title: Dissociating explicit and procedural-learning based systems of perceptual category learning

abstract: A fundamental question is whether people have available one category learning system, or many. Most multiple systems advocates postulate one explicit and one implicit system. Although there is much agreement about the nature of the explicit system, there is less agreement about the nature of the implicit system. In this article, we review a dual systems theory of category learning called competition between verbal and implicit systems (COVIS) developed by Ashby et al. (1998). The explicit system dominates the learning of verbalizable, rule-based category structures and is mediated by frontal brain areas such as the anterior cingulate, prefrontal cortex (PFC), and head of the caudate nucleus. The implicit system, which uses procedural learning, dominates the learning of non-verbalizable, information-integration category structures, and is mediated by the tail of the caudate nucleus and a dopamine-mediated reward signal. We review nine studies that test six a priori predictions from COVIS, each of which is supported by the data.


title: Gullstrand's optical schematic system of the eye--modified by Vojniković & Tamajo.

abstract: According to early examination (Vojniković, 1978) estimated, that the index of refraction for aqueous and vitreous is not the sum (1.33 or 1.66), than is for aqueous n = 1.334, and for vitreous N = 1.336. This measurements are made with Abbe's refractometer, by temperature of 33 degrees C for aqueous, and 36 degrees C for vitreous, as it's in human eye. In the principle of these results, the authors calculated the new values of cardinal points for the eye, and compared with Gullstrand's optical schematic eye. So, the refractive power for the eye F = 59.98 D, first focal length f1 = -16.67 mm and second focal length f2 = +22.27 mm


title: Information visualization to support management decisions

abstract: Information visualization can accelerate perception, provide insight and control, and harness this flood of valuable data to gain a competitive advantage in making business decisions. Although such a statement seems to be obvious, there is a lack in the literature of practical evidence of the benefit of information visualization. The main contribution of this paper is to illustrate how, for a major European apparel retailer, the visualization of performance information plays a critical role in improving business decisions and in extracting insights from Redio Frequency Idetification (RFID)-based performance measures. In this paper, we identify — based on a literature review — three fundamental managerial functions of information visualization, namely as: a communication medium, a knowledge management means, and a decision-support instrument. Then, we provide — based on real industrial case evidence — how information visualization supports business decision-making. Several examples are provided to evidence the benefit of information visualization through its three identified managerial functions. We find that — depending on the way performance information is shaped, communicated, and made interactive — it not only helps decision making, but also offers a means of knowledge creation, as well as an appropriate communication channel.


title: Investigating Business Intelligence in the era of Big Data: concepts, benefits and challenges

abstract: Business intelligence suppose retrieving value from data floating in the organization environment. It provides methods and tools for collecting, storing, formatting and analyzing data for the purpose of helping managers in decision-making. At the start, only data from enterprise internal activities were examined. Now and in this turbulent business environment, organizations should incorporate analysis of the huge amount of external data gathered from multifarious sources. It is argued that BI systems accuracy depends on quantity of data at their disposal, yet some storage and analysis methods are phased out and should be reviewed by academics and practitioners. This paper presents an overview of BI challenges in the context of Big Data (BD) and some available solutions provided, either by using Cloud Computing (CC) or improving Data Warehouse (DW) efficiency.


title: Platform competition in two-sided markets

abstract: Many if not most markets with network externalities are two-sided. To succeed, platforms in industries such as software, portals and media, payment systems and the Internet, must â€œget both sides of the market on board.â€


title: Comparative microRNA detection from precursor-microRNA-transfected hepatocellular carcinoma cells by capillary electrophoresis with dual-color laser-induced fluorescence.

abstract: A dual-LIF (dLIF) setup combined with CE for microRNA (miRNA) detection is proposed in this study. An argon ion laser (488 nm) and a solid state laser (640 nm) were chosen to excite the fluorescent dye-labeled DNA probe after splinted ligation of miRNA. The crosstalk of emission spectrum of Alex Fluor 488 and Alex Fluor 647 is minimized with a zero crosstalk matrix for Alex Fluor 647 to 488 channels. The linear ranges of the device for the fluorescent dye-labeled DNA probe were both from 1.0 nM to 0.1 pM. The limits of detection for Alexa Fluor 488-labeled DNA and Alex Fluor 647-labeled DNA were 9.3 and 31 fM, respectively. The detection of specific miRNA has been accomplished by combining splinted ligation with the fluorescent dye-labeled oligonucleotides. The linear range for the synthetic miRNA is from 1.0 nM to 1.0 pM. Without PCR amplification, CE-dLIF was applied to discriminate a pre-miR-10b*-transfected cells (contains precursor miR-10b*) from hepatocellular carcinoma cell (control cells). Therefore, this result indicates CE-dLIF has great potential to provide a rapid comparative assay for miRNAs detection.


title: Renewable Energy

abstract: The Energy Committee of the Royal Swedish Academy of Sciences has in a series of projects gathered information and knowledge on renewable energy from various sources, both within and outside the academic world. In this article, we synthesize and summarize some of the main points on renewable energy from the various Energy Committee projects and the Committee's Energy 2050 symposium, regarding energy from water and wind, bioenergy, and solar energy. We further summarize the Energy Committee's scenario estimates of future renewable energy contributions to the global energy system, and other presentations given at the Energy 2050 symposium. In general, international coordination and investment in energy research and development is crucial to enable future reliance on renewable energy sources with minimal fossil fuel use.


title: Language learning: Cues or rules?

abstract: Child language researchers have often taken gender and case paradigms to be interesting test cases for theories of language learning. In this paper we develop a computational model of the acquisition of the gender, number, and case paradigm for the German definite article. The computational formalism used is a connectionist algorithm developed by Rumelhart, Hinton, and Williams (1986. In D. Rumelhart & J. McClelland (Eds.), Parallel Distributed Processing; Explorations in the Microstructure of Cognition. Cambridge, MA: MIT Press). Three models are developed. In the first two, various cues to gender studied by Kopcke and Zubin (1983, Zeitschrift fur germanistiche Linguistik, 11, 166–182; 1984, Linguistiche Berichte, 93, 26–50) are entered by hand. In the third, the simulation is given only the raw phonological features of the stem. Despite the elimination of the hand-crafting of the units, the third model outperformed the first two in both training and generalization. All three models showed a good match to the developmental data of Mills (1986, The acquisition of gender: a study of English and German. Berlin: Springer-Verlag) and MacWhinney (1978, Monographs of the Society for Research in Child Development, 43, whole No. 1). Advantages of a connectionist approach over older theories are discussed.


title: An augmented reality application with hand gestures for learning 3D geometry

abstract: Geometry is an interesting area of mathematics. It opens to many different approaches and closely relates to our everyday lives. However, when students recall their experiences of learning geometry, many of them regards it as not only unpleasant experiences but often also difficult experiences. The traditional materials and tools such as pens, papers, blackboards, textbooks and/or classical methodologies like drawing, narrative teaching stills cannot be regarded as a great support for students who learn geometry. In this paper, we propose a framework for learning geometry using a software tool based on augmented reality (AR) [1] and hand gestures recognition technologies [2]. These technologies are combined into a system that can address some current issues in geometry education and provide students with an easier way for studying geometry.


title: Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading

abstract: We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as “powergrading.” We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small “budget” of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.


title: Differentially private summaries for sparse data

abstract: Differential privacy is fast becoming the method of choice for releasing data under strong privacy guarantees. A standard mechanism is to add noise to the counts in contingency tables derived from the dataset. However, when the dataset is sparse in its underlying domain, this vastly increases the size of the published data, to the point of making the mechanism infeasible. We propose a general framework to overcome this problem. Our approach releases a compact summary of the noisy data with the same privacy guarantee and with similar utility. Our main result is an efficient method for computing the summary directly from the input data, without materializing the vast noisy data. We instantiate this general framework for several summarization methods. Our experiments show that this is a highly practical solution: The summaries are up to 1000 times smaller, and can be computed in less than 1% of the time compared to standard methods. Finally, our framework works with various data transformations, such as wavelets or sketches.


title: Max-margin Deep Generative Models

abstract: Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, little work has been done on examining or empowering the discriminative ability of DGMs on making accurate predictions. This paper presents max-margin deep generative models (mmDGMs), which explore the strongly discriminative principle of max-margin learning to improve the discriminative power of DGMs, while retaining the generative capability. We develop an efficient doubly stochastic subgradient algorithm for the piecewise linear objective. Empirical results on MNIST and SVHN datasets demonstrate that (1) max-margin learning can significantly improve the prediction performance of DGMs and meanwhile retain the generative ability; and (2) mmDGMs are competitive to the state-of-the-art fully discriminative networks by employing deep convolutional neural networks (CNNs) as both recognition and generative models.


