title: Evaluating a model for cost-effective data quality management in a real-world CRM setting

abstract: Managing data resources at high quality is usually viewed as axiomatic. However, we suggest that, since the process of improving data quality should attempt to maximize economic benefits as well, high data quality is not necessarily economically-optimal. We demonstrate this argument by evaluating a microeconomic model that links the handling of data quality defects, such as outdated data and missing values, to economic outcomes: utility, cost, and net-benefit. The evaluation is set in the context of Customer Relationship Management (CRM) and uses large samples from a real-world data resource used for managing alumni relations. Within this context, our evaluation shows that all model parameters can be measured, and that all model-related assumptions are, largely, well supported. The evaluation confirms the assumption that the optimal quality level, in terms of maximizing net-benefits, is not necessarily the highest possible. Further, the evaluation process contributes some important insights for revising current data acquisition and maintenance policies.


title: An ERP study on the time course of emotional face processing.

abstract: Using event-related brain potentials (ERPs), we investigated the time course of facial expression processing in human subjects watching photographs of fearful and neutral faces. Upright fearful faces elicited a frontocentral positivity within120 ms after stimulus presentation, which was followed by a broadly distributed sustained positivity beyond 250 ms post-stimulus. Emotional expression eiects were delayed and attenuated when faces were inverted. In contrast, the face-speci¢c N170 component was completely unaiected by facial expression.We conclude that emotional expression analysis and the structural encoding of faces are parallel processes. Early emotional ERP modulations may re£ect the rapid activation of prefrontal areas involved in the analysis of facial expression. NeuroReport 13:1^5 c 2002 Lippincott Williams &W ilkins.


title: Eddy-Current Displacement Transducer With Extended Linear Range and Automatic Tuning

abstract: Design details and measurement results of the eddy-current displacement transducer with extended linear range and automatic tuning are presented. The transducer is based on a resonant impedance inversion method of transfer curve linearization where the displacement probe circuit is kept in resonance by the resonance control loop. The transducer exhibits an extended linear range due to the compensation of displacement probe losses by a negative impedance converter (NIC) at the transducer input. Particular attention is paid to the NIC design and its temperature compensation. The new voltage-controlled NIC circuit is introduced, which can easily be realized with a standard commercial integrated circuit (LM 1496). Details of the transducer's reference generator and the NIC control voltage generator are also presented. The transducer's linear range extends from 0.25 to 3.75 mm (approximately 44% of the 8-mm probe diameter) while maintaining nonlinearity within plusmn2% F.S. This result is, at least, a 75% improvement to the best commercially available eddy-current displacement transducers (a linear range of 80 mil ~2 mm for an 8-mm probe). Further linearization can be achieved by postlinearization of the detected signal.


title: Online EM for Unsupervised Models

abstract: The (batch) EM algorithm plays an important role in unsupervised induction, but it sometimes suffers from slow convergence. In this paper, we show that online variants (1) provide significant speedups and (2) can even find better solutions than those found by batch EM. We support these findings on four unsupervised tasks: part-of-speech tagging, document classification, word segmentation, and word alignment.


title: High efficiency patch antenna for 24 GHz anticollision radar

abstract: This paper presents design and measured results in synphase patch antenna for anticollision radar at 24 GHz. The antenna consists of two linear arrays of 24 serially connected patches. The aim was to design an antenna that in azimuth has lobes below -20 dB in ISM range of 24.05-24.25 GHz, at which the anticollision radar operates. The realized antenna has gain of 21.75 dB and efficiency of 60% at the frequency of 24.1 GHz. Three-decibel bandwidth in azimuth is 3.6/spl deg/ and in elevation is 46/spl deg/. In the range of 24.05-24.25 GHz, gain is higher than 21.15 dB and the reflection coefficient is below -14 dB.


title: Theorizing Language Teacher Identity: Three Perspectives and Beyond

abstract: Language teacher identity is an emerging subject of interest in research on language teacher education and teacher development. Yet relatively little attention has been paid to the ways in which teacher identity is theorized. The present article explores ways of theorizing language teacher identity by presenting three data-based studies of teacher identity and juxtaposing the three different theoretical frameworks that they use: Tajfel's (1978) social identity theory, Lave and Wenger's (1991) theory of situated learning, and Simon's (1995) concept of the image-text. It is seen that each theoretical perspective allows us to investigate different substantive and theoretical aspects of language teacher identity and that there are strong conceptual resonances among the different approaches. While in isolation each theory has its limitations, an openness to multiple theoretical approaches allows a richer and more useful understanding of the processes and contexts of teacher identity.


title: NScale: neighborhood-centric large-scale graph analytics in the cloud

abstract: There is an increasing interest in executing complex analyses over large graphs, many of which require processing a large number of multi-hop neighborhoods or subgraphs. Examples include ego network analysis, motif counting, finding social circles, personalized recommendations, link prediction, anomaly detection, analyzing influence cascades, and others. These tasks are not well served by existing vertex-centric graph processing frameworks, where user programs are only able to directly access the state of a single vertex at a time, resulting in high communication, scheduling, and memory overheads in executing such tasks. Further, most existing graph processing frameworks ignore the challenges in extracting the relevant portions of the graph that an analysis task is interested in, and loading those onto distributed memory. This paper introduces NScale, a novel end-to-end graph processing framework that enables the distributed execution of complex subgraph-centric analytics over large-scale graphs in the cloud. NScale enables users to write programs at the level of subgraphs rather than at the level of vertices. Unlike most previous graph processing frameworks, which apply the user program to the entire graph, NScale allows users to declaratively specify subgraphs of interest. Our framework includes a novel graph extraction and packing (GEP) module that utilizes a cost-based optimizer to partition and pack the subgraphs of interest into memory on as few machines as possible. The distributed execution engine then takes over and runs the user program in parallel on those subgraphs, restricting the scope of the execution appropriately, and utilizes novel techniques to minimize memory consumption by exploiting overlaps among the subgraphs. We present a comprehensive empirical evaluation comparing against three state-of-the-art systems, namely Giraph, GraphLab, and GraphX, on several real-world datasets and a variety of analysis tasks. Our experimental results show orders-of-magnitude improvements in performance and drastic reductions in the cost of analytics compared to vertex-centric approaches.


AQUA: An underwater walking robot


title: Graph Connectivity Measures for Unsupervised Word Sense Disambiguation

abstract: Word sense disambiguation (WSD) has been a long-standing research objective for natural language processing. In this paper we are concerned with developing graph-based unsupervised algorithms for alleviating the data requirements for large scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most "important" node among the set of graph nodes representing its senses. We propose a variety of measures that analyze the connectivity of graph structures, thereby identifying the most relevant word senses. We assess their performance on standard datasets, and show that the best measures perform comparably to state-of-the-art.


title: "And We Will Fight For Our Race!"A Measurement Study of Genetic Testing Conversations on Reddit and 4chan

abstract: Progress in genomics has enabled the emergence of a booming market for "direct-to-consumer" genetic testing. Nowadays, companies like 23andMe and AncestryDNA provide affordable health, genealogy, and ancestry reports, and have already tested tens of millions of customers. At the same time, alt- and far-right groups have also taken an interest in genetic testing, using them to attack minorities and prove their genetic "purity." In this paper, we present a measurement study shedding light on how genetic testing is being discussed on Web communities in Reddit and 4chan. We collect 1.3M comments posted over 27 months on the two platforms, using a set of 280 keywords related to genetic testing. We then use NLP and computer vision tools to identify trends, themes, and topics of discussion. ::: Our analysis shows that genetic testing attracts a lot of attention on Reddit and 4chan, with discussions often including highly toxic language expressed through hateful, racist, and misogynistic comments. In particular, on 4chan's politically incorrect board (/pol/), content from genetic testing conversations involves several alt-right personalities and openly antisemitic rhetoric, often conveyed through memes. Finally, we find that discussions build around user groups, from technology enthusiasts to communities promoting fringe political views.


title: Low-Cost Circularly Polarized Origami Antenna

abstract: In this letter, we present a novel circularly polarized (CP) origami antenna. We fold paper in the form of an origami tetrahedron to serve as the substrate of the antenna. The antenna comprises two triangular monopole elements that are perpendicular to each other. Circular polarization characteristics are achieved by exciting both elements with equal magnitudes and with a phase difference of 90°. In this letter, we explain the origami folding steps in detail. We also verify the proposed concept of the CP origami antenna by performing simulations and measurements using a fabricated prototype. The antenna exhibits a 10-dB impedance bandwidth of 70.2% (2.4–5 GHz), and a 3-dB axial-ratio bandwidth of 8% (3.415–3.7 GHz). The measured left-hand circular polarization gain of the antenna is in the range of 5.2–5.7 dBi for the 3-dB axial-ratio bandwidth.


