title: Business ecosystem analysis framework

abstract: The concept of business ecosystems has gained popularity from researchers and practitioners since first introduced in Moore's article in Harvard Business Review in 1993 “Predators and prey: a new ecology of competition”. Inspired by biology, the concept of business ecosystems provides a metaphor to comprehend the inter-wined nature of industries [1]. The reason behind the popularity of this concept and its differentiation from other business theories like Osterwalder's Business Model Canvas [2] and Porter's Value Chain Approach [3] is its comprehensiveness and inclusion of several other components surrounding any business. In this paper, the necessary methods, components and tools for creating, analyzing and managing a business ecosystem successfully will be introduced. The aim of the paper is to propose a Business Ecosystem Analysis Framework that covers all the steps needed to analyze, manage and ensure the feasibility and sustainability of any ecosystem. This framework can be used to locate strengths and weaknesses of an ecosystem, identify its main actors and define how their maturity, interconnectedness and continuity can be improved. The results of the research can be applied to any industry sector.


title: Joint CT/CBCT deformable registration and CBCT enhancement for cancer radiotherapy

abstract: This paper details an algorithm to simultaneously perform registration of computed tomography (CT) and cone-beam computed (CBCT) images, and image enhancement of CBCT. The algorithm employs a viscous fluid model which naturally incorporates two components: a similarity measure for registration and an intensity correction term for image enhancement. Incorporating an intensity correction term improves the registration results. Furthermore, applying the image enhancement term to CBCT imagery leads to an intensity corrected CBCT with better image quality. To achieve minimal processing time, the algorithm is implemented on a graphic processing unit (GPU) platform. The advantage of the simultaneous optimization strategy is quantitatively validated and discussed using a synthetic example. The effectiveness of the proposed algorithm is then illustrated using six patient datasets, three head-and-neck datasets and three prostate datasets.


title: Iterative guidance normal filter for point cloud

abstract: 3D point clouds have become increasingly popular in recent year due to the rapid development of low-cost 3D sensors. One of the most interesting challenges is to filter point cloud, which undoubtedly becomes a crucial part of the point cloud processing pipeline. Based on normal information, this paper proposes a simple but effective point cloud filter framework. In this framework, a kd-tree structure is constructed for representing point cloud to search neighborhood and estimate normal for each point at first. Then, iteratively performing the processing that a bilateral filter is applied to the normal field obtained from the previous iteration, using the same normal field as the guidance; afterward, adjusting point positions is performed depending on the filtered normals. Experimental results indicate the effectiveness of our algorithms.


title: Towards workload-aware self-management: Predicting significant workload shifts

abstract: The workloads of enterprise DBS often show periodic patterns, e.g. because there are mainly OLTP transactions during day-time and analysis operations at night. However, current DBS self-management functions do not consider these periodic patterns in their analysis. Instead, they either adapt the DBS configuration to an overall “average” workload, or they reactively try to adapt the DBS configuration after every periodic change as if the workload had never been observed before. In this paper we propose a periodicity detection approach, which allows the prediction of workload changes for DBS self-management functions. For this purpose, we first describe how recurring DBS workloads, i.e. workloads that are similar to workloads that have been observed in the past, can be identified. We then propose two different approaches for detecting periodic patterns in the history of recurring DBS workloads. Finally we show how this knowledge on periodic patterns can be used to predict workload changes, and how it can be adapted to changes in the periodic patterns over time.


title: Analysis of SSL certificate reissues and revocations in the wake of heartbleed

abstract: Central to the secure operation of a public key infrastructure (PKI) is the ability to revoke certificates. While much of users' security rests on this process taking place quickly, in practice, revocation typically requires a human to decide to reissue a new certificate and revoke the old one. Thus, having a proper understanding of how often systems administrators reissue and revoke certificates is crucial to understanding the integrity of a PKI. Unfortunately, this is typically difficult to measure: while it is relatively easy to determine when a certificate is revoked, it is difficult to determine whether and when an administrator should have revoked. In this paper, we use a recent widespread security vulnerability as a natural experiment. Publicly announced in April 2014, the Heartbleed OpenSSL bug, potentially (and undetectably) revealed servers' private keys. Administrators of servers that were susceptible to Heartbleed should have revoked their certificates and reissued new ones, ideally as soon as the vulnerability was publicly announced. Using a set of all certificates advertised by the Alexa Top 1 Million domains over a period of six months, we explore the patterns of reissuing and revoking certificates in the wake of Heartbleed. We find that over 73% of vulnerable certificates had yet to be reissued and over 87% had yet to be revoked three weeks after Heartbleed was disclosed. Moreover, our results show a drastic decline in revocations on the weekends, even immediately following the Heartbleed announcement. These results are an important step in understanding the manual processes on which users rely for secure, authenticated communication.


title: An Empirical Study on Software Failure Classification with Multi-label and Problem-Transformation Techniques

abstract: Classification techniques have been used in software-engineering research to perform tasks such as categorizing software executions. Traditionally, existing work has proposed single-label failure classification techniques, in which the training and subsequent executions are labeled with a singular fault attribution. Although such approaches have received substantial attention in research on automated software engineering, in reality, recent work shows that the assumption of such a single attribution is often unrealistic: in practice, the inherent characteristics of software behavior, such as multiple faults that contribute to failures and fault interactions, may negatively influence the effectiveness of these techniques. To relax this unrealistic assumption, in the machine learning field, researchers have proposed new approaches for multi-label classification. However, the effectiveness and efficiency of such approaches varies widely based upon application domains. In this paper, we empirically investigate the performance of these new approaches on the failure classification task under different application settings. We conducted experiments using eight classification techniques on five subject programs with more than 8,000 faulty versions to investigate how each such technique accounts for the intricacies of software behavior. Our experimental results show that multi-label techniques provide improved accuracy over single-label. We also evaluated the efficiency of the training and prediction phases of each technique, and offer guidance as to the applicability for each technique for different usage contexts.


title: Spike-based indirect training of a spiking neural network-controlled virtual insect

abstract: Spiking neural networks (SNNs) have been shown capable of replicating the spike patterns observed in biological neuronal networks, and of learning via biologically-plausible mechanisms, such as synaptic time-dependent plasticity (STDP). As result, they are commonly used to model cultured neural network, and memristor-based neuromorphic computer chips that aim at replicating the scalability and functionalities of biological circuitries. These examples of SNNs, however, do not allow for the direct manipulation of the synaptic strengths (or weights) as required by existing training algorithms. Therefore, this paper presents an indirect training algorithm that, instead, is designed to manipulate input spike trains (stimuli) that can be implemented by patterns of blue light, or controlled input voltages, to induce the desired synaptic weights changes via STDP. The approach is demonstrated by training an SNN to control a virtual insect that seeks to reach a target location in an obstacle populated environment, without any prior control or navigation knowledge. The simulation results illustrate the feasibility and efficiency of the proposed indirect training algorithm for a biologically-plausible sensorimotor system.


Analysis : Determining People ' s Emotions in Facebook


title: AdaNet: Adaptive Structural Learning of Artificial Neural Networks

abstract: We present new algorithms for adaptively learning artificial neural networks. Our algorithms (AdaNet) adaptively learn both the structure of the network and its weights. They are based on a solid theoretical analysis, including data-dependent generalization guarantees that we prove and discuss in detail. We report the results of large-scale experiments with one of our algorithms on several binary classification tasks extracted from the CIFAR-10 dataset. The results demonstrate that our algorithm can automatically learn network structures with very competitive performance accuracies when compared with those achieved for neural networks found by standard approaches.


title: Improving JavaScript performance by deconstructing the type system

abstract: Increased focus on JavaScript performance has resulted in vast performance improvements for many benchmarks. However, for actual code used in websites, the attained improvements often lag far behind those for popular benchmarks. This paper shows that the main reason behind this short-fall is how the compiler understands types. JavaScript has no concept of types, but the compiler assigns types to objects anyway for ease of code generation. We examine the way that the Chrome V8 compiler defines types, and identify two design decisions that are the main reasons for the lack of improvement: (1) the inherited prototype object is part of the current object's type definition, and (2) method bindings are also part of the type definition. These requirements make types very unpredictable, which hinders type specialization by the compiler. Hence, we modify V8 to remove these requirements, and use it to compile the JavaScript code assembled by JSBench from real websites. On average, we reduce the execution time of JSBench by 36%, and the dynamic instruction count by 49%.


title: Supervised semantic indexing

abstract: In this article we propose Supervised Semantic Indexing (SSI), an algorithm that is trained on (query, document) pairs of text documents to predict the quality of their match. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained with a supervised signal directly on the ranking task of interest, which we argue is the reason for our superior results. As the query and target texts are modeled separately, our approach is easily generalized to different retrieval tasks, such as online advertising placement. Dealing with models on all pairs of words features is computationally challenging. We propose several improvements to our basic model for addressing this issue, including low rank (but diagonal preserving) representations, and correlated feature hashing (CFH). We provide an empirical study of all these methods on retrieval tasks based on Wikipedia documents as well as an Internet advertisement task. We obtain state-of-the-art performance while providing realistically scalable methods.


