The Role of Trust in Customer Online Shopping Behavior: Perspective of Technology Acceptance Model


title: A SURVEY OF ALGORITHMS FOR KEYWORD SEARCH ON GRAPH DATA

abstract: In this chapter, we survey methods that perform keyword search on graph data. Keyword search provides a simple but user-friendly interface to retrieve information from complicated data structures. Since many real life datasets are represented by trees and graphs, keyword search has become an attractive mechanism for data of a variety of types. In this survey, we discuss methods of keyword search on schema graphs, which are abstract representation for XML data and relational data, and methods of keyword search on schema-free graphs. In our discussion, we focus on three major challenges of keyword search on graphs. First, what is the semantics of keyword search on graphs, or, what qualifies as an answer to a keyword search; second, what constitutes a good answer, or, how to rank the answers; third, how to perform keyword search efficiently. We also discuss some unresolved challenges and propose some new research directions on this topic.


title: Body-IMU autocalibration for inertial hip and knee joint tracking

abstract: Sensor to body calibration is a key requirement for capturing accurate body movements in applications based on wearable systems. In this paper, we consider the specific problem of estimating the positions of multiple inertial measurement units (IMUs) relative to the adjacent body joints. To derive an efficient, robust and precise method based on a practical procedure is a crucial as well as challenging task when developing a wearable system with multiple embedded IMUs. In this work, first, we perform a theoretical analysis of an existing position calibration method, showing its limited applicability for the hip and knee joint. Based on this, we propose a method for simultaneously estimating the positions of three IMUs (mounted on pelvis, upper leg, lower leg) relative to these joints. The latter are here considered as an ensemble. Finally, we perform an experimental evaluation based on simulated and real data, showing the improvements of our calibration method as well as lines of future work.


title: Leveraging High-Dimensional Side Information for Top-N Recommendation

abstract: Top-$N$ recommender systems typically utilize side information to address the problem of data sparsity. As nowadays side information is growing towards high dimensionality, the performances of existing methods deteriorate in terms of both effectiveness and efficiency, which imposes a severe technical challenge. In order to take advantage of high-dimensional side information, we propose in this paper an embedded feature selection method to facilitate top-$N$ recommendation. In particular, we propose to learn feature weights of side information, where zero-valued features are naturally filtered out. We also introduce non-negativity and sparsity to the feature weights, to facilitate feature selection and encourage low-rank structure. Two optimization problems are accordingly put forward, respectively, where the feature selection is tightly or loosely coupled with the learning procedure. Augmented Lagrange Multiplier and Alternating Direction Method are applied to efficiently solve the problems. Experiment results demonstrate the superior recommendation quality of the proposed algorithm to that of the state-of-the-art alternatives.


title: A Fully Pipelined and Dynamically Composable Architecture of CGRA

abstract: Future processor chips will not be limited by the transistor resources, but will be mainly constrained by energy efficiency. Reconfigurable fabrics bring higher energy efficiency than CPUs via customized hardware that adapts to user applications. Among different reconfigurable fabrics, coarse-grained reconfigurable arrays (CGRAs) can be even more efficient than fine-grained FPGAs when bit-level customization is not necessary in target applications. CGRAs were originally developed in the era when transistor resources were more critical than energy efficiency. Previous work shares hardware among different operations via modulo scheduling and time multiplexing of processing elements. In this work, we focus on an emerging scenario where transistor resources are rich. We develop a novel CGRA architecture that enables full pipelining and dynamic composition to improve energy efficiency by taking full advantage of abundant transistors. Several new design challenges are solved. We implement a prototype of the proposed architecture in a commodity FPGA chip for verification. Experiments show that our architecture can fully exploit the energy benefits of customization for user applications in the scenario of rich transistor resources.


title: An Approach for Schema Extraction of JSON and Extended JSON Document Collections

abstract: JSON documents are raising as a common format for data representation due to the increasing popularity of NoSQL document-oriented databases. One of the reasons for such a popularity is their ability to handle large volumes of data at the absence of an explicit data schema. However, schema information is sometimes essential for applications during data retrieval, integration and analysis tasks, for example. Given this context, this paper presents an approach that extracts a schema from a JSON or Extended JSON document collection stored in a NoSQL document-oriented database or other document repository. Aggregation operations are considered in order to obtain a schema for each distinct structure in the collection, and a hierarchical data structure is proposed to group these schemas in order to generate a global schema in JSON Schema format. Experiments conducted on actual datasets, like DBPedia and Foursquare, demonstrate that the accuracy of the generated schemas is equivalent or even superior than related work.


title: Missing Value Imputation Based on Deep Generative Models

abstract: Missing values widely exist in many real-world datasets, which hinders the performing of advanced data analytics. Properly filling these missing values is crucial but challenging, especially when the missing rate is high. Many approaches have been proposed for missing value imputation (MVI), but they are mostly heuristics-based, lacking a principled foundation and do not perform satisfactorily in practice. In this paper, we propose a probabilistic framework based on deep generative models for MVI. Under this framework, imputing the missing entries amounts to seeking a fixed-point solution between two conditional distributions defined on the missing entries and latent variables respectively. These distributions are parameterized by deep neural networks (DNNs) which possess high approximation power and can capture the nonlinear relationships between missing entries and the observed values. The learning of weight parameters of DNNs is performed by maximizing an approximation of the log-likelihood of observed values. We conducted extensive evaluation on 13 datasets and compared with 11 baselines methods, where our methods largely outperforms the baselines.


title: Basic tasks of sentiment analysis

abstract: Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about.


title: Community Mining from Signed Social Networks

abstract: Many complex systems in the real world can be modeled as signed social networks that contain both positive and negative relations. Algorithms for mining social networks have been developed in the past; however, most of them were designed primarily for networks containing only positive relations and, thus, are not suitable for signed networks. In this work, we propose a new algorithm, called FEC, to mine signed social networks where both positive within-group relations and negative between-group relations are dense. FEC considers both the sign and the density of relations as the clustering attributes, making it effective for not only signed networks but also conventional social networks including only positive relations. Also, FEC adopts an agent-based heuristic that makes the algorithm efficient (in linear time with respect to the size of a network) and capable of giving nearly optimal solutions. FEC depends on only one parameter whose value can easily be set and requires no prior knowledge on hidden community structures. The effectiveness and efficacy of FEC have been demonstrated through a set of rigorous experiments involving both benchmark and randomly generated signed networks.


title: Unsupervised Learning of Spoken Language with Visual Context

abstract: Humans learn to speak before they can read or write, so why can’t computers do the same? In this paper, we present a deep neural network model capable of rudimentary spoken language acquisition using untranscribed audio training data, whose only supervision comes in the form of contextually relevant visual images. We describe the collection of our data comprised of over 120,000 spoken audio captions for the Places image dataset and evaluate our model on an image search and annotation task. We also provide some visualizations which suggest that our model is learning to recognize meaningful words within the caption spectrograms.


title: Evaluation of the Effects of Vaccinium arctostaphylos L. Fruit Extract on Serum Lipids and hs-CRP Levels and Oxidative Stress in Adult Patients with Hyperlipidemia: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial

abstract: Background. Dyslipidemia produces atherosclerosis, which in turn results in coronary artery disease (CAD). Atherosclerosis is being considered as an inflammatory disease. Vaccinium arctostaphylos L. is a plant with fruits rich in anthocyanins. The aim of this study was to evaluate the effects of fruit extract of this plant on serum levels of lipids, hs-CRP, and malondialdehyde (MDA) as a marker of oxidative stress, in hyperlipidemic adult patients. Methods. In this randomized, double-blind, placebo-controlled clinical trial, 50 hyperlipidemic adult patients were randomly and equally assigned to receive either medicinal (V. arctostaphylos fruit extract) or placebo capsules twice daily for 4 weeks. Each medicinal capsule contained 45 ± 2 mg of anthocyanins. Fasting serum levels of total cholesterol, TG, LDL-C, HDL-C, hs-CRP, and MDA were obtained before and after the intervention and compared. Results. V. arctostaphylos fruit extract significantly reduced total cholesterol (), LDL-C (), TG (), and MDA () compared to placebo but did not have any significant effect on HDL-C () and hs-CRP (). Conclusion. Fruit extract of Vaccinium arctostaphylos has beneficial effects on serum lipid profile and oxidative stress in hyperlipidemic adult patients. Therefore, it could be considered as a supplement for treatment of dyslipidemia and prevention of atherosclerosis development.


title: Sensor pillow and bed sheet system: Unconstrained monitoring of respiration rate and posture movements during sleep

abstract: In this paper, we have developed a low-cost sleep monitoring system for patient based on polysomnography which will be useful for patient communication with healthcare personals and/or relatives. In particular, we have presented the sensor pillow and bed sheet system that employs wireless networks based on low-cost ZigBee technology and a sensor array of force sensitive resistors (FSR) based on polymer thick film (PTF) device, for classifying and specifically verifying the respiration rate during sleep. This paper also proposes a simple motion model that explains the change of head and body pressure distribution. In addition, we can detect some physiological parameters during the sleep stages and wakefulness as well as record respiration rate as related to different physiological factors. The integration of this sensor system and wireless technology with computer software could make this healthcare monitoring system a commercial product valuable for point-of-care applications.


