title: Stemming for Kurdish Information Retrieval

abstract: Resource scarcity along with diversity –in both dialect and script– are the two primary challenges in Kurdish language processing. In this paper we aim at addressing these two problems by building stemmers for the two main dialects of the Kurdish language (i.e. Sorani and Kurmanji) and investigate their effectiveness on Kurdish Information Retrieval.


title: NLP based sentiment analysis on Twitter data using ensemble classifiers

abstract: Most sentiment analysis systems use bag-of-words approach for mining sentiments from the online reviews and social media data. Rather considering the whole sentence/ paragraph for analysis, the bag-of-words approach considers only individual words and their count as the feature vectors. This may mislead the classification algorithm especially when used for problems like sentiment classification. Traditional machine learning algorithms like Naive Bayes, Maximum Entropy, SVM etc. are widely used to solve the classification problems. These machine learning algorithms often suffer from biasness towards a particular class. In this paper, we propose Natural Language (NLP) based approach to enhance the sentiment classification by adding semantics in feature vectors and thereby using ensemble methods for classification. Adding semantically similar words and context-sense identities to the feature vectors will increase the accuracy of prediction. Experiments conducted demonstrate that the semantics based feature vector with ensemble classifier outperforms the traditional bag-of-words approach with single machine learning classifier by 3–5%.


title: The QCAE: A Questionnaire of Cognitive and Affective Empathy

abstract: Empathy has been inconsistently defined and inadequately measured. This research aimed to produce a new and rigorously developed questionnaire. Exploratory (n 1= 640) and confirmatory (n 2= 318) factor analyses were employed to develop the Questionnaire of Cognitive and Affective Empathy (QCAE). Principal components analysis revealed 5 factors (31 items). Confirmatory factor analysis confirmed this structure in an independent sample. The hypothesized 2-factor structure (cognitive and affective empathy) was tested and provided the best and most parsimonious fit to the data. Gender differences, convergent validity, and construct validity were examined. The QCAE is a valid tool for assessing cognitive and affective empathy.


title: Applying Vector Space Model (VSM) Techniques in Information Retrieval for Arabic Language

abstract: Information Retrieval (IR) allows the storage, management, processing and retrieval of information, documents, websites, etc. Building an IR system for any language is imperative. This is evident through the massive conducted efforts to build IR systems using any of its models that are valid for certain languages. This report presents an implementation for a core IR technique which is Vector Space Model (VSM). We have chosen VSM model for our project since it is a term weighting scheme, and the retrieved documents could be sorted according to their relevancy degree. One other significant feature for such technique is the ability to get a relevance feedback from the users of the system; users can judge whether the retrieved document is relative to their need or not. The developed system has been validated through building an Arabic IR website using server side scripting. The experiments verifies the effectiveness of our system to apply all techniques of vector space model and valid over Arabic language.


title: Edge-based visual-inertial odometry

abstract: In this paper we propose a method for monocular visual-inertial odometry that utilizes image edges as measurements. In contrast to previous feature-based approaches, the proposed method does not employ any assumption on the geometry of the scene (e.g., it does not assume straight lines). It can thus use measurements from all image areas with significant gradient, similarly to direct semi-dense methods. However, in contrast to direct semi-dense approaches, the proposed method's measurement model is invariant to linear changes in the image intensity. The novel edge parameterization and measurement model we propose explicitly account for the fact that edge points can only provide useful information in the direction of the image gradient. We present both Monte-Carlo simulations, as well as results from real-world experimental testing, which demonstrate that the proposed edge-based approach to visual-inertial odometry is consistent, and outperforms the point-based one.


title: Integrated Facility Location and Vehicle Routing Models: Recent Work and Future Prospects

abstract: SYNOPTIC ABSTRACTFacility location and vehicle routing are two widely used and studied management science resource planning models. According to the 1986 Statistical Abstract of the United States, freight transportation outlays for local trucking totaled 80.9 billion dollars in 1983. Thus, location/routing decisions have considerable economic importance in domains such as distribution systems planning. Since location and routing decisions are closely related, integrated models that consider the two aspects simultaneously offer the promise of more effective and economical decisions. However, such integrated models are complex and their design poses challenges in combining the short-term operational considerations of vehicle routing with the medium/long-term strategic issues of facility location. This paper discusses recent research related to various modeling approaches for location/routing problems, including comprehensive mathematical programming formulations, analytical approximations, and modified faci...


title: Grounding object perception in a naive agent's sensorimotor experience

abstract: Artificial object perception usually relies on a priori defined models and feature extraction algorithms. We study how the concept of object can be grounded in the sensorimotor experience of a naive agent. Without any knowledge about itself or the world it is immersed in, the agent explores its sensorimotor space and identifies objects as consistent networks of sensorimotor transitions, independent from their context. A fundamental drive for prediction is assumed to explain the emergence of such networks from a developmental standpoint. An algorithm is proposed and tested to illustrate the approach.


title: Efficient Disfluency Detection with Transition-based Parsing

abstract: Automatic speech recognition (ASR) outputs often contain various disfluencies. It is necessary to remove these disfluencies before processing downstream tasks. In this paper, an efficient disfluency detection approach based on right-to-left transitionbased parsing is proposed, which can efficiently identify disfluencies and keep ASR outputs grammatical. Our method exploits a global view to capture long-range dependencies for disfluency detection by integrating a rich set of syntactic and disfluency features with linear complexity. The experimental results show that our method outperforms state-of-the-art work and achieves a 85.1% f-score on the commonly used English Switchboard test set. We also apply our method to in-house annotated Chinese data and achieve a significantly higher f-score compared to the baseline of CRF-based approach.


title: WebShodh: A Code Mixed Factoid Question Answering System for Web

abstract: Code-Mixing (CM) is a natural phenomenon observed in many multilingual societies and is becoming the preferred medium of expression and communication in online and social media fora. In spite of this, current Question Answering (QA) systems do not support CM and are only designed to work with a single interaction language. This assumption makes it inconvenient for multi-lingual users to interact naturally with the QA system especially in scenarios where they do not know the right word in the target language. In this paper, we present WebShodh - an end-end web-based Factoid QA system for CM languages. We demonstrate our system with two CM language pairs: Hinglish (Matrix language: Hindi, Embedded language: English) and Tenglish (Matrix language: Telugu, Embedded language: English). Lack of language resources such as annotated corpora, POS taggers or parsers for CM languages poses a huge challenge for automated processing and analysis. In view of this resource scarcity, we only assume the existence of bi-lingual dictionaries from the matrix languages to English and use it for lexically translating the question into English. Later, we use this loosely translated question for our downstream analysis such as Answer Type(AType) prediction, answer retrieval and ranking. Evaluation of our system reveals that we achieve an MRR of 0.37 and 0.32 for Hinglish and Tenglish respectively. We hosted this system online and plan to leverage it for collecting more CM questions and answers data for further improvement.


title: The comparative investigation of the stick insect and cockroach models in the study of insect locomotion

abstract: Evolution has perfected very different six-legged walking machines from common neural and biomechanical components in an ancestral insect, as exemplified by the American cockroach and the stick insect. Comparative experimental investigations of both intact animals and reduced neural preparations, integrated with mathematical modeling, have been instrumental in advancing our understanding of insect locomotion. Locomotion in stick insects and cockroaches can be described by a related series of mathematical models that describe and quantify the effects of central coupling and feedback, and help explore the role of descending inputs (from higher neuronal centers) and neuromodulation. Insights into sensory–motor interactions and adaptive motor control in insects are useful in designing more responsive, robust, and adaptable bio-robots, which, in turn, can contribute to hypothesis-testing in biology.


title: Bilingualism as a protection against the onset of symptoms of dementia

abstract: This study examined the effect of lifelong bilingualism on maintaining cognitive functioning and delaying the onset of symptoms of dementia in old age. The sample was selected from the records of 228 patients referred to a Memory Clinic with cognitive complaints. The final sample consisted of 184 patients diagnosed with dementia, 51% of whom were bilingual. The bilinguals showed symptoms of dementia 4 years later than monolinguals, all other measures being equivalent. Additionally, the rate of decline in Mini-Mental State Examination (MMSE) scores over the 4 years subsequent to the diagnosis was the same for a subset of patients in the two groups, suggesting a shift in onset age with no change in rate of progression.


title: Assessing the Effects of UTAUT and Self-Determination Predictor on Students Continuance Intention to Use Student Portal

abstract: Higher Education Institution, with the advent of information system technology, is putting in an effort to introduce a student portal system for use by students. Continued usage considered as a measure of success in information system implementation. The objective of this study is to propose an integrated research framework to investigate the factors that can motivate students to continue to utilize UCSA student portal system. Two streams of research provide the basis for this integrated framework namely Unified theory of acceptance and use of technology (UTAUT) and Self-Determination theory. A total sample of 279 students from UCSA (University College ShahPutra) responded to 20-item questionnaires containing 6 constructs: continuance intention to use, performance expectancy, effort expectancy, social influence, facilitating condition and intrinsic motivation. Covariance-based SEM (Structural Equation Modeling) was employed as the main method of analysis. Results revealed that the performance expectancy and intrinsic motivation do not have any statistically significant effect on continuance intention to use UCSA student portal. However, effort expectancy, social influence and facilitating condition were shown to significantly influence continuance intention. The model which derived from the data explained 53 % of variance of student portal continuance intention.


