title: Filter Before You Parse: Faster Analytics on Raw Data with Sparser

abstract: Exploratory big data applications often run on raw unstructured or semi-structured data formats, such as JSON files or text logs. These applications can spend 80--90% of their execution time parsing the data. In this paper, we propose a new approach for reducing this overhead: apply filters on the data's raw bytestream before parsing. This technique, which we call raw filtering, leverages the features of modern hardware and the high selectivity of queries found in many exploratory applications. With raw filtering, a user-specified query predicate is compiled into a set of filtering primitives called raw filters (RFs). RFs are fast, SIMD-based operators that occasionally yield false positives, but never false negatives. We combine multiple RFs into an RF cascade to decrease the false positive rate and maximize parsing throughput. Because the best RF cascade is data-dependent, we propose an optimizer that dynamically selects the combination of RFs with the best expected throughput, achieving within 10% of the global optimum cascade while adding less than 1.2% overhead. We implement these techniques in a system called Sparser, which automatically manages a parsing cascade given a data stream in a supported format (e.g., JSON, Avro, Parquet) and a user query. We show that many real-world applications are highly selective and benefit from Sparser. Across diverse workloads, Sparser accelerates state-of-the-art parsers such as Mison by up to 22 × and improves end-to-end application performance by up to 9 ×.


title: Towards Information Diffusion in Mobile Social Networks

abstract: The emerging of mobile social networks opens opportunities for viral marketing. However, before fully utilizing mobile social networks as a platform for viral marketing, many challenges have to be addressed. In this paper, we address the problem of identifying a small number of individuals through whom the information can be diffused to the network as soon as possible, referred to as the diffusion minimization problem. Diffusion minimization under the probabilistic diffusion model can be formulated as an asymmetric $k$ -center problem which is NP-hard, and the best known approximation algorithm for the asymmetric $k$ -center problem has approximation ratio of $\log ^*n$ and time complexity $O(n^5)$ . Clearly, the performance and the time complexity of the approximation algorithm are not satisfiable in large-scale mobile social networks. To deal with this problem, we propose a community based algorithm and a distributed set-cover algorithm. The performance of the proposed algorithms is evaluated by extensive experiments on both synthetic networks and a real trace. The results show that the community based algorithm has the best performance in both synthetic networks and the real trace compared to existing algorithms, and the distributed set-cover algorithm outperforms the approximation algorithm in the real trace in terms of diffusion time.


title: A 10-kV/400-V 500-kVA Electronic Power Transformer

abstract: This paper presents the design and development of a three-phase 10-kV/400-V 500-kVA electronic power transformer (EPT). The power circuit is designed in a modular fashion, i.e., the main circuit consists of many identical ac–dc–dc–ac modules (abbreviated as power modules). Each power module consists of a high-voltage power cell, a low-voltage power cell (LVPC), a medium-frequency isolation transformer, and a filter. The corresponding control and protection system is developed. A special three-stage startup strategy is designed to shorten the startup time and reduce the startup inrush current. The negative-sequence current compensation is introduced in the input stage to handle the unbalanced loads. To keep the dc-link voltages balanced, an individual dc voltage balancing controller based on regulating the output power of each parallel LVPC is proposed. The detailed control hardware design and software implementation are discussed. The functions of this 10-kV EPT prototype are verified through the laboratory and field tests. The results are shown in this paper. Currently, the prototype is operating in the industrial power grid.


title: Monitoring Kinematic Changes With Fatigue in Running Using Body-Worn Sensors

abstract: In this paper, we investigate monitoring of kinematic changes evoked by fatigue in running using wearable technology. Movement data were recorded with ETHOS devices. ETHOS is the ETH Orientation Sensor, a customized inertial measurement unit for unconstrained monitoring of human movement. We perform two real-world experiments, in which 21 runners of different skill levels participated. The real-world experiments capture two exhausting 45-min runs: one on a treadmill and one on a conventional outdoor track. We describe and evaluate algorithms to extract kinematic parameters from the sensor data. We identified parameters that change with fatigue for all runners, ones that change for runners of distinct skill levels, and ones that are dependent on an individual's running technique. Overall, we found that observations from treadmill running are not always generalizable to outdoor running. We, thus, argue for using wearable technology to provide athletes and trainers with continuous, quantitative objective measurements of running technique. These could be used to further gain insight into the complex relationship of running kinematics, injury risk, fatigue, and running economy.


Practice of self-medication with antibiotics in the Colombo district, Sri Lanka


title: Tactical Language and Culture Training Systems: Using AI to Teach Foreign Languages and Cultures

abstract: The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire communicative skills in foreign languages and cultures. More than 40,000 learners worldwide have used TLCTS courses. TLCTS utilizes artificial intelligence technologies during the authoring process, and at run time to process learner speech, engage in dialog, and evaluate and assess learner performance. This paper describes the architecture of TLCTS and the artificial intelligence technologies that it employs, and presents results from multiple evaluation studies that demonstrate the benefits of learning foreign language and culture using this approach.


title: Prime Box Parallel Search Algorithm: Searching Dynamic Dictionary in O(lg m) Time

abstract: Hashing and Trie tree data structures are among the preeminent ::: data mining techniques considered for the ideal search. Hashing techniques have ::: the amortized time complexity of O(1). Although in worst case, searching a hash ::: table can take as much as θ(n) time [1]. On the other hand, Trie tree data ::: structure is also well renowned data structure. The ideal lookup time for ::: searching a string of length m in database of n strings using Trie data ::: structure is O(m) [2]. In the present study, we have proposed a novel Prime Box ::: parallel search algorithm for searching a string of length m in a dictionary of ::: dynamically increasing size, with a worst case search time complexity of ::: O(log2m). We have exploited parallel techniques over this novel algorithm to ::: achieve this search time complexity. Also this prime Box search is independent ::: of the total words present in the dictionary, which makes it more suitable for ::: dynamic dictionaries with increasing size.


title: Eccentric exercises; why do they work, what are the problems and how can we improve them?

abstract: Eccentric exercises (EE) have proved successful in the management of chronic tendinopathy, particularly of the Achilles and patellar tendons, where they have been shown to be effective in controlled trials. However, numerous questions regarding EE remain. The standard protocols are time-consuming and require very motivated patients. EE are effective in some tendinopathies but not others. Furthermore, the location of the lesion can have a profound effect on efficacy; for example, standard EE in insertional lesions of the Achilles are ineffective. Until recently little was known of the effect of EE on tendinopathic tendons, although a greater understanding of this process is emerging. Additionally, recent in vivo evidence directly comparing eccentric and concentric exercises provides a possible explanation for the therapeutic benefit of EE. The challenge now is to make EE more effective. Suggestions on areas of future research are made.


title: Animal Metaphors and Metaphorizing Animals: An Integrated Literary, Cognitive, and Evolutionary Analysis of Making and Partaking of Stories

abstract: Humans use metaphors to explore their relationship with nature. Our ability to make and understand metaphors appears to be an automatic cognitive process, one that likely evolved along with our ability to create and understand language. Because metaphors are processed automatically, without conscious appraisal, they can be used to rapidly communicate, or manipulate. Applying theories of evolutionary psychology and cognitive science to literary texts, we explored the role of animal metaphors in the making and partaking of stories in the context of a course in environmental studies. We investigated how humans are animals and yet use culture to shield themselves from this reality. We read and analyzed literature in which animal metaphors are central, such as Honore de Balzac’s short story Passion in the Desert and Langdon Smith’s poem “Evolution.” Throughout the course, the overarching theme is that animal metaphors are powerful tools for framing our relationship with the environment and that they can be best understood in the context of humans as evolved animals.


title: ConvNets with Smooth Adaptive Activation Functions for Regression

abstract: Within Neural Networks (NN), the parameters of Adaptive Activation Functions (AAF) control the shapes of activation functions. These parameters are trained along with other parameters in the NN. AAFs have improved performance of Convolutional Neural Networks (CNN) in multiple classification tasks. In this paper, we propose and apply AAFs on CNNs for regression tasks. We argue that applying AAFs in the regression (second-to-last) layer of a NN can significantly decrease the bias of the regression NN. However, using existing AAFs may lead to overfitting. To address this problem, we propose a Smooth Adaptive Activation Function (SAAF) with a piecewise polynomial form which can approximate any continuous function to arbitrary degree of error, while having a bounded Lipschitz constant for given bounded model parameters. As a result, NNs with SAAF can avoid overfitting by simply regularizing model parameters. We empirically evaluated CNNs with SAAFs and achieved state-of-the-art results on age and pose estimation datasets.


title: Management innovation

abstract: This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.


