title: Optimal and Low-Complexity Algorithms for Dynamic Spectrum Access in Centralized Cognitive Radio Networks with Fading Channels

abstract: In this paper, we develop a centralized spectrum sensing and Dynamic Spectrum Access (DSA) scheme for secondary users (SUs) in a Cognitive Radio (CR) network. Assuming that the primary channel occupancy follows a Markovian evolution, the channel sensing problem is modeled as a Partially Observable Markov Decision Process (POMDP). We assume that each SU can sense only one channel at a time by using energy detection, and the sensing outcomes are then reported to a central unit, called the secondary system decision center (SSDC), that determines the channel sensing/accessing policies. We derive both the optimal channel assignment policy for secondary users to sense the primary channels, and the optimal channel access rule. Our proposed optimal sensing and accessing policies alleviate many shortcomings and limitations of existing proposals: (a) ours allows fully utilizing all available primary spectrum white spaces, (b) our model, and thus the proposed solution, exploits the temporal and spatial diversity across different primary channels, and (c) is based on realistic local sensing decisions rather than complete knowledge of primary signalling structure. As an alternative to the high complexity of the optimal channel sensing policy, a suboptimal sensing policy is obtained by using the Hungarian algorithm iteratively, which reduces the complexity of the channel assignment from an exponential to a polynomial order. We also propose a heuristic algorithm that reduces the complexity of the sensing policy further to a linear order. The simulation results show that the proposed algorithms achieve a near-optimal performance with a significant reduction in computational time.


title: ORIGINAL ARTICLE Campaign Ads, Online Messaging, and Participation: Extending the Communication Mediation Model

abstract: Political communication researchers have devoted a great deal of attention to the role of political advertising, the Internet, and political discussion in civic and political life. In this article, we integrate and extend this research by developing a campaign communication mediation model of civic and campaign participation. Two data sets are merged for this inquiry: (a) content-coded ad-buy data on the placement of campaign messages on a market-by-market and program-by-program basis and (b) a national panel study concerning patterns of traditional and digital media consumption and levels of civic and campaign participation. Exposure to televised campaign advertising is estimated by developing an algorithm based on the market and program placement of specific ads and geocoded survey respondents’ viewing of certain categories of television content in which these ads were concentrated. Structural equation models reveal that advertising exposure drives online news use in ways that complement conventional news influences on political discussion and political messaging. However, campaign exposure emphasizing ‘‘attack’’ messages appears to diminish information seeking motivations via broadcast and print media, yet only indirectly and weakly suppresses participation in civic and political life. Further, alternative specifications reveal that our original model produces the best fit, empirically and theoretically. We use these insights to propose an O-S-R-O-R (orientations-stimuli-reasoning-orientations-responses) framework as an alternative to the longstanding O-S-O-R model in communication and social psychology.


title: A Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation

abstract: Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in Location-Based Social Networks (LBSNs) such as Yelp and Gowalla. Bayesian Personalised Ranking (BPR) is a popular pairwise recommendation technique that is used to generate the ranked list of venues of interest to a user, by leveraging the user's implicit feedback such as their check-ins as instances of positive feedback, while randomly sampling other venues as negative instances. To alleviate the sparsity that affects the usefulness of recommendations by BPR for users with few check-ins, various approaches have been proposed in the literature to incorporate additional sources of information such as the social links between users, the textual content of comments, as well as the geographical location of the venues. However, such approaches can only readily leverage one source of additional information for negative sampling. Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR. In particular, we apply a multi-centre Gaussian model and a power-law distribution method, to capture geographical influence and social correlation when sampling negative venues, respectively. Finally, we conduct comprehensive experiments using three large-scale datasets from the Yelp, Gowalla and Brightkite LBSNs. The experimental results demonstrate the effectiveness of fusing both geographical influence and social correlation in our proposed PRFMC framework and its superiority in comparison to BPR-based and other similar ranking approaches. Indeed, our PRFMC approach attains a 37% improvement in MRR over a recently proposed approach that identifies negative venues only from social links.