2 cross-cultural psychology of consumer behavior


title: Predicting Abnormal Returns From News Using Text Classification

abstract: We show how text from news articles can be used to predict intraday price movements of financial assets using support vector machines. Multiple kernel learning is used to combine equity returns with text as predictive features to increase classification performance and we develop an analytic center cutting plane method to solve the kernel learning problem efficiently. We observe that while the direction of returns is not predictable using either text or returns, their size is, with text features producing significantly better performance than historical returns alone.


title: A review of analgesic and emotive breathing: a multidisciplinary approach

abstract: The diaphragm is the primary muscle involved in breathing and other non-primarily respiratory functions such as the maintenance of correct posture and lumbar and sacroiliac movement. It intervenes to facilitate cleaning of the upper airways through coughing, facilitates the evacuation of the intestines, and promotes the redistribution of the body’s blood. The diaphragm also has the ability to affect the perception of pain and the emotional state of the patient, functions that are the subject of this article. The aim of this article is to gather for the first time, within a single text, information on the nonrespiratory functions of the diaphragm muscle and its analgesic and emotional response functions. It also aims to highlight and reflect on the fact that when the diaphragm is treated manually, a daily occurrence for manual operators, it is not just an area of musculature that is treated but the entire body, including the psyche. This reflection allows for a multidisciplinary approach to the diaphragm and the collaboration of various medical and nonmedical practitioners, with the ultimate goal of regaining or improving the patient’s physical and mental well-being.


title: Augmented Vehicular Reality: Enabling Extended Vision for Future Vehicles

abstract: Like today's autonomous vehicle prototypes, vehicles in the future will have rich sensors to map and identify objects in the environment. For example, many autonomous vehicle prototypes today come with line-of-sight depth perception sensors like 3D cameras. These cameras are used for improving vehicular safety in autonomous driving, but have fundamentally limited visibility due to occlusions, sensing range, and extreme weather and lighting conditions. To improve visibility and performance, not just for autonomous vehicles but for other Advanced Driving Assistance Systems (ADAS), we explore a capability called Augmented Vehicular Reality (AVR). AVR broadens the vehicle's visual horizon by enabling it to share visual information with other nearby vehicles, but requires careful techniques to align coordinate frames of reference, and to detect dynamic objects. Preliminary evaluations hint at the feasibility of AVR and also highlight research challenges in achieving AVR's potential to improve autonomous vehicles and ADAS.


title: Transparent user models for personalization

abstract: Personalization is a ubiquitous phenomenon in our daily online experience. While such technology is critical for helping us combat the overload of information we face, in many cases, we may not even realize that our results are being tailored to our personal tastes and preferences. Worse yet, when such a system makes a mistake, we have little recourse to correct it. In this work, we propose a framework for addressing this problem by developing a new user-interpretable feature set upon which to base personalized recommendations. These features, which we call badges, represent fundamental traits of users (e.g., "vegetarian" or "Apple fanboy") inferred by modeling the interplay between a user's behavior and self-reported identity. Specifically, we consider the microblogging site Twitter, where users provide short descriptions of themselves in their profiles, as well as perform actions such as tweeting and retweeting. Our approach is based on the insight that we can define badges using high precision, low recall rules (e.g., "Twitter profile contains the phrase 'Apple fanboy'"), and with enough data, generalize to other users by observing shared behavior. We develop a fully Bayesian, generative model that describes this interaction, while allowing us to avoid the pitfalls associated with having positive-only data. Experiments on real Twitter data demonstrate the effectiveness of our model at capturing rich and interpretable user traits that can be used to provide transparency for personalization.


Comparing Time-Series Clustering Algorithms in R Using the dtwclust Package


title: Effects of a cognitive-behavioral exposure-based body image therapy for overweight females with binge eating disorder: a pilot study

abstract: Background ::: Although not part of the diagnostic criteria of the DSM-5, body image disturbance seems to be a relevant feature of Binge Eating Disorder (BED) as well as of other eating disorders such as Anorexia Nervosa (AN) or Bulimia Nervosa (BN). Hence, the aim of the present pilot study was to assess the changeability of body image disturbance in a sample of overweight females with BED by a cognitive-behavioral treatment, directly addressing body image disturbance. ::: ::: ::: Method ::: Overweight females (N = 34) with BED were randomized to a manualized body image therapy or a waiting-list control group. The final sample consisted of n = 15 participants in the intervention group and n = 19 in the control group due to two drop-outs in the control condition. Before and after the intervention or the waiting period, respectively, participants filled out a questionnaire battery assessing several body image and eating disorder related features. To quantify the perceptual component of body image disturbance, a digital photo distortion technique based on a picture of each participant taken in a standardized suit was applied. ::: ::: ::: Results ::: In a two-way ANOVA, significant Time × Group interactions were found for eating and shape concerns, drive for thinness, body dissatisfaction, depressiveness and low self-esteem. Follow-up t-tests indicated a significant symptom reduction of a generally high magnitude in the intervention group. No significant changes concerning body checking and the estimations of one's own "real", "felt" and "ideal" body dimensions were found. ::: ::: ::: Conclusion ::: The strong symptom reduction in the cognitive-affective component of body image disturbance indicates that an exposure-based cognitive-behavioral body image intervention is a promising treatment module for overweight females with BED, but future research with a larger sample size is needed to quantify possible changes in all components of body image.


title: Efficient Orchestration Mechanisms for Congestion Mitigation in NFV: Models and Algorithms

abstract: Network Functions Virtualization (NFV) has recently gained momentum among network operators as a means to share their physical infrastructure among virtual operators, which can independently compose and configure their communication services. However, the spatio-temporal correlation of traffic demands and computational loads can result in high congestion and low network performance for virtual operators, thus leading to service level agreement breaches. In this paper, we analyze the congestion resulting from the sharing of the physical infrastructure and propose innovative orchestration mechanisms based on both centralized and distributed approaches, aimed at unleashing the potential of the NFV technology. In particular, we first formulate the network functions composition problem as a non-linear optimization model to accurately capture the congestion of physical resources. To further simplify the network management, we also propose a dynamic pricing strategy of network resources, proving that the resulting system achieves a stable equilibrium in a completely distributed fashion, even when all virtual operators independently select their best network configuration. Numerical results show that the proposed approaches consistently reduce resource congestion. Furthermore, the distributed solution well approaches the performance that can be achieved using a centralized network orchestration system.


title: Classification of tongue images based on doublet and color space dictionary

abstract: Recently, pathological diagnosis plays a crucial role in many areas of medicine, and some researchers have proposed many models and algorithms for improving classification accuracy by extracting excellent feature or modifying the classifier. They have also achieved excellent results on pathological diagnosis using tongue images. However, pixel values can't express intuitive features of tongue images and different classifiers for training samples have different adaptability. Accordingly, this paper presents a robust approach to infer the pathological characteristics by observing tongue images. Our proposed method makes full use of the local information and similarity of tongue images. Firstly, tongue images in RGB color space are converted to Lab. Then, we compute tongue statistics information. In the calculation process, Lab space dictionary is created at first, through it, we compute statistic value for each dictionary value. After that, a method based on Doublets is taken for feature optimization. At last, we use XGBOOST classifier to predict the categories of tongue images. We achieve classification accuracy of 95.39% using statistics feature and the improved classifier, which is helpful for TCM (Traditional Chinese Medicine) diagnosis.


title: Power-Up SRAM State as an Identifying Fingerprint and Source of True Random Numbers

abstract: Intermittently powered applications create a need for low-cost security and privacy in potentially hostile environments, supported by primitives including identification and random number generation. Our measurements show that power-up of SRAM produces a physical fingerprint. We propose a system of fingerprint extraction and random numbers in SRAM (FERNS) that harvests static identity and randomness from existing volatile CMOS memory without requiring any dedicated circuitry. The identity results from manufacture-time physically random device threshold voltage mismatch, and the random numbers result from runtime physically random noise. We use experimental data from high-performance SRAM chips and the embedded SRAM of the WISP UHF RFID tag to validate the principles behind FERNS. For the SRAM chip, we demonstrate that 8-byte fingerprints can uniquely identify circuits among a population of 5,120 instances and extrapolate that 24-byte fingerprints would uniquely identify all instances ever produced. Using a smaller population, we demonstrate similar identifying ability from the embedded SRAM. In addition to identification, we show that SRAM fingerprints capture noise, enabling true random number generation. We demonstrate that a 512-byte SRAM fingerprint contains sufficient entropy to generate 128-bit true random numbers and that the generated numbers pass the NIST tests for runs, approximate entropy, and block frequency.


