title: A Study of prevalence of malnutrition in government School children in the field area of Azad nagar Bangalore, India

abstract: Malnutrition is a problem at varying proportions in developing countries, and anthropometry is a simple tool to assess its magnitude in children. This study was aimed at identifying the prevalence of malnutrition among 500 children of govt schols of Azad Nagar, bangalore south Asia. The value of using various field based formulae and of various anthropometric indicators used for classification of malnutrition was also studied. The study was focused on children aged 8-14 years studying in class 1 st to 8 th in govt schools. Anthropometric data and eating practices of children were collected with the help of a pretested questionnaire and food intake diary. Selected anthropometric measurements were taken using standard techniques. Their Body Mass Index (B.M.I) for age was calculated and compared with WHO (2007) standards. Compared to WHO standards, mean BMI of school children in Azad Nagar and its surrounding area was inferior at all ages. The prevalence of malnutrition was 68%, males recorded a relatively high high rate of malnutrition 57.94% (197) than females 42.06% (143). The study reveals that the average of govt school children in Azad Nagar are underweighted. Poor nutrition of children do not only affects the cognitive development of children but also likely to reduce the work capacity in future.


title: An energy-efficient mobile recommender system

abstract: The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (green knowledge), which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors. However, extracting green knowledge from location traces is not a trivial task. Conventional data analysis tools are usually not customized for handling the massive quantity, complex, dynamic, and distributed nature of location traces. To that end, in this paper, we provide a focused study of extracting energy-efficient transportation patterns from location traces. Specifically, we have the initial focus on a sequence of mobile recommendations. As a case study, we develop a mobile recommender system which has the ability in recommending a sequence of pick-up points for taxi drivers or a sequence of potential parking positions. The goal of this mobile recommendation system is to maximize the probability of business success. Along this line, we provide a Potential Travel Distance (PTD) function for evaluating each candidate sequence. This PTD function possesses a monotone property which can be used to effectively prune the search space. Based on this PTD function, we develop two algorithms, LCP and SkyRoute, for finding the recommended routes. Finally, experimental results show that the proposed system can provide effective mobile sequential recommendation and the knowledge extracted from location traces can be used for coaching drivers and leading to the efficient use of energy.


Fingerprint image analysis: Role of orientation patch and ridge structure dictionaries


title: Fraud Detection in Mobile Payments Utilizing Process Behavior Analysis

abstract: Generally, fraud risk implies any intentional deception made for financial gain. In this paper, we consider this risk in the field of services which support transactions with electronic money. Specifically, we apply a tool for predictive security analysis at runtime which observes process behavior with respect to transactions within a money transfer service and tries to match it with expected behavior given by a process model. We analyze deviations from the given behavior specification for anomalies that indicate a possible misuse of the service related to money laundering activities. We evaluate the applicability of the proposed approach and provide measurements on computational and recognition performance of the tool - Predictive Security Analyser - produced using real operational and simulated logs. The goal of the experiments is to detect misuse patterns reflecting a given money laundering scheme in synthetic process behavior based on properties captured from real world transaction events.


title: Autonomous indoor helicopter flight using a single onboard camera

abstract: We consider the problem of autonomously flying a helicopter in indoor environments. Navigation in indoor settings poses two major challenges. First, real-time perception and response is crucial because of the high presence of obstacles. Second, the limited free space in such a setting places severe restrictions on the size of the aerial vehicle, resulting in a frugal payload budget. We autonomously fly a miniature RC helicopter in small known environments using an on-board light-weight camera as the only sensor. We use an algorithm that combines data-driven image classification with optical flow techniques on the images captured by the camera to achieve real-time 3D localization and navigation. We perform successful autonomous test flights along trajectories in two different indoor settings. Our results demonstrate that our method is capable of autonomous flight even in narrow indoor spaces with sharp corners.


title: Portable Trust: biometric-based authentication and blockchain storage for self-sovereign identity systems

abstract: We devised a mobile biometric-based authentication system only relying on local processing. Our Android open source solution explores the capability of current smartphones to acquire, process and match fingerprints using only its built-in hardware. Our architecture is specifically designed to run completely locally and autonomously, not requiring any cloud service, server, or permissioned access to fingerprint reader hardware. It involves three main stages, starting with the fingerprint acquisition using the smartphone camera, followed by a processing pipeline to obtain minutiae features and a final step for matching against other locally stored fingerprints, based on Oriented FAST and Rotated BRIEF (ORB) descriptors. We obtained a mean matching accuracy of 55%, with the highest value of 67% for thumb fingers. Our ability to capture and process a finger fingerprint in mere seconds using a smartphone makes this work usable in a wide range of scenarios, for instance, offline remote regions. This work is specifically designed to be a key building block for a self-sovereign identity solution and integrate with our permissionless blockchain for identity and key attestation.


title: Interactive Video Object Segmentation in the Wild

abstract: In this paper we present our system for human-in-the-loop video object segmentation. The backbone of our system is a method for one-shot video object segmentation. While fast, this method requires an accurate pixel-level segmentation of one (or several) frames as input. As manually annotating such a segmentation is impractical, we propose a deep interactive image segmentation method, that can accurately segment objects with only a handful of clicks. On the GrabCut dataset, our method obtains 90% IOU with just 3.8 clicks on average, setting the new state of the art. Furthermore, as our method iteratively refines an initial segmentation, it can effectively correct frames where the video object segmentation fails, thus allowing users to quickly obtain high quality results even on challenging sequences. Finally, we investigate usage patterns and give insights in how many steps users take to annotate frames, what kind of corrections they provide, etc., thus giving important insights for further improving interactive video segmentation.


title: ICT for Sustainability: An Emerging Research Field

abstract: This introductory chapter provides definitions of sustainability, sustainable development, decoupling, and related terms; gives an overview of existing interdisciplinary research fields related to ICT for Sustainability, including Environmental Informatics, Computational Sustainability, Sustainable HCI, and Green ICT; introduces a conceptual framework to structure the effects of ICT on sustainability; and provides an overview of this book.


title: Video event retrieval from a small number of examples using rough set theory

abstract: In this paper, we develop an example-based event retrieval method which constructs a model for retrieving events of interest in a video archive, by using examples provided by a user. But, this is challenging because shots of an event are characterized by significantly different features, due to camera techniques, settings and so on. That is, the video archive contains a large variety of shots of the event, while the user can only provide a small number of examples. Considering this, we use "rough set theory" to capture various characteristics of the event. Specifically, by using rough set theory, we can extract classification rules which can correctly identify different subsets of positive examples. Furthermore, in order to extract a larger variety of classification rules, we incorporate "bagging" and "random subspace method" into rough set theory. Here, we define indiscernibility relations among examples based on outputs of classifiers, built on different subsets of examples and different subsets of feature dimensions. Experimental results on TRECVID 2009 video data validate the effectiveness of our example-based event retrieval method.


title: Screening for industrially important enzymes from thermophilic bacteria; selection of lipase-producing microorganisms and optimization of culture conditions

abstract: In this study, 201 thermophilic bacteria that were isolated from natural hot springs in Aydin/Turkey and registered in Adnan Menderes University Department of Biology culture stocks were used. It was determined that 43 of these bacteria exhibited lipolytic activity and 22 of them exhibited lipase activity. These 22 lipase positive isolates were grown in LB broth medium and the quantitative lipase activities were determined. HBB 134 was chosen as the best lipase producing isolate with the activity of 19.925 U/mL. According to 16S rRNA sequences, it was found that the isolate showed maximum similarity (% 99) with Anoxybacillus flavithermus. The best enzyme production from HBB 134 was determined in medium including 0.5% olive oil as carbon source and 0.5% pepton as nitrogen source, pH 6.50 and 45 °C. When the isolate HBB 134 was grown in optimum culture conditions it was determined that production of the lipase started at the beginning of the logarithmic growth phase and it reached maximum level in the middle (12 hour) of the logarithmic phase. It was determined that most of the enzyme activity


title: Incremental temporal logic synthesis of control policies for robots interacting with dynamic agents

abstract: We consider the synthesis of control policies from temporal logic specifications for robots that interact with multiple dynamic environment agents. Each environment agent is modeled by a Markov chain whereas the robot is modeled by a finite transition system (in the deterministic case) or Markov decision process (in the stochastic case). Existing results in probabilistic verification are adapted to solve the synthesis problem. To partially address the state explosion issue, we propose an incremental approach where only a small subset of environment agents is incorporated in the synthesis procedure initially and more agents are successively added until we hit the constraints on computational resources. Our algorithm runs in an anytime fashion where the probability that the robot satisfies its specification increases as the algorithm progresses.


title: Logistic discriminant analysis

abstract: Linear discriminant analysis (LDA) is one of the well known methods to extract the best features for the multi-class discrimination. Otsu derived the optimal nonlinear discriminant analysis (ONDA) by assuming the underlying probabilities and showed that the ONDA was closely related to Bayesian decision theory (the posterior probabilities). Also Otsu pointed out that LDA could be regarded as a linear approximation of the ONDA through the linear approximations of the Bayesian posterior probabilities. Based on this theory, we propose a novel nonlinear discriminant analysis named logistic discriminant analysis (LgDA) in which the posterior probabilities are estimated by multi-nominal logistic regression (MLR). The experimental results are shown by comparing the discriminant spaces constructed by LgDA and LDA for the standard repository datasets.


