title: Forward and pressure retarded osmosis: potential solutions for global challenges in energy and water supply.

abstract: Osmotically driven membrane processes (ODMP) have gained renewed interest in recent years and they might become a potential solution for the world's most challenging problems of water and energy scarcity. Though the concept of utilizing osmotic pressure difference between high and low salinity streams across semipermeable membranes has been explored for several decades, lack of optimal membranes and draw solutions hindered competition between forward osmosis (FO) and pressure retarded osmosis (PRO) with existing water purification and power generation technologies, respectively. Driven by growing global water scarcity and by energy cost and negative environmental impacts, novel membranes and draw solutions are being developed for ODMPs, mass and heat transfer in osmotic process are becoming better understood, and new applications of ODMPs are emerging. Therefore, OMDPs might become promising green technologies to provide clean water and clean energy from abundantly available renewable resources. This review focuses primarily on new insights into osmotic membrane transport mechanisms and on novel membranes and draw solutions that are currently being developed. Furthermore, the effects of operating conditions on the overall performance of osmotic membranes will be highlighted and future perspectives will be presented.


title: The facial width‐to‐height ratio determines interpersonal distance preferences in the observer

abstract: Facial width-to-height ratio (fWHR) is correlated with a number of aspects of aggressive behavior in men. Observers appear to be able to assess aggressiveness from male fWHR, but implications for interpersonal distance preferences have not yet been determined. This study utilized a novel computerized stop-distance task to examine interpersonal space preferences of female participants who envisioned being approached by a man; men's faces photographed posed in neutral facial expressions were shown in increasing size to mimic approach. We explored the effect of the men's fWHR, their behavioral aggression (measured previously in a computer game), and women's ratings of the men's aggressiveness, attractiveness, and masculinity on the preferred interpersonal distance of 52 German women. Hierarchical linear modelling confirmed the relationship between the fWHR and trait judgements (ratings of aggressiveness, attractiveness, and masculinity). There were effects of fWHR and actual aggression on the preferred interpersonal distance, even when controlling statistically for men's and the participants’ age. Ratings of attractiveness, however, was the most influential variable predicting preferred interpersonal distance. Our results extend earlier findings on fWHR as a cue of aggressiveness in men by demonstrating implications for social interaction. In conclusion, women are able to accurately detect aggressiveness in emotionally neutral facial expressions, and adapt their social distance preferences accordingly.


title: Phishing URL detection by using artificial neural network with PSO

abstract: Phishing is a serious threat in the virtual world and it has affected the information system. Phishing is mostly conducted via email, other methods such as instant messaging and social networking sites as Facebook twitter. In this paper, we proposed a method to classify the Uniform Resource Locator (URL) into Phishing URL or Non phishing URL. Artificial neural network has been trained by using particle swarm optimization (PSO) to classify URLs to improve the performance of ANN. The proposed model has been run on different ratio of learning and different activation function on number of hidden layer, output layer. Accuracy and RMSE criteria have been selected for evaluating the artificial neural network with particle swarm optimization (ANN_PSO) model. ANN_PSO model performs better training in terms of accuracy with respect to Back Propagation Neural Network (BPNN).


title: Fujifilm-Kodak Duopolistic Competition in Japan and the United States

abstract: This article analyzes the duopolistic competition of Fujifilm and Kodak according to the product life cycle and “game theoretic” exchange-of-hostage hypothesis of international trade and investment. It shows how each firm used its home-team and first-mover advantages to compete with the other. It shows that Fujifilm's strategic use of marketing logistics and innovative products reduced Kodak's market share in Japan and elsewhere. This article has implications for the global competition of multinationals' policies and for market competition and trade policies of the U.S., Japan, and other countries.


title: A framework for improved video text detection and recognition

abstract: Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.


title: MQTT based vehicle accident detection and alert system

abstract: Now-a-days as we open the newspaper, we find atleast one news of a road accident. With vehicles becoming increasingly affordable, there has been a surge in the number of vehicles on roads on an average all over the world. Accidents bring devastation upon victims, causing loss of precious time and money. It has been established, after extensive research, that a majority of accidents become fatalities because of lack of communication to the concerned medical authorities and the consequent lack of immediate medical support. This application helps sense the possible occurence of an accident on the road, with the help of sensors attached to the vehicle. This occurence will be immediately communicated to the concerned people so that further action can be taken without any further ado.


title: Manufacturing of Manganese-Zinc Soft Ferrite by Powder Metallurgy

abstract: Objective of this paper is, improvement of quality of Mn-Zn soft ferrites manufactured by powder metallurgy and overall output yield of it's plant. The efforts have been made to synthesize the crucial parameters which are responsible for better material preparation, pressing and sintering. By adopting these recommendations, the rejection rate is substantially reduced and the variation in magnetic properties is less. Data, which give more uniformity in bigger lots and are responsible for more uniform magnetic properties, have been discussed. Simple, quality-control instruments and their measurement methods which can be incorporated for stage inspection have been explained. The additives for better ferrite powder preparation, granules making and to obtain better magnetic have been discussed. Improved pressing, sintering, porosity, density and permeability relationship have been drawn. A sintering method to obtain better sintered density and high permeability in ferrites is also explained.


title: Meditation-induced neuroplastic changes in amygdala activity during negative affective processing

abstract: ABSTRACTRecent evidence suggests that the effects of meditation practice on affective processing and resilience have the potential to induce neuroplastic changes within the amygdala. Notably, literature speculates that meditation training may reduce amygdala activity during negative affective processing. Nonetheless, studies have thus far not verified this speculation. In this longitudinal study, participants (N = 21, 9 men) were trained in awareness-based compassion meditation (ABCM) or matched relaxation training. The effects of meditation training on amygdala activity were examined during passive viewing of affective and neutral stimuli in a non-meditative state. We found that the ABCM group exhibited significantly reduced anxiety and right amygdala activity during negative emotion processing than the relaxation group. Furthermore, ABCM participants who performed more compassion practice had stronger right amygdala activity reduction during negative emotion processing. The lower right amygdala activity...


title: TWIICE — A lightweight lower-limb exoskeleton for complete paraplegics

abstract: This paper introduces TWIICE, a lower-limb exoskeleton that enables people suffering from complete paraplegia to stand up and walk again. TWIICE provides complete mobilization of the lower-limbs, which is a first step toward enabling the user to regain independence in activities of the daily living. The tasks it can perform include level and inclined walking (up to 20° slope), stairs ascent and descent, sitting on a seat, and standing up. Participation in the world's first Cybathlon (Zurich, 2016) demonstrated good performance at these demanding tasks. In this paper, we describe the implementation details of the device and comment on preliminary results from a single user case study.


title: Neural embedding-based indices for semantic search

abstract: Abstract Traditional information retrieval techniques that primarily rely on keyword-based linking of the query and document spaces face challenges such as the vocabulary mismatch problem where relevant documents to a given query might not be retrieved simply due to the use of different terminology for describing the same concepts. As such, semantic search techniques aim to address such limitations of keyword-based retrieval models by incorporating semantic information from standard knowledge bases such as Freebase and DBpedia. The literature has already shown that while the sole consideration of semantic information might not lead to improved retrieval performance over keyword-based search, their consideration enables the retrieval of a set of relevant documents that cannot be retrieved by keyword-based methods. As such, building indices that store and provide access to semantic information during the retrieval process is important. While the process for building and querying keyword-based indices is quite well understood, the incorporation of semantic information within search indices is still an open challenge. Existing work have proposed to build one unified index encompassing both textual and semantic information or to build separate yet integrated indices for each information type but they face limitations such as increased query process time. In this paper, we propose to use neural embeddings-based representations of term, semantic entity, semantic type and documents within the same embedding space to facilitate the development of a unified search index that would consist of these four information types. We perform experiments on standard and widely used document collections including Clueweb09-B and Robust04 to evaluate our proposed indexing strategy from both effectiveness and efficiency perspectives. Based on our experiments, we find that when neural embeddings are used to build inverted indices; hence relaxing the requirement to explicitly observe the posting list key in the indexed document: (a) retrieval efficiency will increase compared to a standard inverted index, hence reduces the index size and query processing time, and (b) while retrieval efficiency, which is the main objective of an efficient indexing mechanism improves using our proposed method, retrieval effectiveness also retains competitive performance compared to the baseline in terms of retrieving a reasonable number of relevant documents from the indexed corpus.


