title: Disentangled Person Image Generation

abstract: Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information. In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangled representation of the aforementioned image factors and generates novel person images at the same time. First, a multi-branched reconstruction network is proposed to disentangle and encode the three factors into embedding features, which are then combined to re-compose the input image itself. Second, three corresponding mapping functions are learned in an adversarial manner in order to map Gaussian noise to the learned embedding feature space, for each factor, respectively. Using the proposed framework, we can manipulate the foreground, background and pose of the input image, and also sample new embedding features to generate such targeted manipulations, that provide more control over the generation process. Experiments on the Market-1501 and Deepfashion datasets show that our model does not only generate realistic person images with new foregrounds, backgrounds and poses, but also manipulates the generated factors and interpolates the in-between states. Another set of experiments on Market-1501 shows that our model can also be beneficial for the person re-identification task1.


title: Deep speaker verification: Do we need end to end?

abstract: End-to-end learning treats the entire system as a whole adaptable black box, which, if sufficient data are available, may learn a system that works very well for the target task. This principle has recently been applied to several prototype research on speaker verification (SV), where the feature learning and classifier are learned together with an objective function that is consistent with the evaluation metric. An opposite approach to end-to-end is feature learning, which firstly trains a feature learning model, and then constructs a back-end classifier separately to perform SV. Recently, both approaches achieved significant performance gains on SV, mainly attributed to the smart utilization of deep neural networks. However, the two approaches have not been carefully compared, and their respective advantages have not been well discussed. In this paper, we compare the end-to-end and feature learning approaches on a text-independent SV task. Our experiments on a dataset sampled from the Fisher database and involving 5,000 speakers demonstrated that the feature learning approach outperformed the end-to-end approach. This is a strong support for the feature learning approach, at least with data and computation resources similar to ours.


title: Money talks? Competing discourses in the implementation of direct payments

abstract: The Community Care (Direct Payments) Act 1996 enables local authorities to make cash payments to service users with physical and sensory impairments and learning difficulties under the age of 65. This gives users control over money spent on meeting their community care needs, rather than receiving services arranged for them by the local authority. The policy is often represented as a victory for the disability movement and as a push towards user empowerment and social justice. However, direct payments also need to be understood as part of a wider market discourse prominent in the restructuring of welfare. Therefore, a growing culture of localized care markets with increasing ideological diversity may ultimately erode its scope for a meaningful level of user empowerment. By examining these market and social justice discourses, this article draws on analysis of two local authority approaches to direct payments and examines the level of meanings of control.


title: Feature-preserving procedural texture

abstract: This paper presents how to synthesize a texture in a procedural way that preserves the features of the input exemplar. The exemplar is analyzed in both spatial and frequency domains to be decomposed into feature and non-feature parts. Then, the non-feature parts are reproduced as a procedural noise, whereas the features are independently synthesized. They are combined to output a non-repetitive texture that also preserves the exemplar's features. The proposed method allows the user to control the extent of extracted features and also enables a texture to edited quite effectively.


title: An Integrated Semantic Web Service Discovery and Composition Framework

abstract: In this paper we present a theoretical analysis of graph-based service composition in terms of its dependency with service discovery. Driven by this analysis we define a composition framework by means of integration with fine-grained I/O service discovery that enables the generation of a graph-based composition which contains the set of services that are semantically relevant for an input-output request. The proposed framework also includes an optimal composition search algorithm to extract the best composition from the graph minimising the length and the number of services, and different graph optimisations to improve the scalability of the system. A practical implementation used for the empirical analysis is also provided. This analysis proves the scalability and flexibility of our proposal and provides insights on how integrated composition systems can be designed in order to achieve good performance in real scenarios for the web.


title: Green supply chain management: A state of the art review

abstract: Green supply chain management (GSCM) has become a topic of interest for both business leaders and academic researchers alike. Over the last decade or so, the GSCM literature has grown considerably. Although sufficient literature exists about various aspects and facets of GSCM, earlier works and reviews have a limited focus and narrow perspective. This paper presents a comprehensive integrated view of the published literature of GSCM so as to facilitate further study, practice and research. First, we introduce the different definitions of GSCM and its' development. Second, we focus on reviewing the research on GSCM's decisions and models. Finally, we discuss the limitations of existing research on GSCM and indicate future research opportunities.


title: Prebiotics and gut microbiota in chickens.

abstract: Prebiotics are non-digestible feed ingredients that are metabolized by specific members of intestinal microbiota and provide health benefits for the host. Fermentable oligosaccharides are best known prebiotics that have received increasing attention in poultry production. They act through diverse mechanisms, such as providing nutrients, preventing pathogen adhesion to host cells, interacting with host immune systems and affecting gut morphological structure, all presumably through modulation of intestinal microbiota. Currently, fructooligosaccharides, inulin and mannanoligosaccharides have shown promising results while other prebiotic candidates such as xylooligosaccharides are still at an early development stage. Despite a growing body of evidence reporting health benefits of prebiotics in chickens, very limited studies have been conducted to directly link health improvements to prebiotic-dependent changes in the gut microbiota. This article visits the current knowledge of the chicken gastrointestinal microbiota and reviews most recent publications related to the roles played by prebiotics in modulation of the gut microbiota and immune functions. Progress in this field will help us better understand how the gut microbiota contributes to poultry health and productivity, and support the development of new prebiotic products as an alternative to in-feed antibiotics.


title: Systematic review on the effectiveness of augmented reality applications in medical training

abstract: BACKGROUND ::: Computer-based applications are increasingly used to support the training of medical professionals. Augmented reality applications (ARAs) render an interactive virtual layer on top of reality. The use of ARAs is of real interest to medical education because they blend digital elements with the physical learning environment. This will result in new educational opportunities. The aim of this systematic review is to investigate to which extent augmented reality applications are currently used to validly support medical professionals training. ::: ::: ::: METHODS ::: PubMed, Embase, INSPEC and PsychInfo were searched using predefined inclusion criteria for relevant articles up to August 2015. All study types were considered eligible. Articles concerning AR applications used to train or educate medical professionals were evaluated. ::: ::: ::: RESULTS ::: Twenty-seven studies were found relevant, describing a total of seven augmented reality applications. Applications were assigned to three different categories. The first category is directed toward laparoscopic surgical training, the second category toward mixed reality training of neurosurgical procedures and the third category toward training echocardiography. Statistical pooling of data could not be performed due to heterogeneity of study designs. Face-, construct- and concurrent validity was proven for two applications directed at laparoscopic training, face- and construct validity for neurosurgical procedures and face-, content- and construct validity in echocardiography training. In the literature, none of the ARAs completed a full validation process for the purpose of use. ::: ::: ::: CONCLUSION ::: Augmented reality applications that support blended learning in medical training have gained public and scientific interest. In order to be of value, applications must be able to transfer information to the user. Although promising, the literature to date is lacking to support such evidence.


title: XiOS: Extended Application Sandboxing on iOS

abstract: Until very recently it was widely believed that iOS malware is effectively blocked by Apple's vetting process and application sandboxing. However, the newly presented severe malicious app attacks (e.g., Jekyll) succeeded to undermine these protection measures and steal private data, post Twitter messages, send SMS, and make phone calls. Currently, no effective defenses against these attacks are known for iOS. The main goal of this paper is to systematically analyze the recent attacks against iOS sandboxing and provide a practical security framework for iOS app hardening which is fully independent of the Apple's vetting process and particularly benefits enterprises to protect employees' iOS devices. The contribution of this paper is twofold: First, we show a new and generalized attack that significantly reduces the complexity of the recent attacks against iOS sandboxing. Second, we present the design and implementation of a novel and efficient iOS app hardening service, XiOS, that enables fine-grained application sandboxing, and mitigates the existing as well as our new attacks. In contrast to previous work in this domain (on iOS security), our approach does not require to jailbreak the device. We demonstrate the efficiency and effectiveness of XiOS by conducting several benchmarks as well as fine-grained policy enforcement on real-world iOS applications.


title: On the implementation of BRS codes in Ceph

abstract: Ceph is a reliable, scalable, unified distributed storage system, and recently has become one of OpenStack's standard open source storage solutions. For the reason of the low performance, Ceph Filesystem (Cephfs) cannot employ erasure codes directly, especially for binary Reed-Solomon (BRS) codes, whose size of the parity block is larger than that of the data block. To address these problems, we implemented efficient BRS codes, and filled the gap between BRS codes and Cephfs by a simple conversion. In addition to that, an efficient framework consisting of filesystem, cache tier and storage tier was adopted in Ceph to ensure that the file data is finally stored with the erasure coding technology. The experimental results show that such a design spends a small amount of additional cost but obtains much better effects.


