title: Generic Probabilistic Interactive Situation Recognition and Prediction: From Virtual to Real

abstract: Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making. Although there exist a lot of works dealing with modeling driver behavior of a single object, it remains a challenge to make predictions for multiple highly interactive agents that react to each other simultaneously. In this work, we propose a generic probabilistic hierarchical recognition and prediction framework which employs a two-layer Hidden Markov Model (TLHMM) to obtain the distribution of potential situations and a learning-based dynamic scene evolution model to sample a group of future trajectories. Instead of predicting motions of a single entity, we propose to get the joint distribution by modeling multiple interactive agents as a whole system. Moreover, due to the decoupling property of the layered structure, our model is suitable for knowledge transfer from simulation to real world applications as well as among different traffic scenarios, which can reduce the computational efforts of training and the demand for a large data amount. A case study of highway ramp merging scenario is demonstrated to verify the effectiveness and accuracy of the proposed framework.


title: Efficient and Robust Parallel DNN Training through Model Parallelism on Multi-GPU Platform

abstract: The training process of Deep Neural Network (DNN) is compute-intensive, often taking days to weeks to train a DNN model. Therefore, parallel execution of DNN training on GPUs is a widely adopted approach to speed up process nowadays. Due to the implementation simplicity, data parallelism is currently the most commonly used parallelization method. Nonetheless, data parallelism suffers from excessive inter-GPU communication overhead due to frequent weight synchronization among GPUs. Another approach is model parallelism, which partitions model among GPUs. This approach can significantly reduce inter-GPU communication cost compared to data parallelism, however, maintaining load balance is a challenge. Moreover, model parallelism faces the staleness issue; that is, gradients are computed with stale weights. In this paper, we propose a novel model parallelism method, which achieves load balance by concurrently executing forward and backward passes of two batches, and resolves the staleness issue with weight prediction. The experimental results show that our proposal achieves up to 15.77x speedup compared to data parallelism and up to 2.18x speedup compared to the state-of-the-art model parallelism method without incurring accuracy loss.


title: Use of M-CHAT for a multinational screening of young children with autism in the Arab countries

abstract: There has been a decline in the mortality rate among infants and children under five years of age in the last decade in many developing countries. This has led to a shift in focus to look beyond survival to the prevention or reduction of impairment and promotion of children's health. With the apparent rise in the prevalence of autism spectrum disorders (ASD) in the Arab countries the development of an Arabic tool for early diagnosis and intervention was sought as part of an effort to better understand the prevalence of this disorder. The Modified Checklist for Autism in Toddlers (M-CHAT) was chosen. The study was conducted in nine Arabic speaking countries. The final analysis included 228 children (122 screened positive for ASD). The sensitivity (0.86), the specificity (0.80) and positive predictive value (0.88) were very similar to Robins et al. study (2001). Maternal health problems during pregnancy and labour were found to be more significant for ASD mothers than their control. In addition, child health problems were more evident among ASD subjects as reported by their parents with significant differences from controls. The limitation of the study is that the sample size is not large enough to generalize the results to all countries of the region. The strength of the study is that it is the first known study where Arab countries undertook a collaborative mental health study using the same tool for screening for a specific disorder.


title: Do facial expressions signal specific emotions : Judging emotion from the face in context

abstract: In the face-to-face encounters of everyday life, each person monitors the emotional reactions of others. Is she pleased? Is he annoyed? Are my listeners getting bored already? Ordinarily, when one person observes the emotional reaction of another, a variety of sources of information are available. In this article, we consider the case in which the observer has two such sources: information about the expresser's situation and about the expresser's facial expression. The specific question we address is the relative influence of these two cues when each alone would suggest a different emotional interpretation. We think that an answer to this question is interesting not only for its own sake but also for what it implies in regard to the deeper issue of the precise message contained in a facial expression. Let us begin our discussion with the specific question of relative influence. To illustrate, suppose that you are the observer. You see a woman receive a gift, and then you see her facial reaction to the gift. A photograph of her expression is shown in Figure 1. What is the woman's emotional reaction? When the situation and the face are put into this sort of competition, the face has a normative preeminence. The situation is a guide, not a guarantee. The situation can suggest how persons in general might typically react, but the facial reaction is part of the very reaction to be judged. Different individuals can react differently to a given situation. Suppose that the gift given to


Age, Gender, and Technology Attitude as Factors for Acceptance of Smart Interactive Textiles in Home Environments - Towards a Smart Textile Technology Acceptance Model


title: [From Morvan's disease to potassium channelopathies].

abstract: The term Morvan's disease, first coined in 1890, is still in use, although the generic term neuromyotonia--which is not exempt from criticism--has largely superseded it. Symptoms and signs are variable, ranging from benign painful fasciculations, pseudomyotonic cases, rigid forms, cases in which central nervous system features are also present (with, in addition to nerve hyperexcitability, agitation, confusion, delirium, insomnia, hyperhidrosis and tachycardia). A distal peripheral motor nerve is the origin of nerve hyperexcitability. There is growing evidence that autoimmunity is involved in the pathogenesis of many cases. Antibodies to voltage-gated potassium channels are detected in the serum of many patients with peripheral nerve hyperexcitability. Other cases are probably genetic. Inherited disorders are related to episodic dominant ataxia type 1, with the same mutation of a gene coding for potassium channel subunit Kv 1-1. Many inappropriate or non specific names are used to refer to peripheral nerve hyperexcitability. Isaacs syndrome, voltage-gated potassium channelopathy, or Morvan's syndrome are suggested.


title: Geodesic flow kernel for unsupervised domain adaptation

abstract: In real-world applications of visual recognition, many factors — such as pose, illumination, or image quality — can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross-validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods.


title: RFID based e-monitoring system for municipal solid waste management

abstract: With the increase of population of a country, proper management of cumulative of Municipal Solid Waste (MSW) becomes more acute for maintaining green environment. In conventional approach a number of trucks collect the MSW and then transport and transfer these MSW in a pre-specified location, but all the above jobs are not properly monitored. It is very important to monitor the trucks and record the information related to the collecting time and area from a central location to ensure the job well done. This project exploits the tremendous power of RFID technology and presents the development of an electronic monitoring (e-monitoring) system to overcome the above problem in the conventional approach. The proposed e-monitoring system is an embedded system that consists of RFID technology interfaced with PIC micro-controller and a web based computerized software. It has been tested in the laboratory environment as well as in the field environment. The test results show that the system functions properly and is working real time. Municipal authority can monitor the SW collecting status through the system and can generate different reports to improve the performance of their service. The prototype developed in this project can be further improved and used for commercial purpose.


c-Fos mapping of brain regions activated by multi-modal and electric foot shock stress


The aging bladder insights from animal models


title: Trends in the Surgical Treatment of Pathologic Proximal Femur Fractures Among Musculoskeletal Tumor Society Members

abstract: BACKGROUND ::: Several strategies for the treatment of pathologic proximal femur fractures are practiced but treatment outcomes have not been rigorously compared. ::: ::: ::: QUESTIONS/PURPOSES ::: Major variations in the use of intramedullary fixation, extramedullary/plate-screw fixation, and endoprosthetic reconstruction techniques for pathologic proximal femur fractures in patients with skeletal metastases are reported. The clinical and surgical variables that influence this choice differ among treating surgeons. To characterize the technique preferences and to identify areas of consensus regarding specific clinical presentations, we administered an online survey to the Musculoskeletal Tumor Society (MSTS) membership. We also tested whether responses correlated with the respondents' years in practice and asked about the indications for wide tumor resection and the role of tumor debulking and adjuvant cementation. ::: ::: ::: METHODS ::: A 10-minute, web-based survey was sent via email to 244 MSTS members. The survey queried participants' musculoskeletal oncology training and experience and presented case scenarios illustrating different combinations of four variables that influence decision-making: cancer type, estimated patient survival, fracture displacement, and anatomic region of involvement. ::: ::: ::: RESULTS ::: Forty-one percent (n = 98) of MSTS members completed the survey. Intramedullary nail fixation (IMN; 45%) and proximal femur resection and reconstruction (34%) were the most commonly recommended techniques followed by long-stem cemented hemiarthroplasty/cemented hemiarthroplasty (15%) and open reduction and internal fixation (7%). Most respondents (56%) recommended use of cementation with IMN. Differences of opinion on recommended treatment were associated with variations in cancer type, fracture displacement, and anatomic region of involvement. ::: ::: ::: CONCLUSIONS ::: Our online survey showed a trend among MSTS members for selecting IMN and arthroplasty-related techniques to treat pathologic fractures of the proximal femur, but major differences in preferred operative technique exist. Prospective studies are needed to develop consistent, evidence-based treatment recommendations.


