression, and anorexia nervosa. In conclusion, the eCB system plays a critical role in psychiatry; however, detrimental consequences of manipulating this endogenous system cannot be underestimated over the potential and promising perspectives of its therapeutic manipulation.


title: Big Data Security and Privacy Issues in Healthcare

abstract: With the ever-increasing cost for healthcare and increased health insurance premiums, there is a need for proactive healthcare and wellness. In addition, the new wave of digitizing medical records has seen a paradigm shift in the healthcare industry. As a result, the healthcare industry is witnessing an increase in sheer volume of data in terms of complexity, diversity and timeliness. As healthcare experts look for every possible way to lower costs while improving care process, delivery and management, big data emerges as a plausible solution with the promise to transform the healthcare industry. This paradigm shift from reactive to proactive healthcare can result in an overall decrease in healthcare costs and eventually lead to economic growth. While the healthcare industry harnesses the power of big data, security and privacy issues are at the focal point as emerging threats and vulnerabilities continue to grow. In this paper, we present the state-of-the-art security and privacy issues in big data as applied to healthcare industry.


title: The Psychometric Properties of the Smartphone Application-Based Addiction Scale (SABAS)

abstract: The goal of the study was to validate the English version of the Smartphone Application-Based Addiction Scale (SABAS; Csibi et al. 2016), which is a short and easy-to-use tool for screening the risk of smartphone application-based addiction. Another aim was to identify the most frequently used smartphone applications and their perceived importance by the participants. Data were collected online from 240 English-speaking volunteers, aged 18 to 69 years. The instruments used were the SABAS, the Nomophobia Questionnaire (NMP-Q), the Brief Sensation Seeking Scale (BSSS), the Deprivation Sensation Scale (DSS), and the Patient Health Questionnaire (PHQ-9). Participants also ranked the importance of their most frequently used smartphone applications. The six items of the SABAS yielded one component, which accounted for 52.38% of the total variance. The internal reliability of the scale was good (Cronbach’s alpha 0.81). NMP-Q was a significant predictor of SABAS, explaining 17.6% of the total variance. The regression analysis, with SABAS score as the dependent variable and NMP-Q, DSS, PHQ-9, and BSSS scores as predictors, indicated that approximately 47% of the variance in SABAS was accounted for by the predictors (R 2 = 0.47). The English version of the SABAS appears to be a valid and reliable ultra-brief tool for a quick and easy assessment of smartphone application-based addiction symptoms.


title: Real-Time Fisheye Lens Distortion Correction Using Automatically Generated Streaming Accelerators

abstract: Fisheye lenses are often used in scientific or virtual reality applications to enlarge the field of view of a conventional camera. Fisheye lens distortion correction is an image processing application which transforms the distorted fisheye images back to the natural-looking perspective space. This application is characterized by non-linear streaming memory access patterns that make main memory bandwidth a key performance limiter. We have developed a fisheye lens distortion correction system on a custom board that includes a Xilinx Virtex-4 FPGA. We express the application in a high level streaming language, and we utilize Proteus, an architectural synthesis tool, to quickly explore the design space and generate the streaming accelerator best suited for our cost and performance constraints. This paper shows that appropriate ESL tools enable rapid prototyping and design of real-life, performance critical and cost sensitive systems with complex memory access patterns and hardware-software interaction mechanisms.


title: â€œI am borrowing ya mixing ?â€ An Analysis of English-Hindi Code Mixing in Facebook

abstract: Code-Mixing is a frequently observed phenomenon in social media content generated by multi-lingual users. The processing of such data for linguistic analysis as well as computational modelling is challenging due to the linguistic complexity resulting from the nature of the mixing as well as the presence of non-standard variations in spellings and grammar, and transliteration. Our analysis shows the extent of Code-Mixing in English-Hindi data. The classification of Code-Mixed words based on frequency and linguistic typology underline the fact that while there are easily identifiable cases of borrowing and mixing at the two ends, a large majority of the words form a continuum in the middle, emphasizing the need to handle these at different levels for automatic processing of the data.


