title: 5G City: A Novel 5G-Enabled Architecture for Ultra-High Definition and Immersive Media on City Infrastructure

abstract: The rapid adoption of cutting edge media services such as ultra-high definition (UHD/4K) video and immersive media i.e, virtual and augmented reality (VR/AR), will demand large investments in a scalable, ubiquitous, and robust communication infrastructure and services. The H2020 5GCity project aims to solve such issues and beyond while designing, developing, and deploying a sliceable, distributed cloud & edge and radio 5G-platform with neutral hosting capability to support the sharing between information technology (IT) infrastructure owners and media service providers i.e., vertical media actors. In this paper we discuss how 5G technology will facilitate the rise of highly demanding media use cases and its implication on how service providers typically operate (in terms of business model). After a brief introduction of the 5GCity's architecture we present how three media-related use cases will be deployed in the cities of Barcelona, Bristol and Lucca by considering the project's architecture and the underlying neutral host parading to understand the benefits of such model for infrastructure owners and media service providers.


title: An IoT Agriculture System Using FIWARE

abstract: The industrial panorama is evolving. Using IoT sensors and actuators it is possible to increase product's value, by controlling its production chain. Internet technologies such as FIWARE can provide the services that modern industry needs to process and evaluate sensor data to apply higher production standards, which increase product value. Although FIWARE enablers provide the tools to fulfil modern industry needs, there is an interoperability gap a gap between applications and enablers, as different enablers have different protocols and needs. This article describes an agriculture system developed using vf-OS (virtual factory Operating System), a platform that aims to become the bridge between applications and enablers, as it provides the means to interact with them. The developed system is composed of different applications, that use enablers (integrated using the vf-OS system), current context management and fruit quality theories, to control product quality during the whole fruit production chain.


title: Understanding performance in touch selections: Tap, drag and radial pointing drag with finger, stylus and mouse

abstract: Touch-based interaction with computing devices is becoming more and more common. In order to design for this setting, it is critical to understand the basic human factors of touch interactions such as tapping and dragging; however, there is relatively little empirical research in this area, particularly for touch-based dragging. To provide foundational knowledge in this area, and to help designers understand the human factors of touch-based interactions, we conducted an experiment using three input devices (the finger, a stylus, and a mouse as a performance baseline) and three different pointing activities. The pointing activities were bidirectional tapping, one-dimensional dragging, and radial dragging (pointing to items arranged in a circle around the cursor). Tapping activities represent the elemental target selection method and are analysed as a performance baseline. Dragging is also a basic interaction method and understanding its performance is important for touch-based interfaces because it involves relatively high contact friction. Radial dragging is also important for touch-based systems as this technique is claimed to be well suited to direct input yet radial selections normally involve the relatively unstudied dragging action, and there have been few studies of the interaction mechanics of radial dragging. Performance models of tap, drag, and radial dragging are analysed. For tapping tasks, we confirm prior results showing finger pointing to be faster than the stylus/mouse but inaccurate, particularly with small targets. In dragging tasks, we also confirm that finger input is slower than the mouse and stylus, probably due to the relatively high surface friction. Dragging errors were low in all conditions. As expected, performance conformed to Fitts' Law. Our results for radial dragging are new, showing that errors, task time and movement distance are all linearly correlated with number of items available. We demonstrate that this performance is modelled by the Steering Law (where the tunnel width increases with movement distance) rather than Fitts' Law. Other radial dragging results showed that the stylus is fastest, followed by the mouse and finger, but that the stylus has the highest error rate of the three devices. Finger selections in the North-West direction were particularly slow and error prone, possibly due to a tendency for the finger to stick-slip when dragging in that direction.


The Emotional Dynamics of Narcissism


title: Configurations of identity among sexual minority youth: context, desire, and narrative.

abstract: Youth with same-sex desire undergo a process of narrative engagement as they construct configurations of identity that provide meaning and coherence with available sexual taxonomies. This article presents a theoretical analysis and four case studies centering on the relationship among context, desire, and identity for youth with same-sex desire. Through an interpretive, holistic analysis of the personal narratives of youth, we examine the integration of same-sex desire, behavior, and identity in the general life story and the selective appropriation of elements of “master narratives” of sexual identity development. Narratives were characterized by challenges to integrate desire, behavior, and identity into a configuration that conformed to the received sexual taxonomy. Implications for theory and further research on sexual identity development are discussed.


title: Theoretical and Empirical Challenges in Studying : The HR Practice – Firm Performance Relationship

abstract: Over the past 10 years a plethora of research has been conducted seeking to establish a relationship between human resource (HR) practices and firm performance. While this research has demonstrated promising results, a significant number of problems exist. This paper seeks to identify the theoretical and empirical challenges facing researchers who wish to further establish the impact of HR practices on firm performance. We conclude with some recommendations for future research in this area that might more accurately assess this relationship in ways that will be useful for both researchers and practitioners.


title: Factored Language Models And Generalized Parallel Backoff

abstract: We introduce factored language models (FLMs) and generalized parallel backoff (GPB). An FLM represents words as bundles of features (e.g., morphological classes, stems, data-driven clusters, etc.), and induces a probability model covering sequences of bundles rather than just words. GPB extends standard backoff to general conditional probability tables where variables might be heterogeneous types, where no obvious natural (temporal) backoff order exists, and where multiple dynamic backoff strategies are allowed. These methodologies were implemented during the JHU 2002 workshop as extensions to the SRI language modeling toolkit. This paper provides initial perplexity results on both CallHome Arabic and on Penn Treebank Wall Street Journal articles. Significantly, FLMs with GPB can produce bigrams with significantly lower perplexity, sometimes lower than highly-optimized baseline trigrams. In a multi-pass speech recognition context, where bigrams are used to create first-pass bigram lattices or N-best lists, these results are highly relevant.


title: Microstrip Patch Antennas on Tunable Electromagnetic Band-Gap Substrates

abstract: A tunable metamaterial in integrated circuit structures is investigated through an example of a microstrip patch antenna on a mushroom-type electromagnetic band-gap (EBG) structure. The patch antenna is designed as a half-wavelength resonator of an EBG loaded microstrip transmission line. The operating frequency of a patch antenna can be switched and controlled dynamically by loading diode switches in between vias and the ground plane. When the switches are on, the EBG surface is short to the ground and is at its on-state; while when the switches are turned off, the EBG surface is dc open and is at its off-state. It is found that the resulting patch antenna in-band resonant frequencies are very different at these two states. Antenna dual-band frequencies, gain, efficiency, and radiation patterns are characterized. The designed switchable EBG-patch antenna is fabricated and tested in these two states. The measurement is found in good agreement with simulation. An example is also given for the case of selected switches within the EBG elements to tune dynamically the resonant frequency.


title: Analytical Design Method of Multiway Dual-Band Planar Power Dividers With Arbitrary Power Division

abstract: In this paper, a novel closed-form design method of generalized Wilkinson power dividers is proposed. By using this method, the power divider could be designed to be arbitrary-way (N-way) with arbitrary power division, and arbitrary dual-band operations in a pure planar structure. A previous dual-band unequal Wilkinson power divider is extended to arbitrary terminal impedances case, thus, it can be used to construct multiway planar power dividers through the combination of the two-section dual-frequency transformers. To obtain three-way (or any odd-way) power dividers with dual-band and unequal power division features, a new developed recombinant structure is employed. This recombinant structure consists of a two-way dual-band unequal power divider/combiner without any isolation structures. Furthermore, the complete design procedures and analytical equations of these proposed generalized power dividers are presented. To verify our proposed design approach in theory, several three-way and four-way power dividers with different dual-band applications and various power divisions are designed and simulated. Finally, a practical three-way power divider operating at both 0.6 and 2.45 GHz with a power dividing ratio of 3:5:1 is fabricated in microstrip technology as a typical example. The measured results of the fabricated power divider verify our proposed idea.


title: Offset cancellation with subthreshold-operated feedback circuit for fully differential amplifiers

abstract: An offset cancellation technique for fully differential amplifiers is proposed. This technique uses subthreshold-operated operational amplifiers (subth-OP amps) for feedback biasing. Two subth-OP amps sense the two outputs of the differential amplifier and accordingly adjust the load currents in the amplifier to fix the outputs to a given reference voltage. The feedback operation is established only at dc and low frequencies because the subth-OP amps operate very slowly. The differential amplifier consequently operates as a high-pass filter and therefore shows no dc offset in its output, while it can normally amplify ac input signals. The results for the simulation and fabrication of the device are described.