title: COGNITIVE NEUROSCIENCE OF HUMAN MEMORY

abstract: Current knowledge is summarized about long-term memory systems of the human brain, with memory systems defined as specific neural networks that support specific mnemonic processes. The summary integrates convergent evidence from neuropsychological studies of patients with brain lesions and from functional neuroimaging studies using positron emission tomography (PET) or functional magnetic resonance imaging (fMRI). Evidence is reviewed about the specific roles of hippocampal and parahippocampal regions, the amygdala, the basal ganglia, and various neocortical areas in declarative memory. Evidence is also reviewed about which brain regions mediate specific kinds of procedural memory, including sensorimotor, perceptual, and cognitive skill learning; perceptual and conceptual repetition priming; and several forms of conditioning. Findings are discussed in terms of the functional neural architecture of normal memory, age-related changes in memory performance, and neurological conditions that affect memory such as amnesia, Alzheimeris disease, Parkinsonis disease, and Huntingtonis disease.


title: Research information in nurses' clinical decision-making: what is useful?

abstract: Research information in nurses’ clinical decision-making: what is useful? ::: ::: Aim. To examine those sources of information which nurses find useful for reducing the uncertainty associated with their clinical decisions. ::: ::: Background. Nursing research has concentrated almost exclusively on the concept of research implementation. Few, if any, papers examine the use of research knowledge in the context of clinical decision-making. There is a need to establish how useful nurses perceive information sources are, for reducing the uncertainties they face when making clinical decisions. ::: ::: Design. Cross-case analysis involving qualitative interviews, observation, documentary audit and Q methodological modelling of shared subjectivities amongst nurses. The case sites were three large acute hospitals in the north of England, United Kingdom. One hundred and eight nurses were interviewed, 61 of whom were also observed for a total of 180 hours and 122 nurses were involved in the Q modelling exercise. ::: ::: Results. Text-based and electronic sources of research-based information yielded only small amounts of utility for practising clinicians. Despite isolating four significantly different perspectives on what sources were useful for clinical decision-making, it was human sources of information for practice that were overwhelmingly perceived as the most useful in reducing the clinical uncertainties of nurse decision-makers. ::: ::: Conclusions. It is not research knowledge per se that carries little weight in the clinical decisions of nurses, but rather the medium through which it is delivered. Specifically, text-based and electronic resources are not viewed as useful by nurses engaged in making decisions in real time, in real practice, but those individuals who represent a trusted and clinically credible source are. More research needs to be carried out on the qualities of people regarded as clinically important information agents (specifically, those in clinical nurse specialist and associated roles) whose messages for practice appear so useful for clinicians.


title: Review on wrapper feature selection approaches

abstract: The main objective of feature selection process consists of investigating the optimal feature subset leading to better classification quality while spending less computational cost compared to maintaining the whole initial feature set. The problem of feature selection has been extensively researched since the early beginnings of machine learning. Even though several methods were proposed to handle the issue of feature selection using a variety of techniques, it is difficult to identify a specific method as the most fitted one regarding the feature subset selection issue. In this study, we provide an overview of existing wrapper methods pointing out their weaknesses and their strengths.


title: Diamonds in the rough: Social media visual analytics for journalistic inquiry

abstract: Journalists increasingly turn to social media sources such as Facebook or Twitter to support their coverage of various news events. For large-scale events such as televised debates and speeches, the amount of content on social media can easily become overwhelming, yet still contain information that may aid and augment reporting via individual content items as well as via aggregate information from the crowd's response. In this work we present a visual analytic tool, Vox Civitas, designed to help journalists and media professionals extract news value from large-scale aggregations of social media content around broadcast events. We discuss the design of the tool, present the text analysis techniques used to enable the presentation, and provide details on the visual and interaction design. We provide an exploratory evaluation based on a user study in which journalists interacted with the system to explore and report on a dataset of over one hundred thousand twitter messages collected during the U.S. State of the Union presidential address in 2010.


title: On-road vehicle detection: a review

abstract: Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.


title: Automated Image Registration Based on Pseudoinvariant Metrics of Dynamic Land-Surface Features

abstract: Accurate assessment of land-cover/land-use change is essential for understanding the impacts of global change and necessitates the use of satellite data. Satellite change detection requires large volumes of multitemporal images to be precisely registered. Image registration is particularly difficult in dynamic (i.e., rapidly time varying) landscapes since the changes themselves interfere with the process of tie-point identification. Despite the existence of sophisticated registration algorithms, it is still problematic to register images acquired over such areas due to a dearth of stable features. Hence, we propose an automated image registration method using tie points derived from pseudoinvariant features (PIFs) and apply the method to register satellite images for hydrologic change detection in the Arctic, where abundant shallow lakes dominate the landscape but change significantly over time. A key to the method is the identification of ldquoshape-stablerdquo lakes as PIFs, which preserve their geometric shape even though the shorelines may migrate significantly. The proposed method automatically identifies PIFs based on scale-invariant shape descriptors and employs their center points for establishing the registration model. Our method thus consists of water-body feature extraction, PIF detection based on feature shape criteria, and image registration using tie points derived from the PIFs. The approach is used to register 1978 and 2000 Landsat images in Alaska, where lakes dominate the landscape and change significantly over time. The performance of the proposed approach is evaluated quantitatively, and a high subpixel registration accuracy of 0.66 pixel at Enhanced Thematic Mapper Plus resolution (i.e., 19 m) is achieved. A comparative evaluation indicates that the proposed approach outcompetes the conventional manual tie-point selection method and automated image registration techniques based on fast Fourier transform.


Security Issues and Solutions for Cloud Computing


title: Predicting Trust and Distrust in Social Networks

abstract: As user-generated content and interactions have overtaken the web as the default mode of use, questions of whom and what to trust have become increasingly important. Fortunately, online social networks and social media have made it easy for users to indicate whom they trust and whom they do not. However, this does not solve the problem since each user is only likely to know a tiny fraction of other users, we must have methods for inferring trust - and distrust - between users who do not know one another. In this paper, we present a new method for computing both trust and distrust (i.e., positive and negative trust). We do this by combining an inference algorithm that relies on a probabilistic interpretation of trust based on random graphs with a modified spring-embedding algorithm. Our algorithm correctly classifies hidden trust edges as positive or negative with high accuracy. These results are useful in a wide range of social web applications where trust is important to user behavior and satisfaction.


title: The Tapered TEM Horn Antenna Design for EMC and Radiation Measurement

abstract: The semi-rigid cable for microwave band as a feeding network structure that effectively excited tapered TEM horn antenna geometry is proposed. The semi-rigid cable to parallel waveguide transition then to fed tapered TEM horn antenna. The designed tapered TEM horn antenna has the merits such as wideband, simple feeding network, low profile compact size with fairly good antenna performances such as return loss, peak gain and radiation patterns.


title: A Comparison of Online Trust Building Factors between Potential Customers and Repeat Customers

abstract: While vendors on the Internet may have enjoyed an increase in the number of clicks on their Web sites, they have also faced disappointments in converting these clicks into purchases. Lack of trust is identified as one of the greatest barriers inhibiting Internet transactions. Thus, it is essential to understand how trust is created and how it evolves in the Electronic Commerce (EC) context throughout a customer’s purchase experience with an Internet store. As the first step in studying the dynamics of online trust building, this research aims to compare online trust-building factors between potential customers and repeat customers. For this purpose, we classify trust in an Internet store into potential customer trust and repeat customer trust, depending on the customer’s purchase experience with the store. We find that trust building differs between potential customers and repeat customers in terms of antecedents. We also compare the effects of shared antecedents on trust between potential customers and repeat customers. We find that customer satisfaction has a stronger effect on trust building for repeat


title: Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries

abstract: According to official statistics, cancer is considered as the second leading cause of human fatalities. Among different types of cancer, brain tumor is seen as one of the deadliest forms due to its aggressive nature, heterogeneous characteristics, and low relative survival rate. Determining the type of brain tumor has significant impact on the treatment choice and patient's survival. Human-centered diagnosis is typically error-prone and unreliable resulting in a recent surge of interest to automatize this process using convolutional neural networks (CNNs). CNNs, however, fail to fully utilize spatial relations, which is particularly harmful for tumor classification, as the relation between the tumor and its surrounding tissue is a critical indicator of the tumor's type. In our recent work, we have incorporated newly developed CapsNets to overcome this shortcoming. CapsNets are, however, highly sensitive to the miscellaneous image background. The paper addresses this gap. The main contribution is to equip CapsNet with access to the tumor surrounding tissues, without distracting it from the main target. A modified CapsNet architecture is, therefore, proposed for brain tumor classification, which takes the tumor coarse boundaries as extra inputs within its pipeline to increase the CapsNet's focus. The proposed approach noticeably outperforms its counterparts.


title: Conditional Restricted Boltzmann Machines for Structured Output Prediction

abstract: Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic models that have recently been applied to a wide range of problems, including collaborative filtering, classification, and modeling motion capture data. While much progress has been made in training non-conditional RBMs, these algorithms are not applicable to conditional models and there has been almost no work on training and generating predictions from conditional RBMs for structured output problems. We first argue that standard Contrastive Divergence-based learning may not be suitable for training CRBMs. We then identify two distinct types of structured output prediction problems and propose an improved learning algorithm for each. The first problem type is one where the output space has arbitrary structure but the set of likely output configurations is relatively small, such as in multi-label classification. The second problem is one where the output space is arbitrarily structured but where the output space variability is much greater, such as in image denoising or pixel labeling. We show that the new learning algorithms can work much better than Contrastive Divergence on both types of problems.


