title: Action Recognition with Image Based CNN Features

abstract: Most of human actions consist of complex temporal compositions of more simple actions. Action recognition tasks usually relies on complex handcrafted structures as features to represent the human action model. Convolutional Neural Nets (CNN) have shown to be a powerful tool that eliminate the need for designing handcrafted features. Usually, the output of the last layer in CNN (a layer before the classification layer -known as fc7) is used as a generic feature for images. In this paper, we show that fc7 features, per se, can not get a good performance for the task of action recognition, when the network is trained only on images. We present a feature structure on top of fc7 features, which can capture the temporal variation in a video. To represent the temporal components, which is needed to capture motion information, we introduced a hierarchical structure. The hierarchical model enables to capture sub-actions from a complex action. At the higher levels of the hierarchy, it represents a coarse capture of action sequence and lower levels represent fine action elements. Furthermore, we introduce a method for extracting key-frames using binary coding of each frame in a video, which helps to improve the performance of our hierarchical model. We experimented our method on several action datasets and show that our method achieves superior results compared to other state-of-the-arts methods.


title: Hybrid chat and task dialogue for more engaging HRI using reinforcement learning

abstract: Most of today's task-based spoken dialogue systems perform poorly if the user goal is not within the system's task domain. On the other hand, chatbots cannot perform tasks involving robot actions but are able to deal with unforeseen user input. To overcome the limitations of each of these separate approaches and be able to exploit their strengths, we present and evaluate a fully autonomous robotic system using a novel combination of task-based and chat-style dialogue in order to enhance the user experience with human-robot dialogue systems. We employ Reinforcement Learning (RL) to create a scalable and extensible approach to combining chat and task-based dialogue for multimodal systems. In an evaluation with real users, the combined system was rated as significantly more “pleasant” and better met the users' expectations in a hybrid task+chat condition, compared to the task-only condition, without suffering any significant loss in task completion.


Connecting Supervised and Unsupervised Sentence Embeddings


MobSTer: A model-based security testing framework for web applications


title: Total synthesis of the potent anti-inflammatory lipid mediator Protectin D1

abstract: Abstract The total synthesis of the potent anti-inflammatory lipid mediator Protectin D1 derived from docosahexaenoic acid, has been achieved. The chiral hydroxy-groups at C10 and C17 were obtained via a chiral pool strategy from (4 R )-4-(2-hydroxyethyl)-2,2-dimethyl-1,3-dioxolane and 3,4- O -isopropylidene-2-deoxy- d -ribose, respectively. Wittig reactions, Takai olefination, Pd 0 /Cu I Sonogashira coupling, and Zn(Cu/Ag) reduction completed the total synthesis of Protectin D1.


title: Multiple face recognition in real-time using cloud computing, Emgu CV and Windows Azure

abstract: Multiple face recognition has several applications, such as in the areas of security and robotics. Recognition and classification techniques have been developed in recent years, through different programming languages and approaches. However, the level of detailing often requires a high processing power. This article proposes the use of cloud computing - more specifically, Windows Azure platform - to identify possible performance gains while testing EmguCV framework.


title: A Coplanar Reconfigurable Folded Slot Antenna Without Bias Network for WLAN Applications

abstract: The design of a reconfigurable single folded slot antenna is shown. Metal strips are used in the slot to manipulate the ground size around the slot, which yields to changing the slot's perimeter and thus changing the resonant frequency of the antenna. The design of a single folded slot antenna is first illustrated. Later, the design is modified to be reconfigurable. The resonant frequencies for the reconfigurable design are chosen to be applicable with the WLAN applications. The antenna design, simulation, and measurements are described. The simulated results both for the return loss and maximum gain agree with the measurements. The antenna has similar radiation patterns in both bands. The advantage of this design is that the length of the bias lines does not affect the antenna performance, thus making its design, feeding, and matching an extremely simple and low-cost procedure for antenna designers.


Image Generation from Caption


title: Adolescent Peer Relations, Friendships, and Romantic Relationships: Do They Predict Social Anxiety and Depression?

abstract: This study examined multiple levels of adolescents' interpersonal functioning, including general peer relations (peer crowd affiliations, peer victimization), and qualities of best friendships and romantic relationships as predictors of symptoms of depression and social anxiety. An ethnically diverse sample of 421 adolescents (57% girls; 14 to 19 years) completed measures of peer crowd affiliation, peer victimization, and qualities of best friendships and romantic relationships. Peer crowd affiliations (high and low status), positive qualities in best friendships, and the presence of a dating relationship protected adolescents against feelings of social anxiety, whereas relational victimization and negative interactions in best friendships predicted high social anxiety. In contrast, affiliation with a high-status peer crowd afforded some protection against depressive affect; however, relational victimization and negative qualities of best friendships and romantic relationships predicted depressive symptom...


title: Adtranz: a mobile computing system for maintenance and collaboration

abstract: The paper describes the mobile information and communication aspects of a next generation train maintenance and diagnosis system, discusses the working prototype features, and research results. Wearable/Mobile computers combined with the wireless technology improve efficiency and accuracy of the maintenance work. This technology enables maintenance personnel at the site to communicate with a remote helpdesk/expertise center through digital data, audio, and image.


title: Improving the Accuracy of UML Class Model Recovery

abstract: The gap between UML class models and their implementations impedes program understanding and analysis, and is a source of program errors. Although many reverse engineering techniques were proposed to bridge this gap, two major problems still exist. First, the accuracy of association inference from container classes is not adequate without considering iterators. Second, associations implemented by inherited fields are missed by existing techniques. In this paper, we present an approach to precisely and automatically recover a class model from Java byte code. Our approach tackles the above problems and improves the accuracy of the recovered models. The preliminary empirical results show that our approach achieved a higher accuracy for association inference than existing reverse engineering tools.


title: A qualitative investigation of changes in the belief systems of families of children with autism or Down syndrome

abstract: Background There have been few reports of the world views, values and priorities of families of children with autism or Down syndrome, despite the fact that family belief systems are considered to be among the most important factors affecting the adaptation and resilience of families. ::: ::: Methods Transcripts from three focus groups involving 19 key informants (15 parents of children with autism or Down syndrome, and 4 service providers) were analysed using qualitative methods. ::: ::: Results The themes indicated that raising a child with a disability can be a life-changing experience that spurs families to examine their belief systems. Parents can come to gain a sense of coherence and control through changes in their world views, values and priorities that involve different ways of thinking about their child, their parenting role, and the role of the family. Although parents may grapple with lost dreams, over time positive adaptations can occur in the form of changed world views concerning life and disability, and an appreciation of the positive contributions made by children to family members and society as a whole. Parents’ experiences indicate the importance of hope and of seeing possibilities that lie ahead. ::: ::: Conclusions The information from this study may be used to provide families with an advance understanding of the changes in beliefs that they might undergo, and assists service providers in providing individualized and family-centred services and supports to families.


title: Learning to push by grasping: Using multiple tasks for effective learning

abstract: Recently, end-to-end learning frameworks are gaining prevalence in the field of robot control. These frameworks input states/images and directly predict the torques or the action parameters. However, these approaches are often critiqued due to their huge data requirements for learning a task. The argument of the difficulty in scalability to multiple tasks is well founded, since training these tasks often require hundreds or thousands of examples. But do end-to-end approaches need to learn a unique model for every task? Intuitively, it seems that sharing across tasks should help since all tasks require some common understanding of the environment. In this paper, we attempt to take the next step in data-driven end-to-end learning frameworks: move from the realm of task-specific models to joint learning of multiple robot tasks. In an astonishing result we show that models with multi-task learning tend to perform better than task-specific models trained with same amounts of data. For example, a deep-network learned with 2.5K grasp and 2.5K push examples performs better on grasping than a network trained on 5K grasp examples.