title: Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness

abstract: Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems. However, recent research on adversarial examples poses enormous challenges on the robustness of SER systems by showing the susceptibility of deep neural networks to adversarial examples as they rely only on small and imperceptible perturbations. In this study, we evaluate how adversarial examples can be used to attack SER systems and propose the first black-box adversarial attack on SER systems. We also explore potential defenses including adversarial training and generative adversarial network (GAN) to enhance robustness. Experimental evaluations suggest various interesting aspects of the effective utilization of adversarial examples useful for achieving robustness for SER systems opening up opportunities for researchers to further innovate in this space.


title: Quantitative Graph Theory: A new branch of graph theory and in network science

abstract: In this paper, we describe some highlights of the new branch quantitative graph theory and explain its significant different features compared to classical graph theory. The main goal of quantitative graph theory is the structural quantification of information contained in complex networks by employing a measurement approach based on numerical invariants and comparisons. Furthermore, the methods as well as the networks do not need to be deterministic but can be statistic. As such this complements the field of classical graph theory, which is descriptive and deterministic in nature. We provide examples of how quantitative graph theory can be used for novel applications in the context of the overarching concept network science.


title: Language profiles in nonspeaking individuals of normal intelligence with severe cerebral palsy

abstract: Evidence is presented that verbal intelligence may be relatively superior to nonverbal intelligence in many nonspeaking individuals of normal intelligence with severe cerebral palsy and that unqualified generalizations about language abilities in this patient population should be avoided. Standardized tests of different levels of processing oral language (phonemic analysis, vocabulary knowledge, sentence syntax, discourse comprehension), of processing written language (word decoding out of sentence context and sentence and paragraph comprehension), and of producing written language (spelling single dictated words) were administered to a nonspeaking adult, adolescent, and child of normal intelligence with severe cerebral palsy. Intraindividual differences and common patterns across language profiles were examined. Not all subjects showed the same pattern within their language profiles, but all showed relatively better receptive oral language at the discourse level than any other level and were relatively b...


title: GRETA: Graph-Based Tag Assignment for GitHub Repositories

abstract: GitHub is a well-known software community where a large number of software repositories are hosted. Since large amounts of documents and code in GitHub repositories are in a mess, users cannot search or understand them efficiently. One solution is to employ a tag system, which annotates each repository with several tags. Thus, the GitHub repositories can be more efficiently accessed and understood. However, GitHub does not provide any automated tools of tagging repositories. In order to tackle this problem, we propose GRETA, a novel graph-based approach to assigning tags for repositories on GitHub. The core insight of GRETA is (1) to construct an Entity-Tag Graph (ETG) for GitHub using the domain knowledge from StackOverflow, and (2) to assign tags for repositories by taking a random walk algorithm. We have implemented GRETA and also developed a repository search engine for GitHub using the tag assignment results of GRETA. We have evaluated GRETA against several baseline methods to investigate its effectiveness of tagging GitHub repositories. The results show GRETA achieves up to 35% of F-Measure, outperforming the baseline methods. Besides, the GRETA-based search engine gains a higher NDCG value than the search engine provided by GitHub, indicating that it significantly enhances the search ability on GitHub with the tagged repositories.


title: Odds Ratio or Prevalence Ratio? An Overview of Reported Statistical Methods and Appropriateness of Interpretations in Cross-sectional Studies with Dichotomous Outcomes in Veterinary Medicine

abstract: One of the most commonly observational study designs employed in veterinary is the cross-sectional study with binary outcomes. To measure an association with exposure, the use of prevalence ratios (PR) or odds ratios (OR) are possible. In human epidemiology, much has been discussed about the use of the OR exclusively for case-control studies and some authors reported that there is no good justification for fitting logistic regression when the prevalence of the disease is high, in which OR overestimate the PR. Nonetheless, interpretation of OR is difficult since confusing between risk and odds can lead to incorrect quantitative interpretation of data such as "the risk is X times greater," commonly reported in studies that use OR. The aims of this study were (1) to review articles with cross-sectional designs to assess the statistical method used and the appropriateness of the interpretation of the estimated measure of association and (2) to illustrate the use of alternative statistical methods that estimate PR directly. An overview of statistical methods and its interpretation using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted and included a diverse set of peer-reviewed journals among the veterinary science field using PubMed as the search engine. From each article, the statistical method used and the appropriateness of the interpretation of the estimated measure of association were registered. Additionally, four alternative models for logistic regression that estimate directly PR were tested using our own dataset from a cross-sectional study on bovine viral diarrhea virus. The initial search strategy found 62 articles, in which 6 articles were excluded and therefore 56 studies were used for the overall analysis. The review showed that independent of the level of prevalence reported, 96% of articles employed logistic regression, thus estimating the OR. Results of the multivariate models indicated that logistic regression was the method that most overestimated the PR. The findings of this study indicate that although there are methods that directly estimate PR, many studies in veterinary science do not use these methods and misinterpret the OR estimated by the logistic regression.


Comparisons of Relational Databases with Big Data : a Teaching Approach


title: Localized strain sensing using high spatial resolution, highly-sensitive MEMS resonant strain gauges for failure prevention

abstract: Experimental measurements of the first-ever MEMS double ended tuning fork (DETF) resonant strain gauge, successfully bonded to steel by rapid inductive heating are presented in this paper. The localized strain-sensing results are compared with a finite element model. With a gauge length of just 200µm, the MEMS strain gauge enables higher spatial resolution and improved localized strain detection in comparison with commonly used metal-foil strain gauges, which average strain measurement over the entire metal-foil area. This sensing improvement enables the accurate detection of localized strains and may help improve crack propagation detection, and prevent catastrophic failures in structural elements.


title: Memristor-based IMPLY logic design procedure

abstract: Memristors can be used as logic gates. No design methodology exists, however, for memristor-based combinatorial logic. In this paper, the design and behavior of a memristive-based logic gate - an IMPLY gate - are presented and design issues such as the tradeoff between speed (fast write times) and correct logic behavior are described, as part of an overall design methodology. A memristor model is described for determining the write time and state drift. It is shown that the widely used memristor model - a linear ion drift memristor - is impractical for characterizing an IMPLY logic gate, and a different memristor model is necessary such as a memristor with a current threshold.


Organizational innovation


title: Testing Adaptive Toolbox Models : A Bayesian Hierarchical Approach

abstract: Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.


title: Research on customer segmentation model by clustering

abstract: In the paper, we use credit card consumption data as our model-building samples and present a modeling framework for building segment-level predictive models that utilize pattern-based clustering approach and signature discovery techniques. We devise monetary matrix and fluctuate-rate matrix to study various modes. Through clustering on both matrixes, we uncover different customer characteristics. Utilizing these characteristics, we can build two-dimension Consumption-Based customer segmentation model.


title: The Karnofsky performance status scale re-examined: a cross-validation with the EORTC-C30

abstract: A cross-validation of the Karnofsky Performance Status (KPS) and quality of life (QOL) as measured by item 30 of the quality of life questionnaire developed by the European Organization for Research and Treatment of Cancer Study Group (EORTC QLQ-C30) was conducted using ordered logit analysis and prospective data from a continuous sample of 139 lung cancer patients. The QOL is found to be a much broader concept than the KPS, since it likely captures not only physical functioning but also functioning in the non-physical dimensions of social, emotional, and possibly cognitive well-being as well as the level of distress in the physical dimensions of pain, breathing and fatigue. These results suggest that the palliative treatment of advanced cancer and the terminally ill should be guided by a broad concept of well-being that goes beyond one based only on physical functioning.


title: Developing University Ontology using protégé OWL Tool: Process and Reasoning

abstract: The current web is based on html which can display information simply. Researchers are working towards the semantic web which is an intelligent and meaningful web proposed by Tim burner's lee. Ontology and ontology based application are its basic ingredients. With the ontology we can focus on only main concepts and its relationship rather than information. Protege is a most popular tool for ontology editing and for developing ontology (1). It has a GUI which enables ontology developers to concentrate on conceptual terms without thinking about syntax of an output language. Protege has flexible knowledge model and extensible plug-in architecture. This paper explains the terms of university through university ontology. We will focus on creating an university ontology using protege. Rajiv Gandhi Technical University Bhopal, India has been taken an example for the ontology development and various aspects like: super class and subclass hierarchy, creating a subclass instances for class illustration, query retrieval process visualization view and graph view have been demonstrated.