title: Evolution in Quantum Computing

abstract: With the skyrocketing needs of rapid processing speed and miniaturization, classical computers are not able to keep up the pace with these few necessary parameters. As classical computers work on classical mechanics there expansion is at zenith. Due to these limitation quantum mechanics is taking the role of game changer in race of computation. Quantum computing studies quantum computer with the quantum mechanics phenomenon of superposition, entanglement, tunneling and annealing to solve problems that can't be solved in life span of human beings. This review aims for summarizing timeline of the quantum computing along with quantum mechanics behind it.


title: Green tea consumption and the risk of liver cancer in Japan: the Ohsaki Cohort study

abstract: OBJECTIVES ::: To investigate the association between green tea consumption and liver cancer incidence. ::: ::: ::: METHODS ::: We prospectively followed 41,761 Japanese adults aged 40-79 years, without a history of cancer at the baseline or any missing data for green tea consumption frequency. Cox proportional hazards models were used to calculate hazard ratios (HR) with 95% confidence intervals (CI), adjusted for age, alcohol drinking, smoking, the consumption of coffee, vegetables, dairy products, fruit, fish, and soybean. ::: ::: ::: RESULTS ::: Over 9 years of follow-up, among 325,947 accrued person-years, the total incidence of liver cancer was 247 cases. We found that green tea consumption was inversely associated with the incidence of liver cancer. In men, the multivariate-adjusted HRs (95% CIs) for liver cancer incidence with different green tea consumption categories were 1.00 (reference) for <1 cup/day, 0.83 (0.53-1.30) for 1-2 cups/day, 1.11 (0.73-1.68) for 3-4 cups/day, and 0.63 (0.41-0.98) for >/=5 cups/day (p for trend = 0.11). The corresponding data among women were 1.00 (reference), 0.68 (0.35-1.31), 0.79 (0.44-1.44), 0.50 (0.27-0.90) (p for trend = 0.04). ::: ::: ::: CONCLUSIONS ::: Green tea consumption is associated with a reduced risk of liver cancer incidence.


title: An X-band, 23.8-dBm fully integrated power amplifier with 25.8% PAE in 0.18-µm CMOS technology

abstract: An X-band high efficiency power amplifier with the highest PAE is presented in this letter. The single-stage power amplifier is implemented in TSMC standard bulk 0.18-µm 1P6M CMOS technology. In order to obtain wide bandwidth at PAE and output power, broadband output and input matching network are adopted in the design. From the measurements, the power amplifier obtained the best PAE of 25.8% and saturation output power of 23.8 dBm at 9.5 GHz. Besides, this PA demonstrates a 1-dB power bandwidth from 7.8 to 11 GHz and the PAE within the band all exceed 20%. To our knowledge, this power amplifier has the highest PAE, the smallest chip size, and wide bandwidth of output power and PAE in CMOS amplifiers at X-band to date.


title: Timing-sync protocol for sensor networks

abstract: Wireless ad-hoc sensor networks have emerged as an interesting and important research area in the last few years. The applications envisioned for such networks require collaborative execution of a distributed task amongst a large set of sensor nodes. This is realized by exchanging messages that are time-stamped using the local clocks on the nodes. Therefore, time synchronization becomes an indispensable piece of infrastructure in such systems. For years, protocols such as NTP have kept the clocks of networked systems in perfect synchrony. However, this new class of networks has a large density of nodes and very limited energy resource at every node; this leads to scalability requirements while limiting the resources that can be used to achieve them. A new approach to time synchronization is needed for sensor networks.In this paper, we present Timing-sync Protocol for Sensor Networks (TPSN) that aims at providing network-wide time synchronization in a sensor network. The algorithm works in two steps. In the first step, a hierarchical structure is established in the network and then a pair wise synchronization is performed along the edges of this structure to establish a global timescale throughout the network. Eventually all nodes in the network synchronize their clocks to a reference node. We implement our algorithm on Berkeley motes and show that it can synchronize a pair of neighboring motes to an average accuracy of less than 20ms. We argue that TPSN roughly gives a 2x better performance as compared to Reference Broadcast Synchronization (RBS) and verify this by implementing RBS on motes. We also show the performance of TPSN over small multihop networks of motes and use simulations to verify its accuracy over large-scale networks. We show that the synchronization accuracy does not degrade significantly with the increase in number of nodes being deployed, making TPSN completely scalable.


Creating a Grammar-Based Speech Recognition Parser for Mexican Spanish Using HTK, Compatible with CMU Sphinx-III System


title: Section mixture models for scientific document summarization

abstract: In this paper, we present a system for summarization of scientific and structured documents that has three components: section mixture models are used for estimation of the weights of terms; a hypothesis test to select a subset of these terms; and a sentence extractor based on techniques for combinatorial optimization. The section mixture models approach is an adaptation of a bigram mixture model based on the main sections of a scientific document and a collection of citing sentences (citances) from papers that reference the document. The model was adapted from earlier work done on Biomedical documents used in the summarization task of the 2014 Text Analysis Conference (TAC 2014). The mixture model trained on the Biomedical data was used also on the data for the Computational Linguistics scientific summarization task of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (CL-SciSumm 2016). This model gives rise to machine-generated summaries with ROUGE scores that are nearly as strong as those seen on the Biomedical data and was also the highest scoring submission to the task of generating a human summary. For sentence extraction, we use the OCCAMS algorithm (Davis et al., in: Vreeken, Ling, Zaki, Siebes, Yu, Goethals, Webb, Wu (eds) ICDM workshops, IEEE Computer Society, pp 454–463, 2012) which takes the sentences from the original document and the assignment of weights of the terms computed by the language models and outputs a set of minimally overlapping sentences whose combined term coverage is maximized. Finally, we explore the importance of an appropriate background model for the hypothesis test to select terms to achieve the best quality summaries.


title: Evaluating SentiStrength for Arabic Sentiment Analysis

abstract: Social networking websites are used today as platforms enabling their users to write down almost anything about everything. Social media users express their opinions and feelings about lots of events occurring in their daily lives. Lots of studies are conducted to study the sentiments presented by social media users regarding different topics. Sentiment Analysis (SA) is a new field that is concerned with measuring the sentiment presented in a given text. Due to their wide set of applications, several SA tools are available. Most of them are designed for English text. As for other languages such as Arabic, the case is different since only few tools are available. In fact, many of these tools were originally designed for English and were later adapted to deal with Arabic. SentiStrength is an example of tools that are successful for English and were later adapted to Arabic. However, the adaptation has been done in a crude manner and no deep studies are available to measure the effectiveness of such tools for Arabic text. In this paper, we perform a comprehensive evaluation of SentiStrength using 11 Arabic datasets consisting of tens of thousands of reviews/comments from different domains and in different dialects. We perform the evaluation in terms of positive and negative sentiments. The evaluation results show that overall SentiStrength achieves 62% accuracy, 83.7% precision, 64% recall (positive correct), 68% F1 measure and 55% negative correct.


title: The Behavioral Regulation in SportQuestionnaire (BRSQ): InstrumentDevelopment and Initial Validity Evidence

abstract: The purpose of the four studies described in this article was to develop and test a new measure of competitive sport participants’ intrinsic motivation, extrinsic motivation, and amotivation (self-determination theory; Deci & Ryan, 1985). The items for the new measure, named the Behavioral Regulation in Sport Questionnaire (BRSQ), were constructed using interviews, expert review, and pilot testing. Analyses supported the internal consistency, test–retest reliability, and factorial validity of the BRSQ scores. Nomological validity evidence was also supportive, as BRSQ subscale scores were correlated in the expected pattern with scores derived from measures of motivational consequences. When directly compared with scores derived from the Sport Motivation Scale (SMS; Pelletier, Fortier, Vallerand, Tuson, & Blais, 1995) and a revised version of that questionnaire (SMS-6; Mallett, Kawabata, Newcombe, Otero-Forero, & Jackson, 2007), BRSQ scores demonstrated equal or superior reliability and factorial validity as well as better nomological validity.


title: A model predictive control approach for combined braking and steering in autonomous vehicles

abstract: In this paper we present a Model Predictive Control (MPC) approach for combined braking and steering systems in autonomous vehicles. We start from the result presented in F. Borrelli et al. (2005) and P. Falcone et al. (2006), where a Model Predictive Controller (MPC) for autonomous steering systems has been presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle and the brakes at the four wheels independently, while fulfilling various physical and design constraints.


title: A comparative study of users' microblogging behavior on sina weibo and twitter

abstract: In this article, we analyze and compare user behavior on two different microblogging platforms: (1) Sina Weibo which is the most popular microblogging service in China and (2) Twitter. Such a comparison has not been done before at this scale and is therefore essential for understanding user behavior on microblogging services. In our study, we analyze more than 40 million microblogging activities and investigate microblogging behavior from different angles. We (i) analyze how people access microblogs and (ii) compare the writing style of Sina Weibo and Twitter users by analyzing textual features of microposts. Based on semantics and sentiments that our user modeling framework extracts from English and Chinese posts, we study and compare (iii) the topics and (iv) sentiment polarities of posts on Sina Weibo and Twitter. Furthermore, (v) we investigate the temporal dynamics of the microblogging behavior such as the drift of user interests over time. Our results reveal significant differences in the microblogging behavior on Sina Weibo and Twitter and deliver valuable insights for multilingual and culture-aware user modeling based on microblogging data. We also explore the correlation between some of these differences and cultural models from social science research.


