title: Knowledge Organization Systems (KOS) in the Semantic Web: a multi-dimensional review

abstract: Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009, a significant number of conventional Knowledge Organization Systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses “LOD KOS” as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).


title: Accelerated Testing and Evaluation of Autonomous Vehicles via Imitation Learning

abstract: In this paper, we investigate the use of surrogate agents to accelerate test scenario generation for autonomous vehicles. Our goal is to train the surrogate to replicate the true performance modes of the system. We create these surrogates by utilizing imitation learning with deep neural networks. By using imitator surrogates in place of the true agent, we are capable of predicting mission performance more quickly, gaining greater throughput for simulation-based testing. We demonstrate that using on-line imitation learning with Dataset Aggregation (DAgger) can not only correctly encode a policy that executes a complex mission, but can also encode multiple different behavioral modes. To improve performance for the target vehicle and mission, we manipulate the training set during each iteration to remove samples which do not contribute to the final policy. We call this approach Quantile-DAgger (Q-DAgger) and demonstrate its ability to replicate the behaviors of an autonomous vehicle in a collision avoidance scenario.


title: A Novel Algorithm for Unbiased Learning to Rank

abstract: Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i.e., learning-to-rank. Recently, a number of authors have proposed new techniques referred to as 'unbiased learning-to-rank', which can reduce position bias and train a relatively high-performance ranker using click data. Most of the algorithms, based on the inverse propensity weighting (IPW) principle, first estimate the click bias at each position, and then train an unbiased ranker with the estimated biases using a learning-to-rank algorithm. However, there has not been a method for pairwise learning-to-rank that can jointly conduct debiasing of click data and training of a ranker using a pairwise loss function. In this paper, we propose a novel algorithm, which can jointly estimate the biases at click positions and the biases at unclick positions, and learn an unbiased ranker. Experiments on benchmark data show that our algorithm can significantly outperform existing algorithms. In addition, an online A/B Testing at a commercial search engine shows that our algorithm can effectively conduct debiasing of click data and enhance relevance ranking.


title: Error analysis of multi-hop free-space optical communication

abstract: In this paper we analyze the error performance of free-space optical (FSO) communication over multiple hops. We first develop an error model for a single hop based on visibility, atmospheric attenuation, and geometric spread of the light beam. We model atmospheric visibility by Gaussian distributions with mean and variance values to reflect clear and adverse weather conditions. Based on this, we find the end-to-end bit error distribution of the FSO link for single hop and multi-hop scenarios. We present simulation results for decoded relaying, where each hop decodes the signal before retransmitting. We demonstrate that multi-hop FSO communication achieves a significant reduction in the mean bit error rate and also reduces the variance of the bit error rate. We argue that by lowering mean error and error variance, multi-hop operation facilitates an efficient system design and improves the reliability of the FSO link by application of specific coding schemes (such as forward error correction techniques).


title: Obfuscated malicious javascript detection using classification techniques

abstract: As the World Wide Web expands and more users join, it becomes an increasingly attractive means of distributing malware. Malicious javascript frequently serves as the initial infection vector for malware. We train several classifiers to detect malicious javascript and evaluate their performance. We propose features focused on detecting obfuscation, a common technique to bypass traditional malware detectors. As the classifiers show a high detection rate and a low false alarm rate, we propose several uses for the classifiers, including selectively suppressing potentially malicious javascript based on the classifier's recommendations, achieving a compromise between usability and security.


title: A 4.6Tbits/s 3.6GHz single-cycle NoC router with a novel switch allocator in 65nm CMOS

abstract: As chip multiprocessors (CMPs) become the only viable way to scale up and utilize the abundant transistors made available in current microprocessors, the design of on-chip networks is becoming critically important. These networks face unique design constraints and are required to provide extremely fast and high bandwidth communication, yet meet tight power and area budgets. In this paper, we present a detailed design of our on-chip network router targeted at a 36-core shared-memory CMP system in 65 nm technology. Our design targets an aggressive clock frequency of 3.6 GHz, thus posing tough design challenges that led to several unique circuit and microarchitectural innovations and design choices, including a novel high throughput and low latency switch allocation mechanism, a non-speculative single-cycle router pipeline which uses advanced bundles to remove control setup overhead, a low-complexity virtual channel allocator and a dynamically-managed shared buffer design which uses prefetching to minimize critical path delay. Our router takes up 1.19 mm2 area and expends 551 mW power at 10% activity, delivering a single-cycle no-load latency at 3.6 GHz clock frequency while achieving apeak switching data rate in excess of 4.6 Tbits/sper router node.


title: Design of high torque density variable flux permanent magnet machine using Alnico magnets

abstract: The paper proposes a high torque density design for variable flux machines with Alnico magnets. The proposed design uses tangentially magnetized magnets in order to achieve high air gap flux density and to avoid demagnetization by the armature field. Barriers are also inserted in the rotor to limit the armature flux and to allow the machine to utilize both reluctance and magnet torque components. An analytical procedure is first applied to obtain the initial machine design parameters. Then several modifications are applied to the stator and rotor designs through finite element simulations (FEA) in order to improve the machine efficiency and torque density.


title: Fire and smoke detection without sensors: Image processing based approach

abstract: In this paper, novel models for fire and smoke detection using image processing is provided. The models use different colour models for both fire and smoke. The colour models are extracted using a statistical analysis of samples extracted from different type of video sequences and images. The extracted models can be used in complete fire/smoke detection system which combines colour information with motion analysis.


title: Flexible job-shop scheduling with parallel variable neighborhood search algorithm

abstract: Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. FJSP is NP-hard and mainly presents two difficulties. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on the machines. This paper proposes a parallel variable neighborhood search (PVNS) algorithm that solves the FJSP to minimize makespan time. Parallelization in this algorithm is based on the application of multiple independent searches increasing the exploration in the search space. The proposed PVNS uses various neighborhood structures which carry the responsibility of making changes in assignment and sequencing of operations for generating neighboring solutions. The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the FJSP.


title: Auditory acclimatization and hearing aids: Late auditory evoked potentials and speech recognition following unilateral and bilateral amplification

abstract: The aim of this study was to investigate changes in central auditory processing following unilateral and bilateral hearing aid fitting using a combination of physiological and behavioral measures: late auditory event-related potentials (ERPs) and speech recognition in noise, respectively. The hypothesis was that for fitted ears, the ERP amplitude would increase over time following hearing aid fitting in parallel with improvement in aided speech recognition. The N1 and P2 ERPs were recorded to 500 and 3000 Hz tones presented at 65, 75, and 85 dB sound pressure level to either the left or right ear. New unilateral and new bilateral hearing aid users were tested at the time of first fitting and after 12 weeks hearing aid use. A control group of long-term hearing aid users was tested over the same time frame. No significant changes in the ERP were observed for any group. There was a statistically significant 2% improvement in aided speech recognition over time for all groups, although this was consistent with a general test-retest effect. This study does not support the existence of an acclimatization effect observable in late ERPs following 12 weeks' hearing aid use.


title: Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory

abstract: Dopamine (DA) D1 receptor (D1R) stimulation in prefrontal cortex (PFC) produces an 'inverted-U' dose-response, whereby either too little or too much D1R stimulation impairs spatial working memory. This response has been observed across species, including genetic linkages with human cognitive abilities, PFC activation states and DA synthesis. The cellular basis for the inverted U has long been sought, with in vitro intracellular recordings supporting a variety of potential mechanisms. The current study demonstrates that the D1R agonist inverted-U response can be observed in PFC neurons of behaving monkeys: low levels of D1R stimulation enhance spatial tuning by suppressing responses to nonpreferred directions, whereas high levels reduce delay-related firing for all directions, eroding tuning. These sculpting actions of D1R stimulation are mediated in monkeys and rats by cyclic AMP intracellular signaling. The evidence for an inverted U at the cellular level in behaving animals promises to bridge in vitro molecular analyses with human cognitive experience.


title: Intelligent interface based speech recognition for home automation using android application

abstract: The home automation system plays an important role in maintaining living standards and provide secure and flexible environment. The aim of this project is to design a home automation system which makes operating of electrical appliances in home through android application. The home automation system allows controlling of home appliances by using voice commands by recognizing the input speech. The speech recognition is done by Support Vector Machine. The home automation system is implemented wirelessly using General packet radio service (GPRS) technology. The electrical appliances such as fan, light switches, light sensors, current sensors are integrated in a system which then connected to microcontroller which act as a within the home to control and perform the user commands.


title: Web Spam Detection with Anti-Trust Rank

abstract: Spam pages on the web use various techniques to artificially achieve high rankings in search engine results. Human experts can do a good job of identifying spam pages and pages whose information is of dubious quality, but it is practically infeasible to use human effort for a large number of pages. Similar to the Trust Rank algorithm [1], we propose a method of selecting a seed set of pages to be evaluated by a human. We then use the link structure of the web and the manually labeled seed set, to detect other spam pages. Our experiments on the WebGraph dataset [3] show that our approach is very effective at detecting spam pages from a small seed set and achieves higher precision of spam page detection than the Trust Rank algorithm, apart from detecting pages with higher pageranks [10, 11], on an average.


title: The role of gamification in education–a literature review

abstract: “Gamification” is a concept which aims to increase user experience and engagement with a system, while education is an area with high prospective for application of this concept. A lot of research is ongoing since it seeks to improve the quality of learning of students. Current situation on research of “Gamification in education” is still at initial stages where all of them aim to improve the effectiveness of education activity using game elements. Several ga ps on the studies


title: Psychometric Properties and Validation of the Arabic Social Media Addiction Scale

abstract: This study investigated the psychometric properties of the Arabic version of the SMAS. SMAS is a variant of IAT customized to measure addiction to social media instead of the Internet as a whole. Using a self-report instrument on a cross-sectional sample of undergraduate students, the results revealed the following. First, the exploratory factor analysis showed that a three-factor model fits the data well. Second, concurrent validity analysis showed the SMAS to be a valid measure of social media addiction. However, further studies and data should verify the hypothesized model. Finally, this study showed that the Arabic version of the SMAS is a valid and reliable instrument for use in measuring social media addiction in the Arab world.