title: The business model dilemma of technology shifts

abstract: Technology shifts are lethal to many manufacturing companies. Previous research indicates that this is not purely a problem of technological innovation, but is also closely related to the inertia of business models and business model innovation. This paper inquires into the dynamics of this intersection between technology and business models. Anchored in a case study in the automotive industry, it reveals how a potential technology shift constitutes a business model dilemma for firms leading in the existing technology. The paper illustrates why technology shifts are so difficult to master and contributes to theory by suggesting that managing technology shifts does not require either technology or service innovation in order to create a viable business model, but instead a compound of both. Furthermore, the paper applies a business model perspective to illustrate the explanatory power of analyzing the challenges of technology shifts faced by incumbent firms.


title: Multi-View 3D Object Retrieval With Deep Embedding Network

abstract: In multi-view 3D object retrieval, each object is characterized by a group of 2D images captured from different views. Rather than using hand-crafted features, in this paper, we take advantage of the strong discriminative power of convolutional neural network to learn an effective 3D object representation tailored for this retrieval task. Specifically, we propose a deep embedding network jointly supervised by classification loss and triplet loss to map the high-dimensional image space into a low-dimensional feature space, where the Euclidean distance of features directly corresponds to the semantic similarity of images. By effectively reducing the intra-class variations while increasing the inter-class ones of the input images, the network guarantees that similar images are closer than dissimilar ones in the learned feature space. Besides, we investigate the effectiveness of deep features extracted from different layers of the embedding network extensively and find that an efficient 3D object representation should be a tradeoff between global semantic information and discriminative local characteristics. Then, with the set of deep features extracted from different views, we can generate a comprehensive description for each 3D object and formulate the multi-view 3D object retrieval as a set-to-set matching problem. Extensive experiments on SHREC’15 data set demonstrate the superiority of our proposed method over the previous state-of-the-art approaches with over 12% performance improvement.


title: Text analytics in industry : Challenges , desiderata and trends

abstract: HighlightsState-of-the art review of industrial applications of text analytics (natural language processing, text mining).Identification of challenges, constraints of industrial text analytics applications.Formulation of desiderata for industrial text analytics applications.Discussion of future trends in industrial text analytics applications. The recent decades have witnessed an unprecedented expansion in the volume of unstructured data in digital textual formats. Companies are now starting to recognize the potential economic value lying untapped in their text data repositories and sources, including external ones, such as social media platforms, and internal ones, such as safety reports and other company-specific document collections. Information extracted from these textual data sources is valuable for a range of enterprise application and for informed decision making. In this article we provide a systematic review of the current state of the art in the application of text analytics in industry. Our review is structured along three dimensions: the application context, the methods and techniques utilized, and the evaluation procedure. Based on the review, we identify the different challenges and constraints that an real-world, industrial environment imposes on text analytics techniques, as opposed to their deployment in more controlled, research environments. In addition, we formulate a set of desiderata that text analytics techniques should satisfy in order to alleviate these challenges and to ensure their successful deployment in industry. Furthermore, we discuss future trends in text analytics and their potential application in industry.


title: Linear spatial pyramid matching using sparse coding for image classification

abstract: Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n2 ~ n3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algorithms to handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and propose a linear SPM kernel based on SIFT sparse codes. This new approach remarkably reduces the complexity of SVMs to O(n) in training and a constant in testing. In a number of image categorization experiments, we find that, in terms of classification accuracy, the suggested linear SPM based on sparse coding of SIFT descriptors always significantly outperforms the linear SPM kernel on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors.


title: An exact algorithm for the fixed-charge multiple knapsack problem

abstract: We formulate the fixed-charge multiple knapsack problem (FCMKP) as an extension of the multiple knapsack problem (MKP). The Lagrangian relaxation problem is easily solved, and together with a greedy heuristic we obtain a pair of upper and lower bounds quickly. We make use of these bounds in the pegging test to reduce the problem size. We also present a branch-and-bound (B&B) algorithm to solve FCMKP to optimality. This algorithm exploits the Lagrangian upper bound as well as the pegging result for pruning, and at each terminal subproblem solve MKP exactly by invoking MULKNAP code developed by Pisinger [Pisinger, D., 1999. An exact algorithm for large multiple knapsack problems. European Journal of Operational Research 114, 528-541]. As a result, we are able to solve almost all test problems with up to 32,000 items and 50 knapsacks within a few seconds on an ordinary computing environment, although the algorithm remains some weakness for small instances with relatively many knapsacks.


title: The Creation and Analysis of a Website Privacy Policy Corpus

abstract: Website privacy policies are often ignored by Internet users, because these documents tend to be long and difficult to understand. However, the significance of privacy policies greatly exceeds the attention paid to them: these documents are binding legal agreements between website operators and their users, and their opaqueness is a challenge not only to Internet users but also to policy regulators. One proposed alternative to the status quo is to automate or semi-automate the extraction of salient details from privacy policy text, using a combination of crowdsourcing, natural language processing, and machine learning. However, there has been a relative dearth of datasets appropriate for identifying data practices in privacy policies. To remedy this problem, we introduce a corpus of 115 privacy policies (267K words) with manual annotations for 23K fine-grained data practices. We describe the process of using skilled annotators and a purpose-built annotation tool to produce the data. We provide findings based on a census of the annotations and show results toward automating the annotation procedure. Finally, we describe challenges and opportunities for the research community to use this corpus to advance research in both privacy and language technologies.


title: Image in Painting Techniques: A survey

abstract: Inpainting is the process of reconstructing lost or deteriorated part of images based on the background information. i. e. image Inpainting fills the missing or damaged region in an image utilizing spatial information of its neighbouring region. Inpainting algorithm have numerous applications. It is helpfully used for restoration of old films and object removal in digital photographs. It is also applied to red-eye correction, super resolution, compression etc. The main goal of the Inpainting algorithm is to modify the damaged region in an image in such a way that the inpainted region is undetectable to the ordinary observers who are not familiar with the original image. There have been several approaches proposed for the image inpainting. This proposed work presents a brief survey of different image inpainting techniques and comparative study of these techniques. In this paper we provide a review of different techniques used for image Inpainting. We discuss different inpainting techniques like image PDE based image inpainting, Exemplar based image inpainting, hybrid inpainting, and texture synthesis based image inpainting and semi-automatic and fast digital Inpainting.


title: Cascaded one-vs-rest detection network for fine-grained recognition without part annotations

abstract: Fine-grained recognition is a challenging task due to the small intra-category variances. Most of top-performing fine-grained recognition methods leverage parts of objects for better performance. Therefore, part annotations which are extremely computationally expensive are required. In this paper, we propose a novel cascaded deep CNN detection framework for fine-grained recognition which is trained to detect the whole object without considering parts. Nevertheless, most of current top-performing detection networks use the N+1 class (N object categories plus background) softmax loss, and the background category with much more training samples dominates the feature learning progress so that the features are not good for object categories with fewer samples. To bridge this gap, we introduce a cascaded structure to eliminate background and exploit a one-vs-rest loss to capture more minute variances among different subordinate categories. Experiments show that our proposed recognition framework achieves comparable performance with state-of-the-art, part-free, fine-grained recognition methods on the CUB-200-2011 Bird dataset. Moreover, our method even outperforms most of part-based methods while does not need part annotations at the training stage and is free from any annotations at test stage.


title: A smart lock system using Wi-Fi security

abstract: In large apartment complexes, fraternities, or even for an owner having many keys for each and every apartment, car, or gate he owns, maintaining entry to authorized personnel only is a problem. Besides the costs involved in fabrication, duplication, and distribution of keys, there are security problems in case of lost keys. In this paper an innovative lock system prototype using today's technologies will be presented. The novelty of this prototype relies on the fact that using new technologies along with old ones will result in a smart and more efficient. We propose a smart digital door lock system for any lock system. A digital door lock system is any equipment that uses the digital information such as a secret code instead of the legacy key system. In our proposed system, a Central Control module is embedded in the door itself, this is required to prevent additional complications and more robust mechanism for the door as a whole. Technically, this system embeds itself in the Local Area Network of the house. This adds extra security layers and prevents access to the system only through the network. Furthermore, the biggest advantage of the proposed system over existing ones is that it can be easily installed with minimal requirement of infrastructures and planning.


title: A unifying view of sparse approximate Gaussian process regression

abstract: We provide a new unifying view, including all existing proper probabilistic sparse approximations for Gaussian process regression. Our approach relies on expressing the effective prior which the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically justified ranking of the closeness of the known approximations to the corresponding full GPs. Finally we point directly to designs of new better sparse approximations, combining the best of the existing strategies, within attractive computational constraints.


