title: Actions speak as loud as words: Predicting relationships from social behavior data

abstract: In recent years, new studies concentrating on analyzing user personality and finding credible content in social media have become quite popular. Most such work augments features from textual content with features representing the user's social ties and the tie strength. Social ties are crucial in understanding the network the people are a part of. However, textual content is extremely useful in understanding topics discussed and the personality of the individual. We bring a new dimension to this type of analysis with methods to compute the type of ties individuals have and the strength of the ties in each dimension. We present a new genre of behavioral features that are able to capture the "function" of a specific relationship without the help of textual features. Our novel features are based on the statistical properties of communication patterns between individuals such as reciprocity, assortativity, attention and latency. We introduce a new methodology for determining how such features can be compared to textual features, and show, using Twitter data, that our features can be used to capture contextual information present in textual features very accurately. Conversely, we also demonstrate how textual features can be used to determine social attributes related to an individual.


title: An LCD column driver using a switch capacitor DAC

abstract: LCD column drivers have traditionally used nonlinear R-string style digital-to-analog converters (DAC). This paper describes an architecture that uses 840 linear charge redistribution 10/12-bit DACs to implement a 420-output column driver. Each DAC performs its conversion in less than 15 /spl mu/s and draws less than 5 /spl mu/A. This architecture allows 10-bit independent color control in a 17 mm/sup 2/ die for the LCD television market.


title: Random walks on adjacency graphs for mining lexical relations from big text data

abstract: Lexical relations, or semantic relations of words, are useful knowledge fundamental to all applications since they help to capture inherent semantic variations of vocabulary in human languages. Discovering such knowledge in a robust way from arbitrary text data is a significant challenge in big text data mining. In this paper, we propose a novel general probabilistic approach based on random walks on word adjacency graphs to systematically mine two fundamental and complementary lexical relations, i.e., paradigmatic and syntagmatic relations between words from arbitrary text data. We show that representing text data as an adjacency graph opens up many opportunities to define interesting random walks for mining lexical relation patterns, and propose specific random walk algorithms for mining paradigmatic and syntagmatic relations. Evaluation results on multiple corpora show that the proposed random walk-based algorithms can discover meaningful paradigmatic and syntagmatic relations of words from text data.


Analysis on DNA based Cryptography to Secure Data Transmission


title: Proposed application of big data analytics in healthcare at Maharaja Yeshwantrao Hospital

abstract: This paper gives an insight of how we can store healthcare data digitally like patient's records as an Electronic Health Record (EHR) and how we can generate useful information from these records by using analytics techniques and tools which will help in saving time and money of patients as well as the doctors. This paper is fully focused towards the Maharaja Yeshwantrao Hospital (M.Y.) located in Indore, Madhya Pradesh, India. M.Y hospital is the central India's largest government hospital. It generates large amount of heterogeneous data from different sources like patients health records, laboratory test result, electronic medical equipment, health insurance data, social media, drug research, genome research, clinical outcome, transaction and from Mahatma Gandhi Memorial medical college which is under MY hospital. To manage this data, data analytics may be used to make it useful for retrieval. Hence the concept of "big data" can be applied. Big data is characterized as extremely large data sets that can be analysed computationally to find patterns, trends, and associations, visualization, querying, information privacy and predictive analytics on large wide spread collection of data. Big data analytics can be done using Hadoop which plays an effective role in performing meaningful real-time analysis on the large volume of this data to predict the emergency situations before it happens. This paper also discusses about the EHR and the big data usage and its analytics at M.Y. hospital.


title: Handling Anti-Virtual Machine Techniques in Malicious Software

abstract: Malware analysis relies heavily on the use of virtual machines (VMs) for functionality and safety. There are subtle differences in operation between virtual and physical machines. Contemporary malware checks for these differences and changes its behavior when it detects a VM presence. These anti-VM techniques hinder malware analysis. Existing research approaches to uncover differences between VMs and physical machines use randomized testing, and thus cannot guarantee completeness. ::: In this article, we propose a detect-and-hide approach, which systematically addresses anti-VM techniques in malware. First, we propose cardinal pill testing—a modification of red pill testing that aims to enumerate the differences between a given VM and a physical machine through carefully designed tests. Cardinal pill testing finds five times more pills by running 15 times fewer tests than red pill testing. We examine the causes of pills and find that, while the majority of them stem from the failure of VMs to follow CPU specifications, a small number stem from under-specification of certain instructions by the Intel manual. This leads to divergent implementations in different CPU and VM architectures. Cardinal pill testing successfully enumerates the differences that stem from the first cause. Finally, we propose VM Cloak—a WinDbg plug-in which hides the presence of VMs from malware. VM Cloak monitors each execute malware command, detects potential pills, and at runtime modifies the command’s outcomes to match those that a physical machine would generate. We implemented VM Cloak and verified that it successfully hides VM presence from malware.


title: Defining Digital Forensic Examination and Analysis Tool Using Abstraction Layers

abstract: This paper uses the theory of abstraction layers to describe the purpose and goals of digital forensic analysis tools. Using abstraction layers, we identify where tools can introduce errors and provide requirements that the tools must follow. Categories of forensic analysis types are also defined based on the abstraction layers. Abstraction layers are not a new concept, but their usage in digital forensic analysis is not well documented. What does it mean to be a Digital Forensic Analysis Tool? How do we categorize the different types of analysis tools? For example, an investigator can view the files and directories of a suspect system by using either specialized forensic software or by using the operating system (OS) of an analysis system and viewing the files by mounting the drive. Both methods allow the investigator to view evidence in allocated files, but only the specialized forensic software allows him to easily view unallocated files. Additional tools are required if he is relying on the OS. Clearly both allow the investigator to find evidence and therefore should be considered forensic tools, but it is unclear how we should compare and categorize them. The high-level process of digital forensics includes the acquisition of data from a source, analysis of the data and extraction of evidence, and preservation and presentation of the evidence. Previous work has been done on the theory and requirements of data acquisition [7] and the preservation of evidence [4]. This paper addresses the tools that are used for the analysis of data and extraction of evidence. This paper examines the nature of tools in digital forensics and proposes definitions and requirements. Current digital forensic tools produce results that have been successfully used in prosecutions, but lack designs that were created with forensic science needs. They provide the investigator with access to evidence, but typically do not provide access to methods for verifying that the evidence is reliable. This is necessary when approaching digital forensics from a scientific point of view and could be a legal requirement in the future. The core concept of this paper is the basic notion of abstraction layers. Abstraction layers exist in all forms of digital data and therefore in the tools used to analyze them. The idea of using tools for layers of abstraction is not new, but a discussion of the definitions, properties, and error types of abstraction layers when used with digital


Fast plagiarism detection in large scale data


title: Relative Positioning Enhancement in VANETs: A Tight Integration Approach

abstract: Position information is a fundamental requirement for many vehicular applications such as navigation, intelligent transportation systems (ITSs), collision avoidance, and location-based services (LBSs). Relative positioning is effective for many applications, including collision avoidance and LBSs. Although Global Navigation Satellite Systems (GNSSs) can be used for absolute or relative positioning, the level of accuracy does not meet the requirements of many applications. Cooperative positioning (CP) techniques, fusing data from different sources, can be used to improve the performance of absolute or relative positioning in a vehicular ad hoc network (VANET). VANET CP systems are mostly based on radio ranging, which is not viable, despite being assumed in much of the literature. Considering this and emerging vehicular communication technologies, a CP method is presented to improve the relative positioning between two vehicles within a VANET, fusing the available low-level Global Positioning System (GPS) data. The proposed method depends on no radio ranging technique. The performance of the proposed method is verified by analytical and experimental results. Although the principles of the proposed method are similar to those of differential solutions such as differential GPS (DGPS), the proposed technique outperforms DGPS with about 37% and 45% enhancement in accuracy and precision of relative positioning, respectively.


title: Bangla text document categorization using Stochastic Gradient Descent (SGD) classifier

abstract: This paper describes the Bangla Document Categorization using Stochastic Gradient Descent (SGD) classifier. Here, document categorization is the task in which text documents are classified into one or more of predefined categories based on their contents. The proposed system can be divided into three steps: 1. feature extraction incorporating term frequency (TF) and inverse document frequency (IDF), 2. classifier design using the Stochastic Gradient Descent (SGD) algorithm by learning the distinct features, and 3. performance measure using F1-score. In the experiments on BDNews24 documents, it is observed that our proposed method provides higher accuracy in comparison with the methods based on Support Vector Machine (SVM) and Naive Bayesian (NB) classifier.


title: Image and Texture Segmentation Using Local Spectral Histograms

abstract: We present a method for segmenting images consisting of texture and nontexture regions based on local spectral histograms. Defined as a vector consisting of marginal distributions of chosen filter responses, local spectral histograms provide a feature statistic for both types of regions. Using local spectral histograms of homogeneous regions, we decompose the segmentation process into three stages. The first is the initial classification stage, where probability models for homogeneous texture and nontexture regions are derived and an initial segmentation result is obtained by classifying local windows. In the second stage, we give an algorithm that iteratively updates the segmentation using the derived probability models. The third is the boundary localization stage, where region boundaries are localized by building refined probability models that are sensitive to spatial patterns in segmented regions. We present segmentation results on texture as well as nontexture images. Our comparison with other methods shows that the proposed method produces more accurate segmentation results


title: ORIGINAL ARTICLE Can You Hear Me Now? The Impact of Voice in an Online Gaming Community

abstract: This paper reports the results of a controlled field experiment in which voice communication was introduced into an existing online community (online gaming guilds within the popular game ‘‘World of Warcraft’’), comparing a mix of voice and text with text only. Quantitative results suggest increases in liking and trust due to the addition of voice, as well as insulation from unexpected negative impacts of text-only play. The findings are discussed with respect to social capital, cyberbalkanization, and the general computer-mediated communication literature, with special attention paid to social information processing theory.


