title: Pseudo-gustatory display system based on cross-modal integration of vision, olfaction and gustation

abstract: In this paper, we propose a pseudo-gustatory display based on the cross-modal interactions that underlie the perception of flavor. Although several studies on visual, auditory, haptic, and olfactory displays have been conducted, gustatory displays have seldom been studied. This scarcity has been attributed to the fact that synthesizing arbitrary taste from basic tastants is difficult. On the other hand, it has been noted that the perception of taste is influenced by visual cues, auditory cues, smell, the trigeminal system, and touch. In our research, we aim at utilizing this influence between modalities for realizing a "pseudo-gustatory" system that enables the user to experience various tastes without changing the chemical composition of foods. Based on this concept, we built a "Meta Cookie" system to change the perceived taste of a cookie by overlaying visual and olfactory information onto a real cookie which an AR marker pattern. We performed an experiment that investigates how people experience the flavor of a plain cookie by using our system. The result suggests that our system can change the perceived taste based on the effect of the cross-modal interaction of vision, olfaction and gustation.


title: RASCAL: Calculation of graph similarity using maximum common edge subgraphs

abstract: A new graph similarity calculation procedure is introduced for comparing labeled graphs. Given a minimum similarity threshold, the procedure consists of an initial screening process to determine whether it is possible for the measure of similarity between the two graphs to exceed the minimum threshold, followed by a rigorous maximum common edge subgraph (MCES) detection algorithm to compute the exact degree and composition of similarity. The proposed MCES algorithm is based on a maximum clique formulation of the problem and is a significant improvement over other published algorithms. It presents new approaches to both lower and upper bounding as well as vertex selection.


title: Polygraph: automatically generating signatures for polymorphic worms

abstract: It is widely believed that content-signature-based intrusion detection systems (IDS) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match polymorphic worms. Polygraph generates signatures that consist of multiple disjoint content substrings. In doing so, Polygraph leverages our insight that for a real-world exploit to function properly, multiple invariant substrings must often be present in all variants of a payload; these substrings typically correspond to protocol framing, return addresses, and in some cases, poorly obfuscated code. We contribute a definition of the polymorphic signature generation problem; propose classes of signature suited for matching polymorphic worm payloads; and present algorithms for automatic generation of signatures in these classes. Our evaluation of these algorithms on a range of polymorphic worms demonstrates that Polygraph produces signatures for polymorphic worms that exhibit low false negatives and false positives.


title: An Examination of the Public Transport Information Requirements of Users

abstract: This paper focuses on the provision of public transport information in Dublin, Ireland. It examines both existing and potential methods of accessing information, with particular focus on the implementation of various intelligent transport systems applications. One of the main objectives of this paper is examining the stages a passenger goes through when deciding to undertake a public transport trip and in what form they require information at each stage. This paper defines these stages as "pre-trip to destination," "at-stop," "onboard," and "pre-trip to origin" (this is the return journey). Each of these four stages is examined in this paper. A web-based survey was used to collect data on passenger preferences and describes the methods of information delivery each passenger requires at each stage. This paper primarily deals with the respondents' stated preference for public transport information and does not examine revealed preferences. The survey also details results of passengers' opinions of the different information provision formats such as call centers, mobile phones, the Internet, and paper-based methods. This paper concludes with the results of this exploratory research


title: Emotional remapping of music to facial animation

abstract: We propose a method to extract the emotional data from a piece of music and then use that data via a remapping algorithm to automatically animate an emotional 3D face sequence. The method is based on studies of the emotional aspect of music and our parametric-based behavioral head model for face animation. We address the issue of affective communication remapping in general, i.e. translation of affective content (eg. emotions, and mood) from one communication form to another. We report on the results of our MusicFace system, which use these techniques to automatically create emotional facial animations from multi-instrument polyphonic music scores in MIDI format and a remapping rule set.


title: Talking to, not about, citizens: experiences of focus groups in public e-service development

abstract: This paper focuses deficient understanding of citizens' needs regarding public e-services. In Sweden e-government efforts are motivated by dual goals of citizen benefit and agencies' internal efficiency. Rhetorical, this is a persuasive ambition, but in practice it seems to be easier to focus agency efficiency and redesign of business processes and information systems than to find out what citizens really want. Citizens, i.e. the future users of the e-service, are in best case represented in the project by citizen organizations. More seldom do individual citizens take part in the project. User needs are, thus, sometimes "guessed" instead of analyzed. We report from an e-government project which started with little understanding of the future users. To overcome this we introduced focus groups as a method to meet and talk to citizens and find out their needs regarding the e-service. The paper discusses how focus groups can be used in e-government projects.


title: Population Cost Prediction on Public Healthcare Datasets

abstract: The increasing availability of digital health records should ideally improve accountability in healthcare. In this context, the study of predictive modeling of healthcare costs forms a foundation for accountable care, at both population and individual patient-level care. In this research we use machine learning algorithms for accurate predictions of healthcare costs on publicly available claims and survey data. Specifically, we investigate the use of the regression trees, M5 model trees and random forest, to predict healthcare costs of individual patients given their prior medical (and cost) history. Overall, three observations showcase the utility of our research: (a) prior healthcare cost alone can be a good indicator for future healthcare cost, (b) M5 model tree technique led to very accurate future healthcare cost prediction, and (c) although state-of-the-art machine learning algorithms are also limited by skewed cost distributions in healthcare, for a large fraction (75%) of population, we were able to predict with higher accuracy using these algorithms. In particular, using M5 model trees we were able to accurately predict costs within less than $125 for 75% of the population when compared to prior techniques. Since models for predicting healthcare costs are often used to ascertain overall population health, our work is useful to evaluate future costs for large segments of disease populations with reasonably low error as demonstrated in our results on real-world publicly available datasets.


title: Fuzzy Group-Based Intersection Control via Vehicular Networks for Smart Transportations

abstract: Vehicular network has been recently used to achieve high efficient and flexible traffic scheduling at intersection roads for smart transportation systems. Different from existing works, where traffic signal is used to schedule waiting vehicles at each lane, we propose to divide vehicles in the same lane into small groups and schedule vehicle groups via wireless communication rather than traffic lights. Such direct scheduling of vehicles can reduce waiting time and improve fairness, especially when the traffic volume in different lanes is imbalanced. The key challenge in such a design lies in determining appropriate size of groups with respect to real-time traffic conditions. To cope with this issue, we propose a neuro-fuzzy network-based grouping mechanism, where the network is trained using reinforcement learning technique. Also, vehicle groups are scheduled via a neuro-fuzzy network. Simulations using ns3 are conducted to evaluate the performance of our algorithm and compare it with similar works. The results show that our algorithm can reduce waiting time and at the same time improve fairness in various cases, and the advantage against traffic light algorithms can be up to 40%.


title: Prediction of sentinel lymph node status using single-photon emission computed tomography (SPECT)/computed tomography (CT) imaging of breast cancer

abstract: PURPOSE ::: Single-photon emission computed tomography (SPECT)/computed tomography (CT) improves the anatomical identification of sentinel lymph nodes (SNs). We aimed to evaluate the possibility of predicting the SN status using SPECT/CT. ::: ::: ::: METHODS ::: SN mapping using a SPECT/CT system was performed in 381 cases of clinically node-negative, operable invasive breast cancer. We evaluated and compared the values of SN mapping on SPECT/CT, the findings of other modalities and clinicopathological factors in predicting the SN status. ::: ::: ::: RESULTS ::: Patients with SNs located in the Level I area were evaluated. Of the 355 lesions (94.8 %) assessed, six cases (1.6 %) were not detected using any imaging method. According to the final histological diagnosis, 298 lesions (78.2 %) were node negative and 83 lesions (21.7 %) were node positive. The univariate analysis showed that SN status was significantly correlated with the number of SNs detected on SPECT/CT in the Level I area (P = 0.0048), total number of SNs detected on SPECT/CT (P = 0.011), findings of planar lymphoscintigraphy (P = 0.011) and findings of a handheld gamma probe during surgery (P = 0.012). According to the multivariate analysis, the detection of multiple SNs on SPECT/CT imaging helped to predict SN metastasis. ::: ::: ::: CONCLUSIONS ::: The number of SNs located in the Level I area detected using the SPECT/CT system may be a predictive factor for SN metastasis.


title: Färber, Keim, Weitzel: Automated Recommendation in Personnel Recruitment AN AUTOMATED RECOMMENDATION APPROACH TO SELECTION IN PERSONNEL RECRUITMENT

abstract: Many online recruitment platforms suffer from the inappropriateness of Boolean search methods for matching candidates with job requirements. While such platforms have so far been a successful means for decreasing personnel advertising cost, the huge amount of electronic candidate profiles has not yet been exploited to optimize search quality. In this paper, using findings from an empirical survey on modern recruitment practices among Germany's top 1,000 enterprises and supported by findings from personnel selection theory, we identify a gap between the actual requirements of matching people with jobs and current e-recruitment procedures. Based on information systems research and drawing from selection and assessment theory, a framework for developing new matching methods is proposed. We describe the elements of a matching method using a probabilistic automated recommendation approach and then present first quite promising results from applying the algorithm to synthetic data.