Switched Reluctance Starter/Generator System Modeling Results


title: Thermal management and cooling of windings in electrical machines for electric vehicle and traction application

abstract: Conventional electrical machine cooling includes housing fins, shaft-mounted fan, and water jacket. In these cases, heat from the copper loss of windings needs to pass through either the stator core or the air between windings and the housing. Because of the large thermal resistance in the path, these methods are sometimes not efficient enough for high torque density machines. Overheat in windings causes failure in the insulation and damages the machine. Many technologies that facilitate winding cooling have been investigated in the literature, such as winding topologies with more efficient heat dissipation capability, impregnation material with high thermal conductivity, and advanced direct winding. This paper reviews and classifies thermal management and cooling methods to provide a guideline for high torque density electrical machine design for a better winding thermal management.


title: An overview of decoding techniques for large vocabulary continuous speech recognition

abstract: Abstract A number of decoding strategies for large vocabulary continuous speech recognition (LVCSR) are examined from the viewpoint of their search space representation. Different design solutions are compared with respect to the integration of linguistic and acoustic constraints, as implied by m -gram language models (LM) and cross-word (CW) phonetic contexts. This study is structured along two main axes: the network expansion and the search algorithm itself. The network can be expanded statically or dynamically while the search can proceed either time-synchronously or asynchronously which leads to distinct architectures. Three broad classes of decoding methods are briefly reviewed: the use of weighted finite state transducers (WFST) for static network expansion, the time-synchronous dynamic-expansion search and the asynchronous stack decoding. Heuristic methods for further reducing the search space are also considered. The main approaches are compared and some prospective views are formulated regarding possible future avenues.


title: Numerical integration using sparse grids

abstract: We present new and review existing algorithms for the numerical integration of multivariate functions defined over d-dimensional cubes using several variants of the sparse grid method first introduced by Smolyak [49]. In this approach, multivariate quadrature formulas are constructed using combinations of tensor products of suitable one-dimensional formulas. The computing cost is almost independent of the dimension of the problem if the function under consideration has bounded mixed derivatives. We suggest the usage of extended Gauss (Patterson) quadrature formulas as the one‐dimensional basis of the construction and show their superiority in comparison to previously used sparse grid approaches based on the trapezoidal, Clenshaw–Curtis and Gauss rules in several numerical experiments and applications. For the computation of path integrals further improvements can be obtained by combining generalized Smolyak quadrature with the Brownian bridge construction.


title: ORIGINAL ARTICLE Virtual Virgins and Vamps: The Effects of Exposure to Female Characters ’ Sexualized Appearance and Gaze in an Immersive Virtual Environment

abstract: This experiment exposed a sample of U.S. undergraduates (43 men, 40 women) to suggestively or conservatively clad virtual females who exhibited either responsive, high eye gaze or nonresponsive, low gaze in an immersive virtual environment. Outside the virtual world, men and women who encountered a highly stereotypical character—a suggestively clad, high gaze agent (“vamp”) or conservatively clad, low gaze character (“virgin”)—demonstrated more sexism and greater rape myth acceptance than participants who saw a suggestively clad nonresponsive or conservatively clad, responsive character. Results suggest that gender-stereotypical virtual females enhance negative attitudes toward women, whereas those that violate expectations and break stereotypes do not.


title: r Human Brain Mapping 33:1375–1383 (2012) r Exploring Commonalities Across Participants in the Neural Representation of Objects

abstract: r r Abstract: The question of whether the neural encodings of objects are similar across different people is one of the key questions in cognitive neuroscience. This article examines the commonalities in the in- ternal representation of objects, as measured with fMRI, across individuals in two complementary ways. First, we examine the commonalities in the internal representation of objects across people at the level of interobject distances, derived from whole brain fMRI data, and second, at the level of spatially localized anatomical brain regions that contain sufficient information for identification of object catego- ries, without making the assumption that their voxel patterns are spatially matched in a common space. We examine the commonalities in internal representation of objects on 3T fMRI data collected while participants viewed line drawings depicting various tools and dwellings. This exploratory study revealed the extent to which the representation of individual concepts, and their mutual similarity, is


title: A Dynamic Self-Structuring Neural Network

abstract: Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, the trial and error structuring method have several difficulties such as time and availability of experts. In this article, an algorithm that simplifies structuring neural network classification models has been proposed. The algorithm aims at creating a large enough structure to learn models from the training dataset that can be generalised well on the testing dataset. Our algorithm dynamically tunes the structure parameters during the training phase aiming to derive accurate non-overfitting classifiers. The proposed algorithm has been applied to phishing websites classification problem and it shows competitive results with respect to various evaluation measures such as Harmonic Mean (F1-score), precision, accuracy, etc.


title: Boosting Sensitivity of a Retinal Vessel Segmentation Algorithm with Convolutional Neural Network

abstract: Accurate vessel segmentation is a tough task for various medical images applications especially the segmentation of retinal images vessels. A computerised algorithm is required for analysing the progress of eye diseases. A variety of computerised retinal segmentation methods have been proposed but almost all methods to date show low sensitivity for narrowly low contrast vessels. We propose a new retinal vessel segmentation algorithm to address the issue of low sensitivity. The proposed method introduces a deep learning model along with pre-processing and post-processing. The pre-processing is used to handle the issue of uneven illuminations. We design a fully Convolutional Neural Network (CNN) and train it to get fine vessels observation.The post-processing step is used to remove the background noise pixels to achieve well-segmented vessels. The proposed segmentation method gives good segmented images especially for detecting tiny vessels. We evaluate our method on the commonly used publicly available databases: DRIVE and STARE databases. The higher sensitivity of 75% leads to proper detection of tiny vessels with an accuracy of 94.7%.


title: Career Trajectory Analysis of Information Technology Alumni: A LinkedIn Perspective

abstract: Understanding alumni's career path is critical for an Information Technology (IT) program to engage existing students, get connected to alumni and adapt the curriculum to the ever-changing technology field. LinkedIn, the largest professional networking site, provides a wealth of publicly available career outcomes of educations from higher education institutions. In the paper, we present an alumni database architecture that can extract the data from alumni LinkedIn profiles, transform and load the data in a database for analysis. We collected over 600 LinkedIn profiles of alumni who have graduated with Bachelor of Science (BS) degrees and Master of Science (MS) degrees from an IT program. We analyzed and compared the career trajectory of BSIT and MSIT graduates. Our findings show that graduates from both programs in general were moving towards managerial positions as their career progressed. However, BSIT and MSIT alumni showed some different career paths. BSIT graduates were developed into two different job titles: engineers and managers; while MSIT alumni were gradually growing into more managerial and business types of positions, such as managers and analysts


title: Psychological resilience, positive emotions, and successful adaptation to stress in later life.

abstract: In 3 studies, the authors investigated the functional role of psychological resilience and positive emotions in the stress process. Studies la and 1b explored naturally occurring daily stressors. Study 2 examined data from a sample of recently bereaved widows. Across studies, multilevel random coefficient modeling analyses revealed that the occurrence of daily positive emotions serves to moderate stress reactivity and mediate stress recovery. Findings also indicated that differences in psychological resilience accounted for meaningful variation in daily emotional responses to stress. Higher levels of trait resilience predicted a weaker association between positive and negative emotions, particularly on days characterized by heightened stress. Finally, findings indicated that over time, the experience of positive emotions functions to assist high-resilient individuals in their ability to recover effectively from daily stress. Implications for research into protective factors that serve to inhibit the scope, severity, and diffusion of daily stressors in later adulthood are discussed.


title: Fast Optimal Transport Averaging of Neuroimaging Data

abstract: Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before group linear averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of an entropic smoothing approach. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.


title: A knowledge-based decision system for healthcare diagnosis and advisory

abstract: In this paper, we present a knowledge-based decision system for healthcare. It not only performs intelligent diagnoses but also produces inferential advices for the interrelated diseases involving overweight or obese, diabetes, high blood pressure and high cholesterol conditions. Moreover, it performs deep diagnoses for the pregnant Asian women; for the unknown type of diabetes and for the risk of a heart attack and stroke increases. Also, it generates a risk report to remind patients to pay attention on their health. Our knowledge-based decision system provides the efficient and effective way to take care of the patient's health, to promote the human's quality of life and to provide disease monitoring and control to alleviate or to reduce the medical condition. In the long-term, this system will help us to reduce our medicare investments and to provide high quality healthy lives.


