title: Photometric Stereo with General, Unknown Lighting

abstract: Work on photometric stereo has shown how to recover the shape and reflectance properties of an object using multiple images taken with a fixed viewpoint and variable lighting conditions. This work has primarily relied on known lighting conditions or the presence of a single point source of light in each image. In this paper we show how to perform photometric stereo assuming that all lights in a scene are distant from the object but otherwise unconstrained. Lighting in each image may be an unknown and may include arbitrary combination of diffuse, point and extended sources. Our work is based on recent results showing that for Lambertian objects, general lighting conditions can be represented using low order spherical harmonics. Using this representation we can recover shape by performing a simple optimization in a low-dimensional space. We also analyze the shape ambiguities that arise in such a representation. We demonstrate our method by reconstructing the shape of objects from images obtained under a variety of lightings. We further compare the reconstructed shapes against shapes obtained with a laser scanner.


title: The Composition of Hemp Seed Oil and Its Potential as an Important Source of Nutrition

abstract: The fatty acid and natural product content of hemp seed oil was analyzed by GC-MS and LC-MS. The presence of linoleic acid (LA) and -linolenic acid (LNA) were confirmed in their previously reported ratio of 3:1 LA:LNA. The presence of -caryophyllene (740 mg/L), myrcene (160 mg/L), -sitosterol (100-148 g/L) and trace amounts of methyl salicylate was observed in the oil which had not been previously reported. Trace amounts of cannabidiol (CBD) were also detected. Bioassays were performed with the oil to determine its effectiveness as an antimicrobial agent. Some bioactivity was observed during the primary screening. (Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: Website: )


title: Quotient inductive-inductive types

abstract: In type theory one usually defines data types inductively. Over the years, many principles have been invented, such as inductive families, and inductive-recursive and inductive-inductive definitions. More recently, higher inductive types have been proposed in the context of homotopy type theory. Specific instances of higher inductive types have been successfully used in formalisations of homotopy theory and in formalising type theory in type theory. However, a general definition and theory of higher inductive types is still lacking. As an intermediate goal, we give a principle which unifies ordinary, indexed and inductive-inductive definitions, extended with path constructors from higher inductive types, but with no non-trivial structure between paths, i.e. we consider sets only. For this reason, we call these types quotient inductive-inductive types. We give an initial algebra semantics for these definitions, give the induction principle, and show that these two interpretations coincide.


title: A T-Type Isolated Zero Voltage Switching DC–DC Converter With Capacitive Output

abstract: A novel isolated dc–dc converter is proposed, using the T-Type topology with zero voltage switching and pulse width modulated with a capacitive output filter. It uses four switches, two of which are subjected to the input voltage, and the other two to half the input voltage. All switches commutate under zero voltage over a wide load range. The proposed converter has the following features: (a) symmetrical operation of the isolation transformer, (b) modulation by pulse width with constant frequency, (c) zero voltage switching, and (d) three-level voltage applied to the primary winding of the transformer. Theoretical analysis, design example, and experimental data for a 2 kW, 400 VDC input, 400 VDC output, and 50 kHz switching frequency laboratory prototype, working in two operating points are included in this paper. The measured efficiency was 95% at 950 W.


title: Communication with emblematic gestures: Shared and distinct neural correlates of expression and reception

abstract: Emblematic (or symbolic) gestures allow individuals to convey a variety of thoughts and emotions ranging from approval to hostility. The use of such gestures involves the execution of a codified motor act by the addresser and its perception and decoding by the addressee. To examine underlying common and distinct neural correlates, we used fMRI tasks in which subjects viewed video clips of emblematic one-hand gestures. They were asked to (1) take the perspective of the addresser and imagine executing the gestures (“expression” condition), and to (2) take the perspective of the addressee and imagine being confronted with the gestures (“reception” condition). Common areas of activation were found in inferior frontal, medial frontal, and posterior temporal cortices with left-hemispheric predominance as well as in the cerebellum. The distinction between regions specifically involved in the expression or reception condition partly resembled the dorsal and ventral stream dichotomy of visual processing with junctions in inferior frontal and medial prefrontal cortices. Imagery of gesture expression involved the dorsal visual stream as well as higher-order motor areas. In contrast, gesture reception encompassed regions related to semantic processing, and medial prefrontal areas known to be involved in the process of understanding the intentions of others. In conclusion, our results provide evidence for a dissociation in representations of emblematic gesture processing between addresser and addressee in addition to shared components in language-related areas. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.


title: A GUI modeling DSL for pattern-based GUI testing PARADIGM

abstract: Today's software feature user interface (UI) patterns. Those patterns describe generic solutions for common recurrent problems. However, to the best of our knowledge, there is no specific testing methodology that is particularly suited for testing those patterns providing generic testing solutions that can be reused after minor configurations in order to test slightly different implementations. Pattern-Based Graphical User Interface Testing (PBGT) is a recent methodology that aims at systematizing and automating the GUI testing process, by sampling the input space using "UI Test Patterns" that express generic solutions to test common recurrent GUI's behaviour. This paper describes the development process of PARADIGM, a domain specific language (DSL) to be used in the context of PBGT and empirically evaluates PARADIGM to assess its diminished modeling efforts, usefulness, graphical power, and acceptability.


title: A Taxonomy of Attacks on the DNP3 Protocol

abstract: Distributed Network Protocol (DNP3) is the predominant SCADA protocol in the energy sector – more than 75% of North American electric utilities currently use DNP3 for industrial control applications. This paper presents a taxonomy of attacks on the protocol. The attacks are classified based on targets (control center, outstation devices and network/communication paths) and threat categories (interception, interruption, modification and fabrication). To facilitate risk analysis and mitigation strategies, the attacks are associated with the specific DNP3 protocol layers they exploit. Also, the operational impact of the attacks is categorized in terms of three key SCADA objectives: process confi- dentiality, process awareness and process control. The attack taxonomy clarifies the nature and scope of the threats to DNP3 systems, and can provide insights into the relative costs and benefits of implementing mitigation strategies.


Psychological Reactance And User Workarounds. A Study In The Context Of Electronic Medical Records Implementations


Empirical exploitation of live virtual machine migration


title: Steampunk as design fiction

abstract: In this paper we look at the Steampunk movement and consider is relevance as a design strategy for HCI and interaction design. Based on a study of online practices of Steampunk, we consider how, as a design fiction, Steampunk provides an explicit model for how to physically realize an ideological and imagined world through design practice. We contend that the practices of DIY and appropriation that are evident in Steampunk design provide a useful set of design strategies and implications for HCI.


title: Removal of false positive in object detection with contour-based classifiers

abstract: This paper proposes a method of constructing a contour-based classifier to remove the false positive objects after Haar-based detection. The classifier is learned with the discrete AdaBoost. During the training, the oriented chamfer is introduced to construct strong learners. Experimental results have demonstrated that the proposed method is feasible and promising in the removal of the false positive.


title: A Hybrid Data Center Architecture for Big Data

abstract: In the past few years, Big Data analytics have changed the way computing services and resources are being used. New users are getting into the cloud services provided by data centers on a daily basis, and the number of applications hosted on the clouds is rapidly increasing. However, existing data center architectures cannot cope with the black-box nature of the cloud nor with Big Data analytics. Current data center networks using electrical packet switches have many disadvantages. Electrical links consume too much power to handle the increased bandwidth demanded by emerging applications. They are often not suitable for providing high bandwidth at low latency and have bottleneck issues. A promising solution involves adding optical interconnections to the infrastructure in data centers offering high throughput, low latency, and incredibly low energy consumption compared to the current data center networks based on commodity switches. In this work, we propose a hybrid electrical and optical networking architecture for data centers hosting Cloud Computing and Big Data applications. Furthermore, we analyze and compare application behaviors with current and proposed hybrid architectures. The results show that hybrid architecture provides a viable solution to data center issues and easily outperforms existing data center architectures. The results reflect that hybrid architecture reduces the delay by nearly 39% and decreases the cost of cooling systems from 49.57% to 27% of the total costs, while reducing the overall long-term costs of data centers.


title: ATTITUDES AND SOCIAL COGNITION Culture and Cause: American and Chinese Attributions for Social and Physical Events

abstract: The authors argue that attribution patterns reflect implicit theories acquired from induction and socialization and hence differentially distributed across human cultures. In particular, the authors tested the hypothesis that dispositionalism in attribution for behavior reflects a theory of social behavior more widespread in individualist than collectivist cultures


title: Proliferation, cell cycle and apoptosis in cancer

abstract: Beneath the complexity and idiopathy of every cancer lies a limited number of 'mission critical' events that have propelled the tumour cell and its progeny into uncontrolled expansion and invasion. One of these is deregulated cell proliferation, which, together with the obligate compensatory suppression of apoptosis needed to support it, provides a minimal 'platform' necessary to support further neoplastic progression. Adroit targeting of these critical events should have potent and specific therapeutic consequences.


title: Implementation Matters: A Review of Research on the Influence of Implementation on Program Outcomes and the Factors Affecting Implementation

abstract: The first purpose of this review was to assess the impact of implementation on program outcomes, and the second purpose was to identify factors affecting the implementation process. Results from over quantitative 500 studies offered strong empirical support to the conclusion that the level of implementation affects the outcomes obtained in promotion and prevention programs. Findings from 81 additional reports indicate there are at least 23 contextual factors that influence implementation. The implementation process is affected by variables related to communities, providers and innovations, and aspects of the prevention delivery system (i.e., organizational functioning) and the prevention support system (i.e., training and technical assistance). The collection of implementation data is an essential feature of program evaluations, and more information is needed on which and how various factors influence implementation in different community settings.


