title: Prosody in the Comprehension of Spoken Language: A Literature Review:

abstract: Research on the exploitation of prosodic information in the comprehension of spoken language is reviewed. The research falls into three main areas: the use of prosody in the recognition of spoken words, in which most attention has been paid to the question of whether the prosodic structure of a word plays a role in initial activation of stored lexical representations; the use of prosody in the computation of syntactic structure, in which the resolution of global and local ambiguities has formed the central focus; and the role of prosody in the processing of discourse structure, in which there has been a preponderance of work on the contribution of accentuation and deaccentuation to integration of concepts with an existing discourse model. The review reveals that in each area progress has been made towards new conceptions of prosody's role in processing, and in particular this has previously held deterministic views of the relationship between prosodic of linguistic structure.


title: Canonical Abstract Syntax Trees

abstract: This paper presents GOM, a language for describing abstract syntax trees and generating a Java implementation for those trees. GOM includes features allowing the user to specify and modify the interface of the data structure. These features provide in particular the capability to maintain the internal representation of data in canonical form with respect to a rewrite system. This explicitly guarantees that the client program only manipulates normal forms for this rewrite system, a feature which is only implicitly used in many implementations.


title: A New Perspective on Semantics of Data Provenance

abstract: Data Provenance refers to the "origin", "lineage", and "source" of data. In this work, we examine provenance from a semantics perspective and present the W7 model, an ontological model of data provenance. In the W7 model, provenance is conceptualized as a combination of seven interconnected elements including "what", "when", "where", "how", "who", "which" and "why". Each of these components may be used to track events that affect data during its lifetime. The W7 model is general and extensible enough to capture provenance semantics for data in different domains. Using the example of the Wikipedia, we illustrate how the W7 model can capture domain or application specific provenance.


title: Election Bias: Comparing Polls and Twitter in the 2016 U.S. Election

abstract: While the polls have been the most trusted source for election predictions for decades, in the recent presidential election they were called inaccurate and biased. How inaccurate were the polls in this election and can social media beat the polls as an accurate election predictor? Polls from several news outlets and sentiment analysis on Twitter data were used, in conjunction with the results of the election, to answer this question and outline further research on the best method for predicting the outcome of future elections.


title: Brain tumor detection by using a modified FCM and Level set algorithms

abstract: Segmentation is a vital role in medical image processing, where clustering technique widely used in medical application particularly for brain tumor detection in MRI. The aim of this research is to carry out a new technique for detecting a brain tumor from MRI images in the goal to design a new cooperative approach for the biomedical framework. First, we introduce the notion of FCM which incorporates the spatial information to get a better estimation of the clusters centers then the results obtained are considered as an initialization of the active edge for the Level set. During the application of this method, multiple initializations have been carried out to set the initial parameters. The results found after the implementation are satisfactory, which allows us to say that the use of a combined method of several segmentation algorithms allows to have better results and also a high diagnosis accuracy.


Identification of autism spectrum disorder using deep learning and the ABIDE dataset


title: How to Write and Use the Ontology Requirements Specification Document

abstract: The goal of the ontology requirements specification activity is to state why the ontology is being built, what its intended uses are, who the end-users are, and which requirements the ontology should fulfill. The novelty of this paper lies in the systematization of the ontology requirements specification activity since the paper proposes detailed methodological guidelines for specifying ontology requirements efficiently. These guidelines will help ontology engineers to capture ontology requirements and produce the ontology requirements specification document (ORSD). The ORSD will play a key role during the ontology development process because it facilitates, among other activities, (1) the search and reuse of existing knowledge-aware resources with the aim of re-engineering them into ontologies, (2) the search and reuse of existing ontological resources (ontologies, ontology modules, ontology statements as well as ontology design patterns), and (3) the verification of the ontology along the ontology development. In parallel to the guidelines, we present the ORSD that resulted from the ontology requirements specification activity within the SEEMP project, and how this document facilitated not only the reuse of existing knowledge-aware resources but also the verification of the SEEMP ontologies. Moreover, we present some use cases in which the methodological guidelines proposed here were applied.


title: A New Approach for Resource Scheduling with Deep Reinforcement Learning

abstract: With the rapid development of deep learning, deep reinforcement learning (DRL) began to appear in the field of resource scheduling in recent years. Based on the previous research on DRL in the literature, we introduce online resource scheduling algorithm DeepRM2 and the offline resource scheduling algorithm DeepRM_Off. Compared with the state-of-the-art DRL algorithm DeepRM and heuristic algorithms, our proposed algorithms have faster convergence speed and better scheduling efficiency with regarding to average slowdown time, job completion time and rewards.


title: Developers' code context models for change tasks

abstract: To complete a change task, software developers spend a substantial amount of time navigating code to understand the relevant parts. During this investigation phase, they implicitly build context models of the elements and relations that are relevant to the task. Through an exploratory study with twelve developers completing change tasks in three open source systems, we identified important characteristics of these context models and how they are created. In a second empirical analysis, we further examined our findings on data collected from eighty developers working on a variety of change tasks on open and closed source projects. Our studies uncovered, amongst other results, that code context models are highly connected, structurally and lexically, that developers start tasks using a combination of search and navigation and that code navigation varies substantially across developers. Based on these findings we identify and discuss design requirements to better support developers in the initial creation of code context models. We believe this work represents a substantial step in better understanding developers' code navigation and providing better tool support that will reduce time and effort needed for change tasks.


title: Parallel Local Algorithms for Core, Truss, and Nucleus Decompositions

abstract: Finding the dense regions of a graph and relations among them is a fundamental task in network analysis. Nucleus decomposition is a principled framework of algorithms that generalizes the k-core and k-truss decompositions. It can leverage the higher-order structures to locate the dense subgraphs with hierarchical relations. Computation of the nucleus decomposition is performed in multiple steps, known as the peeling process, and it requires global information about the graph at any time. This prevents the scalable parallelization of the computation. Also, it is not possible to compute approximate and fast results by the peeling process, because it does not produce the densest regions until the algorithm is complete. In a previous work, Lu et al. proposed to iteratively compute the h-indices of vertex degrees to obtain the core numbers and prove that the convergence is obtained after a finite number of iterations. In this work, we generalize the iterative h-index computation for any nucleus decomposition and prove convergence bounds. We present a framework of local algorithms to obtain the exact and approximate nucleus decompositions. Our algorithms are pleasingly parallel and can provide approximations to explore time and quality trade-offs. Our shared-memory implementation verifies the efficiency, scalability, and effectiveness of our algorithms on real-world networks. In particular, using 24 threads, we obtain up to 4.04x and 7.98x speedups for k-truss and (3, 4) nucleus decompositions.


EzPC: Programmable, Efficient, and Scalable Secure Two-Party Computation


title: Privacy-aware and blockchain connected gateways for users to access legacy IoT devices

abstract: To achieve privacy requirements, IoT application providers may need to spend a lot of money to replace existing IoT devices. To address this problem, this study proposes the Blockchain Connected Gateways (BC Gateways) to protect users from providing personal data to IoT devices without user consent. In addition, the gateways store user privacy preferences on IoT devices in the blockchain network. Therefore, this study can utilize the blockchain technology to resolve the disputes of privacy issues. In conclusion, this paper can contribute to improving user privacy and trust in IoT applications with legacy IoT devices.


title: Low power link layer security for IoT: Implementation and performance analysis

abstract: In this paper, we present the implementation and performance evaluation of security functionalities at the link layer of IEEE 802.15.4-compliant IoT devices. Specifically, we implement the required encryption and authentication mechanisms entirely in software and as well exploit the hardware ciphers that are made available by our IoT platform. Moreover, we present quantitative results on the memory footprint, the execution time and the energy consumption of selected implementation modes and discuss some relevant tradeoffs. As expected, we find that hardware-based implementations are not only much faster, leading to latencies shorter than two orders of magnitude compared to software-based security suites, but also provide substantial savings in terms of ROM memory occupation, i.e. up to six times, and energy consumption. Furthermore, the addition of hardware-based security support at the link layer only marginally impacts the network lifetime metric, leading to worst-case reductions of just 2% compared to the case where no security is employed. This is due to the fact that energy consumption is dominated by other factors, including the transmission and reception of data packets and the control traffic that is required to maintain the network structures for routing and data collection. On the other hand, entirely software-based implementations are to be avoided as the network lifetime reduction in this case can be as high as 25%.


title: Semi-supervised vision-language mapping via variational learning

abstract: Understanding the semantic relations between vision and language data has become a research trend in artificial intelligence and robotic systems. The lack of training data is an essential issue for vision-language understanding. We address the problem of image and sentence cross-modal retrieval when paired training samples are not sufficient. Inspired by recent works in variational inference, in this paper, the autoencoding variational Bayes framework is novelly extended to a semi-supervised model for image-sentence mapping task. Our method does not require all training images and sentences to be paired. The proposed model is an end-to-end system, and consists of a two-level variational embedding structure where unpaired data are involved in the first level embedding to give support to intra-modality statistics so that the lower bound of the joint marginal likelihood of paired data embeddings can be better approximated. The proposed retrieval model is evaluated on two popular datasets, i.e. Flickr30K and Flickr8K, producing superior performances compared with related state-of-the-art methods.


Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks


title: Explaining the Gibbs Sampler

abstract: Abstract Computer-intensive algorithms, such as the Gibbs sampler, have become increasingly popular statistical tools, both in applied and theoretical work. The properties of such algorithms, however, may sometimes not be obvious. Here we give a simple explanation of how and why the Gibbs sampler works. We analytically establish its properties in a simple case and provide insight for more complicated cases. There are also a number of examples.


title: Quality-aware aggregation & predictive analytics at the edge

abstract: We investigate the quality of aggregation and predictive analytics in edge computing environments. Edge analytics require pushing processing and inference to the edge of a network of sensing & actuator nodes, which enables huge amount of contextual data to be processed in real time that would be prohibitively complex and costly to transfer on centralized locations. We propose a quality-aware, time-optimized edge analytics model that supports communication efficient predictive modeling within the edge network. Our idea rests on the capability of edge nodes to intelligently decide when and which data to deliver and process in light of minimizing the communication overhead and maximizing the quality of analytics results. We provide mathematical modeling, performance and comparative assessment over real datasets showing its benefits in edge computing environments.


title: Alarm prediction in large-scale sensor networks — A case study in railroad

abstract: Sensor network is broadly used across industries to monitor equipment conditions. Huge volume of information collected from a large set of sensors poses great challenges to make inferences for prediction of significant events. We collaborate with a US Class I railway company and apply advanced analytics techniques to be able to predict alarms associated with catastrophic equipment failures several days ahead of time. We use the case study in railroad to demonstrate the techniques to address the big data concerns. In addition, the alarm-prediction rule development needs to satisfy the critical constraints to meet the high standards of prediction accuracy combined with human interpretability in railroad industry. We build customized SVM algorithm to meet the requirements. By adding a unique and remarkable feature of human interpretability to the rules we develop, our solution is able to facilitate the decision making process of operators and lead to efficient operational decision support.


title: Variational Lossy Autoencoder

abstract: Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. ::: For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. ::: In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. ::: Our proposed VAE model allows us to have control over what the global latent code can learn and , by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the code only ``autoencodes'' data in a lossy fashion. ::: In addition, by leveraging autoregressive models as both prior distribution $p(z)$ and decoding distribution $p(x|z)$, we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 as well as competitive results on CIFAR10.


title: Migrating to Cloud-Native Architectures Using Microservices: An Experience Report

abstract: Migration to the cloud has been a popular topic in industry and academia in recent years. Despite many benefits that the cloud presents, such as high availability and scalability, most of the on-premise application architectures are not ready to fully exploit the benefits of this environment, and adapting them to this environment is a non-trivial task. Microservices have appeared recently as novel architectural styles that are native to the cloud. These cloud-native architectures can facilitate migrating on-premise architectures to fully benefit from the cloud environments because non-functional attributes, like scalability, are inherent in this style. The existing approaches on cloud migration does not mostly consider cloud-native architectures as their first-class citizens. As a result, the final product may not meet its primary drivers for migration. In this paper, we intend to report our experience and lessons learned in an ongoing project on migrating a monolithic on-premise software architecture to microservices. We concluded that microservices is not a one-fit-all solution as it introduces new complexities to the system, and many factors, such as distribution complexities, should be considered before adopting this style. However, if adopted in a context that needs high flexibility in terms of scalability and availability, it can deliver its promised benefits.


title: A review of power converter topologies for wind generators

abstract: Wind energy conversion systems have become a focal point in the research of renewable energy sources. This is in no small part due to the rapid advances in the size of wind generators as well as the development of power electronics and their applicability in wind energy extraction. This paper provides a comprehensive review of past and present converter topologies applicable to permanent magnet generators, induction generators, synchronous generators and doubly fed induction generators. The many different generator–converter combinations are compared on the basis of topology, cost, efficiency, power consumption and control complexity. The features of each generator–converter configuration are considered in the context of wind turbine systems.


title: Determination of simple thresholds for accelerometry-based parameters for fall detection

abstract: The increasing population of elderly people is mainly living in a home-dwelling environment and needs applications to support their independency and safety. Falls are one of the major health risks that affect the quality of life among older adults. Body attached accelerometers have been used to detect falls. The placement of the accelerometric sensor as well as the fall detection algorithms are still under investigation. The aim of the present pilot study was to determine acceleration thresholds for fall detection, using triaxial accelerometric measurements at the waist, wrist, and head. Intentional falls (forward, backward, and lateral) and activities of daily living (ADL) were performed by two voluntary subjects. The results showed that measurements from the waist and head have potential to distinguish between falls and ADL. Especially, when the simple threshold-based detection was combined with posture detection after the fall, the sensitivity and specificity of fall detection were up to 100 %. On the contrary, the wrist did not appear to be an optimal site for fall detection.


title: Forensic Acquisition of Cloud Drives

abstract: Cloud computing and cloud storage services, in particular, pose a new challenge to digital forensic investigations. Currently, evidence acquisition for such services still follows the traditional method of collecting artifacts on a client device. This approach requires labor-intensive reverse engineering efforts, and ultimately results in an acquisition that is inherently incomplete. Specifically, it makes the incorrect assumption that all storage content for an account is fully replicated on the client; further, there are no means to acquire historical data in the form of document revisions, nor is there a way to acquire cloud-native artifacts, such as Google Docs. ::: In this work, we introduce the concept of API-based evidence acquisition for cloud services, which addresses these concerns by utilizing the officially supported API of the service. To demonstrate the utility of this approach, we present a proof-of-concept acquisition tool, kumodd, which can acquire evidence from four major cloud drive providers: Google Drive, Microsoft OneDrive, Dropbox, and Box. The implementation provides both command-line and web user interfaces, and can be readily incorporated into established forensic processes.


title: Twin Support Vector Machines for Pattern Classification

abstract: We propose twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data


title: STNR: A suffix tree based noise resilient algorithm for periodicity detection in time series databases

abstract: Periodicity detection has been used extensively in predicting the behavior and trends of time series databases. In this paper, we present a noise resilient algorithm for periodicity detection using suffix trees as an underlying data structure. The algorithm not only calculates symbol and segment periodicity, but also detects the partial (or sequence) periodicity in time series. Most of the existing algorithms fail to perform efficiently in presence of noise; although noise is an inevitable constituent of real world data. The conducted experiments demonstrate that our algorithm performs more efficiently compared to other algorithms in presence of replacement, insertion, deletion or a mixture of any of these types of noise.


title: Tc-99m MDP uptake in liver metastases from adenocarcinoma of the duodenum.

abstract: A 45-year-old man with histopathologically confirmed adenocarcinoma of the duodenum underwent Tc-99m methylene diphosphonate bone scintigraphy to evaluate possible bone metastases. Both abdominal ultrasonography and CT showed hepatomegaly and massive metastases in the liver with multiple areas of tumor necrosis. Liver biopsy established the diagnosis of liver metastases from adenocarcinoma. Bone scintigraphy revealed multiple areas of increased uptake in the liver, consistent with multiple hepatic metastases.


title: RAISR: Rapid and Accurate Image Super Resolution

abstract: Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the Single Image Super-Resolution (SISR) problem. The idea is that with sufficient training data (corresponding pairs of low and high resolution images) we can learn set of filters (i.e. a mapping) that when applied to given image that is not in the training set, will produce a higher resolution version of it, where the learning is preferably low complexity. In our proposed approach, the run-time is more than one to two orders of magnitude faster than the best competing methods currently available, while producing results comparable or better than state-of-the-art. ::: A closely related topic is image sharpening and contrast enhancement, i.e., improving the visual quality of a blurry image by amplifying the underlying details (a wide range of frequencies). Our approach additionally includes an extremely efficient way to produce an image that is significantly sharper than the input blurry one, without introducing artifacts such as halos and noise amplification. We illustrate how this effective sharpening algorithm, in addition to being of independent interest, can be used as a pre-processing step to induce the learning of more effective upscaling filters with built-in sharpening and contrast enhancement effect.


title: Unified contact force control approach for cable-driven parallel robots using an impedance/admittance control strategy

abstract: In this paper a unified contact force control approach for cable-driven parallel robots (CDPR) is proposed. The contact force controller is governed by two control loops: 1) An inner impedance control loop enforcing a dynamical relationship between the internal forces and the end-effector velocities to maintain a desired tension level in the cable system. 2) An outer admittance control loop considering the contact wrench by altering the desired end-effector trajectory. For the parametrization of the admittance controller the desired stiffness of the virtual manipulator system is defined by the mechanical stiffness of the end-effector. Further, the influences of changing the stiffness of the virtual system are discussed. To reconstruct the contact wrench a disturbance observer based on the generalized momentum approach is incorporated into both control loops. Experiments with a 6-DOF CDPR with industrial electric synchronous machines are presented validating the proposed contact force control approach.