title: Affine image registration using a new information metric

abstract: We present a new information metric for multimodality image registration. The metric is technically a pseudometric since it satisfies the properties, (i) nonnegativity, (ii) symmetry, (iii) triangle inequality and is (iv) zero if (but not only if) the two image intensities are identical. Information metrics are rarely used in image registration and notably, the widely used mutual information measure is not a metric. Given images A and B, the metric used here is the sum of the conditional entropies H(A/spl bsol/B) and H(B/spl bsol/A). We show that when compared to mutual information which can even become negative in the multiple image case, it is easier to extend our metric to the registration of multiple images. And, after using an upper bound, we show that the sum of the conditional entropies can be efficiently computed even in the multiple image case. We use the metric to simultaneously register multiple 2D slice images obtained from proton density (PD), magnetic resonance (MR) T2 and MR T1 3D volumes and to match human face images obtained wider different illuminations. Our results demonstrate the efficacy of the metric in affine, multiple image registration.


title: Abstract Extracting 3D information from broadcast soccer video

abstract: Abstract In this paper, we propose a new method to estimate players' and ball's positions from monocular broadcast soccer video. With the relationship between objects and the camera in perspective projection, we derive the formula for estimating the moving objects' positions in real world, even when the ball is in the air. This method calibrates the camera's position in the stadium through the homography between the image and the playfield, and the self-calibration for rotating and zooming camera. Thus, the method can estimate the ball's position in the air without referring to other reference object with known height. In order to reduce manual interference, the players are detected based on the playfield detection. For the ball, we combine the detection procedure and tracking procedure organically. First, we extract candidate regions in each frame, then search the most likely regions in consecutive frames using Viterbi decoding algorithm. Once detected, the ball will be tracked by Kalman filter, which can help improve the detection recall. The system checks whether the ball is lost automatically. If it is lost, the detection procedure restarts. Experiments on synthesized data verify the proposed method, and promising results are obtained on real video data.


title: Specification of Non-Functional Requirements: A Hybrid Approach

abstract: When specifying user requirements, not only it is critical to ensure correct and unambiguous specification of functional requirements, but also that of non-functional requirements (NFRs). In fact, resolving ambiguities from user specified natural language NFRs and specifying the correct ones in a formal language have attracted significant attention. Our current research focuses on the issues pertaining the same. We observe that it is a usual practice for a user to narrate the NFRs in natural language and the requirement engineers manually try to express the same, using some semi-formal or formal language notations. However, inaccurate and the laborious manual approach may fail to detect all the NFRs and correctly remove the ambiguities in those detected. Hence, current research attempts have focused on automating the conversion of natural language NFRs to formal notations. In literature, there exist numerous approaches that take requirements as input and output the extended UML counterpart including NFRs. However, majority of the approaches do not support ambiguity resolution and verification of the extracted NFRs that are fairly essential. In this paper, we propose and discuss a hybrid approach viz. NFRs-Specifier, that attempts to resolve ambiguities, extract NFR’s, perform verification and generate NFRs specification by means of the extended UML model.


The DNA sequence of a 762 bp fragment containing the SUP11-1 gene


title: Privacy-Aware Scheduling SaaS in High Performance Computing Environments

abstract: Hybrid clouds have gained popularity in recent times in a variety of organizations due to their ability to provide additional capacity in a public cloud, to augment private cloud capacity, when it is needed. However, scheduling distributed applications’ jobs (e.g, workflow tasks) on hybrid cloud resources introduces new challenges. One key problem is the danger of exposing private data and jobs in a third-party public cloud infrastructure, for example in healthcare applications. In this article, we tackle the problem of designing workflow scheduling algorithms to meet customers’ deadlines, while not compromising data and task privacy requirements. Our work is different from most studies on workflow scheduling where the main goal is to achieve a balance between desirable, yet incompatible constraints, such as meeting the deadline and/or minimizing the execution time. Although many others have addressed the trade-off between cost and time, or privacy and cost, their work still suffers from an insufficient consideration of the trade-off between privacy and time. To address such shortcomings in the literature, we present a new SaaS scheduling broker composed of MPHC-P1, MPHC-P2, and MPHC-P3 policies to preserve privacy while scheduling the workflows’ tasks under customers’ deadlines. We evaluated our approach using real workflows running on a VMware based hybrid cloud. Results demonstrate that under our scheduling policies, MPHC-P2 and MPHC-P3 are promising in time-critical scenarios by reducing the total cost by 10-20 percent compared to alternatives. Overall, results show that our approach is efficient in reducing the cost of executing workflows while satisfying both their privacy and deadline constraints.


title: A two-stage approach for discriminating melanocytic skin lesions using standard cameras

abstract: In this paper, we propose a novel approach to discriminate malignant melanomas and benign atypical nevi, since both types of melanocytic skin lesions have very similar characteristics. Recent studies involving the non-invasive diagnosis of melanoma indicate that the concentrations of the two main classes of melanin present in the human skin, eumelanin and pheomelanin, can potentially be used in the computation of relevant features to differentiate these lesions. So, we describe how these features can be estimated using only standard camera images. Moreover, we demonstrate that using these features in conjunction with features based on the well known ABCD rule, it is possible to achieve 100% of sensitivity and more than 99% accuracy in melanocytic skin lesion discrimination, which is a highly desirable characteristic in a prescreening system.


title: Velodyne SLAM

abstract: Estimating a vehicles' own trajectory and generating precise maps of the environment are both important tasks for intelligent vehicles. Especially for the second task laser scanners are the sensor of choice as they provide precise range measurements. This work proposes an approach for simultaneous localization and mapping (SLAM) specifically designed for the Velodyne HDL-64E laser scanner which exhibits characteristics not present in most other systems. This comprises the continuous, spinning data acquisition and the relative high sensor noise. Together, these make standard SLAM approaches generate noisy maps and inaccurate trajectories. We show that it is possible to generate precise maps and localize therein in spite of not using wheel speed sensors or other information. The presented approach is evaluated on a novel, challenging 3D data set being made publicly available.


title: Design of an Expert System to Calculate Inheritance Shares Based on Islamic Law

abstract: Death is an inevitable end that comes only when it comes. Part of the preparation to death is ensuring that one’s assets are shared among the benefactors without rancor. Islam has defined the sharing formula of the properties of the deceased. The concept of inheritance from Islamic perspectives calls for no debate. The major challenge is how to get this knowledge to all in a more convenient and unambiguous form. This paper discusses the modeling of a rule-based expert system to calculate inheritance shares based on Islamic law.


title: RDF-4X: a scalable solution for RDF quads store in the cloud

abstract: Resource Description Framework (RDF) represents a flexible and concise model for representing the metadata of resources on the web. Over the past years, with the increasing amount of RDF data, efficient and scalable RDF data management has become a fundamental challenge to achieve the Semantic Web vision. However, multiple approaches for RDF storage have been suggested, ranging from simple triple stores to more advanced techniques like vertical partitioning on the predicates or centralized approaches. Unfortunately, it is still a challenge to store a huge quantity of RDF quads due, in part, to the query processing for RDF data. This paper proposes a scalable solution for RDF data management that uses Apache Accumulo. We focus on introducing storage methods and indexing techniques that scale to billions of quads across multiple nodes, while providing fast and easy access to the data through conventional query mechanisms such as SPARQL. Our performance evaluation shows that in most cases our approach works well against large RDF datasets.


title: A New Multi-modal Technique for Bib Number/Text Detection in Natural Images

abstract: The detection and recognition of racing bib number/text, which is printed on paper, cardboard tag, or t-shirt in natural images in marathon, race and sports, is challenging due to person movement, non-rigid surface, distortion by non-illumination, severe occlusions, orientation variations etc. In this paper, we present a multi-modal technique that combines both biometric and textual features to achieve good results for bib number/text detection. We explore face and skin features in a new way for identifying text candidate regions from input natural images. For each text candidate region, we propose to use text detection and recognition methods for detecting and recognizing bib numbers/texts, respectively. To validate the usefulness of the proposed multi-modal technique, we conduct text detection and recognition experiments before text candidate region detection and after text candidate region detection in terms of recall, precision and f-measure. Experimental results show that the proposed multi-modal technique outperforms the existing bib number detection method.


title: A Theory of Secure Control Flow

abstract: Control-Flow Integrity (CFI) means that the execution of a program dynamically follows only certain paths, in accordance with a static policy. CFI can prevent attacks that, by exploiting buffer overflows and other vulnerabilities, attempt to control program behavior. This paper develops the basic theory that underlies two practical techniques for CFI enforcement, with precise formulations of hypotheses and guarantees.


