title: PPP: Joint Pointwise and Pairwise Image Label Prediction

abstract: Pointwise label and pairwise label are both widely used in computer vision tasks. For example, supervised image classification and annotation approaches use pointwise label, while attribute-based image relative learning often adopts pairwise labels. These two types of labels are often considered independently and most existing efforts utilize them separately. However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels. For example, an image labeled with "coast" and annotated with "beach, sea, sand, sky" is more likely to have a higher ranking score in terms of the attribute "open", while "men shoes" ranked highly on the attribute "formal" are likely to be annotated with "leather, lace up" than "buckle, fabric". The existence of potential relations between pointwise labels and pairwise labels motivates us to fuse them together for jointly addressing related vision tasks. In particular, we provide a principled way to capture the relations between class labels, tags and attributes, and propose a novel framework PPP(Pointwise and Pairwise image label Prediction), which is based on overlapped group structure extracted from the pointwise-pairwise-label bipartite graph. With experiments on benchmark datasets, we demonstrate that the proposed framework achieves superior performance on three vision tasks compared to the state-of-the-art methods.


title: Eye Tracking in Human-Computer Interaction and Usability Research

abstract: The objective of the tutorial is to give an overview on how eye tracking is currently used and how it can be used as a method in human computer interaction research and especially in usability research. An eye tracking system records how the eyes move while a subject is completing a task for example on a web site. By analyzing these eye movements we are able to gain an objective insight into the behavior of that person.


title: Some results on optimal locally repairable codes

abstract: In a linear code, a code symbol is said to have locality r if it can be repaired by accessing at most r other code symbols. For an (n, k, r) locally repairable codes (LRC), the most important bounds on minimum distances might be the well-known Singleton-like bound and the Cadambe-Mazumdar bound which takes the field size into account. In this paper, we study the constructions of optimal LRCs from the view of parity-check matrices. Firstly, all the optimal binary LRCs meeting the Singleton-like bound are found in the sense of equivalence of linear codes, i.e., except the proposed 4 classes of LRCs, there is no other binary (n, k, r) LRC with minimum distance d = n − k − ⌈k/r⌉+2. Then a class of binary LRCs with distance 4 and arbitrary locality is proposed and shown to be optimal with respect to the Cadambe-Mazumdar bound. Moreover, we give a class of high rate optimal q-ary LRCs meeting the Singleton-like bound with minimum distance 4 while the required field size is only q ≥ r − 1. Finally, several methods to obtain short optimal LRCs from long optimal LRCs are proposed at the end of this paper.


title: DLOW: Domain Flow for Adaptation and Generalization

abstract: In this work, we present a domain flow generation(DLOW) model to bridge two different domains by generating a continuous sequence of intermediate domains flowing from one domain to the other. The benefits of our DLOW model are two-fold. First, it is able to transfer source images into different styles in the intermediate domains. The transferred images smoothly bridge the gap between source and target domains, thus easing the domain adaptation task. Second, when multiple target domains are provided for training, our DLOW model is also able to generate new styles of images that are unseen in the training data. We implement our DLOW model based on CycleGAN. A domainness variable is introduced to guide the model to generate the desired intermediate domain images. In the inference phase, a flow of various styles of images can be obtained by varying the domainness variable. We demonstrate the effectiveness of our model for both cross-domain semantic segmentation and the style generalization tasks on benchmark datasets. Our implementation is available at https://github.com/ETHRuiGong/DLOW .


Approaches to Glycemic Treatment. Sec. 7. In Standards of Medical Care in Diabetesd2016. Diabetes Care 2016


title: ADVIO: An authentic dataset for visual-inertial odometry

abstract: The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods. Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems. We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry. For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device. We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums. The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.


title: Anomaly based intrusion detection for 802.11 networks with optimal features using SVM classifier

abstract: The Wireless Fidelity (WiFi) is a widely used wireless technology due to its flexibility and mobility in the presence of vulnerable security features. Several attempts to secure 802.11 standard ends up with the inadequate security mechanisms that are vulnerable to various attacks and intrusions. Thus, integration of external defense mechanism like intrusion detection system (IDS) is inevitable. An anomaly-based IDS employs machine learning algorithms to detect attacks. Selecting the best set of features is central to ensure the performance of the classifier in terms of speed of learning, accuracy, and reliability. This paper proposes a normalized gain based IDS for MAC Intrusions (NMI) to improve the IDS performance significantly. The proposed NMI includes two primary components OFSNP and DCMI. The first component is optimal feature selection using NG and PSO (OFSNP) and the second component is Detecting and Categorizing MAC 802.11 Intrusions (DCMI) using SVM classifier. The OFSNP ranks the features using an independent measure as normalized gain (NG) and selects the optimal set of features using semi-supervised clustering (SSC). The SSC is based on particle swarm optimization (PSO) that uses labeled and unlabeled features simultaneously to find a group of optimal features. Using the optimal set of features, the proposed DCMI utilizes a rapid and straightforward support vector machine (SVM) learning that classifies the attacks under the appropriate classes. Thus, the proposed NMI achieves a better trade-off between detection accuracy and learning time. The experimental results show that the NMI accurately detects and classifies the 802.11 specific intrusions and also, it reduces the false positives and computation complexity by decreasing the number of features.


title: Image provenance inference through content-based device fingerprint analysis

abstract: We have introduced different intrinsic device fingerprints and their applications in image provenance inference. Although with varying levels of accuracy, the device fingerprints arising from optical aberration, CFA interpolation, CRF, and in-device image compression are effective in differentiating devices of different brands or models. Although they cannot uniquely identify the source device of an image, they do provide useful information about the image provenance and are effective at narrowing down the image source to a smaller set of possible devices. More than half of the chapter was spent on SPN, which is the only fingerprint that distinguishes devices of the same model. Because of its merits, such as the uniqueness to individual device and the robustness against common image operations, it has attracted much attention from researches and been successfully used for source device identification, device linking, source-oriented image clustering, and image forgery detection. In spite of the effectiveness of SPN, it is by nature a very weak signal and may have been contaminated by image content and other interferences. Its successful application requires jointly processing a large number of pixels, which results in very high dimensionality of SPN. This may bring huge difficulties in practice, e.g., in large-scale source-oriented image clustering based on SPN, so it is essential to conduct research on the compact representation of SPN for fast search and clustering.


title: doi:10.1155/2007/87046 Research Article Design and Implementation of Numerical Linear Algebra Algorithms on Fixed Point DSPs

abstract: Numerical linear algebra algorithms use the inherent elegance of matrix formulations and are usually implemented using C/C++ floating point representation. The system implementation is faced with practical constraints because these algorithms usually need to run in real time on fixed point digital signal processors (DSPs) to reduce total hardware costs. Converting the simulation model to fixed point arithmetic and then porting it to a target DSP device is a difficult and time-consuming process. In this paper, we analyze the conversion process. We transformed selected linear algebra algorithms from floating point to fixed point arithmetic, and compared real-time requirements and performance between the fixed point DSP and floating point DSP algorithm implementations. We also introduce an advanced code optimization and an implementation by DSP-specific, fixed point C code generation. By using the techniques described in the paper, speed can be increased by a factor of up to 10 compared to floating point emulation on fixed point hardware.


title: Anatomical and biomechanical mechanisms of subacromial impingement syndrome

abstract: Abstract Subacromial impingement syndrome is the most common disorder of the shoulder, resulting in functional loss and disability in the patients that it affects. This musculoskeletal disorder affects the structures of the subacromial space, which are the tendons of the rotator cuff and the subacromial bursa. Subacromial impingement syndrome appears to result from a variety of factors. Evidence exists to support the presence of the anatomical factors of inflammation of the tendons and bursa, degeneration of the tendons, weak or dysfunctional rotator cuff musculature, weak or dysfunctional scapular musculature, posterior glenohumeral capsule tightness, postural dysfunctions of the spinal column and scapula and bony or soft tissue abnormalities of the borders of the subacromial outlet. These entities may lead to or cause dysfunctional glenohumeral and scapulothoracic movement patterns. These various mechanisms, singularly or in combination may cause subacromial impingement syndrome.


title: Coping theory and research: Past, present, and future

abstract: In this essay in honor of Donald Oken, I emphasize coping as a key concept for theory and research on adaptation and health. My focus will be the contrasts between two approaches to coping, one that emphasizes style—that is, it treats coping as a personality characteristic—and another that emphasizes process—that is, efforts to manage stress that change over time and are shaped by the adaptational context out of which it is generated. I begin with an account of the style and process approaches, discuss their history briefly, set forth the principles of a process approach, describe my own efforts at measurement, and define coping and its functions from a process standpoint. This is followed by a digest of major generalizations that resulted from coping process research. The essay concludes with a discussion of special issues of coping measurement, in particular, the limitations of both coping style and process approaches and how these limitations might be dealt with. There has been a prodigious volume of coping research in the last decade or two, which I can only touch on very selectively. In this essay, I also ignore a host of important developmental issues that have to do with the emergence of coping and its cognitive and motivational bases in infants, as well as a growing literature on whether, how, and why the coping process changes with aging.


