title: Concept Extraction to Identify Adverse Drug Reactions in Medical Forums: A Comparison of Algorithms

abstract: Social media is becoming an increasingly important source of information to complement traditional pharmacovigilance methods. In order to identify signals of potential adverse drug reactions, it is necessary to first identify medical concepts in the social media text. Most of the existing studies use dictionary-based methods which are not evaluated independently from the overall signal detection task. ::: We compare different approaches to automatically identify and normalise medical concepts in consumer reviews in medical forums. Specifically, we implement several dictionary-based methods popular in the relevant literature, as well as a method we suggest based on a state-of-the-art machine learning method for entity recognition. MetaMap, a popular biomedical concept extraction tool, is used as a baseline. Our evaluations were performed in a controlled setting on a common corpus which is a collection of medical forum posts annotated with concepts and linked to controlled vocabularies such as MedDRA and SNOMED CT. ::: To our knowledge, our study is the first to systematically examine the effect of popular concept extraction methods in the area of signal detection for adverse reactions. We show that the choice of algorithm or controlled vocabulary has a significant impact on concept extraction, which will impact the overall signal detection process. We also show that our proposed machine learning approach significantly outperforms all the other methods in identification of both adverse reactions and drugs, even when trained with a relatively small set of annotated text.


title: Optimization of a human-powered aircraft using fluid–structure interaction simulations

abstract: The special type of aircrafts in which the human power of the pilot is sufficient to take off and sustain flight are known as Human-Powered Aircrafts (HPAs). To explore the peculiarities of these aircrafts, the aerodynamic performance of an existing design is evaluated first, using both the vortex lattice method and computational fluid dynamics. In a second step, it is attempted to design and optimize a new HPA capable of winning the Kremer International Marathon Competition. The design will be special in that it allows one to include a second pilot on board the aircraft. As the structural deflection of the wing is found to be a key aspect during design, fluid–structure interaction simulations are performed and included in the optimization procedure. To assess the feasibility of winning the competition, the physical performance of candidate pilots is measured and compared with the predicted required power.


title: Simulation of four quadrant operation & speed control of BLDC motor on MATLAB / SIMULINK

abstract: BLDC motors have been gaining attention from various industrial and household appliance manufacturers, because of its high efficiency, high power density and low maintenance cost. After many research and developments in the fields of magnetic materials and power electronics, their applications to electric drives have increased to a significant extent. In this paper, the modeling of Brushless DC motor drive system along with control system for speed and current has been presented using MATLAB/SIMULINK. In order to evaluate the model, various cases of simulation studies are carried out. Test results thus obtained show that, the model performance is satisfactory.


title: Towards a Neural Network Approach to Abstractive Multi-Document Summarization

abstract: Till now, neural abstractive summarization methods have achieved great success for single document summarization (SDS). However, due to the lack of large scale multi-document summaries, such methods can be hardly applied to multi-document summarization (MDS). In this paper, we investigate neural abstractive methods for MDS by adapting a state-of-the-art neural abstractive summarization model for SDS. We propose an approach to extend the neural abstractive model trained on large scale SDS data to the MDS task. Our approach only makes use of a small number of multi-document summaries for fine tuning. Experimental results on two benchmark DUC datasets demonstrate that our approach can outperform a variety of baseline neural models.


title: Generalized Deep Image to Image Regression

abstract: We present a Deep Convolutional Neural Network architecture which serves as a generic image-to-image regressor that can be trained end-to-end without any further machinery. Our proposed architecture, the Recursively Branched Deconvolutional Network (RBDN), develops a cheap multi-context image representation very early on using an efficient recursive branching scheme with extensive parameter sharing and learnable upsampling. This multi-context representation is subjected to a highly non-linear locality preserving transformation by the remainder of our network comprising of a series of convolutions/deconvolutions without any spatial downsampling. The RBDN architecture is fully convolutional and can handle variable sized images during inference. We provide qualitative/quantitative results on 3 diverse tasks: relighting, denoising and colorization and show that our proposed RBDN architecture obtains comparable results to the state-of-the-art on each of these tasks when used off-the-shelf without any post processing or task-specific architectural modifications.


title: Seamless Fusion of LiDAR and Aerial Imagery for Building Extraction

abstract: Although many efforts have been made on the fusion of Light Detection and Ranging (LiDAR) and aerial imagery for the extraction of houses, little research on taking advantage of a building's geometric features, properties, and structures for assisting the further fusion of the two types of data has been made. For this reason, this paper develops a seamless fusion between LiDAR and aerial imagery on the basis of aspect graphs, which utilize the features of houses, such as geometry, structures, and shapes. First, 3-D primitives, standing for houses, are chosen, and their projections are represented by the aspects. A hierarchical aspect graph is then constructed using aerial image processing in combination with the results of LiDAR data processing. In the aspect graph, the note represents the face aspect and the arc is described by attributes obtained by the formulated coding regulations, and the coregistration between the aspect and LiDAR data is implemented. As a consequence, the aspects and/or the aspect graph are interpreted for the extraction of houses, and then the houses are fitted using a planar equation for creating a digital building model (DBM). The experimental field, which is located in Wytheville, VA, is used to evaluate the proposed method. The experimental results demonstrated that the proposed method is capable of effectively extracting houses at a successful rate of 93%, as compared with another method, which is 82% effective when LiDAR spacing is approximately 7.3 by 7.3 ft $^{2}$ . The accuracy of 3-D DBM is higher than the method using only single LiDAR data.


title: SemEval-2017 Task 3: Community Question Answering

abstract: We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Similarity,(C) Question-External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A-D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A-C.


title: Disclosure, ambiguity and risk reduction in real-time dating sites

abstract: While social network capabilities are proliferating on many online services, research has focused on just a few popular social network sites. In this note, we consider a different kind of social network site, explicitly designed to support particular types of risky sexual activity among men who have sex with men (MSM). We consider the role of ambiguity built into the interface in how users manage self-disclosure and its association with articulating more friends-only or sexual connections on the site. Despite the site's explicit orientation toward risky sexual practices, we find indications that users mitigate potential public health issues through the practice of sero-sorting. We discuss how design considerations that may allow for easier entrance into a community can cause problems for long-term users, or generate potential public health issues.


title: Extending AIS research to management accounting and control issues: A research note

abstract: The purpose of this commentary is to initiate discussion on the status of research on the interface between management control, especially management accounting, and information technology, and on how we could proceed to understand this relationship and changes in it. The commentary also points to the need for expanding the sphere of theories and methodologies applied in the accounting information systems field, if we want to explain and understand the complex and mutually constitutive nature of the relationship, especially how new technologies are actually designed and adapted to work in practice. The mainstream tradition tends to largely ignore the design and implementation processes and especially the struggles therein.


title: Voiceless Speech Recognition Using Dynamic Visual Speech Features

abstract: This paper describes a voiceless speech recognition technique that utilizes dynamic visual features to represent the facial movements during phonation. The dynamic features extracted from the mouth video are used to classify utterances without using the acoustic data. The audio signals of consonants are more confusing than vowels and the facial movements involved in pronunciation of consonants are more discernible. Thus, this paper focuses on identifying consonants using visual information. This paper adopts a visual speech model that categorizes utterances into sequences of smallest visually distinguishable units known as visemes. The viseme model used is based on the viseme model of Moving Picture Experts Group 4 (MPEG-4) standard. The facial movements are segmented from the video data using motion history images (MHI). MHI is a spatio-temporal template (grayscale image) generated from the video data using accumulative image subtraction technique. The proposed approach combines discrete stationary wavelet transform (SWT) and Zernike moments to extract rotation invariant features from the MHI. A feed forward multilayer perceptron (MLP) neural network is used to classify the features based on the patterns of visible facial movements. The preliminary experimental results indicate that the proposed technique is suitable for recognition of English consonants.


title: Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks

abstract: Speech enhancement (SE) aims to reduce noise in speech signals. Most SE techniques focus only on addressing audio information. In this work, inspired by multimodal learning, which utilizes data from different modalities, and the recent success of convolutional neural networks (CNNs) in SE, we propose an audio-visual deep CNNs (AVDCNN) SE model, which incorporates audio and visual streams into a unified network model. We also propose a multi-task learning framework for reconstructing audio and visual signals at the output layer. Precisely speaking, the proposed AVDCNN model is structured as an audio-visual encoder-decoder network, in which audio and visual data are first processed using individual CNNs, and then fused into a joint network to generate enhanced speech (the primary task) and reconstructed images (the secondary task) at the output layer. The model is trained in an end-to-end manner, and parameters are jointly learned through back-propagation. We evaluate enhanced speech using five instrumental criteria. Results show that the AVDCNN model yields a notably superior performance compared with an audio-only CNN-based SE model and two conventional SE approaches, confirming the effectiveness of integrating visual information into the SE process. In addition, the AVDCNN model also outperforms an existing audio-visual SE model, confirming its capability of effectively combining audio and visual information in SE.


