title: Why Sleep Matters-The Economic Costs of Insufficient Sleep: A Cross-Country Comparative Analysis

abstract: Abstract The Centers for Disease Control and Prevention (CDC) in the United States has declared insufficient sleep a "public health problem." Indeed, according to a recent CDC study, more than a third of American adults are not getting enough sleep on a regular basis. However, insufficient sleep is not exclusively a US problem, and equally concerns other industrialised countries such as the United Kingdom, Japan, Germany, or Canada. According to some evidence, the proportion of people sleeping less than the recommended hours of sleep is rising and associated with lifestyle factors related to a modern 24/7 society, such as psychosocial stress, alcohol consumption, smoking, lack of physical activity and excessive electronic media use, among others. This is alarming as insufficient sleep has been found to be associated with a range of negative health and social outcomes, including success at school and in the labour market. Over the last few decades, for example, there has been growing evidence suggesting a strong association between short sleep duration and elevated mortality risks. Given the potential adverse effects of insufficient sleep on health, well-being and productivity, the consequences of sleep-deprivation have far-reaching economic consequences. Hence, in order to raise awareness of the scale of insufficient sleep as a public-health issue, comparative quantitative figures need to be provided for policy- and decision-makers, as well as recommendations and potential solutions that can help tackling the problem.


title: Megaspeed Drive Systems: Pushing Beyond 1 Million r/min

abstract: The latest research in mesoscale drive systems is targeting rotational speeds toward 1 million r/min for a power range of 1-1 kW. Emerging applications for megaspeed drives (MegaNdrives) are to be found in future turbo compressor systems for fuel cells and heat pumps, generators/starters for portable nanoscale gas turbines, printed circuit board drilling and machining spindles, and electric power generation from pressurized gas flow. The selection of the machine type and the challenges involved in designing a machine for megaspeed operation such as the winding concepts, a mechanical rotor design capable of 1 000 000 r/min, the selection of magnetic materials for the stator, and the optimization concerning high-frequency losses and torque density are presented. Furthermore, a review of the advantageous inverter topologies, taking into account the extremely low stator inductance and possible high-speed bearing types such as ball bearings, air bearings, foil bearings, and magnetic bearings, are given. Finally, prototypes and experimental results originating from MegaNdrive research at Swiss Federal Institute of Technology Zurich are discussed and extreme temperature operation and power microelectricalmechanical system are identified as targets for future research.


title: VISUALIZING UNCERTAIN INFORMATION

abstract: When a GIS is used to drive map-based visualization, exploration of potential relationships takes precedence over presentation of facts. In these early stages of scientific analysis or policy formulation, providing a way for analysts to assess uncertainty in the data they are exploring is critical to the perspectives they form and the approaches they decide to pursue. As a basis from which to develop methods for visualizing uncertain information, this paper addresses the difference between data quality and uncertainty, the application of Berlin's graphic variables to the representation of uncertainty, conceptual models of spatial uncertainty as they relate to kinds of cartographic symbolization, and categories of user interfaces suited to presenting data and uncertainty about that data. Also touched on is the issue of how we might evaluate our attempts to depict uncertain information on maps.


title: Big Data Computing Using Cloud-Based Technologies, Challenges and Future Perspectives

abstract: The excessive amounts of data generated by devices and Internet-based sources at a regular basis constitute, big data. This data can be processed and analyzed to develop useful applications for specific domains. Several mathematical and data analytics techniques have found use in this sphere. This has given rise to the development of computing models and tools for big data computing. However, the storage and processing requirements are overwhelming for traditional systems and technologies. Therefore, there is a need for infrastructures that can adjust the storage and processing capability in accordance with the changing data dimensions. Cloud Computing serves as a potential solution to this problem. However, big data computing in the cloud has its own set of challenges and research issues. This chapter surveys the big data concept, discusses the mathematical and data analytics techniques that can be used for big data and gives taxonomy of the existing tools, frameworks and platforms available for different big data computing models. Besides this, it also evaluates the viability of cloud-based big data computing, examines existing challenges and opportunities, and provides future research directions in this field.


title: Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms This paper reviews approaches to develop a sensorimotor rhythm-based BCI , including mapping of scalp EEG signals to the cortical sources and control of physical devices for increasing users ’ engagement

abstract: Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm electroencephalography (EEG)-based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.


title: Performance analysis of NSL-KDD dataset using ANN

abstract: Anomalous traffic detection on internet is a major issue of security as per the growth of smart devices and this technology. Several attacks are affecting the systems and deteriorate its computing performance. Intrusion detection system is one of the techniques, which helps to determine the system security, by alarming when intrusion is detected. In this paper performance of NSL-KDD dataset is evaluated using ANN. The result obtained for both binary class as well as five class classification (type of attack). Results are analyzed based on various performance measures and better accuracy was found. The detection rate obtained is 81.2% and 79.9% for intrusion detection and attack type classification task respectively for NSL-KDD dataset. The performance of the proposed scheme has been compared with existing scheme and higher detection rate is achieved in both binary class as well as five class classification problems.


title: Why are some people happier than others? The role of cognitive and motivational processes in well-being.

abstract: Addressing the question of why some people are happier than others is important for both theoretical and practical reasons and should be a central goal of a comprehensive positive psychology. Following a construal theory of happiness, the author proposes that multiple cognitive and motivational processes moderate the impact of the objective environment on well-being. Thus, to understand why some people are happier than others, one must understand the cognitive and motivational processes that serve to maintain, and even enhance, enduring happiness and transient mood. The author's approach has been to explore hedonically relevant psychological processes, such as social comparison, dissonance reduction, self-reflection, self-evaluation, and person perception, in chronically happy and unhappy individuals. In support of a construal framework, self-rated happy and unhappy people have been shown to differ systematically in the particular cognitive and motivational strategies they use. Promising research directions for positive psychology in pursuit of the sources of happiness, as well as the implications of the construal approach for prescriptions for enhancing well-being, are discussed.


title: A System for a Hand Gesture-Manipulated Virtual Reality Environment

abstract: Extensive research has been done using machine learning techniques for hand gesture recognition (HGR) using camera-based devices; such as the Leap Motion Controller (LMC). However, limited research has investigated machine learning techniques for HGR in virtual reality applications (VR). This paper reports on the design, implementation, and evaluation of a static HGR system for VR applications using the LMC. The gesture recognition system incorporated a lightweight feature vector of five normalized tip-to-palm distances and a k-nearest neighbour (kNN) classifier. The system was evaluated in terms of response time, accuracy and usability using a case-study VR stellar data visualization application created in the Unreal Engine 4. An average gesture classification time of 0.057ms with an accuracy of 82.5% was achieved on four distinct gestures, which is comparable with previous results from Sign Language recognition systems. This shows the potential of HGR machine learning techniques applied to VR, which were previously applied to non-VR scenarios such as Sign Language recognition.


title: Tag Completion for Image Retrieval

abstract: Many social image search engines are based on keyword/tag matching. This is because tag-based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR is highly dependent on the availability and quality of manual tags. Recent studies have shown that manual tags are often unreliable and inconsistent. In addition, since many users tend to choose general and ambiguous tags in order to minimize their efforts in choosing appropriate words, tags that are specific to the visual content of images tend to be missing or noisy, leading to a limited performance of TBIR. To address this challenge, we study the problem of tag completion, where the goal is to automatically fill in the missing tags as well as correct noisy tags for given images. We represent the image-tag relation by a tag matrix, and search for the optimal tag matrix consistent with both the observed tags and the visual similarity. We propose a new algorithm for solving this optimization problem. Extensive empirical studies show that the proposed algorithm is significantly more effective than the state-of-the-art algorithms. Our studies also verify that the proposed algorithm is computationally efficient and scales well to large databases.


Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts


title: Self-Gain or Self-Regulation Impairment? Tests of Competing Explanations of the Supervisor Abuse and Employee Deviance Relationship Through Perceptions of Distributive Justice

abstract: Two competing explanations for deviant employee responses to supervisor abuse are tested. A self-gain view is compared with a self-regulation impairment view. The self-gain view suggests that distributive justice (DJ) will weaken the abusive supervision–employee deviance relationship, as perceptions of fair rewards offset costs of abuse. Conversely, the self-regulation impairment view suggests that DJ will strengthen the relationship, as experiencing abuse drains self-resources needed to maintain appropriate behavior, and this effect intensifies when employees receive inconsistent information about their organizational membership (fair outcomes). Three field studies using different samples, measures, and designs support the self-regulation impairment view. Two studies found that the Abusive Supervision DJ interaction was mediated by self-regulation impairment variables (ego depletion and intrusive thoughts). Implications for theory and research are discussed.