Models for the digital transformation


title: Growth and Human Development in North-East India

abstract: One of the most comprehensive works in the vast area of human development in the North-Eastern region of India, this volume brings new thinking on the approach to poverty, measurement of poverty, relationship between economic growth and human development, socio-political transition, politics of growth and growth trends, and development attainment. The status of HD across and within the eight North-Eastern states (the study also includes Orissa), have been provided with large empirical data. Academicians from within and outside the region contributing to this volume broadly divide the literature into three sections: (i) concept and measurement of growth and human development; (ii) the issue at the national level; and (iii) the issue in the North-East region. Meaningful for formulating development policies that will have larger implications for the country as a whole, the volume is enriched with HD indices of each major state, and comparative HD analysis at all-India level.


title: Truth, Amnesty and Prosecutions: Models for Dealing with the Past

abstract: This report was produced by the authors to contribute to the negotiations on contending with the past that were led by Dr Richard Haass and Prof Meghan O’Sullivan. Throughout 2012, As part of their ongoing project Amnesties, Prosecutions and the Public Interest in the Northern Ireland Transition, the authors met with victims, ex-security force personnel, former combatants, civil society groups, criminal justice leaders and all the main political parties to discuss these matters. At a number of these encounters, the authors were asked to provide ‘models’ as to how the issues of truth, prosecution and amnesty might intersect as part of efforts to deal with the past in Northern Ireland. In response to those requests, this report outlines are four possible models which chart how these issues might be managed.


Automatic Image Alignment and Stitching of Medical Images with Seam Blending


title: Fear, hope and social desirability bias among women at high risk for HIV in West Africa

abstract: BACKGROUND ::: Self-reports are widely used for measuring behaviour in HIV research and prevention, yet the accuracy of these measures has been shown to be questionable in many cases. Social desirability bias (SDB) is one of the key factors identified as affecting self-report accuracy. ::: ::: ::: METHODS ::: Using in-depth interviews, we examined SDB from the perspective of 60 women at high risk for HIV in two West African countries: Ghana and Nigeria. We solicited suggestions for reducing SDB in the context of HIV research and prevention, and asked for feedback regarding methods currently being employed to reduce SDB. ::: ::: ::: RESULTS ::: Themes pertaining to fear and a desire to have a better life were pervasive throughout the data. Thematic structure was similar between sites and age groups, although younger women tended to be more concerned about the interview context. ::: ::: ::: CONCLUSIONS ::: Vulnerability of a population should be considered when asking sensitive questions. Audio-computer-assisted self-interviews may not be appropriate for vulnerable populations in developing countries, particularly for older respondents.


title: Analyzing Adaptive Cache Replacement Strategies

abstract: Adaptive Replacement Cache (ARC) and CLOCK with Adaptive Replacement (CAR) are state-of-the- art "adaptive" cache replacement algorithms invented to improve on the shortcomings of classical cache replacement policies such as LRU, LFU and CLOCK. By separating out items that have been accessed only once and items that have been accessed more frequently, both ARC and CAR are able to control the harmful effect of single-access items flooding the cache and pushing out more frequently accessed items. Both ARC and CAR have been shown to outperform their classical and popular counterparts in practice. Both algorithms are complex, yet popular. Even though they can be treated as online algorithms with an "adaptive" twist, a theoretical proof of the competitiveness of ARC and CAR remained unsolved for over a decade. We show that the competitiveness ratio of CAR (and ARC) has a lower bound of N + 1 (where N is the size of the cache) and an upper bound of 18N (4N for ARC). If the size of cache offered to ARC or CAR is larger than the one provided to OPT, then we show improved competitiveness ratios. The important implication of the above results are that no "pathological" worst-case request sequences exist that could deteriorate the performance of ARC and CAR by more than a constant factor as compared to LRU.


title: Amyotrophic lateral sclerosis

abstract: Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal neurodegenerative disease of the human motor system. In this Seminar, we summarise current concepts about the origin of the disease, what predisposes patients to develop the disorder, and discuss why all cases of ALS are not the same. In the 150 years since Charcot originally described ALS, painfully slow progress has been made towards answering these questions. We focus on what is known about ALS and where research is heading-from the small steps of extending longevity, improving therapies, undertaking clinical trials, and compiling population registries to the overarching goals of establishing the measures that guard against onset and finding the triggers for this neurodegenerative disorder.


title: An approach to Korean license plate recognition based on vertical edge matching

abstract: License plate recognition (LPR) has many applications in traffic monitoring systems. In this paper, a vertical edge matching based algorithm to recognize a Korean license plate from an input gray-scale image is proposed. The algorithm is able to recognize license plates in normal shape, as well as plates that are out of shape due to the angle of view. The proposed algorithm is fast enough and the recognition unit of the LPR system can be implemented only in software so that the cost of the system is reduced.


title: Retrieving Attitudes: Sentiment Analysis from Clinical Narratives

abstract: Physicians and nurses express their judgments and observations towards a patient’s health status in clinical narratives. Thus, their judgments are explicitly or implicitly included in patient records. To get impressions on the current health situation of a patient or on changes in the status, analysis and retrieval of this subjective content is crucial. In this paper, we approach this question as sentiment analysis problem and analyze the feasibility of assessing these judgments in clinical text by means of general sentiment analysis methods. Specifically, the word usage in clinical narratives and in a general text corpus is compared. The linguistic characteristics of judgments in clinical narratives are collected. Besides, the requirements for sentiment analysis and retrieval from clinical narratives are derived.


title: Neuroimaging in sleep medicine.

abstract: The development of neuroimaging techniques has made possible the characterization of cerebral function throughout the sleep-wake cycle in normal human subjects. Indeed, human brain activity during sleep is segregated within specific cortical and subcortical areas in relation to the sleep stage, sleep physiological events and previous waking activity. This approach has allowed sleep physiological theories developed from animal data to be confirmed, but has also introduced original concepts about the neurobiological mechanisms of sleep, dreams and memory in humans. In contrast, at present, few neuroimaging studies have been dedicated to human sleep disorders. The available work has brought interesting data that describe some aspects of the pathophysiology and neural consequences of disorders such as insomnia, sleep apnea and narcolepsy. However, the interpretation of many of these results is restricted by limited sample size and spatial/temporal resolution of the employed technique. The use of neuroimaging in sleep medicine is actually restrained by concerns resulting from the technical experimental settings and the characteristics of the diseases. Nevertheless, we predict that future studies, conducted with state of the art techniques on larger numbers of patients, will be able to address these issues and contribute significantly to the understanding of the neural basis of sleep pathologies. This may finally offer the opportunity to use neuroimaging, in addition to the clinical and electrophysiological assessments, as a helpful tool in the diagnosis, classification, treatment and monitoring of sleep disorders in humans. 2007 Elsevier B.V. All rights reserved.


title: Heart Disease Prediction System Using Supervised Learning Classifier

abstract: Cardiovascular disease remains the biggest cause of deaths worldwide and the Heart Disease Prediction at the early stage is importance. In this paper Supervised Learning Algorithm is adopted for heart disease prediction at the early stage using the patient's medical record is proposed and the results are compared with the known supervised classifier Support Vector Machine (SVM). The information in the patient record is classified using a Cascaded Neural Network (CNN) classifier. In the classification stage 13 attributes are given as input to the CNN classifier to determine the risk of heart disease. The proposed system will provide an aid for the physicians to diagnosis the disease in a more efficient way. The efficiency of the classifier is tested using the records collected from 270 patients. The results show the CNN classifier can predict the likelihood of patients with heart disease in a more efficient way.


title: Automated Analysis and Debugging of Network Connectivity Policies

abstract: Network connectivity policies are crucial for assuring the security and availability of large-scale datacenter. Managing these policies is fraught with complexity and operator errors. The difficulties are exacerbated when deploying large scale offerings of public cloud services where multiple tenants are hosted within customized isolation boundaries. In these large-scale settings it is impractical to depend on human effort or trial and error to maintain the correctness and consistency of policies. We describe an approach for automatically validating network connectivity policies and its implementation in a tool called SecGuru. SecGuru can check selected properties of policies, e.g., is some traffic permitted or denied, and it can compare two policies yielding a semantic diff to summarize drifts. We use bit-vector logic to encode policies and semantic diffs; and the theorem prover Z3 as the underlying solver. A key contribution is a new algorithm for compactly enumerating symbolic diffs. We finally describe the experience of using SecGuru in Azure, a public cloud provider. Azure uses SecGuru for continuously monitoring policy configurations and alerting on errors, and also as a regression test suite to check policies before deployment. As a result of using SecGuru, today Azure proactively detects and avoids policy misconfigurations that lead to security and availability issues.