title: Middleware to support sensor network applications

abstract: Current trends in computing include increases in both distribution and wireless connectivity, leading to highly dynamic, complex environments on top of which applications must be built. The task of designing and ensuring the correctness of applications in these environments is similarly becoming more complex. The unified goal of much of the research in distributed wireless systems is to provide higher-level abstractions of complex low-level concepts to application programmers, easing the design and implementation of applications. A new and growing class of applications for wireless sensor networks require similar complexity encapsulation. However, sensor networks have some unique characteristics, including dynamic availability of data sources and application quality of service requirements, that are not common to other types of applications. These unique features, combined with the inherent distribution of sensors, and limited energy and bandwidth resources, dictate the need for network functionality and the individual sensors to be controlled to best serve the application requirements. In this article, we describe different types of sensor network applications and discuss existing techniques for managing these types of networks. We also overview a variety of related middleware and argue that no existing approach provides all the management tools required by sensor network applications. To meet this need, we have developed a new middleware called MiLAN. MiLAN allows applications to specify a policy for managing the network and sensors, but the actual implementation of this policy is effected within MiLAN. We describe MiLAN and show its effectiveness through the design of a sensor-based personal health monitor.


title: Face it, don't Facebook it: Impacts of Social Media Addiction on Mindfulness, Coping Strategies and the Consequence on Emotional Exhaustion

abstract: Addiction to social media has now become a problem that societies are concerned with. The aim of the present study is to investigate the impacts that social media addiction has on mindfulness and choice of coping strategy, as well as to explore the consequences on emotional exhaustion. The survey data were collected from 211 employees in 13 enterprises in Thailand. Results from partial least square structural equation modelling revealed that people who are highly addicted to social media tended to have lower mindfulness and tended to use emotion-focused coping to deal with stress. Lack of mindfulness and the decision to use emotion-coping strategy are also subsequently associated with higher emotional exhaustion. Copyright © 2015 John Wiley & Sons, Ltd.


Microcontroller based Digital Trigger Circuit for Converter


title: Design of dielectric resonators oscillator for mobile WiMAX at 2,3 GHz with additional coupling λ/4

abstract: Oscillator is the source of energy for all microwave communication systems. In this research, oscillator using dielectric resonator oscillator (DRO) will be designed. Comparing to other types of oscillators, DRO has a bigger value Q factor. Designed DRO is used as the carrier for the mobile WiMAX 802.16e at frequency 2,3 GHz. The dielectric resonator Trans-Tech 8500 Series Temperature Stable will be used. To obtain a high power fundamental, it is proposed to use an additional coupling λ/4 and double-stub as matching for reducing the harmonic power. Meanwhile, to obtain low phase noise, BFR 380T BJT low-phase noise with the bias of Vcc = 5 V, Vce = 3 V, and Ic = 40 mA is used. DRO is simulated using ADS software. The phase noise is −144 dBc / Hz at 10 kHz frequency carrier with a value of Q factor is 7316. The output power at the fundamental frequency is 13 dBm and second harmonic power is −40 dBm. A Monte-Carlo Yield Analysis simulation is used to simulate the performances varied with all the variation of tolerance. It is shown from Monte-Carlo-Yield Analysis simulation, DRO with an additional coupling λ/4 producing variations as same as specification with an average percentage is 84.5%. It is also shown from the results that the designed DRO with coupling λ/4 is better than DRO without additional coupling λ/4 with single-stub matching.


title: Look around you: Saliency maps for omnidirectional images in VR applications

abstract: Understanding visual attention has always been a topic of great interest in the graphics, image/video processing, robotics and human-computer interaction communities. By understanding salient image regions, the compression, transmission and rendering algorithms can be optimized. This is particularly important in omnidirectional images (ODIs) viewed with a head-mounted display (HMD), where only a fraction of the captured scene is displayed at a time, namely viewport. In order to predict salient image regions, saliency maps are estimated either by using an eye tracker to collect eye fixations during subjective tests or by using computational models of visual attention. However, eye tracking developments for ODIs are still in the early stages and although a large list of saliency models are available, no particular attention has been dedicated to ODIs. Therefore, in this paper, we consider the problem of estimating saliency maps for ODIs viewed with HMDs, when the use of an eye tracker device is not possible. We collected viewport center trajectories (VCTs) of 32 participants for 21 ODIs and propose a method to transform the gathered data into saliency maps. The obtained saliency maps are compared in terms of image exposition time used to display each ODI in the subjective tests. Then, motivated by the equator bias tendency in ODIs, we propose a post-processing method, namely fused saliency maps (FSM), to adapt current saliency models to ODIs requirements. We show that the use of FSM on current models improves their performance by up to 20%. The developed database and testbed are publicly available with this paper.


title: Provenance Information in Geodata Infrastructures

abstract: When it comes to usability evaluation of geodata information about its provenance or lineage are vital. Nevertheless, in practice the corresponding metadata elements are often neglected. Even if available, the tabular or listed metadata representations in current metadata catalogue user interfaces do not sufficiently support the users browsing and comparing metadata. This chapter proposes an interactive application for data provenance visualization called MetaViz. As a foundation for the MetaViz design the chapter provides a detailed analysis on modeling aspects, available standards and specifications for data provenance and presents possible design and implementation choices. A scientific geodata infrastructure that supports researchers sharing results of numerical simulations of different environmental phenomena serves as the underlying use case.


title: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

abstract: This paper presents a database containing 'ground truth' segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region properties.


ebusiness@mit.edu or 617-253-7054Agent-Based Models of Financial Markets: A Comparison with Experimental Markets


Heart-Brain Neurodynamics: The Making of Emotion


title: Ontologies for transportation research: A survey

abstract: Abstract Transportation research relies heavily on a variety of data. From sensors to surveys, data supports day-to-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transportation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.


Explainable IR for Personalizing Professional Search


title: Detection and Analysis of Emotion From Speech Signals

abstract: Recognizing emotion from speech has become one the active research themes in speech processing and in applications based on human-computer interaction. This paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include neutral, anger, joy and sadness. The distinuishability of emotional features in speech were studied first followed by emotion classification performed on a custom dataset. The classification was performed for different classifiers. One of the main feature attribute considered in the prepared dataset was the peak-to-peak distance obtained from the graphical representation of the speech signals. After performing the classification tests on a dataset formed from 30 different subjects, it was found that for getting better accuracy, one should consider the data collected from one person rather than considering the data from a group of people.


title: DroidLegacy: Automated Familial Classification of Android Malware

abstract: We present an automated method for extracting familial signatures for Android malware, i.e., signatures that identify malware produced by piggybacking potentially different benign applications with the same (or similar) malicious code. The APK classes that constitute malware code in a repackaged application are separated from the benign code and the Android API calls used by the malicious modules are extracted to create a signature. A piggybacked malicious app can be detected by first decomposing it into loosely coupled modules and then matching the Android API calls called by each of the modules against the signatures of the known malware families. Since the signatures are based on Android API calls, they are related to the core malware behavior, and thus are more resilient to obfuscations. In triage, AV companies need to automatically classify large number of samples so as to optimize assignment of human analysts. They need a system that gives low false negatives even if it is at the cost of higher false positives. Keeping this goal in mind, we fine tuned our system and used standard 10 fold cross validation over a dataset of 1,052 malicious APKs and 48 benign APKs to verify our algorithm. Results show that we have 94% accuracy, 97% precision, and 93% recall when separating benign from malware. We successfully classified our entire malware dataset into 11 families with 98% accuracy, 87% precision, and 94% recall.


