title: Computer vision based vehicle detection for toll collection system using embedded Linux

abstract: Many highway toll collection systems have already been developed and are widely used in India. Some of these include Manual toll collection, RF tags, Barcodes, Number plate recognition. All these systems have disadvantages that lead to some errors in the corresponding system. This paper presents a brief review of toll collection systems present in India, their advantages and disadvantages and also aims to design and develop a new efficient toll collection system which will be a good low cost alternative among all other systems. The system is based on Computer Vision vehicle detection using OpenCV library in Embedded Linux platform. The system is designed using Embedded Linux development kit (Raspberry pi). In this system, a camera captures images of vehicles passing through toll booth thus a vehicle is detected through camera. Depending on the area occupied by the vehicle, classification of vehicles as light and heavy is done. Further this information is passed to the Raspberry pi which is having web server set up on it. When raspberry pi comes to know the vehicle, then it access the web server information and according to the type of the vehicle, appropriate toll is charged. This system can also made to count moving vehicles from pre-recorded videos or stored videos by using the same algorithm and procedure that we follow in this paper.


title: Robust clustering methods: a unified view

abstract: Clustering methods need to be robust if they are to be useful in practice. In this paper, we analyze several popular robust clustering methods and show that they have much in common. We also establish a connection between fuzzy set theory and robust statistics, and point out the similarities between robust clustering methods and statistical methods such as the weighted least-squares technique, the M estimator, the minimum volume ellipsoid algorithm, cooperative robust estimation, minimization of probability of randomness, and the epsilon contamination model. By gleaning the common principles upon which the methods proposed in the literature are based, we arrive at a unified view of robust clustering methods. We define several general concepts that are useful in robust clustering, state the robust clustering problem in terms of the defined concepts, and propose generic algorithms and guidelines for clustering noisy data. We also discuss why the generalized Hough transform is a suboptimal solution to the robust clustering problem.


title: Pediatric submersion injuries: emergency care and resuscitation.

abstract: Drowning and submersion injuries are highly prevalent, yet preventable, causes of childhood mortality and morbidity. Although much of the resuscitation of the drowning pediatric victim is basic to all respiratory and cardiac arrest situations, there are some caveats for treatment of this type of injury. Risk factors for drowning victims include epilepsy, underlying cardiac dysrhythmias, hyperventilation, hypoglycemia, hypothermia, and alcohol and illicit drug use. Prehospital care should focus on restoring normal ventilation and circulation as quickly as possible to limit the extent of hypoxic insult. Diagnostic testing for symptomatic patients may include blood glucose level, arterial blood gas level, complete blood count, electrolytes levels, chest radiography, and cardiorespiratory monitoring with pulse oximetry and a rhythm strip. In this review, passive external, active external, and active internal rewarming techniques for treatment of hypothermic patients are discussed. A systematic approach to treatment and disposition or admission of pediatric drowning victims is also included, with extensive clinical pathways for quick reference.


title: Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

abstract: With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide “suggestion” reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction “management” obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.


title: Facial emotion processing in major depression: a systematic review of neuroimaging findings

abstract: Background ::: Cognitive models of depression suggest that major depression is characterized by biased facial emotion processing, making facial stimuli particularly valuable for neuroimaging research on the neurobiological correlates of depression. The present review provides an overview of functional neuroimaging studies on abnormal facial emotion processing in major depression. Our main objective was to describe neurobiological differences between depressed patients with major depressive disorder (MDD) and healthy controls (HCs) regarding brain responsiveness to facial expressions and, furthermore, to delineate altered neural activation patterns associated with mood-congruent processing bias and to integrate these data with recent functional connectivity results. We further discuss methodological aspects potentially explaining the heterogeneity of results.


title: InverseFaceNet: Deep Single-Shot Inverse Face Rendering From A Single Image

abstract: We introduce InverseFaceNet, a deep convolutional inverse rendering framework for faces that jointly estimates facial pose, shape, expression, reflectance and illumination from a single input image in a single shot. By estimating all these parameters from just a single image, advanced editing possibilities on a single face image, such as appearance editing and relighting, become feasible. Previous learning-based face reconstruction approaches do not jointly recover all dimensions, or are severely limited in terms of visual quality. In contrast, we propose to recover high-quality facial pose, shape, expression, reflectance and illumination using a deep neural network that is trained using a large, synthetically created dataset. Our approach builds on a novel loss function that measures model-space similarity directly in parameter space and significantly improves reconstruction accuracy. In addition, we propose an analysis-by-synthesis breeding approach which iteratively updates the synthetic training corpus based on the distribution of real-world images, and we demonstrate that this strategy outperforms completely synthetically trained networks. Finally, we show high-quality reconstructions and compare our approach to several state-of-the-art approaches.


title: An exact method for the two-echelon, single-source, capacitated facility location problem

abstract: Facility location problems form an important class of integer programming problems, with applications in the telecommunication, distribution and transportation industries. In this paper we are conc ...


title: Cinnamon Treatment Upregulates Neuroprotective Proteins Parkin and DJ-1 and Protects Dopaminergic Neurons in a Mouse Model of Parkinson’s Disease

abstract: Upregulation and/or maintenance of Parkinson’s disease (PD)-related beneficial proteins such as Parkin and DJ-1 in astrocytes during neurodegenerative insults may have therapeutic efficacy in PD. Cinnamon is a commonly used natural spice and flavoring material throughout the world. Here we have explored a novel use of cinnamon in upregulating Parkin and DJ-1 and protecting dopaminergic neurons in MPTP mouse model of PD. Recently we have delineated that oral feeding of cinnamon (Cinnamonum verum) powder produces sodium benzoate (NaB) in blood and brain of mice. Proinflammatory cytokine IL-1β decreased the level of Parkin/DJ-1 in mouse astrocytes. However, cinnamon metabolite NaB abrogated IL-1β-induced loss of these proteins. Inability of TNF-α to produce nitric oxide (NO) and decrease the level of Parkin/DJ-1 in wild type (WT) astrocytes, failure of IL-1β to reduce Parkin/DJ-1 in astrocytes isolated from iNOS (−/−) mice, and decrease in Parkin/DJ-1 in WT astrocytes by NO donor DETA-NONOate suggest that NO is a negative regulator of Parkin/DJ-1. Furthermore, suppression of IL-1β-induced expression of iNOS in astrocytes by NaB and reversal of NaB-mediated protection of Parkin/DJ-1 by DETA-NONOate in astrocytes indicate that NaB protects Parkin/DJ-1 in activated astrocytes via suppressing iNOS. Similarly MPTP intoxication also increased the level of iNOS and decreased the level of Parkin/DJ-1 in vivo in the nigra. However, oral treatment of MPTP-intoxicated mice with cinnamon powder and NaB reduced the expression of iNOS and protected Parkin/DJ-1 in the nigra. These findings paralleled dopaminergic neuronal protection, normalized striatal neurotransmitters, and improved motor functions by cinnamon in MPTP-intoxicated mice. These results suggest that cinnamon may be beneficial for PD patients.


title: Learning from small data sets to improve assembly semiconductor manufacturing processes

abstract: The use of machine learning algorithms such as Gradient Boosting Trees (GBT) [1] and Random Forest (RF) [2] have enjoyed some successes [3, 4] for Intel class test yield prediction and analysis. However, we have not attempted it on Intel assembly data mainly due to the lack of Design of Experiment (DOE) data. In addition, it is a continual effort to reduce the cost of DOE for data-driven engineering decision. Clearly inadequacy of training data discourages proper machine learning for predictive modeling. We propose the use of machine learning algorithm to implement an adaptive-sequential (A-S) process and accuracy guard band concept for improved recipe generation process development. A chipset assembly process recipe generation process development use case is examined. It has demonstrated a 13–20 folds DOE sample size reduction with equal or better accuracy and coverage than the least square curve fitting method. The model is useful for accurate “What-if” process development simulation.


Medicinal Plants Disease Identification Using Canny Edge Detection Algorithm , Histogram Analysis and CBIR


title: CDMA/FDMA-interconnects for future ULSI communications

abstract: Future inter- and intra-ULSI interconnect systems demand extremely high data rates as well as bidirectional multi-I/O concurrent service, reconfigurable computing/processing architecture, and total compatibility with mainstream silicon SOC (system-on-chip) and SIP (system-in-package) technologies. In this talk, we review recent advances in CDMA and FDMA interconnect schemes that promise to meet all of the above system requirements. The physical transmission line is no longer limited to a direct-coupled metal wire. Rather, it can be accomplished via either wired or wireless mediums through capacitor couplers that reduce the baseband noise and DC power consumption. These new advances in interconnect schemes would fundamentally alter the paradigm of ULSI data communications and enable the design of next generation computing/processing systems.


title: Image-based façade modeling

abstract: We propose in this paper a semi-automatic image-based approach to façade modeling that uses images captured along streets and relies on structure from motion to recover camera positions and point clouds automatically as the initial stage for modeling. We start by considering a building façade as a flat rectangular plane or a developable surface with an associated texture image composited from the multiple visible images. A façade is then decomposed and structured into a Directed Acyclic Graph of rectilinear elementary patches. The decomposition is carried out top-down by a recursive subdivision, and followed by a bottom-up merging with the detection of the architectural bilateral symmetry and repetitive patterns. Each subdivided patch of the flat façade is augmented with a depth optimized using the 3D points cloud. Our system also allows for an easy user feedback in the 2D image space for the proposed decomposition and augmentation. Finally, our approach is demonstrated on a large number of façades from a variety of street-side images.