title: Tangicons: algorithmic reasoning in a collaborative game for children in kindergarten and first class

abstract: In this paper, we describe the use of Tangicons, nonelectronic physical programming cubes for kindergarten and first grade children. Tangicons have been developed with the help of kindergarten children during various sessions of observing, playing and talking to them. Most tangible computing environments are too complex for young children. We developed an appropriate educational environment on a pedagogical basis resulting in easy to use tangible bricks, integrated in a physical game. Tangicons are haptic programmable bricks for programming a sequence of operations. Their symbol design is related to real world objects. With Tangicons, children are able to learn first steps of programming in a playful way.


title: Hamartoma of the Tongue: Two Case Reports

abstract: Hamartoma designates an excessive focal overgrowth of mature normal cells and tissues in an organ or tissue composed of identical cellular elements. Two cases of hamartoma at the back of the tongue are reported. Histologically they comprised fibrous tissue, fat, salivary gland lobules, and smooth muscle. The literature is reviewed and discussed.


title: Culture as situated cognition: Cultural mindsets, cultural fluency, and meaning making

abstract: Culture is a human universal, a “good enough” solution to universal needs. It is also a specific meaning-making framework, a “mindset” that influences what feels fluent, what is attended to, which goals or mental procedures are salient. Cross-national comparisons demonstrate both universality and between-group difference (specificity) but cannot address underlying process or distinguish fixed from context-dependent effects. I use a situated cognition framework and experimental methods to address these gaps, demonstrating that salient cultural mindsets have causal downstream consequences for meaning making, self-processes, willingness to invest in relationships, and complex mental procedures. Moreover, individualistic and collectivistic mindsets are accessible cross-culturally so both can be primed. Between-group differences arise in part from momentary cues that make either individualistic or collectivistic mindset accessible.


title: Multidimensional Dirichlet Process-Based Non-Parametric Signal Classification for Autonomous Self-Learning Cognitive Radios

abstract: In this paper, we propose a Bayesian non-parametric signal classification approach for spectrum sensing in cognitive radios (CR's). The proposed classification approach is based on the Dirichlet process mixture model (DPMM) that allows inferring the number and types of signals from their spectral and cyclic properties. The proposed algorithm is completely autonomous and does not require any prior knowledge of the existing signals or the number of distinct signal classes. We assume that the cluster parameters are drawn from a mixture model, where each mixture component parameterizes a specific observation model, including both Gaussian and non-Gaussian models. By using the Gibbs sampling, we estimate the observation model and cluster parameters that best fit the observed data. Given N data points, under certain regularity conditions, we derive an upper bound for the mean-squared error (MSE) in estimating the clusters means. A Bayesian prediction method is also developed to estimate the probability distribution of the data points. The proposed algorithm is applied to detect and classify WiFi and Bluetooth signals in the ISM band. Simulation results validate the proposed classification approach and show its robustness against channel impairments such as Rayleigh channel fading.


Representations and estimates for inverse operators of the potential theory integral equations in a polyhedron


title: A Web-based Bayesian Intelligent Tutoring System for Computer Programming

abstract: In this paper, we present a Web-based intelligent tutoring system, called BITS. The decision making process conducted in our intelligent system is guided by a Bayesian network approach to support students in learning computer programming. Our system takes full advantage of Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. We discuss how to employ Bayesian networks as an inference engine to guide the students' learning processes. In addition, we describe the architecture of BITS and the role of each module in the system. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, our intelligent system can help a student navigate through the online course materials, recommend learning goals, and generate appropriate reading sequences.


title: Measuring Marketing Productivity: Current Knowledge and Future Directions

abstract: Abstract For too long, marketers have not been held accountable for showing how marketing expenditures add to shareholder value. As time has gone by, this lack of accountability has undermined marketers’ credibility, threatened the standing of the marketing function within the firm, and even threatened marketing’s existence as a distinct capability within the firm. This article proposes a broad framework for assessing marketing productivity, cataloging what is already known, and suggesting areas for further research. The authors conclude that it is possible to show how marketing expenditures add to shareholder value. The effective dissemination of new methods of assessing marketing productivity to the business community will be a major step toward raising marketing’s vitality in the firm and, more important, toward raising the performance of the firm itself. The authors also suggest many areas in which further research is essential to making methods of evaluating marketing productivity increasingly valid,...


title: Detecting clusters of anomalies on low-dimensional feature subsets with application to network traffic flow data

abstract: In a variety of applications, one desires to detect groups of anomalous data samples, with a group potentially manifesting its atypicality (relative to a reference model) on a low-dimensional subset of the full measured set of features. Samples may only be weakly atypical individually, whereas they may be strongly atypical when considered jointly. What makes this group anomaly detection problem quite challenging is that it is a priori unknown which subset of features jointly manifests a particular group of anomalies. Moreover, it is unknown how many anomalous groups are present in a given data batch. In this work, we develop a group anomaly detection (GAD) scheme to identify the subset of samples and subset of features that jointly specify an anomalous cluster. We apply our approach to network intrusion detection to detect BotNet and peer-to-peer flow clusters. Unlike previous studies, our approach captures and exploits statistical dependencies that may exist between the measured features. Experiments on real world network traffic data demonstrate the advantage of our proposed system, and highlight the importance of exploiting feature dependency structure, compared to the feature (or test) independence assumption made in previous studies.


Ranking Kernels for Structures and Embeddings: A Hybrid Preference and Classification Model


title: Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning

abstract: Ultrasound imaging makes use of backscattering of waves during their interaction with scatterers present in biological tissues. Simulation of synthetic ultrasound images is a challenging problem on account of inability to accurately model various factors of which some include intra-/inter scanline interference, transducer to surface coupling, artifacts on transducer elements, inhomogeneous shadowing and nonlinear attenuation. Current approaches typically solve wave space equations making them computationally expensive and slow to operate. We propose a generative adversarial network (GAN) inspired approach for fast simulation of patho-realistic ultrasound images. We apply the framework to intravascular ultrasound (IVUS) simulation. A stage 0 simulation performed using pseudo B-mode ultrasound image simulator yields speckle mapping of a digitally defined phantom. The stage I GAN subsequently refines them to preserve tissue specific speckle intensities. The stage II GAN further refines them to generate high resolution images with patho-realistic speckle profiles. We evaluate patho-realism of simulated images with a visual Turing test indicating an equivocal confusion in discriminating simulated from real. We also quantify the shift in tissue specific intensity distributions of the real and simulated images to prove their similarity.


title: Texture synthesis using convolutional neural networks with long-range consistency and spectral constraints

abstract: Procedural texture generation enables the creation of more rich and detailed virtual environments without the help of an artist. However, finding a flexible generative model of real world textures remains an open problem. We present a novel Convolutional Neural Network based texture model consisting of two summary statistics (the Gramian and Translation Gramian matrices), as well as spectral constraints. We investigate the Fourier Transform or Window Fourier Transform in applying spectral constraints, and find that the Window Fourier Transform improved the quality of the generated textures. We demonstrate the efficacy of our system by comparing generated output with that of related state of the art systems.


Integrating Structure with Deep Reinforcement and Imitation Learning


title: Advanced QR Code Based Identity Card: A New Era for Generating Student ID Card in Developing Countries

abstract: Educational institution in developing countries is a vast sector and it is expanding rapidly. With the rapid growth of technologies, educational institutions are still lagging behind. For identification of students they still lie on manual handwritten ID card and files. If proper ID card system is introduced then it would be much easier to identify a student and can track his her progress. Therefore, in this research we implemented advanced using of QR code and student ID generation. We tried level best to make right implementation of QR code with student ID card. Automated student ID had image capturing facility and requirements of an ID and there is no need of hand written ID card. QR code is attached into ID card and student personal details can be found by scanning the QR code with smart phones (downloaded & installed QR reader software). QR code is said to be the next generation of bar code and using QR code with ID card brought positive outcomes. While using software tools, identity cards works fine and suitable for an educational institution as it is free. Thus, ID card will be a perfect use of modern technology in developing countries.


title: Robotic versus Open Partial Nephrectomy: A Systematic Review and Meta-Analysis

abstract: Objectives ::: To critically review the currently available evidence of studies comparing robotic partial nephrectomy (RPN) and open partial nephrectomy (OPN). ::: ::: ::: Materials and Methods ::: A comprehensive review of the literature from Pubmed, Web of Science and Scopus was performed in October 2013. All relevant studies comparing RPN with OPN were included for further screening. A cumulative meta-analysis of all comparative studies was performed and publication bias was assessed by a funnel plot. ::: ::: ::: Results ::: Eight studies were included for the analysis, including a total of 3418 patients (757 patients in the robotic group and 2661 patients in the open group). Although RPN procedures had a longer operative time (weighted mean difference [WMD]: 40.89; 95% confidence interval [CI], 14.39–67.40; p = 0.002), patients in this group benefited from a lower perioperative complication rate (19.3% for RPN and 29.5% for OPN; odds ratio [OR]: 0.53; 95%CI, 0.42–0.67; p<0.00001), shorter hospital stay (WMD: −2.78; 95%CI, −3.36 to −1.92; p<0.00001), less estimated blood loss(WMD: −106.83; 95%CI, −176.4 to −37.27; p = 0.003). Transfusions, conversion to radical nephrectomy, ischemia time and estimated GFR change, margin status, and overall cost were comparable between the two techniques. The main limitation of the present meta-analysis is the non-randomization of all included studies. ::: ::: ::: Conclusions ::: RPN appears to be an efficient alternative to OPN with the advantages of a lower rate of perioperative complications, shorter length of hospital stay and less blood loss. Nevertheless, high quality prospective randomized studies with longer follow-up period are needed to confirm these findings.