title: Modified double skeletal staining protocols with Alizarin red and Alcian blue in laboratory animals

abstract: Purpose: Skeletal staining is a way to study the effect of many chemical or herbal substances on development of bones and cartilages in order to record the level of probable deformity. This article aimed to present a modified protocol to make all skeletal studies on laboratory animals possible. Materials and Methods: The important notes about skeletal staining were fixing by ethanol and clearing by potassium hydroxide in embryonic and newborn samples. In adults it was fixing by neutral formalin buffer, then ethanol after washing the samples in ddH2O, and clearing the samples by trypsin and potassium hydroxide in separated stages. The amount of colors used for cartilages and bones was different in animals with different ages for a good stained sample. Results: The mentioned procedures resulted incompletely clear stained samples whose skeletal parts, i.e. cartilages and bones, were stained blue and red, respectively. Conclusion: Although most reviewed investigations have used the same protocol in different laboratory animals with different ages, the used materials and also their concentrations for skeletal staining procedures differ in embryos and adults.


title: Failure and reliability analysis of STT-MRAM

abstract: Abstract Spin Transfer Torque Magnetic RAM (STT-MRAM) promises low power, great miniaturization prospective (e.g. 22 nm) and easy integration with CMOS process. It becomes actually a strong non-volatile memory candidate for both embedded and standalone applications. However STT-MRAM suffers from important failure and reliability issues compared with the conventional solutions based on magnetic field switching. For example, a read current could write erroneously the stored data, the variability of ultra-thin oxide barrier drives high resistance variation and the injected current in the nanopillar induces lower lifetime etc. This paper classifies firstly all the possible failures of STT-MRAM into “soft errors” and “hard errors”, and analyzes their impact on the memory reliability. Based on this work, we can find some efficient design solutions to address respectively these two types of errors and improve the reliability of STT-MRAM.


title: Vision-based vehicle detection and inter-vehicle distance estimation

abstract: In this paper, we propose a vision-based robust vehicle detection and inter-vehicle distance estimation algorithm for driving assistance system. We use the haar-like features of car rear-shadows, as well as the edge features for detecting of vehicles. The use of additional vehicle edge features greatly reduces the false-positive errors in the vehicle detection. And, after analyzing the conventional two inter-vehicle distance estimation methods: the location-based and the vehicle width-based, an improved inter-vehicle distance estimation algorithm which has the advantage of both method is proposed. Several experimental results show the effectiveness of the proposed method.


title: MPC-based approach to active steering for autonomous vehicle systems

abstract: In this paper a novel approach to autonomous steering systems is presented. A model predictive control (MPC) scheme is designed in order to stabilize a vehicle along a desired path while fulfilling its physical constraints. Simulation results show the benefits of the systematic control methodology used. In particular we show how very effective steering manoeuvres are obtained as a result of the MPC feedback policy. Moreover, we highlight the trade off between the vehicle speed and the required preview on the desired path in order to stabilize the vehicle. The paper concludes with highlights on future research and on the necessary steps for experimental validation of the approach.


title: Robust and Discriminative Self-Taught Learning

abstract: The lack of training data is a common challenge in many machine learning problems, which is often tackled by semi-supervised learning methods or transfer learning methods. The former requires unlabeled images from the same distribution as the labeled ones and the latter leverages labeled images from related homogenous tasks. However, these restrictions often cannot be satisfied. To address this, we propose a novel robust and discriminative self-taught learning approach to utilize any unlabeled data without the above restrictions. Our new approach employs a robust loss function to learn the dictionary, and enforces the structured sparse regularization to automatically select the optimal dictionary basis vectors and incorporate the supervision information contained in the labeled data. We derive an efficient iterative algorithm to solve the optimization problem and rigorously prove its convergence. Promising results in extensive experiments have validated the proposed approach.


title: Development of future short range radar technology

abstract: First cars equipped with 24 GHz short range radar (SRR) systems in combination with 77 GHz long range radar (LRR) system enter the market in autumn 2005 enabling new safety and comfort functions. In Europe the 24 GHz ultra wideband (UWB) frequency band is temporally allowed only till end of June 2013 with a limitation of the car pare penetration of 7%. From middle of 2013 new cars have to be equipped with SRR sensors which operate in the frequency band of 79 GHz (77 GHz to 81 GHz). The development of the 79 GHz SRR technology within the German government (BMBF) funded project KOKON is described


title: On ideal binary mask as the computational goal of auditory scene analysis

abstract: In his famous treatise of computational vision, Marr (1982) makes a compelling argument for separating different levels of analysis in order to understand complex information processing. In particular, the computational theory level, concerned with the goal of computation and general processing strategy, must be separated from the algorithm level, or the separation of what from how. This chapter is an attempt at a computational-theory analysis of auditory scene analysis, where the main task is to understand the character of the CASA problem.


title: Primary Prevention of HypertensionClinical and Public Health Advisory From the National High Blood Pressure Education Program

abstract: The National High Blood Pressure Education Program Coordinating Committee ::: published its first statement on the primary prevention of hypertension in ::: 1993. This article updates the 1993 report, using new and further evidence ::: from the scientific literature. Current recommendations for primary prevention ::: of hypertension involve a population-based approach and an intensive targeted ::: strategy focused on individuals at high risk for hypertension. These 2 strategies ::: are complementary and emphasize 6 approaches with proven efficacy for prevention ::: of hypertension: engage in moderate physical activity; maintain normal body ::: weight; limit alcohol consumption; reduce sodium intake; maintain adequate ::: intake of potassium; and consume a diet rich in fruits, vegetables, and low-fat ::: dairy products and reduced in saturated and total fat. Applying these approaches ::: to the general population as a component of public health and clinical practice ::: can help prevent blood pressure from increasing and can help decrease elevated ::: blood pressure levels for those with high normal blood pressure or hypertension.


title: Ultrasound detection and identification of cosmetic fillers in the skin.

abstract: BACKGROUND ::: While the incidence of cosmetic filler injections is rising world-wide, neither exact details of the procedure nor the agent used are always reported or remembered by the patients. Thus, although complications are reportedly rare, availability of a precise diagnostic tool to detect cutaneous filler deposits could help clarify the association between the procedure and the underlying pathology. ::: ::: ::: OBJECTIVES ::: The aim of this study was to evaluate cutaneous sonography in the detection and identification of cosmetic fillers deposits and, describe dermatological abnormalities found associated with the presence of those agents. ::: ::: ::: METHODS ::: We used ultrasound in a porcine skin model to determine the sonographic characteristics of commonly available filler agents, and subsequently applied the analysis to detect and identify cosmetic fillers among patients referred for skin disorders. ::: ::: ::: RESULTS ::: Fillers are recognizable on ultrasound and generate different patterns of echogenicity and posterior acoustic artefacts. Cosmetic fillers were identified in 118 dermatological patients; most commonly hyaluronic acid among degradable agents and silicone oil among non-degradable. Fillers deposits were loosely scattered throughout the subcutaneous tissue, with occasional infiltration of local muscles and loco-regional lymph nodes. Accompanying dermatopathies were represented by highly localized inflammatory processes unresponsive to conventional treatment, morphea-like reactions, necrosis of fatty tissue and epidermal cysts; in the case of non-degradable agents, the associated dermatopathies were transient, resolving upon disappearance of the filler. ::: ::: ::: CONCLUSIONS ::: Cosmetic filler agents may be detected and identified during routine ultrasound of dermatological lesions; the latter appear to be pathologically related to the cosmetic procedure.


title: The effects of ferrocene addition on soot particle inception and growth in premixed ethylene flames

abstract: Abstract The effects of the addition of dicyclopentadienyl iron, ferrocene, on soot particle inception and growth were studied in atmospheric pressure, premixed ethylene flames with C O = 0.71–0.83 at 0.015–0.46% ferrocene by weight of the fuel. The rate of surface growth of soot in undoped and doped flames was measured using laser light scattering and light extinction techniques to determine the particle size and number density as a function of residence time in the flame. The final soot yields in the ferrocene doped flames were enhanced by factors of 13.5-1.2 over the range of flame C O ratios studied. This enhancement decreased with increasing C O ratio for the same concentration of iron in the flames. The spatial distribution of the elements iron and carbon in the particles was determined with Auger spectroscopy. The iron was found to be concentrated in the cores of the particles. The results of a steady-state one-dimensional nucleation model which indicated that the iron oxide particles would nucleate well in advance of the soot inception time were consistent with this elemental stratification and the observation of the onset of carbon deposition in doped flames at earlier residence times than in undoped flames of the same C O ratio. The iron oxide particle sizes and number densities predicted from the model indicated that these particles were both numerous and small enough to be seed particles on which carbon deposition could take place. The importance of the activity of the surfaces of the solid in determining the rate of surface growth of soot was investigated. Lower rates of surface growth measured in doped flames than in undoped flames of the same C O ratio at early residence times indicated that the nucleated iron oxide particles were a less active surface on which carbon deposition took place than were the young soot particles. The rate of surface growth decreased by a factor of two over the residence time in the flames in all undoped flames and in doped flames of C O ratio above 0.80. For the doped flames of C O ratio below 0.80, the rate of surface growth did not decay in the late residence time regions of the flames. The break between the doped flames which did and did not display the persistent growth rates correlates with the relative magnitudes of the characteristic diffusion times for iron through carbon based on the final particle size formed in the flames and the particle growth time.