title: Human body heat for powering wearable devices: From thermal energy to application

abstract: Abstract Energy harvesting is the key technology to enable self-sustained wearable devices for the Internet of Things and medical applications. Among various types of harvesting sources such as light, vibration and radio frequency, thermoelectric generators (TEG) are a promising option due to their independence of light conditions or the activity of the wearer. This work investigates scavenging of human body heat and the optimization of the power conversion efficiency from body core to the application. We focus on the critical interaction between thermal harvester and power conditioning circuitry and compare two approaches: (1) a high output voltage, low thermal resistance μTEG combined with a high efficiency actively controlled single inductor DC-DC converter, and (2) a high thermal resistance, low electric resistance mTEG in combination with a low-input voltage coupled inductors based DC-DC converter. The mTEG approach delivers up to 65% higher output power per area in a lab setup and 1–15% in a real-world experiment on the human body depending on physical activity and environmental conditions. Using off-the-shelf and low-cost components, we achieve an average power of 260 μW (μTEG) to 280 μW (mTEG) and power densities of 13 μW cm −2 (μTEG) to 14 μW cm −2 (mTEG) for systems worn on the human wrist. With the small and lightweight harvesters optimized for wearability, 16% (mTEG) to 24% (μTEG) of the theoretical maximum efficiency is achieved in a worst-case scenario. This efficiency highly depends on the application specific conditions and requires careful system design. The harvesters can power wearables in different use cases, for example a multi-sensor bracelet that measures activity, acquires images and displays results.


title: Database Mining: A Performance Perspective

abstract: The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented. Three classes of database mining problems involving classification, associations, and sequences are described. It is argued that these problems can be uniformly viewed as requiring discovery of rules embedded in massive amounts of data. A model and some basic operations for the process of rule discovery are described. It is shown how the database mining problems considered map to this model, and how they can be solved by using the basic operations proposed. An example is given of an algorithm for classification obtained by combining the basic rule discovery operations. This algorithm is efficient in discovering classification rules and has accuracy comparable to ID3, one of the best current classifiers. >


title: Bank-to-Turn Control for a Small UAV using Backstepping and Parameter Adaptation

abstract: Abstract In this research we consider the problem of path following control for a small fixed-wing unmanned aerial vehicle (UAV). Assuming the UAV is equipped with an autopilot for low level control, we adopt a kinematic error model with respect to the moving Serret-Frenet frame attached to a path for tracking controller design. A kinematic path following control law that commands heading rate is presented. Backstepping is applied to derive the roll angle command by taking into account the approximate closed-loop roll dynamics. A parameter adaptation technique is employed to account for the inaccurate time constant of the closed-loop roll dynamics during actual implementation. The path following control algorithm is validated in real-time through a high-fidelity hardware-in-the-loop simulation (HILS) environment showing the applicability of the algorithm on a real system.


title: Integrated 16-Channel Transmit and Receive Beamforming ASIC for Ultrasound Imaging

abstract: In commercial ultrasound systems, the multielement transducer array is connected to analog front end electronics using long-wire high voltage coaxial cables. This paper presents the circuit design of 16-channel Transmit (Tx) and Receive (Rx) beam forming ASIC (Application Specific Integrated Circuit) that can be integrated in ultrasound probe head which reduces the number of coaxial cables. The proposed modular design for programmable 16-channel transmit beam former operates at medical frequencies in pulse-echo mode and provides user control of transmit parameters such as transmit pulse length, pulse pattern, transmit frequency, and mode of excitation. The receive beam former implements delay and coherent sum of the digitized echoes from 16 adjacent transducer elements to form scan lines required for image reconstruction. The proposed architecture of the Rx beam former design provides great flexibility for beam forming, such as receive focusing with predetermined delay profile. Each transmit channel can be programmable to give a maximum delay of 163.85 s with 1.25 ns delay resolution. The proposed design implements dynamic receive focusing with minimum time delay resolution of 3.125 ns for 40 MHz input data rate. The proposed ASIC of integrated Tx and Rx beam former is implemented in UMC 130 nm technology using Synopsys ICC and Design Compiler. The implementation reports show that the area is 5.29 mm2, power dissipation is 38 mW.


title: Pyogenic granuloma of the scrotum.

abstract: Pyogenic granuloma is a benign, vascular, over-exuberant, inflammatory, solitary skin tumor common in infants and children. It is a rapidly growing solitary, sessile, or polypoidal vascular nodule that will bleed easily. The exact pathogenesis of this condition is unknown. Various predisposing factors, such as trauma, hormones, infection, viral oncogenesis, microscopic anastomosis, and others may be responsible for its growth.


title: Uncertainty in Multitask Transfer Learning

abstract: Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks. The result is a rich and meaningful prior capable of few-shot learning on new tasks. The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments. Analysis on toy tasks shows that it can learn from significantly different tasks while finding similarities among them. Experiments of Mini-Imagenet yields the new state of the art with 74.5% accuracy on 5 shot learning. Finally, we provide experiments showing that other existing methods can fail to perform well in different benchmarks.


Smart Home Security System using Fuzzy Logic


title: CPG-Inspired Workspace Trajectory Generation and Adaptive Locomotion Control for Quadruped Robots

abstract: This paper deals with the locomotion control of quadruped robots inspired by the biological concept of central pattern generator (CPG). A control architecture is proposed with a 3-D workspace trajectory generator and a motion engine. The workspace trajectory generator generates adaptive workspace trajectories based on CPGs, and the motion engine realizes joint motion imputes. The proposed architecture is able to generate adaptive workspace trajectories online by tuning the parameters of the CPG network to adapt to various terrains. With feedback information, a quadruped robot can walk through various terrains with adaptive joint control signals. A quadruped platform AIBO is used to validate the proposed locomotion control system. The experimental results confirm the effectiveness of the proposed control architecture. A comparison by experiments shows the superiority of the proposed method against the traditional CPG-joint-space control method.


title: Theories of Lexical Semantics

abstract: Introduction 1. Historical-Philological Semantics 2. Structuralist Semantics 3. Generativist Semantics 4. Neostructuralist Semantics 5. Cognitive Semantics Conclusion References


title: When the map is better than the territory

abstract: The causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions of causal structure may be useful to observers, they are at best a compressed description and at worse leave out critical information. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus (be more informative) at a macroscale (Hoel et al. 2013). That is, a macro model of a system (a map) can be more informative than a fully detailed model of the system (the territory). This has been called causal emergence. While causal emergence may at first glance seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon's discovery of the channel capacity. I argue that systems have a particular causal capacity, and that different causal models of those systems take advantage of that capacity to various degrees. For some systems, only macroscale causal models use the full causal capacity. Such macroscale causal models can either be coarse-grains, or may leave variables and states out of the model (exogenous) in various ways, which can improve the model's efficacy and its informativeness via the same mathematical principles of how error-correcting codes take advantage of an information channel's capacity. As model choice increase, the causal capacity of a system approaches the channel capacity. Ultimately, this provides a general framework for understanding how the causal structure of some systems cannot be fully captured by even the most detailed microscopic model.


title: 3D Scene Reconstruction from Multiple Spherical Stereo Pairs

abstract: We propose a 3D environment modelling method using multiple pairs of high-resolution spherical images. Spherical images of a scene are captured using a rotating line scan camera. Reconstruction is based on stereo image pairs with a vertical displacement between camera views. A 3D mesh model for each pair of spherical images is reconstructed by stereo matching. For accurate surface reconstruction, we propose a PDE-based disparity estimation method which produces continuous depth fields with sharp depth discontinuities even in occluded and highly textured regions. A full environment model is constructed by fusion of partial reconstruction from spherical stereo pairs at multiple widely spaced locations. To avoid camera calibration steps for all camera locations, we calculate 3D rigid transforms between capture points using feature matching and register all meshes into a unified coordinate system. Finally a complete 3D model of the environment is generated by selecting the most reliable observations among overlapped surface measurements considering surface visibility, orientation and distance from the camera. We analyse the characteristics and behaviour of errors for spherical stereo imaging. Performance of the proposed algorithm is evaluated against ground-truth from the Middlebury stereo test bed and LIDAR scans. Results are also compared with conventional structure-from-motion algorithms. The final composite model is rendered from a wide range of viewpoints with high quality textures.


title: Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings

abstract: As an important branch of deep learning, convolutional neural network has largely improved the performance of building detection. For further accelerating the development of building detection toward automatic mapping, a benchmark dataset bears significance in fair comparisons. However, several problems still remain in the current public datasets that address this task. First, although building detection is generally considered equivalent to extracting roof outlines, most datasets directly provide building footprints as ground truths for testing and evaluation; the challenges of these benchmarks are more complicated than roof segmentation, as relief displacement leads to varying degrees of misalignment between roof outlines and footprints. On the other hand, an image dataset should feature a large quantity and high spatial resolution to effectively train a high-performance deep learning model for accurate mapping of buildings. Unfortunately, the remote sensing community still lacks proper benchmark datasets that can simultaneously satisfy these requirements. In this paper, we present a new large-scale benchmark dataset termed Aerial Imagery for Roof Segmentation (AIRS). This dataset provides a wide coverage of aerial imagery with 7.5 cm resolution and contains over 220,000 buildings. The task posed for AIRS is defined as roof segmentation. We implement several state-of-the-art deep learning methods of semantic segmentation for performance evaluation and analysis of the proposed dataset. The results can serve as the baseline for future work.


