title: Look-Up Table Unit Activation Function for Deep Convolutional Neural Networks

abstract: Activation functions provide deep neural networks the non-linearity that is necessary to learn complex distributions. It is still inconclusive what is the optimal shape for the activation function. In this work, we introduce a novel type of activation function of which the shape is learned with network training. The proposed Look-up Table Unit (LuTU) stores a set of anchor points in a look-up table like structure, and the activation function is generated from the anchor points by either linear interpolation or smoothing with a single period cosine mask function. LuTU is in theory able to approximate any univariate function. By observing the learned shapes of LuTU, we further propose a Mixture of Gaussian Unit (MoGU) that can learn similar non-linear shapes with much fewer parameters. Finally, we use a multiple activation function fusion framework that combines multiple types of functions to achieve better performance. The inference complexity of multiple activation function fusion is constant with linear interpolation approximation. Our experiments on a synthetic dataset, ImageNet, and CIFAR-10 demonstrate that the proposed method outperforms traditional ReLU family activation functions. On the ImageNet dataset, our method achieves 1.47% and 1.0% higher accuracy on ResNet-18 and ResNet-34 models, respectively. With the proposed activation function, we can design a network that has the same performance as ResNet-34 but 8 fewer convolutional layers.


Claim Synthesis via Predicate Recycling


title: Personalized Privacy-Preserving Data Aggregation for Histogram Estimation

abstract: Histogram estimation is one of the fundamental tasks in crowdsourcing data aggregation. Since contributing data reveal more or less information about individuals' identifications and activities, participants need to preserve privacy of data according to their own levels of privacy concern. However, most of the existing work only aggregates data with an identical privacy level. In this paper, we propose an aggregation scheme for histogram estimation, wherein participants can publish their data at personalized differential-privacy levels. The aggregator also benefits from potential wider engagement or more honest data. Specially, since privacy levels under personalized privacy policy are sensitive information for participants, our scheme permits participants to keep their privacy levels secret even from the aggregator. We also show how to further optimize the estimation accuracy under given privacy levels by choosing specific randomization strategies.


title: Network function shapes network structure: the case of the Arabidopsis flower organ specification genetic network

abstract: The reconstruction of many biological networks has allowed detailed studies of their structural properties. Several features exhibited by these networks have been interpreted to be the result of evolutionary dynamics. For instance the degree distributions may follow from a preferential attachment of new genes to older ones during evolution. Here we argue that even in the absence of any evolutionary dynamics, the presence of atypical features may follow from the fact that the network implements certain functions. To examine this network function shapes network structure scenario, we focus on the Arabidopsis genetic network controlling early flower organogenesis in which gene expression dynamics has been modelled using a Boolean framework. Specifically, for a system with 15 master genes, the phenotype consists of 10 experimentally determined steady-state expression patterns, considered here as the functional constraints on the network. The space of genetic networks satisfying these constraints is sometimes referred to as the neutral or genotype network. We sample this space using Markov Chain Monte Carlo which allows us to demonstrate how the functional (phenotypic) constraints shape the gene network structure. We find that this shaping is strongest for the edge (interaction) usage, with effects that are functionally interpretable. In contrast, higher order features such as degree assortativity and network motifs are hardly shaped by the phenotypic constraints.


title: Tackling interoperability in composite applications from an enterprise mash-up perspective

abstract: The idea of a global mesh of enterprise composite applications is being impeded by the delay of next-generation interoperable mash-up platforms, providing intuitive human-guided resource interaction, reuse and composition. In this paper we revisit current mash-up platforms and analyze their major shortcomings with regard to the development of real composite applications. Then, we elaborate on a novel reference architecture for a mash-up platform called EzWeb/FAST. EzWeb/FAST facilitates the establishment of a user-centered mesh of interoperable resources that can be composed flexibly and easily to create and execute composite applications based on complex mashupable gadgets. The release of EzWeb/FAST will help enterprises to realize how SOA front-end innovations could make them more productive, responsive and, ultimately, more competitive.


title: Recent and future research trends in planar multi-beam antennas in the millimeter wave range at IETR-France

abstract: A review of some flat multi-beam antennas developed at IETR during the last 5 years (2010–2015) is reported. These antennas are based on quasi-optical and circuit-based beam forming networks (BFNs). The enhanced scanning capabilities of the proposed solutions will be shown. In addition, some antennas integrate antenna functions like monopulse techniques to enhance the angular resolution for tracking applications. The main concepts and features of the developed antennas will be reported. These antennas operate in different frequency band in the millimeter wave range and are realized with different fabrication processes.


title: Compact and Closely Spaced Metamaterial MIMO Antenna With High Isolation for Wireless Applications

abstract: This letter presents a design of a two-element multiple-input-multiple-output (MIMO) metamaterial-based antenna. The two antenna elements operate at 5.8 GHz for wireless applications. The two antenna elements are designed employing only one left-handed unit cell. The reduction of mutual coupling between the two antenna elements is achieved by using a simple defected ground structure between them to limit the surface waves between them. The distance between the two antenna elements is only 1.8 mm (0.034 λ0). The designed antenna elements have better than -45 dB coupling isolation between the two inputs. Moreover, the proposed MIMO antenna has the advantage of compactness (its size is only 2.36 × 2.6 cm2). The design represents size reduction of more than 50% compared to conventional patch antennas operating at the same frequency. The proposed MIMO system has -55 dB correlation coefficient between its two elements.


Forecasting Aviation Spare Parts Demand Using Croston Based Methods and Artificial Neural Networks


title: The Java Context Awareness Framework (JCAF) - A Service Infrastructure and Programming Framework for Context-Aware Applications

abstract: Context-awareness is a key concept in ubiquitous computing. But to avoid developing dedicated context-awareness sub-systems for specific application areas there is a need for more generic programming frameworks. Such frameworks can help the programmer develop and deploy context-aware applications faster. This paper describes the Java Context-Awareness Framework – JCAF, which is a Java-based context-awareness infrastructure and programming API for creating context-aware computer applications. The paper presents the design goals of JCAF, its runtime architecture, and its programming model. The paper presents some applications of using JCAF in three different applications and discusses lessons learned from using JCAF.


title: From gut dysbiosis to altered brain function and mental illness: mechanisms and pathways

abstract: The human body hosts an enormous abundance and diversity of microbes, which perform a range of essential and beneficial functions. Our appreciation of the importance of these microbial communities to many aspects of human physiology has grown dramatically in recent years. We know, for example, that animals raised in a germ-free environment exhibit substantially altered immune and metabolic function, while the disruption of commensal microbiota in humans is associated with the development of a growing number of diseases. Evidence is now emerging that, through interactions with the gut–brain axis, the bidirectional communication system between the central nervous system and the gastrointestinal tract, the gut microbiome can also influence neural development, cognition and behaviour, with recent evidence that changes in behaviour alter gut microbiota composition, while modifications of the microbiome can induce depressive-like behaviours. Although an association between enteropathy and certain psychiatric conditions has long been recognized, it now appears that gut microbes represent direct mediators of psychopathology. Here, we examine roles of gut microbiome in shaping brain development and neurological function, and the mechanisms by which it can contribute to mental illness. Further, we discuss how the insight provided by this new and exciting field of research can inform care and provide a basis for the design of novel, microbiota-targeted, therapies.


title: Epitaxial growth of UHV magnetron sputtered Mo thin films on MgO(001) substrates, oxygen segregation and surface reconstructions

abstract: Studies of epitaxial growth of Mo thin films on MgO(001) substrates by ultrahigh Vacuum (UHV) d.c. magnetron sputter deposition have shown independently by in situ low energy electron diffraction ( ...


title: Longitudinal melanonychia in childhood: a clinical and histopathological review of Korean patients

abstract: Longitudinal melanonychia is not frequently observed in children, and few studies exist on longitudinal melanonychia in paediatric patients, especially in Asian populations. We aimed to evaluate the clinical and histological findings of longitudinal melanonychia in Korean paediatric patients. A retrospective review of paediatric patients (≤18 years old) with longitudinal melanonychia who underwent nail biopsy between January 2010 and October 2015 was performed. Of a total of 21 patients with longitudinal melanonychia, there were 10 cases of functional melanonychia, eight of lentigo, and three of nevus, as determined by microscopic examination. Mild nuclear atypia was found in four cases, as well as a low degree of pagetoid spread in one case. No patient was diagnosed with melanoma. During the follow-up period, no signs of malignancy were noticed and one patient with nail matrix nevus showed complete spontaneous regression of the pigmented band. Clinical and histological features, generally accepted as signs suggestive of melanoma in adults, were more readily observed in benign conditions in children.


