title: A Machine Learning Approach for an Indonesian-English Cross Language Question Answering System

abstract: We have built a CLQA (Cross Language Question Answering) system for a source language with limited data resources (e.g. Indonesian) using a machine learning approach. The CLQA system consists of four modules: question analyzer, keyword translator, passage retriever and answer finder. We used machine learning in two modules, the question classifier (part of the question analyzer) and the answer finder. In the question classifier, we classify the EAT (Expected Answer Type) of a question by using SVM (Support Vector Machine) method. Features for the classification module are basically the output of our shallow question parsing module. To improve the classification score, we use statistical information extracted from our Indonesian corpus. In the answer finder module, using an approach different from the common approach in which answer is located by matching the named entity of the word corpus with the EAT of question, we locate the answer by text chunking the word corpus. The features for the SVM based text chunking process consist of question features, word corpus features and similarity scores between the word corpus and the question keyword. In this way, we eliminate the named entity tagging process for the target document. As for the keyword translator module, we use an Indonesian-English dictionary to translate Indonesian keywords into English. We also use some simple patterns to transform some borrowed English words. The keywords are then combined in boolean queries in order to retrieve relevant passages using IDF scores. We first conducted an experiment using 2,837 questions (about 10% are used as the test data) obtained from 18 Indonesian college students. We next conducted a similar experiment using the NTCIR (NII Test Collection for IR Systems) 2005 CLQA task by translating the English questions into Indonesian. Compared to the Japanese-English and Chinese-English CLQA results in the NTCIR 2005, we found that our system is superior to others except for one system that uses a high data resource employing 3 dictionaries. Further, a rough comparison with two other Indonesian-English CLQA systems revealed that our system achieved higher accuracy score.


title: Linearization of excitatory synaptic integration at no extra cost

abstract: In many theories of neural computation, linearly summed synaptic activation is a pervasive assumption for the computations performed by individual neurons. Indeed, for certain nominally optimal models, linear summation is required. However, the biophysical mechanisms needed to produce linear summation may add to the energy-cost of neural processing. Thus, the benefits provided by linear summation may be outweighed by the energy-costs. Using voltage-gated conductances in a relatively simple neuron model, this paper quantifies the cost of linearizing dendritically localized synaptic activation. Different combinations of voltage-gated conductances were examined, and many are found to produce linearization; here, four of these models are presented. Comparing the energy-costs to a purely passive model, reveals minimal or even no additional costs in some cases.


title: The effect of DPD bandwidth limitation on EVM for a 28 nm WLAN 802.11ac transmitter

abstract: Digital predistortion (DPD) performance for an IEEE 802.11ac WLAN transmitter in 28 nm CMOS, using 20 and 80 MHz bandwidth with 256QAM modulation, is analyzed under different bandwidth limitations. Due to band-limited conditions in the transmitter chain, the DPD linearization performance may be reduced. For a minimum transmitter error vector magnitude (EVM) requirement of −32 dB (2.5%) for 256QAM modulation, a reduction in the maximum linear output power of 1.6 dB due to bandwidth limitations in the transmitter chain is estimated using simulations and measurement results.


title: Framing QA as Building and Ranking Intersentence Answer Justifications

abstract: We propose a question answering (QA) approach for standardized science exams that both identifies correct answers and produces compelling human-readable justifications for why those answers are correct. Our method first identifies the actual information needed in a question using psycholinguistic concreteness norms, then uses this information need to construct answer justifications by aggregating multiple sentences from different knowledge bases using syntactic and lexical information. We then jointly rank answers and their justifications using a reranking perceptron that treats justification quality as a latent variable. We evaluate our method on 1,000 multiple-choice questions from elementary school science exams, and empirically demonstrate that it performs better than several strong baselines, including neural network approaches. Our best configuration answers 44% of the questions correctly, where the top justifications for 57% of these correct answers contain a compelling human-readable justification...


title: On the discovery of the nature of cholera toxin in India.

abstract: This brief account looks at the discovery of the nature of cholera toxin in India. During the late nineteenth and early twentieth centuries research on cholera was being carried out in Bombay and Calcutta. Robert Koch visited India in 1883 and isolated and purified vibrio cholera the following year.This paper considers the contribution of Dr Sambhu Nath De, known best as S. N. De, (1915-1985) to cholera toxin discovery and notes the 2009 Conference which honoured his memory.


Thirty years in hemostasis research in Cluj Napoca


title: Automatic Section Recognition in Obituaries

abstract: Obituaries contain information about people's values across times and cultures, which makes them a useful resource for exploring cultural history. They are typically structured similarly, with sections corresponding to Personal Information, Biographical Sketch, Characteristics, Family, Gratitude, Tribute, Funeral Information and Other aspects of the person. To make this information available for further studies, we propose a statistical model which recognizes these sections. To achieve that, we collect a corpus of 20058 English obituaries from TheDaily Item, this http URL and The London Free Press. The evaluation of our annotation guidelines with three annotators on 1008 obituaries shows a substantial agreement of Fleiss k = 0.87. Formulated as an automatic segmentation task, a convolutional neural network outperforms bag-of-words and embedding-based BiLSTMs and BiLSTM-CRFs with a micro F1 = 0.81.


title: Language model verbalization for automatic speech recognition

abstract: Transcribing speech in properly formatted written language presents some challenges for automatic speech recognition systems. The difficulty arises from the conversion ambiguity between verbal and written language in both directions. Non-lexical vocabulary items such as numeric entities, dates, times, abbreviations and acronyms are particularly ambiguous. This paper describes a finite-state transducer based approach that improves proper transcription of these entities. The approach involves training a language model in the written language domain, and integrating verbal expansions of vocabulary items as a finite-state model into the decoding graph construction. We build an inverted finite-state transducer to map written vocabulary items to alternate verbal expansions using rewrite rules. Then, this verbalizer transducer is composed with the n-gram language model to obtain a verbalized language model, whose input labels are in the verbal language domain while output labels are in the written language domain. We show that the proposed approach is very effective in improving the recognition accuracy of numeric entities.


title: A partitioning approach to improve reconfigurable neuron-inspired online BIST

abstract: Two of the most challenging issues in online testing are deriving a general tester scheme for various circuits and reducing the area overhead. This paper presents a novel reconfigurable online tester using artificial neural networks to test combinational hardware. Our proposed BIST architecture has the capability of testing a number of arbitrary sub-modules of a big design simultaneously by time-multiplexing between them. Output partitioning method is proposed as a powerful technique to reduce neural network training time and the tester area overhead. Our experimental results show that after proper partitioning the average area overhead is reduced by 16% in data-path and 33% in memory area. Also average fault detection latency has been improved by 14%.


title: Power divider in ridge gap waveguide technology

abstract: A power divider in ridge gap waveguide technology has been designed to work at 15GHz. Measurements and simulations are provided. Besides, a broad numerical study of the characteristic impedance of a ridge gap waveguide is presented.


title: The Yo-Yo intermittent recovery test in basketball players.

abstract: The purpose of this study was to examine the physiological correlates of the Yo-Yo intermittent recovery test level 1 (Yo-Yo IR1) in basketball players. Twenty-two male basketball players (means+/-S.D., body mass 72.4+/-11.4kg, height 181.7+/-6.9cm, age 16.8+/-2.0 years) were tested for maximal oxygen uptake (VO(2max)), ventilatory threshold (VT) and running economy (RE) on a motorized treadmill. Lower limb explosive strength and anaerobic-capacity was assessed using vertical jumps (CMJ), 15m shuttle running sprint (15mSR) and line drill (LD), respectively. The same test battery was replicated after an experimental basketball game in order to assess selective effect of fatigue on physical performance. Pre to post-game CMJ (40.3+/-5.7 versus 39.9+/-5.9cm) and 15mSR (5.80+/-0.25 versus 5.77+/-0.22s) performances were not significantly different (p>0.05). LD performance decreased significantly post-game (from 26.7+/-1.3 to 27.7+/-2.7s, p<0.001). Yo-Yo IR1 performances (m) were significantly related to VO(2max) (r=0.77, p=0.0001), speed at VO(2max) (r=0.71, p=0.0001) and %VO(2max) at VT (r=-0.60, p=0.04). Yo-Yo IR1 performance was significantly correlated to post-game LD decrements (r=-0.52, p=0.02). These findings show that Yo-Yo IR1 may be considered as a valid basketball-specific test for the assessment of aerobic fitness and game-related endurance.


title: ATHYNOS: Helping Children with Dyspraxia Through an Augmented Reality Serious Game

abstract: Emerging technologies and ICT have changed the lifestyle of society, all scientific areas are taking advantage of technology to get a real development. Therapists realize the benefits of using serious games as an assistive tool in psychotherapy. Thus, this research examines relevant issues regarding Dyspraxia disorders in children and presents a comparative study between two therapies methods, one using a manual puzzle and other using ATHYNOS, an Augmented Reality Serious Game developed to help children with the Dyspraxia to improve their motor skills and hand-eye coordination through technology. The analysis of data results showed that exist a significant difference between both methods, proving that children playing with ATHYNOS got less time in the activity execution and also better performance.


title: Optical character recognition for model-based object recognition applications

abstract: This paper discusses the performance of Fourier descriptors and Hu's seven moment invariants for an Optical Character Recognition (OCR) engine developed for 3D model-based object recognition applications.


title: An adaptive neural network approach to the tracking control of micro aerial vehicles in constrained space

abstract: ABSTRACTThis paper presents an adaptive neural network approach to the trajectory tracking control of micro aerial vehicles especially when they are flying in a limited indoor area. Differing from conventional controllers, the proposed controller employs the outer position loop to directly generate angular velocity commands in the presence of unknown aerodynamics and disturbances and then the fast inner loop to handle the angular rate control. Adaptive neural networks are deployed to estimate all the uncertain factors with the adaptation law derived from the Lyapunov function. To achieve a real-time performance, a norm estimation approach of ideal weights is designed to achieve a high bandwidth and lighten the burden of computation burden. Meanwhile, a barrier Lyapunov function is introduced to guarantee the constraint of vehicle positions as well as the validity of the neural network estimation. Simulations and practical flight tests are conducted to verify the feasibility and effectiveness of the propos...