DATA MINING TECHNIQUES


title: Big Data, Deep Learning – At the Edge of X-Ray Speaker Analysis

abstract: With two years, one has roughly heard a thousand hours of speech – with ten years, around ten thousand. Similarly, an automatic speech recogniser’s data hunger these days is often fed in these dimensions. In stark contrast, however, only few databases to train a speaker analysis system contain more than ten hours of speech. Yet, these systems are ideally expected to recognise the states and traits of speakers independent of the person, spoken content, language, cultural background, and acoustic disturbances at human parity or even super-human levels. While this is not reached at the time for many tasks such as speaker emotion recognition, deep learning – often described to lead to ‘dramatic improvements’ – in combination with sufficient learning data satisfying the ‘deep data cravings’ holds the promise to get us there. Luckily, every second, more than five hours of video are uploaded to the web and several hundreds of hours of audio and video communication in most languages of the world take place. If only a fraction of these data would be shared and labelled reliably, ‘x-ray’-alike automatic speaker analysis could be around the corner for next gen human-computer interaction, mobile health applications, and many further benefits to society. In this light, first, a solution towards utmost efficient exploitation of the ‘big’ (unlabelled) data available is presented. Small-world modelling in combination with unsupervised learning help to rapidly identify potential target data of interest. Then, gamified dynamic cooperative crowdsourcing turn its labelling into an entertaining experience, while reducing the amount of required labels to a minimum by learning alongside the target task also the labellers’ behaviour and reliability. Further, increasingly autonomous deep holistic end-to-end learning solutions are presented for the task at hand. Benchmarks are given from the nine research challenges co-organised by the author over the years at the annual Interspeech conference since 2009. The concluding discussion will contain some crystal ball gazing alongside practical hints not missing out on ethical aspects.


Aerodynamic Performance of the Underbody and Wings of an Open-Wheel Race Car


title: Convolutional Image Captioning

abstract: Image captioning is an important but challenging task, applicable to virtual assistants, editing tools, image indexing, and support of the disabled. Its challenges are due to the variability and ambiguity of possible image descriptions. In recent years significant progress has been made in image captioning, using Recurrent Neural Networks powered by long-short-term-memory (LSTM) units. Despite mitigating the vanishing gradient problem, and despite their compelling ability to memorize dependencies, LSTM units are complex and inherently sequential across time. To address this issue, recent work has shown benefits of convolutional networks for machine translation and conditional image generation. Inspired by their success, in this paper, we develop a convolutional image captioning technique. We demonstrate its efficacy on the challenging MSCOCO dataset and demonstrate performance on par with the baseline, while having a faster training time per number of parameters. We also perform a detailed analysis, providing compelling reasons in favor of convolutional language generation approaches.


title: 3-D ICs: A Novel Chip Design for Improving Deep-Submicrometer Interconnect Performance and Systems-on-Chip Integration

abstract: Performance of deep-submicrometer very large scale integrated (VLSI) circuits is being increasingly dominated by the interconnects due to decreasing wire pitch and increasing die size. Additionally, heterogeneous integration of different technologies in one single chip is becoming increasingly desirable, for which planar (two-dimensional) ICs may not be suitable. This paper analyzes the limitations of the existing interconnect technologies and design methodologies and presents a novel three-dimensional (3-D) chip design strategy that exploits the vertical dimension to alleviate the interconnect related problems and to facilitate heterogeneous integration of technologies to realize a system-on-a-chip (SoC) design. A comprehensive analytical treatment of these 3-D ICs has been presented and it has been shown that by simply dividing a planar chip into separate blocks, each occurring a separate physical level interconnected by short and vertical interlayer interconnects (VILICs), significant improvement in performance and reduction in wire-limited chip area can be achieved, without the aid of any other circuit or design innovations. A scheme to optimize the interconnect distribution among different interconnect tiers is presented and the effect of transferring the repeaters to upper Si layers has been quantified in this analysis for a two-layer 3-D chip. Furthermore, one of the major concerns in 3-D ICs arising due to power dissipation problems has been analyzed and an analytical model has been presented to estimate the temperatures of the different active layers. It is demonstrated that advancement in heat sinking technology will be necessary in order to extract maximum performance from these chips. Implications of 3-D device architecture on several design issues have also been discussed with special attention to SoC design strategies. Finally some of the promising technologies for manufacturing 3-D ICs have been outlined.


title: Exploiting Spatial-Temporal Modelling and Multi-Modal Fusion for Human Action Recognition

abstract: In this report, our approach to tackling the task of ActivityNet 2018 Kinetics-600 challenge is described in detail. Though spatial-temporal modelling methods, which adopt either such end-to-end framework as I3D \cite{i3d} or two-stage frameworks (i.e., CNN+RNN), have been proposed in existing state-of-the-arts for this task, video modelling is far from being well solved. In this challenge, we propose spatial-temporal network (StNet) for better joint spatial-temporal modelling and comprehensively video understanding. Besides, given that multi-modal information is contained in video source, we manage to integrate both early-fusion and later-fusion strategy of multi-modal information via our proposed improved temporal Xception network (iTXN) for video understanding. Our StNet RGB single model achieves 78.99\% top-1 precision in the Kinetics-600 validation set and that of our improved temporal Xception network which integrates RGB, flow and audio modalities is up to 82.35\%. After model ensemble, we achieve top-1 precision as high as 85.0\% on the validation set and rank No.1 among all submissions.


title: Dexmo: An Inexpensive and Lightweight Mechanical Exoskeleton for Motion Capture and Force Feedback in VR

abstract: We present Dexmo: an inexpensive and lightweight mechanical exoskeleton system for motion capturing and force feedback in virtual reality applications. Dexmo combines multiple types of sensors, actuation units and link rod structures to provide users with a pleasant virtual reality experience. The device tracks the user's motion and uniquely provides passive force feedback. In combination with a 3D graphics rendered environment, Dexmo provides the user with a realistic sensation of interaction when a user is for example grasping an object. An initial evaluation with 20 participants demonstrate that the device is working reliably and that the addition of force feedback resulted in a significant reduction in error rate. Informal comments by the participants were overwhelmingly positive.


title: Simulation of Automotive Radar Target Lists using a Novel Approach of Object Representation

abstract: The development of radar signal processing algorithms for target tracking and higher-level automotive applications is mainly done based on real radar data. A data basis has to be acquired during cost-expensive and time-consuming test runs. For a comparably simple application like the adaptive cruise control (ACC), the variety of significant traffic situations can sufficiently be covered by test runs. But for more advanced applications like intersection assistance, the effort for the acquisition of a representative set of radar data will be unbearable. In this paper, we propose a way of simulating radar target lists in a realistic but computationally undemanding way, which will allow to significantly reduce the amount of real radar data needed.


title: Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number

abstract: Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not the both. In general, it is a nontrivial task to perform clustering on mixed data composed of numerical and categorical attributes because there exists an awkward gap between the similarity metrics for categorical and numerical data. This paper therefore presents a general clustering framework based on the concept of object-cluster similarity and gives a unified similarity metric which can be simply applied to the data with categorical, numerical, and mixed attributes. Accordingly, an iterative clustering algorithm is developed, whose outstanding performance is experimentally demonstrated on different benchmark data sets. Moreover, to circumvent the difficult selection problem of cluster number, we further develop a penalized competitive learning algorithm within the proposed clustering framework. The embedded competition and penalization mechanisms enable this improved algorithm to determine the number of clusters automatically by gradually eliminating the redundant clusters. The experimental results show the efficacy of the proposed approach.


title: Post-Capture Lighting Manipulation using Flash Photography

abstract: Real-world lighting often consists of multiple illuminants with different colors. Separating and manipulating these illuminants in post-process is a challenging problem that requires either significant manual input or calibrated scene geometry and lighting. In this work, we leverage a flash/no-flash image pair to analyze and edit scene illuminants based on their color differences. We derive a novel physics-based relationship between color variations in the observed flash/no-flash intensities and the chromaticies and surface shading corresponding to individual scene illuminants. Our technique uses this constraint to automatically separate an image into constituent images lit by each illuminant. Each light component can then be edited independently to enable applications like white-balance, lighting editing, and intrinsic image decompositions. We demonstrate that this technique outperforms state-of-the-art techniques for some applications and enables other applications that are not possible with previous work.