What design science is not


title: Research Note: Democracy with Adjectives: Conceptual Innovation in Comparative Research

abstract: The recent trend toward democratization in countries across the globe has challenged scholars to pursue two potentially contradictory goals. On the one hand, they seek to increase analytic differentiation in order to capture the diverse forms of democracy that have emerged. On the other hand, they are concerned with conceptual validity. Specifically, they seek to avoid the problem of conceptual stretching that arises when the concept of democracy is applied to cases for which, by relevant scholarly standards, it is not appropriate. This article argues that the pursuit of these two goals has led to a proliferation of conceptual innovations, including numerous subtypes of democracy--that is to say, democracy "with adjectives." The article explores the strengths and weaknesses of alternative strategies of conceptual innovation that have emerged: descending and climbing Sartori's ladder of generality, generating "diminished" subtypes of democracy, "precising" the definition of democracy by adding defining attributes, and shifting the overarching concept with which democracy is associated. The goal of the analysis is to make more comprehensible the complex structure of these strategies, as well as to explore trade-offs among the strategies. Even when scholars proceed intuitively, rather than self-consciously, they tend to operate within this structure. Yet it is far more desirable for them to do so self-consciously, with a full awareness of these trade-offs.


title: Road-Network Aware Trajectory Clustering: Integrating Locality, Flow, and Density

abstract: Mining trajectory data has been gaining significant interest in recent years. However, existing approaches to trajectory clustering are mainly based on density and Euclidean distance measures. We argue that when the utility of spatial clustering of mobile object trajectories is targeted at road-network aware location-based applications, density and Euclidean distance are no longer the effective measures. This is because traffic flows in a road network and the flow-based density characterization become important factors for finding interesting trajectory clusters. We propose NEAT–a road-network aware approach for fast and effective clustering of trajectories of mobile objects traveling in road networks. Our approach carefully considers the traffic locality characterized by the physical constraints of the road network, the traffic flow among consecutive road segments, and the flow-based density to organize trajectories into spatial clusters in a comprehensive three-phase clustering framework. NEAT discovers spatial clusters as groups of sub-trajectories which describe both dense and highly continuous flows of mobile objects. We perform extensive experiments with mobility traces generated using different scales of real road networks. Experimental results demonstrate the flexibility of the NEAT system and show that NEAT is highly accurate and runs orders of magnitude faster than existing density-based trajectory clustering approaches.


Of Information Systems School of Information Systems 8-2012 Online Feature Selection for Mining Big Data


title: Synthetic aperture radar for lane boundary detection in driver assistance systems

abstract: In this paper we investigate the feasibility for using a Synthetic Aperture Radar (SAR) to detect radar scatterers in support of advanced driver assistance systems. Specifically, we consider the detection of radar scatterers physically embedded into lane and carriageway boundaries similar to way optical retroreflectors (cats eyes) are used in present infrastructure. We use simulations to generate high resolution SAR images for detecting and localizing radar scatterers. The simulated results presented here highlight the feasibility of the technique and provide a platform for further investigation. This paper facilitates the realization of the role of modified infrastructure for improving the sensing capability of highly assisted and autonomous vehicles.


title: How Privacy Flaws Affect Consumer Perception

abstract: We examine how consumers perceive publicized instances of privacy flaws and private information data breaches.Using three real-world privacy breach incidents, we study how these flaws affected consumers' future purchasing behavior and perspective on a company's trustworthiness. We investigate whether despite a lack of widespread privacy enhancing technology (PET) usage, consumers are taking some basic security precautions when making purchasing decisions. We survey 600participants on three well-known privacy breaches. Our results show that, in general, consumers are less likely to purchase products that had experienced some form of privacy breach.We find evidence of a slight bias toward giving products the consumers owned themselves more leeway, as suggested by the endowment effect hypothesis.


title: Control of Mobile Robots Using the Soar Cognitive Architecture

abstract: This paper describes the development of a system that uses computational psychology (the Soar cognitive architecture) for the control of unmanned vehicles.A multithreaded software system written using Java and integrated with the Soar cognitive architecture has been implemented on two types of mobile robots. Soar can be used as a general purpose robotic intelligence system and can handle a wide variety of sensor inputs and motor-control outputs. The use of existing computational psychology methods (such as Soar) may be a more efficient approach to developing robotic software, rather than developing new software for every new unmanned vehicle application. Results from the application of this software system (named the cognitive robotic system, or CRS) to a practical unmanned ground vehicle mission,navigatingtoatargetGPSlocationwhileavoidingobstacles,aredescribed.TheCRS has been designed so that its capabilities can be expanded in the future through the inclusion of additional sensors and motors, additional software systems, and more sophisticated Soar agents.


title: Multi30K: Multilingual English-German Image Descriptions

abstract: We introduce the Multi30K dataset to stimulate multilingual multimodal research. Recent advances in image description have been demonstrated on English-language datasets almost exclusively, but image description should not be limited to English. This dataset extends the Flickr30K dataset with i) German translations created by professional translators over a subset of the English descriptions, and ii) descriptions crowdsourced independently of the original English descriptions. We outline how the data can be used for multilingual image description and multimodal machine translation, but we anticipate the data will be useful for a broader range of tasks.


title: Distributed k-core decomposition

abstract: Among the novel metrics used to study the relative importance of nodes in complex networks, k-core decomposition has found a number of applications in areas as diverse as sociology, proteinomics, graph visualization, and distributed system analysis and design. This paper proposes new distributed algorithms for the computation of the k-core decomposition of a network, with the purpose of (i) enabling the run-time computation of k-cores in "live" distributed systems and (ii) allowing the decomposition, over a set of connected machines, of very large graphs, that cannot be hosted in a single machine. Lower bounds on the algorithms complexity are given, and an exhaustive experimental analysis on real-world graphs is provided.


title: Argumentation mining

abstract: Argumentation mining aims to automatically detect, classify and structure argumentation in text. Therefore, argumentation mining is an important part of a complete argumentation analyisis, i.e. understanding the content of serial arguments, their linguistic structure, the relationship between the preceding and following arguments, recognizing the underlying conceptual beliefs, and understanding within the comprehensive coherence of the specific topic. We present different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural analysis of the detected argumentation. Different state-of-the-art techniques on machine learning and context free grammars are applied to solve the challenges of argumentation mining. We also highlight fundamental questions found during our research and analyse different issues for future research on argumentation mining.


title: Semi-supervised distance metric learning for Collaborative Image Retrieval

abstract: Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called ldquoCollaborative Image Retrievalrdquo (CIR). To effectively explore the log data, we propose a novel semi-supervised distance metric learning technique, called ldquoLaplacian Regularized Metric Learningrdquo (LRML), for learning robust distance metrics for CIR. Different from previous methods, the proposed LRML method integrates both log data and unlabeled data information through an effective graph regularization framework. We show that reliable metrics can be learned from real log data even they may be noisy and limited at the beginning stage of a CIR system. We conducted extensive evaluation to compare the proposed method with a large number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information.


title: Survey evidence on the willingness of U.S. consumers to pay for automotive fuel economy

abstract: Prospect theory holds that human beings faced with a risky bet will tend to value potential losses about twice as much as potential gains. Previous research has demonstrated that prospect theory could be sufficient to explain an energy paradox in the market for automotive fuel economy. This paper analyzes data from questions added to four commercial, multi-client surveys of 1000 U.S. households each in 2004, 2011, 2012 and 2013. Households were asked about willingness to pay for future fuel savings as well as the annual fuel savings necessary to justify a given upfront payment. Payback periods inferred from household responses are generally consistent over time and across different formulations of questions. Mean calculated payback periods are about 3 years, but there is substantial dispersion among individual responses. The calculated payback periods do not appear to be correlated with the attributes of respondents. Respondents were able to quantitatively describe their uncertainty about both vehicle fuel economy and future fuel prices. Simulation of loss averse behavior based on respondents’ stated uncertainty illustrates how loss aversion could lead consumers to substantially undervalue future fuel savings relative to their expected value.


title: Multiphase Space Vector Pulse Width Modulation

abstract: Pole-phase modulation adjusts the pole-phase ratio of an induction machine and requires a multileg, multiphase inverter. This paper analyzes an n-leg, n-phase inverter, and presents techniques for n-phase space vector pulse width modulation (SVPWM). In particular, nine-phase SVPWM is developed and implemented on a nine winding induction machine. The nine-phase SVPWM is compared to nine phase sine-triangle PWM in terms of DC bus utilization.