title: Proposed diagnostic criteria for internet addiction

abstract: OBJECTIVE ::: The objective of this study was to develop diagnostic criteria for internet addiction disorder (IAD) and to evaluate the validity of our proposed diagnostic criteria for discriminating non-dependent from dependent internet use in the general population. ::: ::: ::: METHODS ::: This study was conducted in three stages: the developmental stage (110 subjects in the survey group; 408 subjects in the training group), where items of the proposed diagnostic criteria were developed and tested; the validation stage (n = 405), where the proposed criteria were evaluated for criterion-related validity; and the clinical stage (n = 150), where the criteria and the global clinical impression of IAD were evaluated by more than one psychiatrist to determine inter-rater reliability. ::: ::: ::: RESULTS ::: The proposed internet addiction diagnostic criteria consisted of symptom criterion (seven clinical symptoms of IAD), clinically significant impairment criterion (functional and psychosocial impairments), course criterion (duration of addiction lasting at least 3 months, with at least 6 hours of non-essential internet usage per day) and exclusion criterion (exclusion of dependency attributed to psychotic disorders). A diagnostic score of 2 + 1, where the first two symptoms (preoccupation and withdrawal symptoms) and at least one of the five other symptoms (tolerance, lack of control, continued excessive use despite knowledge of negative effects/affects, loss of interests excluding internet, and use of the internet to escape or relieve a dysphoric mood) was established. Inter-rater reliability was 98%. ::: ::: ::: CONCLUSION ::: Our findings suggest that the proposed diagnostic criteria may be useful for the standardization of diagnostic criteria for IAD.


title: Instagram #Instasad?: Exploring Associations Among Instagram Use, Depressive Symptoms, Negative Social Comparison, and Strangers Followed

abstract: Abstract As the use and influence of social networking continues to grow, researchers have begun to explore its consequences for psychological well-being. Some research suggests that Facebook use can have negative consequences for well-being. Instagram, a photo-sharing social network created in 2010, has particular characteristics that may make users susceptible to negative consequences. This study tested a theoretically grounded moderated meditation model of the association between Instagram use and depressive symptoms through the mechanism of negative social comparison, and moderation by amount of strangers one follows. One hundred and seventeen 18–29 year olds completed online questionnaires containing demographics, frequency of Instagram use, amount of strangers followed on Instagram, the Center for Epidemiological Resources Scale for Depression, and the Social Comparison Rating Scale. Instagram use was marginally positively associated with depressive symptoms, and positive social comparison was signi...


title: A Low-Distortion High-Efficiency Class-D Audio Amplifier Based on Sliding Mode Control

abstract: This brief presents a low-power low-distortion high-efficiency class-D audio amplifier. The proposed architecture uses an integral sliding mode controller with a novel on-chip continuous current sensor. A full-bridge output stage is used to increase the output power, and an adaptive nonoverlapping-clock generation technique is presented to eliminate the short circuit current. Moreover, the switching frequency is chosen to achieve the highest possible linearity without efficiency degradation. The proposed architecture contains the minimum number of loop amplifiers to reduce quiescent current consumption. The proposed class-D audio amplifier has been implemented using 65-nm CMOS technology and operated from a single 2.7-V voltage supply (using thick oxide transistors) while occupying an active area of 0.31 mm ::: 2 ::: . Post-layout simulations show that the proposed architecture achieves a total harmonic distortion of 0.002% for a 2.2 Vpp 1-kHz input signal. It achieves a peak efficiency of 96% and a maximum output power of 400 mW at an 8-Ω differential load.


title: Event detection and localization for small mobile robots using reservoir computing

abstract: Reservoir Computing (RC) techniques use a fixed (usually randomly created) recurrent neural network, or more generally any dynamic system, which operates at the edge of stability, where only a linear static readout output layer is trained by standard linear regression methods. In this work, RC is used for detecting complex events in autonomous robot navigation. This can be extended to robot localization tasks which are solely based on a few low-range, high-noise sensory data. The robot thus builds an implicit map of the environment (after learning) that is used for efficient localization by simply processing the input stream of distance sensors. These techniques are demonstrated in both a simple simulation environment and in the physically realistic Webots simulation of the commercially available e-puck robot, using several complex and even dynamic environments.


title: Pervasive negative effects of rewards on intrinsic motivation: The myth continues

abstract: A major concern in psychology and education is that rewards decrease intrinsic motivation to perform activities. Over the past 30 years, more than 100 experimental studies have been conducted on this topic. In 1994, Cameron and Pierce conducted a meta-analysis of this literature and concluded that negative effects of reward were limited and could be easily prevented in applied settings. A more recent meta-analysis of the literature by Deci, Koestner, and Ryan (1999) shows pervasive negative effects of reward. The purpose of the present article is to resolve differences in previous meta-analytic findings and to provide a meta-analysis of rewards and intrinsic motivation that permits tests of competing theoretical explanations. Our results suggest that in general, rewards are not harmful to motivation to perform a task. Rewards given for low-interest tasks enhance free-choice intrinsic motivation. On high-interest tasks, verbal rewards produce positive effects on free-choice motivation and self-reported task interest. Negative effects are found on high-interest tasks when the rewards are tangible, expected (offered beforehand), and loosely tied to level of performance. When rewards are linked to level of performance, measures of intrinsic motivation increase or do not differ from a nonrewarded control group. Overall, the pattern of results indicates that reward contingencies do not have pervasive negative effects on intrinsic motivation. Theoretical and practical implications of the findings are addressed.


title: Developmental trajectories of DSM-IV symptoms of attention-deficit/hyperactivity disorder: genetic effects, family risk and associated psychopathology

abstract: BACKGROUND: DSM-IV specifies three ADHD subtypes; the combined, the hyperactive-impulsive and the inattentive. Little is known about the developmental relationships underlying these subtypes. The o ...


title: Implementation and recent progress in cloud-based smart home automation systems

abstract: Consumers' interest in smart home concepts has been increasing due to the rapidly expanding home appliances industry that introduces Wi-Fi enabled appliances. Manufacturers provide firmware that allows users to control appliances using smartphones from anywhere. Smart appliances, firmware, and smartphones connected to a cloud server for data storage to form a simple smart home automation system (SHAS). This paper reviews how SHAS is implemented and its recent progress. The authors' observation found that there is a growing interest among researchers and developers to study software-defined network, web services, and end-user development tools within 2016 and 2018. This progress caused by researchers' and developers' interest to mitigate heterogeneity issues in SHAS.


title: A Credibility Analysis System for Assessing Information on Twitter

abstract: Information credibility on Twitter has been a topic of interest among researchers in the fields of both computer and social sciences, primarily because of the recent growth of this platform as a tool for information dissemination. Twitter has made it increasingly possible to offer near-real-time transfer of information in a very cost-effective manner. It is now being used as a source of news among a wide array of users around the globe. The beauty of this platform is that it delivers timely content in a tailored manner that makes it possible for users to obtain news regarding their topics of interest. Consequently, the development of techniques that can verify information obtained from Twitter has become a challenging and necessary task. In this paper, we propose a new credibility analysis system for assessing information credibility on Twitter to prevent the proliferation of fake or malicious information. The proposed system consists of four integrated components: a reputation-based component, a credibility classifier engine, a user experience component, and a feature-ranking algorithm. The components operate together in an algorithmic form to analyze and assess the credibility of Twitter tweets and users. We tested the performance of our system on two different datasets from 489,330 unique Twitter accounts. We applied 10-fold cross-validation over four machine learning algorithms. The results reveal that a significant balance between recall and precision was achieved for the tested dataset.


title: Fogernetes: Deployment and management of fog computing applications

abstract: Devices used in fog and edge computing are heterogeneous and distributed. These computing environments are unpredictable and their applications are becoming more complex. This leads to challenges regarding deployment and management of fog and edge applications. It is important to ensure that quality of service, availability, reliability and real-time characteristics are guaranteed during deployment to take advantage of fog computing. In this paper, we present Fogernetes, a fog computing platform that enables management and deployment of fog applications with specific requirements on heterogeneous devices with different capabilities. Fogernetes allows matching the requirements of application components with device capabilities by using a labeling system. Based on a case study, we evaluate and test Fogernetes and examine its practical applicability for the deployment and management of fog computing applications. Fodeo serves as an example application. Fodeo analyzes video streams from multiple cameras and optimizes the video delivery. Fogernetes enables the deployment of Fodeo components on appropriate devices by matching requirements and capabilities.