title: ManyEyes: a Site for Visualization at Internet Scale

abstract: We describe the design and deployment of Many Eyes, a public Web site where users may upload data, create interactive visualizations, and carry on discussions. The goal of the site is to support collaboration around visualizations at a large scale by fostering a social style of data analysis in which visualizations not only serve as a discovery tool for individuals but also as a medium to spur discussion among users. To support this goal, the site includes novel mechanisms for end-user creation of visualizations and asynchronous collaboration around those visualizations. In addition to describing these technologies, we provide a preliminary report on the activity of our users.


title: Dynamic Facet Ordering for Faceted Product Search Engines

abstract: Faceted browsing is widely used in Web shops and product comparison sites. In these cases, a fixed ordered list of facets is often employed. This approach suffers from two main issues. First, one needs to invest a significant amount of time to devise an effective list. Second, with a fixed list of facets, it can happen that a facet becomes useless if all products that match the query are associated to that particular facet. In this work, we present a framework for dynamic facet ordering in e-commerce. Based on measures for specificity and dispersion of facet values, the fully automated algorithm ranks those properties and facets on top that lead to a quick drill-down for any possible target product. In contrast to existing solutions, the framework addresses e-commerce specific aspects, such as the possibility of multiple clicks, the grouping of facets by their corresponding properties, and the abundance of numeric facets. In a large-scale simulation and user study, our approach was, in general, favorably compared to a facet list created by domain experts, a greedy approach as baseline, and a state-of-the-art entropy-based solution.


title: Stream-based wandering monitoring system for elderly people with dementia

abstract: Statistics reported by the United Nations indicate that the elderly population has grown rapidly and continuously. These indicators illustrate the fact that we are entering into the era of aging population. The elderly are usually faced with many problems resulting from the deterioration of health with increasing age. One of the major problems comes in the form of a decline in mental ability, commonly called dementia. This problem leads to wandering behavior - aimless movement, repetitive locomotion with no identifiable goal and disorientation to time and place. The wandering behavior often results in negative consequences such as getting lost and serious injuries or eventually death. To control and reduce negative consequences associated with wandering, we propose a novel wandering monitoring system that timely detect wandering patterns in the elderly with dementia movement. Our system applies a stream processing technique together with GPS technology to identify location and detect wandering behavior of the elderly in real-time. The experimental results show that our wandering monitoring system offers low-latency processing and can provide real-time and instantaneous responses. The average latency about 1 ms per additional elderly patient was observed with reasonable processing overheads.


title: A conditional approach to dispositional constructs: the local predictability of social behavior.

abstract: A conditional approach to dispositions is developed in which dispositional constructs are viewed as clusters of if-then propositions. These propositions summarize contingencies between categories of conditions and categories of behavior rather than generalized response tendencies. A fundamental unit for investigating dispositions is therefore the conditional frequency of acts that are central to a given behavior category in circumscribed situations, not the overall frequency of behaviors. In an empirical application of the model, we examine how people's dispositional judgments are linked to extensive observations of targets' behavior in a range of natural social situations. We identify categories of these social situations in which targets' behavior may be best predicted from observers' dispositional judgements, focusing on the domains of aggression and withdrawal. One such category consists of subjectively demanding or stressful situations that tax people's performance competencies. As expected, children judged to be aggressive or withdrawn were variable across situations in dispositionally relevant behaviors, but they diverged into relatively predictable aggressive and withdrawn actions in situations that required the social, self-regulatory, and cognitive competencies they lacked. Implications of the conditional approach for personality assessment and person perception research are considered.


title: The effect of betamethasone on myocardial viability during cold cardioplegia in canine donor heart preservation

abstract: This study examined the effect of betamethasone on myocardial viability of reperfused isolated canine hearts following a 6-h hypothermic cardioplegia. The dogs were divided into two groups: group I (n = 9) received nondepolarizing cardioplegia containing betamethasone 250 mg/l while group II (n = 7) was administered cardioplegia without betamethasone. The myocardial concentrations of calcium, ATP, ADP, total adenine nucleotide, cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) were identical in the two groups throughout the experiment. The coronary sinus plasma concentrations of MB fraction off creatine kinase (MB-CK), cAMP and cGMP after 2 h of reperfusion were significantly lower in group I than in group II. The myocardial mitochondrial ultrastructure, as assessed by semiquantitative morphometry, was found to be significantly better preserved in group I than in group II at the end of both preservation and reperfusion. In addition, the left ventricular end-systolic pressure volume relation (ESPVR) showed a higher slope and lower intercept in group I than in group II. These results suggest that the addition of betamethasone to nondepolarizing cold cardioplegia enhances myocardial protection via membrane stabilization without affecting the adenine nucleotide metabolism.


title: Measuring Distance with Mobile Phones Using Single-Camera Stereo Vision

abstract: Computer stereo vision is an important technique for robotic navigation and other mobile scenarios where depth perception is needed, but it usually requires two cameras with a known horizontal displacement. In this paper, we present a solution for mobile devices with just one camera, which is a first step towards making computer stereo vision available to a wide range of devices that are not equipped with stereo cameras. We have built a prototype using a state-of-the-art mobile phone, which has to be manually displaced in order to record images from different lines of sight. Since the displacement between the two images is not known in advance, it is measured using the phone's inertial sensors. We evaluated the accuracy of our single-camera approach by performing distance calculations to everyday objects in different indoor and outdoor scenarios, and compared the results with that of a stereo camera phone. As a main advantage of a single moving camera is the possibility to vary its relative position between taking the two pictures, we investigated the effect of different camera displacements on the accuracy of distance measurements.


title: A Dataset for Improved RGBD-Based Object Detection and Pose Estimation for Warehouse Pick-and-Place

abstract: An important logistics application of robotics involves manipulators that pick-and-place objects placed in warehouse shelves. A critical aspect of this task corre- sponds to detecting the pose of a known object in the shelf using visual data. Solving this problem can be assisted by the use of an RGB-D sensor, which also provides depth information beyond visual data. Nevertheless, it remains a challenging problem since multiple issues need to be addressed, such as low illumination inside shelves, clutter, texture-less and reflective objects as well as the limitations of depth sensors. This paper provides a new rich data set for advancing the state-of-the-art in RGBD- based 3D object pose estimation, which is focused on the challenges that arise when solving warehouse pick- and-place tasks. The publicly available data set includes thousands of images and corresponding ground truth data for the objects used during the first Amazon Picking Challenge at different poses and clutter conditions. Each image is accompanied with ground truth information to assist in the evaluation of algorithms for object detection. To show the utility of the data set, a recent algorithm for RGBD-based pose estimation is evaluated in this paper. Based on the measured performance of the algorithm on the data set, various modifications and improvements are applied to increase the accuracy of detection. These steps can be easily applied to a variety of different methodologies for object pose detection and improve performance in the domain of warehouse pick-and-place.


title: Editing Training Data for kNN Classifiers with Neural Network Ensemble

abstract: Since kNN classifiers are sensitive to outliers and noise contained in the training data set, many approaches have been proposed to edit the training data so that the performance of the classifiers can be improved. In this paper, through detaching the two schemes adopted by the Depuration algorithm, two new editing approaches are derived. Moreover, this paper proposes to use neural network ensemble to edit the training data for kNN classifiers. Experiments show that such an approach is better than the approaches derived from Depuration, while these approaches are better than or comparable to Depuration.


title: Unsupervised Training for 3D Morphable Model Regression

abstract: We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.


title: Brownout Approach for Adaptive Management of Resources and Applications in Cloud Computing Systems: A Taxonomy and Future Directions

abstract: Cloud computing has been regarded as an emerging approach to provisioning resources and managing applications. It provides attractive features, such as an on-demand model, scalability enhancement, and management cost reduction. However, cloud computing systems continue to face problems such as hardware failures, overloads caused by unexpected workloads, or the waste of energy due to inefficient resource utilization, which all result in resource shortages and application issues such as delays or saturation. A paradigm, the brownout, has been applied to handle these issues by adaptively activating or deactivating optional parts of applications or services to manage resource usage in cloud computing system. Brownout has successfully shown that it can avoid overloads due to changes in workload and achieve better load balancing and energy saving effects. This article proposes a taxonomy of the brownout approach for managing resources and applications adaptively in cloud computing systems and carries out a comprehensive survey. It identifies open challenges and offers future research directions.