STATEMENT ON THE SECURITY THREAT POSED BY UNMANNED AERIAL SYSTEMS AND POSSIBLE COUNTERMEASURES


A survey of Knowledge Discovery and Data Mining process models


title: Structure-Texture Image Decomposition—Modeling, Algorithms, and Parameter Selection

abstract: This paper explores various aspects of the image decomposition problem using modern variational techniques. We aim at splitting an original image f into two components u and ?, where u holds the geometrical information and ? holds the textural information. The focus of this paper is to study different energy terms and functional spaces that suit various types of textures. Our modeling uses the total-variation energy for extracting the structural part and one of four of the following norms for the textural part: L2, G, L1 and a new tunable norm, suggested here for the first time, based on Gabor functions. Apart from the broad perspective and our suggestions when each model should be used, the paper contains three specific novelties: first we show that the correlation graph between u and ? may serve as an efficient tool to select the splitting parameter, second we propose a new fast algorithm to solve the TV ? L1 minimization problem, and third we introduce the theory and design tools for the TV-Gabor model.


title: Path-space manipulation of physically-based light transport

abstract: Industry-quality content creation relies on tools for lighting artists to quickly prototype, iterate, and refine final renders. As industry-leading studios quickly adopt physically-based rendering (PBR) across their art generation pipelines, many existing tools have become unsuitable as they address only simple effects without considering underlying PBR concepts and constraints. We present a novel light transport manipulation technique that operates directly on path-space solutions of the rendering equation. We expose intuitive direct and indirect manipulation approaches to edit complex effects such as (multi-refracted) caustics, diffuse and glossy indirect bounces, and direct/indirect shadows. With our sketch- and object-space selection, all built atop a parameterized regular expression engine, artists can search and isolate shading effects to inspect and edit. We classify and filter paths on the fly and visualize the selected transport phenomena. We survey artists who used our tool to manipulate complex phenomena on both static and animated scenes.


title: Artificial Intelligence Expert Systems And Languages In Modelling And Simulation

abstract: Thank you very much for downloading artificial intelligence expert systems and languages in modelling and simulation. Maybe you have knowledge that, people have search hundreds times for their chosen readings like this artificial intelligence expert systems and languages in modelling and simulation, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop.


title: Enthymemes: Body and Soul

abstract: To Aristotle, the enthymeme holds a position of unquestioned prominence; it is the "substance of rhetorical persuasion,"1 "the very body of proof,"2 and "the orator's proper modes of persuasion/*3 Although studies of the enthymeme clarify some important points, they do not fully account for the centrality of the enthymeme in Aristotle's treatise. Specifically, no one has set forth Aristotle's rationale for viewing the enthymeme as the primary engine of rhetorical proof and practical reasoning,4 that is, the means by which the orator influences the judgments and actions of the audience. Thus, this essay develops the thesis that the affective comportent inherent in the enthymeme is the essence of Aristotle's concept of the enthmeme as practical reasoning. Within the context of this thesis, "affective component" denotes the area of feelings and emotions. One may discover support for this thesis in the etymology of "enthymeme" and terms related to practical reasoning; from Aristotle's discussion of human action; and, from the Rhetoric. The first source of support for the presence of an affective component in the enthymeme is the etymology of the term. To be sure, one must be careful not to make too much out of the history of a word. But such information can be informative; and in the case of "enthymeme," the etymology is a considerable aid in understanding what Aristotle thought its composition and function to be. "Enthymeme" is Englished from Ινθνμημα ( iv + θυμός = en -\thymos), the more important part of which is the stem, thymos.5 The basic meaning of thymos is "soul, spirit, as the principle of life, feeling and thought, esp. of strong feeling and passion."6 More specifie uses of thymos by itself include "in physical sense, breath, life," and "spirit, strength." Another major meaning is "soul, as shown by the feelings and passions; and so, 1. desire or inclination, esp. desire for meat and drink, appetite, ... 2. mind, temper, will, ... 3. spirit, courage, ... 4. the seat of anger, ... 5. the heart, as the seat of the emotions, esp. joy or grief, ... 6. mind, soul, as the seat of thought. . . ."


title: Internet marketing: a content analysis of the research

abstract: The amount of research related to Internet marketing has grown rapidly since the dawn of the Internet Age. A review of the literature base will help identify the topics that have been explored as well as identify topics for further research. This research project collects, synthesizes, and analyses both the research strategies (i.e., methodologies) and content (e.g., topics, focus, categories) of the current literature, and then discusses an agenda for future research efforts. We analyzed 411 articles published over the past eighteen years (1994-present) in thirty top Information Systems (IS) journals and 22 articles in the top 5 Marketing journals. The results indicate an increasing level of activity during the 18-year period, a biased distribution of Internet marketing articles focused on exploratory methodologies, and several research strategies that were either underrepresented or absent from the pool of Internet marketing research. We also identified several subject areas that need further exploration. The compilation of the methodologies used and Internet marketing topics being studied can serve to motivate researchers to strengthen current research and explore new areas of this research.


title: An algebraic theory of graph reduction

abstract: We show how membership in classes of graphs definable in monadic second order logic and of bounded treewidth can be decided by finite sets of terminating reduction rules. The method is constructive in the sense that we describe an algorithm which will produce, from a formula in monadic second order logic and an integer k such that the class defined by the formula is of treewidth ≤ k, a set of rewrite rules that reduces any member of the class to one of finitely many graphs, in a number of steps bounded by the size of the graph. This reduction system corresponds to an algorithm that runs in time linear in the size of the graph.


title: Automatic speech recognition with deep neural networks for impaired speech

abstract: Automatic Speech Recognition has reached almost human performance in some controlled scenarios. However, recognition of impaired speech is a difficult task for two main reasons: data is (i) scarce and (ii) heterogeneous. In this work we train different architectures on a database of dysarthric speech. A comparison between architectures shows that, even with a small database, hybrid DNN-HMM models outperform classical GMM-HMM according to word error rate measures. A DNN is able to improve the recognition word error rate a 13 % for subjects with dysarthria with respect to the best classical architecture. This improvement is higher than the one given by other deep neural networks such as CNNs, TDNNs and LSTMs. All the experiments have been done with the Kaldi toolkit for speech recognition for which we have adapted several recipes to deal with dysarthric speech and work on the TORGO database. These recipes are publicly available.


title: Miniaturized Dual-Band Wilkinson Power Divider With Self-Compensation Structure

abstract: A miniaturized dual-band Wilkinson power divider with a parallel $LC$ circuit at the midpoints of two coupled-line sections is proposed in this paper. General design equations for parallel inductor $L$ and capacitor $C$ are derived from even- and odd-mode analysis. Generally speaking, characteristic impedances between even and odd modes are different in two coupled-line sections, and their electrical lengths are also different in inhomogeneous medium. This paper proved that a parallel $LC$ circuit compensates for the characteristic impedance differences and the electrical length differences for dual-band operation. In other words, the proposed model provides self-compensation structure, and no extra compensation circuits are needed. Moreover, the upper limit of the frequency ratio range can be adjusted by two coupling strengths, where loose coupling for the first coupled-line section and tight coupling for the second coupled-line section are preferred for a wider frequency ratio range. Finally, an experimental circuit shows good agreement with the theoretical simulation.


title: On the Connection between Differential Privacy and Adversarial Robustness in Machine Learning.

abstract: Adversarial examples in machine learning has been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth. Most past defenses are best-effort, heuristic approaches that have all been shown to be vulnerable to sophisticated attacks. More recently, rigorous defenses that provide formal guarantees have emerged, but are hard to scale or generalize. A rigorous and general foundation for designing defenses is required to get us off this arms race trajectory. We propose leveraging differential privacy (DP) as a formal building block for robustness against adversarial examples. We observe that the semantic of DP is closely aligned with the formal definition of robustness to adversarial examples. We propose PixelDP, a strategy for learning robust deep neural networks based on formal DP guarantees. PixelDP networks give theoretical guarantees for a subset of their predictions regarding the robustness against adversarial perturbations of bounded size. Our evaluation with MNIST, CIFAR-10, and CIFAR-100 shows that PixelDP networks achieve accuracy under attack on par with the best-performing defense to date, but additionally certify robustness against meaningful-size 1-norm and 2-norm attacks for 40-60% of their predictions. Our experience points to DP as a rigorous, broadly applicable, and mechanism-rich foundation for robust machine learning.


title: Training LDCRF model on unsegmented sequences using connectionist temporal classification

abstract: Many machine learning problems such as speech recognition, gesture recognition, and handwriting recognition are concerned with simultaneous segmentation and labeling of sequence data. Latent-dynamic conditional random field (LDCRF) is a well-known discriminative method that has been successfully used for this task. However, LDCRF can only be trained with pre-segmented data sequences in which the label of each frame is available apriori. In the realm of neural networks, the invention of connectionist temporal classification (CTC) made it possible to train recurrent neural networks on unsegmented sequences with great success. In this paper, we use CTC to train an LDCRF model on unsegmented sequences. Experimental results on two gesture recognition tasks show that the proposed method outperforms LDCRFs, hidden Markov models, and conditional random fields.


