title: Boils at Frictional Locations in a Patient with Hidradenitis Suppurativa.

abstract: Dear Editor, Hidradenitis suppurativa (HS) is a chronic, recurrent, inflammatory skin disease. The primary clinical presentation are painful inflamed nodules or boils of inverse areas, i.e. the axillary and anogenito-crural regions, but it can also involve the infra- and inter-mammary regions (1,2). The etiology of HS is not clearly defined. Obesity, smoking, and genetic factors are considered important risk factors. In addition, it has also been suggested that friction may contribute to the development of HS, especially in the obese, but this is based on highly anecdotal reports (3-5). We describe a case with classic HS, obesity, and HS-like lesions at the position of the bra strap, suggesting that mechanical stress was an external pathogenic factor for HS development. A 33-year old woman presented with an 18-year history of chronic, recurrent, inflammatory nodules in the axillae, the groin, the pubic region, and to a lesser extent the abdomen and buttocks. She was obese with as result of 33.2 kg/m-2 of 33.2, had a positive family history of two first grade family members with HS, and was a smoker (19 Pack years). There were no other known comorbidities. The inflamed lesions had been treated with several courses of oral antibiotics (minocycline, erythromycin, and combination therapy of clindamycin and rifampicine) and surgical treatments: lancing, deroofing, and excisions (2,6). On examination, there were nodules, folliculitis, cysts, and depressed scars in the axillae and groins, including the inner thighs (Figure 1). On the chest, corresponding to where the lower edge of the patient's bra was usually located, a superficial nodule and follicular papules were observed, exactly coinciding with the red stripe caused by mechanical stress (friction and pressure) of the bra edge. There was no skin fold present on the location of the HS lesions, and there were no lesions observed in the intermammary region or on the side of the breasts in contact with the skin of the thorax (skin to skin contact) (Figure 2). Cultures from skin swabs showed commensal skin flora and moderate mixed anaerobic bacteria, as would be expected in a HS lesion. It is well documented that HS is a disease of the obese. However, the role of friction as an environmental factor is poorly documented. Patients report that environmental factors such as tight-fitting clothing or friction could cause flares in the disease (2). Furthermore, it has been postulated that friction may contribute to the development of HS by stimulating interfollicular hyperplasia (7). HS lesions arranged in a linear pattern suggest an environmental influence and suggests an etiopathogenic role for mechanical stress. Waistline, or as in this case the chest line, distribution indicates that wearing of tight waistbands, wide belts, or bras may induce HS in predisposed individuals. To our knowledge, there is only one case report describing an obese patient with classic HS (typical lesions in predilection areas) who developed HS like lesions on the upper abdomen (waist) at the height of the waistband as well as under the lower abdominal apron (skin on skin contact) (8). Two other reports suggesting a pathogenic role for mechanical stress are flawed, however, as neither of the cases showed signs of concomitant classic HS or had a family history, bringing into question the implied association of HS (9,10). In summary, we presented a case with classic HS locations (typical lesions on typical locations, i.e. the axillae and inguino-crural regions) developing inflammatory lesions on the chest at the location closely corresponding to where the bra strap was exerting mechanical pressure and friction on the skin. The lesions were clinically and microbiologically compatible with ectopic HS lesions. The chest is an atypical HS location free of apocrine sweat glands. It is postulated that these lesions may have been induced by mechanical stress, additionally triggered by the pro-inflammatory state of the obese body. Patients are encouraged to avoid friction from environmental factors such as tight clothing.


title: Alzheimer's Disease: The Role of Microglia in Brain Homeostasis and Proteopathy

abstract: Brain aging is central to late-onset Alzheimer’s disease (LOAD), although the mechanisms by which it occurs at protein or cellular levels are not fully understood. Alzheimer’s disease is the most common proteopathy and is characterized by two unique pathologies: senile plaques and neurofibrillary tangles, the former accumulating earlier than the latter. Aging alters the proteostasis of amyloid- peptides and microtubule-associated protein tau, which are regulated in both autonomous and non-autonomous manners. Microglia, the resident phagocytes of the central nervous system, play a major role in the non-autonomous clearance of protein aggregates, whose function is significantly altered by aging and neurodegeneration. This is genetically supported by the association of microglia-specific genes, TREM2 and CD33, and late onset Alzheimer’s disease. Here, we propose that the functional characterization of microglia, and their contribution to proteopathy, will lead to a new therapeutic direction in Alzheimer’s disease research.


title: Molecular devices featuring sequential photoinduced charge separations for the storage of multiple redox equivalents

abstract: Abstract The present review focuses on one facet of artificial photosynthesis: the charge photo-accumulation function, which has been much less investigated than photoinduced energy or electron transfer reactions. However, charge accumulation is certainly of fundamental importance to perform reactions involving multiple redox equivalents, such as most fuel making reactions. Indeed, the charge photo-accumulation function is specifically relevant to the efficiency of artificial photosynthetic devices and represents an essential property of the photosystem II in oxygenic photosynthetic organisms. We introduce the general concept of charge photo-accumulation, which consists in the collection of multi-redox equivalents at specific sites upon consecutive multiple photon absorptions. We survey the existing artificial molecular systems capable of multiple charge storage upon successive light excitations and new directions for future developments in this area are presented.


title: Is it really about me?: message content in social awareness streams

abstract: In this work we examine the characteristics of social activity and patterns of communication on Twitter, a prominent example of the emerging class of communication systems we call "social awareness streams." We use system data and message content from over 350 Twitter users, applying human coding and quantitative analysis to provide a deeper understanding of the activity of individuals on the Twitter network. In particular, we develop a content-based categorization of the type of messages posted by Twitter users, based on which we examine users' activity. Our analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.


title: Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno

abstract: In this paper, we present a flexible approach to the efficient and exhaustive manual annotation of text documents. For this purpose, we extend WebAnno (Yimam et al., 2013) an open-source web-based annotation tool. 1 While it was previously limited to specific annotation layers, our extension allows adding and configuring an arbitrary number of layers through a web-based UI. These layers can be annotated separately or simultaneously, and support most types of linguistic annotations such as spans, semantic classes, dependency relations, lexical chains, and morphology. Further, we tightly integrate a generic machine learning component for automatic annotation suggestions of span annotations. In two case studies, we show that automatic annotation suggestions, combined with our split-pane UI concept, significantly reduces annotation time.


title: Privacy concerns in smart cities

abstract: textabstractIn this paper a framework is constructed to hypothesize if and how smart city technologies and urban big data produce privacy concerns among the people in these cities (as inhabitants, workers, visitors, and otherwise). The framework is built on the basis of two recurring dimensions in research about people's concerns about privacy: one dimensions represents that people perceive particular data as more personal and sensitive than others, the other dimension represents that people's privacy concerns differ according to the purpose for which data is collected, with the contrast between service and surveillance purposes most paramount. These two dimensions produce a 2 × 2 framework that hypothesizes which technologies and data-applications in smart cities are likely to raise people's privacy concerns, distinguishing between raising hardly any concern (impersonal data, service purpose), to raising controversy (personal data, surveillance purpose). Specific examples from the city of Rotterdam are used to further explore and illustrate the academic and practical usefulness of the framework. It is argued that the general hypothesis of the framework offers clear directions for further empirical research and theory building about privacy concerns in smart cities, and that it provides a sensitizing instrument for local governments to identify the absence, presence, or emergence of privacy concerns among their citizens.


A Retrieval Model for Both Recognition and Recall.


title: WAIS-IV and WISC-IV Structural Validity Alternate Methods, Alternate Results. Commentary on Weiss et al. (2013a) and Weiss et al. (2013b)

abstract: Weiss, Keith, Zhu, and Chen (2013a) and Weiss, Keith, Zhu, and Chen (2013b), this issue, report examinations of the factor structure of the Wechsler Adult Intelligence Scale–Fourth Edition (WAIS-IV) and Wechsler Intelligence Scale for Children–Fourth Edition (WISC-IV), respectively; comparing Wechsler Hierarchical Model (W-HM) and Cattell–Horn–Carroll Hierarchical Model (CHC-HM), subtest cross-loadings, and factorial invariance between clinical and normative groups from the respective standardizations. Both studies suffer from a number of theoretical, methodological, and practical problems that significantly limit conclusions and recommendations for practitioner interpretations of these instruments. Additional analyses and research are required to better inform practitioners for WAIS-IV and WISC-IV use.


title: Supply chain risk management: outlining an agenda for future research.