EASL-EORTC Clinical Practice Guidelines: Management of hepatocellular carcinoma European Association for the Study of the Liver ⇑ , European Organisation for Research and Treatment of Cancer


title: Image-based reconstruction of wire art

abstract: Objects created by connecting and bending wires are common in furniture design, metal sculpting, wire jewelry, etc. Reconstructing such objects with traditional depth and image based methods is extremely difficult due to their unique characteristics such as lack of features, thin elements, and severe self-occlusions. We present a novel image-based method that reconstructs a set of continuous 3D wires used to create such an object, where each wire is composed of an ordered set of 3D curve segments. Our method exploits two main observations: simplicity - wire objects are often created using only a small number of wires, and smoothness - each wire is primarily smoothly bent with sharp features appearing only at joints or isolated points. In light of these observations, we tackle the challenging image correspondence problem across featureless wires by first generating multiple candidate 3D curve segments and then solving a global selection problem that balances between image and smoothness cues to identify the correct 3D curves. Next, we recover a decomposition of such curves into a set of distinct and continuous wires by formulating a multiple traveling salesman problem, which finds smooth paths, i.e., wires, connecting the curves. We demonstrate our method on a wide set of real examples with varying complexity and present high-fidelity results using only 3 images for each object. We provide the source code and data for our work in the project website.


title: Review of developments in BLDC motor controllers along with study of four-quadrant operation and active Power Factor correction

abstract: This paper presents a review on recent developments in BLDC motor controllers and studies on four quadrant operation of BLDC drive along with active PFC. The main areas reviewed include Sensor-less control, Direct Torque Control (DTC), Fuzzy logic control, controller for four quadrant operation and active Power Factor Corrected (PFC) converter fed BLDC motor drive. A comprehensive study has been done on four quadrant operation and active PFC converter fed BLDC motor drive with simulation in MATLAB/SIMULINK. The proposed control algorithm for four quadrant operation detects the speed reversal requirement and changes the quadrant of operation accordingly. In PFC converter fed BLDC motor drive, a Boost converter working in continuous current mode is designed to improve the supply power factor.


title: Planning Formalisms and Authoring in Interactive Storytelling

abstract: Interacting Storytelling systems integrate AI techniques such as planning with narrative representations to generate stories. In this paper, we discuss the use of planning formalisms in Interactive Storytelling from the perspective of story generation and authoring. We compare two different planning formalisms, Hierarchical Task Network (HTN) planning and Heuristic Search Planning (HSP). While HTN provide a strong basis for narrative coherence in the context of interactivity, HSP offer additional flexibility and the generation of stories and the mechanisms for generating comic situations.


title: The Password Life Cycle: User Behaviour in Managing Passwords

abstract: Users need to keep track of many accounts and passwords. We conducted a series of interviews to investigate how users cope with these demanding tasks, and used Grounded Theory to analyze the interview results. We found that most users cope by reusing passwords and writing them down, but with a rich variety of behaviour and diverse personalized strategies. These approaches seem to disregard security advice, but at a detailed level they involve perceptive behaviour and careful self-management of user resources. We identify a password life cycle that follows users’ password behaviour and how it develops over time as users adapt to changing circumstances and demands. Users’ strategies have their limitations, but we suggest they indicate a rational response to the requirements of password authentication. We suggest that instead of simply advising against such behaviour, new approaches could be designed that harness existing user behaviour while limiting negative consequences.


title: An efficient projection for l1, ∞ regularization

abstract: In recent years the l1, ∞ norm has been proposed for joint regularization. In essence, this type of regularization aims at extending the l1 framework for learning sparse models to a setting where the goal is to learn a set of jointly sparse models. In this paper we derive a simple and effective projected gradient method for optimization of l1, ∞ regularized problems. The main challenge in developing such a method resides on being able to compute efficient projections to the l1, ∞ ball. We present an algorithm that works in O(n log n) time and O(n) memory where n is the number of parameters. We test our algorithm in a multi-task image annotation problem. Our results show that l1, ∞ leads to better performance than both l2 and l1 regularization and that it is is effective in discovering jointly sparse solutions.


title: The debate over dopamine’s role in reward: the case for incentive salience

abstract: Introduction ::: Debate continues over the precise causal contribution made by mesolimbic dopamine systems to reward. There are three competing explanatory categories: ‘liking’, learning, and ‘wanting’. Does dopamine mostly mediate the hedonic impact of reward (‘liking’)? Does it instead mediate learned predictions of future reward, prediction error teaching signals and stamp in associative links (learning)? Or does dopamine motivate the pursuit of rewards by attributing incentive salience to reward-related stimuli (‘wanting’)? Each hypothesis is evaluated here, and it is suggested that the incentive salience or ‘wanting’ hypothesis of dopamine function may be consistent with more evidence than either learning or ‘liking’. In brief, recent evidence indicates that dopamine is neither necessary nor sufficient to mediate changes in hedonic ‘liking’ for sensory pleasures. Other recent evidence indicates that dopamine is not needed for new learning, and not sufficient to directly mediate learning by causing teaching or prediction signals. By contrast, growing evidence indicates that dopamine does contribute causally to incentive salience. Dopamine appears necessary for normal ‘wanting’, and dopamine activation can be sufficient to enhance cue-triggered incentive salience. Drugs of abuse that promote dopamine signals short circuit and sensitize dynamic mesolimbic mechanisms that evolved to attribute incentive salience to rewards. Such drugs interact with incentive salience integrations of Pavlovian associative information with physiological state signals. That interaction sets the stage to cause compulsive ‘wanting’ in addiction, but also provides opportunities for experiments to disentangle ‘wanting’, ‘liking’, and learning hypotheses. Results from studies that exploited those opportunities are described here.


title: Bundled camera paths for video stabilization

abstract: We present a novel video stabilization method which models camera motion with a bundle of (multiple) camera paths. The proposed model is based on a mesh-based, spatially-variant motion representation and an adaptive, space-time path optimization. Our motion representation allows us to fundamentally handle parallax and rolling shutter effects while it does not require long feature trajectories or sparse 3D reconstruction. We introduce the 'as-similar-as-possible' idea to make motion estimation more robust. Our space-time path smoothing adaptively adjusts smoothness strength by considering discontinuities, cropping size and geometrical distortion in a unified optimization framework. The evaluation on a large variety of consumer videos demonstrates the merits of our method.


title: Limiting, leaving, and (re)lapsing: an exploration of facebook non-use practices and experiences

abstract: Despite the abundance of research on social networking sites, relatively little research has studied those who choose not to use such sites. This paper presents results from a questionnaire of over 400 Internet users, focusing specifically on Facebook and those users who have left the service. Results show the lack of a clear, binary distinction between use and non-use, that various practices enable diverse ways and degrees of engagement with and disengagement from Facebook. Furthermore, qualitative analysis reveals numerous complex and interrelated motivations and justifications, both for leaving and for maintaining some type of connection. These motivations include: privacy, data misuse, productivity, banality, addiction, and external pressures. These results not only contribute to our understanding of online sociality by examining this under-explored area, but they also build on previous work to help advance how we conceptually account for the sociological processes of non-use.


title: An Enactive Model of Creativity for Computational Collaboration and Co-creation

abstract: The modern landscape of computing has rapidly evolved with breakthroughs in new input modalities and interaction designs, but the fundamental model of humans giving commands to computers is still largely dominant. A small but growing number of projects in the computational creativity field are beginning to study and build creative computers that are able to collaborate with human users as partners by simulating, to various degrees, the collaboration that naturally occurs between humans in creative domains (Biles, Leonardo, 36:43–45, 2003; Lubart, Int J Hum Comput Stud, 63:365–369, 2005; Hoffman and Weinberg, Shimon: an interactive improvisational robotic marimba player. In: CHI’10 extended abstracts on human factors in computing systems, ACM, New York, pp 3097–3102, 2010; Zook et al., Understanding human creativity for computational play. In: 8th ACM conference on creativity and cognition, 2011; Davis et al., Building artistic computer colleagues with an enactive model of creativity, 2014). If this endeavor proves successful, the implications for HCI and the field of computing in general could be significant. Creative computers could understand and work alongside humans in a new hybrid form of human-computer co-creativity that could inspire, motivate, and perhaps even teach creativity to human users through collaboration.


title: The Impact of Imbalanced Training Data for Convolutional Neural Networks

abstract: This thesis empirically studies the impact of imbalanced training data on Convolutional Neural Network (CNN) performance in image classification. Images from the CIFAR-10 dataset, a set containing ...


title: An Integrated Approach For Arabic-English Named Entity Translation

abstract: Translation of named entities (NEs), such as person names, organization names and location names is crucial for cross lingual information retrieval, machine translation, and many other natural language processing applications. Newly named entities are introduced on daily basis in newswire and this greatly complicates the translation task. Also, while some names can be translated, others must be transliterated, and, still, others are mixed. In this paper we introduce an integrated approach for named entity translation deploying phrase-based translation, word-based translation, and transliteration modules into a single framework. While Arabic based, the approach introduced here is a unified approach that can be applied to NE translation for any language pair.


title: Multiple weighted prediction models for video coding with brightness variations

abstract: Weighted prediction (WP) is a tool introduced in H.264 to improve motion-compensation performance for video sequences with brightness variations. Various WP models to estimate the parameter set have been discussed in the literature. However, no single WP model works well for all types of brightness variations. A single reference frame multiple WP models (SRefMWP) scheme is proposed to facilitate the use of multiple WP models in a single reference frame. The proposed scheme makes a new arrangement of the multiple frame buffers in multiple reference frame motion estimation. It enables different macroblocks in the same frame to use different WP models even when they are predicted from the same reference frame. Furthermore, a new re-ordering mechanism for SRefMWP is also proposed to guarantee that the list of the reference picture is in the best order for further decreasing the bit rate. To reduce the implementation cost, a reduction in the memory requirement is achieved via look-up tables (LUTs). Experimental results show that SRefMWP can achieve significant coding gain in scenes with different types of brightness variations. Furthermore, SRefMWP with LUTs can reduce the memory requirement by about 80% while keeping the same coding efficiency as SRefMWP.