Paxos Made Simple


title: Robust front-end processing for Speech Recognition in noisy conditions

abstract: In this paper, we investigate the applicability and effectiveness of advanced feature compensation techniques in devising a robust front-end for Automatic Speech Recognition (ASR). First, the Vector Taylor Series (VTS) equations are altered by bringing in the auditory masking factor. The resultant VTS approximation is used to compensate the parameters of a clean speech model and a Minimum Mean Square Error (MMSE) estimate is used to estimate the clean speech features from noisy features. Second, we apply root-compression instead of conventional log-compression to the mel-filter banks energy. Third, we apply a frame selection method to eliminate the noise dominated frames to improve the performance in high noise scenarios. The proposed algorithms are validated on noise corrupted Librispeech and TIMIT speech recognition databases and are shown to provide significant gain in performance.


title: Attribute-Guided Face Generation Using Conditional CycleGAN

abstract: We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the CycleGAN and propose conditional CycleGAN, which is designed to 1) handle unpaired training data because the training low/high-res and high-res attribute images may not necessarily align with each other, and to 2) allow easy control of the appearance of the generated face via the input attributes. We demonstrate impressive results on the attribute-guided conditional CycleGAN, which can synthesize realistic face images with appearance easily controlled by user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using the attribute image as identity to produce the corresponding conditional vector and by incorporating a face verification network, the attribute-guided network becomes the identity-guided conditional CycleGAN which produces impressive and interesting results on identity transfer. We demonstrate three applications on identity-guided conditional CycleGAN: identity-preserving face superresolution, face swapping, and frontal face generation, which consistently show the advantage of our new method.


title: Efficient image splicing detection algorithm based on markov features

abstract: Image splicing is one of the most common methods for digital image tampering. In this paper, an efficient Markov features based algorithm is proposed for image splicing detection. The proposed algorithm first extracts two types of Markov features, coefficient-wise Markov features and block-wise Markov features in the discrete cosine transform (DCT) domain. The former are obtained by exploiting correlations between consecutive coefficients and the latter are computed by exploiting coefficient correlations between adjacent blocks. Then, a feature vector is obtained by combining these two Markov features and it is fed into support vector machine (SVM) for the classification of authentic and spliced images. The experimental results show that the proposed method not only achieves much higher detection accuracy but also reduces the total running time significantly in comparison with state-of-the-art methods.


title: Genotyping-by-sequencing highlights patterns of genetic structure and domestication in artichoke and cardoon

abstract: Exploiting the biodiversity of crops and their wild relatives is fundamental for maintaining and increasing food security. The species Cynara cardunculus includes three taxa: the globe artichoke, one of the most important Mediterranean vegetables, the leafy cardoon, and the wild cardoon. In this study, genotyping by sequencing (GBS) was successfully applied to reveal thousands of polymorphisms in a C. cardunculus germplasm collection, including 65 globe artichoke, 9 leafy cardoon, and 21 wild cardoon samples. The collection showed a strong population structure at K = 2, separating the globe artichoke from the leafy and wild cardoon. At higher K values, further substructures were observed, in which the wild cardoon was separated from the leafy cardoon, and the latter included the Spanish wild cardoons, while the wild sample from Portugal was admixed. Moreover, subpopulations within the globe artichoke set were highlighted. Structure analysis restricted to the globe artichoke dataset pointed out genetic differentiation between the ˝Catanesi˝ typology and all the other samples (K = 2). At higher values of K, the separation of the ˝Catanesi˝ group still held true, and green headed landraces from Apulia region, Italy (˝Green Apulian˝) formed a distinct subpopulation. ˝Romaneschi˝ artichoke types fell in a variable group with admixed samples, indicating that they should not be considered as a genetically uniform typology. The results of principal component analysis and Neighbor-Joining hierarchical clustering were consistent with structure results, and in addition provided a measure of genetic relationships among individual genotypes. Both analyses attributed the wild material from Spain and Portugal to the cultivated cardoon group, supporting the idea that this might be indeed a feral form of the leafy cardoon. Different reproductive habit and possibly selective pressure led to a slower LD decay in artichoke compared to cardoon. Genotyping by sequencing has proven a reliable methodology to obtain valuable SNPs and assess population genetics in C. cardunculus.


title: Algorithmic Design of Low-Power Variable-Stiffness Mechanisms

abstract: Compliant actuators enabling low-power stiffness adaptation are missing ingredients and key enablers of next generation robotic systems. One of the key components of these actuators is the mechanism implementing stiffness adaptation that requires sophisticated control and nontrivial mechanical design. However, despite recent advances in controlling these systems, their design remains experience based and not well understood. In this paper, we present an optimization-based computational framework for the design of intrinsically low-power compliant variable stiffness mechanisms. The core ingredient of this framework is the mathematical formulation of the design problem—provided by a constrained nonlinear parameter optimization—which is computationally solved here to identify optimal variable stiffness designs. We show the basic capability of this formulation in finding parameters for variable stiffness mechanisms that require the least power by design. Further, we demonstrate the generality of this method in cross-comparing mechanisms with different kinematic topology to identify the one that requires the least power by design.


title: Relative Entropy Inverse Reinforcement Learning

abstract: We consider the problem of imitation learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an ecient tool for generalizing the demonstration, based on the assumption that the expert is optimally acting in a Markov Decision Process (MDP). Most of the past work on IRL requires that a (near)optimal policy can be computed for dierent reward functions. However, this requirement can hardly be satised in systems with a large, or continuous, state space. In this paper, we propose a model-free IRL algorithm, where the relative entropy between the empirical distribution of the state-action trajectories under a baseline policy and their distribution under the learned policy is minimized by stochastic gradient descent. We compare this new approach to well-known IRL algorithms using learned MDP models. Empirical results on simulated car racing, gridworld and ball-in-a-cup problems show that our approach is able to learn good policies from a small number of demonstrations.


title: Exploring self-compassion as a refuge against recalling the body-related shaming of caregiver eating messages on dimensions of objectified body consciousness in college women

abstract: Guided by an overarching body-related shame regulation framework, the present investigation examined the associations between caregiver eating messages and dimensions of objectified body consciousness and further explored whether self-compassion moderated these links in a sample of 322 U.S. college women. Correlational findings indicated that retrospective accounts of restrictive/critical caregiver eating messages were positively related to body shame and negatively related to self-compassion and appearance control beliefs. Recollections of experiencing pressure to eat from caregivers were positively correlated with body shame and inversely associated with appearance control beliefs. Higher self-compassion was associated with lower body shame and body surveillance. Self-compassion attenuated the associations between restrictive/critical caregiver eating messages and both body surveillance and body shame. Implications for advancing our understanding of the adaptive properties of a self-compassionate self-regulatory style in mitigating recall of familial body-related shaming on the internalized body-related shame regulating processes of body objectification in emerging adulthood are discussed.


title: Predicting healthcare trajectories from medical records: a deep learning approach.

abstract: Abstract Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden – diabetes and mental health – the results show improved prediction accuracy.


title: A survey on heuristic malware detection techniques

abstract: Malware is a malicious code which is developed to harm a computer or network. The number of malwares is growing so fast and this amount of growth makes the computer security researchers invent new methods to protect computers and networks. There are three main methods used to malware detection: Signature based, Behavioral based and Heuristic ones. Signature based malware detection is the most common method used by commercial antiviruses but it can be used in the cases which are completely known and documented. Behavioral malware detection was introduced to cover deficiencies of signature based method. However, because of some shortcomings, the heuristic methods have been introduced. In this paper, we discuss the state of the art heuristic malware detection methods and briefly overview various features used in these methods such as API Calls, OpCodes, N-Grams etc. and discuss their advantages and disadvantages.