title: Vib-bracelet: a passive absorber for attenuating forearm tremor

abstract: Tremor is a rhythmic, involuntary, oscillatory movement of a limb produced by alternating contractions of reciprocally innervated muscles. More than 4% of the population over 40 years old suffer from tremor. There is no cure for most tremors, and while psychological therapy is sometimes helpful, tremors are usually treated with either medication or invasive surgery including thalamotomy and deep brain stimulation. Both medications and surgery may have adverse effects, and thus, there is a growing interest in developing non-invasive vibration attenuation devices. This paper presents a passive absorber device for attenuating pronation/supination tremor, dubbed Vib-bracelet. It is based on the principles of dynamic vibration absorption and is tuned to the frequency of the tremor. Prototypes were manufactured and tested on a mechanical model of the human forearm. Simulations and experiments demonstrate the efficiency of the device in attenuating vibrations in the range of 4-6 Hz, which is the range of frequency of observed tremor, with maximum amplitude attenuation of 85%.


title: Negativity Bias, Negativity Dominance, and Contagion

abstract: We hypothesize that there is a general bias, based on both innate predispositions and experience, in animals and humans, to give greater weight to negative entities (e.g., events, objects, personal traits). This is manifested in 4 ways: (a) negative potency (negative entities are stronger than the equivalent positive entities), (b) steeper nega tive gradients (the negativity of negative events grows more rapidly with approach to them in space or time than does the positivity of positive events, (c) negativity domi nance (combinations of negative and positive entities yield evaluations that are more negative than the algebraic sum of individual subjective valences would predict), and (d) negative differentiation (negative entities are more varied, yield more complex conceptual representations, and engage a wider response repertoire). We review evi dence for this taxonomy, with emphasis on negativity dominance, including literary, historical, religious, and cultural sources, as well as the psychological literatures on learning, attention, impression formation, contagion, moral judgment, development, and memory. We then consider a variety of theoretical accounts for negativity bias. We suggest that 1 feature of negative events that make them dominant is that negative entities are more contagious than positive entities.


title: Ensemble based classifiers using dictionary learning

abstract: Dictionary learning is used in signal, image, audio and video processing applications to represent signals by a sparse set of atoms where sparse representations are managed for the problems of compression, denoising, feature extraction and data classification. In many machine learning applications, classifier ensembles are shown to be superior than their single classifier counterparts. In this paper, we propose to use dictionary learning as a base classifier in ensemble learning methods and introduce Random Subspace Dictionary Learning (RDL) and Bagging Dictionary Learning (BDL) algorithms by learning ensembles of dictionaries for each class using feature/instance subspaces. The experimental results show that the ensemble based dictionary learning methods outperform the single dictionary learning (DL), Support Vector Machines (SVM), and SVM based ensemble classifiers.


title: A key point based copy-move forgery detection using HOG features

abstract: Image forgery detection is always a challenging task especially when it comes to the blind image forgery detection techniques which do not contain any water marks or signatures. Copy move forgery is a popular blind image forgery detection technique which is widely used by forgers for performing easy and efficient forgery. A copy-move forgery followed by rotation and scaling of the forged part is even more challenging to detect. In this paper, we present a copy move forgery detection algorithm which detects and localizes the forgery with reasonably good accuracy and in less time compared to other state-of-the-art methods. This method uses Harris Corner points to detect regions of interest and identifies a feature vector around this corner points using the Histogram of Oriented Gradients (HOG) descriptor. A comparison of the feature vectors is performed in the entire feature space to find a suitable match by using Sum of Squared Differences (SSD) and Nearest Neighbour Distance Ratio (NDR). The outlier matches in the previous step is eliminated by using RANSAC. The results are evaluated based on various performance measures like True Positive Rate (TPR), False Positive Rate (FPR), Precision and Recall. Datasets proposed by Adrizzone et al. and CoMoFoD(small) are used for evaluation. The results achieved show the efficiency of our method against moderate rotation and scaling with a reasonably good True Positive Rate.


title: Practitioners' expectations on automated fault localization

abstract: Software engineering practitioners often spend significant amount of time and effort to debug. To help practitioners perform this crucial task, hundreds of papers have proposed various fault localization techniques. Fault localization helps practitioners to find the location of a defect given its symptoms (e.g., program failures). These localization techniques have pinpointed the locations of bugs of various systems of diverse sizes, with varying degrees of success, and for various usage scenarios. Unfortunately, it is unclear whether practitioners appreciate this line of research. To fill this gap, we performed an empirical study by surveying 386 practitioners from more than 30 countries across 5 continents about their expectations of research in fault localization. In particular, we investigated a number of factors that impact practitioners' willingness to adopt a fault localization technique. We then compared what practitioners need and the current state-of-research by performing a literature review of papers on fault localization techniques published in ICSE, FSE, ESEC-FSE, ISSTA, TSE, and TOSEM in the last 5 years (2011-2015). From this comparison, we highlight the directions where researchers need to put effort to develop fault localization techniques that matter to practitioners.


title: Enhancing Query Formulation for Spoken Document Retrieval

abstract: The popularity and ubiquity of multimedia associated with spoken documents has spurred a lot of research interest in spoken document retrieval (SDR) in the recent past. Beyond much effort devoted to developing robust indexing and modeling techniques for representing spoken documents, a recent line of thought targets at the improvement of query modeling for better reflecting the user's information need. Pseudo-relevance feedback is by far the most commonly-used paradigm for query reformulation, which assumes that a small amount of top-ranked feedback documents obtained from the initial round of retrieval are relevant and can be utilized for this purpose. Nevertheless, simply taking all of the top-ranked feedback documents obtained from the initial retrieval for query modeling does not always perform well, especially when the top-ranked documents contain much redundant or non-relevant information. In the view of this, we explore in this paper an interesting problem of how to effectively glean useful cues from the top-ranked documents so as to achieve more accurate query modeling. Towards this end, various sources of information cues are considered and integrated into the process of feedback document selection so as to achieve better retrieval effectiveness. Furthermore, we also investigate representing the query and documents with different granularities of index features to work in conjunction with the query and document models. A series of experiments conducted on the TDT (Topic Detection and Tracking) task seem to demonstrate the effectiveness of our query modeling framework for SDR.


title: Comparison of Various Encryption Algorithms and Techniques for Secured Data Communication

abstract: For a secured system it has been desired to make the combination of data and keys secured. One can hack the data by knowing the information about the radiations of a machine, key length, encryption time, number of stations and block size. Few algorithms creates dummy file which has been generated along with the encrypted data in order to misguide the hacker and acts as overheads. Short length keys are normally exposed to the hacker very easily that‟s why large key lengths have been preferred. For the encryption and transmission of data in Multinode Network (MN) various techniques are used. This paper covers the various techniques and algorithms used for the data security in MN.


title: Full Duplex Communication System for Deaf & Dumb People

abstract: One of the important problems that our society faces is that people with disabilities are finding it hard to cope up with the fast growing technology. The access to communication technologies has become essential for the handicapped people. Generally deaf and dumb people use sign language for communication but they find difficulty in communicating with others who don't understand sign language. Sign language is an expressive and natural way for communication between normal and dumb people (information majorly conveyed through the hand gesture). So, we need a translator to understand what they speak and communicate with us. The sign language translation system translates the normal sign language to speech and hence makes the communication between normal person and dumb people easier. But the question arises, how the deaf person understands the speech of a normal person and hence we need a system which converts the speech of normal person to text and the corresponding gesture is displayed on display. So, the whole idea is to build a device that enables two way communications between deaf-mute person and a normal person


title: 3D Face Morphable Models "In-the-Wild"

abstract: 3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful 3D facial shape models can be learnt from such data, it is difficult to build statistical texture models that are sufficient to reconstruct faces captured in unconstrained conditions (in-the-wild). In this paper, we propose the first, to the best of our knowledge, in-the-wild 3DMM by combining a powerful statistical model of facial shape, which describes both identity and expression, with an in-the-wild texture model. We show that the employment of such an in-the-wild texture model greatly simplifies the fitting procedure, because there is no need to optimise with regards to the illumination parameters. Furthermore, we propose a new fast algorithm for fitting the 3DMM in arbitrary images. Finally, we have captured the first 3D facial database with relatively unconstrained conditions and report quantitative evaluations with state-of-the-art performance. Complementary qualitative reconstruction results are demonstrated on standard in-the-wild facial databases.


title: A maturity model for blockchain adoption

abstract: Background ::: The rapid development of the blockchain technology and its various applications has rendered it important to understand the guidelines for adopting it.


title: Poppy humanoid platform: Experimental evaluation of the role of a bio-inspired thigh shape

abstract: In this paper, we present an experimental evaluation of the role of the morphology in the Poppy humanoid platform. More precisely, we have investigated the impact of the bio-inspired thigh, bended of 6°, on the balance and biped locomotion. We compare this design with a more traditional straight thigh. We describe both the theoretical model and real experiments showing that the bio-inspired thigh allows the reduction of falling speed by almost 60% (single support phase) and the decrease of the lateral motion needed for the mass transfer from one foot to the other by 30% (double support phase). We also present an experiment where the robot walks on a treadmill thanks to the social and physical guidance of expert users and we show that the bended thigh reduces the upper body motion by about 45% indicating a more stable walk.


