Internet Addiction and Relationships with Insomnia, Anxiety, Depression, Stress and Self-Esteem in University Students: A Cross-Sectional Designed Study

BACKGROUND AND AIMS
Internet addiction (IA) could be a major concern in university medical students aiming to develop into health professionals. The implications of this addiction as well as its association with sleep, mood disorders and self-esteem can hinder their studies, impact their long-term career goals and have wide and detrimental consequences for society as a whole. The objectives of this study were to: 1) Assess potential IA in university medical students, as well as factors associated with it; 2) Assess the relationships between potential IA, insomnia, depression, anxiety, stress and self-esteem.

METHODS
Our study was a cross-sectional questionnaire-based survey conducted among 600 students of three faculties: medicine, dentistry and pharmacy at Saint-Joseph University. Four validated and reliable questionnaires were used: the Young Internet Addiction Test, the Insomnia Severity Index, the Depression Anxiety Stress Scales (DASS 21), and the Rosenberg Self Esteem Scale (RSES).

RESULTS
The average YIAT score was 30 ± 18.474; Potential IA prevalence rate was 16.8% (95% confidence interval: 13.81-19.79%) and it was significantly different between males and females (p-value = 0.003), with a higher prevalence in males (23.6% versus 13.9%). Significant correlations were found between potential IA and insomnia, stress, anxiety, depression and self-esteem (p-value < 0.001); ISI and DASS sub-scores were higher and self-esteem lower in students with potential IA.

CONCLUSIONS
Identifying students with potential IA is important because this addiction often coexists with other psychological problems. Therefore, interventions should include not only IA management but also associated psychosocial stressors such as insomnia, anxiety, depression, stress, and self-esteem.


Sensorless current estimation and sharing in multiphase input-parallel output-parallel DC-DC converters

This paper introduces a sensorless current-sharing strategy for multiphase input-parallel output-parallel (IPOP) DC-DC converters. A dual active bridge (DAB) DC-DC converter is chosen as the basic DC-DC converter. With this strategy, by perturbing the duty cycles in (n-1) out of n phases in turn and measuring the changes of duty cycles respectively, the parameters mismatches among phases are estimated. According to the mismatches, a set of variables, which are proportional to the per-phase output currents, are calculated. Then with a current-sharing regulator, parameter mismatches are compensated, thus achieving current sharing without current sensing. The strategy is verified through both simulation and experimental implementation with a 30V-to-70V, 272W, 20kHz, three-phase IPOP DC-DC converter.


Neural mechanisms of selective visual attention.

The two basic phenomena that define the problem of visual attention can be illustrated in a simple example. Consider the arrays shown in each panel of Figure 1. In a typical experiment, before the arrays were presented, subjects would be asked to report letters appearing in one color (targets, here black letters), and to disregard letters in the other color (nontargets, here white letters). The array would then be briefly flashed, and the subjects, without any opportunity for eye movements, would give their report. The display mimics our. usual cluttered visual environment: It contains one or more objects that are relevant to current behavior, along with others that are irrelevant. The first basic phenomenon is limited capacity for processing information. At any given time, only a small amount of the information available on the retina can be processed and used in the control of behavior. Subjectively, giving attention to any one target leaves less available for others. In Figure 1, the probability of reporting the target letter N is much lower with two accompa nying targets (Figure la) than with none (Figure Ib). The second basic phenomenon is selectivity-the ability to filter out un wanted information. Subjectively, one is aware of attended stimuli and largely unaware of unattended ones. Correspondingly, accuracy in identifying an attended stimulus may be independent of the number of nontargets in a display (Figure la vs Ie) (see Bundesen 1990, Duncan 1980).


Discrete Delaunay: Boundary extraction from voxel objects

We present a discrete approach for boundary extraction from 3D image data. The proposed technique is based on the duality between the Voronoi graph computed accross the digital boundary and the Delaunay triangulation. The originality of the approach is that algorithms perform only integer arithmetic and the method does not suffer from standard round problems and numerical instabilities in the case of floating point computations. This method has been applied both on segmented anatomical structures and on manufactured objects presenting corners and edges. The experimental results show that the method allows to produce a polygonal boundary representation which is guaranteed to be a 2-manifold. This representation is successfully transformed into a triangular quality mesh which meets all topological and geometrical requirements of applications such as augmented reality or simulation.


Fundamental Limits of Wideband Localization— Part I: A General Framework

The availability of position information is of great importance in many commercial, public safety, and military applications. The coming years will see the emergence of location-aware networks with submeter accuracy, relying on accurate range measurements provided by wide bandwidth transmissions. In this two-part paper, we determine the fundamental limits of localization accuracy of wideband wireless networks in harsh multipath environments. We first develop a general framework to characterize the localization accuracy of a given node here and then extend our analysis to cooperative location-aware networks in Part II. In this paper, we characterize localization accuracy in terms of a performance measure called the squared position error bound (SPEB), and introduce the notion of equivalent Fisher information (EFI) to derive the SPEB in a succinct expression. This methodology provides insights into the essence of the localization problem by unifying localization information from individual anchors and that from a priori knowledge of the agent's position in a canonical form. Our analysis begins with the received waveforms themselves rather than utilizing only the signal metrics extracted from these waveforms, such as time-of-arrival and received signal strength. Hence, our framework exploits all the information inherent in the received waveforms, and the resulting SPEB serves as a fundamental limit of localization accuracy.


Coupled Bayesian Sets Algorithm for Semi-supervised Learning and Information Extraction

Our inspiration comes from Nell (Never Ending Language Learning), a computer program running at Carnegie Mellon University to extract structured information from unstructured web pages. We consider the problem of semi-supervised learning approach to extract category instances (e.g. country(USA), city(New York)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semisupervised approaches using a small number of labeled examples together with many unlabeled examples are often unreliable as they frequently produce an internally consistent, but nevertheless, incorrect set of extractions. We believe that this problem can be overcome by simultaneously learning independent classifiers in a new approach named Coupled Bayesian Sets algorithm, based on Bayesian Sets, for many different categories and relations (in the presence of an ontology defining constraints that couple the training of these classifiers). Experimental results show that simultaneously learning a coupled collection of classifiers for random 11 categories resulted in much more accurate extractions than training classifiers through original Bayesian Sets algorithm, Naive Bayes, BaS-all and Coupled Pattern Learner (the category extractor used in NELL).


Polarity Lexicon Building: to what Extent Is the Manual Effort Worth?

Polarity lexicons are a basic resource for analyzing the sentiments and opinions expressed in texts in an automated way. This paper explores three methods to construct polarity lexicons: translating existing lexicons from other languages, extracting polarity lexicons from corpora, and annotating sentiments Lexical Knowledge Bases. Each of these methods require a different degree of human effort. We evaluate how much manual effort is needed and to what extent that effort pays in terms of performance improvement. Experiment setup includes generating lexicons for Basque, and evaluating them against gold standard datasets in different domains. Results show that extracting polarity lexicons from corpora is the best solution for achieving a good performance with reasonable human effort.


Exploring specialized near-memory processing for data intensive operations

Emerging 3D stacked memory systems provide significantly more bandwidth than current DDR modules. However, general purpose processors do not take full advantage of these resources offered by the memory modules. Taking advantage of the increased bandwidth requires the use of specialized processing units. In this paper, we evaluate the benefits of placing hardware accelerators at the bottom layer of a 3D stacked memory system compared to accelerators that are placed external to the memory stack. Our evaluation of the design using cycle-accurate simulation and RTL synthesis shows that, for important data intensive kernels, near-memory accelerators inside a single 3D memory package provide 3x-13x speedup over a Quad-core Xeon processor. Most of the benefits are from the application of accelerators, as the near-memory configurations provide marginal benefits compared to the same number of accelerators placed on a die external to the memory package. This comparable performance for external accelerators is due to the high bandwidth afforded by the high-speed off-chip links. On the other hand, near-memory accelerators consume 7%-39% less energy than the external accelerators.


Towards an Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with qualitative and quantitative descriptions of uncertainty and domain knowledge. An action language is used for the architecture’s low-level (LL) and high-level (HL) system descriptions, and the HL definition of recorded history is expanded to allow prioritized defaults. For any given objective, tentative plans created in the HL using commonsense reasoning are implemented in the LL using probabilistic algorithms, and the corresponding observations are added to the HL history. Tight coupling between the levels helps automate the selection of relevant variables and the generation of policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in complex domains. The architecture is evaluated in simulation and on robots moving objects in indoor domains.


