title: Hesitant Triangular Fuzzy Information Aggregation Operators Based on Bonferroni Means and Their Application to Multiple Attribute Decision Making

abstract: We investigate the multiple attribute decision-making (MADM) problems with hesitant triangular fuzzy information. Firstly, definition and some operational laws of hesitant triangular fuzzy elements are introduced. Then, we develop some hesitant triangular fuzzy aggregation operators based on Bonferroni means and discuss their basic properties. Some existing operators can be viewed as their special cases. Next, we apply the proposed operators to deal with multiple attribute decision-making problems under hesitant triangular fuzzy environment. Finally, an illustrative example is given to show the developed method and demonstrate its practicality and effectiveness.


title: An empirical study of anomaly detection in online games

abstract: In data mining, anomaly detection aims to identify the data samples that do not conform to an expected behavior. Anomaly detection has successfully been applied to many real world applications such as fraud detection for credit cards and intrusion detection in security. However, there are very little research on using anomaly detection techniques to detect cheating in online games. In this paper, we present an empirical study of anomaly detection in online games. Four unsupervised anomaly detection techniques were used to detect abnormal players. A method for evaluating the performance these detection techniques was introduced and analysed. The experiments were conducted on one artificial dataset and two real online games at VNG company. The results show the good capability of detection techniques used in this paper in detecting abnormal players in online games.


title: Socially-Aware Multimedia Content Delivery for the Cloud

abstract: Most Content Delivery Networks (CDNs) are operated as a Software as a Service (SaaS): Many cloud providers build their custom CDNs to benefit from content users, as well as reduce demand on their own telecommunications infrastructure. More importantly, though, CDNs contribute to cloud adoption, as they can address network problems of cloud computing. With multimedia content providers requiring CDN services to enable the delivery of bandwindth-demanding media to end-users, and the growth of HTTP traffic due to media files circulating over Online Social Networks (OSNs), a social-awareness mechanism over a CDN becomes essential, to mitigate the considerable weight placed on bandwidth. A social awareness mechanism augmented to a stand-alone CDN traffic simulator addresses the issue of which content will be copied in the surrogate servers of a CDN infrastructure and to what extent. Hence, it ensures an optimized content diffusion placement. Herein, we further address the issue of temporal diffusion, related to the most efficient timing of the content placement. We exploit the knowledge of peak times for upload and download, so that content is prefetched in the hours with less traffic. We also incorporate other contextual information, such as the viewership within the media service, to ensure performance optimization. Our variations are experimentally proven to contribute toward maximization of CDNs' performance and minimization of content replication costs.


title: Control of Wheeled Mobile Robots: An Experimental Overview

abstract: The subject of this chapter is the motion control problem of wheeled mobile robots (WMRs). With reference to the unicycle kinematics, we review and compare several control strategies for trajectory tracking and posture stabilization in an environment free of obstacles. Experiments are reported for SuperMARIO, a two-wheel differentially-driven mobile robot. From the comparison of the obtained results, guidelines are provided for WMR end-users.


title: Nature, nurture, and development: from evangelism through science toward policy and practice.

abstract: During the second half of the 20th century there was an immense increase in both empirical findings on, and conceptual understanding of, the effects of nature, nurture, and developmental processes on psychological functioning—both normal and abnormal. Unfortunately, the good science has also been accompanied by excessive polarizing claims and by unwarranted extrapolations. This article provides a summary review of the real gains in knowledge, outlines some of the misleading claims, and notes the potential for research and for science-led improvements in policies and practice. The need to bring about a better interpretation of genetic, psychosocial, and developmental research strategies and theoretical concepts is emphasized.


title: Mathematical model of rubberless pneumatic artificial muscle

abstract: This paper describes a mathematical model of a rubberless pneumatic artificial muscle. This artificial muscle has characteristics resembling those of a McKibben-type artificial muscle. Because of its exclusion of rubber material, however, the rubberless artificial muscle can be driven by low pressure. Although it uses a braided mesh similar to that of a McKibben artificial muscle, its contractive mechanism differs slightly. We derived a mathematical model of the rubberless artificial muscle while devoting attention to its unique contractive mechanisms. We confirmed the validity of the mathematical model through experimentation. Results show that this proposed mathematical model can be expressed in a simple form, and that it has sufficient accuracy.


title: Learning Latent Personas of Film Characters

abstract: We present two latent variable models for learning character types, or personas, in film, in which a persona is defined as a set of mixtures over latent lexical classes. These lexical classes capture the stereotypical actions of which a character is the agent and patient, as well as attributes by which they are described. As the first attempt to solve this problem explicitly, we also present a new dataset for the text-driven analysis of film, along with a benchmark testbed to help drive future work in this area.


title: Performance anomaly of 802.11b

abstract: The performance of the IEEE 802.11b wireless local area networks is analyzed. We have observed that when some mobile hosts use a lower bit rate than the others, the performance of all hosts is considerably degraded. Such a situation is a common case in wireless local area networks in which a host far away from an access point is subject to important signal fading and interference. To cope with this problem, the host changes its modulation type, which degrades its bit rate to some lower value. Typically, 802.11b products degrade the bit rate from 11 Mb/s to 5.5, 2, or 1 Mb/s when repeated unsuccessful frame transmissions are detected. In such a case, a host transmitting for example at 1 Mb/s reduces the throughput of all other hosts transmitting at 11 Mb/s to a low value below 1 Mb/s. The basic CSMA/CA channel access method is at the root of this anomaly: it guarantees an equal long term channel access probability to all hosts. When one host captures the channel for a long time because its bit rate is low, it penalizes other hosts that use the higher rate. We analyze the anomaly theoretically by deriving simple expressions for the useful throughput, validate them by means of simulation, and compare with several performance measurements.


title: Microorganisms in Fermented Apple Beverages: Current Knowledge and Future Directions

abstract: Production of fermented apple beverages is spread all around the world with specificities in each country. 'French ciders' refer to fermented apple juice mainly produced in the northwest of France and often associated with short periods of consumption. Research articles on this kind of product are scarce compared to wine, especially on phenomena associated with microbial activities. The wine fermentation microbiome and its dynamics, organoleptic improvement for healthy and pleasant products and development of starters are now widely studied. Even if both beverages seem close in terms of microbiome and process (with both alcoholic and malolactic fermentations), the inherent properties of the raw materials and different production and environmental parameters make research on the specificities of apple fermentation beverages worthwhile. This review summarizes current knowledge on the cider microbial ecosystem, associated activities and the influence of process parameters. In addition, available data on cider quality and safety is reviewed. Finally, we focus on the future role of lactic acid bacteria and yeasts in the development of even better or new beverages made from apples.


title: Nintendo Super Smash Bros. Melee: An"Untouchable"Agent

abstract: Nintendo's Super Smash Bros. Melee fighting game can be emulated on modern hardware allowing us to inspect internal memory states, such as character positions. We created an AI that avoids being hit by training using these internal memory states and outputting controller button presses. After training on a month's worth of Melee matches, our best agent learned to avoid the toughest AI built into the game for a full minute 74.6% of the time.


title: Static balancing of a manipulator with hemispherical work space

abstract: This paper proposes a gravity compensator for the roll-pitch rotation which can be operated in hemispherical work space. The gravity compensator comprises two 1-dof gravity compensators and a bevel differential. It is revealed from analyses of energy and torque that the proposed gravity compensator can perform static balancing completely. The gravity compensation of a 4-dofs manipulator is briefly introduced for example. Experiments show that the proposed compensator can counterbalance the gravitational torques effectively and can be operated in hemispherical work space.


Spin-based quantum dot quantum computing, in


title: Importance of audio feature reduction in automatic music genre classification

abstract: Multimedia database retrieval is rapidly growing and its popularity in online retrieval systems is consequently increasing. Large datasets are major challenges for searching, retrieving, and organizing the music content. Therefore, a robust automatic music-genre classification method is needed for organizing this music data into different classes according to specific viable information. Two fundamental components are to be considered for genre classification: audio feature extraction and classifier design. In this paper, we propose diverse audio features to precisely characterize the music content. The feature sets belong to four groups: dynamic, rhythmic, spectral, and harmonic. From the features, five statistical parameters are considered as representatives, including the fourth-order central moments of each feature as well as covariance components. Ultimately, insignificant representative parameters are controlled by minimum redundancy and maximum relevance. This algorithm calculates the score level of all feature attributes and orders them. Only high-score audio features are considered for genre classification. Moreover, we can recognize those audio features and distinguish which of the different statistical parameters derived from them are important for genre classification. Among them, mel frequency cepstral coefficient statistical parameters, such as covariance components and variance, are more frequently selected than the feature attributes of other groups. This approach does not transform the original features as do principal component analysis and linear discriminant analysis. In addition, other feature reduction methodologies, such as locality-preserving projection and non-negative matrix factorization are considered. The performance of the proposed system is measured based on the reduced features from the feature pool using different feature reduction techniques. The results indicate that the overall classification is competitive with existing state-of-the-art frame-based methodologies.