title: Race directed random testing of concurrent programs

abstract: Bugs in multi-threaded programs often arise due to data races. Numerous static and dynamic program analysis techniques have been proposed to detect data races. We propose a novel randomized dynamic analysis technique that utilizes potential data race information obtained from an existing analysis tool to separate real races from false races without any need for manual inspection. Specifically, we use potential data race information obtained from an existing dynamic analysis technique to control a random scheduler of threads so that real race conditions get created with very high probability and those races get resolved randomly at runtime. Our approach has several advantages over existing dynamic analysis tools. First, we can create a real race condition and resolve the race randomly to see if an error can occur due to the race. Second, we can replay a race revealing execution efficiently by simply using the same seed for random number generation--we do not need to record the execution. Third, our approach has very low overhead compared to other precise dynamic race detection techniques because we only track all synchronization operations and a single pair of memory access statements that are reported to be in a potential race by an existing analysis. We have implemented the technique in a prototype tool for Java and have experimented on a number of large multi-threaded Java programs. We report a number of previously known and unknown bugs and real races in these Java programs.


title: First report on the diversity of insects trapped by a sticky exudate of the inflorescences of Vriesea bituminosa Wawra (Bromeliaceae: Tillandsioideae)

abstract: Vriesea bituminosa Wawra (Bromeliaceae) inflorescences produce a sticky and abundant mucilage, covering their entire surface. Insects get trapped in this exudate when visiting the inflorescences and eventually die. The aim of this study was to record the diversity of insects captured in, and those tolerant to, the mucilage of V. bituminosa inflorescences and to discuss the ecological role of this natural trap. The study was conducted in the Atlantic Forest, in Rio de Janeiro State, Brazil. Insects were collected from 25 inflorescences of V. bituminosa. We obtained 268 individuals of 137 morphospecies of insects; ~50 % have phytophagous habits. We suggest that the exudate acts as a protective barrier against herbivory for this bromeliad species. However, some species such as the Epimorius suffusus moth (Pyralidae), Plunentis porosus (Coreidae), and two species of Diptera (Lauxaniidae and Drosophilidae) freely feed and reproduce on these inflorescences which suggests that they have suitable properties for utilization of the resources on the reproductive structures of this plant. The possible role of insects glued to inflorescences as a source of nutrients for this bromeliad is also discussed.


title: The Influence of IT and Knowledge on Firm Agility and Performance

abstract: In this conceptual paper, we describe how IT and knowledge strategies, resources and capabilities coevolve and collectively influence firm agility. We briefly examine links between business strategy and IT and knowledge strategies, and also between agility and firm performance. To do so, we conduct a comprehensive literature review. Using the resource-based view of the firm and a dynamic capabilities perspective, we build a research model and put forward eight propositions to be tested in future studies. We argue that a holistic view that takes into account interacting, co-evolving aspects of business, IT and knowledge is necessary if we are to understand the drivers and impacts of firm agility in complex, dynamic business ecosystems.


title: Achieving application-centric performance targets via consolidation on multicores: myth or reality?

abstract: Consolidation of multiple applications with diverse and changing resource requirements is common in multicore systems as hardware resources are abundant and opportunities for better system usage are plenty. Can we maximize resource usage in such a system while respecting individual application performance targets or is it an oxymoron to simultaneously meet such conflicting measures? In this work we provide a solution to the above difficult problem by constructing a queueing-theory based tool that we use to accurately predict application scalability on multicores and that can also provide the optimal consolidation suggestions to maximize system resource usage while meeting simultaneously application performance targets. The proposed methodology is light-weight and relies on capturing application resource demands using standard tools, via nonintrusive low-level measurements. We evaluate our approach on an IBM Power7 system using the DaCapo and SPECjvm benchmark suites where each benchmark exhibits different patterns of parallelism. From 900 different consolidations of application instances, our tool accurately predicts the average iteration time of allocated applications with an average error below 10%.


title: Multi-Perspective Neural Architecture for Recommendation System

abstract: Abstract Currently, there starts a research trend to leverage neural architecture for recommendation systems. Though several deep recommender models are proposed, most methods are too simple to characterize users’ complex preference. In this paper, for a fine-grained analysis, users’ ratings are explained from multiple perspectives, based on which, we propose our neural architectures. Specifically, our model employs several sequential stages to encode the user and item into hidden representations. In one stage, the user and item are represented from multiple perspectives and in each perspective, the representation of user and that of item put attentions to each other. Last, we metric the output representations from the final stage to approach the users’ ratings. Extensive experiments demonstrate that our method achieves substantial improvements against baselines.


title: Instance Segmentation by Deep Coloring

abstract: We propose a new and, arguably, a very simple reduction of instance segmentation to semantic segmentation. This reduction allows to train feed-forward non-recurrent deep instance segmentation systems in an end-to-end fashion using architectures that have been proposed for semantic segmentation. Our approach proceeds by introducing a fixed number of labels (colors) and then dynamically assigning object instances to those labels during training (coloring). A standard semantic segmentation objective is then used to train a network that can color previously unseen images. At test time, individual object instances can be recovered from the output of the trained convolutional network using simple connected component analysis. In the experimental validation, the coloring approach is shown to be capable of solving diverse instance segmentation tasks arising in autonomous driving (the Cityscapes benchmark), plant phenotyping (the CVPPP leaf segmentation challenge), and high-throughput microscopy image analysis. ::: The source code is publicly available: this https URL.


title: Toward an optimal use of artificial intelligence techniques within a clinical decision support system

abstract: Intelligent clinical decision support systems have been increasingly used in health care organisations. These systems are intended to help physicians in their diagnosis procedures; making decisions more accurate and effective, minimising medical errors, improving patient safety and reducing costs. However, the effectiveness and accuracy of these systems largely depend on the underlying AI technique that has been used, where same clinical-related problem can be solved using more than one AI technique which may provide different outcomes. Consequently, it is crucial to figure out the ideal utilisation of AI techniques in the clinical decision support systems. Our research study reviews various researches which utilised Artificial Intelligence techniques in clinical decision support systems with the aim of identifying basic criterion for adequate use of intelligent techniques within such systems. This paper presents a yes/no inquiry approach based on observations of previous research studies. The objective of this inquiry is to facilitate the selection of the most beneficial and effective AI technique that can be applied in the medical decision support system to provide the best outcomes.


title: Use of machine learning in big data analytics for insider threat detection

abstract: In current enterprise environments, information is becoming more readily accessible across a wide range of interconnected systems. However, trustworthiness of documents and actors is not explicitly measured, leaving actors unaware of how latest security events may have impacted the trustworthiness of the information being used and the actors involved. This leads to situations where information producers give documents to consumers they should not trust and consumers use information from non-reputable documents or producers. The concepts and technologies developed as part of the Behavior-Based Access Control (BBAC) effort strive to overcome these limitations by means of performing accurate calculations of trustworthiness of actors, e.g., behavior and usage patterns, as well as documents, e.g., provenance and workflow data dependencies. BBAC analyses a wide range of observables for mal-behavior, including network connections, HTTP requests, English text exchanges through emails or chat messages, and edit sequences to documents. The current prototype service strategically combines big data batch processing to train classifiers and real-time stream processing to classifier observed behaviors at multiple layers. To scale up to enterprise regimes, BBAC combines clustering analysis with statistical classification in a way that maintains an adjustable number of classifiers.


title: Information security governance on national cyber physical systems

abstract: Cyber-physical systems (CPS) are smart systems that include engineered interacting networks of physical and computational components. CPS and related systems (including the Internet of Things (IoT) and the Industrial Internet) are widely recognized as having great potential to enable innovative applications and impact multiple economic sectors in the worldwide economy. Systems that are interconnected and integrated to provide new functionality to improve the quality of life and allow increased technology advances. Developments of CPS turn out accompanied by the development of the attacks and the risks to it. This phenomenon has become a trigger for many to secure the system. The leakage of security breach or CPS can provide information on the impact on system performance and financial losses. In addition, security issues can result in the leaking of information that can be misused by unauthorized parties. Information security problems are generally solved by partial and limited. This paper discusses the CPS security framework that is holistic and integrated, so it is necessary for the successful achievement of the objectives of the CPS system.


title: Examining How the Great Firewall Discovers Hidden Circumvention Servers

abstract: Recently, the operators of the national censorship infrastructure of China began to employ "active probing" to detect and block the use of privacy tools. This probing works by passively monitoring the network for suspicious traffic, then actively probing the corresponding servers, and blocking any that are determined to run circumvention servers such as Tor. We draw upon multiple forms of measurements, some spanning years, to illuminate the nature of this probing. We identify the different types of probing, develop fingerprinting techniques to infer the physical structure of the system, localize the sensors that trigger probing---showing that they differ from the "Great Firewall" infrastructure---and assess probing's efficacy in blocking different versions of Tor. We conclude with a discussion of the implications for designing circumvention servers that resist such probing mechanisms.


title: Can callous-unemotional traits enhance the understanding, diagnosis, and treatment of serious conduct problems in children and adolescents? A comprehensive review.