Simulation of Temperature Distribution Inhomogeneity in Aluminum Alloy During Hot Rolling


title: Keratosis Pilaris and its Subtypes: Associations, New Molecular and Pharmacologic Etiologies, and Therapeutic Options

abstract: Keratosis pilaris is a common skin disorder comprising less common variants and rare subtypes, including keratosis pilaris rubra, erythromelanosis follicularis faciei et colli, and the spectrum of keratosis pilaris atrophicans. Data, and critical analysis of existing data, are lacking, so the etiologies, pathogeneses, disease associations, and treatments of these clinical entities are poorly understood. The present article aims to fill this knowledge gap by reviewing literature in the PubMed, EMBASE, and CINAHL databases and providing a comprehensive, analytical summary of the clinical characteristics and pathophysiology of keratosis pilaris and its subtypes through the lens of disease associations, genetics, and pharmacologic etiologies. Histopathologic, genomic, and epidemiologic evidence points to keratosis pilaris as a primary disorder of the pilosebaceous unit as a result of inherited mutations or acquired disruptions in various biomolecular pathways. Recent data highlight aberrant Ras signaling as an important contributor to the pathophysiology of keratosis pilaris and its subtypes. We also evaluate data on treatments for keratosis pilaris and its subtypes, including topical, systemic, and energy-based therapies. The effectiveness of various types of lasers in treating keratosis pilaris and its subtypes deserves wider recognition.


Room Temperature Gas Sensing Using Pure and Modified Metal Oxide Nanowires


title: Hierarchical Attentive Recurrent Tracking

abstract: Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate"where"and"what"processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos. The first layer of attention discards the majority of background by selecting a region containing the object of interest, while the subsequent layers tune in on visual features particular to the tracked object. This framework is fully differentiable and can be trained in a purely data driven fashion by gradient methods. To improve training convergence, we augment the loss function with terms for a number of auxiliary tasks relevant for tracking. Evaluation of the proposed model is performed on two datasets: pedestrian tracking on the KTH activity recognition dataset and the more difficult KITTI object tracking dataset.


title: Towards a Linked-Data based Visualization Wizard

abstract: Datasets published in the LOD cloud are recommended to follow some best practice in order to be 4-5 stars Linked Data compliant. They can often be consumed and accessed by different means such as API access, bulk download or as linked data fragments, but most of the time, a SPARQL endpoint is also provided. While the LOD cloud keeps growing, having a quick glimpse of those datasets is getting harder and there is a need to develop new methods enabling to detect automatically what an arbitrary dataset is about and to recommend visualizations for data samples. We consider that "a visualization is worth a million triples", and in this paper, we propose a novel approach that mines the content of datasets and automatically generates visualizations. Our approach is directly based on the usage of SPARQL queries that will detect the important categories of a dataset and that will specifically consider the properties used by the objects which have been interlinked via owl:sameAs links. We then propose to associate type of visualization for those categories. We have implemented this approach into a so-called Linked Data Vizualization Wizard (LDVizWiz).


title: An image-rejection down-converter for low-IF receivers

abstract: Implemented in 4.1 mm2 in 130-nm CMOS, a dual-conversion image-rejection down-converter applied to the wireless code-division multiple-access (WCDMA) standard draws 13 mA from a single 1.8-V power supply. Using an IF of 2.5 MHz, the WCDMA image-rejection band is widened from 0.58-4.42 MHz to 0.48-5.2 MHz-a bandwidth ratio of 10.8-to accommodate process, voltage, and temperature variations in high-volume production. Ten samples are measured and fully characterized: average image-rejection ratio (IRR) is 46.6 dBc, gain is 12 dB, and noise figure is 10.8 dB. Typical IRR is 44.8, 46.5, and 47.5 dBc at -20degC, 25degC, and 80degC, respectively


title: The 10 deadly sins of information security management

abstract: This paper identifies 10 essential aspects, which, if not taken into account in an information security governance plan, will surely cause the plan to fail, or at least, cause serious flaws in the plan. These 10 aspects can be used as a checklist by management to ensure that a comprehensive plan has been defined and introduced.


title: Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective

abstract: The rapid progress of mobile, wireless communication and sensing technologies has enabled the development of context-aware ubiquitous learning (u-learning) environments, which are able to detect the real-world learning status of students as well as the environmental contexts. Accordingly, appropriate information can be provided to individual students in the right place and at the right time. However, researchers have indicated that, to support students to learn in real-world contexts in smart ways, more factors need to be taken into account when designing and developing learning systems. In this paper, the definition and criteria of smart learning environments are presented from the perspective of context-aware ubiquitous learning. A framework is also presented to address the design and development considerations of smart learning environments to support both online and real-world learning activities. Moreover, some emerging technologies that might facilitate the development of smart learning environments as well as the features and criteria of smart learning are addressed. Finally, research issues related to smart learning are provided.


title: Dentin demineralization inhibition at restoration margins of Vitremer, Dyract and Compoglass.

abstract: PURPOSE ::: To examine the in vitro caries inhibition of a resin-modified glass ionomer cement (Vitremer-3M) and two compomers (Dyract-Dentsply; Compoglass-Ivoclar). ::: ::: ::: MATERIALS AND METHODS ::: Standardized Class V preparations were placed in 40 molars, the gingival margin placed below the cementoenamel junction. Randomly, 10 Vitremer, 10 Dyract and 10 Compoglass restorations were placed according to manufacturer's instructions, in 30 teeth. Ten teeth received P-50 composite resin (3M) restorations and acted as the control. All teeth had an acid-resistant varnish placed to within 1 mm of restoration margins and they were then placed into artificial saliva for 4 weeks, the saliva being replenished every 48 hours. All teeth were subjected to an artificial caries challenge (pH 4.4) for 5 days. Sections of 100 microns were obtained, photographed under polarized light microscopy, and then digitized to quantitate demineralized areas adjacent to the restoration. ::: ::: ::: RESULTS ::: The mean (+/- S.D) area (microns 2) demineralization 100 microns from the dentin/gingival margin was: Vitremer 4965 +/- 841, Compoglass 4981 +/- 2209, Dyract 5375 +/- 516, P-50 8088 +/- 2083. ANOVA and Duncan's (P < 0.05) indicated all three fluoride-releasing materials examined in this study had significantly less demineralization adjacent to restoration margins than the P-50 composite resin control. Seventy percent of glass ionomer cement restorations demonstrated adjacent dentin inhibition zones, while no dentin inhibition zones were demonstrated with the compomer restorations.


Technology Orientation , Dynamic Capabilities and SMEs Performance


Bootstrapping Entity Alignment with Knowledge Graph Embedding


title: A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

abstract: Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.


title: Low-cost solutions for preventing simple side-channel analysis: side-channel atomicity

abstract: We introduce simple methods to convert a cryptographic algorithm into an algorithm protected against simple side-channel attacks. Contrary to previously known solutions, the proposed techniques are not at the expense of the execution time. Moreover, they are generic and apply to virtually any algorithm. In particular, we present several novel exponentiation algorithms, namely, a protected square-and-multiply algorithm, its right-to-left counterpart, and several protected sliding-window algorithms. We also illustrate our methodology applied to point multiplication on elliptic curves. All these algorithms share the common feature that the complexity is globally unchanged compared to the corresponding unprotected implementations.


title: Examining Cooperation in Visual Dialog Models

abstract: In this work we propose a blackbox intervention method for visual dialog models, with the aim of assessing the contribution of individual linguistic or visual components. Concretely, we conduct structured or randomized interventions that aim to impair an individual component of the model, and observe changes in task performance. We reproduce a state-of-the-art visual dialog model and demonstrate that our methodology yields surprising insights, namely that both dialog and image information have minimal contributions to task performance. The intervention method presented here can be applied as a sanity check for the strength and robustness of each component in visual dialog systems.