CFOA-Based Lossless and Lossy Inductance Simulators F ı rat


title: Lenselet image compression scheme based on subaperture images streaming

abstract: Plenoptic cameras capture the light field in a scene with a single shot and produce lenselet images. From a lenselet image, light field can be reconstructed, with which we can render images with different viewpoints and focal length. Because of large volume data, high efficient image compression scheme for storage and transmission is urgent. Containing 4D light field information, lenselet images have much more redundant information than traditional 2D images. In this paper, we propose a subaperture images streaming scheme to compress lenselet images, in which rotation scan mapping is adopted to further improve compression efficiency. The experiment results show our approach can efficient compress the redundancy in lenselet images and outperform traditional image compression method.


title: Is the Pedestrian going to Cross? Answering by 2D Pose Estimation

abstract: Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-of-the-art results.


The Knowledge-Learning-Instruction (KLI) framework: Toward bridging the science-practice chasm to enhance robust student learning (CMU-HCII Tech Rep. No. 10-102). Available from http://reportsarchive.adm.cs.cmu.edu/hcii.html


Biomolecular Computing Systems


title: VBoom: Creating a Virtual Machine Real Estate Boom

abstract: Cloud providers sell identically configured virtual machines (VMs) for the same price. Customers purchasing these VMs expect that they perform similarly and are allocated the same amount of virtual resources. In practice, however, the real performance of identically provisioned VMs depends on the underlying hardware, i.e., how the hardware is configured, and how much shared resources are consumed by co-located VMs. As workloads often have different resource requirements (e.g., CPU or disk I/O bound), a physical machine can be a better host to one VM than another, and swapping the locations of the two VMs can improve the performance (or any other metrics) of both VMs. However, cloud providers are unlikely to offer a VM swapping service since it is a tacit admission of providing different quality of services while charging the same rate. We propose, VBoom, a cloud provider-agnostic system in which VMs can dynamically relocate themselves (via location swapping) in a cloud environment if the new location better serves the needs of the VM. We discuss technical and business benefits and challenges in building such a system. When location matters, we believe that through VBoom, real estate of virtual machines can be established in a completely market-driven fashion.


title: Current solutions for Web service composition

abstract: Web service composition lets developers create applications on top of service-oriented computing's native description, discovery, and communication capabilities. Such applications are rapidly deployable and offer developers reuse possibilities and users seamless access to a variety of complex services. There are many existing approaches to service composition, ranging from abstract methods to those aiming to be industry standards. The authors describe four key issues for Web service composition.


title: Segmentation of color image using adaptive thresholding and masking with watershed algorithm

abstract: The individualization of an object from a digital image is a common problem in the field of image processing. We propose a modified version of the watershed algorithm for image segmentation. We have presented an adaptive masking and a threshoding mechanism over each color channel to overcome over segmentation problem, before combining the segmentation from each channel into the final one. Our proposed method ensures accuracy and quality of the 10 different kinds of color images. The experimental results are obtained using image quality assessment (IQA) metrics such as PSNR, MSE, PSNRRGB and Color Image Quality Measure (CQM) based on reversible YUV color transformation. Consequently, the proposed modified watershed approach can enhance the image segmentation performance. Similarly, it is worth noticing that our proposed MWS approach is faster than many other segmentation algorithms, which makes it appropriate for real-time application. According to the visual and quantitative verification, the proposed algorithm is performing better than three other algorithms.


title: Extending CRISP-DM to incorporate temporal data mining of multidimensional medical data streams: A neonatal intensive care unit case study

abstract: Using a Neonatal Intensive Care Unit (NICU) case study, this work investigates the current CRoss Industry Standard Process for Data Mining (CRISP-DM) approach for modeling Intelligent Data Analysis (IDA)-based systems that perform temporal data mining (TDM). The case study highlights the need for an extended CRISP-DM approach when modeling clinical systems applying Data Mining (DM) and Temporal Abstraction (TA). As the number of such integrated TA/DM systems continues to grow, this limitation becomes significant and motivated our proposal of an extended CRISP-DM methodology to support TDM, known as CRISP-TDM. This approach supports clinical investigations on multi-dimensional time series data. This research paper has three key objectives: 1) Present a summary of the extended CRISP-TDM methodology; 2) Demonstrate the applicability of the proposed model to the NICU data, focusing on the challenges associated with multi-dimensional time series data; and 3) Describe the proposed IDA architecture for applying integrated TDM.


title: Order patterns recurrence plots in the analysis of ERP data

abstract: Recurrence quantification analysis (RQA) is an established tool for data analysis in various behavioural sciences. In this article we present a refined notion of RQA based on order patterns. The use of order patterns is commonplace in time series analysis. Exploiting this concept in combination with recurrence plots (RP) and their quantification (RQA) allows for advances in contemporary EEG research, specifically in the analysis of event related potentials (ERP), as the method is known to be robust against non-stationary data. The use of order patterns recurrence plots (OPRPs) on EEG data recorded during a language processing experiment exemplifies the potentials of the method. We could show that the application of RQA to ERP data allows for a considerable reduction of the number of trials required in ERP research while still maintaining statistical validity.


title: A smart meter design and implementation using ZigBee based Wireless Sensor Network in Smart Grid

abstract: In this paper Wireless Sensor Home Area Network (WSHAN) with ZigBee interfaced smart meter is designed and implemented. Because of the increasing demands on electricity, traditional electric grid needs to be replaced with intelligent, robust, reliable and costly effective smart grid applications. Wireless Sensor Networks (WSN) has a critical role to set up a reliable and costly effective smart electric power grid applications. Our system measures energy usage, logs data real time and shows time of use (TOU) values. The system also controls any device connected to power outputs. While powering on and off, zero-cross of AC signal is detected to calculate phase shift. The smart meter provides correct power usage and transmits data with ZigBee to PC (Personal Computer). The user monitors the power information and remotely controls the system.


title: GA-Based Q-Attack on Community Detection

abstract: Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on the other hand, it arises the concern that individual information may be overmined, and the concept community deception has been proposed to protect individual privacy on social networks. Here, we introduce and formalize the problem of community detection attack and develop efficient strategies to attack community detection algorithms by rewiring a small number of connections, leading to privacy protection. In particular, we first give two heuristic attack strategies, i.e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively. Then, we propose an attack strategy called “genetic algorithm (GA)-based Q-Attack,” where the modularity $Q$ is used to design the fitness function. We launch community detection attack based on the above three strategies against six community detection algorithms on several social networks. By comparison, our Q-Attack method achieves much better attack effects than CDA and DBA, in terms of the larger reduction of both modularity $Q$ and normalized mutual information (NMI). In addition, we further take transferability tests and find that adversarial networks obtained by Q-Attack on a specific community detection algorithm also show considerable attack effects while generalized to other algorithms.


title: Bilingual Word Embeddings for Phrase-Based Machine Translation

abstract: We introduce bilingual word embeddings: semantic embeddings associated across two languages in the context of neural language models. We propose a method to learn bilingual embeddings from a large unlabeled corpus, while utilizing MT word alignments to constrain translational equivalence. The new embeddings significantly out-perform baselines in word semantic similarity. A single semantic similarity feature induced with bilingual embeddings adds near half a BLEU point to the results of NIST08 Chinese-English machine translation task.


title: Progressive Learning for Systematic Design of Large Neural Networks

abstract: We develop an algorithm for systematic design of a large artificial neural network using a progression property. We find that some non-linear functions, such as the rectifier linear unit and its derivatives, hold the property. The systematic design addresses the choice of network size and regularization of parameters. The number of nodes and layers in network increases in progression with the objective of consistently reducing an appropriate cost. Each layer is optimized at a time, where appropriate parameters are learned using convex optimization. Regularization parameters for convex optimization do not need a significant manual effort for tuning. We also use random instances for some weight matrices, and that helps to reduce the number of parameters we learn. The developed network is expected to show good generalization power due to appropriate regularization and use of random weights in the layers. This expectation is verified by extensive experiments for classification and regression problems, using standard databases.