Mammogram Classification Using Convolutional Neural Networks


title: Doping engineering for improved immunity against BV softness and BV shift in trench power MOSFET

abstract: In this paper, we report typical soft breakdown and BVdss walk-in/walk-out phenomena observed in the development of ON semiconductor's T8 60V trench power MOSFET. These breakdown behaviors show strong correlation with doping profile. We propose two 1D location-dependent variables, Qint(y) and Cave(y), to assist the study, and demonstrate the effectiveness of them in revealing hidden information behind regular SIMS data. Our study details the methodology of engineering doping profiles for improved breakdown stability.


title: Image processing technique for determining cold thyroid nodules

abstract: Image processing is a technique that can be applied on medical images for detecting abnormalities such as tumors. Due to risk of malignancy, detecting and diagnosing of cold nodules in thyroid gland are important. We applied Image Enhancement (circular averaging filter, morphological opening by diamond structure element, division of results, transforming colorful image in to gray image), Image segmentation (thresholding) and feature extraction (hill climbing algorithm) to determine cold thyroid nodules automatically with high accuracy.


title: Independent components analysis for fetal electrocardiogram extraction: a case for the data efficient Mermaid algorithm

abstract: Fetal heart rate (FHR) monitoring is currently the primary methodology for antenatal determination of fetal well-being. Currently, the FHR can be detected with ultrasonography, but the additional information from fetal electrocardiogram (FECG) is only available via an invasive scalp electrode. A cost effective noninvasive monitoring through standard ECG electrodes could be used on nearly every patient in lieu of the ultrasound monitors. In this method, a number of electrodes are positioned on the abdomen of the mother to collect, simultaneously, various combinations of the signals including the heartbeats of the mother and the fetus. For accurate fetal heart-rate estimation, a clean FECG must be extracted from the collected mixtures. It is well known that this can be achieved using blind source separation (BSS) techniques. In this paper, the performance of the Mermaid algorithm, which is based on minimizing Renyi's mutual information, is evaluated on this problem of great practical importance. The effectiveness and data efficiency of Mermaid and its superiority over alternative information theoretic BSS algorithms are illustrated using artificially mixed ECG signals as well as fetal heart rate estimates in real ECG mixtures.


title: A study on various data mining techniques for crop yield prediction

abstract: India is a country where agriculture and agriculture related industries are the major source of living for the people. Agriculture is a major source of economy of the country. It is also one of the country which suffer from major natural calamities like drought or flood which damages the crop. This leads to huge financial loss for the farmers thus leading to the suicide. Predicting the crop yield well in advance prior to its harvest can help the farmers and Government organizations to make appropriate planning like storing, selling, fixing minimum support price, importing/exporting etc. Predicting a crop well in advance requires a systematic study of huge data coming from various variables like soil quality, pH, EC, N, P, K etc. As Prediction of crop deals with large set of database thus making this prediction system a perfect candidate for application of data mining. Through data mining we extract the knowledge from the huge size of data. This paper presents the study about the various data mining techniques used for predicting the crop yield. The success of any crop yield prediction system heavily relies on how accurately the features have been extracted and how appropriately classifiers have been employed. This paper summarizes the results obtained by various algorithms which are being used by various authors for crop yield prediction, with their accuracy and recommendation.


title: A novel approach for monitoring and improving the quality of welded joint in gas metal arc welding process using adaptive neuro-fuzzy systems

abstract: In gas metal arc welding, like other welding techniques, the quality of welded joint may be described in terms of weld bead geometry and the presence of welding defects. In turn, the characteristics of welding signals, such as voltage, current and sound, may be used to predict and improve the quality of welded joint. In this work, two sets of adaptive neuro-fuzzy inference system have been used to predict and improve the weld quality characteristics. The required data for modeling were obtained from 57 experiments based on D-optimal design of experiments. The first set is developed to predict the possible welding defects (discontinuity, lack of fusion and overlap) and shape factor of the weld bead. These “predicting adaptive neuro-fuzzy inference system models” have been developed using 13 statistical parameters of the sound, voltage and current signals. The objective of the second set of models, called “improving adaptive neuro-fuzzy inference system models,” is to adjust the input welding parameters in ...


title: On Threat Modeling and Mitigation of Medical Cyber-Physical Systems

abstract: Medical Cyber Physical Systems (MCPS) are life-critical networked systems of medical devices. These systems are increasingly used in hospitals to provide high-quality healthcare for patients. However, MCPS also bring concerns about security and safety and new challenges to protect patients from acts of theft or malice. In this paper, we focus our investigation on a thorough understanding of threat modeling in MCPS. We examine the roles of stakeholders and system components and sketch an abstract architecture of a MCPS to demonstrate various threat modeling options. We also discuss possible security techniques and their applicability and utility for the design of secure MCPS. This work forms a basis for understanding threatening conditions in MCPS, and embarks on promising state-of-the-art research trends for addressing MCPS security concerns.


title: Approximation Vector Machines for Large-scale Online Learning

abstract: One of the most challenging problems in kernel online learning is to bound the model size and to promote the model sparsity. Sparse models not only improve computation and memory usage, but also enhance the generalization capacity, a principle that concurs with the law of parsimony. However, inappropriate sparsity modeling may also significantly degrade the performance. In this paper, we propose Approximation Vector Machine (AVM), a model that can simultaneously encourage the sparsity and safeguard its risk in compromising the performance. When an incoming instance arrives, we approximate this instance by one of its neighbors whose distance to it is less than a predefined threshold. Our key intuition is that since the newly seen instance is expressed by its nearby neighbor the optimal performance can be analytically formulated and maintained. We develop theoretical foundations to support this intuition and further establish an analysis to characterize the gap between the approximation and optimal solutions. This gap crucially depends on the frequency of approximation and the predefined threshold. We perform the convergence analysis for a wide spectrum of loss functions including Hinge, smooth Hinge, and Logistic for classification task, and $l_1$, $l_2$, and $\epsilon$-insensitive for regression task. We conducted extensive experiments for classification task in batch and online modes, and regression task in online mode over several benchmark datasets. The results show that our proposed AVM achieved a comparable predictive performance with current state-of-the-art methods while simultaneously achieving significant computational speed-up due to the ability of the proposed AVM in maintaining the model size.


A Brief Description of the Levenberg-Marquardt Algorithm Implemented by levmar. Foundation for Research and Technology


title: Algorithms for association rule mining — a general survey and comparison

abstract: ABSTRACT Today there are several eAE ient algorithms that ope with the popular and omputationally expensive task of asso iation rule mining. A tually, these algorithms are more or less des ribed on their own. In this paper we explain the fundamentals of asso iation rule mining and moreover derive a general framework. Based on this we des ribe today's approa hes in ontext by pointing out ommon aspe ts and di eren es. After that we thoroughly investigate their strengths and weaknesses and arry out several runtime experiments. It turns out that the runtime behavior of the algorithms is mu h more similar as to be expe ted.


title: Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study

abstract: Comparison of image processing techniques is critically important in deciding which algorithm, method, or metric to use for enhanced image assessment. Image fusion is a popular choice for various image enhancement applications such as overlay of two image products, refinement of image resolutions for alignment, and image combination for feature extraction and target recognition. Since image fusion is used in many geospatial and night vision applications, it is important to understand these techniques and provide a comparative study of the methods. In this paper, we conduct a comparative study on 12 selected image fusion metrics over six multiresolution image fusion algorithms for two different fusion schemes and input images with distortion. The analysis can be applied to different image combination algorithms, image processing methods, and over a different choice of metrics that are of use to an image processing expert. The paper relates the results to an image quality measurement based on power spectrum and correlation analysis and serves as a summary of many contemporary techniques for objective assessment of image fusion algorithms.


title: DeepFont: Identify Your Font from An Image

abstract: As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers. We study the Visual Font Recognition (VFR) problem [4] LFE, and advance the state-of-the-art remarkably by developing the DeepFont system. First of all, we build up the first available large-scale VFR dataset, named AdobeVFR, consisting of both labeled synthetic data and partially labeled real-world data. Next, to combat the domain mismatch between available training and testing data, we introduce a Convolutional Neural Network (CNN) decomposition approach, using a domain adaptation technique based on a Stacked Convolutional Auto-Encoder (SCAE) that exploits a large corpus of unlabeled real-world text images combined with synthetic data preprocessed in a specific way. Moreover, we study a novel learning-based model compression approach, in order to reduce the DeepFont model size without sacrificing its performance. The DeepFont system achieves an accuracy of higher than 80% (top-5) on our collected dataset, and also produces a good font similarity measure for font selection and suggestion. We also achieve around 6 times compression of the model without any visible loss of recognition accuracy.