DaLi : Database as a Library


title: New Privacy Threat on 3G, 4G, and Upcoming 5G AKA Protocols

abstract: Abstract Mobile communications are used by more than two-thirds of the world population who expect security and privacy guarantees. The 3rd Generation Partnership Project (3GPP) responsible for the worldwide standardization of mobile communication has designed and mandated the use of the AKA protocol to protect the subscribers’ mobile services. Even though privacy was a requirement, numerous subscriber location attacks have been demonstrated against AKA, some of which have been fixed or mitigated in the enhanced AKA protocol designed for 5G. In this paper, we reveal a new privacy attack against all variants of the AKA protocol, including 5G AKA, that breaches subscriber privacy more severely than known location privacy attacks do. Our attack exploits a new logical vulnerability we uncovered that would require dedicated fixes. We demonstrate the practical feasibility of our attack using low cost and widely available setups. Finally we conduct a security analysis of the vulnerability and discuss countermeasures to remedy our attack.


title: To prune, or not to prune: exploring the efficacy of pruning for model compression

abstract: Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model's dense connection structure, exposing a similar trade-off in model size and accuracy. We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process. We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint. Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10x reduction in number of non-zero parameters with minimal loss in accuracy.


title: Towards Exploratory OLAP Over Linked Open Data – A Case Study

abstract: Business Intelligence (BI) tools provide fundamental support for analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more information is available on the Web in the form of Resource Description Framework (RDF), and BI tools have a huge potential of achieving better results by integrating real-time data from web sources into the analysis process. In this paper, we describe a framework for so-called exploratory OLAP over RDF sources. We propose a system that uses a multidimensional schema of the OLAP cube expressed in RDF vocabularies. Based on this information the system is able to query data sources, extract and aggregate data, and build a cube. We also propose a computer-aided process for discovering previously unknown data sources and building a multidimensional schema of the cube. We present a use case to demonstrate the applicability of the approach.


title: Learning Semantic Deformation Flows with 3D Convolutional Networks

abstract: Shape deformation requires expert user manipulation even when the object under consideration is in a high fidelity format such as a 3D mesh. It becomes even more complicated if the data is represented as a point set or a depth scan with significant self occlusions. We introduce an end-to-end solution to this tedious process using a volumetric Convolutional Neural Network (CNN) that learns deformation flows in 3D. Our network architectures take the voxelized representation of the shape and a semantic deformation intention (e.g., make more sporty) as input and generate a deformation flow at the output. We show that such deformation flows can be trivially applied to the input shape, resulting in a novel deformed version of the input without losing detail information. Our experiments show that the CNN approach achieves comparable results with state of the art methods when applied to CAD models. When applied to single frame depth scans, and partial/noisy CAD models we achieve \({\sim }60\,\%\) less error compared to the state-of-the-art.


title: Evaluating the language resources of chatbots for their potential in english as a second language

abstract: This paper investigates the linguistic worth of current ‘chatbot’ programs – software programs which attempt to hold a conversation, or interact, in English – as a precursor to their potential as an ESL (English as a second language) learning resource. After some initial background to the development of chatbots, and a discussion of the Loebner Prize Contest for the most ‘human’ chatbot (the ‘Turing Test’), the paper describes an in-depth study evaluating the linguistic accuracy of a number of chatbots available online. Since the ultimate purpose of the current study concerns chatbots' potential with ESL learners, the analysis of language embraces not only an examination of features of language from a native-speaker's perspective (the focus of the Turing Test), but also aspects of language from a second-language-user's perspective. Analyses indicate that while the winner of the 2005 Loebner Prize is the most able chatbot linguistically, it may not necessarily be the chatbot most suited to ESL learners. The paper concludes that while substantial progress has been made in terms of chatbots' language-handling, a robust ESL ‘conversation practice machine’ (Atwell, 1999) is still some way off being a reality.


Improving the Effectiveness of Online Data Collection by Mixing Survey Modes


title: China's Informal Stock Market: How it Developed, how it Works and how it Might Grow

abstract: China has a highly active informal market in shares. The shares of over 1,000 companies are traded and some 3m investors take part in a market in corporate equities organised in various locales outside the two stock exchanges in Shanghai and Shenzhen. China's informal share market has appeared in a number of different guises since the establishment of the stock market in the late 1980s, partly as a result of the government's restrictions as to which type and what number of firms can publicly issue shares. While the central government has at various times attempted to close down these informal markets, they continue to flourish as shareholding firms seek to raise capital and owners of firms seek to transfer their ownership rights outside the bounds of the formal financial sector. This suggests that the government should move to formalise these markets, a move that would recognise the incipient demand for shares and allow the government to introduce a basic regulatory framework for a market which evidently can not be easily suppressed. This paper is divided into three parts. The first provides a brief historical overview of the development of informal share markets in reform China, from the kerb markets of the mid-1980s to the Internet-based brokers of 2003. The second section looks in detail at the organisation of the market today; how shares are issued, traded and settled. Third, and last, the paper identifies the problems inherent in the market at present and outlines the possibilities for its future.


title: Automating e-Learning: The Higher Education Revolution

abstract: As educational institutions throughout the world increasingly offer courses via the Internet, a global lifelong learning economy is emerging in which institutions must compete for students worldwide. This competition is especially noticeable in the offering of postgraduate courses aimed at satisfying the continuing professional development needs of practitioners in a wide range of disciplines. Fifth generation distance education (The Intelligent Flexible Learning Model), incorporating the use of automated response systems and reusable learning objects’ databases, has the potential to provide students with a valuable pedagogical experience at significantly lower cost than traditional approaches to distance education and conventional face-to-face education. Such e-learning initiatives will be irresistible to students, politicians and the business community alike – the higher education revolution is inexorable.


title: Ferrite and wire baluns with under 1 dB loss to 2.5 GHz

abstract: This paper presents a ferrite and wire balun with performance similar to coaxial baluns. This balun makes two significant improvements over current technology: the wire impedance is adjusted for the ferrite and wire winding loading; and a trifilar wire is used to minimize skin effect losses. Insertion loss below 0.9 dB is achieved from 5 MHz to 2.5 GHz.


title: Game strategies in network security

abstract: This paper presents a game-theoretic method for analyzing the security of computer networks. We view the interactions between an attacker and the administrator as a two-player stochastic game and construct a model for the game. Using a nonlinear program, we compute Nash equilibria or best-response strategies for the players (attacker and administrator). We then explain why the strategies are realistic and how administrators can use these results to enhance the security of their network.


title: Regional coherence changes in the early stages of Alzheimer’s disease: A combined structural and resting-state functional MRI study

abstract: Recent functional imaging studies have indicated that the pathophysiology of Alzheimer's disease (AD) can be associated with the changes in spontaneous low-frequency (< 0.08 Hz) blood oxygenation level-dependent fluctuations (LFBF) measured during a resting state. The purpose of this study was to examine regional LFBF coherence patterns in early AD and the impact of regional brain atrophy on the functional results. Both structural MRI and resting-state functional MRI scans were collected from 14 AD subjects and 14 age-matched normal controls. We found significant regional coherence decreases in the posterior cingulate cortex/precuneus (PCC/PCu) in the AD patients when compared with the normal controls. Moreover, the decrease in the PCC/PCu coherence was correlated with the disease progression measured by the Mini-Mental State Exam scores. The changes in LFBF in the PCC/PCu may be related to the resting hypometabolism in this region commonly detected in previous positron emission tomography studies of early AD. When the regional PCC/ PCu atrophy was controlled, these results still remained significant but with a decrease in the statistical power, suggesting that the LFBF results are at least partly explained by the regional atrophy. In addition, we also found increased LFBF coherence in the bilateral cuneus, right lingual gyrus and left fusiform gyrus in the AD patients. These regions are consistent with previous findings of AD-related increased activation during cognitive tasks explained in terms of a compensatory- recruitment hypothesis. Finally, our study indicated that regional brain atrophy could be an important consideration in functional imaging studies of neurodegenerative diseases. (c) 2006 Elsevier Inc. All rights reserved.


title: Funneling and saltation effects for tactile interaction with virtual objects

abstract: Funneling and saltation are two major illusory feedback techniques for vibration-based tactile feedback. They are often put into practice e.g. to reduce the number of vibrators to be worn on the body and thereby build a less cumbersome feedback device. Recently, these techniques have been found to be applicable to eliciting "out of the body" experiences as well (e.g. through user-held external objects). This paper examines the possibility of applying this phenomenon to interacting with virtual objects. Two usability experiments were run to test the effects of funneling and saltation respectively for perceiving tactile sensation from a virtual object in an augmented reality setting. Experimental results have shown solid evidences for phantom sensations from virtual objects with funneling, but mixed results for saltation.


title: 'Choosing' to work when sick: workplace presenteeism.