title: Function after motor vehicle accidents : A prospective study of mild head injury and posttraumatic stress

abstract: Relationships among mild traumatic brain injury (MTBI), posttraumatic stress (PTS), and function were examined in 99 motor vehicle accident (MVA) admissions: 64 in an MTBI group and 35 in a no-MTBI comparison group. Assessments occurred within the first month and at 6 to 9 months. At follow-up, the sample was moderately disabled on the Sickness Impact Profile (SIP), 71% satisfied on the Reintegration of Normal Living Index (RNL), and 42% had returned to work. Only the SIP Psychosocial score was significantly different for MTBI groups; 24% of the sample showed definite symptoms of PTS. This group was significantly more disabled on the SIP, less satisfied on the RNL, and less likely to return to work. The proportion of variance in outcome explained in each model ranged from 32% (Physical SIP) to 44% (RNL). Results suggest the need for clinicians to be more aware of the strong influence of PTS on functional outcomes. Language: en


title: Exploring the relationship between knowledge management practices and innovation performance

abstract: The process of innovation depends heavily on knowledge, and the management of knowledge and human capital should be an essential element of running any type of business. Recent research indicates that organisations are not consistent in their approach to knowledge management (KM), with KM approaches being driven predominantly within an information technology (IT) or humanist framework, with little if any overlap. This paper explores the relationship between KM approaches and innovation performance through a preliminary study focusing on the manufacturing industry. The most significant implication that has emerged from the study is that managers in manufacturing firms should place more emphasis on human resource management (HRM) practices when developing innovation strategies for product and process innovations. The study shows that KM contributes to innovation performance when a simultaneous approach of “soft HRM practices” and “hard IT practices” are implemented.


title: New Optimal and Suboptimal Resource Allocation Techniques for Downlink Non-orthogonal Multiple Access

abstract: This paper investigates several new strategies for the allocation of radio resources (bandwidth and transmission power) using a non-orthogonal multiple access (NOMA) scheme with successive interference cancellation (SIC) in a cellular downlink system. In non-orthogonal access with SIC, the same subband is allocated to multiple users, which requires elaborate multiuser scheduling and subband assignment techniques, compared to orthogonal multiplexing. While taking into account various design issues, we propose and compare several optimum and suboptimum power allocation schemes. These are jointly implemented with multiple user scheduling strategies. Besides, a minimization of the total amount of used bandwidth is targeted. Also, to increase the total achieved system throughput, a hybrid orthogonal-non orthogonal scheme is introduced. This hybrid scheme enables a dynamic switching to orthogonal signaling whenever the non-orthogonal cohabitation in the power domain does not improve the achieved data rate per subband. Extensive simulation results show that the proposed strategies for resource allocation can improve both the spectral efficiency and the cell-edge user throughput, especially when compared to previous schemes employing either orthogonal signaling or NOMA with static inter-subband power allocation. They also prove to be robust in the context of crowded areas.


title: A Review of Mutual Coupling in MIMO Systems

abstract: This paper provides a systematic review of the mutual coupling in multiple-input multiple-output (MIMO) systems, including the effects on performances of MIMO systems and various decoupling techniques. The mutual coupling changes the antenna characteristics in an array, and therefore, degrades the system performance of the MIMO system and causes the spectral regrowth. Although the system performance can be partially improved by calibrating out the mutual coupling in the digital domain, it is more effective to use decoupling techniques (from the antenna point) to overcome the mutual coupling effects. Some popular decoupling techniques for MIMO systems (especially for massive MIMO base station antennas) are also presented.


title: Joint shape segmentation with linear programming

abstract: We present an approach to segmenting shapes in a heterogenous shape database. Our approach segments the shapes jointly, utilizing features from multiple shapes to improve the segmentation of each. The approach is entirely unsupervised and is based on an integer quadratic programming formulation of the joint segmentation problem. The program optimizes over possible segmentations of individual shapes as well as over possible correspondences between segments from multiple shapes. The integer quadratic program is solved via a linear programming relaxation, using a block coordinate descent procedure that makes the optimization feasible for large databases. We evaluate the presented approach on the Princeton segmentation benchmark and show that joint shape segmentation significantly outperforms single-shape segmentation techniques.


title: A social^cognitive approach to motivation and personality.

abstract: Past work has documented and described major patterns of adaptive and maladaptive behavior: the mastery-oriented and the helpless patterns. In this article, we present a research-based model that accounts for these patterns in terms of underlying psychological processes. The model specifies how individuals' implicit theories orient them toward particular goals and how these goals set up the different patterns. Indeed, we show how each feature (cognitive, affective, and behavioral) of the adaptive and maladaptive patterns can be seen to follow directly from different goals. We then examine the generality of the model and use it to illuminate phenomena in a wide variety of domains. Finally, we place the model in its broadest context and examine its implications for our understanding of motivational and personality processes. The task for investigators of motivation and personality is to identify major patterns of behavior and link them to underlying psychological processes. In this article we (a) describe a research-based model that accounts for major patterns of behavior, (b) examine the generality of this model—its utility for understanding domains beyond the ones in which it was originally developed, and (c) explore the broader implications of the model for motivational and personality processes.


title: Decentralized Authentication for Secure Cloud Data Sharing

abstract: Cloud data sharing is a prevalent topic these days due to its immense benefits. But regardless of this, industry is facing a huge setback due to deficiencies in present security frameworks. Achieving security becomes favorable if we put together a strong authentication mechanism. Authentication techniques as much random and complex they get are still breakable, so a truly random and strong technique is required. For this purpose, a new decentralized approach consisting of multiple authentication factors (username and password, biometrics and tokens) is being proposed for authentication which gives strong foundation of security and is difficult to break. The analysis showed significantly better comparative results.


title: Estimation of eyeglassless facial images using principal component analysis

abstract: For facial image analysis, facial parts such as eyes, nose, and mouth are generally focused and used. When these facial parts are hindered by additional objects (eyeglasses, beard, injury, etc.), the feature extraction from facial image will not be accurate. In this paper, we focus on the eyeglass faces because they account for 40% of population in Japan, and present a method of the removal of eyeglass frame in facial images with eyeglasses using principal component analysts. Two approaches are discussed for removal of eyeglasses in facial image. The first method calculates basis vectors from many eyeglassless facial images and one eyeglass facial image, and reconstructs the facial image with the basis vectors which include no feature of eyeglass frame. The second method calculates basis vectors from a set of eyeglassless facial images, and reconstructs the facial image using the values of inner product of the basis vectors and an eyeglass facial image. The former obtains the images which restrain. The features of eyeglass frame while loses the facial individuality a little. The latter obtains a natural eyeglassless facial image.


Therac-25 and the security of the computer controlled equipment Ethics of Science and Technology (WM0314IN)


Sleep quality and psychosocial correlates among elderly attendees of an urban primary care centre in Malaysia


title: Packaging a $W$ -Band Integrated Module With an Optimized Flip-Chip Interconnect on an Organic Substrate

abstract: This paper, for the first time, presents successful integration of a W-band antenna with an organically flip-chip packaged silicon-germanium (SiGe) low-noise amplifier (LNA). The successful integration requires an optimized flip-chip interconnect. The interconnect performance was optimized by modeling and characterizing the flip-chip transition on a low-loss liquid crystal polymer organic substrate. When the loss of coplanar waveguide (CPW) lines is included, an insertion loss of 0.6 dB per flip-chip-interconnect is measured. If the loss of CPW lines is de-embedded, 0.25 dB of insertion loss is observed. This kind of low-loss flip-chip interconnect is essential for good performance of W-band modules. The module, which we present in this paper, consists of an end-fire Yagi-Uda antenna integrated with an SiGe BiCMOS LNA. The module is 3 mm × 1.9 mm and consumes only 19.2 mW of dc power. We present passive and active E- and H-plane radiation pattern measurements at 87, 90, and 94 GHz. Passive and active antennas both showed a 10-dB bandwidth of 10 GHz. The peak gain of passive and active antennas was 5.2 dBi at 90 GHz and 21.2 dBi at 93 GHz, respectively. The measurements match well with the simulated results.


title: Dynamic user-level affect analysis in social media: Modeling violence in the Dark Web

abstract: Affect represents a person's emotions toward objects, issues or other persons. Recent years have witnessed a surge in studies of users' affect in social media, as marketing literature has shown that users' affect influences decision making. The current literature in this area, however, has largely focused on the message level, using text-based features and various classification approaches. Such analyses not only overlook valuable information about the user who posts the messages, but also fail to consider that users' affect may change over time. To overcome these limitations, we propose a new research design for social media affect analysis by specifically incorporating users' characteristics and the time dimension. We illustrate our research design by applying it to a major Dark Web forum of international Jihadists. Empirical results show that our research design allows us to draw on theories from other disciplines, such as social psychology, to provide useful insights on the dynamic change of users' affect in social media.


title: First record of Colletotrichum sansevieriae on Sansevieria sp. (mother-in-law’s tongue) in Australia

abstract: Colletotrichum sansevieriae was detected for the first time on nursery-grown plants of mother-in-law’s tongue (Sansevieria sp.) in Australia.