Artificial Intelligence Safety and Security


title: Reading Beside the Lines: Indentation as a Proxy for Complexity Metric

abstract: Maintainers face the daunting task of wading through a collection of both new and old revisions, trying to ferret out revisions which warrant personal inspection. One can rank revisions by size/lines of code (LOC), but often, due to the distribution of the size of changes, revisions will be of similar size. If we can't rank revisions by LOC perhaps we can rank by Halstead's and McCabe's complexity metrics? However, these metrics are problematic when applied to code fragments (revisions) written in multiple languages: special parsers are required which may not support the language or dialect used; analysis tools may not understand code fragments. We propose using the statistical moments of indentation as a lightweight, language independent, revision/diff friendly metric which actually proxies classical complexity metrics. We have extensively evaluated our approach against the entire CVS histories of the 278 of the most popular and most active SourceForge projects. We found that our results are linearly correlated and rank-correlated with traditional measures of complexity, suggesting that measuring indentation is a cheap and accurate proxy for code complexity of revisions. Thus ranking revisions by the standard deviation and summation of indentation will be very similar to ranking revisions by complexity.


title: USE OF MOBILE IMMERSION IN FOREIGN LANGUAGE TEACHING TO ENHANCE TARGET LANGUAGE VOCABULARY LEARNING

abstract: Smart phones have an essential role in day-to-day lives. Mobile phone applications have several and wide ranging features. The use of these smart phone applications provides new insights for innovation and opportunities in language teaching and learning process. The present study intended to examine the efficiency of a mobile application on teaching 40 phrases from Nation and Martinez phrase test 1-5 compared to conventional activities. Quasi-experimental research design with pre-test and post-test was employed to determine the differences between the scores of the control group (N=20) and the experimental group (N=20) developed by using random sampling. Results indicated that participants in the experimental group performed significantly better in the post-test, demonstrating the effectiveness of the mobile application used in this study on learning idioms. The results showed that learners in the experimental group achieved considerably better development in the post-test, representing the efficiency of the smartphone application that was involved in the study. Suggestions have been given for using mobile phone applications more effectively for vocabulary learning.


title: Antecedents of Online Game Dependency: The Implications of Multimedia Realism and Uses and Gratifications Theory

abstract: Massively Multiplayer Online Game MMOG dependency has been widely studied but research results suggest inconclusive antecedent causes. This study proposes and empirically tests three predictive models of MMOG dependency using a survey of online gaming participants. It finds multimedia realism for social interaction serves as an original antecedent factor affecting other mediating factors to cause MMOG dependency. These mediating factors derive from Uses and Gratifications theory and include: 1 participation in a virtual community, 2 diversion from everyday life, and 3 a pleasant aesthetic experience. Of these, participation in a virtual community has a strong positive relationship with MMOG dependency, and aesthetics has a modest negative relationship. Moderator analyses suggest neither gender nor "frequency of game playing" are significant but experience playing online games is a significant moderating factor of MMOG dependency.


title: A framework for community identification in dynamic social networks

abstract: We propose frameworks and algorithms for identifying communities in social networks that change over time. Communities are intuitively characterized as "unusually densely knit" subsets of a social network. This notion becomes more problematic if the social interactions change over time. Aggregating social networks over time can radically misrepresent the existing and changing community structure. Instead, we propose an optimization-based approach for modeling dynamic community structure. We prove that finding the most explanatory community structure is NP-hard and APX-hard, and propose algorithms based on dynamic programming, exhaustive search, maximum matching, and greedy heuristics. We demonstrate empirically that the heuristics trace developments of community structure accurately for several synthetic and real-world examples.


title: Stochastic direct reinforcement: Application to simple games with recurrence

abstract: We investigate repeated matrix games with stochastic players as a microcosm for studying dynamic, multi-agent interactions using the Stochastic Direct Reinforcement (SDR) policy gradient algorithm. SDR is a generalization of Recurrent Reinforcement Learning (RRL) that supports stochastic policies. Unlike other RL algorithms, SDR and RRL use recurrent policy gradients to properly address temporal credit assignment resulting from recurrent structure. Our main goals in this paper are to (1) distinguish recurrent memory from standard, non-recurrent memory for policy gradient RL, (2) compare SDR with Q-type learning methods for simple games, (3) distinguish reactive from endogenous dynamical agent behavior and (4) explore the use of recurrent learning for interacting, dynamic agents. We find that SDR players learn much faster and hence outperform recently-proposed Q-type learners for the simple game Rock, Paper, Scissors (RPS). With more complex, dynamic SDR players and opponents, we demonstrate that recurrent representations and SDR’s recurrent policy gradients yield better performance than non-recurrent players. For the Iterated Prisoners Dilemma, we show that non-recurrent SDR agents learn only to defect (Nash equilibrium), while SDR agents with recurrent gradients can learn a variety of interesting behaviors, including cooperation.


title: A parallel implementation of the modified augmented Lagrangian preconditioner for the incompressible Navier–Stokes equations

abstract: We describe a parallel implementation of a block triangular preconditioner based on the modified augmented Lagrangian approach to the steady incompressible Navier---Stokes equations. The equations are linearized by Picard iteration and discretized with various finite element and finite difference schemes on two- and three-dimensional domains. We report strong scalability results for up to 64 cores.


title: Parosteal Osteoma of the Clavicle

abstract: Introduction. Osteoma is a benign, slowly growing, asymptomatic, osteogenic neoplasm. Osteoma of a bone other than the skull and facial bones is extremely rare. An extremely rare case of parosteal osteoma is reported. Case Presentation. A 51-year-old woman presented with a large mass in the left supraclavicular fossa. Radiographs and computed tomography revealed a well-defined, 9 × 6 cm, lobed mass in the midportion of the clavicle. Magnetic resonance imaging revealed that it had the same density as cortical bone. An open biopsy was performed to rule out malignant bone tumours, and parosteal osteoma was diagnosed. Four years after the biopsy, the patient was asymptomatic. Conclusion. A rare case of parosteal osteoma of the clavicle was described. Open biopsy is required to rule out a malignant bone tumour, even if parosteal osteoma is suspected based on the clinical course and imaging findings.


title: GramFuzz: Fuzzing testing of web browsers based on grammar analysis and structural mutation

abstract: Fuzz testing is an automated black-box testing technique providing random data as input to a software system in the hope to find vulnerability. In order to be effective, the fuzzed input must be common enough to pass elementary consistency checks. Web Browser accepts JavaScript, CSS files as well as the html as input, which must be considered in fuzzing testing, while traditional fuzzing technology generates test cases using simple mutation strategies, ignoring the grammar and code structure. In this article, vulnerability patterns are summarized and a new fuzzing testing method are proposed based on grammar analysis of input data and mutation of code structure. Combining methods of generation and mutation, test cases will be more effective in the fuzzing testing of web browsers. Applied on the Mozilla and IE web browsers, it discovered a total of 36 new severe vulnerabilities(and thus became one of the top security bug bounty collectors within this period).


title: BrainID: Development of an EEG-based biometric authentication system

abstract: Authentication is a crucial consideration when securing data or any kind of information system. Though existing approaches for authentication are user-friendly, they have vulnerabilities such as the possibility & criminally threatening a user. We propose a novel approach which uses Electroencephalogram (EEG) brain signals for an authentication process. Unique features of EEG data for distinguishing brain activities can potentially be used to authenticate a user. Compared to other biometric systems, this approach is very robust and more secure because response is significantly changed according to instantaneous mental condition. In the proposed approach, the system user is asked to visualize a number while corresponding EEG signals are captured. Captured signals are used to train the system, compared in the authentication process. This approach mainly focuses on the 8 to 30 Hz Alpha and Beta combined frequency band across all EEG channels, since it is the most appropriate band for EEG signals in Brain Computer Interfaces (BCI). Common Spatial Patterns (CSP) values were used as main features to train the model. Linear discriminant analysis (LDA) was used as a classification algorithm for a given set of user data. A trained set of models for each user is embedded in the system as a parameter database. Each user selects the profile attached to their trained model before the authentication session. Eventually, a trained model can authenticate a user after memorization of pre-determined four digits which the user is asked to think at the first stage of the process. The maximum accuracy recorded with the existing data was 96.97%.


title: Enhancing Recurrent Neural Networks with Positional Attention for Question Answering

abstract: Attention based recurrent neural networks (RNN) have shown a great success for question answering (QA) in recent years. Although significant improvements have been achieved over the non-attentive models, the position information is not well studied within the attention-based framework. Motivated by the effectiveness of using the word positional context to enhance information retrieval, we assume that if a word in the question (i.e., question word) occurs in an answer sentence, the neighboring words should be given more attention since they intuitively contain more valuable information for question answering than those far away. Based on this assumption, we propose a positional attention based RNN model, which incorporates the positional context of the question words into the answers' attentive representations. Experiments on two benchmark datasets show the great advantages of our proposed model. Specifically, we achieve a maximum improvement of 8.83% over the classical attention based RNN model in terms of mean average precision. Furthermore, our model is comparable to if not better than the state-of-the-art approaches for question answering.


title: Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

abstract: This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.