title: Dual-inductive snubber circuit design for three-level inverter

abstract: A new circuit design for the protection of e.g. multi level converters will be presented in this paper. The so-called 'dual-inductive snubber circuit design for three-level converter' will optimize the number of the total snubber circuit elements and the behavior RCD/RLD snubber circuit especially in the direction of over-voltage and over-current, and will be suitable to be used in high power inverters as well as FACTS systems due to its features. The new design comprises most of the advantages of the common snubber circuit as a low number of components, improved efficiency and power semiconductor losses due to the low number of snubber elements, reduced over-voltage across the semiconductor devices and no balancing problems. The presented snubber circuit has been analyzed, compared, and confronted to the output of a standardized three level converter system to verify its advantages.


title: Neural Simpletrons: Learning in the Limit of Few Labels with Directed Generative Networks

abstract: We explore classifier training for data sets with very few labels. We investigate this task using a neural network for nonnegative data. The network is derived from a hierarchical normalized Poisso...


title: AdGraph: A Machine Learning Approach to Automatic and Effective Adblocking

abstract: Filter lists are widely deployed by adblockers to block ads and other forms of undesirable content in web browsers. However, these filter lists are manually curated based on informal crowdsourced feedback, which brings with it a significant number of maintenance challenges. To address these challenges, we propose a machine learning approach for automatic and effective adblocking called AdGraph. Our approach relies on information obtained from multiple layers of the web stack (HTML, HTTP, and JavaScript) to train a machine learning classifier to block ads and trackers. Our evaluation on Alexa top-10K websites shows that AdGraph automatically and effectively blocks ads and trackers with 97.7% accuracy. Our manual analysis shows that AdGraph has better recall than filter lists, it blocks 16% more ads and trackers with 65% accuracy. We also show that AdGraph is fairly robust against adversarial obfuscation by publishers and advertisers that bypass filter lists.


title: Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

abstract: Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.


title: Developing an intelligent data discriminating system of anti-money laundering based on SVM

abstract: Statistical learning theory (SLT) is introduced to improve the embarrassments of anti-money laundering (AML) intelligence collection. A set of unusual behavior detection algorithm is presented in this paper based on support vector machine (SVM) in order to take the place of traditional predefined-rule suspicious transaction data filtering system. It could efficiently surmount the worst forms of suspicious data analyzing and reporting mechanism among bank branches including enormous data volume, dimensionality disorder with massive variances and feature overload.


title: Forward Reasoning and Dependency-Directed Backtracking in a System for Computer-Aided Circuit Analysis

abstract: Abstract We present a rule-based system for computer-aided circuit analysis. The set of rules, called EL, is written in a rule language called ARS. Rules are implemented by ARS as pattern-directed invocation demons monitoring an associative data base. Deductions are performed in an antecedent manner, giving EL's analysis a catch-as-catch-can flavour suggestive of the behavior of expert circuit analyzers. We call this style of circuit analysis propagation of constraints. The system threads deduced facts with justifications which mention the antecedent facts and the rule used. These justifications may be examined by the user to gain insight into the operation of the set of rules as they apply to a problem. The same justifications are used by the system to determine the currently active data-base context for reasoning in hypothetical situations. They are also used by the system in the analysis of failures to reduce the search space. This leads to effective control of combinatorial search which we call dependency-directed backtracking.


title: Stress Tensor Field Visualization for Implant Planning in Orthopedics

abstract: We demonstrate the application of advanced 3D visualization techniques to determine the optimal implant design and position in hip joint replacement planning. Our methods take as input the physiological stress distribution inside a patient's bone under load and the stress distribution inside this bone under the same load after a simulated replacement surgery. The visualization aims at showing principal stress directions and magnitudes, as well as differences in both distributions. By visualizing changes of normal and shear stresses with respect to the principal stress directions of the physiological state, a comparative analysis of the physiological stress distribution and the stress distribution with implant is provided, and the implant parameters that most closely replicate the physiological stress state in order to avoid stress shielding can be determined. Our method combines volume rendering for the visualization of stress magnitudes with the tracing of short line segments for the visualization of stress directions. To improve depth perception, transparent, shaded, and antialiased lines are rendered in correct visibility order, and they are attenuated by the volume rendering. We use a focus+context approach to visually guide the user to relevant regions in the data, and to support a detailed stress analysis in these regions while preserving spatial context information. Since all of our techniques have been realized on the GPU, they can immediately react to changes in the simulated stress tensor field and thus provide an effective means for optimal implant selection and positioning in a computational steering environment.


title: 2D laser based road obstacle classification for road safety improvement

abstract: Vehicle and pedestrian collisions often result in fatality to the vulnerable road users (VRU), indicating a strong need of technologies to protect such persons. Laser sensors have been extensively used for moving obstacles detection and tracking. Laser impacts are produced by reflection on these obstacles which suggests that more information is available for their classification. This paper proposes a new system to address this issue. We introduce the design of our system that is divided in three parts : definition of geometric features describing road obstacles, multiclass object classification from an adaboost trained classifier and track class assignment by integrating consecutive classification decision values. During this study, we show how specific features adapted to urban obstacles enhance the state of the art method for person detection in 2D laser data. Hence, in this paper, we evaluate usefulness of each feature and list the best ones. Moreover, we investigate the influence of laser height for each class showing that classification performance depends on the sensor position. Finally, we tested our system on some laser sequences and showed that it can estimate the class of some road obstacles around the vehicle with an accuracy of 87.4%.


title: Hepatic hydrothorax in the absence of ascites: Respiratory failure in a cirrhotic patient

abstract: The frequency of hepatic hydrothorax in cirrhotic patients is reported to be approximately 5%. The pleural effusion is predominantly right-sided (85% of cases) but may be bilateral. Although most often accompanied by significant ascites, it can occur in its absence. We report a case of a right-sided acute hepatic hydrothorax as a result of residual motor blockade during anesthesia recovery and without previous evidence of clinical ascites. This complication should be considered by the anesthesiologist in every cirrhotic patient, with or without clinical evidence of ascites.


title: Crop Reflectance as a tool for the online monitoring of LAI and PAR interception in two different greenhouse Crops

abstract: Methods for the online monitoring of Leaf Area Index (LAI) and photosynthetically active radiation (PAR) interception of the canopy, in greenhouse conditions, using reflectance measurements of the PAR part of the spectrum for tomato and sweet pepper were investigated. LAI and PAR interception were measured at the same moments as reflectance at six wavelengths at different plant developmental stages in greenhouses. The normalised difference vegetation index (NDVI) was also calculated. Relationships between the measured parameters were established in experimental greenhouses and subsequently tested in commercial greenhouses. The best estimates for LAI and PAR interception for both tomato and sweet pepper were obtained from reflectance at 460 nm. The goodness of the fit was validated with data from the commercial greenhouses was also tested. The divergence of the results from those reported from field experiments can be explained by the special environment that occurs in the greenhouse, where there are more sources of reflectance due to the greenhouse frame and the white plastic that covered the floor. Thus, this new approach to estimating LAI and PAR interception using 460 nm is promising and could play a role in the decision support systems that are used in modern greenhouse management.


title: Recognizing Visual Signatures of Spontaneous Head Gestures

abstract: Head movements are an integral part of human nonverbal communication. As such, the ability to detect various types of head gestures from video is important for robotic systems that need to interact with people or for assistive technologies that may need to detect conversational gestures to aid communication. To this end, we propose a novel Multi-Scale Deep Convolution-LSTM architecture, capable of recognizing short and long term motion patterns found in head gestures, from video data of natural and unconstrained conversations. In particular, our models use Convolutional Neural Networks (CNNs) to learn meaningful representations from short time windows over head motion data. To capture longer term dependencies, we use Recurrent Neural Networks (RNNs) that extract temporal patterns across the output of the CNNs. We compare against classical approaches using discriminative and generative graphical models and show that our model is able to significantly outperform baseline models.


title: Performance scaling of multi-objective evolutionary algorithms

abstract: MOEAs are getting immense popularity in the recent past, mainly because of their ability to find a wide spread of Pareto-optimal solutions in a single simulation run. Various evolutionary approaches to multi-objective optimization have been proposed since 1985. Some of fairly recent ones are NSGA-II, SPEA2, PESA (which are included in this study) and others. They all have been mainly applied to two to three objectives. In order to establish their superiority over classical methods and demonstrate their abilities for convergence and maintenance of diversity, they need to be tested on higher number of objectives. In this study, these state-of-the-art MOEAs have been investigated for their scalability with respect to the number of objectives (2 to 8). They have also been compared on the basis of -(1) Their ability to converge to Pareto front, (2) Diversity of obtained non-dominated solutions and (3) Their running time. Four scalable test problems (DTLZ1, 2, 3 and 6) are used for the comparative study.


title: Front-End Factor Analysis for Speaker Verification

abstract: This paper presents an extension of our previous work which proposes a new speaker representation for speaker verification. In this modeling, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis. This space is named the total variability space because it models both speaker and channel variabilities. Two speaker verification systems are proposed which use this new representation. The first system is a support vector machine-based system that uses the cosine kernel to estimate the similarity between the input data. The second system directly uses the cosine similarity as the final decision score. We tested three channel compensation techniques in the total variability space, which are within-class covariance normalization (WCCN), linear discriminate analysis (LDA), and nuisance attribute projection (NAP). We found that the best results are obtained when LDA is followed by WCCN. We achieved an equal error rate (EER) of 1.12% and MinDCF of 0.0094 using the cosine distance scoring on the male English trials of the core condition of the NIST 2008 Speaker Recognition Evaluation dataset. We also obtained 4% absolute EER improvement for both-gender trials on the 10 s-10 s condition compared to the classical joint factor analysis scoring.