title: M2Onto: An Approach and a Tool to Learn OWL Ontology from MongoDB Database

abstract: Ontologies provide shared and reusable pieces of knowledge about a specific domain. Building an ontology by hand is a very hard and prone to errors task. Ontology learning from existing resources provides a good solution to this issue. Databases are widely used to store data. They were often considered as the most reliable sources for knowledge extraction. NOSQL databases are more and more used to store data. MongoDB database is emerging as the fastest growing NOSQL database in the world. It belongs to the document oriented databases variant. This paper proposes an approach to learn OWL ontology from data in MongoDB database and describes a tool implementing transformation rules.


title: GRAM: Graph-based Attention Model for Healthcare Representation Learning

abstract: Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: - Data insufficiency: Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results. Interpretation: The representations learned by deep learning methods should align with medical knowledge. To address these challenges, we propose GRaph-based Attention Model (GRAM) that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism. We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.


title: IBBE-SGX: Cryptographic Group Access Control using Trusted Execution Environments

abstract: While many cloud storage systems allow users to protect their data by making use of encryption, only few support collaborative editing on that data. A major challenge for enabling such collaboration is the need to enforce cryptographic access control policies in a secure and efficient manner. In this paper, we introduce IBBE-SGX, a new cryptographic access control extension that is efficient both in terms of computation and storage even when processing large and dynamic workloads of membership operations, while at the same time offering zero knowledge guarantees. IBBE-SGX builds upon Identity-Based Broadcasting Encryption (IBBE). We address IBBE's impracticality for cloud deployments by exploiting Intel Software Guard Extensions (SGX) to derive cuts in the computational complexity. Moreover, we propose a group partitioning mechanism such that the computational cost of membership update is bound to a fixed constant partition size rather than the size of the whole group. We have implemented and evaluated our new access control extension. Results highlight that IBBE-SGX performs membership changes 1.2 orders of magnitude faster than the traditional approach of Hybrid Encryption (HE), producing group metadata that are 6 orders of magnitude smaller than HE, while at the same time offering zero knowledge guarantees.


title: Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution

abstract: This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life images (from a novel computational imager) confirm the robustness and effectiveness of the proposed method.


title: A Probabilistic Co-Bootstrapping Method for Entity Set Expansion

abstract: Entity Set Expansion (ESE) aims at automatically acquiring instances of a specific target category. Unfortunately, traditional ESE methods usually have the expansion boundary problem and the semantic drift problem. To resolve the above two problems, this paper proposes a probabilistic Co-Bootstrapping method, which can accurately determine the expansion boundary using both the positive and the discriminant negative instances, and resolve the semantic drift problem by effectively maintaining and refining the expansion boundary during bootstrapping iterations. Experimental results show that our method can achieve a competitive performance.


title: On-Manifold Preintegration for Real-Time Visual-Inertial Odometry

abstract: Current approaches for visual--inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time; this problem is further emphasized by the fact that inertial measurements come at high rate, hence, leading to the fast growth of the number of variables in the optimization. In this paper, we address this issue by preintegrating inertial measurements between selected keyframes into single relative motion constraints. Our first contribution is a preintegration theory that properly addresses the manifold structure of the rotation group. We formally discuss the generative measurement model as well as the nature of the rotation noise and derive the expression for the maximum a posteriori state estimator. Our theoretical development enables the computation of all necessary Jacobians for the optimization and a posteriori bias correction in analytic form. The second contribution is to show that the preintegrated inertial measurement unit model can be seamlessly integrated into a visual--inertial pipeline under the unifying framework of factor graphs. This enables the application of incremental-smoothing algorithms and the use of a structureless model for visual measurements, which avoids optimizing over the 3-D points, further accelerating the computation. We perform an extensive evaluation of our monocular VIO pipeline on real and simulated datasets. The results confirm that our modeling effort leads to an accurate state estimation in real time, outperforming state-of-the-art approaches.


title: Botnet spam campaigns can be long lasting: evidence, implications, and analysis

abstract: Accurately identifying spam campaigns launched by a large number of bots in a botnet allows for accurate spam campaign signature generation and hence is critical to defeating spamming botnets. The straight-forward approach of clustering all spam containing the same label such as an URL into a campaign can be easily defeated by techniques such as simple obfuscations of URLs. In this paper, we perform a comprehensive study of content-agnostic characteristics of spam campaigns, e.g. duration and source-network distribution of spammers, in order to ascertain whether and how they can assist the simple label-based clustering methods in identifying campaigns and generating campaign signatures. In particular, from a five-month trace collected by a relay sinkhole, we manually identified and then analyzed seven URL-based botnet spam campaigns consisting of 52 million spam messages sent over 2.09 million SMTP connections originated from over 150,000 non-proxy spamming hosts and destined to about 200,000 end domains. Our analysis shows that the spam campaigns, when observed from large destination domains, exhibit durations far longer than the five-day period as reported in a recent study. We analyze the implications of this finding on spam campaign signature generation. We further study other characteristics of these long-lasting campaigns. Our analysis reveals several new findings regarding workload distribution, sending patterns, and coordination among the spamming machines.


title: Style-Markers in Authorship Attribution A Cross-Language Study of the Authorial Fingerprint

abstract: The present study addresses one of the theoretical problems of computer-assisted authorship attribution, namely the question which traceable features of language can betray authorial uniqueness (a stylistic fingerprint) of literary texts. A number of recen


title: Thread-Specific Storage for C/C++ -- An Object Behavioral Pattern for . . .

abstract: In theory, multi-threading an application can improve performance (by executing multiple instruction streams simultaneously) and simplify program structure (by allowing each thread to execute synchronously rather than reactively or asynchronously). In practice, multi-threaded applications often perform no better, or even worse, than single-threaded applications due to the overhead of acquiring and releasing locks. In addition, multi-threaded applications are hard to program due to the complex concurrency control protocols required to avoid race conditions and deadlocks. This paper describes the Thread-Specific Storage pattern, which alleviates several problems with multi-threading performance and programming complexity. The Thread-Specific Storage pattern improves performance and simplifies multithreaded applications by allowing multiple threads to use one logically global access point to retrieve thread-specific data without incurring locking overhead for each access.


title: The Neuropathology and Neurobiology of Traumatic Brain Injury

abstract: The acute and long-term consequences of traumatic brain injury (TBI) have received increased attention in recent years. In this Review, we discuss the neuropathology and neural mechanisms associated with TBI, drawing on findings from sports-induced TBI in athletes, in whom acute TBI damages axons and elicits both regenerative and degenerative tissue responses in the brain and in whom repeated concussions may initiate a long-term neurodegenerative process called dementia pugilistica or chronic traumatic encephalopathy (CTE). We also consider how the neuropathology and neurobiology of CTE in many ways resembles other neurodegenerative illnesses such as Alzheimer's disease, particularly with respect to mismetabolism and aggregation of tau, β-amyloid, and TDP-43. Finally, we explore how translational research in animal models of acceleration/deceleration types of injury relevant for concussion together with clinical studies employing imaging and biochemical markers may further elucidate the neurobiology of TBI and CTE.


title: The risk of misinterpreting genital signs of sexual abuse in cadavers: a case report

abstract: The significance of genital findings in a case of suspected child sexual abuse has been widely debated in the past decades, as shown by the different classifications available in literature. In the case of postmortem examination, the search for signs of sexual abuse is considerably more difficult because of the superimposition of postmortem modifications, which may determine tissue modifications that can be mistaken for traumatic lesions. This study aims at reporting a case where presumed findings of the first autopsy were denied by histological analysis; in detail, what looked like a possible bruise of the hymen was correctly recognized as hypostasis (livor) of the hymenal tissue by histological analysis. This case report suggests caution in the analysis and discussion of genital lesions found during postmortem examination since the superimposition of cadaveric modifications may radically modify the morphology of soft tissues.