title: Smart alarms: multivariate medical alarm integration for post CABG surgery patients

abstract: In order to monitor patients in the Intensive Care Unit, healthcare practitioners set threshold alarms on each of many individual vital sign monitors. The current alarm algorithms elicit numerous false positive alarms producing an inefficient healthcare system, where nurses habitually ignore low level alarms due to their overabundance. In this paper, we describe an algorithm that considers multiple vital signs when monitoring a post coronary artery bypass graft (post-CABG) surgery patient. The algorithm employs a Fuzzy Expert System to mimic the decision processes of nurses. In addition, it includes a Clinical Decision Support tool that uses Bayesian theory to display the possible CABG-related complications the patient might be undergoing at any point in time, as well as the most relevant risk factors. As a result, this multivariate approach decreases clinical alarms by an average of 59% with a standard deviation of 17% (Sample of 32 patients, 1,451 hours of vital sign data). Interviews comparing our proposed system with the approach currently used in hospitals have also confirmed the potential efficiency gains from this approach.


title: Self-Report After Randomly Assigned Supervision Does not Predict Ability to Practice Motivational Interviewing.

abstract: Abstract The objective of this study was to investigate the relation between self-report and objective assessment of motivational interviewing (MI) skills following training and supervision. After an MI workshop, 96 clinicians from 26 community programs (age 21–68, 65% female, 40.8% Black, 29.6% Caucasian, 24.5% Hispanic, 2.0% Asian, 3.1% other) were randomized to supervision (tele-conferencing or tape-based), or workshop only. At four time points, trainees completed a self-report of MI skill, using items from the MI understanding questionnaire (MIU), and were objectively assessed by raters using the Motivational Interviewing Treatment Integrity (MITI) system. Correlations were calculated between MIU and MITI scores. A generalized linear mixed model was tested on MIU scores, with MITI scores, supervision condition and time as independent variables. MIU scores increased from pre-workshop (mean=4.74, SD =1.79) to post-workshop (mean=6.31, SD =1.03) ( t =8.69, p SD =0.91, t =5.60, p SD =0.94, t =2.43, p =.02). However, MIU scores did not significantly correlate with MITI scores, with or without supervision. Self-reported ability increased with supervision, but self-report was not an indicator of objectively measured skill. This suggests that training does not increase correspondence between self-report and objective assessment, so community treatment programs should not rely on clinician self-report to assess the need for ongoing training and supervision and it may be necessary to train clinicians to accurately assess their own skill.


title: Sliding mode control for a quadrotor slung load system

abstract: This paper studies the modeling and control of a quadrotor with a suspended load. It is shown that the excessive load swing is the part of the system dynamics, which may degrade the quadrotor control performance. A detailed analysis about the system modeling with the load swing effect is carried out based on Newton-Euler and Euler-Lagrange method. A framework for sliding mode control of the quadrotor slung load system is then developed. The simulation results show the excellent modeling and control performance.


Extreme learning machine: A review


title: Dengue Reporter Virus Particles for Measuring Neutralizing Antibodies against Each of the Four Dengue Serotypes

abstract: The lack of reliable, high-throughput tools for characterizing anti-dengue virus (DENV) antibodies in large numbers of serum samples has been an obstacle in understanding the impact of neutralizing antibodies on disease progression and vaccine efficacy. A reporter system using pseudoinfectious DENV reporter virus particles (RVPs) was previously developed by others to facilitate the genetic manipulation and biological characterization of DENV virions. In the current study, we demonstrate the diagnostic utility of DENV RVPs for measuring neutralizing antibodies in human serum samples against all four DENV serotypes, with attention to the suitability of DENV RVPs for large-scale, long-term studies. DENV RVPs used against human sera yielded serotype-specific responses and reproducible neutralization titers that were in statistical agreement with Plaque Reduction Neutralization Test (PRNT) results. DENV RVPs were also used to measure neutralization titers against the four DENV serotypes in a panel of human sera from a clinical study of dengue patients. The high-throughput capability, stability, rapidity, and reproducibility of assays using DENV RVPs offer advantages for detecting immune responses that can be applied to large-scale clinical studies of DENV infection and vaccination.


title: Full-GRU Natural Language Video Description for Service Robotics Applications

abstract: Enabling effective human–robot interaction is crucial for any service robotics application. In this context, a fundamental aspect is the development of a user-friendly human–robot interface, such as a natural language interface. In this letter, we investigate the robot side of the interface, in particular the ability to generate natural language descriptions for the scene it observes. We achieve this capability via a deep recurrent neural network architecture completely based on the gated recurrent unit paradigm. The robot is able to generate complete sentences describing the scene, dealing with the hierarchical nature of the temporal information contained in image sequences. The proposed approach has fewer parameters than previous state-of-the-art architectures, thus it is faster to train and smaller in memory occupancy. These benefits do not affect the prediction performance. In fact, we show that our method outperforms or is comparable to previous approaches in terms of quantitative metrics and qualitative evaluation when tested on benchmark publicly available datasets and on a new dataset we introduce in this letter.


title: End-to-End Adversarial Retinal Image Synthesis

abstract: In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality.


Speech-Language Profile of A Child With Coffin-Siris Syndrome


title: Tunnel FET based ultra-low-leakage compact 2T1C SRAM

abstract: In this paper, an ultra-low-leakage 2T1C compact SRAM is proposed using Tunnel FETs (TFETs). Proposed design utilizes negative differential resistance property of TFETs and capacitor leakage to implement 1T1C latch. Additional 1T read port is added for reading to avoid data stability issues during read operation. Proposed SRAM design is scalable and easily adaptable for lower technology nodes. Ultra-low leakage below 1fA/bit is achieved in the proposed design. Read and write cycle times of sub-2ns and sub-4ns are designed.


title: Do UN Interventions Cause Peace? Using Matching to Improve Causal Inference ∗

abstract: Previous statistical studies of the effects of UN peacekeeping have generally suggested that UN interventions have a positive effect on building a sustainable peace after civil war. Recent methodological developments have questioned this result because the cases in which the United Nations intervenedwere quite different from those in which they did not. Therefore the estimated causal effect may be due to the assumptions of the model that the researchers chose rather than to peacekeeping itself. The root of the problem is that UN missions are not randomly assigned. We argue that standard approaches for dealing with this problem (Heckman regression and instrumental variables) are invalid and impracticable in the context of UN peacekeeping and would lead to estimates of the effects of UN operations that are largely a result of the assumptions of the statistical model rather than the data. We correct for the effects of nonrandom assignment with matching techniques on a sample of UN interventions in post-Cold-War conflicts and find that UN interventions are indeed effective in the sample of post-civil-conflict interventions, but that UN interventions while civil wars are still ongoing have no causal effect.


TOWARDS UNFOLDING CRM IMPLEMENTATION


title: High efficiency isolated DC/DC converter inherently optimized for fuel cell applications

abstract: The isolated full-bridge boost converter has been suggested as the best choice for fuel cell applications. Comparisons have been carried out in the literature using both stress factors and experimental verified designs to determine the optimal converter. Never the less, this paper suggests a different topology not previous used for fuel cell applications with some clear advantages. Taking into account the I-V characteristics of the fuel cell only emphasized the performance of the proposed converter and reveals its self as an optimal candidate for the fuel cell application.


title: CrowdHuman: A Benchmark for Detecting Human in a Crowd

abstract: Human detection has witnessed impressive progress in recent years. However, the occlusion issue of detecting human in highly crowded environments is far from solved. To make matters worse, crowd scenarios are still under-represented in current human detection benchmarks. In this paper, we introduce a new dataset, called CrowdHuman, to better evaluate detectors in crowd scenarios. The CrowdHuman dataset is large, rich-annotated and contains high diversity. There are a total of $470K$ human instances from the train and validation subsets, and $~22.6$ persons per image, with various kinds of occlusions in the dataset. Each human instance is annotated with a head bounding-box, human visible-region bounding-box and human full-body bounding-box. Baseline performance of state-of-the-art detection frameworks on CrowdHuman is presented. The cross-dataset generalization results of CrowdHuman dataset demonstrate state-of-the-art performance on previous dataset including Caltech-USA, CityPersons, and Brainwash without bells and whistles. We hope our dataset will serve as a solid baseline and help promote future research in human detection tasks.


title: Four Applications of Embodied Cognition

abstract: This article presents the views of four sets of authors, each taking concepts of embodied cognition into problem spaces where the new paradigm can be applied. The first considers consequences of embodied cognition on the legal system. The second explores how embodied cognition can change how we interpret and interact with art and literature. The third examines how we move through archi- tectural spaces from an embodied cognition perspective. And the fourth addresses how music cogni- tion is influenced by the approach. Each contribution is brief. They are meant to suggest the potential reach of embodied cognition, increase the visibility of applications, and inspire potential avenues for research.


title: Spectral regression: a unified subspace learning framework for content-based image retrieval

abstract: Relevance feedback is a well established and effective framework for narrowing down the gap between low-level visual features and high-level semantic concepts in content-based image retrieval. In most of traditional implementations of relevance feedback, a distance metric or a classifier is usually learned from user's provided negative and positive examples. However, due to the limitation of the user's feedbacks and the high dimensionality of the feature space, one is often confront with the issue of the curse of the dimensionality. Recently, several researchers have considered manifold ways to address this issue, such as Locality Preserving Projections, Augmented Relation Embedding, and Semantic Subspace Projection. In this paper, by using techniques from spectral graph embedding and regression, we propose a unified framework, called spectral regression, for learning an image subspace. This framework facilitates the analysis of the differences and connections between the algorithms mentioned above. And more crucially, it provides much faster computation and therefore makes the retrieval system capable of responding to the user's query more efficiently.