title: Fuzzy Adaptive Sliding Mode Control of 6 DOF Parallel Manipulator with Electromechanical Actuators in Cartesian Space Coordinates

abstract: This paper proposes a fuzzy adaptive sliding mode controller in Cartesian space coordinates for trajectory tracking of the 6 DOF parallel manipulator with considering dynamics of electromechanical actuators. The 6-DOF sensors, may be very expensive or impossible to find at the desired accuracies; and also, especially at constant speeds, will contain errors; therefore, it is better to use LVDT position sensors and then, solving the forward kinematics problem using an iterative artificial neural network strategy, Instead of using the Numerical methods such as the Newton-Raphson method, that heavy computational load imposes to the system. This controller consists of a sliding mode control and adaptive learning algorithms to adjust uncertain parameters of system, that relax the requirement of bounding parameter values and not depending upon any parameter initialization conditions, and fuzzy approximators to estimate the plant's unknown nonlinear functions, and robustifying control terms to compensate of approximation errors; so that closed-loop stability and finite-time convergence of tracking errors can be guaranteed. Simulation results demonstrate that the proposed control strategy can achieve favorable control performance with regard to uncertainties, nonlinearities and external disturbances.


title: Tone-mapping high dynamic range images by novel histogram adjustment

abstract: In this paper, we present novel histogram adjustment methods for displaying high dynamic range image. We first present a global histogram adjustment based tone mapping operator, which well reproduces global contrast for high dynamic range images. We then segment images and carry out adaptive contrast adjustment using our global tone mapping operator in the local regions to reproduce local contrast and ensure better quality. We demonstrate that our methods are fast, easy to use and a fixed set of parameter values produce good results for a wide variety of images.


title: Computation Offloading and Resource Allocation in Wireless Cellular Networks With Mobile Edge Computing

abstract: Mobile edge computing has risen as a promising technology for augmenting the computational capabilities of mobile devices. Meanwhile, in-network caching has become a natural trend of the solution of handling exponentially increasing Internet traffic. The important issues in these two networking paradigms are computation offloading and content caching strategies, respectively. In order to jointly tackle these issues in wireless cellular networks with mobile edge computing, we formulate the computation offloading decision, resource allocation and content caching strategy as an optimization problem, considering the total revenue of the network. Furthermore, we transform the original problem into a convex problem and then decompose it in order to solve it in a distributed and efficient way. Finally, with recent advances in distributed convex optimization, we develop an alternating direction method of multipliers-based algorithm to solve the optimization problem. The effectiveness of the proposed scheme is demonstrated by simulation results with different system parameters.


title: Large-scale Analysis of Chess Games with Chess Engines: A Preliminary Report

abstract: The strength of chess engines together with the availability of numerous chess games have attracted the attention of chess players, data scientists, and researchers during the last decades. State-of-the-art engines now provide an authoritative judgement that can be used in many applications like cheating detection, intrinsic ratings computation, skill assessment, or the study of human decision-making. A key issue for the research community is to gather a large dataset of chess games together with the judgement of chess engines. Unfortunately the analysis of each move takes lots of times. In this paper, we report our effort to analyse almost 5 millions chess games with a computing grid. During summer 2015, we processed 270 millions unique played positions using the Stockfish engine with a quite high depth (20). We populated a database of 1+ tera-octets of chess evaluations, representing an estimated time of 50 years of computation on a single machine. Our effort is a first step towards the replication of research results, the supply of open data and procedures for exploring new directions, and the investigation of software engineering/scalability issues when computing billions of moves.


title: CITRIC: A low-bandwidth wireless camera network platform

abstract: In this paper, we propose and demonstrate a novel wireless camera network system, called CITRIC. The core component of this system is a new hardware platform that integrates a camera, a frequency-scalable (up to 624 MHz) CPU, 16MB FLASH, and 64MB RAM onto a single device. The device then connects with a standard sensor network mote to form a camera mote. The design enables in-network processing of images to reduce communication requirements, which has traditionally been high in existing camera networks with centralized processing. We also propose a back-end client/server architecture to provide a user interface to the system and support further centralized processing for higher-level applications. Our camera mote enables a wider variety of distributed pattern recognition applications than traditional platforms because it provides more computing power and tighter integration of physical components while still consuming relatively little power. Furthermore, the mote easily integrates with existing low-bandwidth sensor networks because it can communicate over the IEEE 802.15.4 protocol with other sensor network platforms. We demonstrate our system on three applications: image compression, target tracking, and camera localization.


title: Taxonomy of information security risk assessment (ISRA)

abstract: Information is a perennially significant business asset in all organizations. Therefore, it must be protected as any other valuable asset. This is the objective of information security, and an information security program provides this kind of protection for a company's information assets and for the company as a whole. One of the best ways to address information security problems in the corporate world is through a risk-based approach. In this paper, we present a taxonomy of security risk assessment drawn from 125 papers published from 1995 to May 2014. Organizations with different size may face problems in selecting suitable risk assessment methods that satisfy their needs. Although many risk-based approaches have been proposed, most of them are based on the old taxonomy, avoiding the need for considering and applying the important criteria in assessing risk raised by rapidly changing technologies and the attackers knowledge level. In this paper, we discuss the key features of risk assessment that should be included in an information security management system. We believe that our new risk assessment taxonomy helps organizations to not only understand the risk assessment better by comparing different new concepts but also select a suitable way to conduct the risk assessment properly. Moreover, this taxonomy will open up interesting avenues for future research in the growing field of security risk assessment.


Comparative study and Security Limitations of 4 G Network ( Case Study LTE and WIMAX )


title: Development of Automatic Visual Inspection Sensor in Robotic Tailored Blank Welding

abstract: This paper describes the development of a vision inspection sensor system for the automation of laser welding processes in heavy industries. The system consists of PC based vision camera and stripe type laser diode. Its structure, basic principle of this system and the mathematic model and error analysis are also deascribed. A mathematic model is established in order to analyse the influence on sensor error from the geometric parameters. The simulation results are shown the relationship between error and coordinate of image points. Finally, the experiments results illustrate the mismatch size, weld slope, convexity , concavity, overthickness. Undercut and the bead width of the weld for the butt joint. And the feasibility and effectiveness of automatic visual inspection sensor are proved.


title: Ethics, character, and authentic transformational leadership behavior

abstract: Abstract The morality of transformational leadership has been sharply questioned, particularly by libertarians, “grass roots” theorists, and organizational development consultants. This paper argues that to be truly transformational, leadership must be grounded in moral foundations. The four components of authentic transformational leadership (idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration) are contrasted with their counterfeits in dissembling pseudo -transformational leadership on the basis of (1) the moral character of the leaders and their concerns for self and others; (2) the ethical values embedded in the leaders' vision, articulation, and program, which followers can embrace or reject; and (3) the morality of the processes of social ethical choices and action in which the leaders and followers engage and collectively pursue. The literature on transformational leadership is linked to the long-standing literature on virtue and moral character, as exemplified by Socratic and Confucian typologies. It is related as well to the major themes of the modern Western ethical agenda: liberty, utility, and distributive justice Deception, sophistry, and pretense are examined alongside issues of transcendence, agency, trust, striving for congruence in values, cooperative action, power, persuasion, and corporate governance to establish the strategic and moral foundations of authentic transformational leadership.


title: Anomaly Detection for Road Traffic: A Visual Analytics Framework

abstract: The analysis of large amounts of multidimensional road traffic data for anomaly detection is a complex task. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in road traffic, making the data analysis process more transparent. In this paper, we present a visual analytics framework that provides support for: 1) the exploration of multidimensional road traffic data; 2) the analysis of normal behavioral models built from data; 3) the detection of anomalous events; and 4) the explanation of anomalous events. We illustrate the use of this framework with examples from a large database of real road traffic data collected from several areas in Europe. Finally, we report on feedback provided by expert analysts from Volvo Group Trucks Technology, regarding its design and usability.


title: A Multiple-Goal Reinforcement Learning Method for Complex Vehicle Overtaking Maneuvers

abstract: In this paper, we present a learning method to solve the vehicle overtaking problem, which demands a multitude of abilities from the agent to tackle multiple criteria. To handle this problem, we propose to adopt a multiple-goal reinforcement learning (MGRL) framework as the basis of our solution. By considering seven different goals, either Q-learning (QL) or double-action QL is employed to determine action decisions based on whether the other vehicles interact with the agent for that particular goal. Furthermore, a fusion function is proposed according to the importance of each goal before arriving to an overall but consistent action decision. This offers a powerful approach for dealing with demanding situations such as overtaking, particularly when a number of other vehicles are within the proximity of the agent and are traveling at different and varying speeds. A large number of overtaking cases have been simulated to demonstrate its effectiveness. From the results, it can be concluded that the proposed method is capable of the following: 1) making correct action decisions for overtaking; 2) avoiding collisions with other vehicles; 3) reaching the target at reasonable time; 4) keeping almost steady speed; and 5) maintaining almost steady heading angle. In addition, it should also be noted that the proposed method performs lane keeping well when not overtaking and lane changing effectively when overtaking is in progress.