title: Good Question! Statistical Ranking for Question Generation

abstract: We address the challenge of automatically generating questions from reading materials for educational practice and assessment. Our approach is to overgenerate questions, then rank them. We use manually written rules to perform a sequence of general purpose syntactic transformations (e.g., subject-auxiliary inversion) to turn declarative sentences into questions. These questions are then ranked by a logistic regression model trained on a small, tailored dataset consisting of labeled output from our system. Experimental results show that ranking nearly doubles the percentage of questions rated as acceptable by annotators, from 27% of all questions to 52% of the top ranked 20% of questions.


title: DeepMind Lab

abstract: DeepMind Lab is a first-person 3D game platform designed for research and development of general artificial intelligence and machine learning systems. DeepMind Lab can be used to study how autonomous artificial agents may learn complex tasks in large, partially observed, and visually diverse worlds. DeepMind Lab has a simple and flexible API enabling creative task-designs and novel AI-designs to be explored and quickly iterated upon. It is powered by a fast and widely recognised game engine, and tailored for effective use by the research community.


title: General factorization framework for context-aware recommendations

abstract: Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to solve the problem, although most of them assume explicit feedback which strongly limits their real-world applicability. While these algorithms apply various loss functions and optimization strategies, the preference modeling under context is less explored due to the lack of tools allowing for easy experimentation with various models. As context dimensions are introduced beyond users and items, the space of possible preference models and the importance of proper modeling largely increases. In this paper we propose a general factorization framework (GFF), a single flexible algorithm that takes the preference model as an input and computes latent feature matrices for the input dimensions. GFF allows us to easily experiment with various linear models on any context-aware recommendation task, be it explicit or implicit feedback based. The scaling properties makes it usable under real life circumstances as well. We demonstrate the framework's potential by exploring various preference models on a 4-dimensional context-aware problem with contexts that are available for almost any real life datasets. We show in our experiments--performed on five real life, implicit feedback datasets--that proper preference modelling significantly increases recommendation accuracy, and previously unused models outperform the traditional ones. Novel models in GFF also outperform state-of-the-art factorization algorithms. We also extend the method to be fully compliant to the Multidimensional Dataspace Model, one of the most extensive data models of context-enriched data. Extended GFF allows the seamless incorporation of information into the factorization framework beyond context, like item metadata, social networks, session information, etc. Preliminary experiments show great potential of this capability.


title: A Novel Breast Tissue Density Classification Methodology

abstract: It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment.


A Geographic Information System-Based Decision Support System for Mapping Philippine ' s Higher Education Institutions by


Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization


title: SOCIAL MEDIA MINING FOR PUBLIC HEALTH MONITORING AND SURVEILLANCE

abstract: This paper describes topics pertaining to the session, \Social Media Mining for Public Health Monitoring and Surveillance," at the Pacic Symposium on Biocomputing (PSB) 2016. In addition to summarizing the content of the session, this paper also surveys recent research on using social media data to study public health. The survey is organized into sections describing recent progress in public health problems, computational methods, and social implications.


title: Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

abstract: In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset.


title: Towards Formal-Based Semantic Interoperability in Multi-Clouds: The FCLOUDS Framework

abstract: Multi-cloud computing has been proposed as a way to reduce vendor lock-in, to improve resiliency during outages and geo-presence, to boost performance and to lower costs. However, semantic differences between cloud providers, as well as their heterogeneous management interfaces, make changing from one provider to another very complex and costly. This is quite challenging for the implementation of multi-cloud systems. ::: In this paper, we aim to take advantage of formal methods to define a precise semantics for multi-clouds. We propose fclouds, a formal-based framework for semantic interoperability in multi-clouds. This framework contains a catalogue of formal models that mathematically describe cloud APIs and reason over them. A precise alignment can be described between their concepts, which promotes semantic interoperability.


title: An upconversion nanoparticle - Zinc phthalocyanine based nanophotosensitizer for photodynamic therapy

abstract: Recent advances in NIR triggering upconversion-based photodynamic therapy have led to substantial improvements in upconversion-based nanophotosensitizers. How to obtain the high efficiency of singlet oxygen generation under low 980 nm radiation dosage still remains a challenge. A highly efficient nanophotosensitizer, denoted as UCNPs-ZnPc, was constructed for photodynamic therapy, which is based on near infrared (NIR) light upconversion nanoparticle (UCNP) and Zn(II)-phthalocyanine (ZnPc) photosensitizer (PS). The high 102 production efficiency came from the enhancement of the 660 nm upconversion emission of NaYF4:Yb3+, Er3+ UCNP with 25% Yb3+ doping, covalent assemblage of UCNP and ZnPc which significantly shortened the distance and enhanced the energy transfer between the two. The high O-1(2) production led to a secure and efficient PDT treatment, as evidenced by the in vivo test where UCNPs-ZnPc of 50 mg per kg body weight was locally injected into the liver tumor in mice, a low 980 nm radiation dose of 351 j/cm(2) (039 W/cm(2)) and short irradiation duration of 15 min were sufficient to perform image-guided PDT and caused the liver tumor inhibitory ratio of approximately 80.1%. Histological analysis revealed no pathological changes and inflammatory response in heart, lung, kidney, liver or spleen. (c) 2014 Elsevier Ltd. All rights reserved.


title: Tear metabolite changes in keratoconus.

abstract: While efforts have been made over the years, the exact cause of keratoconus (KC) remains unknown. The aim of this study was to identify alterations in endogenous metabolites in the tears of KC patients compared with age-matched healthy subjects. Three groups were tested: 1) Age-matched controls with no eye disease (N = 15), 2) KC - patients wearing Rigid Gas permeable lenses (N = 16), and 3) KC - No Correction (N = 14). All samples were processed for metabolomics analysis using LC-MS/MS. We identified a total of 296 different metabolites of which >40 were significantly regulated between groups. Glycolysis and gluconeogenesis had significant changes, such as 3-phosphoglycerate and 1,3 diphosphateglycerate. As a result the citric acid cycle (TCA) was also affected with notable changes in Isocitrate, aconitate, malate, and acetylphosphate, up regulated in Group 2 and/or 3. Urea cycle was also affected, especially in Group 3 where ornithine and aspartate were up-regulated by at least 3 fold. The oxidation state was also severely affected. Groups 2 and 3 were under severe oxidative stress causing multiple metabolites to be regulated when compared to Group 1. Group 2 and 3, both showed significant down regulation in GSH-to-GSSG ratio when compared to Group 1. Another indicator of oxidative stress, the ratio of lactate - pyruvate was also affected with Groups 2 and 3 showing at least a 2-fold up regulation. Overall, our data indicate that levels of metabolites related to urea cycle, TCA cycle and oxidative stress are highly altered in KC patients.


title: Cloaker: Hardware Supported Rootkit Concealment

abstract: Rootkits are used by malicious attackers who desire to run software on a compromised machine without being detected. They have become stealthier over the years as a consequence of the ongoing struggle between attackers and system defenders. In order to explore the next step in rootkit evolution and to build strong defenses, we look at this issue from the point of view of an attacker. We construct Cloaker, a proof-of-concept rootkit for the ARM platform that is non- persistent and only relies on hardware state modifications for concealment and operation. A primary goal in the design of Cloaker is to not alter any part of the host operating system (OS) code or data, thereby achieving immunity to all existing rootkit detection techniques which perform integrity, behavior and signature checks of the host OS. Cloaker also demonstrates that a self-contained execution environment for malicious code can be provided without relying on the host OS for any services. Integrity checks of hardware state in each of the machine's devices are required in order to detect rootkits such as Cloaker. We present a framework for the Linux kernel that incorporates integrity checks of hardware state performed by device drivers in order to counter the threat posed by rootkits such as Cloaker.