title: Pathologic Features of Fatal Falls From Height

abstract: Deaths due to falls from height are common in urban settings. At the time the body is found, it is often unclear whether the mode of death is accident, suicide, or homicide. To assess the injury pattern in fatal falls from height with special regard to criteria that might be helpful in discrimination between accident, suicide, and homicide, respectively, we reviewed 68 medicolegal autopsy cases (22 females, 46 males, age range 13-89 years) of fatal falls from height regarding demographic data, findings at the death scene, results of the postmortem examination, psychiatric history, and toxicologic findings. Among the 68 cases, there were 34 suicides, 23 accidents, and 11 unclarified cases, in 3 of which homicide was suspected. In general, suicides were from greater heights than accidents (mean height 22.7 m for suicides and 10.8 m for accidents, respectively; 79% of suicides from more than 16 m). Strikingly, severe head injuries predominantly occurred in falls from heights below 10 m (84%) and above 25 m (90%), whereas in the group of falls from 10 to 25 m, these lesions were seen less frequently (28%). Neck injuries like muscle bleeds and fractures of the hyoid bone were found in 33% of falls from more than 10 m and did not occur from less than 10 m. Our data stress that the evaluation of pathologic features alone is not sufficient to assess the mode of death in fatal falls from height. Instead, postmortem findings have to be considered within the framework of the subject's social, medical, and psychiatric history in conjunction with findings at the death scene and toxicology results to obtain the clearest possible picture of the circumstances of death.


title: Dynamic traversal of large gaps by insects and legged robots reveals a template

abstract: It is well known that animals can use neural and sensory feedback via vision, tactile sensing, and echolocation to negotiate obstacles. Similarly, most robots use deliberate or reactive planning to avoid obstacles, which relies on prior knowledge or high-fidelity sensing of the environment. However, during dynamic locomotion in complex, novel, 3-D terrains such as forest floor and building rubble, sensing and planning suffer bandwidth limitation and large noise and are sometimes even impossible. Here, we study rapid locomotion over a large gap, a simple, ubiquitous obstacle, to begin to discover general principles of dynamic traversal of large 3-D obstacles. We challenged the discoid cockroach and an open-loop six-legged robot to traverse a large gap of varying length. Both the animal and the robot could dynamically traverse a gap as large as 1 body length by bridging the gap with its head, but traversal probability decreased with gap length. Based on these observations, we developed a template that well captured body dynamics and quantitatively predicted traversal performance. Our template revealed that high approach speed, initial body pitch, and initial body pitch angular velocity facilitated dynamic traversal, and successfully predicted a new strategy of using body pitch control that increased the robot maximal traversal gap length by 50%. Our study established the first template of dynamic locomotion beyond planar surfaces and is an important step in expanding terradynamics into complex 3-D terrains.


title: Delusional boot: securing hypervisors without massive re-engineering

abstract: The set of virtual devices offered by a hypervisor to its guest VMs is a virtualization component ripe with security exploits -- more than half of all vulnerabilities of today's hypervisors are found in this codebase. This paper presents Min-V, a hypervisor that disables all virtual devices not critical to running VMs in the cloud. Of the remaining devices, Min-V takes a step further and eliminates all remaining functionality not needed for the cloud. To implement Min-V, we had to overcome an obstacle: the boot process of many commodity OSes depends on legacy virtual devices absent from our hypervisor. Min-V introduces delusional boot, a mechanism that allows guest VMs running commodity OSes to boot successfully without developers having to re-engineer the initialization code of these commodity OSes, as well as the BIOS and pre-OS (e.g., bootloader) code. We evaluate Min-V and demonstrate that our security improvements incur no performance overhead except for a small delay during reboot of a guest VM. Our reliability tests show that Min-V is able to run unmodified Linux and Windows OSes on top of this minimal virtualization interface.


title: Comparative Study of the Offset-Geophone and Down-deep Hydrophone Seismic Refraction Survey with Application to the Niger Delta Basin, Nigeria

abstract: This work discusses an uphole survey with the aim of comparing seismic refraction data acquired with the offset-geophone procedure and that acquired using the down-deep hydrophone procedure, and subsequent examination of the validity of each data set for seismic reflection data quality control. Data was obtained at Grid 5594/1961 on the Trans-Ramos river 3-D prospect in the Niger Delta Basin, Nigeria. Interpretation of the offsetgeophone data shows that as shots are taken uphole, vertical time of the shots are initially greater than the time of intercept, and decreases uphole. However, at a particular depth, as shots move up, the intercept time overtakes the vertical time. This would happen only if the shot is taken within the consolidated layer. Therefore, the greatest depth at which vertical time is exceeded by the intercept time qualitatively gives a clue to the base of weathering. This value can then be compared with the value obtained from a quantitative interpretation of the data to ascertain the correctness of the results. Interpretation of the down-deep hydrophone data is only based on theoretical equations and it is nearly impossible to check the computed weathering depth. (


ANALOG AND DIGITAL MODULATION FORMATS OF OPTICAL FIBER COMMUNICATION WITHIN AND BEYOND 100 GB / S : A COMPARATIVE OVERVIEW


A Real Time Survey On Attacks And Detection Strategies For Primary User Emulator In Cognitive Radio Networks


title: Home intercommunication system using visible light communications

abstract: The fast growing technology of wireless data transmission through the illumination of light emitting diodes (LEDs) is known as visible light communications (VLC). This technology makes use of visible light as the carrier for transmitting signals over a wireless channel. VLC uses the visible light spectrum for transmission and one of the main advantages of VLC over radio frequency (RF) based communications is that VLC is not susceptible to electromagnetic interference. In this project, VLC is used for the transmission of audio over an optical wireless channel. This is achieved by designing and implementing a simplex intercom system that comprises of a transmitter, a channel and a receiver and the system transmits audio through visible light using on-off keying (OOK) modulation. In this modulation scheme, the intensity of light is varied by switching the LED on and off at a high frequency such that the switching is not detectable by the human eye. The results of the project demonstrate that audio transmission is achievable with visible light.


title: An Analysis Framework for Content-based Job Recommendation

abstract: 22nd International Conference on Case-Based Reasoning (ICCBR), Cork, Ireland, 29 September - 01 October 2014


title: A neurobiologist’s attempt to understand persistent pain

abstract: Abstract This topical review starts with a warning that despite an impressive wealth of neuroscientific data, a reductionist approach can never fully explain persistent pain. One reason is the complexity of clinical pain (in contrast to experimentally induced pain). Another reason is that the “pain system” shows degeneracy, which means that an outcome can have several causes. Problems also arise from lack of conceptual clarity regarding words like nociceptors, pain, and perception. It is, for example, argued that “homeoceptor” would be a more meaningful term than nociceptor. Pain experience most likely depends on synchronized, oscillatory activity in a distributed neural network regardless of whether the pain is caused by tissue injury, deafferentation, or hypnosis. In experimental pain, the insula, the second somatosensory area, and the anterior cingulate gyrus are consistently activated. These regions are not pain-specific, however, and are now regarded by most authors as parts of the so-called salience network, which detects all kinds of salient events (pain being highly salient). The networks related to persistent pain seem to differ from the those identified experimentally, and show a more individually varied pattern of activations. One crucial difference seems to be activation of regions implicated in emotional and body-information processing in persistent pain. Basic properties of the “pain system” may help to explain why it so often goes awry, leading to persistent pain. Thus, the system must be highly sensitive not to miss important homeostatic threats, it cannot be very specific, and it must be highly plastic to quickly learn important associations. Indeed, learning and memory processes play an important role in persistent pain. Thus, behaviour with the goal of avoiding pain provocation is quickly learned and may persist despite healing of the original insult. Experimental and clinical evidence suggest that the hippocampal formation and neurogenesis (formation of new neurons) in the dentate gyrus are involved in the development and maintenance of persistent pain. There is evidence that persistent pain in many instances may be understood as the result of an interpretation of the organism's state of health. Any abnormal pattern of sensory information as well as lack of expected correspondence between motor commands and sensory feedback may be interpreted as bodily threats and evoke pain. This may, for example, be an important mechanism in many cases of neuropathic pain. Accordingly, many patients with persistent pain show evidence of a distorted body image. Another approach to understanding why the “pain system” so often goes awry comes from knowledge of the dynamic and nonlinear behaviour of neuronal networks. In real life the emergence of persistent pain probably depends on the simultaneous occurrence of numerous challenges, and just one extra (however small) might put the network into a an inflexible state with heightened sensitivity to normally innocuous inputs. Finally, the importance of seeking the meaning the patient attributes to his/her pain is emphasized. Only then can we understand why a particular person suffers so much more than another with very similar pathology, and subsequently be able to help the person to alter the meaning of the situation.


title: Kalman particle swarm optimized polynomials for data classification

abstract: Abstract Data classification is an important area of data mining. Several well known techniques such as decision tree, neural network, etc. are available for this task. In this paper we propose a Kalman particle swarm optimized (KPSO) polynomial equation for classification for several well known data sets. Our proposed method is derived from some of the findings of the valuable information like number of terms, number and combination of features in each term, degree of the polynomial equation etc. of our earlier work on data classification using polynomial neural network. The KPSO optimizes these polynomial equations with a faster convergence speed unlike PSO. The polynomial equation that gives the best performance is considered as the model for classification. Our simulation result shows that the proposed approach is able to give competitive classification accuracy compared to PNN in many datasets.