title: Complementary countermeasures for detecting scenic face spoofing attacks

abstract: The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks. The number of countermeasures is gradually increasing and fairly good results have been reported on the publicly available databases. There exists no superior antispoofing technique due to the varying nature of attack scenarios and acquisition conditions. Therefore, it is important to find out complementary countermeasures and study how they should be combined in order to construct an easily extensible anti-spoofing framework. In this paper, we address this issue by studying fusion of motion and texture based countermeasures under several types of scenic face attacks. We provide an intuitive way to explore the fusion potential of different visual cues and show that the performance of the individual methods can be vastly improved by performing fusion at score level. The Half-Total Error Rate (HTER) of the best individual countermeasure was decreased from 11.2% to 5.1% on the Replay Attack Database. More importantly, we question the idea of using complex classification schemes in individual countermeasures, since nearly same fusion performance is obtained by replacing them with a simple linear one. In this manner, the computational efficiency and also probably the generalization ability of the resulting anti-spoofing framework are increased.


title: Emotion and motivation I: Defensive and appetitive reactions in picture processing.

abstract: Emotional reactions are organized by underlying motivational states—defensive and appetitive—that have evolved to promote the survival of individuals and species. Affective responses were measured while participants viewed pictures with varied emotional and neutral content. Consistent with the motivational hypothesis, reports of the strongest emotional arousal, largest skin conductance responses, most pronounced cardiac deceleration, and greatest modulation of the startle reflex occurred when participants viewed pictures depicting threat, violent death, and erotica. Moreover, reflex modulation and conductance change varied with arousal, whereas facial patterns were content specific. The findings suggest that affective responses serve different functions—mobilization for action, attention, and social communication—and reflect the motivational system that is engaged, its intensity of activation, and the specific emotional context. Emotion is considered here to be fundamentally organized around two motivational systems, one appetitive and one defensive, that have evolved to mediate transactions in the environment that either promote or threaten physical survival (Lang, Bradley, & Cuthbert, 1997). The defense system is primarily activated in contexts involving threat, with a basic behavioral repertoire built on withdrawal, escape, and attack. Conversely, the appetitive system is activated in contexts that promote survival, including sustenance, procreation, and nurturance, with a basic behavioral repertoire of ingestion, copulation, and caregiving. These systems are implemented by neural circuits in the brain, presumably with common outputs to structures mediating the somatic and autonomic physiological systems involved in attention and action (see Davis, 2000; Davis & Lang, 2001;


title: Synthetic Modeling Method for Large Scale Terrain Based on Hydrology

abstract: Generating large scale terrains that conform to the morphology of real scenes is a great challenge for terrain modeling, as simulating complex geometric details is time-consuming and the realistic geographical features are hard to be controlled. In this paper, we propose an efficient modeling method for large scale terrain visualization based on hydrology. To simulate real geographic features, we introduce the hydrology-based Tokunaga river network to guide the terrain generation, and propose a production rule set of river network using procedural modeling. The distribution and structure of river network can be adjusted by user interactions. Ridges are extracted based on river network to provide more skeleton features, and the enrichment method of skeleton features is presented to maintain the morphology of valleys and ridges. Based on the enriched features, diffusion equation is exploited to compute the full elevation field, which can achieve the nature transitions of the regions between skeleton features. Large scale terrain with real morphological features can be generated online through the parallel implementation of diffusion equation. According to user requirements, the augmented virtual terrain can be obtained by blending the selected real terrain with the synthesis terrain seamlessly. The experiments are conducted on digital elevation model, and the results show that the proposed methods can generate large scale terrains that conform to the morphology of real terrain and can well simulate various natural scenes.


A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data


Towards an equal start? Addressing childhood poverty and deprivation


title: ConFirm: Detecting Firmware Modifications in Embedded Systems using Hardware Performance Counters

abstract: Critical infrastructure components nowadays use microprocessor-based embedded control systems. It is often infeasible, however, to employ the same level of security measures used in general purpose computing systems, due to the stringent performance and resource constraints of embedded control systems. Furthermore, as software sits atop and relies on the firmware for proper operation, software-level techniques cannot detect malicious behavior of the firmware. In this work, we propose ConFirm, a low-cost technique to detect malicious modifications in the firmware of embedded control systems by measuring the number of low-level hardware events that occur during the execution of the firmware. In order to count these events, ConFirm leverages the Hardware Performance Counters (HPCs), which readily exist in many embedded processors. We evaluate the detection capability and performance overhead of the proposed technique on various types of firmware running on ARM- and PowerPC-based embedded processors. Experimental results demonstrate that ConFirm can detect all the tested modifications with low performance overhead.


Efficient simulation of light transport in scenes with participating media using photon maps


title: Energy adaptive MAC protocol for wireless sensor networks with RF energy transfer

abstract: Recently, various energy harvesting techniques from ambient environments were proposed as alternative methods for powering sensor nodes, which convert the ambient energy from environments into electricity to power sensor nodes. However, those techniques are not applicable to the wireless sensor networks (WSNs) in the environment with no ambient energy source. To overcome this problem, an RF energy transfer method was proposed to power wireless sensor nodes. However, the RF energy transfer method also has a problem of unfairness among sensor nodes due to the significant difference between their energy harvesting rates according to their positions. In this paper, we propose a medium access control (MAC) protocol for WSNs based on RF energy transfer. The proposed MAC protocol adaptively manages the duty cycle of sensor nodes according to their the amount of harvested energy as well as the contention time of sensor nodes considering fairness among them. Through simulations, we show that our protocol can achieve a high degree of fairness, while maintaining duty cycle of sensor nodes appropriately according to the amount of their harvested energy.


title: Survey on UAV navigation in GPS denied environments

abstract: In the Unmanned Air Vehicle (UAV) navigation the main challenge is estimating and maintaining the accurate values of UAVs position and orientation. The onboard Inertial Measurement Unit (IMU) provide the measurements but it is mainly affected from the accumulated error due to drift in measurements. Traditionally the Global Position System (GPS) measurements of vehicles position data can be fused with IMU measurements to compensate the accumulated error, But the GPS signals is not available everywhere and it will be degraded or fully not available in hostile areas, building structures and water bodies. Researchers already evolved methods to handle the UAV navigation in GPS denied environment by using Vision based navigation like Visual Odometry (VO) and Simultaneous Localisation and Mapping (SLAM). In this survey paper we attempted to understand the existing research towards vision based navigation and finally proposed a Modular Multi-Sensor Data Fusion technique for UAV navigation in the GPS denied environment.


The Great Metamorphosis of The Logos of Life in Ontopoietic Timing


Analysis of the Effect of Comparator Offset on the ENOB of Charge-Sharing SAR ADCs


title: Comparison on physical property, dissolution and disintegration of four launched orally disintegration film (ODF) products for erectile dysfunction

abstract: Commercially available orally disintegration films (ODF) for erectile dysfunction were Viagra L® 50 and 100 mg by Pfizer Pharmaceuticals Korea Ltd., and Mvix S® 50 and 100 mg by SK Chemicals Co. The comparative studies of physical property, disintegration and dissolution time of four ODFs were carried out to evaluate film formulation. For film physical property, thickness and weight uniformity, tensile strength, dissolving pH, hygroscopicity, morphology, flexibility and polymeric characteristics were investigated. In vitro disintegration studies were implemented by six methods. Conventional disintegration method was carried out using USP disintegration apparatus. One side disintegration method and both side disintegration method were performed by using shaking water bath. Slide frame disintegration method was conducted by contact angle measurement. Mouth disintegration method was performed by using two different mediums. One medium was α-amylase free medium, another was add α-amylase. In vitro dissolution studies were carried out using pH 2.0 HCl and pH 6.8 PBS. The results were validated statistically. Film thickness of Mvix S® 50 and 100 mg was thinner than that of Viagra L 50 and 100 mg, respectively. Film tensile strength of Mvix S® 50 mg and 100 mg was much higher than that of Viagra L 50 and 100 mg. However, Viagra L® 50 and 100 mg were more flexible so that it is easier to carry and store. High hygroscopicity and high porosity in Viagra L® could contribute to disintegrate film quickly. In six kinds of disintegration studies, both Viagra L® 50 and 100 mg showed not only muche faster disintegration time but also much faster dissolution rate.


title: A Review of Communication, Driver Characteristics, and Controls Aspects of Cooperative Adaptive Cruise Control (CACC)

abstract: Cooperative adaptive cruise control (CACC) systems have the potential to increase traffic throughput by allowing smaller headway between vehicles and moving vehicles safely in a platoon at a harmonized speed. CACC systems have been attracting significant attention from both academia and industry since connectivity between vehicles will become mandatory for new vehicles in the USA in the near future. In this paper, we review three basic and important aspects of CACC systems: communications, driver characteristics, and controls to identify the most challenging issues for their real-world deployment. Different routing protocols that support the data communication requirements between vehicles in the CACC platoon are reviewed. Promising and suitable protocols are identified. Driver characteristics related issues, such as how to keep drivers engaged in driving tasks during CACC operations, are discussed. To achieve mass acceptance, the control design needs to depict real-world traffic variability such as communication effects, driver behavior, and traffic composition. Thus, this paper also discusses the issues that existing CACC control modules face when considering close to ideal driving conditions.


title: Spatial-temporal mode transmission based upon the QR decomposition of a polynomial channel matrix with uncertainties

abstract: This paper presents an entirely time domain approach for communicating over frequency selective multiple-input multiple-output (MIMO) channels. The proposed system applies a paraunitary matrix to the received signals, which is specifically designed to transform the effective polynomial channel matrix for the system into an upper triangular polynomial matrix. This then enables the MIMO channel equalisation problem to be transformed into a set of single-input single-output (SISO) equalisation problems, by exploiting the upper triangular structure of the transformed channel matrix, which can then be individually solved using Turbo equalisation. In particular, this paper investigates how the system performs when there is error present in the channel matrix state information and discusses the possible errors that are encountered when formulating the QR decomposition (QRD) of a polynomial matrix.