Further Normalization of the Data Base Relational Model


title: Diagnosis and management of uncomplicated urinary tract infections.

abstract: Most uncomplicated urinary tract infections occur in women who are sexually active, with far fewer cases occurring in older women, those who are pregnant, and in men. Although the incidence of urinary tract infection has not changed substantially over the last 10 years, the diagnostic criteria, bacterial resistance patterns, and recommended treatment have changed. Escherichia coli is the leading cause of urinary tract infections, followed by Staphylococcus saprophyticus. Trimethoprim-sulfamethoxazole has been the standard therapy for urinary tract infection; however, E. coli is becoming increasingly resistant to medications. Many experts support using ciprofloxacin as an alternative and, in some cases, as the preferred first-line agent. However, others caution that widespread use of ciprofloxacin will promote increased resistance.


title: How to train your dragon: example-guided control of flapping flight

abstract: Imaginary winged creatures in computer animation applications are expected to perform a variety of motor skills in a physically realistic and controllable manner. Designing physics-based controllers for a flying creature is still very challenging particularly when the dynamic model of the creatures is high-dimensional, having many degrees of freedom. In this paper, we present a control method for flying creatures, which are aerodynamically simulated, interactively controllable, and equipped with a variety of motor skills such as soaring, gliding, hovering, and diving. Each motor skill is represented as Deep Neural Networks (DNN) and learned using Deep Q-Learning (DQL). Our control method is example-guided in the sense that it provides the user with direct control over the learning process by allowing the user to specify keyframes of motor skills. Our novel learning algorithm was inspired by evolutionary strategies of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to improve the convergence rate and the final quality of the control policy. The effectiveness of our Evolutionary DQL method is demonstrated with imaginary winged creatures flying in a physically simulated environment and their motor skills learned automatically from user-provided keyframes.


title: Theoretical study of the electronic spectra of small molecules that incorporate analogues of the copper-cysteine bond.

abstract: The copper-sulfur bond that binds cysteinate to the metal center is a key factor in the spectroscopy of blue copper proteins. We present theoretical calculations describing the electronically excited states of small molecules, including CuSH, CuSCH(3), (CH(3))(2)SCuSH, (imidazole)-CuSH, and (imidazole)(2)-CuSH, derived from the active site of blue copper proteins that contain the copper-sulfur bond in order to identify small molecular systems that have electronic structure that is analogous to the active site of the proteins. Both neutral and cationic forms are studied since these represent the reduced and oxidized forms of the protein, respectively. For CuSH and CuSH(+), excitation energies from time-dependent density functional theory with the B97-1 exchange-correlation functional agree well with the available experimental data and multireference configuration interaction calculations. For the positive ions, the singly occupied molecular orbital is formed from an antibonding combination of a 3d orbital on copper and a 3p(π) orbital on sulfur, which is analogous to the protein. This leads several of the molecules to have qualitatively similar electronic spectra to the proteins. For the neutral molecules, changes in the nature of the low lying virtual orbitals leads the predicted electronic spectra to vary substantially between the different molecules. In particular, addition of a ligand bonded directly to copper results in the low-lying excited states observed in CuSH and CuSCH(3) to be absent or shifted to higher energies.


title: Serendipity in the Research Literature: A Phenomenology of Serendipity Reporting

abstract: The role of information sciences is to connect people with the information they need to accomplish the tasks that contribute to the greater societal good. While evidence of the wonderful contributions arising from serendipitous events abound, the framework describing the information behaviors exhibited during a serendipitous experience is just emerging and additional detail regarding the factors influencing those behaviors is needed in order to support these experiences effectively. Furthermore, it is important to understand the whole process of serendipity to fully appreciate the impact of research policies, disciplinary traditions and academic reporting practices on this unique type of information behavior. This study addresses those need by examining the phenomenon of serendipity as it is reported by biomedical and radiography researchers. A mixed method content analysis of existing research reports will be incorporated with semi-structured interviews of serendipity reporters to gain a robust understanding of the phenomenon of serendipity, and provide detail that may inform the design of information environments.


title: Measuring mental toughness in sport: A psychometric examination of the Psychological Performance Inventory-A and its predecessor

abstract: Touted as a multidimensional measure of mental toughness in sport, this study explored the psychometric properties of the Psychological Performance Inventory (PPI; Loehr, 1986) and its successor the Psychological Performance Inventory–A (PPI–A; Golby, Sheard, & Van Wersch, 2007). Confirmatory factor analysis was employed to examine the extent to which data collected with 333 Australian footballers aged between 15 and 18 years (M = 16.88, SD = .71) fitted the a priori measurement models of both inventories. The results did not support the psychometric properties of the PPI both in terms of model fit and internal consistency. Although model fit data for the PPI–A were encouraging, inadequate levels of internal consistency were evidenced. Convergent validity analyses involving measures of achievement goals and global mental toughness generally supported the validity of the PPI and PPI–A subscales. Taken together with previous research (e.g., Middleton et al., 2004), caution is urged when considering the use ...