title: A Developmental Perspective on Technology in Language Education

abstract: Is technology a tool for language learning, or is language learning a tool with which people can access technology? This article suggests that both language and technology are tools for individual and societal development. The article introduces a developmental approach to integrating technology in language education, based on consideration of both product and process. It then illustrates these concepts through analysis of a large, U.S.-funded English language developmental program in Egypt. Two projects are examined: a teacher education program on computers in English language teaching and a basic English methodology course taught via videoconferencing. The analysis indicates that a developmental approach is critical to successful integration and use of technology in language education programs.


title: An Ultra-Wide-Band 0.4–10-GHz LNA in 0.18-$\mu$m CMOS

abstract: A two-stage ultra-wide-band CMOS low-noise amplifier (LNA) is presented. With the common-gate configuration employed as the input stage, the broad-band input matching is obtained and the noise does not rise rapidly at higher frequency. By combining the common-gate and common-source stages, the broad-band characteristic and small area are achieved by using two inductors. This LNA has been fabricated in a 0.18-mum CMOS process. The measured power gain is 11.2-12.4 dB and noise figure is 4.4-6.5 dB with -3-dB bandwidth of 0.4-10 GHz. The measured IIP3 is -6 dBm at 6 GHz. It consumes 12 mW from a 1.8-V supply voltage and occupies only 0.42 mm2


title: A Model of Inductive Bias Learning

abstract: A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem being learnt, yet small enough to ensure reliable generalization from reasonably-sized training sets. Typically such bias is supplied by hand through the skill and insights of experts. In this paper a model for automatically learning bias is investigated. The central assumption of the model is that the learner is embedded within an environment of related learning tasks. Within such an environment the learner can sample from multiple tasks, and hence it can search for a hypothesis space that contains good solutions to many of the problems in the environment. Under certain restrictions on the set of all hypothesis spaces available to the learner, we show that a hypothesis space that performs well on a sufficiently large number of training tasks will also perform well when learning novel tasks in the same environment. Explicit bounds are also derived demonstrating that learning multiple tasks within an environment of related tasks can potentially give much better generalization than learning a single task.


title: Social Question Answering: Textual, User, and Network Features for Best Answer Prediction

abstract: Community question answering (CQA) sites use a collaborative paradigm to satisfy complex information needs. Although the task of matching questions to their best answers has been tackled for more than a decade, the social question-answering practice is a complex process. The factors influencing the accuracy of question-answer matching are many and hard to disentangle. We approach the task from an application-oriented perspective, probing the space of several dimensions relevant to this problem: features, algorithms, and topics. We gather under a learning to rank framework the most extensive feature set used in literature to date, including 225 features from five different families. We test the power of such features in predicting the best answer to a question on the largest dataset from Yahoo Answers used for this task so far (40M answers) and provide a faceted analysis of the results along different topical areas and question types. We propose a novel family of distributional semantics measures that most of the time can seamlessly replace widely used linguistic similarity features, being more than one order of magnitude faster to compute and providing greater predictive power. The best feature set reaches an improvement between 11p and 26p in P@1 compared to recent well-established state-of-the-art methods.


title: Design of an Energy-Efficient Accelerator for Training of Convolutional Neural Networks using Frequency-Domain Computation

abstract: Convolutional neural networks (CNNs) require high computation and memory demand for training. This paper presents the design of a frequency-domain accelerator for energy-efficient CNN training. With Fourier representations of parameters, we replace convolutions with simpler pointwise multiplications. To eliminate the Fourier transforms at every layer, we train the network entirely in the frequency domain using approximate frequency-domain nonlinear operations. We further reduce computation and memory requirements using sinc interpolation and Hermitian symmetry. The accelerator is designed and synthesized in 28nm CMOS, as well as prototyped in an FPGA. The simulation results show that the proposed accelerator significantly reduces training time and energy for a target recognition accuracy.


title: Human Interaction With Robot Swarms: A Survey

abstract: Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human–swarm interaction (HSI) and identifies the core concepts needed to design a human–swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human–swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.


title: Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks

abstract: Classification of plants based on a multi-organ approach is very challenging. Although additional data provide more information that might help to disambiguate between species, the variability in shape and appearance in plant organs also raises the degree of complexity of the problem. Despite promising solutions built using deep learning enable representative features to be learned for plant images, the existing approaches focus mainly on generic features for species classification, disregarding the features representing plant organs. In fact, plants are complex living organisms sustained by a number of organ systems. In our approach, we introduce a hybrid generic-organ convolutional neural network (HGO-CNN), which takes into account both organ and generic information, combining them using a new feature fusion scheme for species classification. Next, instead of using a CNN-based method to operate on one image with a single organ, we extend our approach. We propose a new framework for plant structural learning using the recurrent neural network-based method. This novel approach supports classification based on a varying number of plant views, capturing one or more organs of a plant, by optimizing the contextual dependencies between them. We also present the qualitative results of our proposed models based on feature visualization techniques and show that the outcomes of visualizations depict our hypothesis and expectation. Finally, we show that by leveraging and combining the aforementioned techniques, our best network outperforms the state of the art on the PlantClef2015 benchmark. The source code and models are available at https://github.com/cs-chan/Deep-Plant .


title: Long term intensive exercise training leads to a higher plasma malate/lactate dehydrogenase (M/L) ratio and increased level of lipid mobilization in horses

abstract: Continuous high intensity training may induce alterations to enzyme activities related to glucose and lipid metabolism in horses. In our study, five Thoroughbred race horses (3 male and 2 female, avg age = 5 yrs old) were compared against five riding horses (1 male, 1 female, 3 gelding, avg age = 13 yrs old) in terms of energy metabolism, by examining plasma malate (MDH) and lactate (LDH) dehydrogenase activities and M/L ratio. MDH is involved in NADH and ATP generation, whereas LDH can convert NADH back into NAD+ for ATP generation. An increase in plasma M/L ratio can reflect heightened energy metabolism in the liver and skeletal muscle of horses adapted to continuous intensive exercise. Moreover, plasma lipid metabolism analytes (adiponectin, NEFA, total cholesterol (T-Cho), and triglycerides (TG)) can reflect changes to lipolysis rate, which can also indicate a change in energy metabolism. Overall, race horses demonstrated increased MDH and LDH activity in plasma (4x and 2x greater, respectively), in addition to a plasma M/L ratio twice as high as that of riding horses (2.0 vs 1.0). In addition, race horses also demonstrated significantly higher levels of plasma NEFA (50% greater), TG (2x greater), and T-Cho (20% greater) as compared to riding horses. Therefore, race horse muscles may have adapted to prolonged high intensity endurance exercise by gaining a higher oxidative capacity and an increased capacity for fat utilization as an energy source, resulting in heightened energy metabolism and increased rate of lipid mobilization.


title: Recognition confidence analysis of handwritten Chinese character with CNN

abstract: In this paper, we present an effective method to analyze the recognition confidence of handwritten Chinese character, based on the softmax regression score of a high performance convolutional neural network (CNN). Through careful and thorough statistics of 827,685 testing samples that randomly selected from total 8836 different classes of Chinese characters, we find that the confidence measurement based on CNN is an useful metric to know how reliable the recognition results are. Furthermore, we find by experiments that the recognition confidence can be used to find out similar and confusable character-pairs, to check wrongly or cursively written samples, and even to discover and correct mislabeled samples. Many interesting observation and statistics are given and analyzed in this study.


title: INFORMATION EXTRACTION METHODS AND EXTRACTION TECHNIQUES IN THE CHEMICAL DOCUMENT'S CONTENTS: SURVEY

abstract: The volume of electronic documents has rapidly increased and the scientific literature has increased too. These huge documents contain considerable information, but it has to be retrieved and managed in a constructive and useful way. Information Extraction (IE) is the field of extracting useful information using different methods and approaches by means of Natural Language Processing (NLP). Researchers still continue to try to identify proper methods to extract information from texts, such as opinions on the internet, medical data, clinical reports, medical reports, notes, papers, patents, etc. Recently a new trend to expand working in IE is taking place by enriching the extraction process to include the extraction of information from images and videos. In this paper, the classification of IE tasks is discussed, as well as the proposed methods and techniques of IE from chemical documents. A more focused approach is then taken into consideration regarding biomedical language processing and ontology. Finally, the paper discusses some of the challenges that the IE field is facing.


title: Learning to Integrate Occlusion-Specific Detectors for Heavily Occluded Pedestrian Detection

abstract: It is a challenging problem to detect partially occluded pedestrians due to the diversity of occlusion patterns. Although training occlusion-specific detectors can help handle various partial occlusions, it is a non-trivial problem to integrate these detectors properly. A direct combination of all occlusion-specific detectors can be affected by unreliable detectors and usually does not favor heavily occluded pedestrian examples, which can only be recognized by few detectors. Instead of combining all occlusion-specific detectors into a generic detector for all occlusions, we categorize occlusions based on how pedestrian examples are occluded into K groups. Each occlusion group selects its own occlusion-specific detectors and fuses them linearly to obtain a classifer. An L1-norm linear support vector machine (SVM) is adopted to select and fuse occlusion-specific detectors for the K classifiers simultaneously. Thanks to the L1-norm linear SVM, unreliable and irrelevant detectors are removed for each group. Experiments on the Caltech dataset show promising performance of our approach for detecting heavily occluded pedestrians.