title: Hierarchical Control of Limbless Locomotion Using a Bio-inspired CPG Model

abstract: Limbless robots have the potential ability to perform various highly efficient movements in different environments, taking advantage of the features of limbless locomotion, such as a low center of gravity, a large contact area and a distributed mass. This thesis deals with the locomotion control of limbless robots, concentrating specifically on the study of a hierarchical control architecture as steps toward developing limbless robots capable of 3D locomotion, fast reflex responses and sophisticated responses to environmental stimuli. ::: ::: First, an overview of limbless robots is presented. Various limbless robots found in the literature are investigated. The survey not only introduces some potential applications for limbless robots, but also establishes a classification of limbless locomotion according to the limbless robots\\\' configurations and auxiliary equipment. Moreover, different approaches to autonomously generate motion patterns for limbless robots are discussed. One type of control approaches based on Central Pattern Generators (CPGs) is emphasized, since it is ideally suited to being applied to a hierarchical control architecture. ::: ::: Then, a bio-inspired CPG model is proposed. The key problem for developing such a hierarchical control architecture is how to design a CPG based controller that can not only generate various gaits, but also provide a solution for realizing reflex mechanisms as well as integrating sensory feedback. To this end, a CPG model inspired by the neuronal circuit diagram in the spinal cord of swimming lampreys is designed. A set of interneurons described with sigmoid functions and leaky integrators is incorporated into the design of the neural oscillator for rhythmic signal generation. Furthermore, according to the connection between neural oscillators, a chained type and a cyclic type of CPG circuits are developed. The chained type CPG circuit is used for generating traveling waves between oscillators, while the cyclic type CPG circuit is used for producing synchronization and maintenance activities. Through numerical simulations, the control parameters over relevant characteristics of the two types of CPG circuits are studied in detail. ::: ::: Next, the proposed CPG model is further designed for limbless gait implementation. Considering the configuration of limbless robots with pitch modules and yaw modules connected alternatively, two CPG circuits are applied to the pitch grouped modules and the yaw grouped modules, respectively. Both the necessary conditions for cooperation between the two CPG circuits and the control parameters for fast limbless locomotion are investigated. Four types of limbless gaits, i.e. side winding, rolling, turning and flapping are realized. Results of simulations and experiments show the effectiveness of the proposed CPG circuits in generating limbless locomotion. ::: ::: After that, in order to realize fast sensory reflex responses, the concept of both sensory neurons and reflex arcs are utilized. Since the proposed CPG model is derived from neural circuit in the spinal cord of lampreys and the existence of sensory neurons in lampreys has been proven, it is simple and natural to add sensory neurons into the proposed CPG model at the neuronal level. Based on the design of the sensory neurons, a reflex mechanism taking advantage of reflex arcs forms short pathways to bridge external stimuli and the CPG model. Thus fast responses can be made when the external stimuli are afferent to the CPG model. A ball hitting experiment and a corridor passing experiment confirm the feasibility of the reflex mechanism. ::: ::: Finally, the development of sophisticated responses to environmental stimuli is presented. A framework that combines the CPG model with a learning method is proposed for achieving adaptive limbless locomotion. The key issue of the framework is to find a mapping that converts external stimuli to proper sensory input, so as to modify the output of the CPG model and thus enable the limbless robot to adapt to environments. Two types of learning methods, i.e. a genetic algorithm (GA) based method and a reinforcement learning (RL) based method are applied to the framework, respectively. Through a slope climbing experiment, it is verified that both of them can achieve adaptive limbless locomotion. Furthermore, the performance of adaptive limbless locomotion under the two methods is compared and analyzed, which provides future work with the possible solutions for promoting the performance of adaptive limbless locomotion. ::: ::: From the results of simulations and experiments, the hierarchical control architecture is confirmed to be a solid platform for improving the locomotive behaviors of limbless robots.


title: Exploring the Relationship between Organizational Culture and Information Security Culture

abstract: Managing Information Security is becoming more challenging in today’s business because people are both a cause of information security incidents as well as a key part of the protection from them. As the impact of organizational culture (OC) on employees is significant, many researchers have called for the creation of information security culture (ISC) in organizations to influence the actions and behaviour of employees towards better organizational information security. Although researchers have called for the creation of ISC to be embedded in organizations, nonetheless, literature suggests that little past research examining the relationship between the nature of OC and ISC. This paper seeks to explore the relationship between the nature of OC and ISC and argues that organizations that have a medium to high security risk profile need to embed the ISC to influence employee actions and behaviours in relation to information security practices. In addition, this paper also introduces a framework to assist organizations in determining the extent to which the desired ISC is embedded into OC.


title: Imaging modalities to diagnose carotid artery stenosis: progress and prospect

abstract: In the past few decades, imaging has been developed to a high level of sophistication. Improvements from one-dimension (1D) to 2D images, and from 2D images to 3D models, have revolutionized the field of imaging. This not only helps in diagnosing various critical and fatal diseases in the early stages but also contributes to making informed clinical decisions on the follow-up treatment profile. Carotid artery stenosis (CAS) may potentially cause debilitating stroke, and its accurate early detection is therefore important. In this paper, the technical development of various CAS diagnosis imaging modalities and its impact on the clinical efficacy is thoroughly reviewed. These imaging modalities include duplex ultrasound (DUS), computed tomography angiography (CTA) and magnetic resonance angiography (MRA). For each of the imaging modalities considered, imaging methodology (principle), critical imaging parameters, and the extent of imaging the vulnerable plaque are discussed. DUS is usually the initial recommended CAS diagnostic examination. However, for the therapeutic intervention, either MRA or CTA is recommended for confirmation, and for added information on intracranial cerebral circulation and aortic arch condition for procedural planning. Over the past few decades, the focus of CAS diagnosis has also shifted from pure stenosis quantification to plaque characterization. This has led to further advancement in the existing imaging tools and development of other potential imaging tools like Optical coherence tomography (OCT), photoacoustic tomography (PAT), and infrared (IR) thermography.


title: Augmented Reality: A Class of Displays on the Reality-Virtuality Continuum

abstract: In this paper we discuss augmented reality (AR) displays in a general sense, within the context of a reality-virtuality (RV) continuum, encompassing a large class of `mixed reality' (MR) displays, which also includes augmented virtuality (AV). MR displays are defined by means of seven examples of existing display concepts in which real objects and virtual objects are juxtaposed. Essential factors which distinguish different MR display systems from each other are presented, first by means of a table in which the nature of the underlying scene, how it is viewed, and the observer's reference to it are compared, and then by means of a three dimensional taxonomic framework comprising: extent of world knowledge, reproduction fidelity, and extent of presence metaphor. A principal objective of the taxonomy is to clarify terminology issues and to provide a framework for classifying research across different disciplines.


title: Get Out of my Picture! Internet-based Inpainting

abstract: Often when we review our holiday photos, we notice things we wish we could have avoided, such as vehicles, construction work, or simply other tourists. We cannot go back and retake the photo, so what can we do if we want to remove these things from our photos? We want to replace these sections of the image in a convincing way, preferably with what would really have been seen there without the occlusions. Previous work on this problem, often referred to as “inpainting”, are mainly applicable to small image regions, and rely largely on models of the local behaviour of natural images, e.g. [3, 5]. Recently, replacing large occlusions in photographs has been approached using images from the Internet [2, 7] or by combining several images captured at approximately the same time [1, 9]. In this paper we leverage recent advances in viewpoint invariant image search [8] to find other images of the same scene on the Internet. Beginning with a query image containing a target region to be replaced, we first use an online image search engine to retrieve images of the same scene, and take these to be a set of oracles. Since these images may have significant variations in viewpoint and lighting, we register each oracle to the query image using multiple homographies and a simple global photometric correction. We then use each oracle to propose a solution, by copying image data into the target region using Poisson blending. Finally, we use a Markov random field (MRF) formulation to combine the proposals into a single, occlusion-free result. Figure 1 shows the main stages of our system, and compares our result to those of two other methods.


The Impact of Color on Learning


title: Heuristic-Based Resource Reservation Strategies for Public Cloud

abstract: Cloud service providers (CSPs) adapt different pricing models for their offered services. Some of the models are suitable for short term requirement while others may be suitable for the cloud service user's (CSU) long term requirement. For example, reservation-based pricing model is appropriate for a CSU's long term demand for resources. Finding the optimal amount of resources to be reserved in advance, to minimize the total cost, needs sufficient research effort. Various algorithms were discussed in the last couple of years to solve the resource reservation problem but most of them are based on integer programming problem (IPP) which is NP in nature. In this paper, we derive some heuristic-based polynomial time algorithms to find some near optimal solution to this problem. We show that the cost for CSU using our approach is comparable to the solution obtained using optimal IPP.


title: Very fast ellipse detection for embedded vision applications

abstract: Real-time ellipse detection is an important yet challenging task, since the estimation of the five parameters of an ellipse requires heavy computation. This task is even more challenging when the processing must be done on a mobile device with limited computational power. The typical trade-off between accuracy, efficiency and limited resources of embedded vision programming must be accounted. In this paper we present a novel strategy for edge point selection, which allows to drastically reduce the number of edge points to be evaluated for parameters estimation, making embedded mobile vision applications feasible. Extensive results show the increased efficiency of the proposed method over state-of-the-art ellipse detectors, in synthetic and challenging real images, and in a live mobile application.


title: An Effective Way of using LSB Steganography in images along with Cryptography

abstract: Hiding data is a very important thing nowadays as the data travels over various unsecure networks. To avoid this problem cryptography is used which hides the data or scrambles your message, but cryptography alone cannot provide total security as the message is still available to perform cryptanalysis. Steganography hides the existence of the message. Most of the peoples use either cryptography or steganography to improve security. But a combination of both the techniques provides better security. This paper will describe an effective way of using cryptography along with a modification in LSB steganography in images. General Terms Image steganography


title: A taxonomy of behaviour change methods: an Intervention Mapping approach

abstract: In this paper, we introduce the Intervention Mapping (IM) taxonomy of behaviour change methods and its potential to be developed into a coding taxonomy. That is, although IM and its taxonomy of behaviour change methods are not in fact new, because IM was originally developed as a tool for intervention development, this potential was not immediately apparent. Second, in explaining the IM taxonomy and defining the relevant constructs, we call attention to the existence of parameters for effectiveness of methods, and explicate the related distinction between theory-based methods and practical applications and the probability that poor translation of methods may lead to erroneous conclusions as to method-effectiveness. Third, we recommend a minimal set of intervention characteristics that may be reported when intervention descriptions and evaluations are published. Specifying these characteristics can greatly enhance the quality of our meta-analyses and other literature syntheses. In conclusion, the dynamics of behaviour change are such that any taxonomy of methods of behaviour change needs to acknowledge the importance of, and provide instruments for dealing with, three conditions for effectiveness for behaviour change methods. For a behaviour change method to be effective: (1) it must target a determinant that predicts behaviour; (2) it must be able to change that determinant; (3) it must be translated into a practical application in a way that preserves the parameters for effectiveness and fits with the target population, culture, and context. Thus, taxonomies of methods of behaviour change must distinguish the specific determinants that are targeted, practical, specific applications, and the theory-based methods they embody. In addition, taxonomies should acknowledge that the lists of behaviour change methods will be used by, and should be used by, intervention developers. Ideally, the taxonomy should be readily usable for this goal; but alternatively, it should be clear how the information in the taxonomy can be used in practice. The IM taxonomy satisfies these requirements, and it would be beneficial if other taxonomies would be extended to also meet these needs.