title: Power System Load Flow Analysis using Microsoft Excel

abstract: This paper presents the design and development of a Microsoft Excel based Power System Load Flow Analysis (MSEBPSLF) tool and its application for system planning and operation. This is a simple desktop tool which provides an interactive and simplified interface for users to store different systems with different operating conditions and then to observe the response of the system. Four different load flow algorithms have been implemented to provide wider choice for the users. All intermediate numerical results are made available for verification purposes. End results are verified and benchmarked with standard applications such as PSS/E, PowerWorld, InterPSS etc. The standard IEEE 14-bus system is provided with the spreadsheet to provide a head start for users.


title: Effect of Coffee and Caffeine Ingestion on Resistance Exercise Performance.

abstract: AbstractRichardson, DL and Clarke, ND. Effect of coffee and caffeine ingestion on resistance exercise performance. J Strength Cond Res 30(10): 2892–2900, 2016—The aim of the present study was to determine the effect of ingesting caffeine dose-matched anhydrous caffeine, coffee, or decaffeinated coff


title: CU-LTE: Spectrally-efficient and fair coexistence between LTE and Wi-Fi in unlicensed bands

abstract: To cope with the increasing scarcity of spectrum resources, researchers have been working to extend LTE/LTE-A cellular systems to unlicensed bands, leading to so-called unlicensed LTE (U-LTE). However, this extension is by no means straightforward, primarily because the radio resource management schemes used by LTE and by systems already deployed in unlicensed bands are incompatible. Specifically, it is well known that coexistence with scheduled systems like LTE degrades considerably the throughput of Wi-Fi networks that are based on carrier-sense medium access schemes. To address this challenge, we propose for the first time a cognitive coexistence scheme to enable spectrum sharing between U-LTE and Wi-Fi networks, referred to as CU-LTE. The proposed scheme is designed to jointly determine dynamic channel selection, carrier aggregation and fractional spectrum access for U-LTE networks, while guaranteeing fair spectrum access for Wi-Fi based on a newly designed cross-technology fairness criterion. We first derive a mathematical model of the spectrum sharing problem for the coexisting networks; we then design a solution algorithm to solve the resulting fairness constrained mixed integer nonlinear optimization problem. The algorithm, based on a combination of branch and bound and convex relaxation techniques, maximizes the network utility with guaranteed optimality precision that can be set arbitrarily to 1 at the expense of computational complexity. Performance evaluation indicates that near-optimal spectrum access can be achieved with guaranteed fairness between U-LTE and Wi-Fi. Issues regarding implementation of CU-LTE are also discussed.


title: Robotic movement elicits automatic imitation

abstract: Recent behavioural and neuroimaging studies have found that observation of human movement, but not of robotic movement, gives rise to visuomotor priming. This implies that the Fmirror neuron_ or Faction observation–execution matching_ system in the premotor and parietal cortices is entirely unresponsive to robotic movement. The present study investigated this hypothesis using an Fautomatic imitation_ stimulus–response compatibility procedure. Participants were required to perform a prespecified movement (e.g. opening their hand) on presentation of a human or robotic hand in the terminal posture of a compatible movement (opened) or an incompatible movement (closed). Both the human and the robotic stimuli elicited automatic imitation; the prespecified action was initiated faster when it was cued by the compatible movement stimulus than when it was cued by the incompatible movement stimulus. However, even when the human and robotic stimuli were of comparable size, colour and brightness, the human hand had a stronger effect on performance. These results suggest that effector shape is sufficient to allow the action observation–matching system to distinguish human from robotic movement. They also indicate, as one would expect if this system develops through learning, that to varying degrees both human and robotic action can be Fsimulated_ by the premotor and parietal cortices. D 2005 Elsevier B.V. All rights reserved. Theme: Neural basis of behaviour Topic: Cognition


title: CVXGEN : a code generator for embedded convex optimization

abstract: CVXGEN is a software tool that takes a high level description of a convex optimization problem family, and automatically generates custom C code that compiles into a reliable, high speed solver for the problem family. The current implementation targets problem families that can be transformed, using disciplined convex programming techniques, to convex quadratic programs of modest size. CVXGEN generates simple, flat, library-free code suitable for embedding in real-time applications. The generated code is almost branch free, and so has highly predictable run-time behavior. The combination of regularization (both static and dynamic) and iterative refinement in the search direction computation yields reliable performance, even with poor quality data. In this paper we describe how CVXGEN is implemented, and give some results on the speed and reliability of the automatically generated solvers.