title: Occlusion and Motion Reasoning for Long-Term Tracking

abstract: Object tracking is a reoccurring problem in computer vision. Tracking-by-detection approaches, in particular Struck [20], have shown to be competitive in recent evaluations. However, such approaches fail in the presence of long-term occlusions as well as severe viewpoint changes of the object. In this paper we propose a principled way to combine occlusion and motion reasoning with a tracking-by-detection approach. Occlusion and motion reasoning is based on state-of-the-art long-term trajectories which are labeled as object or background tracks with an energy-based formulation. The overlap between labeled tracks and detected regions allows to identify occlusions. The motion changes of the object between consecutive frames can be estimated robustly from the geometric relation between object trajectories. If this geometric change is significant, an additional detector is trained. Experimental results show that our tracker obtains state-of-the-art results and handles occlusion and viewpoints changes better than competing tracking methods.


title: Monkey Algorithm for Global Numerical Optimization

abstract: In this paper, monkey algorithm (MA) is designed to solve global numerical optimization problems with continuous variables. The algorithm mainly consists of climb process, watch-jump process, and somersault process in which the climb process is employed to search the local optimal solution, the watch-jump process to look for other points whose objective values exceed those of the current solutions so as to accelerate the monkeys’ search courses, and the somersault process to make the monkeys transfer to new search domains rapidly. The proposed algorithm is applied to efiectively solve the benchmark problems of global optimization with 30, 1000 or even 10000 dimensions. The computational results show that the MA can flnd optimal or near-optimal solutions to the problems with a large dimensions and very large numbers of local optima. c


title: Seizure Semiology: An Overview of the ‘Inverse Problem’

abstract: In clinical practice, a classification of seizures based on clinical signs and symptoms leads to an improved understanding of epilepsy-related issues and therefore strongly contributes to a better pat


title: A Combined Random Forest and OBIA Classification Scheme for Mapping Smallholder Agriculture at Different Nomenclature Levels Using Multisource Data (Simulated Sentinel-2 Time Series, VHRS and DEM)

abstract: Sentinel-2 images are expected to improve global crop monitoring even in challenging tropical small agricultural systems that are characterized by high intra- and inter-field spatial variability and where satellite observations are disturbed by the presence of clouds. To overcome these constraints, we analyzed and optimized the performance of a combined Random Forest (RF) classifier/object-based approach and applied it to multisource satellite data to produce land use maps of a smallholder agricultural zone in Madagascar at five different nomenclature levels. The RF classifier was first optimized by reducing the number of input variables. Experiments were then carried out to (i) test cropland masking prior to the classification of more detailed nomenclature levels, (ii) analyze the importance of each data source (a high spatial resolution (HSR) time series, a very high spatial resolution (VHSR) coverage and a digital elevation model (DEM)) and data type (spectral, textural or other), and (iii) quantify their contributions to classification accuracy levels. The results show that RF classifier optimization allowed for a reduction in the number of variables by 1.5- to 6-fold (depending on the classification level) and thus a reduction in the data processing time. Classification results were improved via the hierarchical approach at all classification levels, achieving an overall accuracy of 91.7% and 64.4% for the cropland and crop subclass levels, respectively. Spectral variables derived from an HSR time series were shown to be the most discriminating, with a better score for spectral indices over the reflectances. VHSR data were only found to be essential when implementing the segmentation of the area into objects and not for the spectral or textural features they can provide during classification.


title: One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

abstract: In this study, a 1-D Convolutional Neural Network (CNN) architecture was developed, trained and utilized to classify single (summer) and three seasons (spring, summer, fall) of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF) and Support Vector Machine (SVM) classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI) imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM), 1.9% (CNN) to 3.5% (RF) over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products.


title: Role analysis using the ego-ERGM: A look at environmental interest group coalitions

abstract: Abstract Interest groups coordinate to achieve political goals. However, these groups are heterogeneous, and the division of labor within these coalitions varies. We explore the presence of distinct roles in coalitions of environmental interest groups, and analyse which factors predict if an organization takes on a particular role. To model these latent dynamics, we introduce the ego-ERGM. We find that a group's budget, member size, staff size, and degree centrality are influential in distinguishing between three role assignments. These results provide insight into the roles adopted in carrying out coalition tasks. This approach shows promise for understanding a host of networks.


title: Artificial potential fields for the obstacles avoidance system of an AUV using a mechanical scanning sonar

abstract: Obstacle avoidance is one of the most relevant problems in mobile robotics field. The solution of this problem allows successful trajectory generation, detecting and avoiding those regions where a path is unable due to the presence of obstacles. The purpose of this paper is the application of the potential fields method for obstacles avoidance using a mechanical scanning sonar device for an autonomous underwater vehicle. Particular sonar’s features are used to instantiate the proposed method. Different tests in simulated environment provided preliminary results that have proved its feasibility for the future implementation of this method in real environments, within the obstacle avoidance system of an autonomous underwater vehicle (AUV).


title: Cervical and oral teratoma in the fetus: a systematic review of etiology, pathology, diagnosis, treatment and prognosis

abstract: INTRODUCTION ::: The aim of the study was to produce a systematic review about etiology, pathology, diagnosis, prognosis and clinical management regarding oral and cervical teratomas. ::: ::: ::: MATERIALS AND METHODS ::: A systematic review of Pubmed/Medline using the following keywords was made: epignathus, cervical teratoma, fetus, oral teratoma, prenatal diagnosis, prognosis, treatment, ultrasound. ::: ::: ::: CONCLUSION ::: The following clinical conclusions can be reached: (1) teratomas are rare, usually benign congenital tumors which recognized multifactorial etiology; (2) prenatal ultrasound diagnosis can be made early in pregnancy (15-16 weeks); (3) 3D ultrasound and MRI may enhance the accuracy of the antenatal diagnosis (location, extension and intracranial spread) and may aid in the selection of patients requiring treatment; (4) prenatal karyotype and search for associated abnormalities is mandatory in all teratomas; (5) delivery should involve elective Cesarean section with ex utero intrapartum treatment procedure or resection of the tumor mass, which may be performed on placental support operation on placental support procedure to increase the chances of postnatal survival.


title: How to integrate domain-specific languages into the game development process

abstract: Domain-specific languages make the relevant details of a domain explicit while omitting the distracting ones. This implies many benefits regarding development speed and quality as well as the exchange of information between expert groups. In order to utilize these benefits for game development, we present a language engineering workflow that describes best practices to identify a reasonable domain abstraction, illustrated by means of a language for 2D Point &Click Adventures. We discuss how this process can be integrated into an agile, iterative development process and what thereby needs to be considered.


Keep polio eradication at the top of WHO's agenda


Supervised Restricted Boltzmann Machines


title: Authenticity and the construct's dimensions in public relations and communication research

abstract: Purpose – The purpose of this paper is to define the construct authenticity and its dimensions and to propose an index to measure levels of authenticity in organisational messages and actions, as well as perceived authenticity from the stakeholders' perspective.Design/methodology/approach – The paper takes the form of a comprehensive literature review.Findings – The construct authenticity and its dimensions are defined from a multidisciplinary perspective, including literature from advertising, communication studies, marketing, and public relations.Research limitations/implications – Items for an authenticity index are proposed. Such a scale could be used to measure the effectiveness of public relations efforts and techniques and, therefore, the perceived authenticity of organisations, including its actions, operations, product, services, and spokespeople in the mind of stakeholders.Practical implications – The consistency between authentic claims, offerings, and promises and the management philosophy and...


title: Terrain sketching

abstract: Procedural methods for terrain synthesis are capable of creating realistic depictions of heightfield terrains with little user intervention. However, users often do wish to intervene in controlling the placement and shape of landforms, but without sacrificing realism. In this paper, we present a sketching interface to procedural terrain generation. This system enables users to draw the silhouette, spine and bounding curves of both extruding (hills and mountains) and embedding landforms (river courses and canyons). Terrain is interactively generated to match the sketched constraints using multiresolution surface deformation. In addition, the wavelet noise characteristics of silhouette strokes are propagated to the surrounding terrain. With terrain sketching users can interactively create or modify landscapes incorporating varied and complex land-forms.


title: Near-Infrared Light-Responsive Poly(N-isopropylacrylamide)/Graphene Oxide Nanocomposite Hydrogels with Ultrahigh Tensibility

abstract: Novel near-infrared (NIR) light-responsive poly(N-isopropylacrylamide)/graphene oxide (PNIPAM-GO) nanocomposite hydrogels with ultrahigh tensibility are prepared by incorporating sparse chemical cross-linking of small molecules with physical cross-linking of graphene oxide (GO) nanosheets. Combination of the GO nanosheets and thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) polymeric networks provides the hydrogels with an excellent NIR light-responsive property. The ultrahigh tensibility of PNIPAM-GO nanocomposite hydrogels is achieved by simply using a very low concentration of N,N′-methylenebis(acrylamide) (BIS) molecules as chemical cross-linkers to generate a relatively homogeneous structure with flexible long polymer chains and rare chemically cross-linked dense clusters. Moreover, the oxidized groups of GO nanosheets enable the formation of a hydrogen bond interaction with the amide groups of PNIPAM chains, which could physically cross-link the PNIPAM chains to increase the toughness of the hy...