title: People and organizational culture : A profile comparison approach to assessing person-organization fit

abstract: This article brings together three current themes in organizational behavior: (1) a renewed interest in assessing person-situation interactional constructs, (2) the quantitative assessment of organizational culture, and (3) the application of “Q-sort,” or template-matching, approaches to assessing person-situation interactions. Using longitudinal data from accountants and M.B.A. students and cross-sectional data from employees of government agencies and public accounting firms, we developed and validated an instrument for assessing person-organization fit, the Organizational Culture Profile (OCP). Results suggest that the dimensionality of individual preferences for organizational cultures and the existence of these cultures are interpretable. Further, person-organization fit predicts job satisfaction and organizational commitment a year after fit was measured and actual turnover after two years. This evidence attests to the importance of understanding the fit between individuals' preferences and organiza...


title: Humanoid robot Lola: Design and walking control

abstract: In this paper we present the humanoid robot LOLA, its mechatronic hardware design, simulation and real-time walking control. The goal of the LOLA-project is to build a machine capable of stable, autonomous, fast and human-like walking. LOLA is characterized by a redundant kinematic configuration with 7-DoF legs, an extremely lightweight design, joint actuators with brushless motors and an electronics architecture using decentralized joint control. Special emphasis was put on an improved mass distribution of the legs to achieve good dynamic performance. Trajectory generation and control aim at faster, more flexible and robust walking. Center of mass trajectories are calculated in real-time from footstep locations using quadratic programming and spline collocation methods. Stabilizing control uses hybrid position/force control in task space with an inner joint position control loop. Inertial stabilization is achieved by modifying the contact force trajectories.


title: Security and Privacy Issues in Deep Learning

abstract: With the development of machine learning (ML), expectations for artificial intelligence (AI) technology have been increasing daily. In particular, deep neural networks have shown outstanding performance results in many fields. Many applications are deeply involved in our daily life, such as making significant decisions in application areas based on predictions or classifications, in which a DL model could be relevant. Hence, if a DL model causes mispredictions or misclassifications due to malicious external influences, then it can cause very large difficulties in real life. Moreover, training DL models involve an enormous amount of data and the training data often include sensitive information. Therefore, DL models should not expose the privacy of such data. In this paper, we review the vulnerabilities and the developed defense methods on the security of the models and data privacy under the notion of secure and private AI (SPAI). We also discuss current challenges and open issues.


Prerequisite Skills for Reading Comprehension: Multi-Perspective Analysis of MCTest Datasets and Systems.


title: Building blocks for electronic spiking neural networks

abstract: We present an electronic circuit modelling the spike generation process in the biological neuron. This simple circuit is capable of simulating the spiking behaviour of several different types of biological neurons. At the same time, the circuit is small so that many neurons can be implemented on a single silicon chip. This is important, as neural computation obtains its power not from a single neuron, but from the interaction between a large number of neurons. Circuits that model these interactions are also presented in this paper. They include the circuits for excitatory, inhibitory and shunting inhibitory synapses, a circuit which models the regeneration of spikes on the axon, and a circuit which models the reduction of input strength with the distance of the synapse to the cell body on the dendrite of the cell. Together these building blocks allow the implementation of electronic spiking neural networks.


title: Camera as the instrument: the rising trend of vision based measurement

abstract: Due to continuing and rapid advances of both hardware and software technologies in camera and computing systems, we continue to have access to cheaper, faster, higher quality, and smaller cameras and computing units. As a result, vision based methods consisting of image processing and computational intelligence can be implemented more easily and affordably than ever using a camera and its associated operations units. Among their various applications, such systems are also being used more and more by researchers and practitioners as generic instruments to measure and monitor physical phenomena. In this article, we take a look at this rising trend and how cameras and vision are being used for instrumentation and measurement, and we also cast a glance at the metrological gauntlet thrown down by vision-based instruments.


title: Non-convex Robust PCA

abstract: We propose a new method for robust PCA -- the task of recovering a low-rank matrix from sparse corruptions that are of unknown value and support. Our method involves alternating between projecting appropriate residuals onto the set of low-rank matrices, and the set of sparse matrices; each projection is {\em non-convex} but easy to compute. In spite of this non-convexity, we establish exact recovery of the low-rank matrix, under the same conditions that are required by existing methods (which are based on convex optimization). For an $m \times n$ input matrix ($m \leq n)$, our method has a running time of $O(r^2mn)$ per iteration, and needs $O(\log(1/\epsilon))$ iterations to reach an accuracy of $\epsilon$. This is close to the running time of simple PCA via the power method, which requires $O(rmn)$ per iteration, and $O(\log(1/\epsilon))$ iterations. In contrast, existing methods for robust PCA, which are based on convex optimization, have $O(m^2n)$ complexity per iteration, and take $O(1/\epsilon)$ iterations, i.e., exponentially more iterations for the same accuracy. ::: Experiments on both synthetic and real data establishes the improved speed and accuracy of our method over existing convex implementations.


title: A Study of Structured Lecture Podcasting to Facilitate Active Learning

abstract: This paper presents an overview of a study of educational podcasting in higher education. This research aims to remedy pedagogical deficiencies in lecture podcasting by structuring content with chapter features designed to facilitate active learning behaviors. Also as mobile media players are quickly becoming ubiquitous, so is media multitasking. However, media multitasking, the processing of multiple information streams, presents challenges for human cognition. This study further investigated relationships in the usage of the chapter features through multitasking preferences and aspects of psychological flow. The study revealed that students used the chapter features as a supporting resource in their lecture study strategies for selection and review behaviors and also to support both their multitasking and flow behavioral preferences. The theory driven method and instrument developed to conduct this research can facilitate future studies.


title: An Autofocus Method for Backprojection Imagery in Synthetic Aperture Radar

abstract: In this letter, we present an autofocus routine for backprojection imagery from spotlight-mode synthetic aperture radar data. The approach is based on maximizing image sharpness and supports the flexible collection and imaging geometries of BP, including wide-angle apertures and the ability to image directly onto a digital elevation map. While image-quality-based autofocus approaches can be computationally intensive, in the backprojection setting, we demonstrate a natural geometric interpretation that allows for optimal single-pulse phase corrections to be derived in closed form as the solution of a quartic polynomial. The approach is applicable to focusing standard backprojection imagery, as well as providing incremental focusing in sequential imaging applications based on autoregressive backprojection. An example demonstrates the efficacy of the approach applied to real data for a wide-aperture backprojection image.


title: Synthetic Word Parsing Improves Chinese Word Segmentation

abstract: We present a novel solution to improve the performance of Chinese word segmentation (CWS) using a synthetic word parser. The parser analyses the internal structure of words, and attempts to convert out-of-vocabulary words (OOVs) into in-vocabulary fine-grained sub-words. We propose a pipeline CWS system that first predicts this fine-grained segmentation, then chunks the output to reconstruct the original word segmentation standard. We achieve competitive results on the PKU and MSR datasets, with substantial improvements in OOV recall.


NoSQL in a Mobile World : Benchmarking Embedded Mobile Databases


title: #Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem

abstract: This study examined how social media use related to sleep quality, self-esteem, anxiety and depression in 467 Scottish adolescents. We measured overall social media use, nighttime-specific social media use, emotional investment in social media, sleep quality, self-esteem and levels of anxiety and depression. Adolescents who used social media more – both overall and at night – and those who were more emotionally invested in social media experienced poorer sleep quality, lower self-esteem and higher levels of anxiety and depression. Nighttime-specific social media use predicted poorer sleep quality after controlling for anxiety, depression and self-esteem. These findings contribute to the growing body of evidence that social media use is related to various aspects of wellbeing in adolescents. In addition, our results indicate that nighttime-specific social media use and emotional investment in social media are two important factors that merit further investigation in relation to adolescent sleep and wellbeing.


title: Passive IP traceback: capturing the origin of anonymous traffic through network telescopes

abstract: IP traceback can be used to find the origin of anonymous traffic; however, Internet-scale IP traceback systems have not been deployed due to a need for cooperation between Internet Service Providers (ISPs). This article presents an Internet-scale Passive IP Trackback (PIT) mechanism that does not require ISP deployment. PIT analyzes the ICMP messages that may scattered to a network telescope as spoofed packets travel from attacker to victim. An Internet route model is then used to help re-construct the attack path. Applying this mechanism to data collected by Cooperative Association for Internet Data Analysis (CAIDA), we found PIT can construct a trace tree from at least one intermediate router in 55.4% the fiercest packet spoofing attacks, and can construct a tree from at least 10 routers in 23.4% of attacks. This initial result shows PIT is a promising mechanism.


title: Codescanner: Detecting (Hidden) x86/x64 code in arbitrary files

abstract: Disassembly is indispensable for the proper analysis of malware. However, a common problem concerning the x86/x64 architecture is that disassemblers produce partially incorrect results. This is used by malware authors who nowadays routinely generate binaries with anti-disassembly measures. In this paper, we derive general constraints on x86 code which are not based on disassembly but on byte level. Based on these constraints we develop a set of classifiers able to locate code in any kind of files. Operating on byte level, our approach is independent of assembly semantics. Our evaluation shows that we are able to precisely locate code and provide anti-disassembly resistance independent of the operating system or compiler. Our tool can be used to detect the code sections of malware, improve code coverage for disassemblers, detect hidden code in files and in memory, or identify malware with anti-disassembly techniques.