Achieving the full MIMO diversity-multiplexing frontier with rotation-based space-time codes


title: Modelling and design of a wideband Marchand balun

abstract: This paper presents a new lumped element model for conventional Marchand baluns. Analyzed by even- and odd-mode method, the equivalent equations are derived for λ/4 coupled lines. Based on the proposed 5th-order equivalent circuit for λ/4 coupled lines, the model parameters can be easily calculated by the derived equations. A GaAs monolithic wideband Marchand balun was implemented to verify the model. The EM simulation and experimental results show a good agreement with the model simulation. This model is available in a wide frequency range and makes the design procedure of the Marchand balun faster and more easily.


title: INTEGRATING COMPUTATIONAL THINKING WITH K-12 SCIENCE EDUCATION - A Theoretical Framework

abstract: Computational thinking (CT) draws on concepts that are fundamental to computing and computer science, however, as an approach, it includes practices, such as problem representation, abstraction, decomposition, simulation, verification, and prediction that are also central to modelling, reasoning, and problem solving in many scientific and mathematical disciplines. Recently, arguments have been made in favour of integrating programming and CT with K-12 curricula. In this paper, we present a theoretical investigation of key issues that need to be considered for integrating CT with K-12 science. We identify the synergies between programming and CT on one hand, and scientific expertise on the other. We then present a critical review of literature on educational computing, and propose a set of guidelines for designing learning environments in science that can jointly foster the development of computational thinking with scientific expertise. Finally, we describe the design of a learning environment that supports CT through modelling and simulation to help middle school students learn physics and biology.


title: Transfer Joint Matching for Unsupervised Domain Adaptation

abstract: Visual domain adaptation, which learns an accurate classifier for a new domain using labeled images from an old domain, has shown promising value in computer vision yet still been a challenging problem. Most prior works have explored two learning strategies independently for domain adaptation: feature matching and instance reweighting. In this paper, we show that both strategies are important and inevitable when the domain difference is substantially large. We therefore put forward a novel Transfer Joint Matching (TJM) approach to model them in a unified optimization problem. Specifically, TJM aims to reduce the domain difference by jointly matching the features and reweighting the instances across domains in a principled dimensionality reduction procedure, and construct new feature representation that is invariant to both the distribution difference and the irrelevant instances. Comprehensive experimental results verify that TJM can significantly outperform competitive methods for cross-domain image recognition problems.


title: A 1mW CMOS limiting amplifier and RSSI for ZigBee™ applications

abstract: This paper presents a low-power intermediate frequency (IF) limiting amplifier (LA) and received signal strength indicator (RSSI). The LA and RSSI are designed for ZigBee™ receiver at 2MHz IF. To save power, two local loops for offset correction are used in LA chain and a sensitivity of -56dBm is achieved. Each LA gain stage employs cascade diodes load to avoid driving the diode load into velocity saturation region. The indication rang is 50dB within ±2dB linearity error. The core area is 0.11×0.31mm2 using a SMIC 0.18-μm CMOS technology. The overall power consumption is 1mW from a 1.8V supply voltage.


title: Improving App Privacy: Nudging App Developers to Protect User Privacy

abstract: Smartphone app developers make many privacy-related decisions on what data to collect about users and how that data is used. Based on interviews and a survey of app developers, the authors identify several hurdles preventing app developers from improved privacy behaviors. These include the difficulties of reading and writing privacy policies as well as privacy not being their primary task. The authors suggest some nudges that would help app developers improve user privacy as well as public policy focus on incentivizing all players in the app development ecosystem to help developers implement better privacy behaviors.


title: The Effects of Teaching Programming via Scratch on Problem Solving Skills: A Discussion from Learners' Perspective.

abstract: Computer programming is perceived as an important competence for the development of problem solving skills in addition to logical reasoning. Hence, its integration throughout all educational levels, as well as the early ages, is considered valuable and research studies are carried out to explore the phenomenon in more detail. In light of these facts, this study is an exploratory effort to investigate the effect of Scratch programming on 5th grade primary school students' prob� lem solving skills. Moreover, the researchers wondered what 5th grade primary school students think about programming. This study was carried out in an explanatory sequential mixed methods design with the participation of 49 primary school students. According to the quantitative results, programming in Scratch platform did not cause any significant differences in the problem solving skills of the primary school students. There is only a non-significant increase in the mean of the fac � tor of "self- confidence in their problem solving ability". When the thoughts of the primary students were considered, it can be clearly stated that all the students liked programming and wanted to improve their programming. Finally, most of the students found the Scratch platform easy to use.


title: Analysis of configuration of planar cable-driven parallel robot on natural frequency

abstract: This paper studies the configuration of a planar cable-driven parallel robot to reduce the vibration of an end-effector. Since cables are more flexible than rigid links, external disturbances may cause a significant vibration during operation. The characteristics of the vibration are closely related to the natural frequency of the end-effector. And, we consider that the shapes of the frame and the end-effector, and the connection methods of the cables can affect the vibration of the cable-driven parallel robot. Therefore, the first natural frequencies in a required workspace are analyzed by the configuration change of a planar cable-driven parallel robot. The simulation shows that planar cable robots can be designed to reduce vibration by utilizing robots configurations.


title: The Effects of Boredom, Loneliness, and Distress Tolerance on Problem Internet Use Among University Students

abstract: The purpose of this study was to examine whether boredom proneness and/or loneliness predict problem internet use (PIU) and whether these possible associations are moderated by distress tolerance. The study used a sample of 169 undergraduate university students known to be regular internet users, and measured the impact of PIU on their life by examining the relationship between PIU and academic performance. As predicted, boredom proneness was significantly associated with PIU and was a significant predictor of PIU in a model that included loneliness and distress tolerance. Loneliness was also significantly associated with both boredom and PIU, but was not a significant predictor of PIU in the model. There was no evidence that distress tolerance moderated either of these associations. As predicted, higher levels of PIU were associated with lower levels of academic performance, leading us to the conclusion that university students who are prone to experiencing boredom tend to use the internet to seek out more stimulating and satisfying activities, which in turn can lead to problematic internet use patterns that can negatively affect their academic performance.


title: Self-consumption: the interplay of autophagy and apoptosis

abstract: Autophagy and apoptosis control the turnover of organelles and proteins within cells, and of cells within organisms, respectively. It is now clear that these processes often occur sequentially, and that crosstalk between the signalling pathways regulating them generally enables autophagy to block the induction of apoptosis, whereas apoptosis-associated caspase activation shuts off autophagy.


title: Towards Personality-driven Persuasive Health Games and Gamified Systems

abstract: Persuasive games and gamified systems are effective tools for motivating behavior change using various persuasive strategies. Research has shown that tailoring these systems can increase their efficacy. However, there is little knowledge on how game-based persuasive systems can be tailored to individuals of various personality traits. To advance research in this area, we conducted a large-scale study of 660 participants to investigate how different personalities respond to various persuasive strategies that are used in persuasive health games and gamified systems. Our results reveal that people's personality traits play a significant role in the perceived persuasiveness of different strategies. Conscientious people tend to be motivated by goal setting, simulation, self-monitoring and feedback; people who are more open to experience are more likely to be demotivated by rewards, competition, comparison, and cooperation. We contribute to the CHI community by offering design guidelines for tailoring persuasive games and gamified designs to a particular group of personalities.


title: Semi-global matching based disparity estimate using fast Census transform

abstract: The Census transform is very useful in stereo matching algorithms. But the traditional Census transform cost too much computation, and its accuracy and robustness are not satisfying. In this paper, a modified semi-global matching (SGM) algorithm based on the fast Census transform is proposed to improve the quality of the range map and decrease the computation. In addition, we modify the disparity refinement to enhance the quality of the estimated range map. Our experiments show that the proposed method performs better than the original SGM algorithm in terms of the accuracy and the anti-noise capability.