abstract: In recent years the issue of supply chain risk has been pushed to the fore, initially by fears related to possible disruptions from the much publicised “millennium bug”. Y2K passed seemingly without incident, though the widespread disruptions caused by fuel protests and then Foot and Mouth Disease in the UK, and by terrorist attacks on the USA have underlined the vulnerability of modern supply chains. Despite increasing awareness among practitioners, the concepts of supply chain vulnerability and its managerial counterpart supply chain risk management are still in their infancy. This paper seeks to identify an agenda for future research and to that end the authors go on to clarify the concept of supply chain risk management and to provide a working definition. The existing literature on supply chain vulnerability and risk management is reviewed and compared with findings from exploratory interviews undertaken to discover practitioners' perceptions of supply chain risk and current supply chain risk managem...


title: Clinical Applications of Resting State Functional Connectivity

abstract: During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm.


title: Thermal response properties and surface insulation failure mechanism of epoxy resin under arc ablation

abstract: Arc ablation during the operation of HVDC circuit breakers can cause irreversible damage to the epoxy resin (EP) insulation pull rod. This is one of the critical factors that affect the reliability and lifetime of a HVDC circuit breaker. In this work, we built an arc ablation experimental platform and studied the thermal response properties of EP insulation under arc ablation. The surface charge evolution characteristics and the dielectric properties were analyzed to reveal the underlying surface insulation failure mechanism of EP after arc ablation. The results indicate that arc ablation can cause severe carbonization to EP insulation. With increasing arc duration, the EP gradually transforms into carbide and pore products. Arc ablation can decrease the molecular moment orientation polarizability and the relative dielectric constant of EP insulation, and thereby increasing the current density formed by Schottky injection. Therefore, when the external electric field is applied, the charge on EP insulation surface migrates from high voltage electrode to the ablating area, causing the regional accumulation of surface charge and the formation of local failure area. This can decrease the surface electrical performance of EP insulation and lead to insulation failure finally.


title: Users' eye gaze pattern in organization-based recommender interfaces

abstract: In this paper, we report the hotspot and gaze path of users' eye-movements on three different layouts for recommender interfaces. One is the standard list layout, as appearing in most of current recommender systems. The other two are variations of organization interfaces where recommended items are organized into categories and each category is annotated by a title. Gaze plots infer that the organization interfaces, especially the quadrant layout, are likely to arouse users' attentions to more recommendations. In addition, more users chose products from the organization layouts. Combining the results with our prior works, we suggest a set of design guidelines and practical implications to our future work.


title: Human-supervised control of the ATLAS humanoid robot for traversing doors

abstract: Door traversal is generally a trivial task for human beings but particularly challenging for humanoid robots. This paper describes a holistic approach for a full-sized humanoid robot to traverse through a door in an outdoor semi-structured environment as specified by the requirements of the DARPA Robotics Challenge. Door traversal can be broken down into four sub-tasks; door detection, walk to the door, door opening, and walk through the door. These topics are covered in detail along with the approaches used for perception and motion planning. Results from lab tests and the DRC Finals are shown. The approach presented in this paper can be further extended to a wide range of door types and configurations.


title: Simplifying Particle Swarm Optimization

abstract: The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex, as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, whereas previous approaches to meta-optimization have tuned behavioural parameters to work well on just a single optimization problem. It is then found that not only the PSO method and its simplified variant have comparable performance for optimizing a number of Artificial Neural Network problems, but also the simplified variant appears to offer a small improvement in some cases.


title: Implementing Human-like Intuition Mechanism in Artificial Intelligence

abstract: Human intuition has been simulated by several research projects using artificial intelligence techniques. Most of these algorithms or models lack the ability to handle complications or diversions. Moreover, they also do not explain the factors influencing intuition and the accuracy of the results from this process. In this paper, we present a simple series based model for implementation of human-like intuition using the principles of connectivity and unknown entities. By using Poker hand datasets and Car evaluation datasets, we compare the performance of some well-known models with our intuition model. The aim of the experiment was to predict the maximum accurate answers using intuition based models. We found that the presence of unknown entities, diversion from the current problem scenario, and identifying weakness without the normal logic based execution, greatly affects the reliability of the answers. Generally, the intuition based models cannot be a substitute for the logic based mechanisms in handling such problems. The intuition can only act as a support for an ongoing logic based model that processes all the steps in a sequential manner. However, when time and computational cost are very strict constraints, this intuition based model becomes extremely important and useful, because it can give a reasonably good performance. Factors affecting intuition are analyzed and interpreted through our model.


title: A Novel and Successful Credit Card Fraud Detection System Implemented in a Turkish Bank

abstract: We developed a credit card fraud detection solution for a major bank in Turkey. The study was completed in about three years and the developed system has been in use since February 2013. It had a great impact in the rule based fraud detection process used by the bank. Indeed, while eighty percent of the rules have been eliminated and the number of alerts has been reduced to half, a significant increase in fraud detection has been recorded. As of now the system can catch ninety seven percent of fraud attempts online or, nearly online. The study is interesting in both the formulation of the problem and the algorithms implemented. In fact, we noticed that the standard classification algorithms are not fully suitable for the fraud detection problem (as the cost of every individual false negative can be different from the others), and we looked for alternative methods, especially the meta-heuristics. Among them the newly introduced migrating birds optimization algorithm (MBO) turned out to be superior and was implemented. In addition, during the study a cost sensitive decision tree algorithm was developed and introduced to the literature.


Char2Wav: End-to-End Speech Synthesis.


title: Dual-Tree wavelet scattering network with parametric log transformation for object classification

abstract: We introduce a ScatterNet that uses a parametric log transformation with Dual-Tree complex wavelets to extract translation invariant representations from a multi-resolution image. The parametric transformation aids the OLS pruning algorithm by converting the skewed distributions into relatively mean-symmetric distributions while the Dual-Tree wavelets improve the computational efficiency of the network. The proposed network is shown to outperform Mallat's ScatterNet [1] on two image datasets, both for classification accuracy and computational efficiency. The advantages of the proposed network over other supervised and some unsupervised methods are also presented using experiments performed on different training dataset sizes.


title: Evaluating spoken dialogue systems according to de-facto standards: A case study

abstract: In the present paper, we investigate the validity and reliability of de-facto evaluation standards, defined for measuring or predicting the quality of the interaction with spoken dialogue systems. Two experiments have been carried out with a dialogue system for controlling domestic devices. During these experiments, subjective judgments of quality have been collected by two questionnaire methods (ITU-T Rec. P.851 and SASSI), and parameters describing the interaction have been logged and annotated. Both metrics served the derivation of prediction models according to the PARADISE approach. Although the limited database allows only tentative conclusions to be drawn, the results suggest that both questionnaire methods provide valid measurements of a large number of different quality aspects; most of the perceptive dimensions underlying the subjective judgments can also be measured with a high reliability. The extracted parameters mainly describe quality aspects which are directly linked to the system, environmental and task characteristics. Used as an input to prediction models, the parameters provide helpful information for system design and optimization, but not general predictions of system usability and acceptability. © 2005 Elsevier Ltd. All rights reserved.


title: Factors influencing the development of a personal tailored microbiota in the neonate, with particular emphasis on antibiotic therapy.

abstract: AbstractIn recent years, it has been clearly evidenced that most cells in a human being are not human: they are microbial, represented by more than 1000 microbial species. The vast majority of microbial species give rise to symbiotic host-bacterial interactions that are fundamental for human health. The complex of these microbial communities has been defined as microbiota or microbiome. These bacterial communities, forged over millennia of co-evolution with humans, are at the basis of a partnership with the developing human newborn, which is based on reciprocal molecular exchanges and cross-talking. Recent data on the role of the human microbiota in newborns and children clearly indicate that microbes have a potential importance to pediatrics, contributing to host nutrition, developmental regulation of intestinal angiogenesis, protection from pathogens, and development of the immune system. This review is aimed at reporting the most recent data on the knowledge of microbiota origin and development in the ...


title: Dual Quaternion Variational Integrator for Rigid Body Dynamic Simulation

abstract: We introduce a symplectic dual quaternion variational integrator(DQVI) for simulating single rigid body motion in all six degrees of freedom. Dual quaternion is used to represent rigid body kinematics and one-step Lie group variational integrator is used to conserve the geometric structure, energy and momentum of the system during the simulation. The combination of these two becomes the first Lie group variational integrator for rigid body simulation without decoupling translations and rotations. Newton-Raphson method is used to solve the recursive dynamic equation. This method is suitable for real-time rigid body simulations with high precision under large time step. DQVI respects the symplectic structure of the system with excellent long-term conservation of geometry structure, momentum and energy. It also allows the reference point and 6-by-6 inertia matrix to be arbitrarily defined, which is very convenient for a variety of engineering problems.