title: Torque Pulsation Minimization in Spoke-type Interior Permanent Magnet Motors With Skewing and Sinusoidal Permanent Magnet Configurations

abstract: This paper proposes two approaches to minimize torque pulsations, including cogging torque and electromagnetic torque ripple, for spoke-type interior permanent magnet (IPM) motors. The first approach is an improved skewing method, in which the rotor PMs are skewed by being symmetrical in the axial direction within one magnet pole pitch for not only reducing torque pulsations but also maintaining maximum available torque and eliminating unbalanced axial electromagnetic forces. The second one is a sinusoidal PM-shaping method with the aim of achieving the same advantageous effects as the proposed skewing method. Both the proposed methods adopt stepped rotor-PM schemes, and the effects of the rotor-PM step numbers on motor performance are investigated. To highlight the advantages of the proposed approaches, a spoke-type IPM motor with the conventional PM configuration is adopted as the basic model. All the motor characteristics, such as back electromotive force, cogging torque, and electromagnetic torque, are predicted by a 3-D finite-element method with the aid of JMAG-Designer.


title: Upper and lower extremity robotic devices for rehabilitation and for studying motor control

abstract: Purpose of reviewThe successful motor rehabilitation of stroke, traumatic brain-injured and spinal cord-injured patients requires an intensive and task-specific therapy approach. Budget constraints limit a hand-to-hand therapy approach, so that intelligent machines may offer a solution to promote mo


title: Comparison between Traditional Approach and Object-Oriented Approach in Software Engineering Development

abstract: This paper discusses the comparison between Traditional approaches and Object-Oriented approach. Traditional approach has a lot of models that deal with different types of projects such as waterfall, spiral, iterative and v-shaped, but all of them and other lack flexibility to deal with other kinds of projects like Object-Oriented. Object-oriented Software Engineering (OOSE) is an object modeling language and methodology. The approach of using object - oriented techniques for designing a system is referred to as object-oriented design. Object-oriented development approaches are best suited to projects that will imply systems using emerging object technologies to construct, manage, and assemble those objects into useful computer applications. Object oriented design is the continuation of object-oriented analysis, continuing to center the development focus on object modeling techniques.


title: New Robust Clustering Model for Identifying Cancer Genome Landscapes

abstract: In recent decades, the availability of comprehensive genomic data has facilitated the insight of molecular portraits of cancer. Specifically, by conducting cancer clustering, cancer samples can be divided into several groups according to their differences and similarities in molecular characteristics. Traditional cancer clustering usually analyzes cancer samples from a single tissue, but such analysis cannot reveal the connections among different types of cancer. Landscape analysis across human cancers can help discover molecular signatures shared across cancer tissues, providing an opportunity to design new gene therapy tailored for different cancer patients. However, the noise level in genomic data is high. The robust clustering method is crucial to tackle this problem. In this paper, we propose a new robust clustering method to approach the landscape analysis for TCGA cancer data from a novel view, which is to eliminate the noise and then perform clustering on the cleaned data rather than weaken the effect of noise as existing noise-resistant norm methods. Extensive experiments on both genomic datasets and clustering benchmark datasets confirm the effectiveness and correctness of our proposed method.


title: The potential use of augmented reality in gamification

abstract: The use of augmented reality (AR) and gamification in various fields is currently gaining popularity for its capability in engaging users.Gamification is a term that defines the use of game-based elements, such as game mechanics, aesthetics, dynamics, and game thinking in the non-game ::: context environment.Meanwhile, AR is a technology that has an ability to overlap computer graphics onto the real environment.However, as a newly ::: emerging concept, gamification seems to have some arguments related to its elements, concepts, and effectiveness in a similar intervention. Therefore, this paper discusses gamification. Although dozens of studies have implemented ::: AR games, there is still an obvious lack in discussing and relating it to a gamified platform. Nevertheless, previous adaptation of games in AR ::: seems that there are also potentials to utilise gamification and AR concepts and elements, as well as AR and AR games in brief.This paper also justifies several previous empirical studies in AR and gamification to look into its elements, research design, and the potentials of AR and gamification combination


Identifying the Most Dominant Event in a News Article by Mining Event Coreference Relations


title: Towards minimalist game design

abstract: In this paper, we describe a design methodology that we have termed Minimalist Game Design. Minimalist games have small rulesets, narrow decision spaces, and abstract audiovisual representations, yet they do not compromise on depth of play or possibility space. We begin with a motivation for and definition of minimalist games, including terms such as "rules," "mechanics," "control," and "interface," and illustrate the importance of artificial design constraints. Using a number of examples, we show the strengths of minimalist game elements in systems, controls, visuals, and audio. Adhering to these constraints, these games feature a small set of mechanics and one core mechanic, while still being sufficiently deep and allowing for player exploration and performance. This depth comes from procedural methods, combinatorial complexity, probability, obfuscation, challenge, or any combination thereof. Our methodology embraces principles of holistic design, where there is no "filler," and where every element of the game contributes to the play experience in some meaningful, deliberate way.


title: Urban point-of-interest recommendation by mining user check-in behaviors

abstract: In recent years, researches on recommendation of urban Points-Of-Interest (POI), such as restaurants, based on social information have attracted a lot of attention. Although a number of social-based recommendation techniques have been proposed in the literature, most of their concepts are only based on the individual or friends' check-in behaviors. It leads to that the recommended POIs list is usually constrained within the users' or friends' living area. Furthermore, since context-aware and environmental information changes quickly, especially in urban areas, how to extract appropriate features from such kind of heterogeneous data to facilitate the recommendation is also a critical and challenging issue. In this paper, we propose a novel approach named Urban POI-Mine (UPOI-Mine) that integrates location-based social networks (LBSNs) for recommending users urban POIs based on the user preferences and location properties simultaneously. The core idea of UPOI-Mine is to build a regression-tree-based predictor in the normalized check-in space, so as to support the prediction of interestingness of POI related to each user's preference. Based on the LBSN data, we extract the features of places in terms of i) Social Factor, ii) Individual Preference, and iii) POI Popularity for model building. To our best knowledge, this is the first work on urban POI recommendation that considers social factor, individual preference and POI popularity in LBSN data, simultaneously. Through comprehensive experimental evaluations on a real dataset from Gowalla, the proposed UPOI-Mine is shown to deliver excellent performance.


title: Dual action of prajmalium on the Ca currents in frog isolated cardiomyocytes

abstract: Abstract The effects of N-n-propylajmaline (prajmalium) on the Na and Ca currents of single frog atrial and ventricular cells were studied by means of the whole-cell patch-clamp technique. Prajmalium (10 −9 to 10 −6 m ) depressed the Na current ( I Na ) in a dose- and use-dependent manner. In the same range of concentrations, prajmalium induced a dual effect on the high ( I CaL ) and low ( I CaT ) threshold Ca currents (the latter being only present in atrial cells). At a low concentration (10 −9 m ), prajmalium increased both Ca currents while at high concentrations (10 −6 m ) it depressed them. Prajmalium appeared very potent on I CaT although this current is generally reported to be barely sensitive to agonists and drugs. The action of the drug was also accompanied by a shortening in the half-time of inactivation of the Ca currents and a slight hyperpolarizing shift of their availability curves. The increase in I CaL by prajmalium was not prevented by prazosin (10 −7 m ) nor by propranolol (10 −6 m ), and it was also observed after I CaL had been fully stimulated by isoproterenol (10 −7 m ). Nifedipine (10 −6 m ), however, was able to prevent or block the prajmalium-induced increase in I CaL . Some similarities between the actions of prajmalium and dihydropyridine agonists on Ca currents are discussed.


title: Using robots to investigate the evolution of adaptive behavior

abstract: In the field of evolutionary robotics, investigators evolve populations of autonomous machines to exhibit some desired behavior. The neurology, morphology, or both may be placed under evolutionary control, and different behaviors can be selected for. Results from this approach can generate unique and surprising hypotheses about why certain behaviors evolve, regardless of whether they emerge in organisms or machines. To illustrate this approach, I describe recent work on the evolution of modularity, morphological computation, and prospection.


title: The Domain Specificity of Academic Emotional Experiences

abstract: The authors analyzed the domain specificity of emotions and focused on experiences of enjoyment, anxiety, and boredom in the domains of mathematics, Latin, German, and English. On the basis of assumptions in R. Pekrun's (2000; in press) control-value theory and findings of pilot studies, the authors hypothesized the existence of a largely domain-specific organization of emotional experiences. The sample consisted of 721 students from grades 7 to 10. Confirmatory multitrait-multimethod factor analysis of the 2-faceted dataset (emotions and domains) corroborated assumptions of domain specificity. Furthermore, using multilevel analysis, the authors found that emotions were significantly more domain-specific than students' grades, with enjoyment being the most domain-specific of the three emotions under investigation. The authors discuss implications for future research and practice.


title: Ten years of search based software engineering: a bibliometric analysis

abstract: Despite preceding related publications, works dealing with the resolution of software engineering problems by search techniques has especially risen since 2001. By its first decade, the Search Based Software Engineering (SBSE) approach has been successfully employed in several software engineering contexts, using various optimization techniques. Aside the relevance of such applications, knowledge regarding the publication patterns on the field plays an important role to its understanding and identity. Such information may also shed light into SBSE trends and future. This paper presents the first bibliometric analysis to SBSE publications. The study covered 740 publications of the SBSE community from 2001 through 2010. The performed bibliometric analysis concerned mainly in four categories: Publication, Sources, Authorship, and Collaboration. Additionally, estimates for the next years of several publication metrics are given. The study also analyzed the applicability of bibliometric laws in SBSE, such as Bradfords and Lotka.