title: Maximizing rumor containment in social networks with constrained time

abstract: The spread of rumor or misinformation in social networks may cause bad effects among the public. Thus, it is necessary to find effective strategies to control the spread of rumor. Specifically, in our paper, we consider such a setting: initially, a subset of nodes is chosen as the set of protectors, and the influence of protector diffuses competitively with the diffusion of rumor. However, in real world, we generally have limited budget (limited number of protectors) and time to fight with rumor. Therefore, we study the problem of maximizing rumor containment within a fixed number of initial protectors and a given time deadline. Generalizing two seminal models in the field—the Independent Cascade (IC) model and the Linear Threshold (LT) model—we propose two new models of competitive influence diffusion in social networks with the following three factors: a time deadline for information diffusion, random time delay of information exchange and personal interests regarding the acceptance of information. Under these two models, we show that the optimization problems are NP-hard. Furthermore, we prove that the objective functions are submodular. As a result, the greedy algorithm is used as constant-factor approximation algorithms with performance guarantee \(1-\frac{1}{e}\) for the two optimization problems.


title: Design of an Unconventional ASV for Underwater Vehicles Recovery: Simulation of the motions for operations in rough seas.

abstract: Paper introduces main characteristics and particulars of an innovative design for an Unmanned Surface Vehicle to autonomously launch and recover AUVs (Autonomous Underwater Vehicles) in open sea. The USV has an unconventional SWATH hull shape and in its smaller size version, it has dedicated hangar at midship that can host one medium size AUV, completely recovered onboard. The focus of this paper is concentrated on the prediction of the steady and unsteady hydrodynamic characteristics in terms of hull resistance and motion in waves. The seakeeping prediction is made by a fully viscous 3D Unsteady NavierStokes solver. The predicted pitch and heave response of the USV-SWATH in relatively high regular waves, in the nonlinear regime, are compared with those of an equivalent catamaran vessel and dramatic reduction in vertical motions and accelerations are found. On this good basis the paper concludes presenting a first hypothesis of the L&R system based on self-tensioning winches with hoists and belt assemblies.


title: Europarl: A Parallel Corpus for Statistical Machine Translation

abstract: We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament, which are published on the web1. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine translation (SMT). We trained SMT systems for 110 language pairs, which reveal interesting clues into the challenges ahead.


title: Face Recognition in Forensic Science

abstract: In this chapter, we will first explain the current means of comparing faces used by forensic science laboratories. It is a nonautomated process performed by forensic examiners and has been referred to as facial “photographic comparison” or forensic facial identification. Next, we will outline the innovative ways in which facial recognition systems are being used by the forensic community. Lastly, we will discuss the growing future of facial biometrics in the legal system and the increasing (not decreasing) need for human examiners to perform facial identification in combination with the automated facial recognition systems.


title: Graph entropy and quantum sorting problems

abstract: Let P = (X, P ) be a partial order on a set of n elements X = x 1 , x 2 ,..., x n . Define the quantum sorting problem QSORT P as: given n distinct numbers x 1 , x 2 ,..., x n consistent with P, sort them by a quantum decision tree using comparisons of the form "x i : x j ". Let Q e (P) be the minimum number of queries used by any quantum decision tree for solving QSORT P with error less than e (where 0 0 is the empty partial order, Q e (P 0 ) ≥ Ω (n log n), i. e., the classical information lower bound holds for quantum decision trees when the input permutations are unrestricted.In this paper we show that the classical information lower bound holds, up to an additive linear term, for quantum decision trees for any partial order P. Precisely, we prove Q e (P) ≥ c log 2 e(P)-c'n where c,c' > 0 are constants and e(P) is the number of linear orderings consistent with P. Our proof builds on an interesting connection between sorting and Korner's graph entropy that was first noted and developed by Kahn and Kim (JCSS 51(1995), 390--399).


title: Editorial: special issue on web content mining

abstract: With the phenomenal growth of the Web, there is an everincreasing volume of data and information published in numerous Web pages. The research in Web mining aims to develop new techniques to effectively extract and mine useful knowledge or information from these Web pages [8]. Due to the heterogeneity and lack of structure of Web data, automated discovery of targeted or unexpected knowledge/information is a challenging task. It calls for novel methods that draw from a wide range of fields spanning data mining, machine learning, natural language processing, statistics, databases, and information retrieval. In the past few years, there was a rapid expansion of activities in the Web mining field, which consists of Web usage mining, Web structure mining, and Web content mining. Web usage mining refers to the discovery of user access patterns from Web usage logs. Web structure mining tries to discover useful knowledge from the structure of hyperlinks. Web content mining aims to extract/mine useful information or knowledge from Web page contents. For this special issue, we focus on Web content mining.


title: The Challenges of Effectively Anonymizing Network Data

abstract: The uncertainties that currently exist about the efficacy of network data anonymization, from both technical and policy perspectives, leave the research community in a vulnerable position. Even as the field marches forward, it does so with little understanding of the implications of publishing anonymized network data on the privacy of the networks being monitored and the utility to researchers. Without that understanding, data publishers are left to wonder what fields must be anonymized to avoid legal fallout, while researchers question the confidence of results gained from the data. However, the extensive work done on micro- data anonymity provides the network research community with several useful insights about how to effectively apply anonymization to published data. At the same time, prior wisdom cannot be applied directly without first overcoming several challenges, including the development of appropriate privacy and utility definitions for the more complex case of network data. Addressing these challenges is essential, in our view, to ensure the continued, yet responsible, availability of network trace data to support security research.


Understanding disentangling in β-VAE.


title: High-Accuracy and Fast-Speed MPPT Methods for PV String Under Partially Shaded Conditions

abstract: The photovoltaic (PV) string under partially shaded conditions exhibits complex output characteristics, i.e., the current–voltage $(I\mbox{--}V)$ curve presents multiple current stairs, whereas the power–voltage $(P\mbox{--}V)$ curve shows multiple power peaks. Thus, the conventional maximum power point tracking (MPPT) method is not acceptable either on tracking accuracy or on tracking speed. In this paper, two global MPPT methods, namely, the search–skip–judge global MPPT (SSJ-GMPPT) and rapid global MPPT (R-GMPPT) methods are proposed in terms of reducing the searching voltage range based on comprehensive study of $I\mbox{--}V$ and $P\mbox{--}V$ characteristics of PV string. The SSJ-GMPPT method can track the real maximum power point under any shading conditions and achieve high accuracy and fast tracking speed without additional circuits and sensors. The R-GMPPT method aims to enhance the tracking speed of long string with vast PV modules and reduces more than 90% of the tracking time that is consumed by the conventional global searching method. The improved performance of the two proposed methods has been validated by experimental results on a PV string. The comparison with other methods highlights the two proposed methods more powerful.


title: Concurrent Activity Recognition with Multimodal CNN-LSTM Structure

abstract: We introduce a system that recognizes concurrent activities from real-world data captured by multiple sensors of different types. The recognition is achieved in two steps. First, we extract spatial and temporal features from the multimodal data. We feed each datatype into a convolutional neural network that extracts spatial features, followed by a long-short term memory network that extracts temporal information in the sensory data. The extracted features are then fused for decision making in the second step. Second, we achieve concurrent activity recognition with a single classifier that encodes a binary output vector in which elements indicate whether the corresponding activity types are currently in progress. We tested our system with three datasets from different domains recorded using different sensors and achieved performance comparable to existing systems designed specifically for those domains. Our system is the first to address the concurrent activity recognition with multisensory data using a single model, which is scalable, simple to train and easy to deploy.


title: Active learning for semantic segmentation with expected change

abstract: We address the problem of semantic segmentation: classifying each pixel in an image according to the semantic class it belongs to (e.g. dog, road, car). Most existing methods train from fully supervised images, where each pixel is annotated by a class label. To reduce the annotation effort, recently a few weakly supervised approaches emerged. These require only image labels indicating which classes are present. Although their performance reaches a satisfactory level, there is still a substantial gap between the accuracy of fully and weakly supervised methods. We address this gap with a novel active learning method specifically suited for this setting. We model the problem as a pairwise CRF and cast active learning as finding its most informative nodes. These nodes induce the largest expected change in the overall CRF state, after revealing their true label. Our criterion is equivalent to maximizing an upper-bound on accuracy gain. Experiments on two data-sets show that our method achieves 97% percent of the accuracy of the corresponding fully supervised model, while querying less than 17% of the (super-)pixel labels.


title: Cross-Examination for Angle-Closure Glaucoma Feature Detection

abstract: Effective feature selection plays a vital role in anterior segment imaging for determining the mechanism involved in angle-closure glaucoma (ACG) diagnosis. This research focuses on the use of redundant features for complex disease diagnosis such as ACG using anterior segment optical coherence tomography images. Both supervised [minimum redundancy maximum relevance (MRMR)] and unsupervised [Laplacian score (L-score)] feature selection algorithms have been cross-examined with different ACG mechanisms. An AdaBoost machine learning classifier is then used for classifying the five various classes of ACG mechanism such as iris roll, lens, pupil block, plateau iris, and no mechanism using both feature selection methods. The overall accuracy has shown that the usefulness of redundant features by L-score method in improved ACG diagnosis compared to minimum redundant features by MRMR method.