title: Efficient ciphertext policy attribute-based encryption with constant-size ciphertext and constant computation-cost

abstract: Attribute-based encryption provides good solutions to the problem of anonymous access control by specifying access policies among private keys or ciphertexts over encrypted data. In ciphertext-policy attribute-based encryption (CP-ABE), each user is associated with a set of attributes, and data is encrypted with access structures on attributes. A user is able to decrypt a ciphertext if and only if his attributes satisfy the ciphertext access structure. CP-ABE is very appealing since the ciphertext and data access policies are integrated together in a natural and effective way. ::: ::: Most current CP-ABE schemes incur large ciphertext size and computation costs in the encryption and decryption operations which depend at least linearly on the number of attributes involved in the access policy. In this paper, we present two new CP-ABE schemes, which have both constant-size ciphertext and constant computation costs for a nonmonotone AND gate access policy, under chosen plaintext and chosen ciphertext attacks. The security of first scheme can be proven CPA-secure in standard model under the decision n-BDHE assumption. And the security of second scheme can be proven CCA-secure in standard model under the decision n-BDHE assumption and the existence of collision-resistant hash functions. Our scheme can also be extended to the decentralizing multi-authority setting.


title: Efficient Deep Neural Network Serving: Fast and Furious

abstract: The emergence of deep neural networks (DNNs) as a state-of-the-art machine learning technique has enabled a variety of artificial intelligence applications for image recognition, speech recognition and translation, drug discovery, and machine vision. These applications are backed by large DNN models running in serving mode on a cloud computing infrastructure to process client inputs such as images, speech segments, and text segments. Given the compute-intensive nature of large DNN models, a key challenge for DNN serving systems is to minimize the request response latencies. This paper characterizes the behavior of different parallelism techniques for supporting scalable and responsive serving systems for large DNNs. We identify and model two important properties of DNN workloads: 1) homogeneous request service demand and 2) interference among requests running concurrently due to cache/memory contention. These properties motivate the design of serving deep learning systems fast (SERF), a dynamic scheduling framework that is powered by an interference-aware queueing-based analytical model. To minimize response latency for DNN serving, SERF quickly identifies and switches to the optimal parallel configuration of the serving system by using both empirical and analytical methods. Our evaluation of SERF using several well-known benchmarks demonstrates its good latency prediction accuracy, its ability to correctly identify optimal parallel configurations for each benchmark, its ability to adapt to changing load conditions, and its efficiency advantage (by at least three orders of magnitude faster) over exhaustive profiling. We also demonstrate that SERF supports other scheduling objectives and can be extended to any general machine learning serving system with the similar parallelism properties as above.


Cross-Lingual Learning-to-Rank with Shared Representations


title: Learning to Extract Coherent Summary via Deep Reinforcement Learning

abstract: Coherence plays a critical role in producing a high-quality summary from a document. In recent years, neural extractive summarization is becoming increasingly attractive. However, most of them ignore the coherence of summaries when extracting sentences. As an effort towards extracting coherent summaries, we propose a neural coherence model to capture the cross-sentence semantic and syntactic coherence patterns. The proposed neural coherence model obviates the need for feature engineering and can be trained in an end-to-end fashion using unlabeled data. Empirical results show that the proposed neural coherence model can efficiently capture the cross-sentence coherence patterns. Using the combined output of the neural coherence model and ROUGE package as the reward, we design a reinforcement learning method to train a proposed neural extractive summarizer which is named Reinforced Neural Extractive Summarization (RNES) model. The RNES model learns to optimize coherence and informative importance of the summary simultaneously. Experimental results show that the proposed RNES outperforms existing baselines and achieves state-of-the-art performance in term of ROUGE on CNN/Daily Mail dataset. The qualitative evaluation indicates that summaries produced by RNES are more coherent and readable.


title: TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling

abstract: We present TopicNets, a Web-based system for visual and interactive analysis of large sets of documents using statistical topic models. A range of visualization types and control mechanisms to support knowledge discovery are presented. These include corpus- and document-specific views, iterative topic modeling, search, and visual filtering. Drill-down functionality is provided to allow analysts to visualize individual document sections and their relations within the global topic space. Analysts can search across a dataset through a set of expansion techniques on selected document and topic nodes. Furthermore, analysts can select relevant subsets of documents and perform real-time topic modeling on these subsets to interactively visualize topics at various levels of granularity, allowing for a better understanding of the documents. A discussion of the design and implementation choices for each visual analysis technique is presented. This is followed by a discussion of three diverse use cases in which TopicNets enables fast discovery of information that is otherwise hard to find. These include a corpus of 50,000 successful NSF grant proposals, 10,000 publications from a large research center, and single documents including a grant proposal and a PhD thesis.


title: Improvement of dexterity by single session low-frequency repetitive transcranial magnetic stimulation over the contralesional motor cortex in acute stroke: A double-blind placebo-controlled crossover trial

abstract: Purpose: Increasing evidence suggests that the contralesional motor cortex (M1) inhibits the ipsilesional M1 in stroke patients. This inhibition could impair motor function of the affected hand. We investigated if inhibitory 1 Hz repetitive transcranial magnetic stimulation (rTMS) over the contralesional M1 improved motor performance of the affected hand in acute stroke. Methods: A double-blind study of real versus placebo rTMS was conducted. Twelve patients early after subcortical stroke (mean: 7 days) received 1200 stimuli of real and placebo rTMS in a crossover design. The sequence of stimulations was counterbalanced across subjects. Stimulus intensity was subthreshold (90% of motor threshold at rest). Motor function was tested by grip strength recordings and Nine Hole Peg Test (NHPT) executions before and after each rTMS session. Results: Compared to sham stimulation, real rTMS improved NHPT results but not grip strength in the affected hand. No change of performance was observed for the unaffected hand. NHPT baseline repetitions in a subgroup of patients indicated stable motor performance prior to the rTMS sessions. Conclusions: The study suggests that therapeutic rTMS applications over the contralesional hemisphere are feasible in acute stroke patients and can transiently improve dexterity of the affected hand. RTMS may become an additional tool for early neurorehabilitation.


title: Multicamera object detection and tracking with object size estimation

abstract: A number of multi-camera solutions exist for tracking objects of interest in surveillance scenes. Generally, the approach follows the idea of either early fusion (where all cameras are used to make a decision about detection and tracking) or late fusion (where objects are detected and tracked in individual cameras independently, and then the results combined). This paper describes an early fusion approach derived from the common approach of projecting foreground mask into a common coordinate system. The described approach extends prior work to suppress false detections and automatically estimate the size of the object under tracking, thus enabling it to work in environments containing a mix of people and vehicles.


title: Comparing Statistical and Machine Learning Classifiers: Alternatives for Predictive Modeling in Human Factors Research

abstract: Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches - genetic programming and decision tree induction - were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this r...


title: Radar plots : a useful way for presenting multivariate health care data

abstract: Abstract Objective Health care researchers have not taken full advantage of the potential to effectively convey meaning in their multivariate data through graphical presentation. The aim of this paper is to translate knowledge from the fields of analytical chemistry, toxicology, and marketing research to the field of medicine by introducing the radar plot, a useful graphical display method for multivariate data. Study Design and Setting Descriptive study based on literature review. Results The radar plotting technique is described, and examples are used to illustrate not only its programming language, but also the differences in tabular and bar chart approaches compared to radar-graphed data displays. Conclusion Radar graphing, a form of radial graphing, could have great utility in the presentation of health-related research, especially in situations in which there are large numbers of independent variables, possibly with different measurement scales. This technique has particular relevance for researchers who wish to illustrate the degree of multiple-group similarity/consensus, or group differences on multiple variables in a single graphical display.


title: Software Engineering Process Theory: A Multi-Method Comparison of Sensemaking-CoevoIution-Implementation Theory and Function-Behavior-Structure Theory

abstract: ContextSoftware engineering has experienced increased calls for attention to theory, including process theory and general theory. However, few process theories or potential general theories have been proposed and little empirical evaluation has been attempted. ObjectiveThe purpose of this paper is to empirically evaluate two previously untested software development process theories - Sensemaking-Coevolution-Implementation Theory (SCI) and the Function-Behavior-Structure Framework (FBS). MethodA survey of more than 1300 software developers is combined with four longitudinal, positivist case studies to achieve a simultaneously broad and deep empirical evaluation. Instrument development, statistical analysis of questionnaire data, case data analysis using a closed-ended, a priori coding scheme and data triangulation are described. ResultsCase data analysis strongly supports SCI, as does analysis of questionnaire response distributions (p<0.001; chi-square goodness of fit test). Furthermore, case-questionnaire triangulation found no evidence that support for SCI varied by participants' gender, education, experience, nationality or the size or nature of their projects. ConclusionsSCI is supported. No evidence of an FBS subculture was found. This suggests that instead of iterating between weakly-coupled phases (analysis, design, coding, testing), it is more accurate and useful to conceptualize development as ad hoc oscillation between making sense of the project context (Sensemaking), simultaneously improving mental representations of the context and design space (Coevolution) and constructing, debugging and deploying software artifacts (Implementation).


title: Educating physicians internationally in the diagnosis of child sexual abuse: evaluation of a brief educational intervention in Malawi.

abstract: We designed this pilot study to assess physician knowledge and comfort evaluating child sexual abuse in Malawi and to evaluate the feasibility of a brief educational intervention to improve physician knowledge. A survey was administered to 21 physicians before receiving training on child sexual abuse. The survey was administered again, 3 days later, to assess improvement. Prior to the training, 48% correctly identified the hymen in a photograph, and 24% correctly interpreted the photograph as normal. Of the 11 physicians available for the posttest 3 days later, significant improvement was found in their ability to correctly identify the hymen (6 of 11 pretraining and all 11 posttraining, p = 0.011) and to interpret the photograph correctly (1 of 11 correct pretraining, 9 of 11 posttraining, p = 0.001). This study demonstrates the feasibility of designing trainings on child sexual abuse in a low-income country.