title: The roles of body and mind in abstract thought

abstract: The Roles of Body and Mind in Abstract Thought Lera Boroditsky (lera@psych.stanford.edu) Department of Psychology, Jordan Hall, Bldg420 Stanford, CA 94305-2130 USA Michael Ramscar (michael@dai.ed.ac.uk) University of Edinburgh, 2 Buccleuch Place Edinburgh, EH8 9LW, Scotland Michael C. Frank (mcfrank@stanford.edu) Department of Psychology, Jordan Hall, Bldg420 Stanford, CA 94305-2130 USA Abstract How are we able to think about things we've never seen or touched? We demonstrate that abstract knowledge is built analogically from more experience- based knowledge. People’s understanding of the abstract domain of time, for example, is so intimately dependent on the more experience-based domain of space, that when people make a an air journey or bet on a racehorse, they also unwittingly (and dramatically) change their thinking about time. Further, it appears that abstract thinking is built o n representations of more experience-based domains that are functionally separable from those involved directly in sensorimotor experience itself. How are we able to think about things we've never seen or touched? Whether we are theorizing about invisible forces, studying the behaviors of atoms, or trying to characterize the nature of private mental experience, much scientific progress depends on generating new ways of describing and conceptualizing phenomena which are not perceivable through the senses. We face the same problems in everyday life with abstract notions like time, justice, and love. How do we come to represent and reason about abstract domains despite the dearth of sensory information available about them? One suggestion is that abstract domains are understood through analogical extensions from richer, more experience-based domains (Boroditsky, 2000; Gentner et al., in press; Gibbs, 1994; Holyoak & Thagard, 1995; Lakoff & Johnson, 1980, 1999). This experience-based structuring view can be formulated in several strengths. A very strong “embodied” formulation might be that knowledge of abstract domains is tied directly to the body such that abstract notions are understood directly through image schemas and motor schemas (Lakoff & Johnson, 1999). A milder view might be that abstract knowledge is based on representations of more experience-based domains that are functionally separable from those directly involved in sensorimotor experience. The studies reported in this paper show that people’s understanding of the abstract domain of time is substrated by their knowledge and experiences in the more concrete domain of space 1 . In fact, people’s representations of time are so intimately dependent on space that when people engage in particular types of spatial thinking (e.g., embarking on a train journey, or urging on a horse in a race), they unwittingly also change how they think about time. Further (and contrary to the very strong embodied view), it appears that abstract thinking is built on representations of more experience-based domains, and not necessarily on the physical experience itself. Suppose you are told that next Wednesday's meeting has been moved forward two days. What day is the meeting now that it has been rescheduled? The answer to this question depends on how you choose to think about time. If you think of yourself as moving forward through time (the ego-moving perspective), then moving a meeting “forward” is moving it further in your direction of motion—that is, from Wednesday to Friday. If, on the other hand, you think of time as coming toward you (the time-moving perspective), then moving a meeting “forward” is moving it closer to you—that is, from Wednesday to Monday (Boroditsky, 2000; McGlone & Harding, 1998; McTaggart, 1908). In a neutral context, people are equally likely to think of themselves as moving through time as they are to think of time as coming toward them, and so are equally This paper only examines one aspect of how people think about time. The domain of time comprises an incredibly complex, heterogeneous and sophisticated set of representations, and much more investigation will be necessary to properly characterize the domain as a whole.


title: Message Domains as an Architectural Solution toComplexity in Distributed Message-Oriented Systems

abstract: Effectiveness of message passing as the means to provide communication in distributed computing motivates the use of such strategy in highly distributed service environments such as grid and cloud computing. Grids and clouds share some important features such as multiplicity of administrative domains and complexity to create applications to be executed over these platforms. In this paper we argue that message domains can provide architectural simplicity to split distributed applications into layers. Interdependence between layers is controlled by visibility policies in each message domain. This architecture allows for the creation of infrastructures of distributed agents in which applications can be modular. I. I NTRODUCTION Highly distributed computing models such as grid and cloud computing are becoming popular as companies and academic institutions start to realize the benefits of computational resources virtualization, platform as a service, and systems as a service. But computation as a commodity comes at a price. As systems scale, so the complexity to keep them working and evolving increases. Popular systems tend to have a large number of customers, therefore, developers of those systems are expected to receive constant requests for enhancements. Although message passing is largely used to create distributed applications in a more controlled scenario, the principles of message passing can be also applied to grids and clouds. Therefore, we need to adapt system distribution to address the new questions introduced by the global scale. When computational resources are dynamically provisioned, the exchanged messages are not only about data and execution control, but may be also about resource finding, reservation, and payment. When nodes are separated by continents, multicasting must be used wisely. If public networks are utilized to transport the messages, extra care should be taken with security. If more than one organization is exchanging messages in the same grid, message exchange within nodes that belong to the same organization should be isolated from messages that provide communication between organizations. As customer expectations increase due to a high offer of remote services, so does the struggle of development teams to keep the costs of software development and software change under control. To address these problems, we advocate that global scale distributed systems should be based on reliable multicast domains. Those domains should be flexible to allow for application designers to create custom configurations that will support decisions over module partitioning, user roles, secrecy of data, and division of concerns into independent layers. The next section will present our argument for the adoption of multicast domains to address those issues. Section III presents an example of message domains. Section IV presents some details of our experience to implement the message domains using JMS. Section V presents related research. Finally, we present our conclusions in Section VI.


title: PacketCloud: an open platform for elastic in-network services

abstract: The Internet was designed with the end-to-end principle where the network layer provided merely the best-effort forwarding service. This design makes it challenging to add new services to the network layer. However, as the Internet connectivity becomes a commodity, users and applications increasingly demand new in-network services. This paper proposes PacketCloud, a cloudlet-based open platform to host elastic in-network services. PacketCloud can help both Internet Service Providers (ISPs) and emerging application/content providers deploy their services to strategic network locations. We have implemented a proof-of-concept prototype of PacketCloud based on the MobilityFirst architecture. PacketCloud introduces a small additional delay, and can scale well to handle high-throughput data traffic.


title: Current Evidence and Opportunities for Expanding the Role of Occupational Therapy for Adults With Musculoskeletal Conditions

abstract: Musculoskeletal conditions are the second greatest cause of disability worldwide, and chronic musculoskeletal conditions affect nearly the same percentage of the general population as chronic circulatory and respiratory conditions combined. Moreover, people with musculoskeletal conditions experience a significant decline in independence with daily activities and occupational performance, key areas targeted by occupational therapy interventions. This special issue of the American Journal of Occupational Therapy provides comprehensive summaries of evidence for the care of common musculoskeletal conditions, highlights important implications that support evidence-informed practice, and proposes ways to advance the practice of occupational therapy to improve the lives of people with musculoskeletal conditions.


title: The Privacy Implications of Cyber Security Systems: A Technological Survey

abstract: Cyber-security systems, which protect networks and computers against cyber attacks, are becoming common due to increasing threats and government regulation. At the same time, the enormous amount of data gathered by cyber-security systems poses a serious threat to the privacy of the people protected by those systems. To ground this threat, we survey common and novel cyber-security technologies and analyze them according to the potential for privacy invasion. We suggest a taxonomy for privacy risks assessment of information security technologies, based on the level of data exposure, the level of identification of individual users, the data sensitivity and the user control over the monitoring, and collection and analysis of the data. We discuss our results in light of the recent technological trends and suggest several new directions for making these mechanisms more privacy-aware.


CONCURRENT ENGINEERING DEVELOPMENT AND PRACTICES FOR AIRCRAFT DESIGN AT AIRBUS


title: Variance-Spectra based Normalization for I-vector Standard and Probabilistic Linear Discriminant Analysis

abstract: I-vector extraction and Probabilistic Linear Discriminant Analysis (PLDA) has become the state-of-the-art configuration for speaker verification. Recently, Gaussian-PLDA has been improved by a preliminary length normalization of i-vectors. This normalization, known to increase the Gaussianity of the i-vector distribution, also improves performance of systems based on standard Linear Discriminant Analysis (LDA) and ”two-covariance model” scoring. But this technique follows a standardization of the i-vectors (centering and whitening ivectors based on the first and second order moments of the development data). We propose in this paper two techniques of normalization based on total, between- and within-speaker variance spectra 1 . These ”spectral” techniques both normalize the i-vectors length for Gaussianity, but the first adapts the ivectors representation to a speaker recognition system based on LDA and two-covariance scoring when the second adapts it to a Gaussian-PLDA model. Significant performance improvements are demonstrated on the male and female telephone portion of NIST SRE 2010. Index Terms: i-vectors, probabilistic linear discriminant analysis, speaker recognition.