abstract: This article provides a comprehensive review of the research on the use of callous and unemotional (CU) traits for designating an important subgroup of children and adolescents with severe conduct problems. It focuses on the etiological significance of recognizing this subgroup of youths with severe conduct problems, its implications for diagnostic classification, and the treatment implications of this research. The review highlights limitations in existing research and provides directions for future research. The available research suggests that children and adolescents with severe conduct problems and elevated CU traits show distinct genetic, cognitive, emotional, biological, environmental, and personality characteristics that seem to implicate different etiological factors underlying their behavior problems relative to other youths with severe conduct problems. Recognizing these subgroups could be critical for guiding future research on the causes of severe conduct problems in children and adolescents. Further, children and adolescents with both severe conduct problems and elevated CU traits appear to be at risk for more severe and persistent antisocial outcomes, even controlling for the severity of their conduct problems, the age of onset of their conduct problems, and common comorbid problems, which supports the clinical importance of designating this group in diagnostic classification systems. Finally, although children and adolescents with both severe conduct problems and elevated CU traits tend to respond less positively to typical interventions provided in mental health and juvenile justice settings, they show positive responses to certain intensive interventions tailored to their unique emotional and cognitive characteristics.


title: Constructing empirical tests of randomness

abstract: In this paper we introduce a general framework for automatic construction of empirical tests of randomness. Our new framework generalises and improves a previous approach (Svenda et al., 2013) and it also provides a clear statistical interpretation of its results. This new approach was tested on selected stream ciphers from the eSTREAM competition. Results show that our approach can lay foundations to randomness testing and it is comparable to the Statistical Test Suite developed by NIST. Additionally, the proposed approach is able to perform randomness analysis even when presented with sequences shorter by several orders of magnitude than required by the NIST suite. Although the Dieharder battery still provides a slightly better randomness analysis, our framework is able to detect non-randomness for stream ciphers with limited number of rounds (Hermes, Fubuki) where both above-mentioned batteries fail.


title: Adapt Cases 4 BPM - A Modeling Framework for Process Flexibility in IIoT

abstract: The digital transformation of manufacturing systems has been prominently discussed by academia and industry in recent years. In this context, software systems can be treated as special kind of self-adaptive systems with an enhanced process flexibility, since interconnected resources have the ability to access data to fulfill non-functional requirements. This enhanced flexibility introduces new issues in the domain of BPM, since processes have to orchestrate business logic as well as adaptation logic that ensure the desired flexibility. This leads to a high complexity, which cannot be handled by classical BPM approaches adequately. In this paper, we present a modeling language based approach that supports modeling flexible and adaptive processes and managing the increased complexity. Additionally, we introduce a corresponding software engineering process that can be used to design flexible and adaptive processes. Since our approach is based on existing concepts of the languages UML and BPMN2.0, it is easy to adopt in new and existent projects that are based on them.


title: Doing Better but Feeling Worse Looking for the “Best” Job Undermines Satisfaction

abstract: Expanding upon Simon's (1955) seminal theory, this investigation compared the choice-making strategies of maximizers and satisficers, finding that maximizing tendencies, although positively correlated with objectively better decision outcomes, are also associated with more negative subjective evaluations of these decision outcomes. Specifically, in the fall of their final year in school, students were administered a scale that measured maximizing tendencies and were then followed over the course of the year as they searched for jobs. Students with high maximizing tendencies secured jobs with 20% higher starting salaries than did students with low maximizing tendencies. However, maximizers were less satisfied than satisficers with the jobs they obtained, and experienced more negative affect throughout the job-search process. These effects were mediated by maximizers' greater reliance on external sources of information and their fixation on realized and unrealized options during the search and selection pro...


title: Fog computing based efficient IoT scheme for the Industry 4.0

abstract: Industry 4.0 aims to dramatically enhance the productivity of manufacturing technologies through the collection and analysis of real-time data. This combines the ubiquity of the IoT with the processing capabilities of cloud computing to generate insights that help to optimize the decision making process. The increasing demand of data and the explosion in the number of sensing devices, which might be highly constrained in terms of communication, battery and computational power, introduce new challenges that need efficient IoT-Cloud architectures. With this in mind, we extend MQTT, the de facto IoT communication protocol, using a fog computing approach that introduces a low complexity computational layer between the Cloud and IoT nodes. In this approach, the MQTT broker, which is in charge of relaying data from publishers to subscribers, is placed at the fog layer. The purpose of introducing an intermediate layer to this particular scenario is to: i) predict future data measurements through prediction techniques; ii) operate as a gateway to upper layers; and iii) provide the capability to offload computationally expensive data processing jobs from the Cloud to the Fog, minimizing additional latency and operational expenses. With this architecture, the transmissions required from IoT devices may be reduced, since the publishers would only need to update the predicted data in case of mismatching. We validate our approach with an energy consumption analysis and simulations of different Machine Learning algorithms on a real dataset, and compare it with the traditional MQTT scheme.


title: It’s Written on Your Face: Detecting Affective States from Facial Expressions while Learning Computer Programming

abstract: We built detectors capable of automatically recognizing affective states of novice computer programmers from student-annotated videos of their faces recorded during an introductory programming tutoring session. We used the Computer Expression Recognition Toolbox (CERT) to track facial features based on the Facial Action Coding System, and machine learning techniques to build classification models. Confusion/Uncertainty and Frustration were distinguished from all other affective states in a student-independent fashion at levels above chance (Cohen’s kappa = .22 and .23, respectively), but detection accuracies for Boredom, Flow/Engagement, and Neutral were lower (kappas = .04, .11, and .07). We discuss the differences between detection of spontaneous versus fixed (polled) judgments as well as the features used in the models.


title: Meet Mike: epic avatars

abstract: Meet Mike uses the latest techniques in advanced motion capture to drive complex facial rigs to enable detailed interaction in VR. This allows participants to meet, talk in VR and experience new levels of photorealistic interaction. The installation uses new advances in real time rigs, skin shaders, facial capture, deep learning and real-time rendering in UE4.


title: Evaluation of sentence embeddings in downstream and linguistic probing tasks

abstract: Despite the fast developmental pace of new sentence embedding methods, it is still challenging to find comprehensive evaluations of these different techniques. In the past years, we saw significant improvements in the field of sentence embeddings and especially towards the development of universal sentence encoders that could provide inductive transfer to a wide variety of downstream tasks. In this work, we perform a comprehensive evaluation of recent methods using a wide variety of downstream and linguistic feature probing tasks. We show that a simple approach using bag-of-words with a recently introduced language model for deep context-dependent word embeddings proved to yield better results in many tasks when compared to sentence encoders trained on entailment datasets. We also show, however, that we are still far away from a universal encoder that can perform consistently across several downstream tasks.


title: Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs)

abstract: Human interaction proofs (HIPs) have become commonplace on the internet for protecting free online services from abuse by automated scripts/bots. They are challenges designed to be easily solved by humans, while remaining too hard for computers to solve. Reading based HIPs comprise a segmentation problem and one or more recognition problems. Recent studies have shown that computers are better at solving the recognition problem than the segmentation problem (Chellapilla and Simard, 2004; Chellapilla et al, 2005a). In this paper we compare human and computer single character recognition abilities through a sequence of human user studies and computer experiments using convolutional neural networks. In these experiments, we assume that segmentation has been solved and the approximate locations of individual HIP characters are known. Results show that computers are as good as or better than humans at single character recognition under all commonly used distortion and clutter scenarios used in todays HIPs.


title: Shaping and reshaping the aesthetic brain: Emerging perspectives on the neurobiology of embodied aesthetics

abstract: Less than two decades after its inception, the burgeoning field of neuroaesthetics continues to grow in interest and momentum. Despite the biological and social importance of the human body and the attention people pay to its appearance in daily life, only recently has neuroaesthetic inquiry turned its attention to questions concerning the aesthetic appraisal of the human body. We review evidence illustrating that the complexity of aesthetic experience is reflected by dynamic interplay between brain systems involved in reward, perceptual and motor processing, with a focus on aesthetic perception involving the human body. We then evaluate work demonstrating how these systems are modulated by beholders’ expertise or familiarity. Finally, we discuss seminal studies revealing the plasticity of behavioural and neural responses to beauty after perceptual and motor training. This research highlights the rich potential for neuroaesthetic inquiry to extend beyond its typical realm of the fine arts to address important questions regarding the relationship between embodiment, aesthetics and performing arts. We conclude by considering some of the criticisms and limitations of neuroaesthetics, and highlight several outstanding issues for future inquiry.


title: Factors of Workplace Environment that Affect Employees Performance: A Case Study of Miyazu Malaysia

abstract: This study aims to investigate the effect of workplace environment’s factors towards employees’ performance. Data was collected through the survey method; total 139 employees were participated from three main workplace of Miyazu (M) Sdn. Bhd. Based on the findings it shows that only supervisor support is not significant towards the employees’ performance. Meanwhile, job aid and physical workplace environment are having a significant relationship towards the employees’ performance.