title: A Fast Implementation of Parallel Discrete-Event Simulation on GPGPU

abstract: Modern General Purpose Graphics Processing Units(GPGPUs) offer much more computational power than recent CPUs by providing a vast number of simple, data parallel, multithreaded cores. In this study, we focus on the use of a GPGPU to perform parallel discrete-event simulation. Our approach is to use a modified service time distribution function to allow more independent events to be processed in parallel. The implementation issues and alternative strategies will be discussed in detail. We use Thrust, an open-source parallel algorithms library which resembles the C++ Standard Template Library (STL), to build our tool. The experimental results show that our implementation can be more than 60 times faster than the sequential simulation. Furthermore, the speedup curve scales well which indicates that our implementation is suitable for large-scale discreteevent simulation models.


title: Gender Differences in Internet Addiction Associated with Psychological Health Indicators Among Adolescents Using a National Web-based Survey

abstract: Internet addiction, especially its prevalence among adolescents and its predictors, has been the focus of much research. Few studies have investigated gender differences in the relationship between Internet addiction and psychological health among adolescents. The present study investigated gender differences in Internet addiction associated with self-rated health, subjective happiness, and depressive symptoms among Korean adolescents aged 12 to 18 years using a nationally representative dataset. Data from 56,086 students (28,712 boys and 27,374 girls) from 400 middle schools and 400 high schools were analyzed. We found that 2.8 % of the students (3.6 % boys and 1.9 % girls) were addicted users, and the prevalence of Internet addiction was higher in boys than in girls. In multiple logistic regression analysis, three psychological health indicators including poor self-rated health, subjective unhappiness, and depressive symptoms were significantly related with Internet addiction in boys and girls. Girls with emotional difficulties such as subjective unhappiness or depressive symptoms had much higher risks of Internet addiction than did boys with similar problems. Further attention should be given to developing Internet addiction prevention and intervention programs that are tailored to fit boys’ and girls’ different needs.


Parallel Distributed Processing


title: Air-filled substrate integrated waveguide — A flexible and low loss technological platform

abstract: In this paper, the air-filled substrate integrated waveguide (AFSIW), offering a technological platform for the design of high performance millimeterwave self-packaged substrate integrated components and systems, is presented. AFSIW is based on low-cost printed circuit board (PCB). Several components, including antennas, filters and couplers, have been theoretically studied and experimentally demonstrated up to 60 GHz. This technology, surpassing its dielectric-filled SIW (DFSIW) counterpart, is expected by the authors to replace the rectangular waveguide technology for future low-cost extremely high data rate communication systems and highly sensitive sensors operating at millimeterwave and beyond.


title: Why Do Individuals Exhibit Investment Biases? ∗

abstract: Where do behavioral biases aecting investment decisions come from? We nd that a signicant proportion (about 20 to 56%) of investment biases such as home bias, loss aversion, performance chasing as well as trading intensity is attributable to genetic factors as opposed to the environment. We nd that higher socioeconomic status, measured by net worth, reduces the importance of a genetic predisposition, while education does not seem to moderate the inuence of genes. Consistent with the importance of underlying genetic factors, we nd that the correlation between familiarity expressed in nancial portfolios and in home location choices is essentially genetic. Our evidence contributes to a deeper understanding of investment biases, with potential implications for public policy.


title: Planar inverted-F antenna and quarter wavelength slot antenna

abstract: An approach of reducing the size of slot antennas has been proposed, which is based on the concept of “open circuit plane” by removing half of the slot antennas. Several examples have been examined to demonstrate the effectiveness of size reduction for slot antennas.


title: Deep Learning of Vortex Induced Vibrations

abstract: Vortex-induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier–Stokes equations coupled with the structure’s dynamic motion equation. In the first case, given scattered data in space–time on the velocity field and the structure’s motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure’s dynamic motion. In the second case, given scattered data in space–time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.


title: Impact of Soil Salinity on the Relation Between Soil Moisture and Dielectric Permittivity

abstract: Soil complex dielectric permittivity spectrum depends mainly on soil volumetric water content and salinity, but other soil properties such as temperature, density and clay content can also significantly impact its dielectric properties. Especially, the influence of soil salinity and texture can have significant impact on the accuracy of moisture determination with the use of soil moisture-dielectric permittivity calibration curves. The paper deals with the impact of soil salinity on the real part of complex dielectric permittivity of soils of various texture. Soil samples of various moisture content and salinity were measured with the use of a coaxial transmission-line cell connected to a one-port vector-network-analyzer in the 0.01–3 GHz frequency range. The relations between soil moisture and the real part of dielectric permittivity at frequencies of 30, 70, 150, 200, 500 and 1000 MHz were examined and the frequency bands with the smallest impact of soil salinity and particle-size distribution were obtained. For the tested soils it occurred that the optimal frequency bands were located between 150 and 500 MHz.


title: Digital Audio Restoration

abstract: This chapter is concerned with the application of modern signal processing techniques to the restoration of degraded audio signals. Although attention is focussed on gramophone recordings, film sound tracks and tape recordings, many of the techniques discussed have applications in other areas where degraded audio signals occur, such as speech transmission, telephony and hearing aids.


title: Visual Servoing from Deep Neural Networks

abstract: We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the same scene. The output of the network is then employed in a visual servoing control scheme. The method converges robustly even in difficult real-world settings with strong lighting variations and occlusions.A positioning error of less than one millimeter is obtained in experiments with a 6 DOF robot.


title: Effect of comfort pads and incubator design on neonatal radiography

abstract: Background ::: There has been increasing interest in patient dose reduction in neonatal intensive care units. Removing comfort pads for radiography has been identified as a potential means to decrease patient dose.


title: A Dual-Notch +27dBm Tx-Power Electrical-Balance Duplexer

abstract: An electrical-balance duplexer uses series connected step-down transformers to enhance linearity and power handling capability by reducing the voltage swing across nonlinear components. Wideband, dual-notch Tx-to-Rx isolation is demonstrated experimentally with a planar inverted-F antenna. The 0.18μm CMOS prototype achieves >50dB isolation for 220MHz aggregated bandwidth or >40dB dual-notch isolation for 160MHz bandwidth, +49dBm Tx-path IIP3 and -48dBc ACLR1 for +27dBm at the antenna.


title: Context-theoretic Semantics for Natural Language: an Overview

abstract: We present the context-theoretic framework, which provides a set of rules for the nature of composition of meaning based on the philosophy of meaning as context. Principally, in the framework the composition of the meaning of words can be represented as multiplication of their representative vectors, where multiplication is distributive with respect to the vector space. ::: ::: We discuss the applicability of the framework to a range of techniques in natural language processing, including subsequence matching, the lexical entailment model of Dagan et al. (2005), vector-based representations of taxonomies, statistical parsing and the representation of uncertainty in logical semantics.


title: Web Services

abstract: Like many other incipient technologies, Web services are still surrounded by a substantial level of noise. This noise results from the always dangerous combination of wishful thinking on the part of research and industry and of a lack of clear understanding of how Web services came to be. On the one hand, multiple contradictory interpretations are created by the many attempts to realign existing technology and strategies with Web services. On the other hand, the emphasis on what could be done with Web services in the future often makes us lose track of what can be really done with Web services today and in the short term. These factors make it extremely difficult to get a coherent picture of what Web services are, what they contribute, and where they will be applied.Alonso and his co-authors deliberately take a step back. Based on their academic and industrial experience with middleware and enterprise application integration systems, they describe the fundamental concepts behind the notion of Web services and present them as the natural evolution of conventional middleware, necessary to meet the challenges of the Web and of B2B application integration. Rather than providing a reference guide or a "how to write your first Web service" kind of book, they discuss the main objectives of Web services, the challenges that must be faced to achieve them, and the opportunities that this novel technology provides. Established, as well as recently proposed, standards and techniques (e.g., WSDL, UDDI, SOAP, WS-Coordination, WS-Transactions, and BPEL), are then examined in the context of this discussion in order to emphasize their scope, benefits, and shortcomings. Thus, the book is ideally suited both for professionals considering the development of application integration solutions and for research and students interesting in understanding and contributing to the evolution of enterprise application technologies.