title: Blocking Transferability of Adversarial Examples in Black-Box Learning Systems

abstract: Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars. To further broaden the use of ML models, cloud-based services offered by Microsoft, Amazon, Google, and others have developed ML-as-a-service tools as black-box systems. However, ML classifiers are vulnerable to adversarial examples: inputs that are maliciously modified can cause the classifier to provide adversary-desired outputs. Moreover, it is known that adversarial examples generated on one classifier are likely to cause another classifier to make the same mistake, even if the classifiers have different architectures or are trained on disjoint datasets. This property, which is known as transferability, opens up the possibility of attacking black-box systems by generating adversarial examples on a substitute classifier and transferring the examples to the target classifier. Therefore, the key to protect black-box learning systems against the adversarial examples is to block their transferability. To this end, we propose a training method that, as the input is more perturbed, the classifier smoothly outputs lower confidence on the original label and instead predicts that the input is "invalid". In essence, we augment the output class set with a NULL label and train the classifier to reject the adversarial examples by classifying them as NULL. In experiments, we apply a wide range of attacks based on adversarial examples on the black-box systems. We show that a classifier trained with the proposed method effectively resists against the adversarial examples, while maintaining the accuracy on clean data.


title: Not quite so blind: Semantic processing despite inattentional blindness.

abstract: We often fail to detect clearly visible, yet unexpected objects when our attention is otherwise engaged, a phenomenon widely known as inattentional blindness. The potentially devastating consequences and the mediators of such failures of awareness have been studied extensively. Surprisingly, however, hardly anything is known about whether and how we process the objects that go unnoticed during inattentional blindness. In 2 experiments, we demonstrate that the meaning of objects undetected due to inattentional blindness interferes with the classification of attended stimuli. Responses were significantly slower when the semantic content of an undetected stimulus contradicted that of the attended, to-be-judged object. We thus clarify the depth of the "blindness" caused by inattention, as we provide compelling evidence that failing to detect the unexpected does not preclude its processing, even at postperceptual stages. Despite inattentional blindness, our mind obviously still has access to something as refined as meaning. (PsycINFO Database Record Keywords: Driver distraction; Language: en


title: Generic transposition from data source toward ontology

abstract: This paper presents a part of a generic approach for transposition models of any data source to the ontology and vice versa using Model Driven Architecture (MDA). We present the general process of our approach by focusing on the transposition from data source to ontology: Data source is based on a unique and single model also for ontology, the cooperation of software engineering and knowledge engineering is then reduced to the determination of transition between software model and ontology. Our approach is based on a transition managed by meta modeling: From any data source, we extract a model which will be transposed to an ontology model by a succession of treatment steps. Data source model is validating against a metamodel then it will undergo transformations to generate the model of ontology which is validating against another metamodel.


title: Implementation of stereo camera-based automatic unmanned ground vehicle system for adaptive target detection

abstract: In this paper, an integrated automatic stereo target-detection system is proposed, in which a stereo camera embedded on the mobile robot system is employed and using this system a potential 3D target object is detected and discriminated from the other objects and backgrounds, and then its moving trajectory and direction data is transferred to the host vehicle so as to warn a potential collision with a target to the UGV (unmanned ground vehicle). The face area of a target person can be tracked by using the YCbCr color model, centroid method and the depth map. In addition, a distance and area coordinates of the target face can be also calculated, in which the calculated coordinates are used for extraction of 3D information that can be used for plan paths, construction of the coordinates maps and exploration of the indoor environment. From some experiments on person tracking by using the sequential stereo image pairs of 1,280 frames, it is analyzed that error ratio between the calculated and measured values of the distance is found to be very low value of 0.7% on average.


A Comprehensive Review on Cyclodextrin-Based Carriers for Delivery of Chemotherapeutic Cytotoxic Anticancer Drugs


SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality.


title: A Mindfulness Course Decreases Burnout and Improves Well-Being among Healthcare Providers

abstract: Objective:Healthcare providers are under increasing stress and work-related burnout has become common. Mindfulness-based interventions have a potential role in decreasing stress and burnout. The purpose of this study was to determine if a continuing education course based on mindfulness-based stress reduction could decrease burnout and improve mental well-being among healthcare providers, from different professions.Design:This was a pre-post observational study conducted in a university medical center. A total of 93 healthcare providers, including physicians from multiple specialties, nurses, psychologists, and social workers who practiced in both university and community settings, participated. The intervention was a continuing education course based on mindfulness-based stress reduction that met 2.5 hours a week for 8 weeks plus a 7-hour retreat. The classes included training in four types of formal mindfulness practices, including the body scan, mindful movement, walking meditation and sitting meditati...


title: Unsupervised Discovery of Mid-Level Discriminative Patches

abstract: The goal of this paper is to discover a set of discriminative patches which can serve as a fully unsupervised mid-level visual representation. The desired patches need to satisfy two requirements: 1) to be representative, they need to occur frequently enough in the visual world; 2) to be discriminative, they need to be different enough from the rest of the visual world. The patches could correspond to parts, objects, "visual phrases", etc. but are not restricted to be any one of them. We pose this as an unsupervised discriminative clustering problem on a huge dataset of image patches. We use an iterative procedure which alternates between clustering and training discriminative classifiers, while applying careful cross-validation at each step to prevent overfitting. The paper experimentally demonstrates the effectiveness of discriminative patches as an unsupervised mid-level visual representation, suggesting that it could be used in place of visual words for many tasks. Furthermore, discriminative patches can also be used in a supervised regime, such as scene classification, where they demonstrate state-of-the-art performance on the MIT Indoor-67 dataset.


title: Characteristics of Nested Living Systems

abstract: Living systems are nested and consist of basic materials, cells, organisms, ecosystems, and their environments, continuously interacting in time and space. Life is an integrated process of nested living systems. We synthesize and discuss exergy capturing and accumulation of organizational exergy; the structuring of the system towards maximum entropy production and export of high entropy products; autopoiesis; emergent attractors or optimum operating points; characteristics of nested systems and holarcic levels; and the role of working and latent information. It is concluded that it is only possible to describe the livingness of a system in a continuous way and that living matter should be defined by the processes of which it is a part. Hence, from the perspective of self-organizing and nested living systems it is difficult to draw boundaries between living and non-living as well as human and non-human systems. Implications of this worldview is discussed in relation to environmental management.


title: Semantic-based IoT device discovery and recommendation mechanism

abstract: The Internet of Things is about data, different devices from different places and the connectivity between them. Our goal is to find a way to interact with the devices and their data, according to the customer's requirements, in the IoT context, all this supported by the use of Web services. We present a broker based architecture for service selection which facilitates devices to specify both functional and non-functional requirements. We develop a device discovery and recommendation mechanism based on a proposed web service similarity metric.


title: Brain-Computer Interfaces in Medicine

abstract: Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function.


title: Load balancing in cloud computing

abstract: Cloud computing is the emerging internet based technology which emphasizes commercial computing. Load balancing helps in improving the performance of the centralized server. In the present work, various algorithms are analyzed using an analysis tool, namely, cloud analyst. Comparison is also made for algorithms load balancing.


Glyph Design for Temporal and Multi-Dimensional Data : Design Considerations and Evaluation


Optimizations and fitting procedures for the liljencrants-fant model for statistical parametric speech synthesis.


title: Design and implementation of an extrusion-based break-in detector for personal computers

abstract: An increasing variety of malware, such as worms, spyware and adware, threatens both personal and business computing. Remotely controlled bot networks of compromised systems are growing quickly. In this paper, we tackle the problem of automated detection of break-ins caused by unknown malware targeting personal computers. We develop a host based system, BINDER (Break-IN DEtectoR), to detect break-ins by capturing user unintended malicious outbound connections (referred to as extrusions). To infer user intent, BINDER correlates outbound connections with user-driven input at the process level under the assumption that user intent is implied by user-driven input. Thus BINDER can detect a large class of unknown malware such as worms, spyware and adware without requiring signatures. We have successfully used BINDER to detect real world spyware on daily used computers and email worms on a controlled testbed with very small false positives


title: Efficient Subjective Bayesian network belief propagation for trees

abstract: Subjective Bayesian networks extend Bayesian networks by incorporating uncertainty in the conditional probabilities. This paper develops subjective belief propagation (SBP) that extends regular belief propagation (BP) to efficiently infer uncertain marginal probabilities in subjective Bayesian networks. It is shown that SBP's runtime exhibits only slightly slower performance than standard BP but is able to effectively characterize a distribution for the marginals. Simulations affirm that unlike the valuation-based system, a previous uncertain probabilistic reasoning framework, SBP is able to effectively capture bounds for the actual error in a consistent manner.