title: Facial Feature Point Detection: A Comprehensive Survey

abstract: Abstract This paper presents a comprehensive survey of facial feature point detection with the assistance of abundant manually labeled images. Facial feature point detection favors many applications such as face recognition, animation, tracking, hallucination, expression analysis and 3D face modeling. Existing methods are categorized into two primary categories according to whether there is the need of a parametric shape model: parametric shape model-based methods and nonparametric shape model-based methods. Parametric shape model-based methods are further divided into two secondary classes according to their appearance models: local part model-based methods (e.g. constrained local model) and holistic model-based methods (e.g. active appearance model). Nonparametric shape model-based methods are divided into several groups according to their model construction process: exemplar-based methods, graphical model-based methods, cascaded regression-based methods, and deep learning based methods. Though significant progress has been made, facial feature point detection is still limited in its success by wild and real-world conditions: large variations across poses, expressions, illuminations, and occlusions. A comparative illustration and analysis of representative methods provides us a holistic understanding and deep insight into facial feature point detection, which also motivates us to further explore more promising future schemes.


title: Automated Assessment of Patients’ Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining

abstract: Patients’ narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms—including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model—were used ::: in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners’ diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients’ self-expression behavior, thus helping clinicians identify potential patients from an early stage.


title: The representation of polysemous words

abstract: Words that have a number of related senses are polysemous. For example, paper refers to both a substance and a publication printed on that substance. Five experiments investigated whether different senses are represented distinctly in the lexicon or if there is a common, core meaning. In all experiments, a polysemous word was used twice, in phrases that selected the same or different senses. Experiment 1 showed that sense consistency aided memory for the polysemous word. Experiment 2 extended this result to a timed sensicality judgment task. Experiment 3 demonstrated that the effects for polysemous words were very similar to those for homonyms. Experiment 4 ruled out the possibility of modifier‐modifier priming. Experiment 5 showed that sense consistency facilitates comprehension relative to a neutral baseline, while sense inconsistency inhibits comprehension. These experiments provide evidence that polysemous words have separate representations for each sense and that any core meaning is minimal. © 2001 Academic Press


title: Nadir CA-125 level as prognosis indicator of high-grade serous ovarian cancer

abstract: The capacity of nadir CA-125 levels to predict the prognosis of epithelial ovarian cancer remains controversial. This study aimed to explore whether the nadir CA-125 serum levels could predict the durations of overall survival (OS) and progression free survival (PFS) in patients with high-grade serous ovarian cancer (HG-SOC) from the USA and PRC. A total of 616 HG-SOC patients from the MD Anderson Cancer Center (MDACC, USA) between 1990 and 2011 were retrospectively analyzed. The results of 262 cases from the Jiangsu Institute of Cancer Research (JICR, PRC) between 1992 and 2011 were used to validate the MDACC data. The CA-125 immunohistochemistry assay was performed on 280 tissue specimens. The Cox proportional hazards model and the log-rank test were used to assess the associations between the clinicopathological characteristics and duration of survival. The nadir CA-125 level was an independent predictor of OS and PFS (p < 0.01 for both) in the MDACC patients. Lower nadir CA-125 levels (≤10 U/mL) were associated with longer OS and PFS (median: 61.2 and 16.8 months with 95% CI: 52.0–72.4 and 14.0–19.6 months, respectively) than their counterparts with shorter OS and PFS (median: 49.2 and 10.5 months with 95% CI: 41.7–56.7 and 6.9–14.1 months, respectively). The nadir CA-125 levels in JICR patients were similarly independent when predicting the OS and PFS (p < 0.01 for both). Nadir CA-125 levels less than or equal to 10 U/mL were associated with longer OS and PFS (median: 59.9 and 15.5 months with 95% CI: 49.7–70.1 and 10.6–20.4 months, respectively), as compared with those more than 10 U/mL (median: 42.0 and 9.0 months with 95% CI: 34.4–49.7 and 6.6–11.2 months, respectively). Baseline serum CA-125 levels, but not the CA-125 expression in tissues, were associated with the OS and PFS of HG-SOC patients in the MDACC and JICR groups. However, these values were not independent. Nadir CA-125 levels were not associated with the tumor burden based on second-look surgery (p = 0.09). Patients who achieved a pathologic complete response had longer OS and PFS (median: 73.7 and 20.7 months with 95% CI: 63.7–83.7 and 9.5–31.9 months, respectively) than those with residual tumors (median: 34.6 and 10.6 months with 95% CI: 6.9–62.3 and 4.9–16.3 months, respectively). The nadir CA-125 level was an independent predictor of OS and PFS in HG-SOC patients. Further prospective studies are required to clinically optimize the chances for a complete clinical response of HG-SOC cases with higher CA-125 levels (>10 U/mL) at the end of primary treatment.


title: An Intrusion Detection System for IEC61850 Automated Substations

abstract: This paper proposes the use of an intrusion detection system (IDS) tailored to counter the threats to an IEC61850-automated substation based upon simulated attacks on intelligent electronic devices (IEDs). Intrusion detection (ID) is the process of detecting a malicious attacker. It is an effective and mature security mechanism. However, it is not harnessed when securing IEC61850-automated substations. The IDS of this paper is developed by using data collected by launching simulated attacks on IEDs and launching packet sniffing attacks using forged address resolution protocol (ARP) packets. The detection capability of the system is then tested by simulating attacks and through genuine user activity. A new method for evaluating the temporal risk of an intrusion for an electric substation based upon the statistical analysis of known attacks is also proposed.


title: A Powered Leg Orthosis for Gait Rehabilitation of Motor-Impaired Patients

abstract: This paper describes a powered leg orthosis for gait rehabilitation of patients with walking disabilities. The paper proposes controllers which can apply suitable forces on the leg so that it moves on a desired trajectory. The description of the controllers, simulations and experimental results with the powered orthosis are presented in the paper. Currently, experiments have been performed with a dummy leg in the orthosis. In the coming months, this powered orthosis will be used on healthy subjects and stroke patients.


title: An intelligent educational software for automatic problem solving in Linear algebra

abstract: Systems of automatic problem solving based on knowledge base play an important role in application of artificial intelligent; especially in education, those systems support effectively to students and teachers in learning and teaching mathematics. Linear algebra is an important basic knowledge for all students in most of disciplines, particularly in information technology. In this paper, a knowledge representation model and inference algorithm are presented; and they have been used to design and implement a system of automatic solving for Linear algebra, as a result that its solutions are suitable with thinking and writing of human. The system is implemented by the algebraic computation system of Maple and C# programming language through connecting technique between Maple and C#. This program has been tested and appreciated completely.


title: Dynamic Social-Aware Peer Selection Scheme for Cooperative Device-to-Device Communications

abstract: Device-to-device (D2D) communication is a promising technology to improve spectrum efficiency, energy efficiency, and transmission delay due to proximity of user equipments (UEs). With cooperative D2D communications, a UE needs to select an optimal peer to act as a relay to forward data to the base station (BS). Therefore, it is essential to take into account the social relationships among UEs during peer selection so as to enhance data privacy. In this paper, we investigate a dynamic social-aware peer selection problem by formulating it as a dynamic optimization problem and proposing the drift-plus-penalty ratio algorithm to solve it. Simulation results show that the proposed peer selection scheme outperforms other existing schemes while keeping the handover frequency lower than others.


title: Soldercubes: a self-soldering self-reconfiguring modular robot system

abstract: Soldercubes are a self-reconfiguring modular robot (MR) system whose modules are light weight, low cost, and designed with manufacturability for large batch production in mind. The frequently cited promises of modular robotics--versatility, robustness, and low cost--assume the availability of large numbers of modules. However, modules in most MR prototypes are large, mechanically complex, expensive, and difficult to manufacture. Soldercubes partially overcome this contradiction through optimizing some components for volume manufacturing processes. With the integration of a soldering connector which weighs only 2 g and has no moving parts, Soldercubes are among the cheapest, lightest and smallest among comparable self-reconfiguring MR systems. This paper describes the Soldercube module design in detail, reports on experiments in a lattice configuration, explores non-lattice applications of the system, and discusses the effects of utilising volume manufacturing processes in module production. All Soldercubes design files are released as open source hardware.


title: Mining feature-opinion pairs and their reliability scores from web opinion sources

abstract: Due to proliferation of Web 2.0, there is an exponential growth in user generated contents in the form of customer reviews on the Web, containing precious information useful for both customers and manufacturers. However, most of the contents are stored in either unstructured or semi-structured format due to which distillation of knowledge from this huge repository is a challenging task. In this paper, we propose a text mining approach to mine product features, opinions and their reliability scores from Web opinion sources. A rule-based system is implemented, which applies linguistic and semantic analysis of texts to mine feature-opinion pairs that have sentence-level co-occurrence in review documents. The extracted feature-opinion pairs and source documents are modeled using a bipartite graph structure. Considering feature-opinion pairs as hubs and source documents as authorities, Hyperlink-Induced Topic Search (HITS) algorithm is applied to generate reliability score for each feature-opinion pair with respect to the underlying corpus. The efficacy of the proposed system is established through experimentation over customer reviews on different models of electronic products.