Web-based Collaborative Corpus Annotation: Requirements and a Framework Implementation


title: Resource Allocation in NFV: A Comprehensive Survey

abstract: Network functions virtualization (NFV) is a new network architecture framework where network function that traditionally used dedicated hardware (middleboxes or network appliances) are now implemented in software that runs on top of general purpose hardware such as high volume server. NFV emerges as an initiative from the industry (network operators, carriers, and manufacturers) in order to increase the deployment flexibility and integration of new network services with increased agility within operator’s networks and to obtain significant reductions in operating expenditures and capital expenditures. NFV promotes virtualizing network functions such as transcoders, firewalls, and load balancers, among others, which were carried out by specialized hardware devices and migrating them to software-based appliances. One of the main challenges for the deployment of NFV is the resource allocation of demanded network services in NFV-based network infrastructures. This challenge has been called the NFV resource allocation (NFV-RA) problem. This paper presents a comprehensive state of the art of NFV-RA by introducing a novel classification of the main approaches that pose solutions to solve it. This paper also presents the research challenges that are still subject of future investigation in the NFV-RA realm.


Piezoelectric Approach on Harvesting Acoustic Energy


CS 224N Assignment 4: Question Answering on SQuAD


title: Near zero turn-on voltage high-efficiency UHF RFID rectifier in silicon-on-sapphire CMOS

abstract: A UHF RFID rectifier which turns on at near zero input voltage is demonstrated. The rectifier is fabricated in 0.25-µm silicon-on-sapphire (SOS) CMOS technology using intrinsic, near zero threshold devices. A novel improved cross-coupled bridge topology is used to minimize the leakage incurred through the use of intrinsic devices while maintaining their low power turn on characteristics. The fabricated rectifier demonstrates a peak power conversion efficiency (PCE) of 71.5% at 915MHz with a RF input of −4 dBm and a 30 kΩ load. More importantly, a PCE ≫ 30% was measured for all RF input powers between −28 and −4 dBm demonstrating state-of-the-art efficiency across a wide range of input powers.


title: An 84 GHz Bandwidth and 20 dB Gain Broadband Amplifier in SiGe Bipolar Technology

abstract: This paper reports on the design, fabrication, and characterization of a lumped broadband amplifier in SiGe bipolar technology. The measured differential gain is 20 dB with a 3-dB bandwidth of more than 84 GHz, which is the highest bandwidth reported so far for broadband SiGe bipolar amplifiers. The resulting gain bandwidth product (GBW) is more than 840 GHz. The amplifier consumes a power of 990 mW at a supply of -5.5 V.


title: Stability Analysis for Neural Networks With Time-Varying Delay Based on Quadratic Convex Combination

abstract: In this paper, a novel method is developed for the stability problem of a class of neural networks with time-varying delay. New delay-dependent stability criteria in terms of linear matrix inequalities for recurrent neural networks with time-varying delay are derived by the newly proposed augmented simple Lyapunov-Krasovski functional. Different from previous results by using the first-order convex combination property, our derivation applies the idea of second-order convex combination and the property of quadratic convex function which is given in the form of a lemma without resorting to Jensen's inequality. A numerical example is provided to verify the effectiveness and superiority of the presented results.


title: Specificities of secretion and uptake of exosomes and other extracellular vesicles for cell-to-cell communication

abstract: The ability of exosomes to transfer cargo from donor to acceptor cells, thereby triggering phenotypic changes in the latter, has generated substantial interest in the scientific community. However, the extent to which exosomes differ from other extracellular vesicles in terms of their biogenesis and functions remains ill-defined. Here, we discuss the current knowledge on the specificities of exosomes and other types of extracellular vesicles, and their roles as important agents of cell-to-cell communication. In this Perspective, Thery and co-authors discuss our current understanding of the biogenesis, secretion and uptake of exosomes and extracellular vesicles.


title: Intelligent stock data prediction using predictive data mining techniques

abstract: Cloud computing is the one of the admired paradigms of current era, which facilitates the users with on demand services and pay as you use services. It has tremendous applications in almost every sphere such as education, gaming, social networking, transportation, medical, business, stock market, pattern matching, etc. Stock market is such an industry where lots of data is generated and benefits are reaped on the basis of accurate prediction. So prediction is a vital part of stock market. Therefore, an attempt is being made to predict the stock market based on the given data set of stock market along with some features; using the techniques available for predictive data mining. Machine learning is one of the upcoming trends of data mining; hence few machine learning algorithms have been used such as Decision tree, Linear model, Random forest and further their results have been compared using the classification evaluation parameters such as H, AUC, ROC, TPR, FPR, etc. Random forest have been consider as the most effective model as it yield the highest accuracy of 54.12% whereas decision tree and linear model gives the accuracy of 51.87% and 52.83% respectively.


title: A new generation of microwave bandpass filters using multilayer ring resonators

abstract: A new generation of compact microwave bandpass filters (BPF) is proposed. The filters are based on multilayer ring resonators (MRR) technique which is applicable to both narrow band and ultra-wideband (UWB) bandpass filters. This class of filters has very compact circuit size and simple structure for fabrication. Multiple attenuation poles exist and can be simply placed to the left or right of the passband. The dimensions of the filters can be designed to around 2 mm × 2 mm × 1 mm. Out of the five filters presented, two of them are fabricated and measured. Good agreement between the simulation and measurements confirmed the design approach.


title: Scale-space and edge detection using anisotropic diffusion

abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >


title: Flipping bits in memory without accessing them: An experimental study of DRAM disturbance errors

abstract: Memory isolation is a key property of a reliable and secure computing system--an access to one memory address should not have unintended side effects on data stored in other addresses. However, as DRAM process technology scales down to smaller dimensions, it becomes more difficult to prevent DRAM cells from electrically interacting with each other. In this paper, we expose the vulnerability of commodity DRAM chips to disturbance errors. By reading from the same address in DRAM, we show that it is possible to corrupt data in nearby addresses. More specifically, activating the same row in DRAM corrupts data in nearby rows. We demonstrate this phenomenon on Intel and AMD systems using a malicious program that generates many DRAM accesses. We induce errors in most DRAM modules (110 out of 129) from three major DRAM manufacturers. From this we conclude that many deployed systems are likely to be at risk. We identify the root cause of disturbance errors as the repeated toggling of a DRAM row's wordline, which stresses inter-cell coupling effects that accelerate charge leakage from nearby rows. We provide an extensive characterization study of disturbance errors and their behavior using an FPGA-based testing platform. Among our key findings, we show that (i) it takes as few as 139K accesses to induce an error and (ii) up to one in every 1.7K cells is susceptible to errors. After examining various potential ways of addressing the problem, we propose a low-overhead solution to prevent the errors


title: Bacterial Foraging Optimization Based Radial Basis Function Neural Network (BRBFNN) for Identification and Classification of Plant Leaf Diseases: An Automatic Approach Towards Plant Pathology

abstract: The contribution of a plant is highly important for both human life and environment. Plants do suffer from diseases, like human beings and animals. There is the number of plant diseases that occur and affects the normal growth of a plant. These diseases affect complete plant including leaf, stem, fruit, root, and flower. Most of the time when the disease of a plant has not been taken care of, the plant dies or may cause leaves drop, flowers, and fruits drop. Appropriate diagnosis of such diseases is required for accurate identification and treatment of plant diseases. Plant pathology is the study of plant diseases, their causes, procedures for controlling and managing them. But, the existing method encompasses human involvement for classification and identification of diseases. This procedure is time-consuming and costly. Automatic segmentation of diseases from plant leaf images using soft computing approach can be reasonably useful than the existing one. In this paper, we have introduced a method named as bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases automatically. For assigning optimal weight to radial basis function neural network we use bacterial foraging optimization that further increases the speed and accuracy of the network to identify and classify the regions infected of different diseases on the plant leafs. The region growing algorithm increases the efficiency of the network by searching and grouping of seed points having common attributes for feature extraction process. We worked on fungal diseases like common rust, cedar apple rust, late blight, leaf curl, leaf spot, and early blight. The proposed method attains higher accuracy in identification and classification of diseases.


title: Mention-anomaly-based Event Detection and tracking in Twitter

abstract: The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (Mention-Anomaly-Based Event Detection), a novel method that leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect important events and estimate the magnitude of their impact over the crowd. The main advantages of MABED over prior works are that (i) it relies solely on tweets, meaning no external knowledge is required, and that (ii) it dynamically estimates the period of time during which each event is discussed rather than assuming a predefined fixed duration. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Last, we show that MABED helps with the interpretation of detected events by providing clear and precise descriptions.