title: Cost-effective optical transceiver subassembly with lens-integrated high-k, low-Tg glass for optical interconnection

abstract: Optical interconnection is a key technology to overcome bandwidth bottlenecks in high-performance computing and high-end servers. An optical subassembly that consists of a transparent substrate and optical devices is attractive for optical interconnection owing to the ease of optical coupling between an integrated lens and optical devices. However, there are difficulties associated with the fabrication of the substrate. To solve these problems, we propose an optical transceiver subassembly using high-k, low-Tg glass. The transceiver subassembly helps in overcoming not only the difficulty in processing conventionally used substrates but also the limitation on high-speed signal transmission by the coplanar waveguide on those substrates. We successfully demonstrate error-free operation at 10 Gb/s and 14 Gb/s with the fabricated optical transceiver subassembly that contains a vertical-cavity surface-emitting laser (VCSEL) array, a driver array, a photodiode (PD) array, and a transimpedance amplifier (TIA) array.


title: Algebraic MACs and Keyed-Verification Anonymous Credentials

abstract: We consider the problem of constructing anonymous credentials for use in a setting where the issuer of credentials is also the verifier, or more generally where the issuer and verifier have a shared key. In this setting we can use message authentication codes (MACs) instead of public key signatures as the basis for the credential system. To this end, we construct two algebraic MACs in prime-order groups, along with efficient protocols for issuing credentials, asserting possession of a credential, and proving statements about hidden attributes (e.g., the age of the credential owner). We prove the security of the first scheme in the generic group model, and prove the security of the second scheme\dash using a dual-system-based approach\dash under decisional Diffie-Hellman (DDH). Our MACs are of independent interest, as they are the only uf-cmva-secure MACs with efficient proofs of knowledge. Finally, we compare the efficiency of our new systems to two existing constructions of anonymous credentials: U-Prove and Idemix. We show that the performance of the new schemes is competitive with U-Prove (which does not have multi-show unlinkability), and many times faster than Idemix.


title: Interactivity and user participation in the television lifecycle: creating, sharing, and controlling content

abstract: Interactive TV research encompasses a rather diverse body of work (e.g. multimedia, HCI, CSCW, UIST, user modeling, media studies) that has accumulated over the past 20 years. In this article, we highlight the state-of-the-art and consider two basic issues: What is interactive TV research? Can it help us reinvent the practices of creating, sharing and watching TV? We survey the literature and identify three concepts that have been inherent in interactive TV research: 1) interactive TV as content creation, 2) interactive TV as a content and experience sharing process, and 3) interactive TV as control of audiovisual content. We propose this simple taxonomy (create-share-control) as an evolutionary step over the traditional hierarchical produce-distribute-consume paradigm. Moreover, we highlight the importance of sociability in all phases of the create-share-control model.


title: PERCIVAL: proactive and reactive attack and response assessment for cyber incidents using visual analytics

abstract: Situational awareness is a key concept in cyber-defence. Its goal is to make the user aware of different and complex aspects of the network he or she is monitoring. This paper proposes PERCIVAL, a novel visual analytics environment that contributes to situational awareness by allowing the user to understand the network security status and to monitor security events that are happening on the system. The proposed visualization allows for comparing the proactive security analysis with the actual attack progress, providing insights on the effectiveness of the mitigation actions the system has triggered against the attack and giving an overview of the possible attack’s evolution. Moreover, the same visualization can be fruitfully used in the proactive analysis since it allows for getting details on computed attack paths and evaluating the mitigation actions that have been proactively computed by the system. A preliminary user study provided a positive feedback on the prototype implementation of the system. A video of the system is available at: https://youtu.be/uMpYCJCX95k.


n-3 Fatty Acid Intake and Periodontal Disease


title: A folded-channel MOSFET for deep-sub-tenth micron era

abstract: Deep-sub-tenth micron MOSFETs with gate length down to 20 nm are reported. To improve the short channel effect immunities, a novel folded channel transistor structure is proposed. The quasi-planar nature of this new variant of the vertical double-gate SOI MOSFETs simplified the fabrication process. The special features of the structure are: (1) a transistor is formed in a vertical ultra-thin Si fin and is controlled by a double-gate, which suppresses short channel effects; (2) the two gates are self-aligned and are aligned to the S/D; (3) S/D is raised to reduce the parasitic resistance; (4) new low-temperature gate or ultra-thin gate dielectric materials can be used because they are deposited after the S/D; and (5) the structure is quasi-planar because the Si fins are relatively short.


title: Learning local descriptors by optimizing the keypoint-correspondence criterion

abstract: Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint bags extracted from two images of the same object under different views form a matching pair, and keypoint bags extracted from images of different objects form a non-matching pair. On average, matching pairs should contain more corresponding keypoints than non-matching pairs. We describe an end-to-end differentiable architecture that enables the learning of local keypoint descriptors from such weakly-labeled data. Our implementation is available at this https URL


title: WebIoT: A web application framework for the internet of things

abstract: After the burst of the dot-com bubble in the Fall of 2001, the Internet has become a participative medium, which allows users to interact with one another and with the services from anywhere and at any time. The potential of such a change is still to be fully exploited, and phenomena such as social networks and cloud computing are just two of the many innovative solutions that have been born from the Web 2.0. At the same time, a new class of users is establishing itself in the Internet landscape: in fact, with the advent of the Internet of Things (IoT), smart objects are becoming the new, and possibly the biggest, Internet community. In this paper, we propose WebIoT, a novel web application framework, based on Google Web Toolkit, aimed at enhancing the interaction among things and between humans and things. Our framework leverages on the following principles: thing-centric design, modularity and web service communications. We will describe the main components of the framework, their interactions and how simple it is to develop any custom IoT application integrating any number and type of smart things. In particular, we will show how different things can be integrated in the framework, how they interact and how users can exploit these interactions to develop any complex functionality. Finally, we describe a typical HealthCare application for the IoT realized using WebIoT.


title: Ubiquitous tagless object locating with ambient harmonic tags

abstract: Extremely low-cost passive RFID tags have become key components for the Internet of Things (IoT), where ubiquitous object locating is one of the most important functions. Although the number of tags in indoor environment continues growing fast, objects cannot be always assumed tagged, whether intentionally or unintentionally. Compared to tagged objects, a tagless target which neither emits nor modulates signal is much more difficult to locate, especially when the size is small. In this paper, we achieve accurate tagless object locating through a dense and wide non-uniform sampling in the Fourier domain of target reflectivity with the help of ambient passive RFID tags as landmarks. Unlike conventional RFID systems, we leverage nonlinearity in passive tags to backscatter second harmonic signals. The frequency separation of uplink and downlink in harmonic RFID allows ready interference cancellation. We embrace spatial diversity enabled by ambient tags and frequency diversity by broadband harmonic backscattering to improve accuracy and robustness. We present the fundamental theory and a prototype system to verify the proposed approach.


title: #greysanatomy vs. #yankees: Demographics and Hashtag Use on Twitter

abstract: Demographics, in particular, gender, age, and race, are a key predictor of human behavior. Despite the significant effect that demographics plays, most scientific studies using online social media do not consider this factor, mainly due to the lack of such information. In this work, we use state-of-the-art face analysis software to infer gender, age, and race from profile images of 350K Twitter users from New York. For the period from November 1, 2014 to October 31, 2015, we study which hashtags are used by different demographic groups. Though we find considerable overlap for the most popular hashtags, there are also many group-specific hashtags.


title: We used Neural Networks to Detect Clickbaits: You won't believe what happened Next!

abstract: Online content publishers often use catchy headlines for their articles in order to attract users to their websites. These headlines, popularly known as clickbaits, exploit a user’s curiosity gap and lure them to click on links that often disappoint them. Existing methods for automatically detecting clickbaits rely on heavy feature engineering and domain knowledge. Here, we introduce a neural network architecture based on Recurrent Neural Networks for detecting clickbaits. Our model relies on distributed word representations learned from a large unannotated corpora, and character embeddings learned via Convolutional Neural Networks. Experimental results on a dataset of news headlines show that our model outperforms existing techniques for clickbait detection with an accuracy of 0.98 with F1-score of 0.98 and ROC-AUC of 0.99.


title: A Low-Profile Wideband Substrate-Integrated Waveguide Cavity-Backed E-Shaped Patch Antenna for the Q-LINKPAN Applications

abstract: A low-profile substrate-integrated waveguide cavity-backed E-shaped patch antenna is proposed for the Q-LINKPAN application in this paper. In order to expand the operating bandwidth, a co-planar waveguide (CPW) is used to feed the proposed antenna with a metallized via for generating one more resonant mode. In addition, a substrate-integrated cavity is employed to suppress the surface wave and improve the radiation efficiency. A differential feeding network is used in the design to improve the symmetry of the E-plane radiation pattern and the H-plane cross polarization. A $2 \times 2$ prototype is designed, fabricated, and measured for a demonstration. The measured results show that the prototype has a 10 dB impedance bandwidth of 34.4%, a gain of around 12.5 dBi with a narrow E-plane radiation beam within 37.5–46 GHz for long distance applications, and a gain of around 8 dBi with a broad E-plane radiation beam within 47–53 GHz for short distance applications. The proposed technique can be used to develop compact planar antenna for meeting both the short- and long-rang communication requirements of the emerging Q-LINKPAN wireless system.