title: Pokémon GO Forensics: An Android Application Analysis

abstract: As the geolocation capabilities of smartphones continue to improve, developers have continued to create more innovative applications that rely on this location information for their primary function. This can be seen with Niantic’s release of Pokemon GO, which is a massively multiplayer online role playing and augmented reality game. This game became immensely popular within just a few days of its release. However, it also had the propensity to be a distraction to drivers, resulting in numerous accidents, and was used as a tool by armed robbers to lure unsuspecting users into secluded areas. This facilitates the need for forensic investigators to be able to analyze the data within the application in order to determine if it may have been involved in these incidents. Because this application is new, limited research has been conducted regarding the artifacts that can be recovered from the application. In this paper, we aim to fill the gaps within the current research by assessing what forensically-relevant information may be recovered from the application and understanding the circumstances behind the creation of this information. Our research focuses primarily on the artifacts generated by the Upsight analytics platform, those contained within the bundles directory and the Pokemon Go Plus accessory. Moreover, we present our new application-specific analysis tool that is capable of extracting forensic artifacts from a backup of the Android application and presenting them to an investigator in an easily-readable format. This analysis tool exceeds the capabilities of the well known mobile forensic tool Cellebrite’s UFED (Universal Forensic Extraction Device) Physical Analyzer in processing Pokemon GO application data.


title: A triboelectric motion sensor in wearable body sensor network for human activity recognition

abstract: The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.


title: Data Deduplication Mechanism for Cloud Storage Systems

abstract: Cloud storage systems are able to provide low-cost and convenient network storage service for users, which makes them more and more popular. However, the storage pressure on cloud storage system caused by the explosive growth of data is growing by the day, especially a vast amount of redundant data waste plenty of storage space. Data deduplication can effectively reduce the size of data by eliminating redundant data in storage systems. However, current researches on data deduplication, which mainly focus on the static scenes such as the backup and archive systems, are not suitable for cloud storage system due to the dynamic nature of data. In this paper, we propose the architecture of deduplication system for cloud storage environment and give the process of avoiding duplication at the file-level and chunk-level on the client side. In the storage nodes (Snodes), Delay Dedupe, a delayed target-deduplication scheme based on the chunk-level deduplication and the access frequency of chunks, are proposed to reduce the response time. Combined with replica management, this method determines whether new duplicated chunks for data modification are hot and removes the hot duplicated chucks when they aren't hot. The experiment results demonstrate that the Delay Dedupe mechanism can effectively reduce the response time and achieve the storage load of Snodes more balanced.


title: Privacy Preserving Data Aggregation in Wireless Sensor Networks

abstract: Privacy preservation is an important issue in today’s context of extreme penetration of Internet and mobile technologies. It is more important in the case of Wireless Sensor Networks (WSNs) where collected data often requires in-network processing and collaborative computing. Researches in this area are mostly concentrated in applying data mining techniques to preserve the privacy content of the data. These techniques are mostly computationally expensive and not suitable for resource limited WSN nodes. In this paper, a scheme is developed to provide privacy preservation in a much simpler way with the help of a secure key management scheme and randomized data perturbation technique. We consider a scenario in which two or more parties owning confidential data need to share only for aggregation purpose to a third party, without revealing the content of the data. Through simulation results the efficacy of our scheme is shown by comparing the results with one of the established schemes.


title: What Makes Buttocks Beautiful? A Review and Classification of the Determinants of Gluteal Beauty and the Surgical Techniques to Achieve Them

abstract: Background ::: No reports can be found in medical literature describing the anatomic details that make the buttocks beautiful, nor the defects in this region that can help the surgeon decide whether gluteal prostheses are necessary or not, and, if so, which type, design, and volume of prostheses should be used, in which plane they should be placed, and so forth.


title: A Strategic Model of Social and Economic Networks

abstract: We study the stability and efficiency of social and economic networks, when self-interested individuals have the discretion to form or sever links. First, in the context of two stylized models, we characterize the sets of stable networkds (immune to incentives to form or sever links) and the sets of efficient networks (those which maximize total production or utility). The sets of stable networks and efficients networks do not always intersect. Next, we show that this tension is not unique to these models, but persists generally. In order to assure that there is always at least one efficient graph which is stable, one is forced to allocate resources to nodes (players) who are not responsible for any of the production. We characterize one such allocations rule: the equal split rule. We characterize another rule which fails to assure that efficient graphs are stable, but arises naturally if the allocations result from the bargaining of players.


title: Visual secret sharing scheme for (k, n) threshold based on QR code with multiple decryptions

abstract: In this paper, a novel visual secret sharing (VSS) scheme based on QR code (VSSQR) with (k, n) threshold is investigated. Our VSSQR exploits the error correction mechanism in the QR code structure, to generate the bits corresponding to shares (shadow images) by VSS from a secret bit in the processing of encoding QR. Each output share is a valid QR code that can be scanned and decoded utilizing a QR code reader, which may reduce the likelihood of attracting the attention of potential attackers. Due to different application scenarios, two different recovered ways of the secret image are given. The proposed VSS scheme based on QR code can visually reveal secret image with the abilities of stacking and XOR decryptions as well as scan every shadow image, i.e., a QR code, by a QR code reader. The secret image could be revealed by human visual system without any computation based on stacking when no lightweight computation device. On the other hand, if the lightweight computation device is available, the secret image can be revealed with better visual quality based on XOR operation and could be lossless revealed when sufficient shares are collected. In addition, it can assist alignment for VSS recovery. The experiment results show the effectiveness of our scheme.


title: Deep Unfolding for Topic Models

abstract: Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.


title: Low-Cost Binary128 Floating-Point FMA Unit Design with SIMD Support

abstract: Binary64 arithmetic is rapidly becoming inadequate to cope with today's large-scale computations due to an accumulation of errors. Therefore, binary128 arithmetic is now required to increase the accuracy and reliability of these computations. At the same time, an obvious trend emerging in modern processors is to extend their instruction sets by allowing single instruction multiple data (SIMD) execution, which can significantly accelerate the data-parallel applications. To address the combined demands mentioned above, this paper presents the architecture of a low-cost binary128 floating-point fused multiply add (FMA) unit with SIMD support. The proposed FMA design can execute a binary128 FMA every other cycle with a latency of four cycles, or two binary64 FMAs fully pipelined with a latency of three cycles, or four binary32 FMAs fully pipelined with a latency of three cycles. We use two binary64 FMA units to support binary128 FMA which requires much less hardware than a fully pipelined binary128 FMA. The presented binary128 FMA design uses both segmentation and iteration hardware vectorization methods to trade off performance, such as throughput and latency, against area and power. Compared with a standard binary128 FMA implementation, the proposed FMA design has 30 percent less area and 29 percent less dynamic power dissipation.


title: Postural instability precedes motion sickness

abstract: We evaluated the hypothesis that postural instability precedes the onset of motion sickness. Subjects standing in a "moving room" were exposed to nearly global oscillating optical flow. In the experimental condition, the optical oscillations were a complex sum-of-sines between 0.1 and 0.3 Hz, with an excursion of 1.8 cm. This optical motion was of such low frequency and magnitude that it was sometimes not noticed by subjects. However, in two experiments, exposure to the moving room produced significant increases in scores on a standard motion sickness questionnaire. In addition, approximately half of subjects reported motion sickness. Analysis of postural motion during exposure to the moving room revealed increases in postural sway before the onset of subjective motion sickness symptoms. This confirms a key prediction of the postural instability theory of motion sickness.


title: A High-Density SNP Genotyping Array for Rice Biology and Molecular Breeding

abstract: ABSTRACT A high-density single nucleotide polymorphism (SNP) array is critically important for geneticists and molecular breeders. With the accumulation of huge amounts of genomic re-sequencing data and available technologies for accurate SNP detection, it is possible to design high-density and high-quality rice SNP arrays. Here we report the development of a high-density rice SNP array and its utility. SNP probes were designed by screening more than 10 000 000 SNP loci extracted from the re-sequencing data of 801 rice varieties and an array named RiceSNP50 was produced on the Illumina Infinium platform. The array contained 51 478 evenly distributed markers, 68% of which were within genic regions. Several hundred rice plants with parent/F1 relationships were used to generate a high-quality cluster file for accurate SNP calling. Application tests showed that this array had high genotyping accuracy, and could be used for different objectives. For example, a core collection of elite rice varieties was clustered with fine resolution. Genome-wide association studies (GWAS) analysis correctly identified a characterized QTL. Further, this array was successfully used for variety verification and trait introgression. As an accurate high-throughput genotyping tool, RiceSNP50 will play an important role in both functional genomics studies and molecular breeding. SUMMARY A high-density rice single nucleotide polymorphism (SNP) array with 51 478 markers has been developed on the Illumina Infinium platform. This array can be used widely in both functional genomics studies and molecular breeding.


title: Developing a CTG simulator app: theory and practice.

abstract: BACKGROUND ::: Cardiotocograph (CTG) interpretation is a core skill for health care professionals working on the labour ward; however, training appears to be deficient in respect of the development of decision-making skills relating to CTG findings. Simulation offers a potential solution to address such 'human factors'. Current access to simulators is limited by cost and operational complexity. We therefore decided to develop an accessible CTG simulator application ('app') that could be operated on a smartphone, a technology already possessed by the majority of health care professionals in the UK and elsewhere. CTG interpretation is a core skill for health care professionals working on the labour ward CONTEXT: A multidisciplinary team with backgrounds in obstetrics, anaesthesia and information technology was assembled to undertake the software development process. An evaluation of the pilot software was undertaken by trainee obstetric doctors. ::: ::: ::: INNOVATION ::: A software development project was undertaken in order to produce a mobile app that simulates CTG accurately and dynamically. This process was based on Davis' Technology Acceptance Model, in which usefulness and ease of use are the central principles. ::: ::: ::: IMPLICATIONS ::: Mobile technology can be used to run simulation software that is both useful and easy to use; however, our evaluation indicated that in order to use the app effectively the operator requires some expertise in the behaviour of the CTG in response to interventions, in common with all current patient simulators. We envisage using this app in hybrid simulation scenarios, in which a live actor and a simulated monitor are used together.