Investigating elevated potassium values.


title: Improving Graph Convolutional Networks with Non-Parametric Activation Functions

abstract: Graph neural networks (GNNs) are a class of neural networks that allow to efficiently perform inference on data that is associated to a graph structure, such as, e.g., citation networks or knowledge graphs. While several variants of GNNs have been proposed, they only consider simple nonlinear activation functions in their layers, such as rectifiers or squashing functions. In this paper, we investigate the use of graph convolutional networks (GCNs) when combined with more complex activation functions, able to adapt from the training data. More specifically, we extend the recently proposed kernel activation function, a non-parametric model which can be implemented easily, can be regularized with standard $\ell_{p}$ -norms techniques, and is smooth over its entire domain. Our experimental evaluation shows that the proposed architecture can significantly improve over its baseline, while similar improvements cannot be obtained by simply increasing the depth or size of the original GCN.


title: A 2.45GHz microstrip patch antenna evolved for WiFi application

abstract: This paper evolves a 2.4GHz microstrip antenna for WiFi application by using the differential evolution (DE). The antenna structure is square substrate with a size of 50mm×50mm×1.6mm. The bottom side of the substrate is printed with a microstrip feed line. The top is metallic painted where a ellipse cut off. The structure is parameterized as a solution vector, and the requirements of the antenna are modeled as objective and constraints. Then a constrained optimization problem (COP) is formulated. The DE algorithm finds an antenna which satisfy the requirements in the simulating. The measured performance of the fabricated antenna matches the simulated one on the whole. This evolved antenna can be applied as a antenna for router and personal computer in indoor use. Notably, an exceptional achievement is the bandwidth of the antenna reaches over 1GHz.


title: Violence, Crime, and Abuse Exposure in a National Sample of Children and Youth

abstract: IMPORTANCE ::: Because exposure to violence, crime, and abuse has been shown to have serious consequences on child development, physicians and policymakers need to know the kinds of exposure that occur at various developmental stages. ::: ::: ::: OBJECTIVES ::: To provide updated estimates of and trends for childhood exposure to a broad range of violence, crime, and abuse victimizations. ::: ::: ::: DESIGN ::: The National Survey of Children's Exposure to Violence was based on a cross-sectional, US national telephone survey conducted in 2011. ::: ::: ::: SETTING ::: Interviews by telephone. ::: ::: ::: PARTICIPANTS ::: The experiences of 4503 children and youth aged 1 month to 17 years were assessed by interviews with caregivers and with youth in the case of those aged 10 to 17 years. ::: ::: ::: RESULTS ::: Two-fifths (41.2%) of children and youth experienced a physical assault in the last year, and 1 in 10 (10.1%) experienced an assault-related injury. Two percent experienced sexual assault or sexual abuse in the last year, but the rate was 10.7% for girls aged 14 to 17 years. More than 1 in 10 (13.7%) experienced maltreatment by a caregiver, including 3.7% who experienced physical abuse. Few significant changes could be detected in rates since an equivalent survey in 2008, but declines were documented in peer flashing, school bomb threats, juvenile sibling assault, and robbery and total property victimization. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: The variety and scope of children's exposure to violence, crime, and abuse suggest the need for better and more comprehensive tools in clinical and research settings for identifying these experiences and their effects.


title: Registration of Non-Uniform Density 3D Point Clouds using Approximate Surface Reconstruction

abstract: 3D laser scanners composed of a rotating 2D laser range scanner exhibit different point densities within and between individual scan lines. Such non-uniform point densities influence neighbor searches which in turn may negatively affect feature estimation and scan registration. To reliably register such scans, we extend a state-of-the-art registration algorithm to include topological information from approximate surface reconstructions. We show that our approach outperforms related approaches in both refining a good initial pose estimate and registering badly aligned point clouds if no such estimate is available. In an example application, we demonstrate local 3D mapping with a micro aerial vehicle by registering sequences of non-uniform density point clouds acquired in-flight with a continuously rotating lightweight 3D scanner.


title: Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

abstract: Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induction approaches have attempted to address this problem, but are typically limited to differentiable memory, and consequently cannot scale beyond small synthetic tasks. In this work, we propose the Manager-Programmer-Computer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface. Specifically, we introduce a Neural Symbolic Machine, which contains a sequence-to-sequence neural "programmer", and a non-differentiable "computer" that is a Lisp interpreter with code assist. To successfully apply REINFORCE for training, we augment it with approximate gold programs found by an iterative maximum likelihood training process. NSM is able to learn a semantic parser from weak supervision over a large knowledge base. It achieves new state-of-the-art performance on WebQuestionsSP, a challenging semantic parsing dataset, with weak supervision. Compared to previous approaches, NSM is end-to-end, therefore does not rely on feature engineering or domain specific knowledge.


title: PD-L1 biomarker testing for non-small cell lung cancer: truth or fiction?

abstract: Research in cancer immunology is currently accelerating following a series of cancer immunotherapy breakthroughs during the last 5 years. Various monoclonal antibodies which block the interaction between checkpoint molecules PD-1 on immune cells and PD-L1 on cancer cells have been used to successfully treat non-small cell lung cancer (NSCLC), including some durable responses lasting years. Two drugs, nivolumab and pembrolizumab, are now FDA approved for use in certain patients who have failed or progressed on platinum-based or targeted therapies while agents targeting PD-L1, atezolizumab and durvalumab, are approaching the final stages of clinical testing. Despite impressive treatment outcomes in a subset of patients who receive these immune therapies, many patients with NSCLC fail to respond to anti-PD-1/PD-L1 and the identification of a biomarker to select these patients remains highly sought after. In this review, we discuss the recent clinical trial results of pembrolizumab, nivolumab, and atezolizumab for NSCLC, and the significance of companion diagnostic testing for tumor PD-L1 expression.


title: Do Functional Movement Screen (FMS) composite scores predict subsequent injury? A systematic review with meta-analysis

abstract: Aim This paper aims to systematically review studies investigating the strength of association between FMS composite scores and subsequent risk of injury, taking into account both methodological quality and clinical and methodological diversity. Design Systematic review with meta-analysis. Data sources A systematic search of electronic databases was conducted for the period between their inception and 3 March 2016 using PubMed, Medline, Google Scholar, Scopus, Academic Search Complete, AMED (Allied and Complementary Medicine Database), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Health Source and SPORTDiscus. Eligibility criteria for selecting studies Inclusion criteria: (1) English language, (2) observational prospective cohort design, (3) original and peer-reviewed data, (4) composite FMS score, used to define exposure and non-exposure groups and (5) musculoskeletal injury, reported as the outcome. Exclusion criteria: (1) data reported in conference abstracts or non-peer-reviewed literature, including theses, and (2) studies employing cross-sectional or retrospective study designs. Results 24 studies were appraised using the Quality of Cohort Studies assessment tool. In male military personnel, there was ‘strong’ evidence that the strength of association between FMS composite score (cut-point ≤14/21) and subsequent injury was ‘small’ (pooled risk ratio=1.47, 95% CI 1.22 to 1.77, p I 2 =57%). There was ‘moderate’ evidence to recommend against the use of FMS composite score as an injury prediction test in football (soccer). For other populations (including American football, college athletes, basketball, ice hockey, running, police and firefighters), the evidence was ‘limited’ or ‘conflicting’. Conclusion The strength of association between FMS composite scores and subsequent injury does not support its use as an injury prediction tool. Trial registration number PROSPERO registration number CRD42015025575.


title: Fog computing security: a review of current applications and security solutions

abstract: Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.


title: Latent Cross: Making Use of Context in Recurrent Recommender Systems

abstract: The success of recommender systems often depends on their ability to understand and make use of the context of the recommendation request. Significant research has focused on how time, location, interfaces, and a plethora of other contextual features affect recommendations. However, in using deep neural networks for recommender systems, researchers often ignore these contexts or incorporate them as ordinary features in the model. In this paper, we study how to effectively treat contextual data in neural recommender systems. We begin with an empirical analysis of the conventional approach to context as features in feed-forward recommenders and demonstrate that this approach is inefficient in capturing common feature crosses. We apply this insight to design a state-of-the-art RNN recommender system. We first describe our RNN-based recommender system in use at YouTube. Next, we offer "Latent Cross," an easy-to-use technique to incorporate contextual data in the RNN by embedding the context feature first and then performing an element-wise product of the context embedding with model's hidden states. We demonstrate the improvement in performance by using this Latent Cross technique in multiple experimental settings.