title: BiCoS: A Bi-level co-segmentation method for image classification

abstract: The objective of this paper is the unsupervised segmentation of image training sets into foreground and background in order to improve image classification performance. To this end we introduce a new scalable, alternation-based algorithm for co-segmentation, BiCoS, which is simpler than many of its predecessors, and yet has superior performance on standard benchmark image datasets.


title: An advanced musculoskeletal humanoid Kojiro

abstract: We have been promoting a project of musculoskeletal humanoids. The project aims at the long-term goal of human-symbiotic robots as well as the mid-term goal of necessary design and control concepts for musculoskeletal robots. This paper presents the concepts and aim of the project and also shows the outline of our latest results about development of new musculoskeletal humanoid Kojiro, which is the succeeding version of our previous Kotaro.


title: Complex Descartes Circle Theorem

abstract: We present a short proof of the Descartes circle theorem on the "curvature-centers" of four mutually tangent circles. Key to the proof is associating an octahedral configuration of spheres to four mutually tangent circles. We also prove an analog for spheres.


title: Random forests versus Neural Networks — What's best for camera localization?

abstract: This work addresses the task of camera localization in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step, Random Forests (RFs) are typically used. On the other hand, Neural Networks (NNs) reign in many dense regression tasks, but are not test-time efficient. We ask the question: which of the two is best for camera localization? To address this, we make two method contributions: (1) a test-time efficient NN architecture which we term a ForestNet that is derived and initialized from a RF, and (2) a new fully-differentiable robust averaging technique for regression ensembles which can be trained end-to-end with a NN. Our experimental findings show that for scene coordinate regression, traditional NN architectures are superior to test-time efficient RFs and ForestNets, however, this does not translate to final 6D camera pose accuracy where RFs and ForestNets perform slightly better. To summarize, our best method, a ForestNet with a robust average, which has an equivalent fast and lightweight RF, improves over the state-of-the-art for camera localization on the 7-Scenes dataset. While this work focuses on scene coordinate regression for camera localization, our innovations may also be applied to other continuous regression tasks.


title: User interface goals, AI opportunities

abstract: This is an opinion piece about the relationship between the fields of human-computer interaction (HCI), and artificial intelligence (AI). The ultimate goal of both fields is to make user interfaces more effective and easier to use for people. But historically, they have disagreed about whether "intelligence" or "direct manipulation" is the better route to achieving this. There is an unjustified perception in HCI that AI is unreliable. There is an unjustified perception in AI that interfaces are merely cosmetic. This disagreement is counterproductive. This article argues that AI's goals of intelligent interfaces would benefit enormously by the user-centered design and testing principles of HCI. It argues that HCI's stated goals of meeting the needs of users and interacting in natural ways, would be best served by application of AI. Peace.


title: Emerging trends and new developments in information science: a document co-citation analysis (2009–2016)

abstract: Characterizing the structure of knowledge, the evolution of research topics, and the emergence of topics has always been an important part of information science (IS). Our previous scientometric review of IS provided a snapshot of this fast-growing field up to the end of 2008. This new study aims to identify emerging trends and new developments appearing in the subsequent 7574 articles published in 10 IS journals between 2009 and 2016, including 20,960 references. The results of a document co-citation analysis show great changes in the research topics in the IS domain. The positions of certain core topics found in the previous study, namely, information retrieval, webometrics, and citation behavior, have been replaced by scientometric indicators (H-index), citation analysis (citation performance and bibliometrics), scientific collaboration, and information behavior in the most recent period of 2009–2016. Dual-map overlays of journals show that the knowledge base of IS research has shifted considerably since 2010, with emerging topics including scientific evaluation indicators, altmetrics, science mapping and visualization, bibliometrics, citation analysis, and scientific collaboration.


title: A framework for efficient storage security in RDBMS

abstract: With the widespread use of e-business coupled with the public’s awareness of data privacy issues and recent database security related legislations, incorporating security features into modern database products has become an increasingly important topic. Several database vendors already offer integrated solutions that provide data privacy within existing products. However, treating security and privacy issues as an afterthought often results in inefficient implementations. Some notable RDBMS storage models (such as the N-ary Storage Model) suffer from this problem. In this work, we analyze issues in storage security and discuss a number of trade-offs between security and efficiency. We then propose a new secure storage model and a key management architecture which enable efficient cryptographic operations while maintaining a very high level of security. We also assess the performance of our proposed model by experimenting with a prototype implementation based on the well-known TPC-H data set.


title: Improved Disk-Drive Failure Warnings

abstract: Improved methods are proposed for disk-drive failure prediction. The SMART (self monitoring and reporting technology) failure prediction system is currently implemented in disk-drives. Its purpose is to predict the near-term failure of an individual hard disk-drive, and issue a backup warning to prevent data loss. Two experimental tests of SMART show only moderate accuracy at low false-alarm rates. (A rate of 0.2% of total drives per year implies that 20% of drive returns would be good drives, relative to /spl ap/1% annual failure rate of drives). This requirement for very low false-alarm rates is well known in medical diagnostic tests for rare diseases, and methodology used there suggests ways to improve SMART. Two improved SMART algorithms are proposed. They use the SMART internal drive attribute measurements in present drives. The present warning-algorithm based on maximum error thresholds is replaced by distribution-free statistical hypothesis tests. These improved algorithms are computationally simple enough to be implemented in drive microprocessor firmware code. They require only integer sort operations to put several hundred attribute values in rank order. Some tens of these ranks are added up and the SMART warning is issued if the sum exceeds a prestored limit. These new algorithms were tested on 3744 drives of 2 models. They gave 3-4 times higher correct prediction accuracy than error thresholds on will-fail drives, at 0.2% false-alarm rate. The highest accuracies achievable are modest (40%-60%). Care was taken to test will-fail drive prediction accuracy on data independent of the algorithm design data. Additional work is needed to verify and apply these algorithms in actual drive design. They can also be useful in drive failure analysis engineering. It might be possible to screen drives in manufacturing using SMART attributes. Marginal drives might be detected before substantial final test time is invested in them, thereby decreasing manufacturing cost, and possibly decreasing overall field failure rates.


title: Dynamic eccentricity in squirrel cage induction motors – Simulation and analytical study of its spectral signatures on stator currents

abstract: Most of faults in three-phase induction motors have relationship with airgap eccentricity. There are two forms of airgap eccentricity: static (SE) and dynamic (DE). According to the literatures, the well known signatures of dynamic eccentricity, on the stator current spectra, are sidebands around the principal slot harmonics (PSH). However, many other researches have shown that DE induces also spectral components around the fundamental, but few are reported on the sources and the causes of these components. In this direction and since it is difficult to study experimentally the DE separately from the SE; the present paper attempts to explain, analytically and by simulation, the generation process of all frequency components that are a function of only DE. For that reason, a detailed analytical study for three-phase induction motors working under DE is performed. This study is based on rotating field approach. A general theoretical analysis of the interaction between all harmonics of the eccentric airgap permeance and the stator and rotor MMF components is put forward. The simulation results, obtained from an accurate model, confirm the existence of specific frequency components around the fundamental, caused by the dynamic airgap eccentricity. The interactions between the DE and the inherent SE are also illustrated using this mathematical model.


title: Experimental Case Studies for Investigating E-Banking Phishing Techniques and Attack Strategies

abstract: Phishing is a form of electronic identity theft in which a combination of social engineering and Web site spoofing techniques is used to trick a user into revealing confidential information with economic value. The problem of social engineering attack is that there is no single solution to eliminate it completely, since it deals largely with the human factor. This is why implementing empirical experiments is very crucial in order to study and to analyze all malicious and deceiving phishing Web site attack techniques and strategies. In this paper, three different kinds of phishing experiment case studies have been conducted to shed some light into social engineering attacks, such as phone phishing and phishing Web site attacks for designing effective countermeasures and analyzing the efficiency of performing security awareness about phishing threats. Results and reactions to our experiments show the importance of conducting phishing training awareness for all users and doubling our efforts in developing phishing prevention techniques. Results also suggest that traditional standard security phishing factor indicators are not always effective for detecting phishing websites, and alternative intelligent phishing detection approaches are needed.


title: Real-time volume graphics

abstract: The tremendous evolution of programmable graphics hardware has made high-quality real-time volume graphics a reality. In addition to the traditional application of rendering volume data in scientific visualization, the interest in applying these techniques for real-time rendering of atmospheric phenomena and participating media such as fire, smoke, and clouds is growing rapidly. This course covers both applications in scientific visualization, e.g., medical volume data, and real-time rendering, such as advanced effects and illumination in computer games, in detail. Course participants will learn techniques for harnessing the power of consumer graphics hardware and high-level shading languages for real-time rendering of volumetric data and effects. Beginning with basic texture-based approaches including hardware ray casting, the algorithms are improved and expanded incrementally, covering local and global illumination, scattering, pre-integration, implicit surfaces and non-polygonal isosurfaces, transfer function design, volume animation and deformation, dealing with large volumes, high-quality volume clipping, rendering segmented volumes, higher-order filtering, and non-photorealistic volume rendering. Course participants are provided with documented source code covering details usually omitted in publications.