The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making


title: Broadband HBT Doherty Power Amplifiers for Handset Applications

abstract: A Doherty power amplifier for IEEE 802.16e mobile worldwide interoperability for microwave access (m-WiMAX) is fully integrated on a 1.2 × 1.2 × mm2 die using a 2-μm InGaP/GaAs hetero-junction bipolar transistor (HBT) process. The direct input power dividing technique is employed on the chip. Broadband input and output matching techniques are used for broadband Doherty operation, and their effects are analyzed. A peaking amplifier 1.5 times larger than a carrier amplifier delivers high efficiency for m-WiMAX signal with a 9.6-dB crest factor and an 8.75-MHz bandwidth (BW). The PA with a supply voltage of 3.4 V has an EVM of 2.3% and a PAE of 31.5% at an output power of 24.75 dBm as well as an operating frequency of 2.6 GHz. A PAE over 30.3% and an output power of greater than 24.6 dBm with an EVM less than 3.15% and a gain variation of 0.2 dB are achieved across 2.5-2.7 GHz without any assisting linearization technique. After compensating AM-AM and AM-PM nonlinearity using a digital feedback predistortion algorithm, the PA exhibits a PAE of over 27% and an output power of over 23.6 dBm across 2.2-2.8 GHz while maintaining an EVM below 2.7%.


title: Collaborative prediction and ranking with non-random missing data

abstract: A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative filtering and recommender systems is founded on the assumption that missing ratings are missing at random. The statistical theory of missing data shows that incorrect assumptions about missing data can lead to biased parameter estimation and prediction. In a recent study, we demonstrated strong evidence for violations of the missing at random condition in a real recommender system. In this paper we present the first study of the effect of non-random missing data on collaborative ranking, and extend our previous results regarding the impact of non-random missing data on collaborative prediction.


title: Determinants of Online Social Business Network Usage Behavior—Applying the Technology Acceptance Model and Its Extensions

abstract: Usage of online social business networks like LinkedIn and XING have become commonplace in today’s workplace. This research addresses the question of what factors drive the intention to use online social business networks. Theoretical frame of the study is the Technology Acceptance Model (TAM) and its extensions, most importantly the TAM2 model. Data has been collected via a Web Survey among users of LinkedIn and XING from January to April 2010. Of 541 initial responders 321 finished the questionnaire. Operationalization was tested using confirmatory factor analyses and causal hypotheses were evaluated by means of structural equation modeling. Core result is that the TAM2 model generally holds in the case of online social business network usage behavior, explaining 73% of the observed usage intention. This intention is most importantly driven by perceived usefulness, attitude towards usage and social norm, with the latter effecting both directly and indirectly over perceived usefulness. However, perceived ease of use has—contrary to hypothesis—no direct effect on the attitude towards usage of online social business networks. Social norm has a strong indirect influence via perceived usefulness on attitude and intention, creating a network effect for peer users. The results of this research provide implications for online social business network design and marketing. Customers seem to evaluate ease of use as an integral part of the usefulness of such a service which leads to a situation where it cannot be dealt with separately by a service provider. Furthermore, the strong direct impact of social norm implies application of viral and peer-to-peer marketing techniques while it’s also strong indirect effect implies the presence of a network effect which stabilizes the ecosystem of online social business service vendors.


Lexicons on Demand: Neural Word Embeddings for Large-Scale Text Analysis


Summarization with Attention-based Deep Recurrent Neural Networks


title: A Survey of Neural Network Techniques for Feature Extraction from Text

abstract: This paper aims to catalyze the discussions about text feature extraction techniques using neural network architectures. The research questions discussed in the paper focus on the state-of-the-art neural network techniques that have proven to be useful tools for language processing, language generation, text classification and other computational linguistics tasks.


Dynamic Resource Allocation in Software Defined and Virtual Networks : A Comparative Analysis


title: Home-Based Virtual Reality Exposure Therapy with Virtual Health Agent Support

abstract: To increase the accessibility and efficiency of virtual reality exposure therapy (VRET) this paper proposes a system for home-based use where patients with social phobia are supported by a virtual health agent. We present an overview of our system design, and discuss key techniques such as (1) dialogue techniques to create automated free speech dialogue between virtual characters and patients in virtual reality worlds; (2) a multi-modal automatic anxiety feedback-loop mechanism to control patients’ anxiety level; and (3) motivational techniques applied by a virtual health agent. The system was evaluated in a pilot study where five patients with social phobia utilized our home-based VRET system. The results showed that the system was able to evoke the required anxiety in patients and that over time self-reported anxiety and heart rate gradually decreased as expected in exposure therapy.


title: Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results

abstract: The ever growing traffic explosion in mobile communications has recently drawn increased attention to the large amount of underutilized spectrum in the millimeter-wave frequency bands as a potentially viable solution for achieving tens to hundreds of times more capacity compared to current 4G cellular networks. Historically, mmWave bands were ruled out for cellular usage mainly due to concerns regarding short-range and non-line-of-sight coverage issues. In this article, we present recent results from channel measurement campaigns and the development of advanced algorithms and a prototype, which clearly demonstrate that the mmWave band may indeed be a worthy candidate for next generation (5G) cellular systems. The results of channel measurements carried out in both the United States and Korea are summarized along with the actual free space propagation measurements in an anechoic chamber. Then a novel hybrid beamforming scheme and its link- and system-level simulation results are presented. Finally, recent results from our mmWave prototyping efforts along with indoor and outdoor test results are described to assert the feasibility of mmWave bands for cellular usage.


title: Designing a new generalized battery management system

abstract: Battery management systems (BMSs) are used in many battery-operated industrial and commercial systems to make the battery operation more efficient and the estimation of battery state nondestructive. The existing BMS techniques are examined in this paper and a new design methodology for a generalized reliable BMS is proposed. The main advantage of the proposed BMS compared to the existing systems is that it provides a fault-tolerant capability and battery protection. The proposed BMS consists of a number of smart battery modules (SBMs) each of which provides battery equalization, monitoring, and battery protection to a string of battery cells. An evaluation SBM was developed and tested in the laboratory and experimental results verify the theoretical expectations.


title: A Personalized E-Learning System Based on User Profile Constructed Using Information Fusion

abstract: In this paper, we describe a personalized e-learning system which can automatically adapt to the interests and levels of learners. The system is designed based on the IEEE Learning Technology Systems Architecture (IEEE LTSA) to achieve high scalability and reusability. A feedback extractor with fusion capability is proposed to combine multiple feedback measures to infer user preferences. User profile, which stores user preferences and levels of expertise, is collected by user profiler to deliver personalized information using the collaborative filtering algorithm.


title: FE based eccentricity analysis of Rogowski Coil

abstract: The Rogowski Coil (RC) has air as core around which coil is wound. It works on the principle of Faraday's and Ampere's law. RC can be used to measure both high speed impulse and alternating currents. RC offers some very important properties like wide bandwidth, galvanic isolation, linearity, lightness and cheap. Presence of all these properties makes RC better than conventional current transducers. It also offers many other applications viz., energy management, protection systems, CT calibration, current sharing, resistance, welding process, measurement of partial discharges and earth resistance of transmission towers. It can measure currents ranging from mili-amperes (mA) to mega-amperes (MA) without any saturation. The construction and measurement conditions decide the application of RC. The eccentricity of coil is the main parameter considered for modelling of RC. The eccentricity effect becomes prominent when the conductor gets displaced away from the centre due to the deviation from circular shape. Change in conductor position affects the measurements done, so this analysis ensures the accuracy of RC. In this paper, eccentricity analysis of RC is done using ANSYS software tool. The observed deviation of mutual inductance during the analysis can be justified with the theoretical existing data.


title: Use of impervious surface in urban land-use classification

abstract: Impervious surface has been recognized as a key indicator in assessing urban environments. However, accurate impervious surface extraction is still a challenge. Effectiveness of impervious surface in urban land-use classification has not been well addressed. This paper explored extraction of impervious surface information from Landsat Enhanced Thematic Mapper data based on the integration of fraction images from linear spectral mixture analysis and land surface temperature. A new approach for urban land-use classification, based on the combined use of impervious surface and population density, was developed. Five urban land-use classes (i.e., low-, medium-, high-, and very-high-intensity residential areas, and commercial/industrial/transportation uses) were developed in the city of Indianapolis, Indiana, USA. Results showed that the integration of fraction images and surface temperature provided substantially improved impervious surface image. Accuracy assessment indicated that the root-mean-square error and system error yielded 9.22% and 5.68%, respectively, for the impervious surface image. The overall classification accuracy of 83.78% for five urban land-use classes was obtained.