Information Extraction Over the Internet for a Dynamic Game


title: Context-Aware Recommender Systems: A Service-Oriented Approach

abstract: Recommender systems are ecient tools that overcome the information overload problem by providing users with the most relevant contents. This is generally done through user’s preferences/ratings acquired from log les of his former sessions. Besides these preferences, taking into account the interaction context of the user will improve the relevancy of recommendation process. In this paper, we propose a context-aware recommender system based on both user prole and context. The approach we present is based on a previous work on data personalization which leads to the denition of a Personalized Access Model that provides a set of personalization services. We show how these services can be deployed in order to provide advanced context-aware recommender systems.


title: A design of switched-beam Yagi-Uda antenna for wireless sensor networks

abstract: In this paper, we present a design of the Yagi-Uda antenna, which can be switched to achieve radiation patterns toward different angles with a step of 45 degree. The antenna covers the ISM band for SRDs with a center frequency of 2.45 GHz. There are eight Yagi-Uda antennas combined in a circle form. Each Yagi-Uda antenna consists of a bended reflector, a bended driven and a bended director. The bended reflector is shared for different Yagi-Uda antenna by PIN diodes. Simulated results show that eight switching beams covered all directions in E plane with particular direction beam-width around of 60 degree. The gain of the main lobe is greater than 5.5dBi, whereas the side lobe is kept 11dB smaller than the main lobe. Thanks to the pattern switchable ability and its simple design, the antenna can be used for the master sensors in wireless sensor networks, or for communicating devices in cognitive radios.


title: Automatically generating abstract paintings in Malevich Style

abstract: This paper presents an approach of automatically generating abstract paintings of Malevich Style. Through parameterized program and flexible production processes, we successfully generated images mimicking the styles of the abstract artist Kazimir Malevich, having classified different styles of his paintings. The generated results using the basic components in Malevich's style based on his art theory are shown and the reason why some colors are chosen are also discussed.


title: Evolving Deep Recurrent Neural Networks Using Ant Colony Optimization

abstract: This paper presents a novel strategy for using ant colony optimization (ACO) to evolve the structure of deep recurrent neural networks. While versions of ACO for continuous parameter optimization have been previously used to train the weights of neural networks, to the authors’ knowledge they have not been used to actually design neural networks. The strategy presented is used to evolve deep neural networks with up to 5 hidden and 5 recurrent layers for the challenging task of predicting general aviation flight data, and is shown to provide improvements of 63 % for airspeed, a 97 % for altitude and 120 % for pitch over previously best published results, while at the same time not requiring additional input neurons for residual values. The strategy presented also has many benefits for neuro evolution, including the fact that it is easily parallizable and scalable, and can operate using any method for training neural networks. Further, the networks it evolves can typically be trained in fewer iterations than fully connected networks.


Identification, characterization, and grounding of gradable terms in clinical text


title: the human brain: International Consortium for Brain Mapping (ICBM)

abstract: Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital form, we embarked upon a program in 1992 to develop a four–dimensional probabilistic atlas and reference system for the human brain. Through an International Consortium for Brain Mapping (ICBM) a dataset is being collected that includes 7000 subjects between the ages of eighteen and ninety years and including 342 mono– and dizygotic twins. Data on each subject includes detailed demographic, clinical, behavioural and imaging information. DNA has been collected for genotyping from 5800 subjects. A component of the programme uses post–mortem tissue to determine the probabilistic distribution of microscopic cyto– and chemoarchitectural regions in the human brain. This, combined with macroscopic information about structure and function derived from subjects in vivo , provides the first large scale opportunity to gain meaningful insights into the concordance or discordance in micro– and macroscopic structure and function. The philosophy, strategy, algorithm development, data acquisition techniques and validation methods are described in this report along with database structures. Examples of results are described for the normal adult human brain as well as examples in patients with Alzheimer's disease and multiple sclerosis. The ability to quantify the variance of the human brain as a function of age in a large population of subjects for whom data is also available about their genetic composition and behaviour will allow for the first assessment of cerebral genotype–phenotype–behavioural correlations in humans to take place in a population this large. This approach and its application should provide new insights and opportunities for investigators interested in basic neuroscience, clinical diagnostics and the evaluation of neuropsychiatric disorders in patients.


title: Dating Violence Experiences of Lesbian, Gay, Bisexual, and Transgender Youth

abstract: Media attention and the literature on lesbian, gay, and bisexual youth overwhelmingly focus on violence involving hate crimes and bullying, while ignoring the fact that vulnerable youth also may be at increased risk of violence in their dating relationships. In this study, we examine physical, psychological, sexual, and cyber dating violence experiences among lesbian, gay, and bisexual youth—as compared to those of heterosexual youth, and we explore variations in the likelihood of help-seeking behavior and the presence of particular risk factors among both types of dating violence victims. A total of 5,647 youth (51 % female, 74 % White) from 10 schools participated in a cross-sectional anonymous survey, of which 3,745 reported currently being in a dating relationship or having been in one during the prior year. Results indicated that lesbian, gay, and bisexual youth are at higher risk for all types of dating violence victimization (and nearly all types of dating violence perpetration), compared to heterosexual youth. Further, when looking at gender identity, transgender and female youth are at highest risk of most types of victimization, and are the most likely perpetrators of all forms of dating violence but sexual coercion, which begs further exploration. The findings support the development of dating violence prevention programs that specifically target the needs and vulnerabilities of lesbian, gay, and bisexual youth, in addition to those of female and transgender youth.


title: Active Learning: Any Value for Classification of Remotely Sensed Data?

abstract: Active learning, which has a strong impact on processing data prior to the classification phase, is an active research area within the machine learning community, and is now being extended for remote sensing applications. To be effective, classification must rely on the most informative pixels, while the training set should be as compact as possible. Active learning heuristics provide capability to select unlabeled data that are the “most informative” and to obtain the respective labels, contributing to both goals. Characteristics of remotely sensed image data provide both challenges and opportunities to exploit the potential advantages of active learning. We present an overview of active learning methods, then review the latest techniques proposed to cope with the problem of interactive sampling of training pixels for classification of remotely sensed data with support vector machines (SVMs). We discuss remote sensing specific approaches dealing with multisource and spatially and time-varying data, and provide examples for high-dimensional hyperspectral imagery.


title: Functional requirements of future automotive radar systems

abstract: Automotive radar systems are starting to divide into two groups: Highly specialized stand alone low-cost sensors targeting high volume markets and high-performance multi purpose sensors used in sophisticated data fusion architectures. While the first group focuses on ultimate cost reduction, the latter provides the basis for future high-performance driver assistance functions which might be deployed in middle- to luxury-class cars. This paper addresses both sensor groups and lists OEM requirements resulting from the specifications of future driver assistance functions and from upcoming perception system architectures. The second part of this contribution addresses recent trends in automotive radar sensing technology.


title: A CMOS hysteresis undervoltage lockout with current source inverter structure

abstract: This paper describes a simple architecture and low power consumption undervoltage lockout (UVLO) circuit with hysteretic threshold. The UVLO circuit monitors the supply voltage and determines whether or not the supply voltage satisfies a predetermined condition. The under voltage lockout circuit is designed based on CSMC 0.5um CMOS technology, utilizing a relatively few amount of circuitry. It is realized with a current source inverter. The threshold voltage is determined by the W/L ratio of current source inverter and resistor in reference generator. The hysteresis is realized by using a feedback circuit to overcome the bad disturbance and noise rejection of the single threshold. Hysteretic threshold range is 40mV. The quiescent current is about 1uA at 3V supply voltage,while the power of circuit consumes only 3uW.