Markov Models for Written Language Identification


Data Analysis Services Related to the IoT and Big Data: Potential Business Opportunities for Third Parties


title: CiteSeerX data: semanticizing scholarly papers

abstract: Scholarly big data is, for many, an important instance of Big Data. Digital library search engines have been built to acquire, extract, and ingest large volumes of scholarly papers. This paper provides an overview of the scholarly big data released by CiteSeerX, as of the end of 2015, and discusses various aspects such as how the data is acquired, its size, general quality, data management, and accessibility. Preliminary results on extracting semantic entities from body text of scholarly papers with Wikifier show biases towards general terms appearing in Wikipedia and against domain specific terms. We argue that the latter will play a more important role in extracting important facts from scholarly papers.


title: Time-Series Plots Integrated in Parallel-Coordinates Displays

abstract: We present a natural extension of two-dimensional parallel-coordinates plots for revealing relationships in time-dependent multi-attribute data by building on the idea that time can be considered as the third dimension. A time slice through the visualization represents a certain point in time and can be viewed as a regular parallel-coordinates display. A vertical slice through one of the axes of the parallel-coordinates display would show a time-series plot. For a focus-and-context Integration of both views, we embed time-series plots between two adjacent axes of the parallel-coordinates plot. Both time-series plots are drawn using a pseudo three-dimensional perspective with a single vanishing point. An independent parallel-coordinates panel that connects the two perspectively displayed time-series plots can move forward and backward in time to reveal changes in the relationship between the time-dependent attributes. The visualization of time-series plots in the context of the parallel-coordinates plot facilitates the exploration of time-related aspects of the data without the need to switch to a separate display. We provide a consistent set of tools for selecting and contrasting subsets of the data, which are important for various application domains.


title: Combating discrimination using Bayesian networks

abstract: Discrimination in decision making is prohibited on many attributes (religion, gender, etc?), but often present in historical decisions. Use of such discriminatory historical decision making as training data can perpetuate discrimination, even if the protected attributes are not directly present in the data. This work focuses on discovering discrimination in instances and preventing discrimination in classification. First, we propose a discrimination discovery method based on modeling the probability distribution of a class using Bayesian networks. This measures the effect of a protected attribute (e.g., gender) in a subset of the dataset using the estimated probability distribution (via a Bayesian network). Second, we propose a classification method that corrects for the discovered discrimination without using protected attributes in the decision process. We evaluate the discrimination discovery and discrimination prevention approaches on two different datasets. The empirical results show that a substantial amount of discrimination identified in instances is prevented in future decisions.


title: Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks

abstract: This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the sparsity encountered in the input. To this end, we examine the trade-off between accuracy and speed for different architectures and additionally propose to use an L1 penalty on the filter activations to further encourage sparsity in the intermediate representations. To the best of our knowledge, this is the first work to propose sparse convolutional layers and L1 regularisation for efficient large-scale processing of 3D data. We demonstrate the efficacy of our approach on the KITTI object detection benchmark and show that Vote3Deep models with as few as three layers outperform the previous state of the art in both laser and laser-vision based approaches by margins of up to 40% while remaining highly competitive in terms of processing time.


title: A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes

abstract: The approach proposed by Śliwerski, Zimmermann, and Zeller (SZZ) for identifying bug-introducing changes is at the foundation of several research areas within the software engineering discipline. Despite the foundational role of SZZ, little effort has been made to evaluate its results. Such an evaluation is a challenging task because the ground truth is not readily available. By acknowledging such challenges, we propose a framework to evaluate the results of alternative SZZ implementations. The framework evaluates the following criteria: (1) the earliest bug appearance, (2) the future impact of changes, and (3) the realism of bug introduction. We use the proposed framework to evaluate five SZZ implementations using data from ten open source projects. We find that previously proposed improvements to SZZ tend to inflate the number of incorrectly identified bug-introducing changes. We also find that a single bug-introducing change may be blamed for introducing hundreds of future bugs. Furthermore, we find that SZZ implementations report that at least 46 percent of the bugs are caused by bug-introducing changes that are years apart from one another. Such results suggest that current SZZ implementations still lack mechanisms to accurately identify bug-introducing changes. Our proposed framework provides a systematic mean for evaluating the data that is generated by a given SZZ implementation.


title: Optimizing the Latent Space of Generative Networks

abstract: Generative Adversarial Networks (GANs) have been shown to be able to sample impressively realistic images. GAN training consists of a saddle point optimization problem that can be thought of as an adversarial game between a generator which produces the images, and a discriminator, which judges if the images are real. Both the generator and the discriminator are commonly parametrized as deep convolutional neural networks. The goal of this paper is to disentangle the contribution of the optimization procedure and the network parametrization to the success of GANs. To this end we introduce and study Generative Latent Optimization (GLO), a framework to train a generator without the need to learn a discriminator, thus avoiding challenging adversarial optimization problems. We show experimentally that GLO enjoys many of the desirable properties of GANs: learning from large data, synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors.


title: TicTac: Accelerating Distributed Deep Learning with Communication Scheduling

abstract: State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communication-heavy systems depends on the computation time, communication time and the extent of overlap of computation and communication. ::: In this work, we identify a shortcoming in systems with graph representation for computation, such as TensorFlow and PyTorch, that result in high variance in iteration time --- random order of received parameters across workers. We develop a system, TicTac, to improve the iteration time by fixing this issue in distributed deep learning with Parameter Servers while guaranteeing near-optimal overlap of communication and computation. TicTac identifies and enforces an order of network transfers which improves the iteration time using prioritization. Our system is implemented over TensorFlow and requires no changes to the model or developer inputs. TicTac improves the throughput by up to $37.7\%$ in inference and $19.2\%$ in training, while also reducing straggler effect by up to $2.3\times$. Our code is publicly available.


title: Proactive Highly Ambulatory Sensor Routing (PHASeR) protocol for mobile wireless sensor networks

abstract: This paper presents a novel multihop routing protocol for mobile wireless sensor networks called PHASeR (Proactive Highly Ambulatory Sensor Routing). The proposed protocol ::: uses a simple hop-count metric to enable the dynamic and robust routing of data towards the sink in mobile environments. It is motivated by the application of radiation mapping by unmanned vehicles, which requires the reliable and timely delivery of regular measurements to the sink. PHASeR maintains a gradient metric in mobile environments by using a global TDMA MAC layer. It also uses the technique of blind forwarding to pass messages through the network in a multipath manner. PHASeR is analysed mathematically based on packet delivery ratio, average packet delay, throughput and overhead. It is then simulated with varying mobility, scalability and traffic loads. The protocol gives good results over all measures, which suggests that it may also be suitable for a wider array of emerging applications.


title: Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis

abstract: Abstract We show that the support vector machine (SVM) classification algorithm, a recent development from the machine learning community, proves its potential for structure–activity relationship analysis. In a benchmark test, the SVM is compared to several machine learning techniques currently used in the field. The classification task involves predicting the inhibition of dihydrofolate reductase by pyrimidines, using data obtained from the UCI machine learning repository. Three artificial neural networks, a radial basis function network, and a C5.0 decision tree are all outperformed by the SVM. The SVM is significantly better than all of these, bar a manually capacity-controlled neural network, which takes considerably longer to train.


title: Shakey 2016—How Much Does it Take to Redo Shakey the Robot?

abstract: Shakey the robot was one of the first autonomous robots that showed impressive capabilities of navigation and mobile manipulation. Since then, robotics research has made great progress, showing more and more capable robotic systems for a large variety of application domains and tasks. In this letter, we look back on decades of research by rebuilding Shakey with modern robotics technology in the open-source Shakey 2016 system. Hereby, we demonstrate the impact of research by showing that ideas from the original Shakey are still alive in state-of-the-art systems, while robotics in general has improved to deliver more robust and more capable software and hardware. Our Shakey 2016 system has been implemented on real robots and leverages mostly open-source software. We experimentally evaluate the system in real-world scenarios on a PR2 robot and a Turtlebot-based robot and particularly investigate the development effort. The experiments documented in this letter demonstrate that results from robotics research are readily available for building complex robots such as Shakey within a short amount of time and little effort.


title: The Quandary of Agricultural Biotechnology, Pure Economic Loss, and Non-Adopters: Comparing Australia, Canada, and the United States

abstract: Innovations impact societies in a variety of ways. Successful innovations are utility enhancing, in that they create a higher degree of benefits that offset any of the potential disadvantages of the innovation. Unsuccessful innovations suffer from the reverse, in that they result in more disadvantages than benefits and therefore, are ultimately rejected by society. The innovation of agricultural biotechnology and genetically modified (GM) crops has triggered substantial discussion regarding the advantages and disadvantages of the technology. Numerous financial and economic benefits are starting to be recognized by adopters, but some non-adopters are growing increasingly concerned about their ability to profit given the high levels of GM crop adoption. While some might argue that non-adopters of GM crops are the conventional economic losers of this innovation, the reality is that demand for non-GM products is higher, in large part, because of consumer desires to avoid GM food products. The concept of pure economic loss in relation to innovation posits that those negatively impacted by the innovation of GM crops are entitled to compensation that offsets the externality. In undertaking a thorough assessment of pure economic loss and GM crops, this article evaluates the logic for, and efficiencies of, having compensation funded via the use of courts versus government regulations. This article considers whether non-adopter rights are developing in the case of GM crops and what governance response mechanism is best suited to those claims. It is concluded that the decision over whether to support or reject an innovation is too important to the larger society as a whole to be decided by the courts.


title: Improvement on Speech Emotion Recognition Based on Deep Convolutional Neural Networks

abstract: Speech emotion recognition (SER) is to study the formation and change of speaker's emotional state from the speech signal perspective, so as to make the interaction between human and computer more intelligent. SER is a challenging task that has encountered the problem of less training data and low prediction accuracy. Here we propose a data processing algorithm based on the imaging principle of the retina and convex lens (DPARIP), to acquire the different sizes of spectrogram and get different training data by changing the distance between the spectrogram and the convex lens. Meanwhile, with the help of deep learning to get the high-level features, we apply the AlexNet on the IEMOCAP database and achieve the average accuracy over 48.8% on six emotions. The experimental results indicate that our proposed data preprocessing algorithm is effective and more accurate compared to existing emotion recognition algorithms.