title: Streaming small-footprint keyword spotting using sequence-to-sequence models

abstract: We develop streaming keyword spotting systems using a recurrent neural network transducer (RNN-T) model: an all-neural, end-to-end trained, sequence-to-sequence model which jointly learns acoustic and language model components. Our models are trained to predict either phonemes or graphemes as subword units, thus allowing us to detect arbitrary keyword phrases, without any out-of-vocabulary words. In order to adapt the models to the requirements of keyword spotting, we propose a novel technique which biases the RNN-T system towards a specific keyword of interest. ::: Our systems are compared against a strong sequence-trained, connectionist temporal classification (CTC) based "keyword-filler" baseline, which is augmented with a separate phoneme language model. Overall, our RNN-T system with the proposed biasing technique significantly improves performance over the baseline system.


title: Characteristics and Principles of Scaled Agile

abstract: The Agile Manifesto and Agile Principles are typically referred to as the definitions of "agile" and "agility". There is research on agile values and agile practises, but how should “Scaled Agility” be defined, and what might be the characteristics and principles of Scaled Agile? This paper examines the characteristics of scaled agile, and the principles that are used to build up such agility. It also gives suggestions as principles upon which Scaled Agility can be built.


title: Development of an Improved P&O Algorithm Assisted Through a Colony of Foraging Ants for MPPT in PV System

abstract: The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed maximum power point (MPP) tracking (MPPT) schemes for photovoltaic (PV) power-generation systems. However, under partially shaded conditions (PSCs), P&O method miserably fails to recognize global MPP (GMPP) and gets trapped in one of the local MPPs (LMPPs). This paper proposes ant-colony-based search in the initial stages of tracking followed by P&O method. In such a hybrid approach, the global search ability of ant-colony optimization (ACO) and local search capability of P&O method are integrated to yield faster and efficient convergence. A theoretical analysis of the static and dynamic convergence behavior of the proposed algorithm is presented together with computed and measured results.


title: Real-time multiple sound source localization using a circular microphone array based on single-source confidence measures

abstract: We propose a novel real-time adaptative localization approach for multiple sources using a circular array, in order to suppress the localization ambiguities faced with linear arrays, and assuming a weak sound source sparsity which is derived from blind source separation methods. Our proposed method performs very well both in simulations and in real conditions at 50% real-time.


title: Hierarchical relational models for document networks

abstract: We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed vocabulary. For each pair of documents, the RTM models their link as a binary random variable that is conditioned on their contents. The model can be used to summarize a network of documents, predict links between them, and predict words within them. We derive efficient inference and estimation algorithms based on variational methods that take advantage of sparsity and scale with the number of links. We evaluate the predictive performance of the RTM for large networks of scientific abstracts, web documents, and geographically tagged news.


title: FPGA design of single-phase matrix converter operating as a frequency changer

abstract: This paper is concerned on field programmable gate array (FPGA) design for control implementations of the single-phase matrix converter (SPMC) operating as a frequency changer. Sinusoidal pulse width modulation (SPWM) technique is used to synthesize the output voltage with increased frequency. The power circuit uses insulated gate bipolar transistor (IGBT) as switching device in implementation. Safe-commutation strategy was incorporated to minimize switching transients. Selected simulation and experimental results are presented to verify operation with.


title: A Robust Lane Detection Method Based on Vanishing Point Estimation Using the Relevance of Line Segments

abstract: In this paper, a robust lane detection method based on vanishing point estimation is proposed. Estimating a vanishing point can be helpful in detecting lanes, because parallel lines converge on the vanishing point in a projected 2-D image. However, it is not easy to estimate the vanishing point correctly in an image with a complex background. Thus, a robust vanishing point estimation method is proposed that uses a probabilistic voting procedure based on intersection points of line segments extracted from an input image. The proposed voting function is defined with line segment strength that represents relevance of the extracted line segments. Next, candidate line segments for lanes are selected by considering geometric constraints. Finally, the host lane is detected by using the proposed score function, which is designed to remove outliers in the candidate line segments. Also, the detected host lane is refined by using inter-frame similarity that considers location consistency of the detected host lane and the estimated vanishing point in consecutive frames. Furthermore, in order to reduce computational costs in the vanishing point estimation process, a method using a lookup table is proposed. Experimental results show that the proposed method efficiently estimates the vanishing point and detects lanes in various environments.


title: Design and fabrication of closed loop Two-Phase Interleaved Boost Converter with Type-III controller

abstract: This paper presents design, fabrication and testing of a Two-Phase Interleaved Boost Converter (IBC) with Type-III controller by utilizing voltage mode control. The small-signal modeling of IBC is derived in continuous conduction mode (CCM) of operation. A right half plane (RHP) zero is appeared in the control to output plant transfer function of the IBC. Due to non-minimum phase problem, the Type-III controller is designed by using ‘k’ factor approach and is implemented for closed loop operation of IBC for achieving better dynamic performance and stability. The closed loop IBC with designed Type-III controller is fabricated and tested in the laboratory. Simulation and experimental results have been produced to show the efficacy of the proposed converter control system.


title: Using Grounded Theory as a Method for Rigorously Reviewing Literature

abstract: This paper offers guidance to conducting a rigorous literature review. We present this in the form of a five-stage process in which we use Grounded Theory as a method. We first probe the guidelines explicated by Webster and Watson, and then we show the added value of Grounded Theory for rigorously analyzing a carefully chosen set of studies; it assures solidly legitimized, in-depth analyses of empirical facts and related insights. This includes, the emergence of new themes, issues and opportunities; interrelationships and dependencies in or beyond a particular area; as well as inconsistencies. If carried out meticulously, reviewing a well-carved out piece of literature by following this guide is likely to lead to more integrated and fruitful theory emergence, something that would enrich many fields in the social sciences.


title: Interactive Perception: Leveraging Action in Perception and Perception in Action

abstract: Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.


ModelarDB: Modular Model-Based Time Series Management with Spark and Cassandra


title: $L_0$ -Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond

abstract: We propose a simple yet effective $L_0$ -regularized prior based on intensity and gradient for text image deblurring. The proposed image prior is based on distinctive properties of text images, with which we develop an efficient optimization algorithm to generate reliable intermediate results for kernel estimation. The proposed algorithm does not require any heuristic edge selection methods, which are critical to the state-of-the-art edge-based deblurring methods. We discuss the relationship with other edge-based deblurring methods and present how to select salient edges more principally. For the final latent image restoration step, we present an effective method to remove artifacts for better deblurred results. We show the proposed algorithm can be extended to deblur natural images with complex scenes and low illumination, as well as non-uniform deblurring. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art image deblurring methods.


title: A 9-Bit 150-MS/s Subrange ADC Based on SAR Architecture in 90-nm CMOS

abstract: This paper presents a 9-bit subrange analog-to-digital converter (ADC) consisting of a 3.5-bit flash coarse ADC, a 6-bit successive-approximation-register (SAR) fine ADC, and a differential segmented capacitive digital-to-analog converter (DAC). The flash ADC controls the thermometer coarse capacitors of the DAC and the SAR ADC controls the binary fine ones. Both theoretical analysis and behavioral simulations show that the differential non-linearity (DNL) of a SAR ADC with a segmented DAC is better than that of a binary ADC. The merged switching of the coarse capacitors significantly enhances overall operation speed. At 150 MS/s, the ADC consumes 1.53 mW from a 1.2-V supply. The effective number of bits (ENOB) is 8.69 bits and the effective resolution bandwidth (ERBW) is 100 MHz. With a 1.3-V supply voltage, the sampling rate is 200 MS/s with 2.2-mW power consumption. The ENOB is 8.66 bits and the ERBW is 100 MHz. The FOMs at 1.3 V and 200 MS/s, 1.2 V and 150 MS/s and 1 V and 100 MS/s are 27.2, 24.7, and 17.7 fJ/conversion-step, respectively.


title: A prospective, open-label, multicenter, observational, postmarket study of the use of a 15 mg/mL hyaluronic acid dermal filler in the lips

abstract: Summary ::: ::: Hyaluronic acid (HA)-based injectable fillers three-dimensionally restore the natural contours of the lips and perioral area, thereby reducing some signs of aging lips. To evaluate the short-term aesthetic impact of treatment with the HA dermal filler Juvederm® VOLBELLA® with Lidocaine, formulated utilizing VYCROSS™ technology, for enhancement or correction of asymmetry of the lips, evaluated using a patient-centric approach. Sixty-two subjects were enrolled in this study, conducted at two sites in Germany. Primary endpoints were satisfaction with improvement, look and feel of the lips, assessed by subject and physician at first visit and 4 weeks post-treatment. Immediately after injection at first visit, 83.6% of subjects were Extremely Satisfied, Very Satisfied or Satisfied with improvement in the lips, which increased to 94.1% and 93.0% of subjects with/without top-up treatment at follow-up, respectively. After injection at first visit, 61.7% of subjects rated the look and feel of their lips as Extremely Natural or Very Natural, which increased to 75.0% and 93.0% of subjects with/without top-up treatment, respectively. The HA dermal filler was associated with minimal discomfort, bruising or swelling of the lips; almost two-thirds of subjects (62%) returned to social engagements on the same day. The high degree of subject satisfaction with aesthetic improvement in the lips, as well as the natural look and feel, indicates that this HA dermal filler represents an effective treatment option for patients requiring lip enhancement.