An Empirical Study on End-to-End Sentence Modelling


title: Neural Text Generation: A Practical Guide

abstract: Deep learning methods have recently achieved great empirical success on machine translation, dialogue response generation, summarization, and other text generation tasks. At a high level, the technique has been to train end-to-end neural network models consisting of an encoder model to produce a hidden representation of the source text, followed by a decoder model to generate the target. While such models have significantly fewer pieces than earlier systems, significant tuning is still required to achieve good performance. For text generation models in particular, the decoder can behave in undesired ways, such as by generating truncated or repetitive outputs, outputting bland and generic responses, or in some cases producing ungrammatical gibberish. This paper is intended as a practical guide for resolving such undesired behavior in text generation models, with the aim of helping enable real-world applications.


The EPC II theory. Data structure in enterprise process control systems


title: How Well do Experienced Software Developers Predict Software Change?

abstract: Abstract Requirements-driven impact analysis ( rdia) identifies the set of software entities needed to be changed to implement a new requirement in an existing system. The input is a set of requirements and the existing system. The output is, for each requirement, a set of software entities that have to be changed. The output is used as input to many project-planning activities, for example cost estimation based on change volume. This paper quantifies how well experienced software developers predict change by conducting rdia, where rdia in this case is the general activity of predicting changes based on change request. The means has been an empirical study of rdia in the industrial object-oriented pmr- project. rdia has been carried out in two releases, R4 and R6, of this project as a normal part of project developers' work. This in-depth case-study has been carried out over four years and in close contact with project developers. The correctness of the prediction is high while problems with underprediction have been identified – many more classes than predicted are changed. We have also found that project developers are unaware of their own positive and negative capabilities in predicting change.


title: Developing a GIS-Based Visual-Acoustic 3D Simulation for Wind Farm Assessment

abstract: Public landscape impact assessment of renewable energy installations is crucial for their acceptance. Thus, a sound assessment basis is crucial in the implementation process. For valuing landscape perception, the visual sense is the dominant human sensory component. However, the visual sense provides only partial information about our environment. Especially when it comes to wind farm assessments, noise produced by the rotating turbine blades is another major impact factor. Therefore, an integrated visual and acoustic assessment of wind farm projects is needed to allow lay people to perceive their impact adequately. This paper presents an approach of linking spatially referenced auralizations to a GIS-based virtual 3D landscape model. We demonstrate how to utilize a game engine for 3D visualization of wind parks, using geodata as a modeling basis. In particular, the controlling and recording of specific parameters in the game engine is shown in order to establish a link to the acoustical model. The resulting prototype has high potential to complement conventional tools for an improved public impact assessment of wind farms.


title: Resolving Giant Molecular Clouds in NGC 300: A First Look with the Submillimeter Array

abstract: We present the first high angular resolution study of giant molecular clouds (GMCs) in the nearby spiral galaxy NGC 300, based on observations from the Submillimeter Array (SMA). We target eleven 500 pc-sized regions of active star formation within the galaxy in the CO(J=2-1) line at 40 pc spatial and 1 km/s spectral resolution and identify 45 individual GMCs. We characterize the physical properties of these GMCs, and find that they are similar to GMCs in the disks of the Milky Way and other nearby spiral galaxies. For example, the GMC mass spectrum in our sample has a slope of 1.80+/-0.07. Twelve clouds are spatially resolved by our observations, of which ten have virial mass estimates that agree to within a factor of two with mass estimates derived directly from CO integrated intensity, suggesting that the majority of these GMCs are bound. The resolved clouds show consistency with Larson's fundamental relations between size, linewidth, and mass observed in the Milky Way. We find that the linewidth scales with the size as DeltaV ~ R^(0.52+/-0.20), and the median surface density in the subsample is 54 Msun/pc^(-2). We detect 13CO in four GMCs and find a mean 12CO/13CO flux ratio of 6.2. Our interferometric observations recover between 30% and 100% of the integrated intensity from the APEX single dish CO observations of Faesi et al. 2014, suggesting the presence of low-mass GMCs and/or diffuse gas below our sensitivity limit. The fraction of APEX emission recovered increases with the SMA total intensity as well as with the star formation rate.


title: Mathematical basics of motion and deformation in computer graphics

abstract: This synthesis lecture presents an intuitive introduction to the mathematics of motion and deformation in computer graphics. Starting with familiar concepts in graphics, such as Euler angles, quaternions, and affine transformations, we illustrate that a mathematical theory behind these concepts enables us to develop the techniques for efficient/effective creation of computer animation. This book, therefore, serves as a good guidepost to mathematics (differential geometry and Lie theory) for students of geometric modeling and animation in computer graphics. Experienced developers and researchers will also benefit from this book, since it gives a comprehensive overview of mathematical approaches that are particularly useful in character modeling, deformation, and animation. Table of Contents: Preface / Symbols and Notations / Introduction / Rigid Transformation / Affine Transformation / Exponential and Logarithm of Matrices / 2D Affine Transformation between Two Triangles / Global 2D Shape Interpolation / Parametrizing 3D Positive Affine Transformations / Further Readings / Bibliography / Authors' Biographies


title: Diagnosis , Management , and Treatment of Hepatitis C : An Update

abstract: This document has been approved by the AASLD, the Infectious Diseases Society of America, and the American College of Gastroenterology.


title: 3D SLAM using planar segments

abstract: This paper presents an improved feature-based 3D SLAM approach for a mobile robot equipped with a rotating laser scanner. The features are represented using the SPmodel, with associated planar segment information based on decimated polygon sets. An Extended Kalman Filter is used to build a three-dimensional map of the environment and track the robot's pose. As shown, the resulting maps are highly detailed, useful for higher-level robotic tasks and small in size.


title: Micromachined One-Port Aluminum Nitride Lamb Wave Resonators Utilizing the Lowest-Order Symmetric Mode

abstract: The characteristics of one-port aluminum nitride (AlN) Lamb wave resonators utilizing the lowest-order symmetric mode with electrically open, grounded, and floating bottom electrode configurations are theoretically and experimentally investigated in this paper. Finite element analysis is performed to take an insight into the static capacitance characteristics of the AlN Lamb wave resonators with various bottom surface conditions. Without sacrificing the transduction efficiency, the floating bottom electrode is capable of reducing the static capacitance in the AlN thin plate and then promotes an efficient improvement in the effective coupling coefficient (k2eff). In addition, in comparison with the grounded bottom electrode, the employment of the floating bottom electrode offers simple fabrication processes for the micromachined Lamb wave resonators. Experimentally, the AlN Lamb wave resonators without the bottom electrode exhibit an average loaded quality factor (Q) as high as 2676 at the series resonance frequency, but a low average k2eff of 0.19%. On the contrary, the Lamb wave resonators with the electrically floating bottom electrode show the largest average k2eff up to 1.13% among the three topologies but a low average loaded Q of 800 at the series resonance frequency. In contrast to the floating bottom electrode, the Lamb wave resonators with the electrically grounded bottom electrode show a smaller average k2eff of 0.78% and a similar average loaded Q of 758 at the series resonance frequency.


title: Fingerprint matching using minutiae and interpolation-based square tessellation fingercode

abstract: To improve the overall accuracy, a hybrid fingerprint-matching scheme using both minutiae and square-tessellation-fingercode has been proposed in the literature. However, for identification applications, the matching process is time-consuming since the fingercode of the query fingerprint is repeatedly extracted when it is compared with different template fingerprints in a large database. In addition, the matching accuracy is influenced by nonlinear distortions in fingerprint images. In this paper, we propose a new approach to solve the problem. We extract the fignercode of the query fingerprint only once. When compared with the template fingerprints, the corresponding fingercodes are generated by interpolation and resampling on the extracted fingercode according to the optimally estimated mapping functions with respect to the minutiae matching results. Experimental results on NIST-4 and FVC2002 demonstrate that our algorithm outperforms the original approach in terms of both accuracy and running time.


title: Impact of helical boundary conditions on nonlinear 3D magnetohydrodynamic simulations of reversed-field pinch

abstract: Helical self-organized reversed-field pinch (RFP) regimes emerge both numerically—in 3D visco-resistive magnetohydrodynamic (MHD) simulations—and experimentally, as in the RFX-mod device at high current (IP above 1 MA). These states, called quasi-single helicity (QSH) states, are characterized by the action of a MHD mode that impresses a quasi-helical symmetry to the system, thus allowing a high degree of magnetic chaos healing. This is in contrast with the multiple helicity (MH) states, where magnetic fluctuations create a chaotic magnetic field degrading the confinement properties of the RFP. This paper reports an extensive numerical study performed in the frame of 3D visco-resistive MHD which considers the effect of helical magnetic boundary conditions, i.e. of a finite value of the radial magnetic field at the edge (magnetic perturbation, MP). We show that the system can be driven to a selected QSH state starting from both spontaneous QSH and MH regimes. In particular, a high enough MP can force a QSH helical self-organization with a helicity different from the spontaneous one. Moreover, MH states can be turned into QSH states with a selected helicity. A threshold in the amplitude of MP is observed above which is able to influence the system. Analysis of the magnetic topology of these simulations indicates that the dominant helical mode is able to temporarily sustain conserved magnetic structures in the core of the plasma. The region occupied by conserved magnetic surfaces increases reducing secondary modes' amplitude to experimental-like values.