title: Local Edge Betweenness Based Label Propagation for Community Detection in Complex Networks

abstract: Nowadays, identification and detection community structures in complex networks is an important factor in extracting useful information from networks. Label propagation algorithm with near linear-time complexity is one of the most popular methods for detecting community structures, yet its uncertainty and randomness is a defective factor. Merging LPA with other community detection metrics would improve its accuracy and reduce instability of LPA. Considering this point, in this paper we tried to use edge betweenness centrality to improve LPA performance. On the other hand, calculating edge betweenness centrality is expensive, so as an alternative metric, we try to use local edge betweenness and present LPA-LEB (Label Propagation Algorithm Local Edge Betweenness). Experimental results on both real-world and benchmark networks show that LPA-LEB possesses higher accuracy and stability than LPA when detecting community structures in networks.


title: Efficient Exact Inference for 3D Indoor Scene Understanding

abstract: In this paper we propose the first exact solution to the problem of estimating the 3D room layout from a single image. This problem is typically formulated as inference in a Markov random field, where potentials count image features (e.g., geometric context, orientation maps, lines in accordance with vanishing points) in each face of the layout. We present a novel branch and bound approach which splits the label space in terms of candidate sets of 3D layouts, and efficiently bounds the potentials in these sets by restricting the contribution of each individual face. We employ integral geometry in order to evaluate these bounds in constant time, and as a consequence, we not only obtain the exact solution, but also in less time than approximate inference tools such as message-passing. We demonstrate the effectiveness of our approach in two benchmarks and show that our bounds are tight, and only a few evaluations are necessary.


Reliability of Graduate Nursing Students in Coding Health Problems By Use of ICDA


title: Print vs. electronic resources: a study of user perceptions, preferences, and use

abstract: The arrival and proliferation of electronic resources and digital libraries have a number of significant impacts on the use of print resources and traditional libraries. This study explores the extent to which graduate students in a metropolitan university setting use print and electronic resources. Reading preferences and use of print and electronic resources vary among different disciplines. Graduate students seem to expect a hybrid of print and electronic resources. They desire to meet their information needs through a mix of print and online resources, even though reasons for supplementing another type of resource differ. Circumstances that affect the selection of use between digital libraries and traditional libraries are also discussed.


title: GameFlow experience model: understanding player enjoyment in pervasive adventure geocaching game

abstract: Game developers have identified, explored, and discussed many of the key issues that arise for players interacting in game worlds and the physical world. This study presents the GameFlow experience model, which is empirically tested by six case studies, with the focus on the interplay between player enjoyment and social context. Geocaching is technology-supported treasure hunt activity that uses a Global Positioning System (GPS) receiver or a smartphone with a geocaching application to find something hidden by other players (Geocaching.com). The results provide a deeper understanding of enjoyment in real-time gaming played in the physical world and other users could follow your experiences virtually, along with the identification of the strengths and weaknesses of the GameFlow experience model as an evaluation tool. This study concluded that the GameFlow experience model could be used in its current form to evaluate pervasive adventure games. This study presents design guidelines for designing and evaluating enjoyment in adventure games.


title: PolArt: A Robust Tool for Sentiment Analysis

abstract: We introduce PolArt, a robust tool for sentiment analysis. PolArt is a pattern-based approach designed to cope with polarity composition. In order to determine the polarity of larger text units, a cascade of rewrite operations is carried out: word polarities are combined to NP, VP and sentence polarities. Moreover, PolArt is able to cope with the target-specific polarity of phrases, where two neutral words combine to a non-neutral phrase. Target detection is done with the Wikipedia category system, but also user defined target hierarchies are allowed. PolArt is based on the TreeTagger chunker output, and is customised for English and German. In this paper we evaluate PolArt’s compositional capacity.


title: Techniques for efficiently querying scientific workflow provenance graphs

abstract: A key advantage of scientific workflow systems over traditional scripting approaches is their ability to automatically record data and process dependencies introduced during workflow runs. This information is often represented through provenance graphs, which can be used by scientists to better understand, reproduce, and verify scientific results. However, while most systems record and store data and process dependencies, few provide easy-to-use and efficient approaches for accessing and querying provenance information. Instead, users formulate provenance graph queries directly against physical data representations (e.g., relational, XML, or RDF), leading to queries that are difficult to express and expensive to evaluate. We address these problems through a high-level query language tailored for expressing provenance graph queries. The language is based on a general model of provenance supporting scientific workflows that process XML data and employ update semantics. Query constructs are provided for querying both structure and lineage information. Unlike other languages that return sets of nodes as answers, our query language is closed, i.e., answers to lineage queries are sets of lineage dependencies (edges) allowing answers to be further queried. We provide a formal semantics for the language and present novel techniques for efficiently evaluating lineage queries. Experimental results on real and synthetic provenance traces demonstrate that our lineage based optimizations outperform an in-memory and standard database implementation by orders of magnitude. We also show that our strategies are feasible and can significantly reduce both provenance storage size and query execution time when compared with standard approaches.


Firefly Algorithm for Unconstrained Optimization


title: Platelet-rich plasma (PRP) for the treatment of vulvar lichen sclerosus in a premenopausal woman: A case report

abstract: Abstract The use of platelet-rich plasma (PRP) for the treatment of lichen sclerosus (LS) in a 38-year-old premenopausal woman is reported. The diagnosis was confirmed histologically and the symptoms documented using the ICIQ Vaginal Symptoms Questionnaire (ICIQ-VS) and the Female Sexual Function Index (FSFI) questionnaire. PRP was prepared from autologous blood using the Regen Cellular Matrix Kit. PRP was administered twice over two months. Histology at follow-up one month after the second administration showed the epidermis was nearly normal and upper dermal cellularity had been restored. The patient was symptom-free and both her ICIQ-VS and her FSFI scores had improved significantly. PRP is a potential new treatment option for LS which needs further assessment in randomized controlled trials.


title: Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps

abstract: We propose a new algorithm to learn a one-hidden-layer convolutional neural network where both the convolutional weights and the outputs weights are parameters to be learned. Our algorithm works for a general class of (potentially overlapping) patches, including commonly used structures for computer vision tasks. Our algorithm draws ideas from (1) isotonic regression for learning neural networks and (2) landscape analysis of non-convex matrix factorization problems. We believe these findings may inspire further development in designing provable algorithms for learning neural networks and other complex models.


title: The Economics of Digital Business Models: A Framework for Analyzing the Economics of Platforms

abstract: The paper proposes an analytical framework for comparing different business models for producing information goods and digital services. It is based on three dimensions that also refer to contrasted literature: the economics of matching, the economics of assembling and the economics of knowledge management. Our framework attempts to identify the principal trade-offs at the core of choices among alternative digital business models, and to compare them in terms of competitiveness and efficiency. It also highlights the role played by users in the production of information goods and competition with pure suppliers.


title: Energy management of cooperative microgrids with P2P energy sharing in distribution networks

abstract: To handle the mismatch problem between local demand and local generation in microgrids (MGs), the paradigm of peer-to-peer (P2P) energy sharing among neighboring MGs has been considered as a promising solution for improving the utilization of local distributed energy resources and saving the energy bills for all MGs. Existing works on cooperative MGs usually consider the high-level energy sharing and trading strategies but little about the physical constraints (e.g. voltage tolerance and power flow constraints) in the underlying distribution network. Hence, their solutions may not be applicable to practical power systems. This paper proposes an optimization problem that aims at minimizing the overall energy cost and the P2P energy sharing losses in a distribution network consisting of multiple MGs and explicitly incorporates the practical constraints (e.g., power balance and battery's operational constraints). The proposed optimization problem is difficult to solve directly due to the non-convex constraints. Nevertheless, motivated by the very recent result in radial distribution networks, the proposed non-convex optimization problem can be relaxed to a second-order cone programming (SOCP) problem without incurring any loss of optimality. We apply the proposed problem to a radial distribution network testbed and obtain the corresponding optimal energy management strategy, which exploits the diversified energy consumption profiles to dynamically coordinate multiple MGs and reduces the total energy bill of all MGs. Moreover, an interesting observation from the simulation results is that the cooperation scheme in the P2P sharing network is significantly affected by the MGs' relative locations in the distribution network.