Regression Models for Ordinal Data: A Machine Learning Approach


title: Building brand identity in competitive markets: a conceptual model

abstract: Purpose – The purpose of this conceptual paper is to identify important elements of brand building based on a literature review and case studies of successful brands in India.Design/methodology/approach – This paper is based on a review of the literature and takes a case study approach. The paper suggests the framework for building brand identity in sequential order, namely, positioning the brand, communicating the brand message, delivering the brand performance, and leveraging the brand equity.Findings – Brand‐building effort has to be aligned with organizational processes that help deliver the promises to customers through all company departments, intermediaries, suppliers, etc., as all these play an important role in the experience customers have with the brand.Originality/value – The paper uses case studies of leading Indian brands to illustrate the importance of action elements in building brands in competitive markets.


title: Self-powered ultra-low power DC-DC converter for RF energy harvesting

abstract: In this paper, an autonomous low voltage and ultra-low power DC-DC converter is presented. This novel topology is inspired from the classical Armstrong oscillator structure. In addition to be self-powered and autonomous, this converter is suitable for high-impedance sources. Theoretical and simulation-based optimizations are used in order to design the converter. A fabricated prototype is tested. It harvests RF energy from a low power rectenna (rectifying antenna). High output voltage and good performances are achieved in the range of 4μW to 1mW of input power.


title: Causal Graph Based Decomposition of Factored MDPs

abstract: We present Variable Influence Structure Analysis, or VISA, an algorithm that performs hierarchical decomposition of factored Markov decision processes. VISA uses a dynamic Bayesian network model of actions, and constructs a causal graph that captures relationships between state variables. In tasks with sparse causal graphs VISA exploits structure by introducing activities that cause the values of state variables to change. The result is a hierarchy of activities that together represent a solution to the original task. VISA performs state abstraction for each activity by ignoring irrelevant state variables and lower-level activities. In addition, we describe an algorithm for constructing compact models of the activities introduced. State abstraction and compact activity models enable VISA to apply efficient algorithms to solve the stand-alone subtask associated with each activity. Experimental results show that the decomposition introduced by VISA can significantly accelerate construction of an optimal, or near-optimal, policy.


title: Evaluation of Word Vector Representations by Subspace Alignment

abstract: Unsupervisedly learned word vectors have proven to provide exceptionally effective features in many NLP tasks. Most common intrinsic evaluations of vector quality measure correlation with similarity judgments. However, these often correlate poorly with how well the learned representations perform as features in downstream evaluation tasks. We present QVEC—a computationally inexpensive intrinsic evaluation measure of the quality of word embeddings based on alignment to a matrix of features extracted from manually crafted lexical resources—that obtains strong correlation with performance of the vectors in a battery of downstream semantic evaluation tasks.1


title: Learning control of finite Markov chains with an explicit trade-off between estimation and control

abstract: An efficient scheme is presented for a learning control problem of finite Markov chains with unknown dynamics, i.e. with unknown transition probabilities. The scheme is designed to optimize the asymptotic system performance and for easy application to models with relatively many states and decisions. In this scheme a control policy is determined each time through maximization of a simple performance criterion that explicitly incorporates a tradeoff between estimation of the unknown probabilities and control of the system. The policy determination can be easily performed even in the case of large-size models, since the maximizing operation can be greatly simplified by use of the policy-iteration method. It is proven that this scheme becomes epsilon -optimal as well as optimal by suitable choice of control parameter values in the sense that a relative frequency coefficient of making optimal decisions tends to the maximum. >


title: Social isolation in parents of children with hemangiomas: Effects of coping styles and emotional distress

abstract: This study investigated factors associated with social isolation in parents of children with hemangiomas. Eighty-one parents completed questionnaires assessing their emotional distress, social isolation, and coping styles. To explore the relationships between these variables, a path analysis was used to test a model in which clinical characteristics of hemangiomas and parents’ coping strategies do not have direct effects on their social isolation but indirect effects via their emotional distress. Bootstrapping was used to assess indirect effects. Time since onset and lesional complications had positive direct effects on parents’ social isolation. Lesional visibility and emotion-focused coping had negative indirect effects on parents’ social isolation via their emotional distress, while problem-focused coping showed a positive indirect effect. These findings may have implications for clinicians managing parents of children with hemangiomas.


title: Substrate-Independent Microwave Components in Substrate Integrated Waveguide Technology for High-Performance Smart Surfaces

abstract: Although all existing air-filled substrate integrated waveguide (AFSIW) topologies yield a substrate-independent electrical performance, they rely on dedicated, expensive, laminates to form air-filled regions that contain the electromagnetic fields. This paper proposes a novel substrate-independent AFSIW manufacturing technology, enabling straightforward integration of high-performance microwave components into a wide range of general-purpose commercially available surface materials by means of standard additive (3-D printing) or subtractive (computer numerically controlled milling/laser cutting) manufacturing processes. First, an analytical formula is derived for the effective permittivity and loss tangent of the AFSIW waveguide. This allows the designer to reduce substrate losses to levels typically encountered in high-frequency laminates. Then, several microwave components are designed and fabricated. Measurements of multiple AFSIW waveguides and a four-way power divider/combiner, both relying on a new coaxial-to-air-filled SIW transition, prove that this novel approach yields microwave components suitable for direct integration into everyday surfaces, with low insertion loss, and excellent matching and isolation over the entire [5.15–5.85] GHz band. Hence, this innovative approach paves the way for a new generation of cost-effective, high-performance, and invisibly integrated smart surface systems that efficiently exploit the area and the materials available in everyday objects.


Chapter 6 HR MetRics and WoRkfoRce analytics


title: Similarity learning based on pool-based active learning for manga character retrieval

abstract: Manga is Japanese style comics which is one of the most popular publications in Japan. There are some e-book services for selling manga. Unfortunately, most of them handle manga only as the set of images. Some digital media use tags or text data for retrieval, where the tags and text data are produced by handmade input. Our final goal is developing a practical content-based manga retrieval system based on automatic tagging of the contents of manga, for example, place, character, expression, situation, and other information readers are interested in. At the firsts step of this goal, we focus on effective tagging for manga character retrieval because characters are most important elements in manga. We employ the measurement "similarity" to measure the difference between line drawings representing characters. Similarity becomes higher when the two line drawings represent a same character. Similarity is obtained as weighted distance of CMR-HOG features, where the weights are obtained by AdaBoost learning. We use pool-based active learning for selecting training data efficiently because the training data should be labeled by handmade which is high cost. Experimental results demonstrates the effectiveness of similarity and the selection of distinctive training samples based on pool-based active learning.


title: Prefrontal–hippocampal interactions in episodic memory

abstract: The roles of the hippocampus and prefrontal cortex (PFC) in memory processing - individually or in concert - are a major topic of interest in memory research. These brain areas have distinct and complementary roles in episodic memory, and their interactions are crucial for learning and remembering events. Considerable evidence indicates that the PFC and hippocampus become coupled via oscillatory synchrony that reflects bidirectional flow of information. Furthermore, newer studies have revealed specific mechanisms whereby neural representations in the PFC and hippocampus are mediated through direct connections or through intermediary regions. These findings suggest a model of how the hippocampus and PFC, along with their intermediaries, operate as a system that uses the current context of experience to retrieve relevant memories.


title: Recognizing handwritten Arabic words using grapheme segmentation and recurrent neural networks

abstract: The Arabic alphabet is used in around 27 languages, including Arabic, Persian, Kurdish, Urdu, and Jawi. Many researchers have developed systems for recognizing cursive handwritten Arabic words, using both holistic and segmentation-based approaches. This paper introduces a system that achieves high accuracy using efficient segmentation, feature extraction, and recurrent neural network (RNN). We describe a robust rule-based segmentation algorithm that uses special feature points identified in the word skeleton to segment the cursive words into graphemes. We show that careful selection from a wide range of features extracted during and after the segmentation stage produces a feature set that significantly reduces the label error. We demonstrate that using same RNN recognition engine, the segmentation approach with efficient feature extraction gives better results than a holistic approach that extracts features from raw pixels. We evaluated this segmentation approach against an improved version of the holistic system MDLSTM that won the ICDAR 2009 Arabic handwritten word recognition competition. On the IfN/ENIT database of handwritten Arabic words, the segmentation approach reduces the average label error by 18.5 %, the sequence error by 22.3 %, and the execution time by 31 %, relative to MDLSTM. This approach also has the best published accuracies on two IfN/ENIT test sets.


title: Systematic evaluation of convolution neural network advances on the Imagenet

abstract: Abstract The paper systematically studies the impact of a range of recent advances in convolution neural network (CNN) architectures and learning methods on the object categorization (ILSVRC) problem. The evaluation tests the influence of the following choices of the architecture: non-linearity (ReLU, ELU, maxout, compatability with batch normalization), pooling variants (stochastic, max, average, mixed), network width, classifier design (convolutional, fully-connected, SPP), image pre-processing, and of learning parameters: learning rate, batch size, cleanliness of the data, etc. The performance gains of the proposed modifications are first tested individually and then in combination. The sum of individual gains is greater than the observed improvement when all modifications are introduced, but the “deficit” is small suggesting independence of their benefits. We show that the use of 128 × 128 pixel images is sufficient to make qualitative conclusions about optimal network structure that hold for the full size Caffe and VGG nets. The results are obtained an order of magnitude faster than with the standard 224 pixel images.