Intuitionistic Fuzzy Soft Matrix and Its Application in Decision Making Problems


title: The Three Generations of Field-Level Networks—Evolution and Compatibility Issues

abstract: Field-level networks have been one of the keys to modern automation systems. Be it in factory, process, or building automation, networks allow for horizontal and vertical integration of distributed devices and functions. This paper reviews the evolution of field-level networks comprising fieldbus systems, industrial Ethernet, and recent industrial wireless networks. The main focus is on demonstrating the continuity in the development of the three generations that ensured backward compatibility at the expense of radical innovation. Given the wide set of modern communication technologies, this paper then discusses how architectures for future automation networks might look. Particular emphasis is put on hybrid architectures for combined wired/wireless networks. A generic concept for integration of multiple wireless segments will be presented that supports seamless roaming for mobile nodes in industrial environment.


title: Ensemble Methods for Multi-label Classification

abstract: Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection by achieving better coverage of labels and inter-label correlations. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared to the RAKEL algorithm and to other state-of-the-art algorithms.


title: Detection and handling of MAC layer misbehavior in wireless networks

abstract: Wireless Medium Access Control (MAC) protocols such as IEEE 802.11 use distributed contention resolution mechanisms for sharing the wireless channel. In this environment, selfish hosts that fail to adhere to the MAC protocol may obtain an unfair share of the channel bandwidth. For example, IEEE 802.11 requires nodes competing for access to the channel to wait for a “backoff” interval, randomly selected from a specified range, before initiating a transmission. Selfish nodes may wait for smaller backoff intervals than well-behaved nodes, thereby obtaining an unfair advantage. We present modifications to the IEEE 802.11 protocol to simplify detection of such selfish hosts. We also present a correction scheme for penalizing selfish misbehavior. Simulation results indicate that our detection and correction schemes are successful in handling MAC layer misbehavior.


title: Learning Stochastic Feedforward Neural Networks

abstract: Multilayer perceptrons (MLPs) or neural networks are popular models used for nonlinear regression and classification tasks. As regressors, MLPs model the conditional distribution of the predictor variables Y given the input variables X. However, this predictive distribution is assumed to be unimodal (e.g. Gaussian). For tasks involving structured prediction, the conditional distribution should be multi-modal, resulting in one-to-many mappings. By using stochastic hidden variables rather than deterministic ones, Sigmoid Belief Nets (SBNs) can induce a rich multimodal distribution in the output space. However, previously proposed learning algorithms for SBNs are not efficient and unsuitable for modeling real-valued data. In this paper, we propose a stochastic feedforward network with hidden layers composed of both deterministic and stochastic variables. A new Generalized EM training procedure using importance sampling allows us to efficiently learn complicated conditional distributions. Our model achieves superior performance on synthetic and facial expressions datasets compared to conditional Restricted Boltzmann Machines and Mixture Density Networks. In addition, the latent features of our model improves classification and can learn to generate colorful textures of objects.


Homomorphic Encryption Standard.


title: The Origin of Cultural Differences in Cognition: The Social Orientation Hypothesis

abstract: A large body of research documents cognitive differences between Westerners and East Asians. Westerners tend to be more analytic and East Asians tend to be more holistic. These findings have often been explained as being due to corresponding differences in social orientation. Westerners are more independent and Easterners are more interdependent. However, comparisons of the cognitive tendencies of Westerners and East Asians do not allow us to rule out alternative explanations for the cognitive differences, such as linguistic and genetic differences, as well as cultural differences other than social orientation. In this review we summarize recent developments which provide stronger support for the social orientation hypothesis.


title: Design and fabrication of X-band Substrate Integrated Waveguide directional coupler

abstract: The compact size of four-port Substrate Integrated Waveguide (SIW) directional coupler designed for X-band application have been simulated and fabricated. The millimeter-wave circuits such as resonator, filter, coupler, power divider and the antenna can be designed by using SIW techniques. This technique appears the past few years and having the smaller size of circuits. It can straightforwardly integrate with the other planar circuit. Ordinary Printed Circuit Board (PCB) method can be utilized in order to complete the fabrication process. The cost of the manufacturing process for SIW is lower compared by using conventional waveguide. Usually, implementation of the directional coupler can be seen at satellite, radar and point-to-point radio. The simulations and measurement of this coupler design have shown the great performances by having the low insertion loss and return loss. Moreover, it also has high isolation and broad operational bandwidth.


title: 3 D hand tracking using Kalman filter in depth space

abstract: Hand gestures are an important type of natural language used in many research areas such as human-computer interaction and computer vision. Hand gestures recognition requires the prior determination of the hand position through detection and tracking. One of the most efficient strategies for hand tracking is to use 2D visual information such as color and shape. However, visual-sensor-based hand tracking methods are very sensitive when tracking is performed under variable light conditions. Also, as hand movements are made in 3D space, the recognition performance of hand gestures using 2D information is inherently limited. In this article, we propose a novel real-time 3D hand tracking method in depth space using a 3D depth sensor and employing Kalman filter. We detect hand candidates using motion clusters and predefined wave motion, and track hand locations using Kalman filter. To verify the effectiveness of the proposed method, we compare the performance of the proposed method with the visual-based method. Experimental results show that the performance of the proposed method out performs visual-based method.


title: Emotive captioning

abstract: Television and film have become important equalization mechanisms for the dissemination and distribution of cultural materials. Closed captioning has allowed people who are deaf and hard of hearing to be included as audience members. However, some of the audio information such as music, sound effects, and speech prosody are not generally provided for in captioning. To include some of this information in closed captions, we generated graphical representations of the emotive information that is normally represented with nondialog sound. Eleven deaf and hard of hearing viewers watched two different video clips containing static and dynamic enhanced captions and compared them with conventional closed captions of the same clips. These viewers then provided verbal and written feedback regarding positive and negative aspects of the various captions. We found that hard of hearing viewers were significantly more positive about this style of captioning than deaf viewers and that some viewers believed that these augmentations were useful and enhanced their viewing experience.


title: Compressed Inverted Indexes for In-Memory Search Engines

abstract: We present the algorithmic core of a full text data base that allows fast Boolean queries, phrase queries, and document reporting using less space than the input text. The system uses a carefully choreographed combination of classical data compression techniques and inverted index based search data structures. It outperforms suffix array based techniques for all the above operations for real world (natural language) texts.


title: Question generation for adaptive assessment for student knowledge modeling in probabilistic domains

abstract: In this paper a question generation approach for adaptive assessment is purposed to estimate the student knowledge model in a probabilistic domain within an intelligent tutoring system. Assessing questions are generated adaptively according to the student knowledge based on two factors (i) the student misconceptions that are entailed in the student knowledge model, and (ii) the information gain maximizing. Updating and verification of the student model are conducted based on the matching between the student’s and model answers to assessing questions. Comparison between using the adapted questions and random questions is investigated. Results suggest that utilizing adapted generated questions increases the approximation accuracy of the student model by 40% in addition to decreasing of the required assessing questions by 50% compared to using fixed questions.


title: A general model of modified Wilkinson power dividers with additional transmission lines

abstract: A generalized Wilkinson power divider with additional transmission lines between the isolation resistor and output ports is presented that decreases parasitic effects. The design equations for the generalized model are given. The experimental results showed the validity of this circuit.


title: A Taxonomy of Attacks in RPL-based Internet of Things

abstract: The growing interest for the Internet of Things is contributing to the large-scale deployment of Low power and Lossy Networks (LLN). These networks support communications amongst objects from the real world, such as home automation devices and embedded sensors, and their interconnection to the Internet. An open standard routing protocol, called RPL, has been specified by the IETF in order to address the specific properties and constraints of these networks. However, this protocol is exposed to a large variety of attacks. Their consequences can be quite significant in terms of network performance and resources. In this paper, we propose to establish a taxonomy of the attacks against this protocol, considering three main categories including attacks targeting network resources, attacks modifying the network topology and attacks related to network traffic. We describe these attacks, analyze and compare their properties, discuss existing counter-measures and their usage from a risk management perspective.