Identification of Non-linear Structures Using Recurrent Neural Networks


title: A New Golden Age in Computer Architecture: Empowering the Machine-Learning Revolution

abstract: The end of Moores law and Dennard scaling has led to the end of rapid improvement in general-purpose program performance. Machine learning (ML), and in particular deep learning, is an attractive alternative for architects to explore. It has recently revolutionized vision, speech, language understanding, and many other fields, and it promises to help with the grand challenges facing our society. The computation at its core is low-precision linear algebra. Thus, ML is both broad enough to apply to many domains and narrow enough to benefit from domain-specific architectures, such as Googles Tensor Processing Unit (TPU). Moreover, the growth in demand for ML computing exceeds Moores law at its peak, just as it is fading. Hence, ML experts and computer architects must work together to design the computing systems required to deliver on the potential of ML. This article offers motivation, suggestions, and warnings to computer architects on how to best contribute to the ML revolution.


title: Evaluating CoBlox: A Comparative Study of Robotics Programming Environments for Adult Novices

abstract: A new wave of collaborative robots designed to work alongside humans is bringing the automation historically seen in large-scale industrial settings to new, diverse contexts. However, the ability to program these machines often requires years of training, making them inaccessible or impractical for many. This project rethinks what robot programming interfaces could be in order to make them accessible and intuitive for adult novice programmers. We created a block-based interface for programming a one-armed industrial robot and conducted a study with 67 adult novices comparing it to two programming approaches in widespread use in industry. The results show participants using the block-based interface successfully implemented robot programs faster with no loss in accuracy while reporting higher scores for usability, learnability, and overall satisfaction. The contribution of this work is showing the potential for using block-based programming to make powerful technologies accessible to a wider audience.


title: The structure and dynamics of multilayer networks

abstract: In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.


title: Crater detection algorithm with part PHOG features for safe landing

abstract: Crater detection from images is a challenging problem due to variations in the geometry shape, illumination, and scale. An algorithm with part based features to automatically detecting craters on landing surfaces is presented in this paper. It is build up a coarse-to-precise approach by learning pyramid histogram of oriented gradient features (PHOG) with part based crescent like structure, whose simplicity combined with an original learning strategy leads to a fast and high accuracy detect results. The approach is verified with images data sets from Mars captured by NASA.


title: Detecting similar repositories on GitHub

abstract: GitHub contains millions of repositories among which many are similar with one another (i.e., having similar source codes or implementing similar functionalities). Finding similar repositories on GitHub can be helpful for software engineers as it can help them reuse source code, build prototypes, identify alternative implementations, explore related projects, find projects to contribute to, and discover code theft and plagiarism. Previous studies have proposed techniques to detect similar applications by analyzing API usage patterns and software tags. However, these prior studies either only make use of a limited source of information or use information not available for projects on GitHub. In this paper, we propose a novel approach that can effectively detect similar repositories on GitHub. Our approach is designed based on three heuristics leveraging two data sources (i.e., GitHub stars and readme files) which are not considered in previous works. The three heuristics are: repositories whose readme files contain similar contents are likely to be similar with one another, repositories starred by users of similar interests are likely to be similar, and repositories starred together within a short period of time by the same user are likely to be similar. Based on these three heuristics, we compute three relevance scores (i.e., readme-based relevance, stargazer-based relevance, and time-based relevance) to assess the similarity between two repositories. By integrating the three relevance scores, we build a recommendation system called RepoPal to detect similar repositories. We compare RepoPal to a prior state-of-the-art approach CLAN using one thousand Java repositories on GitHub. Our empirical evaluation demonstrates that RepoPal achieves a higher success rate, precision and confidence over CLAN.


title: CHESS—Calibrating the Hand-Eye Matrix With Screw Constraints and Synchronization

abstract: Hand–eye calibration is a classic problem in robotics that aims to find the transformation between two rigidly attached reference frames, usually a camera and a robot end-effector or a motion tracker. Most hand–eye calibration techniques require two data streams, one containing the eye (camera) motion and the other containing the hand (robot/tracker) motion, and the classic hand–eye formulation assumes that both data streams are fully synchronized. However, different motion capturing devices and cameras often have variable capture rates and timestamps that cannot always be easily triggered in sync. Although probabilistic approaches have been proposed to solve for nonsynchronized data streams, they are not able to deal with different capture rates. We propose a new approach for unsynchronized hand–eye calibration that is able to deal with different capture rates and time delays. Our method interpolates and resamples the signal with the lowest capture rate in a way that is consistent with the twist motion constraints of the hand–eye problem. Cross-correlation techniques are then used to produce two fully synchronized data streams that can be used to solve the hand–eye problem with classic methods. In our experimental section, we show promising validation results on simulation data and also on real data obtained from a robotic arm holding a camera.


title: Corporate social responsibility as a source of employee satisfaction

abstract: Corporate social responsibility has received an increasing amount of attention from practitioners and scholars alike in recent years. However, very little is known about whether or how corporate social responsibility affects employees. Because employees are primary stakeholders who directly contribute to the success of the company, understanding employee reactions to corporate social responsibility may help answer lingering questions about the potential effects of corporate social responsibility on firms as well as illuminate some of the processes responsible for them. To begin our chapter, we provide a brief history of scholarship on corporate social responsibility and highlight some of the major challenges researchers in this area currently face. We then discuss why corporate social responsibility may represent a special opportunity to influence employees’ general impression of their company. Next, we identify four distinct paths through which corporate social responsibility may affect employees’ relationship with their company that correspond to four universal psychological needs: security, self-esteem, belongingness, and a meaningful existence. Finally, we offer an agenda for micro-level research on corporate social responsibility. # 2012 Elsevier Ltd. All rights reserved.


title: The Hidden Agenda User Simulation Model

abstract: A key advantage of taking a statistical approach to spoken dialogue systems is the ability to formalise dialogue policy design as a stochastic optimization problem. However, since dialogue policies are learnt by interactively exploring alternative dialogue paths, conventional static dialogue corpora cannot be used directly for training and instead, a user simulator is commonly used. This paper describes a novel statistical user model based on a compact stack-like state representation called a user agenda which allows state transitions to be modeled as sequences of push- and pop-operations and elegantly encodes the dialogue history from a user's point of view. An expectation-maximisation based algorithm is presented which models the observable user output in terms of a sequence of hidden states and thereby allows the model to be trained on a corpus of minimally annotated data. Experimental results with a real-world dialogue system demonstrate that the trained user model can be successfully used to optimise a dialogue policy which outperforms a hand-crafted baseline in terms of task completion rates and user satisfaction scores.


title: Compact Wideband Circularly Polarized Patch Antenna for CNSS Applications

abstract: A compact wideband circularly polarized (CP) patch antenna utilizing a quad-feed network (QFN) and quadruple semi-fan-annulus (QSFA) patches is proposed. For using a coupled-line dual-band power divider (PD) and improved phase shifter (PS) with stepped-impedance-open-stub (SIOS), the QFN has a compact size and exhibits the electromagnetic (EM) simulated bandwidth over 96% for the power distribution -6.5±0.5 dB, return loss (RL) > 14 dB, and a consistent 90° (±9°) phase deviation. Moreover, the QSFA patches can expand the CP bandwidth and reduce the size of the antenna effectively. Measurement results show that the proposed antenna achieves CP bandwidth of 72% from 1.15 to 2.45 GHz for RL > 10 dB, axial ratio dB, and 3-dB gain variation (gain > 4.4 dBi). Therefore, the proposed antenna is a good candidate for Compass Navigation Satellite System (CNSS) applications.


title: Performance Study of Class-E Power Amplifier With a Shunt Inductor at Subnominal Condition

abstract: This paper presents analytical expressions for the class-E power amplifier with a shunt inductor for satisfying the subnominal condition and 50% duty ratio. The subnominal condition means that only the zero-current switching condition (ZCS) is achieved, though the nominal conditions mean that both the ZCS and zero-current derivative switching (ZCDS) are satisfied. The design values for achieving the subnominal condition are expressed as a function of the phase shift between the input and output voltages. The class-E amplifier with subnominal condition increases one design degree of freedom compared with that with the nominal conditions. Because of the increase in the design degree of freedom, one more relationship can be specified as a design specification. It is shown analytically that the dc-supply voltage and the current are always proportional to the amplitude of the output voltage and the current as a function of the phase shift. Additionally, the output power capability is affected by the phase shift, and the peak switch voltage has influence on the phase shift as well. This paper gives a circuit design example based on our proposed design expression by specifying the peak switch voltage instead of the ZCDS condition. The measurement and PSpice simulation results agree with the analytical expressions quantitatively, which show the validity of our analytical expressions.