abstract: Presenteeism is a concept used to describe the phenomenon of working through illness and injury. This paper is based on interviews and focus groups undertaken at three different work sites in New Zealand: a small private hospital, a large public hospital and a small factory. The research suggests that presenteeism is a prominent phenomenon in the lives of workers at these different sites, but the way in which it is rationalised and the factors that foster presenteeism are quite distinct. Exploring the way in which presenteeism links to economic and social constraints and workplace cultures provides insights into these rationalisations. The powerful forces promoting presenteeism tempers the research community's concern with absenteeism. A presenteeism discourse needs to be more prominently articulated to oppose both the absenteeism discourse, and to moderate the views taken by some postmodernist theorists on choice in relation to health practices in workplace settings.


title: Investigation of Locked Rotor Test for Estimation of Magnet PWM Carrier Eddy Current Loss in Synchronous Machines

abstract: The measurement method on the basis of locked rotor tests has been developed for the estimation of magnet PWM carrier eddy current loss in synchronous machines. In the measurement, the magnet eddy current loss is separated from the other losses by subtracting the loss of the machine without the magnets from that with the magnets under the appropriate control of armature currents and voltages. The 3-D finite element analysis is also carried out in order to confirm the validity of the proposed measurement method. The measured and calculated results are found to be in good agreement. It is clarified that the magnet eddy current loss of the machine can be predicted from the results of the proposed measurement method.


title: PaDNet: Pan-Density Crowd Counting

abstract: Crowd counting is a highly challenging problem in computer vision and machine learning. Most previous methods have focused on consistent density crowds, i.e. , either a sparse or a dense crowd, meaning they performed well in global estimation while neglecting local accuracy. To make crowd counting more useful in the real world, we propose a new perspective, named pan-density crowd counting, which aims to count people in varying density crowds. Specifically, we propose the Pan-Density Network (PaDNet) which is composed of the following critical components. First, the Density-Aware Network (DAN) contains multiple subnetworks pretrained on scenarios with different densities. This module is capable of capturing pan-density information. Second, the Feature Enhancement Layer (FEL) effectively captures the global and local contextual features and generates a weight for each density-specific feature. Third, the Feature Fusion Network (FFN) embeds spatial context and fuses these density-specific features. Further, the metrics Patch MAE (PMAE) and Patch RMSE (PRMSE) are proposed to better evaluate the performance on the global and local estimations. Extensive experiments on four crowd counting benchmark datasets, the ShanghaiTech, the UCF_CC_50, the UCSD, and the UCF-QNRF, indicate that PaDNet achieves state-of-the-art recognition performance and high robustness in pan-density crowd counting.


title: Building Height Retrieval From VHR SAR Imagery Based on an Iterative Simulation and Matching Technique

abstract: Experimental airborne synthetic aperture radar (SAR) systems achieve spatial resolutions of approximately 10 cm, whereas the new spaceborne very high spatial resolution (VHR) SAR sensors onboard the TerraSAR-X and COSMO-SkyMed satellites achieve spatial resolutions down to 1 m. In VHR SAR data, features from individual urban structures (i.e., buildings) can be identified by their characteristic settings in urban settlement patterns. In this paper, we present a novel concept for the height estimation of generic man-made structures from single detected SAR data. The proposed approach is based on the definition of a hypothesis on the height of the building and on the simulation of a SAR image for testing that hypothesis. A matching procedure is applied between the estimated and the actual SAR image in order to test the height hypothesis. The process is iterated for different height assumptions until the matching function is optimized, and thus, the building height is estimated. The efficiency of the proposed method is demonstrated on a set of 40 flat- and gable-roof buildings using two submeter VHR airborne and two 1-m resolution TerraSAR-X SAR scenes all acquired from the same residential area in Dorsten, Germany. The results show that, in the absence of string disturbing effects, the method is able to estimate the height of flat- and gable-roof buildings in the submeter data to the order of a meter, while the accuracy for the meter resolution spaceborne data is lower but still sufficient to estimate the number of floors of a building.


title: Paraphrase-Driven Learning for Open Question Answering

abstract: We study question answering as a machine learning problem, and induce a function that maps open-domain questions to queries over a database of web extractions. Given a large, community-authored, question-paraphrase corpus, we demonstrate that it is possible to learn a semantic lexicon and linear ranking function without manually annotating questions. Our approach automatically generalizes a seed lexicon and includes a scalable, parallelized perceptron parameter estimation scheme. Experiments show that our approach more than quadruples the recall of the seed lexicon, with only an 8% loss in precision.


title: Terminology extraction: an analysis of linguistic and statistical approaches

abstract: Are linguistic properties and behaviors important to recognize terms? Are statistical measures effective to extract terms? Is it possible to capture a sort of termhood with computation linguistic techniques? Or maybe, terms are too much sensitive to exogenous and pragmatic factors that cannot be confined in computational linguistic? All these questions are still open. This study tries to contribute in the search of an answer, with the belief that it can be found only through a careful experimental analysis of real case studies and a study of their correlation with theoretical insights.


title: Beyond native apps: web technologies to the rescue! (keynote)

abstract: As of today, mobile software development teams can follow a number of different development and distribution strategies, ranging from native apps, to mobile web apps, hybrid apps, and the recently emerging progressive web apps. This talk provides a state-of-the-art overview of the development strategies and technologies for developing mobile apps, each of them with its own advantages and drawbacks. In this context, the use of web technologies is discussed as a promising investment for moving forward one of the most intriguing challenges in the world of mobile apps: its fragmentation with respect to mobile platforms. A discussion of research challenges, and thus opportunities, closes the talk.


title: ProvUSB: Block-level Provenance-Based Data Protection for USB Storage Devices

abstract: Defenders of enterprise networks have a critical need to quickly identify the root causes of malware and data leakage. Increasingly, USB storage devices are the media of choice for data exfiltration, malware propagation, and even cyber-warfare. We observe that a critical aspect of explaining and preventing such attacks is understanding the provenance of data (i.e., the lineage of data from its creation to current state) on USB devices as a means of ensuring their safe usage. Unfortunately, provenance tracking is not offered by even sophisticated modern devices. This work presents ProvUSB, an architecture for fine-grained provenance collection and tracking on smart USB devices. ProvUSB maintains data provenance by recording reads and writes at the block layer and reliably identifying hosts editing those blocks through attestation over the USB channel. Our evaluation finds that ProvUSB imposes a one-time 850 ms overhead during USB enumeration, but approaches nearly-bare-metal runtime performance (90% of throughput) on larger files during normal execution, and less than 0.1% storage overhead for provenance in real-world workloads. ProvUSB thus provides essential new techniques in the defense of computer systems and USB storage devices.


title: Inkjet Printing of Novel Wideband and High Gain Antennas on Low-Cost Paper Substrate

abstract: A complete characterization of the inkjet printing process using metallic nanoparticle inks on a paper substrate for microwave frequencies up to 12.5 GHz as well as its application to low-cost, high gain and wideband antenna design are demonstrated in this work. Laser and heat sintering of metallic nanoparticles are compared on paper substrate for the first time which demonstrate immense cost and time benefits of laser sintering. The antennas fabricated using the characterized process include a Vivaldi for the UWB band which exhibits a significantly higher gain of up to 8 dBi as compared to the currently published inkjet printed antennas, and a novel slow-wave log periodic dipole array which employs a new miniaturization technique to show 20% width reduction.


title: Terrain coverage with ant robots: a simulation study

abstract: In this paper, we study a simple means for coordinating teams of simple agents. In particular, we study ant robots and how they can cover terrain once or repeatedly by leaving markings in the terrain, similar to what ants do. These markings can be sensed by all robots and allow them to cover terrain even if they do not communicate with each other except via the markings, do not have any kind of memory, do not know the terrain, cannot maintain maps of the terrain, nor plan complete paths. The robots do not even need to be localized, which completely eliminates solving difficult and time-consuming localization problems. In this paper, we use real-time heuristic search methods to implement ant robots and present a simulation study with several real-time heuristic search methods to study their properties for terrain coverage. Our experiments show that all of the real-time heuristic search methods robustly cover terrain even if the robots are moved without realizing this, some robots fail, and some markings get destroyed. These results demonstrate that terrain coverage with real-time heuristic search methods is an interesting alternative to more conventional terrain coverage methods.


title: Social Media as It Interfaces with Psychosocial Development and Mental Illness in Transitional Age Youth.

abstract: For transitional age individuals, social media (SM) is an integral component of connecting with others. There are 2 billion SM users worldwide. SM users may experience an increase in perceived social support and life satisfaction. Use of SM may facilitate forming connections among people with potentially stigmatizing mental disorders. However, epidemiologic studies suggest that increased SM use is associated with conditions such as depression, anxiety, and sleep disturbance. Future research should examine directionality of these associations and the role of contextual factors. It also will be useful to leverage SM to provide mental health care and surveillance of mental health concerns.