A multi-hierarchical symbolic model of the environment for improving mobile robot operation


title: Ambiguities in Spacebornene Synthetic Aperture Radar Systems

abstract: Several aspects of range and azimuth (time delay and Doppler) ambiguities in spaceborne synthetic aperture radars (SARs) are examined. An accurate method to evaluate the ratio of the intensities of the ambiguities to that of the signal is described. This method has been applied to the nominal SAR system on SEASAT and the variations of this ratio as a function of orbital latitude and attitude control error are discussed. It is also shown that the detailed range migration-azimuth phase history of an ambiguity is different from that of a signal. The images of ambiguities are, therefore, dispersed. Several examples of such dispersed images observed by the SEASAT SAR are presented. These dispersions are eliminated when the processing parameters are adjusted appropriately. Finally, a method is described which uses a set of multiple pulse repetition frequencies to determine the absolute values of the Doppler centroid frequencies for SARs with high carrier frequencies and relatively poor attitude measurements.


title: QR Code Security: A Survey of Attacks and Challenges for Usable Security

abstract: QR (Quick Response) codes are two-dimensional barcodes with the ability to encode different types of information. Because of their high information density and robustness, QR codes have gained popularity in various fields of application. Even though they offer a broad range of advantages, QR codes pose significant security risks. Attackers can encode malicious links that lead e.g. to phishing sites. Such malicious QR codes can be printed on small stickers and replace benign ones on billboard advertisements. Although many real world examples of QR code based attacks have been reported in the media, only little research has been conducted in this field and almost no attention has been paid on the interplay of security and human-computer interaction. In this work, we describe the manifold use cases of QR codes. Furthermore, we analyze the most significant attack scenarios with respect to the specific use cases. Additionally, we systemize the research that has already been conducted and identified usable security and security awareness as the main research challenges. Finally we propose design requirements with respect to the QR code itself, the reader application and usability aspects in order to support further research into to making QR code processing both secure and usable.


title: An Intermediate Guide to Estimating Multilevel Models for Categorical Data using SAS ® PROC GLIMMIX

abstract: This paper expands upon Ene et al.’s (2015) SAS Global Forum proceeding paper “Multilevel Models for Categorical Data using SAS® PROC GLIMMIX: The Basics” in which the authors presented an overview of estimating two-level models with non-normal outcomes via PROC GLIMMIX. In their paper, the authors focused on how to use GLIMMIX to estimate two-level organizational models; however, they did not include random slopes, address more complex organizational models (e.g., three-level models) or models used to estimate longitudinal data. Hence, the need for the current paper; by building from the examples in Ene et al. (2015), the current paper presents users detailed discussions and illustrations about how to use GLIMMIX to estimate models with random slopes, organizational models in situations with three levels of data, as well as two-level longitudinal data. Consistent with Ene et al.’s paper, we will present the syntax and interpretation of the estimates using a model with a dichotomous outcome as well as a model with a polytomous outcome. Concrete examples will be used to illustrate how PROC GLIMMIX can be used to estimate these models and how key pieces of the output can be used to answer corresponding research questions.


title: A wearable fingertip haptic device with 3 DoF asymmetric 3-RSR kinematics

abstract: A novel wearable haptic device for modulating skin stretch at the fingertip is presented. Rendering of skin stretch in 3 degrees of freedom (DoF), with contact - no contact capabilities, was implemented through rigid parallel kinematics. The novel asymmetrical three revolute-spherical-revolute (3-RSR) configuration allowed compact dimensions with minimum encumbrance of the hand workspace and minimum inter-finger interference. A differential method for solving the non-trivial inverse kinematics is proposed and implemented in real time for controlling the position of the skin tactor. Experiments involving the grasping of a virtual object were conducted using two devices (thumb and index fingers) in a group of 4 subjects: results showed that participants performed the grasping task more precisely and with grasping forces closer to the expected natural behavior when the proposed device provided haptic feedback.


title: Effects of calcium antagonist TMB-8 on active Na and Cl transport in rabbit ileum.

abstract: The effects of 3,4,5-trimethoxybenzoate 8-(N,N-diethylamino)octyl ester (TMB-8), an agent that traps calcium within intracellular stores, were studied on active electrolyte transport in rabbit ileum under basal conditions and after altering transport by increasing the intracellular cAMP content or by exposure to two agonists that act by altering intracellular Ca2+ (carbachol and serotonin). TMB-8 decreased the ileal short-circuit current and increased active Na and Cl absorption by increasing the mucosal-to-serosal Na and Cl fluxes. These effects were reversed by increasing the bathing solution Ca2+ to 4 mM, a concentration that itself did not alter basal ileal transport. The maximum glucose- and amino acid (alanine)-induced increase in Na absorption in the ileum was not affected by TMB-8. The effects on basal transport of TMB-8 were not associated with a change in 45Ca2+ entry across the ileal serosal surface. TMB-8 did not alter cAMP-induced secretion, as judged by its lack of effect on the increase in short-circuit current caused by 8-bromo-cAMP (10(-4) M). TMB-8 totally prevented the transport effects of carbachol but did not inhibit the effects of serotonin. These data suggest a role for intracellular Ca2+ in regulation of basal ileal Na and Cl transport but not in cAMP-induced secretion. There appear to be several pools of intracellular Ca2+ involved in neurohumoral effects on active electrolyte transport.


title: Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural-Symbolic Computing as Examples

abstract: This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic normative contexts.


title: Everything you always wanted to know about SEM (structural equations modeling) but were afraid to ask

abstract: This article is intended to serve as a primer for structural equations models for the behavioral researcher. The technique is not mysterious—it is a natural extension of factor analysis and regression. The measurement part of a structural equations model is essentially a confirmatory factor analysis, and the structural part of the model is like a regression but vastly more flexible in the types of theoretical models that may be tested. The models and notation are introduced and the syntax is provided to replicate the analyses in the paper. Part II of this article will appear in the next issue of the Journal of Consumer Psychology, and it covers advanced issues, including fit indices, sample size, moderators, longitudinal data, mediation, and so forth.


title: Volunteer GIS (VGIS) based waste management: A conceptual design and use of web 2.0 for smart waste management in Dhaka City

abstract: This Waste management is a major issue for rapidly growing megacities in developing countries. Unorganized and poor management system leads towards negative environmental impact, and lowering livability of these cities. How-ever, there are scope for integrating general people in the waste management to improve overall performance. In such context, the aim of this study is to develop and detail, a volunteer GIS based smart waste management system for Dhaka city; in order to ensure active participation of the citizens in resolving poor solid waste management conditions. The study concentrated in developing a Geoweb (Web 2.0) design for collecting waste related problems from the citizens. VGIS concept is developed using web-server and VGIS interface, with JavaScript and Google Maps APIs. The developed Geo-web would allow to report waste related problems (e.g., Illegal waste dumping) by the citizens using geo-tagged marker over a customized Google Map. Additionally, the website would also be used to disseminate waste collection schedule, contact information of the waste collectors for different wards in the city. The system would provide a common platform for citizens' active participation in waste management, and also help bridge the gap between citizens and local government, thus would open opportunities for active e-governance. Successful implementation of the system can help reducing solid waste management problems and create awareness among the citizens regarding waste management and use of internet in improving their surroundings.


title: Immediate dentin sealing improves bond strength of indirect restorations

abstract: Statement of problem Delayed dentin sealing is traditionally performed with indirect restorations. With this technique, dentin is sealed after the provisional phase at the cementation appointment. It was demonstrated that this chronology does not provide optimal conditions for bonding procedures. Immediate dentin sealing (IDS) is a new approach in which dentin is sealed immediately following tooth preparation, before making the impression. Purpose The purpose of this study was to determine whether there were differences in microtensile bond strength to human dentin using IDS technique compared to delayed dentin sealing (DDS). Material and methods Fifteen freshly extracted human molars were obtained and divided into 3 groups of 5 teeth. A 3-step etch-and-rinse dentin bonding agent (DBA) (OptiBond FL) was used for all groups. The control (C) specimens were prepared using a direct immediate bonding technique. The DDS specimens were prepared using an indirect approach with DDS. Preparation of the IDS specimens also used an indirect approach with IDS immediately following preparation. All teeth were prepared for a nontrimming microtensile bond strength test. Specimens were stored in water for 24 hours. Eleven beams (0.9 × 0.9 × 11 mm) from each tooth were selected for testing. Bond strength data (MPa) were analyzed with a Kruskal-Wallis test, and post hoc comparison was done using the Mann-Whitney U test (α=.05). Specimens were also evaluated for mode of fracture using scanning electron microscope (SEM) analysis. Results The mean microtensile bond strengths of C and IDS groups were not statistically different from one another at 55.06 and 58.25 MPa, respectively. The bond strength for DDS specimens, at 11.58 MPa, was statistically different ( P =.0081) from the other 2 groups. Microscopic evaluation of failure modes indicated that most failures in the DDS group were interfacial, whereas failures in the C and IDS groups were both cohesive and interfacial. SEM analysis indicated that for C and IDS specimens, failure was mixed within the adhesive and cohesively failed dentin. For DDS specimens, failure was generally at the top of the hybrid layer in the adhesive. SEM analysis of intact slabs demonstrated a well-organized hybrid layer 3 to 5 μm thick for the C and IDS groups. For DDS specimens the hybrid layer presented a marked disruption with the overlying resin. Conclusions When preparing teeth for indirect bonded restorations, IDS with a 3-step etch-and-rinse filled DBA, prior to impression making, results in improved microtensile bond strength compared to DDS. This technique also eliminates any concerns regarding the film thickness of the dentin sealant.