title: New building blocks for modular design of elliptic and self-equalized filters

abstract: This paper introduces building blocks for modular design of elliptic, pseudoelliptic, and self-equalized filters. The first building block is of second order and generates two transmission zeros (TZs), which are either on the real or imaginary axis. Moving the zeros from the real axis (linear phase response) to the imaginary axis (attenuation poles) requires changing the sign of one coupling coefficient. The second building block is a structure of order three, called extended doublet, which allows the generation of two TZs practically anywhere in the complex plane. An important property of this block is its ability to move the two TZs from the real axis to the imaginary axis of the complex s-plane without changing the signs of its coupling coefficients. The third building block is of third order and generates two TZs, which can be moved from the real to the imaginary axis by changing the sign of one coupling coefficient. Simple waveguide structures to implement these blocks are introduced for validation, although this general approach is feasible for all resonator filter types. Higher order filters are designed modularly by cascading an arbitrary number of these building blocks. A novel concept, which allows the independent control of each pair of TZs in higher order filters, is then introduced. It is shown that the new concept allows a filter of order N to generate N TZs without directly coupling the source to the load even when the coupling coefficients are all assumed frequency independent. The same approach can be used to design and reduce the sensitivity of higher order elliptic and pseudoelliptic filters using other building blocks such as doublets or a mixture of building blocks of different orders and properties. Measured results and extensive computer simulation are presented to demonstrate the validity of the concept and the performance of the designed filters.


title: Working memory training improves visual short-term memory capacity

abstract: Since antiquity, philosophers, theologians, and scientists have been interested in human memory. However, researchers today are still working to understand the capabilities, boundaries, and architecture. While the storage capabilities of long-term memory are seemingly unlimited (Bahrick, J Exp Psychol 113:1–2, 1984), working memory, or the ability to maintain and manipulate information held in memory, seems to have stringent capacity limits (e.g., Cowan, Behav Brain Sci 24:87–185, 2001). Individual differences, however, do exist and these differences can often predict performance on a wide variety of tasks (cf. Engle What is working-memory capacity? 297–314, 2001). Recently, researchers have promoted the enticing possibility that simple behavioral training can expand the limits of working memory which indeed may also lead to improvements on other cognitive processes as well (cf. Morrison and Chein, Psychol Bull Rev 18:46–60 2011). However, initial investigations across a wide variety of cognitive functions have produced mixed results regarding the transferability of training-related improvements. Across two experiments, the present research focuses on the benefit of working memory training on visual short-term memory capacity—a cognitive process that has received little attention in the training literature. Data reveal training-related improvement of global measures of visual short-term memory as well as of measures of the independent sub-processes that contribute to capacity (Awh et al., Psychol Sci 18(7):622–628, 2007). These results suggest that the ability to inhibit irrelevant information within and between trials is enhanced via n-back training allowing for selective improvement on untrained tasks. Additionally, we highlight a potential limitation of the standard adaptive training procedure and propose a modified design to ensure variability in the training environment.


title: Text detection and localization in natural scene images using MSER and fast guided filter

abstract: Textual matter present in a natural scene image provides indispensable information about it. The semantics and information present in the natural scene images can be perceived by extracting the text regions in them. Detection and localization of text from natural scene images is a challenging task for analysis of images due to various font size, font type, and illumination. In this paper, we propose a hybrid approach for text detection and localization based on text confidence score using three attributes namely stroke width dissimilarity, color dissimilarity and occupy rate convex area to discern text and non-text constituents. The aim of this paper is to achieve fast detection and localization of text regions in low resolution and blurred images. To accomplish this, the possible candidate regions are extracted using edge smoothing by fast guided filter followed by MSER. The text confidence score on these constituents is calculated using the Bayesian framework with the help of above mentioned three attributes. Experimental results on benchmark ICDAR 2013 testing dataset shows the efficacy of our method in the form of precision, recall, and f-measure.


Computational Intelligence in architectural and interior design : a state-ofthe-art and outlook on the field


Integrating – VPN and IDS – An approach to Networks Security


title: An empirical investigation into the difficulties experienced by visually impaired Internet users

abstract: In this paper, an empirical based study is described which has been conducted to gain a deeper understanding of the challenges faced by the visually impaired community when accessing the Web. The study, involving 30 blind and partially sighted computer users, has identified navigation strategies, perceptions of page layout and graphics using assistive devices such as screen readers. Analysis of the data has revealed that current assistive technologies impose navigational constraints and provide limited information on web page layout. Conveying additional spatial information could enhance the exploration process for visually impaired Internet users. It could also assist the process of collaboration between blind and sighted users when performing web-based tasks. The findings from the survey have informed the development of a non-visual interface, which uses the benefits of multimodal technologies to present spatial and navigational cues to the user.