title: Action recognition in cluttered dynamic scenes using Pose-Specific Part Models

abstract: We present an approach to recognizing single actor human actions in complex backgrounds. We adopt a Joint Tracking and Recognition approach, which track the actor pose by sampling from 3D action models. Most existing such approaches require large training data or MoCAP to handle multiple viewpoints, and often rely on clean actor silhouettes. The action models in our approach are obtained by annotating keyposes in 2D, lifting them to 3D stick figures and then computing the transformation matrices between the 3D keypose figures. Poses sampled from coarse action models may not fit the observations well; to overcome this difficulty, we propose an approach for efficiently localizing a pose by generating a Pose-Specific Part Model (PSPM) which captures appropriate kinematic and occlusion constraints in a tree-structure. In addition, our approach also does not require pose silhouettes. We show improvements to previous results on two publicly available datasets as well as on a novel, augmented dataset with dynamic backgrounds.


title: Cross-Domain Feature Learning in Multimedia

abstract: In the Web 2.0 era, a huge number of media data, such as text, image/video, and social interaction information, have been generated on the social media sites (e.g., Facebook, Google, Flickr, and YouTube). These media data can be effectively adopted for many applications (e.g., image/video annotation, image/video retrieval, and event classification) in multimedia. However, it is difficult to design an effective feature representation to describe these data because they have multi-modal property (e.g., text, image, video, and audio) and multi-domain property (e.g., Flickr, Google, and YouTube). To deal with these issues, we propose a novel cross-domain feature learning (CDFL) algorithm based on stacked denoising auto-encoders. By introducing the modal correlation constraint and the cross-domain constraint in conventional auto-encoder, our CDFL can maximize the correlations among different modalities and extract domain invariant semantic features simultaneously. To evaluate our CDFL algorithm , we apply it to three important applications: sentiment classification, spam filtering, and event classification. Comprehensive evaluations demonstrate the encouraging performance of the proposed approach.


RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem


title: Improving the sensitivity of the Barthel Index for stroke rehabilitation.

abstract: The Barthel Index is considered to be the best of the ADL measurement scales. However, there are some scales that are more sensitive to small changes in functional independence than the Barthel Index. The sensitivity of the Barthel Index can be improved by expanding the number of categories used to record improvement in each ADL function. Suggested changes to the scoring of the Barthel Index, and guidelines for determining the level of independence are presented. These modifications and guidelines were applied in the assessment of 258 first stroke patients referred for inpatient comprehensive rehabilitation in Brisbane, Australia during 1984 calendar year. The modified scoring of the Barthel Index achieved greater sensitivity and improved reliability than the original version, without causing additional difficulty or affecting the implementation time. The internal consistency reliability coefficient for the modified scoring of the Barthel Index was 0.90, compared to 0.87 for the original scoring.


Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft


title: Sentiment analysis on Italian tweets

abstract: We describe TWITA, the first corpus of Italian ::: tweets, which is created via a completely automatic procedure, portable to any other language. We experiment with sentiment analysis on two datasets from TWITA: a generic ::: collection and a topic-specific collection. The ::: only resource we use is a polarity lexicon, ::: which we obtain by automatically matching ::: three existing resources thereby creating the ::: first polarity database for Italian. We observe ::: that albeit shallow, our simple system captures ::: polarity distinctions matching reasonably well ::: the classification done by human judges, with ::: differences in performance across polarity values and on the two sets.


title: The hp-Multigrid Method Applied to hp-Adaptive Refinement of Triangular Grids

abstract: Recently the hp version of the finite element method, in which adaptivity occurs in both the size, h, of the elements and in the order, p, of the approximating piecewise polynomials, has received increasing attention. It is desirable to combine this optimal order discretization method with an optimal order algebraic solution method like multigrid. An intriguing notion is to use the values of p as the levels of a multilevel method. In this paper we present such a method, known as hp-multigrid, for high-order finite elements and hp-adaptive grids. We present a survey of the development of p-multigrid and hp-multigrid, define an hp-multigrid algorithm based on the p-hierarchical basis for the p levels and h-hierarchical basis for an h-multigrid solution of the p=1 ‘coarse grid’ equations, and present numerical convergence results using hp-adaptive grids. The numerical results suggest that the method has a convergence rate of for Poisson's equation. Published in 2010 by John Wiley & Sons, Ltd.


title: PVC flooring is related to human uptake of phthalates in infants

abstract: Poly Vinyl Chloride (PVC) flooring material contains phthalates and it has been shown that such materials are important sources for phthalates in indoor dust. Phthalates are suspected endocrine disrupting chemicals (EDCs). Consecutive infants between two and six months old and their mothers were invited. A questionnaire about indoor environmental factors and family life style was used. Urinary metabolites of the phthalates di-ethyl phthalate (DEP), di-butyl phthalate (DBP), butylbenzyl phthalate (BBzP) and di-etylhexyl phthalate (DEHP) were measured in the urine of the children. Of 209 invited children, 110 (52%) participated. Urine samples were obtained from 83 of these. Urine levels of the BBzP metabolite monobenzyl phthalate (MBzP) was significantly higher in infants with PVC flooring in their bedrooms (p<0.007) and related to the body area of the infant. Levels of the DEHP metabolites MEHHP (p<0.01) and MEOHP (p<0.04) were higher in the two months old infants who were not exclusively breastfed when compared with breastfed children. The findings indicate that the use of soft PVC as flooring material may increase the human uptake of phthalates in infants. Urinary levels of phthalate metabolites during early life are associated to the use of PVC flooring in the bedroom, body area and the use of infant formula. © 2012 John Wiley & Sons A/S. (Less)


title: Spark: Cluster computing with working sets

abstract: MapReduce and its variants have been highly successful in implementing large-scale data-intensive applications on commodity clusters. However, most of these systems are built around an acyclic data flow model that is not suitable for other popular applications. This paper focuses on one such class of applications: those that reuse a working set of data across multiple parallel operations. This includes many iterative machine learning algorithms, as well as interactive data analysis tools. We propose a new framework called Spark that supports these applications while retaining the scalability and fault tolerance of MapReduce. To achieve these goals, Spark introduces an abstraction called resilient distributed datasets (RDDs). An RDD is a read-only collection of objects partitioned across a set of machines that can be rebuilt if a partition is lost. Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.


title: On the reliability and intuitiveness of aggregated search metrics

abstract: Aggregating search results from a variety of diverse verticals such as news, images, videos and Wikipedia into a single interface is a popular web search presentation paradigm. Although several aggregated search (AS) metrics have been proposed to evaluate AS result pages, their properties remain poorly understood. In this paper, we compare the properties of existing AS metrics under the assumptions that (1) queries may have multiple preferred verticals; (2) the likelihood of each vertical preference is available; and (3) the topical relevance assessments of results returned from each vertical is available. We compare a wide range of AS metrics on two test collections. Our main criteria of comparison are (1) discriminative power, which represents the reliability of a metric in comparing the performance of systems, and (2) intuitiveness, which represents how well a metric captures the various key aspects to be measured (i.e. various aspects of a user's perception of AS result pages). Our study shows that the AS metrics that capture key AS components (e.g., vertical selection) have several advantages over other metrics. This work sheds new lights on the further developments and applications of AS metrics.


A Hierarchy of Hyperbolic Macrodynamic Equations as a Model for Network Training


title: Modelling User Behaviour for Web Recommendation Using LDA Model

abstract: Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analysis of Web usage data will lead to discover Web user access pattern and facilitate users locate more preferable Web pages via collaborative recommending technique. Meanwhile, latent semantic analysis techniques provide a powerful means to capture user access pattern and associated task space. In this paper, we propose a collaborative Web recommendation framework, which employs Latent Dirichlet Allocation (LDA) to model underlying topic-simplex space and discover the associations between user sessions and multiple topics via probability inference. Experiments conducted on real Website usage dataset show that this approach can achieve better recommendation accuracy in comparison to existing techniques. The discovered topic-simplex expression can also provide a better interpretation of user navigational preference.


title: Walk, Look, Remember: The Influence of the Gallery’s Spatial Layout on Human Memory for an Art Exhibition

abstract: The spatial organisation of museums and its influence on the visitor experience has been the subject of numerous studies. Previous research, despite reporting some actual behavioural correlates, rarely had the possibility to investigate the cognitive processes of the art viewers. In the museum context, where spatial layout is one of the most powerful curatorial tools available, attention and memory can be measured as a means of establishing whether or not the gallery fulfils its function as a space for contemplating art. In this exploratory experiment, 32 participants split into two groups explored an experimental, non-public exhibition and completed two unanticipated memory tests afterwards. The results show that some spatial characteristics of an exhibition can inhibit the recall of pictures and shift the focus to perceptual salience of the artworks.


title: A model for concentric tube continuum robots under applied wrenches

abstract: Continuum robots made from telescoping precurved elastic tubes enable base-mounted actuators to specify the curved shapes of robots as thin as standard surgical needles. While free space beam mechanics-based models of the shape of these ‘active cannulas’ exist, current models cannot account for external forces and torques applied to the cannula by the environment. In this paper we apply geometrically exact beam theory to solve the statics problem for concentric-tube continuum robots. This yields the equivalent of forward kinematics for an active cannula with general tube precurvature functions and arbitrarily many tubes, under loading from a general wrench distribution. The model achieves average experimental tip errors of less than 3 mm over the workspace of a prototype active cannula subject to various tip forces.