title: Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking

abstract: Recently, the region proposal networks (RPN) have been combined with the Siamese network for tracking, and shown excellent accuracy with high efficiency. Nevertheless, previously proposed one-stage Siamese-RPN trackers degenerate in presence of similar distractors and large scale variation. Addressing these issues, we propose a multi-stage tracking framework, Siamese Cascaded RPN (C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to shallow low-level layers in a Siamese network. Compared to previous solutions, C-RPN has several advantages: (1) Each RPN is trained using the outputs of RPN in the previous stage. Such process stimulates hard negative sampling, resulting in more balanced training samples. Consequently, the RPNs are sequentially more discriminative in distinguishing difficult background (i.e.,, similar distractors). (2) Multi-level features are fully leveraged through a novel feature transfer block (FTB) for each RPN, further improving the discriminability of C-RPN using both high-level semantic and low-level spatial information. (3) With multiple steps of regressions, C-RPN progressively refines the location and shape of the target in each RPN with adjusted anchor boxes in the previous stage, which makes localization more accurate. C-RPN is trained end-to-end with the multi-task loss function. In inference, C-RPN is deployed as it is, without any temporal adaption, for real-time tracking. In extensive experiments on OTB-2013, OTB-2015, VOT-2016, VOT-2017, LaSOT and TrackingNet, C-RPN consistently achieves state-of-the-art results and runs in real-time.


title: A systematic methodology for privacy impact assessments: a design science approach

abstract: For companies that develop and operate IT applications that process the personal data of customers and employees, a major problem is protecting these data and preventing privacy breaches. Failure to adequately address this problem can result in considerable damage to the company’s reputation and finances, as well as negative effects for customers or employees (data subjects). To address this problem, we propose a methodology that systematically considers privacy issues by using a step-by-step privacy impact assessment (PIA). Existing PIA approaches cannot be applied easily because they are improperly structured or imprecise and lengthy. We argue that companies that employ our PIA can achieve ‘privacy-by-design’, which is widely heralded by data protection authorities. In fact, the German Federal Office for Information Security (BSI) ratified the approach we present in this article for the technical field of RFID and published it as a guideline in November 2011. The contribution of the artefacts we created is twofold: First, we provide a formal problem representation structure for the analysis of privacy requirements. Second, we reduce the complexity of the privacy regulation landscape for practitioners who need to make privacy management decisions for their IT applications.


title: Secure multiparty protocols and zero-knowledge proof systems tolerating a faulty minority

abstract: A multiparty protocol to compute a function f(x 1, ..., x n ) operates as follows: each of n processors holds an input x i , and jointly they must compute and reveal f(x 1, ..., x n ) without revealing any additional information about the inputs. The processors are connected by secure communication lines but some number of processors may be corrupted by a resource-unbounded adversary that may attempt to interfere with the protocol or to gain extra information. Ben-Or, Goldwasser, Wigderson, Chaum, Crepeau, and Damgard have given protocols tolerating faults in t<n/3 processors. We improve the bound to t<n/2; as long as a majority remains uncorrupted, general and secure computations are achievable. To address and prove the security of our results, we introduce concise definitions for security and fault-tolerance. In particular, our notion of relative resilience--a means to compare the security and fault-tolerance of one protocol with that of another in a formal manner--provides a key tool for understanding and proving protocol security.


title: Numerical Methods for Least Squares Problems

abstract: Preface 1. Mathematical and statistical properties of least squares solutions 2. Basic numerical methods 3. Modified least squares problems 4. Generalized least squares problems 5. Constrained least squares problems 6. Direct methods for sparse problems 7. Iterative methods for least squares problems 8. Least squares problems with special bases 9. Nonlinear least squares problems Bibliography Index.


title: Assessing exploitation experiences of girls and boys seen at a Child Advocacy Center

abstract: The primary aim of this study was to describe the abuse experiences of sexually exploited runaway adolescents seen at a Child Advocacy Center (N=62). We also sought to identify risk behaviors, attributes of resiliency, laboratory results for sexually transmitted infection (STI) screens, and genital injuries from colposcopic exams. We used retrospective mixed-methods with in-depth forensic interviews, together with self-report survey responses, physical exams and chart data. Forensic interviews were analyzed using interpretive description analytical methods along domains of experience and meaning of sexual exploitation events. Univariate descriptive statistics characterized trauma responses and health risks. The first sexual exploitation events for many victims occurred as part of seemingly random encounters with procurers. Older adolescent or adult women recruited some youth working for a pimp. However, half the youth did not report a trafficker involved in setting up their exchange of sex for money, substances, or other types of consideration. 78% scored positive on the UCLA PTSD tool; 57% reported DSM IV criteria for problem substance use; 71% reported cutting behaviors, 75% suicidal ideation, and 50% had attempted suicide. Contrary to common depictions, youth may be solicited relatively quickly as runaways, yet exploitation is not always linked to having a pimp. Avoidant coping does not appear effective, as most patients exhibited significant symptoms of trauma. Awareness of variations in youth's sexual exploitation experiences may help researchers and clinicians understand potential differences in sequelae, design effective treatment plans, and develop community prevention programs. Language: en


title: Plastic bag clip discovered in partial colectomy accompanying proposal for phylogenic plastic bag clip classification

abstract: A plastic bag clip was incidentally found anchored in the mucosa of a partial colectomy specimen 2.6 cm proximal to a ruptured diverticulum for which the patient, a mentally retarded, diabetic, 58-year-old man, underwent surgery. Over 20 cases of accidental ingestion of plastic bag clips have been published. Known complications include small bowel perforation, obstruction, dysphagia, gastrointestinal bleeding and colonic impaction. Preoperative diagnosis of plastic clips lodged in the gastrointestinal tract is frustrated due to radiographic translucency. This occult threat could likely be prevented by the design of gastrointestinally safe, plastic-bag-sealing devices. Presented here is a morphologically based classification of bag clips as a possible guide for determining the most hazardous varieties and to aid further discussions of their impact on health.


title: Smart agent based prepaid wireless energy meter

abstract: Prepaid meter (PM) is getting very popular especially in developing countries. There are many advantages to use prepaid meter as opposed to postpaid meter both to the utility provider and to the consumer. Brunei Darussalam has adopted PM but it is not intelligent and not wireless enabled. Reading meters and topping up balance are still done manually. Utility provider does not have information on the usage statistics and has only limited functionalities in the grid control. So accordingly a novel software agent based wireless prepaid energy meter was developed using Java Agent Development Environment (JADE-LEAP) allowing agent from utility provider to query wireless energy meter for energy values of every household. These statistics can be used for statistical computation of the power consumed and for policy and future planning.


HYDROGEN MASER AND CESIUM BEAM TUBE FREQUENCY STANDARDS COMPARISON


Successful implementation of e-Learning Pedagogical considerations


Experience Design


title: Video Deraining and Desnowing Using Temporal Correlation and Low-Rank Matrix Completion

abstract: A novel algorithm to remove rain or snow streaks from a video sequence using temporal correlation and low-rank matrix completion is proposed in this paper. Based on the observation that rain streaks are too small and move too fast to affect the optical flow estimation between consecutive frames, we obtain an initial rain map by subtracting temporally warped frames from a current frame. Then, we decompose the initial rain map into basis vectors based on the sparse representation, and classify those basis vectors into rain streak ones and outliers with a support vector machine. We then refine the rain map by excluding the outliers. Finally, we remove the detected rain streaks by employing a low-rank matrix completion technique. Furthermore, we extend the proposed algorithm to stereo video deraining. Experimental results demonstrate that the proposed algorithm detects and removes rain or snow streaks efficiently, outperforming conventional algorithms.


title: A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing

abstract: In this paper, we develop a binary convolutional encoder-decoder network (B-CEDNet) for natural scene text processing (NSTP). It converts a text image to a class-distinguished salience map that reveals the categorical, spatial and morphological information of characters. The existing solutions are either memory consuming or run-time consuming that cannot be applied to real-time applications on resource-constrained devices such as advanced driver assistance systems. The developed network can process multiple regions containing characters by one-off forward operation, and is trained to have binary weights and binary feature maps, which lead to both remarkable inference run-time speedup and memory usage reduction. By training with over 200, 000 synthesis scene text images (size of $32\times128$), it can achieve $90\%$ and $91\%$ pixel-wise accuracy on ICDAR-03 and ICDAR-13 datasets. It only consumes $4.59\ ms$ inference run-time realized on GPU with a small network size of 2.14 MB, which is up to $8\times$ faster and $96\%$ smaller than it full-precision version.