title: A Fuzzy Model With Online Incremental SVM and Margin-Selective Gradient Descent Learning for Classification Problems

abstract: This paper proposes a new incremental learning approach to endow a Takagi-Sugeno-type fuzzy classification model with high generalization ability. The proposed fuzzy model is learned through incremental support vector machine (SVM) and margin-selected gradient descent learning and is called FM3. In this learning approach, training samples are fed incrementally one-by-one instead of all in one batch. The FM3 evolves from an empty rule set. A one-pass clustering algorithm is used to determine the number of rules and initial fuzzy sets in the rule antecedent part. An online incremental linear SVM is proposed to tune the rule consequent parameters to endow the FM3 with high generalization ability. The use of incremental instead of batch SVM enables the FM3 to handle online training problems with only one new sample available at a time. For antecedent parameter learning, a margin-selected gradient descent algorithm is proposed to prevent overtraining. Simulation results and comparisons with SVMs and fuzzy classifiers with different learning algorithms demonstrate the advantage of the FM3.


title: The Velo gripper: A versatile single-actuator design for enveloping, parallel and fingertip grasps

abstract: We design, optimize and demonstrate the behavior of a tendon-driven robotic gripper performing parallel, enveloping and fingertip grasps. The gripper consists of two fingers, each with two links, and is actuated using a single active tendon. During unobstructed closing, the distal links remain parallel, for parallel grasps. If the proximal links are stopped by contact with an object, the distal links start flexing, creating a stable enveloping grasp. We optimize the route of the active flexor tendon and the route and stiffness of a passive extensor tendon in order to achieve this behavior. We show how the resulting gripper can also execute fingertip grasps for picking up small objects off a flat surface, using contact with the surface to its advantage through passive adaptation. Finally, we introduce a method for optimizing the dimensions of the links in order to achieve enveloping grasps of a large range of objects, and apply it to a set of common household objects.


title: MODEL-BASED IT GOVERNANCE MATURITY ASSESSMENTS WITH COBIT

abstract: IT governance is an executive level concern in many enterprises today, but a method for credible, reliable, and cost-efficient IT governance maturity assessment has been lacking. Control Objectives for Information and related Technology (Cobit) is best practice in the area, but the method requires an experienced analyst to perform the assessment and the provided analysis framework is vague and ambiguous. This paper presents a Cobit based method designed to overcome these featured problems. It comprises a modeling language for IT governance based on Cobit, and a transparent analysis framework which enables aggregation of single metrics into comprehensive maturity scores. The applicability was tested in a small case study. Results demonstrate that the method can be used to conduct time-efficient, valid and reliable IT governance maturity assessments without the help of an experienced analyst.


title: A boosting algorithm for item recommendation with implicit feedback

abstract: Many recommendation tasks are formulated as top-N item recommendation problems based on users' implicit feedback instead of explicit feedback. Here explicit feedback refers to users' ratings to items while implicit feedback is derived from users' interactions with items, e.g., number of times a user plays a song. In this paper, we propose a boosting algorithm named AdaBPR (Adaptive Boosting Personalized Ranking) for top-N item recommendation using users' implicit feedback. In the proposed framework, multiple homogeneous component recommenders are linearly combined to create an ensemble model, for better recommendation accuracy. The component recommenders are constructed based on a fixed collaborative filtering algorithm by using a re-weighting strategy, which assigns a dynamic weight distribution on the observed user-item interactions. AdaBPR demonstrates its effectiveness on three datasets compared with strong baseline algorithms.


title: Learning and Generalization with the Information Bottleneck

abstract: The Information Bottleneck is an information theoretic framework that finds concise representations for an `input' random variable that are as relevant as possible for an `output' random variable. This framework has been used successfully in various supervised and unsupervised applications. However, its learning theoretic properties and justification remained unclear as it differs from standard learning models in several crucial aspects, primarily its explicit reliance on the joint input-output distribution. In practice, an empirical plug-in estimate of the underlying distribution has been used, so far without any finite sample performance guarantees. In this paper we present several formal results that address these difficulties. We prove several finite sample bounds, which show that the information bottleneck can provide concise representations with good generalization, based on smaller sample sizes than needed to estimate the underlying distribution. The bounds are non-uniform and adaptive to the complexity of the specific model chosen. Based on these results, we also present a preliminary analysis on the possibility of analyzing the information bottleneck method as a learning algorithm in the familiar performance-complexity tradeoff framework. In addition, we formally describe the connection between the information bottleneck and minimal sufficient statistics.


title: The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis

abstract: A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple- perspective method increases the interpretability and accountability of both ACA and DCA networks.


title: Low-Voltage Bulk-Driven Operational Amplifier With Improved Transconductance

abstract: This paper presents two low-voltage bulk-driven amplifier input stages with enhanced transconductance. The idea is to introduce auxiliary differential pairs into a conventional bulk-driven stage to boost its transconductance. A low-voltage cascode biasing circuitry based on EKV models is also employed to ensure proper operation of the proposed input stages. An operational amplifier is then implemented with the proposed input stages and biasing circuits as its core building blocks and including a modified low-voltage class AB output amplifier to guarantee rail-to-rail output voltage range. The overall amplifier was implemented in a 0.35 μm n-well CMOS process using 1-V power supply. The measurement results show significant improvement in the performance of the operational amplifier compared to prior arts.


title: Efficient descriptor of histogram of salient edge orientation map for finger vein recognition

abstract: Finger vein images are rich in orientation and edge features. Inspired by the edge histogram descriptor proposed in MPEG-7, this paper presents an efficient orientation-based local descriptor, named histogram of salient edge orientation map (HSEOM). HSEOM is based on the fact that human vision is sensitive to edge features for image perception. For a given image, HSEOM first finds oriented edge maps according to predefined orientations using a well-known edge operator and obtains a salient edge orientation map by choosing an orientation with the maximum edge magnitude for each pixel. Then, subhistograms of the salient edge orientation map are generated from the nonoverlapping submaps and concatenated to build the final HSEOM. In the experiment of this paper, eight oriented edge maps were used to generate a salient edge orientation map for HSEOM construction. Experimental results on our available finger vein image database, MMCBNU_6000, show that the performance of HSEOM outperforms that of state-of-the-art orientation-based methods (e.g., Gabor filter, histogram of oriented gradients, and local directional code). Furthermore, the proposed HSEOM has advantages of low feature dimensionality and fast implementation for a real-time finger vein recognition system.


title: Community Detection in Social Networks: An In-depth Benchmarking Study with a Procedure-Oriented Framework

abstract: Revealing the latent community structure, which is crucial to understanding the features of networks, is an important problem in network and graph analysis. During the last decade, many approaches have been proposed to solve this challenging problem in diverse ways, i.e. different measures or data structures. Unfortunately, experimental reports on existing techniques fell short in validity and integrity since many comparisons were not based on a unified code base or merely discussed in theory. ::: ::: We engage in an in-depth benchmarking study of community detection in social networks. We formulate a generalized community detection procedure and propose a procedure-oriented framework for benchmarking. This framework enables us to evaluate and compare various approaches to community detection systematically and thoroughly under identical experimental conditions. Upon that we can analyze and diagnose the inherent defect of existing approaches deeply, and further make effective improvements correspondingly. ::: ::: We have re-implemented ten state-of-the-art representative algorithms upon this framework and make comprehensive evaluations of multiple aspects, including the efficiency evaluation, performance evaluations, sensitivity evaluations, etc. We discuss their merits and faults in depth, and draw a set of take-away interesting conclusions. In addition, we present how we can make diagnoses for these algorithms resulting in significant improvements.


title: Lipschitz Properties for Deep Convolutional Networks

abstract: In this paper we discuss the stability properties of convolutional neural networks. Convolutional neural networks are widely used in machine learning. In classification they are mainly used as feature extractors. Ideally, we expect similar features when the inputs are from the same class. That is, we hope to see a small change in the feature vector with respect to a deformation on the input signal. This can be established mathematically, and the key step is to derive the Lipschitz properties. Further, we establish that the stability results can be extended for more general networks. We give a formula for computing the Lipschitz bound, and compare it with other methods to show it is closer to the optimal value.