Using Convolutional Neural Networks to Classify Dog Breeds


title: Bandera: extracting finite-state models from Java source code

abstract: Finite-state verification techniques, such as model checking, have shown promise as a cost-effective means for finding defects in hardware designs. To date, the application of these techniques to software has been hindered by several obstacles. Chief among these is the problem of constructing a finite-state model that approximates the executable behavior of the software system of interest. Current best-practice involves hand-construction of models which is expensive (prohibitive for all but the smallest systems), prone to errors (which can result in misleading verification results), and difficult to optimize (which is necessary to combat the exponential complexity of verification algorithms). In this paper, we describe an integrated collection of program analysis and transformation components, called Bandera, that enables the automatic extraction of safe, compact finite-state models from program source code. Bandera takes as input Java source code and generates a program model in the input language of one of several existing verification tools; Bandera also maps verifier outputs back to the original source code. We discuss the major components of Bandera and give an overview of how it can be used to model check correctness properties of Java programs.


title: Acquiring a Dictionary of Emotion-Provoking Events

abstract: This paper is concerned with the discovery and aggregation of events that provoke a particular emotion in the person who experiences them, or emotion-provoking events. We first describe the creation of a small manually-constructed dictionary of events through a survey of 30 subjects. Next, we describe first attempts at automatically acquiring and aggregating these events from web data, with a baseline from previous work and some simple extensions using seed expansion and clustering. Finally, we propose several evaluation measures for evaluating the automatically acquired events, and perform an evaluation of the effectiveness of automatic event extraction.


title: Joint Image and Text Representation for Aesthetics Analysis

abstract: Image aesthetics assessment is essential to multimedia applications such as image retrieval, and personalized image search and recommendation. Primarily relying on visual information and manually-supplied ratings, previous studies in this area have not adequately utilized higher-level semantic information. We incorporate additional textual phrases from user comments to jointly represent image aesthetics utilizing multimodal Deep Boltzmann Machine. Given an image, without requiring any associated user comments, the proposed algorithm automatically infers the joint representation and predicts the aesthetics category of the image. We construct the AVA-Comments dataset to systematically evaluate the performance of the proposed algorithm. Experimental results indicate that the proposed joint representation improves the performance of aesthetics assessment on the benchmarking AVA dataset, comparing with only visual features.


title: Framing Signals - A Return to Portable Shellcode

abstract: Signal handling has been an integral part of UNIX systems since the earliest implementation in the 1970s. Nowadays, we find signals in all common flavors of UNIX systems, including BSD, Linux, Solaris, Android, and Mac OS. While each flavor handles signals in slightly different ways, the implementations are very similar. In this paper, we show that signal handling can be used as an attack method in exploits and backdoors. The problem has been a part of UNIX from the beginning, and now that advanced security measures like ASLR, DEP and stack cookies have made simple exploitation much harder, our technique is among the lowest hanging fruit available to an attacker. Specifically, we describe Sigreturn Oriented Programming (SROP), a novel technique for exploits and backdoors in UNIX-like systems. Like return-oriented programming (ROP), sigreturn oriented programming constructs what is known as a 'weird machine' that can be programmed by attackers to change the behavior of a process. To program the machine, attackers set up fake signal frames and initiate returns from signals that the kernel never really delivered. This is possible, because UNIX stores signal frames on the process' stack. Sigreturn oriented programming is interesting for attackers, OS developers and academics. For attackers, the technique is very versatile, with pre-conditions that are different from those of existing exploitation techniques like ROP. Moreover, unlike ROP, sigreturn oriented programming programs are portable. For OS developers, the technique presents a problem that has been present in one of the two main operating system families from its inception, while the fixes (which we also present) are non-trivial. From a more academic viewpoint, it is also interesting because we show that sigreturn oriented programming is Turing complete. We demonstrate the usefulness of the technique in three applications. First, we describe the exploitation of a vulnerable web server on different Linux distributions. Second, we build a very stealthy proof-of-concept backdoor. Third, we use SROP to bypass Apple's code signing and security vetting process by building an app that can execute arbitrary system calls. Finally, we discuss mitigation techniques.


title: XenTune: Detecting Xen Scheduling Bottlenecks for Media Applications

abstract: Virtualization provides enormous economic and ecological benefits by enabling server consolidation and supporting low-cost green data centers. A key component of the hypervisor in a virtualized system is the scheduler. A typical scheduler provides parameters to influence the hypervisor's behavior. Specifically, an application's performance on the popular open-source Xen virtualization platform can be influenced by tuning its scheduler behavior using the weight, cap, and processor pinning variables. However, determining which parameters to tune and how to do that is non-trivial. In this paper, we introduce XenTune, a monitoring tool for the credit scheduler in Xen that helps in understanding application behavior in scheduler terms and assists in determining scheduler parameters. We demonstrate how the tool is used with application domains - specifically a media application domain. We demonstrate experimental results using a real work-load that shows the considerable benefits of correct scheduler parameter choices. XenTune had helped in the design of a recently proposed scheduler S and in this paper we show how scheduler S interacts with the media application to optimize its performance.


title: A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor Data to End-Users Applications

abstract: Smart cities are becoming more and more popular. Currently, there is no unified and interoperable system which could be reused and redeployed in future smart cities. Having an interoperable: (1) system, (2) architecture, (3) workflow to process IoT data, (4) interoperable applications and services, and (5) secure access to data is becoming essential to reduce development cost in smart cities and ensure interoperability among vertical IoT applications (i.e., domain-specific). Firstly, we survey existing work integrating semantic web technologies to Internet of Things, also called 'Semantic Web of Things' and applying it to smart cities. Secondly, we share in this paper, our vision of Semantic Web of Things applied to smart cities and highlight main research challenges. Finally, the main contribution of this work is the semantic engine applied to IoT and smart cities. Moreover, the proposed semantic engine is applied to three use cases: Machine-to-Machine Measurement (M3) framework, FIESTA-IOT EU project and VITAL EU project.


title: Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN

abstract: Saliency detection is a long standing problem in computer vision. Tremendous efforts have been focused on exploring a universal saliency model across users despite their differences in gender, race, age, etc. Yet recent psychology studies suggest that saliency is highly specific than universal: individuals exhibit heterogeneous gaze patterns when viewing an identical scene containing multiple salient objects. In this paper, we first show that such heterogeneity is common and critical for reliable saliency prediction. Our study also produces the first database of personalized saliency maps (PSMs). We model PSM based on universal saliency map (USM) shared by different participants and adopt a multitask CNN framework to estimate the discrepancy between PSM and USM. Comprehensive experiments demonstrate that our new PSM model and prediction scheme are effective and reliable.


title: Multisensory Approach to Handwriting Remediation: Perceptions of School-Based Occupational Therapists

abstract: OBJECTIVE. The purpose of this study was to describe the current practices of school-based occupational therapists regarding their use of multisensory modalities and activities in handwriting remediation. METHOD. A surveywas sent to 313 school-based occupational therapist members of the American Occupational Therapy Association who identified themselves as working in a school system as their primary employment setting. Of these, 198 surveys were returned and analyzed descriptively, resulting in a response rate of 63.3%. RESULTS. More than 130 different multisensory modalities and activities were documented. Twenty-five of these had previously been reported in the literature, the other 114 were documented, by respondents, within the "other" category. Most respondents reported using 5 or more modalities and activities per student, the most frequent being chalk and chalkboard. No consensus among respondents is apparent about the primary sensory systems stimulated by the modalities and activities. No difference in modality and activity use was found on the basis of demographic variables. CONCLUSION. Overall findings indicate that the breadth of modalities and activities being used is far greater than that currently found in the literature. The results suggest a need for completing studies designed to examine the use and effectiveness of multisensory modalities and activities in handwriting remediation.


title: A concept for PLC hardware-in-the-loop testing using an extension of structured text

abstract: Hardware-in-the-loop (HiL) Simulation is a powerful method to reduce the risk of system failures leading to expensive damages. Many existing HiL tools require the tester to use proprietary test specification languages, usually unknown to the domain of a PLC programmer. This work presents a novel approach of HiL test specification using programming languages specified by IEC 61131-3. By doing this, PLC programmers are able to specify test cases in the domain they are already familiar with.


title: Adding Semantics to Microblog Posts (Abstract)

abstract: In recent years Twitter has become one of the largest online microblogging platforms. Microblogging streams have become invaluable sources for many kinds of analyses, including online reputation management, news and trend detection, and targeted marketing and customer services [4, 9]. Searching and mining microblog streams oers interesting technical challenges, because of the sheer volume of the data, its dynamic nature, the creative language usage, and the length of individual posts [3]. In many microblog search scenarios the goal is to nd out what people are saying about concepts such as products, brands, persons, et cetera. Here, it is important to be able to accurately retrieve tweets that are on topic, including all possible naming and other lexical variants. So, it is common to manually construct lengthy keyword queries that hopefully capture all possible variants. We propose an alternative approach, namely to determine what a microblog post is about by automatically identifying concepts in them. We take a concept to be any item that has a unique and unambiguous entry in a well-known large-scale knowledge source, Wikipedia. Little research exists on understanding and modeling the semantics of individual microblog posts. Linking free text to knowledge resources, on the other hand, has received an increasing amount of attention in recent years. Starting from the domain of named entity recognition, current approaches establish links not just to entity types, but to the actual entities themselves [5]. With over 3.5 million articles, Wikipedia has become a rich source of knowledge and a common target for linking; automatic linking approaches using Wikipedia have met with considerable success [2, 6, 8]. Most, if not all, of the linking methods assume that the input text is relatively clean and grammatically correct and that it provides sucient context for the purposes of identifying concepts. Microblog posts are short, noisy, and full of


title: SAR ADC design using Golden ratio weight algorithm

abstract: This paper describes redundant successive approximation register (SAR) ADC design methods which enable high-reliability and high-speed AD conversion using digital error correction. Especially we propose to apply Fibonacci sequence and its property called Golden ratio to SAR ADC design to optimize redundant search algorithms. We present some interesting properties for well-balanced redundant SAR ADC design, as well as golden-ratio-weighted DAC topologies for its internal usage.