title: Multi-Task Pose-Invariant Face Recognition

abstract: Face images captured in unconstrained environments usually contain significant pose variation, which dramatically degrades the performance of algorithms designed to recognize frontal faces. This paper proposes a novel face identification framework capable of handling the full range of pose variations within ±90° of yaw. The proposed framework first transforms the original pose-invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is then developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the proposed multi-task learning scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace. Finally, face matching is performed at patch level rather than at the holistic level. Extensive and systematic experimentation on FERET, CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task-based baselines as well as state-of-the-art methods for the pose problem. We further extend the proposed algorithm for the unconstrained face verification problem and achieve top-level performance on the challenging LFW data set.


title: Cryptography: A Comparative Analysis for Modern Techniques

abstract: Cryptography plays a vital role for ensuring secure communication between multiple entities. In many contemporary studies, researchers contributed towards identifying best cryptography mechanisms in terms of their performance results. Selection of cryptographic technique according to a particular context is a big question; to answer this question, many existing studies have claimed that technique selection is purely dependent on desired quality attributes such as efficiency and security. It has been identified that existing reviews are either focused only towards symmetric or asymmetric encryption types. Another limitation is found that a criterion for performance comparisons only covers common parameters. In this paper, we have evaluated the performance of different symmetric and asymmetric algorithms by covering multiple parameters such as encryption/decryption time, key generation time and file size. For evaluation purpose, we have performed simulations in a sample context in which multiple cryptography algorithms have been compared. Simulation results are visualized in a way that clearly depicts which algorithm is most suitable while achieving a particular quality attribute.


Beliefs , perceptions and expectations among acne patients


title: High-Gain Wideband Partially Reflecting Surface Antenna for 60 GHz Systems

abstract: In this letter, a high-gain wideband antenna for 60 GHz wireless personal area networks is proposed. It consists of an inset-fed microstrip antenna with a partially reflecting surface (PRS) acting as a Fabry-Perot cavity. Good agreement was achieved between simulated and measured data, showing a wide bandwidth, covering two channels of the 60 GHz band and a maximum experimental gain of 16.4 dBi in the broadside direction at 60 GHz. The proposed structure meets the IEEE 802.15.3c standard requirements in terms of gain and bandwidth while the novel shape of the PRS array makes it smaller compared to existing 60 GHz Fabry-Perot antennas. Moreover, a low-cost printed circuit board technology is used to manufacture antenna prototype.


title: Fast graspability evaluation on single depth maps for bin picking with general grippers

abstract: We present a method that estimates graspability measures on a single depth map for grasping objects randomly placed in a bin. Our method represents a gripper model by using two mask images, one describing a contact region that should be filled by a target object for stable grasping, and the other describing a collision region that should not be filled by other objects to avoid collisions during grasping. The graspability measure is computed by convolving the mask images with binarized depth maps, which are thresholded differently in each region according to the minimum height of the 3D points in the region and the length of the gripper. Our method does not assume any 3-D model of objects, thus applicable to general objects. Our representation of the gripper model using the two mask images is also applicable to general grippers, such as multi-finger and vacuum grippers. We apply our method to bin picking of piled objects using a robot arm and demonstrate fast pick-and-place operations for various industrial objects.


title: Nanomaterials for Photo-Based Diagnostic and Therapeutic Applications

abstract: Photo-based diagnosis and treatment methods are gaining prominence due to increased spatial imaging resolution, minimally invasive modalities involved as well as localized treatment. Recently, nanoparticles (NPs) have been developed and used in photo-based therapeutic applications. While some nanomaterials have inherent photo-based imaging capabilities, others including polymeric NPs act as nanocarriers to deliver various fluorescent dyes or photosensitizers for photoimaging and therapeutic applications. These applications can vary from Magnetic Resonance Imaging (MRI) and optical imaging to photothermal therapy (PTT) and chemotherapy. Materials commonly used for development of photo-based NPs ranges from metal-based (gold, silver and silica) to polymer-based (chitosan, dextran, poly ethylene glycol (PEG) and poly lactic-co-glycolic acid (PLGA)). Recent research has paved the way for multi-modal 'theranostic' (a combination of therapy and diagnosis) nano-carriers capable of active targeting using cell-specific ligands and carrying multiple therapeutic and imaging agents for accurate diagnosis and controlled drug delivery. This review summarizes the different materials used today to synthesize photo-based NPs, their diagnostic and therapeutic applications as well as the current challenges faced in bringing these novel nano-carriers into clinical practices.


title: Going Deeper into First-Person Activity Recognition

abstract: We bring together ideas from recent work on feature design for egocentric action recognition under one framework by exploring the use of deep convolutional neural networks (CNN). Recent work has shown that features such as hand appearance, object attributes, local hand motion and camera ego-motion are important for characterizing first-person actions. To integrate these ideas under one framework, we propose a twin stream network architecture, where one stream analyzes appearance information and the other stream analyzes motion information. Our appearance stream encodes prior knowledge of the egocentric paradigm by explicitly training the network to segment hands and localize objects. By visualizing certain neuron activation of our network, we show that our proposed architecture naturally learns features that capture object attributes and hand-object configurations. Our extensive experiments on benchmark egocentric action datasets show that our deep architecture enables recognition rates that significantly outperform state-of-the-art techniques -- an average $6.6\%$ increase in accuracy over all datasets. Furthermore, by learning to recognize objects, actions and activities jointly, the performance of individual recognition tasks also increase by $30\%$ (actions) and $14\%$ (objects). We also include the results of extensive ablative analysis to highlight the importance of network design decisions..


title: User Acceptance of Computer Technology: A Comparison of Two Theoretical Models

abstract: Computer systems cannot improve organizational performance if they aren't used. Unfortunately, resistance to end-user systems by managers and professionals is a widespread problem. To better predict, explain, and increase user acceptance, we need to better understand why people accept or reject computers. This research addresses the ability to predict peoples' computer acceptance from a measure of their intentions, and the ability to explain their intentions in terms of their attitudes, subjective norms, perceived usefulness, perceived ease of use, and related variables. In a longitudinal study of 107 users, intentions to use a specific system, measured after a one-hour introduction to the system, were correlated 0.35 with system use 14 weeks later. The intention-usage correlation was 0.63 at the end of this time period. Perceived usefulness strongly influenced peoples' intentions, explaining more than half of the variance in intentions at the end of 14 weeks. Perceived ease of use had a small but significant effect on intentions as well, although this effect subsided over time. Attitudes only partially mediated the effects of these beliefs on intentions. Subjective norms had no effect on intentions. These results suggest the possibility of simple but powerful models of the determinants of user acceptance, with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology.


title: Swing-up and control of Rotary Inverted Pendulum using pole placement with integrator

abstract: Rotary Inverted Pendulum (RIP) is highly nonlinear, unstable, multivariable and an under actuated system in the field of control theory. RIP is in one of the imperative systems for testing various control technique. It is most helpful for testing Self tuning regulator kind of control technique. This paper describe steps to design controllers for swing up and stabilization of RIP using Counter based and pole placement with integrator respectively. Finally, experimental and MATLAB based simulation results are used to validate the performance of controllers. A mode controller is also used to switch between Swing up and stabilization controllers. Mode controller is implemented using a switch in simulink imposing angle condition of pendulum. It is used to decide which controller actuate the plant out of two mentioned above.


title: Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

abstract: Abstract Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.


Fast Hydraulic and Thermal Erosion on the GPU Balázs Jákó


title: Learning by analogy: Discriminating between potential analogs

abstract: The ability to successfully discriminate between multiple potentially relevant source analogs when solving new problems is crucial to proficiency in a mathematics domain. Experimental findings in two different mathematical contexts demonstrate that providing cues to support comparative reasoning during an initial instructional analogy, relative to teaching the same analogs and solution strategies without such cues, led to increased ability to discriminate between relevant analogs at a later test. Specifically, providing comparative gestures and visibly aligned source and target problems during initial learning led to higher rates of positive extension of learning to new contexts, and lower rates of susceptibility to misleading contextual features, both immediately and after a week delay.


title: Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network

abstract: The identification, segmentation and detection of infecting area in brain tumor MRI images are a tedious and time-consuming task. The different anatomy structure of human body can be visualized by an image processing concepts. It is very difficult to have vision about the abnormal structures of human brain using simple imaging techniques. Magnetic resonance imaging technique distinguishes and clarifies the neural architecture of human brain. MRI technique contains many imaging modalities that scans and capture the internal structure of human brain. In this study, we have concentrated on noise removal technique, extraction of gray-level co-occurrence matrix (GLCM) features, DWT-based brain tumor region growing segmentation to reduce the complexity and improve the performance. This was followed by morphological filtering which removes the noise that can be formed after segmentation. The probabilistic neural network classifier was used to train and test the performance accuracy in the detection of tumor location in brain MRI images. The experimental results achieved nearly 100% accuracy in identifying normal and abnormal tissues from brain MR images demonstrating the effectiveness of the proposed technique.