title: Reliability, Validity and Sensitivity to Change of Turkish Dizziness Handicap Inventory (DHI) in Patients with Unilateral Peripheral Vestibular Disease

abstract: Objective: The Dizziness Handicap Inventory (DHI) is a condition specific health status measure for patients with vestibular disease. The purpose of this study is to evaluate the internal consistency, test-retest reliability, construct validity and sensitivity to change of the DHI in people with peripheral vestibular disorder. Materials and Methods: Thirty-three patients with unilateral peripheral vestibular disease were included in the study. For analysis of test-retest reliability, Turkish version of DHI inventory developed by “translation-back translation” method was performed to patients on the day of admission and one week after admission. To assess validity, patients were also evaluated with the visual Analog Scale (VAS), Romberg/tandem Romberg test (eyes open/closed), standing on foam (eyes open/closed), static posturography, five times sit to stand test (FTSTS), timed up to go test (TUG), gait speed, dynamic Gait Index (DGI), functional gait assessment (FGA) and Activities-specific Balance Confidence Scale (ABC). To assess sensitivity to change, 27 patients were involved in a 4 week customized vestibular rehabilitation program. Results: Crohnbach alpha for DHI total was 0.92, Crohnbach alpha for emotional subscore was 0.83, Crohnbach alpha for functional subscore was 0.88 and Crohnbach alpha for physical subscore was 0.67. Although ABC showed significant correlation with DHI (r=0.62-0.53, p 0.05). Significant improvement was achieved in DHI and other parameters by customized exercise program (p<0.05). Conclusions: Although Turkish DHI scale showed high internal consistency and sensitivity change, its test-retest reliability and validity was relatively poor.


title: Knowledge sharing and firm innovation capability: an empirical study

abstract: Purpose – The study sets out to examine the influence of individual factors (enjoyment in helping others and knowledge self‐efficacy), organizational factors (top management support and organizational rewards) and technology factors (information and communication technology use) on knowledge sharing processes and whether more leads to superior firm innovation capability. Design/methodology/approach – Based on a survey of 172 employees from 50 large organizations in Taiwan, this study applies the structural equation modeling (SEM) to investigate the research model. Findings – The results show that two individual factors (enjoyment in helping others and knowledge self‐efficacy) and one of the organizational factors (top management support) significantly influence knowledge‐sharing processes. The results also indicate that employee willingness to both donate and collect knowledge enable the firm to improve innovation capability. Research limitations/implications – Future research can examine how personal traits (such as age, level of education, and working experiences) and organizational characteristics (such as firm size and industry type) may moderate the relationships between knowledge enablers and processes. Practical implications – From a practical perspective, the relationships among knowledge‐sharing enablers, processes, and firm innovation capability may provide a clue regarding how firms can promote knowledge‐sharing culture to sustain their innovation performance. Originality/value – The findings of this study provide a theoretical basis, and simultaneously can be used to analyze relationships among knowledge‐sharing factors, including enablers, processes, and firm innovation capability. From a managerial perspective, this study identified several factors essential to successful knowledge sharing, and discussed the implications of these factors for developing organizational strategies that encourage and foster knowledge sharing.


title: Effect of AFO Design on Walking after Stroke: Impact of Ankle Plantar Flexion Contracture

abstract: This study was conducted to compare the effects of three ankle-foot orthosis (AFO) designs on walking after stroke and determine whether an ankle plantar flexion contracture impacts response to the AFOs. A total of 30 individuals, ranging from 6–215 months post-stroke, were tested in four conditions: shoes only (SH), dorsi-assist/dorsi-stop AFO (DA-DS), plantar stop/free dorsiflexion AFO (PS), and rigid AFO (Rigid). Kinematics, kinetics, and electromyographic (EMG) activity were recorded from the hemiparetic lower extremity while participants walked at a self-selected pace. Gait parameters were compared between conditions and between participants with and without a moderate ankle plantar flexion contracture. All AFOs increased ankle dorsiflexion in swing and early stance. Anterior tibialis EMG was reduced only in the PS AFO. Both PS and Rigid AFOs restricted ankle plantar flexion and increased knee flexion in loading. Peak ankle dorsiflexion in stance and soleus EMG intensity were greatest in the PS AFO. The Rigid AFO tended to restrict dorsiflexion in stance and knee flexion in swing only in participants without a plantar flexion contracture. Individuals without a contracture benefit from an AFO that permits dorsiflexion mobility in stance and those with quadriceps weakness may more easily tolerate an AFO with plantar flexion mobility in loading.


title: MyoGym: introducing an open gym data set for activity recognition collected using myo armband

abstract: The activity recognition research has remained popular although the first steps were taken almost two decades ago. While the first ideas were more like a-proof-of-concept studies the area has become a fruitful soil to novel methods of machine learning, to adaptive modeling, signal fusion and several different types of application areas. Nevertheless, one of the slowing aspects in methodology development is the burden in collecting and labeling enough versatile data sets. In this article, a MyoGym data set is introduced to be used in activity recognition classifier development, in development of models for unseen activities, in signal fusion, and many other areas not yet known. The data set includes 6D motion signals and 8 channel electromyogram data from 10 persons and from 30 different gym exercises, each of them consisting a set of ten repetitions. The benchmark results provided, in this article, are in purpose made straightforward that their repetitiveness should be easy for any newcomer in the area.


title: Interface for heterogeneous kernels: A framework to enable hybrid OS designs targeting high performance computing on manycore architectures

abstract: Turning towards exascale systems and beyond, it has been widely argued that the currently available systems software is not going to be feasible due to various requirements such as the ability to deal with heterogeneous architectures, the need for systems level optimization targeting specific applications, elimination of OS noise, and at the same time, compatibility with legacy applications. To cope with these issues, a hybrid design of operating systems where light-weight specialized kernels can cooperate with a traditional OS kernel seems adequate, and a number of recent research projects are now heading into this direction. This paper presents Interface for Heterogeneous Kernels (IHK), a general framework enabling hybrid kernel designs in systems equipped with manycore processors and/or accelerators. IHK provides a range of capabilities, such as resource partitioning, management of heterogeneous OS kernels, as well as a low-level communication layer among the kernels. We describe IHK's interface and demonstrate its feasibility for hybrid kernel designs through executing various different lightweight OS kernels on top of it, which are specialized for certain types of applications. We use the Intel Xeon Phi, Intel's latest manycore coprocessor, as our experimental platform.


title: Bringing the Cloud to Rural and Remote Areas - Cloudlet by Cloudlet

abstract: Instead of relying on huge and expensive data centers for rolling out cloudbased services to rural and remote areas, we propose a hardware platform based on small single-board computers. The role of these micro-data centers is twofold. On the one hand, they act as intermediaries between cloud services and clients, improving availability in the case of network or power outages. On the other hand, they run community-based services on local infrastructure. We illustrate how to build such a system without incurring high costs, high power consumption, or single points of failure. Additionally, we opt for a system that is extendable and scalable as well as easy to deploy, relying on an open design.


title: Relevance of watermarking in medical imaging

abstract: Because of the importance of the security issues in the management of medical information, we suggest the use of watermarking techniques to complete the existing measures for protecting medical images. We discuss the necessary requirements for such a system to be accepted by medical staff and its complementary role with respect with existing security systems. We present different scenarios, one devoted to the authentication and tracing of the images, the second to the integrity control of the patient's record.


title: Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models

abstract: In this paper, we address an important step toward our goal of automatic musical accompaniment-the segmentation problem. Given a score to a piece of monophonic music and a sampled recording of a performance of that score, we attempt to segment the data into a sequence of contiguous regions corresponding to the notes and rests in the score. Within the framework of a hidden Markov model, we model our prior knowledge, perform unsupervised learning of the data model parameters, and compute the segmentation that globally minimizes the posterior expected number of segmentation errors. We also show how to produce "online" estimates of score position. We present examples of our experimental results, and readers are encouraged to access actual sound data we have made available from these experiments.


title: Cardiotocographic Diagnosis of Fetal Health based on Multiclass Morphologic Pattern Predictions using Deep Learning Classification

abstract: Medical complications of pregnancy and pregnancy-related deaths continue to remain a major global challenge today. Internationally, about 830 maternal deaths occur every day due to pregnancy-related or childbirth-related complications. In fact, almost 99% of all maternal deaths occur in developing countries. In this research, an alternative and enhanced artificial intelligence approach is proposed for cardiotocographic diagnosis of fetal assessment based on multiclass morphologic pattern predictions, including 10 target classes with imbalanced samples, using deep learning classification models. The developed model is used to distinguish and classify the presence or absence of multiclass morphologic patterns for outcome predictions of complications during pregnancy. The testing results showed that the developed deep neural network model achieved an accuracy of 88.02%, a recall of 84.30%, a precision of 85.01%, and an F-score of 0.8508 in average. Thus, the developed model can provide highly accurate and consistent diagnoses for fetal assessment regarding complications during pregnancy, thereby preventing and/or reducing fetal mortality rate as well as maternal mortality rate during and following pregnancy and childbirth, especially in low-resource settings and developing countries.