title: Physically based computer graphics for realistic image formation to simulate optical measurement systems

abstract: Abstract. Physically based image synthesis methods, a research direction in computer graphics (CG), are capable of simulating optical measuring systems in their entirety and thus constitute an interesting approach for the development, simulation, optimization, and validation of such systems. In addition, other CG methods, so-called procedural modeling techniques, can be used to quickly generate large sets of virtual samples and scenes thereof that comprise the same variety as physical testing objects and real scenes (e.g., if digitized sample data are not available or difficult to acquire). Appropriate image synthesis (rendering) techniques result in a realistic image formation for the virtual scenes, considering light sources, material, complex lens systems, and sensor properties, and can be used to evaluate and improve complex measuring systems and automated optical inspection (AOI) systems independent of a physical realization. In this paper, we provide an overview of suitable image synthesis methods and their characteristics, we discuss the challenges for the design and specification of a given measuring situation in order to allow for a reliable simulation and validation, and we describe an image generation pipeline suitable for the evaluation and optimization of measuring and AOI systems.


title: A Novel Unsupervised Algorithm for Biological Process-based Analysis on Cancer

abstract: The aberrant alterations of biological functions are well known in tumorigenesis and cancer development. Hence, with advances in high-throughput sequencing technologies, capturing and quantifying the functional alterations in cancers based on expression profiles to explore cancer malignant process is highlighted as one of the important topics among cancer researches. In this article, we propose an algorithm for quantifying biological processes by using gene expression profiles over a sample population, which involves the idea of constructing principal curves to condense information of each biological process by a novel scoring scheme on an individualized manner. After applying our method on several large-scale breast cancer datasets in survival analysis, a subset of these biological processes extracted from corresponding survival model is then found to have significant associations with clinical outcomes. Further analyses of these biological processes enable the study of the interplays between biological processes and cancer phenotypes of interest, provide us valuable insights into cancer biology in biological process level and guide the precision treatment for cancer patients. And notably, prognosis predictions based on our method are consistently superior to the existing state of art methods with the same intention.


title: Designing for Disadvantaged Job Seekers: Insights from Early Investigations

abstract: The Internet plays a pervasive role in job search and employment, especially for professionals and for those who are highly qualified. While job seekers from all occupational groups and employment status rely on aspects of the Internet for employment, past research suggests that disadvantaged job seekers are being "left behind" and will continue to be left behind as the Internet takes on a more eminent role in the employment process. To mitigate this outcome, we extended prior literature and took a user-centered design approach to design and implement a web-based employment application that provides job seekers with resume feedback from local volunteers. We piloted our application to understand: 1) the context and circumstances of our application's shortcomings and 2) UX principles that address these shortcomings. We extend employment research that aims to alleviate the negative effects of technological advancement on disadvantaged job seekers.


title: Image Splicing Forgery Detection Using DCT Coefficients with Multi-Scale LBP

abstract: Image forensics is an active research area due to the large number of shared images online. These images can be easily manipulated with advanced image editing tools and the changes cannot be captured easily by bare human eyes. In this paper, a novel model is proposed based on features extracted from DCT coefficients and Multi-Scale LBP image transform to blindly detect image splicing, where two or more images are combined into one. The experiments were performed on two publicly available datasets CASIA v.1.0 and v2.0. Using k-fold cross validation, several performance measures were computed and compared with other state-of-the-art techniques. The proposed technique has demonstrated improved performance with more than 97.3% accuracy and 0.99 area under the ROC curve.


title: Unbundling the corporation.

abstract: The forces that fractured the computer industry are bearing down on all industries. In the face of changing interaction costs and the new economics of electronic networks, companies must ask themselves the most basic of all questions: what business are we in? In the late 1970s, the computer industry was dominated by huge vertically integrated companies such as IBM, Burroughs, and Digital Equipment. With their vast scale advantages and huge installed bases, they seemed unassailable. Yet just ten years later, power in the industry had shifted: the behemoths were struggling to survive while an army of smaller, highly specialized companies was thriving. What happened? The industry's sea change can be traced back to 1978, when a then-tiny company, Apple Computer, launched the Apple II PC. The Apple II's open architecture unlocked the computer business, creating opportunities for many new companies that specialized in producing specific hardware and software components. Immediately, the advantages of the generalist--size, reputation, integration--began to wither. The new advantages--creativity, speed, flexibility--belonged to the specialist. The story of the computer industry illustrates the crucial role played by interaction costs in shaping industries and companies. Interaction costs represent the money and time expended whenever people and companies exchange goods, services, or ideas [1] The exchanges can occur within companies, among companies, and between companies and customers, and they can take many everyday forms, including management meetings, conferences, telephone conversations, sales calls, reports, and memos. In a real sense, interaction costs are the friction in the economy. Taken together, interaction costs determine the way companies organize themselves and form relationships with other parties. When the interaction costs of undertaking an activity internally are lower than the costs of undertaking it externally, companies tend to incorporate it into their own organization rather than contract it to outside parties. All else being equal, a company will organize itself in whatever way minimizes its overall interaction costs. Apple's open architecture sharply reduced interaction costs in the computer industry. By conforming to a set of well-documented standards, companies could, for the first time, work together easily to produce complementary products and services. As a result, tightly coordinated webs of specialized companies, with names such as Adobe Systems, Apple, Intel, Microsoft, Novell, and Sun Microsystems, could form and ultimately compete effectively against the entrenched, vertically integrated giants. Many of the new companies grew very large very quickly but never lost their focus on specialized activities. The moral of the story is that changes in interaction costs can cause entire industries to reorganize rapidly and dramatically. Today that fact should give all managers pause, for we are on the verge of a broad, systemic reduction in interaction costs throughout the world economy. Electronic networks, combined with powerful PCs, are permitting companies to communicate and exchange data more quickly and cheaply than ever before. As business interactions move on to electronic networks such as the Internet, basic assumptions about corporate organization will be overturned. Activities that companies have always believed to be central to their business will suddenly be offered by new, specialized competitors that can undertake those activities better, faster, and more efficiently. Executives will be forced to ask the most basic and discomforting question about their companies: what business are we really in? The answer will decide their fate in an increasingly frictionless economy. One company, three businesses Look beneath the surface of most companies and you will find three kinds of businesses: a customer relationship business, a product innovation business, and an infrastructure business. …


title: Klopfenstein tapered 2–18 GHz microstrip balun

abstract: Tapered microstrip Balun is used as an impedance transformer network in feeding sections of spiral antennas as it provides impedance transformation over a large range of frequency and also serves the purpose of conversion of single ended port to a symmetric port. The conversion from unbalanced to balanced line relies on a gradual change of cross section of the line. This paper presents a 2–18 GHz microstrip Balun that uses the Klopfenstein equations for the taper. The Balun structure was first simulated in Ansoft HFSS and then fabricated; there was a close match between the simulated and measured results.


User Experience in the Work Domain: A Longitudinal Field Study


title: A universal-input high-power-factor PFC pre-regulator without electrolytic capacitor for PWM dimming LED lighting application

abstract: High brightness white LED has attracted a lot of attention for its high efficacy, simple to drive, environmentally friendly, long lifespan and small size. The power supply for LED lighting also requires long life while maintaining high efficiency, high power factor and low cost. However, a typical design employs electrolytic capacitor as storage capacitor, which is not only bulky, but also with short lifespan, thus hampering the entire LED lighting system. To prolong the lifespan of power supply, it has to use film capacitor with small capacitance to replace electrolytic capacitor. In this paper, a universal input high efficiency, high power factor LED driver is proposed based on the modified SEPIC converter. Along with a relatively large voltage ripple allowable in a PFC design, the proposal of LED lamp driver is able to eliminate the electrolytic capacitor while maintaining high power factor. To increase the efficiency of LED driver, the presented SEPIC-derived converter is modified further as the twin-bus output stage for matching ultra-high efficiency twin-bus LED current regulator. The operation principle and related analysis is described in detail. A 50-W prototype has been built and tested to verify the proposed LED Driver.


title: Mass-produced parts traceability system based on automated scanning of “Fingerprint of Things”

abstract: This paper presents a prototype of parts traceability system which employs the “Fingerprint of Things”-based individual identification technique. Traceability of mass-produced tiny parts such as bolts and nuts are required to ensure quality and safety of big machines. However conventional systems using ID tags or serial marking are not applicable because of quantity and tiny size. To overcome this problem, we propose a tag-less traceability system which uses their appearance images as “fingerprints” to identify each of them. Our traceability system consists of three components; (i) automated fingerprints scanning machine for enrollment, (ii) mobile device for query and (iii) cloud server for identification from database. The key to success of our traceability system is enabling us to capture repeatable image features from the same parts in both of (i) enrollment and (ii) query. To this end, we designed the two lighting mechanisms; one for fast scanning of numerous bolts by automatic feeding, and another for a mobile device to capture one parts by hand. In our experiments, we achieve that 1,000 metal bolts produced with the same mold are perfectly identified by matching their surface images captured with our automatic scanning machine and a smart phone.