title: Path-Loss and Throughput Prediction of IEEE 802.11ad Systems

abstract: This paper presents a path-loss prediction analysis and a performance evaluation of the 60GHz IEEE 802.11ad standard. Received signal strength measurements were performed in indoor locations and the decay coefficients were estimated over a number of measured routes. Furthermore, real world maps were incorporated into a 3-D mm wave ray- tracing tool for the purpose of employing realistic channel matrices into an 802.11ad bit level simulator. Finally, performance metrics for various MCS modes are presented and analyzed for both Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier (SC) transmissions.


title: Biomechanical Comparison of a First- and a Second-Generation All-Soft Suture Glenoid Anchor

abstract: Background: All–soft tissue suture anchors provide advantages of decreased removal of bone and decreased glenoid volume occupied compared with traditional tap or screw-in suture anchors. Previous published data have led to biomechanical concerns with the use of first-generation all-soft suture anchors. Purpose/Hypothesis: The purpose of this study was to evaluate the load to 2-mm displacement and ultimate load to failure of a second-generation all-soft suture anchor, compared with a first-generation anchor and a traditional PEEK (polyether ether ketone) anchor. The null hypothesis was that the newer second-generation anchor will demonstrate no difference in loads to 2-mm displacement after cycling compared with first-generation all-soft suture anchors. Study Design: Controlled laboratory study. Methods: Twenty human cadaveric glenoids were utilized to create 97 total suture anchor sites, and 1 of 3 anchors were randomized and placed into each site: (1) first-generation all-soft suture anchor (Juggerknot; Biomet), (2) second-generation all-soft suture anchor (Suturefix; Smith & Nephew), and (3) a control PEEK anchor (Bioraptor; Smith & Nephew). After initial cyclic loading, load to 2 mm of displacement and ultimate load to failure were measured for each anchor. Results: After cyclic loading, the load to 2-mm displacement was significantly less in first-generation anchors compared with controls (P < .01). However, the load to 2-mm displacement was significantly greater in second-generation anchors compared with controls (P < .01). There was no difference in ultimate load to failure between the first- and second-generation all-soft suture anchors (P > .05). Conclusion: The newer generation all-soft suture anchors with a theoretically more rigid construct and deployment configuration demonstrate biomechanical characteristics (specifically, with load to 2-mm displacement after cyclic loading) that are improved over first-generation all-soft suture anchors and similar to a traditional solid tap-in anchor. The configuration of these newer generation all-soft suture anchors appears to mitigate the biomechanical concerns of decreased load to failure with first-generation all–soft tissue suture anchors. Clinical Relevance: The theoretical advantages of all-soft anchors may be particularly valuable in revision surgery or in cases where multiple anchors are being placed into a small anatomic area.


title: Sentiment Perception of Readers and Writers in Emoji use

abstract: Previous research has traditionally analyzed emoji sentiment from the point of view of the reader of the content not the author. Here, we analyze emoji sentiment from the point of view of the author and present a emoji sentiment benchmark that was built from an employee happiness dataset where emoji happen to be annotated with daily happiness of the author of the comment. The data spans over 3 years, and 4k employees of 56 companies based in Barcelona. We compare sentiment of writers to readers. Results indicate that, there is an 82% agreement in how emoji sentiment is perceived by readers and writers. Finally, we report that when authors use emoji they report higher levels of happiness. Emoji use was not found to be correlated with differences in author moodiness.


title: Scalable regular expression matching on data streams

abstract: Regular Expression (RE) matching has important applications in the areas of XML content distribution and network security. In this paper, we present the end-to-end design of a high performance RE matching system. Our system combines the processing efficiency of Deterministic Finite Automata (DFA) with the space efficiency of Non-deterministic Finite Automata (NFA) to scale to hundreds of REs. In experiments with real-life RE data on data streams, we found that a bulk of the DFA transitions are concentrated around a few DFA states. We exploit this fact to cache only the frequent core of each DFA in memory as opposed to the entire DFA (which may be exponential in size). Further, we cluster REs such that REs whose interactions cause an exponential increase in the number of states are assigned to separate groups -- this helps to improve cache hits by controlling the overall DFA size. To the best of our knowledge, ours is the first end-to-end system capable of matching REs at high speeds and in their full generality. Through a clever combination of RE grouping, and static and dynamic caching, it is able to perform RE matching at high speeds, even in the presence of limited memory. Through experiments with real-life data sets, we show that our RE matching system convincingly outperforms a state-of-the-art Network Intrusion Detection tool with support for efficient RE matching.


title: A fuzzy model for supplier selection in quantity discount environments

abstract: Traditionally, supplier selection should simultaneously take into account numerous heterogeneous criteria, and then is a tedious task for the purchasing decision makers. It becomes especially complicated when quantity discounts are considered at the same time. Under such manner, most studies often formulate such a problem as a Multi-Objective Linear Programming (MOLP) problem, and then scale it down to a Mixed Integer Programming (MIP) problem to handle the inherited multi-objectives simultaneously. However, this approach often neglects to consider scaling and subjective weighting issues. In order to ease the problem mentioned above and to obtain a more reasonable compromise solution for allocating order quantities among suppliers with their quantity discount rate offered, the Analytical Hierarchy Process (AHP) and fuzzy compromise programming are introduced in this study. An illustrated example is presented to demonstrate the proposed model and to illuminate two kinds of attitudes for decision makers. The information from the experiments can be utilized further to explain the suppliers' possible improvement and to help create win-win policies.


title: DR-Cloud: Multi-cloud based disaster recovery service

abstract: With the rapid popularity of cloud computing paradigm, disaster recovery using cloud resources becomes an attractive approach. This paper presents a practical multi-cloud based disaster recovery service model: DRCloud. With DR-Cloud, resources of multiple cloud service providers can be utilized cooperatively by the disaster recovery service provider. A simple and unified interface is exposed to the customers of DR-Cloud to adapt the heterogeneity of cloud service providers involved in the disaster recovery service, and the internal processes between clouds are invisible to the customers. DR-Cloud proposes multiple optimization scheduling strategies to balance the disaster recovery objectives, such as high data reliability, low backup cost, and short recovery time, which are also transparent to the customers. Different data scheduling strategies based on DR-Cloud are suitable for different kinds of data disaster recovery scenarios. Experimental results show that the DR-Cloud model can cooperate with cloud service providers with various parameters effectively, while its data scheduling strategies can achieve their optimization objectives efficiently and are widely applicable.


Image Recognition by a Second-Order Convolutional Neural Network with Dynamic Receptive Fields


title: Outlier detection in complex categorical data by modelling the feature value couplings

abstract: This paper introduces a novel unsupervised outlier detection method, namely Coupled Biased Random Walks (CBRW), for identifying outliers in categorical data with diversified frequency distributions and many noisy features. Existing pattern-based outlier detection methods are ineffective in handling such complex scenarios, as they misfit such data. CBRW estimates outlier scores of feature values by modelling feature value level couplings, which carry intrinsic data characteristics, via biased random walks to handle this complex data. The outlier scores of feature values can either measure the outlierness of an object or facilitate the existing methods as a feature weighting and selection indicator. Substantial experiments show that CBRW can not only detect outliers in complex data significantly better than the state-of-the-art methods, but also greatly improve the performance of existing methods on data sets with many noisy features.


title: Passive-Aggressive Sequence Labeling with Discriminative Post-Editing for Recognising Person Entities in Tweets

abstract: Recognising entities in social media text is difficult. NER on newswire text is conventionally cast as a sequence labeling problem. This makes implicit assumptions regarding its textual structure. Social media text is rich in disfluency and often has poor or noisy structure, and intuitively does not always satisfy these assumptions. We explore noise-tolerant methods for sequence labeling and apply discriminative post-editing to exceed state-of-the-art performance for person recognition in tweets, reaching an F1 of 84%.


title: Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

abstract: We propose deep distributed recurrent Q-networks (DDRQN), which enable teams of agents to learn to solve communication-based coordination tasks. In these tasks, the agents are not given any pre-designed communication protocol. Therefore, in order to successfully communicate, they must first automatically develop and agree upon their own communication protocol. We present empirical results on two multi-agent learning problems based on well-known riddles, demonstrating that DDRQN can successfully solve such tasks and discover elegant communication protocols to do so. To our knowledge, this is the first time deep reinforcement learning has succeeded in learning communication protocols. In addition, we present ablation experiments that confirm that each of the main components of the DDRQN architecture are critical to its success.


title: The Challenge of Enterprise Social Networking (Non-)Use at Work: A Case Study of How to Positively Influence Employees' Enterprise Social Networking Acceptanc

abstract: Enterprise social networking (ESN) platforms have been implemented by organizations to support employees' collaboration. However, there has been a reported lack of user participation on these platforms. This study focuses on interventions that can be used to overcome barriers of usage. A case study was conducted at IBM to investigate the effect of several interventions to positively influence ESN acceptance within IBM's Human Resources (HR) function. In this study we combined two rounds of interview data with activity log data. We interviewed the initiators of interventions, and we interviewed the employees targeted by those interventions. The activity logs allowed us to contrast actual usage of the ESN by employees from HR vs. other organizational functions. Compared to other ESN studies this study explicitly identifies factors driving or inhibiting ESN use, and shows how these factors can be addressed by interventions to significantly increase ESN use. Our results can inform and enable other organizations interested in the design of interventions to increase the use of ESN.