title: Construction of a genetic map for pearl millet, Pennisetum glaucum (L.) R. Br., using a genotyping-by-sequencing (GBS) approach

abstract: Pearl millet is the main component of traditional farming systems and a staple grain in the diet of sub-Saharan Africa and India. To facilitate breeding work in this crop, a genetic map consisting of single nucleotide polymorphism (SNP) markers was constructed using an F2 population of 93 progenies, from a wild × cultivated pearl millet cross. We used a modified genotyping-by-sequencing (GBS) protocol involving two restriction enzymes (PstI–MspI) and PCR amplification with primers including three selective bases to generate 3,321 SNPs. Of these, 2,809 high-quality SNPs exhibited a minor allele frequency ≥0.3. In total, 314 non-redundant haplotypes and 85 F2 individuals were used to construct a genetic map spanning a total distance of 640 cM. These SNPs were evenly distributed over seven linkage groups ranging considerably in size (62–123 cM). The average density for this map was 0.51 SNP/cM, and the average interval between SNP markers was 2.1 (±0.6) cM. Finally, to establish bridges between the linkage groups of this and previous maps, 19 SSR markers were examined for polymorphism between the parents of this population. We could only tentatively suggest a correspondence between four of our linkage groups and those of previous maps. Overall, GBS enabled us to quickly produce a genetic map with a density and uniformity of markers greater than previously published maps. The availability of such a map will be useful for the identification of genomic regions associated with Striga resistance and other important agronomic traits.


title: A prospective study of 39 hips with between 1 and 4 years of follow-up

abstract: Background and purpose Operative findings during revision of metal-on-metal hip arthroplasty (MOMHA) vary widely and can involve massive soft tissue and bone disruption. As a result, planning of theater time and resources is difficult, surgery is chal-lenging, and outcomes are often poor. We describe our experi-ence with revision of MOMHA and provide recommendations for management.Patients and methods We present the findings and outcomes of 39 consecutive MOMHAs (in 35 patients) revised in a tertiary unit (median follow-up time 30 (12–54) months). The patients under-went a preoperative work-up including CT, metal artifact reduc-tion sequence (MARS) MRI, and blood metal ion levels. Results We determined 5 categories of failure. 8 of 39 hips had conventional failure mechanisms including infection and impinge-ment. Of the other 31 hips, 14 showed synovitis without signifi-cant disruption of soft tissue; 6 had a cystic pseudotumor with significant soft tissue disruption; 7 had significant osteolysis; and 4 had a solid pseudotumor. Each category of failure had specific surgical hazards that could be addressed preoperatively. There were 2 reoperations and 1 patient (2 hips) died of an unrelated cause. Median Oxford hip score (OHS) was 37 (9–48); median change (


title: Suspicious Behavior Detection: Current Trends and Future Directions

abstract: Different real-world applications have varying definitions of suspicious behaviors. Detection methods often look for the most suspicious parts of the data by optimizing scores, but quantifying the suspiciousness of a behavioral pattern is still an open issue.


title: A Comparison of Voice Conversion Methods for Transforming Voice Quality in Emotional Speech Synthesis

abstract: This paper presents a comparison of methods for transforming voice quality in neutral synthetic speech to match cheerful, aggressive, and depressed expressive styles. Neutral speech is generated using the unit selection system in the MARY TTS platform and a large neutral database in German. The output is modified using voice conversion techniques to match the target expressive styles, the focus being on spectral envelope conversion for transforming the overall voice quality. Various improvements over the state-of-the-art weighted codebook mapping and GMM based voice conversion frameworks are employed resulting in three algorithms. Objective evaluation results show that all three methods result in comparable reduction in objective distance to target expressive TTS outputs whereas weighted frame mapping and GMM based transformations were perceived slightly better than the weighted codebook mapping outputs in generating the target expressive style in a listening test.


title: Pleated pneumatic artificial muscles: compliant robotic actuators

abstract: Pleated pneumatic artificial muscles (PPAMs), developed at the Vrije Universiteit Brussel, Department of Mechanical Engineering, are used as robotic actuators. Their distinguishing feature is their pleated design, as a result of which their contraction forces and maximum displacement are very high compared to other pneumatic artificial muscles. The PPAM design, operation and characteristics are presented. A rotative joint actuator, made of two antagonistically coupled PPAMs, is discussed to demonstrate their suitability for robotics. It has several properties that are similar to those of skeletal joint actuators. Positioning tasks are seen to be performed very accurately using a simple PI control. Furthermore, the antagonistic actuator can easily be made to have a soft or careful touch, contributing greatly to a safe robot operation. In view of all the characteristics PPAMs are very well suited for automation and robotic applications.


title: SMART-JaCaMo: an organization-based team for the multi-agent programming contest

abstract: The Multi-Agent Programming Contest in 2017 expanded upon the Agents in the City scenario used in the 2016 edition of the contest. In the Agents in the City contest, two teams compete to accomplish logistic tasks in simulations using realistic city maps from OpenStreetMap. The new version of the scenario shifted emphasis to include jobs that require assembled items, new types of facilities, and larger teams, resulting in a significantly more complex scenario than before. In this paper, we describe the strategies used by our team, highlighting our adaptations and any new additions from our participation in the previous year. One such new addition, is that now we have fully explored the use of all three programming dimensions (agent, environment, and organization) found in JaCaMo, the multi-agent system development platform that we used to implement our team. We also provide some discussion and analysis on what we believe were some of our most influential matches.


The Noise Clinic: a Blind Image Denoising Algorithm


title: Pedestrian detection and tracking with night vision

abstract: This paper presents a method for pedestrian detection and tracking using a single night-vision video camera installed on the vehicle. To deal with the nonrigid nature of human appearance on the road, a two-step detection/tracking method is proposed. The detection phase is performed by a support vector machine (SVM) with size-normalized pedestrian candidates and the tracking phase is a combination of Kalman filter prediction and mean shift tracking. The detection phase is further strengthened by information obtained by a road-detection module that provides key information for pedestrian validation. Experimental comparisons (e.g., grayscale SVM recognition versus binary SVM recognition and entire-body detection versus upper-body detection) have been carried out to illustrate the feasibility of our approach.


title: Building high-performance smartphones via non-volatile memory: the swap approach

abstract: Smartphones are getting increasingly high-performance with advances in mobile processors and larger main memories to support feature-rich applications. However, the storage subsystem has always been a prohibitive factor that slows down the pace of reaching even higher performance while maintaining good user experience. Despite today's smartphones are equipped with larger-than-ever main memories, they consume more energy and still run out of memory. But the slow NAND flash based storage vetoes the possibility of swapping---an important technique to extend main memory---and leaves a system that constantly terminates user applications under memory pressure. In this paper, we revisit swapping for smartphones with fast, byte-addressable, non-volatile memory (NVM) technologies. Instead of using flash, we build the swap area with NVM, to allow high performance without sacrificing user experience. Based on NVM's high performance and byte-addressability, we show that a copy-on-write swap-in scheme can achieve even better performance by avoiding unnecessary memory copy operations. To avoid fast worn-out of certain NVMs, we also propose Heap-Wear, a wear leveling algorithm that more evenly distributes writes in NVM. Evaluation results based on the Google Nexus 5 smartphone show that our solution can effectively enhance smartphone performance and give better wear-leveling of NVM.


title: Evaluation of 3D interest point detection techniques

abstract: In this paper, we compare the results of five 3D interest point detection techniques to the interest points marked by human subjects. This comparison is used to quantitatively evaluate the interest point detection algorithms. We asked human subjects to look at a number of 3D models, and mark interest points on the models via a web-based interface. We propose a voting-based method to construct ground truth out of humans' selections of interest points. Evaluation measures, namely False Positive and False Negative Errors, are then defined based on the geodesic distance between the interest points detected by a particular algorithm and the human-generated ground truth.