title: The effect of online social networks and competency-based management on innovation capability

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to explore the effect of online social networks and competency-based management on innovation capability. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The paper is theory-confirming. Theoretical relationships were tested using an empirical study of 289 firms from the Spanish biotechnology and telecommunications industries. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Results confirm that online social network use for internal cognitive processes (e.g. reading, searching and storing information) and external cognitive processes (e.g. sharing and co-creating knowledge) positively affects knowledge transfer. This knowledge helps firms to achieve superior competency in R&D to succeed in innovation programs. ::: ::: ::: ::: ::: Research Limitations/implications ::: ::: ::: ::: ::: All survey respondents were from Spain, which may limit the generalizability of findings. A longitudinal approach was not used. However, doing so would make it possible to explore time lags between online social network use, competency-based management and innovation. ::: ::: ::: ::: ::: Practical Implications ::: ::: ::: ::: ::: This paper highlights the potential as well as the limitations of online social networks and competency-based management in promoting innovation capability. Businesses must consciously manage the assimilation and use of online social networks to benefit from them. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The study contributes to the literature by identifying effects on innovation capability at the meso-level (i.e. online social networks). Findings highlight the need for a shift in focus away from collaborating and interacting in online social networks (micro-level) and organizational contexts (macro-level) so as to improve innovation capability.


ROTE: Rollback Protection for Trusted Execution.


title: Pimecrolimus for the treatment of vulvar lichen sclerosus in a premenarchal girl.

abstract: Abstract Background Lichen sclerosus is a chronic cutaneous disorder with a predilection for the vulva. Lichen sclerosus affects more than one in 900 girls. Superpotent corticosteroids like clobetasol propionate are the most effective treatment for vulvar lichen sclerosus. However, recurrence after stopping steroids is very high. As repeated courses of corticosteroids are frequently needed, there are concerns about potential side effects. Therefore, a treatment regimen that does not rely on corticosteroids may be beneficial. As lichen sclerosus is a T-lymphocyte mediated disorder, it has been suggested that pimecrolimus, a topical T-lymphocyte inhibitor, may be safe and effective for the treatment of lichen sclerosus in children. Case report A 10-year-old girl with lichen sclerosus was initially treated with clobetasol. Remission was achieved, but 3 months later she had a recurrence. Subsequent treatment with clobetasol led to a breakdown of her peri-anal skin with a superimposed infection. She was then treated with pimecrolimus and remission was achieved. She has had no recurrence of active lichen sclerosus and has less burning with pimecrolimus than with clobetasol. Conclusion Pimecrolimus may be an effective treatment of vulvar lichen sclerosus. Pimecrolimus has been shown to be very safe in the pediatric population for the treatment of mild to moderate eczema, without causing dermal atrophy, tachyphylaxis, striae, rebound flares, or hypothalamic-pituitary axis suppression. As the recurrence rate of active lichen sclerosus in prepubertal girls treated with topical corticosteroids is high, and the majority of prepubertal girls with lichen sclerosus continue to have disease after menarche, a treatment regimen that does not rely on corticosteroids may be beneficial.


title: FPGA based real-time signal processor for Pulse Doppler Radar

abstract: With the advancements of VLSI chips and powerful computational devices, new complex algorithms and systems are being devised to tap the available computational power and to get the maximum performance. One particular system is the signal processor of Pulse Doppler Radar. This paper aims at finding the range and radial velocity of a moving target using Pulse Doppler Radar for land surveillance. Different optimization techniques have been employed to increase the accuracy of the system. The design was first tested on MATLAB for software verification and then on Xilinx Virtex II Pro FPGA to verify if the system meets the real-time constraints.


title: Adaptive Load Balancing Algorithm Based on Prediction Model in Cloud Computing

abstract: In cloud computing, load balancing is required to distribute the dynamic local workload evenly across all the nodes. It helps to achieve a high user satisfaction and resource utilization by ensuring an efficient and fair allocation of every computing resource. Although many load balancing schemes have been presented in Cloud computing, there is no scheme providing the elasticity and adaptive adjustment in cloud computing. In this paper, an Adaptive Load Balancing Algorithm based on load prediction model (ALBA) was proposed to improve the resource utilization. When the load in the cluster of virtual machines is lower than the minimal threshold, the ALBA scheme can callback the resources of the cluster. While the load in the cluster of virtual machines is higher than the maximum threshold, the ALBA will adaptively add new virtual machines to balance the computation load and ensure the response time. To avoid the data fluctuation causing by the real-time load acquisition, a load prediction model was introduced and used to improve the accuracy of load prediction. The extensive experiments with CloudSim demonstrate that the proposed adaptive load balancing algorithm -- ALBA, can improve the resource utilization as well as reduce the respond time of tasks.


title: Classification and compensation of amplitude imbalance and imperfect quadrature in resolver signals

abstract: This paper proposes a detection and compensation algorithm of two non-ideal output signals of a resolver to reduce position error. Practically, a resolver generates position errors because of the amplitude imbalance and imperfect quadrature between the two output signals of the resolver. In this paper, a digital signal processor (DSP) based resolver-to-digital converter (RDC) is used to reconstruct the two output signals of the resolver. The proposed algorithm does not need any additional hardware. Furthermore, this algorithm can be applied to both the steady and transient states. The simulation results verify the effectiveness of the proposed algorithm.


title: 2D electronics - opportunities and limitations

abstract: 2D materials (2Ds), in particular the 2Ds beyond graphene, have attracted considerable attention in the transistor community. While rapid progress on 2D transistors has been achieved recently, the prospects of 2D electronics is still under debate. In the present paper we discuss our view of the potential of 2D transistors for digital CMOS and for flexible electronics. We show that 2D MOSFETs show promise for ultimately scaled CMOS due their excellent electrostatic integrity and their ability to suppress source-drain tunneling. Moreover, the 2Ds represent a promising option for radio frequency (RF) flexible electronics since they offer an attractive combination of mechanical (bendability, stretchability), and electrical (reasonable mobility) properties.


title: Increased amygdala activation to angry and contemptuous faces in generalized social phobia.

abstract: Background Generalized social phobia (GSP) is characterized by fear of social interactions and sensitivity to disapproval by others. Given the established role of the amygdala as part of a distributed neural system for the processing of emotional cues, we hypothesized that subjects with GSP would exhibit greater amygdala activation in response to harsh (angry, fearful, and contemptous) vs accepting(happy) facial emotional expressions compared with healthy control subjects(HCs). Methods Fifteen subjects with DSM-IV GSP and 15 age-, sex-, handedness-, and education-matched HCs, free of psychotropic medication for at least 12 weeks, viewed 60 color photographs from a standardized set of human facial stimuli, during which the task was to identify the sex of the person in the photograph. Data were collected across 3 functional (echo-planar) runs using a Siemens 1.5-T magnet, and analyzed using Analysis of Functional Neuroimaging software (Medical College of Wisconsin, Milwaukee). Results In the left allocortex (including the amygdala, uncus, and parahippocampal gyrus), subjects with GSP produced a significantly greater percent blood oxygen level–dependent signal change than did HCs for contemptous compared with happy faces (GSP: 0.72% vs HC: −0.01%; F 1,29 = 9.56, P = .004, Cohen d = 1.15) and for angry compared with happy faces (GSP: 0.45% vs HC: −0.09%; F 1,29 = 6.78, P = .02, Cohen d = 1.00). Subjects with GSP and HCs did not produce a statistically different percent signal change for fearful or nonexpressive faces compared with the happy faces in this region. Conclusions These findings are consistent with a role for differential amygdala(and associated limbic) functioning in GSP. The pronounced response to contemptuous and angry facial expressions suggests that the amygdala in GSP may be particularly active in the processing of disorder-salient stimuli.


title: Local Fisher discriminant analysis for supervised dimensionality reduction

abstract: Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised dimensionality reduction problem where samples are accompanied with class labels. Traditional Fisher discriminant analysis is a popular and powerful method for this purpose. However, it tends to give undesired results if samples in some class form several separate clusters, i.e., multimodal. In this paper, we propose a new dimensionality reduction method called local Fisher discriminant analysis (LFDA), which is a localized variant of Fisher discriminant analysis. LFDA takes local structure of the data into account so the multimodal data can be embedded appropriately. We also show that LFDA can be extended to non-linear dimensionality reduction scenarios by the kernel trick.


title: Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification

abstract: We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multiforme brain tumor.