title: Mismatch of Expectations: How Modern Learning Resources Fail Conversational Programmers

abstract: Conversational programmers represent a class of learners who are not required to write any code, yet try to learn programming to improve their participation in technical conversations. We carried out interviews with 23 conversational programmers to better understand the challenges they face in technical conversations, what resources they choose to learn programming, how they perceive the learning process, and to what extent learning programming actually helps them. Among our key findings, we found that conversational programmers often did not know where to even begin the learning process and ended up using formal and informal learning resources that focus largely on programming syntax and logic. However, since the end goal of conversational programmers was not to build artifacts, modern learning resources usually failed these learners in their pursuits of improving their technical conversations. Our findings point to design opportunities in HCI to invent learner-centered approaches that address the needs of conversational programmers and help them establish common ground in technical conversations.


title: Minimizing power consumption in digital CMOS circuits

abstract: An approach is presented for minimizing power consumption for digital systems implemented in CMOS which involves optimization at all levels of the design. This optimization includes the technology used to implement the digital circuits, the circuit style and topology, the architecture for implementing the circuits and at the highest level the algorithms that are being implemented. The most important technology consideration is the threshold voltage and its control which allows the reduction of supply voltage without significant impact on logic speed. Even further supply reductions can be made by the use of an architecture-based voltage scaling strategy, which uses parallelism and pipelining, to tradeoff silicon area and power reduction. Since energy is only consumed when capacitance is being switched power can be reduced by minimizing this capacitance through operation reduction choice of number representation, exploitation of signal correlations, resynchronization to minimize glitching, logic design, circuit design, and physical design. The low-power techniques that are presented have been applied to the design of a chipset for a portable multimedia terminal that supports pen input, speech I/O and full-motion video. The entire chipset that performs protocol conversion, synchronization, error correction, packetization, buffering, video decompression and D/A conversion operates from a 1.1 V supply and consumes less than 5 mW. >


title: Integrating AHP and data mining for product recommendation based on customer lifetime value

abstract: Product recommendation is a business activity that is critical in attracting customers. Accordingly, improving the quality of a recommendation to fulfill customers' needs is important in fiercely competitive environments. Although various recommender systems have been proposed, few have addressed the lifetime value of a customer to a firm. Generally, customer lifetime value (CLV) is evaluated in terms of recency, frequency, monetary (RFM) variables. However, the relative importance among them varies with the characteristics of the product and industry. We developed a novel product recommendation methodology that combined group decision-making and data mining techniques. The analytic hierarchy process (AHP) was applied to determine the relative weights of RFM variables in evaluating customer lifetime value or loyalty. Clustering techniques were then employed to group customers according to the weighted RFM value. Finally, an association rule mining approach was implemented to provide product recommendations to each customer group. The experimental results demonstrated that the approach outperformed one with equally weighted RFM and a typical collaborative filtering (CF) method.


title: Exploring personal impact for group recommendation

abstract: Group activities are essential ingredients of people's social life. The rapid growth of online social networking services has greatly boosted group activities by providing convenient platform for users to organize and participate in such activities. Therefore, recommender systems, as a critical component in social networking services, now face new challenges in supporting group activities. In this paper, we study the group recommendation problem, i.e., making recommendations to a group of people in social networking services. We analyze the decision making process in a group to propose a personal impact topic (PIT) model for group recommendations. The PIT model effectively identifies the group preference profile for a given group by considering the personal preferences and personal impacts of group members. Moreover, we further enhance the discovery of personal impact with social network information to obtain an extended personal impact topic (E-PIT) model. We have conducted comprehensive data analysis and evaluations on three real datasets. The results show that our proposed group recommendation techniques outperform baseline approaches.


title: Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges

abstract: The rapid advancements in communication technologies and the explosive growth of the Internet of Things have enabled the physical world to invisibly interweave with actuators, sensors, and other computational elements while maintaining continuous network connectivity. The continuously connected physical world with computational elements forms a smart environment. A smart environment aims to support and enhance the abilities of its dwellers in executing their tasks, such as navigating through unfamiliar space and moving heavy objects for the elderly, to name a few. Researchers have conducted a number of efforts to use IoT to facilitate our lives and to investigate the effect of IoT-based smart environments on human life. This article surveys the state-of-the-art research efforts to enable IoT-based smart environments. We categorize and classify the literature by devising a taxonomy based on communication enablers, network types, technologies, local area wireless standards, objectives, and characteristics. Moreover, the article highlights the unprecedented opportunities brought about by IoT-based smart environments and their effect on human life. Some reported case studies from different enterprises are also presented. Finally, we discuss open research challenges for enabling IoT-based smart environments.


title: Effective and Efficient User Account Linkage across Location Based Social Networks

abstract: Sources of complementary information are connected when we link the user accounts belonging to the same user across different domains or devices. The expanded information promotes the development of a wide range of applications, such as cross-domain prediction, cross-domain recommendation, and advertisement. Due to the great significance of user account linkage, there are increasing research works on this study. With the widespread popularization of GPS-enabled mobile devices, linking user accounts with location data has become an important and promising research topic. Being different from most existing studies in this domain that only focus on the effectiveness, we propose novel approaches to improve both effectiveness and efficiency of user account linkage. In this paper, a kernel density estimation (KDE) based method has been proposed to improve the accuracy by alleviating the data sparsity problem in measuring users' similarities. To improve the efficiency, we develop a grid-based structure to organize location data to prune the search space. The extensive experiments conducted on two real-world datasets demonstrate the superiority of the proposed approach in terms of both effectiveness and efficiency compared with the state-of-art methods.


title: Risk factors for small-for-gestational-age and preterm births among 19,269 Tanzanian newborns

abstract: Few studies have differentiated risk factors for term-small for gestational age (SGA), preterm-appropriate for gestational age (AGA), and preterm-SGA, despite evidence of varying risk of child mortality and poor developmental outcomes. We analyzed birth outcome data from singleton infants, who were enrolled in a large randomized, double-blind, placebo-controlled trial of neonatal vitamin A supplementation conducted in Tanzania. SGA was defined as birth weight <10th percentile for gestation age and sex using INTERGROWTH standards and preterm birth as delivery at <37 complete weeks of gestation. Risk factors for term-SGA, preterm-AGA, and preterm-SGA were examined independently using log-binomial regression. Among 19,269 singleton Tanzanian newborns included in this analysis, 68.3 % were term-AGA, 15.8 % term-SGA, 15.5 % preterm-AGA, and 0.3 % preterm-SGA. In multivariate analyses, significant risk factors for term-SGA included maternal age <20 years, starting antenatal care (ANC) in the 3rd trimester, short maternal stature, being firstborn, and male sex (all p < 0.05). Independent risk factors for preterm-AGA were maternal age <25 years, short maternal stature, firstborns, and decreased wealth (all p < 0.05). In addition, receiving ANC services in the 1st trimester significantly reduced the risk of preterm-AGA (p = 0.01). Significant risk factors for preterm-SGA included maternal age >30 years, being firstborn, and short maternal stature which appeared to carry a particularly strong risk (all p < 0.05). Over 30 % of newborns in this large urban and rural cohort of Tanzanian newborns were born preterm and/or SGA. Interventions to promote early attendance to ANC services, reduce unintended young pregnancies, increased maternal height, and reduce poverty may significantly decrease the burden of SGA and preterm birth in sub-Saharan Africa. Australian New Zealand Clinical Trials Registry (ANZCTR) – ACTRN12610000636055, registered on 3rd August 2010.


title: Devices, systems, and methods for automated monitoring enabling precision agriculture

abstract: Addressing the challenges of feeding the burgeoning world population with limited resources requires innovation in sustainable, efficient farming. The practice of precision agriculture offers many benefits towards addressing these challenges, such as improved yield and efficient use of such resources as water, fertilizer and pesticides. We describe the design and development of a light-weight, multi-spectral 3D imaging device that can be used for automated monitoring in precision agriculture. The sensor suite consists of a laser range scanner, multi-spectral cameras, a thermal imaging camera, and navigational sensors. We present techniques to extract four key data products — plant morphology, canopy volume, leaf area index, and fruit counts — using the sensor suite. We demonstrate its use with two systems: multi-rotor micro aerial vehicles and on a human-carried, shoulder-mounted harness. We show results of field experiments conducted in collaboration with growers and agronomists in vineyards, apple orchards and orange groves.


title: Clustering Data Streams Based on Shared Density between Micro-Clusters

abstract: As more and more applications produce streaming data, clustering data streams has become an important technique for data and knowledge engineering. A typical approach is to summarize the data stream in real-time with an online process into a large number of so called micro-clusters. Micro-clusters represent local density estimates by aggregating the information of many data points in a defined area. On demand, a (modified) conventional clustering algorithm is used in a second offline step to recluster the micro-clusters into larger final clusters. For reclustering, the centers of the micro-clusters are used as pseudo points with the density estimates used as their weights. However, information about density in the area between micro-clusters is not preserved in the online process and reclustering is based on possibly inaccurate assumptions about the distribution of data within and between micro-clusters (e.g., uniform or Gaussian). This paper describes DBSTREAM, the first micro-cluster-based online clustering component that explicitly captures the density between micro-clusters via a shared density graph. The density information in this graph is then exploited for reclustering based on actual density between adjacent micro-clusters. We discuss the space and time complexity of maintaining the shared density graph. Experiments on a wide range of synthetic and real data sets highlight that using shared density improves clustering quality over other popular data stream clustering methods which require the creation of a larger number of smaller micro-clusters to achieve comparable results.