Investigation and Manipulation of Different Analog Behaviors of Memristor as Electronic Synapse for Neuromorphic Applications


title: Fast and robust laser stripe extraction for 3D reconstruction in industrial environments

abstract: The use of 3D reconstruction based on active laser triangulation techniques is very complex in industrial environments. The main problem is that most of these techniques are based on laser stripe extraction methods which are highly sensitive to noise, which is virtually inevitable in these conditions. In industrial environments, variable luminance, reflections which show up in the images as noise, and uneven surfaces are common. These factors modify the shape of the laser profile. This work proposes a fast, accurate, and robust method to extract laser stripes in industrial environments. Specific procedures are proposed to extract the laser stripe projected on the background, using a boundary linking process, and on the foreground, using an improved Split-and-Merge approach with different approximation functions including linear, quadratic, and Akima splines. Also, a novel procedure to automatically define the region of interest in the image is proposed. The real-time performance of the proposed method is analyzed by measuring the time taken by the tasks involved in their application. Finally, the proposed extraction method is applied to two real applications: 3D reconstruction of steel strips and weld seam tracking.


title: Estimation of Simultaneously Sparse and Low Rank Matrices

abstract: The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Such structures arise in the context of social networks or protein interactions where underlying graphs have adjacency matrices which are block-diagonal in the appropriate basis. We introduce a convex mixed penalty which involves l1-norm and trace norm simultaneously. We obtain an oracle inequality which indicates how the two effects interact according to the nature of the target matrix. We bound generalization error in the link prediction problem. We also develop proximal descent strategies to solve the optimization problem efficiently and evaluate performance on synthetic and real data sets.


title: Prenatal ionizing irradiaton and early postnatal growth of Colombian and Bolivian squirrel monkeys (Saimiri sciureus).

abstract: Fetal squirrel monkeys of Bolivian and Colombian origin were exposed to 100 rad cobalt-60 radiation in utero and carried to term by the dams. Body weight, head circumference, crown-rump length, and food intake were measured during the first 28 postnatal days. Significant subspecies differences were noted in both mothers and offspring, the Colombian squirrel monkeys being generally larger than the Bolivians. The irradiated offspring weighed significantly less, had significantly smaller crania, and consumed significantly less food than control animals. A statistically nonsignificant decrement was also evident in the crown-rump length of radiation-treated infants.


title: Air-Writing Recognition—Part II: Detection and Recognition of Writing Activity in Continuous Stream of Motion Data

abstract: Air-writing refers to writing of characters or words in the free space by hand or finger movements. We address air-writing recognition problems in two companion papers. Part 2 addresses detecting and recognizing air-writing activities that are embedded in a continuous motion trajectory without delimitation. Detection of intended writing activities among superfluous finger movements unrelated to letters or words presents a challenge that needs to be treated separately from the traditional problem of pattern recognition. We first present a dataset that contains a mixture of writing and nonwriting finger motions in each recording. The LEAP from Leap Motion is used for marker-free and glove-free finger tracking. We propose a window-based approach that automatically detects and extracts the air-writing event in a continuous stream of motion data, containing stray finger movements unrelated to writing. Consecutive writing events are converted into a writing segment. The recognition performance is further evaluated based on the detected writing segment. Our main contribution is to build an air-writing system encompassing both detection and recognition stages and to give insights into how the detected writing segments affect the recognition result. With leave-one-out cross validation, the proposed system achieves an overall segment error rate of 1.15% for word-based recognition and 9.84% for letter-based recognition.


An Improved Real-Time Blob Detection for Visual Surveillance


title: Bifurcation Analysis of Large Equilibrium Systems in Matlab

abstract: The Continuation of Invariant Subspaces (CIS) algorithm produces a smoothly varying basis for an invariant subspace $\mathcal{R}(s)$ of a parameter-dependent matrix A(s). In the case when A(s) is the Jacobian matrix for a system that comes from a spatial discretization of a partial differential equation, it will typically be large and sparse. Cl_matcont is a user-friendly matlab package for the study of dynamical systems and their bifurcations. We incorporate the CIS algorithm into cl_atcont to extend its functionality to large scale bifurcation computations via subspace reduction.


title: Multi-speaker modeling and speaker adaptation for DNN-based TTS synthesis

abstract: In DNN-based TTS synthesis, DNNs hidden layers can be viewed as deep transformation for linguistic features and the output layers as representation of acoustic space to regress the transformed linguistic features to acoustic parameters. The deep-layered architectures of DNN can not only represent highly-complex transformation compactly, but also take advantage of huge amount of training data. In this paper, we propose an approach to model multiple speakers TTS with a general DNN, where the same hidden layers are shared among different speakers while the output layers are composed of speaker-dependent nodes explaining the target of each speaker. The experimental results show that our approach can significantly improve the quality of synthesized speech objectively and subjectively, comparing with speech synthesized from the individual, speaker-dependent DNN-based TTS. We further transfer the hidden layers for a new speaker with limited training data and the resultant synthesized speech of the new speaker can also achieve a good quality in term of naturalness and speaker similarity.


title: A Prism‐Free Method for Silhouette Rendering in Inverse Displacement Mapping

abstract: Silhouette is a key feature that distinguishes displacement mapping from normal mapping. However the silhouette rendering in the GPU implementation of displacement mapping (which is often called inversed displacement mapping) is tricky. Previous approaches rely mostly on construction of additional extruding prism-like geometry, which slows down the rendering significantly. In this paper, we proposed a method for solving the silhouette rendering problem in inverse displace mapping without using any extruding prism-like geometry. At each step of intersection finding, we continuously bends the viewing ray according to the current local tangent space associated with the surface. Thus, it allows mapping a displacement map onto an arbitrary curved surface with more accurate silhouette. While our method is simple, it offers surprisingly good results over Curved Relief Map (CRM) [OP05] in many difficult or degenerated cases.


The Object-oriented Tool MOTES DM and its Use for Diagnosis of Sewage Plant Failures


title: A novel oddball paradigm for affective BCIs using emotional faces as stimuli

abstract: The studies of P300-based brain computer interfaces (BCIs) have demonstrated that visual attention to an oddball event can enhance the event-related potential (ERP) time-locked to this event. However, it was unclear that whether the more sophisticated face-evoked potentials can also be modulated by related mental tasks. This study examined ERP responses to objects, faces, and emotional faces when subjects performs visual attention, face recognition and categorization of emotional facial expressions respectively in an oddball paradigm. The results revealed the significant difference between target and non-target ERPs for each paradigm. Furthermore, the significant difference among three mental tasks was observed for vertex-positive potential (VPP) (p<0.01), late positive potential (LPP) / P3b (p<0.05) at the centro-parietal regions and N250 (p<0.003) at the occipito-temporal regions. The high classification performance for single-trial emotional face-related ERP demonstrated facial emotion processing can be used as a novel oddball paradigm for the affective BCIs.


title: Chronic violent video game exposure and desensitization to violence: Behavioral and event-related brain potential data

abstract: Numerous studies have shown that exposure to media violence increases aggression, though the mechanisms of this effect have remained elusive. One theory posits that repeated exposure to media violence desensitizes viewers to real world violence, increasing aggression by blunting aversive reactions to violence and removing normal inhibitions against aggression. Theoretically, violence desensitization should be reflected in the amplitude of the P300 component of the event-related brain potential (ERP), which has been associated with activation of the aversive motivational system. In the current study, violent images elicited reduced P300 amplitudes among violent, as compared to nonviolent video game players. Additionally, this reduced brain response predicted increased aggressive behavior in a later task. Moreover, these effects held after controlling for individual differences in trait aggressiveness. These data are the first to link media violence exposure and aggressive behavior to brain processes hypothetically associated with desensitization.