title: Predatory fish sounds can alter crab foraging behaviour and influence bivalve abundance

abstract: The risk of predation can have large effects on ecological communities via changes in prey behaviour, morphology and reproduction. Although prey can use a variety of sensory signals to detect predation risk, relatively little is known regarding the effects of predator acoustic cues on prey foraging behaviour. Here we show that an ecologically important marine crab species can detect sound across a range of frequencies, probably in response to particle acceleration. Further, crabs suppress their resource consumption in the presence of experimental acoustic stimuli from multiple predatory fish species, and the sign and strength of this response is similar to that elicited by water-borne chemical cues. When acoustic and chemical cues were combined, consumption differed from expectations based on independent cue effects, suggesting redundancies among cue types. These results highlight that predator acoustic cues may influence prey behaviour across a range of vertebrate and invertebrate taxa, with the potential for cascading effects on resource abundance.


title: A complementing approach for identifying ethical issues in care robotics - grounding ethics in practical use

abstract: We use a long-term study of a robotic eating-aid for disabled users to illustrate how empirical use give rise to a set of ethical issues that might be overlooked in ethic discussions based on theoretical extrapolation of the current state-of-the-art in robotics. This approach provides an important complement to the existing robot ethics by revealing new issues as well as providing actionable guidance for current and future robot design. We discuss our material in relation to the literature on robot ethics, specifically the risk of robots performing care taking tasks and thus causing increased isolation for care recipients. Our data identifies a different set of ethical issues such as independence, privacy, and identity where robotics, if carefully designed and developed, can make positive contributions.


title: Younger Persons are More Likely to Adopt the Mobile Wallet than Older Persons, or are they? The Moderating Role of Age.

abstract: Smartphones, when loaded with a secure software application, can act as a mobile wallet. When face-toface in a retail store, consumers are able to swipe their phone over the terminal and complete payment without having to produce the physical card. The number of terminals that accept contactless payments in the USA is growing as the country moves to the adoption of the ‘chip and pin’ standard. Consumers can choose from wallet providers such as Google, PayPal and ApplePay. This research seeks to explain the factors that influence adoption. Data from an empirical study of US consumers is analyzed with PLS and the influence of trust, word of mouth and facilitating conditions are determined to be significant. After segmenting the data into two groups based on age and with the help of multigroup analysis, age is determined not to be a moderating factor for owners of smartphones.


title: EXTRACTION OF RESPIRATORY RATE FROM PPG SIGNALS USING PCA AND EMD

abstract: Photoplethysmography is a non-invasive electro-optic method developed by Hertzman, which provides information on the blood volume flowing at a particular test site on the body close to the skin. PPG waveform contains two components; one, attributable to the pulsatile component in the vessels, i.e. the arterial pulse, which is caused by the heartbeat, and gives a rapidly alternating signal (AC component). The second one is due to the blood volume and its change in the skin which gives a steady signal that changes very slowly (DC component). PPG signal consists of not only the heart-beat information but also a respiratory signal. Estimation of respiration rates from Photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information.. There have been several efforts on PPG Derived Respiration (PDR), these methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals. PCA identifies patterns in data, and expresses the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing such data. Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications. Test results on PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method.


title: Interdigital sensors and transducers

abstract: This review paper focuses on interdigital electrodes-a geometric structure encountered in a wide variety of sensor and transducer designs. Physical and chemical principles behind the operation of these devices vary so much across different fields of science and technology that the common features present in all devices are often overlooked. This paper attempts to bring under one umbrella capacitive, inductive, dielectric, piezoacoustic, chemical, biological, and microelectromechanical interdigital sensors and transducers. The paper also provides historical perspective, discusses fabrication techniques, modeling of sensor parameters, application examples, and directions of future research.


title: Real Time Robust Template Matching

abstract: One of the most popular methods to extract useful informations from an image sequence is the template matching approach. In this well known method the tracking of a certain feature or target over time is based on the comparison of the content of each image with a sample template. In this article, we propose an efficient robust template matching algorithm that is able to track targets in real time. Special attention is paid to occlusions handling and illumination variations.


title: Opinion Mining on Social Media Data

abstract: Microblogging (Twitter or Facebook) has become a very popular communication tool among Internet users in recent years. Information is generated and managed through either computer or mobile devices by one person and is consumed by many other persons, with most of this user-generated content being textual information. As there are a lot of raw data of people posting real time messages about their opinions on a variety of topics in daily life, it is a worthwhile research endeavor to collect and analyze these data, which may be useful for users or managers to make informed decisions, for example. However this problem is challenging because a micro-blog post is usually very short and colloquial, and traditional opinion mining algorithms do not work well in such type of text. Therefore, in this paper, we propose a new system architecture that can automatically analyze the sentiments of these messages. We combine this system with manually annotated data from Twitter, one of the most popular microblogging platforms, for the task of sentiment analysis. In this system, machines can learn how to automatically extract the set of messages which contain opinions, filter out nonopinion messages and determine their sentiment directions (i.e. positive, negative). Experimental results verify the effectiveness of our system on sentiment analysis in real microblogging applications.


title: Interface Design for Mobile Applications

abstract: Interface design is arguably one of the most important issues in the development of mobile applications. Mobile users often suffer from the poor interface design that seriously hinders the usability of those mobile applications. The major challenge in the interface design of mobile applications is caused by the unique features of mobile devices, such as small screen size, low resolution, and inefficient data entry methods. Therefore, there is a pressing need of theoretical frameworks or guidelines for designing effective and user-friendly interfaces for mobile applications. Based on a comprehensive literature review, this paper proposes a novel framework for the design of effective mobile interfaces. This framework consists of four major components, namely information presentation, data entry methods, mobile users, and context. We also provide a set of practical interface design guidelines and some insights into what factors should be taken into consideration while designing interfaces for mobile applications.


title: Accelerator-centric deep learning systems for enhanced scalability, energy-efficiency, and programmability

abstract: Deep learning (DL) has been successfully deployed in various application domains ranging from computer vision, speech recognition, to natural language processing. As the network models and the datasets used to train these models scale, system architects are faced with new challenges in designing a scalable and energy-efficient high-performance computing (HPC) system for training DL algorithms. One of the key obstacles that DL researchers are facing is the memory capacity bottleneck, where the limited physical memory size of the PCIe-attached DL accelerator (whether it be a discrete GPU or ASIC accelerator like Google's Tensor Processing Unit) constrains the algorithm that can be studied. In this paper, and the associated invited special session talk, we first discuss recent research literature geared towards designing scalable HPC systems for DL. In this context, we then discuss the memory capacity wall problem and introduce the work on virtualized deep neural networks, a memory virtualization solution that systematically reduces the memory consumption of DNN training. We conclude by providing projections on future challenges DNN memory virtualization will encounter upon and suggest accelerator-centric DL system as a promising research direction for the development of a scalable and energy-efficient deep learning system architecture.


title: Evidence for multiple prolactin receptor transcripts in the turkey.

abstract: Multiple prolactin receptor (PRL-R) mRNA transcript isoforms have been identified in mammals, but there are conflicting reports concerning the number of avian PRL-R isoforms. We hypothesized that multiple turkey PRL-R transcript isoforms exist and that PRL-R mRNA abundance may be related to reproductive status. Two turkey PRL-R cDNA fragments were generated using reverse transcriptase polymerase chain reaction (RT-PCR) that displayed a high degree of similarity to mammalian and avian PRL-R. Northern blot analysis of poly A+ mRNA hybridized to a turkey PRL-R riboprobe revealed a 3.1-kb band in the liver, oviduct, and testes. Additional 1.5- and 10.7-kb transcripts were found in the liver and testes, respectively. Hybridization of the same Northern blot to a chicken PRL-R probe verified the presence of a 3.1-kb transcript in all three tissues. A Northern blot was used to examine turkey PRL-R transcript isoform expression in laying hens. A 3.1-kb band was found in the pineal, infundibulum, magnum, isthmus, kidney, and intestine. In addition, 10.7- and 7.3-kb bands were detected in the pineal, magnum, isthmus, and intestine. Turkey PRL-R transcript isoforms were also examined throughout the reproductive cycle. The 10.7-, 7.3-, and 3.1-kb isoforms were detected in the oviduct, intestine, and pineal during each reproductive state. Turkey PRL-R mRNA levels were also compared during the reproductive cycle. Turkey PRL-R mRNA levels were greatest in laying hen pineal glands (P<0.05) and in incubating hen oviducts. This study provides the first evidence for multiple PRL-R mRNA transcript isoforms in turkeys.


title: Games with a purpose

abstract: Through online games, people can collectively solve large-scale computational problems. Such games constitute a general mechanism for using brain power to solve open problems. In fact, designing such a game is much like designing an algorithm - it must be proven correct, its efficiency can be analyzed, a more efficient version can supersede a less efficient one, and so on. "Games with a purpose" have a vast range of applications in areas as diverse as security, computer vision, Internet accessibility, adult content filtering, and Internet search. Any game designed to address these and other problems must ensure that game play results in a correct solution and, at the same time, is enjoyable. People will play such games to be entertained, not to solve a problem - no matter how laudable the objective


title: Programmable analog front-end system for ultrasonic SoC hardware

abstract: This study presents a scheme for enhancing the capabilities of an ultrasonic hardware platform leading to a fully reconfigurable system allowing for dynamic control over the analog front-end. This system presents a robust approach for nondestructive testing, data analysis and imaging applications. The fully programmable analog front-end system proposed in this paper supports up to 8 ultrasonic sensors. The components of this system are dynamically configurable by a Zynq system-on-chip (SoC) module for real-time ultrasonic data acquisition and analysis. This reconfigurable system enables ultrasonic researchers to efficiently prototype different experiments and to incorporate high performance ultrasonic signal and image processing algorithms.