Gendering cybercrime


title: Cloud computing: Security model comprising governance, risk management and compliance

abstract: Cloud security is a broad topic and any combination of policies, technologies, and controls to protect data, infrastructure and services from possible attacks. Security requirements in the cloud are very much different from traditional environments. Since cloud has a dynamic nature with small customer ownership of infrastructure, has broken traditional security architecture. We believe Security should be the joint responsibility of service provider and organization, no matter what kind of service model you are using. Security will be more effective when layered at each level of cloud technology and integrated with a common management platform. There is a hype of cloud in the market, but companies are not still ready to put their business in the cloud just because of security is prominent issues which does not grow the cloud computing business in the market. We focus on the area, i.e. application security, information security, infrastructure security and security monitoring by giving our own security model. This model surely protects our organizational physical as well as virtual assets by providing better security options.


Feature Extraction from Product Reviews using Feature Similarity and Polarity


title: Preparation and characterization of hollow fibers of silicalite-1

abstract: Hollow fibers consisting solely of silicalite-1 were prepared by a novel method consisting of four basic steps. Carbon fibers were first surface modified to facilitate adsorption of colloidal seed crystals of silicalite-1. Such particles were then adsorbed as a monolayer on the fiber surface and induced to grow into a continuous film of intergrown crystals of silicalite-1. Finally, the carbon fiber used as a template was removed by calcination in air, yielding hollow fibers of silicalite-1. Hollow fibers were prepared in two experimental series employing different synthesis conditions resulting in two different modes of growth. The first experimental series was performed with a clear homogeneous TPAOH/silica solution crystallizing at 100°C, and the second series with a NaOH-TPABr/silica gel crystallizing at 150°C. Materials produced in the first series showed a smooth and homogeneous surface and a relatively uniform fiber wall with a final thickness of 1.1 μm. In these materials the film was formed by crystals growing outward from the surface of the carbon fiber. Materials produced in the second series were less uniform with a final thickness of the fiber wall of about 3.2 μm. The crystal growth in these materials appeared to take place both in an inward and outward direction starting from the seed crystals adsorbed on the carbon fiber. The hollow fibers prepared were characterized by SEM, XRD, FT i.r., and nitrogen adsorption measurements.


title: Towards Predicting the Best Answers in Community-based Question-Answering Services

abstract: Community-based question-answering (CQA) services contribute to solving many difficult questions we have. For each question in such services, one best answer can be designated, among all answers, often by the asker. However, many questions on typical CQA sites are left without a best answer even if when good candidates are available. In this paper, we attempt to address the problem of predicting if an answer may be selected as the best answer, based on learning from labeled data. The key tasks include designing features measuring important aspects of an answer and identifying the most importance features. Experiments with a Stack Overflow dataset show that the contextual information among the answers should be the most important factor to consider.


title: Big Data Analytics for Security

abstract: Big data is changing the landscape of security tools for network monitoring, security information and event management, and forensics; however, in the eternal arms race of attack and defense, security researchers must keep exploring novel ways to mitigate and contain sophisticated attackers.


title: Group RFM analysis as a novel framework to discover better customer consumption behavior

abstract: The RFM model provides an effective measure for customers' consumption behavior analysis, where three variables, namely, consumption interval, frequency, and money amount are used to quantify a customer's loyalty and contribution. Based on the RFM value, customers can be clustered into different groups and the group information is very useful in market decision making. However, most previous works completely left out important characteristics of purchased products, such as their prices and lifetimes, and apply the RFM measure on all of a customer's purchased products. This renders the calculation of the RFM value unreasonable or insignificant for customer analysis. In this paper, we propose a new framework called GRFM (for group RFM) analysis to alleviate the problem. The new measure method takes into account the characteristics of the purchased items so that the calculated the RFM value for the customers are strongly related to their purchased items and can correctly reflect their actual consumption behavior. Moreover, GRFM employs a constrained clustering method PICC (for Purchased Items-Constrained Clustering) that could base on a cleverly designed purchase pattern table to adjust original purchase records to satisfy various clustering constraints as well as to decrease re-clustering time. The GRFM allows a customer to belong to different clusters, and thus to be associated with different loyalties and contributions with respect to different characteristics of purchased items. Finally, the clustering result of PICC contains extra information about the distribution status inside each cluster that could help the manager to decide when is most proper to launch a specific sales promotion campaign. Our experiments have confirmed the above observations and suggest that GRFM can play an important role in building a personalized purchasing management system and an inventory management system.


title: Systems Biology in the Context of Big Data and Networks

abstract: Science is going through two rapidly changing phenomena: one is the increasing capabilities of the computers and software tools from terabytes to petabytes and beyond, and the other is the advancement in high-throughput molecular biology producing piles of data related to genomes, transcriptomes, proteomes, metabolomes, interactomes, and so on. Biology has become a data intensive science and as a consequence biology and computer science have become complementary to each other bridged by other branches of science such as statistics, mathematics, physics, and chemistry. The combination of versatile knowledge has caused the advent of big-data biology, network biology, and other new branches of biology. Network biology for instance facilitates the system-level understanding of the cell or cellular components and subprocesses. It is often also referred to as systems biology. The purpose of this field is to understand organisms or cells as a whole at various levels of functions and mechanisms. Systems biology is now facing the challenges of analyzing big molecular biological data and huge biological networks. This review gives an overview of the progress in big-data biology, and data handling and also introduces some applications of networks and multivariate analysis in systems biology.


Style-based Text Categorization: What Newspaper Am I Reading?


title: Abstract On the quest for easy-to-understand splitting rules

abstract: Decision trees are probably the most popular and commonly used classification model. They are built recursively following a top-down approach (from general concepts to particular examples) by repeated splits of the training dataset. The chosen splitting criterion may affect the accuracy of the classifier, but not significantly. In fact, none of the proposed splitting criteria in the literature has proved to be universally better than the rest. Although they all yield similar results, their complexity varies significantly, and they are not always suitable for multi-way decision trees. Here we propose two new splitting rules which obtain similar results to other well-known criteria when used to build multi-way decision trees, while their simplicity makes them ideal for non-expert users.


title: The role of educational software as a support for teaching and learning conversations

abstract: Much recent educational research focuses on teaching and learning within classroom conversations. This raises the question of the role of ICT as a support for such conversations. The central argument of this paper is that the dual nature of computers, as machines (objects) which can be made to act as if they were people (subjects), allows them to play a potentially distinctive and valuable role within educational conversations. This role is to resource and, at the same time, to frame and direct, learning conversations amongst small groups of children. Evidence in support of this argument is provided through the findings of an empirical study. In the study preparation for group work at computers was combined with the use of principles for the selection and design of software in order to develop educational activities to support discussion within the science and maths curricula over one year. One hundred and nineteen children aged between nine and ten participated in the study. The evaluation included video-recording, transcript analysis and a matching control group who covered the same areas of the curriculum without the intervention. The qualitative findings show learning occurring in the talk of the children working around computers and the quantitative findings suggest that this approach can produce significant learning gains within the normal curriculum.


title: A NEW BINARY SUPPORT VECTOR SYSTEM FOR INCREASING DETECTION RATE OF CREDIT CARD FRAUD

abstract: Recently, a new personalized model has been developed to prevent credit card fraud. This model is promising; however, there remains some problems. Existing approaches cannot identify well credit card frauds from few data with skewed distributions. This paper proposes to address the problem using a binary support vector system (BSVS). The proposed BSVS is based on the support vectors in the support vector machines (SVM) and the genetic algorithm (GA) is employed to select support vectors. To obtain a high true negative rate, self-organizing mapping (SOM) is first employed to estimate the distribution model of the input data. Then BSVS is used to best train the data according to the input data distribution to obtain a high detection rate. Experimental results show that the proposed BSVS is effective especially for predicting a high true negative rate.