Fast Similarity Search in Large Dictionaries


title: IEEE 802.15.4 security sublayer for OMNET++

abstract: Most network simulators do not support security features. In this paper, we introduce a new security module for OMNET++ that implements the IEEE 802.15.4 security suite. This module, developed using the C++ language, can simulate all devices and sensors that implement the IEEE 802.15.4 standard. The OMNET++ security module is also evaluated in terms of quality of services in the presence of physical hop attacks. Results show that our module is reliable and can safely be used by researchers.


title: E-Commerce Systems Success: An Attempt to Extend and Respecify the Delone and MaClean Model of IS Success

abstract: E-commerce success is an issue that has attracted the interest of many pundits. There is a general scarcity of models and frameworks for evaluating e-commerce success. Whether traditional information systems success models can be extended to investigating e-commerce success is yet to be investigated. This paper proposes a partial extension and respecification of the Delone and Maclean model of IS success to e-commerce systems. Customer Ecommerce Satisfaction (CES) is proposed as a dependent variable to e-commerce success and its relationships with e-commerce system quality, content quality, use, trust and support are defined and discussed. Further research into developing, validating and empirically testing the model is proposed.


title: Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization

abstract: When approaching a novel visual recognition problem in a specialized image domain, a common strategy is to start with a pre-trained deep neural network and fine-tune it to the specialized domain. If the target domain covers a smaller visual space than the source domain used for pre-training (e.g. ImageNet), the fine-tuned network is likely to be over-parameterized. However, applying network pruning as a post-processing step to reduce the memory requirements has drawbacks: fine-tuning and pruning are performed independently; pruning parameters are set once and cannot adapt over time; and the highly parameterized nature of state-of-the-art pruning methods make it prohibitive to manually search the pruning parameter space for deep networks, leading to coarse approximations. We propose a principled method for jointly fine-tuning and compressing a pre-trained convolutional network that overcomes these limitations. Experiments on two specialized image domains (remote sensing images and describable textures) demonstrate the validity of the proposed approach.


title: EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection

abstract: As news reading on social media becomes more and more popular, fake news becomes a major issue concerning the public and government. The fake news can take advantage of multimedia content to mislead readers and get dissemination, which can cause negative effects or even manipulate the public events. One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events. Unfortunately, most of the existing approaches can hardly handle this challenge, since they tend to learn event-specific features that can not be transferred to unseen events. In order to address this issue, we propose an end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events. It consists of three main components: the multi-modal feature extractor, the fake news detector, and the event discriminator. The multi-modal feature extractor is responsible for extracting the textual and visual features from posts. It cooperates with the fake news detector to learn the discriminable representation for the detection of fake news. The role of event discriminator is to remove the event-specific features and keep shared features among events. Extensive experiments are conducted on multimedia datasets collected from Weibo and Twitter. The experimental results show our proposed EANN model can outperform the state-of-the-art methods, and learn transferable feature representations.


title: User profile model: A user dimension based classification

abstract: As the amount of information offered by information systems is increasing exponentially, the need of personalized approaches for information access increases. This work discusses user profiles designed for providing personalized information access. We first present a general classification of research directions on adaptive systems, followed by a state-of-the-art study about user profiling. We propose then a new classification approach of user profile model. This classification is based on the user dimensions considered to build the user profile.


title: 3D visualization for concept location in source code

abstract: The paper presents a set of tools that work in conjunction to support concept location in software. One of the tools, IRiSS (Information Retrieval based Software Search), is a search engine, designed and implemented to allow searching the source code of a software system. The other tool, sv3D (source viewer 3D), is a visualization front end, designed to represent software data with 3D renderings.The two tools are integrated with MS Visual Studio, with IRiSS providing the infrastructure for indexing the source code and querying, while sv3D helps the user in visually navigating the results of the queries and keeps track of the navigation path.


title: IS THERE A DUTY TO RESPECT “HISTORICAL” FAITH? CHRISTIAN PROSELYTISM OF AN OLDER JEWISH WOMAN WITH DEMENTIA

abstract: In this article, we present a short case study based on an incident that occurred in Israel several years ago. The incident did not reach the courts but was made public by the family members of the older woman at the center of it. The family argued that the actions taken by one of the parties involved should have been defined as elder abuse, but no criminal charges were ever brought. Yet the issues concern key legal and ethical questions about law, religion, and older persons. More specifically, the incident raises the issue of the moral commitment to one's past religious beliefs in reference to one's current choices and preferences once living with dementia. We contend in this article that an Aristotelean account of human dignity would have provided the most satisfactory way to resolve the tensions created by this incident.


title: Autonomous robotic stone stacking with online next best object target pose planning

abstract: Predominately, robotic construction is applied as prefabrication in structured indoor environments with standard building materials. Our work, on the other hand, focuses on utilizing irregular materials found on-site, such as rubble and rocks, for autonomous construction. We present a pipeline that detects randomly placed objects in a scene that are used by our next best stacking pose searching method employing gradient descent with a random initial orientation, exploiting a physics engine. This approach is validated in an experimental setup using a robotic manipulator by constructing balancing vertical stacks without mortars and adhesives. We show the results of eleven consecutive trials to form such towers autonomously using four arbitrarily in front of the robot placed rocks.


title: A CPS Leaky-Wave Antenna With Reduced Beam Squinting Using NRI-TL Metamaterials

abstract: A reduced beam-squinting printed leaky-wave antenna (LWA) is proposed, comprising cascaded negative-refractive-index transmission-line (NRI-TL) metamaterial unit cells. Each NRI-TL unit cell is implemented in co-planar strip (CPS) technology and consists of a host TL loaded with series interdigitated capacitors and shunt meandered inductors. Periodic analysis is applied to the NRI-TL unit cell in order to extract the dispersion and Bloch impedance characteristics. Subsequently, the angular variation of the main radiated beam with frequency or ldquobeam squintingrdquo is derived, based on the expression for the Bloch propagation constant of the NRI-TL line. It is shown that by operating the LWA in the upper right-handed band where the phase and group velocities are closest to the speed of light, the beam squinting that the antenna experiences can be minimized. The theoretical performance of the LWA is verified through full-wave simulations and measurements of a fabricated prototype designed to produce a radiated beam at an angle of at GHz. This 20-element NRI-TL LWA exhibits a measured return-loss bandwidth below dB of 0.91 GHz (18.2%), and an average beam squint of 0.031/MHz. The proposed NRI-TL LWA is uniplanar, differential and broadband, and therefore suitable for integration with other microwave components and devices.


title: Superresolution reconstruction of hyperspectral images

abstract: Hyperspectral images are used for aerial and space imagery applications including target detection, tracking, agricultural and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles and sensor noise degrade the quality of these images. We introduce a novel superresolution reconstruction method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then a method for applying superresolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene.


title: Enhanced Perceptual Functioning in Autism: An Update, and Eight Principles of Autistic Perception

abstract: We propose an ‘‘Enhanced Perceptual Functioning’’ model encompassing the main differences between autistic and non-autistic social and non-social perceptual processing: locally oriented visual and auditory perception, enhanced low-level discrimination, use of a more posterior network in ‘‘complex’’ visual tasks, enhanced perception of first order static stimuli, diminished perception of complex movement, autonomy of low-level information processing toward higher-order operations, and differential relation between perception and general intelligence. Increased perceptual expertise may be implicated in the choice of special ability in savant autistics, and in the variability of apparent presentations within PDD (autism with and without typical speech, Asperger syndrome) in non-savant autistics. The overfunctioning of brain regions typically involved in primary perceptual functions may explain the autistic perceptual endophenotype.


title: The lexicon-based sentiment analysis for fan page ranking in Facebook

abstract: The traditional methods to rank a Facebook fan page only rely on the user engagement including the number of posts, comments, and “likes”. The polarity of each comment, which can be positive, neutral, or negative, is ignored in these methods. In this paper, we propose a content-based ranking method in which the user engagement and the comment polarity are all considered. The user comment is analyzed using a lexicon-based approach. We apply the proposed method for the real Facebook dataset collected using the Social Packets crawler. The result shows that the ranks of pages estimated by our method is close to the ranks estimated by engagement based method. More importantly, by concerning the comment polarity, our page ranking is more accurate regarding user opinion.


title: MTCProv: a practical provenance query framework for many-task scientific computing

abstract: Scientific research is increasingly assisted by computer-based experiments. Such experiments are often composed of a vast number of loosely-coupled computational tasks that are specified and automated as scientific workflows. This large scale is also characteristic of the data that flows within such "many-task" computations (MTC). Provenance information can record the behavior of such computational experiments via the lineage of process and data artifacts. However, work to date has focused on lineage data models, leaving unsolved issues of recording and querying other aspects, such as domain-specific information about the experiments, MTC behavior given by resource consumption and failure information, or the impact of environment on performance and accuracy. In this work we contribute with MTCProv, a provenance query framework for many-task scientific computing that captures the runtime execution details of MTC workflow tasks on parallel and distributed systems, in addition to standard prospective and data derivation provenance. To help users query provenance data we provide a high level interface that hides relational query complexities. We evaluate MTCProv using an application in protein science, and describe how important query patterns such as correlations between provenance, runtime data, and scientific parameters are simplified and expressed.


title: Real-Time Mental Arithmetic Task Recognition From EEG Signals

abstract: Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.


Early Detection and Prevention of Cancer using Data Mining Techniques


title: Agentbased control for decentralised demand side management in the smart grid

abstract: Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e., it exhibits a similar efficiency).