A framework to build bespoke auto-tuners with structured Bayesian optimisation

Due to their complexity, modern computer systems expose many configuration parameters which users must manually tune to maximise the performance of their applications. To relieve users of this burden, auto-tuning has emerged as an alternative in which a black-box optimiser iteratively evaluates configurations to find efficient ones. A popular auto-tuning technique is Bayesian optimisation, which uses the results to incrementally build a probabilistic model of the impact of the parameters on performance. This allows the optimisation to quickly focus on efficient regions of the configuration space. Unfortunately, for many computer systems, either the configuration space is too large to develop a good model, or the time to evaluate performance is too long to be executed many times. In this dissertation, I argue that by extracting a small amount of domain specific knowledge about a system, it is possible to build a bespoke auto-tuner with significantly better performance than its off-the-shelf counterparts. This could be performed, for example, by a system engineer who has a good understanding of the underlying system behaviour and wants to provide performance portability. This dissertation presents BOAT, a framework to build BespOke Auto-Tuners. BOAT offers a novel set of abstractions designed to make the exposition of domain knowledge easy and intuitive. First, I introduce Structured Bayesian Optimisation (SBO), an extension of the Bayesian optimisation algorithm. SBO can leverage a bespoke probabilistic model of the system's behaviour, provided by the system engineer, to rapidly converge to high performance configurations. The model can benefit from observing many runtime measurements per evaluation of the system, akin to the use of profilers. Second, I present Probabilistic-C++ a lightweight, high performance probabilistic programming library. It allows users to declare a probabilistic models of their system's behaviour and expose it to an SBO. Probabilistic programming is a recent tool from the Machine Learning community making the declaration of structured probabilistic models intuitive. Third, I present a new optimisation scheduling abstraction which offers a structured way to express optimisations which themselves execute other optimisations. For example, this is useful to express Bayesian optimisations, which each iteration execute a numerical optimisation. Furthermore, it allows users to easily implement decompositions which exploit the loose coupling of subsets of the configuration parameters to optimise them almost independently.


A Novel Technique for English Font Recognition Using Support Vector Machines

Font Recognition is one of the Challenging tasks in Optical Character Recognition. Most of the existing methods for font recognition make use of local typographical features and connected component analysis. In this paper, English font recognition is done based on global texture analysis. The main objective of this proposal is to employ support vector machines (SVM) in identifying various fonts. The feature vectors are extracted by making use of Gabor filters and the proposed SVM is trained using these features. The method is found to give superior performance over neural networks by avoiding local minima points. The SVM model is formulated tested and the results are presented in this paper. It is observed that this method is content independent and the SVM classifier shows an average accuracy of 93.54%.


Mobile ad hoc networking: imperatives and challenges

Mobile ad hoc networks (MANETs) represent complex distributed systems that comprise wireless mobile nodes that can freely and dynamically self-organize into arbitrary and temporary, ‘‘ad-hoc’’ network topologies, allowing people and devices to seamlessly internetwork in areas with no pre-existing communication infrastructure, e.g., disaster recovery environments. Ad hoc networking concept is not a new one, having been around in various forms for over 20 years. Traditionally, tactical networks have been the only communication networking application that followed the ad hoc paradigm. Recently, the introduction of new technologies such as the Bluetooth, IEEE 802.11 and Hyperlan are helping enable eventual commercial MANET deployments outside the military domain. These recent evolutions have been generating a renewed and growing interest in the research and development of MANET. This paper attempts to provide a comprehensive overview of this dynamic field. It first explains the important role that mobile ad hoc networks play in the evolution of future wireless technologies. Then, it reviews the latest research activities in these areas, including a summary of MANET s characteristics, capabilities, applications, and design constraints. The paper concludes by presenting a set of challenges and problems requiring further research in the future. 2003 Elsevier B.V. All rights reserved.


Single-Phase Seven-Level Grid-Connected Inverter for Photovoltaic System

This paper proposes a single-phase seven-level inverter for grid-connected photovoltaic systems, with a novel pulsewidth-modulated (PWM) control scheme. Three reference signals that are identical to each other with an offset that is equivalent to the amplitude of the triangular carrier signal were used to generate the PWM signals. The inverter is capable of producing seven levels of output-voltage levels (Vdc, 2Vdc/3, Vdc/3, 0, -Vdc, -2Vdc/3, -Vdc/3) from the dc supply voltage. A digital proportional-integral current-control algorithm was implemented in a TMS320F2812 DSP to keep the current injected into the grid sinusoidal. The proposed system was verified through simulation and implemented in a prototype.


A new model for learning in graph domains

In several applications the information is naturally represented by graphs. Traditional approaches cope with graphical data structures using a preprocessing phase which transforms the graphs into a set of flat vectors. However, in this way, important topological information may be lost and the achieved results may heavily depend on the preprocessing stage. This paper presents a new neural model, called graph neural network (GNN), capable of directly processing graphs. GNNs extends recursive neural networks and can be applied on most of the practically useful kinds of graphs, including directed, undirected, labelled and cyclic graphs. A learning algorithm for GNNs is proposed and some experiments are discussed which assess the properties of the model.


Wavelet Feature Based Confusion Character Sets for Gujarati Script

Indic script recognition is a difficult task due to the large number of symbols that result from concatenation of vowel modifiers to basic consonants and the conjunction of consonants with modifiers etc. Recognition of Gujarati script is a less studied area and no attempt is made so far to constitute confusion sets of Gujarati glyphs. In this paper, we present confusion sets of glyphs in printed Gujarati. Feature vector made up of Daubechies D4 wavelet coefficients were subjected to two different classifiers, giving more than 96% accuracy for a larger set of symbols. Novel application of GR neural-net architecture allows for fast building of a classifier for the large character data set. The combined approach of wavelet feature extraction and GRNN classification has given the highest recognition accuracy reported on this script.


Intelligent fuzzy controller of a quadrotor

The aim of this work is to describe an intelligent system based on fuzzy logic that is developed to control a quadrotor. A quadrotor is a helicopter with four rotors, that make the vehicle more stable but more complex to model and to control. The quadrotor has been used as a testing platform in the last years for various universities and research centres. A quadrotor has six degrees of freedom, three of them regarding the position: height, horizontal and vertical motions; and the other three are related to the orientation: pitch, roll and yaw. A fuzzy control is designed and implemented to control a simulation model of the quadrotor. The inputs are the desired values of the height, roll, pitch and yaw. The outputs are the power of each of the four rotors that is necessary to reach the specifications. Simulation results prove the efficiency of this intelligent control strategy.


Clearing algorithms for barter exchange markets: enabling nationwide kidney exchanges

In barter-exchange markets, agents seek to swap their items with one another, in order to improve their own utilities. These swaps consist of cycles of agents, with each agent receiving the item of the next agent in the cycle. We focus mainly on the upcoming national kidney-exchange market, where patients with kidney disease can obtain compatible donors by swapping their own willing but incompatible donors. With over 70,000 patients already waiting for a cadaver kidney in the US, this market is seen as the only ethical way to significantly reduce the 4,000 deaths per year attributed to kidney diseas.
 The clearing problem involves finding a social welfare maximizing exchange when the maximum length of a cycle is fixed. Long cycles are forbidden, since, for incentive reasons, all transplants in a cycle must be performed simultaneously. Also, in barter-exchanges generally, more agents are affected if one drops out of a longer cycle. We prove that the clearing problem with this cycle-length constraint is NP-hard. Solving it exactly is one of the main challenges in establishing a national kidney exchange.
 We present the first algorithm capable of clearing these markets on a nationwide scale. The key is incremental problem formulation. We adapt two paradigms for the task: constraint generation and column generation. For each, we develop techniques that dramatically improve both runtime and memory usage. We conclude that column generation scales drastically better than constraint generation. Our algorithm also supports several generalizations, as demanded by real-world kidney exchanges.
 Our algorithm replaced CPLEX as the clearing algorithm of the Alliance for Paired Donation, one of the leading kidney exchanges. The match runs are conducted every two weeks and transplants based on our optimizations have already been conducted.


PeerSpace - An Online Collaborative Learning Environment for Computer Science Students

The aim of Peer Space is to promote peer support and peer learning in introductory Computer Science (CS) courses by providing the students with online collaborative tools for convenient synchronous and asynchronous interactions on course related topics and social matters. This paper presents the development of various social and learning components in Peer Space that are unique in promoting collaborative learning. Analysis of preliminary results is presented.


Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition

Interpretation of images and videos containing humans interacting with different objects is a daunting task. It involves understanding scene or event, analyzing human movements, recognizing manipulable objects, and observing the effect of the human movement on those objects. While each of these perceptual tasks can be conducted independently, recognition rate improves when interactions between them are considered. Motivated by psychological studies of human perception, we present a Bayesian approach which integrates various perceptual tasks involved in understanding human-object interactions. Previous approaches to object and action recognition rely on static shape or appearance feature matching and motion analysis, respectively. Our approach goes beyond these traditional approaches and applies spatial and functional constraints on each of the perceptual elements for coherent semantic interpretation. Such constraints allow us to recognize objects and actions when the appearances are not discriminative enough. We also demonstrate the use of such constraints in recognition of actions from static images without using any motion information.


Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization

Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint `2,1-norm minimization on both loss function and regularization. The `2,1-norm based loss function is robust to outliers in data points and the `2,1norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence. Our regression based objective makes the feature selection process more efficient. Our method has been applied into both genomic and proteomic biomarkers discovery. Extensive empirical studies are performed on six data sets to demonstrate the performance of our feature selection method.


A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.


Action recognition in the premotor cortex.

We recorded electrical activity from 532 neurons in the rostral part of inferior area 6 (area F5) of two macaque monkeys. Previous data had shown that neurons of this area discharge during goal-directed hand and mouth movements. We describe here the properties of a newly discovered set of F5 neurons ("mirror neurons', n = 92) all of which became active both when the monkey performed a given action and when it observed a similar action performed by the experimenter. Mirror neurons, in order to be visually triggered, required an interaction between the agent of the action and the object of it. The sight of the agent alone or of the object alone (three-dimensional objects, food) were ineffective. Hand and the mouth were by far the most effective agents. The actions most represented among those activating mirror neurons were grasping, manipulating and placing. In most mirror neurons (92%) there was a clear relation between the visual action they responded to and the motor response they coded. In approximately 30% of mirror neurons the congruence was very strict and the effective observed and executed actions corresponded both in terms of general action (e.g. grasping) and in terms of the way in which that action was executed (e.g. precision grip). We conclude by proposing that mirror neurons form a system for matching observation and execution of motor actions. We discuss the possible role of this system in action recognition and, given the proposed homology between F5 and human Brocca's region, we posit that a matching system, similar to that of mirror neurons exists in humans and could be involved in recognition of actions as well as phonetic gestures.


Learning with Local and Global Consistency

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.


DC and RF breakdown voltage characteristics of SiGe HBTs for WiFi PA applications

Breakdown voltage and RF characteristics relevant for RF power amplifiers (PA) are presented in this paper. Typically, DC collector-to-emitter breakdown voltage with base open (BVCEO) or DC collector-to-base breakdown with emitter open (BVCBO) has been presented as the metric for voltage limit of PA devices. In practical PA circuits, the RF envelope voltage can swing well beyond BVCEO without causing a failure. An analysis of output power swing limitations and DC breakdown is presented with attention to biasing and temperature.


Cognitive Reduced Prefrontal Connectivity in Psychopathy

Julian C. Motzkin,1 Joseph P. Newman,2 Kent A. Kiehl,3,4 and Michael Koenigs1 1Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin 53719, 2Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin 53706, 3The MIND Research Network, Albuquerque, New Mexico 87131, and 4Departments of Psychology and Neuroscience, University of New Mexico, Albuquerque, New Mexico 87131


BVFG An Unbiased Detector of Curvilinear Structures

The extraction of curvilinear structures is an important low-level operation in computer vision that has many applications. Most existing operators use a simple model for the line that is to be extracted, i.e., they do not take into account the surroundings of a line. This leads to the undesired consequence that the line will be extracted in the wrong position whenever a line with different lateral contrast is extracted. In contrast, the algorithm proposed in this paper uses an explicit model for lines and their surroundings. By analyzing the scale-space behaviour of a model line profile, it is shown how the bias that is induced by asymmetrical lines can be removed. Furthermore, the algorithm not only returns the precise sub-pixel line position, but also the width of the line for each line point, also with sub-pixel accuracy.


Design and implementation of an Android host-based intrusion prevention system

Android has a dominating share in the mobile market and there is a significant rise of mobile malware targeting Android devices. Android malware accounted for 97% of all mobile threats in 2013 [26]. To protect smartphones and prevent privacy leakage, companies have implemented various host-based intrusion prevention systems (HIPS) on their Android devices. In this paper, we first analyze the implementations, strengths and weaknesses of three popular HIPS architectures. We demonstrate a severe loophole and weakness of an existing popular HIPS product in which hackers can readily exploit. Then we present a design and implementation of a secure and extensible HIPS platform---"Patronus." Patronus not only provides intrusion prevention without the need to modify the Android system, it can also dynamically detect existing malware based on runtime information. We propose a two-phase dynamic detection algorithm for detecting running malware. Our experiments show that Patronus can prevent the intrusive behaviors efficiently and detect malware accurately with a very low performance overhead and power consumption.


Open Source Integrated Planner for Autonomous Navigation in Highly Dynamic Environments


BeAware! - Situation awareness, the ontology-driven way

Available online 18 July 2010 Information overload is a severe problem for human operators of large-scale control systems as, for example, encountered in the domain of road traffic management. Operators of such systems are at risk to lack situation awareness, because existing systems focus on themere presentation of the available information on graphical user interfaces—thus endangering the timely and correct identification, resolution, and prevention of critical situations. In recent years, ontology-based approaches to situation awareness featuring a semantically richer knowledge model have emerged. However, current approaches are either highly domain-specific or have, in case they are domain-independent, shortcomings regarding their reusability. In this paper, we present our experience gained from the development of BeAware!, a framework for ontology-driven information systems aiming at increasing anoperator's situation awareness. In contrast to existing domain-independent approaches, BeAware!'s ontology introduces the concept of spatio-temporal primitive relations betweenobserved real-world objects thereby improving the reusability of the framework. To show its applicability, a prototype of BeAware! has been implemented in thedomain of road trafficmanagement. An overviewof this prototype and lessons learned for the development of ontology-driven information systems complete our contribution. © 2010 Elsevier B.V. All rights reserved.


Using sonification

The idea behind sonification is that synthetic non-verbal sounds can represent numerical data and provide support for information processing activities of many different kinds. This article describes some of the ways that sonification has been used in assistive technologies, remote collaboration, engineering analyses, scientific visualisations, emergency services and aircraft cockpits. Approaches for designing sonifications are surveyed, and issues raised by the existing approaches and applications are outlined. Relations are drawn to other areas of knowledge where similar issues have also arisen, such as human-computer interaction, scientific visualisation, and computer music. At the end is a list of resources that will help you delve further into the topic.


Comparison of Different Classification Techniques on PIMA Indian Diabetes Data

The development of data-mining applications such as classification and clustering has been applied to large scale data. In this research, we present comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. The algorithm or classifiers tested are Multilayer Perceptron, Bayes Network, J48graft (c4.5), Fuzzy Lattice Reasoning (FLR), NaiveBayes, JRip (RIPPER), Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference Systems(ANFIS). A fundamental review on the selected technique is presented for introduction purposes. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods or algorithms. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.


SSD-Assisted Backup and Recovery for Database Systems

Backup and recovery is an important feature of database systems since it protects data against unexpected hardware and software failures. Database systems can provide data safety and reliability by creating a backup and restoring the backup from a failure. Database administrators can use backup/recovery tools that are provided with database systems or backup/recovery methods with operating systems. However, the existing tools perform time-consuming jobs and the existing methods may negatively affect run-time performance during normal operation even though high-performance SSDs are used. In this paper, we present an SSD-assisted backup/recovery scheme for database systems. In our scheme, we extend the out-of-place update characteristics of flash-based SSDs for backup/recovery operations. To this end, we exploit the resources (e.g., flash translation layer and DRAM cache with supercapacitors) inside SSDs, and we call our SSD with new backup/recovery features BR-SSD. We design and implement the backup/recovery functionality in the Samsung enterprise-class SSD (i.e., SM843Tn) for more realistic systems. Furthermore, we conduct a case study of BR-SSDs in replicated database systems and modify MySQL with replication to integrate BR-SSDs. The experimental result demonstrates that our scheme provides fast recovery while it does not negatively affect the run-time performance during normal operation.