Smart Cities and the Ageing Population


title: Optimum Design of Transformer-Type Marchand Balun Using Scalable Integrated Passive Device Technology

abstract: A simple and effective technique for designing transformer-type Marchand baluns is presented in this paper. The coupled-line sections required in a Marchand balun design are realized using planar transformers with a scalable model. The proposed design technique enables the prediction and optimization of Marchand balun performance over a wide range of layout and process parameters. To demonstrate the optimization process, Marchand baluns are designed for implementation using silicon and glass integrated passive device (IPD) technologies. Finally, experimental verification shows very good agreement between the modeled and measured results, thereby validating the IPD design optimization.


title: Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models

abstract: Professional-grade software applications are powerful but complicated$-$expert users can achieve impressive results, but novices often struggle to complete even basic tasks. Photo editing is a prime example: after loading a photo, the user is confronted with an array of cryptic sliders like"clarity","temp", and"highlights". An automatically generated suggestion could help, but there is no single"correct"edit for a given image$-$different experts may make very different aesthetic decisions when faced with the same image, and a single expert may make different choices depending on the intended use of the image (or on a whim). We therefore want a system that can propose multiple diverse, high-quality edits while also learning from and adapting to a user's aesthetic preferences. In this work, we develop a statistical model that meets these objectives. Our model builds on recent advances in neural network generative modeling and scalable inference, and uses hierarchical structure to learn editing patterns across many diverse users. Empirically, we find that our model outperforms other approaches on this challenging multimodal prediction task.


title: Applying Deep Learning to Stereotypical Motor Movement Detection in Autism Spectrum Disorders

abstract: Autism Spectrum Disorders (ASD) are often associated with specific atypical postural or motor behaviors, of which Stereotypical Motor Movements (SMMs) interfere with learning and social interaction. Wireless inertial sensing technology offers a valid infrastructure for real-time SMM detection, whose automation would provide support for tuned intervention and possibly early alert on the onset of meltdown events. The identification and the quantification of SMM patterns remains complex due to strong inter-subject and intra-subject variability, in particular when handcrafted features are considered. This study aims at developing automatic SMM detection systems in a real world setting, based on a deep learning architecture. Here, after a review of the current state of the art of automatic SMM detection, we propose to employ the deep learning paradigm in order to learn the discriminating features from multi-sensor accelerometer signals. Our results with convolutional neural networks provided the preliminary evidence that feature learning and transfer learning embedded in deep architectures can provide accurate SMM detectors in longitudinal scenarios.


MODE REGULARIZED GENERATIVE ADVERSARIAL NETWORKS


title: Systematic review of epidemiological studies on health effects associated with management of solid waste

abstract: BackgroundManagement of solid waste (mainly landfills and incineration) releases a number of toxic substances, most in small quantities and at extremely low levels. Because of the wide range of pollutants, the different pathways of exposure, long-term low-level exposure, and the potential for synergism among the pollutants, concerns remain about potential health effects but there are many uncertainties involved in the assessment. Our aim was to systematically review the available epidemiological literature on the health effects in the vicinity of landfills and incinerators and among workers at waste processing plants to derive usable excess risk estimates for health impact assessment.MethodsWe examined the published, peer-reviewed literature addressing health effects of waste management between 1983 and 2008. For each paper, we examined the study design and assessed potential biases in the effect estimates. We evaluated the overall evidence and graded the associated uncertainties.ResultsIn most cases the overall evidence was inadequate to establish a relationship between a specific waste process and health effects; the evidence from occupational studies was not sufficient to make an overall assessment. For community studies, at least for some processes, there was limited evidence of a causal relationship and a few studies were selected for a quantitative evaluation. In particular, for populations living within two kilometres of landfills there was limited evidence of congenital anomalies and low birth weight with excess risk of 2 percent and 6 percent, respectively. The excess risk tended to be higher when sites dealing with toxic wastes were considered. For populations living within three kilometres of old incinerators, there was limited evidence of an increased risk of cancer, with an estimated excess risk of 3.5 percent. The confidence in the evaluation and in the estimated excess risk tended to be higher for specific cancer forms such as non-Hodgkin's lymphoma and soft tissue sarcoma than for other cancers.ConclusionsThe studies we have reviewed suffer from many limitations due to poor exposure assessment, ecological level of analysis, and lack of information on relevant confounders. With a moderate level confidence, however, we have derived some effect estimates that could be used for health impact assessment of old landfill and incineration plants. The uncertainties surrounding these numbers should be considered carefully when health effects are estimated. It is clear that future research into the health risks of waste management needs to overcome current limitations.


title: Synthetic sequences and ground-truth flow field generation for algorithm validation

abstract: Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems.


title: Step-climbing wheel with axial translation and wheelchair integration

abstract: In this paper, we introduce a mechanical wheel design capable of step climbing toward more convenient utilization of personal aids such as wheelchairs and walkers. Uneven floors with dips and depressions, thresholds, and steps have still been encountered in our daily routines in spite of the promotion of barrier-free environments. To overcome these difficulties, a step-climbing mechanism and its device are developed, allowing wheeled robots to climb a step where its height is higher than the radius of the wheel. By applying the proposed step-climbing device into a personal aid, its potential users enables to minimize a physical burden during step climbing. As a result, this helps the users live independently and maintain their daily routines, not requiring caregivers and/or medical staff. What is the most important aspect from the practical point of view is how to realize the mechanism without additional wheels and linear guides. This paper describes the mechanical design by controlling axial translation in detail. By using a commercial wheelchair equipped with the proposed device, extensive evaluations are tested and analyzed.


Effective Attention Modeling for Aspect-Level Sentiment Classification


title: Using the Wizard-of-Oz method for exploring deep customer experience preferences Analysis and evaluation of customer experience with regard to smartphone-based vehicle telematics services for the Chinese market using the Wizard-of-Oz method

abstract: When almost eighty percent of the senior executives from 362 companies claimed they provided an exceptional customer experience, only eight percent of their customers agreed [1]. It is challenging and necessary to evaluate customer experiences in a proper way, especially under the Chinese cultural background. This paper describes the new application of the Wizard-of-Oz method aimed at exploring deep customer experience preferences. In the following article the authors illustrate the essence of customer experience and present the overview of the Wizard-of-Oz method across the sections. Furthermore the article details the practice of the Wizard-of-Oz experiment based on a development project of smartphone-based vehicle telematics services. Finally the article analyzes the results from the Wizard-of-Oz experiment in the form of sentiment trend matrix.


title: Estimating Mixture Models via Mixtures of Polynomials

abstract: Mixture modeling is a general technique for making any simple model more expressive through weighted combination. This generality and simplicity in part explains the success of the Expectation Maximization (EM) algorithm, in which updates are easy to derive for a wide class of mixture models. However, the likelihood of a mixture model is non-convex, so EM has no known global convergence guarantees. Recently, method of moments approaches offer global guarantees for some mixture models, but they do not extend easily to the range of mixture models that exist. In this work, we present Polymom, an unifying framework based on method of moments in which estimation procedures are easily derivable, just as in EM. Polymom is applicable when the moments of a single mixture component are polynomials of the parameters. Our key observation is that the moments of the mixture model are a mixture of these polynomials, which allows us to cast estimation as a Generalized Moment Problem. We solve its relaxations using semidefinite optimization, and then extract parameters using ideas from computer algebra. This framework allows us to draw insights and apply tools from convex optimization, computer algebra and the theory of moments to study problems in statistical estimation. Simulations show good empirical performance on several models.


title: A Learning-to-Rank Approach for Image Color Enhancement

abstract: We present a machine-learned ranking approach for automatically enhancing the color of a photograph. Unlike previous techniques that train on pairs of images before and after adjustment by a human user, our method takes into account the intermediate steps taken in the enhancement process, which provide detailed information on the person's color preferences. To make use of this data, we formulate the color enhancement task as a learning-to-rank problem in which ordered pairs of images are used for training, and then various color enhancements of a novel input image can be evaluated from their corresponding rank values. From the parallels between the decision tree structures we use for ranking and the decisions made by a human during the editing process, we posit that breaking a full enhancement sequence into individual steps can facilitate training. Our experiments show that this approach compares well to existing methods for automatic color enhancement.


title: Robust real-time underwater digital video streaming using optical communication

abstract: We present a real-time video delivery solution based on free-space optical communication for underwater applications. This solution comprises of AquaOptical II, a high-bandwidth wireless optical communication device, and a two-layer digital encoding scheme designed for error-resistant communication of high resolution images. Our system can transmit digital video reliably through a unidirectional underwater channel, with minimal infrastructural overhead. We present empirical evaluation of this system's performance for various system configurations, and demonstrate that it can deliver high quality video at up to 15 Hz, with near-negligible communication latencies of 100 ms. We further characterize the corresponding end-to-end latencies, i.e. from time of image acquisition until time of display, and reveal optimized results of under 200 ms, which facilitates a wide range of applications such as underwater robot tele-operation and interactive remote seabed monitoring.