title: A dual band antenna design for future millimeter wave wireless communication at 24.25 GHz and 38 GHz

abstract: This paper proposes a dual band antenna for future millimeter wave wireless communication. The performance of this dual band antenna is analyzed in term of reflection coefficient when some of the length of the patch antenna was adjustable, overall gain and total efficiency for both frequencies respectively. The size of this presented patch antenna is 4.9 × 7.6 mm2. The dual band antenna was fabricated on a RTRogers5880 with a dielectric constant of £=2.2 and thickness of the substrate is 0.127 mm. The simulated result obtained the reflection coefficient as a requirement of the antenna which is not less than −10 dB for 24.25 GHz and 38 GHz that capable to cover 5G applications. The proposed antenna has achieved a maximum gain up to 5.5 dBi and 4.5 dBi at desired frequencies respectively. All design and simulation are carried out using CST Microwave Studio software. The proposed antenna design could be suitable to be applied as a device to the 5G wireless system.


title: Could Network Information Facilitate Address Clustering in Bitcoin?

abstract: Address clustering tries to break the privacy of bitcoin users by linking all addresses created by an individual user, based on information available from the blockchain. As an alternative information source, observations of the underlying peer-to-peer network have also been used to attack the privacy of users. In this paper, we assess whether combining blockchain and network information may facilitate the clustering process. For this purpose, we apply all applicable clustering heuristics that are known to us to current blockchain information and associate the resulting clusters with IP address information extracted from observing the message flooding process of the bitcoin network. The results indicate that only a small share of clusters (less than 8%) were conspicuously associated with a single IP address. Also, only a small number of IP addresses showed a conspicuous association with a single cluster.


title: The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization

abstract: While vector quantization (VQ) has been applied widely to generate features for visual recognition problems, much recent work has focused on more powerful methods. In particular, sparse coding has emerged as a strong alternative to traditional VQ approaches and has been shown to achieve consistently higher performance on benchmark datasets. Both approaches can be split into a training phase, where the system learns a dictionary of basis functions, and an encoding phase, where the dictionary is used to extract features from new inputs. In this work, we investigate the reasons for the success of sparse coding over VQ by decoupling these phases, allowing us to separate out the contributions of training and encoding in a controlled way. Through extensive experiments on CIFAR, NORB and Caltech 101 datasets, we compare several training and encoding schemes, including sparse coding and a form of VQ with a soft threshold activation function. Our results show not only that we can use fast VQ algorithms for training, but that we can just as well use randomly chosen exemplars from the training set. Rather than spend resources on training, we find it is more important to choose a good encoder—which can often be a simple feed forward non-linearity. Our results include state-of-the-art performance on both CIFAR and NORB.


title: GRAFT: an approximate graphlet counting algorithm for large graph analysis

abstract: Graphlet frequency distribution (GFD) is an analysis tool for understanding the variance of local structure in a graph. Many recent works use GFD for comparing, and characterizing real-life networks. However, the main bottleneck for graph analysis using GFD is the excessive computation cost for obtaining the frequency of each of the graphlets in a large network. To overcome this, we propose a simple, yet powerful algorithm, called GRAFT, that obtains the approximate graphlet frequency for all graphlets that have upto 5 vertices. Comparing to an exact counting algorithm, our algorithm achieves a speedup factor between 10 and 100 for a negligible counting error, which is, on average, less than 5%; For example, exact graphlet counting for ca-AstroPh takes approximately 3 days; but, GRAFT runs for 45 minutes to perform the same task with a counting accuracy of 95.6%.


title: Mining and Ranking Biomedical Synonym Candidates from Wikipedia

abstract: Biomedical synonyms are important resources for Natural Language Processing in Biomedical domain. Existing synonym resources (e.g., the UMLS) are not complete. Manual efforts for expanding and enriching these resources are prohibitively expensive. We therefore develop and evaluate approaches for automated synonym extraction from Wikipedia. Using the inter-wiki links, we extracted the candidate synonyms (anchor-text e.g., “increased thirst”) in a Wikipedia page and the title (e.g., “polyuria”) of its corresponding linked page. We rank synonym candidates with word embedding and pseudo-relevance feedback (PRF). Our results show that PRF-based reranking outperformed word embedding based approach and a strong baseline using interwiki link frequency. A hybrid method, Rank Score Combination, achieved the best results. Our analysis also suggests that medical synonyms mined from Wikipedia can increase the coverage of existing synonym resources such as UMLS.


title: WiGest: A ubiquitous WiFi-based gesture recognition system

abstract: We present WiGest: a system that leverages changes in WiFi signal strength to sense in-air hand gestures around the user's mobile device. Compared to related work, WiGest is unique in using standard WiFi equipment, with no modi-fications, and no training for gesture recognition. The system identifies different signal change primitives, from which we construct mutually independent gesture families. These families can be mapped to distinguishable application actions. We address various challenges including cleaning the noisy signals, gesture type and attributes detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. We implement a proof-of-concept prototype using off-the-shelf laptops and extensively evaluate the system in both an office environment and a typical apartment with standard WiFi access points. Our results show that WiGest detects the basic primitives with an accuracy of 87.5% using a single AP only, including through-the-wall non-line-of-sight scenarios. This accuracy in-creases to 96% using three overheard APs. In addition, when evaluating the system using a multi-media player application, we achieve a classification accuracy of 96%. This accuracy is robust to the presence of other interfering humans, highlighting WiGest's ability to enable future ubiquitous hands-free gesture-based interaction with mobile devices.


title: The flushing patient: differential diagnosis, workup, and treatment.

abstract: UNLABELLED ::: Cutaneous flushing-a common presenting complaint to dermatologists, allergists, internists, and family practitioners-results from changes in cutaneous blood flow triggered by multiple conditions. Most cases are caused by very common, benign diseases, such as rosacea or climacterum, that are readily apparent after a thorough taking of history and physical examination. However, in some cases, accurate diagnosis requires further laboratory, radiologic, or histopathologic studies to differentiate several important clinicopathologic entities. In particular, the serious diagnoses of carcinoid syndrome, pheochromocytoma, mastocytosis, and anaphylaxis need to be excluded by laboratory studies. If this work-up is unrevealing, rare causes, such as medullary carcinoma of the thyroid, pancreatic cell tumor, renal carcinoma, and others, should be considered. ::: ::: ::: LEARNING OBJECTIVE ::: At the completion of this learning activity, participants should be familiar with the mechanisms of flushing, its clinical differential diagnosis, the approach to establish a definitive diagnosis, and management of various conditions that produce flushing.


title: Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings

abstract: Neural networks achieve the state-of-the-art in image classification tasks. However, they can encode spurious variations or biases that may be present in the training data. For example, training an age predictor on a dataset that is not balanced for gender can lead to gender biased predicitons (e.g. wrongly predicting that males are older if only elderly males are in the training set). We present two distinct contributions: 1) An algorithm that can remove multiple sources of variation from the feature representation of a network. We demonstrate that this algorithm can be used to remove biases from the feature representation, and thereby improve classification accuracies, when training networks on extremely biased datasets. 2) An ancestral origin database of 14,000 images of individuals from East Asia, the Indian subcontinent, sub-Saharan Africa, and Western Europe. We demonstrate on this dataset, for a number of facial attribute classification tasks, that we are able to remove racial biases from the network feature representation.


title: On Pythagorean and Complex Fuzzy Set Operations

abstract: Complex fuzzy logic is a new multivalued logic system that has emerged in the last decade. At this time, there are a limited number of known instances of complex fuzzy logic, and only a partial exploration of their properties. There has also been relatively little progress in developing interpretations of complex-valued membership grades. In this paper, we address both problems by examining the recently developed Pythagorean fuzzy sets (a generalization of intuitionistic fuzzy sets). We first characterize two lattices that have been suggested for Pythagorean fuzzy sets and then extend these results to the unit disc of the complex plane. We thereby identify two new complete, distributive lattices over the unit disc, and explore interpretations of them based on fuzzy antonyms and negations.


title: Design of the Series Resonant Converter for Minimum Component Stress

abstract: For a given output voltage and power, the peak resonant capacitor voltage and peak inductor and switch currents of the series resonant converter depend strongly on the choice of transformer turns ratio and of tank inductance and capacitance. In this paper the particular component values which result in the smallest component stresses are determined, and a simple design strategy is developed. The procedure is illustrated for an off-line 200 W, 5 V application, and it is shown that an incorrect choice of component values can result in significantly higher component stresses than are necessary.


title: Increased fecal calprotectin levels in Crohn’s disease correlate with elevated serum Th1- and Th17-associated cytokines

abstract: BACKGROUND: Patient-reported symptoms and endoscopic disease activity do not correlate well in Crohn's disease (CD). This warrants the need for reliable biomarkers to early detect active intestinal inflammation. Currently, the fecal calprotectin level is the most commonly used biomarker for inflammatory activity in CD. However, the diagnostic accuracy of the fecal calprotectin level is not fully efficacious and diagnosis may be further improved by the identification of other biomarkers for active CD. Here, we studied the association of a variety of serum disease markers with fecal calprotectin levels in CD patients. METHODS: 39 CD patients were included and subdivided into 'normal' (defined as 200 mg/kg feces) fecal calprotectin level groups. Serum levels of 37 different cytokines, chemokines and markers for angiogenesis and vascular injury were quantified by an electrochemiluminescence multiplex assay (V-PLEX Human Biomarker 40-Plex Kit of Meso Scale Discovery ®). Correlations between individual biomarkers and the fecal calprotectin level were assessed using Spearman's correlation coefficient (ρ). RESULTS: A highly significant positive correlation was observed between the pro-inflammatory serum cytokines IFN-γ and CRP and fecal calprotectin levels (P < 0.01). Moreover, fecal calprotectin levels showed a significant positive correlation with IL-6, TNF-β, SAA and IL-17A (P < 0.05). CONCLUSION: We show that a positive correlation exists between multiple serum Th1- and Th17-associated cytokines and the fecal calprotectin level. These cytokines and CRP may serve as additional biomarkers for determining disease activity and evaluating treatment response in CD. Ultimately, this may result in more efficient treatment of active disease in CD patients and prevention of complications.