A Neural Approach to Cross-Lingual Information Retrieval


title: Body image in transgender young people: Findings from a qualitative, community based study

abstract: The goal of this study was to examine the ways in which transgender youth experience their bodies with regard to gender and body size. Ninety transgender youth and young adults completed in-depth interviews in eight metropolitan areas of the United States, Canada, and Ireland. Using a queer perspective, qualitative analyses revealed two broad conceptual categories: body dissatisfaction and body satisfaction. Within these categories, participants focused on body issues related to gender characteristics and body size. Findings revealed evidence of self-criticism and social distress related to body image dissatisfaction and self-acceptance and social acceptance related to body image satisfaction. Data demonstrated how gender, body size, and the intersection of gender and body size influenced personal perceptions of body dissatisfaction and satisfaction. Developmental processes were evident: participants further along in consolidating a gender identity described gaining a sense of social awareness, self-acceptance, and body satisfaction reflecting a sense of resilience.


title: Image Captioning with Deep Bidirectional LSTMs and Multi-Task Learning

abstract: Generating a novel and descriptive caption of an image is drawing increasing interests in computer vision, natural language processing, and multimedia communities. In this work, we propose an end-to-end trainable deep bidirectional LSTM (Bi-LSTM (Long Short-Term Memory)) model to address the problem. By combining a deep convolutional neural network (CNN) and two separate LSTM networks, our model is capable of learning long-term visual-language interactions by making use of history and future context information at high-level semantic space. We also explore deep multimodal bidirectional models, in which we increase the depth of nonlinearity transition in different ways to learn hierarchical visual-language embeddings. Data augmentation techniques such as multi-crop, multi-scale, and vertical mirror are proposed to prevent overfitting in training deep models. To understand how our models “translate” image to sentence, we visualize and qualitatively analyze the evolution of Bi-LSTM internal states over time. The effectiveness and generality of proposed models are evaluated on four benchmark datasets: Flickr8K, Flickr30K, MSCOCO, and Pascal1K datasets. We demonstrate that Bi-LSTM models achieve highly competitive performance on both caption generation and image-sentence retrieval even without integrating an additional mechanism (e.g., object detection, attention model). Our experiments also prove that multi-task learning is beneficial to increase model generality and gain performance. We also demonstrate the performance of transfer learning of the Bi-LSTM model significantly outperforms previous methods on the Pascal1K dataset.


title: UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis

abstract: This paper describes our system used in the Aspect Based Sentiment Analysis (ABSA) task of SemEval 2016. Our system uses Maximum Entropy classifier for the aspect category detection and for the sentiment polarity task. Conditional Random Fields (CRF) are used for opinion target extraction. We achieve state-of-the-art results in 9 experiments among the constrained systems and in 2 experiments among the unconstrained systems.


title: Blue-collar work, career, and success: occupational narratives of Sisu

abstract: This study examined underground iron ore miners' occupational narratives to uncover how their stories socialize miners into blue-collar careers and reinforce their work identities. Through the root theme of sisu (Finnish for inner determination), underground miners create a status hierarchy that is used to construct a sense of pride around their work and to establish milestones of success for themselves and others in their workgroup. Furthermore, they communicatively construct exemplars that guide their performance and decisions during the unfolding of their work experiences. Their discourses provide alternatives to white-collar conceptualizations and practices of careers and success.


title: UniqueID: Decentralized Proof-of-Unique-Human

abstract: Bitcoin and Ethereum are novel mechanisms for decentralizing the concept of money and computation. Extending decentralization to the human identity concept, we can think of using blockchain for creating a list of verified human identities with a one-person-one-ID property. UniqueID is a Decentralized Autonomous Organization(DAO) for maintaining human identities such that every physical human entity can have no more that one account. One part of this identity is simply the user's claim on one of his unique, permanent, and measurable characteristics -biometrics. Blockchain has proved its integrity as a platform for storing and performing computations on such claims. The biggest challenge here is to ensure that the user has submitted his own valid biometric data. Human verifiers can check if there is any inconsistency in other users' data, by peer-to-peer checks. For preventing bad behavior and centralization in the verification process, UniqueID benefits from novel governance mechanisms to choose verifiers and punish unjust ones. Also, there are incentives for honest verifiers and users by newly generated tokens. We show how the users' privacy can be preserved by using state-of-the-art cryptographic techniques, and so they can use their identity without any concerns for votings, financial and banking purposes, social media accounts, reputation systems etc.


title: Optimizing strength training for running and cycling endurance performance: A review.

abstract: Here we report on the effect of combining endurance training with heavy or explosive strength training on endurance performance in endurance-trained runners and cyclists. Running economy is improved by performing combined endurance training with either heavy or explosive strength training. However, heavy strength training is recommended for improving cycling economy. Equivocal findings exist regarding the effects on power output or velocity at the lactate threshold. Concurrent endurance and heavy strength training can increase running speed and power output at VO2max (Vmax and Wmax, respectively) or time to exhaustion at Vmax and Wmax. Combining endurance training with either explosive or heavy strength training can improve running performance, while there is most compelling evidence of an additive effect on cycling performance when heavy strength training is used. It is suggested that the improved endurance performance may relate to delayed activation of less efficient type II fibers, improved neuromuscular efficiency, conversion of fast-twitch type IIX fibers into more fatigue-resistant type IIA fibers, or improved musculo-tendinous stiffness.


title: Color-Based and Recursive Fiducial Marker for Augmented Reality

abstract: The popularity of applications using Augmented Reality, especially due to the dissemination of smartphones with high processing power, introduces the need for Fiducial Markers that can be detected quickly, with good accuracy and can deal with partial occlusion. Fiducial Markers can have different shapes, sizes, structure and colors, and are inserted into a scene to facilitate the detection and consequent projection of virtual objects. In particular, this paper proposes a new Color-based and Recursive Fiducial Marker (CRFM), which is constructed by square-based geometric forms and uses different colors to produce a recursive structure that was designed to still work under partial occlusion. We describe the CRFM design and how its detection mechanism works. Our evaluation results show that CRFM achieves a good level of accuracy. Moreover, we show that the detection of the CRFM can be as fast as a board of ArUco, where only black and white colors are used.


title: Determination of the ideal rat model for spinal cord injury by diffusion tensor imaging

abstract: Four different spinal cord injury (SCI) models (hemisection, contusion, transection, and segment resection) were produced in male Sprague–Dawley rats to determine the most suitable animal model of SCI by analyzing the changes in diffusion tensor imaging (DTI) parameters both qualitatively and quantitatively in vivo. Radiological examinations were performed before surgery and weekly within 4 weeks after surgery to obtain DTI tractography, MRI routine images, and DTI data of fractional anisotropy (FA) and apparent diffusion coefficient (ADC). The Basso, Beattie, and Bresnahan scale was used to evaluate the locomotor outcomes. We found that DTI tractography tracked nerve fibers and showed conspicuous changes in the injured spinal cord in all the model groups, which confirmed that our modeling was successful. A decrease in FA values and an increase in ADC were observed in all the model groups after surgery. There were significant differences in FA and ADC between weeks 1 and 4 in both hemisection and contusion groups (P 0.05). Basso, Beattie, and Bresnahan scores further proved the results because of a significant, positive correlation of the scores with FA (R=0.899, P<0.01) and a significant, negative correlation of the scores with ADC (R=−0.829, P<0.01). Therefore, the transection model, which is more quantified and stable within 4 weeks after injury according to the DTI and behavioral evaluation, should be used as the standard model for SCI animal testing.


title: Robust Support Vector Regression for Uncertain Input and Output Data

abstract: In this paper, a robust support vector regression (RSVR) method with uncertain input and output data is studied. First, the data uncertainties are investigated under a stochastic framework and two linear robust formulations are derived. Linear formulations robust to ellipsoidal uncertainties are also considered from a geometric perspective. Second, kernelized RSVR formulations are established for nonlinear regression problems. Both linear and nonlinear formulations are converted to second-order cone programming problems, which can be solved efficiently by the interior point method. Simulation demonstrates that the proposed method outperforms existing RSVRs in the presence of both input and output data uncertainties.