Chapter 2 REACTIVE POWER AND VOLTAGE CONTROL ISSUES IN ELECTRIC POWER SYSTEMS


title: Leveraging bagging for evolving data streams

abstract: Bagging, boosting and Random Forests are classical ensemble methods used to improve the performance of single classifiers. They obtain superior performance by increasing the accuracy and diversity of the single classifiers. Attempts have been made to reproduce these methods in the more challenging context of evolving data streams. In this paper, we propose a new variant of bagging, called leveraging bagging. This method combines the simplicity of bagging with adding more randomization to the input, and output of the classifiers. We test our method by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples.


Bootstrapping the Blockchain - Directly


title: Interactively shaping agents via human reinforcement: the TAMER framework

abstract: As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without numerous high-cost learning trials. One promising approach to reducing sample complexity of learning a task is knowledge transfer from humans to agents. Ideally, methods of transfer should be accessible to anyone with task knowledge, regardless of that person's expertise in programming and AI. This paper focuses on allowing a human trainer to interactively shape an agent's policy via reinforcement signals. Specifically, the paper introduces "Training an Agent Manually via Evaluative Reinforcement," or TAMER , a framework that enables such shaping. Differing from previous approaches to interactive shaping, a TAMER agent models the human's reinforcement and exploits its model by choosing actions expected to be most highly reinforced. Results from two domains demonstrate that lay users can train TAMER agents without defining an environmental reward function (as in an MDP) and indicate that human training within the TAMER framework can reduce sample complexity over autonomous learning algorithms.


title: How musical training affects cognitive development: rhythm, reward and other modulating variables

abstract: Musical training has recently gained additional interest in education as increasing neuroscientific research demonstrates its positive effects on brain development. Neuroimaging revealed plastic changes in the brains of adult musicians but it is still unclear to what extent they are the product of intensive music training rather than of other factors, such as preexisting biological markers of musicality. In this review, we synthesize a large body of studies demonstrating that benefits of musical training extend beyond the skills it directly aims to train and last well into adulthood. For example, children who undergo musical training have better verbal memory, second language pronunciation accuracy, reading ability and executive functions. Learning to play an instrument as a child may even predict academic performance and IQ in young adulthood. The degree of observed structural and functional adaptation in the brain correlates with intensity and duration of practice. Importantly, the effects on cognitive development depend on the timing of musical initiation due to sensitive periods during development, as well as on several other modulating variables. Notably, we point to motivation, reward and social context of musical education, which are important yet neglected factors affecting the long-term benefits of musical training. Further, we introduce the notion of rhythmic entrainment and suggest that it may represent a mechanism supporting learning and development of executive functions. It also hones temporal processing and orienting of attention in time that may underlie enhancements observed in reading and verbal memory. We conclude that musical training uniquely engenders near and far transfer effects, preparing a foundation for a range of skills, and thus fostering cognitive development.


title: Capacitively Loaded, Inductively Coupled Fed Loop Antenna With an Omnidirectional Radiation Pattern for UHF RFID Tags

abstract: A capacitively loaded, inductively coupled fed loop antenna with an omnidirectional radiation pattern for UHF radio frequency identification (RFID) tags is proposed. The inductively coupled feed structure is beneficial for conjugate impedance matching. The loaded capacitors in the radiating and feeding loops allow for easy control of the resonant frequency and the antenna reactance, respectively. The omnidirectional radiation pattern on the horizontal plane makes the proposed tag insensitive to be mounted on different target objects. With a small size of 43 × 43 mm2, the measured maximum reading range of the prototype is 9.5 m with a total transmitted power of 4.0 W effective isotropic radiated power (EIRP).


title: Sentiment Analysis of Twitter Data: A Survey of Techniques

abstract: With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics,have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide a research on twitter data streams.General challenges and applications of Sentiment Analysis on Twitter are also discussed in this paper.


title: ChaLearn Looking at People and Faces of the World: Face AnalysisWorkshop and Challenge 2016

abstract: We present the 2016 ChaLearn Looking at People and Faces of the World Challenge and Workshop, which ran three competitions on the common theme of face analysis from still images. The first one, Looking at People, addressed age estimation, while the second and third competitions, Faces of the World, addressed accessory classification and smile and gender classification, respectively. We present two crowd-sourcing methodologies used to collect manual annotations. A custom-build application was used to collect and label data about the apparent age of people (as opposed to the real age). For the Faces of the World data, the citizen-science Zooniverse platform was used. This paper summarizes the three challenges and the data used, as well as the results achieved by the participants of the competitions. Details of the ChaLearn LAP FotW competitions can be found at http://gesture.chalearn.org.


Analysis of a Segmented Annular Coplanar Capacitive Tilt Sensor with Increased Sensitivity


title: Performance analysis of VANETs under Rayleigh, Rician, Nakagami-m and Weibull fading

abstract: Vehicular Ad-hoc Networks (VANETs) are considered as one of the promising technologies for reduction of accidents and provisioning of infotainment services on the road. Due to high mobility of vehicles, wireless channel response rapidly changes from time to time which results into random fading. Therefore, this paper provides in depth analysis of performance of VANETs under different fading channels. To be specific, we adopt the cluster based approach to analytically model VANETs. Closed-form expressions of packet loss probability for different fading (i.e. Rayleigh, Rician, Nakagami-m and Weibull channel) models have been provided considering both MAC and Physical Layer characteristics. The derived expressions also encompass the effect of decoding failure and packet collision based on signal to noise ratio (SNR) and back-off timer, respectively. Our results characterizes the impact of channel conditions, vehicle speed, and contention window on the performance of the cluster based VANETs. Extensive simulations are performed to verify our findings.


title: Chinese Characters Mapping Table of Japanese, Traditional Chinese and Simplified Chinese

abstract: Chinese characters are used both in Japanese and Chinese, which are called Kanji and Hanzi respectively. Chinese characters contain significant semantic information, a mapping table between Kanji and Hanzi can be very useful for many Japanese-Chinese bilingual applications, such as machine translation and cross-lingual information retrieval. Because Kanji characters are originated from ancient China, most Kanji have corresponding Chinese characters in Hanzi. However, the relation between Kanji and Hanzi is quite complicated. In this paper, we propose a method of making a Chinese characters mapping table of Japanese, Traditional Chinese and Simplified Chinese automatically by means of freely available resources. We define seven categories for Kanji based on the relation between Kanji and Hanzi, and classify mappings of Chinese characters into these categories. We use a resource from Wiktionary to show the completeness of the mapping table we made. Statistical comparison shows that our proposed method makes a more complete mapping table than the current version of Wiktionary.


title: Getting the Message?: A Study of Explanation Interfaces for Microblog Data Analysis

abstract: In many of today's online applications that facilitate data exploration, results from information filters such as recommender systems are displayed alongside traditional search tools. However, the effect of prediction algorithms on users who are performing open-ended data exploration tasks through a search interface is not well understood. This paper describes a study of three interface variations of a tool for analyzing commuter traffic anomalies in the San Francisco Bay Area. The system supports novel interaction between a prediction algorithm and a human analyst, and is designed to explore the boundaries, limitations and synergies of both. The degree of explanation of underlying data and algorithmic process was varied experimentally across each interface. The experiment (N=197) was performed to assess the impact of algorithm transparency/explanation on data analysis tasks in terms of search success, general insight into the underlying data set and user experience. Results show that 1) presence of recommendations in the user interface produced a significant improvement in recall of anomalies, 2) participants were able to detect anomalies in the data that were missed by the algorithm, 3) participants who used the prediction algorithm performed significantly better when estimating quantities in the data, and 4) participants in the most explanatory condition were the least biased by the algorithm's predictions when estimating quantities.