title: Lost in translation: Maximizing handover effectiveness between paramedics and receiving staff in the emergency department

abstract: Objective: The purpose of this study is to investigate perceptions by paramedics and hospital receiving staff about what enables and constrains handover in the emergency department. ::: Methods: This is a qualitative study of interviews with 19 paramedics, 15 nurses and 16 doctors (n=50) from ambulance services and emergency departments in 2 states of Australia. ::: Results: Three main themes emerged that were evident at both sites and in the 3 professional groups. These were: difficulties in creating a shared cognitive picture, tensions between ‘doing’ and ‘listening’, and fragmenting communication. ::: Conclusion: Recommendations arising from this study as to how handover could be improved are the need for a common language between paramedics and staff in the ED; for shared experiences and understanding between the members of the team, and, for the development of a standardised approach to handover from paramedics to ED receiving staff


title: Deception used for cyber defense of control systems

abstract: Control system cyber security defense mechanisms may employ deception in human system interactions to make it more difficult for attackers to plan and execute successful attacks. These deceptive defense mechanisms are organized and initially explored according to a specific deception taxonomy and the seven abstract dimensions of security previously proposed as a framework for the cyber security of control systems.


title: Maternal Nutrient Restriction Reduces Concentrations of Amino Acids and Polyamines in Ovine Maternal and Fetal Plasma and Fetal Fluids1

abstract: Amino acids and polyamines are essential for placental and fetal growth, but little is known about their availability in the conceptus in response to maternal undernutrition. We hypothesized that maternal nutrient restriction reduces concentrations of amino acids and polyamines in the ovine conceptus. This hypothesis was tested in nutrient-restricted ewes between Days 28 and 78 (experiment 1) and between Days 28 and 135 (experiment 2) of gestation. In both experiments, ewes were assigned randomly on Day 28 of gestation to a control group fed 100% of National Research Council (NRC) nutrient requirements and to an nutrient-restricted group fed 50% of NRC requirements. Every 7 days beginning on Day 28 of gestation, ewes were weighed and rations adjusted for changes in body weight. On Day 78 of gestation, blood samples were obtained from the uterine artery and umbilical vein for analysis. In experiment 2, nutrient-restricted ewes on Day 78 of gestation either continued to be fed 50% of NRC requirements or were realimented to 100% of NRC requirements until Day 135. Fetal weight was reduced in nutrient-restricted ewes at both Day 78 (32%) and Day 135 (15%) compared with controls. Nutritional restriction markedly reduced (P < 0.05) concentrations of total alpha-amino acids (particularly serine, arginine-family amino acids, and branched-chain amino acids) and polyamines in maternal and fetal plasma and in fetal allantoic and amniotic fluids at both mid and late gestation. Realimentation of nutrient-restricted ewes increased (P < 0.05) concentrations of total alpha-amino acids and polyamines in all the measured compartments and prevented intrauterine growth retardation. These novel findings demonstrate that 50% global nutrient restriction decreases concentrations of amino acids and polyamines in the ovine conceptus that could adversely impact key fetal functions. The results have important implications for understanding the mechanisms responsible for both intrauterine growth retardation and developmental origins of adult disease.


title: Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug

abstract: Metformin is widely used in the treatment of type 2 diabetes (T2D), but its mechanism of action is poorly defined. Recent evidence implicates the gut microbiota as a site of metformin action. In a double-blind study, we randomized individuals with treatment-naive T2D to placebo or metformin for 4 months and showed that metformin had strong effects on the gut microbiome. These results were verified in a subset of the placebo group that switched to metformin 6 months after the start of the trial. Transfer of fecal samples (obtained before and 4 months after treatment) from metformin-treated donors to germ-free mice showed that glucose tolerance was improved in mice that received metformin-altered microbiota. By directly investigating metformin–microbiota interactions in a gut simulator, we showed that metformin affected pathways with common biological functions in species from two different phyla, and many of the metformin-regulated genes in these species encoded metalloproteins or metal transporters. Our findings provide support for the notion that altered gut microbiota mediates some of metformin's antidiabetic effects.


title: Different types of wearable sensors and health monitoring systems: A survey

abstract: There is steep rise in diseases due to current lifestyle and issues related with aging. Healthcare systems need to keep pace with the changing requirements of the health problems. Some of the requirements of current health monitoring systems are that they should be portable, consume less power, user friendly and so on. With the rapid development in technologies such as wireless, embedded, nanotechnology and so on it has become possible to develop such systems and devices. In this paper we have done survey of different types of wireless standards, type of sensors used along with some of the recent methodologies used in the field of health monitoring.


title: Nanostructured Materials for Room-Temperature Gas Sensors

abstract: Sensor technology has an important effect on many aspects in our society, and has gained much progress, propelled by the development of nanoscience and nanotechnology. Current research efforts are directed toward developing high-performance gas sensors with low operating temperature at low fabrication costs. A gas sensor working at room temperature is very appealing as it provides very low power consumption and does not require a heater for high-temperature operation, and hence simplifies the fabrication of sensor devices and reduces the operating cost. Nanostructured materials are at the core of the development of any room-temperature sensing platform. The most important advances with regard to fundamental research, sensing mechanisms, and application of nanostructured materials for room-temperature conductometric sensor devices are reviewed here. Particular emphasis is given to the relation between the nanostructure and sensor properties in an attempt to address structure–property correlations. Finally, some future research perspectives and new challenges that the field of room-temperature sensors will have to address are also discussed.


title: Magnetic Field Sensors Based on Microelectromechanical Systems (MEMS) Technology

abstract: Nowdays there is an increasing requirement for magnetic devices with improved sensitivity and resolution, trying to keep as low as possible their cost and power consumption. Additionally there is the need to develop compact devices with several sensors able to measure different parameters including magnetic field, pressure, temperature, acceleration. In this way a multifunctional device could be integrated on the same substrate containing transducers and electronic circuits in a compact configuration without affecting device performances.


title: Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

abstract: The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.


title: Seismic TETwalker mobile robot design and modeling

abstract: The Center of Remote Sensing of Ice Sheets (CReSIS) is studying the use of the TETwalker mobile robot developed by NASA/Goddard Space Flight Center for polar seismic data acquisition. This paper discusses the design process for deploying seismic sensors within the 4-TETwalker mobile robot architecture. The 4-TETwalkerpsilas center payload node was chosen as the deployment medium. An alternative method of deploying seismic sensors that rest on the surface is included. Detailed models were also developed to study robot mobility dynamics and the deployment process. Finally, potential power options of solar sheaths and harvesting vibration energy are proposed.


title: An improved criterion for stability and attractability of memristive neural networks with time-varying delays

abstract: Abstract Memristive neural networks are a novel topic in the design and construction of brain-like circuitry system. This paper addresses a challenging problem: How to derive some less conservative theoretical results on the stability and attractability for memristive neural networks? In this paper, a new comparison method and segmentation method of state space are developed. A succinct criterion is provided to ascertain the global exponential stability and the estimate for location of equilibrium point. The obtained criterion is the improvement and extension of the existing results in the literature. The applicability of the proposed framework can be extended to general memristive neurodynamic systems.


Improved Automatic Speech Recognition Using Subband Temporal Envelope Features and Time-Delay Neural Network Denoising Autoencoder


title: Use of bispectral index system (BIS) to Monitor Enteral Conscious (moderate) sedation during general dental procedures.

abstract: Although dental board regulations for the provision of in-office enteral conscious (oral) sedation vary widely with respect to training and pharmacologic strategies, they agree on the use of drugs that are inherently safe, the use of pulse oximetry and the availability of emergency equipment, including pharmacologic antagonists. Patient safety is of greatest concern and is best addressed by appropriate selection of patients, adequate training of personnel and appropriate monitoring of patients. Readings from bispectral index system (BIS) monitors, which use electroencephalographic signals, correlate accurately with depth of sedation during nondissociative general anesthesia of adults and children in the operating room setting. The usefulness of such monitoring as an adjunct to other forms of monitoring of in-office enteral sedation in the dental setting may represent the next important application of this tool, adding a further level of safety for the patient and another level of predictability for the practitioner. This paper reviews the current evidence supporting this new technique, presenting data from 20 procedures in which BIS monitoring during in-office enteral sedation was employed in a community dental practice.