Traffic Light Priority Control For Emergency Vehicle Using RFID


title: State-of-the-Art Methods for Brain Tissue Segmentation: A Review

abstract: Brain tissue segmentation is one of the most sought after research areas in medical image processing. It provides detailed quantitative brain analysis for accurate disease diagnosis, detection, and classification of abnormalities. It plays an essential role in discriminating healthy tissues from lesion tissues. Therefore, accurate disease diagnosis and treatment planning depend merely on the performance of the segmentation method used. In this review, we have studied the recent advances in brain tissue segmentation methods and their state-of-the-art in neuroscience research. The review also highlights the major challenges faced during tissue segmentation of the brain. An effective comparison is made among state-of-the-art brain tissue segmentation methods. Moreover, a study of some of the validation measures to evaluate different segmentation methods is also discussed. The brain tissue segmentation, content in terms of methodologies, and experiments presented in this review are encouraging enough to attract researchers working in this field.


title: Enteric-coated peppermint-oil capsules in the treatment of irritable bowel syndrome: A prospective, randomized trial

abstract: To determine the efficacy and tolerability of an enteric-coated peppermint-oil formulation (Colpermin), we conducted a prospective, randomized, double-blind, placebo-controlled clinical study in 110 outpatients (66 men/44 women; 18-70 years of age) with symptoms of irritable bowel syndrome. Patients took one capsule (Colpermin or placebo) three to four times daily, 15-30 min before meals, for 1 month. Fifty-two patients on Colpermin and 49 on placebo completed the study. Forty-one patients on Colpermin (79%) experienced an alleviation of the severity of abdominal pain (29 were pain-free); 43 (83%) had less abdominal distension, 43 (83%) had reduced stool frequency, 38 (73%) had fewer borborygmi, and 41 (79%) less flatulence. Corresponding figures for the placebo group were: 21 patients (43%) with reduced pain (4 were pain-free), 14 (29%) with reduced distension, 16 (32%) with reduced stool frequency, 15 (31%) with fewer borborygmi, and 11 (22%) with less flatulence. Symptom improvements after Colpermin were significantly better than after placebo (P < 0.05; Mann-Whitney U-test). One patient on Colpermin experienced heartburn (because of chewing the capsules) and one developed a mild transient skin rash. There were no significant changes in liver function test results. Thus, in this trial, Colpermin was effective and well tolerated.


title: Online banking: a field study of drivers, development challenges, and expectations

abstract: Online banking is the newest and least understood delivery channel for retail banking services. Yet, few, if any, studies were reported quantifying the issues relevant to this cutting-edge technology. This paper reports the results of a quantitative study of the perceptions of banks' executive and IT managers and potential customers with regard to the drivers, development challenges, and expectations of online banking. The findings will be useful for both researchers and practitioners who seek to understand the issues relevant to online banking.


title: Radiation Efficiency Measurement Method for Passive UHF RFID Dipole Tag Antennas

abstract: Concurrently with the continuously developing radio frequency identification (RFID) technology, new types of tag antenna materials and structures are emerging to fulfill the requirements encountered within the new application areas. In this work, a radiation efficiency measurement method is developed and verified for passive ultra-high frequency (UHF) RFID dipole tag antennas. In addition, the measurement method is applied to measure the radiation efficiency of sewed dipole tag antennas for wearable body-centric wireless communication applications. The acquired information from measurements can be used to characterize tag antenna material structures losses and to further both improve and optimize tag antenna performance and reliability.


title: Facial feature parsing and landmark detection via low-rank matrix decomposition

abstract: Facial feature parsing is an active research topic in image understanding. In this paper, we address the feature parsing problem by applying low-rank matrix decomposition on facial images. Specifically, the face is considered as a combination of the skin background which resides in a low dimensional subspace with the salient feature components (e.g., eyes, nose and mouth) as the sparse noise. Given a face image, the feature parsing problem can then be naturally formulated as sparse noise detection when recovering a low-rank matrix. To enhance the feature parsing, a linear transformation matrix is learned to boost the discriminant feature extraction. Furthermore, with the derived parsing maps, the algorithm is easily extended to implement facial landmark detection task. The effectiveness of the proposed algorithm is evaluated through several experiments on comprehensive datasets.


Toward a Model Theory of Actions: How Agents do it in Branching Time


title: Epidemiology, diagnosis, treatment and management of superficial-vein thrombosis of the legs.

abstract: Recent data on lower-limb superficial-vein thrombosis (SVT) may substantially impact its clinical management. Particularly, the clear confirmation that SVT is closely linked to deep-vein thrombosis (DVT) or pulmonary embolism (PE) highlights the potential severity of the disease. DVT or PE is diagnosed in 20–30% of SVT patients. Moreover, clinically relevant symptomatic thromboembolic events complicate isolated SVT (without concomitant DVT or PE at diagnosis) in 4–8% of patients. For the first time, an anticoagulant treatment, once-daily 2.5 mg fondaparinux for 45 days, was demonstrated to be effective and safe for preventing these symptomatic thromboembolic events in patients with lower-limb isolated SVT in the randomized, placebo-controlled CALISTO study. More recent data from another randomized trial support these findings. New recommendations on the management of SVT patients, including complete ultrasonography examination of the legs and, in patients with isolated SVT, prescription of once-daily 2.5 mg fondaparinux subcutaneously for 45 days on top of symptomatic treatments, may be proposed, wherever the cost of fondaparinux is acceptable. Superficial-vein thrombosis (SVT) of the lower limbs has long been regarded as a benign, self-limiting disease, expected to resolve spontaneously and rapidly, and requiring only symptomatic treatments [1,2]. However, the perception of this disease is now changing with the recent publication of data indicating its potential severity [3] and showing for the first time the benefit of a therapeutic strategy based on the administration of an anticoagulant treatment [4]. The overall management of this frequent disease therefore needs to be reconsidered.


title: Visual discrimination learning requires sleep after training

abstract: Performance on a visual discrimination task showed maximal improvement 48–96 hours after initial training, even without intervening practice. When subjects were deprived of sleep for 30 hours after training and then tested after two full nights of recovery sleep, they showed no significant improvement, despite normal levels of alertness. Together with previous findings11 that subjects show no improvement when retested the same day as training, this demonstrates that sleep within 30 hours of training is absolutely required for improved performance.


title: Inverse Optimization : A New Perspective on the Black-Litterman Model

abstract: The Black-Litterman BL model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct “BL”-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new “BL”-type estimators and their corresponding portfolios: a mean variance inverse optimization MV-IO portfolio and a robust mean variance inverse optimization RMV-IO portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward trade-off than their BL counterparts and are more robust to incorrect investor views.


title: Transmission Loss of Sound Into Incubators: Implications for Voice Perception by Infants

abstract: OBJECTIVE:To assess the transmission of sound into incubators as a function of talker position (i.e., standing or sitting), incubator port position (i.e., opened or closed), and center frequency (i.e., 125 to 10,000 Hz in one-third octave steps). The second objective was to estimate the audibility of the human voice inside the incubator.STUDY DESIGN: L eq measures of signal transmission loss and motor noise were obtained from two incubators.RESULTS:In general, signal transmission loss was greater for the standing-talker position, with front portholes closed, and for high-frequency spectra. Motor noise was greater with both front portholes closed and for lower-frequency spectra. The greatest signal delivery to an infant would be obtained when the speaker is sitting using a raised vocal effort while the incubator ports are opened.CONCLUSION:Measured signal transmission loss and motor noise characteristics of two incubators suggest that only mid-frequency speech spectra would be audible to infants and only at a speech-to-noise ratio of approximately 5 to 10 dB with a raised vocal effort.


title: Modeling Cyber–Physical Systems

abstract: This paper focuses on the challenges of modeling cyber-physical systems (CPSs) that arise from the intrinsic heterogeneity, concurrency, and sensitivity to timing of such systems. It uses a portion of an aircraft vehicle management system (VMS), specifically the fuel management subsystem, to illustrate the challenges, and then discusses technologies that at least partially address the challenges. Specific technologies described include hybrid system modeling and simulation, concurrent and heterogeneous models of computation, the use of domain-specific ontologies to enhance modularity, and the joint modeling of functionality and implementation architectures.


title: Content or platform: Why do students complete MOOCs?

abstract: The advent of massive open online courses (MOOCs) poses new learning opportunities for learners as well as challenges for researchers and designers. MOOC students approach MOOCs in a range of fashions, based on their learning goals and preferred approaches, which creates new opportunities for learners but makes it difficult for researchers to figure out what a student’s behavior means, and makes it difficult for designers to develop MOOCs appropriate for all of their learners. Towards better understanding the learners who take MOOCs, we conduct a survey of MOOC learners’ motivations and correlate it to which students complete the course according to the pace set by the instructor/platform (which necessitates having the goal of completing the course, as well as succeeding in that goal). The results showed that course completers tend to be more interested in the course content, whereas non-completers tend to be more interested in MOOCs as a type of learning experience. Contrary to initial hypotheses, however, no substantial differences in mastery-goal orientation or general academic efficacy were observed between completers and non-completers. However, students who complete the course tend to have more self-efficacy for their ability to complete the course, from the beginning.