title: A hybrid Genetic Algorithm for Vehicle Routing Problem with Time Windows

abstract: The Vehicle Routing Problem with Time Windows (VRPTW) consists of a homogenous set of vehicles and a set of customer located in a city. In VRPTW all the vehicles starts from the depot visit the customer and end at the depot. Each customer is visited exactly by one vehicle within the specified time window. The objective is to minimize the number of vehicles and total distance travelled simultaneously. This represents the multiobjective Vehicle Routing Problem with Time Windows. The proposed work consists of Hybrid Genetic Search with Diversity Control using the Genetic Algorithm for solving the VRPTW. The Pareto approach is used for finding the set of optimal solutions for achieving the multiobjective. The crossover operator is used for exchanging the best routes, which have shortest distance. Two mutation operators such as relocation mutation operator and split mutation operator were used in this application. In this, it accounts penalty for an infeasible solutions with respect to time-window and duration constrains. The computations are performed using the instances which are obtained from the VRPLIB.


title: Strategies for designing effective psychotherapeutic gaming interventions for children and adolescents

abstract: A range of face-to-face therapies and interventions for children and adolescents with mental health problems have been developed over the years and include cognitive-behavioral therapy, play therapy and applied behavior analysis. The popularity of computer games has grown exponentially in the last decade and has been widely accepted by children, adolescents and adults alike. Mental health professionals have therefore been exploring the use of these games to complement traditional treatment methods. To date however, there has been little known concrete evidence of the effectiveness of computer games for the treatment of children and adolescents with mental health conditions. Key to the success of such games is that at the outset, they must be well-designed. This paper reviews extant relevant computer gaming literature to propose a set of guidelines and strategies for the design of psychotherapeutic games targeted at children and adolescents. The issues raised concern both the game player as well as the game itself. As part of this review, limitations of existing work and areas of future research are also discussed.


title: Automata Guided Reinforcement Learning With Demonstrations

abstract: Tasks with complex temporal structures and long horizons pose a challenge for reinforcement learning agents due to the difficulty in specifying the tasks in terms of reward functions as well as large variances in the learning signals. We propose to address these problems by combining temporal logic (TL) with reinforcement learning from demonstrations. Our method automatically generates intrinsic rewards that align with the overall task goal given a TL task specification. The policy resulting from our framework has an interpretable and hierarchical structure. We validate the proposed method experimentally on a set of robotic manipulation tasks.


title: Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People

abstract: This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities.


title: Vascusynth: Simulating vascular trees for generating volumetric image data with ground truth segmentation and tree analysis

abstract: Automated segmentation and analysis of tree-like structures from 3D medical images are important for many medical applications, such as those dealing with blood vasculature or lung airways. However, there is an absence of large databases of expert segmentations and analyses of such 3D medical images, which impedes the validation and training of proposed image analysis algorithms. In this work, we simulate volumetric images of vascular trees and generate the corresponding ground truth segmentations, bifurcation locations, branch properties, and tree hierarchy. The tree generation is performed by iteratively growing a vascular structure based on a user-defined (possibly spatially varying) oxygen demand map. We describe the details of the algorithm and provide a variety of example results.


title: Smartphone Addiction in University Students and Its Implication for Learning

abstract: As smartphones are getting popular, a concern for smartphone learner’s addiction to their phones has been raised together with the possibility of Smart Learning. This research is focused on the level of university students’ addiction to their smartphones and to understand the difference between self-regulated learning, learning flow, based on smartphone addiction level. After 210 students of university students in Seoul were participated in this research, it has been found that the higher the addiction level is, the lower level of self -regulated learning the students have, as well as low level of flow when studying. Further interview for smartphone addiction group was conducted, it has been found that the smartphone addict—learners are constantly interrupted by the other applications on the phones when they are studying, and does not have enough control over their smartphone learning plan and its process.


title: E-conic: a perspective-aware interface for multi-display environments

abstract: Multi-display environments compose displays that can be at different locations from and different angles to the user; as a result, it can become very difficult to manage windows, read text, and manipulate objects. We investigate the idea of perspective as a way to solve these problems in multi-display environments. We first identify basic display and control factors that are affected by perspective, such as visibility, fracture, and sharing. We then present the design and implementation of E-conic, a multi-display multi-user environment that uses location data about displays and users to dynamically correct perspective. We carried out a controlled experiment to test the benefits of perspective correction in basic interaction tasks like targeting, steering, aligning, pattern-matching and reading. Our results show that perspective correction significantly and substantially improves user performance in all these tasks.


DETERMINING THE RATE OF VIOLENCE SPREAD AND THE SOCIAL FACTORS AFFECTING IT (A CASE STUDY OF AJABSHIR HIGH SCHOOLS, BOY AND GIRL STUDENTS)


title: Combining organic and printed electronics in Hybrid System in Foil (HySiF) based smart skin for robotic applications

abstract: Hybrid System in Foil (HySiF) technology has the potential to combine compact high-performance circuit integration on thin, flexible silicon chips with thin-film large-area components jointly on a polymer foil substrate. In this manuscript, such a HySiF technology demonstrator known as "Smart Skin" is presented. It contains bending stress sensors along with other modular technology components, such as display, wireless communication and thin-film battery, implemented in a thin foil substrate mounted onto a bionic adaptive robot gripper. For demonstration purposes, we have opted for two distinctly different smart-skin systems, i.e. one connected by wires and the second working autonomously. Here, we have presented the fabrication details and test results of the first mentioned fully functional smart-skin demonstrator. For the second generation of smart skin, the feasibility of combining a matrix of thin-film strain gauges and organic thin-film transistors (OTFTs) along with embedding a silicon chip inside the foil is evaluated.


SparkGalaxy : Workflow-based Big Data Processing


title: Fundamental characteristics of electrostatic wafer chuck with insulating sealant

abstract: In the semiconductor industry, many manufacturing processes, such as CVD or dry etching, are performed in vacuum condition. The electrostatic wafer chuck is the most preferable handling method under such circumstances. It enables retention of a wafer flat and enhanced heat transfer through the whole surface area because the wafer can firmly contact with the chuck. We have investigated the fundamental study of an electrostatic chuck with comb type electrodes and a thin dielectric film. In order to remove the air gap between them, silicone oil is used as a filler to prevent breakdown. The experimental results proved the potential to use the electrostatic chuck for silicon wafer handling. There, however, is a problem which comes from using silicone oil as an insulating filler. The thin dielectric film is easily deformed by tension when the object starts moving. In this report experimental results of the electrostatic wafer chuck are shown when insulating sealant, instead of silicone oil, is used. The electrostatic force acting on the 4 inch silicon wafer is examined with several types of sealant and dielectric films. The electrostatic force increased with the square of the applied voltage for lower voltage and gradually saturated at higher voltage, and the maximum force obtained was approximately 30 N.


title: Toward constructing evidence-based legal arguments using legal decision documents and machine learning

abstract: This paper explores how to extract argumentation-relevant information automatically from a corpus of legal decision documents, and how to build new arguments using that information. For decision texts, we use the Vaccine/Injury Project (V/IP) Corpus, which contains default-logic annotations of argument structure. We supplement this with presuppositional annotations about entities, events, and relations that play important roles in argumentation, and about the level of confidence that arguments would be successful. We then propose how to integrate these semantic-pragmatic annotations with syntactic and domain-general semantic annotations, such as those generated in the DeepQA architecture, and outline how to apply machine learning and scoring techniques similar to those used in the IBM Watson system for playing the Jeopardy! question-answer game. We replace this game-playing goal, however, with the goal of learning to construct legal arguments.


title: Networks in Cognitive Science

abstract: Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex-systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.


title: Efficient Seafloor Classification and Submarine Cable Route Design Using an Autonomous Underwater Vehicle

abstract: This work presents an efficient method for designing submarine cable routes using online seafloor classification based on sidescan sonar scanlines, collected by an autonomous underwater vehicle (AUV). Currently, the cable routes are designed manually by experienced experts. Online seafloor classification and an automated route planning method using an AUV can improve the efficiency of construction of submarine cables. Sidescan sonar is a device commonly used for mapping the seafloor and detecting obstacles. An AUV equipped with a sidescan sonar is used to gather sonar scanlines to perform online seafloor classification and mapping. As the AUV operates, the gathered scanlines are analyzed to classify the seafloor using a probabilistic classifier based on Bayes' theorem with the naive assumption. Based on the seafloor classification map, a probabilistic roadmap is generated. Then, an A* algorithm is applied to determine appropriate cable routes along the cable corridor, using the following five factors: seafloor classification, bathymetry, steepness of slope, angle of change of course, and cable length. Compared with an existing route planning method, the presented method of using an AUV with automatic decision making algorithm can efficiently support submarine cable construction. The favored route determined by the presented method was as acceptable as the route designed by experts.