Smartwatch User Identification as a Means of Authentication


title: A rendering algorithm for visualizing 3D scalar fields

abstract: This paper presents a ray tracing algorithm for rendering 3D scalar fields. An illumination model is developed in which the field is characterized as a varying density emittter with a single level of scattering. This model is equivalent to a particle system in which the particles are sufficiently small. Along each ray cast from the eye, the field is expressed as a function of the ray parameter. The algorithm computes properties of the field along the ray such as the attenuated intensity, the peak density, and the center of gravity, etc., These are mapped into HSV color space to produce an image for visualization.Images produced in this manner are perceived as a varying density 'cloud' where color highlights the computed attributes. The application of this technique is demonstrated for visualizing a three dimensional seismic data set.


title: The determinants of leadership role occupancy: Genetic and personality factors

abstract: Abstract This study investigated the influence of genetic factor and personality on leadership role occupancy among a sample of male twins. Identical twins ( n = 238) who share 100% of their genetic background were compared with fraternal twins ( n = 188) who are expected to share only 50% of their genetic background. Results indicated that 30% of the variance in leadership role occupancy could be accounted for by genetic factor, while non-shared (or non-common) environmental factor accounted for the remaining variance in leadership role occupancy. Genetic influences also contributed to personality variables known to be associated with leadership (i.e., social potency and achievement). Furthermore, the results indicated that the genetic influence on leadership role occupancy was associated with the genetic factors influencing the personality variables, but there was no definitive evidence whether these personality variables partially mediated the relationship between genetic factor and leadership. Results are discussed in terms of the implications for leader selection and training.


title: Development of computational thinking and collaborative learning in kindergarten using programmable educational robots: a teacher training experience

abstract: Nowadays, the teaching-learning processes are constantly changing, one of the latest modifications promises to strengthen the development of digital skills and thinking in the participants, from an early age. In this sense, the present article shows the advances of a study oriented to the formation of programming abilities, computational thinking and collaborative learning in an initial education context. As part of the study it was initially proposed to conduct a training day for teachers who will participate in the experimental phase of the research, considering this human resource as a link of great importance to achieve maximum use of students in the development of curricular themes of the level, using ICT resources and programmable educational robots. The criterion and the positive acceptance expressed by the teaching group after the evaluation applied at the end of the session, constitute a good starting point for the development of the following activities that make up the research in progress.


title: Integrating product data from websites offering microdata markup

abstract: Large numbers of websites have started to markup their content using standards such as Microdata, Microformats, and RDFa. The marked-up content elements comprise descriptions of people, organizations, places, events, products, ratings, and reviews. This development has accelerated in last years as major search engines such as Google, Bing and Yahoo! use the markup to improve their search results. Embedding semantic markup facilitates identifying content elements on webpages. However, the markup is mostly not as fine-grained as desirable for applications that aim to integrate data from large numbers of websites. This paper discusses the challenges that arise in the task of integrating descriptions of electronic products from several thousand e-shops that offer Microdata markup. We present a solution for each step of the data integration process including Microdata extraction, product classification, product feature extraction, identity resolution, and data fusion. We evaluate our processing pipeline using 1.9 million product offers from 9240 e-shops which we extracted from the Common Crawl 2012, a large public Web corpus.


title: Nirikshan: process mining software repositories to identify inefficiencies, imperfections, and enhance existing process capabilities

abstract: Process mining is to extract knowledge about business processes from data stored implicitly in ad-hoc way or explicitly by information systems. The aim is to discover runtime process, analyze performance and perform conformance verification, using process mining tools like ProM and Disco, for single software repository and processes spanning across multiple repositories. Application of process mining to software repositories has recently gained interest due to availability of vast data generated during software development and maintenance. Process data are embodied in repositories which can be used for analysis to improve the efficiency and capability of process, however, involves a lot of challenges which have not been addressed so far. Project team defines workflow, design process and policies for tasks like issue tracking (defect or feature enhancement), peer code review (review the submitted patch to avoid defects before they are injected) etc. to streamline and structure the activities. The reality may not be the same as defined because of imperfections so the extent of non-conformance needs to be measured. We propose a research framework `Nirikshan' to process mine the data of software repositories from multiple perspectives like process, organizational, data and time. We apply process mining on software repositories to derive runtime process map, identify and remove inefficiencies and imperfections, extend the capabilities of existing software engineering tools to make them more process aware, and understand interaction pattern between various contributors to improve the efficiency of project.


title: Towards a Theory of Local and Global in Computation

abstract: Abstract We formulate the rudiments of a method for assessing the difficulty of dividing a computational problem into “independent simpler parts”. This work illustrates measures of complexity which attempt to capture the distinction between “local” and “global” computational problems. One such measure is the covering multiplicity , or average number of partial computations which take account of a given piece of data. Another measure reflects the intuitive notion of a “highly interconnected” computational problem, for which subsets of the data cannot be processed “in isolation”. These ideas are applied in the setting of computational geometry to show that the connectivity predicate has unbounded covering multiplicity and is highly interconnected; and in the setting of numerical computations to measure the complexity of evaluating polynomials and solving systems of linear equations.


title: A multi-case study of agile requirements engineering and the use of test cases as requirements

abstract: ContextIt is an enigma that agile projects can succeed 'without requirements' when weak requirements engineering is a known cause for project failures. While agile development projects often manage well without extensive requirements test cases are commonly viewed as requirements and detailed requirements are documented as test cases. ObjectiveWe have investigated this agile practice of using test cases as requirements to understand how test cases can support the main requirements activities, and how this practice varies. MethodWe performed an iterative case study at three companies and collected data through 14 interviews and two focus groups. ResultsThe use of test cases as requirements poses both benefits and challenges when eliciting, validating, verifying, and managing requirements, and when used as a documented agreement. We have identified five variants of the test-cases-as-requirements practice, namely de facto, behaviour-driven, story-test driven, stand-alone strict and stand-alone manual for which the application of the practice varies concerning the time frame of requirements documentation, the requirements format, the extent to which the test cases are a machine executable specification and the use of tools which provide specific support for the practice of using test cases as requirements. ConclusionsThe findings provide empirical insight into how agile development projects manage and communicate requirements. The identified variants of the practice of using test cases as requirements can be used to perform in-depth investigations into agile requirements engineering. Practitioners can use the provided recommendations as a guide in designing and improving their agile requirements practices based on project characteristics such as number of stakeholders and rate of change.


title: Resource provisioning and scheduling in clouds: QoS perspective

abstract: Resource provisioning of appropriate resources to cloud workloads depends on the quality of service (QoS) requirements of cloud applications and is a challenging task. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounter a problem of allocation of resources, which cannot be addressed with existing resource management frameworks. Resource scheduling, if done after efficient resource provisioning, will be more effective and the cloud resources would be scheduled as per the user requirements (QoS) on provisioned resources. Execution of cloud workloads should be as per QoS parameters to fully satisfy the cloud consumer. Therefore, based on QoS parameters, it is mandatory to predict and verify the resource provisioning before actual resource scheduling. In this paper, a resource provisioning and scheduling framework has been presented which caters to provisioned resource distribution and scheduling of resources. Cloud workloads have been re-clustered using k-means-based clustering algorithm after firstly clustering them through workload patterns to identify the QoS requirements of a workload, and then based on identified QoS requirements resources are provisioned before actual scheduling. Further, scheduling has been done based on different scheduling policies. Finally, the performance of the proposed framework has been evaluated in both real and simulated cloud environment and experimental results show that the framework provisions and schedules resource efficiently by considering energy consumption, execution cost and execution time as QoS parameters.


title: Leveraging Knowledge Bases in LSTMs for Improving Machine Reading

abstract: This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge from KBs encode knowledge as discrete indicator features. Not only do these features generalize poorly, but they require task-specific feature engineering to achieve good performance. We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge and which information from KBs is useful. Experimental results show that our model achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset.


Segmentation Guided Attention Networks for Visual Question Answering


title: AC Power Theory From Poynting Theorem: Accurate Identification of Instantaneous Power Components in Nonlinear-Switched Circuits

abstract: This paper contributes to narrowing the long-standing theoretical gap with power theory (or “power definitions”) for nonlinear ac switching circuits. The true instantaneous energy transformation and storage components of ac circuits are identified from the Poynting Theorem. This paper tackles the problem of power identification from the most general form of energy conservation. Therefore, it is no longer necessary to mathematically “define” powers to fit the engineering solution of a problem. The identification technique does not present problems with physical meaning since it is in full agreement with Maxwell's Equations. In this paper, the method is applied to the identification of the power components of single-phase switched circuits. Instantaneous energy is decomposed only into energy transformed (related to active power) and energy stored (related to reactive power). Examples that have caused physical interpretation problems with other power theories are presented for illustration and validation.


title: Low-Profile Dual Circularly Polarized Antenna Array for Satellite Communications in the X Band

abstract: A planar antenna is introduced that works as a portable system for X-band satellite communications. This antenna is low-profile and modular with dimensions of 40 × 40 × 2.5 × cm. It is composed of a square array of 144 printed circuit elements that cover a wide bandwidth (14.7%) for transmission and reception along with dual and interchangeable circular polarization. A radiation efficiency above 50% is achieved by a low-loss stripline feeding network. This printed antenna has a 3 dB beamwidth of 5°, a maximum gain of 26 dBi and an axial ratio under 1.9 dB over the entire frequency band. The complete design of the antenna is shown, and the measurements are compared with simulations to reveal very good agreement.


title: A novel method for eye tracking and blink detection in video frames

abstract: This paper presents a novel method for eye tracking and blink detection in the video frames obtained from low resolution consumer grade web cameras. It uses a method involving Haar based cascade classifier for eye tracking and a combination of HOG features with SVM classifier for eye blink detection. The presented method is non intrusive and hence provides a comfortable user interaction. The eye tracking method has an accuracy of 92.3% and the blink detection method has an accuracy of 92.5% when tested using standard databases and a combined accuracy of 86% when tested under real world conditions of a normal room.