The Effects of Music on Emotional Response, Brand Attitude, and Purchase Intent in an Emotional Advertising Condition


title: Design and analysis of a coreless flyback converter with a planar printed-circuit-board transformer

abstract: In this paper the development of a 1W DC/DC converter is presented. It is based on a flyback converter for simple applications. The transformer is designed in a form as a spiral coil on a PCB. The special feature of the converter is that the flyback converter is laid out without a core. The area (diameter) of a spiral coil should be bigger as a coil with a core because the air is not a good energy store. The advantage of a coreless transformer is the PCB-Design. The paper describes the theory of the flyback converter and shows a way of the implementation of the coreless planar transformer. An analysis shows some results by different geometry and frequency of the planar transformer.


title: Anxiety and depression among medical students: a cross-sectional study.

abstract: OBJECTIVE ::: To determine the prevalence of anxiety and depression among medical students at Nishtar Medical College, Multan. ::: ::: ::: METHODS ::: A cross-sectional study was carried out at Nishtar Medical College, Multan in 2008. The questionnaire was administered to 815 medical students who had spent more than 6 months in college and had no self reported physical illness. They were present at the time of distribution of the questionnaires and consented. Prevalence of anxiety and depression was assessed using a structured validated questionnaire, the Aga Khan University Anxiety and Depression Scale with a cut-off score of 19. Data Analysis was done using SPSS v. 14. ::: ::: ::: RESULTS ::: Out of 815 students, 482 completed the questionnaire with a response rate of 59.14%. The mean age of students was 20.66 +/- 1.8 years. A high prevalence of anxiety and depression (43.89%) was found amongst medical students. Prevalence of anxiety and depression among students of first, second, third, fourth and final years was 45.86%, 52.58%, 47.14%, 28.75% and 45.10% respectively. Female students were found to be more depressed than male students (OR = 2.05, 95% CI = 1.42-2.95, p = 0.0001). There was a significant association between the prevalence of anxiety and depression and the respective year of medical college (p = 0.0276). It was seen that age, marital status, locality and total family income did not significantly affect the prevalence of anxiety and depression. ::: ::: ::: CONCLUSIONS ::: The results showed that medical students constitute a vulnerable group that has a high prevalence of psychiatric morbidity comprising of anxiety and depression.


title: A Bayesian missing value estimation method for gene expression profile data

abstract: Motivation: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes inference is not new in principle, actual BPCA implementation that makes it possible to estimate arbitrary missing variables is new in terms of statistical methodology. Results: When applied to DNA microarray data from various experimental conditions, the BPCA method exhibited markedly better estimation ability than other recently proposed methods, such as singular value decomposition and K -nearest neighbors. While the estimation performance of existing methods depends on model parameters whose determination is difficult, our BPCA method is free from this difficulty. Accordingly, the BPCA method provides accurate and convenient estimation for missing values. Availability: The software is available at http://hawaii.aist


title: Ontology Learning from Text: A Survey of Methods

abstract: After the vision of the Semantic Web was broadcasted at the turn of the millennium, ontology became a synonym for the solution to many problems concerning the fact that computers do not understand human language: if there were an ontology and every document were marked up with it and we had agents that would understand the markup, then computers would finally be able to process our queries in a really sophisticated way. Some years later, the success of Google shows us that the vision has not come true, being hampered by the incredible amount of extra work required for the intellectual encoding of semantic mark-up – as compared to simply uploading an HTML page. To alleviate this acquisition bottleneck, the field of ontology learning has since emerged as an important sub-field of ontology engineering. It is widely accepted that ontologies can facilitate text understanding and automatic processing of textual resources. Moving from words to concepts not only mitigates data sparseness issues, but also promises appealing solutions to polysemy and homonymy by finding non-ambiguous concepts that may map to various realizations in – possibly ambiguous – words. Numerous applications using lexical-semantic databases like WordNet (Miller, 1990) and its non-English counterparts, e.g. EuroWordNet (Vossen, 1997) or CoreNet (Choi and Bae, 2004) demonstrate the utility of semantic resources for natural language processing. Learning semantic resources from text instead of manually creating them might be dangerous in terms of correctness, but has undeniable advantages: Creating resources for text processing from the texts to be processed will fit the semantic component neatly and directly to them, which will never be possible with general-purpose resources. Further, the cost per entry is greatly reduced, giving rise to much larger resources than an advocate of a manual approach could ever afford. On the other hand, none of the methods used today are good enough for creating semantic resources of any kind in a completely unsupervised fashion, albeit automatic methods can facilitate manual construction to a large extent. The term ontology is understood in a variety of ways and has been used in philosophy for many centuries. In contrast, the notion of ontology in the field of computer science is younger – but almost used as inconsistently, when it comes to the details of the definition. The intention of this essay is to give an overview of different methods that learn ontologies or ontology-like structures from unstructured text. Ontology learning from other sources, issues in description languages, ontology editors, ontology merging and ontology evolving transcend the scope of this article. Surveys on ontology learning from text and other sources can be found in Ding and Foo (2002) and Gomez-Perez