title: Superpixel Sampling Networks

abstract: Superpixels provide an efficient low/mid-level representation of image data, which greatly reduces the number of image primitives for subsequent vision tasks. Existing superpixel algorithms are not differentiable, making them difficult to integrate into otherwise end-to-end trainable deep neural networks. We develop a new differentiable model for superpixel sampling that leverages deep networks for learning superpixel segmentation. The resulting Superpixel Sampling Network (SSN) is end-to-end trainable, which allows learning task-specific superpixels with flexible loss functions and has fast runtime. Extensive experimental analysis indicates that SSNs not only outperform existing superpixel algorithms on traditional segmentation benchmarks, but can also learn superpixels for other tasks. In addition, SSNs can be easily integrated into downstream deep networks resulting in performance improvements.


title: Four Bad Habits of Modern Psychologists

abstract: Four data sets from studies included in the Reproducibility Project were re-analyzed to demonstrate a number of flawed research practices (i.e., “bad habits”) of modern psychology. Three of the four studies were successfully replicated, but re-analysis showed that in one study most of the participants responded in a manner inconsistent with the researchers’ theoretical model. In the second study, the replicated effect was shown to be an experimental confound, and in the third study the replicated statistical effect was shown to be entirely trivial. The fourth study was an unsuccessful replication, yet re-analysis of the data showed that questioning the common assumptions of modern psychological measurement can lead to novel techniques of data analysis and potentially interesting findings missed by traditional methods of analysis. Considered together, these new analyses show that while it is true replication is a key feature of science, causal inference, modeling, and measurement are equally important and perhaps more fundamental to obtaining truly scientific knowledge of the natural world. It would therefore be prudent for psychologists to confront the limitations and flaws in their current analytical methods and research practices.


title: Optimizing protocol interaction using response surface methodology

abstract: Abstract-Response surface methodology (RSM) is a collection of statistical design and numerical optimization techniques traditionally used to optimize industrial processes. In this paper, we demonstrate that the methodology can be successfully applied to the domain of networking. Specifically, we obtain increased throughput with a significant decrease in delay in a ns-2 simulation model of a mobile ad hoc network (MANET) by using RSM to optimize protocol interaction found by factor screening. Whether the experimentation is with a stochastic simulation model or a physical system, such as a MANET or a wireless sensor network test-bed, RSM provides a general and practical methodology to screen factors and robustly and jointly optimize responses.


Using the Middle to Meddle with Mobile


title: Spectrum of Variable-Random Trees

abstract: In this paper, we show that a continuous spectrum of randomisation exists, in which most existing tree randomisations are only operating around the two ends of the spectrum. That leaves a huge part of the spectrum largely unexplored. We propose a base learner VR-Tree which generates trees with variable-randomness. VR-Trees are able to span from the conventional deterministic trees to the complete-random trees using a probabilistic parameter. Using VR-Trees as the base models, we explore the entire spectrum of randomised ensembles, together with Bagging and Random Subspace. We discover that the two halves of the spectrum have their distinct characteristics; and the understanding of which allows us to propose a new approach in building better decision tree ensembles. We name this approach Coalescence, which coalesces a number of points in the random-half of the spectrum. Coalescence acts as a committee of "experts" to cater for unforeseeable conditions presented in training data. Coalescence is found to perform better than any single operating point in the spectrum, without the need to tune to a specific level of randomness. In our empirical study, Coalescence ranks top among the benchmarking ensemble methods including Random Forests, Random Subspace and C5 Boosting; and only Coalescence is significantly better than Bagging and Max-Diverse Ensemble among all the methods in the comparison. Although Coalescence is not significantly better than Random Forests, we have identified conditions under which one will perform better than the other.


title: To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout

abstract: Training deep belief networks (DBNs) requires optimizing a non-convex function with an extremely large number of parameters. Naturally, existing gradient descent (GD) based methods are prone to arbitrarily poor local minima. In this paper, we rigorously show that such local minima can be avoided (upto an approximation error) by using the dropout technique, a widely used heuristic in this domain. In particular, we show that by randomly dropping a few nodes of a one-hidden layer neural network, the training objective function, up to a certain approximation error, decreases by a multiplicative factor. On the flip side, we show that for training convex empirical risk minimizers (ERM), dropout in fact acts as a"stabilizer"or regularizer. That is, a simple dropout based GD method for convex ERMs is stable in the face of arbitrary changes to any one of the training points. Using the above assertion, we show that dropout provides fast rates for generalization error in learning (convex) generalized linear models (GLM). Moreover, using the above mentioned stability properties of dropout, we design dropout based differentially private algorithms for solving ERMs. The learned GLM thus, preserves privacy of each of the individual training points while providing accurate predictions for new test points. Finally, we empirically validate our stability assertions for dropout in the context of convex ERMs and show that surprisingly, dropout significantly outperforms (in terms of prediction accuracy) the L2 regularization based methods for several benchmark datasets.


title: Low cost high performance microcontroller based semiconductor device tester

abstract: This paper deals with design and implementation of a versatile tester system for diode, bipolar junction transistor (BJT), and junction field effect transistor (JFET). The basic building blocks for this tester design include PIC 18F452 microcontroller, analog multiplexer/demultiplexer 74HC4052, and Liquid Crystal Display (LCD). The tester system discussed in this paper provide accurate identification of semiconductor device under test (Diode, BJT, JFET), types of bipolar transistor (NPN, PNP), types of junction field effect transistor (N channel JFET, P channel JFET). The proposed system also detects leads of diode, bipolar junction transistor (BJT), and junction field effect transistor (JFET). Apart from this the tester also measures various parameters of above mentioned semiconductor devices such as forward voltage (VF) and forward current (IF) for diode, current gain (HFE) for BJT, saturation current with the gate shorted to the source (IDSS) for JFET. All measurement and test results are clearly represented on LCD. Low power consumption, simple hardware, user friendly design and compact size are salient features of this semiconductor device tester.


title: Towards a rigorous definition of information system survivability

abstract: The computer systems that provide the information underpinnings for critical infrastructure applications, both military and civilian, are essential to the operation of those applications. Failure of the information systems can cause a major loss of service, and so their dependability is a major concern. Current facets of dependability, such as reliability and availability, do not address the needs of critical information systems adequately because they do not include the notion of degraded service as an explicit requirement. What is needed is a precise notion of what forms of degraded service are acceptable to users, under what circumstances each form is most useful, and the fraction of time such degraded service levels are acceptable. This concept is termed survivability. In this paper, we present the basis for a rigorous definition of survivability and an example of its use.


title: Deep learning bank distress from news and numerical financial data

abstract: In this paper we focus our attention on the exploitation of the information contained in financial news to enhance the performance of a classifier of bank distress. Such information should be analyzed and inserted into the predictive model in the most efficient way and this task deals with all the issues related to text analysis and specifically analysis of news media. Among the different models proposed for such purpose, we investigate one of the possible deep learning approaches, based on a doc2vec representation of the textual data, a kind of neural network able to map the sequential and symbolic text input onto a reduced latent semantic space. Afterwards, a second supervised neural network is trained combining news data with standard financial figures to classify banks whether in distressed or tranquil states, based on a small set of known distress events. Then the final aim is not only the improvement of the predictive performance of the classifier but also to assess the importance of news data in the classification process. Does news data really bring more useful information not contained in standard financial variables? Our results seem to confirm such hypothesis.


title: Risk Factor Analysis Based on Deep Learning Models

abstract: Accurate rendering of diagnosis and prognosis for a disease with respect to a patient requires analysis of complicated, diverse, yet correlated risk factors (RFs). Most of the existing methods for this purpose are based on handcraft RFs by calculating their statistical significance to the disease. However, such methods not only incur intensive labor but also lack capability to discover or infer previously unknown complex relationships and combined effects among correlated RFs. Nowadays, deep learning models have emerged as a hot topic, due to its ability to automatically extract useful and complex features from raw data. In this paper, we explore the effectiveness of deep learning on medical data by building a deep learning based framework to analyze risk factors and study its prediction performance in disease diagnosis. Specifically, we investigate the application of deep learning with a special focus on interpreting the latent features extracted or created from raw data by the model. Experimental results demonstrate that deep learning based methods are able to aggregate features sharing same characteristics, and reduce effects from unimportant and uncorrelated RFs. The abstract features obtained by deep learning methods can represent the essentials of raw inputs, and give a good prediction performance in disease diagnosis.


title: Why do developers use trivial packages? an empirical case study on npm

abstract: Code reuse is traditionally seen as good practice. Recent trends have pushed the concept of code reuse to an extreme, by using packages that implement simple and trivial tasks, which we call `trivial packages'. A recent incident where a trivial package led to the breakdown of some of the most popular web applications such as Facebook and Netflix made it imperative to question the growing use of trivial packages. Therefore, in this paper, we mine more than 230,000 npm packages and 38,000 JavaScript applications in order to study the prevalence of trivial packages. We found that trivial packages are common and are increasing in popularity, making up 16.8% of the studied npm packages. We performed a survey with 88 Node.js developers who use trivial packages to understand the reasons and drawbacks of their use. Our survey revealed that trivial packages are used because they are perceived to be well implemented and tested pieces of code. However, developers are concerned about maintaining and the risks of breakages due to the extra dependencies trivial packages introduce. To objectively verify the survey results, we empirically validate the most cited reason and drawback and find that, contrary to developers' beliefs, only 45.2% of trivial packages even have tests. However, trivial packages appear to be `deployment tested' and to have similar test, usage and community interest as non-trivial packages. On the other hand, we found that 11.5% of the studied trivial packages have more than 20 dependencies. Hence, developers should be careful about which trivial packages they decide to use.