Continuous and Discontinuous Shock Absorber Control through Skyhook Strategy in Semi-Active Suspension System (4DOF Model)


title: Ontologies in biology: design, applications and future challenges

abstract: Biological knowledge is inherently complex and so cannot readily be integrated into existing databases of molecular (for example, sequence) data. An ontology is a formal way of representing knowledge in which concepts are described both by their meaning and their relationship to each other. Unique identifiers that are associated with each concept in biological ontologies (bio-ontologies) can be used for linking to and querying molecular databases. This article reviews the principal bio-ontologies and the current issues in their design and development: these include the ability to query across databases and the problems of constructing ontologies that describe complex knowledge, such as phenotypes.


title: Pen-and-ink textures for real-time rendering

abstract: Simulation of a pen-and-ink illustration style in a realtime rendering system is a challenging computer graphics problem. Tonal art maps (TAMs) were recently suggested as a solution to this problem. Unfortunately, only the hatching aspect of pen-and-ink media was addressed thus far. We extend the TAM approach and enable representation of arbitrary textures. We generate TAM images by distributing stroke primitives according to a probability density function. This function is derived from the input image and varies depending on the TAM’s scale and tone levels. The resulting depiction of textures approximates various styles of pen-and-ink illustrations such as outlining, stippling, and hatching.


title: Load Balancing Under Heavy Traffic in RPL Routing Protocol for Low Power and Lossy Networks

abstract: RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, industrial and environmental monitoring, and wireless sensor networks. RPL allows bi-directional end-to-end IPv6 communication on resource constrained LLN devices, leading to the concept of the Internet of Things (IoT) with thousands and millions of devices interconnected through multihop mesh networks. In this article, we investigate the load balancing and congestion problem of RPL. Specifically, we show that most of the packet losses under heavy traffic are due to congestion, and a serious load balancing problem appears in RPL in terms of routing parent selection. To overcome this problem, this article proposes a simple yet effective queue utilization based RPL ( QU-RPL ) that achieves load balancing and significantly improves the end-to-end packet delivery performance compared to the standard RPL. QU-RPL is designed for each node to select its parent node considering the queue utilization of its neighbor nodes as well as their hop distances to an LLN border router (LBR). Owing to its load balancing capability, QU-RPL is very effective in lowering queue losses and increasing the packet delivery ratio. We implement QU-RPL on a low-power embedded platform, and verify all of our findings through experimental measurements on a real testbed of a multihop LLN over IEEE 802.15.4. We present the impact of each design element of QU-RPL on performance in detail, and also show that QU-RPL reduces the queue loss by up to 84 percent and improves the packet delivery ratio by up to 147 percent compared to the standard RPL.


title: Staffing to Maximize Profit for Call Centers with Alternate Service-Level Agreements

abstract: To ensure quality from outsourced call centers, firms sign service-level agreements (SLAs). These define service measures such as what constitutes an acceptable delay or an acceptable abandonment rate. They may also dictate penalties for failing to meet agreed-upon targets. We introduce a period-based SLA that measures performance over a short duration such as a rush hour. We compare it to alternate SLAs that measure service by individual and over a long horizon. To measure the service levels for these SLAs, we develop several approximations. We approximate the probability an acceptable delay is met by generalizing the heavy-traffic quality and efficiency driven regime. We also provide a new approximation for the abandonment rate. Further, we prove a central limit theorem for the probability of meeting a service level measured by the percentage of customers acceptably served during a period. We demonstrate how an outsourced call center operating in an environment with uncertain demand and abandonment can determine its staffing policy to maximize the expected profit for these SLAs. Numerical experiments demonstrate a high degree of accuracy for the approximations and the resulting staffing levels. We indicate several salient features of the behavior of the period-based SLA.


title: Developmental neurotoxicity of industrial chemicals

abstract: Summary Neurodevelopmental disorders such as autism, attention deficit disorder, mental retardation, and cerebral palsy are common, costly, and can cause lifelong disability. Their causes are mostly unknown. A few industrial chemicals (eg, lead, methylmercury, polychlorinated biphenyls [PCBs], arsenic, and toluene) are recognised causes of neurodevelopmental disorders and subclinical brain dysfunction. Exposure to these chemicals during early fetal development can cause brain injury at doses much lower than those affecting adult brain function. Recognition of these risks has led to evidence-based programmes of prevention, such as elimination of lead additives in petrol. Although these prevention campaigns are highly successful, most were initiated only after substantial delays. Another 200 chemicals are known to cause clinical neurotoxic effects in adults. Despite an absence of systematic testing, many additional chemicals have been shown to be neurotoxic in laboratory models. The toxic effects of such chemicals in the developing human brain are not known and they are not regulated to protect children. The two main impediments to prevention of neurodevelopmental deficits of chemical origin are the great gaps in testing chemicals for developmental neurotoxicity and the high level of proof required for regulation. New, precautionary approaches that recognise the unique vulnerability of the developing brain are needed for testing and control of chemicals.


title: Mixing ideas: a new technique for working with young children as design partners

abstract: This paper sets forth a new technique for working with young children as design partners. Mixing ideas is presented as an additional Cooperative Inquiry design technique used to foster effective collaboration with young children (ages 4-6). The method emerged from our work with children on the Classroom of the Future project at the University of Maryland. A case study of this work is presented along with the implications of this method for future research.


title: TOSCA Solves Big Problems in the Cloud and Beyond!

abstract: TOSCA, the Topology and Orchestration Specification for Cloud Applications offers an OASIS-recognized, open standard domain-specific language (DSL) that enables portability and automated management of applications, services, and resources regardless of underlying cloud platform, software defined environment, or infrastructure. With a growing, interoperable eco-system of open source projects, solutions from leading cloud platform and service providers, and research, TOSCA empowers the definition and modeling of applications and their services (microservices or traditional services) across their entire lifecycle by describing their components, relationships, dependencies, requirements, and capabilities for orchestrating software in the context of associated operational policies. The authors introduce important TOSCA concepts and benefits in the context of commonly understood cloud use cases as a foundation to future discussions regarding advanced TOSCA concepts and additional breakthrough issues.


Temporal Attention as a Scaffold for Language Development


Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers


title: Flexible editing of human motion by three-way decomposition

abstract: This paper proposes a new generative model for flexible editing of human motion. Different from previous work, three intuitive factors of motion, namely, content, identity and style, can be manipulated directly with the new model. With the new generative model, motion editing can be achieved in various aspects, including transferring an unknown style from an actor to another, synthesizing other styles for an unknown actor and generating a new motion with other content. Copyright © 2013 John Wiley & Sons, Ltd.


title: Hierarchical Bayesian Inverse Reinforcement Learning

abstract: Inverse reinforcement learning (IRL) is the problem of inferring the underlying reward function from the expert’s behavior data. The difficulty in IRL mainly arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behavior data as optimal. Another difficulty comes from the noisy behavior data due to sub-optimal experts. We propose a hierarchical Bayesian framework, which subsumes most of the previous IRL algorithms as well as models the sub-optimality of the expert’s behavior. Using a number of experiments on a synthetic problem, we demonstrate the effectiveness of our approach including the robustness of our hierarchical Bayesian framework to the sub-optimal expert behavior data. Using a real dataset from taxi GPS traces, we additionally show that our approach predicts the driving behavior with a high accuracy.


title: DES algorithm security fortification using Elliptic Curve Cryptography

abstract: The Data Encryption Standard (DES) was a widespread symmetric key block cipher algorithm. It was the most popular used cryptographic scheme. DES's security was a highly contentious and controversial aspect until it became an insecure algorithm in 1999. In this paper, a modification that overcomes the security issue of the DES algorithm is introduced. The enhancement is depending on the art of the Elliptic Curve Cryptography (ECC). The ECC approach is also used to achieve the required key generation and distribution to establish a communication session. Our new ECC-based DES algorithm can be applied to any file format, in this paper it is employed to encrypt and decrypt an image file. Experimental results are carried out with detailed analyses, the results demonstrate that the proposed scheme possesses a very large key space to resist the brute-force attack and it is very immune to statistical attacks. The obtained results show that the ECC-based DES algorithm could be used as a highly secure algorithm.


title: Rapidly Scaling Dialog Systems with Interactive Learning

abstract: In personal assistant dialog systems, intent models are classifiers that identify the intent of a user utterance, such as to add a meeting to a calendar or get the director of a stated movie. Rapidly adding intents is one of the main bottlenecks to scaling—adding functionality to—personal assistants. In this paper we show how interactive learning can be applied to the creation of statistical intent models. Interactive learning (Simard, ICE: enabling non-experts to build models interactively for large-scale lopsided problems, 2014) combines model definition, labeling, model building, active learning, model evaluation, and feature engineering in a way that allows a domain expert—who need not be a machine learning expert—to build classifiers. We apply interactive learning to build a handful of intent models in three different domains. In controlled lab experiments, we show that intent detectors can be built using interactive learning and then improved in a novel end-to-end visualization tool. We then applied this method to a publicly deployed personal assistant—Microsoft Cortana—where a non-machine learning expert built an intent model in just over 2 h, yielding excellent performance in the commercial service.