Multiple Views in Immersive Analytics


title: Intelligent cognitive radio: Research on learning and evaluation of CR based on Neural Network

abstract: This paper introduces the research of cognitive engine and application of artificial intelligence techniques in cognitive radio. The limitation of CR engine based on GA is analyzed, propose for improvement is proposed. The decision maker of CR engine should consider both the changeable factors and the unchangeable factors such as cost, bandwidth, signal rate and ARQ. Based on Neural Network, the method of evaluating and learning best decision is proposed. Several key architectural issues for cognitive radio engine based on Neural Network are discussed, including knowledge base information model and learning model Neural Network design.


title: Mule Bite to the Male Genitalia with Complete Penile and Anterior Urethra Amputation: Unusual Case and Review of the Literature

abstract: Animal bite is rare with few cases reported in the literature. The morbidity of animal bites is directly related to the severity of the initial wound. Most victims are boys, and dog bites are the most common injury. Infectious complications are unusual, since treatment is sought early (Wein 2007). Thus, urologists are not usually familiar with management and principles for treating this condition. The authors report the case of a 38-year-old male with a severe mule bite injury to the genitalia causing complete penile and anterior urethra amputation and scrotal wound with no involvement of its contents. To our knowledge, no such case had ever been reported in the medical literature. This kind of emergencies is challenging for urologists.


title: UnrealCV: Connecting Computer Vision to Unreal Engine

abstract: Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated. But creating realistic virtual worlds is not easy. The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with. So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games. To enable this we created an open-source plugin UnrealCV (this http URL) for a popular game engine Unreal Engine 4 (UE4). We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms.


title: Predicting axial capacity of driven piles in cohesive soils using intelligent computing

abstract: An accurate prediction of pile capacity under axial loads is necessary for the design. This paper presents the development of a new model to predict axial capacity of pile foundations driven into cohesive soils. Gene expression programming technique (GEP) has been utilized for this purpose. The data used for development of the GEP model is collected from the literature and comprise a series of in-situ driven piles load tests as well as cone penetration test (CPT) results. The data are divided into two subsets: training set for model calibration and independent validation set for model verification. Predictions from the GEP model are compared with experimental data and with predictions of number of currently adopted CPT-based methods. The results have demonstrated that the GEP model performs well with coefficient of correlation, mean and probability density at 50% equivalent to 0.94, 0.96 and 1.01, respectively, indicating that the proposed model predicts pile capacity accurately.


title: A Survey on Time-of-Flight Stereo Fusion

abstract: Due to the demand for depth maps of higher quality than possible with a single depth imaging technique today, there has been an increasing interest in the combination of different depth sensors to produce a “super-camera” that is more than the sum of the individual parts. In this survey paper, we give an overview over methods for the fusion of Time-of-Flight (ToF) and passive stereo data as well as applications of the resulting high quality depth maps. Additionally, we provide a tutorial-based introduction to the principles behind ToF stereo fusion and the evaluation criteria used to benchmark these methods.


title: DeCNT: Deep Deformable CNN for Table Detection

abstract: This paper presents a novel approach for the detection of tables present in documents, leveraging the potential of deep neural networks. Conventional approaches for table detection rely on heuristics that are error prone and specific to a dataset. In contrast, the presented approach harvests the potential of data to recognize tables of arbitrary layout. Most of the prior approaches for table detection are only applicable to PDFs, whereas, the presented approach directly works on images making it generally applicable to any format. The presented approach is based on a novel combination of deformable CNN with faster R-CNN/FPN. Conventional CNN has a fixed receptive field which is problematic for table detection since tables can be present at arbitrary scales along with arbitrary transformations (orientation). Deformable convolution conditions its receptive field on the input itself allowing it to mold its receptive field according to its input. This adaptation of the receptive field enables the network to cater for tables of arbitrary layout. We evaluated the proposed approach on two major publicly available table detection datasets: ICDAR-2013 and ICDAR-2017 POD. The presented approach was able to surpass the state-of-the-art performance on both ICDAR-2013 and ICDAR-2017 POD datasets with a F-measure of 0.994 and 0.968, respectively, indicating its effectiveness and superiority for the task of table detection.


Implementing A Neural Cache LSTM


title: Syntheses and evaluation of 2,5-disubstituted 4-thiazolidinone analogues as antimicrobial agents

abstract: Two novel series of 4-thiazolidinone derivatives, bearing 2-nitrophenyl imino and 4-nitrophenyl imino groups at position-2 and substituted arylidene groups at position-5, have been synthesized and evaluated for antimicrobial activity against four bacterial and one fungal strain. The success of the synthesis of compounds was confirmed on the basis of spectral analysis. All the newly synthesized compounds were obtained in high yields and exhibited good antibacterial activity; however, the antifungal potential was limited to a few agents.


title: The World Health Organization Health and Work Performance Questionnaire (HPQ).

abstract: This report describes the World Health Organization Health and Work Performance Questionnaire (HPQ), a self-report instrument designed to estimate the workplace costs of health problems in terms of reduced job performance, sickness absence, and work-related accidents-injuries. Calibration data are presented on the relationship between individual-level HPQ reports and archival measures of work performance and absenteeism obtained from employer archives in four groups: airline reservation agents (n = 441), customer service representatives (n = 505), automobile company executives (n = 554), and railroad engineers (n = 850). Good concordance is found between the HPQ and the archival measures in all four occupations. The paper closes with a brief discussion of the calibration methodology used to monetize HPQ reports and of future directions in substantive research based on the HPQ.


Synereo: The Decentralized and Distributed Social Network


title: UPAR7: A knowledge-based system for headline sentiment tagging

abstract: For the Affective Text task at SemEval-2007, University Paris 7's system first evaluates emotion and valence on all words of a news headline (using enriched versions of SentiWordNet and a subset of WordNet-Affect). We use a parser to find the head word, considering that it has a major importance. We also detect contrasts (between positive and negative words) that shift valence. Our knowledge-based system achieves high accuracy on emotion and valence annotation. These results show that working with linguistic techniques and a broad-coverage lexicon is a viable approach to sentiment analysis of headlines.


title: Distance transforms of sampled functions

abstract: We describe linear-time algorithms for solving a class of problems that involve transforming a cost function on a grid using spatial information. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary function on a grid. Alternatively they can be viewed in terms of the minimum convolution of two functions, which is an important operation in grayscale morphology. A consequence of our techniques is a simple and fast method for computing the Euclidean distance transform of a binary image. Our algorithms are also applicable to Viterbi decoding, belief propagation, and optimal control.


title: Stakeholder Theory, Corporate Governance and Public Management: What can the History of State-Run Enterprises Teach us in the PostEnron era

abstract: This paper raises a challenge for those who assume that corporate social responsibility and good corporate governance naturally go hand-in-hand. The recent spate of corporate scandals in the United States and elsewhere has dramatized, once again, the severity of the agency problems that may arise between managers and shareholders. These scandals remind us that even if we adopt an extremely narrow concept of managerial responsibility – such that we recognize no social responsibility beyond the obligation to maximize shareholder value – there may still be very serious difficulties associated with the effective institutionalization of this obligation. It also suggests that if we broaden managerial responsibility, in order to include extensive responsibilities to various other stakeholder groups, we may seriously exacerbate these agency problems, making it even more difficult to impose effective discipline upon managers. Hence, our central question: is a strong commitment to corporate social responsibility institutionally feasible? In searching for an answer, we revisit the history of public management, and in particular, the experience of social-democratic governments during the 1960s and 1970s, and their attempts to impose social responsibility upon the managers of nationalized industries. The results of this inquiry are less than encouraging for proponents of corporate social responsibility. In fact, the history of public-sector management presents a number of stark warnings, which we would do well to heed if we wish to reconcile robust social responsibility with effective corporate governance.


title: Recommendation Systems: A Probabilistic Analysis

abstract: A recommendation system tracks past actions of a group of users to make recommendations to individual members of the group. The growth of computer-mediated marketing and commerce has led to increased interest in such systems. We introduce a simple analytical framework for recommendation systems, including a basis for defining the utility of such a system. We perform probabilistic analyses of algorithms within this framework. These analyses yield insights into how much utility can be derived from knowledge of past user actions.


title: Towards Zero Bit-Error-Rate Physical Unclonable Function: Mismatch-Based vs. Physical-Based Approaches in Standard CMOS Technology

abstract: This paper compares two types of physical unclonable function (PUF) circuits in terms of reliability, mismatch-based PUF vs. physical-based PUF. Most previous PUF circuits utilize device mismatches for generating random responses. Although they have sufficient random features, there is a reliability issue that some portions of bits are changed over time during operation or under noisy environments. To overcome this issue, we previously proposed the differential amplifier PUF (DA-PUF) which improves the reliability by amplifying the small mismatches of the transistors and rejecting the power supply noise through differential operation. In this paper, we first report the experimental results with the fabricated chips in a 0.35 µm CMOS process. The DA-PUF shows 51.30% uniformity, 50.05% uniqueness, and 0.43% maximum BER. For 0% BER, we proposed the physical-based VIA-PUF which is based on the probability of physical connection between the electrical layers. From the experimental results with the fabricated chips in a 0.18 µm CMOS process, we found the VIA-PUF has 51.12% uniformity and 49.64% uniqueness, and 0% BER throughout 1,000-time repeated measurements. Especially, we have no bit change after the stress test at 25 and 125 °C for 96 hours.