FormIT – an Illustration of a Living Lab Methodology


title: Reliability of eWLB (Embedded Wafer Level BGA) for Automotive Radar Applications

abstract: With shrinking of chip sizes, Wafer Level Chip Scale Packaging (WLCSP) becomes an attractive and holistic packaging solutions with various advantages in comparison to conventional packages, such as Ball Grid Array (BGA) with flipchip or wirebonding. With the advancement of various fan-out (FO) WLPs, it has been proven to be a more optimal, low cost, integrated and reliable solution compared to fan-in WLP because of the greater design flexibility in having more input/output (I/O), improved mechanical and thermal performance. In addition, FO-WLP shows superior high-frequency electrical performance due to its shorter, finer and simpler interconnection scheme compared to flipchip packaging. eWLB (embedded wafer level BGA) is a type of FO-WLP that enables applications requiring smaller form factor, excellent thermal performance and thin package profiles and it has the potential to evolve into various configurations with proven yields and manufacturing experience based on more than 8 years of high volume manufacturing of over 1.5B unit shipment. This paper discusses the recent advancements in robust board level reliability performance of eWLB for automotive application, where a review of a Design of Experiment (DOE) study will demonstrate improved Temperature Cycle on Board (TCoB) reliability. Several DOE studies were planned and test vehicles were prepared with various variables, such as materials, redistribution layer (RDL) design, copper (Cu) and under bump metallurgy (UBM) thickness, and a printed circuit board (PCB) pad design. The final test vehicle passed over 1000 times temperature cycles (TC) with optimized design factors of these parametric studies and reliability tests. To investigate potential structural defects after the reliability test, both destructive analyses were performed under industry standard test conditions, Daisy chain test vehicles were used for TCoB reliability performance.


title: Muppet: MapReduce-Style Processing of Fast Data

abstract: MapReduce has emerged as a popular method to process big data. In the past few years, however, not just big data, but fast data has also exploded in volume and availability. Examples of such data include sensor data streams, the Twitter Firehose, and Facebook updates. Numerous applications must process fast data. Can we provide a MapReduce-style framework so that developers can quickly write such applications and execute them over a cluster of machines, to achieve low latency and high scalability? ::: ::: In this paper we report on our investigation of this question, as carried out at Kosmix and WalmartLabs. We describe MapUpdate, a framework like MapReduce, but specifically developed for fast data. We describe Muppet, our implementation of MapUpdate. Throughout the description we highlight the key challenges, argue why MapReduce is not well suited to address them, and briefly describe our current solutions. Finally, we describe our experience and lessons learned with Muppet, which has been used extensively at Kosmix and WalmartLabs to power a broad range of applications in social media and e-commerce.


title: Comparison and Analysis of Single-Phase Transformerless Grid-Connected PV Inverters

abstract: Leakage current minimization is one of the most important considerations in transformerless photovoltaic (PV) inverters. In the past, various transformerless PV inverter topologies have been introduced, with leakage current minimized by the means of galvanic isolation and common-mode voltage (CMV) clamping. The galvanic isolation can be achieved via dc-decoupling or ac-decoupling, for isolation on the dc- or ac-side of the inverter, respectively. It has been shown that the latter provides lower losses due to the reduced switch count in conduction path. Nevertheless, leakage current cannot be simply eliminated by galvanic isolation and modulation techniques, due to the presence of switches’ junction capacitances and resonant circuit effects. Hence, CMV clamping is used in some topologies to completely eliminate the leakage current. In this paper, several recently proposed transformerless PV inverters with different galvanic isolation methods and CMV clamping technique are analyzed and compared. A simple modified H-bridge zero-voltage state rectifier is also proposed, to combine the benefits of the low-loss ac-decoupling method and the complete leakage current elimination of the CMV clamping method. The performances of different topologies, in terms of CMV, leakage current, total harmonic distortion, losses and efficiencies are compared. The analyses are done theoretically and via simulation studies, and further validated with experimental results. This paper is helpful for the researchers to choose the appropriate topology for transformerless PV applications and to provide the design principles in terms of common-mode behavior and efficiency.


title: Understanding Online Interruption-Based Advertising: Impacts of Exposure Timing, Advertising Intent, and Brand Image

abstract: Interruption-based advertising has gained prominence in the online channel. Yet, little attention has been paid to deriving design principles and conceptualizations for online interruption-based advertising. This paper examines three novel design factors related to this phenomenon, namely, exposure timing, advertising intent, and brand image. Exposure timing pertains to the time by which the advertisement (ad) is launched within a website. Advertising intent refers to the explicitness of ad content in portraying the desire to induce purchase behavior. Brand image relates to consumers' overall perceptions of the advertised brand. In a laboratory experiment, participants were exposed to pop-up ads that were operationalized based on these three design considerations. Results reveal three two-way interactions among the study constructs. Online interruption-based ads shown in the predecisional shopping phase are more effective when their contents are designed with implicit advertising intent compared to explicit intent. Brand image is found to moderate the effects of advertising intent on consumer's purchase intention. Participants' responses also show that ads promoting weak brands with less favorable image tend to enjoy higher purchase intention when shown in the predecisional phase compared with the postdecisional phase. Theoretical and practical implications together with suggestions for future research are discussed.


Customer ' Loyalty Forming Mechanism of O 2 O


title: A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty

abstract: Abstract Scientific knowledge is constantly subject to a variety of changes due to new discoveries, alternative interpretations, and fresh perspectives. Understanding uncertainties associated with various stages of scientific inquiries is an integral part of scientists’ domain expertise and it serves as the core of their meta-knowledge of science. Despite the growing interest in areas such as computational linguistics, systematically characterizing and tracking the epistemic status of scientific claims and their evolution in scientific disciplines remains a challenge. We present a unifying framework for the study of uncertainties explicitly and implicitly conveyed in scientific publications. The framework aims to accommodate a wide range of uncertainty types, from speculations to inconsistencies and controversies. We introduce a scalable and adaptive method to recognize semantically equivalent cues of uncertainty across different fields of research and accommodate individual analysts’ unique perspectives. We demonstrate how the new method can be used to expand a small seed list of uncertainty cue words and how the validity of the expanded candidate cue words is verified. We visualize the mixture of the original and expanded uncertainty cue words to reveal the diversity of expressions of uncertainty. These cue words offer a novel resource for the study of uncertainty in scientific assertions.


title: User Identification Across Social Media

abstract: People use various social media sites for different purposes. The information on each site is often partial. When sources of complementary information are integrated, a better profile of a user can be built. This profile can help improve online services such as advertising across sites. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We provide evidence on the existence of a mapping among identities of individuals across social media sites, study the feasibility of finding this mapping, and illustrate and develop means for finding this mapping. Our studies show that effective approaches that exploit information redundancies due to users’ unique behavioral patterns can be utilized to find such a mapping. This study paves the way for analysis and mining across social networking sites, and facilitates the creation of novel online services across sites. In particular, recommending friends and advertising across networks, analyzing information diffusion across sites, and studying specific user behavior such as user migration across sites in social media are one of the many areas that can benefit from the results of this study.


title: Multi-modal & Multi-view & Interactive Benchmark Dataset for Human Action Recognition

abstract: Human action recognition is one of the most active research areas in both computer vision and machine learning communities. Several methods for human action recognition have been proposed in the literature and promising results have been achieved on the popular datasets. However, the comparison of existing methods is often limited given the different datasets, experimental settings, feature representations, and so on. In particularly, there are no human action dataset that allow concurrent analysis on three popular scenarios, namely single view, cross view, and cross domain. In this paper, we introduce a Multi-modal & Multi-view & Interactive (M2I) dataset, which is designed for the evaluation of the performances of human action recognition under multi-view scenario. This dataset consists of 1760 action samples, including 9 person-person interaction actions and 13 person-object interaction actions. Moreover, we respectively evaluate three representative methods for the single-view, cross-view, and cross domain human action recognition on this dataset with the proposed evaluation protocol. It is experimentally demonstrated that this dataset is extremely challenging due to large intraclass variation, multiple similar actions, significant view difference. This benchmark can provide solid basis for the evaluation of this task and will benefit advancing related computer vision and machine learning research topics.


title: Identifying Architectural Bad Smells

abstract: Certain design fragments in software architectures can have a negative impact on system maintainability. In this paper, we introduce the concept of architectural "bad smells," which are frequently recurring software designs that can have non-obvious and significant detrimental effects on system lifecycle properties. We define architectural smells and differentiate them from related concepts, such as architectural antipatterns and code smells. We also describe four representative architectural smells we encountered in the context of reverse-engineering eighteen grid technologies and refactoring one large industrial system.