title: Age diversity , age discrimination climate and performance consequences — a cross organizational study

abstract: This paper deals with the emergence of perceived age discrimination climate on the company level and its performance consequences. In this new approach to the field of diversity research, we investigated (a) the effect of organizational-level age diversity on collective perceptions of age discrimination climate that (b) in turn should influence the collective affective commitment of employees, which is (c) an important trigger for overall company performance. In a large-scale study that included 128 companies, a total of 8,651 employees provided data on their perceptions of age discrimination and affective commitment on the company level. Information on firm-level performance was collected from key informants. We tested the proposed model using structural equation modeling (SEM) procedures and, overall, found support for all hypothesized relationships. The findings demonstrated that age diversity seems to be related to the emergence of an age discrimination climate in companies, which negatively impacts overall firm performance through the mediation of affective commitment. These results make valuable contributions to the diversity and discrimination literature by establishing perceived age discrimination on the company level as a decisive mediator in the age diversity/performance link. The results also suggest important practical implications for the effective management of an increasingly age diverse workforce.


title: Relationships between personality, emotional labor, work engagement and job satisfaction in service professions.

abstract: OBJECTIVES ::: Although there have been studies linking personality to selected aspects of functioning at work, Polish literature reports a shortage of detailed analyses considering, e.g., specific professional groups or certain variables. The aim of our study was to explore the links between personality traits and emotional labor, work engagement and job satisfaction among service workers. ::: ::: ::: MATERIAL AND METHODS ::: The study was based on a cross-sectional, self-report survey of 137 workers representing different service industries in Poland. Each participant received a demographic data sheet and a set of questionnaires: NEO Five-Factor Inventory, the Deep Acting and Surface Acting Scale, the Job Satisfaction Scale and the Utrecht Work Engagement Scale - all in their Polish versions. ::: ::: ::: RESULTS ::: A correlation analysis revealed numerous relationships between the examined variables. However, results of the regression analysis showed that only some personality traits were related with individual aspects of functioning at work. Neuroticism accounted for the phenomenon of faking emotions. Conscientiousness was significantly related to general work engagement, vigor and dedication. Agreeableness and neuroticism significantly predicted job satisfaction. ::: ::: ::: CONCLUSIONS ::: Individual personality traits account for various aspects of work functioning. Int J Occup Med Environ Health 2016;29(5):767-782.


title: Predictive Dialing for Outbound Telephone Call Centers

abstract: In the late 1980s, I used queuing and simulation to invent predictive dialing, a method to determine when computer-directed outbound telephone dialing systems should dial. I included a real-time estimation updating feature that was highly robust against sudden changes in the system's operating environment; thorough validation to ensure that the models tracked all important features of the real systems; and a modular software design that allowed "plug-in" replacement of the control software, eliminating debugging of field upgrades. The improved systems kept operators busier and drastically reduced the number of calls the systems abandoned because no operator was available to talk to the answering party. This invention was critical to the success, in the late 1980s, of International Telesystems Corporation (ITC), a small company founded in 1984, which a competitor, EIS International, bought in 1993 for approximately $12 million.


title: Effective segmentation and classification for HCC biopsy images

abstract: Accurate grading for hepatocellular carcinoma (HCC) biopsy images is important to prognosis and treatment planning. In this paper, we propose an automatic system for grading HCC biopsy images. In preprocessing, we use a dual morphological grayscale reconstruction method to remove noise and accentuate nuclear shapes. A marker-controlled watershed transform is applied to obtain the initial contours of nuclei and a snake model is used to segment the shapes of nuclei smoothly and precisely. Fourteen features are then extracted based on six types of characteristics for HCC classification. Finally, we propose a SVM-based decision-graph classifier to classify HCC biopsy images. Experimental results show that 94.54% of classification accuracy can be achieved by using our SVM-based decision-graph classifier while 90.07% and 92.88% of classification accuracy can be achieved by using k-NN and SVM classifiers, respectively.


title: Signature Morphology Effects of Squint Angle for Arbitrarily Moving Surface Targets in Spotlight Synthetic Aperture Radar

abstract: This investigation examines the phenomenology effects of the squint angle on the morphology of moving target smears in spotlight synthetic aperture radar (SAR). This analysis includes both the smears resulting from standard image formation applied to simulated radar measurements as well as the theoretical predictions for the central contours of the signatures. In particular, this paper generates the down-range and cross-range components of the predicted central 2-D contours of mover signatures, including the locations of the cross-range offsets. The analytics for squinted geometry include additional contributions in the signature contour equations that do not arise for the case of broadside imaging. These terms can affect the overall contour morphology, particularly in terms of shape and extent. Numerous examples are presented to demonstrate that the signature prediction equations yield excellent agreement with standard image formation with simulated radar data. Therefore, this analysis can provide an effective tool in predicting the shape, extent, and location of smears due to arbitrarily moving surface targets for squinted spotlight SAR.


title: An Advanced Current Control Strategy for Three-Phase Shunt Active Power Filters

abstract: This paper proposes an advanced control strategy to enhance performance of shunt active power filter (APF). The proposed control scheme requires only two current sensors at the supply side and does not need a harmonic detector. In order to make the supply currents sinusoidal, an effective harmonic compensation method is developed with the aid of a conventional proportional-integral (PI) and vector PI controllers. The absence of the harmonic detector not only simplifies the control scheme but also significantly improves the accuracy of the APF, since the control performance is no longer affected by the performance of the harmonic tracking process. Furthermore, the total cost to implement the proposed APF becomes lower, owing to the minimized current sensors and the use of a four-switch three-phase inverter. Despite the simplified hardware, the performance of the APF is improved significantly compared to the traditional control scheme, thanks to the effectiveness of the proposed compensation scheme. The proposed control scheme is theoretically analyzed, and a 1.5-kVA APF is built in the laboratory to validate the feasibility of the proposed control strategy.


title: Computing narratives of cognitive user experience for building design analysis KR for industry scale computer-aided architecture design

abstract: We present a cognitive design assistance system equipped with analytical capabilities aimed at anticipating architectural building design performance with respect to people-centred functional design goals. The paper focuses on the system capability to generate narratives of visuo-locomotive user experience from digital computer-aided architecture design (CAAD) models. The system is based on an underlying declarative narrative representation and computation framework pertaining to conceptual, geometric, and qualitative spatial knowledge. The semantics of the declarative narrative model, i.e., the overall representation and computation model, is founded on: (a) conceptual knowledge formalised in an OWL ontology; (b) a general spatial representation and reasoning engine implemented in constraint logic programming; and (c) a declaratively encoded (narrative) construction process (based on search over graph structures) implemented in answer-set programming. ::: ::: We emphasise and demonstrate: complete system implementation, scalability, and robust performance & integration with industry-scale architecture industry tools (e.g., Revit, Archi-CAD) & standards (BIM, IFC).


title: A bibliometric index based on the collaboration distance between cited and citing authors

abstract: The popular h-index used to measure scientific output can be described in terms of a pool of evaluated objects (the papers), a quality function on the evaluated objects (the number of citations received by each paper) and a sentencing line crossing the origin, whose intersection with the graph of the quality function yields the index value (in the h-index this is a line with slope 1). Based on this abstraction, we present a new index, the c-index, in which the evaluated objects are the citations received by an author, a group of authors, a journal, etc., the quality function of a citation is the collaboration distance between the authors of the cited and the citing papers, and the sentencing line can take slopes between 0 and ∞. As a result, the new index counts only those citations which are significant enough, where significance is proportional to collaboration distance. Several advantages of the new c-index with respect to previous proposals are discussed.


title: Detection of blood vessels in retinal images using two-dimensional matched filters.

abstract: Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods.


CLAMI: Defect Prediction on Unlabeled Datasets


title: Wideband and High-Gain Corporate-Fed Gap Waveguide Slot Array Antenna With ETSI Class II Radiation Pattern in $V$ -Band

abstract: We present a $V$ -band multilayer corporate-fed slot array antenna with wide impedance bandwidth and high efficiency. The proposed antenna consists of three unconnected metal layers based on the recently introduced gap waveguide technology. A $2\times 2$ cavity-backed slot subarray acts as the unit cell of the array. The top metal layer contains the radiating slots, the intermediate layer contains the cavities, formed by pins, and the third layer is the ridge gap waveguide corporate-feed network. The latter is realized by a texture of pins and guiding ridges to uniformly excite the cavities with the same amplitude and phase. The proposed antenna fulfills the radiation pattern requirement of the ETSI 320 standard. A prototype consisting of $16\times 16$ slots was manufactured by a fast modern planar 3-D machining method, i.e., die-sink electric discharge machining. The fabricated prototype has a relative impedance bandwidth of 17.6% with input reflection coefficient better than −10 dB. The E- and H-planes radiation patterns satisfy the ETSI class II copolar sidelobe envelope, and the measured cross-polar level is more than −30 dB below the copolar level over the 56–75 GHz frequency band. The measured antenna efficiency is better than 60% over the same band.


title: A Model and Decision Procedure for Data Storage in Cloud Computing

abstract: Cloud computing offers many possibilities for prospective users, there are however many different storage and compute services to choose from between all the cloud providers and their multiple datacenters. In this paper we focus on the problem of selecting the best storage services according to the application's requirements and the user's priorities. In previous work we described a capability based matching process that filters out any service that does not meet the requirements specified by the user. In this paper we introduce a mathematical model that takes this output lists of compatible storage services and constructs an integer linear programming problem. This ILP problem takes into account storage and compute cost as well as performance characteristics like latency, bandwidth, and job turnaround time, a solution to the problem yields an optimal assignment of datasets to storage services and of application runs to compute services. We show that with modern ILP solvers a reasonably sized problem can be solved in one second, even with an order of magnitude increase in cloud providers, number of datacenters, or storage services the problem instances can be solved under a minute. We finish our paper with two use cases, BLAST and MODIS. For MODIS our recommended data allocation leverages both cloud and local resources, it incurs in half the cost of a pure cloud solution and the job turnaround time is 52% faster compared to a pure local solution.