title: MAGAN: Margin Adaptation for Generative Adversarial Networks

abstract: We propose the Margin Adaptation for Generative Adversarial Networks (MAGANs) algorithm, a novel training procedure for GANs to improve stability and performance by using an adaptive hinge loss function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive principled criteria for when to update the margin. We prove that our method converges to its global optimum under certain assumptions. Evaluated on the task of unsupervised image generation, the proposed training procedure is simple yet robust on a diverse set of data, and achieves qualitative and quantitative improvements compared to the state-of-the-art.


Explanatory IRT Analysis Using the SPIRIT Macro in SPSS


title: A Holistic Evaluation of Docker Containers for Interfering Microservices

abstract: Advancement of container technology (e.g. Docker, LXC, etc.) transformed the virtualization concept by providing a lightweight alternative to hypervisors. Docker has emerged as the most popular container management tool. Recent research regarding the comparison of container with hypervisor and bare-metal demonstrates that the container can accomplish bare-metal performance in almost all case. However, the current literature lacks an in-depth study on the experimental evaluation for understanding the performance interference between microservices that are hosted within a single or across multiple containers. In this paper, we have presented the experimental study on the performance evaluation of Docker containers running heterogeneous set of microservices concurrently. We have conducted a comprehensive set of experiments following CEEM (Cloud Evaluation Experiment Methodology) to measure the interference between containers running either competing or independent microservices. We have also considered the effects of constraining the resources of a container by explicitly specifying the cgroups. We have evaluated the performance of containers in terms of inter-container (caused by two concurrent executing containers) and intra-container (caused between two microservices executing inside a container) interference which is almost neglected in the current literature. The evaluation results can be utilized to model the interference effect for smart resource provisioning of microservices in the containerized environment.


title: Innovation versus evidence: to trust direct-to-consumer personal genomic tests?

abstract: The latest genomic technology with massively parallel simultaneous analysis of millions of genetic variants across the genome has revolutionized biomedical science and the biotechnology industry [1–4]. Now, next-generation sequencing (NGS) technology with faster, cheaper and more accurate human genome sequencing permits the unprecedented identification of genome variants. Validation and assessment of causal (driver) mutations involved in disease pathogenesis, progression and therapeutic response to drugs shapes current thinking and opens new ways for personalized disease risk prediction, prevention and treatment. However, there is a big gap in moving from this excellent genotyping approach to clinical applications as multiple problems and challenges need to be overcome [5,6]. Based on multiple novel genetic variants derived from genome-wide association studies (GWAS) [7,8], several companies now promote direct-to-consumer (DTC) genetic tests. It is promoted that these genetic tools can predict an asymptomatic person’s risk for developing common complex diseases, such as cancer, cardiovascular events, diabetes and many other disorders.