title: Synchronization and Power Sharing for Droop-Controlled Inverters in Islanded Microgrids

abstract: Motivated by the recent and growing interest in smart grid technology, we study the operation of DC/AC inverters in an inductive microgrid. We show that a network of loads and DC/AC inverters equipped with power-frequency droop controllers can be cast as a Kuramoto model of phase-coupled oscillators. This novel description, together with results from the theory of coupled oscillators, allows us to characterize the behavior of the network of inverters and loads. Specifically, we provide a necessary and sufficient condition for the existence of a synchronized solution that is unique and locally exponentially stable. We present a selection of controller gains leading to a desirable sharing of power among the inverters, and specify the set of loads which can be serviced without violating given actuation constraints. Moreover, we propose a distributed integral controller based on averaging algorithms which dynamically regulates the system frequency in the presence of a time-varying load. Remarkably, this distributed-averaging integral controller has the additional property that it maintains the power sharing properties of the primary droop controller. Our results hold without assumptions on identical line characteristics or voltage magnitudes.


title: A Survey on Anti-honeypot and Anti-introspection Methods

abstract: Modern virtual machines, debuggers, and sandboxing solutions lend themselves towards more and more inconspicuous ways to run honeypots, and to observe and analyze malware and other malicious activity. This analysis yields valuable data for threat-assessment, malware identification and prevention. However, the use of such introspection methods has caused malware authors to create malicious programs with the ability to detect and evade such environments. This paper presents an overview on existing research of anti-honeypot and anti-introspection methods. We also propose our own taxonomy of detection vectors used by malware.


title: Dynamic Model and Control of a New Quadrotor Unmanned Aerial Vehicle with Tilt-Wing Mechanism

abstract: In this work a dynamic model of a new quadrotor aerial vehicle that is equipped with a tilt-wing mechanism is presented. The vehicle has the capabilities of vertical take-off/landing (VTOL) like a helicopter and flying horizontal like an airplane. Dynamic model of the vehicle is derived both for vertical and horizontal flight modes using Newton-Euler formulation. An LQR controller for the vertical flight mode has also been developed and its performance has been tested with several simulations. Keywords—Control, Dynamic model, LQR, Quadrotor, Tilt-wing, VTOL.


title: Overview of Digital Television Development Worldwide

abstract: This special issue of the proceedings of the IEEE presents a systematical and complete tutorial on digital television (DTV), produced by a team of DTV experts worldwide. This introductory paper puts the current DTV systems into perspective and explains the historical background and different evolution paths that each system took. The main focus is on terrestrial DTV systems, but satellite and cable DTV are also covered,as well as several other emerging services.


title: Resolving Lexical Ambiguity in Tensor Regression Models of Meaning

abstract: This paper provides a method for improving tensor-based compositional distributional models of meaning by the addition of an explicit disambiguation step prior to composition. In contrast with previous research where this hypothesis has been successfully tested against relatively simple compositional models, in our work we use a robust model trained with linear regression. The results we get in two experiments show the superiority of the prior disambiguation method and suggest that the effectiveness of this approach is modelindependent.


title: L Diversity on K-Anonymity with External Database for improving Privacy Preserving Data Publishing

abstract: The data must be secure and measurable at the public when it releases to view. The data table produces personal information and sensitive values. They are maintained for secrecy, the anonymity is the best method to protect the data. There are many anonymity methods to protect the data. k -anonymity is one method to protect the data. The problem in kanonymity method is if data set increases then utility decreases. Also kanonymity data is possible to many attacks like Homogeneity Attack, Background Knowledge Attack. The l diversity is another method to protect the data. Main advantage of ldiversity is the data set increases then the data utility also increases. Based on above advantage, we applied l -diversity concept in k-anonymity applied external data set and we evaluate high efficiency dataset. It shows the l diversity reduces the data losses in k anonymity data sets when data point moves any size.


title: SOCIAL AND PSYCHOLOGICAL EFFECTS OF VASECTOMY

abstract: This is a study of patients admitted to the psychiatric service of the Seattle Veterans Administration Hospital with a history of vasectomy performed sometime prior to their admission to this particular hospital. The hypothesis was that the procedure was less innocuous than commonly believed and that it often failed to solve the problems which allegedly made it desirable. The reason vasectomy has been proposed as an ideal method of birth control has been that it is simple cheap effective and free of side effects. The author agrees that the operation is simple but argues it really is not free of side effects that result from technical problems. Also there is the possibility of psychological effects. The 83 men in this sample were between the ages of 29 and 59; their wives were 21-58. The average patient had been married for 8 years. In 21 instances the husband decided on the operation; most common reasons for the operation were wifes health 24 times and enough children 41 cases. The effects of the operation on sexual behavior was difficult to document. Complications during or after surgery were not common. Psychological testing was advised for potential vasectomy patients. The article ends with some case stories of patients who have had severe psychological problems after surgery. The author feels that the emotional significance of the operation is great. The most significant areas are the way the patient deals with his sterilization including the way the procedure was decided upon.


title: Multi-agent systems: An introduction to distributed artificial intelligence

abstract: From the Publisher: ::: What are multi-agent systems? How do they work? What do they do? If you are looking for the answers to these questions, read on; for Jacques Ferber's authoritative book is the first to provide a single, coherent overview of multi-agent systems. Introduces and defines key concepts throughout the text; provides numerous examples to illustrate core principles; draws on contributions from different disciplines to present a holistic, comprehensive picture of state-of-the art agent technology; and describes all the latest developments in the field and encourages the reader to reflect on possibilities for the future.


title: Real-time hand posture recognition using range data

abstract: A hand posture recognition system using 3D data is described. The system relies on a novel 3D sensor that generates a dense range image of the scene. The main advantage of the proposed system, compared to other gesture recognition techniques, is the capability for robust unconstrained recognition of complex hand postures such as those encountered in sign language alphabets. This is achieved by explicitly utilizing 3D hand geometry. Moreover, the proposed approach does not rely on color information, and guarantees robust segmentation of the hand under varying illumination conditions, and scene content. Several novel 3D image analysis algorithms are presented, covering the complete processing chain: 3D image acquisition, arm segmentation, hand-forearm segmentation, hand pose estimation, 3D feature extraction, and gesture classification. The proposed system is extensively evaluated.


title: Comparing random forest approaches to segmenting and classifyinggestures

abstract: A complete gesture recognition system should localize and classify each gesture from a given gesture vocabulary, within a continuous video stream. In this work, we compare two approaches: a method that performs the tasks of temporal segmentation and classification simultaneously with another that performs the tasks sequentially. The first method trains a single random forest model to recognize gestures from a given vocabulary, as presented in a training dataset of video plus 3D body joint locations, as well as out-of-vocabulary (non-gesture) instances. The second method employs a cascaded approach, training a binary random forest model to distinguish gestures from background and a multi-class random forest model to classify segmented gestures. Given a test input video stream, both frameworks are applied using sliding windows at multiple temporal scales. We evaluated our formulation in segmenting and recognizing gestures from two different benchmark datasets: the NATOPS dataset of 9600 gesture instances from a vocabulary of 24 aircraft handling signals, and the ChaLearn dataset of 7754 gesture instances from a vocabulary of 20Italian communication gestures. The performance of our method compares favorably with state-of-the-art methods that employ Hidden Markov Models or Hidden Conditional Random Fields on the NATOPS dataset. We conclude with a discussion of the advantages of using our model for the task of gesture recognition and segmentation, and outline weaknesses which need to be addressed in the future. Sequential and simultaneous random forest frameworks are compared.Fusing skeletal and appearance features enables accurate gesture representations.Uniform descriptors are created for gestures to account for variability in length.