title: Analysis of Customer Churn in Mobile Industry using Data Mining

abstract: Customer churn prediction in Mobile industry is a latest research topic in recent years. A huge amount of data is generated in Mobile industry every minute. Data mining techniques have also developed in various ways. Customer churn is considered as one of the major issues in Mobile industry. The research signifies that it is more expensive to gain a new customer than to retain an existing one. The knowledge extracted from Mobile industry helps to understand the reasons of customer churn and Telecom providers use the data to retain existing customers. This paper surveys the commonly used data mining techniques to identify customer churn patterns. Classification and association models are the two most commonly used models for data mining in Customer Relationship Management. This study focuses on data mining techniques for reducing customer churn and also likely to reduce error ratio. The recent literature in the area of predictive data mining techniques in customer churn behavior is reviewed and a discussion on the future research directions is offered.


title: Understanding figurative and literal language: The graded salience hypothesis

abstract: Process for the production of film forming synthetic resins for hair fixative compositions by reacting a methylolated 5,5-dialkylhydantoin containing 1-5 carbon atoms in each alkyl group at an elevated temperature in the presence of a bifunctional acid amide and/or amine. Hair fixative compositions containing 2-10% by weight of the resin product as the major or sole resin compound.


title: Transcutaneous Brow Shaping: A Straightforward and Precise Method to Lift and Shape the Eyebrows

abstract: Background ::: The height of the eyebrow is less crucial aesthetically than is the relationship between the lateral and medial portions of the brow. Although various surgical procedures are effective in raising the brow, the authors maintain that transcutaneous brow shaping (TBS) is the only technique that enables precise shaping of the brow and correction of minor asymmetries. ::: ::: ::: Objectives ::: The authors described their experiences with direct TBS alone or in conjunction with blepharoplasty and facelift. ::: ::: ::: Methods ::: A total of 212 patients underwent TBS performed by the senior author (M.P.C). All patients were evaluated clinically and by means of pre- and postoperative photographs. Patients completed questionnaires indicating scar quality and satisfaction with the results. ::: ::: ::: Results ::: There were no major complications. Scar visibility was low, and patients expressed a high level of satisfaction with the aesthetic results of TBS. ::: ::: ::: Conclusions ::: A misconception of transcutaneous browlift procedures is that they yield visible scars. TBS requires accurate planning, preservation of subcutaneous volume, limited undermining, preoperative application of botulinum toxin, and perioperative administration of local vasodilators. When these requirements are fulfilled, the authors have found that TBS does not yield a visible scar and is the easiest, most precise, and most reliable procedure for brow shaping. ::: ::: ::: Level of Evidence ::: 4.


title: From query to question in one click: suggesting synthetic questions to searchers

abstract: In Web search, users may remain unsatisfied for several reasons: the search engine may not be effective enough or the query might not reflect their intent. Years of research focused on providing the best user experience for the data available to the search engine. However, little has been done to address the cases in which relevant content for the specific user need has not been posted on the Web yet. One obvious solution is to directly ask other users to generate the missing content using Community Question Answering services such as Yahoo! Answers or Baidu Zhidao. However, formulating a full-fledged question after having issued a query requires some effort. Some previous work proposed to automatically generate natural language questions from a given query, but not for scenarios in which a searcher is presented with a list of questions to choose from. We propose here to generate synthetic questions that can actually be clicked by the searcher so as to be directly posted as questions on a Community Question Answering service. This imposes new constraints, as questions will be actually shown to searchers, who will not appreciate an awkward style or redundancy. To this end, we introduce a learning-based approach that improves not only the relevance of the suggested questions to the original query, but also their grammatical correctness. In addition, since queries are often underspecified and ambiguous, we put a special emphasis on increasing the diversity of suggestions via a novel diversification mechanism. We conducted several experiments to evaluate our approach by comparing it to prior work. The experiments show that our algorithm improves question quality by 14% over prior work and that adding diversification reduced redundancy by 55%.


title: Artificial Error Generation with Machine Translation and Syntactic Patterns

abstract: Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. We propose treating error generation as a machine translation task, where grammatically correct text is translated to contain errors. In addition, we explore a system for extracting textual patterns from an annotated corpus, which can then be used to insert errors into grammatically correct sentences. Our experiments show that the inclusion of artificially generated errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.


title: Bayes net graphs to understand co-authorship networks?

abstract: Improvements in data collection and the birth of online communities made it possible to obtain very large social networks (graphs). Several communities have been involved in modeling and analyzing these graphs. Usage of graphical models, such as Bayesian Networks (BN), to analyze massive data has become increasingly popular, due to their scalability and robustness to noise. In the literature BNs are primarily used for compact representation of joint distributions and to perform inference, i.e. answer queries about the data. In this work we learn Bayes Nets using the previously proposed SBNS algorithm [14]. We look at the learned networks for the purpose of analyzing the graph structure itself. We also point out a few improvements over the SBNS algorithm. The usefulness of Bayes Net structures to understand social networks is an open area. We discuss possible interpretations using a small subgraph of the Medline publications and hope to provoke some discussion and interest in further analysis.


title: ABSORPTIVE CAPACITY: A REVIEW, RECONCEPTUALIZATION, AND EXTENSION.

abstract: Researchers have used the absorptive capacity construct to explain various organizational phenomena. In this article we review the literature to identify key dimensions of absorptive capacity and offer a reconceptualization of this construct. Building upon the dynamic capabilities view of the firm, we distinguish between a firm's potential and realized capacity. We then advance a model outlining the conditions when the firm's potential and realized capacities can differentially influence the creation and sustenance of its competitive advantage.


title: 122 GHz aperture-coupled stacked patch microstrip antenna in LTCC technology

abstract: An aperture-coupled stacked patch microstrip antenna based on Low-Temperature Co-fired Ceramic (LTCC) technology is presented in this paper. Inspite of using an antenna substrate with high permittivity of 7.1 and only 200 µm thickness, wide bandwidth characteristic is achieved by means of coupled resonances. The antenna physical features contributing to the coupled resonances include a rectangular aperture, two stacked patches and a double via fence. The influence of various antenna parameters is analyzed in order to optimize the antenna design. The measured antenna reflection coefficient and gain shows good agreement with the simulation results, thus verifying the future potential for integrating this off-chip antenna with a Millimeter-Wave Integrated Circuit (MMIC) placed in an LTCC air cavity, for realization of a 122 GHz fully integrated, low-cost and surface mountable LTCC Antenna-in-Package.


title: Computer Architecture Techniques for Power-Efficiency

abstract: In the last few years, power dissipation has become an important design constraint, on par with performance, in the design of new computer systems. Whereas in the past, the primary job of the computer architect was to translate improvements in operating frequency and transistor count into performance, now power efficiency must be taken into account at every step of the design process. While for some time, architects have been successful in delivering 40% to 50% annual improvement in processor performance, costs that were previously brushed aside eventually caught up. The most critical of these costs is the inexorable increase in power dissipation and power density in processors. Power dissipation issues have catalyzed new topic areas in computer architecture, resulting in a substantial body of work on more power-efficient architectures. Power dissipation coupled with diminishing performance gains, was also the main cause for the switch from single-core to multi-core architectures and slowdown in frequency increase. This book aims to document some of the most important architectural techniques that were invented, proposed, and applied to reduce both dynamic power and static power dissipation in processors and memory hierarchies. A significant number of techniques have been proposed for a wide range of situations and this book synthesizes those techniques by focusing on their common characteristics. Table of Contents: Introduction / Modeling, Simulation, and Measurement / Using Voltage and Frequency Adjustments to Manage Dynamic Power / Optimizing Capacitance and Switching Activity to Reduce Dynamic Power / Managing Static (Leakage) Power / Conclusions


title: A five level cascaded H-bridge inverter based on space vector pulse width modulation technique

abstract: Multilevel inverters brought a tremendous revolution in high power superior performance industrial drive applications. Minimizing harmonic distortion is a challenging issue for power electronic drive. This problem can be solved by developing suitable modulation technique. There are number of modulation techniques established in the last decade. Among them, the space vector pulse width modulation (SVPWM) is extensively used because of their easier digital realization, better DC bus utilization and lower THD. Conventional SVPWM requires different number of logical computations of on time calculations and different number of lookup table for each triangle due to having their different number of switching states. In this paper, a simple SVPWM is presented where proposed only four logical computations of on time calculation, four active switching states and four lookup table for each triangle. Five level cascaded h-bridge inverter (CHBI) based on SVPWM technique are modeled and analyzed by using MATLAB/Simulink and origin 6.1 with a passive R-L load that can be extended to any level. These results are compared with other lower levels of inverter and with previous established results. From these results, five level CHBI based on SVPWM shows better performance compared to others in terms of THD.