title: Overview of PAN ’ 16 New Challenges for Authorship Analysis : Cross-genre Profiling , Clustering , Diarization , and Obfuscation

abstract: This paper presents an overview of the PAN/CLEF evaluation lab. During the last decade, PAN has been established as the main forum of digital text forensic research. PAN 2016 comprises three shared tasks: (i) author identification, addressing author clustering and diarization (or intrinsic plagiarism detection); (ii) author profiling, addressing age and gender prediction from a cross-genre perspective; and (iii) author obfuscation, addressing author masking and obfuscation evaluation. In total, 35 teams participated in all three shared tasks of PAN 2016 and, following the practice of previous editions, software submissions were required and evaluated within the TIRA experimentation framework.


title: Modeling and Experimental Evaluation of Bending Behavior of Soft Pneumatic Actuators Made of Discrete Actuation Chambers

abstract: In this article, we have established an analytical model to estimate the quasi-static bending displacement (i.e., angle) of the pneumatic actuators made of two different elastomeric silicones (Elastosil M4601 with a bulk modulus of elasticity of 262 kPa and Translucent Soft silicone with a bulk modulus of elasticity of 48 kPa-both experimentally determined) and of discrete chambers, partially separated from each other with a gap in between the chambers to increase the magnitude of their bending angle. The numerical bending angle results from the proposed gray-box model, and the corresponding experimental results match well that the model is accurate enough to predict the bending behavior of this class of pneumatic soft actuators. Further, by using the experimental bending angle results and blocking force results, the effective modulus of elasticity of the actuators is estimated from a blocking force model. The numerical and experimental results presented show that the bending angle and blocking force models are valid for this class of pneumatic actuators. Another contribution of this study is to incorporate a bistable flexible thin metal typified by a tape measure into the topology of the actuators to prevent the deflection of the actuators under their own weight when operating in the vertical plane.


title: DAUGHMAN‟S ALGORITHM METHOD FOR IRIS RECOGNITION-A BIOMETRIC APPROACH

abstract: Iris recognition is considered to be the most reliable and accurate biometric identification system available. Iris recognition system captures an image of an individual's eye, the iris in the image is then meant for the further segmentation and normalization for extracting its feature. The performance of iris recognition systems depends on the process of segmentation. Segmentation is used for the localization of the correct iris region in the particular portion of an eye and it should be done accurately and correctly to remove the eyelids, eyelashes, reflection and pupil noises present in iris region. In our paper we are using Daughman's Algorithm segmentation method for Iris Recognition. Iris images are selected from the CASIA Database, then the iris and pupil boundary are detected from rest of the eye image, removing the noises. The segmented iris region was normalized to minimize the dimensional inconsistencies between iris regions by using Daugman's Rubber Sheet Model. Then the features of the iris were encoded by convolving the normalized iris region with 1D Log-Gabor filters and phase quantizing the output in order to produce a bit-wise biometric template. The Hamming distance was chosen as a matching metric, which gave the measure of how many bits disagreed between the templates of the iris.


title: Polarity analysis of texts using discourse structure

abstract: Sentiment analysis has applications in many areas and the exploration of its potential has only just begun. We propose Pathos, a framework which performs document sentiment analysis (partly) based on a document's discourse structure. We hypothesize that by splitting a text into important and less important text spans, and by subsequently making use of this information by weighting the sentiment conveyed by distinct text spans in accordance with their importance, we can improve the performance of a sentiment classifier. A document's discourse structure is obtained by applying Rhetorical Structure Theory on sentence level. When controlling for each considered method's structural bias towards positive classifications, weights optimized by a genetic algorithm yield an improvement in sentiment classification accuracy and macro-level F1 score on documents of 4.5% and 4.7%, respectively, in comparison to a baseline not taking into account discourse structure.


The Arabic language and the semantic web : Challenges and opportunities


title: Multipattern string matching with q-grams

abstract: We present three algorithms for exact string matching of multiple patterns. Our algorithms are filtering methods, which apply q-grams and bit parallelism. We ran extensive experiments with them and compared them with various versions of earlier algorithms, e.g., different trie implementations of the Aho--Corasick algorithm. All of our algorithms appeared to be substantially faster than earlier solutions for sets of 1,000--10,000 patterns and the good performance of two of them continues to 100,000 patterns. The gain is because of the improved filtering efficiency caused by q-grams.


title: Neonatal Outcomes of Extremely Preterm Infants From the NICHD Neonatal Research Network

abstract: OBJECTIVE: This report presents data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network on care of and morbidity and mortality rates for very low birth weight infants, according to gestational age (GA). METHODS: Perinatal/neonatal data were collected for 9575 infants of extremely low GA (22–28 weeks) and very low birth weight (401–1500 g) who were born at network centers between January 1, 2003, and December 31, 2007. RESULTS: Rates of survival to discharge increased with increasing GA (6% at 22 weeks and 92% at 28 weeks); 1060 infants died at ≤12 hours, with most early deaths occurring at 22 and 23 weeks (85% and 43%, respectively). Rates of prenatal steroid use (13% and 53%, respectively), cesarean section (7% and 24%, respectively), and delivery room intubation (19% and 68%, respectively) increased markedly between 22 and 23 weeks. Infants at the lowest GAs were at greatest risk for morbidities. Overall, 93% had respiratory distress syndrome, 46% patent ductus arteriosus, 16% severe intraventricular hemorrhage, 11% necrotizing enterocolitis, and 36% late-onset sepsis. The new severity-based definition of bronchopulmonary dysplasia classified more infants as having bronchopulmonary dysplasia than did the traditional definition of supplemental oxygen use at 36 weeks (68%, compared with 42%). More than one-half of infants with extremely low GAs had undetermined retinopathy status at the time of discharge. Center differences in management and outcomes were identified. CONCLUSION: Although the majority of infants with GAs of ≥24 weeks survive, high rates of morbidity among survivors continue to be observed.


title: Electricity price forecasting in deregulated markets: A review and evaluation

abstract: The main methodologies used in electricity price forecasting have been reviewed in this paper. The following price-forecasting techniques have been covered: (i) stochastic time series, (ii) causal models, and (iii) artificial intelligence based models. The quantitative analysis of the work done by various authors has been presented based on (a) time horizon for prediction, (b) input variables, (c) output variables, (d) results, (e) data points used for analysis, (f) preprocessing technique employed, and (g) architecture of the model. The results have been presented in the form of tables for ease of comparison. Classification of various price-influencing factors used by different researchers has been done and put for reference. Application of various models as applied to different electricity markets is also presented for consideration.


title: The influence of technology readiness on satisfaction and behavioral intentions toward self-service technologies

abstract: Today's competitive world is increasingly characterized by technology-assisted services and transactions. Self-service technologies (SSTs) continue to be a critical component of customer-firm interactions but not all customers like to use SSTs or are ready to use them. In this study, we examine the role of customer's technology readiness (TR) and assess the influence of TR on both satisfaction and behavioral intentions toward SSTs. We first review the relevant literature on technology readiness, satisfaction and behavioral intentions, then explore their relationships, and present our research framework and hypotheses. This model was tested with a sample of 413 consumers. The hypothesized model was statistically significantly supported, indicating that TR does influence customer satisfaction and behavioral intention with SSTs. Implications of the results are then discussed.


title: Approximate Query Engines: Commercial Challenges and Research Opportunities

abstract: Recent years have witnessed a surge of interest in Approximate Query Processing (AQP) solutions, both in academia and the commercial world. In addition to well-known open problems in this area, there are many new research challenges that have surfaced as a result of the first interaction of AQP technology with commercial and real-world customers. We categorize these into deployment, planning, and interface challenges. At the same time, AQP settings introduce many interesting opportunities that would not be possible in a database with precise answers. These opportunities create hopes for overcoming some of the major limitations of traditional database systems. For example, we discuss how a database can reuse its past work in a generic way, and become smarter as it answers new queries. Our goal in this talk is to suggest some of the exciting research directions in this field that are worth pursuing.


title: Indoor Positioning and Navigation with Camera Phones

abstract: This low-cost indoor navigation system runs on off-the-shelf camera phones. More than 2,000 users at four different large-scale events have already used it. The system uses built-in cameras to determine user location in real time by detecting unobtrusive fiduciary markers. The required infrastructure is limited to paper markers and static digital maps, and common devices are used, facilitating quick deployment in new environments. The authors have studied the application quantitatively in a controlled environment and qualitatively during deployment at four large international events. According to test users, marker-based navigation is easier to use than conventional mobile digital maps. Moreover, the users' location awareness in navigation tasks improved. Experiences drawn from questionnaires, usage log data, and user interviews further highlight the benefits of this approach.