title: CNN-RNN: a large-scale hierarchical image classification framework

abstract: Objects are often organized in a semantic hierarchy of categories, where fine-level categories are grouped into coarse-level categories according to their semantic relations. While previous works usually only classify objects into the leaf categories, we argue that generating hierarchical labels can actually describe how the leaf categories evolved from higher level coarse-grained categories, thus can provide a better understanding of the objects. In this paper, we propose to utilize the CNN-RNN framework to address the hierarchical image classification task. CNN allows us to obtain discriminative features for the input images, and RNN enables us to jointly optimize the classification of coarse and fine labels. This framework can not only generate hierarchical labels for images, but also improve the traditional leaf-level classification performance due to incorporating the hierarchical information. Moreover, this framework can be built on top of any CNN architecture which is primarily designed for leaf-level classification. Accordingly, we build a high performance network based on the CNN-RNN paradigm which outperforms the original CNN (wider-ResNet) and also the current state-of-the-art. In addition, we investigate how to utilize the CNN-RNN framework to improve the fine category classification when a fraction of the training data is only annotated with coarse labels. Experimental results demonstrate that CNN-RNN can use the coarse-labeled training data to improve the classification of fine categories, and in some cases it even surpasses the performance achieved by fully annotated training data. This reveals that, CNN-RNN can alleviate the challenge of specialized and expensive annotation of fine labels.


title: Universal strategies in research and drug discovery based on protein-fragment complementation assays

abstract: Protein-fragment complementation assays (PCAs) can be used to explore the dynamics of protein–protein interactions, and regulatory responses to intrinsic or extrinsic perturbations of biochemical pathways. Michnick and colleagues discuss the rationale behind the PCA design, and its manifold applications for drug discovery.


title: Fast image-based localization using direct 2D-to-3D matching

abstract: Recently developed Structure from Motion (SfM) reconstruction approaches enable the creation of large scale 3D models of urban scenes. These compact scene representations can then be used for accurate image-based localization, creating the need for localization approaches that are able to efficiently handle such large amounts of data. An important bottleneck is the computation of 2D-to-3D correspondences required for pose estimation. Current stateof- the-art approaches use indirect matching techniques to accelerate this search. In this paper we demonstrate that direct 2D-to-3D matching methods have a considerable potential for improving registration performance. We derive a direct matching framework based on visual vocabulary quantization and a prioritized correspondence search. Through extensive experiments, we show that our framework efficiently handles large datasets and outperforms current state-of-the-art methods.


title: A Comparison Based Study on Biometrics for Human Recognition

abstract: A biometric system provides automatic recognition of an individual based on a unique feature or characteristic possessed by the individual. These biometric characteristic may physiological or behavioral. Unlike other identification methods such as id proof, tokens and password, the distinct aspect of biometric recognition comes into light from randomly distributed features in human being. In this paper, I describe the novel comparison based upon various aspects to make easy selection for biometric device deployment in specific environment. This paper proposes a comparison among all kind of biometric system available in the society. The existing computer security systems used at various places like banking, passport, credit cards, smart cards, PIN , access control and network security are using username and passwords for person identification. Biometric systems also introduce an aspect of user convenience; it means one can be authorized by representing himself or herself. In this paper, the main focus is on working principal of biometric technique, the various biometrics systems and their comparisons.


title: Neural correlates of affective empathy and reinforcement learning in boys with conduct problems: fMRI evidence from a gambling task

abstract: Abstract Background Conduct problems (CP) comprise abnormal behaviors associated with aberrant aspects of affective empathy as well as learning. However, behavioral measures for affective empathy are challenging, and previous results concerning learning in patients with CP are inconsistent. Methods Nineteen boys with CP and 24 typically developing (TD) boys aged 11–17 years (M = 14.34, SD = 1.93) participated in the study. An ultimatum-game was applied in order to elicit the feeling of like or dislike towards the opponent for a subsequent gambling task, which was played by the opponents (OTHER-condition) and by the participants themselves (SELF-condition). Functional MRI data were recorded throughout the experiment. Results In accordance with the model of insensitivity to punishment, hypo-activation of the left amygdala, left anterior insula, ventral medial prefrontal cortex (MPFC), and bilateral temporo-parietal junction (TPJ) was observed as a response to losing in participants with CP during the SELF-condition. Callous-unemotional (CU)-traits correlated negatively with activation of amygdala and right TPJ. During the OTHER-condition, TD participants showed activation in brain areas associated with theory of mind (right TPJ, left IFG), and affect regulation (right DLPFC) rather than areas associated with affective empathy. This pattern was not found in adolescents with CP. Moreover, and independently of individual characteristics of their opponents, adolescents with CP demonstrated reward-associated activation (ventral striatum) observing others win, which was positively correlated with CU-traits. This may be interpreted in line with the theory of reward dominance. Conclusions The current study provides support for the theory of abnormal learning processing in adolescents with CP which yields implications for further research as well as clinical practice. The gambling task did not activate affective empathy networks, but was specific for cognitive empathy, inhibition, and affect regulation.


title: A memory insensitive technique for large model simplification

abstract: The authors propose three simple, but significant improvements to the OoCS (Out-of-Core Simplification) algorithm of P. Lindstrom (2000) which increase the quality of approximations and extend the applicability of the algorithm to an even larger class of compute systems. The original OoCS algorithm has memory complexity that depends on the size of the output mesh, but no dependency on the size of the input mesh. That is, it can be used to simplify meshes of arbitrarily large size, but the complexity of the output mesh is limited by the amount of memory available. Our first contribution is a version of OoCS that removes the dependency of having enough memory to hold (even) the simplified mesh. With our new algorithm, the whole process is made essentially independent of the available memory on the host computer. Our new technique uses disk instead of main memory, but it is carefully designed to avoid costly random accesses. Our two other contributions improve the quality of the approximations generated by OoCS. We propose a scheme for preserving surface boundaries which does not use connectivity information, and a scheme for constraining the position of the "representative vertex" of a grid cell to an optimal position inside the cell.


title: On-line Clustering for Real-Time Topic Detection in Social Media Streaming Data

abstract: The continuous growth of social networks and the active use of social media services result in massive amounts of user-generated data. Worldwide, more and more people report and distribute up-to-date information about almost any topic. At the same time, there is an increasing interest in information that can be gathered from this data. The popularity of new services and technologies that produce and consume data streams imposes new challenges on the analysis, namely, in terms of handling high volumes of noisy data in real-time. Since social media analysis is concerned with investigating current topics and actual events around the world, there is a pronounced need to detect topics in the data and to directly display their occurrence to analysts or other users. In this paper, we present an on-line clustering approach, which builds on traditional data mining methods to address the new requirements of data stream mining: (a) fast incremental processing of incoming stream objects, (b) compactness of data representation, and (c) efficient identification of changes in evolving clustering models.


title: Research of real-time task in Xen virtualization environment

abstract: Aimed at the needs for scheduling real-time tasks in the environment of open real-time system and embedded system, a scheduling mechanism based on messages has been suggested and applied on the Xen virtual machine, and a Multi-Real-Time-Task model based on VCPU is established. Influence on the real-time scheduling is analyzed and solutions will be given. Experiment results prove that scheduling based on this mechanism works well, since in current experiment environment the response time of compute-intensive real-time task is less than 20us, and the response time of event-driven real-time task is less than 30us, both results are quite stable.


title: Application of Social Cognitive Theory in Predicting Childhood Obesity Prevention Behaviors in Overweight and Obese Iranian Adolescents

abstract: The aim of this cross-sectional study was to use social cognitive theory to predict overweight and obesity behaviors in adolescent girls in Iran. Valid and reliable questionnaires about nutritional...


title: Principles of traditional animation applied to 3D computer animation

abstract: This paper describes the basic principles of traditional 2D hand drawn animation and their application to 3D computer animation. After describing how these principles evolved, the individual principles are detailed, addressing their meanings in 2D hand drawn animation and their application to 3D computer animation. This should demonstrate the importance of these principles to quality 3D computer animation.


title: Detecting unknown malicious code by applying classification techniques on OpCode patterns

abstract: In previous studies classification algorithms were employed successfully for the detection of unknown malicious code. Most of these studies extracted features based on byte n-gram patterns in order to represent the inspected files. In this study we represent the inspected files using OpCode n-gram patterns which are extracted from the files after disassembly. The OpCode n-gram patterns are used as features for the classification process. The classification process main goal is to detect unknown malware within a set of suspected files which will later be included in antivirus software as signatures. A rigorous evaluation was performed using a test collection comprising of more than 30,000 files, in which various settings of OpCode n-gram patterns of various size representations and eight types of classifiers were evaluated. A typical problem of this domain is the imbalance problem in which the distribution of the classes in real life varies. We investigated the imbalance problem, referring to several real-life scenarios in which malicious files are expected to be about 10% of the total inspected files. Lastly, we present a chronological evaluation in which the frequent need for updating the training set was evaluated. Evaluation results indicate that the evaluated methodology achieves a level of accuracy higher than 96% (with TPR above 0.95 and FPR approximately 0.1), which slightly improves the results in previous studies that use byte n-gram representation. The chronological evaluation showed a clear trend in which the performance improves as the training set is more updated.