title: Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection

abstract: The rapid growth of consumer videos requires an effective and efficient content summarization method to provide a user-friendly way to manage and browse the huge amount of video data. Compared with most previous methods that focus on sports and news videos, the summarization of personal videos is more challenging because of its unconstrained content and the lack of any pre-imposed video structures. We formulate video summarization as a novel dictionary selection problem using sparsity consistency, where a dictionary of key frames is selected such that the original video can be best reconstructed from this representative dictionary. An efficient global optimization algorithm is introduced to solve the dictionary selection model with the convergence rates as O(1/K2) (where K is the iteration counter), in contrast to traditional sub-gradient descent methods of O(1/√K). Our method provides a scalable solution for both key frame extraction and video skim generation, because one can select an arbitrary number of key frames to represent the original videos. Experiments on a human labeled benchmark dataset and comparisons to the state-of-the-art methods demonstrate the advantages of our algorithm.


title: Efficient In-Memory Indexing with Generalized Prefix Trees.

abstract: Efficient data structures for in-memory indexing gain in importance due to (1) the exponentially increasing amount of data, (2) the growing main-memory capacity, and (3) the gap between main-memory and CPU speed. In consequence, there are high performance demands for in-memory data structures. Such index structures are used—with minor changes—as primary or secondary indices in almost every DBMS. Typically, tree-based or hash-based structures are used, while structures based on prefix-trees (tries) are neglected in this context. For tree-based and hash-based structures, the major disadvantages are inherently caused by the need for reorganization and key comparisons. In contrast, the major disadvantage of trie-based structures in terms of high memory consumption (created and accessed nodes) could be improved. In this paper, we argue for reconsidering prefix trees as in-memory index structures and we present the generalized trie, which is a prefix tree with variable prefix length for indexing arbitrary data types of fixed or variable length. The variable prefix length enables the adjustment of the trie height and its memory consumption. Further, we introduce concepts for reducing the number of created and accessed trie levels. This trie is order-preserving and has deterministic trie paths for keys, and hence, it does not require any dynamic reorganization or key comparisons. Finally, the generalized trie yields improvements compared to existing in-memory index structures, especially for skewed data. In conclusion, the generalized trie is applicable as general-purpose in-memory index structure in many different OLTP or hybrid (OLTP and OLAP) data management systems that require balanced read/write performance.


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title: Variation in vitamin D deficiency among tuberculosis patients by ethnic group and geographical region of birth: evidence from a diverse south London population

abstract: Ethnicity & some geographical regions of birth strongly associated with risk of vitamin D deficiency in TB patients http://ow.ly/MiXR302uUPh


title: Modular Decomposition Method Based on Design Structure Matrix and Application

abstract: Modular decomposition is an effective means to achieve a complex system, but that of current part-component-based does not meet the needs of the positive development of the production. Design Structure Matrix(DSM) can simultaneously reflect the sequence, iteration, and feedback information, and express the parallel, sequential, and coupled relationship between DSM elements. This article, a modular decomposition method, named Design Structure Matrix Clustering modularize method, is proposed, concerned procedures are define, based on sorting calculate and clustering analysis of DSM, according to the rules of rows exchanges and columns exchange with the same serial number. The purpose and effectiveness of DSM clustering modularize method are confirmed through case study of assembly and calibration system for the large equipment. DOI: http://dx.doi.org/10.11591/telkomnika.v10i8.1656 Full Text: PDF


title: UMCC_DLSI: Sentiment Analysis in Twitter using Polirity Lexicons and Tweet Similarity

abstract: This paper describes a system submitted to SemEval-2014 Task 4B: Sentiment Analysis in Twitter, by the team UMCC DLSI Sem integrated by researchers of the University of Matanzas, Cuba and the University of Alicante, Spain. The system adopts a cascade classification process that uses two classifiers, K-NN using the lexical Levenshtein metric and a Dagging model trained over attributes extracted from annotated corpora and sentiment lexicons. Phrases that fit the distance thresholds were automatically classified by the KNN model, the others, were evaluated with the Dagging model. This system achieved over 52.4% of correctly classified instances in the Twitter message-level subtask.


Retentivity as an Indicator of the Capacity of Basins without an Outlet to Accumulate Water Surpluses


title: A secure distributed framework for achieving k-anonymity

abstract: k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model has been that of a single source wanting to publish data. Due to privacy issues, it is common that data from different sites cannot be shared directly. Therefore, this paper presents a two-party framework along with an application that generates k-anonymous data from two vertically partitioned sources without disclosing data from one site to the other. The framework is privacy preserving in the sense that it satisfies the secure definition commonly defined in the literature of Secure Multiparty Computation.


title: Towards Operational Measures of Computer Security

abstract: Ideally, a measure of the security of a system should capture quantitatively the intuitive notion of 'the ability of the system to resist attack'. That is, it should be operational, reflecting the degree to which the system can be expected to remain free of security breaches under particular conditions of operation (including attack). Instead, current security levels at best merely reflect the extensiveness of safeguards introduced during the design and development of a system. Whilst we might expect a system developed to a higher level than another to exhibit 'more secure behaviour' in operation, this cannot be guaranteed; more particularly, we cannot infer what the actual security behaviour will be from knowledge of such a level. In the paper we discuss similarities between reliability and security with the intention of working towards measures of 'operational security' similar to those that we have for reliability of systems. Very informally, these measures could involve expressions such as the rate of occurrence of security breaches (cf rate of occurrence of failures in reliability), or the probability that a specified 'mission' can be accomplished without a security breach (cf reliability function). This new approach is based on the analogy between system failure and security breach. A number of other analogies to support this view are introduced. We examine this duality critically, and have identified a number of important open questions that need to be answered before this quantitative approach can be taken further. The work described here is therefore somewhat tentative, and one of our major intentions is to invite discussion about the plausibility and feasibility of this new approach.


title: An adaptive profile based fraud detection framework for handling concept drift

abstract: As e-commerce continues to grow, so does the opportunity for perpetrating online fraud. As a result many researches have been conducted to make online transactions possible in a risk free environment by proposing different fraud detection methods. Concept drift is an inherent feature in many data streams such as electronic financial transactions. Hence, many fraud detection techniques have tried to detect and preferably manage concept drift. In this paper, a new concept drift management framework has been proposed. In this framework a temporary profile has been introduced in order to retain new concepts in the incoming data stream independently from historical profile. When the historical profile reaches a different decision from the temporary profile this is an indication that most probably a concept drift has occurred. In this case, a window based method is applied as a strategy for managing concept drift. The ability to adapt normal profiles systematically makes this concept drift management framework applicable to any profile based fraud detection method. Simulation results indicate that the proposed scheme is able to reduce the false positives (FPs) of a typical fraud detection method to 4.3% on average in the presence of a wide variety of concept drifts in the incoming transactions. This is an average of 85.7% reduction in FPs for this fraud detection technique.


title: Mirror on the Wall: Finding Similar Questions with Deep Structured Topic Modeling

abstract: Internet users today prefer getting precise answers to their questions rather than sifting through a bunch of relevant documents provided by search engines. This has led to the huge popularity of Community Question Answering cQA services like Yahoo! Answers, Baidu Zhidao, Quora, StackOverflowetc., where forum users respond to questions with precise answers. Over time, such cQA archives become rich repositories of knowledge encoded in the form of questions and user generated answers. In cQA archives, retrieval of similar questions, which have already been answered in some form, is important for improving the effectiveness of such forums. The main challenge while retrieving similar questions is the "lexico-syntactic" gap between the user query and the questions already present in the forum. In this paper, we propose a novel approach called "Deep Structured Topic Model DSTM" to bridge the lexico-syntactic gap between the question posed by the user and forum questions. DSTM employs a two-step process consisting of initially retrieving similar questions that lie in the vicinity of the query and latent topic vector space and then re-ranking them using a deep layered semantic model. Experiments on large scale real-life cQA dataset show that our approach outperforms the state-of-the-art translation and topic based baseline approaches.


title: Positive emotion dispositions differentially associated with Big Five personality and attachment style

abstract: Although theorists have proposed the existence of multiple distinct varieties of positive emotion, dispositional positive affect is typically treated as a unidimensional variable in personality research. We present data elaborating conceptual and empirical differences among seven positive emotion dispositions in their relationships with two core personality constructs, the “Big Five” and adult attachment style. We found that the positive emotion dispositions were differentially associated with self- and peer-rated Extraversion, Conscientiousness, Agreeableness, Openness to Experience, and Neuroticism. We also found that different adult attachment styles were associated with different kinds of emotional rewards. Findings support the theoretical utility of differentiating among several dispositional positive emotion constructs in personality research.


title: Privacy Vulnerabilities in Encrypted HTTP Streams

abstract: Encrypting traffic does not prevent an attacker from performing some types of traffic analysis. We present a straightforward traffic analysis attack against encrypted HTTP streams that is surprisingly effective in identifying the source of the traffic. An attacker starts by creating a profile of the statistical characteristics of web requests from interesting sites, including distributions of packet sizes and inter-arrival times. Later, candidate encrypted streams are compared against these profiles. In our evaluations using real traffic, we find that many web sites are subject to this attack. With a training period of 24 hours and a 1 hour delay afterwards, the attack achieves only 23% accuracy. However, an attacker can easily pre-determine which of trained sites are easily identifiable. Accordingly, against 25 such sites, the attack achieves 40% accuracy; with three guesses, the attack achieves 100% accuracy for our data. Longer delays after training decrease accuracy, but not substantially. We also propose some countermeasures and improvements to our current method. Previous work analyzed SSL traffic to a proxy, taking advantage of a known flaw in SSL that reveals the length of each web object. In contrast, we exploit the statistical characteristics of web streams that are encrypted as a single flow, which is the case with WEP/WPA, IPsec, and SSH tunnels.


title: Study and analysis of copy-move & splicing image forgery detection techniques

abstract: With use of advanced image processing software's and tools, it becomes very easy to perform the tampering on original digital images with different intensions. Such kind of image tampering or manipulation is collectively called as image forgery. The main two types are 1) copy-move forgery and 2) image splicing forgery of image forgery. Copy-move tampering is most generally used by attackers in which object of another image is copy and paste in original image in nearly matching areas. Hence to identify such image threats, it is required to automatic computer vision based method which can classify whether input digital image is original or tampered. The image processing based methods are mainly three important phases like as image pre-processing, image features extraction and detection of forged area on image using features extracted. For copy-move forgery detection there are many methods introduced from last 15 years. This all methods are categorized in two main types is active and passive forgery detection methods. This paper scope is limited to study on passive forgery detection methods. In this paper, aim is to present the study on different old methods of image forgery detection using different approaches like DWT (Discrete Wavelet Transform), SIFT, LBP (Local Binary Pattern) etc. The outcome of this paper is to find the current research challenges based on study of different methods of image forgery detection through the comparative study of all recent methods studied.