title: Ellipse detection through decomposition of circular arcs and line segments

abstract: In this work we propose an efficient and original method for ellipse detection which relies on a recent contour representation based on arcs and line segments [1]. The first step of such a detection is to locate ellipse candidate with a grouping process exploiting geometric properties of adjacent arcs and lines. Then, for each ellipse candidate we extract a compact and significant representation defined from the segment and arc extremities together with the arc middle points. This representation allows then a fast ellipse detection by using a simple least square technique. Finally some first comparisons with other robust approaches are proposed.


abstract: Health monitoring is repeatedly mentioned as one of the main application areas for Pervasive computing. Mobile Health Care is the integration of mobile computing and health monitoring. It is the application of mobile computing technologies for improving communication among patients, physicians, and other health care workers. As mobile devices have become an inseparable part of our life it can integrate health care more seamlessly to our everyday life. It enables the delivery of accurate medical information anytime anywhere by means of mobile devices. Recent technological advances in sensors, low-power integrated circuits, and wireless communications have enabled the design of low-cost, miniature, lightweight and intelligent bio-sensor nodes. These nodes, capable of sensing, processing, and communicating one or more vital signs, can be seamlessly integrated into wireless personal or body area networks for mobile health monitoring. In this paper we present Intelligent Mobile Health Monitoring System (IMHMS), which can provide medical feedback to the patients through mobile devices based on the biomedical and environmental data collected by deployed sensors.


title: Shiftwork-Mediated Disruptions of Circadian Rhythms and Sleep Homeostasis Cause Serious Health Problems

abstract: Shiftwork became common during the last few decades with the growing demands of human life. Despite the social inactivity and irregularity in habits, working in continuous irregular shifts causes serious health issues including sleep disorders, psychiatric disorders, cancer, and metabolic disorders. These health problems arise due to the disruption in circadian clock system, which is associated with alterations in genetic expressions. Alteration in clock controlling genes further affects genes linked with disorders including major depression disorder, bipolar disorder, phase delay and phase advance sleep syndromes, breast cancer, and colon cancer. A diverse research work is needed focusing on broad spectrum changes caused by jet lag in brain and neuronal system. This review is an attempt to motivate the researchers to conduct advanced studies in this area to identify the risk factors and mechanisms. Its goal is extended to make the shift workers aware about the risks associated with shiftwork.


title: Target Classification Using the Deep Convolutional Networks for SAR Images

abstract: The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is generally composed of the extraction of a set of features that transform the raw input into a representation, followed by a trainable classifier. The feature extractor is often hand designed with domain knowledge and can significantly impact the classification accuracy. By automatically learning hierarchies of features from massive training data, deep convolutional networks (ConvNets) recently have obtained state-of-the-art results in many computer vision and speech recognition tasks. However, when ConvNets was directly applied to SAR-ATR, it yielded severe overfitting due to limited training images. To reduce the number of free parameters, we present a new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set illustrate that A-ConvNets can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.


title: Attack Detection and Identification in Cyber-Physical Systems

abstract: Cyber-physical systems integrate computation, communication, and physical capabilities to interact with the physical world and humans. Besides failures of components, cyber-physical systems are prone to malignant attacks, and specific analysis tools as well as monitoring mechanisms need to be developed to enforce system security and reliability. This paper proposes a unified framework to analyze the resilience of cyber-physical systems against attacks cast by an omniscient adversary. We model cyber-physical systems as linear descriptor systems, and attacks as exogenous unknown inputs. Despite its simplicity, our model captures various real-world cyber-physical systems, and it includes and generalizes many prototypical attacks, including stealth, (dynamic) false-data injection and replay attacks. First, we characterize fundamental limitations of static, dynamic, and active monitors for attack detection and identification. Second, we provide constructive algebraic conditions to cast undetectable and unidentifiable attacks. Third, by using the system interconnection structure, we describe graph-theoretic conditions for the existence of undetectable and unidentifiable attacks. Finally, we validate our findings through some illustrative examples with different cyber-physical systems, such as a municipal water supply network and two electrical power grids.


title: Mapping Dependencies Trees: An Application to Question Answering

abstract: We describe an approach for answer selection in a free form question answering task. In order to go beyond the key-word based matching in selecting answers to questions, one would like to incorporate both syntactic and semantic information in the question answering process. We achieve this goal by representing both questions and candidate passages using dependency trees, and incorporating semantic information such as named entities in this representation. The sentence that best answers a question is determined to be the one that minimizes the generalized edit distance between it and the question tree, computed via an approximate tree matching algorithm. We evaluate the approach on question-answer pairs taken from previous TREC Q/A competitions. Preliminary experiments show its potential by significantly outperforming common bag-of-word scoring methods.


title: Deep Gate Recurrent Neural Network

abstract: Abstract : This paper explores the possibility of using multiplicative gate to build two recurrent neural network structures. These two structures are called Deep Simple Gated Unit (DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term dependencies. Compared to traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM, which require more than one gate to control information flow in the network, SGU and DSGU only use one multiplicative gate to control the flow of information. We show that this difference can accelerate the learning speed in tasks that require long dependency information. We also show that DSGU is more numerically stable than SGU. In addition, we also propose a standard way of representing the inner structure of RNN called RNN Conventional Graph (RCG), which helps to analyze the relationship between input units and hidden units of RNN.


title: SVM PMSM Drive With Low Resolution Hall-Effect Sensors

abstract: In this paper, a back-electromotive force-based method is used in conjunction with three very low-cost integrated circuits based on the Hall-effect to estimate the rotor position of the direct-drive permanent-magnet synchronous motors (PMSMs) to be used for traction purpose of an electric wheelchair. A speed estimator based on a rotor frame machine model has been implemented for the PMSM drives, and then the rotor position estimation is achieved by means of a discrete integration of the estimated speed. The three Hall effect sensors are used to detect the rotor initial position as well to reset the error on the rotor position estimation every 60 electrical degrees. In the specific application, support vector machine technique has been implemented together with the field oriented control. Simulations and experimental results are shown in the paper


Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games.


title: Infrared-assisted and roller-based direct metal contact transfer technology for flexible polarizer

abstract: In this paper we report the fabrication of metallic nano-wired polarizer on polymer substrate based on infrared assisted and roller based direct metal contact transfer technology. This method effectively resolves several serious problems in conventional hot-embossing types of nano-imprinting such as uniformly applied pressure and thermal deformation of the flexible substrate due to over heating. In this new contact printing process, we utilize a glass cylinder to keep the PET substrate being in contact with the mold constantly and stably. To avoid over heating and thermal deformation, an infrared light is used to heat up the surface of the PET substrate and achieves localized melting of the polymer surface. After PET heating, the metallic grating patterns which are per-deposited on the Si mold are transferred on the PET substrate, hence forms a flexible polarizer of metallic grating. In addition, this method does not need chemical etching in or after the transfer process so that it significantly simplifies the printing procedures. We have successfully produced a number of metallic nano-wired flexible polarizers easily by this infrared-assisted and roller-based direct metal contact transfer technology, and their optical performance has been experimentally characterized.


title: Adaptive Preemption of Traffic for Emergency Vehicles

abstract: Minimizing travel time is critical for the successful operation of emergency vehicles. Preemption can significantly help emergency vehicles reach the intended destination faster. Majority of the current studies focus on minimizing and/or eliminating delays for EVs and do not consider the negative impacts of preemption on urban traffic. One primary negative impact is extended delays for non-EV traffic due to preemption that is addressed in this paper. We propose an Adaptive Preemption of Traffic (APT) system for Emergency Vehicles in an Intelligent Transportation System. We utilize the knowledge of current traffic conditions in the transportation system to adaptively preempt traffic at signals along the path of EVs so as to minimize, if not eliminate stopped delays for EVs while simultaneously minimizing the delays for non-emergency vehicles in the system. Through extensive simulation results, we show substantial reduction in delays for both EVs.


title: RWC Music Database: Music genre database and musical instrument sound database

abstract: This paper describes the design policy and specifications of the RWC Music Database, a copyright-cleared music database (DB) compiled specifically for research purposes. Shared DBs are common in other research fields and have made significant contributions to progress in those fields. The field of music information processing, however, has lacked a common DB of musical pieces or a large-scale DB of musical instrument sounds. We therefore recently constructed the RWC Music Database comprising four original component DBs: the Popular Music Database (100 pieces), Royalty-Free Music Database (15 pieces), Classical Music Database (50 pieces), and Jazz Music Database (50 pieces). In this paper we report the construction of two additional component DBs: the Music Genre Database (100 pieces) and Musical Instrument Sound Database (50 instruments). For all 315 musical pieces, we prepared original audio signals, corresponding standard MIDI files, and text files of lyrics (for songs). For all 50 instruments, we recorded individual sounds at half-tone intervals with several variations of playing styles, dynamics, instrument manufacturers, and musicians. It is our hope that our DB will make a significant contribution to future advances in the field of music information processing.