title: Facial Expression Recognition

abstract: Facial expression analysis is rapidly becoming an area of intense interest in computer science and human-computer interaction design communities. The most expressive way humans display emotions is through facial expressions. In this paper a method is implemented using 2D appearance-based local approach for the extraction of intransient facial features and recognition of four facial expressions. The algorithm implements Radial Symmetry Transform and further uses edge projection analysis for feature extraction and creates a dynamic spatio- temporal representation of the face, followed by classification into one of the expression classes. The algorithm achieves an accuracy of 81.0% for facial expression recognition from grayscale image.


title: A query integrity assurance scheme for accessing outsourced spatial databases

abstract: With the trend of cloud computing, outsourcing databases to third party service providers is becoming a common practice for data owners to decrease the cost of managing and maintaining databases in-house. In conjunction, due to the popularity of location-based-services (LBS), the need for spatial data (e.g., gazetteers, vector data) is increasing dramatically. Consequently, there is a noticeably new tendency of outsourcing spatial datasets by data collectors. Two main challenges with outsourcing datasets are to keep the data private (from the data provider) and to ensure the integrity of the query result (for the clients). Unfortunately, most of the techniques proposed for privacy and integrity do not extend to spatial data in a straightforward manner. Hence, recent studies proposed various techniques to support either privacy or integrity (but not both) on spatial datasets. In this paper, for the first time, we propose a technique that can ensure both privacy and integrity for outsourced spatial data. In particular, we first use a one-way spatial transformation method based on Hilbert curves, which encrypts the spatial data before outsourcing and, hence, ensures its privacy. Next, by probabilistically replicating a portion of the data and encrypting it with a different encryption key, we devise a technique for the client to audit the trustworthiness of the query results. We show the applicability of our approach for both k-nearest-neighbor queries and spatial range queries, which are the building blocks of any LBS application. We also design solutions to guarantee the freshness of outsourced spatial databases. Finally, we evaluate the validity and performance of our algorithms with security analyses and extensive simulations.


title: Few-Layer MoS2: A Promising Layered Semiconductor

abstract: Due to the recent expanding interest in two-dimensional layered materials, molybdenum disulfide (MoS2) has been receiving much research attention. Having an ultrathin layered structure and an appreciable direct band gap of 1.9 eV in the monolayer regime, few-layer MoS2 has good potential applications in nanoelectronics, optoelectronics, and flexible devices. In addition, the capability of controlling spin and valley degrees of freedom makes it a promising material for spintronic and valleytronic devices. In this review, we attempt to provide an overview of the research relevant to the structural and physical properties, fabrication methods, and electronic devices of few-layer MoS2. Recent developments and advances in studying the material are highlighted.


title: Smart home – digitally engineered domestic life

abstract: The purpose of the Smart Home Project is to devise a set of intelligent home appliances that can provide an awareness of the users' needs, providing them with a better home life experience without overpowering them with complex technologies and intuitive user interfaces. Our main aim for the project is to improve day-to-day home life with smart computer technologies while still keeping the home life as normal as possible – we refer to this as “digitally engineering analogue home life”.


title: The Design and Implementation of a Mobile Learning Resource

abstract: The convergence of mobile communications and handheld computers offers the opportunity to develop technology that will assist individuals and groups to learn anytime, anywhere. We describe the theory-informed design, implementation and evaluation of a handheld learning device. It is intended to support children to capture everyday events such as images, notes and sounds, to relate them to web-based learning resources, to organise these into a visual knowledge map, and to share them with other learners and teachers. A working prototype system, for children aged 9–11, is discussed and evaluated, as an exemplar of personal mobile systems for life-long learning.


title: Urban Public Libraries Do Not Yet Meet Benchmarks for Web Accessibility by Individuals with Disabilities

abstract: A Review of: ::: Maatta Smith, S. L. (2014). Web Accessibility Assessment of Urban Public Library Websites. Public Library Quarterly, 33(3), 187-204. http://dx.doi.org/10.1080/01616846.2014.937207 ::: ::: Abstract ::: ::: Objective – To determine the extent to which urban public libraries in the United States of America provide web sites which are readily accessible to individuals with disabilities with reference to the Urban Library Council’s EDGE initiative (specifically Benchmark 11, “Technology Inclusiveness”). ::: ::: Design – Web site evaluation. ::: ::: Setting – Urban public libraries in the United States of America. ::: ::: Subjects – The 127 library systems, which were both members of the Urban Libraries Council at the time of the study and located in the United States of America. ::: ::: Methods – Using the “everyday life information seeking” conceptual framework, an assessment of each of the web sites of the purposive sample of public library systems was performed by an online evaluation tool as well as visually and physically to determine web accessibility and, by extension, technology inclusiveness. ::: ::: Main Results – The results of the online accessibility evaluation tool revealed that not one of the sites surveyed was free of errors or alerts. Contrast errors (related to color combinations), missing alternative text (providing text alternatives for visual elements), and missing form labels (thereby preventing screen readers from performing searches and navigating to results) were the most common problems. Results of visual and physical scans revealed that many sites lacked specific links and/or resources for persons with disabilities, as well as noting that the resources available used oblique language and required many clicks to access. In addition, the vast majority neglected to feature links to national resources such as the National Library Service for the Blind and Physically Handicapped. ::: ::: Conclusions – The web sites of urban public libraries are not yet completely accessible for persons with disabilities. At the very least they need coding fixes and ongoing maintenance to address the kinds of issues found by the online web evaluation tool used. In addition, resources for disabled persons should be prominently and clearly linked and promoted. Further research is called for, both in non-urban library systems and in testing a wider range of access technologies. Improvement efforts should acknowledge that web design that improves access for persons with disabilities serves the broader community as well.


title: Conversational Exploratory Search via Interactive Storytelling

abstract: Conversational interfaces are likely to become more efficient, intuitive and engaging way for human-computer interaction than today's text or touch-based interfaces. Current research efforts concerning conversational interfaces focus primarily on question answering functionality, thereby neglecting support for search activities beyond targeted information lookup. Users engage in exploratory search when they are unfamiliar with the domain of their goal, unsure about the ways to achieve their goals, or unsure about their goals in the first place. Exploratory search is often supported by approaches from information visualization. However, such approaches cannot be directly translated to the setting of conversational search. ::: In this paper we investigate the affordances of interactive storytelling as a tool to enable exploratory search within the framework of a conversational interface. Interactive storytelling provides a way to navigate a document collection in the pace and order a user prefers. In our vision, interactive storytelling is to be coupled with a dialogue-based system that provides verbal explanations and responsive design. We discuss challenges and sketch the research agenda required to put this vision into life.


title: Distributed scheduling scheme for video streaming over multi-channel multi-radio multi-hop wireless networks

abstract: An important issue of supporting multi-user video streaming over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resources while, at the same time, to meet each video's Quality of Service (QoS) requirement. In this work, we study the problem of video streaming over multi-channel multi-radio multihop wireless networks, and develop fully distributed scheduling schemes with the goals of minimizing the video distortion and achieving certain fairness. We first construct a general distortion model according to the network?s transmission mechanism, as well as the rate distortion characteristics of the video. Then, we formulate the scheduling as a convex optimization problem, and propose a distributed solution by jointly considering channel assignment, rate allocation, and routing. Specifically, each stream strikes a balance between the selfish motivation of minimizing video distortion and the global performance of minimizing network congestions. Furthermore, we extend the proposed scheduling scheme by addressing the fairness problem. Unlike prior works that target at users' bandwidth or demand fairness, we propose a media-aware distortion-fairness strategy which is aware of the characteristics of video frames and ensures max-min distortion-fairness sharing among multiple video streams. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes.


title: Smart parking: Parking occupancy monitoring and visualization system for smart cities

abstract: Parking space is becoming a serious problem due to the day-by-day increase in number of vehicles on the road. Particularly, in cities with large population, or in places where sports or artistic events are scheduled, looking for parking space is a major problem and finding a parking spot can be a frustrating experience. In order to combat this problem, some parking lots have introduced sensors to detect when a car enters or leaves a parking lot in order to track capacity and alert drivers if they are full. This is a partial solution that allows drivers to determine if a parking lot has open parking spots, but not the exact location of those spots. In this paper, we present a prototype of smart parking system using wireless sensor technology and networks. Using a Wireless Sensor Network (WSN), parking spot statuses (occupied or idle) are detected and transmitted to a database. This information then be accessed by users through website or mobile app (application) to receive real-time updates. This system should provide users with near instantaneous updates of available parking spots while the WSN allows for flexibility of sensor placement. With the successful implementation of smart parking, the economical and time costs associated with traffic jams, cost associated with wasted gas fuel, and time looking for an empty parking space that are caused by inefficient parking will be significant reduced.