title: Checklist of the Staphylinidae (Coleoptera) of Colombia

abstract: he beetle family Staphylinidae, or “rove beetles”, is a megadiverse group of more than 48,000 named species worldwide, currently placed in 31 subfamilies (including the recently added Pselaphinae and Scaphidiinae, formerly treated as families) and more than 3,400 genera. Rove beetles are especially diverse and abundant in tropical and temperate forests, in the canopy and in logs as well as in ground litter, but also occur in most other terrestrial or periaquatic habitats. The majority of the species are freeliving predators of other arthropods or invertebrates, but many are saprophagous or feed on fungi or pollen. Many species are commensals of social insects such as ants or termites, or with vertebrates. A few species are economically important through their beneficial or detrimental effects on human activities, for example as biological control agents of pest arthropods that attack crops, or for causing dermatitis (genus Paederus). Summaries of the diversity, distribution, biology, classification and phylogeny of staphylinids can be found in Newton et al. (2000), Navarrete et al. (2002), and Thayer (in press).


title: Top-Down Induction of First-Order Logical Decision Trees

abstract: Although topddown induction of decision trees is a very popular induction method, up till now it has mainly been used for propositional learnings relational decision tree learners are scarce. This dissertation discusses the application domain of decision tree learning and extends it towards the first order logic context of Inductive Logic Programming.


title: Cybercrime: Understanding and addressing the concerns of stakeholders

abstract: Cybercrime and cybercriminal activities continue to impact communities as the steady growth of electronic information systems enables more online business. The collective views of sixty-six computer users and organizations, that have an exposure to cybercrime, were analyzed using concept analysis and mapping techniques in order to identify the major issues and areas of concern, and provide useful advice. The findings of the study show that a range of computing stakeholders have genuine concerns about the frequency of information security breaches and malware incursions (including the emergence of dangerous security and detection avoiding malware), the need for e-security awareness and education, the roles played by law and law enforcement, and the installation of current security software and systems. While not necessarily criminal in nature, some stakeholders also expressed deep concerns over the use of computers for cyberbullying, particularly where younger and school aged users are involved. The government's future directions and recommendations for the technical and administrative management of cybercriminal activity were generally observed to be consistent with stakeholder concerns, with some users also taking practical steps to reduce cybercrime risks.


title: A comparative analysis of smart metering data aggregation performance

abstract: In the Smart Grid era fine-grained energy information pertaining real world processes can be collected and may reveal new insights if these can be analyzed in real-time. Energy “Big Data” analytics can lead to a plethora of new innovative applications and enhance decision making processes. However, to do so, we need new enterprise tools and approaches that can take into consideration the specifics of the energy domain and offer high performance analytics on its raw data. In this work, experiments are conducted to measure the performance of the different levels of energy data aggregation. Thousands of smart meters are aggregated, by usage of the collected energy readings from a real-world trial. Using a selected dataset, the traditional database system (row-based) performance is compared to the emerging column-based approach in order to assess the suitability for real-time analytics in such scenarios.


title: Misery Has More Company Than People Think: Underestimating the Prevalence of Others’ Negative Emotions

abstract: Four studies document underestimations of the prevalence of others’ negative emotions and suggest causes and correlates of these erroneous perceptions. In Study 1a, participants reported that their negative emotions were more private or hidden than were their positive emotions; in Study 1b, participants underestimated the peer prevalence of common negative, but not positive, experiences described in Study 1a. In Study 2, people underestimated negative emotions and overestimated positive emotions even for well-known peers, and this effect was partially mediated by the degree to which those peers reported suppression of negative (vs. positive) emotions. Study 3 showed that lower estimations of the prevalence of negative emotional experiences predicted greater loneliness and rumination and lower life satisfaction and that higher estimations for positive emotional experiences predicted lower life satisfaction. Taken together, these studies suggest that people may think they are more alone in their emotional d...


title: Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations

abstract: Policy search methods in reinforcement learning have demonstrated success in scaling up to larger problems beyond toy examples. However, deploying these methods on real robots remains challenging due to the large sample complexity required during learning and their vulnerability to malicious intervention. We introduce Adversarially Robust Policy Learning (ARPL), an algorithm that leverages active computation of physically-plausible adversarial examples during training to enable robust policy learning in the source domain and robust performance under both random and adversarial input perturbations. We evaluate ARPL on four continuous control tasks and show superior resilience to changes in physical environment dynamics parameters and environment state as compared to state-of-the-art robust policy learning methods. Code, data, and additional experimental results are available at: stanfordvl.github.io/ARPL


FAST AND ROBUST ALGORITHMS FOR HARMONIC ENERGY MINIMIZATION ON GENUS-0 SURFACES


title: An efficient natural language interface to XML database

abstract: In this era databases are used in many fields like banking, human resources, universities etc. Everyone needs to deal with databases for the extraction of required data. But it is very difficult for a common user to retrieve information from the database using database query languages. Access any kind of data from database using natural language like English is a convenient and easy method instead of using formal query languages such as XQuery, SQL etc. The proposed system can accept English language sentences and then it is translated into an XQuery expression. This XQuery statement can be evaluated against an XML database. This query translation is done by mapping the tokens in the dependency parse tree of the natural language query into the XQuery fragments. The existing systems in this field work without handling the multi- sentence queries, yes-no queries and wh-queries. This paper introduces a novel architecture which is capable of translating a wide range of natural language queries into formal database queries.


Don't Be Lazy, Be Consistent: Postgres-R, A New Way to Implement Database Replication


title: A quick artificial bee colony (qABC) algorithm and its performance on optimization problems

abstract: Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability. In this study, the qABC method is described and its performance is analysed depending on the neighbourhood radius, on a set of benchmark problems. And also some analyses about the effect of the parameter limit and colony size on qABC optimization are carried out. Moreover, the performance of qABC is compared with the state of art algorithms' performances.


Natural Language Generation for Medical Reports


title: A 180-mV subthreshold FFT processor using a minimum energy design methodology

abstract: In emerging embedded applications such as wireless sensor networks, the key metric is minimizing energy dissipation rather than processor speed. Minimum energy analysis of CMOS circuits estimates the optimal operating point of clock frequencies, supply voltage, and threshold voltage according to A. Chandrakasan et al. (see ibid., vol.27, no.4, p.473-84, Apr. 1992). The minimum energy analysis shows that the optimal power supply typically occurs in subthreshold (e.g., supply voltages that are below device thresholds). New subthreshold logic and memory design methodologies are developed and demonstrated on a fast Fourier transform (FFT) processor. The FFT processor uses an energy-aware architecture that allows for variable FFT length (128-1024 point), variable bit-precision (8 b and 16 b) and is designed to investigate the estimated minimum energy point. The FFT processor is fabricated using a standard 0.18-/spl mu/m CMOS logic process and operates down to 180 mV. The minimum energy point for the 16-b 1024-point FFT processor occurs at 350-mV supply voltage where it dissipates 155 nJ/FFT at a clock frequency of 10 kHz.


title: An edge detection algorithm for online image analysis

abstract: Online image analysis is used in a wide variety of applications. Edge detection is a fundamental tool used to obtain features of objects as a prerequisite step to object segmentation. This paper presents a simple and relatively fast online edge detection algorithm based on second derivative. The proposed edge detector is less sensitive to noise and may be applied on color, gray and binary images without preprocessing requirements. The merits of the algorithm are demonstrated by comparison with Canny's and Sobel's edge detectors.


title: Invariant Scattering Convolution Networks

abstract: A wavelet scattering network computes a translation invariant image representation which is stable to deformations and preserves high-frequency information for classification. It cascades wavelet transform convolutions with nonlinear modulus and averaging operators. The first network layer outputs SIFT-type descriptors, whereas the next layers provide complementary invariant information that improves classification. The mathematical analysis of wavelet scattering networks explains important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having the same Fourier power spectrum. State-of-the-art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.


title: Integrating MOOC and Flipped Classroom Practice in a Traditional Undergraduate Course: Students’ Experience and Perceptions

abstract: The purpose of this experimental study was to redesign a traditional undergraduate course by integrating MOOC content and flipped classroom practice and to see its effectiveness through students’ experience and perceptions. The course named “Internet and Distance Education” was taught in Winter Semester, 2013 at the Zhejiang University in Hangzhou, China to 15 undergraduate students majoring in education. E-learner satisfaction surveys found that students were generally satisfied with many aspects of the redesigned course, including instructor response timeliness, instructor attitude toward the technology, e-learning course flexibility, technology quality, Internet quality, perceived usefulness, perceived ease of use, and perceived e-learner satisfaction, while learner perceived interaction with others was not so satisfactory. Based on the findings, several suggestions to improve the course design are offered.