CJ.Taylor, "Active Shape Models - "Smart Snakes


title: Improvements of image sharing with steganography and authentication

abstract: Recently, Lin and Tsai proposed an image secret sharing scheme with steganography and authentication to prevent participants from the incidental or intentional provision of a false stego-image (an image containing the hidden secret image). However, dishonest participants can easily manipulate the stego-image for successful authentication but cannot recover the secret image, i.e., compromise the steganography. In this paper, we present a scheme to improve authentication ability that prevents dishonest participants from cheating. The proposed scheme also defines the arrangement of embedded bits to improve the quality of stego-image. Furthermore, by means of the Galois Field GF(2^8), we improve the scheme to a lossless version without additional pixels.


title: Pedestrian classification with 24 GHz chirp sequence radar

abstract: Driver assistance systems based on radar are available today in series production in consumer vehicles. The radar sensor based automotive applications already help to reduce road accidents but is usually limited to highway scenarios. However, there are too many fatalities in public roads. Most of them involve pedestrians and happen in urban road situations. For assisting drivers in those cases, a novel 24 GHz Radar Sensor has been developed within the EU-funded project ARTRAC. The main focus of this paper is to present the pedestrian classification scheme that is incorporated within this sensor. Additionally, the derived requirements and the role of the classification process within the cognitive collision mitigation system is pointed out.


title: Seriously, get off my cloud! Cross-VM RSA Key Recovery in a Public Cloud.

abstract: It has been six years since Ristenpart et al. [29] demonstrated the viability of co-location and provided the first concrete evidence for sensitive information leakage on a commercial cloud. We show that co-location can be achieved and detected by monitoring the last level cache in public clouds. More significantly, we present a full-fledged attack that exploits subtle leakages to recover RSA decryption keys from a colocated instance. We target a recently patched Libgcrypt RSA implementation by mounting Cross-VM Prime and Probe cache attacks in combination with other tests to detect co-location in Amazon EC2. In a preparatory step, we reverse engineer the unpublished nonlinear slice selection function for the 10 core Intel Xeon processor which significantly accelerates our attack (this chipset is used in Amazon EC2). After co-location is detected and verified, we perform the Prime and Probe attack to recover noisy keys from a carefully monitored Amazon EC2 VM running the aforementioned vulnerable libgcrypt library. We subsequently process the noisy data and obtain the complete 2048-bit RSA key used during encryption. This work reaffirms the privacy concerns and underlines the need for deploying stronger isolation techniques in public clouds. Amazon EC2, Co-location Detection, RSA key recovery, Resource Sharing, Prime and Probe


title: SIPHON: Towards Scalable High-Interaction Physical Honeypots

abstract: In recent years, the emerging Internet-of-Things (IoT) has led to rising concerns about the security of networked embedded devices. In this work, we propose the SIPHON architecture---a Scalable high-Interaction Honeypot platform for IoT devices. Our architecture leverages IoT devices that are physically at one location and are connected to the Internet through so-called \emph{wormholes} distributed around the world. The resulting architecture allows exposing few physical devices over a large number of geographically distributed IP addresses. We demonstrate the proposed architecture in a large scale experiment with 39 wormhole instances in 16 cities in 9 countries. Based on this setup, five physical IP cameras, one NVR and one IP printer are presented as 85 real IoT devices on the Internet, attracting a daily traffic of 700MB for a period of two months. A preliminary analysis of the collected traffic indicates that devices in some cities attracted significantly more traffic than others (ranging from 600 000 incoming TCP connections for the most popular destination to less than 50 000 for the least popular). We recorded over 400 brute-force login attempts to the web-interface of our devices using a total of 1826 distinct credentials, from which 11 attempts were successful. Moreover, we noted login attempts to Telnet and SSH ports some of which used credentials found in the recently disclosed Mirai malware.


title: A survey on participating media rendering techniques

abstract: Rendering participating media is important for a number of domains, ranging from commercial applications (entertainment, virtual reality) to simulation systems (driving, flying, and space simulators) and safety analyses (driving conditions, sign visibility). This article surveys global illumination algorithms for environments including participating media. It reviews both appearance-based and physically-based media methods, including the single-scattering and the more general multiple-scattering techniques. The objective of the survey is the characterization of all these methods: identification of their base techniques, assumptions, limitations, and range of utilization. It concludes with some reflections about the suitability of the methods depending on the specific application involved, and possible future research lines.


title: Making students read and review code

abstract: The Praktomat system allows students to read, review, and assess each other's programs in order to improve quality and style. After a successful submission, the student can retrieve and review a program of some fellow student selected by Praktomat. After the review is complete, the student may obtain reviews and re-submit improved versions of his program. The reviewing process is independent of grading; the risk of plagiarism is narrowed by personalized assignments and automatic testing of submitted programs. In a survey, more than two thirds of the students affirmed that reading each other's programs improved their program quality; this is also confirmed by statistical data.


title: Design and implementation of an unmanned tail-sitter

abstract: We present the design and implementation of a small Vertical-Take-Off-and-Landing (VTOL) aircraft. The vehicle requires minimal additional components to achieve the hover capability and is thus very efficient in forward flight. We improve over the state of the art by using a single controller in all flight modes without using blending between hover condition and fixed wing controllers or gain scheduling. We present a compact airflow estimation model for VTOL airframes which rely on the slipstream across control surfaces for hover attitude control. Furthermore we show attitude and position control results in simulation. Finally we show outdoor flight experiments validating our simulation results.


Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks


title: Beam Selection in mm-Wave Multiuser MIMO Systems Using Compressive Sensing

abstract: In this paper, we study beam selection for millimeter-wave (mm-wave) multiuser multiple input multiple output (MIMO) systems where a base station (BS) and users are equipped with antenna arrays. Exploiting a certain sparsity of mm-wave channels, a low-complexity beam selection method for beamforming by low-cost analog beamformers is derived. It is shown that beam selection can be carried out without explicit channel estimation using the notion of compressive sensing (CS). Due to various reasons (e.g., the background noise and interference), some users may choose the same BS beam, which results in high inter-user interference. To overcome this problem, we further consider BS beam selection by users. Through simulations, we show that the performance gap between the proposed approach and the optimal beamforming approach, which requires full channel state information (CSI), becomes narrower for a larger number of users at a moderate/low signal-to-noise ratio (SNR). Since the optimal beamforming approach is difficult to be used due to prohibitively high computational complexity for large antenna arrays with a large number of users, the proposed approach becomes attractive for BSs and users in mm-wave systems where large antenna arrays can be employed.


PADetective: A Systematic Approach to Automate Detection of Promotional Attackers in Mobile App Store


title: Optimized vision-directed deployment of UAVs for rapid traffic monitoring

abstract: The flexibility and cost efficiency of traffic monitoring using Unmanned Aerial Vehicles (UAVs) has made such a proposition an attractive topic of research. To date, the main focus was placed on the types of sensors used to capture the data, and the alternative data processing options to achieve good monitoring performance. In this work we move a step further, and explore the deployment strategies that can be realized for rapid traffic monitoring over particular regions of the transportation network by considering a monitoring scheme that captures data from a visual sensor on-board the UAV, and subsequently analyzes it through a specific vision processing pipeline to extract network state information. These innovative deployment strategies can be used in real-time to assess traffic conditions, while for longer periods, to validate the underlying mobility models that characterise traffic patterns.


title: Endoscopic versus external approach dacryocystorhinostomy: A comparative analysis

abstract: Background: Dacryocystorhinostomy (DCR) consists of creating a lacrimal drainage pathway to the nasal cavity to restore permanent drainage of previously obstructed excreting system. Aim: To compare the result and advantages of both endonasal endoscopic and external DCR regarding the patency rate, patient compliance and complications. Study Design: Prospective non-randomized comparative study. Materials and Methods: Study was conducted for 16 months duration in a teaching hospital with 50 cases of endoscopic and 30 cases of external DCR with a follow-up of minimum 6 months. Data regarding surgical outcome and complications were analysed and compared using χ 2 test. Results: Total 72 patients were included in the study with six having bilateral involvement, out of which 20 were male and 52 were female. The mean age for endoscopic and external DCR was 33.6 years and 46.0 years, respectively. Right eye (63.8%) was involved more commonly than left eye (36.2%). Epiphora was the commonest presenting symptom (63.7%). Mean duration of surgery was much lengthier in external (mean 119.6 minutes) than endoscopic (mean 49.0 minutes) DCR. Bleeding was the most common immediate postoperative complication seen in 33.3% and 10.0% of external and endoscopic DCR cases, respectively. Primary surgical success rate was 90% and 96.7% for endoscopic and external DCR, respectively ( P = 0.046). Among the endoscopic DCR group, four patients underwent revision surgery giving a total successful surgical outcome of 98% at third month of follow-up. However, at 6 month of follow-up, success rate was 92% for endoscopic DCR and 93.3% for external DCR. The difference was not statistically significant ( P = 0.609). Conclusion: Intranasal endoscopic DCR is a simple, minimally invasive, day care procedure and had comparable result with conventional external DCR.