title: Automatically assessing crashes from heap overflows

abstract: Heap overflow is one of the most widely exploited vulnerabilities, with a large number of heap overflow instances reported every year. It is important to decide whether a crash caused by heap overflow can be turned into an exploit. Efficient and effective assessment of exploitability of crashes facilitates to identify severe vulnerabilities and thus prioritize resources. In this paper, we propose the first metrics to assess heap overflow crashes based on both the attack aspect and the feasibility aspect. We further present HCSIFTER, a novel solution to automatically assess the exploitability of heap overflow instances under our metrics. Given a heap-based crash, HCSIFTER accurately detects heap overflows through dynamic execution without any source code or debugging information. Then it uses several novel methods to extract program execution information needed to quantify the severity of the heap overflow using our metrics. We have implemented a prototype HCSIFTER and applied it to assess nine programs with heap overflow vulnerabilities. HCSIFTER successfully reports that five heap overflow vulnerabilities are highly exploitable and two overflow vulnerabilities are unlikely exploitable. It also gave quantitatively assessments for other two programs. On average, it only takes about two minutes to assess one heap overflow crash. The evaluation result demonstrates both effectiveness and efficiency of HC Sifter.


title: Prediction of error probabilities for integrated digital synchronizers

abstract: Synchronization errors occur when asynchronous digital signals are received by clocked digital systems. Digital synchronizers are designed to minimize the probability of such errors. Empirical determination of error probabilities, as used in the past, is not a viable method for large-scale integrated circuit because it is cumbersome, interferes with the circuit performance, and does not account for tolerances of circuit parameters. The authors describe a complete method for predicting synchronization-error probabilities using circuit simulation and theoretical analysis. The analysis includes the efforts of random noise. The simulation model readily takes into account tolerances of the circuit parameters. The direct observability of all parameters of the model enhances the understanding of the synchronization process and the reliability of the predictions.


title: Vision based PID control on a planar cable robot

abstract: In this paper, a visual measuring system is implemented for task space control of a planar cable robot. Since cables are able to apply only tensile forces, the proposed control algorithm shall be designed such that the cables remain in tension in the whole workspace. Furthermore, the position and orientation (pose) of the end-effector in task space coordinates shall be measured directly for precise motion control of the robot. Therefore, in this paper, vision-based measurement system is implemented on a planer cable robot and these measurements are directly used in a controller. Furthermore, the vector of internal forces is used to ensure that all cables remain in tension. Finally, The effectiveness of the proposed control algorithm is examined through some experiments on a planar cable robot and it is shown that the proposed control algorithm is able to provide suitable performance in practice.


title: Dispositional optimism

abstract: Optimism is a cognitive construct (expectancies regarding future outcomes) that also relates to motivation: optimistic people exert effort, whereas pessimistic people disengage from effort. Study of optimism began largely in health contexts, finding positive associations between optimism and markers of better psychological and physical health. Physical health effects likely occur through differences in both health-promoting behaviors and physiological concomitants of coping. Recently, the scientific study of optimism has extended to the realm of social relations: new evidence indicates that optimists have better social connections, partly because they work harder at them. In this review, we examine the myriad ways this trait can benefit an individual, and our current understanding of the biological basis of optimism.


title: Virtual hairy brush for painterly rendering

abstract: We propose a novel "e-brush" for calligraphy and painting, which meets all the criteria for a good e-brush. We use only four attributes to capture the essential features of the brush, and a suitably powerful modeling metaphor for its behavior. The e-brush's geometry, dynamic motions, and pigment changes are all dealt with in a single model. A single model simplifies the synchronization between the various system modules, thus giving rise to a more stable system, and lower costs. By a careful tradeoff between the complexity of the model and computation efficiency, more elaborate simulation of the e-brush's deformation and its recovery for interactive painterly rendering is made possible. We also propose a novel paper-ink model to complement the brush's model, and a machine intelligence module to empower the user to easily create beautiful calligraphy and painting. Despite the complexity of the modeling behind the scene, the high-level user interface has a simplistic and friendly design. The final results created by our e-brush can rival the real artwork.


title: A class-oriented feature selection approach for multi-class imbalanced network traffic datasets based on local and global metrics fusion

abstract: Feature selection is often used as a pre-processing step for machine learning based network traffic classification. Many feature selection techniques have been developed to find an optimal subset of relevant features and to improve overall classification accuracy. But such techniques ignore the class imbalance problem encountered in network traffic classification. The selected feature subset may bias towards the traffic class that occupies the majority of traffic flows on the Internet. To address this issue, this paper proposes a new approach, called class-oriented feature selection (COFS), to identify a relevant feature subset for every class. It combines the proposed local metric and the existing global metric to yield a potentially optimal feature subset for each class, and then removes the redundant features in each feature subset based on the weighted symmetric uncertainty. Additionally, to enhance the generalization on network traffic data, an ensemble learning based scheme is presented with COFS to overcome the negative impacts of the data drift on a traffic classifier. Experiments on real-world network traffic data show that COFS outperforms existing feature selection techniques in most cases. Moreover, our approach achieves >96% flow accuracy and >93% byte accuracy on average.


title: A Real Time Hand Gesture Recognition System Based on DFT and SVM

abstract: Vision based band gesture recognition provides a more nature and powerful means for human-computer interaction. A fast detection process of hand gesture and an effective feature extraction process are presented. The proposed a hand gesture recognition algorithm comprises four main steps. First use Camshift algorithm to track skin color after closing process. Second, in order to extract feature, we use BEA to extract the boundary of the hand. Third, the benefits of Fourier descriptor are invariance to the starting point of the boundary, deformation, and rotation, and therefore transform the starting point of the boundary by Fourier transformation. Finally, outline feature for the nonlinear non-separable type of data was classified by using SVM. Experimental results showed the accuracy is 93.4% in average and demonstrated the feasibility of proposed system.


Comprehensive care by trauma nurse providers: Mission Hospital's ten-year experience.


title: Integer programming formulations for the elementary shortest path problem

abstract: Given a directed graph G=(V,A) with arbitrary arc costs, the Elementary Shortest Path Problem (ESPP) consists of finding a minimum-cost path between two nodes s and t such that each node of G is visited at most once. If negative costs are allowed, the problem is NP-hard. In this paper, several integer programming formulations for the ESPP are compared. We present analytical results based on a polyhedral study of the formulations, and computational experiments where we compare their linear programming relaxation bounds and their behavior within a branch-and-cut framework. The computational results show that a formulation with dynamically generated cutset inequalities is the most effective.


title: Detecting jute plant disease using image processing and machine learning

abstract: Detecting stem diseases of plants by image analysis are still in an inchoate state in the research field. This research has been conducted on detecting the stem diseases of jute plants which is one of the most important cash crops in some of the Asian countries. An automated system based on an Android application has been implemented to take pictures of the disease affected stems of jute plants and send them to the dedicated server for assaying. On the server side, the affected portion from the image will be segmented using customized thresholding formula based on hue-based segmentation. The consequential feature values will be extracted from the segmented portion for texture analysis using color co-occurrence methodology. The extracted values will be compared with the sample values stored in the pre-defined database which will lead the disease to be identified and classified using Multi-SVM classifier. At the final step, the classification result along with the necessary control measures will be sent back to the farmer within three seconds through the application on their phone.


title: Modelling and control of an open-loop stepper motor in Matlab/Simulink

abstract: With the advent of increased industrial the automation and microprocessor applications, the interest in digital motion control systems has also expanded. Hybrid Stepper Motors are widely used in open-loop position applications. This article presents a description and a functional model of a hybrid stepping motor. The motor drive methods of full stepping and back stepping are presented in this paper. Test methods for system characterization are described and response characteristics for the simulated and experimental results are compared to verify the model. This paper shows that an open-loop stepping motor can be accurate simulation to predict real hardware performance.


title: Outlier Detection over Massive-Scale Trajectory Streams

abstract: The detection of abnormal moving objects over high-volume trajectory streams is critical for real-time applications ranging from military surveillance to transportation management. Yet this outlier detection problem, especially along both the spatial and temporal dimensions, remains largely unexplored. In this work, we propose a rich taxonomy of novel classes of neighbor-based trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real-time applications. Our theoretical analysis and empirical study on two real-world datasets—the Beijing Taxi trajectory data and the Ground Moving Target Indicator data stream—and one generated Moving Objects dataset demonstrate the effectiveness of our taxonomy in effectively capturing different types of abnormal moving objects. Furthermore, we propose a general strategy for efficiently detecting these new outlier classes called the m inimal ex amination (MEX) framework. The MEX framework features three core optimization principles, which leverage spatiotemporal as well as the predictability properties of the neighbor evidence to minimize the detection costs. Based on this foundation, we design algorithms that detect the outliers based on these classes of new outlier semantics that successfully leverage our optimization principles. Our comprehensive experimental study demonstrates that our proposed MEX strategy drives the detection costs 100-fold down into the practical realm for applications that analyze high-volume trajectory streams in near real time.