A Comprehensive Survey of Neighborhood-based Recommendation Methods


title: Personality-Based User Modeling for Music Recommender Systems

abstract: Applications are getting increasingly interconnected. Al-though the interconnectedness provide new ways to gather information about the user, not all user information is ready to be directly implemented in order to provide a personalized experience to the user. Therefore, a general model is needed to which users’ behavior, preferences, and needs can be connected to. In this paper we present our works on a personality-based music recommender system in which we use users’ personality traits as a general model. We identified relationships between users’ personality and their behavior, preferences, and needs, and also investigated different ways to infer users’ personality traits from user-generated data of social networking sites (i.e., Facebook, Twitter, and Instagram). Our work contributes to new ways to mine and infer personality-based user models, and show how these models can be implemented in a music recommender system to positively contribute to the user experience.


title: Zero-Inflated Generalized Poisson Regression Model with an Application to Domestic Violence Data

abstract: The generalized Poisson regression model has been used to model dispersed count data. It is a good competitor to the negative binomial re- gression model when the count data is over-dispersed. Zero-inflated Poisson and zero-inflated negative binomial regression models have been proposed for the situations where the data generating process results into too many zeros. In this paper, we propose a zero-inflated generalized Poisson (ZIGP) regression model to model domestic violence data with too many zeros. Es- timation of the model parameters using the method of maximum likelihood is provided. A score test is presented to test whether the number of zeros is too large for the generalized Poisson model to adequately fit the domestic violence data.


Using Clustering for Categorization of Support Tickets


title: The Influence of Initial Soft Tissue Thickness on Peri-Implant Bone Remodeling

abstract: Aim ::: ::: To elucidate the influence of initial soft tissue thickness on peri-implant bone remodeling. The research hypothesis was that implants installed in patients or at sites with thin mucosal tissues would show increased peri-implant bone loss. ::: ::: ::: ::: Material and Methods ::: ::: 79 edentulous patients were consecutively treated with two non-splinted implants supporting an overdenture in the mandible. During recall-visits, peri-implant health was determined by means of probing pocket depth and the modified plaque/bleeding index. Digital peri-apical radiographs were taken from individual implants. Bone level changes were measured from a reference point (lower border of the smooth implant collar) to the marginal bone-to-implant contact level. The linear mixed-effect model analysis was adopted to analyze the influence of clinical parameters and transmucosal abutment height on peri-implant bone loss. ::: ::: ::: ::: Results ::: ::: 67 patients attended the 1-year and 66 the 2-year recall-visit. Mean bone level changes were 0.89 mm (SD 0.62) and 0.90 mm (SD 0.66), plaque scores 0.82 (SD 0.94) and 0.87 (SD 0.92), bleeding scores 0.46 (SD 0.68) and 0.56 (SD 0.72) and PPD 1.65 mm (SD 0.60) and 1.78 mm (SD 0.59) after 1 year and 2 years respectively. The linear mixed-effect model revealed increasing bone level changes with decreasing abutment heights. Peri-implant bone level changes were significantly higher for implants with abutments of <2 mm (1.17 mm, p < .01; 1.23 mm, p < .01), 2 mm (0.86 mm, p < .01; 1.03 mm, p < .01) or 3 mm (0.38 mm, p = .046; 0.41 mm, p = .044) compared to ≥4 mm-abutments (bone level changes set to zero as reference value) both after 1 year and 2 years and bone level changes were significantly influenced by probing pocket depth (p < .01, p < .01), but not by plaque (p = .31, p = .09) and bleeding scores (p = .30, p = .40). ::: ::: ::: ::: Conclusion ::: ::: The present study suggests that implants with lower abutments, reflecting the initial gingival thickness, lose more peri-implant bone, possibly by a re-establishment of the biological width.


title: Emotions, Arousal, and Frontal Alpha Rhythm Asymmetry During Beethoven’s 5th Symphony

abstract: Music is capable of inducing emotional arousal. While previous studies used brief musical excerpts to induce one specific emotion, the current study aimed to identify the physiological correlates of continuous changes in subjective emotional states while listening to a complete music piece. A total of 19 participants listened to the first movement of Ludwig van Beethoven’s 5th symphony (duration: ~7.4 min), during which a continuous 76-channel EEG was recorded. In a second session, the subjects evaluated their emotional arousal during the listening. A fast fourier transform was performed and covariance maps of spectral power were computed in association with the subjective arousal ratings. Subjective arousal ratings had good inter-individual correlations. Covariance maps showed a right-frontal suppression of lower alpha-band activity during high arousal. The results indicate that music is a powerful arousal-modulating stimulus. The temporal dynamics of the piece are well suited for sequential analysis, and could be necessary in helping unfold the full emotional power of music.


title: Science and morality: The role of values in science and the scientific study of moral phenomenon

abstract: This article contributes to the debate over values in science. A critical co-constructivist frameworkis proposed for conceptualizing the role that debate over values plays in all science. Using the psycho-logical literature on moral development, it is shown that although debate over values is an integralpart of all scientific discourse, it plays a more explicit role in fields within the human sciences (e.g.,anthropology, psychology, sociology, etc.) that touch on moral phenomena. Debate over values thusraises a central issue for modern science, namely the need to develop consensually agreed-on meth-ods for resolving such debate.


title: Object perception for intelligent vehicle applications: A multi-sensor fusion approach

abstract: The paper addresses the problem of object perception for intelligent vehicle applications with main tasks of detection, tracking and classification of obstacles where multiple sensors (i.e.: lidar, camera and radar) are used. New algorithms for raw sensor data processing and sensor data fusion are introduced making the most information from all sensors in order to provide a more reliable and accurate information about objects in the vehicle environment. The proposed object perception module is implemented and tested on a demonstrator car in real-life traffics and evaluation results are presented.


title: A column approximate minimum degree ordering algorithm

abstract: Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero pattern of A, that limits the worst-case number of nonzeros in the factorization. The fill-in also depends on P, but Q is selected to reduce an upper bound on the fill-in for any subsequent choice of P. The choice of Q can have a dramatic impact on the number of nonzeros in L and U. One scheme for determining a good column ordering for A is to compute a symmetric ordering that reduces fill-in in the Cholesky factorization of ATA. A conventional minimum degree ordering algorithm would require the sparsity structure of ATA to be computed, which can be expensive both in terms of space and time since ATA may be much denser than A. An alternative is to compute Q directly from the sparsity structure of A; this strategy is used by MATLAB's COLMMD preordering algorithm. A new ordering algorithm, COLAMD, is presented. It is based on the same strategy but uses a better ordering heuristic. COLAMD is faster and computes better orderings, with fewer nonzeros in the factors of the matrix.


title: Tight bounds on quantum searching

abstract: We provide a tight analysis of Grover''s recent algorithm for quantum database searching. We give a simple closed-form formula for the probability of success after any given number of iterations of the algorithm. This allows us to determine the number of iterations necessary to achieve almost certainty of finding the answer. Furthermore, we analyse the behaviour of the algorithm when the element to be found appears more than once in the table and we provide a new algorithm to find such an element even when the number of solutions is not known ahead of time. Using techniques from Shor''s quantum factoring algorithm in addition to Grover''s approach, we introduce a new technique for approximate quantum counting, which allows to estimate the number of solutions. Finally we provide a lower bound on the efficiency of any possible quantum database searching algorithm and we show that Grover''s algorithm nearly comes within a factor 2 of being optimal in terms of the number of probes required in the table.


title: Frontal fibrosing alopecia versus lichen planopilaris: a clinicopathological study.