title: A color similarity measure for robust shadow removal in real time

abstract: We introduce an approach for realtime segmentation of a scene into foreground objects, background, and object shadows superimposed on the background. To segment foreground objects, we use an adaptive thresholding method, which is able to deal with rapid changes of the overall brightness. The segmented image usually includes shadows cast by the objects onto the background. Our approach is able to robustly remove the shadow from the background while preserving the silhouette of the foreground object. We discuss a similarity measure for comparing color pixels, which improves the quality of shadow removal significantly. As the image segmentation is part of a real-time interaction environment, real-time processing is needed. Our implementation allows foreground segmentation and robust shadow removal with 15 Hz.


title: A portable, arbitrary waveform, multichannel constant current electrotactile stimulator

abstract: In this paper, we present the design and performance of a portable, arbitrary waveform, multichannel constant current electrotactile stimulator that costs less than $30 in components. The stimulator consists of a stimulation controller and power supply that are less than half the size of a credit card and can produce ±15 mA at ±150 V. The design is easily extensible to multiple independent channels that can receive an arbitrary waveform input from a digital-to-analog converter, drawing only 0.9 W/channel (lasting 4–5 hours upon continuous stimulation using a 9 V battery). Finally, we compare the performance of our stimulator to similar stimulators both commercially available and developed in research.


title: Fine-grained metadata journaling on NVM

abstract: Journaling file systems have been widely used where data consistency must be assured. However, we observed that the overhead of journaling can cause up to 48.2% performance drop under certain kinds of workloads. On the other hand, the emerging high-performance, byte-addressable Non-volatile Memory (NVM) has the potential to minimize such overhead by being used as the journal device. The traditional journaling mechanism based on block devices is nevertheless unsuitable for NVM due to the write amplification of metadata journal we observed. In this paper, we propose a fine-grained metadata journal mechanism to fully utilize the low-latency byte-addressable NVM so that the overhead of journaling can be significantly reduced. Based on the observation that conventional block-based metadata journal contains up to 90% clean metadata that is unnecessary to be journalled, we design a fine-grained journal format for byte-addressable NVM which contains only modified metadata. Moreover, we redesign the process of transaction committing, checkpointing and recovery in journaling file systems utilizing the new journal format. Therefore, thanks to the reduced amount of ordered writes to NVM, the overhead of journaling can be reduced without compromising the file system consistency. Experimental results show that our NVM-based fine-grained metadata journaling is up to 15.8× faster than the traditional approach under FileBench workloads.


title: Breast cancer metastasis: markers and models

abstract: Breast cancer starts as a local disease, but it can metastasize to the lymph nodes and distant organs. At primary diagnosis, prognostic markers are used to assess whether the transition to systemic disease is likely to have occurred. The prevailing model of metastasis reflects this view — it suggests that metastatic capacity is a late, acquired event in tumorigenesis. Others have proposed the idea that breast cancer is intrinsically a systemic disease. New molecular technologies, such as DNA microarrays, support the idea that metastatic capacity might be an inherent feature of breast tumours. These data have important implications for prognosis predicition and our understanding of metastasis.


title: Predicting football scores using machine learning techniques

abstract: Predicting the results of football matches poses an interesting challenge due to the fact that the sport is so popular and widespread. However, predicting the outcomes is also a difficult problem because of the number of factors which must be taken into account that cannot be quantitatively valued or modeled. As part of this work, a software solution has been developed in order to try and solve this problem. During the development of the system, a number of tests have been carried out in order to determine the optimal combination of features and classifiers. The results of the presented system show a satisfactory capability of prediction which is superior to the one of the reference method (most likely a priori outcome).


title: Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating

abstract: This paper presents the novel development of an embedded system that aims at digital TV content recommendation based on descriptive metadata collected from versatile sources. The described system comprises a user profiling subsystem identifying user preferences and a user agent subsystem performing content rating. TV content items are ranked using a combined multimodal approach integrating classification-based and keyword-based similarity predictions so that a user is presented with a limited subset of relevant content. Observable user behaviors are discussed as instrumental in user profiling and a formula is provided for implicitly estimating the degree of user appreciation of content. A new relation-based similarity measure is suggested to improve categorized content rating precision. Experimental results show that our system can recommend desired content to users with significant amount of accuracy.


title: End-to-End Instance Segmentation with Recurrent Attention

abstract: While convolutional neural networks have gained impressive success recently in solving structured prediction problems such as semantic segmentation, it remains a challenge to differentiate individual object instances in the scene. Instance segmentation is very important in a variety of applications, such as autonomous driving, image captioning, and visual question answering. Techniques that combine large graphical models with low-level vision have been proposed to address this problem; however, we propose an end-to-end recurrent neural network (RNN) architecture with an attention mechanism to model a human-like counting process, and produce detailed instance segmentations. The network is jointly trained to sequentially produce regions of interest as well as a dominant object segmentation within each region. The proposed model achieves competitive results on the CVPPP, KITTI, and Cityscapes datasets.


title: Real-time virtual fitting with body measurement and motion smoothing

abstract: Abstract We present a novel virtual fitting room framework using a depth sensor, which provides a realistic fitting experience with customized motion filters, size adjustments and physical simulation. The proposed scaling method adjusts the avatar and determines a standardized apparel size according to the user׳s measurements, prepares the collision mesh and the physics simulation, with a total of 1 s preprocessing time. The real-time motion filters prevent unnatural artifacts due to the noise from depth sensor or self-occluded body parts. We apply bone splitting to realistically render the body parts near the joints. All components are integrated efficiently to keep the frame rate higher than previous works while not sacrificing realism.


title: Towards Detecting Wheel-Spinning: Future Failure in Mastery Learning

abstract: Wheel-spinning refers to a phenomenon in which a student has spent a considerable amount of time practicing a skill, yet displays little or no progress towards mastery. Wheel-spinning has been shown to be a common problem affecting a significant number of students in different tutoring systems and is negatively associated with learning. In this study, we construct a model of wheel-spinning, using generic features easily calculated from most tutoring systems. We show that for two different systems' data, the model generalizes to future students very well and can detect wheel-spinning in an early stage with high accuracy. We also refine the scope of the wheel-spinning problem in two systems using the model's predictions.


title: Stable models and an alternative logic programming paradigm

abstract: In this paper we reexamine the place and role of stable model semantics in logic programming and contrast it with a least Herbrand model approach to Horn programs. We demonstrate that inherent features of stable model semantics naturally lead to a logic programming system that offers an interesting alternative to more traditional logic programming styles of Horn logic programming, stratified logic programming and logic programming with well-founded semantics. The proposed approach is based on the interpretation of program clauses as constraints. In this setting programs do not describe a single intended model, but a family of stable models. These stable models encode solutions to the constraint satisfaction problem described by the program. Our approach imposes restrictions on the syntax of logic programs. In particular, function symbols are eliminated from the language. We argue that the resulting logic programming system is well-attuned to problems in the class NP, has a well-defined domain of applications, and an emerging methodology of programming. We point out that what makes the whole approach viable is recent progress in implementations of algorithms to compute stable models of propositional logic programs.


title: A space-optimal data-stream algorithm for coresets in the plane

abstract: Given a point set P⊆R2, a subset Q⊆ P is an e-kernel of P if for every slab W containing Q, the (1+e)-expansion of W also contains P. We present a data-stream algorithm for maintaining an e-kernel of a stream of points in R2 that uses O(1/√ e) space and takes O(log (1/e)) amortized time to process each point. This is the first space-optimal data-stream algorithm for this problem.


title: Predicting defect-prone software modules using support vector machines

abstract: Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models.


title: Using latent semantic analysis to improve access to textual information

abstract: This paper describes a new approach for dealing with the vocabulary problem in human-computer interaction. Most approaches to retrieving textual materials depend on a lexical match between words in users' requests and those in or assigned to database objects. Because of the tremendous diversity in the words people use to describe the same object, lexical matching methods are necessarily incomplete and imprecise [5]. The latent semantic indexing approach tries to overcome these problems by automatically organizing text objects into a semantic structure more appropriate for matching user requests. This is done by taking advantage of implicit higher-order structure in the association of terms with text objects. The particular technique used is singular-value decomposition, in which a large term by text-object matrix is decomposed into a set of about 50 to 150 orthogonal factors from which the original matrix can be approximated by linear combination. Terms and objects are represented by 50 to 150 dimensional vectors and matched against user queries in this “semantic” space. Initial tests find this completely automatic method widely applicable and a promising way to improve users' access to many kinds of textual materials, or to objects and services for which textual descriptions are available.