Anodal Stimulation of the Left DLPFC Increases IGT Scores and Decreases Delay Discounting Rate in Healthy Males


title: A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization

abstract: Semantic segmentation and vision-based geolocalization in aerial images are challenging tasks in computer vision. Due to the advent of deep convolutional nets and the availability of relatively low cost UAVs, they are currently generating a growing attention in the field. We propose a novel multi-task multi-stage neural network that is able to handle the two problems at the same time, in a single forward pass. The first stage of our network predicts pixelwise class labels, while the second stage provides a precise location using two branches. One branch uses a regression network, while the other is used to predict a location map trained as a segmentation task. From a structural point of view, our architecture uses encoder-decoder modules at each stage, having the same encoder structure re-used. Furthermore, its size is limited to be tractable on an embedded GPU. We achieve commercial GPS-level localization accuracy from satellite images with spatial resolution of 1 square meter per pixel in a city-wide area of interest. On the task of semantic segmentation, we obtain state-of-the-art results on two challenging datasets, the Inria Aerial Image Labeling dataset and Massachusetts Buildings.


title: Rigid-Foldable Thick Origami

abstract: In this paper, a method is proposed for geometrically constructing thick panel structures that follow the kinetic behavior of rigid origami by using tapered or two-ply panels and hinges located at their edges. The proposed method can convert generalized pattern of rigid-foldable origami into thick panels structure with kinetic motion, which leads to novel designs of origami for various engineering purposes including architecture.


title: INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data

abstract: Predictive modeling techniques are increasingly being used by data scientists to understand the probability of predicted outcomes. However, for data that is high-dimensional, a critical step in predictive modeling is determining which features should be included in the models. Feature selection algorithms are often used to remove non-informative features from models. However, there are many different classes of feature selection algorithms. Deciding which one to use is problematic as the algorithmic output is often not amenable to user interpretation. This limits the ability for users to utilize their domain expertise during the modeling process. To improve on this limitation, we developed INFUSE, a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers. We demonstrate how our system can lead to important insights in a case study involving clinical researchers predicting patient outcomes from electronic medical records.


title: The polycystic ovary syndrome. A position statement from the European Society of Endocrinology

abstract: Polycystic ovary syndrome (PCOS) is the most common ovarian disorder associated with androgen excess in women, which justifies the growing interest of endocrinologists. Great efforts have been made in the last 2 decades to define the syndrome. The presence of three different definitions for the diagnosis of PCOS reflects the phenotypic heterogeneity of the syndrome. Major criteria are required for the diagnosis, which in turn identifies different phenotypes according to the combination of different criteria. In addition, the relevant impact of metabolic issues, specifically insulin resistance and obesity, on the pathogenesis of PCOS, and the susceptibility to develop earlier than expected glucose intolerance states, including type 2 diabetes, has supported the notion that these aspects should be considered when defining the PCOS phenotype and planning potential therapeutic strategies in an affected subject. The NIH Experts Panel recommended the maintenance of the broad diagnostic criteria of Rotterdam, but focused on the need for specific identification of the phenotype of each patient. Four different phenotypes of PCOS are now identified: 1) hyperandrogenism (clinical or biochemical) and CA (H-CA); 2) hyperandrogenism and polycystic ovaries on ultrasound (PCOm) but with ovulatory cycles (H-PCOm); 3) CA and polycystic ovaries without hyperandrogenism (CA-PCOm); and, finally, 4) hyperandrogenism, CA and polycystic ovaries (H-CA-PCOm). This paper offers a critical endocrine on the debate on the definition of PCOS and summarises all major aspects related to aetiological factors, including early life events, potentially involved in the development of the disorder. Diagnostic tools of PCOS are also discussed, with emphasis on the laboratory evaluation of androgens and other potential biomarkers of ovarian and metabolic dysfunctions. We have also paid specific attention to the role of obesity, sleep disorders and neuropsychological aspects of PCOS and on the relevant pathogenetic aspects of cardiovascular risk factors. In addition, we have discussed how to target treatment choices based according to the phenotype and individual patient’s needs.


title: More Attention and Less Repetitive and Stereotyped Behaviors using a Robot with Children with Autism

abstract: The aim of the present study was to assess the usefulness of QTrobot, a socially assistive robot, in interventions with children with autism spectrum disorder (ASD) by assessing children's attention, imitation, and presence of repetitive and stereotyped behaviors. Fifteen children diagnosed with ASD, aged from 4 to 14 years participated in two short interactions, one with a person and one with the robot. Statistical analyses revealed that children directed more attention towards the robot than towards the person, imitated the robot as much as the person, and engaged in fewer repetitive or stereotyped behaviors with the robot than with the person. These results support previous research demonstrating the usefulness of robots in short interactions with children with ASD and provide new evidence to the usefulness of robots in reducing repetitive and stereotyped behaviors in children with ASD, which can affect children's learning.


title: A Tight Bound of Hard Thresholding

abstract: This paper is concerned with the hard thresholding technique which sets all but the $k$ largest absolute elements to zero. We establish a tight bound that quantitatively characterizes the deviation of the thresholded solution from a given signal. Our theoretical result is universal in the sense that it holds for all choices of parameters, and the underlying analysis only depends on fundamental arguments in mathematical optimization. We discuss the implications for the literature: Compressed Sensing. On account of the crucial estimate, we bridge the connection between restricted isometry property (RIP) and the sparsity parameter of $k$ for a vast volume of hard thresholding based algorithms, which renders an improvement on the RIP condition especially when the true sparsity is unknown. This suggests that in essence, many more kinds of sensing matrices or fewer measurements are admissible for the data acquisition procedure. Machine Learning. In terms of large-scale machine learning, a significant yet challenging problem is producing sparse solutions in online setting. In stark contrast to prior works that attempted the $\ell_1$ relaxation for promoting sparsity, we present a novel algorithm which performs hard thresholding in each iteration to ensure such parsimonious solutions. Equipped with the developed bound for hard thresholding, we prove global linear convergence for a number of prevalent statistical models under mild assumptions, even though the problem turns out to be non-convex.


title: Scalable Parallel Programming with CUDA

abstract: The advent of multicore CPUs and manycore GPUs means that mainstream processor chips are now parallel systems. Furthermore, their parallelism continues to scale with Moore's law. The challenge is to develop mainstream application software that transparently scales its parallelism to leverage the increasing number of processor cores, much as 3D graphics applications transparently scale their parallelism to manycore GPUs with widely varying numbers of cores.


title: Comparative Analysis of Intelligent Transportation Systems for Sustainable Environment in Smart Cities

abstract: In recent works on the Internet of Vehicles (IoV), “intelligent” and “sustainable” have been the buzzwords in the context of transportation. Maintaining sustainability in IoV is always a challenge. Sustainability in IoV can be achieved not only by the use of pollution-free vehicular systems, but also by maintenance of road traffic safety or prevention of accidents or collisions. With the aim of establishing an effective sustainable transportation planning system, this study performs a short analysis of existing sustainable transportation methods in the IoV. This study also analyzes various characteristics of sustainability and the advantages and disadvantages of existing transportation systems. Toward the end, this study provides a clear suggestion for effective sustainable transportation planning aimed at the maintenance of an eco-friendly environment and road traffic safety, which, in turn, would lead to a sustainable transportation system.


title: Cognitive behaviour therapy for eating disorders: a “transdiagnostic” theory and treatment

abstract: This paper is concerned with the psychopathological processes that account for the persistence of severe eating disorders. Two separate but interrelated lines of argument are developed. One is that the leading evidence-based theory of the maintenance of eating disorders, the cognitive behavioural theory of bulimia nervosa, should be extended in its focus to embrace four additional maintaining mechanisms. Specifically, we propose that in certain patients one or more of four additional maintaining processes interact with the core eating disorder maintaining mechanisms and that when this occurs it is an obstacle to change. The additional maintaining processes concern the influence of clinical perfectionism, core low self-esteem, mood intolerance and interpersonal difficulties. The second line of argument is that in the case of eating disorders shared, but distinctive, clinical features tend to be maintained by similar psychopathological processes. Accordingly, we suggest that common mechanisms are involved in the persistence of bulimia nervosa, anorexia nervosa and the atypical eating disorders. Together, these two lines of argument lead us to propose a new transdiagnostic theory of the maintenance of the full range of eating disorders, a theory which embraces a broader range of maintaining mechanisms than the current theory concerning bulimia nervosa. In the final sections of the paper we describe a transdiagnostic treatment derived from the new theory, and we consider in principle the broader relevance of transdiagnostic theories of maintenance.


title: Minimizing Polarization and Disagreement in Social Networks

abstract: The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question: “ Given n agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes polarization and disagreement simultaneously? ” This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the others viewpoint of the world, and decrease overall levels of polarization? Such decisions have an important global effect on society williams2007social. Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a graph with O(n/\epsilon^2) edges that is a (1+\epsilon) approximation to the optimum. Our formulation is an instance of optimization over graph topologies, also considered e.g., in boyd2004fastest,daitch2009fitting,sun2006fastest. For a fixed graph, we additionally show how to optimize our objective function over the agents’ innate opinions in polynomial time. We perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both polarization and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a ?60,000-fold reduction in polarization and disagreement. Our code is available at https://github.com/tsourolampis/polarization-disagreement.


title: Rediscovering ACL Discoveries Through the Lens of ACL Anthology Network Citing Sentences

abstract: The ACL Anthology Network (AAN) is a comprehensive manually curated networked database of citations and collaborations in the field of Computational Linguistics. Each citation edge in AAN is associated with one or more citing sentences. A citing sentence is one that appears in a scientific article and contains an explicit reference to another article. In this paper, we shed the light on the usefulness of AAN citing sentences for understanding research trends and summarizing previous discoveries and contributions. We also propose and motivate several different uses and applications of citing sentences.