title: Investigating the climate impacts of global land cover change in the community climate system model

abstract: Recently, (Pitman et al., 2009) found a wide range of bio-geophysical climate impacts from historical land cover change when modelled in a suite of current global climate models (GCMs). The bio-geophysical climate impacts of human land cover change, however, have been investigated by a wide range of general circulation modelling, regional climate modelling, and observational studies. In this regard the IPCC 4th assessment report specifies radiative cooling of 0.2 W/m2 as the dominant global impact of human land cover change since 1750, but states this has a low to medium level of scientific understanding. To further contribute to the understanding of the possible climatic impacts of anthropogenic land cover change, we have performed a series of land cover change experiments with the community land model (CLM) within the community climate system model (CCSM). To do this we have developed a new set of potential vegetation land surface parameters to represent land cover change in CLM. The new parameters are consistent with the potential vegetation biome mapping of (Ramankutty and Foley, 1999), with the plant functional types (PFTs) and plant phenology consistent with the current day Moderate Resolution Imaging Spectroradiometer (MODIS) land surface parameters of (Lawrence and Chase, 2007). We found that land cover change in CCSM resulted in widespread regional warming of the near surface atmosphere, but with limited global impact on near surface temperatures. The experiments also found changes in precipitation, with drier conditions regionally, but with limited impact on average global precipitation. Analysis of the surface fluxes in the CCSM experiments found the current day warming was predominantly driven by changes in surface hydrology through reduced evapo-transpiration and latent heat flux, with the radiative forcing playing a secondary role. We show that these finding are supported by a wide range of observational field studies, satellite studies and regional and global climate modelling studies. Copyright © 2010 Royal Meteorological Society


title: On the best rank-1 approximation of higher-order supersymmetric tensors

abstract: Recently the problem of determining the best, in the least-squares sense, rank-1 approximation to a higher-order tensor was studied and an iterative method that extends the well-known power method for matrices was proposed for its solution. This higher-order power method is also proposed for the special but important class of supersymmetric tensors, with no change. A simplified version, adapted to the special structure of the supersymmetric problem, is deemed unreliable, as its convergence is not guaranteed. The aim of this paper is to show that a symmetric version of the above method converges under assumptions of convexity (or concavity) for the functional induced by the tensor in question, assumptions that are very often satisfied in practical applications. The use of this version entails significant savings in computational complexity as compared to the unconstrained higher-order power method. Furthermore, a novel method for initializing the iterative process is developed which has been observed to yield an estimate that lies closer to the global optimum than the initialization suggested before. Moreover, its proximity to the global optimum is a priori quantifiable. In the course of the analysis, some important properties that the supersymmetry of a tensor implies for its square matrix unfolding are also studied.


title: Internet Addiction among Greek Adolescent Students

abstract: Abstract This research aimed to assess the prevalence of Internet addiction among Greek adolescent students, ages 12 to 18. The sample of 2,200 students was recruited from 120 classes among 85 schools in Thessaly, Greece. The sample included 10% of all classes in schools of Thessaly. The method of randomized stratified selection in every school was used for its constitution. Participants were asked to complete the Diagnostic Questionnaire for Internet Addiction (YDQ), based on eight criteria, as well as an inventory that included sociodemographic factors and questions about the use of Internet, their social life, and their habits. In Greece, 70.8% of adolescents had access to the Internet. The consistency of the YDQ was tested with Cronbach's alpha (0.719), with standardized item alpha (0.728). Proportions are also calculated only on the frequent Internet users, which results in 11% fulfilling five YDQ criteria. The most frequent type of Internet use is online games, representing 50.9% of Internet users, ...


title: Integration of Mitigation, Intention, and Outcome Damage Information, by Students and Circuit Court Judges1

abstract: This article examines the problem of how undergraduate students and State Circuit Court Judges combine three specific components of information (pertaining to mitigation, intention, and seriousness of outcome damage) to make a net evaluative judgment of blame. The problem is considered within the framework and philosophy of Anderson's information integration and functional measurement theory. In three experiments, two levels each of the three information components identified were factorially crossed for two independent cases: self-defense under provocation, and altruistic defense of another. Target stimuli consisted of 100-word scenarios each embodying one factorial treatment combination. Experiment 1 used an independent group and Experiment 2 a repeated measures design. Experiment 3 replicated the second experiment using Judges as subjects. It was found that students integrated mitigation, intention, and outcome damage information by an adding process, and that the results concerning these three independent variables were essentially similar for both types of design. The most striking differences between Judges' and students' judgments centered on the differential use of mitigation information. Judges showed a far broader range of nonadditive, simple interaction effects between mitigation and both outcome damage and type of case. These results presumably reflect the primary concerns and responsibilities of the Judiciary. Questions of mitigation may thus complicate the otherwise straightforward additive integration of information in the intent-damage paradigm, and in judgments of seriousness.