title: Natural signal statistics and sensory gain control

abstract: We describe a form of nonlinear decomposition that is well-suited for efficient encoding of natural signals. Signals are initially decomposed using a bank of linear filters. Each filter response is then rectified and divided by a weighted sum of rectified responses of neighboring filters. We show that this decomposition, with parameters optimized for the statistics of a generic ensemble of natural images or sounds, provides a good characterization of the nonlinear response properties of typical neurons in primary visual cortex or auditory nerve, respectively. These results suggest that nonlinear response properties of sensory neurons are not an accident of biological implementation, but have an important functional role.


title: Measurement and parameter characterization of pseudo-resistor based CCIA for biomedical applications

abstract: This paper performs the detailed analysis and measurement of a pseudo-resistor based CCIA used for bio-potential signal acquisition. The pseudo-resistor is implemented with MOS transistors operating in deep sub-threshold region, whose characteristic is not well-modeled in simulation. The resistor value versus control voltage and the effect of leakage current at OPAMP input node are discussed and characterized in this paper. The tested chip consumes 1.3µA from a 1-V supply, and the total integrated noise from 0.5 to 100 Hz is 3.26µVrms. It achieves a noise efficiency factor of 14.6.


title: SmARTweet: A Location-Based Smart Application for Exhibits and Museums

abstract: In this paper, we present "Smartweed", a Locationbased application developed within DATABENC, a high technology district for Cultural Heritage management. In particular, the project aims at exploiting several location-based services and technologies to realize a smart multimedia guide system able to detect the closest artworks to an user, make them able to "tweet" and "talk" during tourists' visit and capable of automatically telling their stories using multimedia facilities. Moreover, we have deployed and tested the installation of some sensors that, using Wi-Fi technology, allow to the users' mobile devices to detect the closest artwork in a museum environment. Once an artwork has been detected, the related identifier is retrieved and a multimedia story is delivered to user by means of proper multimedia delivery and user-profiling techniques, in order to facilitate and make more stimulating the visit. The artworks detection was performed by a localization algorithm that we designed and tested in our laboratory rooms. As case of study, we show an example of "tweeting and talking artworks" as a location-based application of a sculptures' art exhibition within the Maschio Angioino castle, in Naples - Italy.


title: Variation in Long-Term Acute Care Hospital Use After Intensive Care

abstract: Long-term acute care hospitals (LTACs) are an increasingly common discharge destination for patients recovering from intensive care. In this article the authors use U.S. Medicare claims data to examine regional- and hospital-level variation in LTAC utilization after intensive care to determine factors associated with their use. Using hierarchical regression models to control for patient characteristics, this study found wide variation in LTAC utilization across hospitals, even controlling for LTAC access within a region. Several hospital characteristics were independently associated with increasing LTAC utilization, including increasing hospital size, for-profit ownership, academic teaching status, and colocation of the LTAC within an acute care hospital. These findings highlight the need for research into LTAC admission criteria and the incentives driving variation in LTAC utilization across hospitals.


title: A behavior model for persuasive design

abstract: This paper presents a new model for understanding human behavior. In this model (FBM), behavior is a product of three factors: motivation, ability, and triggers, each of which has subcomponents. The FBM asserts that for a person to perform a target behavior, he or she must (1) be sufficiently motivated, (2) have the ability to perform the behavior, and (3) be triggered to perform the behavior. These three factors must occur at the same moment, else the behavior will not happen. The FBM is useful in analysis and design of persuasive technologies. The FBM also helps teams work together efficiently because this model gives people a shared way of thinking about behavior change.


title: Validation of the Individualized Numeric Rating Scale (INRS): A pain assessment tool for nonverbal children with intellectual disability

abstract: Clinical observations suggest that nonverbal children with severe intellectual disability exhibit pain in a wide variety yet uniquely individual ways. Here, we investigate the feasibility and describe the initial psychometrics properties of the Individualized Numeric Rating Scale (INRS), a personalized pain assessment tool for nonverbal children with intellectual disability based on the parent’s knowledge of the child. Parents of 50 nonverbal children with severe intellectual disability scheduled for surgery were able to complete the task of describing then rank ordering their child’s usual and pain indicators. The parent, bedside nurse and research assistant (RA) triad then simultaneously yet independently scored the patient’s post-operative pain using the INRS for a maximum of two sets of pre/post paired observations. A total of 170 triad assessments were completed before (n = 85) and after (n = 85) an intervention to manage the child’s pain. INRS inter-rater agreement between the parents and research nurse was high (ICC 0.82–0.87) across all ratings. Parent and bedside nurse agreement (ICC 0.65–0.74) and bedside nurse and research nurse agreement (ICC 0.74–0.80) also suggest good reliability. A moderate to strong correlation (0.63–0.73) between INRS ratings and NCCPC-PV total scores provides evidence of convergent validity. These results provide preliminary data that the INRS is a valid and reliable tool for assessing pain in nonverbal children with severe intellectual disability in an acute care setting.


title: Flip-Chip Interconnection Technique for Beyond 100-Gb/s (4 × 25.8-Gb/s) EADFB Laser Array Transmitter

abstract: We developed a flip-chip interconnection technique that is suitable for an EADFB laser array transmitter operating at more than 100 Gb/s (4 × 25 Gb/s). The flip-chip interconnection technique provides a good thermal flow comparable to that of the conventional wire interconnection technique but with a higher modulation bandwidth and lower electrical crosstalk. For a flip-chip interconnection 112-Gb/s (4 × 27.9 Gb/s) module, the OTU4 mask margin is only 4% worse for simultaneous four-channel operation than for single-channel operation. For a flip-chip interconnection 400-Gb/s (8 × 50 Gb/s) module, we obtained clear eye openings for all eight lanes with simultaneous eight-channel operation.


title: On Subjective Well-being and Quality of Life

abstract: We integrate the multi-disciplinary fields of quality of life (QoL) and well-being (WB) and appraise the impacts of health factors. Theoretical and methodological limitations are discussed and new conceptual and technical advances identified, These are informed by cross-cultural and community perspectives. Following a definitional review, social inequalities, and links with happiness are examined. Demographic, experiential and personal factors are outlined. Implications for poverty research are addressed. As the concept of SWB recently converged with the longstanding international QoL definition (WHOQOL Group, 1995), we discuss the separate need for SWB. Future collaborative conceptual and pragmatic research is recommended.


title: A deep learning network for right ventricle segmentation in short-axis MRI

abstract: The segmentation of the right ventricle (RV) myocardium on MRI is a prerequisite step for the evaluation of RV structure and function, which is of great importance in the diagnose of most cardiac diseases, such as pulmonary hypertension, congenital heart disease, coronary heart disease, and dysplasia. However, RV segmentation is considered challenging, mainly because of the complex crescent shape of the RV across slices and phases. Hence this study aims to propose a new approach to segment RV endocardium and epicardium based on deep learning. The proposed method contains two subtasks: (1) localizing the region of interest (ROI), the biventricular region which contains more meaningful features and can facilitate the RV segmentation, and (2) segmenting the RV myocardium based on the localization. The two subtasks are integrated into a joint task learning framework, in which each task is solved via two multilayer convolutional neural networks. The experiments results show that the proposed method has big potential to be further researched and applied in clinical diagnosis.


title: The study of urban metabolism and its applications to urban planning and design

abstract: Following formative work in the 1970s, disappearance in the 1980s, and reemergence in the 1990s, a chronological review shows that the past decade has witnessed increasing interest in the study of urban metabolism. The review finds that there are two related, non-conflicting, schools of urban metabolism: one following Odum describes metabolism in terms of energy equivalents; while the second more broadly expresses a city's flows of water, materials and nutrients in terms of mass fluxes. Four example applications of urban metabolism studies are discussed: urban sustainability indicators; inputs to urban greenhouse gas emissions calculation; mathematical models of urban metabolism for policy analysis; and as a basis for sustainable urban design. Future directions include fuller integration of social, health and economic indicators into the urban metabolism framework, while tackling the great sustainability challenge of reconstructing cities.


title: Inquiry process in program development

abstract: This article describes a model for research and development (RD (b) for which the solution is unknown; the development or invention of a solution which requires (c) some time; in which (d) some errors and corrections are anticipated; and in which (e) the process and epistemology of science governs the outcome. We propose a model of R&D drawn by analogy from ecology, and illustrated with the experiences and results of a ten-year project. The model orders and clarifies the complex R&D enterprise, and identifies some conditions which are necessary if psychologists and social scientists are to responsibly advise public agencies.