title: Laminar spread of a circular liquid jet impinging axially on a rotating disc

abstract: The steady laminar annular spread of a thin liquid film generated by a circular jet which impinges perpendicularly in direction of gravity on the centre of a rotating disc is examined both analytically and numerically. Matched asymptotic expansions of the flow quantities provide the proper means for studying the individual flow regimes arising due to the largeness of the Reynolds number formed with the radius of the jet, its slenderness and the relative magnitude of the centrifugal body force. This is measured by a suitably defined Rossby number, $Ro$ ::: . The careful analysis of jet impingement predicts a marked influence of gravity and surface tension on the film flow, considered in the spirit of a shallow-water approach, only through the vorticity imposed by the jet flow. Accordingly, associated downstream conditions are disregarded as the local Froude and Weber numbers are taken to be sufficiently large. Hence, the parabolic problem shaped from the governing equations in a rigorous manner describes the strongly supercritical spread of a developed viscous film past an infinite disc, essentially controlled by $Ro$ ::: . Its numerical solutions are discussed for a wide range of values of $Ro$ ::: . The different flow regimes reflecting varying effects of viscous shear and centrifugal force are elucidated systematically to clarify the surprising richness of flow phenomena. Special attention is paid to the cases $Ro\gg 1$ ::: and $Ro\ll 1$ ::: . The latter, referring to relatively high disc spin, implies a delicate breakdown of the asymptotic flow structure, thus requiring a specific analytical and numerical treatment. Finally, the impact of gravity and capillarity and thus of the disc edge on the film flow is envisaged in brief.


title: A non-isolated buck-boost DC-DC converter with single switch

abstract: In this paper a new Buck-Boost converter with only one active switch is presented. In this topology, control of the proposed converter is simple; also, the total switch stress of the converter is relatively low. Moreover, the voltage gain of proposed converter is higher than that of traditional Buck-Boost, Sepic, Zeta, and Cuk converters. Furthermore, the high voltage gain can be obtained with suitable duty cycles. In this regard, converter topology is presented and analyzed analytically in different modes, then converter is simulated with PLECS software and results are assessed to demonstrate its effectiveness.


title: A Control Approach for Thrust-Propelled Underactuated Vehicles and its Application to VTOL Drones

abstract: A control approach is proposed for a class of underactuated vehicles in order to stabilize reference trajectories either in thrust direction, velocity, or position. The basic modeling assumption is that the vehicle is pro-pulsed via a thrust force along a single body-fixed direction and that it has full torque actuation for attitude control (i.e., a typical actuation structure for aircrafts, vertical take-off and landing (VTOL) vehicles, submarines, etc.). Additional assumptions on the external forces applied to the vehicle are also introduced for the sake of control design and stability analyses. They are best satisfied for vehicles which are subjected to an external force field (e.g., gravity) and whose shape induces lift forces with limited amplitude, unlike airplanes but as in the case of many VTOL drones. The interactions of the vehicle with the surrounding fluid are often difficult to model precisely whereas they may significantly influence and perturb its motion. By using a standard Lyapunov-based approach, novel nonlinear feedback control laws are proposed to compensate for modeling errors and perform robustly against such perturbations. Simulation results illustrating these properties on a realistic model of a VTOL drone subjected to wind gusts are reported.


title: Face anti-spoofing using patch and depth-based CNNs

abstract: The face image is the most accessible biometric modality which is used for highly accurate face recognition systems, while it is vulnerable to many different types of presentation attacks. Face anti-spoofing is a very critical step before feeding the face image to biometric systems. In this paper, we propose a novel two-stream CNN-based approach for face anti-spoofing, by extracting the local features and holistic depth maps from the face images. The local features facilitate CNN to discriminate the spoof patches independent of the spatial face areas. On the other hand, holistic depth map examine whether the input image has a face-like depth. Extensive experiments are conducted on the challenging databases (CASIA-FASD, MSU-USSA, and Replay Attack), with comparison to the state of the art.


Cross Domain Recommendation Using Vector Space Transfer Learning


title: NLP for Shallow Question Answering of Legal Documents Using Graphs*

abstract: Previous work has shown that modeling relationships between articles of a regulation as vertices of a graph network works twice as better than traditional information retrieval systems for returning articles relevant to the question. In this work we experiment by using natural language techniques such as lemmatizing and using manual and automatic thesauri for improving question based document retrieval. For the construction of the graph, we follow the approach of representing the set of all the articles as a graph; the question is split in two parts, and each of them is added as part of the graph. Then several paths are constructed from part A of the question to part B, so that the shortest path contains the relevant articles to the question. We evaluate our method comparing the answers given by a traditional information retrieval system--vector space model adjusted for article retrieval, instead of document retrieval--and the answers to 21 questions given manually by the general lawyer of the National Polytechnic Institute, based on 25 different regulations (academy regulation, scholarships regulation, postgraduate studies regulation, etc.); with the answer of our system based on the same set of regulations. We found that lemmatizing increases performance in around 10%, while the use of thesaurus has a low impact.


title: Designing business models for cloud platforms

abstract: Platform as a service PaaS has become a strategic option for software vendors who expect to benefit from value co-creation with partners by developing complementary components and applications. In reality, however, established and new software vendors are battling to redefine their offering to embrace PaaS. They face the challenges of transforming, configuring and calibrating their PaaS business models to align them with existing business models, customer expectations and competitive pressures. This motivates our research question: How can software providers design viable business models for PaaS? Our study develops a design theory for PaaS business models. This theory is grounded on a 12-month action design research study at one of the largest global software companies here called Alpha with mixed PaaS experiences in the past. Our primary research contribution is a set of design principles that guide software providers to define a viable PaaS business model in order to create a flourishing software ecosystem for their cloud platform. By synthesizing prescriptive knowledge related to business model design for emerging cloud platforms, our study advances PaaS research towards the existing body of research on software platforms and business models.


title: Achieving Human Parity in Conversational Speech Recognition

abstract: Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. In this paper, we measure the human error rate on the widely used NIST 2000 test set, and find that our latest automated system has reached human parity. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion where friends and family members have open-ended conversations. In both cases, our automated system establishes a new state of the art, and edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively. The key to our system's performance is the use of various convolutional and LSTM acoustic model architectures, combined with a novel spatial smoothing method and lattice-free MMI acoustic training, multiple recurrent neural network language modeling approaches, and a systematic use of system combination.


Covariance Matrix Estimation for Reinforcement Learning


title: A Markov process based approach to effective attacking JPEG steganography

abstract: In this paper, a novel steganalysis scheme is presented to effectively detect the advanced JPEG steganography. For this purpose, we first choose to work on JPEG 2-D arrays formed from the magnitudes of quantized block DCT coefficients. Difference JPEG 2-D arrays along horizontal, vertical, and diagonal directions are then used to enhance changes caused by JPEG steganography. Markov process is applied to modeling these difference JPEG 2-D arrays so as to utilize the second order statistics for steganalysis. In addition to the utilization of difference JPEG 2-D arrays, a thresholding technique is developed to greatly reduce the dimensionality of transition probability matrices, i.e., the dimensionality of feature vectors, thus making the computational complexity of the proposed scheme manageable. The experimental works are presented to demonstrate that the proposed scheme has outperformed the existing steganalyzers in attacking OutGuess, F5, and MB1.


title: Factored Translation Models

abstract: We present an extension of phrase-based statistical machine translation models that enables the straight-forward integration of additional annotation at the word-level — may it be linguistic markup or automatically generated word classes. In a number of experiments we show that factored translation models lead to better translation performance, both in terms of automatic scores, as well as more grammatical coherence.


Integrated Repository of Security Information for Network Security Evaluation


title: Continuous multilinguality with language vectors

abstract: Most existing models for multilingual natural language processing (NLP) treat language as a discrete category, and make predictions for either one language or the other. In contrast, we propose using continuous vector representations of language. We show that these can be learned efficiently with a character-based neural language model, and used to improve inference about language varieties not seen during training. In experiments with 1303 Bible translations into 990 different languages, we empirically explore the capacity of multilingual language models, and also show that the language vectors capture genetic relationships between languages.


title: On normal approximations to symmetric hypergeometric laws

abstract: The Kolmogorov distances between a symmetric hypergeometric law with standard deviation $\sigma$ and its usual normal approximations are computed and shown to be less than $1/(\sqrt{8\pi}\,\sigma)$, with the order $1/\sigma$ and the constant $1/\sqrt{8\pi}$ being optimal. The results of Hipp and Mattner (2007) for symmetric binomial laws are obtained as special cases. Connections to Berry-Esseen type results in more general situations concerning sums of simple random samples or Bernoulli convolutions are explained. Auxiliary results of independent interest include rather sharp normal distribution function inequalities, a simple identifiability result for hypergeometric laws, and some remarks related to Levy's concentration-variance inequality.


title: Microfluidics in structural biology: smaller, faster… better

abstract: Microfluidic technologies promise unprecedented savings in cost and time through the integration of complex chemical and biological assays on a microfabricated chip. Recent advances are making elements of this vision a reality, facilitating the first large-scale integration of microfluidic plumbing with biological assays. The power of miniaturization lies not only in achieving an economy of scale, but also in exploiting the unusual physics of fluid flow and mass transport on small length scales to realize precise and efficient assays that are not accessible with macroscopic tools. Diverse applications ranging from time-resolved studies of protein folding to highly efficient protein crystal growth suggest that microfluidics may become an indispensable tool in biology.