title: REVIEW A Meta-Analysis of D-Cycloserine and the Facilitation of Fear Extinction and Exposure Therapy

abstract: Background Translational research suggests that D-cycloserine (DCS), a partial N-methyl-D-aspartate (NMDA) receptor agonist, might facilitate fear extinction and exposure therapy by either enhancing NMDA receptor function during extinction or by reducing NMDA receptor function during fear memory consolidation. This article provides a quantitative review of DCS-augmented fear extinction and exposure therapy literature. Methods English-language journal articles that examined DCS augmented with fear extinction or exposure therapy were identified through public databases from June 1998 through September 2007, through references of originally identified articles and contact with DCS investigators. Data were extracted for study author, title, and year; trial design; type of subject (animal vs. human; clinical vs. nonclinical); sample size, DCS dose, and timing in relation to extinction/exposure procedures; dependent variable; group means and SDs at post-extinction/exposure; and follow-up outcome. Results D-cycloserine enhances fear extinction/exposure therapy in both animals and anxiety-disordered humans. Gains generally were maintained at follow-up, although some lessening of efficacy was noted. D-cycloserine was more effective when administered a limited number of times and when given immediately before or after extinction training/exposure therapy. Conclusions This meta-analysis suggests that DCS is a useful target for translational research on augmenting exposure-based treatment via compounds that impact neuroplasticity. D-cycloserine 's major contribution to exposure-based therapy might be to increase its speed or efficiency, because the effects of DCS seem to decrease over repeated sessions. This information might guide translational researchers in discovering more selective and/or effective agents that effectively enhance (or reduce) NMDA receptor function.


title: Performance Analysis and Evaluation of InfiniBand FDR and 40GigE RoCE on HPC and Cloud Computing Systems

abstract: Communication interfaces of high performance computing (HPC) systems and clouds have been continually evolving to meet the ever increasing communication demands being placed on them by HPC applications and cloud computing middleware (e.g., Hadoop). The PCIe interfaces can now deliver speeds up to 128 Gbps (Gen3) and high performance interconnects (10/40 GigE, InfiniBand 32 Gbps QDR, InfiniBand 54 Gbps FDR, 10/40 GigE RDMA over Converged Ethernet) are capable of delivering speeds from 10 to 54 Gbps. However, no previous study has demonstrated how much benefit an end user in the HPC / cloud computing domain can expect by utilizing newer generations of these interconnects over older ones or how one type of interconnect (such as IB) performs in comparison to another (such as RoCE).In this paper we evaluate various high performance interconnects over the new PCIe Gen3 interface with HPC as well as cloud computing workloads. Our comprehensive analysis done at different levels, provides a global scope of the impact these modern interconnects have on the performance of HPC applications and cloud computing middleware. The results of our experiments show that the latest InfiniBand FDR interconnect gives the best performance for HPC as well as cloud computing applications.


title: Wireless sensor and actuator system for smart irrigation on the cloud

abstract: The number of devices connected to the Internet is experiencing an explosive growth. The interconnection of smart objects embedded with sensors enables them to interact with the environment and among themselves, forming a Wireless Sensor Network (WSN). These network nodes perform acquisition, collection and analysis of data, such as temperature and soil moisture. Such data can be employed to automate the irrigation process in agriculture while decreasing water consumption, resulting in monetary and environmental benefits. The high storage and processing capabilities, the rapid elasticity and payper-use characteristics makes Cloud Computing an attractive solution to the large amount of data generated by the WSN. This paper proposes and evaluates on a real deployment a cloud-based Wireless Sensor and Actuator Network (WSAN) communication system. This solution monitors and controls a set of sensors and actuators, respectively, to assess plants water needs.


title: On Causal Mediation Analysis with a Survival Outcome

abstract: Suppose that having established a marginal total effect of a point exposure on a time-to-event outcome, an investigator wishes to decompose this effect into its direct and indirect pathways, also known as natural direct and indirect effects, mediated by a variable known to occur after the exposure and prior to the outcome. This paper proposes a theory of estimation of natural direct and indirect effects in two important semiparametric models for a failure time outcome. The underlying survival model for the marginal total effect and thus for the direct and indirect effects, can either be a marginal structural Cox proportional hazards model, or a marginal structural additive hazards model. The proposed theory delivers new estimators for mediation analysis in each of these models, with appealing robustness properties. Specifically, in order to guarantee ignorability with respect to the exposure and mediator variables, the approach, which is multiply robust, allows the investigator to use several flexible working models to adjust for confounding by a large number of pre-exposure variables. Multiple robustness is appealing because it only requires a subset of working models to be correct for consistency; furthermore, the analyst need not know which subset of working models is in fact correct to report valid inferences. Finally, a novel semiparametric sensitivity analysis technique is developed for each of these models, to assess the impact on inference, of a violation of the assumption of ignorability of the mediator.


“ ARM HARDWARE PLATFORM FOR VEHICULAR MONITORING AND TRACKING USING GPS AND GSM


title: Cascade Multi-View Hourglass Model for Robust 3D Face Alignment

abstract: Estimating the 3D facial landmarks from a 2D image remains a challenging problem. Even though state-of-the-art 2D alignment methods are able to predict accurate landmarks for semi-frontal faces, the majority of them fail to provide semantically consistent landmarks for profile faces. A de facto solution to this problem is through 3D face alignment that preserves correspondence across different poses. In this paper, we proposed a Cascade Multi-view Hourglass Model for 3D face alignment, where the first Hourglass model is explored to jointly predict semi-frontal and profile 2D facial landmarks, after removing spatial transformations, another Hourglass model is employed to estimate the 3D facial shapes. To improve the capacity without sacrificing the computational complexity, the original residual bottleneck block in the Hourglass model is replaced by a parallel, multi-scale inception-resnet block. Extensive experiments on two challenging 3D face alignment datasets, AFLW2000-3D and Menpo-3D, show the robustness of the proposed method under continuous pose changes.


title: Multimodal Named Entity Recognition for Short Social Media Posts

abstract: We introduce a new task called Multimodal Named Entity Recognition (MNER) for noisy user-generated data such as tweets or Snapchat captions, which comprise short text with accompanying images. These social media posts often come in inconsistent or incomplete syntax and lexical notations with very limited surrounding textual contexts, bringing significant challenges for NER. To this end, we create a new dataset for MNER called SnapCaptions (Snapchat image-caption pairs submitted to public and crowd-sourced stories with fully annotated named entities). We then build upon the state-of-the-art Bi-LSTM word/character based NER models with 1) a deep image network which incorporates relevant visual context to augment textual information, and 2) a generic modality-attention module which learns to attenuate irrelevant modalities while amplifying the most informative ones to extract contexts from, adaptive to each sample and token. The proposed MNER model with modality attention significantly outperforms the state-of-the-art text-only NER models by successfully leveraging provided visual contexts, opening up potential applications of MNER on myriads of social media platforms.


title: String similarity search and join: a survey

abstract: String similarity search and join are two important operations in data cleaning and integration, which extend traditional exact search and exact join operations in databases by tolerating the errors and inconsistencies in the data. They have many real-world applications, such as spell checking, duplicate detection, entity resolution, and webpage clustering. Although these two problems have been extensively studied in the recent decade, there is no thorough survey. In this paper, we present a comprehensive survey on string similarity search and join. We first give the problem definitions and introduce widely-used similarity functions to quantify the similarity. We then present an extensive set of algorithms for string similarity search and join. We also discuss their variants, including approximate entity extraction, type-ahead search, and approximate substring matching. Finally, we provide some open datasets and summarize some research challenges and open problems.


title: Robust eye tracking and location method based on Particle filtering algorithm

abstract: In this paper we present a fast vision-based eye-gaze tracking method based on Particle filtering algorithm in the condition of near-infrared light and single-camera, against to the requirement of real-time eye tracking in engineering, and the fact that presently most of eye tracking methods in video are not precise, target easy to lose. In the initialize step, we use a high accuracy cascaded classifier trained by AdaBoost algorithm to get the primitive information of eye region. Considering the eye region information in the last frame image is valuable to the next frame image analysis, the particle filter algorithm is adopted to accomplish the eye region tracking. Experimental validations show that the processing time for each single frame is effectively reduced by using the constraints between the last and next frames, for it reduce the search range of the human eye. Finally, we design a segmentation method with double thresholds to extract the pupil and Purkinje bright spot from contours, which conduce to pupil positioning and distinguish the eye region.


title: Miniature Quasi-Lumped-Element Wideband Bandpass Filter at 0.5–2-GHz Band Using Multilayer Liquid Crystal Polymer Technology

abstract: Miniature wideband bandpass filters are proposed using multilayer liquid crystal polymer (LCP) technology to cover the very low-frequency band of 0.5-2 GHz. To reduce the filter size at such low frequencies, lumped-element theory is used for the filter design and a value extraction process is developed to accurately get the capacitive or inductive values of different multilayer microstrip quasi-lumped elements. These elements are used to produce the required filter response, and thus the overall design process relies less on the time-consuming EM optimization. A filter with the size 0.058 λg ×0.026 λg ×0.004 λg is demonstrated as an initial design. To further improve the stopband performance, an improved design is then developed while still maintaining the compact sizes within 0.065 λg ×0.026 λg ×0.004 λg. Both filters are fabricated on a five-metal layer LCP construction, which has not been done before, with robust via connections using the newly developed laser-aided fabrication technique. Good agreements between simulation and fabrication are observed, which has proven both the success of the design methodology, as well as the fabrication technique.