Multimodal analysis of the implicit affective channel in computer-mediated textual communication

Computer-mediated textual communication has become ubiquitous in recent years. Compared to face-to-face interactions, there is decreased bandwidth in affective information, yet studies show that interactions in this medium still produce rich and fulfilling affective outcomes. While overt communication (e.g., emoticons or explicit discussion of emotion) can explain some aspects of affect conveyed through textual dialogue, there may also be an underlying implicit affective channel through which participants perceive additional emotional information. To investigate this phenomenon, computer-mediated tutoring sessions were recorded with Kinect video and depth images and processed with novel tracking techniques for posture and hand-to-face gestures. Analyses demonstrated that tutors implicitly perceived students' focused attention, physical demand, and frustration. Additionally, bodily expressions of posture and gesture correlated with student cognitive-affective states that were perceived by tutors through the implicit affective channel. Finally, posture and gesture complement each other in multimodal predictive models of student cognitive-affective states, explaining greater variance than either modality alone. This approach of empirically studying the implicit affective channel may identify details of human behavior that can inform the design of future textual dialogue systems modeled on naturalistic interaction.


phishGILLNET — phishing detection methodology using probabilistic latent semantic analysis , AdaBoost , and co-training

Identity theft is one of the most profitable crimes committed by felons. In the cyber space, this is commonly achieved using phishing. We propose here robust server side methodology to detect phishing attacks, called phishGILLNET, which incorporates the power of natural language processing and machine learning techniques. phishGILLNET is a multi-layered approach to detect phishing attacks. The first layer (phishGILLNET1) employs Probabilistic Latent Semantic Analysis (PLSA) to build a topic model. The topic model handles synonym (multiple words with similar meaning), polysemy (words with multiple meanings), and other linguistic variations found in phishing. Intentional misspelled words found in phishing are handled using Levenshtein editing and Google APIs for correction. Based on term document frequency matrix as input PLSA finds phishing and non-phishing topics using tempered expectation maximization. The performance of phishGILLNET1 is evaluated using PLSA fold in technique and the classification is achieved using Fisher similarity. The second layer of phishGILLNET (phishGILLNET2) employs AdaBoost to build a robust classifier. Using probability distributions of the best PLSA topics as features the classifier is built using AdaBoost. The third layer (phishGILLNET3) further expands phishGILLNET2 by building a classifier from labeled and unlabeled examples by employing Co-Training. Experiments were conducted using one of the largest public corpus of email data containing 400,000 emails. Results show that phishGILLNET3 outperforms state of the art phishing detection methods and achieves F-measure of 100%. Moreover, phishGILLNET3 requires only a small percentage (10%) of data be annotated thus saving significant time, labor, and avoiding errors incurred in human annotation.


Circle grid fractal pattern for calibration at different camera zoom levels

Camera calibration patterns and fiducial markers are basic technique for 3D measurement to create Computer Graphics (CG) and Augmented Reality (AR). A checkerboard is widely used as a calibration pattern and a circle or ring grid provides higher precision [Datta et al. 2009], and matrix codes are common as AR markers.


Biomedical Image Segmentation Using Fully Convolutional Networks on TrueNorth

With the rapid growth of medical and biomedical image data, energy-efficient solutions for analyzing such image data that can be processed fast and accurately on platforms with low power budget are highly desirable. This paper uses segmenting glial cells in brain microscopy images as a case study to demonstrate how to achieve biomedical image segmentation with significant energy saving and minimal comprise in accuracy. Specifically, we design, train, implement, and evaluate Fully Convolutional Networks (FCNs) for biomedical image segmentation on IBM's neurosynaptic DNN processor — TrueNorth (TN). Comparisons in terms of accuracy and energy dissipation of TN with that of a low power NVIDIA TX2 mobile GPU platform have been conducted. Experimental results show that TN can offer at least two orders of magnitude improvement in energy efficiency when compared to TX2 GPU for the same workload.


McRT-STM: a high performance software transactional memory system for a multi-core runtime

Applications need to become more concurrent to take advantage of the increased computational power provided by chip level multiprocessing. Programmers have traditionally managed this concurrency using locks (mutex based synchronization). Unfortunately, lock based synchronization often leads to deadlocks, makes fine-grained synchronization difficult, hinders composition of atomic primitives, and provides no support for error recovery. Transactions avoid many of these problems, and therefore, promise to ease concurrent programming.We describe a software transactional memory (STM) system that is part of McRT, an experimental Multi-Core RunTime. The McRT-STM implementation uses a number of novel algorithms, and supports advanced features such as nested transactions with partial aborts, conditional signaling within a transaction, and object based conflict detection for C/C++ applications. The McRT-STM exports interfaces that can be used from C/C++ programs directly or as a target for compilers translating higher level linguistic constructs.We present a detailed performance analysis of various STM design tradeoffs such as pessimistic versus optimistic concurrency, undo logging versus write buffering, and cache line based versus object based conflict detection. We also show a MCAS implementation that works on arbitrary values, coexists with the STM, and can be used as a more efficient form of transactional memory. To provide a baseline we compare the performance of the STM with that of fine-grained and coarse-grained locking using a number of concurrent data structures on a 16-processor SMP system. We also show our STM performance on a non-synthetic workload -- the Linux sendmail application.


Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization

In this paper we use a Non-negative Matrix Factorization (NMF) based approach to analyze the strokes of the mridangam, a South Indian hand drum, in terms of the normal modes of the instrument. Using NMF, a dictionary of spectral basis vectors are first created for each of the modes of the mridangam. The composition of the strokes are then studied by projecting them along the direction of the modes using NMF. We then extend this knowledge of each stroke in terms of its basic modes to transcribe audio recordings. Hidden Markov Models are adopted to learn the modal activations for each of the strokes of the mridangam, yielding up to 88.40% accuracy during transcription.


Blind image quality assessment by relative gradient statistics and adaboosting neural network

The image gradient is a commonly computed image feature and a potentially predictive factor for image quality assessment (IQA). Indeed, it has been successfully used for both fulland noreference image quality prediction. However, the gradient orientation has not been deeply explored as a predictive source of information for image quality assessment. Here we seek to amend this by studying the quality relevance of the relative gradient orientation, viz., the gradient orientation relative to the surround. We also deploy a relative gradient magnitude feature which accounts for perceptual masking and utilize an AdaBoosting back-propagation (BP) neural network to map the image features to image quality. The generalization of the AdaBoosting BP neural network results in an effective and robust quality prediction model. The new model, called Oriented Gradients Image Quality Assessment (OG-IQA), is shown to deliver highly competitive image quality prediction performance as compared with the most popular IQA approaches. Furthermore, we show that OG-IQA has good database independence properties and a low complexity. & 2015 Elsevier B.V. All rights reserved.


Unconstrained Monocular 3D Human Pose Estimation by Action Detection and Cross-Modality Regression Forest

This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts.


0-Day Vulnerabilities and Cybercrime

This study analyzes 0-day vulnerabilities in the broader context of cybercrime and economic markets. The work is based on the interviews of several leading experts and on a field research of the authors. In particular, cybercrime is considered when involving traditional criminal activities or when military operations are involved. A description of different 0-day vulnerability markets - White, Black and Government markets - is provided, as well as the characteristics of malware factories and their major customers are discussed.


PreFix: Switch Failure Prediction in Datacenter Networks

In modern datacenter networks (DCNs), failures of network devices are the norm rather than the exception, and many research efforts have focused on dealing with failures after they happen. In this paper, we take a different approach by predicting failures, thus the operators can intervene and "fix" the potential failures before they happen. Specifically, in our proposed system, named PreFix, we aim to determine during runtime whether a switch failure will happen in the near future. The prediction is based on the measurements of the current switch system status and historical switch hardware failure cases that have been carefully labelled by network operators. Our key observation is that failures of the same switch model share some common syslog patterns before failures occur, and we can apply machine learning methods to extract the common patterns for predicting switch failures. Our novel set of features (message template sequence, frequency, seasonality and surge) for machine learning can efficiently deal with the challenges of noises, sample imbalance, and computation overhead. We evaluated PreFix on a data set collected from 9397 switches (3 different switch models) deployed in more than 20 datacenters owned by a top global search engine in a 2-year period. PreFix achieved an average of 61.81% recall and 1.84 * 10^-5 false positive ratio. It outperforms the other failure prediction methods for computers and ISP devices.


Semantic Feature Selection for Text with Application to Phishing Email Detection

In a phishing attack, an unsuspecting victim is lured, typically via an email, to a web site designed to steal sensitive information such as bank/credit card account numbers, login information for accounts, etc. Each year Internet users lose billions of dollars to this scourge. In this paper, we present a general semantic feature selection method for text problems based on the statistical t-test and WordNet, and we show its effectiveness on phishing email detection by designing classifiers that combine semantics and statistics in analyzing the text in the email. Our feature selection method is general and useful for other applications involving text-based analysis as well. Our email body-text-only classifier achieves more than 95% accuracy on detecting phishing emails with a false positive rate of 2.24%. Due to its use of semantics, our feature selection method is robust against adaptive attacks and avoids the problem of frequent retraining needed by machine learning classifiers.