title: Restaurant setup business analysis using yelp dataset

abstract: In this paper, we address the issues associated with setting-up of a new restaurant business. To strategize a new restaurant business, we propose a restaurant business framework which comprises of 3 most important tasks, namely, high frequency attributes, most crowded day and location of the restaurant. First, we identify the features/attributes of the restaurants in which the customers are most interested in and provide those facilities and services to increase the profit. Next, we identify the day of the week when the restaurants are heavily crowded so that the best recipes and offers are made available on those days. Finally, since location has a profound effect on the success of a restaurant business, we consider location to be the most important to know the nearby restaurants and their facilities before coming up with a new restaurant business. The performance analysis of the proposed framework was carried out on the standard Yelp dataset. Thus, we found credit card to be the most preferred attribute, the most crowded day to be Monday and Divey to be the most desired ambience among the customers. We also demonstrate how the new restaurant can be setup by identifying the nearest restaurants and the services.


title: DARLA: Improving Zero-Shot Transfer in Reinforcement Learning

abstract: Domain adaptation is an important open problem in deep reinforcement learning (RL). In many scenarios of interest data is hard to obtain, so agents may learn a source policy in a setting where data is readily available, with the hope that it generalises well to the target domain. We propose a new multi-stage RL agent, DARLA (DisentAngled Representation Learning Agent), which learns to see before learning to act. DARLA's vision is based on learning a disentangled representation of the observed environment. Once DARLA can see, it is able to acquire source policies that are robust to many domain shifts - even with no access to the target domain. DARLA significantly outperforms conventional baselines in zero-shot domain adaptation scenarios, an effect that holds across a variety of RL environments (Jaco arm, DeepMind Lab) and base RL algorithms (DQN, A3C and EC).


title: COMPARING PROGRAMMER PRODUCTIVITY IN OPENACC AND CUDA: AN EMPIRICAL INVESTIGATION

abstract: OpenACC has been touted as a "high productivity" API designed to make GPGPU programming accessible to scientific programmers, but to date, no studies have attempted to verify this quantitatively. In this paper, we conduct an empirical investigation of program productivity comparisons between OpenACC and CUDA in the programming time, the execution time and the analysis of independence of OpenACC model in high performance problems. Our results show that, for our programs and our subject pool, this claim is true. We created two assignments called Machine Problem 3(MP3) and Machine Problem 4(MP4) in the classroom environment and instrumented the WebCode website developed by ourselves to record details of students’ coding process. Three hypotheses were supported by the statistical data: for the same parallelizable problem, (1) the OpenACC programming time is at least 37% shorter than CUDA; (2) the CUDA running speed is 9x faster than OpenACC; (3) the OpenACC development work is not significantly affected by previous CUDA experience


title: A Manifold Approach to Learning Mutually Orthogonal Subspaces

abstract: Although many machine learning algorithms involve learning subspaces with particular characteristics, optimizing a parameter matrix that is constrained to represent a subspace can be challenging. One solution is to use Riemannian optimization methods that enforce such constraints implicitly, leveraging the fact that the feasible parameter values form a manifold. While Riemannian methods exist for some specific problems, such as learning a single subspace, there are more general subspace constraints that offer additional flexibility when setting up an optimization problem, but have not been formulated as a manifold. ::: We propose the partitioned subspace (PS) manifold for optimizing matrices that are constrained to represent one or more subspaces. Each point on the manifold defines a partitioning of the input space into mutually orthogonal subspaces, where the number of partitions and their sizes are defined by the user. As a result, distinct groups of features can be learned by defining different objective functions for each partition. We illustrate the properties of the manifold through experiments on multiple dataset analysis and domain adaptation.


A Distributionally-robust approach for finding support vector machine


title: Pleasurable music affects reinforcement learning according to the listener

abstract: Mounting evidence links the enjoyment of music to brain areas implicated in emotion and the dopaminergic reward system. In particular, dopamine release in the ventral striatum seems to play a major role in the rewarding aspect of music listening. Striatal dopamine also influences reinforcement learning, such that subjects with greater dopamine efficacy learn better to approach rewards while those with lesser dopamine efficacy learn better to avoid punishments. In this study, we explored the practical implications of musical pleasure through its ability to facilitate reinforcement learning via non-pharmacological dopamine elicitation. Subjects from a wide variety of musical backgrounds chose a pleasurable and a neutral piece of music from an experimenter-compiled database, and then listened to one or both of these pieces (according to pseudo-random group assignment) as they performed a reinforcement learning task dependent on dopamine transmission. We assessed musical backgrounds as well as typical listening patterns with the new Helsinki Inventory of Music and Affective Behaviors (HIMAB), and separately investigated behavior for the training and test phases of the learning task. Subjects with more musical experience trained better with neutral music and tested better with pleasurable music, while those with less musical experience exhibited the opposite effect. HIMAB results regarding listening behaviors and subjective music ratings indicate that these effects arose from different listening styles: namely, more affective listening in non-musicians and more analytical listening in musicians. In conclusion, musical pleasure was able to influence task performance, and the shape of this effect depended on group and individual factors. These findings have implications in affective neuroscience, neuroaesthetics, learning, and music therapy.


Information Systems Development Failure : A Case Study to Highlight the IS Development Complexities in Simple , Low Risk Projects in Developing Countries


title: External-Excitation Curl Antenna

abstract: A strip curl antenna is investigated for obtaining a circularly-polarized (CP) tilted beam. This curl is excited through a strip line (called the excitation line) that connects the curl arm to a coaxial feed line. The antenna structure has the following features: a small circumference not exceeding two wavelengths and a small antenna height of less than 0.42 wavelength. The antenna arm is printed on a dielectric hollow cylinder, leading to a robust structure. The investigation reveals that an external excitation for the curl using a straight line (ST-line) is more effective for generating a tilted beam than an internal excitation. It is found that the axial ratio of the radiation field from the external-excitation curl is improved by transforming the ST-line into a wound line (WD-line). It is also found that a modification to the end area of the WD-line leads to an almost constant input impedance (50 ohms). Note that these results are demonstrated for the Ku-band (from 11.7 GHz to 12.75 GHz, 8.6% bandwidth).


title: Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM.

abstract: Sequence-to-sequence deep learning has recently emerged as a new paradigm in supervised learning for spoken language understanding. However, most of the previous studies explored this framework for building single domain models for each task, such as slot filling or domain classification, comparing deep learning based approaches with conventional ones like conditional random fields. This paper proposes a holistic multi-domain, multi-task (i.e. slot filling, domain and intent detection) modeling approach to estimate complete semantic frames for all user utterances addressed to a conversational system, demonstrating the distinctive power of deep learning methods, namely bi-directional recurrent neural network (RNN) with long-short term memory (LSTM) cells (RNN-LSTM) to handle such complexity. The contributions of the presented work are three-fold: (i) we propose an RNN-LSTM architecture for joint modeling of slot filling, intent determination, and domain classification; (ii) we build a joint multi-domain model enabling multi-task deep learning where the data from each domain reinforces each other; (iii) we investigate alternative architectures for modeling lexical context in spoken language understanding. In addition to the simplicity of the single model framework, experimental results show the power of such an approach on Microsoft Cortana real user data over alternative methods based on single domain/task deep learning.


title: iSeeYou: disabling the MacBook webcam indicator LED

abstract: The ubiquitous webcam indicator LED is an important privacy feature which provides a visual cue that the camera is turned on. We describe how to disable the LED on a class of Apple internal iSight webcams used in some versions of MacBook laptops and iMac desktops. This enables video to be captured without any visual indication to the user and can be accomplished entirely in user space by an unprivileged (non-root) application. ::: ::: The same technique that allows us to disable the LED, namely reprogramming the firmware that runs on the iSight, enables a virtual machine escape whereby malware running inside a virtual machine reprograms the camera to act as a USB Human Interface Device (HID) keyboard which executes code in the host operating system. ::: ::: We build two proofs-of-concept: (1) an OS X application, iSeeYou, which demonstrates capturing video with the LED disabled; and (2) a virtual machine escape that launches Terminal.app and runs shell commands. To defend against these and related threats, we build an OS X kernel extension, iSightDefender, which prohibits the modification of the iSight's firmware from user space.