A4C: Anticipatory Asynchronous Advantage Actor-Critic


title: “Emotional” Versus “Emotioneel”: Advertising Language and Emotional Appraisal

abstract: The article contributes to current understanding of language effects in advertising by uncovering a previously ignored mechanism shaping consumer response to an increasingly globalized marketplace. Extending recent psycholinguistic research on the emotions of bilinguals, a series of experiments shows that bilingual consumers report greater perceived emotional intensity for stimuli (e.g. ads) presented in their native language than in their second language.


title: Scalable Distributed Monte-Carlo Tree Search

abstract: Monte-Carlo Tree Search (MCTS) is remarkably successful in two-player games, but parallelizing MCTS has been notoriously difficult to scale well, especially in distributed environments. For a distributed parallel search, transposition-table driven scheduling (TDS) is known to be efficient in several domains. We present a massively parallel MCTS algorithm, that applies the TDS parallelism to the Upper Confidence bound Applied to Trees (UCT) algorithm, which is the most representative MCTS algorithm. To drastically decrease communication overhead, we introduce a reformulation of UCT called Depth-First UCT. The parallel performance of the algorithm is evaluated on clusters using up to 1,200 cores in artificial game-trees. We show that this approach scales well, achieving 740-fold speedups in the best case.


title: Comparison of Three Single-Phase PLL Algorithms for UPS Applications

abstract: In this paper, the performance assessment of three software single-phase phase-locked loop (PLL) algorithms is carried out by means of dynamic analysis and experimental results. Several line disturbances such as phase-angle jump, voltage sag, frequency step, and harmonics are generated by a DSP together with a D/A converter and applied to each PLL. The actual minus the estimated phase-angle values are displayed, providing a refined method for performance evaluation and comparison. Guidelines for parameters adjustments are also presented. In addition, practical implementation issues such as computational delay effects, ride-through, and computational load are addressed. The developed models proved to accurately represent the PLLs under real test conditions.


title: Facial attractiveness

abstract: Humans in societies around the world discriminate between potential mates on the basis of attractiveness in ways that can dramatically affect their lives. From an evolutionary perspective, a reasonable working hypothesis is that the psychological mechanisms underlying attractiveness judgments are adaptations that have evolved in the service of choosing a mate so as to increase gene propagation throughout evolutionary history. The main hypothesis that has directed evolutionary psychology research into facial attractiveness is that these judgments reflect information about what can be broadly defined as an individual's health. This has been investigated by examining whether attractiveness judgments show special design for detecting cues that allow us to make assessments of overall phenotypic condition. This review examines the three major lines of research that have been pursued in order to answer the question of whether attractiveness reflects non-obvious indicators of phenotypic condition. These are studies that have examined facial symmetry, averageness, and secondary sex characteristics as hormone markers.


title: Video summarization by learning submodular mixtures of objectives

abstract: We present a novel method for summarizing raw, casually captured videos. The objective is to create a short summary that still conveys the story. It should thus be both, interesting and representative for the input video. Previous methods often used simplified assumptions and only optimized for one of these goals. Alternatively, they used handdefined objectives that were optimized sequentially by making consecutive hard decisions. This limits their use to a particular setting. Instead, we introduce a new method that (i) uses a supervised approach in order to learn the importance of global characteristics of a summary and (ii) jointly optimizes for multiple objectives and thus creates summaries that posses multiple properties of a good summary. Experiments on two challenging and very diverse datasets demonstrate the effectiveness of our method, where we outperform or match current state-of-the-art.


title: Maximal Sparsity with Deep Networks?

abstract: The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural network layer. Consequently, a lengthy sequence of algorithm iterations can be viewed as a deep network with shared, hand-crafted layer weights. It is therefore quite natural to examine the degree to which a learned network model might act as a viable surrogate for traditional sparse estimation in domains where ample training data is available. While the possibility of a reduced computational budget is readily apparent when a ceiling is imposed on the number of layers, our work primarily focuses on estimation accuracy. In particular, it is well-known that when a signal dictionary has coherent columns, as quantified by a large RIP constant, then most tractable iterative algorithms are unable to find maximally sparse representations. In contrast, we demonstrate both theoretically and empirically the potential for a trained deep network to recover minimal `0-norm representations in regimes where existing methods fail. The resulting system is deployed on a practical photometric stereo estimation problem, where the goal is to remove sparse outliers that can disrupt the estimation of surface normals from a 3D scene.


title: Selective pressures on the once and future contents of ethnic stereotypes: Effects of the communicability of traits.

abstract: It is hypothesized that traits that are most likely to be the subject of social discourse (i.e., most communicable) are most likely to persist in ethnic stereotypes over time and that this effect is moderated by the extent to which an ethnic group is the subject of social discourse. Study 1 yielded communicability ratings of 76 traits. Study 2 tested the relation between a trait's communicability and its presence in stereotypes of 4 Canadian ethnic groups. Study 3 tested the relation between a trait's communicability and its persistence over time in stereotypes of 8 American ethnic groups. Results supported the hypotheses. A communication-based analysis of stereotypes appears helpful in predicting persistence and change in the contents of stereotypes of real groups in the real world.


title: Regulation of autophagy by cytoplasmic p53

abstract: Multiple cellular stressors, including activation of the tumour suppressor p53, can stimulate autophagy. Here we show that knockout, knockdown or pharmacological inhibition of p53 can induce autophagy in human, mouse and nematode cells. Enhanced autophagy improved the survival of p53-deficient cancer cells under conditions of hypoxia and nutrient depletion, allowing them to maintain high ATP levels. Inhibition of p53 led to autophagy in enucleated cells, and cytoplasmic, not nuclear, p53 was able to repress the enhanced autophagy of p53(-/-)cells. Many different inducers of autophagy (for example, starvation, rapamycin and toxins affecting the endoplasmic reticulum) stimulated proteasome-mediated degradation of p53 through a pathway relying on the E3 ubiquitin ligase HDM2. Inhibition of p53 degradation prevented the activation of autophagy in several cell lines, in response to several distinct stimuli. These results provide evidence of a key signalling pathway that links autophagy to the cancer-associated dysregulation of p53.


Interpersonal attraction in exchange and communal relationships.


title: Experiments with hierarchical reinforcement learning of multiple grasping policies

abstract: Robotic grasping has attracted considerable interest, but it still remains a challenging task. The data-driven approach is a promising solution to the robotic grasping problem; this approach leverages a grasp dataset and generalizes grasps for various objects. However, these methods often depend on the quality of the given datasets, which are not trivial to obtain with sufficient quality. Although reinforcement learning approaches have been recently used to achieve autonomous collection of grasp datasets, the existing algorithms are often limited to specific grasp types. In this paper, we present a framework for hierarchical reinforcement learning of grasping policies. In our framework, the lower-level hierarchy learns multiple grasp types, and the upper-level hierarchy learns a policy to select from the learned grasp types according to a point cloud of a new object. Through experiments, we validate that our approach learns grasping by constructing the grasp dataset autonomously. The experimental results show that our approach learns multiple grasping policies and generalizes the learned grasps by using local point cloud information.


title: Reach almost sure consensus with only group information

abstract: This brief presents a new distributed scheme to solve the consensus problem for a group of agents if neither their absolute states nor inter-agent relative states are available. The new scheme considers a random partition of agents into two subgroups at each step and then uses the relative group representative state as feedback information for the consensus purpose. It is then shown that almost sure consensus can be achieved under the proposed scheme in both discrete time and continuous time. For the discrete time case, almost sure consensus is achieved if and only if the weighting parameter for state update is greater than one. For the continuous time case, almost sure consensus is realized when the weighting parameter is positive. Moreover, it is shown that if a uniform probability is considered for group selection, then the group of agents can reach average consensus in mean.