title: Phantom limb pain: A nursing perspective

abstract: Phantom limb pain (PLP) is a neuropathic pain condition occurring after amputation of a limb. PLP affects amputees’ quality of life and results in loss of productivity and psychological distress. The origin of pain from a non-existing limb creates a challenging situation for both patients and nurses. It is imperative to provide patients and nurses with the knowledge that PLP is a real phenomenon that requires care and treatment. This knowledge will lead to reduced problems for patients by allowing them to talk about PLP and ask for help when needed. Understanding of this phenomenon will enable nurses to appreciate the unique features of this form of neuropathic pain and apply appropriate techniques to promote effective pain management. Performing accurate and frequent assessments to understand the unique characteristics of PLP, displaying a non-judgemental attitude towards patients and teaching throughout the peri-operative process are significant nursing interventions.


title: SPARQL Query Parallel Processing: A Survey

abstract: In this paper, we survey current parallel processing approaches for SPARQL queries in RDF data. This survey focuses on three important aspects of current 15 SPARQL query engines in performance, namely, system architectures, RDF data storage management, and SPARQL query executive strategies. We classify those 15 engines as three classes of architectures (i.e., cluster, partition, and federation), three kinds of storage (i.e., partition, graph, and DBMS) and two query executive strategies (i.e., partition and DBMS), respectively. This investigation shows that each aspect (architecture, storage, and query) is a key factor of SPARQL query performance. For instance, in data storage, we evaluate the performance of three latest SPARQL query engines over the LUBM benchmark with different storage, namely, TriAD (based on partition), gStoreD (based on graph), S2RDF (based on DBMS) and then the experiments show that TriAD outperforms the other two engines. On the other hand, S2RDF can support Spark which is proved to be an efficient parallel computing architecture. We wonder to know whether there is some partition-based storage like to support Spark since the partition-based storage of TriAD exhibits a high performance. To answer this question, it is interesting to investigate the relations among the three factors of SPARQL query parallel processing. In this sense, our survey would be helpful in inspiring some new ideas in developing more efficient SPARQL query engines.


title: Translation Modeling with Bidirectional Recurrent Neural Networks

abstract: This work presents two different translation models using recurrent neural networks. The first one is a word-based approach using word alignments. Second, we present phrase-based translation models that are more consistent with phrasebased decoding. Moreover, we introduce bidirectional recurrent neural models to the problem of machine translation, allowing us to use the full source sentence in our models, which is also of theoretical interest. We demonstrate that our translation models are capable of improving strong baselines already including recurrent neural language models on three tasks: IWSLT 2013 German!English, BOLT Arabic!English and Chinese!English. We obtain gains up to 1.6% BLEU and 1.7% TER by rescoring 1000-best lists.


title: Multipole Methods For Photonic Crystal Calculations

abstract: There are a variety of general theories for computing band structures of photonic crystals, including finite difference methods[1], plane wave methods[2], and multipole methods[3]. While the finite difference and plane wave methods are more general in terms of the range of geometries to which they can be applied, the multipole (or Rayleigh) methods are computationally more efficient and superior in terms of their analytic tractability, coming into their own in derivations of theoretical modelling in areas such as long-wavelength localization and homogenization, and in predicting numerically-difficult outcomes (e.g. anomalously large absorption in crystals with fine metallic wires). In this paper, we outline the development of a multipole theory in a manner applicable to both finite and infinite, periodic structures. The former is applied in the computation of the local density of states (which gives emission probability as a function of position), while the latter is used in the computation of the band structure (including complex gap states) of photonic crystals and studies of disordered media, localization and homogenization.


title: Service Usage Classification with Encrypted Internet Traffic in Mobile Messaging Apps

abstract: The rapid adoption of mobile messaging Apps has enabled us to collect massive amount of encrypted Internet traffic of mobile messaging. The classification of this traffic into different types of in-App service usages can help for intelligent network management, such as managing network bandwidth budget and providing quality of services. Traditional approaches for classification of Internet traffic rely on packet inspection, such as parsing HTTP headers. However, messaging Apps are increasingly using secure protocols, such as HTTPS and SSL, to transmit data. This imposes significant challenges on the performances of service usage classification by packet inspection. To this end, in this paper, we investigate how to exploit encrypted Internet traffic for classifying in-App usages. Specifically, we develop a system, named CUMMA, for classifying service usages of mobile messaging Apps by jointly modeling user behavioral patterns, network traffic characteristics, and temporal dependencies. Along this line, we first segment Internet traffic from traffic-flows into sessions with a number of dialogs in a hierarchical way. Also, we extract the discriminative features of traffic data from two perspectives: (i) packet length and (ii) time delay. Next, we learn a service usage predictor to classify these segmented dialogs into single-type usages or outliers. In addition, we design a clustering Hidden Markov Model (HMM) based method to detect mixed dialogs from outliers and decompose mixed dialogs into sub- dialogs of single-type usage. Indeed, CUMMA enables mobile analysts to identify service usages and analyze end-user in-App behaviors even for encrypted Internet traffic. Finally, the extensive experiments on real-world messaging data demonstrate the effectiveness and efficiency of the proposed method for service usage classification.


Personality and Individual Differences, in press Multidimensional Perfectionism and Narcissism: Grandiose or Vulnerable?


title: L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks

abstract: Batch Normalization (BN) has been proven to be quite effective at accelerating and improving the training of deep neural networks (DNNs). However, BN brings additional computation, consumes more memory and generally slows down the training process by a large margin, which aggravates the training effort. Furthermore, the nonlinear square and root operations in BN also impede the low bit-width quantization techniques, which draws much attention in deep learning hardware community. In this work, we propose an L1-norm BN (L1BN) with only linear operations in both the forward and the backward propagations during training. L1BN is shown to be approximately equivalent to the original L2-norm BN (L2BN) by multiplying a scaling factor. Experiments on various convolutional neural networks (CNNs) and generative adversarial networks (GANs) reveal that L1BN maintains almost the same accuracies and convergence rates compared to L2BN but with higher computational efficiency. On FPGA platform, the proposed signum and absolute operations in L1BN can achieve 1.5$\times$ speedup and save 50\% power consumption, compared with the original costly square and root operations, respectively. This hardware-friendly normalization method not only surpasses L2BN in speed, but also simplify the hardware design of ASIC accelerators with higher energy efficiency. Last but not the least, L1BN promises a fully quantized training of DNNs, which is crucial to future adaptive terminal devices.


title: Bootstrapped Named Entity Recognition for Product Attribute Extraction

abstract: We present a named entity recognition (NER) system for extracting product attributes and values from listing titles. Information extraction from short listing titles present a unique challenge, with the lack of informative context and grammatical structure. In this work, we combine supervised NER with bootstrapping to expand the seed list, and output normalized results. Focusing on listings from eBay's clothing and shoes categories, our bootstrapped NER system is able to identify new brands corresponding to spelling variants and typographical errors of the known brands, as well as identifying novel brands. Among the top 300 new brands predicted, our system achieves 90.33% precision. To output normalized attribute values, we explore several string comparison algorithms and found n-gram substring matching to work well in practice.


title: Is muscle power related to running speed with changes of direction

abstract: Background. The purpose of this study was to identify the relationships between leg muscle power and sprinting speed with changes of direction. Methods. Experimental design: the study was designed to describe relationships between physical qualities and a component of sports performance. Setting: testing was conducted in an indoor sports hall and a biomechanics laboratory. Participants: 15 male participants were required to be free of injury and have recent experience competing in sports involving sprints with changes of direction. Measures: subjects were timed in 8 m sprints in a straight line and with various changes of direction. They were also tested for bilateral and unilateral leg extensor muscle concentric power output by an isokinetic squat and reactive strength by a drop jump. Results. The correlations between concentric power and straight sprinting speed were non-significant whereas the relationships between reactive strength and straight speed were statistically significant. Correlations between muscle power and speed while changing direction were generally low and non-significant for concentric leg power with some moderate and significant (p<0.05) coefficients found for reactive strength. The participants who turned faster to one side tended to have a reactive strength dominance in the leg responsible for the push-off action. Conclusions. The relationships between leg muscle power and change-of-direction speed were not consistent. Reactive strength as measured by the drop jump appears to have some importance for lateral change-of-direction speed, possibly because of similar push-off actions. It was concluded that reactive strength of the leg extensor muscles has some importance in change-of-direction performance but the other technical and perceptual factors than influence agility performance should also be considered.


title: Case report: Etizolam and its major metabolites in two unnatural death cases.