title: Gamification – A Novel Phenomenon or a New Wrapping for Existing Concepts?

abstract: Adding game elements to products and services has become a popular app roach for enhancing users’ experiences. Accordingly, gamification is widely considered an important method for intrinsically motivating users toward a preferred behavior. But what exactly about gamification is actually novel? In a broad literature review, we compare and contrast gamification to similar concepts such as hedonic, persuasive and intrinsically motivating information systems. By decomposing and classifying game elements found in the literature, we distinguish between already existing elements an d ones that can be considered new. In order to drive this area of research forward, we develop an extended framework for gamification, identify gaps in the literature, and propose future avenues f or research.


title: Vocabulary learning by mobile-assisted authentic content creation and social meaning-making: two case studies

abstract: In recent years, we have witnessed the concomitant rise of communicative and contextualized approaches as well as the paradigmatic development of the mobile-assisted language learning (MALL) framework in analysing language learning. The focus of MALL research has gradu- ally shifted from content-based (delivery of learning content through mobile devices) to design-oriented (authentic and/or social mobile learning activities) study. In this paper, we present two novel case studies of MALL that emphasize learner-created content. In learning English prepositions and Chinese idioms, respectively, the primary school students used the mobile devices assigned to them on a one-to-one basis to take photos in real-life contexts so as to construct sentences with the newly acquired prepositions or idioms. Subsequently, the learners were voraciously engaged in classroom or online discussion of their semantic constructions, thereby enhancing their understanding of the proper usage of the prepositions or idioms. This work shows the potential of transforming language learning into an authentic seamless learning experience.


title: Enhance word representation for out-of-vocabulary on Ubuntu dialogue corpus

abstract: Ubuntu dialogue corpus is the largest public available dialogue corpus to make it feasible to build end-to-end deep neural network models directly from the conversation data. One challenge of Ubuntu dialogue corpus is the large number of out-of-vocabulary words. In this paper we proposed an algorithm which combines the general pre-trained word embedding vectors with those generated on the task-specific training set to address this issue. We integrated character embedding into Chen et al's Enhanced LSTM method (ESIM) and used it to evaluate the effectiveness of our proposed method. For the task of next utterance selection, the proposed method has demonstrated a significant performance improvement against original ESIM and the new model has achieved state-of-the-art results on both Ubuntu dialogue corpus and Douban conversation corpus. In addition, we investigated the performance impact of end-of-utterance and end-of-turn token tags.


An Efficient NIZK Scheme for Privacy-Preserving Transactions over Account-Model Blockchain


title: Action-Sound Latency: Are Our Tools Fast Enough?

abstract: Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Copyright remains with the author(s).


title: Tesseract: Interactive visual exploration of socio-technical relationships in software development

abstract: Software developers have long known that project success requires a robust understanding of both technical and social linkages. However, research has largely considered these independently. Research on networks of technical artifacts focuses on techniques like code analysis or mining project archives. Social network analysis has been used to capture information about relations among people. Yet, each type of information is often far more useful when combined, as when the “goodness” of social networks is judged by the patterns of dependencies in the technical artifacts. To bring such information together, we have developed Tesseract, an interactive exploratory environment that utilizes cross-linked displays to visualize the myriad relationships between artifacts, developers, bugs, and communications. We evaluated Tesseract by (1) demonstrating its feasibility with GNOME project data (2) assessing its usability via informal user evaluations, and (3) verifying its suitability for the open source community via semi-structured interviews.


title: Lightweight multi-DOA tracking of mobile speech sources

abstract: Estimating the directions of arrival (DOAs) of multiple simultaneous mobile sound sources is an important step for various audio signal processing applications. In this contribution, we present an approach that improves upon our previous work that is now able to estimate the DOAs of multiple mobile speech sources, while being light in resources, both hardware-wise (only using three microphones) and software-wise. This approach takes advantage of the fact that simultaneous speech sources do not completely overlap each other. To evaluate the performance of this approach, a multi-DOA estimation evaluation system was developed based on a corpus collected from different acoustic scenarios named Acoustic Interactions for Robot Audition (AIRA).


title: Design of Fault Tolerant Reversible Arithmetic Logic Unit in QCA

abstract: This work targets design of reversible ALU (arithmetic logic unit) in QCA (Quantum-dot Cellular Automata) framework. The design is based on the reversible QCA structure (RQCA) introduced in this paper. A fault tolerant architecture of reversible ALU is also synthesized. The proposed designs are verified and evaluated over the existing ALU designs and found to be more efficient in terms of design complexity and quantum cost.


title: Information-theoretic bounds on sparsity recovery in the high-dimensional and noisy setting

abstract: The problem of recovering the sparsity pattern of a fixed but unknown vector beta* and Rp based on a set of n noisy observations arises in a variety of settings, including subset selection in regression, graphical model selection, signal denoising, compressive sensing, and constructive approximation. Of interest are conditions on the model dimension p, the sparsity index s (number of non-zero entries in beta*), and the number of observations n that are necessary and/or sufficient to ensure asymptotically perfect recovery of the sparsity pattern. This paper focuses on the information-theoretic limits of sparsity recovery: in particular, for a noisy linear observation model based on measurement vectors drawn from the standard Gaussian ensemble, we derive both a set of sufficient conditions for asymptotically perfect recovery using the optimal decoder, as well as a set of necessary conditions that any decoder must satisfy for perfect recovery. This analysis of optimal decoding limits complements our previous work on thresholds for the behavior of l1 -constrained quadratic programming for Gaussian measurement ensembles.


title: TorusE: Knowledge Graph Embedding on a Lie Group

abstract: Knowledge graphs are useful for many artificial intelligence (AI) tasks. However, knowledge graphs often have missing facts. To populate the graphs, knowledge graph embedding models have been developed. Knowledge graph embedding models map entities and relations in a knowledge graph to a vector space and predict unknown triples by scoring candidate triples. TransE is the first translation-based method and it is well known because of its simplicity and efficiency for knowledge graph completion. It employs the principle that the differences between entity embeddings represent their relations. The principle seems very simple, but it can effectively capture the rules of a knowledge graph. However, TransE has a problem with its regularization. TransE forces entity embeddings to be on a sphere in the embedding vector space. This regularization warps the embeddings and makes it difficult for them to fulfill the abovementioned principle. The regularization also affects adversely the accuracies of the link predictions. On the other hand, regularization is important because entity embeddings diverge by negative sampling without it. This paper proposes a novel embedding model, TorusE, to solve the regularization problem. The principle of TransE can be defined on any Lie group. A torus, which is one of the compact Lie groups, can be chosen for the embedding space to avoid regularization. To the best of our knowledge, TorusE is the first model that embeds objects on other than a real or complex vector space, and this paper is the first to formally discuss the problem of regularization of TransE. Our approach outperforms other state-of-the-art approaches such as TransE, DistMult and ComplEx on a standard link prediction task. We show that TorusE is scalable to large-size knowledge graphs and is faster than the original TransE.


title: Effect of occlusal plane on smile attractiveness

abstract: Abstract Objective: To determine the influence of the occlusal plane angle on smile attractiveness as perceived by a group of adult orthodontic patients and dentists. Materials and Methods: The first stage utilized a laboratory approach to record changes in vertical tooth position at different occlusal plane angles using a maxillary model mounted on an articulator. In the second stage, photographic manipulation was undertaken, using data from stage 1, to produce a computerized prediction of the appearance of the smile at differing occlusal plane angles (0, 5, 10, 15, and 20 degrees). Finally, the five developed photographs were assessed by participants. Results: Alterations in the occlusal plane angle did affect relative smile attractiveness for both patients (n = 66) and dentists (n = 66). For patients, the 10 degree smile was rated better than the 0 and 20 degree smiles (P < .01); for dentists, the 15 degree smile was rated better than the 0 and 20 degree smiles (P < .01). The 5, 10, and 15 degree s...


title: How serotonin shapes moral judgment and behavior

abstract: Neuroscientists are now discovering how hormones and brain chemicals shape social behavior, opening potential avenues for pharmacological manipulation of ethical values. Here, we review recent studies showing how altering brain chemistry can alter moral judgment and behavior, focusing in particular on the neuromodulator serotonin and its role in shaping values related to harm and fairness. We synthesize previous findings and consider the potential mechanisms through which serotonin could increase the aversion to harming others. We present a process model whereby serotonin influences social behavior by shifting social preferences in the positive direction, enhancing the value people place on others’ outcomes. This model may explain previous findings relating serotonin function to prosocial behavior, and makes new predictions regarding how serotonin may influence the neural computation of value in social contexts.