title: Development and Deployment at Facebook

abstract: Internet companies such as Facebook operate in a "perpetual development" mindset. This means that the website continues to undergo development with no predefined final objective, and that new developments are deployed so that users can enjoy them as soon as they're ready. To support this, Facebook uses both technical approaches such as peer review and extensive automated testing, and a culture of personal responsibility.


title: BROWSING RNA STRUCTURES BY INTERACTIVE SONIFICATION

abstract: This paper presents a new interactive sonification technique to browse ribonucleic acid secondary structures using a combined auditory and visual interface. Despite the existence of several optimization criteria for searching an optimal structure within the numerous possible structures of an RNA sequence, it is still necessary to manually inspect a huge number of the resulting structures in detail. We describe briefly the background of RNA structure representation and typical search scenarios. Then we discuss the audio-visual browser in detail, with a special focus on the sound design, data-to-sound mapping and interactive aspects. The sonifications we propose turn RNA structures into auditory timbre gestalts according to the shape classes they belong to. Various research-relevant phenomena become clearly audible such as transitions among shape classes and dierent free energies of selected folds. Both can be simultaneously assessed in an interface that allows for an integrated audio-visual perception.


title: Crowd sensing and delay tolerant networks to support decision making at the routing level

abstract: Sensing combines the ubiquity of smartphones and different types of sensors to collect data from different aspects of the city. Sensing is the process of transmitting collected data for processing by a remote centralized system; which requires a constant and wide coverage of the communication network infrastructure, which is not always possible. In this context, Delay Tolerant Networks, where applications need to transmit data in the environment of an intermittent or even non-existent network infrastructure, appear as a complementary and innovative alternative. In Delay Tolerant Networks, applications, in addition to inheriting their benefits, also inherit the challenges of decision-making in message forwarding. In this article we analyze the importance of the Delay Tolerant Networks in the supporting of the decision making at the routing level, making the messaging process proactive.


title: Combining encoded structured light and photometric stereo for underwater 3D reconstruction

abstract: Surface normal maps created by photometric stereo allow for high-quality rendering from certain viewpoints, even when the resolution of original images is low. However, the lack of constraints between multiple disconnected patches, the frequent presence of low-frequency distortion, and some actual conditions often lead to a bias during the photometric stereo reconstruction using direct integration. In this paper, we therefore present a hybrid method, which exploits the depth information that the encoded structured light system produces, in order to correct the photometric stereo bias. On the other hand, this method retains the high-precision normal information. Our experimental results show that the proposed method can not only recover high-frequency details but also avoid, or at least reduce, the low-frequency bias. In particular, the error that our method generates in the underwater environment is tolerant, even in the case that high turbidity values occur.


title: Modeling musicological information as trigrams in a system for simultaneous chord and local key extraction

abstract: In this paper, we discuss the introduction of a trigram musicological model in a simultaneous chord and local key extraction system. By enlarging the context of the musicological model, we hoped to achieve a higher accuracy that could justify the associated higher complexity and computational load of the search for the optimal solution. Experiments on multiple data sets have demonstrated that the trigram model has indeed a larger predictive power (a lower perplexity). This raised predictive power resulted in an improvement in the key extraction capabilities, but no improvement in chord extraction when compared to a system with a bigram musicological model.


title: Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis

abstract: Effective analysis of features in time-varying data is essential in numerous scientific applications. Feature extraction and tracking are two important tasks scientists rely upon to get insights about the dynamic nature of the large scale time-varying data. However, often the complexity of the scientific phenomena only allows scientists to vaguely define their feature of interest. Furthermore, such features can have varying motion patterns and dynamic evolution over time. As a result, automatic extraction and tracking of features becomes a non-trivial task. In this work, we investigate these issues and propose a distribution driven approach which allows us to construct novel algorithms for reliable feature extraction and tracking with high confidence in the absence of accurate feature definition. We exploit two key properties of an object, motion and similarity to the target feature, and fuse the information gained from them to generate a robust feature-aware classification field at every time step. Tracking of features is done using such classified fields which enhances the accuracy and robustness of the proposed algorithm. The efficacy of our method is demonstrated by successfully applying it on several scientific data sets containing a wide range of dynamic time-varying features.


title: A General Non-Binary LDPC Code Optimization Framework Suitable for Dense Flash Memory and Magnetic Storage

abstract: Transmission channels underlying modern dense storage systems, e.g., Flash memory and magnetic recording (MR) systems, significantly differ from canonical channels, like additive white Gaussian noise (AWGN) channels. While existing low-density parity-check (LDPC) codes optimized for symmetric, AWGN-like channels are being actively considered for Flash applications, we demonstrate that, due to channel asymmetry, such approaches are inadequate. We introduce a refined definition of absorbing sets, which we call general absorbing sets of type two (GASTs) , and study the combinatorial properties of GASTs. We then present the weight consistency matrix (WCM) , which succinctly captures key properties in a GAST. Furthermore, we show how to customize the WCM definition such that it suits other special subclasses of GASTs. Based on these new concepts, we then develop a new, general combinatorial code optimization framework, which we call the WCM framework , and demonstrate its effectiveness on the realistic highly-asymmetric normal-Laplace mixture (NLM) Flash channel. Moreover, we show that our framework can be customized to optimize non-binary LDPC (NB-LDPC) codes for other asymmetric channels, channels with memory (incorporated in MR systems), and canonical symmetric channels. For all the channels we have simulated NB-LDPC codes over, the codes optimized using the WCM framework enjoy at least 1 order, and up to nearly 2 orders of magnitude performance gain in the uncorrectable bit error rate (UBER) or the frame error rate (FER) relative to the unoptimized codes. Our simulations also show that codes optimized for symmetric channels are not the best choice for asymmetric channels.


title: Integrating Pinyin to Improve Spelling Errors Detection for Chinese Language

abstract: Most Chinese texts are inputted with keyboard via two input methods: Pinyin and Wubi, especially by Pinyin input method. In this paper, this users' habitation is used to find the spelling errors automatically. We first train a Chinese character form n-gram language model on a large scale Chinese corpus in the traditional way. In order to improve this character based model, we transform the whole corpus into Pinyin to obtain Pinyin based language model. Fatherly, the tone is considered to get the third model. Integrating these three models, we improve the performance of checking spelling error system. Experimental results demonstrate the effeteness of our model.


title: Face Recognition under Varying Illumination with Logarithmic Fractal Analysis

abstract: Face recognition under illumination variations is a challenging research area. This paper presents a new method based on the log function and the fractal analysis (FA) to produce a logarithmic fractal dimension (LFD) image which is illumina- tion invariant. The proposed FA feature-based method is a very effective edge enhancer technique to extract and enhance facial features such as eyes, eyebrows, nose, and mouth. Our extensive experiments show the proposed method achieves the best recog- nition accuracy using one image per subject for training when compared to six recently proposed state-of-the-art methods.


title: Audience Participation Games: Blurring the Line Between Player and Spectator

abstract: Audience Participation Games challenge traditional assumptions about gameplay by blurring the line between audience and player, allowing audience members to impact gameplay in a meaningful way. Their recent rise in popularity has created new opportunities for game research and development. To better understand this design space, we developed several versions of two prototype games as design probes. We livestreamed them to an online audience in order to develop a framework for audience motivations and participation styles, to explore ways in which mechanics can affect audience members' sense of agency, and to identify promising design spaces. Our results show the breadth of opportunities and challenges that designers face in creating engaging Audience Participation Games.