abstract: A simultaneous analytical method for etizolam and its main metabolites (alpha-hydroxyetizolam and 8-hydroxyetizolam) in whole blood was developed using solid-phase extraction, TMS derivatization and ion trap gas chromatography tandem mass spectrometry (GC-MS/MS). Separation of etizolam, TMS derivatives of alpha-hydroxyetizolam and 8-hydroxyetizolam and fludiazepam as internal standard was performed within about 17 min. The inter-day precision evaluated at the concentration of 50 ng/mL etizolam, alpha-hydroxyetizolam and 8-hydroxyetizolam was evaluated 8.6, 6.4 and 8.0% respectively. Linearity occurred over the range in 5-50 ng/mL. This method is satisfactory for clinical and forensic purposes. This method was applied to two unnatural death cases suspected to involve etizolam. Etizolam and its two metabolites were detected in these cases.


title: A visual chaotic system simulation in Arduino platform controlled by Android app

abstract: The application of computer and electronic technologies has grown extensively in recent years being a useful tool for teaching in various fields of knowledge. The use of these technologies aims at bridging the gap between theory and practice, improving learning outcomes. This work demonstrates the project of didactic prototype based on the Arduino platform to study and teach nonlinear dynamical systems. The teaching platform presented here aims at providing visual effects and better understanding of basic concepts of nonlinear dynamical systems, such as chaos. It is also demonstrated when the data loses the precision, also in a visual setup. Additionally, input data may be configured by wireless communication, which makes it very attractive for young students.


title: An Integrated Method of Multiradar Quantitative Precipitation Estimation Based on Cloud Classification and Dynamic Error Analysis

abstract: Relationships between radar reflectivity factor and rainfall are different in various precipitation cloud systems. In this study, the cloud systems are firstly classified into five categories with radar and satellite data to improve radar quantitative precipitation estimation (QPE) algorithm. Secondly, the errors of multiradar QPE algorithms are assumed to be different in convective and stratiform clouds. The QPE data are then derived with methods of -, Kalman filter (KF), optimum interpolation (OI), Kalman filter plus optimum interpolation (KFOI), and average calibration (AC) based on error analysis on the Huaihe River Basin. In the case of flood on the early of July 2007, the KFOI is applied to obtain the QPE product. Applications show that the KFOI can improve precision of estimating precipitation for multiple precipitation types.


title: Loss Sensitivity and Concreteness as Principles of Integration of Prior Outcomes in Risky Decisions

abstract: An experiment was conducted to contrast cognitive and motivational explanations of why a decision maker integrates a prior outcome with the expected outcomes of a current choice. Undergraduate students imagined that, on a fictitious day at a racetrack, they did not gamble in the prior race, that they gambled and won, or that they gambled and lost. Subjects rated in one session the likelihood of choosing to gamble in the current race. In another session, they rated how satisfied they would be with not gambling, with gambling and winning, and with gambling and losing, respectively. In support of a concreteness principle based on the assumption that cognitive effort is minimised, the results showed more integration of the prior outcome for descriptions of the bets which concretely integrated the prior outcomes with all the outcomes of the current choice than for descriptions which did not do that. A motivational explanation labelled the “loss-sensitivity principle” was also empirically supported in that, irr...


title: Do-it-yourself cellphones: an investigation into the possibilities and limits of high-tech diy

abstract: This paper describes our do-it-yourself cellphone and our use of it to investigate the possibilities and limits of high-tech DIY practice. We describe our autobiographical approach -- making the phone and using it in our daily lives -- and our work disseminating the cellphone in workshops and online. This informs a discussion of the implications of technology for DIY practice. We suggest an understanding of DIY as an individual's ability to combine existing technologies into a desired product, enabled and limited by ecosystems of industrial actors and individuals. We distinguish different pathways into high-tech DIY practice, consider the relationship between prototyping and production, and discuss the effect of technology on DIY's relevance and tools, and on notions of transparency. We conclude by reflecting on the relationship between DIY and empowerment: the extent to which making devices gives people control over the technology in their lives.


title: Assessing assumptions in kinematic hand models: A review

abstract: The incredible complexity of the human hand makes accurate modeling difficult. When implementing a kinematic hand model, many simplifications are made, either to provide simpler analytical solutions, to ease implementation, or to speed up computation for real time applications. However, it is important to understand the trade-offs that certain simplifications entail — the kinematic structure chosen can have important implications for the final model accuracy. This paper provides a brief overview of the biomechanics of the human hand, followed by an in-depth review of kinematic models presented in the literature. This review discusses some simplifications that may often be inappropriate, such as assuming no metacarpal bone motion or assuming orthogonal, intersecting thumb axes. This discussion should help researchers select appropriate kinematic models for applications including anthropomorphic hand design, humancomputer interaction, surgery, rehabilitation, and ergonomics. Some modeling issues remain unclear in the current literature. Future work could compare thumb MCP models and better investigate unactuated compliant degrees of freedom in the hand.


title: Combining unit-level symbolic execution and system-level concrete execution for testing nasa software

abstract: We describe an approach to testing complex safety critical software that combines unit-level symbolic execution and system-level concrete execution for generating test cases that satisfy user-specified testing criteria. We have developed Symbolic Java PathFinder, a symbolic execution framework that implements a non-standard bytecode interpreter on top of the Java PathFinder model checking tool. The framework propagates the symbolic information via attributes associated with the program data. Furthermore, we use two techniques that leverage system-level concrete program executions to gather information about a unit's input to improve the precision of the unit-level test case generation. We applied our approach to testing a prototype NASA flight software component. Our analysis helped discover a serious bug that resulted in design changes to the software. Although we give our presentation in the context of a NASA project, we believe that our work is relevant for other critical systems that require thorough testing.


title: Stereo-based road boundary tracking for mobile robot navigation

abstract: This paper describes a method of stereo-based road boundary tracking for mobile robot navigation. Since sensory evidence for road boundaries might change from place to place, we cannot depend on a single cue but have to use multiple sensory features. The method uses color, edge, and height information obtained from a single stereo camera. To cope with a variety of road types and shapes and that of their changes, we adopt a particle filter in which road boundary hypotheses are represented by particles. The proposed method has been tested in various road scenes and conditions, and verified to be effective for autonomous driving of a mobile robot.


title: Metamemory: A Theoretical Framework and New Findings

abstract: Publisher Summary This chapter focuses on research program, providing a description of a theoretical framework that has evolved out of metamemory research, followed by a few remarks about the methodology. Research in metamemory is initiated by the paradoxical findings that people can accurately predict their subsequent likelihood of recognizing nonrecallable items and that they can quickly and accurately decide-on the basis of no more than a cursory search through memory-that they will not retrieve particular sought after items. Those findings lead to develop a methodology based on psychophysical methods that are used to empirically investigate people's feeling of knowing. The results of the experiments convinced that for dealing with only a part of a complex metacognitive system and to account adequately for feeling-of-knowing phenomena, a larger perspective was needed. This eventuated in the present theoretical framework that emphasizes the role of control and monitoring processes. The embedding of the feeling of knowing in a richer framework helped to dissipate the paradoxical nature of the feeling of knowing. The chapter discusses that today there are many capable, active investigators and a wealth of solid empirical findings.


title: Novel UWB Bandpass Filter Using Stub-Loaded Multiple-Mode Resonator

abstract: Novel compact ultra-wideband (UWB) bandpass filter (BPF) using stub-loaded multiple-mode resonator (MMR) is presented in this letter. The MMR is constructed by loading three open stubs in a uniform-impedance resonator, i.e., one stepped-impedance stub at the center and two uniform-impedance stubs at the symmetrical side locations. Five modes, including two odd modes and three even modes, could be designed within UWB band, and two transmission zeros generated by the stepped-impedance stub improve the passband selectivity greatly. The letter shows modal resonant frequencies against the step-impedance-stub-loaded parameters, which can control the even modes flexibly, while the odd modes remain the same. A compact planar UWB BPF is simulated, fabricated and measured. The simulated and measured results are in good agreement and show good in-band filtering performance and sharp selectivity.


title: How Does Fiction Reading Influence Empathy? An Experimental Investigation on the Role of Emotional Transportation

abstract: The current study investigated whether fiction experiences change empathy of the reader. Based on transportation theory, it was predicted that when people read fiction, and they are emotionally transported into the story, they become more empathic. Two experiments showed that empathy was influenced over a period of one week for people who read a fictional story, but only when they were emotionally transported into the story. No transportation led to lower empathy in both studies, while study 1 showed that high transportation led to higher empathy among fiction readers. These effects were not found for people in the control condition where people read non-fiction. The study showed that fiction influences empathy of the reader, but only under the condition of low or high emotional transportation into the story.


title: Sentence Emotion Analysis and Recognition Based on Emotion Words Using Ren-CECps ∗

abstract: Emotion recognition on text has wide applications. In this study, we make an analysis on sentence emotion based on emotion words using Ren-CECps (a Chinese emotion corpus). Some classification methods (including C4.5 decision tree, SVM, NaiveBayes, ZEROR, and DecisionTable) have been compared. Then a supervised machine learning method (Polynomial kernel method) is proposed to recognize the eight basic emotions (Expect, Joy, Love, Surprise, Anxiety, Sorrow, Angry and Hate). Using Ren-CECps, we get the emotion lexicons for the eight basic emotions. Polynomial kernel (PK) method is used to compute the similarities between sentences and the eight emotion lexicons. Then the experiential knowledge derived from Ren-CECps is used to recognize whether the eight emotion categories are present in a sentence. The experiments showed promising results.