title: Parallel plate Transmission Line Transformer

abstract: A Transmission Line Transformer (TLT) can be used to transform high-voltage nanosecond pulses. These transformers rely on the fact that the length of the pulse is shorter than the transmission lines used. This allows connecting the transmission lines in parallel at the input and in series at the output. In the ideal case such structures achieve a voltage gain which equals the number of transmission lines used. To achieve maximum efficiency, mismatch and secondary modes must be suppressed. Here we describe a TLT based on parallel plate transmission lines. The chosen geometry results in a high efficiency, due to good matching and minimized secondary modes. A second advantage of this design is that the electric field strength between the conductors is the same throughout the entire TLT. This makes the design suitable for high voltage applications. To investigate the concept of this TLT design, measurements are done on two different TLT designs. One TLT consists of 4 transmission lines, while the other one has 8 lines. Both designs are constructed of DiBond™. This material consists of a flat polyethylene inner core with an aluminum sheet on both sides. Both TLT's have an input impedance of 3.125 Ω. Their output impedances are 50 and 200 Ω, respectively. The measurements show that, on a matched load, this structure achieves a voltage gain factor of 3.9 when using 4 transmission lines and 7.9 when using 8 lines.


title: Case Study: SoC Performance Verification and Static Verification of RTL Parameters

abstract: Modern SoCs are developed by integrating several hundreds of IPs like hardware accelerators, I/O interfaces, memories, controllers, third party IPs, etc. It mostly uses several interconnects or cache coherent network for integration. Such complicated SoCs are prone to design errors that may affect, not only functionality but also performance. Performance bugs are much more difficult to detect in comparison to functional bugs. Even bug free performance will vary for a given application for a given SoC micro architecture and RTL design parameters. In majority of cases, performance bug detection and localization have been conducted manually and these are very challenging tasks. To localize such performance bugs in micro-architecture of various design components or interconnects using emulator/FPGA based enviornment is very challenging task. In this paper, we are discussing three approaches to deal with such problems. One approach deals with detection of critical data paths of the design by drawing directed graph of data paths. Later, dynamic performance simulations are run on those paths of the design by stressing all relevant design units. The second approach is about localization of performance bug. Third approach is to extract the RTL design parameters of the various components and analyze these parameters with various stakeholders before declaring the logic freeze of the device. On applying these, we found some critical performance bugs in design which were not found with conventional techniques. The proposed methodologies reduced overall performance verification time and improved quality of design.


title: The reacTable*: A Collaborative Musical Instrument

abstract: The reacTable* is a novel multi-user electro-acoustic musical instrument with a tabletop tangible user interface. In this paper we will focus on the various collaborative aspects of this new instrument as well as on some of the related technical details such as the networking infrastructure. The instrument can be played both in local and remote collaborative scenarios and was designed from the very beginning to serve as a musical instrument for several simultaneous players.


title: Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms

abstract: The authors present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because the authors allow for mixtures of materials and treat voxels as regions, their technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume data. It also has the potential to make volume measurements more accurately and classifies noisy, low-resolution data well. There are two unusual aspects to the authors' approach. First, they assume that, due to partial-volume effects, or blurring, voxels can contain more than one material, e.g., both muscle and fat; the authors compute the relative proportion of each material in the voxels. Second, they incorporate information from neighboring voxels into the classification process by reconstructing a continuous function, /spl rho/(x), from the samples and then looking at the distribution of values that /spl rho/(x) takes on within the region of a voxel. This distribution of values is represented by a histogram taken over the region of the voxel; the mixture of materials that those values measure is identified within the voxel using a probabilistic Bayesian approach that matches the histogram by finding the mixture of materials within each voxel most likely to have created the histogram. The size of regions that the authors classify is chosen to match the sparing of the samples because the spacing is intrinsically related to the minimum feature size that the reconstructed continuous function can represent.


title: A submodular function minimization algorithm based on the minimum-norm base

abstract: We consider an application of the minimum-norm-point algorithm to submodular function minimization. Although combinatorial polynomial algorithms for submodular function minimization (SFM) have recently been obtained, there still remain (open) problems of reducing the complexity of the SFM algorithms and of constructing a practically fast SFM algorithms. We show some possible approach to the problems by means of the minimum-norm-point algorithm. Computational results on submodular function minimization reveal that our algorithm outperforms existing polynomial algorithms for SFM.


title: A Compressive Sensing Data Acquisition and Imaging Method for Stepped Frequency GPRs

abstract: A novel data acquisition and imaging method is presented for stepped-frequency continuous-wave ground penetrating radars (SFCW GPRs). It is shown that if the target space is sparse, i.e., a small number of point like targets, it is enough to make measurements at only a small number of random frequencies to construct an image of the target space by solving a convex optimization problem which enforces sparsity through lscr 1 minimization. This measurement strategy greatly reduces the data acquisition time at the expense of higher computational costs. Imaging results for both simulated and experimental GPR data exhibit less clutter than the standard migration methods and are robust to noise and random spatial sampling. The images also have increased resolution where closely spaced targets that cannot be resolved by the standard migration methods can be resolved by the proposed method.


title: Trust or consequences? Causal effects of perceived risk and subjective norms on cloud technology adoption

abstract: Abstract Cloud computing has become a popular alternative information curation solution for organizations. As more corporate proprietary information is stored in the Cloud, concerns about Cloud information security have also increased. This study investigates the causal effect of perceived risk and subjective norms on users' trust intention to adopt Cloud technology. A partial least squares structural equation modeling (PLS-SEM) analysis was performed to assess latent variables and examine moderating effects to Cloud technology adoption. Our findings suggest that a user's perceived risk and subjective norms have a significant effect on their trust intention vis-a-vis Cloud adoption, which leads to their decision on whether to adopt the Cloud technology. While a user's attitudes do influence their intention to trust the Cloud, their attitude is not moderated by either perceived risk or subjective norms. On the other hand, a user's perceived risk of the Cloud environment predominately moderates their knowledge and perceived behavioral control, which results in their knowledge and perceived behavioral control not having a direct effect on their intention to trust the Cloud. Moreover, a user's subjective norms predicate their trust intention, which further moderates their knowledge and perceived behavioral control to trust and to adopt Cloud technology.


title: Fast homography refinement in soccer videos

abstract: Sports video analysis and camera calibration are important applications which rely on accurate homography computation as a challenging task. Homography refinement is an important step in the task of accurate homography computation and homography tracking. Also, in certain applications (such as homography tracking) the process speed is of great importance. A robust and fast method for accurate refinement of highly inaccurate homographies in soccer video frames is proposed in this paper. To achieve that goal, a new homography model fitting method named the point-line (PL) method is proposed. It uses point-line correspondences to compute the homography, rather than point correspondences or line correspondences used by the common direct linear transformation (DLT) method. The method can be used as a fast homography refinement algorithm but it is sensitive to outliers. In order to make it robust to outliers, the PL method is employed in two different schemes: a random sample consensus (RANSAC) scheme and an iterative scheme. The two schemes are then evaluated on a set of video frames and are compared to the state-of-the-art methods. They are proved to be accurate and robust to noise and one of them is at least 3.9 times faster in soccer penalty area scenes.


Phase Noise and Jitter


title: Machine Learning for Plant Disease Incidence and Severity Measurements from Leaf Images

abstract: In many fields, superior gains have been obtained by leveraging the computational power of machine learning techniques to solve expert tasks. In this paper we present an application of machine learning to agriculture, solving a particular problem of diagnosis of crop disease based on plant images taken with a smartphone. Two pieces of information are important here, the disease incidence and disease severity. We present a classification system that trains a 5 class classification system to determine the state of disease of a plant. The 5 classes represent a health class and 4 disease classes. We further extend the classification system to classify different severity levels for any of the 4 diseases. Severity levels are assigned classes 1 - 5, 1 being a healthy plant, 5 being a severely diseased plant. We present ways of extracting different features from leaf images and show how different extraction methods result in different performance of the classifier. We finally present the smartphone-based system that uses the classification model learnt to do real-time prediction of the state of health of a farmers garden. This works by the farmer uploading an image of a plant in his garden and obtaining a disease score from a remote server.