title: Idempotent work stealing

abstract: Load balancing is a technique which allows efficient parallelization of irregular workloads, and a key component of many applications and parallelizing runtimes. Work-stealing is a popular technique for implementing load balancing, where each parallel thread maintains its own work set of items and occasionally steals items from the sets of other threads. The conventional semantics of work stealing guarantee that each inserted task is eventually extracted exactly once. However, correctness of a wide class of applications allows for relaxed semantics, because either: i) the application already explicitly checks that no work is repeated or ii) the application can tolerate repeated work. In this paper, we introduce idempotent work tealing, and present several new algorithms that exploit the relaxed semantics to deliver better performance. The semantics of the new algorithms guarantee that each inserted task is eventually extracted at least once-instead of exactly once. On mainstream processors, algorithms for conventional work stealing require special atomic instructions or store-load memory ordering fence instructions in the owner's critical path operations. In general, these instructions are substantially slower than regular memory access instructions. By exploiting the relaxed semantics, our algorithms avoid these instructions in the owner's operations. We evaluated our algorithms using common graph problems and micro-benchmarks and compared them to well-known conventional work stealing algorithms, the THE Cilk and Chase-Lev algorithms. We found that our best algorithm (with LIFO extraction) outperforms existing algorithms in nearly all cases, and often by significant margins.


title: Anchored mobilities: mobile technology and transnational migration

abstract: Mobile technologies are deployed into diverse social, cultural, political and geographic settings, and incorporated into diverse forms of personal and collective mobility. We present an ethnography of transnational Thai retirees and their uses of mobile technology, highlighting forms of mobility that are spatially, temporally, and infrastructurally anchored, and concepts of the house as a kinship network that may be globally distributed. We conclude in pointing out several ways in which our observations and analysis can influence design.


title: Unsupervised Learning of Video Representations using LSTMs

abstract: We use Long Short Term Memory (LSTM) networks to learn representations of video sequences. Our model uses an encoder LSTM to map an input sequence into a fixed length representation. This representation is decoded using single or multiple decoder LSTMs to perform different tasks, such as reconstructing the input sequence, or predicting the future sequence. We experiment with two kinds of input sequences - patches of image pixels and high-level representations ("percepts") of video frames extracted using a pretrained convolutional net. We explore different design choices such as whether the decoder LSTMs should condition on the generated output. We analyze the outputs of the model qualitatively to see how well the model can extrapolate the learned video representation into the future and into the past. We further evaluate the representations by finetuning them for a supervised learning problem - human action recognition on the UCF-101 and HMDB-51 datasets. We show that the representations help improve classification accuracy, especially when there are only few training examples. Even models pretrained on unrelated datasets (300 hours of YouTube videos) can help action recognition performance.


Speech-to-Singing Synthesis System : Vocal Conversion from Speaking Voices to Singing Voices by Controlling Acoustic Features Unique to Singing Voices *


title: Multiple Fundamental Frequency Estimation Based on Harmonicity and Spectral Smoothness

abstract: A new method for estimating the fundamental frequencies of concurrent musical sounds is described. The method is based on an iterative approach, where the fundamental frequency of the most prominent sound is estimated, the sound is subtracted from the mixture, and the process is repeated for the residual signal. For the estimation stage, an algorithm is proposed which utilizes the frequency relationships of simultaneous spectral components, without assuming ideal harmonicity. For the subtraction stage, the spectral smoothness principle is proposed as an efficient new mechanism in estimating the spectral envelopes of detected sounds. With these techniques, multiple fundamental frequency estimation can be performed quite accurately in a single time frame, without the use of long-term temporal features. The experimental data comprised recorded samples of 30 musical instruments from four different sources. Multiple fundamental frequency estimation was performed for random sound source and pitch combinations. Error rates for mixtures ranging from one to six simultaneous sounds were 1.8%, 3.9%, 6.3%, 9.9%, 14%, and 18%, respectively. In musical interval and chord identification tasks, the algorithm outperformed the average of ten trained musicians. The method works robustly in noise, and is able to handle sounds that exhibit inharmonicities. The inharmonicity factor and spectral envelope of each sound is estimated along with the fundamental frequency.


title: An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning

abstract: We show that linear value-function approximation is equivalent to a form of linear model approximation. We then derive a relationship between the model-approximation error and the Bellman error, and show how this relationship can guide feature selection for model improvement and/or value-function improvement. We also show how these results give insight into the behavior of existing feature-selection algorithms.


title: Novel FPGA-Based Low-Cost Hardware Architecture for the PRESENT Block Cipher

abstract: This paper presents a novel FPGA-based design for the lightweight block cipher PRESENT and its implementation results. The proposed design allows to study area-performance trade-offs and thus constructing smaller or faster implementations. When optimized by area, the proposed design exhibits smaller latency and fewer FPGA resources than representative related works in the literature.


title: Towards event source unobservability with minimum network traffic in sensor networks

abstract: Sensors deployed to monitor the surrounding environment report such information as event type, location, and time when a real event of interest is detected. An adversary may identify the real event source through eavesdropping and traffic analysis. Previous work has studied the source location privacy problem under a local adversary model. In this work, we aim to provide a stronger notion: event source unobservability, which promises that a global adversary cannot know whether a real event has ever occurred even if he is capable of collecting and analyzing all the messages in the network at all the time. Clearly, event source unobservability is a desirable and critical security property for event monitoring applications, but unfortunately it is also very difficult and expensive to achieve for resource-constrained sensor network. Our main idea is to introduce carefully chosen dummy traffic to hide the real event sources in combination with mechanisms to drop dummy messages to prevent explosion of network traffic. To achieve the latter, we select some sensors as proxies that proactively filter dummy messages on their way to the base station. Since the problem of optimal proxy placement is NP-hard, we employ local search heuristics. We propose two schemes (i) Proxy-based Filtering Scheme (PFS) and (ii) Tree-based Filtering Scheme (TFS) to accurately locate proxies. Simulation results show that our schemes not only quickly find nearly optimal proxy placement, but also significantly reduce message overhead and improve message delivery ratio. A prototype of our scheme was implemented for TinyOS-based Mica2 motes.


title: The merge/purge problem for large databases

abstract: Many commercial organizations routinely gather large numbers of databases for various marketing and business analysis functions. The task is to correlate information from different databases by identifying distinct individuals that appear in a number of different databases typically in an inconsistent and often incorrect fashion. The problem we study here is the task of merging data from multiple sources in as efficient manner as possible, while maximizing the accuracy of the result. We call this the merge/purge problem. In this paper we detail the sorted neighborhood method that is used by some to solve merge/purge and present experimental results that demonstrates this approach may work well in practice but at great expense. An alternative method based upon clustering is also presented with a comparative evaluation to the sorted neighborhood method. We show a means of improving the accuracy of the results based upon a multi-pass approach that succeeds by computing the Transitive Closure over the results of independent runs considering alternative primary key attributes in each pass.


Connection Pedagogy: A Pedagogical Shift for Millennial Learners in the Digital Era


title: Cloudlet Scheduling with Particle Swarm Optimization

abstract: Cloud computing is a particularly interesting and truly complex concept of providing services over networks on demand. Many tools have previously been created to simulate the work of the clouds, such as CloudSim. The main use of these tools is evaluation and testing of architectures and services before deployment on network centers and hosts in order to avoid any potential failures or inconveniences. The benefits of using the pay-per-use clouds may be affected by underutilization of the already reserved resources, so the maximization of system utilization while simultaneously minimizing makespan is of great interest to cloud providers wishing to reduce costs. To minimize makespan and increase resource utilization, a hybrid of particle swarm optimization and simulated annealing is implemented inside of CloudSim to advance the work of the already implemented simple broker. The new method maximizes the resource utilization and minimizes the makespan.


Water Quality Monitoring System Using Zigbee Based Wireless Sensor Network


title: Penile size and the ‘small penis syndrome’

abstract: The male is often troubled by concerns that his penis is not large enough to satisfy his partner or himself. He is ashamed to have others view his penis, especially in the flaccid state. Such concerns might be unfounded in reality and might be a presentation of social anxiety or some other clinical problem, such as erectile dysfunction. Concern over the size of the penis, when such concern becomes excessive, might present as the ‘small penis syndrome’, an obsessive rumination with compulsive checking rituals, body dysmorphic disorder, or as part of a psychosis. However, it is often a worry that can be described as within the normal experience of many men. Various potential causal factors are considered. A thorough assessment, normalizing the worry and then exploring the treatment options in detail with the man, is essential to allow the matter to be consolidated satisfactorily within the male ego.