title: Study of automated face recognition system for office door access control application

abstract: The security currently become a very important issue in public or private institutions in which various security systems have been proposed and developed for some crucial processes such as person identifications, verification or recognition especially for building access control, suspect identifications by the police, driver licenses and many others. Face recognitions have been an active area of research with numerous applications since late 1980s and become one of the important elements in security system development. This paper focuses on the study and development on an automated face recognition system with the potential application for office door access control. The technique of eigenfaces based on the principle component analysis (PCA) and artificial neural networks have been applied into the system. The study includes the analysis of the influences of three main factors of face recognition namely illumination, distance and subject's head orientation on the developed face recognition system purposely built for office door access control. The experimental results have shown that the developed system has achieved good performance of face recognition rate of 80% at the distance of camera and subject between 40 cm to 60 cm and the subject's orientation head angle must be within the range of-20 to +20 degrees.


title: NailTactors: eyes-free spatial output using a nail-mounted tactor array

abstract: This paper investigates the feasibility of using a nail-mounted array of tactors, NailTactors, as an eyes-free output device. By rim-attached eccentric-rotating-mass (ERM) vibrators to artificial nails, miniature high-resolution tactile displays were realized as an eyes-free output device. To understand how to deliver rich signals to users for valid signal perception, three user studies were conducted. The results suggest that users can not only recognized absolute and relative directional cues, but also recognized numerical characters in EdgeWrite format with an overall 89% recognition rate. Experiments also identified the optimal placement of ERM actuators for maximizing information transfer.


title: Cross Domain Knowledge Transfer for Person Re-identification

abstract: Person Re-Identification (re-id) is a challenging task in computer vision, especially when there are limited training data from multiple camera views. In this paper, we pro- pose a deep learning based person re-identification method by transferring knowledge of mid-level attribute features and high-level classification features. Building on the idea that identity classification, attribute recognition and re- identification share the same mid-level semantic representations, they can be trained sequentially by fine-tuning one based on another. In our framework, we train identity classification and attribute recognition tasks from deep Convolutional Neural Network (dCNN) to learn person information. The information can be transferred to the person re-id task and improves its accuracy by a large margin. Further- more, a Long Short Term Memory(LSTM) based Recurrent Neural Network (RNN) component is extended by a spacial gate. This component is used in the re-id model to pay attention to certain spacial parts in each recurrent unit. Experimental results show that our method achieves 78.3% of rank-1 recognition accuracy on the CUHK03 benchmark.


title: Digital Library Engine: Adapting Digital Library for Cloud Computing

abstract: With the rapid growth of digital libraries, more data and smart services are involved. People come to recognize the importance of digital libraries and the convenience they might bring to the society. However, the cost of owning a digital library is quite high, and many institutions do not have the ability to run and maintain a digital library by themselves, especially for massive data and complex services which require lots of storage and computing resources. In this paper, we proposed the Digital Library Engine, which aims to provide a new Platform as a Service for fast developing and deploying digital libraries in cloud. To the best of our knowledge, it is the first work to create a PaaS system for digital libraries. With the help of Digital Library Engine, institutions only need to develop some service bundles, which can be deployed in the engine, and then their own digital libraries could be running well with features of scalability, reliability, security, extensibility, availability and manageability. The practice in CADAL and the experiments demonstrate the feasibility and efficiency of our engine.


The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions


title: Recovery from the Great Recession Has Varied around the World

abstract: Since 2009, percentage growth in GDP has been the highest in Asia and Africa and the lowest in Europe, followed by North America. The mediocre performance on the latter two continents could have something to do with their advanced and open financial systems, which might have made it easier for the global financial crisis to spread through them.


title: Logic Programming Petri Nets

abstract: With the purpose of modeling, specifying and reasoning in an integrated fashion with procedural and declarative aspects (both commonly present in cases or scenarios), the paper introduces Logic Programming Petri Nets (LPPN), an extension to the Petri Net notation providing an interface to logic programming constructs. Two semantics are presented. First, a hybrid operational semantics that separates the process component, treated with Petri nets, from the constraint/terminological component, treated with Answer Set Programming (ASP). Second, a denotational semantics maps the notation to ASP fully, via Event Calculus. These two alternative specifications enable a preliminary evaluation in terms of reasoning efficiency.


title: The number of subjects per variable required in linear regression analyses

abstract: Abstract Objectives To determine the number of independent variables that can be included in a linear regression model. Study Design and Setting We used a series of Monte Carlo simulations to examine the impact of the number of subjects per variable (SPV) on the accuracy of estimated regression coefficients and standard errors, on the empirical coverage of estimated confidence intervals, and on the accuracy of the estimated R 2 of the fitted model. Results A minimum of approximately two SPV tended to result in estimation of regression coefficients with relative bias of less than 10%. Furthermore, with this minimum number of SPV, the standard errors of the regression coefficients were accurately estimated and estimated confidence intervals had approximately the advertised coverage rates. A much higher number of SPV were necessary to minimize bias in estimating the model R 2 , although adjusted R 2 estimates behaved well. The bias in estimating the model R 2 statistic was inversely proportional to the magnitude of the proportion of variation explained by the population regression model. Conclusion Linear regression models require only two SPV for adequate estimation of regression coefficients, standard errors, and confidence intervals.


Small and Medium Businesses Informatization Managment : Main Trends of Development in Russia


title: Vengefully Ever After: Destiny Beliefs, State Attachment Anxiety, and Forgiveness

abstract: Two studies examined how destiny beliefs (that potential relationships are or are not “meant to be”) interact with state attachment anxiety to predict forgiveness tendencies. In Study 1, participants experienced an experimental manipulation of attachment anxiety (vs. security) before indicating the degree to which they would forgive a series of hypothetical partner offenses. In Study 2, participants reported every 2 weeks for 6 months (14 waves in total) on offenses enacted by their partner and indicated the degree to which they forgave the partner, both concurrently and 2 weeks later. Consistent with predictions, results revealed Destiny Beliefs State Attachment Anxiety interaction effects: Strong (relative to weak) destiny beliefs predicted reduced forgiveness tendencies for individuals experiencing state attachment anxiety, but such beliefs were not associated with forgiveness for individuals experiencing state attachment security. Results from Study 2 suggest that this interaction effect was significantly mediated through trust in the partner.


Towards Expanded Trust Management for Agents in Vehicular Ad-hoc Networks


title: Dermoscopic Features of Difficult Melanoma

abstract: BACKGROUND ::: The dermoscopic diagnosis of cutaneous melanoma (CM) may be difficult because some CM lack specific dermoscopic features for melanoma diagnosis. ::: ::: ::: OBJECTIVE ::: To evaluate whether a diagnosis of CM could be achieved using the classic dermoscopic melanoma-specific criteria, we conducted a retrospective multicenter study of 508 CM samples. ::: ::: ::: METHODS ::: All the dermoscopic images were analyzed to identify the dermoscopic criteria found in dermoscopically difficult melanomas (DDM) and to examine the possible relation of dermoscopic diagnosis with respect to the difficulty of the dermoscopic diagnosis and the melanoma thickness. ::: ::: ::: RESULTS ::: A significant percentage of melanomas, 89 of 508 (17.5%), were DDM. The criteria leading to a significant increased risk of DDM were presence of streaks [odds ratio (OR), 2.26; 95% confidence interval (CI), 1.15-4.47), absence or presence of regular pigmentation (OR, 3.41; 95% CI, 1.70-6.85), absence of a blue-whitish veil (OR, 4.04; 95% CI, 2.33-6.99), absence of regression structures (OR, 4.31; 95% CI, 2.42-7.66), and the presence of hypopigmentation (OR, 2.61; 95% CI, 1.49-4.58). ::: ::: ::: CONCLUSION ::: A significant number of melanomas defy even dermoscopic diagnosis. Only a meticulous comparative and interactive process based on an assessment of all the individual's other nevi ("ugly ducking" sign) and a knowledge about recent changes can lead to the recognition of DDM.


title: Radial vs. Cartesian Revisited: A Comparison of Space-Filling Visualizations

abstract: Radial visualization continues to be a popular design choice in information visualization systems, due perhaps in part to its aesthetic appeal. However, it is an open question whether radial visualizations are truly more effective than their Cartesian counterparts. In this paper, we describe an initial user trial from an ongoing empirical study of the SQiRL (Simple Query interface with a Radial Layout) visualization system, which supports both radial and Cartesian projections of stacked bar charts. Participants were shown 20 diagrams employing a mixture of radial and Cartesian layouts and were asked to perform basic analysis on each. The participants' speed and accuracy for both visualization types were recorded. Our initial findings suggest that, in spite of the widely perceived advantages of Cartesian visualization over radial visualization, both forms of layout are, in fact, equally usable. Moreover, radial visualization may have a slight advantage over Cartesian for certain tasks. In a follow-on study, we plan to test users' ability to create, as well as read and interpret, radial and Cartesian diagrams in SQiRL.


title: A hybrid multi-objective evolutionary algorithm using an inverse neural network for aircraft control system design

abstract: This study introduces a hybrid multi-objective evolutionary algorithm (MOEA) for the optimization of aircraft control system design. The strategy suggested is composed mainly of two stages. The first stage consists of training an artificial neural network (ANN) with objective values as inputs and decision variables as outputs to model an approximation of the inverse of the objective function used. The second stage consists of a local improvement phase in objective space preserving objectives relationships, and a mapping process to decision variables using the trained ANN. Both the hybrid MOEA and the original MOEA were applied to an aircraft control system design application for assessment