title: Field-Programmable Deep Neural Network (DNN) Learning and Inference accelerator: a concept

abstract: An accelerator is a specialized integrated circuit designed to perform specific computations faster than if those were performed by CPU or GPU. A Field-Programmable DNN learning and inference accelerator (FProg-DNN) using hybrid systolic and non-systolic techniques, distributed information-control and deep pipelined structure is proposed and its microarchitecture and operation presented here. Reconfigurability attends diverse DNN designs and allows for different number of workers to be assigned to different layers as a function of the relative difference in computational load among layers. The computational delay per layer is made roughly the same along pipelined accelerator structure. VGG-16 and recently proposed Inception Modules are used for showing the flexibility of the FProg-DNN reconfigurability. Special structures were also added for a combination of convolution layer, map coincidence and feedback for state of the art learning with small set of examples, which is the focus of a companion paper by the author (Franca-Neto, 2018). The accelerator described is able to reconfigure from (1) allocating all a DNN computations to a single worker in one extreme of sub-optimal performance to (2) optimally allocating workers per layer according to computational load in each DNN layer to be realized. Due the pipelined architecture, more than 50x speedup is achieved relative to GPUs or TPUs. This speed-up is consequence of hiding the delay in transporting activation outputs from one layer to the next in a DNN behind the computations in the receiving layer. This FProg-DNN concept has been simulated and validated at behavioral-functional level.


title: Baselines and test data for cross-lingual inference

abstract: The recent years have seen a revival of interest in textual entailment, sparked by i) the emergence of powerful deep neural network learners for natural language processing and ii) the timely development of large-scale evaluation datasets such as SNLI. Recast as natural language inference, the problem now amounts to detecting the relation between pairs of statements: they either contradict or entail one another, or they are mutually neutral. Current research in natural language inference is effectively exclusive to English. In this paper, we propose to advance the research in SNLI-style natural language inference toward multilingual evaluation. To that end, we provide test data for four major languages: Arabic, French, Spanish, and Russian. We experiment with a set of baselines. Our systems are based on cross-lingual word embeddings and machine translation. While our best system scores an average accuracy of just over 75%, we focus largely on enabling further research in multilingual inference.


title: Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding

abstract: This paper presents the design of the machine learning architecture that underlies the Alexa Skills Kit (ASK), which was the first Spoken Language Understanding (SLU) Software Development Kit (SDK) for a virtual digital assistant, as far as we are aware. At Amazon, the infrastructure powers more than 20,000 skills built through the ASK, as well as AWS's Amazon Lex SLU Service. The ASK emphasizes flexibility, predictability and a rapid iteration cycle for third party developers. It imposes inductive biases that allow it to learn robust SLU models from extremely small and sparse datasets and, in doing so, removes significant barriers to entry for software developers and dialog systems researchers.


Late results of surgical treatment for extracranial carotid artery insufficiency and a report of 31 cases treated surgically at the VA Hospital, Omaha


title: PhotoMesa: a zoomable image browser using quantum treemaps and bubblemaps

abstract: PhotoMesa is a zoomable image browser that uses a novel treemap algorithm to present large numbers of images grouped by directory, or other available metadata. It uses a new interaction technique for zoomable user interfaces designed for novices and family use that makes it straightforward to navigate through the space of images, and impossible to get lost.PhotoMesa groups images using one of two new algorithms that lay out groups of objects in a 2D space-filling manner. Quantum treemaps are designed for laying out images or other objects of indivisible (quantum) size. They are a variation on existing treemap algorithms in that they guarantee that every generated rectangle will have a width and height that are an integral multiple of an input object size. Bubblemaps also fill space with groups of quantum-sized objects, but generate non-rectangular blobs, and utilize space more efficiently.


title: A Solution for Preventing Fraudulent Financial Reporting using Descriptive Data Mining Techniques

abstract: the present age of scams, financial statement fraud represents enormous cost to our economy. The deliberate misstatement of numbers in the accounting books with the help of well planned scheme by an intelligent squad of knowledgeable perpetrators in order to deceive the capital market participants is termed as financial statement fraud. In order to reduce fraud risk which comprehends both detection and prevention of financial statement fraud, this paper implements descriptive data mining techniques such as Association rules and clustering as opposed to predictive data mining techniques used in the literature. Each of these techniques is applied on dataset obtained from financial statements namely balance sheet, income statement and cash flow statement of 114 companies.


title: Large-Scale Liquid Simulation on Adaptive Hexahedral Grids

abstract: Regular grids are attractive for numerical fluid simulations because they give rise to efficient computational kernels. However, for simulating high resolution effects in complicated domains they are only of limited suitability due to memory constraints. In this paper we present a method for liquid simulation on an adaptive octree grid using a hexahedral finite element discretization, which reduces memory requirements by coarsening the elements in the interior of the liquid body. To impose free surface boundary conditions with second order accuracy, we incorporate a particular class of Nitsche methods enforcing the Dirichlet boundary conditions for the pressure in a variational sense. We then show how to construct a multigrid hierarchy from the adaptive octree grid, so that a time efficient geometric multigrid solver can be used. To improve solver convergence, we propose a special treatment of liquid boundaries via composite finite elements at coarser scales. We demonstrate the effectiveness of our method for liquid simulations that would require hundreds of millions of simulation elements in a non-adaptive regime.


title: The Expected Value of Control: An Integrative Theory of Anterior Cingulate Cortex Function

abstract: The dorsal anterior cingulate cortex (dACC) has a near-ubiquitous presence in the neuroscience of cognitive control. It has been implicated in a diversity of functions, from reward processing and performance monitoring to the execution of control and action selection. Here, we propose that this diversity can be understood in terms of a single underlying function: allocation of control based on an evaluation of the expected value of control (EVC). We present a normative model of EVC that integrates three critical factors: the expected payoff from a controlled process, the amount of control that must be invested to achieve that payoff, and the cost in terms of cognitive effort. We propose that dACC integrates this information, using it to determine whether, where and how much control to allocate. We then consider how the EVC model can explain the diverse array of findings concerning dACC function.


title: SleeveAR: Augmented Reality for Rehabilitation using Realtime Feedback

abstract: We present an intelligent user interface that allows people to perform rehabilitation exercises by themselves under the offline supervision of a therapist. Every year, many people suffer injuries that require rehabilitation. This entails considerable time overheads since it requires people to perform specified exercises under the direct supervision of a therapist. Therefore it is desirable that patients continue performing exercises outside the clinic (for instance at home, thus without direct supervision), to complement in-clinic physical therapy. However, to perform rehabilitation tasks accurately, patients need appropriate feedback, as otherwise provided by a physical therapist, to ensure that these unsupervised exercises are correctly executed. Different approaches address this problem, providing feedback mechanisms to aid rehabilitation. Unfortunately, test subjects frequently report having trouble to completely understand the feedback thus provided, which makes it hard to correctly execute the prescribed movements. Worse, injuries may occur due to incorrect performance of the prescribed exercises, which severely hinders recovery. SleeveAR is a novel approach to provide real-time, active feedback, using multiple projection surfaces to provide effective visualizations. Empirical evaluation shows the effectiveness of our approach as compared to traditional video-based feedback. Our experimental results show that our intelligent UI can successfully guide subjects through an exercise prescribed (and demonstrated) by a physical therapist, with performance improvements between consecutive executions, a desirable goal to successful rehabilitation.


title: Primal-Dual Plug-and-Play Image Restoration

abstract: We propose a new plug-and-play image restoration method based on primal-dual splitting. Existing plug-and-play image restoration methods interpret any off-the-shelf Gaussian denoiser as one step of the so-called alternating direction method of multipliers (ADMM). This makes it possible to exploit the power of such a highly-customized Gaussian denoising method for general image restoration tasks in a plug-and-play fashion. However, the ADMM-based plug-and-play approach (ADMMPnP) has several limitations: 1) it often requires a problem-specific iterative method in solving a subproblem, which results in a computationally expensive inner loop; and 2) it is specialized to handle the formulation of a regularization (plug-and-play) term plus a data-fidelity term, so that it does not allow to impose hard constraints useful for image restoration. Our approach resolves these issues by leveraging the nature of primal-dual splitting, yielding a very flexible plug-and-play image restoration method. Experimental results demonstrate that the proposed method is much more efficient than ADMMPnP with an inner loop, whereas it keeps the same efficiency as ADMMPnP in the case where the subproblem of ADMMPnP can be solved efficiently.


title: Line-art illustration of dynamic and specular surfaces

abstract: Line-art illustrations are effective tools for conveying shapes and shading of complex objects. We present a set of new algorithms to render line-art illustrations of dynamic and specular (reflective and refractive) surfaces. We first introduce a real-time principal direction estimation algorithm to determine the line stroke directions on dynamic opaque objects using neighboring normal ray triplets. To render reflections or refractions in a line-art style, we develop a stroke direction propagation algorithm by using multi-perspective projections to propagate the stroke directions from the nearby opaque objects onto specular surfaces. Finally, we present an image-space stroke mapping method to draw line strokes using the computed or propagated stroke directions. We implement these algorithms using a GPU and demonstrate real-time illustrations of scenes with dynamic and specular 3D models in line-art styles.