title: Slotless Protocols for Fast and Energy-Efficient Neighbor Discovery

abstract: In mobile ad-hoc networks, neighbor discovery protocols are used to find surrounding devices and to establish a first contact between them. Since the clocks of the devices are not synchronized and their energy-budgets are limited, usually duty-cycled, asynchronous discovery protocols are applied. Only if two devices are awake at the same point in time, they can rendezvous. Currently, time-slotted protocols, which subdivide time into multiple intervals with equal lengths (slots), are considered to be the most efficient discovery schemes. In this paper, we break away from the assumption of slotted time. We propose a novel, continuous-time discovery protocol, which temporally decouples beaconing and listening. Each device periodically sends packets with a certain interval, and periodically listens for a given duration with a different interval. By optimizing these interval lengths, we show that this scheme can, to the best of our knowledge, outperform all known protocols such as DISCO, U-Connect or Searchlight significantly. For example, Searchlight takes up to 740 % longer than our proposed technique to discover a device with the same duty-cycle. Further, our proposed technique can also be applied in widely-used asymmetric purely interval-based protocols such as ANT or Bluetooth Low Energy.


title: CUSTOMER SATISFACTION WITH E-SERVICES : THE CASE OF AN ONLINE RECRUITMENT PORTAL

abstract: In this contribution we aim to present a state of the art review of academic literature on e-service quality and customer satisfaction with online services. We illustrate some of the research issues by means of a presentation of an empirical study, performed with respect to an online job-search service, or so-called job board. In addition we develop a number of suggestions for further research.


title: Mining long-term search history to improve search accuracy

abstract: Long-term search history contains rich information about a user's search preferences, which can be used as search context to improve retrieval performance. In this paper, we study statistical language modeling based methods to mine contextual information from long-term search history and exploit it for a more accurate estimate of the query language model. Experiments on real web search data show that the algorithms are effective in improving search accuracy for both fresh and recurring queries. The best performance is achieved when using clickthrough data of past searches that are related to the current query.


title: Review: information technology and organizational performance: an integrative model of IT business value

abstract: Despite the importance to researchers, managers, and policy makers of how information technology (IT) contributes to organizational performance, there is uncertainty and debate about what we know and don't know. A review of the literature reveals that studies examining the association between information technology and organizational performance are divergent in how they conceptualize key constructs and their interrelationships. We develop a model of IT business value based on the resource-based view of the firm that integrates the various strands of research into a single framework. We apply the integrative model to synthesize what is known about IT business value and guide future research by developing propositions and suggesting a research agenda. A principal finding is that IT is valuable, but the extent and dimensions are dependent upon internal and external factors, including complementary organizational resources of the firm and its trading partners, as well as the competitive and macro environment. Our analysis provides a blueprint to guide future research and facilitate knowledge accumulation and creation concerning the organizational performance impacts of information technology.


Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems


title: A recommendation mechanism for contextualized mobile advertising

abstract: Abstract Mobile advertising complements the Internet and interactive television advertising and makes it possible for advertisers to create tailor-made campaigns targeting users according to where they are, their needs of the moment and the devices they are using (i.e. contextualized mobile advertising). Therefore, it is necessary that a fully personalized mobile advertising infrastructure be made. In this paper, we present such a personalized contextualized mobile advertising infrastructure for the advertisement of commercial/non-commercial activities. We name this infrastructure MALCR, in which the primary ingredient is a recommendation mechanism that is supported by the following concepts: (1) minimize users' inputs (a typical interaction metaphor for mobile devices) for implicit browsing behaviors to be best utilized; (2) implicit browsing behaviors are then analyzed with a view to understanding the users' interests in the values of features of advertisements; (3) having understood the users' interests, Mobile Ads relevant to a designated location are subsequently scored and ranked; (4) Top-N scored advertisements are recommended. The recommendation mechanism is novel in its combination of two-level Neural Network learning, Neural Network sensitivity analysis, and attribute-based filtering. This recommendation mechanism is also justified (by thorough evaluations) to show its ability in furnishing effective personalized contextualized mobile advertising.


title: Large Displacement Optical Flow from Nearest Neighbor Fields

abstract: We present an optical flow algorithm for large displacement motions. Most existing optical flow methods use the standard coarse-to-fine framework to deal with large displacement motions which has intrinsic limitations. Instead, we formulate the motion estimation problem as a motion segmentation problem. We use approximate nearest neighbor fields to compute an initial motion field and use a robust algorithm to compute a set of similarity transformations as the motion candidates for segmentation. To account for deviations from similarity transformations, we add local deformations in the segmentation process. We also observe that small objects can be better recovered using translations as the motion candidates. We fuse the motion results obtained under similarity transformations and under translations together before a final refinement. Experimental validation shows that our method can successfully handle large displacement motions. Although we particularly focus on large displacement motions in this work, we make no sacrifice in terms of overall performance. In particular, our method ranks at the top of the Middlebury benchmark.


Fecal Microbiota Transplantation Inhibits Multidrug-Resistant Gut Pathogens: Preliminary Report Performed in an Immunocompromised Host


A H IERARCHICAL M ODEL FOR D EVICE P LACEMENT


title: Development of Land-Use Patterns in the Central Andes

abstract: At present, the region is exploited to the upper limit of vegetation and sometimes beyond for mining purposes. The limits of human activity are those imposed by the availability of vegetation for feeding domestic animals, by the effects of cold and wind and, in the south, by the salinity of the soil. The upper limits of exploitation are among the highest in the world and comparable with the northern region of the high chain of the Himalaya (the Tibetan sector). In the Andes, potatoes, chenopodia, and barley are grown as high as 4,200 m; herds of llamas, alpacas, and sheep graze on the last tufts of gramineae to be found between 4,700 and 4,800 m. Mines are worked at altitudes above 5,000 m and several million people live above 3,500 m. So it is here in the tropical Central Andes that millions of human beings encounter the problems of adapting to the constraints of high altitude and to the effects of changes in elevation. The human body encounters problems relating to hypoxia, to the constraints of rapid temperature and humidity changes and, possibly, to detrimental effects from high levels of cosmic radiation. It should be noted, however, that the limitations imposed upon human settlement by altitude are due more to the unavailability of a usable vegetable biomass than to these physiological difficulties. The constraints of life imposed on high-altitude societies have resulted in adaptive responses which have been fashioned over generations. However, the problem confronting present-day populations is not so much that of adapting to high altitude as that of coping with changes of environment, the descent to the hotter low-lying regions, a d the effects of successive movements between regions of iffering elevation. The highltitude area of the Central Andes, which has been inhabited for at least 15,000 years and contains the oldest relics of human settlement on the continent, was the stronghold of peasant chieftainships and states. It still supports a dense human population today. For example, around Lake Titicaca, at 3,800 m, densities of 100 inhabitants per square kilometre are common. This area is economically backward and large numbers of the population are emigrating toward the lower land to the east and w st. Even so, this emigration does not preclude the possibility of areas in the punas being re-established for grazing and farming (Favre, 1977). Is the present low productivity the result of the low capacity for organic production of the high-altitude environment, natural constraints, and lack of development of ecological potential, or does it stem from socio-economic factors in the past and present-day constraints? There is no single answer to this question; for the time being, only a partial answer can be offered and suggestions made of research directions which would help determine the relative importance of natural and social factors. There are two possible approaches: first, it is possible to describe the potential of the natural environment and then examine how this potential has been utilized by societies in the course of history and what techniques were employed for managing and exploiting the available resources. The second approach takes as its starting point a description of how the system functioned at three points in the history of the Andes from the fifteenth to the beginning of the sixteenth century, the seventeenth century, and the mid-twentieth century; from this historic base, the problem of how the present system might be enhanced may be examined. The second approach will be utilized here.


title: Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings

abstract: We consider the task of inferring is-a relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing is-a relationships and to correct wrong extractions. Moreover -- and in contrast with other methods -- the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks.


title: Parallel Support Vector Machines: The Cascade SVM

abstract: We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into subsets and optimized separately with multiple SVMs. The partial results are combined and filtered again in a 'Cascade' of SVMs, until the global optimum is reached. The Cascade SVM can be spread over multiple processors with minimal communication overhead and requires far less memory, since the kernel matrices are much smaller than for a regular SVM. Convergence to the global optimum is guaranteed with multiple passes through the Cascade, but already a single pass provides good generalization. A single pass is 5x - 10x faster than a regular SVM for problems of 100,000 vectors when implemented on a single processor. Parallel implementations on a cluster of 16 processors were tested with over 1 million vectors (2-class problems), converging in a day or two, while a regular SVM never converged in over a week.


title: A Comparison between Bat Algorithm andCuckoo Search for Path Planning

abstract: Here, metaheuristic search techniques are applied for robotic path planning. Robot has to move from its starting point called source point to final point called as destination point with minimum number of moves and iteration. Here both cuckoo search and bat algorithms are applied for the proposed problem and simulation results are compared. The techniques are applied for different number of population and bat algorithm provide better results as compared to cuckoo search.