title: Toward Computation and Memory Efficient Neural Network Acoustic Models with Binary Weights and Activations

abstract: Neural network acoustic models have significantly advanced state of the art speech recognition over the past few years. However, they are usually computationally expensive due to the large number of matrix-vector multiplications and nonlinearity operations. Neural network models also require significant amounts of memory for inference because of the large model size. For these two reasons, it is challenging to deploy neural network based speech recognizers on resource-constrained platforms such as embedded devices. This paper investigates the use of binary weights and activations for computation and memory efficient neural network acoustic models. Compared to real-valued weight matrices, binary weights require much fewer bits for storage, thereby cutting down the memory footprint. Furthermore, with binary weights or activations, the matrix-vector multiplications are turned into addition and subtraction operations, which are computationally much faster and more energy efficient for hardware platforms. In this paper, we study the applications of binary weights and activations for neural network acoustic modeling, reporting encouraging results on the WSJ and AMI corpora.


title: Dual-Band Dual-Sense Circularly-Polarized CPW-Fed Slot Antenna With Two Spiral Slots Loaded

abstract: A novel design of a dual-band dual-sense circularly-polarized CPW-fed slot antenna with two spiral slots loaded is presented. The two loaded spiral slots in the ground plane can result in different senses of circular polarization. The dual-band operation is achieved by using the T-shape strip. The ratio of two frequencies is 1.375. The antenna is designed, fabricated, and measured. It is shown that the proposed antenna has good circular polarization characteristic. The measured 10 dB return loss impedance bandwidths are 8.7% for the lower band (RHCP) and 23% for the upper band (LHCP). The measured 3 dB axial-ratio bandwidths are 8.4% and 19.24%, with respect to 1.6 GHz (RHCP) and 2.2 GHz (LHCP), respectively.


Teaching Robots Using Mixed Reality


title: A two factor theory for Website design

abstract: This study was designed to verify whether an analogy to Herzberg's hygiene-motivational theory about workplace could be used in the Web environment. According to this analogy, the presence of hygiene factors would provide the basic functionality of a Web site, while their absence would create user dissatisfaction. Motivating factors are those that contribute to user satisfaction. They add additional value and may entice users to keep returning to a Web site. In Phase I, we identified 44 core features in the Web environment that were classified into 12 categories by subjects. In Phase II, a different group of subjects in a pilot study were asked to distinguish between the hygiene and motivational characteristics of these features and categories. The preliminary results show that 4 categories and 14 features were judged to be primarily motivational, while 3 categories and 13 features were perceived to be primarily hygiene in nature. The remaining 5 categories and 17 features were perceived to be both.


MOANTOOL : A Matlab Toolbox for Model Predictive Control with Obstacle Avoidance


title: BUSINESS ANALYTICS: ENABLING STRATEGIC ALIGNMENT AND ORGANISATIONAL TRANSFORMATION

abstract: Business analytics systems can potentially create value and provide competitive advantage to organisations. We argue that business analytics systems can also enable alignment between business strategy and IT strategy and support organisational transformation. We explain how a data warehouse, standardised metrics and global reporting supported the strategic transformation of a diversified and multi-layered international mining company to a unified and integrated global organisation. We identify a number of factors critical to the success of the business analytics initiative and to strategic alignment and organisational transformation, including: the early definition of globally standard metrics and dimensions; a high quality technology infrastructure and high quality data; sustained senior management support together with strong leadership within the business analytics project; effective governance structures that support change management and continual renewal of business analytics capabilities; and hybrid people with a mix of technical, business and strong communication skills. A number of implications of the study are discussed and suggestions for future work are provided.


title: Random Survival Forests for High-Dimensional Data

abstract: Minimal depth is a dimensionless order statistic that measures the predictiveness of a variable in a survival tree. It can be used to select variables in high-dimensional problems using Random Survival Forests (RSF), a new extension of Breiman's Random Forests (RF) to survival settings. We review this methodology and demonstrate its use in high-dimensional survival problems using a public domain R-language package randomSurvivalForest . We discuss effective ways to regularize forests and discuss how to properly tune the RF parameters ‘nodesize’ and ‘mtry’. We also introduce new graphical ways of using minimal depth for exploring variable relationships. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 115–132 2011 © 2011 Wiley Periodicals, Inc.