title: Transition Flight Modeling of a Fixed-Wing VTOL UAV

abstract: This paper describes a complete six-degree-of-freedom nonlinear mathematical model of a tilt rotor unmanned aerial vehicle (UAV). The model is specifically tailored for the design of a hover to forward flight and forward flight to hover transition control system. In that respect, the model includes the aerodynamic effect of propeller-induced airstream which is a function of cruise speed, tilt angle and angle of attack. The cross-section area and output velocity of the propeller-induced airstream are calculated with momentum theory. The projected area on the UAV body that is affected by the propeller-induced airstream is specified and 2D aerodynamic analyses are performed for the airfoil profile of this region. Lookup-tables are generated and implemented in the nonlinear mathematical model. In addition, aerodynamic coefficients of the airframe are calculated by using CFD method and these data are embedded into the nonlinear model as a lookup-table form. In the transition flight regime, both aerodynamic and thrust forces act on the UAV body and the superimposed dynamics become very complex. Hence, it is important to define a method for hover-to-cruise and cruise-to-hover transitions. To this end, both transition scenarios are designed and a state-schedule is developed for flight velocity, angle of attack, and thrust levels of each of the thrust-propellers. This transition state schedule is used as a feedforward state for the flight control system. We present the simulation results of the transition control system and show the successful transition of TURAC in experiment.


title: Program complexity metrics and programmer opinions

abstract: Various program complexity measures have been proposed to assess maintainability. Only relatively few empirical studies have been conducted to back up these assessments through empirical evidence. Researchers have mostly conducted controlled experiments or correlated metrics with indirect maintainability indicators such as defects or change frequency. This paper uses a different approach. We investigate whether metrics agree with complexity as perceived by programmers. We show that, first, programmers' opinions are quite similar and, second, only few metrics and in only few cases reproduce complexity rankings similar to human raters. Data-flow metrics seem to better match the viewpoint of programmers than control-flow metrics, but even they are only loosely correlated. Moreover we show that a foolish metric has similar or sometimes even better correlation than other evaluated metrics, which raises the question how meaningful the other metrics really are. In addition to these results, we introduce an approach and associated statistical measures for such multi-rater investigations. Our approach can be used as a model for similar studies.


title: Biomimetic Walking Robot Scorpion: Control and Modeling

abstract: Abstract We present the biomimetic control scheme for the walking robot SCORPION. We used a concept of Basic Motion Patterns, which can be combined in a very flexible manner. Also reflexes are introduced to increase the reactivity. In addition our modeling and simulation approach is described, which has been done based on the ADAMS™ simulator. Especially the motion patterns of real scorpions were analyzed and used for walking patterns and smooth acceleration of the robot.


title: Complex Adaptive Systems

abstract: The field of Complex Adaptive Systems (CAS) is approximately 20 years old, having been established by physicists, economists, and others studying complexity at the Santa Fe Institute in New Mexico, USA. The field has spawned much work, such as Holland's contributions of genetic algorithms, classifier systems, and his ecosystem simulator, which assisted in provoking the fields of evolutionary computation and artificial life. The framework of inducted principles derived from many natural and artificial examples of complex systems has assisted in the investigation in such diverse fields of study as psychology, anthropology, genetic evolution, ecology, and business management theory, although a unified theory of such complex systems still appears to be a long way off. This work reviews the principles of complex adaptive systems as a framework, providing a number of interpretations from eminent researches in the field. Many example works are cited, and the theory is used to phrase some ambiguus work in the field of artificial immune systems and artificial life. The methodology of using simulations of CAS as the starting point for models in the field of biological inspired computation is postulated as an important contribution of CAS to that field.


title: Prototyping of flexible capacitive encoder with un-tethered slider using electrostatic induction

abstract: This paper describes a capacitive linear encoder that is characterized by its un-tethered sensor slider. The main components are made of flexible printed circuit films measuring only 0.2 mm in thickness that allows the sensor to be set up in thin interspaces or on curved surfaces. The sensor consists of a long receiver film and a short transmitter film, respectively containing four-phase and two-phase electrode; the transmitter is used as a slider and the receiver as a stator. To realize an un-tethered slider, the sensor employs a unique approach; electric power is supplied to the transmitter film by electrostatic induction, thus removing electric wires from the slider. This un-tethered slider can facilitate the sensitive applications where mechanical disturbance caused by an electric wire can be a problem. The principle was verified using a prototype encoder, which showed a linearity error of +/- 10 micrometers.


title: A Review of the Role of Sensors in Mobile Context-Aware Recommendation Systems

abstract: Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios.


title: Leaching of plastic additives to marine organisms.

abstract: It is often assumed that ingestion of microplastics by aquatic species leads to increased exposure to plastic additives. However, experimental data or model based evidence is lacking. Here we assess the potential of leaching of nonylphenol (NP) and bisphenol A (BPA) in the intestinal tracts of Arenicola marina (lugworm) and Gadus morhua (North Sea cod). We use a biodynamic model that allows calculations of the relative contribution of plastic ingestion to total exposure of aquatic species to chemicals residing in the ingested plastic. Uncertainty in the most crucial parameters is accounted for by probabilistic modeling. Our conservative analysis shows that plastic ingestion by the lugworm yields NP and BPA concentrations that stay below the lower ends of global NP and BPA concentration ranges, and therefore are not likely to constitute a relevant exposure pathway. For cod, plastic ingestion appears to be a negligible pathway for exposure to NP and BPA.


title: A Broadband Pattern Diversity Annular Slot Antenna

abstract: An annular slot antenna suitable for pattern diversity in wideband applications is presented. Pattern diversity is obtained by feeding the antenna simultaneously with two microstrip lines printed on the back of the substrate, which generate orthogonal radiation patterns. Two shorts placed at 45 degrees between the microstrip lines and in the opposite direction ensure an isolation above 15 dB according to measurements. The antenna operates in the range of frequencies from 3 GHz to 12 GHz (1:4 bandwidth) and covers many different standards (Wireless-LAN, HIPERLAN2, WIMAX, UWB). Simulations and measurements for return loss, isolation and radiation patterns are presented. Furthermore, measurements have been carried out in a real multipath environment in order to evaluate pattern diversity performance. Results of measurements of the correlation coefficient and diversity gain over the entire operating bandwidth show that the antenna is suitable for antenna diversity systems.


title: F/A-18A testing of flight control system reversion to mechanical backup

abstract: The F/A-18 incorporates a digital fly-by-wire primary flight control system (FCS). An emergency mechanical backup/linkage to the horizontal stabilators (stabs) is provided in the event of a major FCS failure. Unfortunately, mechanical backup (MECH) mode reversion incidents have increased dramatically in recent years. A flight test program was performed to identify MECH reversion trigger mechanisms and identify a minimum single engine power setting (RPM) above which reversion was improbable, To do so required forcing in-flight MECH reversions while operating single engine. This was achieved by inducing substantial hydraulic demand through cycling the control stick and/or ancillary equipment such as flaps and landing gear. Risks associated with attempting to force the aircraft into an emergency mode with vigorous flight control inputs while single engine are addressed in this paper along with the risk mitigation, test method, conclusions and lessons learned.


The Voice Conversion Challenge 2016


Cluster Analysis , Data-Mining , Multi-dimensional Visualization of Earthquakes over Space , Time and Feature Space


title: Adversarial Examples: Opportunities and Challenges

abstract: Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs), which are designed by attackers to fool deep learning models. Different from real examples, AEs can mislead the model to predict incorrect outputs while hardly be distinguished by human eyes, therefore threaten security-critical deep-learning applications. In recent years, the generation and defense of AEs have become a research hotspot in the field of artificial intelligence (AI) security. This article reviews the latest research progress of AEs. First, we introduce the concept, cause, characteristics and evaluation metrics of AEs, then give a survey on the state-of-the-art AE generation methods with the discussion of advantages and disadvantages. After that, we review the existing defenses and discuss their limitations. Finally, future research opportunities and challenges on AEs are prospected.


title: New equivalent-electrical circuit model and a practical measurement method for human body impedance

abstract: Human body impedance analysis is an effective tool to extract electrical information from tissues in the human body. This paper presents a new measurement method of impedance using armpit electrode and a new equivalent circuit model for the human body. The lowest impedance was measured by using an LCR meter and six electrodes including armpit electrodes. The electrical equivalent circuit model for the cell consists of resistance R and capacitance C. The R represents electrical resistance of the liquid of the inside and outside of the cell, and the C represents high frequency conductance of the cell membrane. We propose an equivalent circuit model which consists of five parallel high frequency-passing CR circuits. The proposed equivalent circuit represents alpha distribution in the impedance measured at a lower frequency range due to ion current of the outside of the cell, and beta distribution at a high frequency range due to the cell membrane and the liquid inside cell. The calculated values by using the proposed equivalent circuit model were consistent with the measured values for the human body impedance.