Category Detection with PAC Guarantees


title: Soft exosuit for hip assistance

abstract: Abstract Exoskeletons comprised of rigid load-bearing structures have been developed for many years, but a new paradigm is to create “exosuits” that apply tensile forces to the body using textiles and utilize the body’s skeletal structure to support compressive forces. Exosuits are intended to augment the musculature by providing small to moderate levels of assistance at appropriate times in the walking cycle. They have a number of substantial benefits: with their fabric construction, exosuits eliminate problems of needing to align a rigid frame precisely with the biological joints and their inertia can be extremely low. In this paper, we present a fully portable hip-assistance exosuit that uses a backpack frame to attach to the torso, onto which is mounted a spooled-webbing actuator that connects to the back of the users thigh. The actuators, powered by a geared brushless motor connected to a spool via a timing belt, wind up seat-belt webbing onto the spool so that a large travel is possible with a simple, compact mechanism. Designed to be worn over the clothing, the webbing creates a large moment arm around the hip that provides torques in the sagittal plane of up to 30% of the nominal biological torques for level-ground walking. Due to its soft design, the system does not restrict the motion of the hip in the ab- and adduction directions or rotation about the leg axis. Here we present the design of the system along with some initial measurements of the system in use during walking on level ground at 1.25 m/s, where it creates a force of up to 150 N on the thigh, equivalent to a torque of 20.5 Nm to assist hip extension.


title: Augmented Fuzzy Cognitive Maps Supplemented with Case Based Reasoning for Advanced Medical Decision Support

abstract: Fuzzy Cognitive Maps (FCMs) have been used to design Decision Support Systems and particularly for medical informatics to develop Intelligent Diagnosis Systems. Even though they have been successfully used in many different areas, there are situations where incomplete and vague input information may present difficulty in reaching a decision. In this chapter the idea of using the Case Based Reasoning technique to augment FCMs is presented leading to the development of an Advanced Medical Decision Support System. This system is applied in the speech pathology area to diagnose language impairments..


title: Integrating GeoGebra with TPACK in Improving Pre-Service Mathematics Teachers’ Professional Development

abstract: The relationship between the use of Geogebra and Technological Pedagogical Content and Knowledge (TPACK) by teachers has not been fully investigated and understood. Therefore, the aim of this study was to integrate GeoGebra technology to develop the TPACK of secondary school pre-service mathematics teachers. The participants of this study were 60 Indian teachers. Data were collected through the administration of pre-post TPACK surveys using a Google Online survey. Results from the survey revealed that there were changes in teachers' TPACK after undergoing the Geogebra workshop. Practical implications are discussed regarding how to enhance pre-service teachers' TPACK using GeoGebra in mathematics teaching.


title: Real-Time Standard Scan Plane Detection and Localisation in Fetal Ultrasound Using Fully Convolutional Neural Networks

abstract: Fetal mid-pregnancy scans are typically carried out according to fixed protocols. Accurate detection of abnormalities and correct biometric measurements hinge on the correct acquisition of clearly defined standard scan planes. Locating these standard planes requires a high level of expertise. However, there is a worldwide shortage of expert sonographers. In this paper, we consider a fully automated system based on convolutional neural networks which can detect twelve standard scan planes as defined by the UK fetal abnormality screening programme. The network design allows real-time inference and can be naturally extended to provide an approximate localisation of the fetal anatomy in the image. Such a framework can be used to automate or assist with scan plane selection, or for the retrospective retrieval of scan planes from recorded videos. The method is evaluated on a large database of 1003 volunteer mid-pregnancy scans. We show that standard planes acquired in a clinical scenario are robustly detected with a precision and recall of 69 % and 80 %, which is superior to the current state-of-the-art. Furthermore, we show that it can retrospectively retrieve correct scan planes with an accuracy of 71 % for cardiac views and 81 % for non-cardiac views.


title: The APACHE PHPrognostic System * Risk Prediction of Hospital Mortality for Critically Ill Hospitalized Adults

abstract: The objective of this study was to refine the APACHE (Acute Physiology, Age, Chronic Health Evaluation) methodology in order to more accurately predict hospital mortality risk for critically ill hospitalized adults. We prospectively collected data on 17,440 unselected adult medical/surgical intensive care unit (ICU) admissions at 40 US hospitals (14 volunteer tertiary-care institutions and 26 hospitals randomly chosen to represent intensive care services nationwide). We analyzed the relationship between the patient's likelihood of surviving to hospital discharge and the following predictive variables: major medical and surgical disease categories, acute physiologic abnormalities, age, preexisting functional limitations, major comorbidities, and treatment location immediately prior to ICU admission. The APACHE III prognostic system consists of two options: (1) an APACHE III score, which can provide initial risk stratification for severely ill hospitalized patients within independently defined patient groups; and (2) an APACHE III predictive equation, which uses APACHE III score and reference data on major disease categories and treatment location immediately prior to ICU admission to provide risk estimates for hospital mortality for individual ICU patients. A five-point increase in APACHE III score (range, 0 to 299) is independently associated with a statistically significant increase in the relative risk of hospital death (odds ratio, 1.10 to 1.78) within each of 78 major medical and surgical disease categories. The overall predictive accuracy of the first-day APACHE III equation was such that, within 24 h of ICU admission, 95 percent of ICU admissions could be given a risk estimate for hospital death that was within 3 percent of that actually observed (r2 = 0.41; receiver operating characteristic = 0.90). Recording changes in the APACHE III score on each subsequent day of ICU therapy provided daily updates in these risk estimates. When applied across the individual ICUs, the first-day APACHE III equation accounted for the majority of variation in observed death rates (r2 = 0.90, p less than 0.0001).


title: Cloud assisted HetNets toward 5G wireless networks

abstract: With the proliferation of connected devices and emerging data-hungry applications, the volume of mobile data traffic is predicted to have a 1000-fold growth by the year 2020. To address the challenge of this data explosion, industry and academia have initiated research and development of 5G wireless networks, which are envisaged to cater to the massive data traffic volume, while providing ubiquitous connectivity and supporting diverse applications with different quality of service (QoS) requirements. To support the expected massive growth of mobile data, a large number of small cells are expected to be deployed indoors and outdoors, giving rise to heterogeneous networks (HetNets), which are considered to be the key path toward 5G. With such large-scale HetNets, network operators face many serious challenges in terms of operation and management, cost-effective small cell deployment, and intercell interference mitigation. To deal with those issues, a cloud based platform is introduced, aiming to simplify the deployment, operation and management, and facilitate round-the-clock optimization of the network, to pave the way for the development of 5G. Two case studies are provided to illustrate the benefits of the cloud based architecture. Finally, the related standardization activities are provided and some research topics essential for a successful development of 5G are discussed.


A Literature Review on the Design of Spherical Rolling Robots


title: Java Treeview---extensible visualization of microarray data

abstract: Summary: Open source software encourages innovation by allowing users to extend the functionality of existing applications. Treeview is a popular application for the visualization of microarray data, but is closed-source and platform-specific, which limits both its current utility and suitability as a platform for further development. Java Treeview is an open-source, cross-platform rewrite that handles very large datasets well, and supports extensions to the file format that allow the results of additional analysis to be visualized and compared. The combination of a general file format and open source makes Java Treeview an attractive choice for solving a class of visualization problems. An applet version is also available that can be used on any website with no special server-side setup. ::: ::: Availability:http://jtreeview.sourceforge.net under GPL.


title: Polysemous codes

abstract: This paper considers the problem of approximate nearest neighbor search in the compressed domain. We introduce polysemous codes, which offer both the distance estimation quality of product quantization and the efficient comparison of binary codes with Hamming distance. Their design is inspired by algorithms introduced in the 90's to construct channel-optimized vector quantizers. At search time, this dual interpretation accelerates the search. Most of the indexed vectors are filtered out with Hamming distance, letting only a fraction of the vectors to be ranked with an asymmetric distance estimator. The method is complementary with a coarse partitioning of the feature space such as the inverted multi-index. This is shown by our experiments performed on several public benchmarks such as the BIGANN dataset comprising one billion vectors, for which we report state-of-the-art results for query times below 0.3\,millisecond per core. Last but not least, our approach allows the approximate computation of the k-NN graph associated with the Yahoo Flickr Creative Commons 100M, described by CNN image descriptors, in less than 8 hours on a single machine.


title: A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming Over HTTP

abstract: With companies such as Netflix and YouTube accounting for more than 50% of the peak download traffic on North American fixed networks in 2015, video streaming represents a significant source of Internet traffic. Multimedia delivery over the Internet has evolved rapidly over the past few years. The last decade has seen video streaming transitioning from User Datagram Protocol to Transmission Control Protocol-based technologies. Dynamic adaptive streaming over HTTP (DASH) has recently emerged as a standard for Internet video streaming. A range of rate adaptation mechanisms are proposed for DASH systems in order to deliver video quality that matches the throughput of dynamic network conditions for a richer user experience. This survey paper looks at emerging research into the application of client-side, server-side, and in-network rate adaptation techniques to support DASH-based content delivery. We provide context and motivation for the application of these techniques and review significant works in the literature from the past decade. These works are categorized according to the feedback signals used and the end-node that performs or assists with the adaptation. We also provide a review of several notable video traffic measurement and characterization studies and outline open research questions in the field.