A computer algorithm for reconstructing a scene from two projections


title: Background Inpainting for Videos with Dynamic Objects and a Free-moving Camera

abstract: We propose a method for removing marked dynamic objects from videos captured with a free-moving camera, so long as the objects occlude parts of the scene with a static background. Our approach takes as input a video, a mask marking the object to be removed, and a mask marking the dynamic objects to remain in the scene. To inpaint a frame, we align other candidate frames in which parts of the missing region are visible. Among these candidates, a single source is chosen to fill each pixel so that the final arrangement is color-consistent. Intensity differences between sources are smoothed using gradient domain fusion. Our frame alignment process assumes that the scene can be approximated using piecewise planar geometry: A set of homographies is estimated for each frame pair, and one each is selected for aligning pixels such that the color-discrepancy is minimized and the epipolar constraints are maintained. We provide experimental validation with several real-world video sequences to demonstrate that, unlike in previous work, inpainting videos shot with free-moving cameras does not necessarily require estimation of absolute camera positions and per-frame per-pixel depth maps.


title: How do open source communities blog?

abstract: We report on an exploratory study, which aims at understanding how software communities use blogs compared to conventional development infrastructures. We analyzed the behavior of 1,100 bloggers in four large open source communities, distinguishing between committing bloggers and other community members. We observed that these communities intensively use blogs with one new entry every 8 h. A blog entry includes 14 times more words than a commit message. When analyzing the content of the blogs, we found that committers and others bloggers write about similar topics. Most popular topics in committers' blogs represent high-level concepts such as features and domain concepts, while source code related topics are discussed in 15% of their posts. Other community members frequently write about community events and conferences as well as configuration and deployment topics. We found that the blogging peak period is usually after the software is released. Moreover, committers are more likely to blog after corrective engineering than after forward engineering and re-engineering activities. Our findings call for a hypothesis-driven research to (a) further understand the role of social media in dissolving the collaboration boundaries between developers and other stakeholders and (b) integrate social media into development processes and tools.


title: PAC Subset Selection in Stochastic Multi-armed Bandits

abstract: We consider the problem of selecting, from among the arms of a stochastic n-armed bandit, a subset of size m of those arms with the highest expected rewards, based on efficiently sampling the arms. This "subset selection" problem finds application in a variety of areas. In the authors' previous work (Kalyanakrishnan & Stone, 2010), this problem is framed under a PAC setting (denoted "Explore-m"), and corresponding sampling algorithms are analyzed. Whereas the formal analysis therein is restricted to the worst case sample complexity of algorithms, in this paper, we design and analyze an algorithm ("LUCB") with improved expected sample complexity. Interestingly LUCB bears a close resemblance to the well-known UCB algorithm for regret minimization. The expected sample complexity bound we show for LUCB is novel even for single-arm selection (Explore-1). We also give a lower bound on the worst case sample complexity of PAC algorithms for Explore-m.


title: Origami Pop-up Card Generation from 3D Models Using a Directed Acyclic Graph

abstract: Origami is a paper art used to create three-dimensional (3D) objects by cutting and folding a single sheet of paper. Origami is labor-intensive and requires a high skill level to generate two-dimensional (2D) objects that pop-up into realistic 3D objects. However, this special feature makes designing an origami architecture procedure challenging. This paper provides a novel algorithm to create an origami paper card from a 3D model with a user-specified folding line. The algorithm segments the 2D shape from a 3D model and creates layers of the pop-up paper card using a directed acyclic graph. After applying layers to the layout, the algorithm creates connections between layers, and then outputs the origami layout. Based on a directed acyclic graph, the algorithm computes a class of paper architectures containing two sets of layers and connections that approximate the input geometry while guaranteeing that a pop up card is physically realizable. The proposed method is demonstrated with a number of paper pop-ups, and physical experimental results are presented.


title: Scripting Argumentative Knowledge Construction in Computer-Supported Learning Environments

abstract: Computer-supported collaborative learning (CSCL) environments may encourage learners to engage in argumentative knowledge construction. Argumentative knowledge construction means that learners work together to elaborate on concepts by constructing arguments and counterarguments. This is achieved through discourse with the goal of acquiring knowledge within a specific domain. However, learners may encounter problems relating to one of three dimensions of argumentative knowledge construction. First, learners seem to have difficulties in constructing arguments that contribute to solving the task. Second, learners’ arguments may lack important components such as data and warrants. Third, learners rarely build upon the arguments of their learning partners. Structuring argumentative knowledge construction with collaboration scripts is a promising instructional approach for facilitating specific process dimensions of argumentative knowledge construction. Little is known, however, about how to most effectively facilitate the acquisition of knowledge by directing collaboration scripts at specific dimensions of argumentative knowledge construction. This chapter will outline the theoretical background of argumentative knowledge construction and will then describe script components that target different dimensions of argumentative knowledge construction. The chapter will then discuss the empirical findings of two studies regarding the effects of these script components.