title: A New Total Digital Smile Planning Technique (3D-DSP) to Fabricate CAD-CAM Mockups for Esthetic Crowns and Veneers

abstract: Purpose. Recently, the request of patients is changed in terms of not only esthetic but also previsualization therapy planning. The aim of this study is to evaluate a new 3D-CAD-CAM digital planning technique that uses a total digital smile process. Materials and Methods. Study participants included 28 adult dental patients, aged 19 to 53 years, with no oral, periodontal, or systemic diseases. For each patient, 3 intra- and extraoral pictures and intraoral digital impressions were taken. The digital images improved from the 2D Digital Smile System software and the scanner stereolithographic (STL) file was matched into the 3D-Digital Smile System to obtain a virtual previsualization of teeth and smile design. Then, the mockups were milled using a CAM system. Minimally invasive preparation was carried out on the enamel surface with the mockups as position guides. Results. The patients found both the digital smile design previsualization (64.3%) and the milling mockup test (85.7%) very effective. Conclusions. The new total 3D digital planning technique is a predictably and minimally invasive technique, allows easy diagnosis, and improves the communication with the patient and helps to reduce the working time and the errors usually associated with the classical prosthodontic manual step.


title: Bringing Semantics into Focus Using Visual Abstraction

abstract: Relating visual information to its linguistic semantic meaning remains an open and challenging area of research. The semantic meaning of images depends on the presence of objects, their attributes and their relations to other objects. But precisely characterizing this dependence requires extracting complex visual information from an image, which is in general a difficult and yet unsolved problem. In this paper, we propose studying semantic information in abstract images created from collections of clip art. Abstract images provide several advantages. They allow for the direct study of how to infer high-level semantic information, since they remove the reliance on noisy low-level object, attribute and relation detectors, or the tedious hand-labeling of images. Importantly, abstract images also allow the ability to generate sets of semantically similar scenes. Finding analogous sets of semantically similar real images would be nearly impossible. We create 1,002 sets of 10 semantically similar abstract scenes with corresponding written descriptions. We thoroughly analyze this dataset to discover semantically important features, the relations of words to visual features and methods for measuring semantic similarity.


title: Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals

abstract: BackgroundLong-term continuous systolic blood pressure (SBP) and heart rate (HR) monitors are of tremendous value to medical (cardiovascular, circulatory and cerebrovascular management), wellness (emotional and stress tracking) and fitness (performance monitoring) applications, but face several major impediments, such as poor wearability, lack of widely accepted robust SBP models and insufficient proofing of the generalization ability of calibrated models.MethodsThis paper proposes a wearable cuff-less electrocardiography (ECG) and photoplethysmogram (PPG)-based SBP and HR monitoring system and many efforts are made focusing on above challenges. Firstly, both ECG/PPG sensors are integrated into a single-arm band to provide a super wearability. A highly convenient but challenging single-lead configuration is proposed for weak single-arm-ECG acquisition, instead of placing the electrodes on the chest, or two wrists. Secondly, to identify heartbeats and estimate HR from the motion artifacts-sensitive weak arm-ECG, a machine learning-enabled framework is applied. Then ECG-PPG heartbeat pairs are determined for pulse transit time (PTT) measurement. Thirdly, a PTT&HR-SBP model is applied for SBP estimation, which is also compared with many PTT-SBP models to demonstrate the necessity to introduce HR information in model establishment. Fourthly, the fitted SBP models are further evaluated on the unseen data to illustrate the generalization ability. A customized hardware prototype was established and a dataset collected from ten volunteers was acquired to evaluate the proof-of-concept system.ResultsThe semi-customized prototype successfully acquired from the left upper arm the PPG signal, and the weak ECG signal, the amplitude of which is only around 10% of that of the chest-ECG. The HR estimation has a mean absolute error (MAE) and a root mean square error (RMSE) of only 0.21 and 1.20 beats per min, respectively. Through the comparative analysis, the PTT&HR-SBP models significantly outperform the PTT-SBP models. The testing performance is 1.63 ± 4.44, 3.68, 4.71 mmHg in terms of mean error ± standard deviation, MAE and RMSE, respectively, indicating a good generalization ability on the unseen fresh data.ConclusionsThe proposed proof-of-concept system is highly wearable, and its robustness is thoroughly evaluated on different modeling strategies and also the unseen data, which are expected to contribute to long-term pervasive hypertension, heart health and fitness management.


title: G-KVM: A Full GPU Virtualization on KVM

abstract: Graphics processing Units (GPUs), which originally designed for computer graphics applications, have been widely adopted to general purpose computing in many domains owing to their massive computational power. In the era of cloud computing, GPU virtualization becomes an important technique for the better management of GPUs in data centers. However, most of current solutions are not full virtualization. They either need to modify the guest drivers or libraries, or restrict the hardware sharing capability. The only full GPU virtualization solution is GPUvm, which however can only be executed on Xen hypervisors. In this paper, we present a full GPU virtualization solution on KVM (Kernel-based Virtual Machine), called G-KVM. Our work is not merely a direct porting of GPUvm to KVM, since Xen and KVM have fundamental differences in their system architectures. Two major changes of G-KVM are aggregator and QEMU device model. The experiments show that G-KVM has better performance for MMIO operations than GPUvm on Xen hypervisor. For the compute-extensive experiments, execution time of G-KVM can achieve nearly 82% of native performance, which is similar to GPUvm. The performance scaling experiment shows that the performance of single machine with G-KVM can be scaled up to multiple virtual machines.


title: A single layer wideband differential-fed patch antenna array with SIW feeding networks

abstract: A single layer wideband SIW-fed differential patch array is proposed in this paper. A SIW-CPS-CMS (substrate integrated waveguide - coupled lines - coupled microstrip line) transition is designed and has a bandwidth of about 50%, covering the E-band and W-band. The differential phase deviation between the coupled microstrip lines is less than 7.5° within the operation band. A 1×4 array and a 4×4 array are designed. The antenna is composed of SIW parallel power di vider network, SIW-CPS-CMS transition, and series differential-fed patch array. Simulated results show that the bandwidth of the 1×4 array and 4×4 array are 37% and 12%, and the realized gain are 10.5–12 dB and 17.2–20.2dB within the corresponding operation band, respectively. The features of single layer and wideband on impedance and gain of the proposed SIW-fed differential patch array make it a good candidate for automotive radar or other millimeter wave applications.


title: EURIDICE — IoT applied to logistics using the Intelligent Cargo concept

abstract: In an interconnected world, the need to exchange information across domain's boundaries is increasingly common, the concern is rapidly moving towards defining the content that needs to be consumed by numerous and different actors using different platforms and/or software solutions, since the internal processes have been consolidated and optimized. The transport logistics sector is no exception, the main issue in this domain is for ICT applications that allow to tag, monitor and transmit information about the freight along the whole transport chain, thus guaranteeing an efficient communication among the supply chain actors for a prompt intervention and resolution in case of problems and in general to increase the transport efficiency. The paper will present the technical aspects of the solutions developed within the EURIDICE project and will link them to the concrete in-field applications that make the platform a great innovation and a step towards the realization of the Future Internet.


title: User-Independent Recognition of Sports Activities From a Single Wrist-Worn Accelerometer: A Template-Matching-Based Approach

abstract: Goal : To investigate the accuracy of template matching for classifying sports activities using the acceleration signal recorded with a wearable sensor. Methods : A population of 29 normal weight and 19 overweight subjects was recruited to perform eight common sports activities, while body movement was measured using a triaxial accelerometer placed at the wrist. User- and axis-independent acceleration signal templates were automatically extracted to represent each activity category and recognize activity types. Five different similarity measures between example signals and templates were compared: Euclidean distance, dynamic time warping (DTW), derivative DTW, correlation and an innovative index, and combining distance and correlation metrics ( Rce ). Template-based activity recognition was compared to statistical-learning classifiers, such as Naive Bayes, decision tree, logistic regression (LR), and artificial neural network (ANN) trained using time- and frequency-domain signal features. Each algorithm was tested on data from a holdout group of 15 normal weight and 19 overweight subjects. Results : The Rce index outperformed other template-matching metrics by achieving recognition rate above 80% for the majority of the activities. Template matching showed robust classification accuracy when tested on unseen data and in case of limited training examples. LR and ANN achieved the highest overall recognition accuracy $\sim$ 85% but showed to be more vulnerable to misclassification error than template matching on overweight subjects’ data. Conclusion : Template matching can be used to classify sports activities using the wrist acceleration signal. Significance : Automatically extracted template prototypes from the acceleration signal may be used to enhance accuracy and generalization properties of statistical-learning classifiers.


title: Inner Space Preserving Generative Pose Machine

abstract: Image-based generative methods, such as generative adversarial networks (GANs) have already been able to generate realistic images with much context control, specially when they are conditioned. However, most successful frameworks share a common procedure which performs an image-to-image translation with pose of figures in the image untouched. When the objective is reposing a figure in an image while preserving the rest of the image, the state-of-the-art mainly assumes a single rigid body with simple background and limited pose shift, which can hardly be extended to the images under normal settings. In this paper, we introduce an image"inner space"preserving model that assigns an interpretable low-dimensional pose descriptor (LDPD) to an articulated figure in the image. Figure reposing is then generated by passing the LDPD and the original image through multi-stage augmented hourglass networks in a conditional GAN structure, called inner space preserving generative pose machine (ISP-GPM). We evaluated ISP-GPM on reposing human figures, which are highly articulated with versatile variations. Test of a state-of-the-art pose estimator on our reposed dataset gave an accuracy over 80% on PCK0.5 metric. The results also elucidated that our ISP-GPM is able to preserve the background with high accuracy while reasonably recovering the area blocked by the figure to be reposed.