THE INTERTWINED EFFECT OF PERCEIVED USEFULNESS, PERCEIVED EASE OF USE AND TRUST IN A WEBSITE ON THE INTENTION TO RETURN


title: Digital Image Authentication From JPEG Headers

abstract: It is often desirable to determine if an image has been modified in any way from its original recording. The JPEG format affords engineers many implementation trade-offs which give rise to widely varying JPEG headers. We exploit these variations for image authentication. A camera signature is extracted from a JPEG image consisting of information about quantization tables, Huffman codes, thumbnails, and exchangeable image file format (EXIF). We show that this signature is highly distinct across 1.3 million images spanning 773 different cameras and cell phones. Specifically, 62% of images have a signature that is unique to a single camera, 80% of images have a signature that is shared by three or fewer cameras, and 99% of images have a signature that is unique to a single manufacturer. The signature of Adobe Photoshop is also shown to be unique relative to all 773 cameras. These signatures are simple to extract and offer an efficient method to establish the authenticity of a digital image.


title: DC microgrid islands on ships

abstract: Since shipboard power systems are inherently islanded and consist of energy intensive sources and loads, it is an interesting application for dc microgrids. The object of this paper is to explore the merits of dc based shipboard power distribution as compared to conventional ac solutions. A survey of current initiatives, emerging concepts and design challenges is carried out. Based on the review, insight is offered on different aspects like efficiency, safety, and reliability and their relevant tradeoffs. It will be discussed how the interplay of reliability and availability, driven by the computational cost of optimization and restoration goals, forms the heart of reconfigurability.


title: Abstract MatrixFlow: Temporal Network Visual Analytics to Track Symptom Evolution during Disease Progression

abstract: Objective: ::: To develop a visual analytic system to help medical professionals improve disease diagnosis by providing insights for understanding disease progression.


title: Deep Learning for Understanding Faces

abstract: Recent developments in deep convolutional neural networks (DCNNs) have shown impressive performance improvements on various object detection/recognition problems. This has been made possible due to the availability of large annotated data and a better understanding of the nonlinear mapping between images and class labels, as well as the affordability of powerful graphics processing units (GPUs). These developments in deep learning have also improved the capabilities of machines in understanding faces and automatically executing the tasks of face detection, pose estimation, landmark localization, and face recognition from unconstrained images and videos. In this article, we provide an overview of deep-learning methods used for face recognition. We discuss different modules involved in designing an automatic face recognition system and the role of deep learning for each of them. Some open issues regarding DCNNs for face recognition problems are then discussed. This article should prove valuable to scientists, engineers, and end users working in the fields of face recognition, security, visual surveillance, and biometrics.


title: Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

abstract: Aiming at the braking energy feedback control in the optimal energy recovery of the two-motor dual-axis drive electric vehicle (EV), the efficiency numerical simulation model based on the permanent magnet synchronous motor loss was established. At the same time, under different speed and braking conditions, based on maximum recovery efficiency and data calculation of motor system, the optimization motor braking torque distribution model was established. Thus, the distribution rule of the power optimization for the front and rear electric mechanism was obtained. This paper takes the Economic Commission of Europe (ECE) braking safety regulation as the constraint condition, and finally, a new regenerative braking torque distribution strategy numerical simulation was developed. The simulation model of Simulink and CarSim was established based on the simulation object. The numerical simulation results show that under the proposed strategy, the average utilization efficiency of the motor system is increased by 3.24% compared with the I based braking force distribution strategy. Moreover, it is 9.95% higher than the maximum braking energy recovery strategy of the front axle. Finally, through the driving behavior of the driver obtained from the big data platform, we analyze how the automobile braking force matches with the driver’s driving behavior. It also analyzes how the automobile braking force matches the energy recovery efficiency. The research results in this paper provide a reference for the future calculation of braking force feedback control system based on big data of new energy vehicles. It also provides a reference for the modeling of brake feedback control system.


title: Signing RDF graphs

abstract: The Resource Description Framework (RDF) describes graphs of statements about resources. RDF is a fundamental lower layer of the semantic web. This paper explores the equality of two RDF graphs in light of the graph isomorphism literature. We consider anonymous resources as unlabelled vertices in a graph, and show that the standard graph isomorphism algorithms, developed in the 1970's, can be used effectively for comparing RDF graphs. The techniques presented are useful for testing RDF software.


Title Multiscale space vehicle component identification


title: Topic modeling for social media content: A practical approach

abstract: Perceiving the discussed topic in social media brings a great amount of value to different fields, such as marketing, security, education, and management. Topic modeling provides a powerful method for projecting text documents into topic space. In this paper, we explore an unsupervised topic modeling approach that incorporates LDA algorithm toward discovering the topics in social media content. Empirical experiments on social media datasets with 90,527 records reveal that this approach is quite effective for detecting the topic facets and extracting their dynamics over time. By analyzing the studied dataset, five main topics were discovered accurately by the presented algorithm according to the domain experts' comments. The presented model is quite general and can be applied in a wide variety of domains to automatically mining topics from the social media channels.


title: Social rejection shares somatosensory representations with physical pain

abstract: How similar are the experiences of social rejection and physical pain? Extant research suggests that a network of brain regions that support the affective but not the sensory components of physical pain underlie both experiences. Here we demonstrate that when rejection is powerfully elicited—by having people who recently experienced an unwanted break-up view a photograph of their ex-partner as they think about being rejected—areas that support the sensory components of physical pain (secondary somatosensory cortex; dorsal posterior insula) become active. We demonstrate the overlap between social rejection and physical pain in these areas by comparing both conditions in the same individuals using functional MRI. We further demonstrate the specificity of the secondary somatosensory cortex and dorsal posterior insula activity to physical pain by comparing activated locations in our study with a database of over 500 published studies. Activation in these regions was highly diagnostic of physical pain, with positive predictive values up to 88%. These results give new meaning to the idea that rejection “hurts.” They demonstrate that rejection and physical pain are similar not only in that they are both distressing—they share a common somatosensory representation as well.


title: Lava: hardware design in Haskell

abstract: Lava is a tool to assist circuit designers in specifying, designing, verifying and implementing hardware. It is a collection of Haskell modules. The system design exploits functional programming language features, such as monads and type classes, to provide multiple interpretations of circuit descriptions. These interpretations implement standard circuit analyses such as simulation, formal verification and the generation of code for the production of real circuits.Lava also uses polymorphism and higher order functions to provide more abstract and general descriptions than are possible in traditional hardware description languages. Two Fast Fourier Transform circuit examples illustrate this.


title: Fast search in Hamming space with multi-index hashing

abstract: There has been growing interest in mapping image data onto compact binary codes for fast near neighbor search in vision applications. Although binary codes are motivated by their use as direct indices (addresses) into a hash table, codes longer than 32 bits are not being used in this way, as it was thought to be ineffective. We introduce a rigorous way to build multiple hash tables on binary code substrings that enables exact K-nearest neighbor search in Hamming space. The algorithm is straightforward to implement, storage efficient, and it has sub-linear run-time behavior for uniformly distributed codes. Empirical results show dramatic speed-ups over a linear scan baseline and for datasets with up to one billion items, 64- or 128-bit codes, and search radii up to 25 bits.


title: SST: Single-Stream Temporal Action Proposals

abstract: Our paper presents a new approach for temporal detection of human actions in long, untrimmed video sequences. We introduce Single-Stream Temporal Action Proposals (SST), a new effective and efficient deep architecture for the generation of temporal action proposals. Our network can run continuously in a single stream over very long input video sequences, without the need to divide input into short overlapping clips or temporal windows for batch processing. We demonstrate empirically that our model outperforms the state-of-the-art on the task of temporal action proposal generation, while achieving some of the fastest processing speeds in the literature. Finally, we demonstrate that using SST proposals in conjunction with existing action classifiers results in improved state-of-the-art temporal action detection performance.


A Study of Meme Propagation : Statistics , Rates , Authorities , and Spread


title: Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability

abstract: Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the number of samples. A number of kernel approximation methods have thus been proposed in the recent years, among which the random features method gains much popularity due to its simplicity and direct reduction of nonlinear problem to a linear one. The Random Binning (RB) feature, proposed in the first random-feature paper \cite{rahimi2007random}, has drawn much less attention than the Random Fourier (RF) feature. In this work, we observe that the RB features, with right choice of optimization solver, could be orders-of-magnitude more efficient than other random features and kernel approximation methods under the same requirement of accuracy. We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features. In particular, we show that by drawing $R$ random grids with at least $\kappa$ number of non-empty bins per grid in expectation, RB method achieves a convergence rate of $O(1/(\kappa R))$, which not only sharpens its $O(1/\sqrt{R})$ rate from Monte Carlo analysis, but also shows a $\kappa$ times speedup over other random features under the same analysis framework. In addition, we demonstrate another advantage of RB in the L1-regularized setting, where unlike other random features, a RB-based Coordinate Descent solver can be parallelized with guaranteed speedup proportional to $\kappa$. Our extensive experiments demonstrate the superior performance of the RB features over other random features and kernel approximation methods. Our code and data is available at { \url{https://github.com/teddylfwu/RB_GEN}}.