title: The V-network testbed for malware analysis

abstract: This paper presents a virtualised network environment that serves as a stable and re-usable platform for the analysis of malware propagation. The platform, which has been developed using VMware virtualisation technology, enables the use of either a graphical user interface or scripts to create virtual networks, clone, restart and take snapshots of virtual machines, reset experiments, clean virtual machines and manage the entire infrastructure remotely. The virtualised environment uses open source routing software to support the deployment of intrusion detection systems and other malware attack sensors, and is therefore suitable for evaluating countermeasure systems before deployment on live networks. An empirical analysis of network worm propagation has been conducted using worm outbreak experiments on Class A size networks to demonstrate the capability of the developed platform.


title: A relative entropy-based approach to image thresholding

abstract: Abstract In this paper, we present a new image thresholding technique which uses the relative entropy (also known as the Kullback-Leiber discrimination distance function) as a criterion of thresholding an image. As a result, a gray level minimizing the relative entropy will be the desired threshold. The proposed relative entropy approach is different from two known entropy-based thresholding techniques, the local entropy and joint entropy methods developed by N. R. Pal and S. K. Pal in the sense that the former is focused on the matching between two images while the latter only emphasized the entropy of the co-occurrence matrix of one image. The experimental results show that these three techniques are image dependent and the local entropy and relative entropy seem to perform better than does the joint entropy. In addition, the relative entropy can complement the local entropy and joint entropy in terms of providing different details which the others cannot. As far as computing saving is concerned, the relative entropy approach also provides the least computational complexity.


Multilingual Central Repository version 3.0: upgrading a very large lexical knowledge base


Internal physics models guide probabilistic judgments about object dynamics


title: S-box, SET, Match: A Toolbox for S-box Analysis

abstract: Boolean functions and substitution boxes (S-boxes) represent the only nonlinear part in many algorithms and therefore play the crucial role in their security. Despite the fact that some algorithms today reuse theoretically secure and carefully constructed S-boxes, there is a clear need for a tool that can analyze security properties of S-boxes and hence the corresponding primitives. This need is especially evident in the scenarios where the goal is to create new S-boxes. Even in the cases when some common properties of S-boxes are known, we believe it is prudent to exhaustively investigate all possible sets of cryptographic properties. In this paper we present a tool for the evaluation of Boolean functions and S-boxes suitable for cryptography.


title: Dense visual SLAM for RGB-D cameras

abstract: In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photometric and the depth error over all pixels. In contrast to sparse, feature-based methods, this allows us to better exploit the available information in the image data which leads to higher pose accuracy. Furthermore, we propose an entropy-based similarity measure for keyframe selection and loop closure detection. From all successful matches, we build up a graph that we optimize using the g2o framework. We evaluated our approach extensively on publicly available benchmark datasets, and found that it performs well in scenes with low texture as well as low structure. In direct comparison to several state-of-the-art methods, our approach yields a significantly lower trajectory error. We release our software as open-source.


Interactive Multimodal Learning Environments Special Issue on Interactive Learning Environments : Contemporary Issues and Trends


title: Automated visual inspection for metal parts based on morphology and fuzzy rules

abstract: Automated visual inspection system (AVIS) is a method of analyzing, classifying, detection defects for products at the production line. Usually, this inspection is either conducted by human, machine or both. In this paper, we explain an algorithm that capable to classify mechanical products in real time. The system is consists of two parts: hardware and software. The algorithm used the web-camera attaching to an adjustable arm to capture various image. Our main objective is to develop an image processing algorithm and fuzzy reasoning that can compute both the area and circularity of mechanical shapes and hence classify them according to their categories. The result shows the accuracy of classification is 80.5 % for group classification and 98% for individual classification of mechanical parts.


title: Feature Selection and Kernel Learning for Local Learning-Based Clustering

abstract: The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Scholkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.


title: Computing with Categories

abstract: This paper shows how the constructions involved in category theory may be turned into computer programs. Key issues are the computational representation of categories and of universal properties. The approach is illustrated with a program for computing finite limits of an arbitrary category; this is written in the functional programming language ML. We have developed such programs for a number of categorical constructions.


title: Protecting web-based patient portal for the security and privacy of electronic medical records

abstract: Patient portals, such as Myhealthatvanderbilt.com, AdjuvantHealth, Medical Web Experts, are web-based systems that allow both physicians and patients to access and manage patient medical records via the Internet, facilitate clinical workflow and enable data sharing and collaboration. Serving as the front-end to a huge amount of sensitive information (e.g., medical records, billing), the patient portal is an essential link in the chain of ensuring patient data security and privacy. In August 2000, it was reported that over 800 patients’ sensitive information was breached through KPOnline, a web-based healthcare portal, due to a small piece of flawed software that is integrated into the system [3].


title: Feature extraction using Restricted Boltzmann Machine for stock price prediction

abstract: Recently, many different types of artificial neural networks (ANNs) have been applied to forecast stock price and good performance is obtained. However, most of these models use only a small number of features as input and there may not be enough information to make prediction due to the complexity of stock market. If having a larger number of features, the run time of training would be increased and the generalization performance would be deteriorated due to the curse of dimension. Therefore, an effective tool to extract highly discriminative low-dimensional features from the high-dimensional raw input would be a great help in improving the generalization performance of the regression model. Restricted Boltzmann Machine (RBM) is a new type of machine learning tool with strong power of representation, which has been utilized as the feature extractor in a large variety of classification problems. In this paper, we use the RBM to extract discriminative low-dimensional features from raw data with dimension up to 324, and then use the extracted features as the input of Support Vector Machine (SVM) for regression. Experimental results indicate that our approach for stock price prediction has great improvement in terms of low forecasting errors compared with SVM using raw data.


title: WatsonPaths: Scenario-based Question Answering and Inference over Unstructured Information

abstract: We present WatsonPaths, a novel system that can answer scenario-based questions. These include medical questions that present a patient summary and ask for the most likely diagnosis or most appropriate treatment. WatsonPaths builds on the IBM Watson question answering system. WatsonPaths breaks down the input scenario into individual pieces of information, asks relevant subquestions of Watson to conclude new information, and represents these results in a graphical model. Probabilistic inference is performed over the graph to conclude the answer. On a set of medical test preparation questions, WatsonPaths shows a significant improvement in accuracy over multiple baselines.


title: SemEval-2014 Task 3: Cross-Level Semantic Similarity

abstract: This paper introduces a new SemEval task on Cross-Level Semantic Similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. Highquality data sets were constructed for four comparison types using multi-stage annotation procedures with a graded scale of similarity. Nineteen teams submitted 38 systems. Most systems surpassed the baseline performance, with several attaining high performance for multiple comparison types. Further, our results show that comparisons of semantic representation increase performance beyond what is possible with text alone.


title: Differential oscillatory encoding of foreign speech

abstract: Neuronal oscillations play a key role in auditory perception of verbal input, with the oscillatory rhythms of the brain showing synchronization with specific frequencies of speech. Here we investigated the neural oscillatory patterns associated with perceiving native, foreign, and unknown speech. Spectral power and phase synchronization were compared to those of a silent context. Power synchronization to native speech was found in frequency ranges corresponding to the theta band, while no synchronization patterns were found for the foreign speech context and the unknown language context. For phase synchrony, the native and unknown languages showed higher synchronization in the theta-band than the foreign language when compared to the silent condition. These results suggest that neural synchronization patterns are markedly different for native and foreign languages.


title: Practical selection of svm parameters and noise estimation for svm regression

abstract: We investigate practical selection of hyper-parameters for support vector machines (SVM) regression (that is, e-insensitive zone and regularization parameter C). The proposed methodology advocates analytic parameter selection directly from the training data, rather than re-sampling approaches commonly used in SVM applications. In particular, we describe a new analytical prescription for setting the value of insensitive zone e, as a function of training sample size. Good generalization performance of the proposed parameter selection is demonstrated empirically using several low- and high-dimensional regression problems. Further, we point out the importance of Vapnik's e-insensitive loss for regression problems with finite samples. To this end, we compare generalization performance of SVM regression (using proposed selection of e-values) with regression using 'least-modulus' loss (e = 0) and standard squared loss. These comparisons indicate superior generalization performance of SVM regression under sparse sample settings, for various types of additive noise.