title: A Model for Mandatory Use of Software Technologies: An Integrative Approach by Applying Multiple Levels of Abstraction of Informing Science

abstract: Introduction With the increasing integration of information technologies (IT) into business, today's organizations tend to sanction the use of information systems applications over which their employees do not have the privilege of selection. For example, applications such as enterprise resource planning (ERP) systems are widely used and standardize the way software technologies are applied to business processes. Alternatively, an organization may install a standard array of desktop applications, each befitting certain tasks. To study system use in such mandatory environments, researchers often apply the Technology Acceptance Model (TAM) with two approaches: (a) using the TAM as is but adding voluntariness as a moderator, or (b) adding antecedents to perceived usefulness and perceived ease of use. Either way, the constructs of intention to use and use are central to the models, as they are in the TAM. We argue that both approaches are incognizant of the fact that, in mandatory environments, intention and use may not have enough variances to be meaningful criterion variables. Informing science considers systems at three levels of abstraction (E. Cohen, 1999; Gill & Bhattacherjee, 2007, 2009). From this perspective, the centrality on intention and use reflects a limiting view that construes mandatory use only at the first level, i.e., the user is informed via system use. Mandatory systems, however, often aim at goals achieved only when new instances of informing occur. For instance, governments are taking advantage of the ever-increasing capabilities of IT to make their citizenry better informed. However, employees' mandated use of IT systems is no guarantee that benefits made possible by IT are indeed realized. To inform the citizens, first the employees must be properly informed and be able to trust information output from the systems. Second, a new instance of informing takes place when employees use the information to inform citizens. The desired results are benefits to the governments, such as better relationships with their constituencies, cost effectiveness, and positive image of the governments. The purpose of this paper, therefore, is to develop a theoretical model to explain mandatory use of software technologies by employing multiple levels of abstraction in the spirit of informing science. Such a vantage point affords us an integrative view on prior studies. We adapted Wixom and Todd's (2005) model to explain a conceptual gap between system characteristics (specifically, information quality) and system use, which DeLone and McLean's (1992, 2003) milestone models of system success have not addressed in full. Wixom and Todd (2005) filled the gap by reasoning that users' evaluation of system characteristics ("object-based beliefs") impacts their affective feeling toward those characteristics ("object-based attitudes"), which, in turn, exerts influence on two TAM predictors of use - perceived usefulness and perceived ease of use ("behavioral beliefs"). Although Wixom and Todd (2005) did justice to user attitude toward system use ("behavioral attitude"), their model was not specifically designed for mandatory environments and thus treats intention as the ultimate criterion variable. We addressed this by introducing DeLone and McLean's (2003) constructs of user satisfaction and net benefits. The latter is the goal of informing at the second level of abstraction and the former is the connection between the first and second levels. Looking again at government systems, employees who are satisfied processors of information are the key to the successful creation of new instances of informing. This paper is organized as follows. First, we briefly review the informing science framework and the three levels of abstraction, which are the foundation of our theoretical approach. Then, we propose our model and introduce the relevant theories and models that we integrate by using the informing science approach. …


title: Fuzzing: The State of the Art

abstract: Abstract : Fuzzing is an approach to software testing where the system being tested is bombarded with test cases generated by another program. The system is then monitored for any flaws exposed by the processing of this input. While the fundamental principles of fuzzing have not changed since the term was first coined, the complexity of the mechanisms used to drive the fuzzing process have undergone significant evolutionary advances. This paper is a survey of the history of fuzzing, which attempts to identify significant features of fuzzers and recent advances in their development, in order to discern the current state of the art in fuzzing technologies, and to extrapolate them into the future.


title: Summarization Based on Embedding Distributions

abstract: In this study, we consider a summarization method using the document level similarity based on embeddings, or distributed representations of words, where we assume that an embedding of each word can represent its “meaning.” We formalize our task as the problem of maximizing a submodular function defined by the negative summation of the nearest neighbors’ distances on embedding distributions, each of which represents a set of word embeddings in a document. We proved the submodularity of our objective function and that our problem is asymptotically related to the KL-divergence between the probability density functions that correspond to a document and its summary in a continuous space. An experiment using a real dataset demonstrated that our method performed better than the existing method based on sentence-level similarity.


title: The influence of Pilates training on the ability to contract the Transversus Abdominis muscle in asymptomatic individuals

abstract: Abstract The neuromuscular system acts to maintain postural stability and reduce the impact of deleterious loads on the spine. Exercising of the abdominal muscles has become widely used in the management of low back pain in order to provide this supplement to spinal stability. Several exercise programmes have been advocated to promote stabilization but evaluation is difficult. This study evaluates two common forms of exercise effects on the ability to appropriately contract Transversus Abdominis (TrA) muscle, whose normal function is regarded as significant in spinal stability. Thirty-six asymptomatic females were examined. Twelve formed the Pilates trained group, 12 the abdominal curl group (both attending a minimum of 25 classes in 6 months) and 12 were non-training controls. A pressure biofeedback unit (PBU) was used to assess performance of the TrA muscle during an abdominal hollowing activity (TrA isolation test) and under limb load (Lumbo-pelvic stability test). The percentage of subjects passing the TrA isolation test was 10 subjects (83%) from the Pilates group, four subjects (33%) from the abdominal curl group, and three subjects (25%) from the control group. The percentage of subjects passing the lumbo-pelvic stability test was five subjects (42%) from the Pilates group, all the subjects from both the abdominal curl and control groups failed the test. The study appears to indicate that Pilates trained subjects could contract the TrA and maintain better lumbo-pelvic control than do those who perform regular abdominal curl exercises, or no abdominal muscle exercises.


title: Articulation points guided redundancy elimination for betweenness centrality

abstract: Betweenness centrality (BC) is an important metrics in graph analysis which indicates critical vertices in large-scale networks based on shortest path enumeration. Typically, a BC algorithm constructs a shortest-path DAG for each vertex to calculate its BC score. However, for emerging real-world graphs, even the state-of-the-art BC algorithm will introduce a number of redundancies, as suggested by the existence of articulation points. Articulation points imply some common sub-DAGs in the DAGs for different vertices, but existing algorithms do not leverage such information and miss the optimization opportunity. We propose a redundancy elimination approach, which identifies the common sub-DAGs shared between the DAGs for different vertices. Our approach leverages the articulation points and reuses the results of the common sub-DAGs in calculating the BC scores, which eliminates redundant computations. We implemented the approach as an algorithm with two-level parallelism and evaluated it on a multicore platform. Compared to the state-of-the-art implementation using shared memory, our approach achieves an average speedup of 4.6x across a variety of real-world graphs, with the traversal rates up to 45 ~ 2400 MTEPS (Millions of Traversed Edges per Second).


title: Multi-stage Contextual Deep Learning for Pedestrian Detection

abstract: Cascaded classifiers have been widely used in pedestrian detection and achieved great success. These classifiers are trained sequentially without joint optimization. In this paper, we propose a new deep model that can jointly train multi-stage classifiers through several stages of back propagation. It keeps the score map output by a classifier within a local region and uses it as contextual information to support the decision at the next stage. Through a specific design of the training strategy, this deep architecture is able to simulate the cascaded classifiers by mining hard samples to train the network stage-by-stage. Each classifier handles samples at a different difficulty level. Unsupervised pre-training and specifically designed stage-wise supervised training are used to regularize the optimization problem. Both theoretical analysis and experimental results show that the training strategy helps to avoid over fitting. Experimental results on three datasets (Caltech, ETH and TUD-Brussels) show that our approach outperforms the state-of-the-art approaches.


title: From Cellulose Dissolution and Regeneration to Added Value Applications — Synergism Between Molecular Understanding and Material Development

abstract: Modern society is now demanding “greener” materials due to depleting fossil fuels and increasing environmental awareness. In the near future, industries will need to become more resource-conscious by making greater use of available renewable and sustainable raw materials. In this context, agro-forestry and related industries can in‐ deed contribute to solve many resource challenges for society and suppliers in the near future. Thus, cellulose can be predicted to become an important resource for ma‐ terials due to its abundance and versatility as a biopolymer. Cellulose is found in many different forms and applications. However, the dissolution and regeneration of cellulose are key (and challenging) aspects in many potential applications. This chap‐ ter is divided into two parts: (i) achievements in the field of dissolution and regenera‐ tion of cellulose including solvents and underlying mechanisms of dissolution; and (ii) state-of-the-art production of value-added materials and their applications includ‐ ing manmade textile fibers, hydrogels, aerogels, and all-cellulose composites, where the latter is given special attention.


title: Psychological interventions for patients with cancer: psychological flexibility and the potential utility of Acceptance and Commitment Therapy.

abstract: Cancer is an illness affecting patients' physical and psychosocial well-being: high numbers report problematic levels of distress at many points through diagnosis, treatment and survivorship. Conclusive evidence for the long-term benefits of psychological interventions is lacking and this may be because (1) they employ a too limited scope of underlying therapeutic model; or (2) that they are too focused on improving psychopathological outcomes. Acceptance and Commitment Therapy (ACT) may add components not emphasised elsewhere and may provide a more suitable model of adjustment and coping. Following a comprehensive literature search a theoretical and conceptual discussion of the potential for ACT-based oncology interventions is presented. Only a small number of studies have purposively studied the application of ACT within the cancer setting, but this nonetheless presents useful pilot data. The data demonstrate potential clinical- and cost-effectiveness for a range of patients, including those with psychological comorbidity. Within the context of wider cancer adjustment, ACT offers an intervention framework to appropriately build upon the strong empirical base already established for Mindfulness within this specific patient population. The evidence available suggests that the underlying framework of ACT offers an intervention model that is potentially more suited to the individualistic nature of cancer adjustment.