title: Interpretable Deep Learning under Fire

abstract: Providing explanations for complicated deep neural network (DNN) models is critical for their usability in security-sensitive domains. A proliferation of interpretation methods have been proposed to help end users understand the inner workings of DNNs, that is, how a DNN arrives at a particular decision for a specific input. This improved interpretability is believed to offer a sense of security by involving human in the decision-making process. However, due to its data-driven nature, the interpretability itself is potentially susceptible to malicious manipulation, about which little is known thus far. In this paper, we conduct the first systematic study on the security of interpretable deep learning systems (IDLSes). We first demonstrate that existing IDLSes are highly vulnerable to adversarial manipulation. We present ACID attacks, a broad class of attacks that generate adversarial inputs which not only mislead target DNNs but also deceive their coupled interpretation models. By empirically investigating three representative types of interpretation models, we show that ACID attacks are effective against all of them. This vulnerability thus seems pervasive in many IDLSes. Further, using both analytical and empirical evidence, we identify the prediction-interpretation"independency"as one possible root cause of this vulnerability: a DNN and its interpretation model are often not fully aligned, resulting in the possibility for the adversary to exploit both models simultaneously. Moreover, by examining the transferability of adversarial inputs across different interpretation models, we expose the fundamental tradeoff among the attack evasiveness with respect to different interpretation methods. These findings shed light on developing potential countermeasures and designing more robust interpretation methods, leading to several promising research directions.


title: Causes, assessment and treatment of malnutrition in older people

abstract: Malnutrition is a growing problem in the UK with as many as 14% of people aged over 65 at risk. It is of particular concern in care homes where more than one third of residents are undernourished. Weight loss is not the only symptom of malnourishment and nurses should examine any changes to a person's health and well-being to identify causes. Regular monitoring of patients' risk of malnutrition through use of screening assessments, such as the Malnutrition Universal Screening Tool, ensures undernourishment is identified early. As the population ages, it is more important than ever that the implications of malnutrition are recognised and addressed.


No . 227 Anger Management and Violence Prevention : Improving Effectiveness


title: Development of the lower limbs for a humanoid robot

abstract: This paper gives an overview of the development of a novel biped walking machine for a humanoid robot, Roboray. This lower-limb robot is designed as an experimental system for studying biped locomotion based on force and torque controlled joints. The robot has 13 actuated DOF and torque sensors are integrated at all the joints except the waist joint. We designed a new tendon type joint modules as a pitch joint drive module, which is highly back-drivable and elastic. We also built a decentralized control system using the small controller boards named Smart Driver. The forward walking experiment with this lower limbs was conducted to test the mechanical structure and control system.


title: Kernel sparse representation based classification

abstract: Sparse representation has attracted great attention in the past few years. Sparse representation based classification (SRC) algorithm was developed and successfully used for classification. In this paper, a kernel sparse representation based classification (KSRC) algorithm is proposed. Samples are mapped into a high dimensional feature space first and then SRC is performed in this new feature space by utilizing kernel trick. Since samples in the high dimensional feature space are unknown, we cannot perform KSRC directly. In order to overcome this difficulty, we give the method to solve the problem of sparse representation in the high dimensional feature space. If an appropriate kernel is selected, in the high dimensional feature space, a test sample is probably represented as the linear combination of training samples of the same class more accurately. Therefore, KSRC has more powerful classification ability than SRC. Experiments of face recognition, palmprint recognition and finger-knuckle-print recognition demonstrate the effectiveness of KSRC.


An efficient orientation filter for inertial and inertial / magnetic sensor arrays


title: Fast Stochastic Methods for Nonsmooth Nonconvex Optimization

abstract: We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems, where the nonconvex part is smooth and the nonsmooth part is convex. Surprisingly, unlike the smooth case, our knowledge of this fundamental problem is very limited. For example, it is not known whether the proximal stochastic gradient method with constant minibatch converges to a stationary point. To tackle this issue, we develop fast stochastic algorithms that provably converge to a stationary point for constant minibatches. Furthermore, using a variant of these algorithms, we show provably faster convergence than batch proximal gradient descent. Finally, we prove global linear convergence rate for an interesting subclass of nonsmooth nonconvex functions, that subsumes several recent works. This paper builds upon our recent series of papers on fast stochastic methods for smooth nonconvex optimization [22, 23], with a novel analysis for nonconvex and nonsmooth functions.


title: Backdoors to Abduction

abstract: Abductive reasoning (or Abduction, for short) is among the most fundamental AI reasoning methods, with a broad range of applications, including fault diagnosis, belief revision, and automated planning. Unfortunately, Abduction is of high computational complexity; even propositional Abduction is \Sigma_2^P-complete and thus harder than NP and coNP. This complexity barrier rules out the existence of a polynomial transformation to propositional satisfiability (SAT). In this work we use structural properties of the Abduction instance to break this complexity barrier. We utilize the problem structure in terms of small backdoor sets. We present fixed-parameter tractable transformations from Abduction to SAT, which make the power of today's SAT solvers available to Abduction.


title: Some new results on neural network approximation

abstract: We show that standard feedforward networks with as few as a single hidden layer can uniformly approximate continuous functions on compacta provided that the activation function @j is locally Riemann integrable and nonpolynomial, and have universal L^p (@m) approximation capabilities for finite and compactly supported input environment measures @m provided that @j is locally bounded and nonpolynomial. In both cases, the input-to-hidden weights and hidden layer biases can be constrained to arbitrarily small sets; if in addition @j is locally analytic a single universal bias will do.


Active Learning – Modern Learning Theory


title: Unconstrained inverse quadratic programming problem

abstract: The paper covers a formulation of the inverse quadratic programming problem in terms of unconstrained optimization where it is required to find the unknown parameters (the matrix of the quadratic form and the vector of the quasi-linear part of the quadratic form) provided that approximate estimates of the optimal solution of the direct problem and those of the target function to be minimized in the form of pairs of values lying in the corresponding neighborhoods are only known. The formulation of the inverse problem and its solution are based on the least squares method. In the explicit form the inverse problem solution has been derived in the form a system of linear equations. The parameters obtained can be used for reconstruction of the direct quadratic programming problem and determination of the optimal solution and the extreme value of the target function, which were not known formerly. It is possible this approach opens new ways in over applications, for example, in neurocomputing and quadric surfaces fitting. Simple numerical examples have been demonstrated. A scenario in the Octave/MATLAB programming language has been proposed for practical implementation of the method.


title: Reconstruct argorithm of 2D barcode for reading the QR code on cylindrical surface

abstract: Traditional barcode recognition algorithm usually do not fit the cylindrical code but the one on flat surface. This paper proposes a low-cost approach to implement recognition of the curved QR codes printed on bottles or cans. Finder patterns are extracted from detecting module width proportion and corners of contours and an efficient direct least-square ellipse fitting method is employed to extract the elliptic edge and the boundary of code region. Then the code is reconstructed by direct mapping from the stereoscopic coordinates to the image plane using the 3D back-projection, thus the data of code could be restored. Compared with previous approaches, the proposed algorithm outperforms in not only the computation amount but also higher accuracy of the barcode recognition, whether in the flat or the cylindrical surface.


title: Five basic muscle activation patterns account for muscle activity during human locomotion

abstract: An electromyographic (EMG) activity pattern for individual muscles in the gait cycle exhibits a great deal of intersubject, intermuscle and context-dependent variability. Here we examined the issue of common underlying patterns by applying factor analysis to the set of EMG records obtained at different walking speeds and gravitational loads. To this end healthy subjects were asked to walk on a treadmill at speeds of 1, 2, 3 and 5 kmh(-1) as well as when 35-95% of the body weight was supported using a harness. We recorded from 12-16 ipsilateral leg and trunk muscles using both surface and intramuscular recording and determined the average, normalized EMG of each record for 10-15 consecutive step cycles. We identified five basic underlying factors or component waveforms that can account for about 90% of the total waveform variance across different muscles during normal gait. Furthermore, while activation patterns of individual muscles could vary dramatically with speed and gravitational load, both the limb kinematics and the basic EMG components displayed only limited changes. Thus, we found a systematic phase shift of all five factors with speed in the same direction as the shift in the onset of the swing phase. This tendency for the factors to be timed according to the lift-off event supports the idea that the origin of the gait cycle generation is the propulsion rather than heel strike event. The basic invariance of the factors with walking speed and with body weight unloading implies that a few oscillating circuits drive the active muscles to produce the locomotion kinematics. A flexible and dynamic distribution of these basic components to the muscles may result from various descending and proprioceptive signals that depend on the kinematic and kinetic demands of the movements.


title: Evolutionary coalitional games: design and challenges in wireless networks

abstract: The current state of the art of evolutionary coalitional game theory in wireless networking and communications is captured in this article, where solutions are discussed under their related applications. Fully distributed algorithms for global optima in network selection games are examined. Then the problem of dynamic network formation and evolutionary coalitional games in wireless networks are investigated. This article also points out the open research issues, new interests, and developments in this interdisciplinary field.