Domain Adaptive Information Extraction From Text


Metaphor is like analogy


title: Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network

abstract: Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral–spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with hyperspectral data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for hyperspectral data classification.


title: It governance in the public sector: a conceptual model

abstract: Information technology governance (ITG) has been identified as an organizational ability of great importance for IT strategic alignment and the delivery of business though IT. However, the results and contribution of ITG are poorly understood. This work presents an explanatory model of the effects of IT governance mechanisms on IT and organizational performance of public organizations. To test the model, data was collected from a survey conducted in 146 Brazilian public organizations. Partial least squares analysis was applied to test the proposed model. The results indicate that (i) IT performance is positively correlated with organizational performance, (ii) relational mechanisms between IT and a business are determinant factors for the performance of IT, and also positively correlated with organizational performance, and (iii), decision structure and process maturity are not significantly correlated with IT performance.


title: The manhunt network: People tracking in hybrid-overlapping under the vertical top-view depth camera networks

abstract: The importance of surveillance system increases in recent years. The ability of camera tracking has also gone mature enough to produce reliable results. But even so, the automotive tracking algorithm in multiple views is still a late bloomer. In this paper, we are exploring a general form of cameras' coalition tracking. We not only propose a new 3D biometric feature for identification in camera networks, but also focus on reformulating the framework of a camera network. Here, a new hybrid-overlapping coalition of a set of cameras is proposed for either overlapping or non-overlapping tracking. In the meantime, a novel time-varying non-overlapping strategy inspirited by the manhunt procedure of law enforcement is developed. The proposed approach is combining the overlapping and nonoverlapping (hybrid-overlapping) camera networks together to track targets collaboratively for the better efficient.


title: Method card design dimensions: a survey of card-based design tools

abstract: There are many examples of cards used to assist or provide structure to the design process, yet there has not been a thorough articulation of the strengths and weaknesses of the various examples. We review eighteen card-based design tools in order to understand how they might benefit designers. The card-based tools are explained in terms of five design dimensions including the intended purpose and scope of use, duration of use, methodology, customization, and formal/material qualities. Our analysis suggests three design patterns or archetypes for existing card-based design method tools and highlights unexplored areas in the design space. The paper concludes with recommendations for the future development of card-based methods for the field of interaction design.


title: Trusted execution environment-based authentication gauge (TEEBAG)

abstract: We present a new approach to authentication using Trusted Execution Environments (TEEs), by changing the location of authentication from a remote device (e.g. remote authentication server) to user device(s) that are TEE enabled. The authentication takes place locally on the user device and only the outcome is sent back to the remote device. Our approach uses existing features and capabilities of TEEs to enhance the security of user authentication. We reverse the way traditional authentication schemes work: instead of the user presenting their authentication data to a remote device, we request the remote device to send the stored authentication template (s) to the local device. Almost paradoxically, this enhances security of authentication data by supplying it only to a trusted device, and so enabling users to authenticate the intended remote entity. This addresses issues related with bad SSL certificates on local devices, DNS poisoning, and counteracts certain threats posed by the presence of malware. We present a protocol to implement such authentication system discussing its strengths and limitations, before identifying available technologies to implement the architecture.


title: How Emotions Change Time

abstract: Experimental evidence suggests that emotions can both speed-up and slow-down the internal clock. Speeding-up has been observed for to-be-timed emotional stimuli that have the capacity to sustain attention, whereas slowing-down has been observed for to-be-timed neutral stimuli that are presented in the context of emotional distractors. These effects have been explained by mechanisms that involve changes in bodily arousal, attention or sentience. A review of these mechanisms suggests both merits and difficulties in the explanation of the emotion-timing link. Therefore, a hybrid mechanism involving stimulus-specific sentient representations is proposed as a candidate for mediating emotional influences on time. According to this proposal, emotional events enhance sentient representations, which in turn support temporal estimates. Emotional stimuli with a larger share in ones sentience are then perceived as longer than neutral stimuli with a smaller share.


title: Modeling User Arguments, Interactions, and Attributes for Stance Prediction in Online Debate Forums

abstract: Online debate forums are important social media for people to voice their opinions and debate with each other. Mining user stances or viewpoints from these forums has been a popular research topic. However, most current work does not address an important problem: for a specific issue, there may not be many users participating and expressing their opinions. Despite the sparsity of user stances, users may provide rich side information; for example, users may write arguments to back up their stances, interact with each other, and provide biographical information. In this work, we propose an integrated model to leverage side information. Our proposed method is a regression-based latent factor model which jointly models user arguments, interactions, and attributes. Our method can perform stance prediction for both warm-start and cold-start users. We demonstrate in experiments that our method has promising results on both micro-level and macro-level stance prediction.


title: Searching for Common Sense: Populating Cyc from the Web

abstract: The Cyc project is predicated on the idea that effective machine learning depends on having a core of knowledge that provides a context for novel learned information - what is known informally as "common sense." Over the last twenty years, a sufficient core of common sense knowledge has been entered into Cyc to allow it to begin effectively and flexibly supporting its most important task: increasing its own store of world knowledge. In this paper, we present initial work on a method of using a combination of Cyc and the World Wide Web, accessed via Google, to assist in entering knowledge into Cyc. The long-term goal is automating the process of building a consistent, formalized representation of the world in the Cyc knowledge base via machine learning. We present preliminary results of this work and describe how we expect the knowledge acquisition process to become more accurate, faster, and more automated in the future.


title: A Systematic Review of Data Mining Approaches to Credit Card Fraud Detection

abstract: Credit card fraud is a serious and ever-growing problem with billions of dollars lost every year due to ::: fraudulent transactions. Fraud has always been present and will always be. It is also ever changing, as the ::: technology and usage patterns change over time, which makes CCFD (credit card fraud detection) a particularly ::: hard problem. Traditionally, fraud detection relied solely on domain experts’ detection rules, but in the past ::: decade or two, such solutions are being augmented with data mining models for fraud detection. The progress in ::: this area is impeded both by the sensitive nature of the data and great commercial potential – the industrial ::: solutions are understandably kept secret and authentic datasets are rare and few. In this paper we study the ::: CCFD problem with its typical problems and state of the art solution. We survey the recent literature and bring a ::: structured overview of relevant fraud detection features and data mining approaches to this problem.


title: The visions, technologies, applications and security issues of Internet of Things

abstract: In this paper, the originality of the Internet of Things (IoT) is introduced. Then the different visions are discussed. After this, the technologies and applications are analyzed. Based on these, the issues are displayed. In the end, the conclusions are drawn; we should utilize IoT while we must try hard to find the solutions to the issues.


title: Image contrast enhancement for outdoor machine vision applications

abstract: Outdoor machine vision is getting a concern nowadays. Ranging from surveillance and monitoring system to automotive system such as driver assistance system require vision application or artificial eye to keep monitoring the situations. However, most of these applications works very well during clear weather and degrade during bad weather due to the atmospheric particles mitigate the quality of vision system. This paper discuss the state of the art of image enhancement techniques used to adjust the contrast of an outdoor image degrade by fog, haze, and rain. A brief overview of bad weather will be discussed and several recent techniques on removing fog, haze and rain are discussed.


title: Survey of End-to-End Mobile Network Measurement Testbeds

abstract: Mobile (cellular) networks enable innovation, but can also stifle it and lead to user frustration when network performance falls below expectations. As mobile networks become the predominant method of Internet access, developer, research, network operator, and regulatory communities have taken an increased interest in measuring end-to-end mobile network performance to, among other goals, minimize negative impact on application responsiveness. In this survey, we examine current approaches to end-to-end mobile network performance measurement, diagnosis, and application prototyping. We compare available tools and their shortcomings with respect to the needs of developers, researchers, network operators, and regulators. We intend for this survey to provide a comprehensive view of currently active efforts and some auspicious directions for future work in mobile network measurement and mobile application performance evaluation.


title: Simplified intrinsic camera calibration and hand-eye calibration for robot vision

abstract: In this paper we investigate how intrinsic camera calibration and hand-eye calibration can be performed on a robot vision system using the simplest possible motions and a planar calibration object. The standard methods on plane-based camera calibration are extended with theory on how to use pure translational motions for the intrinsic calibration and we see how hand-eye calibration can be performed within the same framework. The calibration of two cameras in a stereo head configuration is shown to be an interesting application of the developed theory. Results of experiments on a real robot vision system are presented.


title: A visualization tool of patent topic evolution using a growing cell structure neural network

abstract: This research used a cell structure map to visualize technological evolution and showed the developmental trend in a technological field. The basic concept was to organize patents into a map produced by growing cell structures. The map was then disassembled into clusters with similar contexts using the Girvan---Newman algorithm. Next, the continuity between clusters in two snapshots was identified and used as the base for establishing a trajectory in the technology. An analysis of patents in the flaw detection field found that the field was composed of several technological trajectories. Among them, ultrasonic flaw detection, wafer inspection and substrate inspection were relatively larger and more continuing technologies, while infrared thermography defect inspection has been an emerging topic in recent years. It is to be hoped that the map of technology constructed in this research provides insights into the history of technological evolution and helps explain the transition patterns through changes in cluster continuity. This can serve a reference point by experts who attempt to visualize the mapping of technological development or identify the latest focus of attention.