title: Lightweight Swarm Attestation: A Tale of Two LISA-s

abstract: In the last decade, Remote Attestation (RA) emerged as a distinct security service for detecting attacks on embedded devices, cyber-physical systems (CPS) and Internet of Things (IoT) devices. RA involves verification of current internal state of an untrusted remote hardware platform (prover) by a trusted entity (verifier). RA can help the latter establish a static or dynamic root of trust in the prover and can also be used to construct other security services, such as software updates and secure deletion. Various RA techniques with different assumptions, security features and complexities, have been proposed for the single-prover scenario. However, the advent of IoT brought about the paradigm of many interconnected devices, thus triggering the need for efficient collective attestation of a (possibly mobile) group or swarm of provers. Though recent work has yielded some initial concepts for swarm attestation, several key issues remain unaddressed, and practical realizations have not been explored. This paper's main goal is to advance swarm attestation by bringing it closer to reality. To this end, it makes two contributions: (1) a new metric, called QoSA: Quality of Swarm Attestation, that captures the information offered by a swarm attestation technique; this allows comparing efficacy of multiple protocols, and (2) two practical attestation protocols -- called LISAa and LISAs -- for mobile swarms, with different QoSA features and communication and computation complexities. Security of proposed protocols is analyzed and their performance is assessed based on experiments with prototype implementations.


title: Robust Multi-pose Facial Expression Recognition

abstract: Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training set by rendering 3D face scans from the BU-4DFE dynamic facial expression database [17] at 49 different viewpoints. We extract Local Binary Pattern (LBP) descriptors and further utilize multiple instance learning to mitigate the influence of inaccurate alignment in this challenging task. Experimental results demonstrate the power and validate the effectiveness of the proposed multi-pose facial expression recognition framework.


title: Synthesis, structural, optical and solid state NMR study of lead bismuth titanate borosilicate glasses

abstract: Abstract Various compositions (0.0 ≤ x ≤ 1.0) in the glassy system 55[(PbxBi1-x)TiO3]-44[2SiO2.B2O3] doped with one mol percent of graphene nanoplatlets (GNPs) have been synthesized using the conventional melt-quenching technique. X-ray diffraction (XRD) study revealed the formation of bulk and transparent lead bismuth titanate borosilicate glasses. Density and molar volume were determined using the liquid displacement method. Structural analyses were carried out in detail using different characterization techniques such as infrared spectroscopy, Raman spectroscopy, UV–vis spectroscopy and solid state nuclear magnetic resonance (SSNMR) spectroscopy. 29Si and 11B-MAS-NMR-spectral analysis for five glass samples with different fraction of PbO reveal that as increases the content of PbO, the silicate and borate network become more polymerized.


title: Inferring Unknown Biological Function by Integration of GO Annotations and Gene Expression Data

abstract: Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inference are needed. We present here a novel computational approach for inferring biological function for a set of genes with previously unknown function, given a set of genes with well-known information. This approach is based on the premise that genes with similar behaviour should be grouped together. This is known as the guilt-by-association principle. Thus, it is possible to take advantage of clustering techniques to obtain groups of unknown genes that are co-clustered with genes that have well-known semantic information (GO annotations). Meaningful knowledge to infer unknown semantic information can therefore be provided by these well-known genes. We provide a method to explore the potential function of new genes according to those currently annotated. The results obtained indicate that the proposed approach could be a useful and effective tool when used by biologists to guide the inference of biological functions for recently discovered genes. Our work sets an important landmark in the field of identifying unknown gene functions through clustering, using an external source of biological input. A simple web interface to this proposal can be found at this http URL


title: Charge-state distributions of metallic electron cyclotron resonance plasmas

abstract: The authors have developed metallic ion beams in the energy range of a few keV to a few MeV using a low-energy ion-beam facility (LEIBF) consisting of a 10GHz all-permanent-magnet electron cyclotron resonance (ECR) ion source placed on a high-voltage (200kV) platform. The metallic vapors were achieved by plasma sputtering, using a micro-oven and volatile compounds to produce multiply charged ECR plasma. The ions were then extracted from the plasma and their energy and momentum were analyzed using a high-resolution dipole magnet. Charge-state distributions (CSDs) of three metallic ECR plasmas (Cu, Ni, and Sn) produced by different techniques are presented to help in using low-energy metallic ions for implantation. We present the CSD studies of metallic ECR plasma and highlight the physics of the process involved.


title: Deep semantic ranking based hashing for multi-label image retrieval

abstract: With the rapid growth of web images, hashing has received increasing interests in large scale image retrieval. Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels. However, most of these hashing methods are designed to handle simple binary similarity. The complex multi-level semantic structure of images associated with multiple labels have not yet been well explored. Here we propose a deep semantic ranking based method for learning hash functions that preserve multilevel semantic similarity between multi-label images. In our approach, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features. Meanwhile, a ranking list that encodes the multilevel similarity information is employed to guide the learning of such deep hash functions. An effective scheme based on surrogate loss is used to solve the intractable optimization problem of nonsmooth and multivariate ranking measures involved in the learning procedure. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods in term of ranking evaluation metrics when tested on multi-label image datasets.


title: Stock market prediction: A big data approach

abstract: The Stock market process is full of uncertainty and is affected by many factors. Hence the Stock market prediction is one of the important exertions in finance and business. There are two types of analysis possible for prediction, technical and fundamental. In this paper both technical and fundamental analysis are considered. Technical analysis is done using historical data of stock prices by applying machine learning and fundamental analysis is done using social media data by applying sentiment analysis. Social media data has high impact today than ever, it can aide in predicting the trend of the stock market. The method involves collecting news and social media data and extracting sentiments expressed by individual. Then the correlation between the sentiments and the stock values is analyzed. The learned model can then be used to make future predictions about stock values. It can be shown that this method is able to predict the sentiment and the stock performance and its recent news and social data are also closely correlated.


title: Convolutional Neural Networks for Steady Flow Approximation

abstract: In aerodynamics related design, analysis and optimization problems, flow fields are simulated using computational fluid dynamics (CFD) solvers. However, CFD simulation is usually a computationally expensive, memory demanding and time consuming iterative process. These drawbacks of CFD limit opportunities for design space exploration and forbid interactive design. We propose a general and flexible approximation model for real-time prediction of non-uniform steady laminar flow in a 2D or 3D domain based on convolutional neural networks (CNNs). We explored alternatives for the geometry representation and the network architecture of CNNs. We show that convolutional neural networks can estimate the velocity field two orders of magnitude faster than a GPU-accelerated CFD solver and four orders of magnitude faster than a CPU-based CFD solver at a cost of a low error rate. This approach can provide immediate feedback for real-time design iterations at the early stage of design. Compared with existing approximation models in the aerodynamics domain, CNNs enable an efficient estimation for the entire velocity field. Furthermore, designers and engineers can directly apply the CNN approximation model in their design space exploration algorithms without training extra lower-dimensional surrogate models.


title: 3D Field D: Improved Path Planning and Replanning in Three Dimensions

abstract: We present an interpolation-based planning and replanning algorithm that is able to produce direct, low-cost paths through three-dimensional environments. Our algorithm builds upon recent advances in 2D grid-based path planning and extends these techniques to 3D grids. It is often the case for robots navigating in full three-dimensional environments that moving in some directions is significantly more difficult than others (e.g. moving upwards is more expensive for most aerial vehicles). Thus, we also provide a facility to incorporate such characteristics into the planning process. Along with the derivation of the 3D interpolation function used by our planner, we present a number of results demonstrating its advantages and real-time capabilities.


title: Communication via eye blinks - detection and duration analysis in real time

abstract: A method for a real-time vision system that automatically detects a user's eye blinks and accurately measures their durations is introduced. The system is intended to provide an alternate input modality to allow people with severe disabilities to access a computer. Voluntary long blinks trigger mouse clicks, while involuntary short blinks are ignored. The system enables communication using "blink patterns:" sequences of long and short blinks which are interpreted as semiotic messages. The location of the eyes is determined automatically through the motion of the user's initial blinks. Subsequently, the eye is tracked by correlation across time, and appearance changes are automatically analyzed in order to classify the eye as either open or closed at each frame. No manual initialization, special lighting, or prior face detection is required. The system has been tested with interactive games and a spelling program. Results demonstrate overall detection accuracy of 95.61% and an average rate of 28 frames per second.


title: A wearable heart rate belt for ambulant ECG monitoring

abstract: Long-term ECG monitoring is desirable in many daily healthcare situations where a wearable device that can continuously record ECG signals is needed. In this work, we proposed a wearable heart rate belt for ambulant ECG monitoring which can be comfortably worn on the chest or the waist. Active textile electrodes were designed for ECG recording. And a battery powered circuit board was developed consisting of ECG signal conditioning circuits, a 3-axis accelerator for body motion detection, a 12-bit AD converter, a DSP for signal processing and a SD card for data storage. The system also includes a wireless communication module that can transmit heart rate data to a sport watch for displaying. Experiments were carried out which shows that the proposed system is unobtrusive and can be comfortably worn by the user during daily activities. When wearing on the waist, ECG signals with reasonably good quality were acquired in rest and walking situations. The proposed system shows promise for long-term ambulant ECG monitoring.