title: Discrete Cross-Modal Hashing for Efficient Multimedia Retrieval

abstract: Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxation step also brings quantization errors, resulting in poor retrieval performances. Other cross-modal hashing methods try to directly optimize the challenging objective function with discrete binary constraints. Inspired by [1], we propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the discrete binary codes without relaxing them. DCMH is formulated through reconstructing the semantic similarity matrix and learning binary codes as ideal features for classification. Furthermore, DCMH alternately updates binary codes of each modality, and iteratively learns the discrete hashing codes bit by bit efficiently, which is quite promising for large-scale datasets. Extensive empirical results on three real-world datasets show that DCMH outperforms the baseline approaches significantly.


title: Improving Explainable Recommendations with Synthetic Reviews

abstract: An important task for a recommender system to provide interpretable explanations for the user. This is important for the credibility of the system. Current interpretable recommender systems tend to focus on certain features known to be important to the user and offer their explanations in a structured form. It is well known that user generated reviews and feedback from reviewers have strong leverage over the users' decisions. On the other hand, recent text generation works have been shown to generate text of similar quality to human written text, and we aim to show that generated text can be successfully used to explain recommendations. In this paper, we propose a framework consisting of popular review-oriented generation models aiming to create personalised explanations for recommendations. The interpretations are generated at both character and word levels. We build a dataset containing reviewers' feedback from the Amazon books review dataset. Our cross-domain experiments are designed to bridge from natural language processing to the recommender system domain. Besides language model evaluation methods, we employ DeepCoNN, a novel review-oriented recommender system using a deep neural network, to evaluate the recommendation performance of generated reviews by root mean square error (RMSE). We demonstrate that the synthetic personalised reviews have better recommendation performance than human written reviews. To our knowledge, this presents the first machine-generated natural language explanations for rating prediction.


title: Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks

abstract: ABSTRACTThe benefits of autonomous vehicles (AVs) are widely acknowledged, but there are concerns about the extent of these benefits and AV risks and unintended consequences. In this article, we fi...


title: Context-aware activity prediction using human behavior pattern in real smart home environments

abstract: Nowadays, close monitoring of daily activities of elders is enabled by employment of the advanced wireless sensor networks, whose large quantity data are then analyzed by activity recognition techniques whereby their behavior patterns can be accurately modelled. In general, behavior patterns are important information about how elders live, and caregivers can thus take care of elders easily with the help from that informaiton. So far, there are many research results related to learning of human behavior; however, their assumptions are usually either too simple or inflexible to account for complex human behaviors in real life, which change dynamically depending on contexts. We here present a context-aware framework for human behavior learning and prediction. Such framework discovers contexts from resident's real life data and adapts correspoinding behavior patterns next accordingly. We evaluate the framework on two public datasets, and the experimental results show promising results.


Marijuana and Coronary Heart Disease


Wireless Body Area Networks for Healthcare: A Feasibility Study


title: Monolithic Low Cost Ka-Band Wilkinson Power Dividers on Flexible Organic Substrates

abstract: A millimeter wave Wilkinson power divider is presented on a thin and durable organic substrate, along with an analysis of bend topologies to reduce coupling between the output paths. The divider provides excellent performance across the entire Ka-band from 27 to 40 GHz. Insertion loss is approximately 0.3 dB and isolation between the output ports is around 30 dB at the design frequency.


title: Wiring the''New Organization'': Integrating Collaborative Technologies and Team-Based Work

abstract: This invention relates to a pivoted double handle self-locking mechanism for operating a valve. Each of a pair of oppositely extending handles is pivotally mounted on a socket member secured to a key that rotates the valve, each handle being provided with a gear segment that meshes with a corresponding gear segment on the other handle whereby rocking of either handle about its pivot by a lever connected thereto effects rocking of the other handle so that a lug on one handle is moved to a position in which it will not engage a stop carried by the valve body upon subsequent arcuate movement of both handles while in their rocked position in response to a manual pull exerted on the lever connected to either one of the handles.


title: A review of algorithms for audio fingerprinting

abstract: An audio fingerprint is a content-based compact signature that summarizes an audio recording. Audio fingerprinting technologies have recently attracted attention since they allow the monitoring of audio independently of its format and without the need of meta-data or watermark embedding. The different approaches to fingerprinting are usually described with different rationales and terminology depending on the background: pattern matching, multimedia (music) information retrieval or cryptography (robust hashing). In this paper, we review different techniques mapping functional parts to blocks of a unified framework.


title: Ultra Dense Small Cell Networks: Turning Density into Energy Efficiency

abstract: In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit energy, in ultra dense small cell networks (UDNs). Due to severe coupling in interference, this problem is formulated as a dynamic stochastic game (DSG) between small cell base stations (SBSs). This game enables capturing the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean-field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable partial differential equations. Exploiting the stochastic nature of the problem, user scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean-field theory, the proposed solution yields an equilibrium control policy per SBS, which maximizes the network utility while ensuring users’ quality-of-service. Simulation results show that the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in the network’s outage probabilities compared to a baseline model, which focuses on improving EE while attempting to satisfy the users’ instantaneous quality-of-service requirements.


title: STEMing the Tide: Using Ingroup Experts to Inoculate Women's Self-Concept in Science, Technology, Engineering, and Mathematics (STEM)

abstract: Three studies tested a stereotype inoculation model, which proposed that contact with same-sex experts (advanced peers, professionals, professors) in academic environments involving science, technology, engineering, and mathematics (STEM) enhances women’s self-concept in STEM, attitudes toward STEM, and motivation to pursue STEM careers. Two cross-sectional controlled experiments and 1 longitudinal naturalistic study in a calculus class revealed that exposure to female STEM experts promoted positive implicit attitudes and stronger implicit identification with STEM (Studies 1–3), greater self-efficacy in STEM (Study 3), and more effort on STEM tests (Study 1). Studies 2 and 3 suggested that the benefit of seeing same-sex experts is driven by greater subjective identification and connectedness with these individuals, which in turn predicts enhanced self-efficacy, domain identification, and commitment to pursue STEM careers. Importantly, women’s own self-concept benefited from contact with female experts even though negative stereotypes about their gender and STEM remained active.


Determinants of customer satisfaction with consultant services


title: A thermal imaging based wireless sensor network for automatic water leakage detection in distribution pipes

abstract: Water leakage is a significant problem in both developing and developed countries causing water loss in water-distribution systems. Leakage causes economic loss in the form of wastage of water, damage to pipe networks and foundations of roads and buildings, and also poses risk to public health due to water contamination. The lost or unaccounted amount of water is typically 20-30 percent of production. Some older systems may lose even up to 50 percent. The water pipe networks in houses as well as public places are generally concealed and hence detecting water leakage in the initial stages before an upcoming damage is difficult. The existing technologies for detecting leakage have various limitations such as efficiency being dependent on size, material and depth of pipes, need for manual intervention, dependency on weather and surface conditions and effect of water pressure. In this paper, we propose an automated water leakage detection system using a wireless sensor network created along the distribution pipes based on thermal (IR) imaging. Thermal imaging has the capability to work in low lighting or dark conditions and helps in capturing the contrast between hot and cold areas created due to water leakage. A network of low cost, low power thermal imaging sensors each having its own processing and Radiofrequency (RF) Transreceiver units and operating independent of pipe, weather or surface conditions is proposed in this paper. A central database is updated on a real-time basis, enabling very early leakage detection and initiating subsequent action to address the problem. An example system evaluation is performed and results highlighting the power and cost impact of the sensor network system are presented.


title: Eating disorder symptomatology among ballet dancers

abstract: Objective: ::: ::: The current study sought to compare eating disorder symptomatology among ballet dancers and individuals with restricting anorexia nervosa (RAN), bulimia nervosa (BN), and no eating pathology. ::: ::: ::: ::: Method: ::: ::: Twenty-nine female ballet dancers completed assessments and were compared with an archival dataset of 26 women with RAN, 47 women with BN, and 44 women with no eating pathology. Eating disorder diagnoses and behaviors were assessed with a semi-structured clinical interview, the Eating Disorder Inventory (EDI), and a weight history interview. ::: ::: ::: ::: Results: ::: ::: Eighty-three percent of dancers met lifetime criteria for AN (6.9%), BN (10.3%), AN+BN (10.3%), or EDNOS (55.0%). Moreover, dancers looked more similar to eating-disordered individuals than to control individuals on measures of eating pathology. ::: ::: ::: ::: Conclusion: ::: ::: Despite previous emphasis on the pathology AN, the current findings suggest that dancers frequently engage in binge eating and purging behaviors. Moreover, it appears that their pathology is as severe as that of non-dancing women with eating disorders. © 2006 by Wiley Periodicals, Inc. Int J Eat Disord 2006


title: Teaching English As Second Language through Drama for Effective Communication Skills: A Pragmatic Perspective

abstract: Literatures have reveal that teaching English Language in a conventional classroom with all the available methodology hardly gives the students opportunity to use the language effectively or gain the competence and confidence of using the language in and outside the classroom and probably develop fluency in it: especially when English is a second language other than the learner’s language like the Nigeria situation. This paper thus opines that with the use of drama as a tool or technique in teaching English Language as second language; learners would be equipped with the essential skills of communication and gain fluency in the language. This paper therefore explores the array of models through which language exploration through drama is related to Language Acquisition theories. The paper examines the theories of Language acquisition to establish the relevance of drama-in-education to the domain of teaching and learning and probably language teaching. It further x-rays the mimesis concept of drama as a basis and model for language learning by explaining the three imitative models of language learning among humans as they relates to communicative activities: to prove the relevance of drama as a potent tool for fostering effective communication skills in English as Second Language Learners.