title: GLCM Textural Features for Brain Tumor Classification

abstract: Automatic recognition system for medical images is challenging task in the field of medical image processing. Medical images acquired from different modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc which are used for the diagnosis purpose. In the medical field, brain tumor classification is very important phase for the further treatment. Human interpretation of large number of MRI slices (Normal or Abnormal) may leads to misclassification hence there is need of such a automated recognition system, which can classify the type of the brain tumor. In this research work, we used four different classes of brain tumors and extracted the GLCM based textural features of each class, and applied to twolayered Feed forward Neural Network, which gives 97.5% classification rate.


title: On marrying ontological and metamodeling technical spaces

abstract: In software engineering, the use of models and metamodeling approaches (e.g., MDA with MOF/UML) for purposes such as software design or software validation is an established practice. Ontologies constitute domain models formalized using expressive logic languages for class definitions and rules. Hence, when seen from an abstract point of view, the two paradigms and their various technological spaces seem closely related. However, in the state-of-the-art research and practice the two technologies are just beginning to converge and the relationship between the two is still under exploration. In this paper, we give an outline of current ontology technologies, such as the Semantic Web standards for a Web Ontology Language (OWL). Then, we describe a domain analysis of the different technical spaces, explaining the features of the different paradigms. Eventually, we describe some avenues for integrating various ontological technical spaces into meta modeling technical spaces.


title: Recommendation in Context-Rich Environment: An Information Network Analysis Approach

abstract: Recommendation has received tremendous attention recently due to its wide and successful applications across different domains. Different from traditional setting of recommendation tasks, modern recommendation tasks are usually exposed in a context-rich environment. For example, in addition to a user-item rating matrix, users and items are connected to other objects via different relationships and they are usually associated with rich attributes, such as text and spatio-temporal information. It turns out that heterogeneous information network serves a natural data model to capture the rich context of these recommendation tasks. In this tutorial, we will systematically introduce the methodologies of using heterogeneous information network mining approach to solve recommendation tasks, and demonstrate the effectiveness of such methods using different applications, ranging from collaboration recommendation in scientific research network to job recommendation in professional social network, and to drug discovery in biomedical networks. The topics to be covered in the tutorial include: (1) overall introduction; (2) recommendation in heterogeneous information networks, which introduces the general methodology of how to model the recommendation problem as a heterogeneous information network mining problem; (3) recommendation in a text-rich setting, where the information network is further enriched by refined analysis of text information; (4) recommendation with spatio-temporal information, where entities and relationships in the network are associated with spatio-temporal attributes; and (5) research frontiers for context-rich recommendation.


title: A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention

abstract: The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully auto- ::: matic, black-box learning machines for feature-based classi cation and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across di erent types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under speci c constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML.


title: Measuring influence on Twitter

abstract: There are currently over 175 million Twitter accounts worldwide, making Twitter one of the most popular and most observed Social Media platform. But Twitter is not so much a social network where the exchange of personal information is facilitated -- in fact, recent surveys state that it's not very social at all with a large amount of inactive accounts and a low motivation of engaging in dialogues [1]. Twitter has rather evolved into a pool of constantly updating information streams consisting of links, short status updates, and eyewitness news. Among the millions of users, a small percentage is what is called the group of influencers or alpha users. They have a large, active audience that consumes and multiplies the content published by the influencer. Thus, an influencer's content -- whether it is plain text or links -- is distributed in a number of micro-networks and receives attention from a large amount of users even though they might not even be direct followers of the influencer. The further the content is spread, the further the influence of the user reaches. There are various tools that enable performance measurement on Social Media. Some only sum up numbers such as the amount of followers or mentions gained on Twitter; others interpret the numbers and rate the performance using a specific algorithm. An example for the latter is Klout, a popular service that will be looked at more closely, focusing on the question of how Klout calculates its scores which serve as a means of measuring success of Twitter usage. The research purpose of this paper is to determine a grounded approach for measuring social networking potential of individual Twitter users.


title: Creating sophisticated development tools with OmniBrowser

abstract: Smalltalk is not only an object-oriented programming language; it is also known for its extensive integrated development environment supporting interactive and dynamic programming. While the default tools are adequate for browsing the code and developing applications, it is often cumbersome to extend the environment to support new language constructs or to build additional tools supporting new ways of navigating and presenting source code. In this paper, we present the OmniBrowser, a browser framework that supports the definition of browsers based on an explicit metamodel. With OmniBrowser a domain model is described in a graph and the navigation in this graph is specified in its associated metagraph. We present how new browsers are built from predefined parts and how new tools are easily described. The browser framework is implemented in the Squeak Smalltalk environment. This paper shows several concrete instantiations of the framework: a remake of the ubiquitous Smalltalk system browser, a coverage browser, the Duo Browser and the dynamic protocols browser.


title: A 26.8 dB Gain 19.7 dBm CMOS Power Amplifier Using 4-way Hybrid Coupling Combiner

abstract: This letter presents a novel compact 4-way hybrid coupling combiner applied in an mm-wave CMOS power amplifier (PA) design with wideband, large output power and high gain. Basing on the principle of both series and parallel combination concurrently working in a concentric distributed active transformer, the size of a 4-way power combiner can be dramatically reduced. Using the proposed power combiner, a four-stage common source PA is implemented in 65 nm CMOS process. In each stage, inductive source degeneration is employed to enhance transistor stability without reducing maximum stable gain. The measurement results show that the PA can offer 26.8 dB linear gain with 3 dB bandwidth of 51–67 GHz. At 1.2 V/1.4 V external supply voltage, the PA is able to deliver 17.5 dBm/19.7 dBm $P_{sat}$ with 10.3%/13.4% power added efficiency (PAE). The chip size of the PA is only $0.32~{\rm mm}^{2}$ .


title: The dynamics of multimodal integration: The averaging diffusion model

abstract: We combine extant theories of evidence accumulation and multi-modal integration to develop an integrated framework for modeling multimodal integration as a process that unfolds in real time. Many studies have formulated sensory processing as a dynamic process where noisy samples of evidence are accumulated until a decision is made. However, these studies are often limited to a single sensory modality. Studies of multimodal stimulus integration have focused on how best to combine different sources of information to elicit a judgment. These studies are often limited to a single time point, typically after the integration process has occurred. We address these limitations by combining the two approaches. Experimentally, we present data that allow us to study the time course of evidence accumulation within each of the visual and auditory domains as well as in a bimodal condition. Theoretically, we develop a new Averaging Diffusion Model in which the decision variable is the mean rather than the sum of evidence samples and use it as a base for comparing three alternative models of multimodal integration, allowing us to assess the optimality of this integration. The outcome reveals rich individual differences in multimodal integration: while some subjects’ data are consistent with adaptive optimal integration, reweighting sources of evidence as their relative reliability changes during evidence integration, others exhibit patterns inconsistent with optimality.


title: Mindfulness meditation training alters stress-related amygdala resting state functional connectivity: a randomized controlled trial

abstract: Recent studies indicate that mindfulness meditation training interventions reduce stress and improve stress-related health outcomes, but the neural pathways for these effects are unknown. The present research evaluates whether mindfulness meditation training alters resting state functional connectivity (rsFC) of the amygdala, a region known to coordinate stress processing and physiological stress responses. We show in an initial discovery study that higher perceived stress over the past month is associated with greater bilateral amygdala-subgenual anterior cingulate cortex (sgACC) rsFC in a sample of community adults (n ¼ 130). A follow-up, single-blind randomized controlled trial shows that a 3-day intensive mindfulness meditation training intervention (relative to a well-matched 3-day relaxation training intervention without a mindfulness component) reduced right amygdala-sgACC rsFC in a sample of stressed unemployed community adults (n ¼ 35). Although stress may increase amygdala-sgACC rsFC, brief training in mindfulness meditation could reverse these effects. This work provides an initial indication that mindfulness meditation training promotes functional neuroplastic changes, suggesting an amygdala-sgACC pathway for stress reduction effects.


title: Mining electronic health records: towards better research applications and clinical care

abstract: Clinical data describing the phenotypes and treatment of patients represents an underused data source that has much greater research potential than is currently realized. Mining of electronic health records (EHRs) has the potential for establishing new patient-stratification principles and for revealing unknown disease correlations. Integrating EHR data with genetic data will also give a finer understanding of genotype-phenotype relationships. However, a broad range of ethical, legal and technical reasons currently hinder the systematic deposition of these data in EHRs and their mining. Here, we consider the potential for furthering medical research and clinical care using EHR data and the challenges that must be overcome before this is a reality.


title: Question classification for Arabic Question Answering Systems

abstract: Due to very fast growth of information in the last few decades, getting precise information in real time is becoming increasingly difficult. Search engines such as Google and Yahoo are helping in finding the information but the information provided by them are in the form of documents which consumes a lot of time of the user. Question Answering Systems have emerged as a good alternative to search engines where they produce the desired information in a very precise way in the real time. This saves a lot of time for the user. There has been a lot of research in the field of English and some European language Question Answering Systems. However, Arabic Question Answering Systems could not match the pace due to some inherent difficulties with the language itself as well as due to lack of tools available to assist the researchers. Question classification is a very important module of Question Answering Systems. In this paper, we are presenting a method to accurately classify the Arabic questions in order to retrieve precise answers. The proposed method gives promising results.