Generating English from Abstract Meaning Representations


title: A 90-μW 16 bit 20 KHz-BW feedforward double-sampled sigma-delta modulator in 0.18 CMOS

abstract: This paper presents a low power, area-efficient feedforward double-sampling sigma delta modulator. A novel double sampled structure that combines two techniques was proposed. The DAC capacitor reset technique is used to eliminate the noise folding caused by the mis match between two sampling branches; a direct summation scheme obviates analog adder in the front of the quantizer, which greatly relaxes the timing constraints in the feedforward path. The modulator is implemented in the SMIC 0.18um technology; the simulation results show the modulator provides 102.5dB SNDR over a 20-KHz signal bandwidth. The power consumption of modulator is 90μW under 1.5V power supply.


title: Re-evaluating the need for Modelling Term-Dependence in Text Classification Problems

abstract: A substantial amount of research has been carried out in developing machine learning algorithms that account for term dependence in text classification. These algorithms offer acceptable performance in most cases but they are associated with a substantial cost. They require significantly greater resources to operate. This paper argues against the justification of the higher costs of these algorithms, based on their performance in text classification problems. In order to prove the conjecture, the performance of one of the best dependence models is compared to several well established algorithms in text classification. A very specific collection of datasets have been designed, which would best reflect the disparity in the nature of text data, that are present in real world applications. The results show that even one of the best term dependence models, performs decent at best when compared to other independence models. Coupled with their substantially greater requirement for hardware resources for operation, this makes them an impractical choice for being used in real world scenarios.


title: Video object annotation, navigation, and composition

abstract: We explore the use of tracked 2D object motion to enable novel approaches to interacting with video. These include moving annotations, video navigation by direct manipulation of objects, and creating an image composite from multiple video frames. Features in the video are automatically tracked and grouped in an off-line preprocess that enables later interactive manipulation. Examples of annotations include speech and thought balloons, video graffiti, path arrows, video hyperlinks, and schematic storyboards. We also demonstrate a direct-manipulation interface for random frame access using spatial constraints, and a drag-and-drop interface for assembling still images from videos. Taken together, our tools can be employed in a variety of applications including film and video editing, visual tagging, and authoring rich media such as hyperlinked video.


title: Keep it simple: Ranking health states yields values similar to cardinal measurement approaches

abstract: Objectives: To examine the relationship between ordinal and cardinal valuation of health states. Study Design and Setting: We analyzed rank, visual analog scale (VAS), and time trade-off (TTO) responses for 52 health states defined using the EQ-5D classification system developed by the EuroQol Group. We analyzed 179,431 responses from 11,483 subjects in eight countries: Slovenia, Argentina, Denmark, Japan, Netherlands, Spain, United Kingdom, and United States. We first compared responses across methods by frequency of ties and values below dead. Ordinal associations between methods were evaluated using Spearman’s correlation and Kendall’s tau. Next, we estimated numerical values from rank responses using country-specific conditional logit models. After anchoring predicted values on a common scale, we further investigated the cardinal relationships between rank, VAS, and TTO-based values using Pearson’s rho and quadratic regression. Results: For each country, rank responses are less likely than TTO responses to be tied and to indicate that states are worse than dead. In all countries, rank responses show a strong linear correlation with both TTO (Pearson’s rho 5 0.88e0.99) and VAS (rho 50.91e0.98) responses. However, rank-based values imply greater decrements in health for mild states than cardinal values. Conclusions: Illiteracy and innumeracy can hinder implementation of complex preference elicitation techniques in diverse settings and populations. These results indicate that ranking exercises may provide an attractive alternative for health-state valuation. 2008 Elsevier Inc. All rights reserved.


title: Multi-task Self-Supervised Visual Learning

abstract: We investigate methods for combining multiple self-supervised tasks--i.e., supervised tasks where data can be collected without manual labeling--in order to train a single visual representation. First, we provide an apples-to-apples comparison of four different self-supervised tasks using the very deep ResNet-101 architecture. We then combine tasks to jointly train a network. We also explore lasso regularization to encourage the network to factorize the information in its representation, and methods for "harmonizing" network inputs in order to learn a more unified representation. We evaluate all methods on ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our results show that deeper networks work better, and that combining tasks--even via a naive multi-head architecture--always improves performance. Our best joint network nearly matches the PASCAL performance of a model pre-trained on ImageNet classification, and matches the ImageNet network on NYU depth prediction.


title: Learning monocular reactive UAV control in cluttered natural environments

abstract: Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike large vehicles, MAVs can only carry very light sensors, such as cameras, making autonomous navigation through obstacles much more challenging. In this paper, we describe a system that navigates a small quadrotor helicopter autonomously at low altitude through natural forest environments. Using only a single cheap camera to perceive the environment, we are able to maintain a constant velocity of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors.


Machine Comprehension using BiDAF


title: Link Prediction in Social Networks Using Computationally Efficient Topological Features

abstract: Online social networking sites have become increasingly popular over the last few years. As a result, new interdisciplinary research directions have emerged in which social network analysis methods are applied to networks containing hundreds millions of users. Unfortunately, links between individuals may be missing due to imperfect acquirement processes or because they are not yet reflected in the online network (i.e., friends in real world did not form a virtual connection.) Existing link prediction techniques lack the scalability required for full application on a continuously growing social network which may be adding everyday users with thousands of connections. The primary bottleneck in link prediction techniques is extracting structural features required for classifying links. In this paper we propose a set of simple, easy-to-compute structural features that can be analyzed to identify missing links. We show that a machine learning classifier trained using the proposed simple structural features can successfully identify missing links even when applied to a hard problem of classifying links between individuals who have at least one common friend. A new friends measure that we developed is shown to be a good predictor for missing links and an evaluation experiment was performed on five large social networks datasets: Face book, Flickr, You Tube, Academia and The Marker. Our methods can provide social network site operators with the capability of helping users to find known, offline contacts and to discover new friends online. They may also be used for exposing hidden links in an online social network.


title: Inspecting GNU radio applications with controlport and performance counters

abstract: Due to differences in the operating system and the effects of sample rate on the computational load of a software radio, we have historically had a difficult time understanding the performance boundaries of software radio applications. This problem further leads to difficulties in debugging, optimization, and profiling analysis of both software radio frameworks and applications. This paper introduces a new tool developed for GNU Radio that starts to solve these problems. Called Performance Counters, GNU Radio now has an inbuilt ability to measure its performance for offline optimization as well as real-time behavioral analysis and adaptation. The Performance Counters are designed such that a GNU Radio application can directly sample them or access them through the use of ControlPort, another new tool that enables remote interaction with GNU Radio. We show in this paper some of the tools we have developed around ControlPort and the Performance Counters that will help us better understand GNU Radio's performance and capabilities as well as lead to better on-line adaptation of radios.


title: Analysis of Sequential Tasks in Use Context of Mobile Apps

abstract: Most of the work on context-aware systems has focused on the context of time, location, and activity. Previous studies on the context flow have been primarily conducted on a qualitative basis. This paper proposes a new approach from a quantitative perspective. We gathered the data from automated task service, "If This Then That (IFTTT)", and analyzed the sequential tasks in terms of event occurrence in smart devices through association rule mining. We found out three consecutive tasks in cross-applications. The results of analysis have potential to find hidden use patterns as telling what kinds of services and channels are associated with each other. The findings provide some insights on the development of design guidelines for context-aware services.


title: Radiological Outcome of Short Segment Posterior Instrumentation and Fusion for Thoracolumbar Burst Fractures

abstract: STUDY DESIGN ::: Retrospective study. ::: ::: ::: PURPOSE ::: To evaluate the radiological outcome of the surgical treatment of thoracolumbar burst fractures by using short segment posterior instrumentation (SSPI) and fusion. ::: ::: ::: OVERVIEW OF LITERATURE ::: The optimal surgical treatment of thoracolumbar burst fractures remains a matter of debate. SSPI is one of a number of possible choices, yet some studies have revealed high rates of poor radiological outcome for this SSPI. ::: ::: ::: METHODS ::: Patients treated using the short segment instrumentation and fusion technique at the Spinal Injuries Center (Iizuka, Fukuoka, Japan) from January 1, 2006 to July 31, 2012 were selected for this study. Radiographic parameters such as local sagittal angle, regional sagittal angle, disc angle, anterior or posterior height of the vertebral body at admission, postoperation and final observation were collected for radiological outcome evaluation. ::: ::: ::: RESULTS ::: There were 31 patients who met the inclusion criteria with a mean follow-up duration of 22.7 months (range, 12-48 months). The mean age of this group was 47.9 years (range, 15-77 years). The mean local sagittal angles at the time of admission, post-operation and final observation were 13.1°, 7.8° and 14.8°, respectively. There were 71% good cases and 29% poor cases based on our criteria for the radiological outcome evaluation. The correction loss has a strong correlation with the load sharing classification score (Spearman rho=0.64, p<0.001). ::: ::: ::: CONCLUSIONS ::: The loss of kyphotic correction following the surgical treatment of thoracolumbar burst fracture by short segment instrumentation is common and has a close correlation with the degree of comminution of the vertebral body. Patients with high load sharing scores are more susceptible to correction loss and postoperative kyphotic deformity than those with low scores.