title: A biologically inspired dual-network memory model for reduction of catastrophic forgetting

abstract: Neural networks encounter serious catastrophic forgetting when information is learned sequentially, which is unacceptable for both a model of human memory and practical engineering applications. In this study, we propose a novel biologically inspired dual-network memory model that can significantly reduce catastrophic forgetting. The proposed model consists of two distinct neural networks: hippocampal and neocortical networks. Information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network. In the hippocampal network, chaotic behavior of neurons in the CA3 region of the hippocampus and neuronal turnover in the dentate gyrus region are introduced. Chaotic recall by CA3 enables retrieval of stored information in the hippocampal network. Thereafter, information retrieved from the hippocampal network is interleaved with previously stored information and consolidated by using pseudopatterns in the neocortical network. The computer simulation results show the effectiveness of the proposed dual-network memory model.


title: CDS-MEC: NFV/SDN-based application management for MEC in 5G Systems

abstract: Abstract This paper presents and evaluates the first open-source Network Function Virtualization (NFV)/Software Defined Networking (SDN)-based Mobile Edge Computing (MEC) platform. Our platform solves the Mobile Edge (ME) management issues with respect to Application (App) provisioning and traffic management. First, the ME Apps are managed as Virtual Network Functions (VNFs) on top of the virtual environment through the Juju VNF Manager (VNFM). Second, we develop an SDN controller to manage traffic on the ME System. Third, unlike other relevant architectures of ME systems, we use the control plane (i.e., S1 interface) to derive appropriate states for traffic management. Finally, we evaluate our solution in two use-cases: ME caching and Information Centric (ICN)/Delay Tolerant (DTN) Public Safety communication (PS). The MEC caching framework displays improved user Quality of Experience, e.g., latency, in comparison to direct communication, while the PS solution provides a residual mean of communication for rescue teams, when the network core (EPC) and a Public Data Network (PDN) are unavailable.


title: Gaussian random paths for real-time motion planning

abstract: In this paper, we propose Gaussian random paths by defining a probability distribution over continuous paths interpolating a finite set of anchoring points using Gaussian process regression. By utilizing the generative property of Gaussian random paths, a Gaussian random path planner is developed to safely steer a robot to a goal position. The Gaussian random path planner can be used in a number of applications, including local path planning for a mobile robot and trajectory optimization for whole body motion planning. We have conducted an extensive set of simulations and experiments, showing that the proposed planner outperforms look-ahead planners which use a pre-defined subset of egocentric trajectories in terms of collision rates and trajectory lengths. Furthermore, we apply the proposed method to existing trajectory optimization methods as an initialization step and demonstrate that it can help produce more cost-efficient trajectories.


title: Electrical machine topologies and technologies for electric, hybrid, and fuel cell vehicles

abstract: This paper overviews various electrical machine technologies, including brushless permanent magnet, induction, switched reluctance, and brushless reluctance machines, with particular emphasis on the concept of hybrid electrical machine topologies, and compares their relative merits, in terms of torque and power density, operating speed range, over load capability, and efficiency, for application in electric, hybrid and fuel cell vehicles. electric vehicles.


title: Two Geometric Algorithms for Layout Analysis

abstract: This paper presents geometric algorithms for solving two key problems in layout analysis: finding a cover of the background whitespace of a document in terms of maximal empty rectangles, and finding constrained maximum likelihood matches of geometric text line models in the presence of geometric obstacles. The algorithms are considerably easier to implement than prior methods, they return globally optimal solutions, and they require no heuristics. The paper also introduces an evaluation function that reliably identifies maximal empty rectangles corresponding to column boundaries. Combining this evaluation function with the two geometric algorithms results in an easy-to-implement layout analysis system. Reliability of the system is demonstrated on documents from the UW3 database.


title: A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots

abstract: An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers that use the orientation of each lower limb segment and the angular velocities of the joints to output the current gait phase. Experiments to obtain the input signals and desired outputs for the learning and validation process are conducted, and two neural network methods (a multilayer perceptron and nonlinear autoregressive with external inputs (NARX)) are used to develop an optimal classifier. Offline and online evaluations using four criteria are used to compare the performance of the classifiers. The proposed NARX-based method exhibits sufficiently good performance to replace foot sensors as a means of classifying gait phases.


title: Effect of fatigue on force production and force application technique during repeated sprints.

abstract: We investigated the changes in the technical ability of force application/orientation against the ground vs. the physical capability of total force production after a multiple-set repeated sprints series. Twelve male physical education students familiar with sprint running performed four sets of five 6-s sprints (24s of passive rest between sprints, 3min between sets). Sprints were performed from a standing start on an instrumented treadmill, allowing the computation of vertical (F(V)), net horizontal (F(H)) and total (F(Tot)) ground reaction forces for each step. Furthermore, the ratio of forces was calculated as RF=F(H)F(Tot)(-1), and the index of force application technique (D(RF)) representing the decrement in RF with increase in speed was computed as the slope of the linear RF-speed relationship. Changes between pre- (first two sprints) and post-fatigue (last two sprints) were tested using paired t-tests. Performance decreased significantly (e.g. top speed decreased by 15.7±5.4%; P<0.001), and all the mechanical variables tested significantly changed. F(H) showed the largest decrease, compared to F(V) and F(Tot). D(RF) significantly decreased (P<0.001, effect size=1.20), and the individual magnitudes of change of D(RF) were significantly more important than those of F(Tot) (19.2±20.9 vs. 5.81±5.76%, respectively; P<0.01). During a multiple-set repeated sprint series, both the total force production capability and the technical ability to apply force effectively against the ground are altered, the latter to a larger extent than the former.


title: Building a HighLevel Dataflow System on top of MapReduce: The Pig Experience

abstract: Increasingly, organizations capture, transform and analyze enormous data sets. Prominent examples include internet companies and e-science. The Map-Reduce scalable dataflow paradigm has become popular for these applications. Its simple, explicit dataflow programming model is favored by some over the traditional high-level declarative approach: SQL. On the other hand, the extreme simplicity of Map-Reduce leads to much low-level hacking to deal with the many-step, branching dataflows that arise in practice. Moreover, users must repeatedly code standard operations such as join by hand. These practices waste time, introduce bugs, harm readability, and impede optimizations. ::: ::: Pig is a high-level dataflow system that aims at a sweet spot between SQL and Map-Reduce. Pig offers SQL-style high-level data manipulation constructs, which can be assembled in an explicit dataflow and interleaved with custom Map- and Reduce-style functions or executables. Pig programs are compiled into sequences of Map-Reduce jobs, and executed in the Hadoop Map-Reduce environment. Both Pig and Hadoop are open-source projects administered by the Apache Software Foundation. ::: ::: This paper describes the challenges we faced in developing Pig, and reports performance comparisons between Pig execution and raw Map-Reduce execution.


title: Mid-term evaluation of "Health Japan 21": focus area for the nutrition and diet.

abstract: This paper provides a review of the mid-term evaluation of "Health Japan 21" and dis-cusses the status of progress towards the goals of items within the "Nutrition and diet" area. Among 14 items investigated, an improving trend was observed in eight items, though there was no improvement in five items. Whilst the percentage of obese individuals significantly increased during the 25 years from 1976 to 1999, secular trend showed that it has become unchanged since 2000, when "Health Japan 21" was enacted, regardless of gender and age. Another favorable finding was a decreasing trend of salt intake especially in the age group of 50-59 years. Besides, the analyses of the proportion of the persons "who have interest in dietary improvement" among the ones "who believe there are problems in their diet" showed that an increased awareness of inappropriate diet and also in the motivation to improve it, especially among males aged 50-59 years. On the other hand, some items showed worsening trend; e.g. decrease in vegetable intake, decrease of persons who are aware of their own optimal weight and practice weight control. Thus, the progress within Health Japan 21 was assessed as not necessarily satisfactory. In order to ensure the progress of "Health Japan 21" towards 2010, it is now crucial to effectively incorporate "Japanese Food Guide Spinning Top" and a new strategy of non-communicable diseases prevention focusing on the control of metabolic syndrome, which will be launched in April 2008, into the national health promotion program.


title: Spectral clustering for multi-type relational data

abstract: Clustering on multi-type relational data has attracted more and more attention in recent years due to its high impact on various important applications, such as Web mining, e-commerce and bioinformatics. However, the research on general multi-type relational data clustering is still limited and preliminary. The contribution of the paper is three-fold. First, we propose a general model, the collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Second, under this model, we derive a novel algorithm, the spectral relational clustering, to cluster multi-type interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects. Extensive experiments demonstrate the promise and effectiveness of the proposed algorithm. Third, we show that the existing spectral clustering algorithms can be considered as the special cases of the proposed model and algorithm. This demonstrates the good theoretic generality of the proposed model and algorithm.


title: Neural Semantic Encoders

abstract: We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders (NSE). NSE has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can access multiple and shared memories depending on the complexity of a task. We demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks, natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.