title: Leveraging behavioral science to mitigate cyber security risk

abstract: Most efforts to improve cyber security focus primarily on incorporating new technological approaches in products and processes. However, a key element of improvement involves acknowledging the importance of human behavior when designing, building and using cyber security technology. In this survey paper, we describe why incorporating an understanding of human behavior into cyber security products and processes can lead to more effective technology. We present two examples: the first demonstrates how leveraging behavioral science leads to clear improvements, and the other illustrates how behavioral science offers the potential for significant increases in the effectiveness of cyber security. Based on feedback collected from practitioners in preliminary interviews, we narrow our focus to two important behavioral aspects: cognitive load and bias. Next, we identify proven and potential behavioral science findings that have cyber security relevance, not only related to cognitive load and bias but also to heuristics and behavioral science models. We conclude by suggesting several next steps for incorporating behavioral science findings in our technological design, development and use.


title: Pseudoangiomatous Stromal Hyperplasia: A Rare Cause of Idiopathic Gigantomastia

abstract: Gigantomastia remains a rare clinical diagnosis with significant physical and psychological impacts on patients. We present the case of a 40-year-old woman with idiopathic breast enlargement. Further histological analysis of the breast tissue revealed pseudoangiomatous stromal hyperplasia. This is the first reported case of diffuse breast enlargement resulting from pseudoangiomatous stromal hyperplasia.


title: The ns-3 Network Simulator

abstract: As networks of computing devices grow larger and more complex, the need for highly accurate and scalable network simulation technologies becomes critical. Despite the emergence of large-scale testbeds for network research, simulation still plays a vital role in terms of scalability (both in size and in experimental speed), reproducibility, rapid prototyping, and education. With simulation based studies, the approach can be studied in detail at varying scales, with varying data applications, varying field conditions, and will result in reproducible and analyzable results.


Analysis of the Drug : H + Antiporter Family 1 ( DHA 1 ) in the hemiascomycetes yeasts


title: Syllable structure development of toddlers with expressive specific language impairment

abstract: A total of 35 children – 20 with expressive specific language impairment (SLI-E) and 15 typically developing (TD) peers – were compared longitudinally from 24 to 36 months with respect to their production of syllable shapes in 10-minute spontaneous speech samples. SLI-E 24-month-olds predominantly produced earlier developing syllable shapes containing vowels, liquids, and glides. TD 24-month-olds and SLI-E 36-month-olds produced approximately the same proportion of syllable types, with the exception of consonant clusters, where TD 24-month-olds produced more than SLI-E 36-month-olds. TD children at 36 months showed the greatest use of syllable shapes containing two different consonants and consonant clusters. Detailed analyses revealed that SLI-E children produced fewer syllable shapes containing final consonants, more than one consonant type, and consonant clusters. Furthermore, the children with SLI-E were found to vocalize less often than their TD peers. The possible relationships between these findings, SLI-E children’s concomitant deficits in morphology and syntax, and the implications for diagnosis and remediation are discussed.


title: Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition

abstract: In this paper we address the problem of human action recognition from video sequences. Inspired by the exemplary results obtained via automatic feature learning and deep learning approaches in computer vision, we focus our attention towards learning salient spatial features via a convolutional neural network (CNN) and then map their temporal relationship with the aid of Long-Short-Term-Memory (LSTM) networks. Our contribution in this paper is a deep fusion framework that more effectively exploits spatial features from CNNs with temporal features from LSTM models. We also extensively evaluate their strengths and weaknesses. We find that by combining both the sets of features, the fully connected features effectively act as an attention mechanism to direct the LSTM to interesting parts of the convolutional feature sequence. The significance of our fusion method is its simplicity and effectiveness compared to other state-of-the-art methods. The evaluation results demonstrate that this hierarchical multi stream fusion method has higher performance compared to single stream mapping methods allowing it to achieve high accuracy outperforming current state-of-the-art methods in three widely used databases: UCF11, UCFSports, jHMDB.


title: Joint Incremental Disfluency Detection and Dependency Parsing

abstract: We present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. For comparison, we evaluated two pipeline systems, using state-of-the-art disfluency detectors. The joint model performed better on both tasks, with a parse accuracy of 90.5% and 84.0% accuracy at disfluency detection. The model runs in expected linear time, and processes over 550 tokens a second.


title: A Survey of Schema Matching Research using Database Schemas and Instances

abstract: Schema matching is considered as one of the essential phases of data integration in database systems. The main aim of the schema matching process is to identify the correlation between schema which helps later in the data integration process. The main issue concern of schema matching is how to support the merging decision by providing the correspondence between attributes through syntactic and semantic heterogeneous in data sources. There have been a lot of attempts in the literature toward utilizing database instances to detect the correspondence between attributes during schema matching process. Many approaches based on instances have been proposed aiming at improving the accuracy of the matching process. This paper set out a classification of schema matching research in database system exploiting database schema and instances. We survey and analyze the schema matching techniques applied in the literature by highlighting the strengths and the weaknesses of each technique. A deliberate discussion has been reported highlights on challenges and the current research trends of schema matching in database. We conclude this paper with some future work directions that help researchers to explore and investigate current issues and challenges related to schema matching in contemporary databases.


title: Analysis of Blockchain technology: pros, cons and SWOT

abstract: Any online transaction that involves digital money is a bit of a challenge these days with the rising threats of hackers trying to steal bank details posted online. This leads to the invention of various kinds of crypto-currency, Bitcoin being one of them. The technology behind using the Bitcoin is popularly called as Blockchain. Blockchain is a digitized, de-centralized, public ledger of all crypto-currency transaction/s. Blockchain tries to create and share all the online transactions, stored in a distributed ledger, as a data structure on a network of computers. It validates the transactions using peer-to-peer network of computers. It allows users to make and verify transactions immediately without a central authority. Blockchain is a transaction database which contains information about all the transactions ever executed in the past and works on Bitcoin protocol. In this analysis paper we discussed what is Blockchain?, SWOT analysis of BC, Types of BC and how Blockchain works along with its advantages and disadvantages.


title: AckSeer: a repository and search engine for automatically extracted acknowledgments from digital libraries

abstract: Acknowledgments are widely used in scientific articles to express gratitude and credit collaborators. Despite suggestions that indexing acknowledgments automatically will give interesting insights, there is currently, to the best of our knowledge, no such system to track acknowledgments and index them. In this paper we introduce AckSeer, a search engine and a repository for automatically extracted acknowledgments in digital libraries. AckSeer is a fully automated system that scans items in digital libraries including conference papers, journals, and books extracting acknowledgment sections and identifying acknowledged entities mentioned within. We describe the architecture of AckSeer and discuss the extraction algorithms that achieve a F1 measure above 83%. We use multiple Named Entity Recognition (NER) tools and propose a method for merging the outcome from different recognizers. The resulting entities are stored in a database then made searchable by adding them to the AckSeer index along with the metadata of the containing paper/book. We build AckSeer on top of the documents in CiteSeerx digital library yielding more than 500,000 acknowledgments and more than 4 million mentioned entities.


title: Fine-grained DVFS using on-chip regulators

abstract: Limit studies on Dynamic Voltage and Frequency Scaling (DVFS) provide apparently contradictory conclusions. On the one hand early limit studies report that DVFS is effective at large timescales (on the order of million(s) of cycles) with large scaling overheads (on the order of tens of microseconds), and they conclude that there is no need for small overhead DVFS at small timescales. Recent work on the other hand—motivated by the surge of on-chip voltage regulator research—explores the potential of fine-grained DVFS and reports substantial energy savings at timescales of hundreds of cycles (while assuming no scaling overhead). This article unifies these apparently contradictory conclusions through a DVFS limit study that simultaneously explores timescale and scaling speed. We find that coarse-grained DVFS is unaffected by timescale and scaling speed, however, fine-grained DVFS may lead to substantial energy savings for memory-intensive workloads. Inspired by these insights, we subsequently propose a fine-grained microarchitecture-driven DVFS mechanism that scales down voltage and frequency upon individual off-chip memory accesses using on-chip regulators. Fine-grained DVFS reduces energy consumption by 12p on average and up to 23p over a collection of memory-intensive workloads for an aggressively clock-gated processor, while incurring an average 0.08p performance degradation (and at most 0.14p). We also demonstrate that the proposed fine-grained DVFS mechanism is orthogonal to existing coarse-grained DVFS policies, and further reduces energy by 6p on average and up to 11p for memory-intensive applications with limited performance impact (at most 0.7p).