title: Non-linear robust control for inverted-pendulum 2D walking

abstract: We present an approach to high-level control for bipedal walking exemplified with a 2D point-mass inextensible-legs inverted-pendulum model. Balance control authority here is only from step position and trailing-leg push-off, both of which are bounded to reflect actuator limits. The controller is defined implicitly as the solution of an optimization problem. The optimization robustly avoids falling for given bounded disturbances and errors and, given that, minimizes the number of steps to reach a given target speed. The optimization can be computed in advance and stored for interpolated real-time use online. The general form of the resulting optimized controller suggests a few simple principles for regulating walking speed: 1) The robot should take bigger steps when speeding up and should also take bigger steps when slowing down 2) push-off is useful for regulating small changes in speed, but it is fully saturated or inactive for larger changes in speed. While the numerically optimized model is simple, the approach should be applicable to, and we plan to use it for, control of bipedal robots in 3D with many degrees of freedom.


title: Toward a Research Agenda in Adversarial Reasoning: Computational Approaches to Anticipating the Opponent's Intent and Actions

abstract: This paper defines adversarial reasoning as computational approaches to inferring and anticipating an enemy's perceptions, intents and actions. It argues that adversarial reasoning transcends the boundaries of game theory and must also leverage such disciplines as cognitive modeling, control theory, AI planning and others. To illustrate the challenges of applying adversarial reasoning to real-world problems, the paper explores the lessons learned in the CADET - a battle planning system that focuses on brigade-level ground operations and involves adversarial reasoning. From this example of current capabilities, the paper proceeds to describe RAID - a DARPA program that aims to build capabilities in adversarial reasoning, and how such capabilities would address practical requirements in Defense and other application areas.


title: Security as a Service Model for Cloud Environment

abstract: Cloud computing is becoming increasingly important for provision of services and storage of data in the Internet. However there are several significant challenges in securing cloud infrastructures from different types of attacks. The focus of this paper is on the security services that a cloud provider can offer as part of its infrastructure to its customers (tenants) to counteract these attacks. Our main contribution is a security architecture that provides a flexible security as a service model that a cloud provider can offer to its tenants and customers of its tenants. Our security as a service model while offering a baseline security to the provider to protect its own cloud infrastructure also provides flexibility to tenants to have additional security functionalities that suit their security requirements. The paper describes the design of the security architecture and discusses how different types of attacks are counteracted by the proposed architecture. We have implemented the security architecture and the paper discusses analysis and performance evaluation results.


title: Event-driven image sensor application: Event-driven image segmentation

abstract: We present through this paper an event-driven image sensor with a built-in and native image processing technique. In fact, digital image analysis and processing became one of the most demanding fields due to its wide and various applications. The activity of our event-driven image sensor is limited to the effective and relevant information in the captured scene. Moreover, its event-driven protocol efficiently suppresses any spatial redundancies. Therefore, the image sensor data flow is drastically reduced. Furthermore, we are introducing an effective and cost-free image segmentation technique by taking advantage of the spatial redundancy suppression method.


The Linguistic Approach to Salish Prehistory


title: Examining cloud computing adoption intention, pricing mechanism, and deployment model

abstract: Abstract Cloud computing is a new information technology (IT) paradigm that promises to revolutionize traditional IT delivery through reduced costs, greater elasticity, and ubiquitous access. On the surface, adopting cloud computing requires a firm to address many of the same concerns they face in adopting any enterprise IT. However, cloud technologies also offer new pricing and deployment strategies that are unavailable in traditional enterprise solutions. It is unclear how previous research frameworks of enterprise IT adoption relate to these new adoption strategies. To bridge this gap in the literature, our study uses the technology–organization–environment (TOE) framework of innovation diffusion theory to develop a cloud service adoption model that deals with not only adoption intention, but also pricing mechanisms and deployment models. Our research model has been empirically tested using 200 Taiwanese firms. We found that: (1) Cloud adoption is still at its initial stage, since the adoption rates are very low; (2) the perceived benefits, business concerns, and IT capability within the TOE framework are significant determinants of cloud computing adoption, while external pressure is not; (3) firms with greater IT capability tend to choose the pay-as-you-go pricing mechanism; (4) business concern is the most important factor influencing the choice of deployment model, with higher concerns leading to private deployment options.


title: Organizational issues in the implementation and adoption of health information technology innovations: An interpretative review

abstract: Abstract Purpose Implementations of health information technologies are notoriously difficult, which is due to a range of inter-related technical, social and organizational factors that need to be considered. In the light of an apparent lack of empirically based integrated accounts surrounding these issues, this interpretative review aims to provide an overview and extract potentially generalizable findings across settings. Methods We conducted a systematic search and critique of the empirical literature published between 1997 and 2010. In doing so, we searched a range of medical databases to identify review papers that related to the implementation and adoption of eHealth applications in organizational settings. We qualitatively synthesized this literature extracting data relating to technologies, contexts, stakeholders, and their inter-relationships. Results From a total body of 121 systematic reviews, we identified 13 systematic reviews encompassing organizational issues surrounding health information technology implementations. By and large, the evidence indicates that there are a range of technical, social and organizational considerations that need to be deliberated when attempting to ensure that technological innovations are useful for both individuals and organizational processes. However, these dimensions are inter-related, requiring a careful balancing act of strategic implementation decisions in order to ensure that unintended consequences resulting from technology introduction do not pose a threat to patients. Conclusions Organizational issues surrounding technology implementations in healthcare settings are crucially important, but have as yet not received adequate research attention. This may in part be due to the subjective nature of factors, but also due to a lack of coordinated efforts toward more theoretically-informed work. Our findings may be used as the basis for the development of best practice guidelines in this area.


title: Positive design of smart interactive fabric artifacts for people with dementia

abstract: Confronting the expected rise of dementia as a major health care problem raises many questions about the best ways to adapt the health system to deal with it. To the extent that intelligent assistive technologies can help, there seems to be value in comforting fabric artifacts enhanced by electronic games and activities designed to support, engage and entertain people with dementia. Local cottage industries which now support the creation of textile crafts should be empowered to scale up to meet the growing demand for such products. New design concepts are required to accomplish this in the face of rising costs and limited resources. This paper proposes a four-step design process that meets this need, and provides practical suggestions about how it could be applied in this context. A number of examples are included.


Recent Themes in Case-Based Reasoning and Knowledge Discovery.


title: Dengue virus pathogenesis: An integrated view

abstract: Summary: Much remains to be learned about the pathogenesis of the different manifestations of dengue virus (DENV) infections in humans. They may range from subclinical infection to dengue fever, dengue hemorrhagic fever (DHF), and eventually dengue shock syndrome (DSS). As both cell tropism and tissue tropism of DENV are considered major determinants in the pathogenesis of dengue, there is a critical need for adequate tropism assays, animal models, and human autopsy data. More than 50 years of research on dengue has resulted in a host of literature, which strongly suggests that the pathogenesis of DHF and DSS involves viral virulence factors and detrimental host responses, collectively resulting in abnormal hemostasis and increased vascular permeability. Differential targeting of specific vascular beds is likely to trigger the localized vascular hyperpermeability underlying DSS. A personalized approach to the study of pathogenesis will elucidate the basis of individual risk for development of DHF and DSS as well as identify the genetic and environmental bases for differences in risk for development of severe disease.


title: Learning Answer-Entailing Structures for Machine Comprehension

abstract: Understanding open-domain text is one of the primary challenges in NLP. Machine comprehension evaluates the system’s ability to understand text through a series of question-answering tasks on short pieces of text such that the correct answer can be found only in the given text. For this task, we posit that there is a hidden (latent) structure that explains the relation between the question, correct answer, and text. We call this the answer-entailing structure; given the structure, the correctness of the answer is evident. Since the structure is latent, it must be inferred. We present a unified max-margin framework that learns to find these hidden structures (given a corpus of question-answer pairs), and uses what it learns to answer machine comprehension questions on novel texts. We extend this framework to incorporate multi-task learning on the different subtasks that are required to perform machine comprehension. Evaluation on a publicly available dataset shows that our framework outperforms various IR and neuralnetwork baselines, achieving an overall accuracy of 67.8% (vs. 59.9%, the best previously-published result.)


title: Combining Structured and Free-text Data for Automatic Coding of Patient Outcomes

abstract: Integrating easy-to-extract structured information such as medication and treatments into current natural language processing based systems can significantly boost coding performance; in this paper, we present a system that rigorously attempts to validate this intuitive idea. Based on recent i2b2 challenge winners, we derive a strong language model baseline that extracts patient outcomes from discharge summaries. Upon incorporating additional clinical cues into this language model, we see a significant boost in performance to F1 of 88.3 and a corresponding reduction in error of 23.52%.