title: The Singularity May Never Be Near

abstract: There is both much optimism and pessimism around artificial intelligence (AI) today. The optimists are investing millions of dollars, and even in some cases billions of dollars into AI. The pessimists, on the other hand, predict that AI will end many things: jobs, warfare, and even the human race. Both the optimists and the pessimists often appeal to the idea of a technological singularity, a point in time where machine intelligence starts to run away, and a new, more intelligent species starts to inhabit the earth. If the optimists are right, this will be a moment that fundamentally changes our economy and our society. If the pessimists are right, this will be a moment that also fundamentally changes our economy and our society. It is therefore very worthwhile spending some time deciding if either of them might be right.


title: Religious Competition and Reallocation: the Political Economy of Secularization in the Protestant Reformation*

abstract: Using novel microdata, we document an unintended, first-order consequence of the Protestant Reformation: a massive reallocation of resources from religious to secular purposes. To understand this process, we propose a conceptual framework in which the introduction of religious competition shifts political markets where religious authorities provide legitimacy to rulers in exchange for control over resources. Consistent with our framework, religious competition changed the balance of power between secular and religious elites: secular authorities acquired enormous amounts of wealth from monasteries closed during the Reformation, particularly in Protestant regions. This transfer of resources had important consequences. First, it shifted the allocation of upper-tail human capital. Graduates of Protestant universities increasingly took secular, especially administrative, occupations. Protestant university students increasingly studied secular subjects, especially degrees that prepared students for public sector jobs, rather than church sector-specific theology. Second, it affected the sectoral composition of fixed investment. Particularly in Protestant regions, new construction shifted from religious toward secular purposes, especially the building of palaces and administrative buildings, which reflected the increased wealth and power of secular lords. Reallocation was not driven by preexisting economic or cultural differences. Our findings indicate that the Reformation played an important causal role in the secularization of the West.


title: Clustering Meets Implicit Generative Models

abstract: Clustering is a cornerstone of unsupervised learning which can be thought as disentangling the multiple generative mechanisms underlying the data. In this paper we introduce an algorithmic framework to train mixtures of implicit generative models which we instantiate for variational autoencoders. Relying on an additional set of discriminators, we propose a competitive procedure in which the models only need to approximate the portion of the data distribution from which they can produce realistic samples. As a byproduct, each model is simpler to train, and a clustering interpretation arises naturally from the partitioning of the training points among the models. We empirically show that our approach splits the training distribution in a reasonable way and increases the quality of the generated samples.


title: THE PROPAGATION AND EXCITATION OF SURFACE WAVES IN AN ABSORBING LAYER

abstract: The present invention encompasses an apparatus and method for supporting multiple wireless data communication networks between a wireless communications device and a fixed communications device or another wireless communications device. Two network elements compose the physical apparatus, a protocol converter and a router. Upon receiving packet data from a fixed communications device, a router will locate a destination wireless communications device. The router will then convert the packet data to a format usable by the wireless data network that the wireless communications device is currently using for wireless access. After conversion, the router will perform actual transmission of the packet data to the correct destination wireless network. Once the converted packet data is received by the wireless network base station, it will be transmitted via RF to the destination wireless communications device. The router will also perform reverse functions of receiving packet data from a wireless communications device across wireless data network and then perform necessary conversion to a format usable by fixed communications device. The router will then perform actual transmission of the converted packet data to the fixed communications device.


title: Customer satisfaction factors of mobile commerce in Korea

abstract: Purpose – The purpose of the paper is to distinguish features of m‐commerce from those of e‐commerce and identify factors to influence customer satisfaction (m‐satisfaction) and loyalty (m‐loyalty) in m‐commerce by empirically‐based case study.Design/methodology/approach – First, based on previous literature, the paper builds sets of customer satisfaction factors for both e‐commerce and m‐commerce. Second, features of m‐commerce are identified by comparing it with current e‐commerce through decision tree (DT). Third, with the derived factors from DT, significant factors and relationships among the factors, m‐satisfaction and m‐loyalty are examined by m‐satisfaction model employing structural equation model.Findings – The paper finds that m‐commerce is partially similar in factors like “transaction process” and “customization” which lead customer satisfaction after connecting an m‐commerce site, but it has unique aspects of “content reliability”, “availability”, and “perceived price level of mobile Interne...


title: Immersive Interactive Virtual Reality and Informal Education

abstract: Research in conceptual learning and virtual reality is a relatively young field, but growing rapidly. The increasing development of virtual reality (VR) technologies has matured enough as to expand research from the military and scientific visualization realm into more multidisciplinary areas, such as education, art, and psychology. Consequently, virtual reality interfaces, interaction techniques, and devices have improved greatly in order to provide more natural and obvious modes of interaction and motivational elements. Nevertheless, the prohibitive costs and inaccessibility of VR technologies, coupled with issues of usability, educator training, operation, and maintenance, present important drawbacks for the educational use of VR making it difficult to incorporate in dwindling school budgets. In spite of these concerns and objections regarding the appropriateness and educational efficacy of virtual reality, there remain compelling reasons for believing that VR learning environments for children warrant serious investigation and can provide strong tools for learning. Institutions of informal education, such as museums, research, and cultural centers are in a better position to make use of such advanced systems and investigate their educational potential while effectively shaping how they deliver public education. This paper describes the interactive learning environments developed for learners of all ages in such an institution of informal education, and discusses the issues involved in using state-of-the-art interactive virtual environments for childhood learning. 1. BACKGROUND The development of new interactive technologies inevitably has an impact on all aspects of teaching and learning. This is more evident in the case of novel interactive technologies that fascinate the broad public, as is Virtual Reality (VR). Research in virtual reality and education is a relatively young field but in recent years it has shown considerable growth.


title: Cognitive Failure and Alexithymia and Predicting High–Risk Behaviors of Students With Learning Disabilities

abstract: BACKGROUND ::: One of the threatening health issues is prevalence of high-risk behaviors in various groups. Because of rapid social changes, it has been considered as of the most important problems of society by health organizations, administrative laws, and social policymakers. ::: ::: ::: OBJECTIVES ::: The aim of this study was to determine the role of cognitive failure and alexithymia in predicting high-risk behaviors of students with learning disabilities. ::: ::: ::: PATIENTS AND METHODS ::: This was a correlational research including all 14-16 years old students during 2012-2013 school year in Arak, IR Iran. Eighty students with learning disabilities were sampled by simply random sampling. The data were collected by cognitive failures questionnaire, Toronto alexithymia scale, and high-risk behavior questionnaire. ::: ::: ::: RESULTS ::: The results showed that high-risk behaviors had significant positive correlations with difficulty identifying feelings (r = 0.321), difficulty describing feelings (r = 0.336), externally oriented thinking (r = 0.248), distractibility (0.292), memory distortion (r = 0.374), blunders (r = 0.335), and names amnesia (r = 0.275). Multiple regression analysis showed that cognitive failure and alexithymia predicted 32% of the total variance of high-risk behaviors. ::: ::: ::: CONCLUSIONS ::: These findings demonstrated that cognitive failure and alexithymia had important roles in strengthening and appearance of high-risk behaviors in students with learning disabilities. Therefore, considering those problems, precautionary actions might be necessary.


title: Reachability Analysis and Safety Verification for Neural Network Control Systems

abstract: Autonomous cyber-physical systems (CPS) rely on the correct operation of numerous components, with state-of-the-art methods relying on machine learning (ML) and artificial intelligence (AI) components in various stages of sensing and control. This paper develops methods for estimating the reachable set and verifying safety properties of dynamical systems under control of neural network-based controllers that may be implemented in embedded software. The neural network controllers we consider are feedforward neural networks called multilayer perceptrons (MLP) with general activation functions. As such feedforward networks are memoryless, they may be abstractly represented as mathematical functions, and the reachability analysis of the network amounts to range (image) estimation of this function provided a set of inputs. By discretizing the input set of the MLP into a finite number of hyper-rectangular cells, our approach develops a linear programming (LP) based algorithm for over-approximating the output set of the MLP with its input set as a union of hyper-rectangular cells. Combining the over-approximation for the output set of an MLP based controller and reachable set computation routines for ordinary difference/differential equation (ODE) models, an algorithm is developed to estimate the reachable set of the closed-loop system. Finally, safety verification for neural network control systems can be performed by checking the existence of intersections between the estimated reachable set and unsafe regions. The approach is implemented in a computational software prototype and evaluated on numerical examples.