Designing recursive networks combinatorially


title: A broadband vibration control using passive circuits

abstract: In this research a broadband passive vibration control technique using a semi-active circuit is presented. A digitally tunable RL shunt circuit and a semi-active mode identifier are developed using 8-bit CMOS microcontrollers to control multiple vibratory modes in a mechanical system. In order to increase the adaptability of the controller, the effects of additional capacitors in the circuit are investigated. The governing equations for the coupled electro-mechanical system are introduced using Hamilton’s Principle and finite element analysis. The system is verified experimentally using and the simulation and experimental results are provided in multiple formats.


title: Compact ConvNets with Ternary Weights and Binary Activations

abstract: Compact architectures, ternary weights and binary activations are two methods suitable for making neural networks more efficient. We introduce a) a dithering binary activation which improves accura ...


title: Using automatic generation of Labanotation to protect folk dance

abstract: Labanotation uses symbols to describe human motion and is an effective means of protecting folk dance. We use motion capture data to automatically generate Labanotation. First, we convert the motion capture data of the biovision hierarchy file into three-dimensional coordinate data. Second, we divide human motion into element movements. Finally, we analyze each movement and find the corresponding notation. Our work has been supervised by an expert in Labanotation to ensure the correctness of the results. At present, the work deals with a subset of symbols in Labanotation that correspond to several basic movements. Labanotation contains many symbols and several new symbols may be introduced for improvement in the future. We will refine our work to handle more symbols. The automatic generation of Labanotation can greatly improve the work efficiency of documenting movements. Thus, our work will significantly contribute to the protection of folk dance and other action arts.


title: Efficient Clustering of Uncertain Data

abstract: We study the problem of clustering data objects whose locations are uncertain. A data object is represented by an uncertainty region over which a probability density function (pdf) is defined. One method to cluster uncertain objects of this sort is to apply the UK-means algorithm, which is based on the traditional K-means algorithm. In UK-means, an object is assigned to the cluster whose representative has the smallest expected distance to the object. For arbitrary pdf, calculating the expected distance between an object and a cluster representative requires expensive integration computation. We study various pruning methods to avoid such expensive expected distance calculation.


title: Lowpass/bandpass signal reconstruction and digital filtering from nonuniform samples

abstract: This paper considers the problem of non uniform sampling in the case of finite energy functions and random processes, not necessarily approaching to zero as time goes to infinity. The proposed method allows to perform exact signal reconstruction, spectral estimation or linear filtering directly from the non-uniform samples. The method can be applied to either lowpass, or bandpass signals.


title: An attack scenario and mitigation mechanism for enterprise BYOD environments

abstract: The recent proliferation of the Internet of Things (IoT) technology poses major security and privacy concerns. Specifically, the use of personal IoT devices, such as tablets, smartphones, and even smartwatches, as part of the Bring Your Own Device (BYOD) trend, may result in severe network security breaches in enterprise environments. Such devices increase the attack surface by weakening the digital perimeter of the enterprise network and opening new points of entry for malicious activities. In this paper we demonstrate a novel attack scenario in an enterprise environment by exploiting the smartwatch device of an innocent employee. Using a malicious application running on a suitable smartwatch, the device imitates a real Wi-Fi direct printer service in the network. Using this attack scenario, we illustrate how an advanced attacker located outside of the organization can leak/steal sensitive information from the organization by utilizing the compromised smartwatch as a means of attack. An attack mitigation process and countermeasures are suggested in order to limit the capability of the remote attacker to execute the attack on the network, thus minimizing the data leakage by the smartwatch.


An efficient approach to model-based hierarchical reinforcement learning


title: "You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks

abstract: Video sharing platforms like YouTube are increasingly targeted by aggression and hate attacks. Prior work has shown how these attacks often take place as a result of "raids," i.e., organized efforts by ad-hoc mobs coordinating from third-party communities. Despite the increasing relevance of this phenomenon, however, online services often lack effective countermeasures to mitigate it. Unlike well-studied problems like spam and phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defense mechanisms that look for automated activity unsuitable. Therefore, the de-facto solution is to reactively rely on user reports and human moderation. In this paper, we propose an automated solution to identify YouTube videos that are likely to be targeted by coordinated harassers from fringe communities like 4chan. First, we characterize and model YouTube videos along several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of videos that were targeted by raids. Then, we use an ensemble of classifiers to determine the likelihood that a video will be raided with very good results (AUC up to 94%). Overall, our work provides an important first step towards deploying proactive systems to detect and mitigate coordinated hate attacks on platforms like YouTube.


title: Cloud computing simulators: A detailed survey and future direction

abstract: Cloud computing is one of the emerging technologies with its ease of access and diverse applicability, letting customers attracted to it and thus posing many challenging issues that need to overcome in this field. Since the evolution of cloud computing: Load balancing, power constrains, program offloading, cost modelling and security issues are the popular research topic in this field. Deploying real cloud for testing or for commercial use is very costly. Cloud simulator helps to model various kinds of cloud application by creating Data Centre, Virtual Machine and many Utilities which can be added to configure it, thus making it very easy to analyse. Till now, many cloud simulators have been proposed and also available to use. These simulators are built for specific purpose, and have varying features in each of them. In this paper we presented a comprehensive study of major cloud simulators by highlighting their important features and analysing their pros and cons. we made a comparison among the simulators by considering their important attributes and finally concluded with our future direction.


Depression , Anxiety and Stress among Undergraduate Nursing Students in a Public University in Sri Lanka


title: The Habitat Requirement of the Genji-Firefly Luciola cruciata (Coleoptera : Lampyridae), a Representative Endemic Species of Japanese Rural Landscapes

abstract: Raising public interest in nature through conserving species of high social interest is crucial in achieving effective conservation of biodiversity. In Japan, the Genji-firefly Luciola cruciata (Coleoptera Lampyridae) in biodiversity rich agricultural landscapes called the satoyama has always attracted exceptional public interest. This study provides rare information on environmental factors associated with the abundance of the Genji-firefly. Stepwise backward multiple regression revealed that firefly abundance increased with increasing pH, DO and prey abundance while decreasing with water depth and the proportion of artificially modified ditch length. These factors are thought to be influential mainly to the larval and pre-pupal periods of the firefly. The implications of the results for the conservation of the Genji-firefly are discussed, with reference to the relationship between Genji-firefly conservation and extensive biodiversity conservation in the satoyama.


title: Applying game achievement systems to enhance user experience in a photo sharing service

abstract: Achievement systems are reward structures providing additional goals for players, and thus extending the play time of videogames. In this paper, we explore how applications other than games could benefit from achievement systems, and how users perceive this additional content in a service. For this purpose, we added an achievement system to a geo-tagged photo sharing service called Nokia Image Space. The results suggest that there is some potential in achievement systems outside the game domain. The achievements triggered some friendly competition and comparison between users. However, many users were not convinced, expressing concerns about the achievements motivating undesirable usage patterns. Therefore, an achievement system poses certain design considerations when applied in nongame software.


title: Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining

abstract: Current patent systems face a serious problem of declining quality of patents as the larger number of ap- plications make it difficult for patent officers to spend enough time for evaluating each application. For building a better patent system, it is necessary to define a public consensus on the quality of patent applications in a quantitative way. In this article, we tackle the problem of assessing the quality of patent applications based on machine learning and text mining techniques. For each patent application, our tool automatically computes a score called patentability, which indicates how likely it is that the application will be approved by the patent office. We employ a new statis- tical prediction model to estimate examination results (approval or rejection) based on a large data set including 0.3 million patent applications. The model computes the patentability score based on a set of feature variables including the text contents of the specification documents. Experimental results showed that our model outperforms a conven- tional method which uses only the structural properties of the documents. Since users can access the estimated result through a Web-browser-based GUI, this system allows both patent examiners and applicants to quickly detect weak applications and to find their specific flaws.


title: Person Reidentification Using Deep Convnets With Multitask Learning

abstract: Person reidentification involves recognizing a person across nonoverlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the reidentification problem. Specifically, a Siamese network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that the use of a novel multitask learning objective is crucial for regularizing the network parameters in order to prevent overfitting due to the small size of the training data set. We complement the verification task, which is at the heart of reidentification, by training the network to jointly perform verification and identification and to recognize attributes related to the clothing and pose of the person in each image. In addition, we show that our proposed approach performs well even in the challenging cross-data set scenario, which may better reflect real-world expected performance.


title: Dynamic lithium-ion battery model for system simulation

abstract: Presents here a complete dynamic model of a lithium ion battery that is suitable for virtual-prototyping of portable battery-powered systems. The model accounts for nonlinear equilibrium potentials, rate- and temperature-dependencies, thermal effects and response to transient power demand. The model is based on publicly available data such as the manufacturers' data sheets. The Sony US18650 is used as an example. The model output agrees both with manufacturer's data and with experimental results. The model can be easily modified to fit data from different batteries and can be extended for wide dynamic ranges of different temperatures and current rates.


title: Millimeter wave array for UAV imaging MIMO radar

abstract: High resolution imaging radars at millimeter-wave frequencies for obstacle avoidance for Unmanned Aerial Vehicles (UAV) utilize a large antenna aperture and sequential mechanical scanning of the aperture to form images with high fidelity. Planar arrays are an attractive alternative. They do not require mechanical scanning, and images are formed digitally using beamforming techniques [1]. To achieve image quality comparable to mechanically scanned radars, fully populated arrays require inordinate numbers of elements. These are costly, a challenge to design, and difficult to produce. Thinned or sparse arrays are a potential solution to this problem. MIMO (Multiple Input Multiple Output) techniques are an efficient method of thinning an array using spreading sequences to create an overlay of paired transmitter and receiver elements, without sacrificing image quality. The use of MIMO techniques to thin an array is a relatively recent development [2]. To be of practical use at millimeter-wave frequencies, MIMO techniques require the development of novel array geometries and architecture. This paper presents designs for the implementation of millimeter-wave planar arrays which have been thinned using MIMO techniques and considerers the limitations on the length of spreading sequences due to the increasingly pronounced effect of Doppler at higher frequencies. These considerations have led to the first instance of the use of the Moreno-Tirkel sequence Family B for high resolution imaging radar.


title: A Security Threats Measurement Model for Reducing Cloud Computing Security Risk

abstract: Cloud computing is the trend of information resources sharing and also an important tool for enterprises or organizations to enhance competitiveness. Users have the flexibility to adjust request and configuration. However, network security and resource sharing issues have continued to threaten the operation of cloud computing, making cloud computing security encounter serious test. How to evaluate cloud computing security has become a topic worthy of further exploration. Combining system, management and technique three levels security factors, this paper proposes a Security Threats Measurement Model (STMM). Applying the STMM, security of cloud computing system environment can be effectively evaluated and cloud computing security defects and problems can be concretely identified. Based on the evaluation results, the user can choice the higher security service provider or request the service provider security improvement actions.