title: Comparative genomic analysis identifies structural features of CRISPR-Cas systems in Riemerella anatipestifer

abstract: BACKGROUND ::: Riemerella anatipestifer infection is a contagious disease that has resulted in major economic losses in the duck industry worldwide. This study attempted to characterize CRISPR-Cas systems in the disease-causing agent, Riemerella anatipestifer (R. anatipestifer). The CRISPR-Cas system provides adaptive immunity against foreign genetic elements in prokaryotes and CRISPR-cas loci extensively exist in the genomes of archaea and bacteria. However, the structure characteristics of R. anatipestifer CRISPR-Cas systems remains to be elucidated due to the limited availability of genomic data. ::: ::: ::: RESULTS ::: To identify the structure and components associated with CRISPR-Cas systems in R. anatipestifer, we performed comparative genomic analysis of CRISPR-Cas systems in 25 R. anatipestifer strains using high-throughput sequencing. The results showed that most of the R. anatipestifer strains (20/25) that were analyzed have two CRISPR loci (CRISPR1 and CRISPR2). CRISPR1 was shown to be flanked on one side by cas genes, while CRISPR2 was designated as an orphan. The other analyzed strains harbored only one locus, either CRISPR1 or CRISPR2. The length and content of consensus direct repeat sequences, as well as the length of spacer sequences associated with the two loci, differed from each other. Only three cas genes (cas1, cas2 and cas9) were located upstream of CRISPR1. CRISPR1 was also shown to be flanked by a 107 bp-long putative leader sequence and a 16 nt-long anti-repeat sequence. Combined with analysis of spacer organization similarity and phylogenetic tree of the R. anatipestifer strains, CRISPR arrays can be divided into different subgroups. The diversity of spacer organization was observed in the same subgroup. In general, spacer organization in CRISPR1 was more divergent than that in CRISPR2. Additionally, only 8 % of spacers (13/153) were homologous with phage or plasmid sequences. The cas operon flanking CRISPR1 was observed to be relatively conserved based on multiple sequence alignments of Cas amino acid sequences. The phylogenetic analysis associated with Cas9 showed Cas9 sequence from R. anatipestifer was closely related to that of Bacteroides fragilis and formed part of the subtype II-C subcluster. ::: ::: ::: CONCLUSIONS ::: Our data revealed for the first time the structural features of R. anatipestifer CRISPR-Cas systems. The illumination of structural features of CRISPR-Cas system may assist in studying the specific mechanism associated with CRISPR-mediated adaptive immunity and other biological functions in R. anatipestifer.


title: RT-Link: A Time-Synchronized Link Protocol for Energy- Constrained Multi-hop Wireless Networks

abstract: We propose RT-link, a time-synchronized link protocol for real-time wireless communication in industrial control, surveillance and inventory tracking. RT-link provides predictable lifetime for battery-operated embedded nodes, bounded end-to-end delay across multiple hops, and collision-free operation. We investigate the use of hardware-based time-synchronization for infrastructure nodes by using an AM carrier-current radio for indoors and atomic clock receivers for outdoors. Mobile nodes are synchronized via in-band software synchronization within the same framework. We identify three key observations in the design and deployment of RT-link: (a) hardware-based global-time synchronization is a robust and scalable option to in-band software-based techniques, (b) achieving global time-synchronization is both economical and convenient for indoor and outdoor deployments, (c) RT-link achieves a practical lifetime of over 2 years. Through analysis and simulation, we show that RT-link outperforms energy-efficient link protocols such as B-MAC in terms of node lifetime and end-to-end latency. The protocol supports flexible services such as on-demand end-to-end rate control and logical topology control. We implemented RT-link on the CMU FireFly sensor platform and have integrated it within the nano-RK real-time sensor OS. A 42-node network with sub-20 mus synchronization accuracy has been deployed for 3 weeks in the NIOSH Mining Research Laboratory and within two 5-story campus buildings


title: On Bitcoin and red balloons

abstract: Many large decentralized systems rely on information propagation to ensure their proper function. We examine a common scenario in which only participants that are aware of the information can compete for some reward, and thus informed participants have an incentive not to propagate information to others. One recent example in which such tension arises is the 2009 DARPA Network Challenge (finding red balloons). We focus on another prominent example: Bitcoin, a decentralized electronic currency system. ::: Bitcoin represents a radical new approach to monetary systems. It has been getting a large amount of public attention over the last year, both in policy discussions and in the popular press. Its cryptographic fundamentals have largely held up even as its usage has become increasingly widespread. We find, however, that it exhibits a fundamental problem of a different nature, based on how its incentives are structured. We propose a modification to the protocol that can eliminate this problem. ::: Bitcoin relies on a peer-to-peer network to track transactions that are performed with the currency. For this purpose, every transaction a node learns about should be transmitted to its neighbors in the network. The current implemented protocol provides an incentive to nodes to not broadcast transactions they are aware of. Our solution is to augment the protocol with a scheme that rewards information propagation. Since clones are easy to create in the Bitcoin system, an important feature of our scheme is Sybil-proofness. ::: We show that our proposed scheme succeeds in setting the correct incentives, that it is Sybil-proof, and that it requires only a small payment overhead, all this is achieved with iterated elimination of dominated strategies. We complement this result by showing that there are no reward schemes in which information propagation and no self-cloning is a dominant strategy.


title: Flow Guided Recurrent Neural Encoder for Video Salient Object Detection

abstract: Image saliency detection has recently witnessed significant progress due to deep convolutional neural networks. However, extending state-of-the-art saliency detectors from image to video is challenging. The performance of salient object detection suffers from object or camera motion and the dramatic change of the appearance contrast in videos. In this paper, we present flow guided recurrent neural encoder (FGRNE), an accurate and end-to-end learning framework for video salient object detection. It works by enhancing the temporal coherence of the per-frame feature by exploiting both motion information in terms of optical flow and sequential feature evolution encoding in terms of LSTM networks. It can be considered as a universal framework to extend any FCN based static saliency detector to video salient object detection. Intensive experimental results verify the effectiveness of each part of FGRNE and confirm that our proposed method significantly outperforms state-of-the-art methods on the public benchmarks of DAVIS and FBMS.


title: The Magma Algebra System I: The User Language

abstract: Abstract In the first of two papers on MAGMA , a new system for computational algebra, we present the MAGMA language, outline the design principles and theoretical background, and indicate its scope and use. Particular attention is given to the constructors for structures, maps, and sets.


title: Remaining useful life estimation - A review on the statistical data driven approaches

abstract: Remaining useful life (RUL) is the useful life left on an asset at a particular time of operation. Its estimation is central to condition based maintenance and prognostics and health management. RUL is typically random and unknown, and as such it must be estimated from available sources of information such as the information obtained in condition and health monitoring. The research on how to best estimate the RUL has gained popularity recently due to the rapid advances in condition and health monitoring techniques. However, due to its complicated relationship with observable health information, there is no such best approach which can be used universally to achieve the best estimate. As such this paper reviews the recent modeling developments for estimating the RUL. The review is centred on statistical data driven approaches which rely only on available past observed data and statistical models. The approaches are classified into two broad types of models, that is, models that rely on directly observed state information of the asset, and those do not. We systematically review the models and approaches reported in the literature and finally highlight future research challenges.


title: Direct control of a computer from the human central nervous system

abstract: Describes an invasive alternative to externally applied brain-computer interface (BCI) devices. This system requires implantation of a special electrode into the outer layers of the human neocortex. The recorded signals are transmitted to a nearby receiver and processed to drive a cursor on a computer monitor in front of the patient. The authors' present patient has learned to control the cursor for the production of synthetic speech and typing.


title: Defining Identification: A Theoretical Look at the Identification of Audiences With Media Characters

abstract: In this article I argue that although the notion of identification with media characters is widely discussed in media research, it has not been carefully conceptualized or rigorously tested in empirical audience studies. This study presents a theoretical discussion of identification, including a definition of identification and a discussion of the consequences of identification with media characters for the development of identity and socialization processes. It is suggested that a useful distinction can be made between identification and other types of reactions that media audiences have to media characters. A critical look at media research involving identification exposes the inherent conceptual problems in this research and leads to hypotheses regarding the antecedents and consequences of identification with media characters. The importance of a theory of identification to media research and communication research, more broadly, is presented.


title: Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning

abstract: In the past few years, Iranian universities have embarked to use e-learning tools and technologies to extend and improve their educational services. After a few years of conducting e-learning programs a debate took place within the executives and managers of the e-learning institutes concerning which activities are of the most influence on the learning progress of online students. This research is aimed to investigate the impact of a number of e-learning activities on the students’ learning development. The results show that participation in virtual classroom sessions has the most substantial impact on the students’ final grades. This paper presents the process of applying data mining methods to the web usage records of students’ activities in a virtual learning environment. The main idea is to rank the learning activities based on their importance in order to improve students’ performance by focusing on the most important ones.


title: Deep exemplar-based colorization

abstract: We propose the first deep learning approach for exemplar-based local colorization. Given a reference color image, our convolutional neural network directly maps a grayscale image to an output colorized image. Rather than using hand-crafted rules as in traditional exemplar-based methods, our end-to-end colorization network learns how to select, propagate, and predict colors from the large-scale data. The approach performs robustly and generalizes well even when using reference images that are unrelated to the input grayscale image. More importantly, as opposed to other learning-based colorization methods, our network allows the user to achieve customizable results by simply feeding different references. In order to further reduce manual effort in selecting the references, the system automatically recommends references with our proposed image retrieval algorithm, which considers both semantic and luminance information. The colorization can be performed fully automatically by simply picking the top reference suggestion. Our approach is validated through a user study and favorable quantitative comparisons to the-state-of-the-art methods. Furthermore, our approach can be naturally extended to video colorization. Our code and models will be freely available for public use.