The Transition to Minimal Consciousness through the Evolution of Associative Learning


title: WDM FSO network with turbulence-accentuated interchannel crosstalk

abstract: A wavelength division multiplexing (WDM) access network using high-speed free-space optical (FSO) communication for the distribution link is proposed. Combining FSO communication with optical fiber can reduce the system cost and provide high-bandwidth access in regions where optical fiber installation is problematic. The WDM channels suffer from interchannel crosstalk, while the FSO communication performance in a clear atmosphere is limited by atmospherically induced scintillation. These impairments, plus the amplified spontaneous emission noise from optical amplification, combine in a potentially problematic way, particularly in the upstream direction, which is investigated here. This turbulence-accentuated crosstalk effect is considered for the cases of 1) signal turbulent but crosstalk not and 2) crosstalk turbulent but signal not. Error floors are obtained in each case. The FSO link length that can be supported in the general case of the hybrid network is investigated.


title: An evaluation of face and ear biometrics

abstract: Face recognition based on principal component analysis is a heavily researched topic in computer vision. The ear has been proposed as a biometric, with claimed advantages over the face. We have applied the PCA approach to images of the face and ear using the same set of subjects. Testing was done with three different gallery/probe combinations. For faces we have: 1) probes of same day but different expression, 2) probes of a different day but similar expression, and 3) probes of different day and different expression. Analogously, for ears, we have: 1) probes of same day but other ear, 2) probes of a different day but same ear, and 3) probes of different day and other ear Results indicate that the face provides a more reliable biometric than the ear.


title: Appraisal of homogeneous techniques in Distributed Data Mining: classifier approach

abstract: In recent years, Distributed Data Mining (DDM) has evolved in large space aiming at minimizing computation cost and memory overhead in processing huge geographically distributed data. There are two approaches of DDM -- homogeneous and heterogeneous classifier approach. This paper presents implementation of four homogeneous classifier techniques for DDM with different Electronic Health Records (EHRs) homogeneous datasets namely diabetes, hepatitis, hypothyroid and further analyzing results based on metric evaluation.


title: Interactive Digital Learning Materials for Kindergarten Students in Bangladesh

abstract: The pedagogy of teaching and learning has changed with the proliferation of communication technology and it is necessary to develop interactive learning materials for children that may improve their learning, catching, and memorizing capabilities. Perhaps, one of the most important innovations in the age of technology is multimedia and its application. It is imperative to create high quality and realistic learning environment for children. Interactive learning materials can be easier to understand and deal with their first learning. We developed some interactive learning materials in the form of a video for Playgroup using multimedia application tools. This study investigated the impact of students' abilities to acquire new knowledge or skills through interactive learning materials. We visited one kindergartens (Nursery schools), interviewed class teachers about their teaching methods and level of students' ability of recognizing English alphabets, pictures, etc. The course teachers were provided interactive learning materials to show their playgroups for a number of sessions. The video included English alphabets with related words and pictures, and motivational fun. We noticed that almost all children were very interested to interact with their leaning video. The students were assessed individually and asked to recognize the alphabets, and pictures. The students adapted with their first alphabets very quickly. However, there were individual differences in their cognitive development. This interactive multimedia can be an alternative to traditional pedagogy for teaching playgroups.


title: Music Recommendation Based on Acoustic Features and User Access Patterns

abstract: Music recommendation is receiving increasing attention as the music industry develops venues to deliver music over the Internet. The goal of music recommendation is to present users lists of songs that they are likely to enjoy. Collaborative-filtering and content-based recommendations are two widely used approaches that have been proposed for music recommendation. However, both approaches have their own disadvantages: collaborative-filtering methods need a large collection of user history data and content-based methods lack the ability of understanding the interests and preferences of users. To overcome these limitations, this paper presents a novel dynamic music similarity measurement strategy that utilizes both content features and user access patterns. The seamless integration of them significantly improves the music similarity measurement accuracy and performance. Based on this strategy, recommended songs are obtained by a means of label propagation over a graph representing music similarity. Experimental results on a real data set collected from http://www.newwisdom.net demonstrate the effectiveness of the proposed approach.


title: Particle Swarm Optimization based K-Prototype Clustering Algorithm

abstract: Clustering in data mining is a discovery process that groups a set of data so as to maximize the intra- cluster similarity and to minimize the inter-cluster similarity. The K-Means algorithm is best suited for clustering large numeric data sets when at possess only numeric values. The K-Modes extends to the K-Means when the domain is categorical. But in some applications, data objects are described by both numeric and categorical features. The K-Prototype algorithm is one of the most important algorithms for clustering this type of data. This algorithm produces locally optimal solution that dependent on the initial prototypes and order of object in the data. Particle Swarm Optimization is one of the simple optimization techniques, which can be effectively implemented to enhance the clustering results. But discrete or binary Particle Swarm Optimization mechanisms are useful for handle mixed data set. This leads to a better cost evaluation in the description space and subsequently enhanced processing of mixed data by the Particle Swarm Optimization. This paper proposes a new variant of binary Particle Swarm Optimization and K-Prototype algorithms to reach global optimal solution for clustering optimization problem. The proposed algorithm is implemented and evaluated on standard benchmark dataset taken from UCI machine learning repository. The comparative analysis proved that Particle Swarm based on K-Prototype algorithm provides better performance than the traditional K-modes and K- Prototype algorithms.


title: Architectural design features of a programmable high throughput reconfigurable SHA-2 Processor

abstract: The continued growth of both wired and wireless communications has triggered the revolution for the generation of new cryptographic algorithms. Hash functions are common and important cryptographic primitives, which are very critical for data integrity assurance and data origin authentication security services. Many hash algorithms have been investigated and developed in the last years. This work is related to hash functions FPGA implementation. SHA-2 hash family is a new standard in the widely used hash functions category. An architecture and the FPGA implementation of this standard are proposed in this work. We propose a reconfigurable SHA-2 processor in the sense that it performs the two hash functions (256, and 512) of the SHA-2 standard for extended signature authentication. This paper investigates optimizations techniques that have recently been proposed in the literature. These techniques consist mostly in operation rescheduling and hardware reutilization, allowing a significant reduction of the critical path while the required area also decreases. As several 64-bit adders are needed in SHA-512 hash value computation, investigations on the adders implementations on FPGAs are analyzed and developed, with a view to reduce the chip area. The proposed processor is compared with the implementation of each hash function in a separate FPGA device to demonstrate that our architecture achieves a performance comparable to separate implementations while requiring much less hardware. Two types of FPGAs are evaluated in order to produce realistic results. Speed/area results from this processor are analyzed and are shown to demonstrate that our designs achieves a favorably performances with other FPGA-based implementations. A fastest data throughput is achieved by our optimized design.


title: Evaluation of the Goal-Oriented Requirements Engineering Method KAOS

abstract: Software engineering is a complex task. But although there is no silver bullet that guarantees accomplishing this task, appropriate methods can support the engineer by addressing the characteristics that make it complex. The objective of this paper is to evaluate whether and how the goal-oriented requirements engineering method KAOS addresses these characteristics of complex tasks and thereby, whether it effectively supports software engineering. For serving this purpose, we conduct a literature analysis, which discloses core concepts underlying to the KAOS method, and we apply KAOS in two software development projects, which provide insights into KAOS in use. Our results show that KAOS, despite of some shortcomings, addresses all characteristics, but that applying it can be work intensive. Consequently, while KAOS supports software engineering, provided support must be weigh up against invested work.


title: Pricing in Agent Economies Using Multi-Agent Q-Learning

abstract: This paper investigates how adaptive software agents may utilize reinforcement learning algorithms such as Q-learning to make economic decisions such as setting prices in a competitive marketplace. For a single adaptive agent facing fixed-strategy opponents, ordinary Q-learning is guaranteed to find the optimal policy. However, for a population of agents each trying to adapt in the presence of other adaptive agents, the problem becomes non-stationary and history dependent, and it is not known whether any global convergence will be obtained, and if so, whether such solutions will be optimal. In this paper, we study simultaneous Q-learning by two competing seller agents in three moderately realistic economic models. This is the simplest case in which interesting multi-agent phenomena can occur, and the state space is small enough so that lookup tables can be used to represent the Q-functions. We find that, despite the lack of theoretical guarantees, simultaneous convergence to self-consistent optimal solutions is obtained in each model, at least for small values of the discount parameter. In some cases, exact or approximate convergence is also found even at large discount parameters. We show how the Q-derived policies increase profitability and damp out or eliminate cyclic price “wars” compared to simpler policies based on zero lookahead or short-term lookahead. In one of the models (the “Shopbot” model) where the sellers' profit functions are symmetric, we find that Q-learning can produce either symmetric or broken-symmetry policies, depending on the discount parameter and on initial conditions.


Diagnostic exercise: head tilt in a gerbil.