title: Software Fault Prediction using Language Processing

abstract: Accurate prediction of faulty modules reduces the cost of software development and evolution. Two case studies with a language-processing based fault prediction measure are presented. The measure, refereed to as a QALP score, makes use of techniques from information retrieval to judge software quality. The QALP score has been shown to correlate with human judgements of software quality. The two case studies consider the measure's application to fault prediction using two programs (one open source, one proprietary). Linear mixed-effects regression models are used to identify relationships between defects and QALP score. Results, while complex, show that little correlation exists in the first case study, while statistically significant correlations exists in the second. In this second study the QALP score is helpful in predicting faults in modules (files) with its usefulness growing as module size increases.


title: Advanced lithium ion battery charger

abstract: A lithium-ion battery charger has been developed for four and eight cell batteries or multiples thereof. This charger has the advantage over those using commercial lithium-ion charging chips in that the individual cells are allowed to be taper charged at their upper charging voltage rather than be cut-off when all cells of the string have reached the upper charging voltage limit. Since 30-60% of the capacity of lithium-ion cells maybe restored during the taper charge, this charger has the distinct benefit of fully charging lithium ion-batteries by restoring all of the available capacity to all of its cells.


title: An ultra-wideband common gate LNA with gm-boosted and noise cancelling techniques

abstract: In this paper, an ultra-wideband (UWB) common gate low-noise amplifier (LNA) with gm-boosted and noise-cancelling techniques is presented In this scheme we utilize gm-boosted stage for cancelling the noise of matching device. The bandwidth extension and flat gain are achieved by using of series and shunt peaking techniques. Simulated in .13 um Cmos technology, the proposed LNA achieved 2.38-3.4dB NF and S11 less than -11dB in the 3.1-10.6 GHz band Maximum power gain(S21) is 11dB and -3dB bandwidth is 1.25-11.33 GHz. The power consumption of LNA is 5.8mW.


title: Quantum-Behaved Flower Pollination Algorithm

abstract: Flower Pollination algorithm (FPA) is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, a new hybrid optimization method called quantum-behaved flower pollination algorithm (QFPA) is proposed. The method combines the standard Flower Pollination algorithm (FPA) with the quantum-behaved search mechanism to improve the global searching ability and accuracy. The simulation experiments show that the proposed QFPA algorithm can improve the convergence speed and the quality of solutions effectively.


title: Challenges for BrainComputer Interface Research for HumanComputer Interaction Applications

abstract: Electro-encephalography (EEG) is unique among functional brain imaging methods in that it promises a means of providing a cost-e cient, safe, portable and easy to use braincomputer interface (BCI) for both healthy users and the disabled. While an already extensive corpus of EEG-BCI experimental work has demonstrated that, to a degree, a person's mental states can be detected in single-trials using sophisticated mathematical tools, clear challenges for the use in human-computer interaction applications have also been outlined. He we discuss challenges, which are relevant for the use of BCIs in wider range of applications beyond rehabilitation.


Exploring Fine-Grained Emotion Detection in Tweets


title: Creativity in algorithmic art

abstract: Early algorithmic art (also called computer art or digital art) is chosen as a case to differentiate three aspects of creative behavior: trivial, personal, and historic creativity. Extending a remark by Marcel Duchamp on the role of the spectator in fully completing a work of art, one - perhaps controversial - position in the history of art of the 20th century claims that the artist only generates the material work, whereas society transforms the work into an accepted work of art. This position leads to differentiation in the concept of creativity. The paper discusses different shades of creativity. It is of interest both to digital art, and to creativity research.


title: ffLink: A Lightweight High-Performance Open-Source PCI Express Gen3 Interface for Reconfigurable Accelerators

abstract: We describe the architecture and implementation of ffLink, a high-performance PCIe Gen3 interface for attaching reconfigurable accelerators on Xilinx Virtex 7 FPGA devices to Linux-based hosts. ffLink encompasses both hardware as well as flexible operating system components that allow a tailoring of the infrastructure to the specific data transfer needs of the application. When configured to use multiple DMA engines to hide transfer latencies, ffLink achieves a throughput of up to 7 GB/s, which is 95% of the maximum throughput of an eight-lane PCIe interface, while requiring just 11% of device area on a mid-size FPGA.


title: An Investigation of Student Satisfaction Factors

abstract: Any company with goals and objectives wants to achieve their targets must be focused customer satisfaction, to satisfy the customers should be one of the most important aims. Around the world generally successful companies' main priority is to meet any need which are customers demand regarding their satisfaction points.This study's main aim is to investigate the factors which affect students' satisfaction or dissatisfaction based on earlier work. It may be suggested that university management can develop new methods utilizing earlier work results to improve the level of student satisfaction.


title: The entanglement of leader character and leader competence and its impact on performance

abstract: Abstract Whereas the micro- and macro-oriented leadership literatures have often studied leader competencies necessary for effective performance, the role of leader character in relation to competencies and performance has been to a large extent neglected. Our work seeks to shift the scholarly dialogue by introducing the concept of character-competence entanglement, which reflects the binding between character and competence over time. The highest degree of entanglement represents the deep and more persistent interconnection and mutually-reinforcing effect between highly-developed leader character and highly-developed leader competence, whereas in cases of low entanglement, character can be activated temporarily in a particular context to help strengthen the relationship between competence and performance. Our core proposition is that high character-competence entanglement will lead to extraordinary performance over time. In addition, we emphasize that relying on naturally-occurring learning opportunities and the processes of “learning-by-living” both outside and inside the organization will positively impact the development of character-competence entanglement.


title: Systematic Risk in Supply Chain Networks

abstract: Industrial production output is generally correlated with the state of the economy. Nonetheless, during times of economic downturn, some industries take the biggest hit, whereas at times of economic boom they reap most benefits. To provide insight into this phenomenon, we map supply networks of industries and firms and investigate how the supply network structure mediates the effect of economy on industry or firm sales. Previous research has shown that retail sales are correlated with the state of the economy. Since retailers source their products from other industries, the sales of their suppliers can also be correlated with the state of the economy. This correlation represents the source of systematic risk for an industry that propagates through a supply chain network. Specifically, we identify the following mechanisms that can affect the correlation between sales and the state of the economy in a supply chain network: propagation of systematic risk into production decisions, aggregation of orders from ...


title: MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification

abstract: We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of word embeddings for sentence classification. MGNC-CNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector. We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets. This model is much simpler than comparable alternative architectures and requires substantially less training time. Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality. We show that MGNC-CNN consistently outperforms baseline models.


title: How unique is your web browser

abstract: We investigate the degree to which modern web browsers are subject to "device fingerprinting" via the version and configuration information that they will transmit to websites upon request. We implemented one possible fingerprinting algorithm, and collected these fingerprints from a large sample of browsers that visited our test side, panopticlick.eff.org. We observe that the distribution of our fingerprint contains at least 18.1 bits of entropy, meaning that if we pick a browser at random, at best we expect that only one in 286,777 other browsers will share its fingerprint. Among browsers that support Flash or Java, the situation is worse, with the average browser carrying at least 18.8 bits of identifying information. 94.2% of browsers with Flash or Java were unique in our sample. ::: ::: By observing returning visitors, we estimate how rapidly browser fingerprints might change over time. In our sample, fingerprints changed quite rapidly, but even a simple heuristic was usually able to guess when a fingerprint was an "upgraded" version of a previously observed browser's fingerprint, with 99.1% of guesses correct and a false positive rate of only 0.86%. ::: ::: We discuss what privacy threat browser fingerprinting poses in practice, and what countermeasures may be appropriate to prevent it. There is a tradeoff between protection against fingerprintability and certain kinds of debuggability, which in current browsers is weighted heavily against privacy. Paradoxically, anti-fingerprinting privacy technologies can be self-defeating if they are not used by a sufficient number of people; we show that some privacy measures currently fall victim to this paradox, but others do not.


title: Attention estimation system via smart glasses

abstract: Attention plays a critical role in effective learning. By means of attention assessment, it helps learners improve and review their learning processes, and even discover Attention Deficit Hyperactivity Disorder (ADHD). Hence, this work employs modified smart glasses which have an inward facing camera for eye tracking, and an inertial measurement unit for head pose estimation. The proposed attention estimation system consists of eye movement detection, head pose estimation, and machine learning. In eye movement detection, the central point of the iris is found by the locally maximum curve via the Hough transform where the region of interest is derived by the identified left and right eye corners. The head pose estimation is based on the captured inertial data to generate physical features for machine learning. Here, the machine learning adopts Genetic Algorithm (GA)-Support Vector Machine (SVM) where the feature selection of Sequential Floating Forward Selection (SFFS) is employed to determine adequate features, and GA is to optimize the parameters of SVM. Our experiments reveal that the proposed attention estimation system can achieve the accuracy of 93.1% which is fairly good as compared to the conventional systems. Therefore, the proposed system embedded in smart glasses brings users mobile, convenient, and comfortable to assess their attention on learning or medical symptom checker.