title: Abstraction of man-made shapes

abstract: Man-made objects are ubiquitous in the real world and in virtual environments. While such objects can be very detailed, capturing every small feature, they are often identified and characterized by a small set of defining curves. Compact, abstracted shape descriptions based on such curves are often visually more appealing than the original models, which can appear to be visually cluttered. We introduce a novel algorithm for abstracting three-dimensional geometric models using characteristic curves or contours as building blocks for the abstraction. Our method robustly handles models with poor connectivity, including the extreme cases of polygon soups, common in models of man-made objects taken from online repositories. In our algorithm, we use a two-step procedure that first approximates the input model using a manifold, closed envelope surface and then extracts from it a hierarchical abstraction curve network along with suitable normal information. The constructed curve networks form a compact, yet powerful, representation for the input shapes, retaining their key shape characteristics while discarding minor details and irregularities.


title: A model of the relationship between psychological characteristics, mobile phone addiction and use of mobile phones by Taiwanese university female students

abstract: While many researches have analyzed the psychological antecedents of mobile phone addiction and mobile phone usage behavior, their relationship with psychological characteristics remains mixed. We investigated the relationship between psychological characteristics, mobile phone addiction and use of mobile phones for 269 Taiwanese female university students who were administered Rosenberg's self-esteem scale, Lai's personality inventory, and a mobile phone usage questionnaire and mobile phone addiction scale. The result showing that: (1) social extraversion and anxiety have positive effects on mobile phone addiction, and self-esteem has negative effects on mobile phone addiction. (2) Mobile phone addiction has a positive predictive effect on mobile phone usage behavior. The results of this study identify personal psychological characteristics of Taiwanese female university students which can significantly predict mobile phone addiction; female university students with mobile phone addiction will make more phone calls and send more text messages. These results are discussed and suggestions for future research for school and university students are provided.


title: In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking

abstract: The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos. In this work, we describe a new method to expose fake face videos generated with neural networks. Our method is based on detection of eye blinking in the videos, which is a physiological signal that is not well presented in the synthesized fake videos. Our method is tested over benchmarks of eye-blinking detection datasets and also show promising performance on detecting videos generated with DeepFake.


title: Efficient sparse-matrix multi-vector product on GPUs

abstract: Sparse Matrix-Vector (SpMV) and Sparse Matrix-Multivector (SpMM) products are key kernels for computational science and data science. While GPUs offer significantly higher peak performance and memory bandwidth than multicore CPUs, achieving high performance on sparse computations on GPUs is very challenging. A tremendous amount of recent research has focused on various GPU implementations of the SpMV kernel. But the multi-vector SpMM kernel has received much less attention. In this paper, we present an in-depth analysis to contrast SpMV and SpMM, and develop a new sparse-matrix representation and computation approach suited to achieving high data-movement efficiency and effective GPU parallelization of SpMM. Experimental evaluation using the entire SuiteSparse matrix suite demonstrates significant performance improvement over existing SpMM implementations from vendor libraries.


Learning to Plan with Portable Symbols


title: Towards Materials for Computational Heirlooms: Blockchains and Wristwatches

abstract: This paper explores the contrasting notions of "permanance and disposability," "the digital and the physical," and "symbolism and function" in the context of interaction design. Drawing from diverse streams of knowledge, we describe a novel design direction for enduring computational heirlooms based on the marriage of decentralized, trustless software and durable mobile hardware. To justify this concept, we review prior research; attempt to redefine the notion of "material;" propose blockchain-based software as a particular digital material to serve as a substrate for computational heirlooms; and argue for the use of mobile artifacts, informed in terms of their materials and formgiving practices by mechanical wristwatches, as its physical embodiment and functional counterpart. This integration is meant to enable mobile and ubiquitous interactive systems for the storing, experiencing, and exchanging value throughout multiple human lifetimes; showcasing the feats of computational sciences and crafts; and enabling novel user experiences.


title: Toward efficient task assignment and motion planning for large-scale underwater missions

abstract: An Autonomous Underwater Vehicle (AUV) needs to acquire a certain degree of autonomy for any particular underwater mission to fulfill the mission objectives successfully and ensure its safety in all stages of the mission in a large scale operating filed. In this paper, a novel combinatorial conflict-free-task assignment strategy consisting an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method is established upon the heuristic search potency of the Particle Swarm Optimisation (PSO) algorithm to address the discrete nature of routing-task assignment approach and the complexity of NP-hard path planning problem. The proposed hybrid method is highly efficient for having a reactive guidance framework that guarantees successful completion of missions specifically in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of vehicle's autonomy by relying on its reactive nature and capability of providing fast feasible solutions.


The Effects of Moral Intensity on Whistleblowing Behaviors of Accounting Professionals


title: Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions

abstract: To test simulation models of pedestrian flows, we have performed experiments for corridors, bottleneck areas, and intersections. Our evaluations of video recordings show that the geometric boundary conditions are not only relevant for the capacity of the elements of pedestrian facilities, they also influence the time gap distribution of pedestrians, indicating the existence of self-organization phenomena. After calibration of suitable models, these findings can be used to improve design elements of pedestrian facilities and egress routes. It turns out that "obstacles" can stabilize flow patterns and make them more fluid. Moreover, intersecting flows can be optimized, utilizing the phenomenon of "stripe formation." We also suggest increasing diameters of egress routes in stadia, theaters, and lecture halls to avoid long waiting times for people in the back, and shock waves due to impatience in cases of emergency evacuation. Moreover, zigzag-shaped geometries and columns can reduce the pressure in panicking crowds. The proposed design solutions are expected to increase the efficiency and safety of train stations, airport terminals, stadia, theaters, public buildings, and mass events in the future. As application examples we mention the evacuation of passenger ships and the simulation of pilgrim streams on the Jamarat bridge. Adaptive escape guidance systems, optimal way systems, and simulations of urban pedestrian flows are addressed as well.


title: A compensatory role for declarative memory in neurodevelopmental disorders

abstract: Abstract Most research on neurodevelopmental disorders has focused on their abnormalities. However, what remains intact may also be important. Increasing evidence suggests that declarative memory, a critical learning and memory system in the brain, remains largely functional in a number of neurodevelopmental disorders. Because declarative memory remains functional in these disorders, and because it can learn and retain numerous types of information, functions, and tasks, this system should be able to play compensatory roles for multiple types of impairments across the disorders. Here, we examine this hypothesis for specific language impairment, dyslexia, autism spectrum disorder, Tourette syndrome, and obsessive–compulsive disorder. We lay out specific predictions for the hypothesis and review existing behavioral, electrophysiological, and neuroimaging evidence. Overall, the evidence suggests that declarative memory indeed plays compensatory roles for a range of impairments across all five disorders. Finally, we discuss diagnostic, therapeutic and other implications.


Instantaneous Magnetic Field Distribution in Brushless Permanent Magnet dc Motors , Part 111 : Effect of Stator


title: Automatic manga colorization with color style by generative adversarial nets

abstract: Many comic books are now published as digital books, which easily provide color contents compared to the physical books. The motivation of automatic colorization of comic books now arises. Previous studies colorize sketches without other clues or with spatial color annotations. They are expected to reduce workloads of comic artists but still require spatial color annotations for desirable colorizations. This study introduces a color style information and combines it with conditional adversarially learned inference. The experimental results demonstrate that the objects are painted with colors depending on the color style information and that the color style information extracted from a color image supports to painting an object with a desirable color.