After School Equine Program for At-Risk Youth


title: Learning 3D Geological Structure from Drill-Rig Sensors for Automated Mining

abstract: This paper addresses one of the key components of the mining process: the geological prediction of natural resources from spatially distributed measurements. We present a novel approach combining undirected graphical models with ensemble classifiers to provide 3D geological models from multiple sensors installed in an autonomous drill rig. Drill sensor measurements used for drilling automation, known as measurement-while-drilling (MWD) data, have the potential to provide an estimate of the geological properties of the rocks being drilled. The proposed method maps MWD parameters to rock types while considering spatial relationships, i.e., associating measurements obtained from neighboring regions. We use a conditional random field with local information provided by boosted decision trees to jointly reason about the rock categories of neighboring measurements. To validate the approach, MWD data was collected from a drill rig operating at an iron ore mine. Graphical models of the 3D structure present in real data sets possess a high number of nodes, edges and cycles, making them intractable for exact inference. We provide a comparison of three approximate inference methods to calculate the most probable distribution of class labels. The empirical results demonstrate the benefits of spatial modeling through graphical models to improve classification performance.


title: Anti-CRISPRdb: a comprehensive online resource for anti-CRISPR proteins

abstract: CRISPR-Cas is a tool that is widely used for gene editing. However, unexpected off-target effects may occur as a result of long-term nuclease activity. Anti-CRISPR proteins, which are powerful molecules that inhibit the CRISPR-Cas system, may have the potential to promote better utilization of the CRISPR-Cas system in gene editing, especially for gene therapy. Additionally, more in-depth research on these proteins would help researchers to better understand the co-evolution of bacteria and phages. Therefore, it is necessary to collect and integrate data on various types of anti-CRISPRs. Herein, data on these proteins were manually gathered through data screening of the literatures. Then, the first online resource, anti-CRISPRdb, was constructed for effectively organizing these proteins. It contains the available protein sequences, DNA sequences, coding regions, source organisms, taxonomy, virulence, protein interactors and their corresponding three-dimensional structures. Users can access our database at http://cefg.uestc.edu.cn/anti-CRISPRdb/ without registration. We believe that the anti-CRISPRdb can be used as a resource to facilitate research on anti-CRISPR proteins and in related fields.


title: A Hybrid Active Gate Drive for Switching Loss Reduction and Voltage Balancing of Series-Connected IGBTs

abstract: Insulated gate bipolar transistors (IGBTs) are usually connected in series to form high-voltage switches in power electronics applications. However, the series operation of IGBTs is not easy due to the unbalanced voltage sharing between them, especially during the switching transients and the tail-current period. In this paper, a hybrid active gate drive is presented for both switching loss reduction and voltage balancing of the series-connected IGBTs. Compared with the conventional gate drive, the proposed method allows dynamical adjustment of the switching speed of IGBTs; thus the switching loss can be suppressed without increasing the current and voltage stresses of the power device. For series connection, the transient voltage sharing is achieved by using an adaptive control method, while the voltage balancing during the tail-current period is optimized by a low-loss snubber circuit. The performance of the proposed hybrid active gate drive and control method has been validated by experimental results.


title: Natural doubling of the apparent switching frequency using three-level ANPC converter

abstract: The 3L-NPC (three-level neutral-point-clamped) is the most popular multilevel converter used in high-power medium-voltage applications. An important disadvantage of this structure is the unequal distribution of losses among the switches. The performances of 3L-NPC structure were improved by developing the 3L-ANPC (Active-NPC) converter which has more degrees of freedom. In this paper the switching states and the loss distribution problem are studied for different PWM strategies. A new PWM strategy is also proposed in the paper. It has numerous advantages: (a) natural doubling of the apparent switching frequency without using the flying-capacitor concept, (b) dead times do not influence the operating mode at 50% of the duty cycle, (c) operating at both high and small switching frequencies without structural modifications and (d) better balancing of loss distribution in switches. The PSIM simulation results are shown in order to validate the proposed PWM strategy and the analysis of the switching states.


title: Price-based Resource Allocation for Edge Computing: A Market Equilibrium Approach

abstract: The emerging edge computing paradigm promises to deliver superior user experience and enable a wide range of Internet of Things (IoT) applications. In this work, we propose a new market-based framework for efficiently allocating resources of heterogeneous capacity-limited edge nodes (EN) to multiple competing services at the network edge. By properly pricing the geographically distributed ENs, the proposed framework generates a market equilibrium (ME) solution that not only maximizes the edge computing resource utilization but also allocates optimal (i.e., utility-maximizing) resource bundles to the services given their budget constraints. When the utility of a service is defined as the maximum revenue that the service can achieve from its resource allotment, the equilibrium can be computed centrally by solving the Eisenberg-Gale (EG) convex program. drawn from the economics literature. We further show that the equilibrium allocation is Pareto-optimal and satisfies desired fairness properties including sharing incentive, proportionality, and envy-freeness. Also, two distributed algorithms are introduced, which efficiently converge to an ME. When each service aims to maximize its net profit (i.e., revenue minus cost) instead of the revenue, we derive a novel convex optimization problem and rigorously prove that its solution is exactly an ME. Extensive numerical results are presented to validate the effectiveness of the proposed techniques.


title: Feasibility of Whole Brain Emulation

abstract: Whole brain emulation (WBE) is the possible future one-to-one modeling of the function of the entire (human) brain. The basic idea is to take a particular brain, scan its structure in detail, and construct a software model of it that is so faithful to the original that, when run on appropriate hardware, it will behave in essentially the same way as the original brain. This would achieve software-based intelligence by copying biological intelligence (without necessarily understanding it).


title: A Modified Naive Bayes to online training assessment in virtual reality simulators

abstract: Training systems based on virtual reality are used in several areas. In these systems the user is immersed into a virtual world to have realistic training through realistic interactions. In such training is important to know the quality of user¿s training. Several approaches to perform assessment in training simulators based on virtual reality have been proposed. However, they did not present a satisfactory solution for special cases as a bone marrow harvest in patients with bones deteriorated by disease. In this paper, we present an approach to online training assessment based on a modified naive Bayes, which can manipulate qualitative and quantitative variables simultaneously.


FULLY AUTOMATIC STEREO-TO-MULTIVIEW CONVERSION IN AUTOSTEREOSCOPIC DISPLAYS


title: Importance sampling for physically-based hair fiber models

abstract: We present a new strategy for importance sampling hair reflectance models. To combine hair reflectance models with increasingly popular physically-based rendering algorithms, an efficient sampling scheme is required to select scattered rays that lead to lower variance and noise. Our new strategy, which is tied closely to the derivation of physically-based fiber functions, works well for both smooth and rough fibers based on the Marschner et al. model and also for Lambertian fibers. It should be directly usable with future hair reflectance models that allow for more general cross-sections and more complex surface properties, provided the lobes are derived in a similar, separable fashion. Our strategy includes lobe selection and can efficiently sample complex lobe shapes like the Marschner TRT function. The scheme is easy to implement and requires no precomputation, allowing fully heterogeneous variation of all fiber parameters.


OF A COMPACT AND LOW COST ELEMENTARY RADIATING CELL FOR SATELLITE BROADCASTING AUTOMOTIVE RECEIVING ARRAYS 33


title: FinFET cells with different transistor sizing techniques against PVT variations

abstract: This paper investigates the impact of the main sources of variation on performance and power consumption for different transistor sizing techniques applied to cells in FinFET technologies. The analysis considers process, voltage and temperature variations, individually. Voltage and temperature variations are combined to obtain an insight into their contributions. Results are useful to define the variability contributions in the early design steps and to select the most appropriate transistor sizing technique for a targeted application. Results provide a quantitative understanding of each contribution considering a 14nm FinFET technology.


title: In-Ear EEG From Viscoelastic Generic Earpieces: Robust and Unobtrusive 24/7 Monitoring

abstract: We introduce a novel in-ear sensor which satisfies key design requirements for wearable electroencephalography (EEG)—it is discreet, unobtrusive, and capable of capturing high-quality brain activity from the ear canal. Unlike our initial designs, which utilize custom earpieces and require a costly and time-consuming manufacturing process, we here introduce the generic earpieces to make ear-EEG suitable for immediate and widespread use. Our approach represents a departure from silicone earmoulds to provide a sensor based on a viscoelastic substrate and conductive cloth electrodes, both of which are shown to possess a number of desirable mechanical and electrical properties. Owing to its viscoelastic nature, such an earpiece exhibits good conformance to the shape of the ear canal, thus providing stable electrode–skin interface, while cloth electrodes require only saline solution to establish low impedance contact. The analysis highlights the distinguishing advantages compared with the current state-of-the-art in ear-EEG. We demonstrate that such a device can be readily used for the measurement of various EEG responses.