Healthcare Insurance Fraud Detection Leveraging Big Data Analytics


Measurement 232 Th ( n , γ ) reaction cross-section at E n = 17 . 28 MeV


title: Fast Image Classification by Boosting Fuzzy Classifiers

abstract: This paper presents a novel approach to visual objects classification based on generating simple fuzzy classifiers using local image features to distinguish between one known class and other classes. Boosting meta learning is used to find the most representative local features. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives better classification accuracy and the time of learning and testing process is more than 30% shorter.


title: Auxetic metamaterial simplifies soft robot design

abstract: Soft materials are being adopted in robotics in order to facilitate biomedical applications and in order to achieve simpler and more capable robots. One route to simplification is to design the robot's body using ‘smart materials’ that carry the burden of control and actuation. Metamaterials enable just such rational design of the material properties. Here we present a soft robot that exploits mechanical metamaterials for the intrinsic synchronization of two passive clutches which contact its travel surface. Doing so allows it to move through an enclosed passage with an inchworm motion propelled by a single actuator. Our soft robot consists of two 3D-printed metamaterials that implement auxetic and normal elastic properties. The design, fabrication and characterization of the metamaterials are described. In addition, a working soft robot is presented. Since the synchronization mechanism is a feature of the robot's material body, we believe that the proposed design will enable compliant and robust implementations that scale well with miniaturization.


title: Mechanisms of the Anatomically Correct Testbed Hand

abstract: We have built an anatomically correct testbed (ACT) hand with the purpose of understanding the intrinsic biomechanical and control features in human hands that are critical for achieving robust, versatile, and dexterous movements, as well as rich object and world exploration. By mimicking the underlying mechanics and controls of the human hand in a hardware platform, our goal is to achieve previously unmatched grasping and manipulation skills. In this paper, the novel constituting mechanisms, unique muscle to joint relationships, and movement demonstrations of the thumb, index finger, middle finger, and wrist of the ACT Hand are presented. The grasping and manipulation abilities of the ACT Hand are also illustrated. The fully functional ACT Hand platform allows for the possibility to design and experiment with novel control algorithms leading to a deeper understanding of human dexterity.


title: Clustering by compression

abstract: We present a new method for clustering based on compression. The method does not use subject-specific features or background knowledge, and works as follows: First, we determine a parameter-free, universal, similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal. However, the optimality comes at the price of using the noncomputable notion of Kolmogorov complexity. We propose axioms to capture the real-world setting, and show that the NCD approximates optimality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (ternary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics, we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.


title: Infinite Wavelength Resonant Antennas With Monopolar Radiation Pattern Based on Periodic Structures

abstract: The analysis of resonant-type antennas based on the fundamental infinite wavelength supported by certain periodic structures is presented. Since the phase shift is zero for a unit-cell that supports an infinite wavelength, the physical size of the antenna can be arbitrary; the antenna's size is independent of the resonance phenomenon. The antenna's operational frequency depends only on its unit-cell and the antenna's physical size depends on the number of unit-cells. In particular, the unit-cell is based on the composite right/left-handed (CRLH) metamaterial transmission line (TL). It is shown that the CRLH TL is a general model for the required unit-cell, which includes a nonessential series capacitance for the generation of an infinite wavelength. The analysis and design of the required unit-cell is discussed based upon field distributions and dispersion diagrams. It is also shown that the supported infinite wavelength can be used to generate a monopolar radiation pattern. Infinite wavelength resonant antennas are realized with different number of unit-cells to demonstrate the infinite wavelength resonance


title: Generating Extractive Summaries of Scientific Paradigms

abstract: Researchers and scientists increasingly find themselves in the position of having to quickly understand large amounts of technical material. Our goal is to effectively serve this need by using bibliometric text mining and summarization techniques to generate summaries of scientific literature. We show how we can use citations to produce automatically generated, readily consumable, technical extractive summaries. We first propose C-LexRank, a model for summarizing single scientific articles based on citations, which employs community detection and extracts salient information-rich sentences. Next, we further extend our experiments to summarize a set of papers, which cover the same scienti fic topic. We generate extractive summaries of a set of Question Answering (QA) and Dependency Parsing (DP) papers, their abstracts, and their citation sentences and show that citations have unique information amenable to creating a summary.


title: Design Recommendations for Self-Monitoring in the Workplace : Studies in Software Development

abstract: One way to improve the productivity of knowledge workers is to increase their self-awareness about productivity at work through self-monitoring. Yet, little is known about expectations of, the experience with, and the impact of self-monitoring in the workplace. To address this gap, we studied software developers, as one community of knowledge workers. We used an iterative, user-feedback-driven development approach (N=20) and a survey (N=413) to infer design elements for workplace self-monitoring, which we then implemented as a technology probe called WorkAnalytics. We field-tested these design elements during a three-week study with software development professionals (N=43). Based on the results of the field study, we present design recommendations for self-monitoring in the workplace, such as using experience sampling to increase the awareness about work and to create richer insights, the need for a large variety of different metrics to retrospect about work, and that actionable insights, enriched with benchmarking data from co-workers, are likely needed to foster productive behavior change and improve collaboration at work. Our work can serve as a starting point for researchers and practitioners to build self-monitoring tools for the workplace.


title: ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks

abstract: Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, recently proposed ResNet architecture and its modifications produce state-of-the-art results in image classification problems. ResNet and most of the previously proposed architectures have a fixed structure and apply the same transformation to all input images. In this work, we develop a ResNet-based model that dynamically selects Computational Units (CU) for each input object from a learned set of transformations. Dynamic selection allows the network to learn a sequence of useful transformations and apply only required units to predict the image label. We compare our model to ResNet-38 architecture and achieve better results than the original ResNet on CIFAR-10.1 test set. While examining the produced paths, we discovered that the network learned different routes for images from different classes and similar routes for similar images.


title: Autonomous Electric Vehicle Using Ultrasonic Sensor Skirt Approach

abstract: The proposed work deals with the designing of a self-driven electric car used to provide automatic shuttle services within an industrial site or even college campus. In this paper, a system is designed to be used within college premises for various multi-disciplinary activities. The vehicle can move around without any manual guidance. The major task in the designing of an autonomous vehicle is to provide it with vision so that it can move around in the environment with ease. The proposed idea uses various electronic sensors to accomplish this task. It is capable of taking decisions which include avoiding of static or dynamic obstacles in between the travel path. Based on these decisions, the further movement of the vehicle is decided. The decision-making ability lies with the logic and controller system of the vehicle. The system also performs the function of detection of side walls to follow a straight path. The vehicle will decide the path based on the user defined destination point. The travel path is mapped using GPS system. The proposed system uses battery power which can be charged using solar energy via solar panels. This work gives a complete description about the system design of an autonomous electrically driven vehicle having a capacity of carrying 4 persons.


title: Evaluation of bedside shift report: A research and evidence-based practice initiative

abstract: This evaluation of bedside shift report describes the process of involving clinical nurses in evidence-based practice (EBP) and research at an academic medical center by using existing structures and resources. Nurse involvement and study findings are described from idea inception to asking the clinical question, searching and synthesizing literature, collecting and analyzing data, interpreting results, and deriving conclusions. Study findings and conclusions demonstrate that nurses' active participation in a clinical relevant project promotes implementation and integration of EBP and research in the practice setting.


title: Parameterless Bat Algorithm and Its Performance Study

abstract: A parameter-free or parameterless bat algorithm is a new variant of the bat algorithm which was recently introduced. Characteristic of this algorithm is that user does not need to specify the control parameters when running this algorithm. Thus, this bat algorithm variant can have wide usability in solving real-world optimization problems. In this chapter, a preliminary study of the proposed parameterless bat algorithm is presented.


title: Collision-streams: fast GPU-based collision detection for deformable models

abstract: We present a fast GPU-based streaming algorithm to perform collision queries between deformable models. Our approach is based on hierarchical culling and reduces the computation to generating different streams. We present a novel stream registration method to compact the streams and efficiently compute the potentially colliding pairs of primitives. We also use a deferred front tracking method to lower the memory overhead. The overall algorithm has been implemented on different GPUs and we have evaluated its performance on non-rigid and deformable simulations. We highlight our speedups over prior CPU-based and GPU-based algorithms. In practice, our algorithm can perform inter-object and intra-object computations on models composed of hundreds of thousands of triangles in tens of milliseconds.