Mining Parallel Corpora from Sina Weibo and Twitter

Microblogs such as Twitter, Facebook, and Sina Weibo (China's equivalent of Twitter) are a remarkable linguistic resource. In contrast to content from edited genres such as newswire, microblogs contain discussions of virtually every topic by numerous individuals in different languages and dialects and in different styles. In this work, we show that some microblog users post “self-translated” messages targeting audiences who speak different languages, either by writing the same message in multiple languages or by retweeting translations of their original posts in a second language. We introduce a method for finding and extracting this naturally occurring parallel data. Identifying the parallel content requires solving an alignment problem, and we give an optimally efficient dynamic programming algorithm for this. Using our method, we extract nearly 3M Chinese–English parallel segments from Sina Weibo using a targeted crawl of Weibo users who post in multiple languages. Additionally, from a random sample of Twitter, we obtain substantial amounts of parallel data in multiple language pairs. Evaluation is performed by assessing the accuracy of our extraction approach relative to a manual annotation as well as in terms of utility as training data for a Chinese–English machine translation system. Relative to traditional parallel data resources, the automatically extracted parallel data yield substantial translation quality improvements in translating microblog text and modest improvements in translating edited news content.


Synthetic aperture radar interferometry-Invited paper


10-kV SiC MOSFET-Based Boost Converter

10-kV silicon carbide (SiC) MOSFETs are currently being developed by a number of organizations in the U.S. with prospective applications in high-voltage and high-frequency power-electronic systems. The aim of this paper is to demonstrate the high-frequency and high-temperature capability of 10-kV SiC MOSFETs in the application of a dc/dc boost converter. In this study, 10-kV SiC MOSFET and junction barrier Schottky (JBS) diode were characterized and modeled in SPICE. Following this, a dc/dc boost converter based on a 10-kV 10-A MOSFET and a 10-kV 5-A JBS diode was designed and tested under continuous operation for frequencies up to 25 kHz. The boost converter had an output voltage of 4 kV, an output power of 4 kW, and operated with a junction temperature of 174degC for the SiC MOSFET. The fast-switching speed, low losses, and high-temperature operation capability of 10-kV SiC MOSFETs demonstrated in the dc/dc boost converter make them attractive for high-frequency and high-voltage power-conversion applications.


Analytical loss model of power MOSFET

An accurate analytical model is proposed in this paper to calculate the power loss of a metal-oxide semiconductor field-effect transistor. The nonlinearity of the capacitors of the devices and the parasitic inductance in the circuit, such as the source inductor shared by the power stage and driver loop, the drain inductor, etc., are considered in the model. In addition, the ringing is always observed in the switching power supply, which is ignored in the traditional loss model. In this paper, the ringing loss is analyzed in a simple way with a clear physical meaning. Based on this model, the circuit power loss could be accurately predicted. Experimental results are provided to verify the model. The simulation results match the experimental results very well, even at 2-MHz switching frequency.


Design And Application Guide For High Speed MOSFET Gate Drive Circuits By

The main purpose of this paper is to demonstrate a systematic approach to design high performance gate drive circuits for high speed switching applications. It is an informative collection of topics offering a “one-stop-shopping” to solve the most common design challenges. Thus it should be of interest to power electronics engineers at all levels of experience. The most popular circuit solutions and their performance are analyzed, including the effect of parasitic components, transient and extreme operating conditions. The discussion builds from simple to more complex problems starting with an overview of MOSFET technology and switching operation. Design procedure for ground referenced and high side gate drive circuits, AC coupled and transformer isolated solutions are described in great details. A special chapter deals with the gate drive requirements of the MOSFETs in synchronous rectifier applications. Several, step-by-step numerical design examples complement the paper.


A High Efficiency Synchronous Buck VRM with Current Source Gate Driver

In this paper, a new current source gate drive circuit is proposed for high efficiency synchronous buck VRMs. The proposed circuit achieves quick turn-on and turn-off transition times to reduce switching loss and conduction loss in power MOSFETS. The driver circuit consists of two sets of four control switches and two very small inductors (typically 50 nH-300 nH each at 1 MHz). It drives both the control MOSFET and synchronous MOSFET in synchronous buck VRMs. An analysis, design procedure, optimization procedure and experimental results are presented for the proposed circuit. Experimental results demonstrate an efficiency of 86.6% at 15 A load and 81.9% at 30 A load for 12 V input and 1.3 V output at 1 MHz.


Tapped-inductor buck converter for high-step-down DC-DC conversion

The narrow duty cycle in the buck converter limits its application for high-step-down dc-dc conversion. With a simple structure, the tapped-inductor buck converter shows promise for extending the duty cycle. However, the leakage inductance causes a huge turn-off voltage spike across the top switch. Also, the gate drive for the top switch is not simple due to its floating source connection. This paper solves all these problems by modifying the tapped-inductor structure. A simple lossless clamp circuit can effectively clamp the switch turn-off voltage spike and totally recover the leakage energy. Experimental results for 12V-to-1.5V and 48V-to-6V dc-dc conversions show significant improvements in efficiency.


Robust and fast visual tracking via spatial kernel phase correlation filter

In this paper, we present a novel robust and fast object tracker called spatial kernel phase correlation based Tracker (SPC). Compared with classical correlation tracking which occupies all spectrums (including both phase spectrum and magnitude spectrum) in frequency domain, our SPC tracker only adopts the phase spectrum by implementing using phase correlation filter to estimate the object's translation. Thanks to circulant structure and kernel trick, we can implement dense sampling in order to train a high-quality phase correlation filter. Meanwhile, SPC learns the object's spatial context model by using new spatial response distribution, achieving superior performance. Given all these elaborate configurations, SPC is more robust to noise and cluster, and achieves more competitive performance in visual tracking. The framework of SPC can be briefly summarized as: firstly, phase correlation filter is well trained with all subwindows and is convoluted with a new image patch; then, the object's translation is calculated by maximizing spatial response; finally, to adapt to changing object, phase correlation filter is updated by reliable image patches. Tracking performance is evaluated by Peak-to-Sidelobe Ratio (PSR), aiming to resolve drifting problem by adaptive model updating. Owing to Fast Fourier Transform (FFT), the proposed tracker can track the object at about 50 frames/s. Numerical experiments demonstrate the proposed algorithm performs favorably against several state-of-the-art trackers in speed, accuracy and robustness. & 2016 Elsevier B.V. All rights reserved.


Menu-Match: Restaurant-Specific Food Logging from Images

Logging food and calorie intake has been shown to facilitate weight management. Unfortunately, current food logging methods are time-consuming and cumbersome, which limits their effectiveness. To address this limitation, we present an automated computer vision system for logging food and calorie intake using images. We focus on the "restaurant" scenario, which is often a challenging aspect of diet management. We introduce a key insight that addresses this problem specifically: restaurant plates are often both nutritionally and visually consistent across many servings. This insight provides a path to robust calorie estimation from a single RGB photograph: using a database of known food items together with restaurant-specific classifiers, calorie estimation can be achieved through identification followed by calorie lookup. As demonstrated on a challenging Menu-Match dataset and an existing third party dataset, our approach outperforms previous computer vision methods and a commercial calorie estimation app. Our Menu-Match dataset of realistic restaurant meals is made publicly available.


Modelling User Satisfaction with an Employee Portal

User satisfaction with general information system (IS) and certain types of information technology (IT) applications has been thoroughly studied in IS research. With the widespread and increasing use of portal technology, however, there is a need to conduct a user satisfaction study on portal use -in particular, the business-to-employee (b2e) portal. In this paper, we propose a conceptual model for determining b2e portal user satisfaction, which has been derived from an extensive literature review of user satisfaction scales and the b2e portal. Nine dimensions of b2e portal user satisfaction are identified and modeled: information content, ease of use, convenience of access, timeliness, efficiency, security, confidentiality, communication, and layout.


Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches

Nowadays, smartphones have become indispensable to everyone, with more and more built-in location-based applications to enrich our daily life. In the last decade, fingerprinting based on RSS has become a research focus in indoor localization, due to its minimum hardware requirement and satisfiable positioning accuracy. However, its time-consuming and labor-intensive site survey is a big hurdle for practical deployments. Fingerprint crowdsourcing has recently been promoted to relieve the burden of site survey by allowing common users to contribute to fingerprint collection in a participatory sensing manner. For its promising commitment, new challenges arise to practice fingerprint crowdsourcing. This article first identifies two main challenging issues, fingerprint annotation and device diversity, and then reviews the state of the art of fingerprint crowdsourcing-based indoor localization systems, comparing their approaches to cope with the two challenges. We then propose a new indoor subarea localization scheme via fingerprint crowdsourcing, clustering, and matching, which first constructs subarea fingerprints from crowdsourced RSS measurements and relates them to indoor layouts. We also propose a new online localization algorithm to deal with the device diversity issue. Our experiment results show that in a typical indoor scenario, the proposed scheme can achieve a 95 percent hit rate to correctly locate a smartphone in its subarea.


Optimizing Soft Real-Time Scheduling Performance for Virtual Machines with SRT-Xen

Multimedia applications are an important part of today's Internet. However, currently most virtualization solutions, including Xen, lack adequate support for soft real-time tasks. Soft real-time applications, e.g. media workloads, are impeded by components of virtualization, such as the increase of scheduling latency. This paper focuses on improving scheduling scheme to support soft real-time workloads in virtualization systems. In this paper, we present an enhanced scheduler SRT-Xen. SRT-Xen can promote the soft real-time domain's performance compared with Xen's existing scheduling. It focuses on not only bringing a new realtime-friendly scheduling framework with corresponding strategies but also improving the management of the virtual CPUs' queuing in order to implement a fair scheduling mechanism for both real-time and non-real-time tasks. Finally, we use PESQ (Perceptual Evaluation of Speech Quality) and other benchmarks to evaluate and compare SRT-Xen with some other works. The results show that SRT-Xen supports soft real-time domains well without penalizing non-real-time ones.


Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe

Advances in Internet of Things (IoT) give rise to a variety of latency-sensitive, closed-loop applications that reside at the edge. These applications often involve a large number of sensors that generate volumes of data, which must be processed and disseminated in real-time to potentially a large number of entities for actuation, thereby forming a closed-loop, publish-process-subscribe system. To meet the response time requirements of such applications, this paper presents techniques to realize a scalable, fog/edge-based broker architecture that balances data publication and processing loads for topic-based, publish-process-subscribe systems operating at the edge, and assures the Quality-of-Service (QoS), specified as the 90th percentile latency, on a per-topic basis. The key contributions include: (a) a sensitivity analysis to understand the impact of features such as publishing rate, number of subscribers, per-sample processing interval and background load on a topic's performance; (b) a latency prediction model for a set of co-located topics, which is then used for the latency-aware placement of topics on brokers; and (c) an optimization problem formulation for k-topic co-location to minimize the number of brokers while meeting each topic's QoS requirement. Here, k denotes the maximum number of topics that can be placed on a broker. We show that the problem is NP-hard for k >=3 and present three load balancing heuristics. Empirical results are presented to validate the latency prediction model and to evaluate the performance of the proposed heuristics.


Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization

There is a natural correlation between the visual and auditive elements of a video. In this work, we use this correlation in order to learn strong and general features via cross-modal self-supervision with carefully chosen neural network architectures and calibrated curriculum learning. We suggest that this type of training is an effective way of pretraining models for further pursuits in video understanding, as they achieve on average 14.8% improvement over models trained from scratch. Furthermore, we demonstrate that these general features can be used for audio classification and perform on par with state-of-the-art results. Lastly, our work shows that using cross-modal self-supervision for pretraining is a good starting point for the development of multi-sensory models.


Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine. One of the goals of genomic medicine is to determine how variations in the DNA of individuals can affect the risk of different diseases, and to find causal explanations so that targeted therapies can be designed. Here we focus on how machine learning can help to model the relationship between DNA and the quantities of key molecules in the cell, with the premise that these quantities, which we refer to as cell variables, may be associated with disease risks. Modern biology allows high-throughput measurement of many such cell variables, including gene expression, splicing, and proteins binding to nucleic acids, which can all be treated as training targets for predictive models. With the growing availability of large-scale data sets and advanced computational techniques such as deep learning, researchers can help to usher in a new era of effective genomic medicine.


Measuring epistemic curiosity and its diversive and specific components.

A questionnaire constructed to assess epistemic curiosity (EC) and perceptual curiosity (PC) curiosity was administered to 739 undergraduates (546 women, 193 men) ranging in age from 18 to 65. The study participants also responded to the trait anxiety, anger, depression, and curiosity scales of the State-Trait Personality Inventory (STPI; Spielberger et al., 1979) and selected subscales of the Sensation Seeking (SSS; Zuckerman, Kolin, Price, & Zoob, 1964) and Novelty Experiencing (NES; Pearson, 1970) scales. Factor analyses of the curiosity items with oblique rotation identified EC and PC factors with clear simple structure. Subsequent analyses of the EC items provided the basis for developing an EC scale, with Diversive and Specific Curiosity subscales. Moderately high correlations of the EC scale and subscales with other measures of curiosity provided strong evidence of convergent validity. Divergent validity was demonstrated by minimal correlations with trait anxiety and the sensation-seeking measures, and essentially zero correlations with the STPI trait anger and depression scales. Male participants had significantly higher scores on the EC scale and the NES External Cognition subscale (effect sizes of r =.16 and.21, respectively), indicating that they were more interested than female participants in solving problems and discovering how things work. Male participants also scored significantly higher than female participants on the SSS Thrill-and-Adventure and NES External Sensation subscales (r =.14 and.22, respectively), suggesting that they were more likely to engage in sensation-seeking activities.


Gait planning and behavior-based control for statically-stable walking robots

A substantial portion of the Earth is inaccessible to any sort of wheeled mechanism— natural obstacles like large rocks, loose soil, deep ravines, and steep slopes conspire to render rolling locomotion ineffective. Hills, mountains, shores, seabeds, as well as the moon and other planets present similar terrain challenges. In many of these natural terrains, legs are well-suited. They can avoid small obstacles by making discrete contacts and passing up undesirable footholds. Legged mechanisms can climb over obstacles and step across ditches, surmounting terrain discontinuities of body-scale while staying level and stable. To achieve their potential, legged robots must coordinate their leg motions to climb over, step across and walk in natural terrain. These coordinated motions, which support and propel the robot, are called a gait. This thesis develops a new method of gait planning and control that enables statically-stable walking robots to produce a gait that is robust and productive in natural terrain. Independent task-achieving processes, called gait behaviors, establish a nominal gait, adapt it to the terrain, and react to disturbances like bumps and slips. Gait controlled in this way enabled the robot Dante II to walk autonomously in natural terrain, including the volcanic crater of Mount Spurr. This method extends to other walking robots as demonstrated by a generalized hexapod that performs the variety of gaits seen in six-legged insects, as well as aperiodic free gaits. The ability to change gait patterns on-the-fly with continuous, stable motion is a new development that enables robots to behave more like animals in adapting their gait to terrain. Finally, this thesis describes why walking robots need predictive plans as well as reflexive behaviors to walk effectively in the real world. It presents a method of guiding the behavior of a walking robot by planning distinct attributes of the desired gait. This partitioning of gait planning avoids the complexity of high degree-of-freedom motion planning. The ability to plan and foresee changes in gait improves performance while maintaining robust safety and stability. Abstract ii iii Acknowledgment


Developing a Taxonomy of Dark Triad Triggers at Work – A Grounded Theory Study Protocol

In past years, research and corporate scandals have evidenced the destructive effects of the dark triad at work, consisting of narcissism (extreme self-centeredness), psychopathy (lack of empathy and remorse) and Machiavellianism (a sense of duplicity and manipulativeness). The dark triad dimensions have typically been conceptualized as stable personality traits, ignoring the accumulating evidence that momentary personality expressions - personality states - may change due to the characteristics of the situation. The present research protocol describes a qualitative study that aims to identify triggers of dark triad states at work by following a grounded theory approach using semi-structured interviews. By building a comprehensive categorization of dark triad triggers at work scholars may study these triggers in a parsimonious and structured way and organizations may derive more effective interventions to buffer or prevent the detrimental effects of dark personality at work.


Web Engineering: A New Discipline for Development of Web-Based Systems

In most cases, development of Web-based systems has been ad hoc, lacking systematic approach and quality control and assurance procedures. Hence, there is now legitimate and growing concern about the manner in which Web-based systems are developed and their long-term quality and integrity. Web Engineering, an emerging new discipline, advocates a process and a systematic approach to development of high quality Web-based systems. It promotes the establishment and use of sound scientific, engineering and management principles, and disciplined and systematic approaches to development, deployment and maintenance of Web-based systems. This paper gives an introductory overview on Web Engineering. It presents the principles and roles of Web Engineering, assesses the similarities and differences between development of traditional software and Web-based systems, identifies key Web engineering activities and reviews some of the ongoing work in this area. It also highlights the prospects of Web engineering and the areas that need further study.