title: Digital receivers and transmitters using polyphase filter banks for wireless communications

abstract: Provides a tutorial overview of multichannel wireless digital receivers and the relationships between channel bandwidth, channel separation, and channel sample rate. The overview makes liberal use of figures to support the underlying mathematics. A multichannel digital receiver simultaneously down-converts a set of frequency-division-multiplexed (FDM) channels residing in a single sampled data signal stream. In a similar way, a multichannel digital transmitter simultaneously up-converts a number of baseband signals to assemble a set of FDM channels in a single sampled data signal stream. The polyphase filter bank has become the architecture of choice to efficiently accomplish these tasks. This architecture uses three interacting processes to assemble or to disassemble the channelized signal set. In a receiver, these processes are an input commutator to effect spectral folding or aliasing due to a reduction in sample rate, a polyphase M-path filter to time align the partitioned and resampled time series in each path, and a discrete Fourier transform to phase align and separate the multiple baseband aliases. In a transmitter, these same processes operate in a related manner to alias baseband signals to high order Nyquist zones while increasing the sample rate with the output commutator.


title: Modeling of Item-Difficulty for Ontology-based MCQs

abstract: Multiple choice questions (MCQs) that can be generated from a domain ontology can significantly reduce human effort & time required for authoring & administering assessments in an e-Learning environment. Even though here are various methods for generating MCQs from ontologies, methods for determining the difficulty-levels of such MCQs are less explored. In this paper, we study various aspects and factors that are involved in determining the difficulty-score of an MCQ, and propose an ontology-based model for the prediction. This model characterizes the difficulty values associated with the stem and choice set of the MCQs, and describes a measure which combines both the scores. Further more, the notion of assigning difficultly-scores based on the skill level of the test taker is utilized for predicating difficulty-score of a stem. We studied the effectiveness of the predicted difficulty-scores with the help of a psychometric model from the Item Response Theory, by involving real-students and domain experts. Our results show that, the predicated difficulty-levels of the MCQs are having high correlation with their actual difficulty-levels.


title: A Novel IoT Authorization Architecture on Hyperledger Fabric With Optimal Consensus Using Genetic Algorithm

abstract: This paper proposes a distributed authorization architecture for Internet of Things adopting a hyperledger fabric framework. This paper also applies Genetic Algorithm to enhance the consensus component, traditionally used for Kafka, in order to determine the best configuration over different parameters, such as input transactions and their success rates. The experimental results confirm our superior performance in that the transaction transfer rate of our optimization, called GA Kafka, outperforms the traditional Kafka by 69.43%, and 89.97% for transaction transfer success rate.


Robust Hashing for Image-based Malware Classification


title: Effective application of process improvement patterns to business processes

abstract: Improving the operational effectiveness and efficiency of processes is a fundamental task of business process management (BPM). There exist many proposals of process improvement patterns (PIPs) as practices that aim at supporting this goal. Selecting and implementing relevant PIPs are therefore an important prerequisite for establishing process-aware information systems in enterprises. Nevertheless, there is still a gap regarding the validation of PIPs with respect to their actual business value for a specific application scenario before implementation investments are incurred. Based on empirical research as well as experiences from BPM projects, this paper proposes a method to tackle this challenge. Our approach toward the assessment of process improvement patterns considers real-world constraints such as the role of senior stakeholders or the cost of adapting available IT systems. In addition, it outlines process improvement potentials that arise from the information technology infrastructure available to organizations, particularly regarding the combination of enterprise resource planning with business process intelligence. Our approach is illustrated along a real-world business process from human resource management. The latter covers a transactional volume of about 29,000 process instances over a period of 1 year. Overall, our approach enables both practitioners and researchers to reasonably assess PIPs before taking any process implementation decision.


title: Color Image Enhancement using Edge Based Histogram Equalization

abstract: Image enhancement is one of the major research areas in the field of digital image processing. The sole objective of this domain is to enhance the quality of a poor contrast image. As a result, the final processed image becomes much more understandable than the original one. A new edge based histogram equalization method is proposed in this study. A high pass filter is used to detect edges with a help of an appropriate gradient operator. In general, a convolution mask is used for this kind of area processes like filtering. This proposed method increases the quality of the poor contrast area. This method does not create an impact on brighter area in the given input image. A few portion of the poor contrast is raised and few others contrast are being reduced. The simulation results of this study are compared with the conventional histogram equalization. Our experimental results show that the Peak Signal-to-Noise Ratio (PSNR) is better than the regular Histogram Equalization (HE) method. All simulation results are obtained using Matlab simulation software tool with standard color test images. Color image enhancement methods are applicable in the areas such as iris recognition, digital photography, remote sensing, biology, medicine, geophysics and microarray techniques, etc.


title: DESIGN OF PLANAR INVERTED-F ANTENNA

abstract: These days communication devices smaller than palm size have appeared in the market. Antenna size is a major factor that limits device miniaturization. Planar inverted F antenna has been designed here which is very popular for handheld wireless devices which are working well for wi-fi and LTE application. it can be easily placed in small space present inside the mobile device. This work concentrates on design and development of compact antenna used for mobile devices. For low profile micro strip patch is popular but it is not good candidate for the portable devices as their designs are based on half wavelength of operation, and not meet the strict small space requirement of these devices. Whereas PIFA design invokes the quarter wavelength operation. Additionally the PIFA offers very high radiation efficiency and sufficient bandwidth in a compact antenna.


title: Standardizing Power Monitoring and Control at Exascale

abstract: Power API--the result of collaboration among national laboratories, universities, and major vendors--provides a range of standardized power management functions, from application-level control and measurement to facility-level accounting, including real-time and historical statistics gathering. Support is already available for Intel and AMD CPUs and standalone measurement devices.


title: Image stitching algorithm research based on OpenCV

abstract: In view of the vast scene and high-resolution image stitching problems, this paper studies a kind of Harris image stitching algorithm which is based on OpenCV configuration environment. First of all, the Harris corner detection is used to extract the feature points. Then Normalized Cross Correlation is used to rough match the feature points and RANSAC algorithm is used to eliminate error matching. Secondly, cylindrical projection transformation model is adopted to realize image registration algorithm. And finally this paper uses an improved weighting average fusion algorithm to fuse images, reducing the computational complexity of image fusion and eliminating seam of stitched images. This algorithm reduces the overlapping rate of images during image stitching and increases the speed of image Registration and Mosaic. It realizes the purpose of giving consideration to both precision and efficiency.


title: Multiple-Robot Simultaneous Localization and Mapping: A Review

abstract: Simultaneous localization and mapping SLAM in unknown GPS-denied environments is a major challenge for researchers in the field of mobile robotics. Many solutions for single-robot SLAM exist; however, moving to a platform of multiple robots adds many challenges to the existing problems. This paper reviews state-of-the-art multiple-robot systems, with a major focus on multiple-robot SLAM. Various issues and problems in multiple-robot SLAM are introduced, current solutions for these problems are reviewed, and their advantages and disadvantages are discussed.


title: Fuzzy logic based emotion classification

abstract: Emotions affect many aspects of our daily lives including decision making, reasoning and physical wellbeing. Researchers have therefore addressed the detection of emotion from individuals’ heart rate, skin conductance, pupil dilation, tone of voice, facial expression and electroencephalogram (EEG). This paper presents an algorithm for classifying positive and negative emotions from EEG. Unlike other algorithms that extract fuzzy rules from the data, the fuzzy rules used in this paper are obtained from emotion classification research reported in the literature and the classification output indicates both the type of emotion and its strength. The results show that the algorithm is more than 90 times faster than the widely used LIBSVM and the obtained average accuracy of 63.52 % is higher than previously reported using the same EEG dataset. This makes this algorithm attractive for real time emotion classification. In addition, the paper introduces a new oscillation feature computed from local minima and local maxima of the signal.


title: New Approaches to Fuzzy-Rough Feature Selection

abstract: There has been great interest in developing methodologies that are capable of dealing with imprecision and uncertainty. The large amount of research currently being carried out in fuzzy and rough sets is representative of this. Many deep relationships have been established, and recent studies have concluded as to the complementary nature of the two methodologies. Therefore, it is desirable to extend and hybridize the underlying concepts to deal with additional aspects of data imperfection. Such developments offer a high degree of flexibility and provide robust solutions and advanced tools for data analysis. Fuzzy-rough set-based feature (FS) selection has been shown to be highly useful at reducing data dimensionality but possesses several problems that render it ineffective for large datasets. This paper proposes three new approaches to fuzzy-rough FS-based on fuzzy similarity relations. In particular, a fuzzy extension to crisp discernibility matrices is proposed and utilized. Initial experimentation shows that the methods greatly reduce dimensionality while preserving classification accuracy.


title: White blood cell segmentation for acute leukemia bone marrow images

abstract: Morphological diagnosis of the blood and bone marrow smear under the microscope is an important preliminary step in the diagnosis of acute leukemia. The features and differential counts of the cells provide valuable information to the specialist to confirm the diagnosis and begin treatment, thus increasing the chance of survival of the patient. Manual diagnosis procedures are often tedious, labor intensive and time consuming. A computerised system can help accelerating the morphological diagnosis process. The proposed method consists of gradient magnitude, thresholding, morphological operations and watershed transform to perform cells segmentation. 50 images from subtypes M2, M5 and M6 were used to test the proposed method and the result showed that the method managed to obtain qualitatively good segmentation results. The segmentation accuracy for the tested image is 94.5% while the overage accuracies for the other subtypes are 94.58%, 95.06% and 95.65% respectively.