The Consumer Motivation Scale: A detailed review of item generation, exploration, confirmation, and validation procedures


title: Mastering social media: An analysis of Jefferson County's communications during the 2013 Colorado floods

abstract: We report on the social media communications and work practices of the Jefferson County Type III Incident Management Team during the September 2013 Colorado Floods. In this case study, we examine flood-related communications across three platforms: Facebook, Twitter, and the team’s blog for insight into how this innovative team coordinated their communications to meet the information needs of a community outside of the media spotlight. Using a mixed method approach of interviews and social media content analysis, we describe their online behaviors in relation to the needs of the emergency response as a whole. We report on adaptations to their work practice that allowed them to extend traditional communications with social media to create an integrated communication plan. Finally, we look to the team’s experiences for direction in how to use social media in emergencies generally.


title: Resource- and Message Size-Aware Scheduling of Stream Processing at the Edge with application to Realtime Microscopy

abstract: Whilst computational resources at the cloud edge can be leveraged to improve latency and reduce the costs of cloud services for a wide variety mobile, web, and IoT applications; such resources are naturally constrained. For distributed stream processing applications, there are clear advantages to offloading some processing work to the cloud edge. Many state of the art stream processing applications such as Flink and Spark Streaming, being designed to run exclusively in the cloud, are a poor fit for such hybrid edge/cloud deployment settings, not least because their schedulers take limited consideration of the heterogeneous hardware in such deployments. In particular, their schedulers broadly assume a homogeneous network topology (aside from data locality consideration in, e.g., HDFS/Spark). Specialized stream processing frameworks intended for such hybrid deployment scenarios, especially IoT applications, allow developers to manually allocate specific operators in the pipeline to nodes at the cloud edge. In this paper, we investigate scheduling stream processing in hybrid cloud/edge deployment settings with sensitivity to CPU costs and message size, with the aim of maximizing throughput with respect to limited edge resources. We demonstrate real-time edge processing of a stream of electron microscopy images, and measure a consistent reduction in end-to-end latency under our approach versus a resource-agnostic baseline scheduler, under benchmarking.


title: IoT based solar energy monitoring system

abstract: The Internet of Things has a vision in which the internet extends into the real world, which incorporates everyday objects. The IoT allows objects to be sensed or controlled remotely over existing network infrastructure, creating opportunities for pure integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention. This technology has many applications like Solar cities, Smart villages, Micro grids and Solar Street lights and so on. As Renewable energy grew at a rate faster than any other time in history during this period. The proposed system refers to the online display of the power usage of solar energy as a renewable energy. This monitoring is done through raspberry pi using flask framework. Smart Monitoring displays daily usage of renewable energy. This helps the user to analysis of energy usage. Analysis impacts on the renewable energy usage and electricity issues.


Dynamic Behaviors of Pneumatic Cylinder (Friction and Vibration Characteristics)


title: Preserving differential privacy in convolutional deep belief networks

abstract: The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing $$\epsilon $$∈-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions.


title: Inverting the Generator of a Generative Adversarial Network

abstract: Generative adversarial networks (GANs) learn a deep generative model that is able to synthesise novel, high-dimensional data samples. New data samples are synthesised by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties, that may be useful for down stream tasks such as classification or retrieval. Unfortunately, GANs do not offer an "inverse model", a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample. In this paper, we introduce a technique, inversion, to project data samples, specifically images, to the latent space using a pre-trained GAN. Using our proposed inversion technique, we are able to identify which attributes of a dataset a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss. We demonstrate how our proposed inversion technique may be used to quantitatively compare performance of various GAN models trained on three image datasets. We provide code for all of our experiments, this https URL


title: Efficient Exploration Through Bayesian Deep Q-Networks

abstract: We propose Bayesian Deep Q-Networks (BDQN), a Thompson sampling approach for Deep Reinforcement Learning (DRL) in Markov decision processes (MDP). BDQN is an efficient exploration-exploitation algorithm which combines Thompson sampling with deep-Q networks (DQN) and directly incorporates uncertainty over the Q-value in the last layer of the DQN, on the feature representation layer. This allows us to efficiently carry out Thompson sampling through Gaussian sampling and Bayesian Linear Regression (BLR), which has fast closed-form updates. We apply our method to a wide range of Atari games and compare BDQN to a powerful baseline: the double deep Q-network (DDQN). Since BDQN carries out more efficient exploration, it is able to reach higher rewards substantially faster: in less than 5M-+1M interactions for almost half of the games to reach DDQN scores. We also establish theoretical guarantees for the special case when the feature representation is d-dimensional and fixed. We provide the Bayesian regret of posterior sampling RL (PSRL) and frequentist regret of the optimism in the face of uncertainty (OFU) for episodic MDPs.


title: A Smart Parking Lot Management System for Scheduling the Recharging of Electric Vehicles

abstract: In this paper, we propose a centralized electric vehicles (EVs) recharge scheduling system for parking lots using a realistic vehicular mobility/parking pattern focusing on individual parking lots. We consider two different types of EV based on their mobility/parking patterns: 1) regular EVs; and 2) irregular EVs. An extensive trace-based vehicular mobility model collected from the Canton of Zurich is used for the regular EVs, and a probabilistic pattern built on top of this trace is used for modeling the behavior of irregular EVs. To the extent of our knowledge, this is the first EV charging scheduling study in the literature that takes into account a realistic vehicular mobility pattern focusing on individual parking lots. We compare the performance of our proposed system with two well-known basic scheduling mechanisms, first come first serve and earliest deadline first, with regard to two objective functions: 1) maximizing the total parking lot revenue; and 2) maximizing the total number of EVs fulfilling their requirements. Comparison results show that our proposed system outperforms well-known basic scheduling mechanisms with regards to both objectives. Parking lots managing the recharging of a high number of EVs will greatly benefit from using such recharge scheduling systems in the context of smart cities.


title: Confidence Driven Unsupervised Semantic Parsing

abstract: Current approaches for semantic parsing take a supervised approach requiring a considerable amount of training data which is expensive and difficult to obtain. This supervision bottleneck is one of the major difficulties in scaling up semantic parsing. ::: ::: We argue that a semantic parser can be trained effectively without annotated data, and introduce an unsupervised learning algorithm. The algorithm takes a self training approach driven by confidence estimation. Evaluated over Geoquery, a standard dataset for this task, our system achieved 66% accuracy, compared to 80% of its fully supervised counterpart, demonstrating the promise of unsupervised approaches for this task.


title: The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans

abstract: A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false information campaigns with targeted manipulation of public opinion on specific topics. These false information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one, and needs increased public awareness, as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community. In this paper, we make a step in this direction by providing a taxonomy of the Web's false information ecosystem, comprising various types of false information, actors, and their motives. We report a comprehensive overview of existing research on the false information ecosystem by identifying several lines of work: 1) how the public perceives false information; 2) understanding the propagation of false information; 3) detecting and containing false information on the Web; and 4) false information on the political stage. In this work, we pay particular attention to political false information as: 1) it can have dire consequences to the community (e.g., when election results are mutated) and 2) previous work show that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false information dissemination on the Web.


title: Sequence determinants of improved CRISPR sgRNA design

abstract: The CRISPR/Cas9 system has revolutionized mammalian somatic cell genetics. Genome-wide functional screens using CRISPR/Cas9-mediated knockout or dCas9 fusion-mediated inhibition/activation (CRISPRi/a) are powerful techniques for discovering phenotype-associated gene function. We systematically assessed the DNA sequence features that contribute to single guide RNA (sgRNA) efficiency in CRISPR-based screens. Leveraging the information from multiple designs, we derived a new sequence model for predicting sgRNA efficiency in CRISPR/Cas9 knockout experiments. Our model confirmed known features and suggested new features including a preference for cytosine at the cleavage site. The model was experimentally validated for sgRNA-mediated mutation rate and protein knockout efficiency. Tested on independent data sets, the model achieved significant results in both positive and negative selection conditions and outperformed existing models. We also found that the sequence preference for CRISPRi/a is substantially different from that for CRISPR/Cas9 knockout and propose a new model for predicting sgRNA efficiency in CRISPRi/a experiments. These results facilitate the genome-wide design of improved sgRNA for both knockout and CRISPRi/a studies.


title: Unsupervised Type and Token Identification of Idiomatic Expressions

abstract: Idiomatic expressions are plentiful in everyday language, yet they remain mysterious, as it is not clear exactly how people learn and understand them. They are of special interest to linguists, psycholinguists, and lexicographers, mainly because of their syntactic and semantic idiosyncrasies as well as their unclear lexical status. Despite a great deal of research on the properties of idioms in the linguistics literature, there is not much agreement on which properties are characteristic of these expressions. Because of their peculiarities, idiomatic expressions have mostly been overlooked by researchers in computational linguistics. In this article, we look into the usefulness of some of the identified linguistic properties of idioms for their automatic recognition. Specifically, we develop statistical measures that each model a specific property of idiomatic expressions by looking at their actual usage patterns in text. We use these statistical measures in a type-based classification task where we automatically separate idiomatic expressions (expressions with a possible idiomatic interpretation) from similar-on-the-surface literal phrases (for which no idiomatic interpretation is possible). In addition, we use some of the measures in a token identification task where we distinguish idiomatic and literal usages of potentially idiomatic expressions in context.