title: Forecasting of ozone concentration in smart city using deep learning

abstract: Clean air is one of the most important needs for the well-being of human being health. In smart cities, timely and precise air pollution levels knowledge is vital for the successful setup of smart pollution systems. Recently, pollution and weather data in smart city have been bursting, and we have truly got into the era of big data. Ozone is considered as one of the most air pollutants with hurtful impact to human health. Existing methods used to predict the level of ozone uses shallow pollution prediction models and are still unsatisfactory in their accuracy to be used in many real-world applications. In order to increase the accuracy of prediction models we come up with the concept of using deep architecture models tested on big pollution and weather data. In this paper, a new deep learning-based ozone level prediction model is proposed, which considers the pollution and weather correlations integrally. This deep learning model is used to learn ozone level features, and it is trained using a grid search technique. A deep architecture model is utilized to represent ozone level features for prediction. Moreover, experiments demonstrate that the proposed method for ozone level prediction has superior performance. The outcome of this study can be helpful in predicting the ozone level pollution in Aarhus city as a model of smart cities for improving accuracy of ozone forecasting tools.


title: An OPC UA cross-domain information model for energy management in automation systems

abstract: Interoperability between different technologies in the industrial and building domains of automation has already become a significant research topic. Extending this thought towards cross-domain interoperability, synergy effects arise from the resulting unified view to process data of both domains. This facilitates savings of energy, working time and materials. In this work a cross-domain information model aiming at this goal is proposed.


title: Antioxidant characterization of native monofloral Cuban honeys.

abstract: Five typical Cuban monofloral honeys were analyzed for their in vitro total antioxidant capacity (TAC), phenolic compounds, and ascorbic acid content. Identification and quantification of phenolics were carried out by HPLC-DAD-ESI/MS. Fourteen phenolic compounds could be identified (eight phenolic acids and six flavonoids), including three glycosylated derivatives. Similar contents of total phenolics were found in the different honeys, although they differed in their qualitative profiles. A significant (positive) correlation was found between the results of TAC obtained by parallel FIA-ABTS system and ORAC assay (r=0.9565, p<0.001). Similar correlations were also established between total phenolics and TAC, determined by either the ORAC (r=0.9633; p<or=0.001) or the TEAC assay (r=0.9582; p<or=0.001). Honeys were fractionated by solid-phase extraction into four fractions, and the relative contribution of each fraction to TAC was calculated. Phenolic compounds were significant contributors to the antioxidant capacity of the honeys, but they were not uniquely responsible for it. The antioxidant activity appeared to be a result of the combined activity of a range of compounds including phenolics and other minor components. Ascorbic acid was not detected.


title: Monitoring Value of Multimodal Magnetic Resonance Imaging in Disease Progression of Amyotrophic Lateral Sclerosis: A Prospective Observational Study

abstract: Background: Ongoing efforts have been made to identify new neuroimaging markers to track amyotrophic lateral sclerosis (ALS) progression. This study aimed to explore the monitoring value of multimodal magnetic resonance imaging (MRI) in the disease progression of ALS. Methods: From September 2015 to March 2017, ten patients diagnosed with ALS in Peking Union Medical College Hospital completed head MRI scans at baseline and during follow-up. Multimodal MRI analyses, including gray matter (GM) volume measured by voxel-based morphometry; cerebral blood flow (CBF) evaluated by arterial spin labeling; functional connectivity, including low-frequency fluctuation (fALFF) and regional homogeneity (ReHo), measured by resting-state functional MRI; and integrity of white-matter (WM) fiber tracts evaluated by diffusion tensor imaging, were performed in these patients. Comparisons of imaging metrics were made between baseline and follow-up using paired t-test. Results: In the longitudinal comparisons, the brain structure (GM volume of the right precentral gyri, left postcentral gyri, and right thalami) and perfusion (CBF of the bilateral temporal poles, left precentral gyri, postcentral gyri, and right middle temporal gyri) in both motor and extramotor areas at follow-up were impaired to different extents when compared with those at baseline (all P < 0.05, false discovery rate adjusted). Functional connectivity was increased in the motor areas (fALFF of the right precentral gyri and superior frontal gyri, and ReHo of right precentral gyri) and decreased in the extramotor areas (fALFF of the bilateral middle frontal gyri and ReHo of the right precuneus and cingulate gyri) (all P < 0.001, unadjusted). No significant changes were detected in terms of brain WM measures. Conclusion: Multimodal MRI could be used to monitor short-term brain changes in ALS patients.


title: Object-Centric Auto-Encoders and Dummy Anomalies for Abnormal Event Detection in Video

abstract: Abnormal event detection in video is a challenging vision problem. Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training. Because of the lack of prior information regarding abnormal events, these methods are not fully-equipped to differentiate between normal and abnormal events. In this work, we formalize abnormal event detection as a one-versus-rest binary classification problem. Our contribution is two-fold. First, we introduce an unsupervised feature learning framework based on object-centric convolutional auto-encoders to encode both motion and appearance information. Second, we propose a supervised classification approach based on clustering the training samples into normality clusters. A one-versus-rest abnormal event classifier is then employed to separate each normality cluster from the rest. For the purpose of training the classifier, the other clusters act as dummy anomalies. During inference, an object is labeled as abnormal if the highest classification score assigned by the one-versus-rest classifiers is negative. Comprehensive experiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and UMN. Our approach provides superior results on all four data sets. On the large-scale ShanghaiTech data set, our method provides an absolute gain of 8.4% in terms of frame-level AUC compared to the state-of-the-art method.


title: Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors

abstract: To make sound economic decisions, the brain needs to compute several different value-related signals. These include goal values that measure the predicted reward that results from the outcome generated by each of the actions under consideration, decision values that measure the net value of taking the different actions, and prediction errors that measure deviations from individuals' previous reward expectations. We used functional magnetic resonance imaging and a novel decision-making paradigm to dissociate the neural basis of these three computations. Our results show that they are supported by different neural substrates: goal values are correlated with activity in the medial orbitofrontal cortex, decision values are correlated with activity in the central orbitofrontal cortex, and prediction errors are correlated with activity in the ventral striatum.


title: Rethinking the concepts of ‘local or global processors’: evidence from Williams syndrome, Down syndrome, and Autism Spectrum Disorders

abstract: Both Williams syndrome (WS) and Autism Spectrum Disorder (ASD) have been characterized as preferentially processing local information, whereas in Down syndrome (DS) the reported tendency is to process stimuli globally. We designed a cross-syndrome, cross-task comparison to reveal similarities and differences in local/global processing in these disorders. Our in-depth study compared local/global processing across modalities (auditory-verbal/visuo-spatial) and levels of processing (high/low) in the three syndromes. Despite claims in the literature, participants with ASD or WS failed to show a consistent local processing bias, while those with DS failed to show a reliable global processing bias. Depending on the nature of the stimuli and the task, both local and global processing biases were evident in all three neurodevelopmental disorders. These findings indicate that individuals with neurodevelopmental disorders cannot simply be characterized as local or global processors.


title: Nucleation of the BCC phase from disorder in a diblock copolymer melt: Testing approximate theories through simulation

abstract: We examine nucleation of the stable body-centred-cubic (BCC) phase from the metastable uniform disordered phase in an asymmetric diblock copolymer melt. Our comprehensive, large-scale simulations of the time-dependent, mean-field Landau-Brazovskii model find that spherical droplets of the BCC phase nucleate directly from disorder. Near the order-disorder transition, the critical nucleus is large and has a classical profile, attaining the bulk BCC phase in an interior that is separated from disorder by a sharp interface. At greater undercooling, the amplitude of BCC order in the interior decreases and the nucleus interface broadens, leading to a diffuse critical nucleus. This diffuse nucleus becomes large as the simulation approaches the disordered phase spinodal. We show that our simulation follows the same nucleation pathway that Cahn and Hilliard found for an incompressible two-component fluid, across the entire metastable region. In contrast, a classical nucleation theory calculation based on the free ...


title: Automated Quantitative Analysis of Capnogram Shape for COPD–Normal and COPD–CHF Classification

abstract: We develop an approach to quantitative analysis of carbon dioxide concentration in exhaled breath, recorded as a function of time by capnography. The generated waveform - or capnogram - is currently used in clinical practice to establish the presence of respiration as well as determine respiratory rate and end-tidal CO 2 concentration. The capnogram shape also has diagnostic value, but is presently assessed qualitatively, by visual inspection. Prior approaches to quantitatively characterizing the capnogram shape have explored the correlation of various geometric parameters with pulmonary function tests. These studies attempted to characterize the capnogram in normal subjects and patients with cardiopulmonary disease, but no consistent progress was made, and no translation into clinical practice was achieved. We apply automated quantitative analysis to discriminate between chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF), and between COPD and normal. Capnograms were collected from 30 normal subjects, 56 COPD patients, and 53 CHF patients. We computationally extract four physiologically based capnogram features. Classification on a hold-out test set was performed by an ensemble of classifiers employing quadratic discriminant analysis, designed through cross-validation on a labeled training set. Using 80 exhalations of each capnogram record in the test set, performance analysis with bootstrapping yields areas under the receiver operating characteristic (ROC) curve of 0.89 (95% CI: 0.72-0.96) for COPD/CHF classification, and 0.98 (95% CI: 0.82- 1.0) for COPD/normal classification. This classification performance is obtained with a run-time sufficiently fast for real- time monitoring.