abstract: BACKGROUND ::: Frontal fibrosing alopecia (FFA) is an acquired scarring alopecia currently considered a clinical variant of lichen planopilaris (LPP). Our purpose was to examine the clinicopathological features of FFA. In addition, we investigated the similarities and differences between FFA and LPP. ::: ::: ::: METHODS ::: Biopsies from the scalp lesions of eight patients with FFA and eight patients with LPP were microscopically analyzed. Two cases of FFA and four cases of LPP were studied using direct immunofluorescence. ::: ::: ::: RESULTS ::: In spite of the completely different clinical characteristics of FFA and LPP patients, the histopathological findings for the two entities were similar. Common microscopic findings for both FFA and LPP included an inflammatory lymphocytic infiltrate involving the isthmus and infundibulum of the hair follicles, the presence of apoptotic cells in the external root sheath, and a concentric fibrosis surrounding the hair follicles that resulted in their destruction with subsequent scarring alopecia. Biopsies taken from FFA patients showed less follicular inflammation and more apoptotic cells than those from LPP patients. In some cases of LPP, the inflammatory infiltrate involved the interfollicular epidermis, a finding never present in our FFA cases. Direct immunofluorescence was negative in the two cases of FFA studied and showed deposits of immunoglobulins and/or complement in two of the four LPP cases examined. ::: ::: ::: CONCLUSIONS ::: The characteristic findings for FFA were more prominent apoptosis and less inflammation than found in LPP, along with spared interfollicular epidermis. FFA cases showed a rather characteristic histopathological pattern, although we could not find any clear-cut histological differences between FFA and LPP.


title: Interactive design of animated plushies

abstract: We present a computational approach to creating animated plushies, soft robotic plush toys specifically-designed to reenact user-authored motions. Our design process is inspired by muscular hydrostat structures, which drive highly versatile motions in many biological systems. We begin by instrumenting simulated plush toys with a large number of small, independently-actuated, virtual muscle-fibers. Through an intuitive posing interface, users then begin animating their plushie. A novel numerical solver, reminiscent of inverse-kinematics, computes optimal contractions for each muscle-fiber such that the soft body of the plushie deforms to best match user input. By analyzing the co-activation patterns of the fibers that contribute most to the plushie's motions, our design system generates physically-realizable winch-tendon networks. Winch-tendon networks model the motorized cable-driven actuation mechanisms that drive the motions of our real-life plush toy prototypes. We demonstrate the effectiveness of our computational approach by co-designing motions and actuation systems for a variety of physically-simulated and fabricated plushies.


title: Temporal Analysis of Language through Neural Language Models

abstract: We provide a method for automatically detecting change in language across time through a chronologically trained neural language model. We train the model on the Google Books Ngram corpus to obtain word vector representations specific to each year, and identify words that have changed significantly from 1900 to 2009. The model identifies words such as cell and gay as having changed during that time period. The model simultaneously identifies the specific years during which such words underwent change.


Diagnosis and Classification of Diabetes Mellitus


title: Improving electromagnetic compatibility performance of packages and SiP modules using a conformal shielding solution

abstract: High-speed digital and wireless devices radiate unintentional electromagnetic noise, which can affect the normal operation of other devices within the same system, causing intra-system electromagnetic interference (EMI) problems, or contribute to the total radiated EMI from the system, resulting in potential system-level EMI issues. PCB and system level shielding may alleviate the system-level EMI between wireless PCB board and the outside environment, but seldom prevent the intra-system EMI within the shielding enclosure. Package and System in Package (SiP) level shielding is desirable to shield the unintended radiation and protect the other circuits on board. Traditionally an external metal lid is employed to isolate the radiation from an IC, but the package cost and the size penalty due to the solder pads for shield attachment make the solution unattractive. In this paper, a new shielding technology for IC packages based on metal spray coating (conformal shielding) is presented. By spraying a conductive material on the sides of the package, a very thin metal layer is constructed around the top and four sides of a package. This very thin sprayed metal layer adds zero penalty to the package size and works similar to a solid metal shielding with very good shielding effectiveness; hence, it is suitable for wireless infrastructure, tele-communications, and high-speed digital applications.


title: Oil Spill Detection in Radarsat and Envisat SAR Images

abstract: We present algorithms for automatic detection of oil spills in synthetic aperture radar (SAR) images. The algorithms consist of three main parts, namely: 1) detection of dark spots; 2) feature extraction from the dark spot candidates; and 3) classification of dark spots as oil spills or look-alikes. The algorithms have been trained on a large number of Radarsat and Envisat Advanced Synthetic Aperture Radar (ASAR) images. The performance of the algorithm is compared to manual and semiautomatic approaches in a benchmark study using 59 Radarsat and Envisat images. The algorithms can be considered to be a good alternative to manual inspection when large ocean areas are to be inspected


title: Clustering intrusion detection alarms to support root cause analysis

abstract: It is a well-known problem that intrusion detection systems overload their human operators by triggering thousands of alarms per day. This paper presents a new approach for handling intrusion detection alarms more efficiently. Central to this approach is the notion that each alarm occurs for a reason, which is referred to as the alarm's root causes. This paper observes that a few dozens of rather persistent root causes generally account for over 90p of the alarms that an intrusion detection system triggers. Therefore, we argue that alarms should be handled by identifying and removing the most predominant and persistent root causes. To make this paradigm practicable, we propose a novel alarm-clustering method that supports the human analyst in identifying root causes. We present experiments with real-world intrusion detection alarms to show how alarm clustering helped us identify root causes. Moreover, we show that the alarm load decreases quite substantially if the identified root causes are eliminated so that they can no longer trigger alarms in the future.


title: Is feature selection secure against training data poisoning?

abstract: Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation of data-driven technologies. Feature selection has been widely used in machine learning for security applications to improve generalization and computational efficiency, although it is not clear whether its use may be beneficial or even counterproductive when training data are poisoned by intelligent attackers. In this work, we shed light on this issue by providing a framework to investigate the robustness of popular feature selection methods, including LASSO, ridge regression and the elastic net. Our results on malware detection show that feature selection methods can be significantly compromised under attack (we can reduce LASSO to almost random choices of feature sets by careful insertion of less than 5% poisoned training samples), highlighting the need for specific countermeasures.


title: User identity linkage across social networks via linked heterogeneous network embedding

abstract: User identity linkage has important implications in many cross-network applications, such as user profile modeling, recommendation and link prediction across social networks. To discover accurate cross-network user correspondences, it is a critical prerequisite to find effective user representations. While structural and content information describe users from different perspectives, there is a correlation between the two aspects of information. For example, a user who follows a celebrity tends to post content about the celebrity as well. Therefore, the projections of structural and content information of a user should be as close to each other as possible, which inspires us to fuse the two aspects of information in a unified space. However, owing to the information heterogeneity, most existing methods extract features from content and structural information respectively, instead of describing them in a unified way. In this paper, we propose a Linked Heterogeneous Network Embedding model (LHNE) to learn the comprehensive representations of users by collectively leveraging structural and content information in a unified framework. We first model the topics of user interests from content information to filter out noise. Next, cross-network structural and content information are embedded into a unified space by jointly capturing the friend-based and interest-based user co-occurrence in intra-network and inter-network, respectively. Meanwhile, LHNE learns user transfer and topic transfer for enhancing information exchange across networks. Empirical results show LHNE outperforms the state-of-the-art methods on both real social network and synthetic datasets and can work well even with little or no structural information.


title: Efficient Encoding of Low-Density Parity-Check Codes

abstract: Low-density parity-check (LDPC) codes can be considered serious competitors to turbo codes in terms of performance and complexity and they are based on a similar philosophy: constrained random code ensembles and iterative decoding algorithms. We consider the encoding problem for LDPC codes. More generally we consider the encoding problem for codes specified by sparse parity-check matrices. We show how to exploit the sparseness of the parity-check matrix to obtain efficient encoders. For the (3,6)-regular LDPC code, for example, the complexity of encoding is essentially quadratic in the block length. However, we show that the associated coefficient can be made quite small, so that encoding codes even of length n/spl sime/100000 is still quite practical. More importantly, we show that "optimized" codes actually admit linear time encoding.


Violent Media Content and Effects


title: Silk-based biomaterials.

abstract: Silk from the silkworm, Bombyx mori, has been used as biomedical suture material for centuries. The unique mechanical properties of these fibers provided important clinical repair options for many applications. During the past 20 years, some biocompatibility problems have been reported for silkworm silk; however, contamination from residual sericin (glue-like proteins) was the likely cause. More recent studies with well-defined silkworm silk fibers and films suggest that the core silk fibroin fibers exhibit comparable biocompatibility in vitro and in vivo with other commonly used biomaterials such as polylactic acid and collagen. Furthermore, the unique mechanical properties of the silk fibers, the diversity of side chain chemistries for 'decoration' with growth and adhesion factors, and the ability to genetically tailor the protein provide additional rationale for the exploration of this family of fibrous proteins for biomaterial applications. For example, in designing scaffolds for tissue engineering these properties are particularly relevant and recent results with bone and ligament formation in vitro support the potential role for this biomaterial in future applications. To date, studies with silks to address biomaterial and matrix scaffold needs have focused on silkworm silk. With the diversity of silk-like fibrous proteins from spiders and insects, a range of native or bioengineered variants can be expected for application to a diverse set of clinical needs.