title: The function of self-disclosure on social network sites: Not only intimate, but also positive and entertaining self-disclosures increase the feeling of connection

abstract: Three alternative models how updates on Facebook create closeness are tested.The more intimate communication on Facebook is hidden in private conversations.Here, the classical link between intimacy and feeling of connection holds.Positive and entertaining updates also increase the feeling of connection.Interaction partner responsiveness plays no significant role on social media. How can social network sites (SNS) foster relationships when most status updates on SNS are mainly entertaining and not very intimate? This finding cannot be explained by classical social psychological theories such as social penetration theory which regard disclosure intimacy as the main driver of relational outcomes. By building on literature on the role of capitalization and humor in relationship formation and maintenance, this paper suggests two alternative paths from public self-disclosure to relational outcomes. Respondents judged the content and relational effects of own and friends' status updates as well as private conversations. In general, all types of messages were mainly positive and entertaining. The more intimate communication took place in private conversations; here, the classical link between disclosure intimacy and feeling connected still held. However, positive and entertaining self-disclosures also increased the feeling of connection, especially when reading friends' updates. Interestingly, interaction partners' responsiveness did not play a significant role, indicating that results from dyadic face-to-face interactions do not hold for public communication on social media. The study contributes to the development of a more differentiated model on the role of self-disclosure on SNS.


title: An overview on trajectory outlier detection

abstract: The task of trajectory outlier detection is to discover trajectories or their segments which differ substantially from or are inconsistent with the remaining set. In this paper, we make an overview on trajectory outlier detection algorithms from three aspects. Firstly, algorithms considering multi-attribute. In this kind of algorithms, as many key attributes as possible, such as speed, direction, position, time, are explored to represent the original trajectory and to compare with the others. Secondly, suitable distance metric. Many researches try to find or develop suitable distance metric which can measure the divergence between trajectories effectively and reliably. Thirdly, other studies attempt to improve existing algorithms to find outliers with less time and space complexity, and even more reliable. In this paper, we survey and summarize some classic trajectory outlier detection algorithms. In order to provide an overview, we analyze their features from the three dimensions above and discuss their benefits and shortcomings. It is hope that this review will serve as the steppingstone for those interested in advancing moving object outlier detection.


title: Discovering Mobile Application Usage Patterns from a Large-Scale Dataset

abstract: The discovering of patterns regarding how, when, and where users interact with mobile applications reveals important insights for mobile service providers. In this work, we exploit for the first time a real and large-scale dataset representing the records of mobile application usage of 5,342 users during 2014. The data was collected by a software agent, installed at the users’ smartphones, which monitors detailed usage of applications. First, we look for general patterns of how users access some of the most popular mobile applications in terms of frequency, duration, diversity, and data traffic. Next, we mine the dataset looking for temporal patterns in terms of when and how often accesses occur. Finally, we exploit the location of each access to detect users’ points of interest and location-based communities. Based on the results, we derive a model to generate synthetic datasets of mobile application usage and evaluate solutions to predict the next application to be launched. We also discuss a series of implications of the findings regarding telecommunication services, mobile advertisements, and smart cities. This is the first time this dataset is used, and we also make it publicly available for other researchers.


title: Understanding Self-Organizing Teams in Agile Software Development

abstract: Traditional software teams consist of independently focused self-managing professionals with high individual but low team autonomy. A challenge with introducing agile software development is that it requires a high level of both individual and team autonomy. This paper studies the barriers with introducing self-organizing teams in agile software development and presents data from a seven month ethnographic study of professional developers in a Scrum team. We found the most important barrier to be the highly specialized skills of the developers and the corresponding division of work. In addition we found a lack of system for team support, and reduced external autonomy to be important barriers for introducing self- organizing teams. These findings have implications for software development managers and practitioners.


title: A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition

abstract: This paper proposes a deterministic particle swarm optimization to improve the maximum power point tracking (MPPT) capability for photovoltaic system under partial shading condition. The main idea is to remove the random number in the accelerations factor of the conventional PSO velocity equation. Additionally, the maximum change in velocity is restricted to a particular value, which is determined based on the critical study of P-V characteristics during partial shading. Advantages of the method include: 1) consistent solution is achieved despite a small number of particles, 2) only one parameter, i.e., the inertia weight, needs to be tuned, and 3) the MPPT structure is much simpler compared to the conventional PSO. To evaluate the idea, the algorithm is implemented on a buck-boost converter and compared to the conventional hill climbing (HC) MPPT method. Simulation results indicate that the proposed method outperforms the HC method in terms of global peak tracking speed and accuracy under various partial shading conditions. Furthermore, it is tested using the measured data of a tropical cloudy day, which includes rapid movement of the passing clouds and partial shading. Despite the wide fluctuations in array power, the average efficiency for the 10-h test profile reaches 99.5%.


title: Interactive Attention for Neural Machine Translation

abstract: Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence. By repeatedly reading the representation of source sentence, which keeps fixed after generated by the encoder (Bahdanau et al., 2015), the attention mechanism has greatly enhanced state-of-the-art NMT. In this paper, we propose a new attention mechanism, called INTERACTIVE ATTENTION, which models the interaction between the decoder and the representation of source sentence during translation by both reading and writing operations. INTERACTIVE ATTENTION can keep track of the interaction history and therefore improve the translation performance. Experiments on NIST Chinese-English translation task show that INTERACTIVE ATTENTION can achieve significant improvements over both the previous attention-based NMT baseline and some state-of-the-art variants of attention-based NMT (i.e., coverage models (Tu et al., 2016)). And neural machine translator with our INTERACTIVE ATTENTION can outperform the open source attention-based NMT system Groundhog by 4.22 BLEU points and the open source phrase-based system Moses by 3.94 BLEU points averagely on multiple test sets.


title: A Broadband Dual-Polarized Base Station Antenna With Sturdy Construction

abstract: A broadband dual-polarized base station antenna with sturdy construction is presented in this letter. The antenna mainly contains four parts: main radiator, feeding baluns, bedframe, and reflector. First, two orthogonal dipoles are etched on a substrate as main radiator forming dual polarization. Two baluns are then introduced to excite the printed dipoles. Each balun has four bumps on the edges for electrical connection and fixation. The bedframe is designed to facilitate the installation, and the reflector is finally used to gain unidirectional radiation. Measured results show that the antenna has a 48% impedance bandwidth with reflection coefficient less than -15 dB and port isolation more than 22 dB. A four-element antenna array with 6° ± 2° electrical down tilt is also investigated for wideband base station application. The antenna and its array have the advantages of sturdy construction, high machining accuracy, ease of integration, and low cost. They can be used for broadband base station in the next-generation wireless communication system.


title: A compact slot loop rectenna for dual-band operation at 2.4- and 5.8-GHz bands

abstract: A compact dual-band square slot loop rectenna fed by a microstrip line (MSL) for 2.4- and 5.8-GHz bands is presented. Instead of exploiting resonances of integer multiples of full wavelength in conventional dual- or multi-band slot loop antenna design, it is for the first time that the half-wavelength and one-wavelength resonant modes of MSL-fed slot loop antennas are exploited in the design of dual-band rectenna. Due to the distinct impedance behaviors at the two resonances, a matching circuit is inserted in between the rectifier circuit and slot loop radiator for simultaneous conjugate match at the two resonances. For verification, a prototype of the proposed compact slot loop rectenna for operation at 2.4- and 5.8-GHz bands is fabricated and tested. The conversion efficiencies of 49% and 66% are achieved, respectively. The prototype rectenna measures only 13 mm × 24 mm or 0.106λ 0 × 0.196λ 0 at 2.45 GHz.


title: Recurrent Models for Situation Recognition

abstract: This work proposes Recurrent Neural Network (RNN) models to predict structured ‘image situations’ – actions and noun entities fulfilling semantic roles related to the action. In contrast to prior work relying on Conditional Random Fields (CRFs), we use a specialized action prediction network followed by an RNN for noun prediction. Our system obtains state-of-the-art accuracy on the challenging recent imSitu dataset, beating CRF-based models, including ones trained with additional data. Further, we show that specialized features learned from situation prediction can be transferred to the task of image captioning to more accurately describe human-object interactions.


title: Recognizing human actions: a local SVM approach

abstract: Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper, we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.


title: Real Time Face Morphing

abstract: Although various techniques of image deformation have been developed and generally applied in animation and morphing, there are a few mechanisms to spread out these techniques to handle videos, especially real-time warping of an expressive moving part in the video like human face. This work presents a system which provides real-time deformation of the shape and appearance of face of people who are standing in front of a D-RGB camera, such as the Microsoft Kinect. This system allows the user to manipulate human face shape parameters such as jaw, chin, nose, mouth, eyes etc. The user is allowed to concentrate only on what they require to change about the face and observe the manipulated appearance in real-time. Thus, instead of posing in front of a real mirror and imagining their appearance, our system allows the users to pose in front of a digital 'virtual mirror' and visualize themselves in different face features.