Making Decisions about Self-Disclosure in Online Social Networks


title: Psychotherapy of Mood Disorders

abstract: In the last decades, psychotherapy has gained increasing acceptance as a major treatment option for mood disorders. Empirically supported treatments for major depression include cognitive behavioural therapy (CBT), interpersonal psychotherapy (IPT), behavioural therapy and, to a lesser extent, short-term psychodynamic psychotherapy. Meta-analytic evidence suggests that psychotherapy has a significant and clinically relevant, though not large, effect on chronic forms of depression. Psychotherapy with chronic patients should take into account several important differences between patients with chronic and acute depression (identification with their depressive illness, more severe social skill deficits, persistent sense of hopelessness, need of more time to adapt to better circumstances). Regarding adolescent depression, the effectiveness of IPT and CBT is empirically supported. Adolescents require appropriate modifications of treatment (developmental approach to psychotherapy, involvement of parents in therapy). The combination of psychotherapy and medication has recently attracted substantial interest; the available evidence suggests that combined treatment has small but significant advantages over each treatment modality alone, and may have a protective effect against depression relapse or recurrence. Psychobiological models overcoming a rigid brain-mind dichotomy may help the clinician give patients a clear rationale for the combination of psychological and pharmacological treatment. In recent years, evidence has accumulated regarding the effectiveness of psychological therapies (CBT, family-focused therapy, interpersonal and social rhythm therapy, psychoeducation) as an adjunct to medication in bipolar disorder. These therapies share several common elements and there is considerable overlap in their actual targets. Psychological interventions were found to be useful not only in the treatment of bipolar depressive episodes, but in all phases of the disorder.


title: Dimensionality reduction through PCA over SIFT and SURF descriptors

abstract: One of the constant challenges in image analysis is to improve the process for obtaining distinctive object characteristics. Feature descriptors usually demand high dimensionality to adequately represent the objects of interest. The higher the dimensionality, the greater the consumption of resources such as memory space and computational time. Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) present algorithms that, besides of detecting interest points accurately, extract well suited feature descriptors. The problem with these feature descriptors is their high dimensionality. There have been several works attempting to confront the curse of dimensionality over some of the developed descriptors. In this paper, we apply Principal Component Analysis (PCA) to reduce SIFT and SURF feature vectors in order to perform the task of having an accurate low-dimensional feature vector. We evaluate such low-dimensional feature vectors in a matching application, as well as their distinctiveness in image retrieval. Finally, the required resources in computational time and memory space to process the original descriptors are compared to those resources consumed by the new low-dimensional descriptors.


title: A Dependency Tree Based Approach for Sentence-Level Sentiment Classification

abstract: Sentiment classification is a much-researched field that identifies positive or negative emotions in a large number of texts. Most existing studies focus on document-based approaches and documents are represented as bag-of word. Therefore, this feature representation fails to obtain the relation or associative information between words and it can't distinguish different opinions of a sentiment word with different targets. In this paper, we present a dependency tree-based sentence-level sentiment classification approach. In contrast to a document, a sentence just contains little information and a small set of features which can be used for the sentiment classification. So we not only capture flat features (bag-of-word), but also extract structured features from the dependency tree of a sentence. We propose a method to add more information to the dependency tree and provide an algorithm to prune dependency tree to reduce the noisy, and then introduce a convolution tree kernel-based approach to the sentence-level sentiment classification. The experimental results show that our dependency tree-based approach achieved significant improvement, particularly for implicit sentiment classification.


Trading Paths: Mapping Chickasaw History in the Eighteenth Century


title: Generalization of rigid foldable quadrilateral mesh origami

abstract: In general, a quadrilateral mesh surface does not enable a continuous rigid motion because an overconstrained system, in which the number of constraints around degree-4 vertices (three for each vertex) exceeds the number of variables (the number of hinges), is constructed. However, it is known that the developable double corrugation surface, called Miura-ori, produces a rigid deployment mechanism. The rigid-foldability of Miura-ori is due to the singularity in its pattern, where a single vertex is repeated. We generalize the geometric condition for enabling rigid motion in general quadrilateral mesh origami without the trivial repeating symmetry. To ensure the existence of a finite motion, we derive the identity of functions from the formula for degree-4 single-vertex origami. This yields a variety of unexplored generalized shapes of quadrilateral mesh origami that preserve finite rigid-foldability in addition to developability and flat-foldability.


title: Transfer characteristics and high frequency modeling of logic gates using carbon nanotube field effect transistors (CNT-FETs)

abstract: In the present work, current model equations from an analytical model for carbon nanotube field effect transistors are utilized to calculate the transfer characteristics of different logic gates such as inverters, NAND gates, and NOR gates. A small signal model has been implemented and the necessary parameters have been calculated to describe the high frequency response for these logic gates. Results show that how different diameters and wrapping angles of carbon nanotubes, determined by the chiral vector (n,m), can change the transfer characteristics, small signal parameters, and high frequency response for devices based on carbon nanotubes.


title: Basic Approaches in Recommendation Systems

abstract: Recommendation systems support users in finding items of interest. In this chapter, we introduce the basic approaches of collaborative filtering, content-based filtering, and knowledge-based recommendation. We first discuss principles of the underlying algorithms based on a running example. Thereafter, we provide an overview of hybrid recommendation approaches which combine basic variants. We conclude this chapter with a discussion of newer algorithmic trends, especially critiquing-based and group recommendation.


title: Real-time decentralized search with inter-agent collision avoidance

abstract: This paper addresses the problem of coordinating a team of mobile autonomous sensor agents performing a cooperative mission while explicitly avoiding inter-agent collisions in a team negotiation process. Many multi-agent cooperative approaches disregard the potential hazards between agents, which are an important aspect to many systems and especially for airborne systems. In this work, team negotiation is performed using a decentralized gradient-based optimization approach whereas safety distance constraints are specifically designed and handled using Lagrangian multiplier methods. The novelty of our work is the demonstration of a decentralized form of inter-agent collision avoidance in the loop of the agents' real-time group mission optimization process, where the algorithm inherits the properties of performing its original mission while minimizing the probability of inter-agent collisions. Explicit constraint gradient formulation is derived and used to enhance computational advantage and solution accuracy. The effectiveness and robustness of our algorithm has been verified in a simulated environment by coordinating a team of UAVs searching for targets in a large-scale environment.


title: An Image Denoising Fast Algorithm for Weighted Total Variation

abstract: The total variation (TV) model is a classical and effective model in image denoising, but the weighted total variation (WTV) model has not attracted much attention. In this paper, we propose a new constrained WTV model for image denoising. A fast denoising dual method for the new constrained WTV model is also proposed. To achieve this task, we combines the well known gradient projection (GP) and the fast gradient projection (FGP) methods on the dual approach for the image denoising problem. Experimental results show that the proposed method outperforms currently known GP andFGP methods, and canbe applicable to both the isotropic and anisotropic WTV functions.


title: OpenARC: open accelerator research compiler for directive-based, efficient heterogeneous computing

abstract: This paper presents Open Accelerator Research Compiler (OpenARC): an open-source framework that supports the full feature set of OpenACC V1.0 and performs source-to-source transformations, targeting heterogeneous devices, such as NVIDIA GPUs. Combined with its high-level, extensible Intermediate Representation (IR) and rich semantic annotations, OpenARC serves as a powerful research vehicle for prototyping optimization, source-to-source transformations, and instrumentation for debugging, performance analysis, and autotuning. In fact, OpenARC is equipped with various capabilities for advanced analyses and transformations, as well as built-in performance and debugging tools. We explain the overall design and implementation of OpenARC, and we present key analysis techniques necessary to efficiently port OpenACC applications. Porting various OpenACC applications to CUDA GPUs using OpenARC demonstrates that OpenARC performs similarly to a commercial compiler, while serving as a general research framework.


title: End-to-End Training of Deep Visuomotor Policies

abstract: Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a partially observed guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.


title: Oral geotrichosis: report of 12 cases

abstract: Oral geotrichosis is an uncommon opportunistic infection caused by Geotrichum candidum, a habitual contaminant and component of the flora of various parts of the body. This communication reports both a 20-year retrospective study of clinically and mycologically proven cases of oral geotrichosis, and a prospective study of fungal oral flora in 200 individuals divided into two groups: normal individuals and individuals with associated conditions. Twelve patients with proven oral geotrichosis were included: 9 females and 3 males, with a mean age of 48.5 years; the associated conditions were diabetes mellitus (66.6%), leukemia, Hodgkin's lymphoma and HIV/AIDS infection. The oral geotrichoses showed three clinical varieties: pseudomembranous (75%), hyperplastic, and palatine ulcer. G. candidum was isolated in 11 cases and G. capitatum in one. Positive fungal cultures were obtained from the two groups, and 48% and 78% of cultures were positive, respectively, for Candida spp. In 2.8% and 6.33% of the cases, G. candidum was isolated, respectively, together with one strain of G. capitatum. Oral geotrichosis is an exceptional infection that clinically presents, and is treated, as oral candidiasis. G. candidum may be isolated from the oral flora of a small proportion of patients, either normal individuals or those with associated conditions.