Feedback research revisited.


title: Uplink contention based SCMA for 5G radio access

abstract: Fifth generation (5G) wireless networks are expected to support very diverse applications and terminals. Massive connectivity with a large number of devices is an important requirement for 5G networks. Current LTE system is not able to efficiently support massive connectivity, especially on the uplink (UL). Among the issues that arise due to massive connectivity is the cost of signaling overhead and latency. In this paper, an uplink contention-based sparse code multiple access (SCMA) design is proposed as a solution. First, the system design aspects of the proposed multiple-access scheme are described. The SCMA parameters can be adjusted to provide different levels of overloading, thus suitable to meet the diverse traffic connectivity requirements. In addition, the system-level evaluations of a small packet application scenario are provided for contention-based UL SCMA. SCMA is compared to OFDMA in terms of connectivity and drop rate under a tight latency requirement. The simulation results demonstrate that contention-based SCMA can provide around 2.8 times gain over contention-based OFDMA in terms of supported active users. The uplink contention-based SCMA scheme can be a promising technology for 5G wireless networks for data transmission with low signaling overhead, low delay, and support of massive connectivity.


title: Antecedents of Brand Recall and Brand Attitude towards Purchase Intention in advergames.

abstract: Research on purchase intention had showed remarkable work in contributing to the theory of buyer behaviour. However, the impact of brand recall and brand attitude on purchase intention in advergames has not yet been well explored especially in a developing economic country such as Malaysia. Furthermore, the influences of selfbrand congruity, entertainment and brand placement acceptance as antecedents to brand recall and brand attitude are expecting to put more “value added” to the brand recall, brand attitude and purchase intention relationships. This paper aims to investigate the impact of brand recall and brand attitude on purchase intention in advergames among Malaysian gamers. Also, the antecedents towards brand recall and brand attitude were examined to measure their influences on purchase intention. A total of 350 Malaysian gamers have been interviewed through an online survey for this study. The results showed that self-brand congruity, entertainment and brand placement acceptance were found to have significant relationship with brand attitude and purchase intention. Also, both entertainment and brand placement acceptance were found to have significant relationship with brand recall. As expected, brand recall and brand attitude have positive relationship with consumer purchase intention in advergames. Applications of the study are discussed.


title: Accurate Dependency Parsing with a Stacked Multilayer Perceptron

abstract: DeSR is a statistical transition-based dependency parser which learns from annotated corpora which actions to perform for building parse trees while scanning a sentence. We describe recent improvements to the parser, in particular stacked parsing, exploiting a beam search strategy and using a Multilayer Perceptron classifier. For the Evalita 2009 Dependency Parsing task DesR was configured to use a combination of stacked parsers. The stacked combination achieved the best accuracy scores in both the main and pilot subtasks. The contribution to the result of various choices is analyzed, in particular for taking advantage of the peculiar features of the TUT Treebank.


title: A survey on CAD methods in 3D garment design

abstract: With the advance in virtual reality applications, garment industry has strived for new developments. This paper reviews state-of-the-art CAD methods in 3D garment design. A large range of techniques are selected and organized into several key modules which form the core of a 3D garment design technology platform. In each module, basic techniques are presented first. Then advanced developments are systematically discussed and commented. The selected key modules - digital human modeling, 3D garment design and modification, numerical integration of draping, 2D pattern generation, geometric details modeling, parallel computation and GPU acceleration - are discussed in turn. Major challenges and solutions that have been addressed over the years are discussed. Finally, some of the ensuing challenges in 3D garment CAD technologies are outlined.


title: A new encryption algorithm for image cryptosystems

abstract: Abstract There are two major differences of the characteristics of the text data and image data. One difference is that the size of image data is usually much larger than that of text data. The other is that plain data rarely permit loss when a compression technique is used, but image data do. In this paper, we design an efficient cryptosystem for images. Our method is based on vector quantization, which is one of the popular image compression techniques. Our method can achieve the following two goals. One goal is to design a high security image cryptosystem. The other goal is to reduce computational complexity of the encryption and decryption algorithms.


title: Where in the World are You? Geolocation and Language Identification in Twitter

abstract: The movements of ideas and content between locations and languages are unquestionably crucial concerns to researchers of the information age, and Twitter has emerged as a central, global platform on which hundreds of millions of people share knowledge and information. A variety of research has attempted to harvest locational and linguistic metadata from tweets to understand important questions related to the 300 million tweets that flow through the platform each day. Much of this work is carried out with only limited understandings of how best to work with the spatial and linguistic contexts in which the information was produced, however. Furthermore, standard, well-accepted practices have yet to emerge. As such, this article studies the reliability of key methods used to determine language and location of content in Twitter. It compares three automated language identification packages to Twitter's user interface language setting and to a human coding of languages to identify common sources of disagreemen...


title: DETECTION OF POWER-LINES IN COMPLEX NATURAL SURROUNDINGS

abstract: Power transmission line inspection using Unmanned Aerial Vehicles (UAV) is taking off as an exciting solution due to advances in sensors and flight technology. Extracting power-lines from aerial images, taken from the UAV, having complex natural surroundings is a critical task in the above problem. In this paper we propose an approach for suppressing natural surrounding that leads to power line detection. The results of applying our method on real-life video frames taken from a UAV demonstrate that our approach is very effective. We believe that our approach can be easily used for line detection in any other real outdoor video as well.


title: Defining and applying prediction performance metrics on a recurrent NARX time series model

abstract: Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series prediction. The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real data output. Some prediction metrics are also proposed to assess the quality of predictions. This metrics enable to compare different prediction schemes and provide an objective way to measure how changes in training or prediction model (neural network architecture) affect the quality of predictions. Results show that the proposed NARX approach consistently outperforms the prediction obtained by the RNN neural network.


title: Low-Cost Mobile Phone Microscopy with a Reversed Mobile Phone Camera Lens

abstract: The increasing capabilities and ubiquity of mobile phones and their associated digital cameras offer the possibility of extending low-cost, portable diagnostic microscopy to underserved and low-resource areas. However, mobile phone microscopes created by adding magnifying optics to the phone's camera module have been unable to make use of the full image sensor due to the specialized design of the embedded camera lens, exacerbating the tradeoff between resolution and field of view inherent to optical systems. This tradeoff is acutely felt for diagnostic applications, where the speed and cost of image-based diagnosis is related to the area of the sample that can be viewed at sufficient resolution. Here we present a simple and low-cost approach to mobile phone microscopy that uses a reversed mobile phone camera lens added to an intact mobile phone to enable high quality imaging over a significantly larger field of view than standard microscopy. We demonstrate use of the reversed lens mobile phone microscope to identify red and white blood cells in blood smears and soil-transmitted helminth eggs in stool samples.


title: A New Trajectory Similarity Measure for GPS Data

abstract: We present a new algorithm for measuring the similarity between trajectories, and in particular between GPS traces. We call this new similarity measure the Merge Distance (MD). Our approach is robust against subsampling and supersampling. We perform experiments to compare this new similarity measure with the two main approaches that have been used so far: Dynamic Time Warping (DTW) and the Euclidean distance.


title: Observational Data Patterns for Time Series Data Quality Assessment

abstract: Observational data are fundamental for scientific research in almost any domain. Recent advances in sensor and data management technologies are enabling unprecedented amounts of observational data to be collected and analyzed. However, an essential part of using observational data is not currently as scalable as data collection and analysis methods: data quality assurance and control. While specialized tools for very narrow domains do exist, general methods are harder to create. This paper explores the identification of data issues that lead to the creation of data tests and tools to perform data quality control activities. Developing this identification step in a systematic manner allows for better and more general quality control tools. As our case study, we use carbon, water, and energy fluxes as well as micro-meteorological data collected at field sites that are part of FLUXNET, a network of over 400 ecosystem-level monitoring stations. In an effort toward the release of a new global data set of fluxes, we are doing data quality control for these data. The experience from this work led to the creation of a catalog of issues identified in the data. This paper presents this catalog and its generalization into a set of patterns of data quality issues that can be detected in observational data.