title: AKT/PKB Signaling: Navigating Downstream

abstract: The serine/threonine kinase Akt, also known as protein kinase B (PKB), is a central node in cell signaling downstream of growth factors, cytokines, and other cellular stimuli. Aberrant loss or gain of Akt activation underlies the pathophysiological properties of a variety of complex diseases, including type-2 diabetes and cancer. Here, we review the molecular properties of Akt and the approaches used to characterize its true cellular targets. In addition, we discuss those Akt substrates that are most likely to contribute to the diverse cellular roles of Akt, which include cell survival, growth, proliferation, angiogenesis, metabolism, and migration.


title: A Managerial Point of View on the Relationship between of Maslow's Hierarchy of Needs and Herzberg's Dual Factor Theory

abstract: This paper aims to investigate the relationship between Maslow's hierarchy of needs, and Herzberg's dual factor theory. Thus, the details that will contribute to management is intended to achieve. The literature research was conducted for this paper. There is a fairly close relationship between Maslow’s hierarchy of needs theory and Herzberg’s motivation hygiene theory. According to this study, if two theories of motivation are applied together to organization, it will be more effective. These theories of motivation are a good idea to implement this. Theories of motivation in order to avoid a sense of harmony within the organization, increasing burnout, and should be known. This article aims to provide insight for both theories of motivation.


title: Parting Crowds: Characterizing Divergent Interpretations in Crowdsourced Annotation Tasks

abstract: Crowdsourcing is a common strategy for collecting the “gold standard” labels required for many natural language applications. Crowdworkers differ in their responses for many reasons, but existing approaches often treat disagreements as "noise" to be removed through filtering or aggregation. In this paper, we introduce the workflow design pattern of crowd parting: separating workers based on shared patterns in responses to a crowdsourcing task. We illustrate this idea using an automated clustering-based method to identify divergent, but valid, worker interpretations in crowdsourced entity annotations collected over two distinct corpora -- Wikipedia articles and Tweets. We demonstrate how the intermediate-level view provide by crowd-parting analysis provides insight into sources of disagreement not easily gleaned from viewing either individual annotation sets or aggregated results. We discuss several concrete applications for how this approach could be applied directly to improving the quality and efficiency of crowdsourced annotation tasks.


title: Hybrid CTC-Attention based End-to-End Speech Recognition using Subword Units

abstract: In this paper, we present an end-to-end automatic speech recognition system, which successfully employs subword units in a hybrid CTC-Attention based system. The subword units are obtained by the byte-pair encoding (BPE) compression algorithm. Compared to using words as modeling units, using characters or subword units does not suffer from the out-of-vocabulary (OOV) problem. Furthermore, using subword units further offers a capability in modeling longer context than using characters. We evaluate different systems over the LibriSpeech 1000h dataset. The subword-based hybrid CTC-Attention system obtains 6.8% word error rate (WER) on the test clean subset without any dictionary or external language model. This represents a significant improvement (a 12.8% WER relative reduction) over the character-based hybrid CTC-Attention system.


title: Coordinated circRNA Biogenesis and Function with NF90/NF110 in Viral Infection

abstract: Summary Circular RNAs (circRNAs) generated via back-splicing are enhanced by flanking complementary sequences. Expression levels of circRNAs vary under different conditions, suggesting participation of protein factors in their biogenesis. Using genome-wide siRNA screening that targets all human unique genes and an efficient circRNA expression reporter, we identify double-stranded RNA-binding domain containing immune factors NF90/NF110 as key regulators in circRNA biogenesis. NF90/NF110 promote circRNA production in the nucleus by associating with intronic RNA pairs juxtaposing the circRNA-forming exon(s); they also interact with mature circRNAs in the cytoplasm. Upon viral infection, circRNA expression is decreased, in part owing to the nuclear export of NF90/NF110 to the cytoplasm. Meanwhile, NF90/NF110 released from circRNP complexes bind to viral mRNAs as part of their functions in antiviral immune response. Our results therefore implicate a coordinated regulation of circRNA biogenesis and function by NF90/NF110 in viral infection.


title: An information theoretic approach to classify cognitive states using fMRI

abstract: In this study, an information theoretic approach is proposed to model brain connectivity during a cognitive processing task, measured by functional Magnetic Resonance Imaging (fMRI). For this purpose, a local mesh of varying size is formed around each voxel. The arc weights of each mesh are estimated using a linear regression model by minimizing the squared error. Then, the optimal mesh size for each sample, that represents the information distribution in the brain, is estimated by minimizing various information criteria which employ the mean square error of linear regression model. The estimated mesh size shows the degree of locality or degree of connectivity of the voxels for the underlying cognitive process. The samples are generated during an fMRI experiment employing item recognition (IR) and judgment of recency (JOR) tasks. For each sample, estimated arc weights of the local mesh with optimal size are used to classify whether it belongs to IR or JOR tasks. Results indicate that the suggested connectivity model with optimal mesh size for each sample represent the information distribution in the brain better than the state-of-the art methods.


title: Language Style Matching as a Predictor of Social Dynamics in Small Groups

abstract: Synchronized verbal behavior can reveal important information about social dynamics. This study introduces the linguistic style matching (LSM) algorithm for calculating verbal mimicry based on an automated textual analysis of function words. The LSM algorithm was applied to language generated during a small group discussion in which 70 groups comprised of 324 individuals engaged in an information search task either face-to-face or via text-based computer-mediated communication. As a metric, LSM predicted the cohesiveness of groups in both communication environments, and it predicted task performance in face-to-face groups. Other language features were also related to the groups’ cohesiveness and performance, including word count, pronoun patterns, and verb tense. The results reveal that this type of automated measure of verbal mimicry can be an objective, efficient, and unobtrusive tool for predicting underlying social dynamics. In total, the study demonstrates the effectiveness of using language to predi...


title: Towards Designing Cooperative and Social Conversational Agents for Customer Service

abstract: The idea of interacting with computers through natural language dates back to the 1960s, but recent technological advances have led to a renewed interest in conversational agents such as chatbots or digital assistants. In the customer service context, conversational agents promise to create a fast, convenient, and cost-effective channel for communicating with customers. Although numerous agents have been implemented in the past, most of them could not meet the expectations and disappeared. In this paper, we present our design science research project on how to design cooperative and social conversational agents to increase service quality in customer service. We discuss several issues that hinder the success of current conversational agents in customer service. Drawing on the cooperative principle of conversation and social response theory, we propose preliminary meta-requirements and design principles for cooperative and social conversational agents. Next, we will develop a prototype based on these design principles.


title: Emulation of obsolete hardware in open source virtualization software

abstract: Nuclear Power plants are built to last 40 years. From the beginning of their lifetime up to now, dozens of specific, but not critical, applications have been developed. Their cost does not only reside in the development but mainly in the qualification and validation processes. Most of the time, once these applications have been commissioned and have fulfilled their functions, no functional or performance reason require them to be modified later on. They should stay unchanged during the whole lifetime of the plant. Meanwhile the hardware has changed many times; the average lifespan of a standard PC is 1 to 2 years. In order to cope with these frequent changes, we have come out with a new solution: emulation of obsolete hardware through virtualization.


title: Faster SGD Using Sketched Conditioning

abstract: We propose a novel method for speeding up stochastic optimization algorithms via sketching methods, which recently became a powerful tool for accelerating algorithms for numerical linear algebra. We revisit the method of conditioning for accelerating first-order methods and suggest the use of sketching methods for constructing a cheap conditioner that attains a significant speedup with respect to the Stochastic Gradient Descent (SGD) algorithm. While our theoretical guarantees assume convexity, we discuss the applicability of our method to deep neural networks, and experimentally demonstrate its merits.


title: Phase control for resonant DC-DC converter with class-DE inverter and class-E rectifier

abstract: In this paper, a phase control scheme for Class-DE-E dc-dc converter is proposed and its performance is clarified. The proposed circuit is composed of phase-controlled Class-DE inverter and Class-E rectifier. The proposed circuit achieves the fixed frequency control without frequency harmonics lower than the switching frequency. Moreover, it is possible to achieve the continuous control in a wide range of the line and load variations. The output voltage decreases in proportion to the increase of the phase shift. The proposed converter keeps the advantages of Class-DE-E dc-dc converter, namely, a high power conversion efficiency under a high-frequency operation and low switch-voltage stress. Especially, high power conversion efficiency can be kept for narrow range control. We present numerical calculations for the design and the numerical analyses to clarify the characteristics of the proposed control. By carrying out circuit experiments, we show a quantitative similarity between the numerical predictions and the experimental results. In our experiments, the measured efficiency is over 84% with 2.5 W output power for 1.0-MHz operating frequency at the nominal operation. Moreover, the output voltage is regulated from 100% to 39%, keeping over 57% power conversion efficiency by using the proposed control scheme.