Toxicity of phenol to fish and aquatic ecosystems.


title: Anger, disgust, and presumption of harm as reactions to taboo-breaking behaviors.

abstract: Three experiments investigated the relationship between the presumption of harm in harm-free violations of creatural norms (taboos) and the moral emotions of anger and disgust. Experiment 1 showed that participants made a presumption of harm to others from taboo violations even in conditions described as harmless and not involving other people; this presumption was predicted by anger and not disgust. Experiment 2 manipulated taboo violation and included a cognitive load task to clarify the post-hoc nature of presumption of harm. Experiment 3 was similar but more accurately measured presumed harm. In Experiments 2 and 3, only without load was symbolic harm presumed, indicating its post-hoc function to justify moral anger, which was not affected by load. In general, manipulations of harmfulness to others predicted moral anger better than moral disgust, while manipulations of taboo predicted disgust better. The presumption of harm was found on measures of symbolic rather than actual harm when a choice existed. These studies clarify our understanding of the relationship between emotions and their justification when people consider victimless, offensive acts.


title: Stable and efficient Paclitaxel nanoparticles for targeted glioblastoma therapy.

abstract: Development of effi cient nanoparticles (NPs) for cancer therapy remains a challenge. NPs are required to have high stability, uniform size, suffi cient drug loading, targeting capability, and ability to overcome drug resistance. In this study, the development of a NP formulation that can meet all these challenging requirements for targeted glioblastoma multiform (GBM) therapy is reported. This multifunctional NP is composed of a polyethylene glycolcoated magnetic iron oxide NP conjugated with cyclodextrin and chlorotoxin (CTX) and loaded with fl uorescein and paclitaxel (PTX) (IONP-PTX-CTX-FL). The physicochemical properties of the IONP-PTX-CTX-FL are characterized by transmission electron microscope, dynamic light scattering, and highperformance liquid chromatography. The cellular uptake of NPs is studied using fl ow cytometry and confocal microscopy. Cell viability and apoptosis are assessed with the Alamar Blue viability assay and fl ow cytometry, respectively. The IONP-PTX-CTX-FL had a uniform size of ≈44 nm and high stability in cell culture medium. Importantly, the presence of CTX on NPs enhanced the uptake of the NPs by GBM cells and improved the effi cacy of PTX in killing both GBM and GBM drug-resistant cells. The IONP-PTX-CTX-FL demonstrated its great potential for brain cancer therapy and may also be used to deliver PTX to treat other cancers.


title: Higher Education Ranking and Leagues Tables: Lessons Learned from Benchmarking

abstract: The paper intends to contribute to the debate on ranking and league tables by adopting a critical approach to ranking methodologies from the point of view of a university benchmarking exercise. The absence of a strict benchmarking exercise in the ranking process has been, in the opinion of the author, one of the major problems encountered in the ranking systems and procedures at all levels, regional, national and international. The benchmarking exercise of ten Canadian research intensive universities at the departmental level should contribute to a re‐examination of the typology of the world class ranking systems and suggest reconstruction paths on several major issues.


title: A facial wearable robot for supporting eye opening and closure movement

abstract: This study proposes a novel facial wearable robot for supporting eyelid movements. The robot is designed for people with facial paralysis, especially on one side of the face. People with facial paralysis are not able to blink, which leads to dry eyes and could cause permanent damage to the cornea. They are also prone to developing synkinesis during recovery, and a biofeedback-enabled rehabilitation is considered to prevent it. Based on these, we developed a robot system to support eyelid movements on the paralyzed side, based on the eye closure on the healthy side. The robot has a novel mechanism for supporting the eyelid control, made from soft material, which is called Eyelid Gating Mechanism (ELGM). ELGM deforms by simple rotational or linear actuation inputs and its deformation is customized to eyelid movements. Therefore, this robot can provide non-invasive and gentle support for eyelid movements. In this paper, we present the development and performance evaluation of the developed facial wearable robot.


title: SNIPER: Efficient Multi-Scale Training

abstract: We present SNIPER, an algorithm for performing efficient multi-scale training in instance level visual recognition tasks. Instead of processing every pixel in an image pyramid, SNIPER processes context regions around ground-truth instances (referred to as chips) at the appropriate scale. For background sampling, these context-regions are generated using proposals extracted from a region proposal network trained with a short learning schedule. Hence, the number of chips generated per image during training adaptively changes based on the scene complexity. SNIPER only processes 30% more pixels compared to the commonly used single scale training at 800x1333 pixels on the COCO dataset. But, it also observes samples from extreme resolutions of the image pyramid, like 1400x2000 pixels. As SNIPER operates on resampled low resolution chips (512x512 pixels), it can have a batch size as large as 20 on a single GPU even with a ResNet-101 backbone. Therefore it can benefit from batch-normalization during training without the need for synchronizing batch-normalization statistics across GPUs. SNIPER brings training of instance level recognition tasks like object detection closer to the protocol for image classification and suggests that the commonly accepted guideline that it is important to train on high resolution images for instance level visual recognition tasks might not be correct. Our implementation based on Faster-RCNN with a ResNet-101 backbone obtains an mAP of 47.6% on the COCO dataset for bounding box detection and can process 5 images per second during inference with a single GPU. Code is available at this https URL.


title: How games for computing education are evaluated? A systematic literature review

abstract: Educational games are assumed to be an effective and efficient instructional strategy for computing education. However, it is essential to systematically evaluate such games in order to obtain sound evidence of their impact. Thus, the objective of this article is to present the state of the art on how games for computing education are evaluated. Therefore, we performed a systematic literature review of a sample of 3617 articles from which 112 relevant articles have been identified, describing 117 studies on the evaluation of games for computing education. Based on these studies we analyzed how evaluations are defined (the analysis factors evaluated, research designs, evaluation models/methods used, kind of data collection instruments, etc.), how they have been executed (sample size and replications) and analyzed (data analysis methods used). As a result, we can confirm that most evaluations use a simple research design in which, typically, the game is used and afterwards subjective feedback is collected via questionnaires from the learners. The majority of the evaluations are run with small samples, without replication, using mostly qualitative methods for data analysis. We also observed that most studies do not use a well-defined evaluation model or method. This shows that there is a need for more rigorous evaluations as well as methodological support in order to assist game creators and instructors to improve such games as well as to systematically support decisions on when or how to include them within instructional units. Exploring how games for computing education are evaluated.No consensus on which quality factors are evaluated.Most evaluations are performed in an ad-hoc manner lacking scientific rigor.Models developed for evaluating educational games: MEEGA and EGameFlow.Need for more rigorous evaluations as well as methodological support.


title: Field tests experience from 1.6MW/400kWh Li-ion battery energy storage system providing primary frequency regulation service

abstract: Lithium-ion battery energy storage systems (BESSs) represent suitable alternatives to conventional generating units for providing primary frequency regulation on the Danish market. This paper presents aspects concerning the operation of the BESSs in the Danish energy market while providing upwards primary frequency regulation. Moreover, the paper presents the experience form field tests dedicated to the evaluation of the BESSs' performance degradation. For this purpose, capacity measurements, Hybrid Pulse Power Characterization (HPPC) measurements, and AC impedance measurements were performed on the BESS demonstrator located in Western Denmark and initial results are introduced and discussed. These measurements can be used to validate models for battery ageing during realistic operation or to develop the diagnostic tools for the BESS.


title: Crowdsourced Data Management: Overview and Challenges

abstract: Many important data management and analytics tasks cannot be completely addressed by automated processes. Crowdsourcing is an effective way to harness human cognitive abilities to process these computer-hard tasks, such as entity resolution, sentiment analysis, and image recognition. Crowdsourced data management has been extensively studied in research and industry recently. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowdsourced data management. We first give an overview of crowdsourcing, and then summarize the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data management. Next we review crowdsourced operators, including selection, collection, join, top-k, sort, categorize, aggregation, skyline, planning, schema matching, mining and spatial crowdsourcing. We also discuss crowdsourcing optimization techniques and systems. Finally, we provide the emerging challenges.


title: Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks

abstract: Time series (particularly multivariate) classification has drawn a lot of attention in the literature because of its broad applications for different domains, such as health informatics and bioinformatics. Thus, many algorithms have been developed for this task. Among them, nearest neighbor classification (particularly 1-NN) combined with Dynamic Time Warping (DTW) achieves the state of the art performance. However, when data set grows larger, the time consumption of 1-NN with DTW grows linearly. Compared to 1-NN with DTW, the traditional feature-based classification methods are usually more efficient but less effective since their performance is usually dependent on the quality of hand-crafted features. To that end, in this paper, we explore the feature learning techniques to improve the performance of traditional feature-based approaches. Specifically, we propose a novel deep learning framework for multivariate time series classification. We conduct two groups of experiments on real-world data sets from different application domains. The final results show that our model is not only more efficient than the state of the art but also competitive in accuracy. It also demonstrates that feature learning is worth to investigate for time series classification.


title: Compiling Quantum Circuits to Realistic Hardware Architectures using Temporal Planners

abstract: To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.


title: Walking balance control for biped robot based on multi-sensor integration

abstract: This paper deals with walking balance control of biped robot inspired by biological concept of sensory integration. The control system is proposed with a trajectory generator and a walking balance controller. The inverted pendulum model is used to generate a more natural and energy efficient biped gait. The walking balance controller, which includes Kalman filter and fuzzy controller, is designed based on sensory fusion to keep the body balance and make the robot walking following the desired reference trajectory. A retrofitted biped platform Bioloid is used to validate the proposed balance control method. The experimental results confirm the effectiveness of the proposed control strategy.