title: Graph structure in the web --- revisited: a trick of the heavy tail

abstract: Knowledge about the general graph structure of the World Wide Web is important for understanding the social mechanisms that govern its growth, for designing ranking methods, for devising better crawling algorithms, and for creating accurate models of its structure. In this paper, we describe and analyse a large, publicly accessible crawl of the web that was gathered by the Common Crawl Foundation in 2012 and that contains over 3.5 billion web pages and 128.7 billion links. This crawl makes it possible to observe the evolution of the underlying structure of the World Wide Web within the last 10 years: we analyse and compare, among other features, degree distributions, connectivity, average distances, and the structure of weakly/strongly connected components. Our analysis shows that, as evidenced by previous research, some of the features previously observed by Broder et al. are very dependent on artefacts of the crawling process, whereas other appear to be more structural. We confirm the existence of a giant strongly connected component; we however find, as observed by other researchers, very different proportions of nodes that can reach or that can be reached from the giant component, suggesting that the "bow-tie structure" is strongly dependent on the crawling process, and to the best of our current knowledge is not a structural property of the web. More importantly, statistical testing and visual inspection of size-rank plots show that the distributions of indegree, outdegree and sizes of strongly connected components are not power laws, contrarily to what was previously reported for much smaller crawls, although they might be heavy-tailed. We also provide for the first time accurate measurement of distance-based features, using recently introduced algorithms that scale to the size of our crawl.


title: Real-Time and High-Accuracy Arctangent Computation Using CORDIC and Fast Magnitude Estimation

abstract: This paper presents an improved VLSI (Very Large Scale of Integration) architecture for real-time and high-accuracy computation of trigonometric functions with fixed-point arithmetic, particularly arctangent using CORDIC (Coordinate Rotation Digital Computer) and fast magnitude estimation. The standard CORDIC implementation suffers of a loss of accuracy when the magnitude of the input vector becomes small. Using a fast magnitude estimator before running the standard algorithm, a pre-processing magnification is implemented, shifting the input coordinates by a proper factor. The entire architecture does not use a multiplier, it uses only shift and add primitives as the original CORDIC, and it does not change the data path precision of the CORDIC core. A bit-true case study is presented showing a reduction of the maximum phase error from 414 LSB (angle error of 0.6355 rad) to 4 LSB (angle error of 0.0061 rad), with small overheads of complexity and speed. Implementation of the new architecture in 0.18 µm CMOS technology allows for real-time and low-power processing of CORDIC and arctangent, which are key functions in many embedded DSP systems. The proposed macrocell has been verified by integration in a system-on-chip, called SENSASIP (Sensor Application Specific Instruction-set Processor), for position sensor signal processing in automotive measurement applications.


title: Toward Carbon Fibers from Single Component Kraft Lignin Systems: Optimization of Chain Extension Chemistry

abstract: Single component softwood kraft lignins have been sought after as precursors to carbon fibers. This noble goal can be achieved by adding carbon onto lignin via propargylation. The reactivity of propargylated lignins may then be modulated via methylation, thus eliminating the onset of gelation via phenoxyl radical initiated random polymerization. This article demonstrates that properly installed propargyl groups of an acetone soluble kraft lignin (ASKL) fraction can be thermally polymerized to high molecular weights in a controlled manner. In order to create single component chain extended softwood kraft lignin systems for carbon fiber applications, one needs to regulate the amount and the positioning of the propargyl groups on the lignin. This became possible, and it is now demonstrated that the propargylation of lignin needs to occur first, followed by methylation and not the other way around. Such a sequence offers substantial benefits for the onset of a Claisen rearrangement to occur between the propar...


title: Supervised Collaboration for Syntactic Annotation of Quranic Arabic 1 Supervised Collaboration for Syntactic Annotation of Quranic Arabic

abstract: The Quranic Arabic Corpus ( http://corpus.quran.com ) is a collaboratively constructed linguistic resource initiated at the University of Leeds, with multiple layers of annotation including part-of-speech tagging, morphological segmentation (Dukes and Habash 2010) and syntactic analysis using dependency grammar (Dukes and Buckwalter 2010). The motivation behind this work is to produce a resource that enables further analysis of the Quran, the 1,400 year-old central religious text of Islam. This project contrasts with other Arabic treebanks by providing a deep linguistic model based on the historical traditional grammar known as i?r?b (?????). By adapting this well-known canon of Quranic grammar into a familiar tagset, it is possible to encourage online annotation by Arabic linguists and Quranic experts. This article presents a new approach to linguistic annotation of an Arabic corpus: online supervised collaboration using a multi-stage approach. The different stages include automatic rule-based tagging, initial manual verification, and online supervised collaborative proofreading. A popular website attracting thousands of visitors per day, the Quranic Arabic Corpus has approximately 100 unpaid volunteer annotators each suggesting corrections to existing linguistic tagging. To ensure a high-quality resource, a small number of expert annotators are promoted to a supervisory role, allowing them to review or veto suggestions made by other collaborators. The Quran also benefits from a large body of existing historical grammatical analysis, which may be leveraged during this review. In this paper we evaluate and report on the effectiveness of the chosen annotation methodology. We also discuss the unique challenges of annotating Quranic Arabic online and describe the custom linguistic software used to aid collaborative annotation.


title: Distribution and importance of microplastics in the marine environment: A review of the sources, fate, effects, and potential solutions

abstract: Abstract The presence of microplastics in the marine environment poses a great threat to the entire ecosystem and has received much attention lately as the presence has greatly impacted oceans, lakes, seas, rivers, coastal areas and even the Polar Regions. Microplastics are found in most commonly utilized products (primary microplastics), or may originate from the fragmentation of larger plastic debris (secondary microplastics). The material enters the marine environment through terrestrial and land-based activities, especially via runoffs and is known to have great impact on marine organisms as studies have shown that large numbers of marine organisms have been affected by microplastics. Microplastic particles have been found distributed in large numbers in Africa, Asia, Southeast Asia, India, South Africa, North America, and in Europe. This review describes the sources and global distribution of microplastics in the environment, the fate and impact on marine biota, especially the food chain. Furthermore, the control measures discussed are those mapped out by both national and international environmental organizations for combating the impact from microplastics. Identifying the main sources of microplastic pollution in the environment and creating awareness through education at the public, private, and government sectors will go a long way in reducing the entry of microplastics into the environment. Also, knowing the associated behavioral mechanisms will enable better understanding of the impacts for the marine environment. However, a more promising and environmentally safe approach could be provided by exploiting the potentials of microorganisms, especially those of marine origin that can degrade microplastics. Capsule The concentration, distribution sources and fate of microplastics in the global marine environment were discussed, so also was the impact of microplastics on a wide range of marine biota.


title: Detecting Engagement in HRI: An Exploration of Social and Task-Based Context

abstract: Despite a large body of existing literature on automatic affect recognition, there seems to be a lack of studies investigating task and social context for the purpose of automatically predicting affect. This work aims to take the current state of the art a step forward and explore the role of task and social context and their interdependencies in the automatic prediction of user engagement in a HRI scenario involving an iCat robot playing chess with young children. We performed an experimental evaluation by training several SVMs-based models with different features extracted from a set of context logs collected in a HRI field experiment. The features include information about the game and the social context at the interaction level (overall features) and at the game turn level (turn-based features). While the overall features capture game and social context in an independent way at the interaction level, turn-based features attempt to encode the interdependencies of game and social context at each turn of the game. Results showed that game and social context-based features can be successfully used to predict engagement with the robot in the showcased scenario. Specifically, overall features proved more successful than turn-based features and game context-based features more effective than social context-based features. Finally the results demonstrated that the integration of game and social context-based features with features encoding their interdependencies leads to higher recognition performances.


title: Loyalty programs and their impact on repeat-purchase loyalty patterns

abstract: Abstract Loyalty programs are currently increasing in popularity around the world. This paper discusses the potential of loyalty programs to alter the normal market patterns of repeat-purchase behaviour which characterise competitive repeat-purchase markets. In line with this thinking, a large scale loyalty program is evaluated in terms of its ability to change normal repeat-purchase patterns by generating ‘excess loyalty’ for brands in the program. Panel data were used to develop Dirichlet estimates of expected repeat-purchase loyalty statistics by brand. These estimates were compared with the observed market repeat-purchase behaviour. Overall a trend towards a weak level of excess loyalty was observed, although the expected deviation was not consistently observed for all the loyalty program brands. Only two of the six loyalty program participant brands showed substantial excess loyalty deviations. However, these deviations in repeat-purchase loyalty were observed for non-members of the loyalty program as well as members and appear likely to be at least partially the result of other loyalty efforts particular to these brands.


title: Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction

abstract: In this survey we present different approaches that allow an intelligent agent to explore autonomous its environment to gather information and learn multiple tasks. Different communities proposed different solutions, that are in many cases, similar and/or complementary. These solutions include active learning, exploration/exploitation, online-learning and social learning. The common aspect of all these approaches is that it is the agent to selects and decides what information to gather next. Applications for these approaches already include tutoring systems, autonomous grasping learning, navigation and mapping and human-robot interaction. We discuss how these approaches are related, explaining their similarities and their differences in terms of problem assumptions and metrics of success. We consider that such an integrated discussion will improve inter-disciplinary research and applications.