Infinite Superimposed Discrete All-Pole Modeling for Multipitch Analysis of Wavelet Spectrograms


title: Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation

abstract: Semantic image segmentation is a fundamental task in image understanding. Per-pixel semantic labelling of an image benefits greatly from the ability to consider region consistency both locally and globally. However, many Fully Convolutional Network based methods do not impose such consistency, which may give rise to noisy and implausible predictions. We address this issue by proposing a dense multi-label network module that is able to encourage the region consistency at different levels. This simple but effective module can be easily integrated into any semantic segmentation systems. With comprehensive experiments, we show that the dense multi-label can successfully remove the implausible labels and clear the confusion so as to boost the performance of semantic segmentation systems.


title: Measuring user acceptance of emerging information technologies: an assessment of possible method biases

abstract: The measurement scales for the perceived usefulness and perceived ease-of-use constructs introduced by F.D. Davis (1989) become widely used for forecasting user acceptance of emerging information technologies. An experiment was conducted to examine whether grouping of items caused artifactual inflation of reliability and validity measures. We found support for our hypothesis that the reliability and validity stemmed not from item grouping but from the constructs of perceived usefulness and perceived ease-of-use being clearly defined, and the items used to measure each of these constructs clearly capturing the essence of the construct. >


Inference for Knowledge Graph Construction


title: Nerf c-means: Non-Euclidean relational fuzzy clustering

abstract: Abstract The relational fuzzy c-means (RFCM) algorithm can be used to cluster a set of n objects described by pair-wise dissimilarity values if (and only if) there exist n points in R n − 1 whose squared Euclidean distances precisely match the given dissimilarity data. This strong restriction on the dissimilarity data renders RFCM inapplicable to most relational clustering problems. This paper substantially improves RFCM by generalizing it to the case of arbitrary (symmetric) dissimilarity data. The generalization is obtained using a computationally efficient modification of the existing algorithm that is equivalent to applying a “spreading” transformation to the dissimilarity data. While the method given applies specifically to dissimilarity data, a simple transformation can be used to convert similarity relations into dissimilarity data, so the method is applicable to any numerical relational data that are positive, reflexive (or anti-reflexive) and symmetric. Numerical examples illustrate and compare the present approach to problems that can be studied with alternatives such as the linkage algorithms.


title: Mining Roles with Multiple Objectives

abstract: With the growing adoption of Role-Based Access Control (RBAC) in commercial security and identity management products, how to facilitate the process of migrating a non-RBAC system to an RBAC system has become a problem with significant business impact. Researchers have proposed to use data mining techniques to discover roles to complement the costly top-down approaches for RBAC system construction. An important problem is how to construct RBAC systems with low complexity. In this article, we define the notion of weighted structural complexity measure and propose a role mining algorithm that mines RBAC systems with low structural complexity. Another key problem that has not been adequately addressed by existing role mining approaches is how to discover roles with semantic meanings. In this article, we study the problem in two primary settings with different information availability. When the only information is user-permission relation, we propose to discover roles whose semantic meaning is based on formal concept lattices. We argue that the theory of formal concept analysis provides a solid theoretical foundation for mining roles from a user-permission relation. When user-attribute information is also available, we propose to create roles that can be explained by expressions of user-attributes. Since an expression of attributes describes a real-world concept, the corresponding role represents a real-world concept as well. Furthermore, the algorithms we propose balance the semantic guarantee of roles with system complexity. Finally, we indicate how to create a hybrid approach combining top-down candidate roles. Our experimental results demonstrate the effectiveness of our approaches.


title: Detecting Phishing Web Pages with Visual Similarity Assessment Based on Earth Mover's Distance (EMD)

abstract: An effective approach to phishing Web page detection is proposed, which uses Earth mover's distance (EMD) to measure Web page visual similarity. We first convert the involved Web pages into low resolution images and then use color and coordinate features to represent the image signatures. We use EMD to calculate the signature distances of the images of the Web pages. We train an EMD threshold vector for classifying a Web page as a phishing or a normal one. Large-scale experiments with 10,281 suspected Web pages are carried out to show high classification precision, phishing recall, and applicable time performance for online enterprise solution. We also compare our method with two others to manifest its advantage. We also built up a real system which is already used online and it has caught many real phishing cases


title: TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

abstract: Heart rate monitoring using wrist-type photoplethysmographic signals during subjects’ intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects’ hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.


title: Percutaneous laser lumbar disc decompression - mechanism of action, indications and contraindications.

abstract: This article describes the development of minimally invasive methods in the treatment of lumbar discopathy, with particular attention to percutaneous laser disc decompression (PLDD). The authors discus the therapeutic operating mechanism of PLDD, emphasizing the importance of the thermal characteristics of laser light, which is responsible for the vaporization and ablation of a small amount of tissue from the nucleus pulposus. This causes a significant reduction in pressure in the closed structure of the disc, and consequently reduced compression exerted by the disk on the dural sac and the nerve roots. Improvement in the flow of cerebro-spinal fluid has also been observed on the level at which the operation is performed. On the basis of our own experience and the reports of other authors, we have specified indications and contra-indications for PLDD. Our conclusion is that PLDD is an effective treatment method for low back pain and ischialgia caused by protrusion or herniation of the nucleus pulposus, with elimination or significant reduction in symptoms in over 75% of those treated; reduction or resolution of neurological deficits that arise in the course of lumbar discopathy has also been observed. This method enables one-stage treatment of multi-level degenerative changes in the intervertebral disc. The only absolute contraindications for PLDD are the presence of sequestration, disturbances in blood coagulation, and bacterial infection.


title: Nonuniform Sampling and Reconstruction in Shift-Invariant Spaces

abstract: This article discusses modern techniques for nonuniform sampling and reconstruction of functions in shift-invariant spaces. It is a survey as well as a research paper and provides a unified framework for uniform and nonuniform sampling and reconstruction in shift-invariant subspaces by bringing together wavelet theory, frame theory, reproducing kernel Hilbert spaces, approximation theory, amalgam spaces, and sampling. Inspired by applications taken from communication, astronomy, and medicine, the following aspects will be emphasized: (a) The sampling problem is well defined within the setting of shift-invariant spaces. (b) The general theory works in arbitrary dimension and for a broad class of generators. (c) The reconstruction of a function from any sufficiently dense nonuniform sampling set is obtained by efficient iterative algorithms. These algorithms converge geometrically and are robust in the presence of noise. (d) To model the natural decay conditions of real signals and images, the sampling theory is developed in weighted L p-spaces.


Forwarding-Loop Attacks in Content Delivery Networks


title: A unified data mining solution for authorship analysis in anonymous textual communications

abstract: The cyber world provides an anonymous environment for criminals to conduct malicious activities such as spamming, sending ransom e-mails, and spreading botnet malware. Often, these activities involve textual communication between a criminal and a victim, or between criminals themselves. The forensic analysis of online textual documents for addressing the anonymity problem called authorship analysis is the focus of most cybercrime investigations. Authorship analysis is the statistical study of linguistic and computational characteristics of the written documents of individuals. This paper is the first work that presents a unified data mining solution to address authorship analysis problems based on the concept of frequent pattern-based writeprint. Extensive experiments on real-life data suggest that our proposed solution can precisely capture the writing styles of individuals. Furthermore, the writeprint is effective to identify the author of an anonymous text from a group of suspects and to infer sociolinguistic characteristics of the author.


title: Semantic Clustering-Based Deep Hypergraph Model for Online Reviews Semantic Classification in Cyber-Physical-Social Systems

abstract: Sentiment classification of online reviews is playing an increasingly important role for both consumers and businesses in cyber-physical-social systems. However, existing works ignore the semantic correlation among different reviews, causing the ineffectiveness for sentiment classification. In this paper, a word embedding clustering-based deep hypergraph model (ECDHG) is proposed for the sentiment analysis of online reviews. The ECDHG introduces external knowledge by employing the pre-training word embeddings to express reviews. Then, semantic units are detected under the supervision of semantic cliques discovered by an improved hierarchical fast clustering algorithm. Convolutional neural networks are connected to extract the high-order textual and semantic features of reviews. Finally, the hypergraph can be constructed based on high-order relations of samples for the sentiment classification of reviews. Experiments are performed on five-domain data sets including movie, book, DVD, kitchen, and electronic to assess the performance of the proposed model compared with other seven models. The results validate that our model outperforms the compared methods in classification accuracy.


OBELISK - One Kernel to Solve Nearly Everything: Unified 3D Binary Convolutions for Image Analysis


title: Correction of phenotype misclassification based on high-discrimination genetic predictive risk models.

abstract: Misclassification of phenotype status can seriously affect accuracy in association studies, including studies of genetic risk factors. A common problem is the classification of participants as nondiseased because of insufficient diagnostic workup or because participants have not been followed up long enough to develop disease. Some validated predictive models may have high discrimination in predicting disease. We suggest that information from such models can be used to predict the risk that a nondiseased participant will eventually develop disease and to recode the status of participants predicted to be at highest risk. We evaluate conditions under which recoding results in a maximal net improvement in the accuracy of phenotype classification. Net improvement is expected only when the positive likelihood ratio of the predictive model is larger than the inverse of the odds of disease among apparently nondiseased controls. We conducted simulations to probe the impact of reclassification on the power to detect new risk factors under several scenarios of classification accuracy of the previously developed models. We also apply this framework to a validated model of progression to advanced age-related macular degeneration that uses genetic and nongenetic variables (area under the curve = 0.915). In the training cohort (n = 2,937) and a separate validation cohort (n = 1,227), 195-272 and 78-91 nonprogressor participants, respectively, were reclassified as progressors. Correction of phenotype misclassification based on highly informative predictive models may be helpful in identifying additional genetic and other risk factors, when there are validated risk factors that provide strong discriminating ability.