title: A new 1.8V fierce-gate crystal oscillator based on the constant cell in 28nm CMOS technology for automotive radar applications

abstract: The majority of analog and digital integrated circuits with built in crystal oscillator use the Gated Pierce design where the oscillator is built around a single CMOS inverting gate. In most applications that require a high level of precision and stability of the performances versus Process, Voltage and Temperature (PVT) variations, this design is not suitable. The inverter cell itself is very sensitive to PVT variations, negatively affecting the overall performances of a crystal oscillator. This paper shows a new Pierce-Gate crystal oscillator based on the constant gm cell, implemented in 28nm CMOS TSMC technology. The oscillator can work with any crystal frequency between 20 MHz and 100 MHz, the power supply is 1.8V and the output is a 50% duty cycle square waveform. Simulations and measurements show that the new designed crystal oscillator is superior in terms of phase noise performances (−123 dBc/Hz at 100 Hz offset at 60 MHz and −163 dBc/Hz at 1MHz offset at 60 MHz), phase noise variation over PVT, Power Supply Rejection Ratio (PSRR), output frequency and output duty cycle stability, and input impedance with respect to the state of the art counterparts. The implemented crystal oscillator is very challenging and suitable for automotive radar applications.


title: Speech Recognition Engineering Issues in Speech to Speech Translation System Design for Low Resource Languages and Domains

abstract: Engineering automatic speech recognition (ASR) for speech to speech (S2S) translation systems, especially targeting languages and domains that do not have readily available spoken language resources, is immensely challenging due to a number of reasons. In addition to contending with the conventional data-hungry speech acoustic and language modeling needs, these designs have to accommodate varying requirements imposed by the domain needs and characteristics, target device and usage modality (such as phrase-based, or spontaneous free form interactions, with or without visual feedback) and huge spoken language variability arising due to socio-linguistic and cultural differences of the users. This paper, using case studies of creating speech translation systems between English and languages such as Pashto and Farsi, describes some of the practical issues and the solutions that were developed for multilingual ASR development. These include novel acoustic and language modeling strategies such as language adaptive recognition, active-learning based language modeling, class-based language models that can better exploit resource poor language data, efficient search strategies, including N-best and confidence generation to aid multiple hypotheses translation, use of dialog information and clever interface choices to facilitate ASR, and audio interface design for meeting both usability and robustness requirements.


title: An Adaptive Mean-Shift Framework for MRI Brain Segmentation

abstract: An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.


Using Intermediate Representations to Solve Math Word Problems


title: Situation-specificities of Vocalizations in Leach's Storm-Petrel Oceanodroma leucorhoa

abstract: The vocalizations of a nocturnally active seabird, Leach's Storm-Petrel (Oceanodroma leucorhoa), were studied at their breeding colony on Daikoku Island, Hokkaido, Japan. Three distinct call types were recognized, namely Chatter-call, Purr-call and Screech-call. All were vocalized by both male and female adults. Purr-call vocalizations and those of Screech-calls showed situation-specificities. In the burrows with Purr-call vocalizations, there were two heterosexual birds, but no broods were found. Therefore, this call may be associated with courtship behavior or pair formation. Screech-calls were always vocalized between two consexual birds and seemed to be vocalized during aggressive encounters. Chatter-calls were usually vocalized together with other call types and in various situations, although the aerial birds vocalized only this call type. But situation-specificities were recognized when these situations were divided according to the calls vocalized with Chatter-calls. Some discussion was made about functional interpretation of vocalizations in Leach's Storm-Petrels.


title: Invariant Representations from Adversarially Censored Autoencoders

abstract: We combine conditional variational autoencoders (VAE) with adversarial censoring in order to learn invariant representations that are disentangled from nuisance/sensitive variations. In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to prevent. Conditioning the decoder on the nuisance variable enables clean separation of the representation, since they are recombined for model learning and data reconstruction. We show this natural approach is theoretically well-founded with information-theoretic arguments. Experiments demonstrate that this method achieves invariance while preserving model learning performance, and results in visually improved performance for style transfer and generative sampling tasks.


title: Peripheral detection as a workload measure in driving: Effects of traffic complexity and route guidance system use in a driving study

abstract: Recent studies have explored a promising method to measure driver workload—the Peripheral Detection Task (PDT). The PDT has been suggested as a standard method to assess safety-relevant workload from the use of in-vehicle information systems (IVIS) while driving. This paper reports the German part of a Swedish-German cooperative study in which the PDT was investigated focusing on its specific sensitivity compared with alternative workload measures. Forty-nine professional drivers performed the PDT while following route guidance system instructions on an inner-city route. The route consisted of both highly demanding and less demanding sections. Two route guidance systems that differed mainly in display size and display organization were compared. Subjective workload ratings (NASA-TLX) as well as physiological measures (heart rate and heart rate variability) were collected as reference data. The PDT showed sensitivity to route demands. Despite their differing displays, both route guidance systems affected PDT performance similarly in intervals of several minutes. However, the PDT proved sensitive to peaks in workload from IVIS use and from the driving task. Peaks in workload were studied by video analyses of four selected subsections on the route. Subjective workload ratings reflected overall route demands and also did not indicate differing effects of the two displays. The physiological measures were less sensitive to workload and indicated emotional strain as well. An assessment of the PDT as a method for the measurement of safety-related workload is given.


title: The potential usefulness of biological markers in risk assessment.

abstract: Substantial data have been generated during the last 5 years in experimental systems and human populations which shed light on the potential usefulness of biological markers in human cancer risk as...


title: election of third party software in Off-The-Shelf-based software evelopment — An interview study with industrial practitioners

abstract: The success of software development using third party components highly depends on the ability to select a suitable component for the intended application. The evidence shows that there is limited knowledge about current industrial OTS selection practices. As a result, there is often a gap between theory and practice, and the proposed methods for supporting selection are rarely adopted in the industrial practice. This paper's goal is to investigate the actual industrial practice of component selection in order to provide an initial empirical basis that allows the reconciliation of research and industrial endeavors. The study consisted of semi-structured interviews with 23 employees from 20 different software-intensive companies that mostly develop web information system applications. It provides qualitative information that help to further understand these practices, and emphasize some aspects that have been overlooked by researchers. For instance, although the literature claims that component repositories are important for locating reusable components; these are hardly used in industrial practice. Instead, other resources that have not received considerable attention are used with this aim. Practices and potential market niches for software-intensive companies have been also identified. The results are valuable from both the research and the industrial perspectives as they provide a basis for formulating well-substantiated hypotheses and more effective improvement strategies.


title: Reinventing grounded theory: some questions about theory, ground and discovery

abstract: Grounded theory's popularity persists after three decades of broad-ranging critique. In this article three problematic notions are discussed—‘theory,’ ‘ground’ and ‘discovery’—which linger in the continuing use and development of grounded theory procedures. It is argued that far from providing the epistemic security promised by grounded theory, these notions—embodied in continuing reinventions of grounded theory—constrain and distort qualitative inquiry, and that what is contrived is not in fact theory in any meaningful sense, that ‘ground’ is a misnomer when talking about interpretation and that what ultimately materializes following grounded theory procedures is less like discovery and more akin to invention. The procedures admittedly provide signposts for qualitative inquirers, but educational researchers should be wary, for the significance of interpretation, narrative and reflection can be undermined in the procedures of grounded theory.


title: Deep Direct Regression for Multi-oriented Scene Text Detection

abstract: In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposals. In the context of multioriented scene text detection, we analyze the drawbacks of indirect regression, which covers the state-of-the-art detection structures Faster-RCNN and SSD as instances, and point out the potential superiority of direct regression. To verify this point of view, we propose a deep direct regression based method for multi-oriented scene text detection. Our detection framework is simple and effective with a fully convolutional network and one-step post processing. The fully convolutional network is optimized in an end-to-end way and has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries. The proposed method is particularly beneficial to localize incidental scene texts. On the ICDAR2015 Incidental Scene Text benchmark, our method achieves the F-measure of 81%, which is a new state-ofthe-art and significantly outperforms previous approaches. On other standard datasets with focused scene texts, our method also reaches the state-of-the-art performance.