title: Computational GOMS modeling of a complex team task: lessons learned

abstract: This paper presents the lessons learned when a computational GOMS modeling tool was used to evaluate user interface concepts and team structure designs for a new class of military shipboard workstations. The lessons are both encouraging and cautionary: For example, computational GOMS models scaled well to a large and complex task involving teams of users. Interruptability and working memory constructs had to be added to conventional GOMS model concepts. However, two surprises emerged: First, the non-psychological aspects of the model construction were the practical bottleneck. Second, user testing data in this domain were difficult to collect and lacked definition, meaning that the model provided a better characterization of the design details than the user testing data. Included in these lessons are recommendations for future model applications and modeling methodology development.


title: Applications of VANETs: Present & Future

abstract: The Vehicular Safety Consortium (VSC), the Crash-Avoidance Metrics Partnership (CAMP) consortium and the Vehicle Infrastructure Initiative (VII) [1] along with the giants of the light-duty vehicle manufactures, are working to develop pre-competitive safety technologies and various applications that can be offered in Vehicular ad-hoc Networks (VANETs), a special kind mobile ad-hoc networks where wireless equipped element called on-board unit (OBU) in vehicles form a network with the Roadside unit (RSU) without any additional infrastructure. In this paper, we are primarily categorizing various possible applications of vehicular network, along with its features, and implementations in the real world.


title: Detection of Wi-Fi transmitter transients using statistical method

abstract: Algorithms used to detect Wi-Fi transmitter transients are discussed in this paper, and the start of a Wi-Fi transmitter transient, in our opinion, has a new definition. Current algorithms, namely Variance Fractal Dimension Threshold Detection, Bayesian Step Change Detection and Phase Detection are analyzed at the beginning of this article. According to the disadvantages such as complexity, accuracy and a threshold needed in the final determination of these traditional methods, an improved algorithm based on Mean Change Point Detection is put forward. Threshold for determination and nonparametric estimation for hypothesis test are not needed in our improved approach, it detects the start of transient only by calculating the maximum of the difference of statistic. Moreover, the experimental results show that this improved method outperforms the other three methods, particularly in the case of low SNR.


title: A deep learning methodology to proliferate golden signoff timing

abstract: Signoff timing analysis remains a critical element in the IC design flow. Multiple signoff corners, libraries, design methodologies, and implementation flows make timing closure very complex at advanced technology nodes. Design teams often wish to ensure that one tool's timing reports are neither optimistic nor pessimistic with respect to another tool's reports. The resulting “correlation” problem is highly complex because tools contain millions of lines of black-box and legacy code, licenses prevent any reverse-engineering of algorithms, and the nature of the problem is seemingly “unbounded” across possible designs, timing paths, and electrical parameters. In this work, we apply a “big-data” approach to the timer correlation problem. We develop a machine learning-based tool, Golden Timer eXtension (GTX), to correct divergence in flip-flop setup time, cell arc delay, wire delay, stage delay, and path slack at timing endpoints between timers. We propose a methodology to apply GTX to two arbitrary timers, and we evaluate scalability of GTX across multiple designs and foundry technologies / libraries, both with and without signal integrity analysis. Our experimental results show reduction in divergence between timing tools from 139.3ps to 21.1ps (i.e., 6.6×) in endpoint slack, and from 117ps to 23.8ps (4.9× reduction) in stage delay. We further demonstrate the incremental application of our methods so that models can be adapted to any outlier discrepancies when new designs are taped out in the same technology / library. Last, we demonstrate that GTX can also correlate timing reports between signoff and design implementation tools.


title: Self-reference by politicians on Twitter: Strategies to adapt to 140 characters

abstract: Abstract This paper studies how language is used on Twitter by Belgian and Spanish politicians in the context of the 2014 European elections campaign. Taking a pragmatic perspective on language use, we investigate three interrelated aspects of political communication on Twitter: (i) how political candidates adapt their language to the technological affordances of Twitter; (ii) which linguistic strategies the politicians use to express self-reference in function of different communicative goals and in relation to contextual factors such as ideological profile or nationality; and (iii) how some of the image building and community building strategies can be interpreted as reflexive signs of metapragmatic awareness. This study shows that the practice of self-referencing is a prominent feature of politicians’ discourse on Twitter, which is used as a tool for professional communication, as most of the self-references refer to professional rather than private identities. When spreading tweets, the studied Belgian and Spanish politicians use a number of adaptability strategies. They use conciseness strategies, such as subject pronoun ellipsis in Dutch and full subject NP constructions of a first person plural verb form in Spanish and Catalan, to accommodate to the 140 character constraint of the microblogging service. To express identities the politicians not only make use of first person pronouns, but also use the Twitter handle, the hashtag or proper nouns, referring to themselves in the third person. This practice shows that Twitter is not only a tool for professional political communication, but also a tool for personal branding. To end, it is argued that different linguistic choices show signs of metapragmatic awareness with the politicians reflecting on the communicative practice in which they are involved, indicating that they creatively use this type of social media to render their campaign talk searchable and ‘followable’.


title: Wireless-control strategy for parallel operation of distributed generation inverters

abstract: In this paper, a method for the parallel operation of inverters in an ac-distributed system is proposed. This paper explores the control of active and reactive power flow through the analysis of the output impedance of the inverters and its impact on the power sharing. As a result, adaptive virtual output impedance is proposed in order to achieve a proper reactive power sharing, regardless of the line-impedance unbalances. A soft-start operation is also included, avoiding the initial current peak, which results in a seamless hot-swap operation. Active power sharing is achieved by adjusting the frequency in load transient situations only, owing to which the proposed method obtains a constant steady-state frequency and amplitude. As opposed to the conventional droop method, the transient response can be modified by acting on the main control parameters. Linear and nonlinear loads can be properly shared due to the addition of a current harmonic loop in the control strategy. Experimental results are presented from a two-6-kVA parallel-connected inverter system, showing the feasibility of the proposed approach


title: Measuring Code Quality to Improve Specification Mining

abstract: Formal specifications can help with program testing, optimization, refactoring, documentation, and, most importantly, debugging and repair. However, they are difficult to write manually, and automatic mining techniques suffer from 90-99 percent false positive rates. To address this problem, we propose to augment a temporal-property miner by incorporating code quality metrics. We measure code quality by extracting additional information from the software engineering process and using information from code that is more likely to be correct, as well as code that is less likely to be correct. When used as a preprocessing step for an existing specification miner, our technique identifies which input is most indicative of correct program behavior, which allows off-the-shelf techniques to learn the same number of specifications using only 45 percent of their original input. As a novel inference technique, our approach has few false positives in practice (63 percent when balancing precision and recall, 3 percent when focused on precision), while still finding useful specifications (e.g., those that find many bugs) on over 1.5 million lines of code.


title: Printed Spiral Winding Inductor With Wide Frequency Bandwidth

abstract: Winding parasitic capacitance is a major factor limiting the bandwidth of an inductor. In this paper, 1) the traditional, 2) the alternating, and 3) the partial alternating winding methods are evaluated for the multilayer printed spiral winding inductors for megahertz operations. The self-capacitances of various winding structures are estimated by the summation of parasitic capacitance among the turns of a winding. The electric field energy distributions in the inductors are derived from the voltage profiles to illustrate the relative magnitudes of winding parasitic capacitances. The results show that parasitic capacitance reduction can be achieved by reducing stored electric field energy. The partial alternating winding method is found to have the widest frequency bandwidth with reduced number of through-hole vias for multilayer printed spiral winding design. The theoretical analysis has been confirmed with practical measurements. The results provide useful information for the optimal design of coreless or core-based high-frequency planar magnetics.


title: Dividing and Aggregating Network for Multi-view Action Recognition

abstract: In this paper, we propose a new Dividing and Aggregating Network (DA-Net) for multi-view action recognition. In our DA-Net, we learn view-independent representations shared by all views at lower layers, while we learn one view-specific representation for each view at higher layers. We then train view-specific action classifiers based on the view-specific representation for each view and a view classifier based on the shared representation at lower layers. The view classifier is used to predict how likely each video belongs to each view. Finally, the predicted view probabilities from multiple views are used as the weights when fusing the prediction scores of view-specific action classifiers. We also propose a new approach based on the conditional random field (CRF) formulation to pass message among view-specific representations from different branches to help each other. Comprehensive experiments on two benchmark datasets clearly demonstrate the effectiveness of our proposed DA-Net for multi-view action recognition.


Voice Activated Virtual Assistants Personality Perceptions and Desires: Comparing Personality Evaluation Frameworks