title: Leadership behaviours: effects on job satisfaction, productivity and organizational commitment

abstract: Background Research in the west has shown that job satisfaction, productivity and organizational commitment are affected by leadership behaviours. The purpose of this study is to determine the effect of leadership behaviours on employee outcomes in Singapore. Very little research related to this subject has been done in health care settings in this country. The comparison of the results of the different types of settings and samples will allow a better understanding of the relationshiop between leadership behaviours and employee outcomes and thus help to determine if leadership is worth the extra effort. ::: ::: Method The study explored the relationships between five leadership behaviours identified by Kouzes and Posner and the employee outcomes of registered nurses practising in the general wards, intensive care units and the coronary care unit in an acute hospital. Survey questionnaires were used to elicit responses from 100 registered nurses and 20 managers belonging to the organization. Data collected included demographic characteristics and the degree to which the five types of leadership behaviours were used as perceived by the nurse managers and the registered nurses. In addition, the level of nurse job satisfaction, the degree of productivity and the extent of organizational commitment are described. ::: ::: Findings The findings show a similar trend to the original studies in the United States of America. Use of leadership behaviours and employee outcomes were significantly correlated. The regression results indicate that 29% of job satisfaction, 22% of organizational commitment and 9% of productivity were explained by the use of leadership behaviours. Recommendations are made in the light of these findings.


title: Controlling chaos and bifurcations of SMIB power system experiencing SSR phenomenon using SSSC

abstract: Abstract This paper presents the effect of Static Synchronous Series Compensation (SSSC) on the bifurcations of heavily loaded Single Machine Infinite Bus (SMIB) power system experiencing Subsynchronous Resonance (SSR). In SSR phenomenon, the series compensation increases the power transfer capability of the transmission line. However, Hopf bifurcation is depicted at certain compensation levels. The system then routes to chaos via torus breakdown scenario in case of conventional compensation (variable series capacitor) scheme. In this study, the effect of replacing the conventional compensation with SSSC is highlighted. Varying the SSSC controller reference voltage changes the compensation degree. The results show that the operating point of the system never loses stability at any realistic compensation degree in case of SSSC which means that all bifurcations of the system have been eliminated. Time domain simulations coincide with the results of the bifurcation analysis. The robustness of the SSSC compensation scheme and its controller is verified by subjecting a single-phase to ground fault at the end of the transmission line. The results are compared with the case of conventional compensation. Additionally, the effect of the SSSC controller gain on the location of the Hopf bifurcation is addressed.


title: Towards Inference-Oriented Reading Comprehension: ParallelQA

abstract: In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.


corpus-tools.org: An Interoperable Generic Software Tool Set for Multi-layer Linguistic Corpora.


title: Estimating 3D Gaze Directions Using Unlabeled Eye Images via Synthetic Iris Appearance Fitting

abstract: Estimating three-dimensional (3D) human eye gaze by capturing a single eye image without active illumination is challenging. Although the elliptical iris shape provides a useful cue, existing methods face difficulties in ellipse fitting due to unreliable iris contour detection. These methods may fail frequently especially with low resolution eye images. In this paper, we propose a synthetic iris appearance fitting (SIAF) method that is model-driven to compute 3D gaze direction from iris shape. Instead of fitting an ellipse based on exactly detected iris contour, our method first synthesizes a set of physically possible iris appearances and then optimizes inside this synthetic space to find the best solution to explain the captured eye image. In this way, the solution is highly constrained and guaranteed to be physically feasible. In addition, the proposed advanced image analysis techniques also help the SIAF method be robust to the unreliable iris contour detection. Furthermore, with multiple eye images, we propose a SIAF-joint method that can further reduce the gaze error by half, and it also resolves the binary ambiguity which is inevitable in conventional methods based on simple ellipse fitting.


title: Detection and classification of voice pathology using feature selection

abstract: The aim of this study is to apply automatic speech recognition (ASR) mechanism to improve the amount of information extracted from the voice and to increase the accuracy of the system by using selective highly discriminative features among different types of acoustic features. For feature extraction, we applied three techniques which are Mel Frequency Cepstral Coefficient (MFCC), Linear Prediction Cepstral Coefficients (LPCC), and RelAtive SpecTrA - Perceptual Linear Predictive (RASTA-PLP) with a number of selected coefficients from each technique by using t-test, Kruskal-Wallis test, or genetic algorithm (GA). Then for classification, either support vector machine (SVM) or Gaussian Mixture Model (GMM) is used. The experimental results on a selected MEEI subset database show that the proposed method gives high accuracies compared with some recent related methods both in detection and classification tasks. The highest accuracy of 99.9875 % with a standard deviation of 0.0263 is achieved in case of detection, and 99.8578 % with a standard deviation of 0.1657 in case of multi-class pathology classification.


title: Audio event detection from acoustic unit occurrence patterns

abstract: In most real-world audio recordings, we encounter several types of audio events. In this paper, we develop a technique for detecting signature audio events, that is based on identifying patterns of occurrences of automatically learned atomic units of sound, which we call Acoustic Unit Descriptors or AUDs. Experiments show that the methodology works as well for detection of individual events and their boundaries in complex recordings.


title: Designing Future Social Wearables with Live Action Role Play (Larp) Designers

abstract: Designing wearable technology that supports physical and social engagement in a collocated setting is challenging. In this research, we reached out to an expert community of crafters of social experiences: larpers (live action role players). Larpers and larp designers have a longstanding tradition of designing and making use of a variety of elements, such as costumes, physical objects, environments, and recently also digital artifacts. These are crafted in support of co-experience values that we argue can inform the design of social wearables. We engaged in a co-design process with a game designer and co-founder of a larp production company, and embedded the resulting social wearables in a larp. Here, we present the results of this design and implementation process, and articulate design affordances that resonate with our larp designer' values. This work may inspire and inform researchers and designers creating wearable technology that is aimed at supporting collocated engagement.


title: Classification of sentence level sentiment analysis using cloud machine learning techniques

abstract: Cloud machine learning (CML) techniques offer contemporary machine learning services, with pre-trained models and a service to generate own personalized models. This paper presents a completely unique emotional modeling methodology for incorporating human feeling into intelligent systems. The projected approach includes a technique to elicit emotion factors from users, a replacement illustration of emotions and a framework for predicting and pursuit user’s emotional mechanical phenomenon over time. The neural network based CML service has better training concert and enlarged exactness compare to other large scale deep learning systems. Opinions are important to almost all human activities and cloud based sentiment analysis is concerned with the automatic extraction of sentiment related information from text. With the rising popularity and availability of opinion rich resources such as personal blogs and online appraisal sites, new opportunities and issues arise as people now, actively use information technologies to explore and capture others opinions. In the existing system, a segmentation ranking model is designed to score the usefulness of a segmentation candidate for sentiment classification. A classification model is used for predicting the sentiment polarity of segmentation. The joint framework is trained directly using the sentences annotated with only sentiment polarity, without the use of any syntactic or sentiment annotations in segmentation level. However the existing system still has issue with classification accuracy results. To improve the classification performance, in the proposed system, cloud integrate the support vector machine, naive bayes and neural network algorithms along with joint segmentation approaches has been proposed to classify the very positive, positive, neutral, negative and very negative features more effectively using important feature selection. Also to handle the outliers we apply modified k-means clustering method on the given dataset. It is used to cloud cluster the outliers and hence the label as well as unlabeled features is handled efficiently. From the experimental result, we conclude that the proposed system yields better performance than the existing system.


title: Semantic 3D occupancy mapping through efficient high order CRFs

abstract: Semantic 3D mapping can be used for many applications such as robot navigation and virtual interaction. In recent years, there has been great progress in semantic segmentation and geometric 3D mapping. However, it is still challenging to combine these two tasks for accurate and large-scale semantic mapping from images. In the paper, we propose an incremental and (near) real-time semantic mapping system. A 3D scrolling occupancy grid map is built to represent the world, which is memory and computationally efficient and bounded for large scale environments. We utilize the CNN segmentation as prior prediction and further optimize 3D grid labels through a novel CRF model. Superpixels are utilized to enforce smoothness and form robust P N high order potential. An efficient mean field inference is developed for the graph optimization. We evaluate our system on the KITTI dataset and improve the segmentation accuracy by 10% over existing systems.


title: Development of Miniature Marx Generator using BJT

abstract: We have developed a small sized all solid-state pulsed power generator for industrial applications and biomedical applications such as an ozonizer and a cell treatment. In this paper, a novel 19 stages Miniature Marx Generator using bipolar junction transistors (BJTs) to produce high voltage and low energy pulses with nano-seconds pulse rise time is described. This pulsed power generator is consisted of a Cockroft-Walton circuit and a Miniature Marx circuit. The Cockroft-Walton circuit charges all capacitors and the charging voltage is about 300 V. The Miniature Marx circuit is consisted of resistors, capacitors, and BJTs as extreme high speed closing switches using avalanche breakdown phenomena. Another set of BJTs are placed in parallel with the load to turning off a load current. The size of Miniature Marx circuit is 72 mm × 95 mm. The pulsed power generator developed here has an output voltage of about −5.2 kV and a pulse duration of 7 ns. The charging energy of primary capacitors is the milli-Joule regime.


title: Consumer satisfaction with internet shopping: a research framework and propositions for future research

abstract: Consumer satisfaction with Internet shopping has been conceptualized in a variety of ways. Studies in this area remain broad and appear relatively fragmented. In view of this, the purpose of this study is to propose a research framework that integrates both end-user computing satisfaction literature and service quality literature. This framework explicitly considers information quality, system quality, and service quality as the key dimensions of consumer satisfaction with Internet shopping. We believe the research framework and propositions serve as salient guidelines for researchers.