title: 24-GHz microstrip grid array antenna for automotive radars application

abstract: A single-ended microstrip grid array antenna has been designed and measured for 24 GHz automotive radars application. It has been designed on Rogers 5880 substrate with dimensions of 60×60×0.787 mm. The antenna exhibits 2.45 GHz impedance bandwidth, 19.26 dBi peak realized gain and broadside pencil-beam radiation pattern with low side lobe and weak cross-polarization.


title: A Fully-Integrated 77-GHz FMCW Radar Transceiver in 65-nm CMOS Technology

abstract: A fully-integrated FMCW radar system for automotive applications operating at 77 GHz has been proposed. Utilizing a fractional- synthesizer as the FMCW generator, the transmitter linearly modulates the carrier frequency across a range of 700 MHz. The receiver together with an external baseband processor detects the distance and relative speed by conducting an FFT-based algorithm. Millimeter-wave PA and LNA are incorporated on chip, providing sufficient gain, bandwidth, and sensitivity. Fabricated in 65-nm CMOS technology, this prototype provides a maximum detectable distance of 106 meters for a mid-size car while consuming 243 mW from a 1.2-V supply.


title: OFDM-based automotive joint radar-communication system

abstract: We propose a novel automotive joint radar-communication (JRC) system, where the system first transmits OFDM sub-carriers for radar processing followed by sub-carriers enabling radar and communication functionalities. The receiver processing includes iterative estimation of parameters to alleviate the shortage of samples to estimate range. The receiver first estimates the target parameters from the sub-carriers dedicated to radar; these parameters then determine the channel for the communication link. The communication data is then extracted, thereby enabling the use of all the carriers for improving the range estimation. It is shown that the range estimation improves significantly after efficient use of all the sub-carriers. Furthermore, for radar parameter estimation, we propose an effective iterative method based on alternating least square (ALS) to recover the angle of arrival (AoA), Doppler and Range. Numerical results demonstrate the feasibility of our proposed system.


title: TangiPaint: A Tangible Digital Painting System

abstract: TangiPaint is a digital painting application that provides the experience of working with real materials such as canvas and oil paint. Using fingers on the touchscreen of an iPad or iPhone, users can lay down strokes of thick, three-dimensional paint on a simulated canvas. Then using the Tangible Display technology introduced by Darling and Ferwerda [1], users can tilt the display screen to see the gloss and relief or "impasto" of the simulated surface, and modify it until they get the appearance they desire. Scene lighting can also be controlled through direct gesture-based interaction. A variety of "paints" with different color and gloss properties and substrates with different textures are available and new ones can be created or imported. The tangiPaint system represents a first step toward developing digital art media that look and behave like real materials.


Learning Latent Representations of Music to Generate Interactive Musical Palettes


title: On the estimation of radar polarization orientation shifts induced by terrain slopes

abstract: In recent studies, D. L. Schuler et al. (2000) applied polarimetric imaging radar-derived orientation angles to measure topography, and J. S. Lee et al. (2000) used orientation angles for polarimetric SAR data compensation, to ensure accurate estimation of geophysical parameters in rugged terrain areas. To support these applications, it is important to accurately estimate shifts in orientation angles induced by the azimuth slope variations. However, in many cases, inconsistency in the estimation of orientation angle shifts was encountered in several areas, introducing noisy and erroneous results. The present authors develop a unified analysis of estimation algorithms based on the circular polarization covariance matrix. The concept of reflection symmetry is used to explain the soundness of the circular polarization method and to show problems associated with other algorithms. L-band polarimetric synthetic aperture radar (SAR) images of Camp Roberts, CA, are used to substantiate this theory.


title: Social Monitoring for Public Health

abstract: Public health thrives on high-quality evidence, yet acquiring meaningful data on a population remains a central challenge of public health research and practice. Social monitoring, the analysis of social media and other user-generated web data, has brought advances in the way we leverage population data to understand health. Social media offers advantages over traditional data sources, including real-time data availability, ease of access, and reduced cost. Social media allows us to ask, and answer, questions we never thought possible. This book presents an overview of the progress on uses of social monitoring to study public health over the past decade. We explain available data sources, common methods, and survey research on social monitoring in a wide range of public health areas. Our examples come from topics such as disease surveillance, behavioral medicine, and mental health, among others. We explore the limitations and concerns of these methods. Our survey of this exciting new field of data-driven research lays out future research directions.


title: Learning Continuous Word Embedding with Metadata for Question Retrieval in Community Question Answering

abstract: Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the web. This paper is concerned with the problem of question retrieval. Question retrieval in cQA archives aims to find the existing questions that are semantically equivalent or relevant to the queried questions. However, the lexical gap problem brings about new challenge for question retrieval in cQA. In this paper, we propose to learn continuous word embeddings with metadata of category information within cQA pages for question retrieval. To deal with the variable size of word embedding vectors, we employ the framework of fisher kernel to aggregated them into the fixedlength vectors. Experimental results on large-scale real world cQA data set show that our approach can significantly outperform state-of-the-art translation models and topic-based models for question re-


External Quality Assessment of Molecular Detection of Ebola Virus in China


title: Stable Control of a Simulated One-Legged Running Robot with Hip and Leg Compliance

abstract: We present a control strategy for a simplified model of a one-legged running robot which features compliant elements in series with hip and leg actuators. For this model, proper spring selection and initial conditions result in "passive dynamic" operation close to the desired motion, without any actuation. However, this motion is not stable. Our controller is based on online calculations of the desired passive dynamic motion which is then parametrized in terms of a normalized "locomotion time". We show in simulation that the proposed controller stabilizes a wide range of velocities and is robust to modeling errors. It also tracks changes in desired robot velocity and remains largely passive despite a fixed set of springs, masses, and inertias. Comparisons of simulated runs with direct hip actuation show 95% hip actuation energy savings at 3 m/s. Such energy savings are critical for the power autonomy of electrically actuated legged robots.


title: Deep Image Harmonization

abstract: Compositing is one of the most common operations in photo editing. To generate realistic composites, the appearances of foreground and background need to be adjusted to make them compatible. Previous approaches to harmonize composites have focused on learning statistical relationships between hand-crafted appearance features of the foreground and background, which is unreliable especially when the contents in the two layers are vastly different. In this work, we propose an end-to-end deep convolutional neural network for image harmonization, which can capture both the context and semantic information of the composite images during harmonization. We also introduce an efficient way to collect large-scale and high-quality training data that can facilitate the training process. Experiments on the synthesized dataset and real composite images show that the proposed network outperforms previous state-of-the-art methods.


title: JavaScript Errors in the Wild: An Empirical Study

abstract: Client-side JavaScript is being widely used in popular web applications to improve functionality, increase responsiveness, and decrease load times. However, it is challenging to build reliable applications using JavaScript. This paper presents an empirical characterization of the error messages printed by JavaScript code in web applications, and attempts to understand their root causes. We find that JavaScript errors occur in production web applications, and that the errors fall into a small number of categories. We further find that both non-deterministic and deterministic errors occur in the applications, and that the speed of testing plays an important role in exposing errors. Finally, we study the correlations among the static and dynamic properties of the application and the frequency of errors in it in order to understand the root causes of the errors.


title: Investigating morphing algorithms for generative music

abstract: This paper presents our research into morphing algorithms for generating musical transitions, including digitally inspired composing techniques as well as models of existing creative processes. We show that it is possible to generate effective morphs between contrasting musical passages and describe some ways in which iterative developments seem to have improved the musicality of the generated material. The paper begins by outlining the potential of musical morphing processes and introducing important considerations. Relevant existing research and music theory is then summarised. Following this, our developments of morphing algorithms are detailed and some evaluation criteria are proposed. The article concludes by discussing the apparent results achieved by these morphing algorithms and what they indicate about directions for further research.


title: Impact of Surrogate Assessments on High-Recall Retrieval

abstract: We are concerned with the effect of using a surrogate assessor to train a passive (i.e., batch) supervised-learning method to rank documents for subsequent review, where the effectiveness of the ranking will be evaluated using a different assessor deemed to be authoritative. Previous studies suggest that surrogate assessments may be a reasonable proxy for authoritative assessments for this task. Nonetheless, concern persists in some application domains---such as electronic discovery---that errors in surrogate training assessments will be amplified by the learning method, materially degrading performance. We demonstrate, through a re-analysis of data used in previous studies, that, with passive supervised-learning methods, using surrogate assessments for training can substantially impair classifier performance, relative to using the same deemed-authoritative assessor for both training and assessment. In particular, using a single surrogate to replace the authoritative assessor for training often yields a ranking that must be traversed much lower to achieve the same level of recall as the ranking that would have resulted had the authoritative assessor been used for training. We also show that steps can be taken to mitigate, and sometimes overcome, the impact of surrogate assessments for training: relevance assessments may be diversified through the use of multiple surrogates; and, a more liberal view of relevance can be adopted by having the surrogate label borderline documents as relevant. By taking these steps, rankings derived from surrogate assessments can match, and sometimes exceed, the performance of the ranking that would have been achieved, had the authority been used for training. Finally, we show that our results still hold when the role of surrogate and authority are interchanged, indicating that the results may simply reflect differing conceptions of relevance between surrogate and authority, as opposed to the authority having special skill or knowledge lacked by the surrogate.