The Effectiveness of Different Assessment Techniques being used to Measure the Impact of Haptic Technologies on Dental Undergraduate Students' 3-D Perceptions, Motor and Clinical Skills


title: Ambient and Wearable Sensor Fusion for Activity Recognition in Healthcare Monitoring Systems

abstract: The use of wearable sensors for home monitoring provides an effective means of inferring a patient’s level of activity. However, wearable sensors have intrinsic ambiguities that prevent certain activities to be recognized accurately. The purpose of this paper is to introduce a robust framework for enhanced activity recognition by integrating an ear-worn activity recognition (e-AR) sensor with ambient blob-based vision sensors. Accelerometer information from the e-AR is fused with features extracted from the vision sensor by using a Gaussian Mixture Model Bayes classifier. The experimental results showed a significant improvement of the classification accuracy compared to the use of the e-AR sensor alone.


title: Evaluating operating system vulnerability to memory errors

abstract: Reliability is of great concern to the scalability of extreme-scale systems. Of particular concern are soft errors in main memory, which are a leading cause of failures on current systems and are predicted to be the leading cause on future systems. While great effort has gone into designing algorithms and applications that can continue to make progress in the presence of these errors without restarting, the most critical software running on a node, the operating system (OS), is currently left relatively unprotected. OS resiliency is of particular importance because, though this software typically represents a small footprint of a compute node's physical memory, recent studies show more memory errors in this region of memory than the remainder of the system. In this paper, we investigate the soft error vulnerability of two operating systems used in current and future high-performance computing systems: Kitten, the lightweight kernel developed at Sandia National Laboratories, and CLE, a high-performance Linux-based operating system developed by Cray. For each of these platforms, we outline major structures and subsystems that are vulnerable to soft errors and describe methods that could be used to reconstruct damaged state. Our results show the Kitten lightweight operating system may be an easier target to harden against memory errors due to its smaller memory footprint, largely deterministic state, and simpler system structure.


title: HardScope: Thwarting DOP with Hardware-assisted Run-time Scope Enforcement

abstract: Widespread use of memory unsafe programming languages (e.g., C ::: and C++) leaves many systems vulnerable to memory corruption ::: attacks. A variety of defenses have been proposed to mitigate attacks ::: that exploit memory errors to hijack the control flow of the code ::: at run-time, e.g., (fine-grained) randomization or Control Flow Integrity. However, recent work on data-oriented programming (DOP) ::: demonstrated highly expressive (Turing-complete) attacks, even in ::: the presence of these state-of-the-art defenses. Although multiple ::: real-world DOP attacks have been demonstrated, no efficient defenses are yet available. We propose run-time scope enforcement ::: (RSE), a novel approach designed to efficiently mitigate all currently ::: known DOP attacks by enforcing compile-time memory safety constraints (e.g., variable visibility rules) at run-time. We present HardScope, a proof-of-concept implementation of hardware-assisted ::: RSE for the new RISC-V open instruction set architecture. We ::: discuss our systematic empirical evaluation of HardScope which ::: demonstrates that it can mitigate all currently known DOP attacks, ::: and has a real-world performance overhead of 3.2% in embedded ::: benchmarks.


title: A Web Services Discovery Approach Based on Mining Underlying Interface Semantics

abstract: In recent years, Web service discovery has been a hot research topic. In this paper, we propose a novel Web services discovery approach, which can mine the underlying semantic structures of interaction interface parameters to help users find and employ Web services, and can match interfaces with high precision when the parameters of those interfaces contain meaningful synonyms, abbreviations, and combinations of disordered fragments. Our approach is based on mining the underlying semantics. First, we propose a conceptual Web services description model in which we include the type path for the interaction interface parameters in addition to the traditional text description. Then, based on this description model, we mine the underlying semantics of the interaction interface to create index libraries by clustering interaction interface names and fragments under the supervision of co-occurrence probability. This index library can help provide a high-efficiency interface that can match not only synonyms but also abbreviations and fragment combinations. Finally, we propose a Web service Operations Discovery algorithm (OpD). The OpD discovery results include two types of Web services: services with “Single” operations and services with “Composite” operations. The experimental evaluation shows that our approach performs better than other Web service discovery methods in terms of both discovery time and precision/recall rate.


title: Compact planar ultra-wideband power dividers with frequency range up to 67 GHz for multichannel receivers

abstract: This paper presents two ultra-wideband power dividers, based on a Wilkinson divider, with ranges of operating frequencies 2–12 GHz and 8–67 GHz. These dividers have been designed for equal splitting a local oscillator signal in four-channel measurement receiver of a vector network analyzer. For 8–67 GHz divider a novel input circuit, based on a resistive splitter and coupled lines was proposed. Using this circuit occupied area of the topology was greatly reduced, and outstanding value of isolation (more than 20 dB from 8 GHz to 67 GHz and above) was obtained.


Churn Identification in Microblogs using Convolutional Neural Networks with Structured Logical Knowledge


title: Business model canvas perspective on big data applications

abstract: Large and complex data that becomes difficult to be handled by traditional data processing applications triggers the development of big data applications which have become more pervasive than ever before. In the era of big data, data exploration and analysis turned into a difficult problem in many sectors such as the smart routing and health care sectors. Companies which can adapt their businesses well to leverage big data have significant advantages over those that lag this capability. The need for exploring new approaches to address the challenges of big data forces companies to shape their business models accordingly. In this paper, we summarize and share our findings regarding the business models deployed in big data applications in different sectors. We analyze existing big data applications by taking into consideration the core elements of a business (via business model canvas) and present how these applications provide value to their customers by making profit out of using big data.


title: No task left behind?: examining the nature of fragmented work

abstract: We present data from detailed observation of 24 information workers that shows that they experience work fragmentation as common practice. We consider that work fragmentation has two components: length of time spent in an activity, and frequency of interruptions. We examined work fragmentation along three dimensions: effect of collocation, type of interruption, and resumption of work. We found work to be highly fragmented: people average little time in working spheres before switching and 57% of their working spheres are interrupted. Collocated people work longer before switching but have more interruptions. Most internal interruptions are due to personal work whereas most external interruptions are due to central work. Though most interrupted work is resumed on the same day, more than two intervening activities occur before it is. We discuss implications for technology design: how our results can be used to support people to maintain continuity within a larger framework of their working spheres.


title: Diffusion-Convolutional Neural Networks

abstract: We present diffusion-convolutional neural networks (DCNNs), a new model for graph-structured data. Through the introduction of a diffusion-convolution operation, we show how diffusion-based representations can be learned from graph-structured data and used as an effective basis for node classification. DCNNs have several attractive qualities, including a latent representation for graphical data that is invariant under isomorphism, as well as polynomial-time prediction and learning that can be represented as tensor operations and efficiently implemented on the GPU. Through several experiments with real structured datasets, we demonstrate that DCNNs are able to outperform probabilistic relational models and kernel-on-graph methods at relational node classification tasks.


title: Automated and unsupervised user interaction logging as basis for usability evaluation of mobile applications

abstract: The evaluation of mobile user interfaces can be a tedious task, especially if usability tests under real-world conditions should be performed. In particular, the evaluation of high-fidelity prototypes provides valuable measures about the quality of mobile applications, which helps designers to identify potentials of improvement for the next revision. Due to their costs or missing expert knowledge evaluation techniques such as cognitive walkthroughs or heuristic evaluation are often not used. Additionally, commercial frameworks provide insufficient details on usability as they only address commercial statistics regarding user loyalty, in-app purchases or demographics. In this paper, we present a novel approach and toolkit for automated and unsupervised evaluation of mobile applications that, in contrast to existing frameworks, is able to trace any user interaction during the entire lifecycle of an application. As a major novelty, our toolkit can be added to mobile applications without changing application source code, which makes it flexible and scalable for all types of applications. Also, our toolkit is able to identify and visualize design flaws such as navigational errors or efficiency for mobile applications.


title: Impossibility of distributed consensus with one faulty process

abstract: The consensus problem involves an asynchronous system of processes, some of which may be unreliable. The problem is for the reliable processes to agree on a binary value. In this paper, it is shown that every protocol for this problem has the possibility of nontermination, even with only one faulty process. By way of contrast, solutions are known for the synchronous case, the “Byzantine Generals” problem.


title: Consumer perceived risk, attitude and online shopping behaviour; Empirical evidence from Malaysia

abstract: The development of e-commerce has increased the popularity of online shopping worldwide. In Malaysia, it was reported that online shopping market size was RM1.8 billion in 2013 and it is estimated to reach RM5 billion by 2015. However, online shopping was rated 11th out of 15 purposes of using internet in 2012. Consumers' perceived risks of online shopping becomes a hot topic to research as it will directly influence users' attitude towards online purchasing, and their attitude will have significant impact to the online purchasing behaviour. The conceptualization of consumers' perceived risk, attitude and online shopping behaviour of this study provides empirical evidence in the study of consumer online behaviour. Four types of risks – product risk, financial, convenience and non-delivery risks – were examined in term of their effect on consumers' online attitude. A web-based survey was employed, and a total of 300 online shoppers of a Malaysia largest online marketplace participated in this study. The findings indicated that product risk, financial and non-delivery risks are hazardous and negatively affect the attitude of online shoppers. Convenience risk was found to have positive effect on consumers' attitude, denoting that online buyers of this site trusted the online seller and they encountered less troublesome with the site. It also implies that consumers did not really concern on non-convenience aspect of online shopping, such as handling of returned products and examine the quality of products featured in the online seller website. The online buyers' attitude was significantly and positively affects their online purchasing behaviour. The findings provide useful model for measuring and managing consumers' perceived risk in internet-based transaction to increase their involvement in online shopping and to reduce their cognitive dissonance in the e-commerce setting.