title: Modeling the Spatial Reach of the LFP

abstract: The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent of the region generating the LFP. Here, we use a detailed biophysical modeling approach to investigate the size of the contributing region by simulating the LFP from a large number of neurons around the electrode. We find that the size of the generating region depends on the neuron morphology, the synapse distribution, and the correlation in synaptic activity. For uncorrelated activity, the LFP represents cells in a small region (within a radius of a few hundred micrometers). If the LFP contributions from different cells are correlated, the size of the generating region is determined by the spatial extent of the correlated activity.


title: Mechanism and jumping pattern of one-legged jumping robot with pneumatic actuators

abstract: This paper describes the mechanism of a one-legged jumping robot that can mimic the muscular and skeletal system of a human leg. The jumping robot has a waist, a thigh, a shin, a foot, and a toe. The McKibben-type pneumatic muscle actuators that are able to generate the maximum output force of 1200 [N] are employed. Two kinds of pneumatic artificial muscles, mono-articular and bi-articular muscles, were installed in the rear and in the front of the thigh and shin. A jumping pattern is also developed in consideration of the performance of the pneumatic actuators. Several jumping experiments were conducted, and the effectiveness of the mechanism and jumping pattern was verified.


title: Simulation and control of a two-wheeled self-balancing robot

abstract: A two-wheeled self-balancing robot is a special type of wheeled mobile robot, its balance problem is a hot research topic due to its unstable state for controlling. In this paper, human transporter model has been established. Kinematic and dynamic models are constructed and two control methods: Proportional-integral-derivative (PID) and Linear-quadratic regulator (LQR) are implemented to test the system model in which controls of two subsystems: self-balance (preventing system from falling down when it moves forward or backward) and yaw rotation (steering angle regulation when it turns left or right) are considered. PID is used to control both two subsystems, LQR is used to control self-balancing subsystem only. By using simulation in Matlab, two methods are compared and discussed. The theoretical investigations for controlling the dynamic behavior are meaningful for design and fabrication. Finally, the result shows that LQR has a better performance than PID for self-balancing subsystem control.


title: Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines

abstract: Deep learning (DL) has achieved notable successes in many machine learning tasks. A number of frameworks have been developed to expedite the process of designing and training deep neural networks (DNNs), such as Caffe, Torch and Theano. Currently they can harness multiple GPUs on a single machine, but are unable to use GPUs that are distributed across multiple machines; as even average-sized DNNs can take days to train on a single GPU with 100s of GBs to TBs of data, distributed GPUs present a prime opportunity for scaling up DL. However, the limited bandwidth available on commodity Ethernet networks presents a bottleneck to distributed GPU training, and prevents its trivial realization. ::: To investigate how to adapt existing frameworks to efficiently support distributed GPUs, we propose Poseidon, a scalable system architecture for distributed inter-machine communication in existing DL frameworks. We integrate Poseidon with Caffe and evaluate its performance at training DNNs for object recognition. Poseidon features three key contributions that accelerate DNN training on clusters: (1) a three-level hybrid architecture that allows Poseidon to support both CPU-only and GPU-equipped clusters, (2) a distributed wait-free backpropagation (DWBP) algorithm to improve GPU utilization and to balance communication, and (3) a structure-aware communication protocol (SACP) to minimize communication overheads. We empirically show that Poseidon converges to same objectives as a single machine, and achieves state-of-art training speedup across multiple models and well-established datasets using a commodity GPU cluster of 8 nodes (e.g. 4.5x speedup on AlexNet, 4x on GoogLeNet, 4x on CIFAR-10). On the much larger ImageNet22K dataset, Poseidon with 8 nodes achieves better speedup and competitive accuracy to recent CPU-based distributed systems such as Adam and Le et al., which use 10s to 1000s of nodes.


Self-Organizing Maps In Natural Language Processing


Alberta LARGE-SCALE FRACTURE OF SEA ICE PLATES


title: Cost-aware depth map estimation for Lytro camera

abstract: Since commercial light field cameras became available, the light field camera has aroused much interest from computer vision and image processing communities due to its versatile functions. Most of its special features are based on an estimated depth map, so reliable depth estimation is a crucial step. However, estimating depth on real light field cameras is a challenging problem due to noise and short baselines among sub-aperture images. We propose a depth map estimation method for light field cameras by exploiting correspondence and focus cues. We aggregate costs among all the sub-aperture images on cost volume to alleviate noise effects. With efficiency of the cost volume, cost-aware depth estimation is quickly achieved by discrete-continuous optimization. In addition, we analyze each property of correspondence and focus cues and utilize them to select reliable anchor points. A well reconstructed initial depth map from the anchors is shown to enhance convergence. We show our method outperforms the state-of-the-art methods by validating it on real datasets acquired with a Lytro camera.


title: Latent Embeddings for Zero-Shot Classification

abstract: We present a novel latent embedding model for learning a compatibility function between image and class embeddings, in the context of zero-shot classification. The proposed method augments the state-of-the-art bilinear compatibility model by incorporating latent variables. Instead of learning a single bilinear map, it learns a collection of maps with the selection, of which map to use, being a latent variable for the current image-class pair. We train the model with a ranking based objective function which penalizes incorrect rankings of the true class for a given image. We empirically demonstrate that our model improves the state-of-the-art for various class embeddings consistently on three challenging publicly available datasets for the zero-shot setting. Moreover, our method leads to visually highly interpretable results with clear clusters of different fine-grained object properties that correspond to different latent variable maps.


title: Color fused multiple features for traffic sign recognition

abstract: In this paper, we design the color fused multiple features to describe a traffic sign, and we further implement this description method to detect traffic signs and to classify multi-class traffic signs. At the detection stage, we utilize the GentleAdaboost classifier to separate traffic signs from the background; at the classification stage, we implement the random forest classifier to classify multi-class traffic signs. We do the extensive experiments on the popular standard traffic sign datasets: the German Traffic Sign Recognition Benchmark and the Swedish Traffic Signs Dataset. We compare eight features which include the HOG feature, the LBP feature, the color cues and their different combinations. We also compare the popular classifiers for traffic sign recognition. The experimental results demonstrate that the color fused feature achieves better classification performance than the feature without color cues, and the GentleAdaboost classifier achieves the better comprehensive performance of the binary classification, and the random forest classifier achieves the best multi-class classification accuracy.


title: Self-supervised learning of grasp dependent tool affordances on the iCub Humanoid robot

abstract: The ability to learn about and efficiently use tools constitutes a desirable property for general purpose humanoid robots, as it allows them to extend their capabilities beyond the limitations of their own body. Yet, it is a topic that has only recently been tackled from the robotics community. Most of the studies published so far make use of tool representations that allow their models to generalize the knowledge among similar tools in a very limited way. Moreover, most studies assume that the tool is always grasped in its common or canonical grasp position, thus not considering the influence of the grasp configuration in the outcome of the actions performed with them. In the current paper we present a method that tackles both issues simultaneously by using an extended set of functional features and a novel representation of the effect of the tool use. Together, they implicitly account for the grasping configuration and allow the iCub to generalize among tools based on their geometry. Moreover, learning happens in a self-supervised manner: First, the robot autonomously discovers the affordance categories of the tools by clustering the effect of their usage. These categories are subsequently used as a teaching signal to associate visually obtained functional features to the expected tool's affordance. In the experiments, we show how this technique can be effectively used to select, given a tool, the best action to achieve a desired effect.


title: The Nasal Ligaments and Tip Support in Rhinoplasty: An Anatomical Study

abstract: Background ::: In 1971, Janeke and Wright1 published a now classic study on the support of the nasal tip in which they found four areas of anatomic support. These findings led to the "tripod concept" of tip support. Recently, surgeons have begun repairing and/or preserving the nasal ligaments as a method to control tip projection and rotation. Therefore, a reassessment of the nasal ligaments and tip support is warranted. ::: ::: ::: Objectives ::: The present study was done to investigate the ligamentous and structural support of the nasal tip. Clinically, surgeons are aware of the role of the nasal ligaments and are beginning to utilize tip suture techniques to achieve greater tip refinement and long-term support. ::: ::: ::: Methods ::: Anatomic studies were conducted on 24 fresh cadavers at the time of autopsy. The two groups consisted of the following: (1) group 1 included dissection of 10 cadavers concentrated on the various ligaments of the nose; and (2) group 2 involved dissections of 14 cadavers analyzing the relationship between the alar domes and the anterior septal angle (ASA). ::: ::: ::: Results ::: Regarding the ligaments of the nose, we were able to consistently identify the following ligaments: (1) interdomal; (2) intercrural; (3) Pitanguy's midline; (4) pyriform; and (5) a scroll ligament complex consisting of the longitudinal and vertical scroll ligaments. We did not find two commonly accepted ligaments: (1) a "footplate ligament" from the footplate of the medial crus to the caudal septum; and (2) a "sesamoid ligament" attachment from the accessory cartilage to the pyriform aperture. Dissections done to study the relationship between the domes and ASA revealed that the domes projected an average of 5.7 mm (range, 2.2-9.6 mm) above the ASA and were longitudinally 5.5 mm (range, 2.9-9.5 mm) caudal to the ASA. Thus, there was no direct support from the ASA to the domes. ::: ::: ::: Conclusions ::: It is our recommendation that surgeons should consider preservation of the nasal ligaments whenever possible and utilize them to manipulate tip projection, position, and rotation. Awareness of the relationship between the dome and the caudal septum will hopefully minimize problems with the tongue-in-groove operation.