“Exascale Computing and Big Data: The Next Frontier,”


title: Dengue disease prediction using weka data mining tool

abstract: Dengue is a life threatening disease prevalent in several developed as well as developing countries like India.In this paper we discuss various algorithm approaches of data mining that have been utilized for dengue disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as dengue, diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms. In this paper we have firstly classified the dengue data set and then compared the different data mining techniques in weka through Explorer, knowledge flow and Experimenter interfaces. Furthermore in order to validate our approach we have used a dengue dataset with 108 instances but weka used 99 rows and 18 attributes to determine the prediction of disease and their accuracy using classifications of different algorithms to find out the best performance. The main objective of this paper is to classify data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. From the findings of this paper it can be concluded that Na\"ive Bayes and J48 are the best performance algorithms for classified accuracy because they achieved maximum accuracy= 100% with 99 correctly classified instances, maximum ROC = 1, had least mean absolute error and it took minimum time for building this model through Explorer and Knowledge flow results


title: Microstrip stepped impedance resonator bandpass filter with an extended optimal rejection bandwidth

abstract: Bandpass filters with an optimal rejection bandwidth are designed using parallel-coupled stepped impedance resonators (SIRs). The fundamental (f/sub o/) and higher order resonant harmonics of an SIR are analyzed against the length ratio of the high-Z and low-Z segments. It is found that an optimal length ratio can be obtained for each high-Z to low-Z impedance ratio to maximize the upper rejection bandwidth. A tapped-line input/output structure is exploited to create two extra transmission zeros in the stopband. The singly loaded Q(Q/sub si/) of a tapped SIR is derived. With the aid of Q/sub si/, the two zeros can be independently tuned over a wide frequency range. When the positions of the two zeros are purposely located at the two leading higher order harmonics, the upper rejection band can be greatly extended. Chebyshev bandpass filters with spurious resonances up to 4.4f/sub o/, 6.5f/sub o/, and 8.2f/sub o/ are fabricated and measured to demonstrate the idea.


title: Modeling and Design of Series Voltage Compensator for Reduction of DC-Link Capacitance in Grid-Tie Solar Inverter

abstract: A grid-tie solar inverter with a series voltage compensator for reducing the high-voltage dc-link capacitance is presented. The compensator obtains energy from the dc link to sustain the voltage on its dc side and generates an ac voltage to counteract the voltage ripple on the dc link. As the compensator processes small ripple voltage on the dc link and reactive power, it can be implemented with low-voltage devices, and thus, its volt-amp rating is small. As the required energy storage of the dc link, formed by a reduced value of the dc-link capacitor and the compensator, is reduced, the architecture allows replacing popularly used electrolytic capacitors with alternatives of longer lifetime, such as power film capacitors, or extending the system lifetime even if there is a significant reduction in the capacitance of electrolytic capacitors due to aging. Detailed mathematical analysis on the static and dynamic behaviors of the overall system, and the control method will be presented. A simplified design procedure for the compensator will be given. A 2-kW, 220-V, 50-Hz prototype has been built and evaluated. The theoretical predictions are compared favorably with experimental results. Finally, the implementation cost with electrolytic-capacitor and compensator for the dc link will be compared.


title: Knowledge Sharing in Cross-Functional Teams and its Antecedents: Role of Mutual Trust as a Moderator

abstract: Growth and success of an organisation are primarily dependent on its ability to develop, leverage, and utilise its knowledge base. This study developed a model to test the impact of antecedents of knowledge Sharing (KS) on team performance. The antecedents were classified as organisational characteristics (structure, learning culture, employee training, reward system, top management support) and individual characteristic (emotional intelligence). The study is based on data collected with the help of structured questionnaires from 582 team members working under 69 teams in 26 surveyed organisations in India. Results suggest that high emotional intelligence increases the extent of KS and hence, has positive impact on team performance among cross-functional team members. Moreover, mutual trust among team members moderates KS behaviour and team performance. Thus, findings confirm the applicability and predictive power of the proposed model. Furthermore, this model contributes to the literature of predicting KS practices in cross-functional projects.


title: Learning Cognitive Models using Neural Networks

abstract: A cognitive model of human learning provides information about skills a learner must acquire to perform accurately in a task domain. Cognitive models of learning are not only of scientific interest, but are also valuable in adaptive online tutoring systems. A more accurate model yields more effective tutoring through better instructional decisions. Prior methods of automated cognitive model discovery have typically focused on well-structured domains, relied on student performance data or involved substantial human knowledge engineering. In this paper, we propose Cognitive Representation Learner (CogRL), a novel framework to learn accurate cognitive models in ill-structured domains with no data and little to no human knowledge engineering. Our contribution is two-fold: firstly, we show that representations learnt using CogRL can be used for accurate automatic cognitive model discovery without using any student performance data in several ill-structured domains: Rumble Blocks, Chinese Character, and Article Selection. This is especially effective and useful in domains where an accurate human-authored cognitive model is unavailable or authoring a cognitive model is difficult. Secondly, for domains where a cognitive model is available, we show that representations learned through CogRL can be used to get accurate estimates of skill difficulty and learning rate parameters without using any student performance data. These estimates are shown to highly correlate with estimates using student performance data on an Article Selection dataset.


Effectiveness of Structured Teaching Program on Knowledge , Attitude and Management Strategies Among Teachers of Primary School toward Children with Attention Deficit Hyperactivity Disorders .


title: From Keyframes to Key Objects: Video Summarization by Representative Object Proposal Selection

abstract: We propose to summarize a video into a few key objects by selecting representative object proposals generated from video frames. This representative selection problem is formulated as a sparse dictionary selection problem, i.e., choosing a few representatives object proposals to reconstruct the whole proposal pool. Compared with existing sparse dictionary selection based representative selection methods, our new formulation can incorporate object proposal priors and locality prior in the feature space when selecting representatives. Consequently it can better locate key objects and suppress outlier proposals. We convert the optimization problem into a proximal gradient problem and solve it by the fast iterative shrinkage thresholding algorithm (FISTA). Experiments on synthetic data and real benchmark datasets show promising results of our key object summarization apporach in video content mining and search. Comparisons with existing representative selection approaches such as K-mediod, sparse dictionary selection and density based selection validate that our formulation can better capture the key video objects despite appearance variations, cluttered backgrounds and camera motions.


title: Dynamics and control of quadrotor with robotic manipulator

abstract: We show that the Lagrange dynamics of quadrotor-manipulator systems can be completely decoupled into: 1) the center-of-mass dynamics in E(3), which, similar to the standard quadrotor dynamics, is point-mass dynamics with under-actuation and gravity effect; and 2) the "internal rota- tional" dynamics of the quadrotor's rotation and manipulator configuration, which assumes the form of standard Lagrange dynamics of robotic manipulator with full-actuation and no gravity effect. Relying on this structure, we propose a novel backstepping-like end-effector tracking control law, which can allow us to assign different roles for the center-of-mass control and for the internal rotational dynamics control according to task objectives. Simulations using a planar quadrotor with a 2-DOF arm are also performed to show the theory. I. INTRODUCTION Quadrotors have been researched extensively in recent years due to their agile performance, relative-easiness to con- trol, and affordability, with the rapid advancements in sen- sors, actuators, materials, and embedded computing. These quadrotors are also promising to extend the ground-bound ability of the typical mobile robots to the three dimensional space. Some application examples include: remote landscape survey, aerial photography, surveillance and reconnaissance,


title: Fast Closed-Form Matting Using a Hierarchical Data Structure

abstract: Image/video matting is one of the key operations in many image/video editing applications. Although previous methods can generate high-quality matting results, their high computational cost in processing high-resolution image and video data often limits their usability. In this paper, we present a unified acceleration method for closed-form image and video matting using a hierarchical data structure, which achieves an excellent compromise between quality and speed. We first apply a Gaussian KD tree to adaptively cluster the input high-dimensional image and video feature space into a low-dimensional feature space. Then, we solve the affinity-weighted Laplacian alpha matting in the reduced feature space. The final matting results are derived using detail-aware alpha interpolation. Our algorithm can be fully parallelized by exploiting advanced graphics hardware, which can further accelerate the matting computation. Our method accelerates existing methods by at least an order of magnitude with good quality, and also greatly reduces the memory consumption. This acceleration strategy is also extended to support other affinity-based matting approaches, which makes it a more general accelerating framework for a variety of matting methods. Finally, we apply the presented method to accelerate image and video dehazing, and image shadow detection and removal.


title: Advances in magnetic sensors

abstract: The most important milestone in the field of magnetic sensors was that AMR sensors started to replace Hall sensors in many application, were larger sensitivity is an advantage. GMR and SDT sensor finally found limited applications. We also review the development in miniaturization of fluxgate sensors and briefly mention SQUIDs, resonant sensors, GMIs and magnetomechanical sensors.


title: Customer relationship management: Emerging practice, process, and discipline

abstract: Customer relationship management (CRM) has once again gained prominence amongst academics and practitioners. However, there is a tremendous amount of confusion regarding its domain and meaning. In this paper, the authors explore the conceptual foundations of CRM by examining the literature on relationship marketing and other disciplines that contribute to the knowledge of CRM. A CRM process framework is proposed that builds on other relationship development process models. CRM implementation challenges as well as CRM's potential to become a distinct discipline of marketing are also discussed in this paper.


title: The name game: Employability evaluations of prototypical applicants with stereotypical feminine and masculine first names

abstract: This study was designed to examine professional human resource managers’ recommendations and inferences about prototypical applicants who had identical qualifications, in which the presence of periods of unemployment and name (feminine, masculine) of applicants were manipulated. Results indicate that although overall income for female applicants was less than male applicants in some conditions, male applicants were penalized and evaluated more harshly than female applicants when they had experienced periods of unemployment. Specifically, male applicants with employment gaps were seen as less committed and as less hirable than their female counterparts. Overall, male applicants were less likely to be recommended for an interview, and, when they experienced multiple gaps, they were less likely to be recommended for further consideration.


title: Bagging predictors

abstract: Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy.