Negotiation and Cooperation in Multi-Agent Environments

Automated intelligent agents inhabiting a shared environment must coordinate their activities Cooperation not merely coordination may improve the performance of the individual agents or the overall behavior of the system they form Research in Distributed Arti cial Intelligence DAI addresses the problem of designing automated intelligent systems which interact e ectively DAI is not the only eld to take on the challenge of understanding cooperation and coordination There are a variety of other multi entity environments in which the entities coordinate their activity and cooperate Among them are groups of people animals particles and computers We argue that in order to address the challenge of building coordinated and collaborated intelligent agents it is bene cial to combine AI techniques with methods and techniques from a range of multi entity elds such as game theory operations research physics and philosophy To support this claim we describe some of our projects where we have successfully taken an interdisciplinary approach We demonstrate the bene ts in applying multi entity methodologies and show the adaptations modi cations and extensions necessary for solving the DAI problems This is an extended version of a lecture presented upon receipt of the Computers and Thought Award at the th International Joint Conference on Arti cial Intelligence in Montreal Canada August


Wikipedia Chemical Structure Explorer: substructure and similarity searching of molecules from Wikipedia

BACKGROUND
Wikipedia, the world's largest and most popular encyclopedia is an indispensable source of chemistry information. It contains among others also entries for over 15,000 chemicals including metabolites, drugs, agrochemicals and industrial chemicals. To provide an easy access to this wealth of information we decided to develop a substructure and similarity search tool for chemical structures referenced in Wikipedia.

RESULTS
We extracted chemical structures from entries in Wikipedia and implemented a web system allowing structure and similarity searching on these data. The whole search as well as visualization system is written in JavaScript and therefore can run locally within a web page and does not require a central server. The Wikipedia Chemical Structure Explorer is accessible on-line at www.cheminfo.org/wikipedia and is available also as an open source project from GitHub for local installation.

CONCLUSIONS
The web-based Wikipedia Chemical Structure Explorer provides a useful resource for research as well as for chemical education enabling both researchers and students easy and user friendly chemistry searching and identification of relevant information in Wikipedia. The tool can also help to improve quality of chemical entries in Wikipedia by providing potential contributors regularly updated list of entries with problematic structures. And last but not least this search system is a nice example of how the modern web technology can be applied in the field of cheminformatics. Graphical abstractWikipedia Chemical Structure Explorer allows substructure and similarity searches on molecules referenced in Wikipedia.


Word Embeddings and Their Use In Sentence Classification Tasks

This paper has two parts. In the first part we discuss word embeddings. We discuss the need for them, some of the methods to create them, and some of their interesting properties. We also compare them to image embeddings and see how word embedding and image embedding can be combined to perform different tasks. In the second part we implement a convolutional neural network trained on top of pre-trained word vectors. The network is used for several sentence-level classification tasks, and achieves state-of-art (or comparable) results, demonstrating the great power of pre-trainted word embeddings over random ones.


A critical review of blockchain and its current applications

Blockchain technology has been known as a digital currency platform since the emergence of Bitcoin, the first and the largest of the cryptocurrencies. Hitherto, it is used for the decentralization of markets more generally, not exclusively for the decentralization of money and payments. The decentralized transaction ledger of blockchain could be employed to register, confirm, and send all kinds of contracts to other parties in the network. In this paper, we thoroughly review state-of-the-art blockchain-related applications emerged in the literature. A number of published works were carefully included based on their contributions to the blockchain's body of knowledge. Several remarks are explored and discussed in the last section of the paper.


Hidden Voice Commands

Voice interfaces are becoming more ubiquitous and are now the primary input method for many devices. We explore in this paper how they can be attacked with hidden voice commands that are unintelligible to human listeners but which are interpreted as commands by devices. We evaluate these attacks under two different threat models. In the black-box model, an attacker uses the speech recognition system as an opaque oracle. We show that the adversary can produce difficult to understand commands that are effective against existing systems in the black-box model. Under the white-box model, the attacker has full knowledge of the internals of the speech recognition system and uses it to create attack commands that we demonstrate through user testing are not understandable by humans. We then evaluate several defenses, including notifying the user when a voice command is accepted; a verbal challenge-response protocol; and a machine learning approach that can detect our attacks with 99.8% accuracy.


Supply Chain Sourcing Under Asymmetric Information

We study a supply chain with two suppliers competing over a contract to supply components to a manufacturer. One of the suppliers is a big company for whom the manufacturer’s business constitutes a small part of his business. The other supplier is a small company for whom the manufacturer’s business constitutes a large portion of his business. We analyze the problem from the perspective of the big supplier and address the following questions: What is the optimal contracting strategy that the big supplier should follow? How does the information about the small supplier’s production cost affect the profits and contracting decision? How does the existence of the small supplier affect profits? By studying various information scenarios regarding the small supplier’s and the manufacturer’s production cost, we show, for example, that the big supplier benefits when the small supplier keeps its production cost private. We quantify the value of information for the big supplier and the manufacturer. We also quantify the cost (value) of the alternative-sourcing option for the big supplier (the manufacturer). We determine when an alternative-sourcing option has more impact on profits than information. We conclude with extensions and numerical examples to shed light on how system parameters affect this supply chain.


A Dependency Graph Approach for Fault Detection and Localization Towards Secure Smart Grid

Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.


Secure and reconfigurable network design for critical information dissemination in the Internet of battlefield things (IoBT)

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.


Multi-task learning of dish detection and calorie estimation

In recent years, a rise in healthy eating has led to various food management applications, which have image recognition to automatically record meals. However, most image recognition functions in existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network, which has improved accuracies of various kinds of image recognition tasks, such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates food calories while simultaneously detecting dishes by multi-task learning of food calorie estimation and food dish detection with a single CNN. It is expected to achieve high speed and save memory by simultaneous estimation in a single network. Currently, there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, so in this work, we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos with bounding-boxes attached and single-dish food photos with food calories. Our results show that our multi-task method achieved higher speed and a smaller network size than a sequential model of food detection and food calorie estimation.


Chernoff-Hoeffding Inequality and Applications

When dealing with modern big data sets, a very common theme is reducing the set through a random process. These generally work by making “many simple estimates” of the full data set, and then judging them as a whole. Perhaps magically, these “many simple estimates” can provide a very accurate and small representation of the large data set. The key tool in showing how many of these simple estimates are needed for a fixed accuracy trade-off is the Chernoff-Hoeffding inequality [2, 6]. This document provides a simple form of this bound, and two examples of its use.


The Unbearable Automaticity of Being

What was noted by E. J. hanger (1978) remains true today: that much of contemporary psychological research is based on the assumption that people are consciously and systematically processing incoming information in order to construe and interpret their world and to plan and engage in courses of action. As did E. J. hanger, the authors question this assumption. First, they review evidence that the ability to exercise such conscious, intentional control is actually quite limited, so that most of moment-to-moment psychological life must occur through nonconscious means if it is to occur at all. The authors then describe the different possible mechanisms that produce automatic, environmental control over these various phenomena and review evidence establishing both the existence of these mechanisms as well as their consequences for judgments, emotions, and behavior. Three major forms of automatic self-regulation are identified: an automatic effect of perception on action, automatic goal pursuit, and a continual automatic evaluation of one's experience. From the accumulating evidence, the authors conclude that these various nonconscious mental systems perform the lion's share of the self-regulatory burden, beneficently keeping the individual grounded in his or her current environment.


Intrinsically Motivated Learning of Hierarchical Collections of Skills

Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies.


An examination of the determinants of customer loyalty in mobile commerce contexts

While the importance of customer loyalty has been recognized in marketing literature for at least three decades, the development and empirical validation of a customer loyalty model in a mobile commerce (m-commerce) context had not been addressed. The purpose of our study was to develop and validate such a customer loyalty model. Based on IS and marketing literature, a comprehensive set of constructs and hypotheses were compiled with a methodology for testing them. A questionnaire was constructed and data were collected from 255 users of m-commerce systems in Taiwan. Structural modeling techniques were then applied to analyze the data. The results indicated that customer loyalty was affected by perceived value, trust, habit, and customer satisfaction, with customer satisfaction playing a crucial intervening role in the relationship of perceived value and trust to loyalty. Based on the findings, its implications and limitations are discussed. # 2005 Elsevier B.V. All rights reserved.


Principles and practice in reporting structural equation analyses.

Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodness-of-fit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.