title: A multiple objective optimization based echo state network tree and application to intrusion detection

abstract: Echo state network tree (ESNTree) is proposed in this paper. ESNTree changes the activation function of the hidden layer and modifies the initialization method of echo state network (ESN). If the number of input features is too large, genetic algorithm is used to extract better features. Thus the complexity of input feature space can be reduced. On the other hand, a divide-and-conquer method is used. Decision tree and ESN are combined to decrease the complexity of the classifier and make the classifier more comprehensible and more interpretable. Experiments show that ESNTree achieves better performance than neural network tree (NNTree). ESNTree has been applied to intrusion detection successfully.


title: Blurred License Plate Recognition based on single snapshot from drive recorder

abstract: Nowadays, drive recorders are becoming a popular form of evidences used by drivers and accepted by court. One common investigation task is to identify vehicles of interest and recognize their license plates (LPs). In this paper, we focus on License Plate Recognition (LPR) based on single snapshot from a drive recorder. As drive recorders are installed on moving vehicles, snapshots by drive recorders usually suffer from serious blur, and the key issue is recognizing the Blurred License Plate (BLP) from single image. A straightforward method is first deblurring the BLP and then recognizing it. However, the first problem with this method is that general image deblurring methods are designed to get a good overall visual effect and the deblurred results may be not good for LPR. The second problem is that general image deblurring methods don't use the features of the LPs, which could be important priors for the deblurring process. To overcome these issues, this paper proposes a novel method that integrates deblurring and recognizing in a closed-loop. The proposed method utilizes characters and patterns of LPs as priors, and the deblurring and recognizing process will stop when a reliable recognition result is obtained from the deblurred image. Furthermore, by analyzing the features of BLPs, this paper proposes a l 0 -norm based deblurring method. Experiments show that, compared to other LPR methods, the proposed method can achieve higher recognition rate on the BLPs.


title: Transparent, lightweight application execution replay on commodity multiprocessor operating systems

abstract: We present Scribe, the first system to provide transparent, low-overhead application record-replay and the ability to go live from replayed execution. Scribe introduces new lightweight operating system mechanisms, rendezvous and sync points, to efficiently record nondeterministic interactions such as related system calls, signals, and shared memory accesses. Rendezvous points make a partial ordering of execution based on system call dependencies sufficient for replay, avoiding the recording overhead of maintaining an exact execution ordering. Sync points convert asynchronous interactions that can occur at arbitrary times into synchronous events that are much easier to record and replay. We have implemented Scribe without changing, relinking, or recompiling applications, libraries, or operating system kernels, and without any specialized hardware support such as hardware performance counters. It works on commodity Linux operating systems, and commodity multi-core and multiprocessor hardware. Our results show for the first time that an operating system mechanism can correctly and transparently record and replay multi-process and multi-threaded applications on commodity multiprocessors. Scribe recording overhead is less than 2.5% for server applications including Apache and MySQL, and less than 15% for desktop applications including Firefox, Acrobat, OpenOffice, parallel kernel compilation, and movie playback.


title: Plant Image Retrieval Using Color, Shape and Texture Features

abstract: We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging. We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered.


The Evolving Definition of Neuromodulation


title: The chronic effects of cannabis on memory in humans: a review. Curr. Drug Abuse Rev

abstract: Abstract Memory problems are frequently associated with cannabis use, in both the short- and long-term. To date, reviews on the long-term cognitive sequelae of cannabis use have examined a broad range of cognitive functions, with none specifically focused on memory. Consequently, this review sought to examine the literature specific to memory function in cannabis users in the nontoxicated state with the aim of identifying the existence and nature of memory impairment in cannabis users and appraising potentially related mediators or moderators. Literature searches were conducted to extract well-controlled studies that investigated memory function in cannabis users outside of the acute intoxication period, with a focus on reviewing studies published within the past 10 years. Most recent studies have examined working memory and verbal episodic memory and cumulatively, the evidence suggests impaired encoding, storage, manipulation and retrieval mechanisms in long-term or heavy cannabis users. These impairments are not dissimilar to those associated with acute intoxication and have been related to the duration, frequency, dose and age of onset of cannabis use. We consider the impact of not only specific parameters of cannabis use in the manifestation of memory dysfunction, but also such factors as age, neurodevelopmental stage, IQ, gender, various vulnerabilities and other substance-use interactions, in the context of neural efficiency and compensatory mechanisms. The precise nature of memory deficits in cannabis users, their neural substrates and manifestation requires much further exploration through a variety of behavioural, functional brain imaging, prospective and genetic studies.


title: Bilinear CNNs for Fine-grained Visual Recognition

abstract: We present a simple and effective architecture for fine-grained visual recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs belong to the class of orderless texture representations but unlike prior work they can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 86.9% and 91.3% per-image accuracy on the Caltech-UCSD birds [67], NABirds [64], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at this http URL.


title: Document clustering by concept factorization

abstract: In this paper, we propose a new data clustering method called concept factorization that models each concept as a linear combination of the data points, and each data point as a linear combination of the concepts. With this model, the data clustering task is accomplished by computing the two sets of linear coefficients, and this linear coefficients computation is carried out by finding the non-negative solution that minimizes the reconstruction error of the data points. The cluster label of each data point can be easily derived from the obtained linear coefficients. This method differs from the method of clustering based on non-negative matrix factorization (NMF) \citeXu03 in that it can be applied to data containing negative values and the method can be implemented in the kernel space. Our experimental results show that the proposed data clustering method and its variations performs best among 11 algorithms and their variations that we have evaluated on both TDT2 and Reuters-21578 corpus. In addition to its good performance, the new method also has the merit in its easy and reliable derivation of the clustering results.


title: Moral Identity and Adolescent Prosocial and Antisocial Behaviors: Interactions with Moral Disengagement and Self-regulation

abstract: Moral identity has been positively linked to prosocial behaviors and negatively linked to antisocial behaviors; but, the processes by which it is linked to such outcomes are unclear. The purpose of the present study was to examine moral identity not only as a predictor, but also as a moderator of relationships between other predictors (moral disengagement and self-regulation) and youth outcomes (prosocial and antisocial behaviors). The sample consisted of 384 adolescents (42 % female), ages 15–18 recruited from across the US using an online survey panel. Latent variables were created for moral identity, moral disengagement, and self-regulation. Structural equation models assessed these latent variables, and interactions of moral identity with moral disengagement and self-regulation, as predictors of prosocial (charity and civic engagement) and antisocial (aggression and rule breaking) behaviors. None of the interactions were significant predicting prosocial behaviors. For antisocial behaviors, the interaction between moral identity and moral disengagement predicted aggression, while the interaction between moral identity and self-regulation was significant in predicting aggression and rule breaking. Specifically, at higher levels of moral identity, the positive link between moral disengagement and aggression was weaker, and the negative link between self-regulation and both antisocial behaviors was weaker. Thus, moral identity may buffer against the maladaptive effects of high moral disengagement and low self-regulation.


title: Crowdsourcing for Usability Testing

abstract: While usability evaluation is critical to designing usable websites, traditional usability testing can be both expensive and time consuming. The advent of crowdsourcing platforms such as Amazon Mechanical Turk and CrowdFlower offer an intriguing new avenue for performing remote usability testing with potentially many users, quick turn-around, and significant cost savings. To investigate the potential of such crowdsourced usability testing, we conducted a usability study which evaluated a graduate school’s website using a crowdsourcing platform. In addition, we performed a similar but not identical traditional lab usability test on the same site. While we find that crowdsourcing exhibits some notable limitations in comparison to the traditional lab environment, its applicability and value for usability testing is clearly evidenced. We discuss both methodological differences for crowdsourced usability testing, as well as empirical contrasts to results from more traditional, face-to-face usability testing.


title: A Simple Model for Intrinsic Image Decomposition with Depth Cues

abstract: We present a model for intrinsic decomposition of RGB-D images. Our approach analyzes a single RGB-D image and estimates albedo and shading fields that explain the input. To disambiguate the problem, our model estimates a number of components that jointly account for the reconstructed shading. By decomposing the shading field, we can build in assumptions about image formation that help distinguish reflectance variation from shading. These assumptions are expressed as simple nonlocal regularizers. We evaluate the model on real-world images and on a challenging synthetic dataset. The experimental results demonstrate that the presented approach outperforms prior models for intrinsic decomposition of RGB-D images.