title: STT-RAM Cache Hierarchy With Multiretention MTJ Designs

abstract: Spin-transfer torque random access memory (STT-RAM) is the most promising candidate to be universal memory due to its good scalability, zero standby power, and radiation hardness. Having a cell area only 1/9 to 1/3 that of SRAM, allows for a much larger cache with the same die footprint. Such reduction of cell size can significantly shrink the cache array size, leading to significant improvement of overall system performance and power consumption, especially in this multicore era where locality is crucial. However, deploying STT-RAM technology in L1 caches is challenging because write operations on STT-RAM are slow and power-consuming. In this paper, we propose a range of cache hierarchy designs implemented entirely using STT-RAM that deliver optimal power saving and performance. In particular, our designs use STT-RAM cells with various data retention times and write performances, made possible by novel magnetic tunneling junction designs. For L1 caches where speed is of utmost importance, we propose a scheme that uses fast STT-RAM cells with reduced data retention time coupled with a dynamic refresh scheme. In the dynamic refresh scheme, another emerging technology, memristor, is used as the counter to monitor the data retention of the low-retention STT-RAM, achieving a higher array area efficiency than an SRAM-based counter. For lower level caches with relatively larger cache capacities, we propose a design that has partitions of different retention characteristics, and a data migration scheme that moves data between these partitions. The experiments show that on the average, our proposed multiretention level STT-RAM cache reduces total energy by as much as 30%–74.2% compared to previous single retention level STT-RAM caches, while improving instruction per cycle performance for both two-level and three-level cache hierarchies.


title: The rise and fall of Hox gene clusters

abstract: Although all bilaterian animals have a related set of Hox genes, the genomic organization of this gene complement comes in different flavors. In some unrelated species, Hox genes are clustered; in others, they are not. This indicates that the bilaterian ancestor had a clustered Hox gene family and that, subsequently, this genomic organization was either maintained or lost. Remarkably, the tightest organization is found in vertebrates, raising the embarrassingly finalistic possibility that vertebrates have maintained best this ancestral configuration. Alternatively, could they have co-evolved with an increased ;organization' of the Hox clusters, possibly linked to their genomic amplification, which would be at odds with our current perception of evolutionary mechanisms? When discussing the why's and how's of Hox gene clustering, we need to account for three points: the mechanisms of cluster evolution; the underlying biological constraints; and the developmental modes of the animals under consideration. By integrating these parameters, general conclusions emerge that can help solve the aforementioned dilemma.


title: An Initial Theory of Value-Based Software Engineering

abstract: This chapter presents an initial “4+1” theory of value-based software engineering (VBSE). The engine in the center is the stakeholder win-win Theory W, which addresses the questions of “which values are important?” and “how is success assured?” for a given software engineering enterprise. The four additional theories that it draws upon are utility theory (how important are the values?), decision theory (how do stakeholders’ values determine decisions?), dependency theory (how do dependencies affect value realization?), and control theory (how to adapt to change and control value realization?). After discussing the motivation and context for developing a VBSE theory and the criteria for a good theory, the chapter discusses how the theories work together into a process for defining, developing, and evolving software-intensive systems. It also illustrates the application of the theory to a supply chain system example, discusses how well the theory meets the criteria for a good theory, and identifies an agenda for further research.


title: Activity analysis based on low sample rate smart meters

abstract: Activity analysis disaggregates utility consumption from smart meters into specific usage that associates with human activities. It can not only help residents better manage their consumption for sustainable lifestyle, but also allow utility managers to devise conservation programs. Existing research efforts on disaggregating consumption focus on analyzing consumption features with high sample rates (mainly between 1 Hz ~ 1MHz). However, many smart meter deployments support sample rates at most 1/900 Hz, which challenges activity analysis with occurrences of parallel activities, difficulty of aligning events, and lack of consumption features. We propose a novel statistical framework for disaggregation on coarse granular smart meter readings by modeling fixture characteristics, household behavior, and activity correlations. This framework has been implemented into two approaches for different application scenarios, and has been deployed to serve over 300 pilot households in Dubuque, IA. Interesting activity-level consumption patterns have been identified, and the evaluation on both real and synthetic datasets has shown high accuracy on discovering washer and shower.


title: Learning object motion patterns for anomaly detection and improved object detection

abstract: We present a novel framework for learning patterns of motion and sizes of objects in static camera surveillance. The proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback. Pixel level probability density functions (pdfs) of appearance have been used for background modelling in the past, but modelling pixel level pdfs of object speed and size from the tracks is novel. Each pdf is modelled as a multivariate Gaussian mixture model (GMM) of the motion (destination location & transition time) and the size (width & height) parameters of the objects at that location. Output of the tracking module is used to perform unsupervised EM-based learning of every GMM. We have successfully used the proposed scene model to detect local as well as global anomalies in object tracks. We also show the use of this scene model to improve object detection through pixel-level parameter feedback of the minimum object size and background learning rate. Most object path modelling approaches first cluster the tracks into major paths in the scene, which can be a source of error. We avoid this by building local pdfs that capture a variety of tracks which are passing through them. Qualitative and quantitative analysis of actual surveillance videos proved the effectiveness of the proposed approach.


title: Empowering Simple Binary Classifiers for Image Set Based Face Recognition

abstract: Face recognition from image sets has numerous real-life applications including recognition from security and surveillance systems, multi-view camera networks and personal albums. An image set is an unordered collection of images (e.g., video frames, images acquired over long term observations and personal albums) which exhibits a wide range of appearance variations. The main focus of the previously developed methods has therefore been to find a suitable representation to optimally model these variations. This paper argues that such a representation could not necessarily encode all of the information contained in the set. The paper, therefore, suggests a different approach which does not resort to a single representation of an image set. Instead, the images of the set are retained in their original form and an efficient classification strategy is developed which extends well-known simple binary classifiers for the task of multi-class image set classification. Unlike existing binary to multi-class extension strategies, which require multiple binary classifiers to be trained over a large number of images, the proposed approach is efficient since it trains only few binary classifiers on very few images. Extensive experiments and comparisons with existing methods show that the proposed approach achieves state of the art performance for image set classification based face and object recognition on a number of challenging datasets.


title: Automatic Misconfiguration Troubleshooting with PeerPressure

abstract: Technical support contributes 17% of the total cost of ownership of today's desktop PCs [25]. An important element of technical support is troubleshooting miscon-figured applications. Misconfiguration troubleshooting is particularly challenging, because configuration information is shared and altered by multiple applications. ::: ::: In this paper, we present a novel troubleshooting system: PeerPressure, which uses statistics from a set of sample machines to diagnose the root-cause misconfigurations on a sick machine. This is in contrast with methods that require manual identification on a healthy machine for diagnosing misconfigurations [30]. The elimination of this manual operation makes a significant step towards automated misconfiguration troubleshooting. ::: ::: In PeerPressure, we introduce a ranking metric for misconfiguration candidates. This metric is based on empirical Bayesian estimation. We have prototyped a PeerPressure troubleshooting system and used a database of 87 machine configuration snapshots to evaluate its performance. With 20 real-world troubleshooting cases, PeerPressure can effectively pinpoint the root-cause misconfigurations for 12 of these cases. For the remaining cases, PeerPressure significantly narrows down the number of root-cause candidates by three orders of magnitude.


title: The use of “overall accuracy” to evaluate the validity of screening or diagnostic tests

abstract: OBJECTIVE ::: Evaluations of screening or diagnostic tests sometimes incorporate measures of overall accuracy, diagnostic accuracy, or test efficiency. These terms refer to a single summary measurement calculated from 2 x 2 contingency tables that is the overall probability that a patient will be correctly classified by a screening or diagnostic test. We assessed the value of overall accuracy in studies of test validity, a topic that has not received adequate emphasis in the clinical literature. ::: ::: ::: DESIGN ::: Guided by previous reports, we summarize the issues concerning the use of overall accuracy. To document its use in contemporary studies, a search was performed for test evaluation studies published in the clinical literature from 2000 to 2002 in which overall accuracy derived from a 2 x 2 contingency table was reported. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Overall accuracy is the weighted average of a test's sensitivity and specificity, where sensitivity is weighted by prevalence and specificity is weighted by the complement of prevalence. Overall accuracy becomes particularly problematic as a measure of validity as 1) the difference between sensitivity and specificity increases and/or 2) the prevalence deviates away from 50%. Both situations lead to an increasing deviation between overall accuracy and either sensitivity or specificity. A summary of results from published studies (N = 25) illustrated that the prevalence-dependent nature of overall accuracy has potentially negative consequences that can lead to a distorted impression of the validity of a screening or diagnostic test. ::: ::: ::: CONCLUSIONS ::: Despite the intuitive appeal of overall accuracy as a single measure of test validity, its dependence on prevalence renders it inferior to the careful and balanced consideration of sensitivity and specificity.


title: ‘When You’re Here, You’re Family’: Culinary Tourism and the Olive Garden Restaurant:

abstract: In this paper, I address the practice of domestic culinary tourism by focusing on a seemingly banal suburban chain restaurant featuring ethnic cuisine. I examine the ways in which the restaurant positions itself as offering ‘real’ Italian food and a ‘real’ Italian experience, and analyze a broad array of cultural texts that chastise the Olive Garden for this assertion and condemn the chain and its patrons. Ultimately, I demonstrate that many of those things that position the restaurant as an object of derision are part of the appeal of the chain, resulting in a complex set of meanings that resides in the Olive Garden as a site of cultural interest.


title: The CES-D Scale A Self-Report Depression Scale for Research in the General Population

abstract: The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depression which have been used in previously validated longer scales. The new scale was tested in household interview surveys and in psychiatric settings. It was found to have very high internal consistency and adequate test- retest repeatability. Validity was established by pat terns of correlations with other self-report measures, by correlations with clinical ratings of depression, and by relationships with other variables which support its construct validity. Reliability, validity, and factor structure were similar across a wide variety of demographic characteristics in the general population samples tested. The scale should be a useful tool for epidemiologic studies of de pression.