title: Energy Efficient Clustering Algorithms for Wireless Sensor Networks

abstract: Energy efficiency is a major concern in wireless sensor networks (WSNs). Many clustering algorithms have been proposed for such a purpose. This paper investigates the existing clustering algorithms. The algorithms have been classified and some representatives are described in each category. After analyzing the strengths and the weaknesses of each category, an important characteristic of WSNs is pointed out for further improvement of energy efficiency for WSNs.


title: Analysis and application of an outsourcing risk framework

abstract: There is much reported research on risk, and risk management but research on strategic IT system development outsourcing risk, from the client perspective, remains unclear. A literature-based conceptual risk framework for strategic IT system development outsourcing from the client perspective has been developed. We then investigate, (1) critical client risks for strategic IT system development outsourcing projects, and (2) the most common critical client risk factors for such projects. In order to identify any serious omissions in our framework an initial validation of the risk framework is provided through a review of nine published cases of unsuccessful strategic IT system development outsourcing projects. The risks critical to a client are associated with complexity, contract, execution, financial, legal, the organizational environment, planning and control, scope and requirements, the team, and the user. Risks manifest in all nine published cases, include (1) complexity and (2) the team. Three risk factors not previously identified in the initial framework are included in a revised framework. The risk framework assisted us in identifying a number of critical risk factors affecting the outcome of strategic IT system development outsourcing projects.


Change of Landsat 8 TIRS calibration parameters and its effect on land surface temperature retrieval


title: SURE-based optimization for adaptive sampling and reconstruction

abstract: We apply Stein's Unbiased Risk Estimator (SURE) to adaptive sampling and reconstruction to reduce noise in Monte Carlo rendering. SURE is a general unbiased estimator for mean squared error (MSE) in statistics. With SURE, we are able to estimate error for an arbitrary reconstruction kernel, enabling us to use more effective kernels rather than being restricted to the symmetric ones used in previous work. It also allows us to allocate more samples to areas with higher estimated MSE. Adaptive sampling and reconstruction can therefore be processed within an optimization framework. We also propose an efficient and memory-friendly approach to reduce the impact of noisy geometry features where there is depth of field or motion blur. Experiments show that our method produces images with less noise and crisper details than previous methods.


title: System-Level Performance of Downlink Non-Orthogonal Multiple Access (NOMA) under Various Environments

abstract: Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for further improving the spectrum efficiency compared to that for orthogonal multiple access (OMA) in the 5th Generation (5G) mobile communication systems. All of the existing evaluations for NOMA focus on the macrocell deployment since NOMA fully utilizes the power domain and the difference in channel gains, e.g., path loss, between users, which is typically sufficiently large in macrocells. Currently, small cells are becoming important and being studied for future Long-Term Evolution (LTE) enhancements in order to improve further the system performance. Thus, it is of great interest to study the performance of NOMA for small cell deployment under various environments. This paper investigates the system level performance of NOMA in small cells considering practical assumptions such as the single user multiple-input multiple-output (SU-MIMO) technique, adaptive modulation and coding (AMC), feedback channel quality indicator (CQI). Some of the key NOMA specific functionalities, including multi-user paring and transmit power allocation are also taken into account in the evaluation. Based on computer simulations, we show that for both macrocell and small cell deployments, NOMA can still provide a larger throughput performance gain compared to that for OMA.


title: Improved executive functions in 6–12-year-old children following cognitively engaging tennis lessons

abstract: ABSTRACTThe aim of this cross-sectional study was to evaluate the relationships between cognitively engaging exercise (i.e., game-based and coordination exercises), executive functions (i.e., inhibitory control and working memory), and physical fitness. Forty junior tennis players (6–12 years old), who regularly participated in tennis lessons (2.55 years, SD = 1.61) prior to the study, were investigated. All participants completed evaluations of executive functions (inhibitory control and working memory) at rest. The duration of each lessons’ instructional activities, including coordination training, game-based exercise, rallying, and non-physical activity (explanations and breaks), was recorded. Physical fitness was evaluated using the Tennis Field Test. A longer duration of game-based exercise was positively correlated with inhibitory control and physical fitness. Coordination training was associated with improved working memory. Non-physical activity was inversely correlated with inhibitory control, wo...


title: Evolving Behaviour Trees for the Mario AI Competition Using Grammatical Evolution

abstract: This paper investigates the applicability of Genetic Programming type systems to dynamic game environments. Grammatical Evolution was used to evolved Behaviour Trees, in order to create controllers for the Mario AI Benchmark. The results obtained reinforce the applicability of evolutionary programming systems to the development of artificial intelligence in games, and in dynamic systems in general, illustrating their viability as an alternative to more standard AI techniques.


title: Treatment Satisfaction of Patients with Psoriasis

abstract: Treatment satisfaction of patients with psoriasis largely depends on the treatment modality, but evidence on preferences for specific medications is scarce. Here we assessed treatment satisfaction of 200 participants with moderate-to-severe psoriasis from a German University hospital with a 5-point scale and the Treatment Satisfaction Questionnaire for Medication (TSQM) and determined sociodemographic and disease-related influence factors. Participants obtaining biologicals and traditional systemic medications were significantly more satisfied than those receiving phototherapy or topical agents (TSQM = 323.3, 288.0, 260.6 or 266.8; p < 0.001). The highest TSQM score was calculated for ustekinumab (350.1), followed by acitretin (338.1), adalimumab (323.0), fumaric acid esters (304.7), infliximab (300.2), etanercept (298.8), and methotrexate (272.3; p < 0.001). High disease-related quality of life impairment (β = -0.437, p < 0.001) and psoriatic arthritis (β = -0.185, p = 0.005) were associated with decreased satisfaction. Optimising satisfaction is essential to improve adherence and outcome. We show high preferences for biologicals, particularly ustekinumab, but also good satisfaction with certain traditional medications.


Big Data and Machine Learning in E-government: Automatic Expert Evaluation Case


title: Extreme reductions: Contraction of disyllables into monosyllables in Taiwan Mandarin

abstract: This study investigates a severe form of segmental reduction known as contraction. In Taiwan Mandarin, a disyllabic word or phrase is often contracted into a monosyllabic unit in conversational speech, just as “do not” is often contracted into “don’t” in English. A systematic experiment was conducted to explore the underlying mechanism of such contraction. Preliminary results show evidence that contraction is not a categorical shift but a gradient undershoot of the articulatory target as a result of time pressure. Moreover, contraction seems to occur only beyond a certain duration threshold. These findings may further our understanding of the relation between duration and segmental reduction. Index Terms: contraction, reduction, undershoot, duration, speech rate, Taiwan Mandarin


title: A new instrument for measuring insight: the Beck Cognitive Insight Scale

abstract: The clinical measurements of insight have focused primarily on patients' unawareness of their having a mental disorder and of their need for treatment ([Acta Psychiatr. Scand. 89 (1994) 62; Am. J. Psychiatry 150 (1993) 873]; etc.). A complementary approach focuses on some of the cognitive processes involved in patients' re-evaluation of their anomalous experiences and of their specific misinterpretations: distancing, objectivity, perspective, and self-correction. The Beck Cognitive Insight Scale (BCIS) was developed to evaluate patients' self-reflectiveness and their overconfidence in their interpretations of their experiences. A 15-item self-report questionnaire was subjected to a principle components analysis, yielding a 9-item self-reflectiveness subscale and a 6-item self-certainty subscale. A composite index of the BCIS reflecting cognitive insight was calculated by subtracting the score for the self-certainty scale from that of the self-reflectiveness scale. The scale demonstrated good convergent, discriminant, and construct validity: (a) the BCIS composite index showed a significant correlation with being aware of having a mental disorder on the Scale to Assess Unawareness of Mental Disorder (SUMD; Arch. Gen. Psychiatry 51 (1994) 826) and the self-reflectiveness subscale was significantly correlated with being aware of delusions on the SUMD, (b) the composite index score of the BCIS differentiated inpatients with psychotic diagnoses from inpatients without psychotic diagnoses, and (c) in a separate study, change scores on the BCIS were significantly correlated with change scores on positive and negative symptoms. The results provided tentative support for the validity of the BCIS. Suggestions were made for further investigation of the cognitive processes involved in identifying and correcting erroneous beliefs and misinterpretations.


title: Node similarity in the citation graph

abstract: Published scientific articles are linked together into a graph, the citation graph, through their citations. This paper explores the notion of similarity based on connectivity alone, and proposes several algorithms to quantify it. Our metrics take advantage of the local neighborhoods of the nodes in the citation graph. Two variants of link-based similarity estimation between two nodes are described, one based on the separate local neighborhoods of the nodes, and another based on the joint local neighborhood expanded from both nodes at the same time. The algorithms are implemented and evaluated on a subgraph of the citation graph of computer science in a retrieval context. The results are compared with text-based similarity, and demonstrate the complementarity of link-based and text-based retrieval.