A Hitchhiker’s Guide to Choosing Parameters of Stochastic Kronecker Graphs


title: Session-Based Recommendations Using Item Embedding

abstract: Recent methods for learning vector space representations of words, word embedding, such as GloVe and Word2Vec have succeeded in capturing fine-grained semantic and syntactic regularities. We analyzed the effectiveness of these methods for e-commerce recommender systems by transferring the sequence of items generated by users' browsing journey in an e-commerce website into a sentence of words. We examined the prediction of fine-grained item similarity (such as item most similar to iPhone 6 64GB smart phone) and item analogy (such as iPhone 5 is to iPhone 6 as Samsung S5 is to Samsung S6) using real life users' browsing history of an online European department store. Our results reveal that such methods outperform related models such as singular value decomposition (SVD) with respect to item similarity and analogy tasks across different product categories. Furthermore, these methods produce a highly condensed item vector space representation, item embedding, with behavioral meaning sub-structure. These vectors can be used as features in a variety of recommender system applications. In particular, we used these vectors as features in a neural network based models for anonymous user recommendation based on session's first few clicks. It is found that recurrent neural network that preserves the order of user's clicks outperforms standard neural network, item-to-item similarity and SVD (recall@10 value of 42% based on first three clicks) for this task.


title: Violence Detection in Video Using Computer Vision Techniques

abstract: Whereas the action recognition community has focused mostly on detecting simple actions like clapping, walking or jogging, the detection of fights or in general aggressive behaviors has been comparatively less studied. Such capability may be extremely useful in some video surveillance scenarios like in prisons, psychiatric or elderly centers or even in camera phones. After an analysis of previous approaches we test the well-known Bag-of-Words framework used for action recognition in the specific problem of fight detection, along with two of the best action descriptors currently available: STIP and MoSIFT. For the purpose of evaluation and to foster research on violence detection in video we introduce a new video database containing 1000 sequences divided in two groups: fights and non-fights. Experiments on this database and another one with fights from action movies show that fights can be detected with near 90% accuracy.


title: Development of Double Side Protection Process with Bump Support Film (BSF) and Backside Coating Tape for WLP

abstract: Wafer Level Package (WLP) demand has been increasing in proportion to cost competitive device's demand year by year. In order to manufacture the WLP which has higher reliability efficiently, assembly processes with Bump Support Film (BSF) and Backside coating tape has been developed. The BSF consists of Back Grinding tape (BG tape) for getting a function as a wafer thinning and Bump Support Layer (BSL) which is formed on circuit side around the bump for preventing a bump cracking. In addition, the BG tape has bump absorption layer which is thick enough for applying to various bump pitches, heights and chip geometries. In this paper, two main investigations are reported. One is process evaluation with Test Element Group (TEG) and Substrate. The other is comparison of reliability after Thermal Cycle Test (TCT). As a result, we succeeded in forming BSL with a little of residue at the bump top with bump absorption layer which has higher elastic modulus at the large deformation region than that of BSL, and one minute of plasma etching as cleaning of bump surface. We also succeeded in manufacturing double side protected package which has higher reliability than a package without protection at wafer level.


title: Logic Programming Approaches for Representing and Solving Constraint Satisfaction Problems: A Comparison

abstract: Many logic programming based approaches can be used to describe and solve combinatorial search problems. On the one hand there is constraint logic programming which computes a solution as an answer substitution to a query containing the variables of the constraint satisfaction problem. On the other hand there are systems based on stable model semantics, abductive systems, and first order logic model generators which compute solutions as models of some theory. This paper compares these different approaches from the point of view of knowledge representation (how declarative are the programs) and from the point of view of performance (how good are they at solving typical problems).


title: Miniature Four-Way and Two-Way 24 GHz Wilkinson Power Dividers in 0.13 $\mu$m CMOS

abstract: This letter presents 24 GHz four-way and two-way miniature Wilkinson power dividers (PDs) in a standard CMOS technology. The chip area is significantly reduced using a lumped-element design, and the effective areas of four-way and two-way Wilkinson dividers are 0.33 times 0.33 mm2 and 0.12 times 0.29 mm2, respectively. The four-way Wilkinson divider results in an insertion loss 24.7 dB from 22 to 26 GHz. The two-way Wilkinson divider results in an insertion loss 14.8 dB from 22 to 26 GHz. To the author's knowledge, this is the first demonstration of 24 GHz four-way Wilkinson PD in a standard CMOS technology.


title: Object-oriented design process model

abstract: Summary Design is a first step in the development phase for any engineered product or system. It is defined as the process and strategies used to manage complexity. Software design process is an iterative process whereby the requirements are transformed into a “blueprint” for constructing the software. A design model is developed based on the combination of intuition and judgment, a set of principles and heuristics, and a process of iteration that leads to final design specifications. Without a proper design, a software system may fail to deliver its intended service and often will lead to some consuming maintenance activities. Therefore it is necessary for software developers to do the design process thoroughly before they start implementing the system. Objectoriented design is not an easy task. It is even difficult for a novice designer or for an experienced designer who wants to shift to object-oriented approach. Throughout literature, there are varying schools of thought on what constitutes object-oriented design. What is the process involved in this phase and what are components or structures? This paper presents four popular object-oriented design methods, and then a process model of object-oriented design for novice designer is proposed. The model consists of a process and four components. The process model is part of the model of a guidance system to assist novice designers in designing object-oriented systems.


title: Measuring the perceived quality of an AR-based learning application: a multidimensional model

abstract: ABSTRACTAugmented reality (AR) technologies could enhance learning in several ways. The quality of an AR-based educational platform is a combination of key features that manifests in usability, usefulness, and enjoyment for the learner. In this paper, we present a multidimensional model to measure the quality of an AR-based application as perceived by students. The model is specified by a second-order factor (perceived quality) and three dimensions: ergonomic quality, learning quality, and hedonic quality. The purpose of this model is to embody previous research into a coherent framework for the evaluation of AR-based educational platforms and to provide guidance for researchers and practitioners. The model was empirically validated on a Chemistry learning scenario and the results confirm the importance of both the learning and hedonic quality.


title: The loss surface and expressivity of deep convolutional neural networks

abstract: We analyze the expressiveness and loss surface of practical deep convolutional neural networks (CNNs) with shared weights and max pooling layers. We show that such CNNs produce linearly independent features at a"wide"layer which has more neurons than the number of training samples. This condition holds e.g. for the VGG network. Furthermore, we provide for such wide CNNs necessary and sufficient conditions for global minima with zero training error. For the case where the wide layer is followed by a fully connected layer, we show that almost every critical point of the empirical loss is a global minimum with zero training error. Our analysis suggests that both depth and width are very important in deep learning. While depth brings more representational power and allows the network to learn high level features, width smoothes the optimization landscape of the loss function in the sense that a sufficiently wide network has a well-behaved loss surface with potentially no bad local minima.


title: Good fonts for dyslexia

abstract: Around 10% of the people have dyslexia, a neurological disability that impairs a person's ability to read and write. There is evidence that the presentation of the text has a significant effect on a text's accessibility for people with dyslexia. However, to the best of our knowledge, there are no experiments that objectively measure the impact of the font type on reading performance. In this paper, we present the first experiment that uses eye-tracking to measure the effect of font type on reading speed. Using a within-subject design, 48 subjects with dyslexia read 12 texts with 12 different fonts. Sans serif, monospaced and roman font styles significantly improved the reading performance over serif, proportional and italic fonts. On the basis of our results, we present a set of more accessible fonts for people with dyslexia.


title: Deep-Learning-Assisted Volume Visualization

abstract: Designing volume visualizations showing various structures of interest is critical to the exploratory analysis of volumetric data. The last few years have witnessed dramatic advances in the use of convolutional neural networks for identification of objects in large image collections. Whereas such machine learning methods have shown superior performance in a number of applications, their direct use in volume visualization has not yet been explored. In this paper, we present a deep-learning-assisted volume visualization to depict complex structures, which are otherwise challenging for conventional approaches. A significant challenge in designing volume visualizations based on the high-dimensional deep features lies in efficiently handling the immense amount of information that deep-learning methods provide. In this paper, we present a new technique that uses spectral methods to facilitate user interactions with high-dimensional features. We also present a new deep-learning-assisted technique for hierarchically exploring a volumetric dataset. We have validated our approach on two electron microscopy volumes and one magnetic resonance imaging dataset.