title: Learning deeply coupled autoencoders for smartphone based robust periocular verification

abstract: Smartphone based periocular recognition has received substantial attention from the biometric research community. In this work, we propose a new scheme for the smartphone based periocular recognition. The proposed scheme is based on the texture features extracted from the periocular images using Maximum Response (MR) filters. These texture features are then classified using a deep neural network based on deeply coupled autoencoders. Extensive experiments are carried out on the large-scale VISOB database with 550 subjects captured using three different smartphones. The obtained results demonstrate the average performance of the proposed scheme with GMR of over 92% at FMR = 10−3.


title: Imbalanced Deep Learning by Minority Class Incremental Rectification

abstract: Model learning from class imbalanced training data is a long-standing and significant challenge for machine learning. In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data. To address this problem, we formulate a class imbalanced deep learning model based on batch-wise incremental minority (sparsely sampled) class rectification by hard sample mining in majority (frequently sampled) classes during model training. This model is designed to minimise the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process. To that end, we introduce a Class Rectification Loss (CRL) function that can be deployed readily in deep network architectures. Extensive experimental evaluations are conducted on three imbalanced person attribute benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object category benchmark dataset (CIFAR-100). These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.


title: Adding numbers to text classification

abstract: Many real-world problems involve a combination of both text- and numerical-valued features. For example, in email classification, it is possible to use instance representations that consider not only the text of each message, but also numerical-valued features such as the length of the message or the time of day at which it was sent. Text-classification methods have thus far not easily incorporated numerical features. In earlier work we described an approach for converting numerical features into bags of tokens so that text classification methods can be applied to numerical classification problems, and showed that the resulting learning methods are competitive with traditional numerical classification methods. In this paper we use this as a way to learn on problems that involve a combination of text and numbers. We show that the results outperform competing methods. Further, we show that selecting a best classification method using text-only features and then adding numerical features to the problem (as might happen if numerical features are only later added to a pre existing text-classification problem) gives performance that rivals a more time-consuming approach of evaluating all classification methods using the full set of both text and numerical features.


title: Crowdsourcing in logistics: concepts and applications using the social crowd

abstract: The introduction of crowdsourcing offers numerous business opportunities. In recent years, manifold forms of crowdsourcing have emerged on the market -- also in logistics. Thereby, the ubiquitous availability and sensor-supported assistance functions of mobile devices support crowdsourcing applications, which promotes contextual interactions between users at the right place at the right time. This paper presents the results of an in-depth-analysis on crowdsourcing in logistics in the course of ongoing research in the field of location-based crowdsourcing (LBCS). This paper analyzes LBCS for both, 'classic' logistics as well as 'information' logistics. Real-world examples of crowdsourcing applications are used to underpin the two evaluated types of logistics using crowdsourcing. Potential advantages and challenges of logistics with the crowd ('crowd-logistics') are discussed. Accordingly, this paper aims to provide the necessary basis for a novel interdisciplinary research field.


title: Nipple-areola reconstruction

abstract: Nipple-areola reconstruction from nonspecialized tissues is presented as a worthwhile component of breast reconstruction. General principles are discussed, as are specific techniques for reconstruction of the areola and the nipple. The author's method is recommended as the most efficient use of local tissue for nipple reconstruction in the prosthetic breast, where skin and fat are both in short supply.


title: Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data

abstract: The classification in class imbalanced data has drawn significant interest in medical application. Most existing methods are prone to categorize the samples into the majority class, resulting in bias, in particular the insufficient identification of minority class. A kind of novel approach, class weights random forest is introduced to address the problem, by assigning individual weights for each class instead of a single weight. The validation test on UCI data sets demonstrates that for imbalanced medical data, the proposed method enhanced the overall performance of the classifier while producing high accuracy in identifying both majority and minority class.


title: INTERSPEECH 2011 Personalizing Model M for Voice-search

abstract: Model M is a recently proposed class based exponential n-gram language model. In this paper, we extend it with personalization features, address the scalability issues present with large data sets, and test its effectiveness on the Bing Mobile voice-search task. We find that Model M by itself reduces both perplexity and word error rate compared with a conventional model, and that the personalization features produce a further significant improvement. The personalization features provide a very large improvement when the history contains a relevant query; thus the overall effect is gated by the number of times a user requeries a past request.


title: Analyzing the effects of disk-pointer corruption

abstract: The long-term availability of data stored in a file system depends on how well it safeguards on-disk pointers used to access the data. Ideally, a system would correct all pointer errors. In this paper, we examine how well corruption-handling techniques work in reality. We develop a new technique called type-aware pointer corruption to systematically explore how a file system reacts to corrupt pointers. This approach reduces the exploration space for corruption experiments and works without source code. We use type-aware pointer corruption to examine Windows NTFS and Linux ext3. We find that they rely on type and sanity checks to detect corruption, and NTFS recovers using replication in some instances. However, NTFS and ext3 do not recover from most corruptions, including many scenarios for which they possess sufficient redundant information, leading to further corruption, crashes, and unmountable file systems. We use our study to identify important lessons for handling corrupt pointers.


title: A comparison of IoT application layer protocols through a smart parking implementation

abstract: Several IoT protocols have been introduced in order to provide an efficient communication for resource-ronstrained applications. However, their performance is not as yet well understood. To address this issue, we evaluated and compared four communication protocols, namely, CoAP, MQTT, XMPP, and WebSocket. For this, we implemented a smart parking application using open source software for these protocols and measured their response time by varying the traffic load.


PHYSICAL AND CHEMICAL CHARACTERISTICS OF SOILS FORMED ON OPHITIC AND SEDIMENTARY MATERIALS IN MEDITERRANEAN CLIMATE


title: AppFlow: using machine learning to synthesize robust, reusable UI tests

abstract: UI testing is known to be difficult, especially as today’s development cycles become faster. Manual UI testing is tedious, costly and error- prone. Automated UI tests are costly to write and maintain. This paper presents AppFlow, a system for synthesizing highly robust, highly reusable UI tests. It leverages machine learning to automatically recognize common screens and widgets, relieving developers from writing ad hoc, fragile logic to use them in tests. It enables developers to write a library of modular tests for the main functionality of an app category (e.g., an “add to cart” test for shopping apps). It can then quickly test a new app in the same category by synthesizing full tests from the modular ones in the library. By focusing on the main functionality, AppFlow provides “smoke testing” requiring little manual work. Optionally, developers can customize AppFlow by adding app-specific tests for completeness. We evaluated AppFlow on 60 popular apps in the shopping and the news category, two case studies on the BBC news app and the JackThreads shopping app, and a user-study of 15 subjects on the Wish shopping app. Results show that AppFlow accurately recognizes screens and widgets, synthesizes highly robust and reusable tests, covers 46.6% of all automatable tests for Jackthreads with the tests it synthesizes, and reduces the effort to test a new app by up to 90%. Interestingly, it found eight bugs in the evaluated apps, including seven functionality bugs, despite that they were publicly released and supposedly went through thorough testing.


Detection of Abrupt Changes: Theory and Application


title: Synthesizing test data for fraud detection systems

abstract: We report an experiment aimed at generating synthetic test data for fraud detection in an IP based video-on-demand service. The data generation verifies a methodology previously developed by the present authors [E. Lundin et al., (2002)] that ensures that important statistical properties of the authentic data are preserved by using authentic normal data and fraud as a seed for generating synthetic data. This enables us to create realistic behavior profiles for users and attackers. The data is used to train the fraud detection system itself, thus creating the necessary adaptation of the system to a specific environment. Here we aim to verify the usability and applicability of the synthetic data, by using them to train a fraud detection system. The system is then exposed to a set of authentic data to measure parameters such as detection capability and false alarm rate as well as to a corresponding set of synthetic data, and the results are compared.


title: Vision-Based Gesture Recognition: A Review

abstract: With the development of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient. Due to the limitation of these devices the useable command set is also limited. Direct use of hands as an input device is an attractive method for providing natural Human Computer Interaction which has evolved from text-based interfaces through 2D graphical-based interfaces, multimedia-supported interfaces, to fully fledged multi-participant Virtual Environment (VE) systems. Imagine the human-computer interaction of the future: A 3D- application where you can move and rotate objects simply by moving and rotating your hand - all without touching any input device. In this paper a review of vision based hand gesture recognition is presented. The existing approaches are categorized into 3D model based approaches and appearance based approaches, highlighting their advantages and shortcomings and identifying the open issues.


title: Structure-preserving image smoothing via region covariances

abstract: Recent years have witnessed the emergence of new image smoothing techniques which have provided new insights and raised new questions about the nature of this well-studied problem. Specifically, these models separate a given image into its structure and texture layers by utilizing non-gradient based definitions for edges or special measures that distinguish edges from oscillations. In this study, we propose an alternative yet simple image smoothing approach which depends on covariance matrices of simple image features, aka the region covariances. The use of second order statistics as a patch descriptor allows us to implicitly capture local structure and texture information and makes our approach particularly effective for structure extraction from texture. Our experimental results have shown that the proposed approach leads to better image decompositions as compared to the state-of-the-art methods and preserves prominent edges and shading well. Moreover, we also demonstrate the applicability of our approach on some image editing and manipulation tasks such as image abstraction, texture and detail enhancement, image composition, inverse halftoning and seam carving.