Osmotic distillation-a low temperature concentration technique


title: Robust lane detection and object tracking In relation to the intelligence transport system

abstract: Every person in this world is concerned about being safe. Increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. In driving assistance systems, obstacle detection especially for moving object detection is a key component of collision avoidance[1]. Many sensors can be used for obstacle detection and lane detection, such as laser, radar and vision sensors. The most frequently used principal approach to detect road boundaries and lanes using vision system on the vehicle. The detecting all kinds of obstacle on the road, mainly include IPM (Inverse Perspective Mapping) method. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes and objects. A versatile methodology is used in order to detecting the lanes and objects. In our research we have developed a simple heuristic method which is more robust in both lane detection object detection and tracking in video. In this method we use clustering methodology to group the detected points in case of lane detection. Heuristic gives effective results in detection and tracking of multiple vehicles at a time irrespective to the distance.


title: Expanding textual entailment corpora fromWikipedia using co-training

abstract: In this paper we propose a novel method to automatically extract large textual entailment datasets homogeneous to existing ones. The key idea is the combination of two intuitions: (1) the use of Wikipedia to extract a large set of textual entailment pairs; (2) the application of semisupervised machine learning methods to make the extracted dataset homogeneous to the existing ones. We report empirical evidence that our method successfully expands existing textual entailment corpora.


title: Beauty Product Advertisements: A Critical Discourse Analysis

abstract: This study examined beauty advertisements in local English magazines from a Critical Discourse Analysis perspective. This study mainly focused on the use of language in beauty advertisements and strategies employed by advertisers to manipulate and influence their customers. The analysis is based on Fairclough’s three-dimensional framework. It demonstrates how the ideology of ‘beauty’ is produced and reproduced through advertisements in popular local women’s magazines. A qualitative research was conducted on beauty product advertisements in two popular local women’s magazines, Cleo and Women’s Weekly. The findings indicated that advertisers used various strategies to manipulate women. The advertisements promote an idealised lifestyle and manipulate readers to a certain extent into believing whatever that is advertised is indeed true. This study revealed how the ideology of beauty is constructed and reconstructed through magazines by stereotyping how beauty products are synonymous with a better life. Advertising language is used to control people’s minds. Thus people in power (advertisers) use language as a means to exercise control over others.


title: Privacy preserving association rule mining in vertically partitioned data

abstract: Privacy considerations often constrain data mining projects. This paper addresses the problem of association rule mining where transactions are distributed across sources. Each site holds some attributes of each transaction, and the sites wish to collaborate to identify globally valid association rules. However, the sites must not reveal individual transaction data. We present a two-party algorithm for efficiently discovering frequent itemsets with minimum support levels, without either site revealing individual transaction values.


title: Survey and Classification of Methods for Building a Semantic Annotation

abstract: Though Arabic is one of the five most spoken languages, little work has been done on building Arabic semantic resources. Currently, there is no agreed-upon method for building such a reliable Arabic semantic resource. The purpose of this paper is to present a comprehensive survey of different methods for building or enriching Arabic semantic resources; to study and analyze each method; and to categorize the methods according to their properties. This work should contribute to the definition of new methods and help researchers on Arabic semantics to fit their work in the panel of existing ones.


title: Bifilar Winding of a Center-Tapped Transformer Including Integrated Resonant Inductance for LLC Resonant Converters

abstract: A bifilar winding method is proposed for the center-tapped transformer which includes integrated resonant inductance in an LLC resonant converter in order to improve the balance of its resonant network and power conversion efficiency. This method can reduce resonance imbalance and transformer inefficiency caused by leakage inductance imbalance in the secondary side of the transformer. As a result, the proposed bifilar winding method employed in the center-tapped transformer maintains the balance of rectifying current and reduces conduction losses in the converter.


title: Controller Area Network Assisted Grid Synchronization of a Microgrid With Renewable Energy Sources and Storage

abstract: Microgrids are emerging as an important constituent of large-scale smart grids. They are equipped with the controls necessary for managing the operation in islanded or grid connected mode and serve the loads with clean, reliable, and uninterruptible power. Microgrid must be equipped with a robust grid synchronization (GS) algorithm so as to allow a smooth transfer from islanded mode to grid tied mode. This gets even more critical when the availability of the power grid is irregular or the power grid is weak. This paper proposes a simple yet highly reliable GS technique based on controller area network (CAN) communication. The grid synchronizer block senses the three phase grid voltages, derives the phase angle ( $\boldsymbol {\theta }$ ) using synchronously rotating reference frame-based phase lock loop, and transmits this information on the CAN network to all micro-sources (MS). The local controller associated with each MS receives the data with small but known and definite time latencies. This allows each MS to energize simultaneously in synchronization with the grid, facilitating easy GS of all the MS, minimizing the time required for GS of the complete microgrid. All the analytical, simulation, and hardware results related to this paper are presented.


title: RDF2Vec-based Classification of Ontology Alignment Changes

abstract: When ontologies cover overlapping topics, the overlap can be represented using ontology alignments. These alignments need to be continuously adapted to changing ontologies. Especially for large ontologies this is a costly task often consisting of manual work. Finding changes that do not lead to an adaption of the alignment can potentially make this process significantly easier. This work presents an approach to finding these changes based on RDF embeddings and common classification techniques. To examine the feasibility of this approach, an evaluation on a real-world dataset is presented. In this evaluation, the best classifiers reached a precision of 0.8.


title: Professional Partnerships and Educational Change: Effective Collaboration Over Time:

abstract: Program directors of 38 collaborative award-winning ATE programs from 1977-1989 were surveyed to determine which programs still existed, which had changed and why. Of the 38 programs, 31 were still in operation. Reasons for program termination included changes in program emphasis, loss of funding, loss of key personnel, and lack of administrative support. Characteristics of successful school/university partnerships are discussed, and a holistic approach to teacher education collaboration is recommended.


title: Towards an Approach to Overcome Software Brittleness

abstract: Development of bug-free, high-surety, complex software is quite difficult using current tools. The brittle nature of the programming constructs in popular languages such as C/C++ is one root cause. Brittle commands force the designer to rigidly specify the minutiae of tasks, e.g. using ''for(index=0;index>total;index++)'', rather than specifying the goals or intent of the tasks, e.g. ''ensure that all relevant data elements have been examined''. Specification of task minutiae makes code hard to comprehend, which in turn encourages design errors/limitations and makes future modifications quite difficult. This report describes an LDRD project to seed the development of a surety computer language, for stand-alone computing environments, to be implemented using the swarm intelligence of autonomous agents. The long term vision of this project was to develop a language with the following surety capabilities: (1) Reliability -- Autonomous agents can appropriate y decide when to act and when a task is complete, provide a natural means for avoiding brittle task specifications, and can overcome many hardware glitches. (2) Safety, security -- Watchdog safety and security agents can monitor other agents to prevent unauthorized or dangerous actions. (3) An immune system -- The small chunks of agent code can have an encryption scheme tomore » enable detection and elimination of unauthorized and corrupted agents. This report describes the progress achieved during this small 9 month project and describes lessons learned.« less


title: Predictive model performance: offline and online evaluations

abstract: We study the accuracy of evaluation metrics used to estimate the efficacy of predictive models. Offline evaluation metrics are indicators of the expected model performance on real data. However, in practice we often experience substantial discrepancy between the offline and online performance of the models. We investigate the characteristics and behaviors of the evaluation metrics on offline and online testing both analytically and empirically by experimenting them on online advertising data from the Bing search engine. One of our findings is that some offline metrics like AUC (the Area Under the Receiver Operating Characteristic Curve) and RIG (Relative Information Gain) that summarize the model performance on the entire spectrum of operating points could be quite misleading sometimes and result in significant discrepancy in offline and online metrics. For example, for click prediction models for search advertising, errors in predictions in the very low range of predicted click scores impact the online performance much more negatively than errors in other regions. Most of the offline metrics we studied including AUC and RIG, however, are insensitive to such model behavior. We designed a new model evaluation paradigm that simulates the online behavior of predictive models. For a set of ads selected by a new prediction model, the online user behavior is estimated from the historic user behavior in the search logs. The experimental results on click prediction model for search advertising are highly promising.


title: Machine Recognition of Human Activities: A Survey

abstract: The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing-make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) "actions" and 2) "activities." "Actions" are characterized by simple motion patterns typically executed by a single human. "Activities" are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.


title: Image Retrieval: Content versus Context

abstract: In this paper, we introduce a new approach to image retrieval. This new approach takes the best from two worlds, combines image features (content) and words from collateral text (context) into one semantic space. Our approach uses Latent Semantic Indexing, a method that uses co-occurrence statistics to uncover hidden semantics. This paper shows how this method, that has proven successful in both monolingual and cross lingual text retrieval, can be used for multi-modal and cross-modal information retrieval. Experiments with an on-line newspaper archive show that Latent Semantic Indexing can outperform both content based and context based approaches and that it is a promising approach for indexing visual and multi-modal data.


title: Combining policy gradient and Q-learning

abstract: Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as ‘PGQL’, for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL. In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.