CMEE-IL: Code Mix Entity Extraction in Indian Languages from Social Media Text @ FIRE 2016 - An Overview


title: Information technology governance — COBIT model

abstract: This paper explains the concept of information technology audit, shows relationship between audit and IT Management. Information systems provide both the means for organizations to transact business and the ability to report the financial results of their operations. Information technology auditing is an integral part of corporate governance. However, information technology auditing is often looked upon as a “necessary evil” or is overlooked entirely by IT management. IT governance and information technology audit is imposed as an imperative for successful business. To improve the management of IT in accordance with regulatory requirements, organizations are using best practice frameworks to facilitate the work.


title: Embedded-Graph Theory

abstract: In this paper, we propose a new type of graph, denoted as"embedded-graph", and its theory, which employs a distributed representation to describe the relations on the graph edges. Embedded-graphs can express linguistic and complicated relations, which cannot be expressed by the existing edge-graphs or weighted-graphs. We introduce the mathematical definition of embedded-graph, translation, edge distance, and graph similarity. We can transform an embedded-graph into a weighted-graph and a weighted-graph into an edge-graph by the translation method and by threshold calculation, respectively. The edge distance of an embedded-graph is a distance based on the components of a target vector, and it is calculated through cosine similarity with the target vector. The graph similarity is obtained considering the relations with linguistic complexity. In addition, we provide some examples and data structures for embedded-graphs in this paper.


title: A Dual-Tree Complex Wavelet Transform Based Convolutional Neural Network for Human Thyroid Medical Image Segmentation

abstract: This research proposes a novel dual-tree complex wavelet transform based Convolutional Neural Network (WCNN) to perform organ tissue segmentation from medical images. Accurate and efficient segmentation on the medical image of human organ is a critical step towards disease diagnosis. For medical image segmentation tasks, conventional Convolutional Neural Networks (CNNs) are: 1) inclined to ignore crucial texture information of the image due to the limitations of typical pooling approaches, and 2) insufficiently robust to noise. To overcome the obstacles, a spectral domain transformation technique is adopted in the CNN. Specifically, a dual-tree complex wavelet pooling layer is concatenated to the traditional pooling process in a CNN. By using wavelet decomposition, the image becomes scalable in the spatial direction, allowing accurate recognition of textures. The WCNN decomposes the image into a number of wavelet subbands, and reduces noisy data by filtering out high-frequency subbands. The performance of WCNN is tested on standard image classification datasets, and applied for human thyroid optical coherence tomography (OCT) image segmentation. Compared to the traditional CNNs using max pooling, experimental results demonstrate that the WCNN approach obtains outstanding consistency and accuracy in the image segmentation domain.


title: A Survey on 5G: The Next Generation of Mobile Communication

abstract: The rapidly increasing number of mobile devices, voluminous data, and higher data rate are pushing to rethink the current generation of the cellular mobile communication. The next or fifth generation (5G) cellular networks are expected to meet high-end requirements. The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and very high-speed data transfer. The 5G networks would provide novel architectures and technologies beyond state-of-the-art architectures and technologies. In this paper, our intent is to find an answer to the question: "what will be done by 5G and how?" We investigate and discuss serious limitations of the fourth generation (4G) cellular networks and corresponding new features of 5G networks. We identify challenges in 5G networks, new technologies for 5G networks, and present a comparative study of the proposed architectures that can be categorized on the basis of energy-efficiency, network hierarchy, and network types. Interestingly, the implementation issues, e.g., interference, QoS, handoff, security-privacy, channel access, and load balancing, hugely effect the realization of 5G networks. Furthermore, our illustrations highlight the feasibility of these models through an evaluation of existing real-experiments and testbeds.


title: Deep auto-encoder neural networks in reinforcement learning

abstract: This paper discusses the effectiveness of deep auto-encoder neural networks in visual reinforcement learning (RL) tasks. We propose a framework for combining the training of deep auto-encoders (for learning compact feature spaces) with recently-proposed batch-mode RL algorithms (for learning policies). An emphasis is put on the data-efficiency of this combination and on studying the properties of the feature spaces automatically constructed by the deep auto-encoders. These feature spaces are empirically shown to adequately resemble existing similarities and spatial relations between observations and allow to learn useful policies. We propose several methods for improving the topology of the feature spaces making use of task-dependent information. Finally, we present first results on successfully learning good control policies directly on synthesized and real images.


title: System structure for software fault tolerance

abstract: The paper presents, and discusses the rationale behind, a method for structuring complex computing systems by the use of what we term “recovery blocks”, “conversations” and “fault-tolerant interfaces”. The aim is to facilitate the provision of dependable error detection and recovery facilities which can cope with errors caused by residual design inadequacies, particularly in the system software, rather than merely the occasional malfunctioning of hardware components.


title: Design of the Quality of Life in Motion (QLIM) study: a randomized controlled trial to evaluate the effectiveness and cost-effectiveness of a combined physical exercise and psychosocial training program to improve physical fitness in children with cancer

abstract: BackgroundChildhood cancer and its treatment have considerable impact on a child's physical and mental wellbeing. Especially long-term administration of chemotherapy and/or radiotherapy impairs physical fitness both during and after therapy, when children often present with muscle weakness and/or low cardiorespiratory fitness. Physical exercise can improve these two elements of physical fitness, but the positive effects of physical exercise might be further increased when a child's wellbeing is simultaneously enhanced by psychosocial training. Feeling better may increase the willingness and motivation to engage in sports activities. Therefore, this multi-centre study evaluates the short and long-term changes in physical fitness of a child with a childhood malignancy, using a combined physical exercise and psychosocial intervention program, implemented during or shortly after treatment. Also examined is whether positive effects on physical fitness reduce inactivity-related adverse health problems, improve quality of life, and are cost-effective.MethodsThis multi-centre randomized controlled trial compares a combined physical and psychosocial intervention program for children with cancer, with care as usual (controls). Children with cancer (aged 8-18 years) treated with chemotherapy and/or radiotherapy, and who are no longer than 1 year post-treatment, are eligible for participation. A total of 100 children are being recruited from the paediatric oncology/haematology departments of three Dutch university medical centres. Patients are stratified according to pubertal stage (girls: age ≤10 or >10 years; boys: ≤11 or >11 years), type of malignancy (haematological or solid tumour), and moment of inclusion into the study (during or after treatment), and are randomly assigned to the intervention or control group.DiscussionChildhood cancer patients undergoing long-term cancer therapy may benefit from a combined physical exercise and psychosocial intervention program since it may maintain or enhance their physical fitness and increase their quality of life. However, the feasibility, patient need, and effectiveness of such a program should be established before the program can be implemented as part of standard care.Trial registration numberNTR1531 (The Netherlands National Trial Register)


title: Portable and Scalable Vision-Based Vehicular Instrumentation for the Analysis of Driver Intentionality

abstract: Probably the most promising breakthroughs in vehicular safety will emerge from intelligent, Advanced Driving Assistance Systems (i-ADAS). Influential research institutions and large vehicle manufacturers work in lockstep to create advanced, on-board safety systems by means of integrating the functionality of existing systems and developing innovative sensing technologies. In this contribution, we describe a portable and scalable vehicular instrumentation designed for on-road experimentation and hypothesis verification in the context of designing i-ADAS prototypes.


title: Simple and strong: twisted silver painted nylon artificial muscle actuated by Joule heating

abstract: Highly oriented nylon and polyethylene fibres shrink in length when heated and expand in diameter. By twisting and then coiling monofilaments of these materials to form helical springs, the anisotropic thermal expansion has recently been shown to enable tensile actuation of up to 49% upon heating. Joule heating, by passing a current through a conductive coating on the surface of the filament, is a convenient method of controlling actuation. In previously reported work this has been done using highly flexible carbon nanotube sheets or commercially available silver coated fibres. In this work silver paint is used as the Joule heating element at the surface of the muscle. Up to 29% linear actuation is observed with energy and power densities reaching 840 kJ m-3 (528 J kg-1) and 1.1 kW kg-1 (operating at 0.1 Hz, 4% strain, 1.4 kg load). This simple coating method is readily accessible and can be applied to any polymer filament. Effective use of this technique relies on uniform coating to avoid temperature gradients.