title: The Role of Data Science in Web Science

abstract: Web science relies on an interdisciplinary approach that seeks to go beyond what any one subject can say about the World Wide Web. By incorporating numerous disciplinary perspectives and relying heavily on domain knowledge and expertise, data science has emerged as an important new area that integrates statistics with computational knowledge, data collection, cleaning and processing, analysis methods, and visualization to produce actionable insights from big data. As a discipline to use within Web science research, data science offers significant opportunities for uncovering trends in large Web-based datasets. A Web science observatory exemplifies this relationship by offering an online platform of tools for carrying out Web science research, allowing users to carry out data science techniques to produce insights into Web science issues such as community development, online behavior, and information propagation. The authors outline the similarities and differences of these two growing subject areas to demonstrate the important relationship developing between them.


title: Arteriovenous malformations of the uterus

abstract: Arterial venous malformations (AVM) of the uterus are uncommon entities and should be considered in patients who present with profuse genital bleeding. There are two types of uterine AVM: acquired and congenital. Acquired uterine AVMs are conformed by communications between the uterine arteries and the myometrial veins, and are caused by an iatrogenic event or a pathological condition. Congenital AVMs are the result of abnormal development of primitive vessels that result in connections between pelvic arteries and veins in the uterus without an interconnecting capillary bed. Ultrasonography is a noninvasive diagnostic method able to demonstrate and characterize AVMs of the uterus. AVM in the pelvis may be noted incidentally by computed tomography (CT) of the pelvis, and magnetic resonance imaging (MRI) is frequently used to confirm and further characterize the sonographic findings of uterine AVM. Catheter angiography and embolization are very effective in defining the vascular anatomy and treating uterine vascular abnormalities.


title: Analysis of Serious Games based Learning Requirements using Feedback and Traces of Users

abstract: Identify the games that best meet the needs and expectations of teachers and objectives of their courses remains a necessity about the integration of serious games among active teaching methods. Indeed, several serious games have developed in recent years, and it is often difficult for a teacher, not a computer scientist in particular, to find a game that meets these specific needs. Our aim is to develop models and tools enabling the teacher to find serious games adapted to his needs, considering user feedback and their traces of interaction with the game. To this end, we have explored the evaluation methods of serious games as well as methods of extracting knowledge from traces and texts. In this paper, we present our method of knowledge extraction of educational objectives. Thus, our proposal is assisting and supporting teachers/trainers to choose serious games and easily integrate them into their learning processes and devices.


SMART HOME : From User's Behavior To Prediction of Energy Cosumption


title: Generating samples under a Wright-Fisher neutral model of genetic variation

abstract: Summary: A Monte Carlo computer program is available to generate samples drawn from a population evolving according to a Wright–Fisher neutral model. The program assumes an infinite-sites model of mutation, and allows recombination, gene conversion, symmetric migration among subpopulations, and a variety of demographic histories. The samples produced can be used to investigate the sampling properties of any sample statistic under these neutral models. Availability: The source code for the program (in the language C) is available at http://home.uchicago.edu/


Gender classification using face images: a review


title: HR sourcing and shoring: strategies, drivers, success factors and implications for HR

abstract: This paper identifies the core strategies pursued as firms make choices about sourcing and shoring of HR services. It draws attention to a range of variables that need to be measured to assess the effectiveness of these sourcing and shoring strategies. It presents a review of the business drivers for human resource outsourcing and identifies the strategic outcomes that should be used to determine success in this. A range of readiness factors are identified and included in Appendix 1. In order to help HR practitioners plan for their own approach, the paper also considers how the market for sourcing and shoring options is developing by analysing some data on the volume and scale of contemporary deals. This analysis is used to identify useful avenues of research that might help explain international differences in the HRO market. Finally, some future developments and challenges for HR functions are signalled by analysing both vendor strategies and issues for the retained HR business.


title: Injury Risk Quantification for Industrial Robots in Collaborative Operation with Humans

abstract: While the topic of human-safe robots has an appreciable academic history, only today are various solutions to this challenge becoming economically relevant. We are witnessing a maturing of technology and market expectations, paired with initiatives in the area of standardization. One of the remaining hurdles to routine deployment of robots for collaborative operation with humans is obtaining a well-founded understanding of low-level injuries of the type that such robots can potentially inflict. For the assessment of collaborative robot designs and systems, the low-level injury potential must be unambiguously quantified. A classification scheme for holding such information is presented and an outlook on its use is given.