Evaluation of Side-Channel Preprocessing Techniques on Cryptographic-Enabled HF and UHF RFID-Tag Prototypes


title: Nonsuicidal self-injury as a gateway to suicide in young adults.

abstract: PURPOSE ::: To investigate the extent to which nonsuicidal self-injury (NSSI) contributes to later suicide thoughts and behaviors (STB) independent of shared risk factors. ::: ::: ::: METHODS ::: One thousand four hundred and sixty-six students at five U.S. colleges participated in a longitudinal study of the relationship between NSSI and suicide. NSSI, suicide history, and common risk/protective factors were assessed annually for three years. Analyses tested the hypotheses that the practice of NSSI prior to STB and suicide behavior (excluding ideation) reduced inhibition to later STB independent of shared risk factors. Analyses also examined factors that predicted subsequent STB among individuals with NSSI history. ::: ::: ::: RESULTS ::: History of NSSI did significantly predict concurrent or later STB (AOR 2.8, 95%, CI 1.9-4.1) independent of covariates common to both. Among those with prior or concurrent NSSI, risk of STB is predicted by > 20 lifetime NSSI incidents (AOR 3.8, 95% CI, 1.4-10.3) and history of mental health treatment (AOR 2.2, 95% CI, 1.9-4.6). Risk of moving from NSSI to STB is decreased by presence of meaning in life (AOR .6, 95% CI, .5-.7) and reporting parents as confidants (AOR, .3, 95% CI, .1-.9). ::: ::: ::: CONCLUSIONS ::: NSSI prior to suicide behavior serves as a "gateway" behavior for suicide and may reduce inhibition through habituation to self-injury. Treatments focusing on enhancing perceived meaning in life and building positive relationships with others, particularly parents, may be particularly effective in reducing suicide risk among youth with a history of NSSI.


title: Comparing CORBA and Web-Services in view of a Service Oriented Architecture

abstract: The concept of Service Oriented Architecture revolves around registering services as tasks. These tasks are accomplished collectively by various disparate components seamlessly connected to one another. The task of interlinking these components may be considered amongst the most convoluted and difficult tasks currently faced by software practitioners. This paper attempts to show that although middleware technologies can be solely utilized to develop service oriented architecture, however such architecture would severely lack quality, interoperability and ease of implementation. In order to resolve these complexities and complications this paper proposes Web Services as an alternative to Middleware, for the realization of a fully functional interoperable and an automated SOA which conforms to the characteristics of a SOA. This paper provides an abstract implementation model of a SOA using both middleware and web services. It then attempts to point out the implementation and accepted benefits of the latter, especially when legacy applications are involved. Emphasize is laid out on the significance of interoperability since it assists in mobility and other corporate benefits. The paper concludes that when interoperability along with its benefits of mobility, expansion, costs, simplicity and enterprise integration are required in the construction of a SOA then web services should be the definite integration choice. The paper also highlights the importance of object oriented middleware, along with situations in which it might be preferred over web services.


title: Investigation of palladium coverage on bonded balls of palladium-coated copper wires

abstract: The application of Palladium coated-Copper (Pd-Cu) wires is a solution to prevent copper oxidation during the copper wire bonding process. This is true for the Second Bond but may not be the situation when it comes to the First Bond, because during the Free Air Ball (FAB) formation, the Pd-Cu wire tip is melted and the Pd distribution on the FAB surface is dependent on several processing factors. The type of the wires used and the electrical current applied during the EFO (Electronic Flame Off) process to form the FAB are believed to be the two major factors affecting the resulting Pd distribution on the FAB. Without an optimized EFO process to form ideal, spherical FABs with nice palladium surface coverage, the copper may be exposed after the First bond. Consequently, after molding, it is subject to corrosion due to the presence of halogen ions inside the mold compound chemistry. Typically, after a pressure cooker test or HAST test, these first bonds may fail. In this paper, we will first present the study of palladium surface coverage during FAB formation and also bonded ball using various EFO parameters with different Pd-Cu wire sizes. During first bond bonding, pure impact of the FAB will determine 75%–80% of the final shape of the bonded ball bond; the subsequent ultrasonic USG vibration accounts only for about 20% of the final shape of the ball bond, depending on the bonding parameters. To reveal the various degrees of palladium coverage on the first bond, we prepared samples and subjected them to weak chloride acids to etch away the exposed copper regions and compare the results using SEM photomicrographs. We found that, even if when the palladium surface coverage on a FAB is incomplete, it is still possible to "seal" off the copper during a good first bond. Therefore, we will present the guidelines on how to achieve improved EFO parameters to form good palladium coverage during FAB formation, as well as how to differentiate and select a palladium coverage in FAB that could still form good first bond with concealed copper not exposed to corrosion attack from the molding compounds, hence forming a more reliable bonding. Such allowable exposed copper regions are presented for three different wire diameters: 0.8, 0.9, and 1.0 mil wires.


title: DB&A: An Open Source Web Service for Meter Data Management

abstract: With the increasing number of initiatives dealing with storing meter data, systems that perform Meter Data Management (MDM) constitute a critical component for realising the potential benefits of the smart grid. However, most of the MDM systems designed today are generalised to such an extent that they do not consider the relational structure of electrical system. Furthermore, these systems do often not restrict themselves to specific data latency requirements. To address this issue, this paper presents the Database & Analytics (DB&A), an open reference implementation for a MDM system that is instantiated as a web service. Its design is based on a set of design goals, an explicitly addressed data latency scope and five real-world scenarios. We present an abstract model of the metering hierarchy that allows MDM systems to be used as compositional services, forming a service tree. Moreover, we provide implementation details about analytic functions to support the service tree. The DB&A is evaluated on a cloud and embedded platform using a subset of data constructed from a case study that includes 8 months of real data. The results show that the DB&A complies with the boundaries of the defined data latency scope for the cloud and embedded platform.


title: NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison

abstract: Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as - NoSQL, NewSQL and Search-based systems. This paper motivation is to provide - classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.


title: Virtualization: Issues, security threats, and solutions

abstract: Although system virtualization is not a new paradigm, the way in which it is used in modern system architectures provides a powerful platform for system building, the advantages of which have only been realized in recent years, as a result of the rapid deployment of commodity hardware and software systems. In principle, virtualization involves the use of an encapsulating software layer (Hypervisor or Virtual Machine Monitor) which surrounds or underlies an operating system and provides the same inputs, outputs, and behavior that would be expected from an actual physical device. This abstraction means that an ideal Virtual Machine Monitor provides an environment to the software equivalent to the host system, but which is decoupled from the hardware state. Because a virtual machine is not dependent on the state of the physical hardware, multiple virtual machines may be installed on a single set of hardware. The decoupling of physical and logical states gives virtualization inherent security benefits. However, the design, implementation, and deployment of virtualization technology have also opened up novel threats and security issues which, while not particular to system virtualization, take on new forms in relation to it. Reverse engineering becomes easier due to introspection capabilities, as encryption keys, security algorithms, low-level protection, intrusion detection, or antidebugging measures can become more easily compromised. Furthermore, associated technologies such as virtual routing and networking can create challenging issues for security, intrusion control, and associated forensic processes. We explain the security considerations and some associated methodologies by which security breaches can occur, and offer recommendations for how virtualized environments can best be protected. Finally, we offer a set of generalized recommendations that can be applied to achieve secure virtualized implementations.


title: Cross-sectional structural analysis for 3D printing optimization

abstract: We propose a novel cross-sectional structural analysis technique that efficiently detects critical stress inside a 3D object. We slice the object into cross-sections and compute stress based on bending momentum equilibrium. Unlike traditional approaches based on finite element methods, our method does not require a volumetric mesh or solution of linear systems, enabling interactive analysis speed. Based on the stress analysis, the orientation of an object is optimized to increase mechanical strength when manufactured with 3D printing.


title: Neural Natural Language Inference Models Enhanced with External Knowledge

abstract: Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.


title: VLSI Architecture for Novel Hopping Discrete Fourier Transform Computation

abstract: The hopping discrete Fourier transform (HDFT) is a new method applied for time-frequency spectral analysis of time-varying signals. In the implementation of HDFT algorithms, the updating vector transform (UVT) plays a key role, and therefore a novel recursive DFT-based UVT formula is introduced in the proposed design for a HDFT algorithm and its architecture. The perceived advantages can be summarized as: 1) the proposed algorithm reduces the number of multiplications, additions, and coefficients by 42.3%, 33.3%, and 50%, respectively, compared with Park’s method under the settings of an M-sample complex input sequence (M = 256), and an N-point recursive DFT computation scheme (N = 64) for time hop L (L = 4); 2) by adopting the hardware-sharing scheme and the register-shifting concept, the proposed design only takes nine multipliers and 12 adders for realization. The proposed hardware accelerator can be implemented using a field-programmable gate array, which can operate at 48.33 MHz clock rate. The resource utilization of combinational logic lookup tables (LUTs) and digital signal processing (DSP) blocks reduced by 11.7% and 42.5% compared with Juang et al. ’s work. For very-large-scale integration realizations, the proposed design would be more powerful than other existing algorithms in future applications focusing on DSP, filtering, and communications.