title: Optimization of the Security-Performance Tradeoff in RC4 Encryption Algorithm

abstract: In this paper, we have investigated different vulnerabilities in RC4 and its enhanced variants to overcome the security attacks. It is established that in spite of several proposals, RC4 is not secure enough and a trade-off is always sought between security and network performance for overall provisioning of the secure communication. The main goal of the work presented in this paper is the optimization of security-performance tradeoff. We have proposed three RC4 variants referred to as RC4-M1, RC4-M2 and RC4-M3. Security of the proposed schemes is analyzed in terms of randomness and computational complexity. All the proposed variants qualify the NIST statistical test suite of randomness satisfactorily. The proposed schemes also offer computational complexity in terms of greater number of operations relative to the existing variants. The strength of the proposed schemes has been analyzed against different cryptanalytic attempts and shown the resistance of proposed schemes against attacks. The security-performance tradeoff has been analyzed in terms of run time, CPU cycles consumed, energy cost, and throughput. Encryption time of the proposed schemes--RC4-M1, RC4-M2 and RC4-M3 is 30.1, 10 and 48.7 % less as compared to RC4+ respectively. The results clearly indicate that the computation load of the proposed variants is significantly reduced as compared to the RC4+, concluding that the proposed schemes are computationally efficient. Our results and their analysis also recognize the suitability of the security algorithms for particular application areas.


title: Gender-Differentiated Parenting Revisited: Meta-Analysis Reveals Very Few Differences in Parental Control of Boys and Girls

abstract: Although various theories describe mechanisms leading to differential parenting of boys and girls, there is no consensus about the extent to which parents do treat their sons and daughters differently. The last meta-analyses on the subject were conducted more than fifteen years ago, and changes in gender-specific child rearing in the past decade are quite plausible. In the current set of meta-analyses, based on 126 observational studies (15,034 families), we examined mothers' and fathers' differential use of autonomy-supportive and controlling strategies with boys and girls, and the role of moderators related to the decade in which the study was conducted, the observational context, and sample characteristics. Databases of Web of Science, ERIC, PsychInfo, Online Contents, Picarta, and Proquest were searched for studies examining differences in observed parental control of boys and girls between the ages of 0 and 18 years. Few differences were found in parents' use of control with boys and girls. Parents were slightly more controlling with boys than with girls, but the effect size was negligible (d = 0.08). The effect was larger, but still small, in normative groups and in samples with younger children. No overall effect for gender-differentiated autonomy-supportive strategies was found (d = 0.03). A significant effect of time emerged: studies published in the 1970s and 1980s reported more autonomy-supportive strategies with boys than toward girls, but from 1990 onwards parents showed somewhat more autonomy-supportive strategies with girls than toward boys. Taking into account parents' gender stereotypes might uncover subgroups of families where gender-differentiated control is salient, but based on our systematic review of the currently available large data base we conclude that in general the differences between parenting of boys versus girls are minimal.


title: A Comparative Evaluation of Term Recognition Algorithms

abstract: Automatic Term Recognition (ATR) is a fundamental processing step preceding more complex tasks such as semantic search and ontology learning. From a large number of methodologies available in the literature only a few are able to handle both single and multi-word terms. In this paper we present a comparison of five such algorithms and propose a combined approach using a voting mechanism. We evaluated the six approaches using two different corpora and show how the voting algorithm performs best on one corpus (a collection of texts from Wikipedia) and less well using the Genia corpus (a standard life science corpus). This indicates that choice and design of corpus has a major impact on the evaluation of term recognition algorithms. Our experiments also showed that single-word terms can be equally important and occupy a fairly large proportion in certain domains. As a result, algorithms that ignore single-word terms may cause problems to tasks built on top of ATR. Effective ATR systems also need to take into account both the unstructured text and the structured aspects and this means information extraction techniques need to be integrated into the term recognition process.


title: An Ontology-Based Approach to Social Media Mining for Crisis Management

abstract: We describe an existing multilingual information extraction system that automatically detects event information on disasters, conflicts and health threats in near-real time from a continuous flow of on-line news articles. We illustrate a number of strategies for customizing the system to process social media texts such as Twitter messages, which are currently seen as a crucial source of information for crisis management applications. On one hand, we explore the mapping of our domain model with standard ontologies in the field. On the other hand, we show how the language resources of such a system can be built, starting from an existing domain ontology and a text corpus, by deploying a semisupervised method for ontology lexicalization. As a result, event detection is turned up into an ontology population process, where crowdsourced content is automatically augmented with a shared structured representation, in accordance with the Linked Open Data principles.


title: Learning and Memory Functions of the Basal Ganglia

abstract: ▪ Abstract Although the mammalian basal ganglia have long been implicated in motor behavior, it is generally recognized that the behavioral functions of this subcortical group of structures are not exclusively motoric in nature. Extensive evidence now indicates a role for the basal ganglia, in particular the dorsal striatum, in learning and memory. One prominent hypothesis is that this brain region mediates a form of learning in which stimulus-response (S-R) associations or habits are incrementally acquired. Support for this hypothesis is provided by numerous neurobehavioral studies in different mammalian species, including rats, monkeys, and humans. In rats and monkeys, localized brain lesion and pharmacological approaches have been used to examine the role of the basal ganglia in S-R learning. In humans, study of patients with neurodegenerative diseases that compromise the basal ganglia, as well as research using brain neuroimaging techniques, also provide evidence of a role for the basal ganglia in hab...


title: Axis-aligned filtering for interactive physically-based diffuse indirect lighting

abstract: We introduce an algorithm for interactive rendering of physically-based global illumination, based on a novel frequency analysis of indirect lighting. Our method combines adaptive sampling by Monte Carlo ray or path tracing, using a standard GPU-accelerated raytracer, with real-time reconstruction of the resulting noisy images. Our theoretical analysis assumes diffuse indirect lighting, with general Lambertian and specular receivers. In practice, we demonstrate accurate interactive global illumination with diffuse and moderately glossy objects, at 1-3 fps. We show mathematically that indirect illumination is a structured signal in the Fourier domain, with inherent band-limiting due to the BRDF and geometry terms. We extend previous work on sheared and axis-aligned filtering for motion blur and shadows, to develop an image-space filtering method for interreflections. Our method enables 5--8X reduced sampling rates and wall clock times, and converges to ground truth as more samples are added. To develop our theory, we overcome important technical challenges---unlike previous work, there is no light source to serve as a band-limit in indirect lighting, and we also consider non-parallel geometry of receiver and reflecting surfaces, without first-order approximations.


title: Grid-Connected Photovoltaic Generation System

abstract: This study addresses a grid-connected photovoltaic (PV) generation system. In order to make the PV generation system more flexible and expandable, the backstage power circuit is composed of a high step-up converter and a pulsewidth-modulation (PWM) inverter. In the dc-dc power conversion, the high step-up converter is introduced to improve the conversion efficiency of conventional boost converters and to allow the parallel operation of low-voltage PV modules. Moreover, an adaptive total sliding-mode control system is designed for the current control of the PWM inverter to maintain the output current with a higher power factor and less variation under load changes. In addition, an adaptive step-perturbation method is proposed to achieve the objective of maximum power point tracking, and an active sun tracking scheme without any light sensors is investigated to make PV plates face the sun directly in order to capture maximum irradiation and enhance system efficiency. Experimental results are given to verify the validity of the high step-up converter, the PWM inverter control, the ASP method, and the active sun tracker for a grid-connected PV generation system.


title: DWT-DCT based blind audio watermarking using Arnold scrambling and Cyclic codes

abstract: Currently over the millions of digital audio files such as digital songs are copied illegally during file-sharing over the networks. It has resulted as the loss of revenue for music and broadcasting industries. The traditional protection schemes are no longer useful to protect copyright and ownership of multimedia objects. These challenges have prompted significant research in digital audio watermarking for protection and authentication. It helps to prevent forgery and impersonation of audio signal. In this paper, a novel audio watermarking based algorithm is proposed using Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). Furthermore, the Arnold transform and error correction technique are utilized to improve the performance of proposed algorithm. The performance is measured using Bit Error Rate (BER), Peak Sound to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the extracted watermark and original watermark. The experimental results show excellent resilience against typical signal processing attacks compared to the previous algorithm. The performance is improved in terms of BER for 0–3.9%, PSNR higher than 62-db and Structural Similarity Index (SSIM) from 0.99 to 1.