Traffic Prediction using a Deep Learning Paradigm


title: DC Microgrid for Wind and Solar Power Integration

abstract: Operational controls are designed to support the integration of wind and solar power within microgrids. An aggregated model of renewable wind and solar power generation forecast is proposed to support the quantification of the operational reserve for day-ahead and real-time scheduling. Then, a droop control for power electronic converters connected to battery storage is developed and tested. Compared with the existing droop controls, it is distinguished in that the droop curves are set as a function of the storage state-of-charge (SOC) and can become asymmetric. The adaptation of the slopes ensures that the power output supports the terminal voltage while at the same keeping the SOC within a target range of desired operational reserve. This is shown to maintain the equilibrium of the microgrid's real-time supply and demand. The controls are implemented for the special case of a dc microgrid that is vertically integrated within a high-rise host building of an urban area. Previously untapped wind and solar power are harvested on the roof and sides of a tower, thereby supporting delivery to electric vehicles on the ground. The microgrid vertically integrates with the host building without creating a large footprint.


title: Psychometric Evaluation of the Connor-Davidson Resilience Scale (CD-RISC) in a Sample of Indian Students

abstract: Resilience refers to an individual's ability to thrive despite adversity. The current study examined the psycho- metric properties of the Connor-Davidson Resilience Scale (CD-RISC) in the Indian context. The sample comprised of 256 students (age M =22.75, SD = 1.36 years) who completed questionnaire measures of trait resilience (CD-RISC), Big Five Inventory (BFI), Positive and Negative Affect Schedule (PANAS) and life satisfaction (Satisfaction with Life Scale). Origi- nal five factor solution did not get confirmation and further a four factor solution, through exploratory factor analysis, was selected as being the most suitable, namely, hardiness, optimism, resourcefulness and purpose. The CD-RISC appeared to be a reliable (a = 0.89) and valid measure. The concurrent validity results supported hypotheses regarding the relationship of resilience to personality dimensions and life satisfaction and positive and negative affects. Resilience was negatively associ- ated with neuroticism, negative affects and positively related to all other variables of the study.


title: Source Identification Using Signal Characteristics in Controller Area Networks

abstract: The CAN (Controller Area Network) bus, i.e., the de facto standard for connecting ECUs inside cars, is increasingly becoming exposed to some of the most sophisticated security threats. Due to its broadcast nature and ID oriented communication, each node is sightless in regards to the source of the received messages and assuring source identification is an uneasy challenge. While recent research has focused on devising security in CAN networks by the use of cryptography at the protocol layer, such solutions are not always an alternative due to increased communication and computational overheads, not to mention backward compatibility issues. In this work we set steps for a distinct approach, namely, we try to take authentication up to unique physical characteristics of the frames that are placed by each node on the bus. For this we analyze the frames by taking measurements of the voltage, filtering the signal and examining mean square errors and convolutions in order to uniquely identify each potential sender. Our experimental results show that distinguishing between certain nodes is clearly possible and by clever choices of transceivers and frame IDs each message can be precisely linked to its sender.


title: Proposal for a High Precision Tensor Processing Unit

abstract: This whitepaper proposes the design and adoption of a new generation of Tensor Processing Unit which has the performance of Google's TPU, yet performs operations on wide precision data. The new generation TPU is made possible by implementing arithmetic circuits which compute using a new general purpose, fractional arithmetic based on the residue number system.


title: Interacting Attention-gated Recurrent Networks for Recommendation

abstract: Capturing the temporal dynamics of user preferences over items is important for recommendation. Existing methods mainly assume that all time steps in user-item interaction history are equally relevant to recommendation, which however does not apply in real-world scenarios where user-item interactions can often happen accidentally. More importantly, they learn user and item dynamics separately, thus failing to capture their joint effects on user-item interactions. To better model user and item dynamics, we present the Interacting Attention-gated Recurrent Network (IARN) which adopts the attention model to measure the relevance of each time step. In particular, we propose a novel attention scheme to learn the attention scores of user and item history in an interacting way, thus to account for the dependencies between user and item dynamics in shaping user-item interactions. By doing so, IARN can selectively memorize different time steps of a user's history when predicting her preferences over different items. Our model can therefore provide meaningful interpretations for recommendation results, which could be further enhanced by auxiliary features. Extensive validation on real-world datasets shows that IARN consistently outperforms state-of-the-art methods.


title: Review of Data Mining Techniques for Churn Prediction in Telecom

abstract: Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting customer churn. This paper provides a review of around 100 recent journal articles starting from year 2000 to present the various data mining techniques used in multiple customer based churn models. It then summarizes the existing telecom literature by highlighting the sample size used, churn variables employed and the findings of different DM techniques. Finally, we list the most popular techniques for churn prediction in telecom as decision trees, regression analysis and clustering, thereby providing a roadmap to new researchers to build upon novel churn management models.


title: MIMO OFDM radar system for drone detection

abstract: This paper presents a system setup based on MIMO OFDM radar that can be used for drone detection and detection of unpiloted aerial vehicles (UAVs) in general. In recent years UAVs have become more and more popular not only for commercial use but also as a private gadget. However, these drones can also be misused as a weapon by criminals, spies and terrorists. Therefore many research is done to be able to detect drones and prevent further harm. One possible approach is radar that has already been used successfully in automotive and military applications and offers many advantages like weather and daylight independent operation. It is also possible to detect drones that are emitting no electrical or optical signals. However, there are still many challenges that have to be solved before the implementation of reliably operating systems. This paper presents a system approach based on MIMO OFDM radar that can be used to survey large areas simultaneously and detect flying drones. Additionally, verification measurements with a drone in a real world scenario are presented demonstrating the successful operation of the system.


title: Sources of European drug consumption data at a country level

abstract: AbstractObjectives ::: This study aimed at outlining the characteristics of nationwide administrative databases monitoring drug consumption in Europe.MethodsInternet and bibliographic databases (April 2010) were searched and experts in drug utilization (DU) research interviewed to find nationwide administrative medicines consumption databases in Europe, with data for the out- and inpatient healthcare sector. A questionnaire was developed to gather additional information. We collected data providers, websites, accessibility, data sources, healthcare settings, population coverage, medicines-related data, patient and prescriber data, periods covered, and linkage to other databases.ResultsThirty-one administrative nationwide medicine consumption databases in 25 countries were identified. Questionnaires were responded for 20 databases. Eleven provided wholesalers’ sales data, 11 on reimbursed, 5 on prescribed, and 4 on dispensing medicines. Fifteen databases provided inpatient drug consumption data, mainly wholesalers’ sales.Conclusions ::: Nationwide administrative databases are of value to all stakeholders involved in the conduct and interpretation of post-marketing safety studies, and in the conduct of DU research. The endorsement of the anatomical therapeutic chemical/defined daily dose methodology by these databases contributes to data harmonization. However, there is still a lack of information on inpatient medicines consumption at a patient-level.


title: Contrast set mining in temporal databases

abstract: Understanding the underlying differences between groups or classes in certain contexts can be of the utmost importance. Contrast set mining relies on discovering significant patterns by contrasting two or more groups. A contrast set is a conjunction of attribute-value pairs that differ meaningfully in its distribution across groups. A previously proposed technique is rules for contrast sets, which seeks to express each contrast set found in terms of rules. This work extends rules for contrast sets to a temporal data mining task. We define a set of temporal patterns in order to capture the significant changes in the contrasts discovered along the considered time line. To evaluate the proposal accuracy and ability to discover relevant information, two different real-life data sets were studied using this approach.


title: RIOT OS: Towards an OS for the Internet of Things

abstract: The Internet of Things (IoT) is characterized by heterogeneous devices. They range from very lightweight sensors powered by 8-bit microcontrollers (MCUs) to devices equipped with more powerful, but energy-efficient 32-bit processors. Neither traditional operating systems (OS) currently running on Internet hosts, nor a typical OS for sensor networks are capable to fulfill the diverse requirements of such a wide range of devices. To leverage the IoT, redundant development should be avoided and maintenance costs should be reduced. In this paper we revisit the requirements for an OS in the IoT. We introduce RIOT OS, an OS that explicitly considers devices with minimal resources but eases development across a wide range of devices. RIOT OS allows for standard C and C++ programming, provides multi- threading as well as real-time capabilities, and needs only a minimum of 1.5 kB of RAM.


title: Hierarchical Document Clustering: A Review

abstract: As text documents are largely increasing in the internet, the process of grouping similar documents for versatile applications have put the eye of researchers in this area. However most clustering methods suffer from challenges in dealing with problems of high dimensionality, scalability, accuracy and meaningful cluster labels. This paper presents a review on all these well known methods of document clustering. Hierarchical document clustering method is explained in detail. Study shows that hierarchical document clustering performs well but still there is a scope to improve above mentioned problems.


title: Player Detection and Tracking in Broadcast Tennis Video

abstract: In this paper, we propose a novel algorithm for player detection and tracking in tennis games. The algorithm utilizes court knowledge as well as player color and edge information to extract deformable player figures. Several new techniques are presented in our algorithm: initially, the court lines are detected and reconstructed. Based on the court model, an adaptive search window is designed for locating the minimum region containing a player figure. After retrieving the region of interest, pixel data are processed by non-dominant color extraction and edge detection filters, respectively. Finally, the non-dominant color map and edge map are refined and combined, and a novel shadow removal method is then applied to isolate the player figure. The algorithm was tested on numerous videos with different courts and light condition. Experiments reveal promising results against various environmental factors.