BRIDGE Risk Analyzer: A Collaborative Tool for Enhanced Risk Analysis in Crisis Situations 1


title: Comparison of Sentinel-1 and TerraSAR-X for ship detection

abstract: Sentinel-1A (S1-A) is the first of a pair of satellites operating a C-band Synthetic Aperture Radar (SAR) developed to give continuity to the European SAR programme. After 6 months commissioning phase from its lunch on April 3rd 2014, S1-A started to systematically deliver data to Copernicus Ocean, Land and Emergency services. Interferometric Wide Swath (IWS) mode in dual-polarization VV+VH is the standard acquisition mode used for the observation of the European marine environment. The objectives of this study are the comparison and synergetic use of the S1-A imagery in IWS mode with TerraSAR-X (TS-X) imagery in StripMap mode for SAR ship detection application. Exemplary dataset acquired over the English Channel during a controlled experiment is used to pursue the objective. Automatic Identification System ships messages received by terrestrial stations in the area are used as ground truth and to identify the SAR detections.


title: Rdf serialization from JSON data: The case of JSON data in Diavgeia.gov.gr

abstract: The continuous increase in the volume of open data and the transition to the next generation of connected data web requires the development of methodologies and tools to exploit the already published data. The most important open data providers publish their data in JSON format. In this paper, we present the main features of JSON data compared with those of JSON LD data and their capabilities. We also present, RDF triples production methodologies from JSON and JSON-LD data as infrastructure to support the linked open data. For this reason, we propose a related algorithm and its implementation in programming environment, and we are testing this on open data retrieved by the Greek open data repository. Finally we present the case of RDF triples production with the help of relevant tools.


title: Adversarial contention resolution for simple channels

abstract: This paper analyzes the worst-case performance of randomized backoff on simple multiple-access channels. Most previous analysis of backoff has assumed a statistical arrival model.For batched arrivals, in which all n packets arrive at time 0, we show the following tight high-probability bounds. Randomized binary exponential backoff has makespan Θ(nlgn), and more generally, for any constant r, r-exponential backoff has makespan Θ(nloglgr n). Quadratic backoff has makespan Θ((n/lg n)3/2), and more generally, for r>1, r-polynomial backoff has makespan Θ((n/lg n)1+1/r). Thus, for batched inputs, both exponential and polynomial backoff are highly sensitive to backoff constants. We exhibit a monotone superpolynomial subexponential backoff algorithm, called loglog-iterated backoff, that achieves makespan Θ(nlglg n/lglglg n). We provide a matching lower bound showing that this strategy is optimal among all monotone backoff algorithms. Of independent interest is that this lower bound was proved with a delay sequence argument.In the adversarial-queuing model, we present the following stability and instability results for exponential backoff and loglog-iterated backoff. Given a (λ,T)-stream, in which at most n=λT packets arrive in any interval of size T, exponential backoff is stable for arrival rates of λ=O(1/lgn) and unstable for arrival rates of λ=Ω(lglgn/lgn); loglog-iterated backoff is stable for arrival rates of λ=O(1/(lglgn\lgn)) and unstable for arrival rates of λ=Ω(1/lgn). Our instability results show that bursty input is close to being worst-case for exponential backoff and variants and that even small bursts can create instabilities in the channel.


title: A comparative study between iterative waterfall and incremental software development life cycle model for optimizing the resources using computer simulation

abstract: To achieve the maximum productivity using minimum resource is the aim of any software industry. Software engineering provides an abstraction process to develop software product. It has introduced various methodologies, principles and concepts. Most of them are the software process models which are also known as software life cycle models. In the software industry different-different types of projects (small, medium, large, complex) arrive in random inter arrival time for development of software products. Before implementing the products, project managers decide an appropriated software process model in documentation that is used in production of products. An empirical study conducted in 2012 that presents various effecting factors for selecting any software life cycle model. One of the important factors is team size. Software industry has a development team which works in co-ordination and depends on each other. Sometimes project managers are not intelligently assigned resources to particular phases of software life cycle model. Therefore to overcome these issues we are simulating iterative waterfall and incremental model to determine optimal resources for every phase of software life cycle model. This study shall enable the project manager to determine optimal resource without implementing software product. As future work, we shall be simulating spiral and iterative incremental model to determine which SDLC model is more appropriate for software industry.


title: Ranking with recursive neural networks and its application to multi-document summarization

abstract: We develop a Ranking framework upon Recursive Neural Networks (R2N2) to rank sentences for multi-document summarization. It formulates the sentence ranking task as a hierarchical regression process, which simultaneously measures the salience of a sentence and its constituents (e.g., phrases) in the parsing tree. This enables us to draw on word-level to sentence-level supervisions derived from reference summaries. In addition, recursive neural networks are used to automatically learn ranking features over the tree, with hand-crafted feature vectors of words as inputs. Hierarchical regressions are then conducted with learned features concatenating raw features. Ranking scores of sentences and words are utilized to effectively select informative and non-redundant sentences to generate summaries. Experiments on the DUC 2001, 2002 and 2004 multi-document summarization datasets show that R2N2 outperforms state-of-the-art extractive summarization approaches.


title: Interactive Augmented Reality Techniques for Construction at a Distance of 3 D Geometry

abstract: This paper presents a series of new augmented reality user interaction techniques to support the capture and creation of 3D geometry of large outdoor structures, part of an overall concept we have named construction at a distance. We use information about the user's physical presence, along with hand and head gestures, to allow the user to capture and create the geometry of objects that are orders of magnitude larger than themselves, with no prior information or assistance. Using augmented reality and these new techniques, users can enter geometry and verify its accuracy in real time. This paper includes a number of examples showing objects that have been modelled in the physical world, demonstrating the usefulness of the techniques.


title: The Radon Cumulative Distribution Transform and Its Application to Image Classification

abstract: Invertible image representation methods (transforms) are routinely employed as low-level image processing operations based on which feature extraction and recognition algorithms are developed. Most transforms in current use (e.g., Fourier, wavelet, and so on) are linear transforms and, by themselves, are unable to substantially simplify the representation of image classes for classification. Here, we describe a nonlinear, invertible, low-level image processing transform based on combining the well-known Radon transform for image data, and the 1D cumulative distribution transform proposed earlier. We describe a few of the properties of this new transform, and with both theoretical and experimental results show that it can often render certain problems linearly separable in a transform space.


title: Integrated Information as a Metric for Group Interaction: Analyzing Human and Computer Groups Using a Technique Developed to Measure Consciousness

abstract: Researchers in many disciplines have previously used a variety of mathematical techniques for analyzing group interactions. Here we use a new metric for this purpose, called "integrated information" or "phi." Phi was originally developed by neuroscientists as a measure of consciousness in brains, but it captures, in a single mathematical quantity, two properties that are important in many other kinds of groups as well: differentiated information and integration. Here we apply this metric to the activity of three types of groups that involve people and computers. First, we find that 4-person work groups with higher measured phi perform a wide range of tasks more effectively, as measured by their collective intelligence. Next, we find that groups of Wikipedia editors with higher measured phi create higher quality articles. Last, we find that the measured phi of the collection of people and computers communicating on the Internet increased over a recent six-year period. Together, these results suggest that integrated information can be a useful way of characterizing a certain kind of interactional complexity that, at least sometimes, predicts group performance. In this sense, phi can be viewed as a potential metric of effective group collaboration.


title: Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding

abstract: In this paper, a joint machine learning and game theory modeling (MLGT) framework is proposed for inter frame coding tree unit (CTU) level bit allocation and rate control (RC) optimization in high efficiency video coding (HEVC). First, a support vector machine-based multi-classification scheme is proposed to improve the prediction accuracy of CTU-level rate-distortion (R-D) model. The legacy “chicken-and-egg” dilemma in video coding is proposed to be overcome by the learning-based R-D model. Second, a mixed R-D model-based cooperative bargaining game theory is proposed for bit allocation optimization, where the convexity of the mixed R-D model-based utility function is proved, and Nash bargaining solution is achieved by the proposed iterative solution search method. The minimum utility is adjusted by the reference coding distortion and frame-level quantization parameter (QP) change. Finally, intra frame QP and inter frame adaptive bit ratios are adjusted to make inter frames have more bit resources to maintain smooth quality and bit consumption in the bargaining game optimization. Experimental results demonstrate that the proposed MLGT-based RC method can achieve much better R-D performances, quality smoothness, bit rate accuracy, buffer control results, and subjective visual quality than the other state-of-the-art one-pass RC methods, and the achieved R-D performances are very close to the performance limits from the FixedQP method.


Image Features From Phase Congruency