154-2008: Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry

The explosion of customer data in the last twenty years has increased the need for data mining aimed at customer relationship management (CRM) and understanding the customer. It is well known that the telecom sector consists of customers with a wide array of customer behaviors. These customers pose different risks, making it imperative to implement different treatment strategies to maximize shareholder profit and improve revenue generation. Segmentation is the process of developing meaningful customer groups that are similar based on individual account characteristics and behaviors. The goal of segmentation is to know your customer better and to apply that knowledge to increase profitability, reduce operational cost, and enhance customer service. Segmentation can provide a multidimensional view of the customer for better treatment targeting. An improved understanding of customer risk and behaviors enables more effective portfolio management and the proactive application of targeted treatments to lift account profitability. In this paper we outline several segmentation techniques using SAS Enterprise MinerTM. INTRODUCTION Rapid advances in computer technology and an explosion of data collection systems over the last thirty years make it more critical for business to understand their customers. Companies employing data driven analytical strategies often enjoy a competitive advantage. Many organizations across several industries widely employ analytical models to gain a better understanding of their customers. They use these models to predict a wide array of events such as behavioral risk, fraud, or the likelihood of response. Regardless of the predictive variable, a single model may not perform optimally across the target population because there may be distinct segments with different characteristics inherent in the population. Segmentation may be done judgmentally based on experience, but such segmentation schema is limited to the use of only a few variables at best. True multivariate segmentation with the goal of identifying the different segments in your population is best achieved through the use of cluster analysis. Clustering and profiling of the customer base can answer the following questions: ♦ Who are my customers? ♦ How profitable are my customers? ♦ Who are my least profitable customers? ♦ Why are my customers leaving? ♦ What do my best customers look like? This paper discusses the use of SAS Enterprise Miner to segment a population of customers using cluster analysis and decision trees. Focus is placed on the methodology rather than the results to ensure the integrity and confidentiality of customer data. Other statistical strategies are presented in this paper which could be employed in the pursuit of further customer intelligence. It has been said that statistical cluster analysis is as much art as it is science because it is performed without the benefit of well established statistical criteria. Data Mining and Predictive Modeling SAS Global Forum 2008


Causality : Models , Reasoning , and Inference

For most researchers in the ever growing fields of probabilistic graphical models, belief networks, causal influence and probabilistic inference, ACM Turing award winner Dr. Pearl and his seminary papers on causality are well-known and acknowledged. Representation and determination of Causality, the relationship between an event (the cause) and a second event (the effect), where the second event is understood as a consequence of the first, is a challenging problem. Over the years, Dr. pearl has written significantly on both Art and Science of Cause and Effect. In this book on "Causality: Models, Reasoning and Inference", the inventor of Bayesian belief networks discusses and elaborates on his earlier workings including but not limited to Reasoning with Cause and Effect, Causal inference in statistics, Simpson's paradox, Causal Diagrams for Empirical Research, Robustness of Causal Claims, Causes and explanations, and Probabilities of causation Bounds and identification.


Information Systems Success: The Quest for the Dependent Variable

A large number of studies have been conducted during the last decade and a half attempting to identify those factors that contribute to information systems success. However, the dependent variable in these studies—I/S success —has been an elusive one to define. Different researchers have addressed different aspects of success, making comparisons difficult and the prospect of building a cumulative tradition for I/S research similarly elusive. To organize this diverse research, as well as to present a more integrated view of the concept of I/S success, a comprehensive taxonomy is introduced. This taxonomy posits six major dimensions or categories of I/S success—SYSTEM OUALITY, INFORMATION QUALITY, USE, USER SATISFACTION, INDIVIDUAL IMPACT, and ORGANIZATIONAL IMPACT. Using these dimensions, both conceptual and empirical studies are then reviewed (a total of 180 articles are cited) and organized according to the dimensions of the taxonomy. Finally, the many aspects of I/S success are drawn together into a descriptive model and its implications for future I/S research are discussed.


Just Feels Good : Customers ’ Affective Response to Touch and Its Influence on Persuasion

Prior research has assumed that touch has a persuasive effect only if it provides attribute or structural information about a product. Under this view, the role of touch as a persuasive tool is limited. The main purpose of this research is to investigate the persuasive influence of touch as an affective tool in the absence of useful product-related information. The authors find that for people who are motivated to touch because it is fun or interesting, a communication that incorporates touch leads to increased affective response and increased persuasion, particularly when the touch provides neutral or positive sensory feedback. People who are not motivated to touch for fun will also be persuaded by a communication that incorporates touch when they are able to make sense of how the touch is related to the message. The authors explore the effectiveness of different types of touch in generating an affective response, and they replicate the effects on attitudes and behavior in a real-world setting. This research suggests that the marketing implications of touch are more substantial than previously believed. The authors present research implications for direct marketing, product packaging, point-of-purchase displays, and print advertising.


Portfolio Choice and the Bayesian Kelly Criterion

We derive optimal gambling and investment policies for cases in which the underlying stochastic process has parameter values that are unobserved random variables. For the objective of maximizing logarithmic utility when the underlying stochastic process is a simple random walk in a random environment, we show that a state-dependent control is optimal, which is a generalization of the celebrated Kelly strategy: The optimal strategy is to bet a fraction of current wealth equal to a linear function of the posterior mean increment. To approximate more general stochastic processes, we consider a continuous-time analog involving Brownian motion. To analyze the continuous-time problem, we study the diffusion limit of random walks in a random environment. We prove that they converge weakly to a Kiefer process, or tied-down Brownian sheet. We then find conditions under which the discrete-time process converges to a diffusion, and analyze the resulting process. We analyze in detail the case of the natural conjugate prior, where the success probability has a beta distribution, and show that the resulting limiting diffusion can be viewed as a rescaled Brownian motion. These results allow explicit computation of the optimal control policies for the continuoustime gambling and investment problems without resorting to continuous-time stochastic-control procedures. Moreover they also allow an explicit quantitative evaluation of the financial value of randomness, the financial gain of perfect information and the financial cost of learning in the Bayesian problem.


Vibration parameter estimation using FMCW radar

Vibration sensing is essential in many applications. Traditional vibration sensors are contact based. With the advance of low-cost and highly integrated CMOS radars, another class of non-contact vibration sensors is emerging. In this paper, we present detailed analysis on obtaining vibration parameters using frequency modulated continuous wave (FMCW) radars. We establish the Cramer Rao lower bounds (CRLB) of the parameter estimation problem and propose an estimation algorithm that achieves the bounds in simulations. These analysis show that vibration sensing using FMCW radars can easily achieve sub-Hertz frequency accuracy and micrometer level amplitude accuracy.


Using Multi-Locators to Increase the Robustness of Web Test Cases

The main reason for the fragility of web test cases is the inability of web element locators to work correctly when the web page DOM evolves. Web elements locators are used in web test cases to identify all the GUI objects to operate upon and eventually to retrieve web page content that is compared against some oracle in order to decide whether the test case has passed or not. Hence, web element locators play an extremely important role in web testing and when a web element locator gets broken developers have to spend substantial time and effort to repair it. While algorithms exist to produce robust web element locators to be used in web test scripts, no algorithm is perfect and different algorithms are exposed to different fragilities when the software evolves. Based on such observation, we propose a new type of locator, named multi-locator, which selects the best locator among a candidate set of locators produced by different algorithms. Such selection is based on a voting procedure that assigns different voting weights to different locator generation algorithms. Experimental results obtained on six web applications, for which a subsequent release was available, show that the multi-locator is more robust than the single locators (about -30% of broken locators w.r.t. the most robust kind of single locator) and that the execution overhead required by the multiple queries done with different locators is negligible (2-3% at most).


lncDML: Identification of long non-coding RNAs by Deep Metric Learning

The next-generation sequencing technologies provide a great deal of transcripts for bioinformatics research. Specially, because of the regulation of long non-coding RNAs (lncRNAs) in various cellular processes, the research on IncRNAs is in full swing. And the solution of IncRNAs identification is the basis for the in-depth study of its functions. In this study, we present an approach to identify the IncRNAs from large scale transcripts, named IncDML which is completely different from previous identification methods. In our model, we extract signal to noise ratio (SNR) and k-mer from transcripts sequences as features. Firstly, we just use the SNR to cluster the original dataset to three parts. In this process, we achieve preliminary identification effect to some extent. Then abandoning traditional feature selection, we directly measure the relationship between each pair of samples by deep metric learning for each part of data. Finally, a novel classifier based on complex network is applied to achieve the final identification. The experiment results show that IncDML is a very effective method for identifying IncRNAs.