title: Generalizing the Convolution Operator in Convolutional Neural Networks

abstract: Convolutional neural networks (CNNs) have become an essential tool for solving many machine vision and machine learning problems. A major element of these networks is the convolution operator which essentially computes the inner product between a weight vector and the vectorized image patches extracted by sliding a window in the image planes of the previous layer. In this paper, we propose two classes of surrogate functions for the inner product operation inherent in the convolution operator and so attain two generalizations of the convolution operator. The first one is based on the class of positive definite kernel functions where their application is justified by the kernel trick. The second one is based on the class of similarity measures defined according to some distance function. We justify this by tracing back to the basic idea behind the neocognitron which is the ancestor of CNNs. Both of these methods are then further generalized by allowing a monotonically increasing function (possibly depending on the weight vector) to be applied subsequently. Like any trainable parameter in a neural network, the template pattern and the parameters of the kernel/distance function are trained with the back-propagation algorithm. As an aside, we use the proposed framework to justify the use of sine activation function in CNNs. Additionally, we discovered a family of generalized convolution operators which is based on the convex combination of the dot-product and the negative squared Euclidean distance functions. Our experiments on the MNIST dataset show that the performance of ordinary CNNs can be achieved by generalized CNNs based on weighted L1/L2 distances, proving the applicability of the proposed generalization of the convolutional neural networks.


title: MVTec D2S: Densely Segmented Supermarket Dataset

abstract: We introduce the Densely Segmented Supermarket (D2S) dataset, a novel benchmark for instance-aware semantic segmentation in an industrial domain. It contains 21,000 high-resolution images with pixel-wise labels of all object instances. The objects comprise groceries and everyday products from 60 categories. The benchmark is designed such that it resembles the real-world setting of an automatic checkout, inventory, or warehouse system. The training images only contain objects of a single class on a homogeneous background, while the validation and test sets are much more complex and diverse. To further benchmark the robustness of instance segmentation methods, the scenes are acquired with different lightings, rotations, and backgrounds. We ensure that there are no ambiguities in the labels and that every instance is labeled comprehensively. The annotations are pixel-precise and allow using crops of single instances for articial data augmentation. The dataset covers several challenges highly relevant in the field, such as a limited amount of training data and a high diversity in the test and validation sets. The evaluation of state-of-the-art object detection and instance segmentation methods on D2S reveals significant room for improvement.


Supertagging With LSTMs


title: m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series

abstract: Multivariate time series (MTS) have become increasingly common in healthcare domains where human vital signs and laboratory results are collected for predictive diagnosis. Recently, there have been increasing efforts to visualize healthcare MTS data based on star charts or parallel coordinates. However, such techniques might not be ideal for visualizing a large MTS dataset, since it is difficult to obtain insights or interpretations due to the inherent high dimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novel framework to visualize high-dimensional MTS data by projecting them into a low-dimensional (2-D or 3-D) space while capturing the underlying data properties. Our framework is easy to use and provides interpretable insights for healthcare professionals to understand MTS data. We evaluate our visualization framework on two real-world datasets and demonstrate that the results of our m-TSNE show patterns that are easy to understand while the other methods' visualization may have limitations in interpretability.


title: Football analysis using spatio-temporal tools

abstract: Analysing a football match is without doubt an important task for coaches, talent scouts, players and even media; and with current technologies more and more match data is collected. Several companies offer the ability to track the position of the players and the ball with high accuracy and high resolution. They also offer software that include basic analysis tools, for example straight-forward statistics about distance run and number of passes. It is, however, a non-trivial task to perform more advanced analysis. We present a collection of tools that we developed specifically for analysing the performance of football players and teams.