title: Compact second harmonic-suppressed bandstop and bandpass filters using open stubs

abstract: Integration of bandstop filters with the bandstop or bandpass filter are presented in this paper. By replacing the series quarter-wavelength connecting lines of conventional open-stub bandpass/bandstop filters with the equivalent T-shaped lines, one could have compact open-stub bandstop/bandpass filters with second harmonic suppression. Transmission-line model calculation is used to derive the design equations of the equivalent T-shaped lines. Experiments have also been done to validate the design concept. Compared with the conventional open-stub bandpass/bandstop filters, over 30-dB improvement of the second harmonic suppression and 28.6% size reduction are achieved


title: Real Time Simulation of Dynamic Voltage Restorer (DVR) utilizing RTDS and dSPACE

abstract: This paper deals with the simulation of a Dynamic Voltage Restorer (DVR) for compensating electric power grid disturbances, in order to protect sensitive loads. The DVR is composed for power electronics converters (AC-DC and DC-AC converters), a serie transformer and a control system. This control system is capable of detecting the disturbances and compensating them. The complete system (grid model, DVR and sensitive load) was simulated on Real-Time Digital Simulator (RTDS), with Hardware in the Loop (HTL) on dSPACE. In RTDS, the power circuits was modeled, and already on dPSACE, the control logic was implemented. In order to analyze the DVR's dynamic behavior, simulation tests have been performed, inserting disturbances with variable amplitude, characterizing a voltage sag, swell and interruption. And finally, the simulation results show a successful compensation for the disturbances inserted in grid, protecting the load.


title: LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace

abstract: Online professional social networks such as LinkedIn serve as a marketplace, wherein job seekers can find right career opportunities and job providers can reach out to potential candidates. LinkedIn's job recommendations product is a key vehicle for efficient matching between potential candidates and job postings. However, we have observed in practice that a subset of job postings receive too many applications (due to several reasons such as the popularity of the company, nature of the job, etc.), while some other job postings receive too few applications. Both cases can result in job poster dissatisfaction and may lead to discontinuation of the associated job posting contracts. At the same time, if too many job seekers compete for the same job posting, each job seeker's chance of getting this job will be reduced. In the long term, this reduces the chance of users finding jobs that they really like on the site. Therefore, it becomes beneficial for the job recommendation system to consider values provided to both job seekers as well as job posters in the marketplace. In this paper, we propose the job application redistribution problem, with the goal of ensuring that job postings do not receive too many or too few applications, while still providing job recommendations to users with the same level of relevance. We present a dynamic forecasting model to estimate the expected number of applications at the job expiration date, and algorithms to either promote or penalize jobs based on the output of the forecasting model. We also describe the system design and architecture for LiJAR, LinkedIn's Job Applications Forecasting and Redistribution system, which we have implemented and deployed in production. We perform extensive evaluation of LiJAR through both offline and online A/B testing experiments. Our production deployment of this system as part of LinkedIn's job recommendation engine has resulted in significant increase in the engagement of users for underserved jobs (6.5%) without affecting the user engagement in terms of the total number of job applications, thereby addressing the needs of job seekers as well as job providers simultaneously.


title: XML Representation of Constraint Networks: Format XCSP 2.1

abstract: We propose a new extended format to represent constraint networks using XML. This format allows us to represent constraints defined either in extension or in intension. It also allows us to reference global constraints. Any instance of the problems CSP (Constraint Satisfaction Problem), QCSP (Quantified CSP) and WCSP (Weighted CSP) can be represented using this format.


title: Automation of Android Applications Testing Using Machine Learning Activities Classification

abstract: Mobile applications are being used every day by more than half of the world's population to perform a great variety of tasks. With the increasingly widespread usage of these applications, the need arises for efficient techniques to test them. Many frameworks allow automating the process of application testing, however existing frameworks mainly rely on the application developer for providing testing scripts for each developed application, thus preventing reuse of these tests for similar applications. In this paper, we present a novel approach for the automation of testing Android applications by leveraging machine learning techniques and reusing popular test scenarios. We discuss and demonstrate the potential benefits of our approach in an empirical study where we show that our developed testing tool, based on the proposed approach, outperforms standard methods in realistic settings.


title: A NOVEL 180Â°HYBRID BASED ON THE MODIFIED GYSEL POWER DIVIDER

abstract: A novel 180 - hybrid is proposed, based on a modifled Gysel power combiner, using phase shifter and ground bridge for the difierence output. Also the Defected Microstrip Structure has been used to increase the hybrid's phase matching. All steps of the design are simulated using HFSS 11 and the flnal design is validated by the fabrication. It has good results between 7GHz to 10GHz and can be used between 6GHz to 11GHz with less accuracy.


title: Causality and counterfactuals in the situation calculus

abstract: Structural causal models offer a popular framework for exploring causal concepts. However, due to their limited expressiveness, structural models have difficulties coping with such concepts as actual (event-to-event) causation. In this article, we propose a new type of causal model, based on embedding structural considerations in the language of situation calculus. By using situation calculus as a basic language, we leverage its power to express complex, dynamically changing situations and, by relying on structural considerations, we can formulate an effective theory of counterfactuals within the situation-calculus.


title: A 5G Cognitive System for Healthcare

abstract: Developments and new advances in medical technology and the improvement of people’s living standards have helped to make many people healthier. However, there are still large design deficiencies due to the imbalanced distribution of medical resources, especially in developing countries. To address this issue, a video conference-based telemedicine system is deployed to break the limitations of medical resources in terms of time and space. By outsourcing medical resources from big hospitals to rural and remote ones, centralized and high quality medical resources can be shared to achieve a higher salvage rate while improving the utilization of medical resources. Though effective, existing telemedicine systems only treat patients’ physiological diseases, leaving another challenging problem unsolved: How to remotely detect patients’ emotional state to diagnose psychological diseases. In this paper, we propose a novel healthcare system based on a 5G Cognitive System (5G-Csys). The 5G-Csys consists of a resource cognitive engine and a data cognitive engine. Resource cognitive intelligence, based on the learning of network contexts, aims at ultra-low latency and ultra-high reliability for cognitive applications. Data cognitive intelligence, based on the analysis of healthcare big data, is used to handle a patient’s health status physiologically and psychologically. In this paper, the architecture of 5G-Csys is first presented, and then the key technologies and application scenarios are discussed. To verify our proposal, we develop a prototype platform of 5G-Csys, incorporating speech emotion recognition. We present our experimental results to demonstrate the effectiveness of the proposed system. We hope this paper will attract further research in the field of healthcare based on 5G cognitive systems.


title: CommAI: Evaluating the first steps towards a useful general AI

abstract: With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal. However, most current research focuses instead on important but narrow applications, such as image classification or machine translation. We believe this to be largely due to the lack of objective ways to measure progress towards broad machine intelligence. In order to fill this gap, we propose here a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.


title: Evaluation of factors influencing dairy biofilm formation in filling hoses of food-processing equipment

abstract: Abstract Biofilms in dairy-processing environments lead to increased opportunities for microbial contamination of processed products. The aim of this work is to investigate the influence of factors that can affect biofilm onset in a dairy-filling test hose. Bacterial composition (monoculture Pseudomonas fragi, polyculture), fat content of milk (1.5%, 3.5%), flow condition (laminar, turbulent), and contact material (PTFE, stainless steel 1.4404) were considered as factors at a constant low temperature of 5.5 ± 0.5 °C. Biofilms were visualized by microscopy (CLSM) and analysed by counting viable cells, ATP-bioluminescence assay, and staining method for biomass quantification. The correlation between these methods was evaluated, since the chosen methods detect different characteristics of biofilms. Furthermore, enzymatic cleaning was applied after each experimental run. The results showed that the bacterial composition, material type, and fat content significantly affect the results of viable-cell counting (p ≤ 0.05). Flow conditions and material type both have significant influence upon biomass-quantification results (p ≤ 0.001). The highest positive Pearson correlation was estimated between the ATP-bioluminescence and biomass methods (0.550), while the lowest correlation was found between viable-cell counting and biomass quantification (0.345). Enzymatic treatment showed a good result for the cleaning of the PTFE-hose liner in the dairy-filling hose.


title: A filtering technique to lower oscillator phase noise

abstract: Based on a physical understanding of phase-noise mechanisms, a passive LC filter is found to lower the phase-noise factor in a differential oscillator to its fundamental minimum. Three fully integrated LC voltage-controlled oscillators (VCOs) serve as a proof of concept. Two 1.1-GHz VCOs achieve -153 dBc/Hz at 3 MHz offset, biased at 3.7 mA from 2.5 V. A 2.1-GHz VCO achieves -148 dBc/Hz at 15 MHz offset, taking 4 mA from a 2.7-V supply. All oscillators use fully integrated resonators, and the first two exceed discrete transistor modules in figure of merit. Practical aspects and repercussions of the technique are discussed.


The Meaningful Mixed Data TOPSIS (TOPSIS-MMD) Method and its Application in Supplier Selection


title: Identifying problems and solutions in scientific text

abstract: Research is often described as a problem-solving activity, and as a result, descriptions of problems and solutions are an essential part of the scientific discourse used to describe research activity. We present an automatic classifier that, given a phrase that may or may not be a description of a scientific problem or a solution, makes a binary decision about problemhood and solutionhood of that phrase. We recast the problem as a supervised machine learning problem, define a set of 15 features correlated with the target categories and use several machine learning algorithms on this task. We also create our own corpus of 2000 positive and negative examples of problems and solutions. We find that we can distinguish problems from non-problems with an accuracy of 82.3%, and solutions from non-solutions with an accuracy of 79.7%. Our three most helpful features for the task are syntactic information (POS tags), document and word embeddings.


On Evaluation System of Innovative Education of Colleges