Statistical Natural Language Generation from Tabular Non-textual Data


Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems


title: Network robustness and fragility: percolation on random graphs.

abstract: Recent work on the Internet, social networks, and the power grid has addressed the resilience of these networks to either random or targeted deletion of network nodes or links. Such deletions include, for example, the failure of Internet routers or power transmission lines. Percolation models on random graphs provide a simple representation of this process but have typically been limited to graphs with Poisson degree distribution at their vertices. Such graphs are quite unlike real-world networks, which often possess power-law or other highly skewed degree distributions. In this paper we study percolation on graphs with completely general degree distribution, giving exact solutions for a variety of cases, including site percolation, bond percolation, and models in which occupation probabilities depend on vertex degree. We discuss the application of our theory to the understanding of network resilience.


title: Are mesh anchoring sutures necessary in ventral hernioplasty? Multicenter study

abstract: BACKGROUND ::: Avoiding mesh fixation to the surrounding tissue in ventral hernioplasty would simplify the operation, decrease the time of the procedure, and decrease the risk of suture-related complications. ::: ::: ::: METHODS ::: Four hospitals included 111 patients according to the common protocol for prospective clinical evaluation of sutureless ventral hernioplasty. Surgical technique involves placement of the polypropylene mesh with flat-shape memory in either the retromuscular or preperitoneal space without suture anchoring. ::: ::: ::: RESULTS ::: Local complication rate was low (12.6%, 14 patients), postoperative pain measured according to the visual analogue scale was minimal (mean 4, range 1-8). Three recurrences (3%) were recorded. Mild scar discomfort, which did not require treatment nor limit physical activity, was recorded in 28 (25%), 18 (17%), and 11 (14%) patients at 6-month, 1- and 2-year follow-up, respectively. ::: ::: ::: CONCLUSIONS ::: Results of the study suggest that the sutureless sublay technique is safe and effective in the treatment of ventral abdominal hernia, especially in small and medium defects.


title: QG-net: a data-driven question generation model for educational content

abstract: The ever growing amount of educational content renders it increasingly difficult to manually generate sufficient practice or quiz questions to accompany it. This paper introduces QG-Net, a recurrent neural network-based model specifically designed for automatically generating quiz questions from educational content such as textbooks. QG-Net, when trained on a publicly available, general-purpose question/answer dataset and without further fine-tuning, is capable of generating high quality questions from textbooks, where the content is significantly different from the training data. Indeed, QG-Net outperforms state-of-the-art neural network-based and rules-based systems for question generation, both when evaluated using standard benchmark datasets and when using human evaluators. QG-Net also scales favorably to applications with large amounts of educational content, since its performance improves with the amount of training data.


title: Being versus appearing socially uninterested: Challenging assumptions about social motivation in autism

abstract: Progress in psychological science can be limited by a number of factors, not least of which are the starting assumptions of scientists themselves. We believe that some influential accounts of autism rest on a questionable assumption that many of its behavioral characteristics indicate a lack of social interest – an assumption that is flatly contradicted by the testimony of many autistic people themselves. In this article, we challenge this assumption by describing alternative explanations for four such behaviors: (a) low levels of eye contact, (b) infrequent pointing, (c) motor stereotypies, and (d) echolalia. The assumption that autistic people's unusual behaviors indicate diminished social motivation has had profound and often negative effects on the ways they are studied and treated. We argue that understanding and supporting autistic individuals will require interrogating this assumption, taking autistic testimony seriously, considering alternative explanations for unusual behaviors, and investigating unconventional – even idiosyncratic – ways in which autistic individuals may express their social interest. These steps are crucial, we believe, for creating a more accurate, humane, and useful science of autism.


title: A Fast Edge Extraction Method for Mobile Lidar Point Clouds

abstract: Edges in mobile light detection and ranging (lidar) point clouds are important for many applications but usually overlooked. In this letter, we propose a fast edge extraction method for mobile lidar. First, an edge index based on geometric center is introduced and then gradients in unorganized 3-D point clouds are defined. By analyzing the ratio between eigenvalues, edge candidates can be detected. Finally, an edge linking algorithm named graph snapping is proposed. The method is tested extensively and the experimental results demonstrate that the proposed method is able to quickly extract most of 3-D edges with higher accuracy than the existing methods.


title: Efficient kNN Classification With Different Numbers of Nearest Neighbors

abstract: ${k}$ nearest neighbor (kNN) method is a popular classification method in data mining and statistics because of its simple implementation and significant classification performance. However, it is impractical for traditional kNN methods to assign a fixed ${k}$ value (even though set by experts) to all test samples. Previous solutions assign different $k$ values to different test samples by the cross validation method but are usually time-consuming. This paper proposes a kTree method to learn different optimal $k$ values for different test/new samples, by involving a training stage in the kNN classification. Specifically, in the training stage, kTree method first learns optimal $k$ values for all training samples by a new sparse reconstruction model, and then constructs a decision tree (namely, kTree) using training samples and the learned optimal $k$ values. In the test stage, the kTree fast outputs the optimal $k$ value for each test sample, and then, the kNN classification can be conducted using the learned optimal $k$ value and all training samples. As a result, the proposed kTree method has a similar running cost but higher classification accuracy, compared with traditional kNN methods, which assign a fixed ${k}$ value to all test samples. Moreover, the proposed kTree method needs less running cost but achieves similar classification accuracy, compared with the newly kNN methods, which assign different ${k}$ values to different test samples. This paper further proposes an improvement version of kTree method (namely, k*Tree method) to speed its test stage by extra storing the information of the training samples in the leaf nodes of kTree, such as the training samples located in the leaf nodes, their kNNs, and the nearest neighbor of these kNNs. We call the resulting decision tree as k*Tree, which enables to conduct kNN classification using a subset of the training samples in the leaf nodes rather than all training samples used in the newly kNN methods. This actually reduces running cost of test stage. Finally, the experimental results on 20 real data sets showed that our proposed methods (i.e., kTree and k*Tree) are much more efficient than the compared methods in terms of classification tasks.


title: Swarm intelligence and robotics

abstract: – The aim of this paper is to provide a review of recent developments in the application of swarm intelligence to robotics., – This paper initially considers swarm intelligence and then discusses its application to robotics through reference to a number of recent research programmes., – Based on the principles of swarm intelligence, which is derived from the swarming behaviour of biological entities, swarm robotics research is widespread but still at an early stage. Much aims to gain an understanding of biological swarming and apply it to autonomous, mobile multi‐robot systems. European activities are particularly strong and several large, collaborative projects are underway. Research in the USA has a military bias and much is funded by defence agencies., – The paper provides an up‐to‐date insight into swarm robot research and development.


title: A soft wearable robot for the shoulder: Design, characterization, and preliminary testing

abstract: In this paper, we present a soft wearable robot for the shoulder which has the potential to assist individuals suffering from a range of neuromuscular conditions affecting the shoulder to perform activities of daily living. This wearable robot combines two types of soft textile pneumatic actuators which were custom developed for this particular application to support the upper arm through shoulder abduction and horizontal flexion/extension. The advantage of a textile-based approach is that the robot can be lightweight, low-profile, comfortable and non-restrictive to the wearer, and easy to don like an item of clothing. The actuator's ability to fold flat when not in use allows the robot to be almost invisible under clothing, potentially allowing the user to avoid any stigma associated with using assistive devices in public. To abduct the arm, a textilebased pneumatic actuator was developed to fit within the axilla to push the arm upwards, while a pair of smaller actuators pivot the abduction actuator to allow for horizontal extension and flexion. The individual textile actuators were experimentally evaluated before being integrated into a wearable garment. Human subject testing was performed to evaluate the ability of the robot to assist the arm by monitoring changes in biological muscle activity when comparing the robot powered on and off. Preliminary results show large reductions in muscular effort in targeted muscles, demonstrating the feasibility and promise of such a soft wearable robot for the shoulder.


title: Automatic failure recovery for software-defined networks

abstract: Tolerating and recovering from link and switch failures are fundamental requirements of most networks, including Software-Defined Networks (SDNs). However, instead of traditional behaviors such as network-wide routing re-convergence, failure recovery in an SDN is determined by the specific software logic running at the controller. While this admits more freedom to respond to a failure event, it ultimately means that each controller application must include its own recovery logic, which makes the code more difficult to write and potentially more error-prone. In this paper, we propose a runtime system that automates failure recovery and enables network developers to write simpler, failure-agnostic code. To this end, upon detecting a failure, our approach first spawns a new controller instance that runs in an emulated environment consisting of the network topology excluding the failed elements. Then, it quickly replays inputs observed by the controller before the failure occurred, leading the emulated network into the forwarding state that accounts for the failed elements. Finally, it recovers the network by installing the difference ruleset between emulated and current forwarding states.


title: Quality of experience for HTTP adaptive streaming services

abstract: The growing consumer demand for mobile video services is one of the key drivers of the evolution of new wireless multimedia solutions requiring exploration of new ways to optimize future wireless networks for video services towards delivering enhanced quality of experience (QoE). One of these key video enhancing solutions is HTTP adaptive streaming (HAS), which has recently been spreading as a form of Internet video delivery and is expected to be deployed more broadly over the next few years. As a relatively new technology in comparison with traditional push-based adaptive streaming techniques, deployment of HAS presents new challenges and opportunities for content developers, service providers, network operators and device manufacturers. One of these important challenges is developing evaluation methodologies and performance metrics to accurately assess user QoE for HAS services, and effectively utilizing these metrics for service provisioning and optimizing network adaptation. In that vein, this article provides an overview of HAS concepts, and reviews the recently standardized QoE metrics and reporting framework in 3GPP. Furthermore, we present an end-to-end QoE evaluation study on HAS conducted over 3GPP LTE networks and conclude with a discussion of future challenges and opportunities in QoE optimization for HAS services.