Deep Aggregation of Local 3D Geometric Features for 3D Model Retrieval


title: In-Car Positioning and Navigation Technologies—A Survey

abstract: In-car positioning and navigation has been a killer application for Global Positioning System (GPS) receivers, and a variety of electronics for consumers and professionals have been launched on a large scale. Positioning technologies based on stand-alone GPS receivers are vulnerable and, thus, have to be supported by additional information sources to obtain the desired accuracy, integrity, availability, and continuity of service. A survey of the information sources and information fusion technologies used in current in-car navigation systems is presented. The pros and cons of the four commonly used information sources, namely, 1) receivers for radio-based positioning using satellites, 2) vehicle motion sensors, 3) vehicle models, and 4) digital map information, are described. Common filters to combine the information from the various sources are discussed. The expansion of the number of satellites and the number of satellite systems, with their usage of available radio spectrum, is an enabler for further development, in combination with the rapid development of microelectromechanical inertial sensors and refined digital maps.


title: A Low-Power Wideband Transmitter Front-End Chip for 80 GHz FMCW Radar Systems With Integrated 23 GHz Downconverter VCO

abstract: A low-power FMCW 80 GHz radar transmitter front-end chip is presented, which was fabricated in a SiGe bipolar production technology ( fT=180 GHz, fmax=250 GHz ). Additionally to the fundamental 80 GHz VCO, a 4:1-frequency divider (up to 100 GHz), a 23 GHz local oscillator (VCO) with a low phase noise of -112 dBc/Hz (1 MHz offset), a PLL-mixer and a static frequency divider is integrated together with several output buffers. This chip was designed for low power consumption (in total <; 0.5 W, i.e., 100 mA at 5 V supply voltage), which is dominated by the 80 GHz VCO due to the demands for high output power (≈ 12 dBm) and low phase noise (minimum -97 dBc/Hz at 1 MHz offset) within the total wide tuning range from 68 GHz to 92.5 GHz (Δf = 24.5 GHz). Measurements of the double-PLL system at 80 GHz showed a low phase noise of -88 dBc/Hz at 10 kHz offset frequency.


title: Network Analysis in the Legal Domain: A complex model for European Union legal sources

abstract: Legislators, designers of legal information systems, as well as citizens face often problems due to the interdependence of the laws and the growing number of references needed to interpret them. Quantifying this complexity is not an easy task. In this paper, we introduce the "Legislation Network" as a novel approach to address related problems. We have collected an extensive data set of a more than 60-year old legislation corpus, as published in the Official Journal of the European Union, and we further analysed it as a complex network, thus gaining insight into its topological structure. Among other issues, we have performed a temporal analysis of the evolution of the Legislation Network, as well as a robust resilience test to assess its vulnerability under specific cases that may lead to possible breakdowns. Results are quite promising, showing that our approach can lead towards an enhanced explanation in respect to the structure and evolution of legislation properties.


title: How do designers and user experience professionals actually perceive and use personas?

abstract: Personas are a critical method for orienting design and development teams to user experience. Prior work has noted challenges in justifying them to developers. In contrast, it has been assumed that designers and user experience professionals - whose goal is to focus designs on targeted users - will readily exploit personas. This paper examines that assumption. We present the first study of how experienced user-centered design (UCD) practitioners with prior experience deploying personas, use and perceive personas in industrial software design. We identify limits to the persona approach in the context studied. Practitioners used personas almost exclusively for communication, but not for design. Participants identified four problems with personas, finding them abstract, impersonal, misleading and distracting. Our findings argue for a new approach to persona deployment and construction. Personas cannot replace immersion in actual user data. And rather than focusing on creating engaging personas, it is critical to avoid persona attributes that mislead or distract.


title: Comparison of position sensorless control based back-EMF estimators in PMSM

abstract: This research approach a study with comparison of position sensorless control schemes based on back-electromotive force (back-EMF) estimation in permanent magnet synchronous motors (PMSM). The characteristics of the estimated back-EMF signals are analyzed using various mathematical models of a PMSM. The transfer functions of the estimators, based on the extended EMF model in the rotor reference frame, are derived to show their similarity. They are then used for the analysis of the effects of both the motor parameter variations and the voltage errors due to inverter nonlinearity on the accuracy of the back-EMF estimation. The differences between a phase-locked-loop (PLL) type estimator and a Luenberger observer type estimator, generally used for extracting rotor speed and position information from estimated back-EMF signals, are also examined. An experimental study with a 250-W interior-permanent-magnet machine has been performed to validate the analyses.


title: Usability Evaluation of Web Based Educational Multimedia by Eye- Tracking Technique

abstract: In cases that education processes are supported by the internet and web technologies, usability is one of the factors that can affect success of students and technology usage status of teachers. In this study, usability factors for effective design and usage of web based multimedia packages created for educational purposes are surveyed. During the research, experiments and analysis are done in the Human – Computer Interaction Laboratory with nine prospective teachers. According to literature review, it is generally possible to find 75% of the usability problems by testing only 5 participants. Experiment data are collected by using screen and voice records, which contain eye and mouse motions. For analysis of data, fixation count, sequence and dwell time values in gridded area of interest, heat map values in whole are considered. The present study demonstrates that large number of fixations and the disperse nature of fixation locations make it hard for users to find the task. It was also found based on eye-movement data that size of some objects and words were too small to notice. There should be options that enable users to decide on the size of objects according to their preference. Finally, this study makes recommendations aimed at making the multimedia more effective, efficient and of higher quality to ensure user satisfaction. It can be said that results obtained from this study can also be used for design and validation of computer aided materials such as learning objects, web based applications and serious games.


title: Developing Millimeter-Wave Planar Antenna With a Cosecant Squared Pattern

abstract: This communication reports a novel planar array antenna having a cosecant squared pattern for millimeter-wave communication systems. The substrate integrated waveguide, which has high quality factor, low transmission loss, and no parasitic radiation, has been adopted to develop the proposed antenna. To improve the design efficiency, the aggressive space mapping (ASM) algorithm is used for synthesizing the desired excitations with low computation cost and high accuracy. A radiation model that considers mutual couplings among element antennas is developed based on the active element pattern for the coarse model of the ASM. And the genetic algorithm is adopted for the parameter mapping and extraction of the ASM process. As a demonstration, a prototype has been designed with a center frequency of 42.6 GHz, and experiments are carried out to verify the design. The measured results show that the measured antenna has a cosecant squared region up to 40°, a gain of 17.7 dBi at 42.6 GHz, and the maximum sidelobe is lower than −16.5 dB. The proposed antenna can be used for the base station of the long-range Qlink-Pan communication system.


title: A Design of U-Shaped Slot Antenna With Broadband Dual Circularly Polarized Radiation

abstract: In this communication, a novel broadband dual circularly polarized (CP) antenna is presented. This antenna consists of an U-shaped slot to achieve broadband CP radiation and two 50- $\Omega $ microstrip-fed ports to realize both right-hand circular polarization and left-hand circular polarization at the same frequency band. The axial ratio bandwidth (ARBW) is greatly broadened by moving the feeding ports to upper portion of the U-shaped slot. To verify this design, the proposed antenna is fabricated and measured. The measured −10 dB reflection coefficient bandwidth is about 114.4% (1.80–6.61 GHz) and the 3-dB ARBW is approximately 110.5% (1.83–6.35 GHz). The isolation between two ports is better than 14.8 dB within the ARBW.


title: Refinement Methodology for Automatic Document Alignment Using Taxonomy in Digital Libraries

abstract: Effective information retrieval in digital libraries requires semantic alignments of documents with taxonomy. The alignments provide the semantic description of documents. The proposed methodology aligns documents using the hierarchical structure of taxonomy. It refines the results of the existing semantic keyphrase extraction algorithm. The evaluation shows promising results.