Computer Science Epistemology in Agent-Based Social Simulation : Computation , Simulation and Intention


Real-Time Photon Mapping on GPU


title: Clustering via Nonparametric Density Estimation

abstract: The R package pdfCluster performs cluster analysis based on a nonparametric estimate of the density of the observed variables. Functions are provided to encompass the whole process of clustering, from kernel density estimation, to clustering itself and subsequent graphical diagnostics. After summarizing the main aspects of the methodology, we describe the features and the usage of the package, and finally illustrate its application with the aid of two data sets.


title: Touch increases autonomic coupling between romantic partners

abstract: Interpersonal touch is of paramount importance in human social bonding and close relationships, allowing a unique channel for affect communication. So far the effect of touch on human physiology has been studied at an individual level. The present study aims at extending the study of affective touch from isolated individuals to truly interacting dyads. We have designed an ecological paradigm where romantic partners interact only via touch and we manipulate their empathic states. Simultaneously, we collected their autonomic activity (skin conductance, pulse, respiration). Fourteen couples participated to the experiment. We found that interpersonal touch increased coupling of electrodermal activity between the interacting partners, regardless the intensity and valence of the emotion felt. In addition, physical touch induced strong and reliable changes in physiological states within individuals. These results support an instrumental role of interpersonal touch for affective support in close relationships. Furthermore, they suggest that touch alone allows the emergence of a somatovisceral resonance between interacting individuals, which in turn is likely to form the prerequisites for emotional contagion and empathy.


title: Relational Forward Models for Multi-Agent Learning

abstract: The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predictions of agents' future behavior in multi-agent environments. Because these models operate on the discrete entities and relations present in the environment, they produce interpretable intermediate representations which offer insights into what drives agents' behavior, and what events mediate the intensity and valence of social interactions. Furthermore, we show that embedding RFM modules inside agents results in faster learning systems compared to non-augmented baselines. As more and more of the autonomous systems we develop and interact with become multi-agent in nature, developing richer analysis tools for characterizing how and why agents make decisions is increasingly necessary. Moreover, developing artificial agents that quickly and safely learn to coordinate with one another, and with humans in shared environments, is crucial.


title: Towards on Cloud of Things: Survey, Challenges, Open Research Issues, and Tools

abstract: In computer, Cloud of Things (CoT) it is a Technique came by integrated two concepts Internet of Things(IoT) and Cloud Computing. Therefore, Cloud of Things is a currently a wide area of research and development. This paper discussed the concept of Cloud of Things (CoT) in detail and explores the challenges, open research issues, and various tools that can be used with Cloud of Things (CoT). As a result, this paper gives a knowledge and platform to explore Cloud of Things (CoT), and it gives new ideas for researchers to find the open research issues and solution to challenges.


title: Data Preprocessing for Efficient Sentimental Analysis

abstract: Sentiment analysis is an important research area that identifies the people's sentiments, opinions and emotions underlying a text. As the use of social media is increasing day by day, it plays an essential role in communication through technology. Twitter, which is one of the popular and largely used social media platforms for communication has more than 200 million tweets per day. Tweets are short in length and due to limited size of tweets people generally commit some mistakes while tweeting so pre-processing is necessary. The use of modern emoticons which are known as emojis that is largely used in social media communications that conveys variety of emotions. The purpose of this paper is to use N -gram method and Hidden Markov Model for Spell-Checking and Correction of tweets and also Emoji Sentiment Ranking method which is used to evaluate sentiment mapping of emojis by using sentiment polarity such as negative, neutral, or positive.


title: Fog Networking: An Overview on Research Opportunities

abstract: The past 15 years have seen the rise of the Cloud, along with rapid increase in Internet backbone traffic and more sophisticated cellular core networks. There are three different types of Clouds: (1) data center, (2) backbone IP network and (3) cellular core network, responsible for computation, storage, communication and network management. Now the functions of these three types of Clouds are descending to be among or near the end users, i.e., to the edge of networks, as Fog. This article presents an overview on research opportunities of Fog networking: an architecture that users one or a collaborative multitude of end-user clients or near-user edge devices to carry out a substantial amount of storage, communication and management. Architecture allocates functionalities, while engineering artifacts that may use a Fog architecture include 5G, home/personal networking, and the Internet of Things.


title: Mental Health of Immigrants and Refugees

abstract: The United States is a country of immigrants. With the exception of Native-Americans, every other American is, or descends from, an immigrant. First and second generation immigrant children are the most rapidly growing segment of the American population, with the great majority of this population being of non-European origin. This paper reviews the unique risk factors and mental health needs of our new immigrant populations, as well as treatment and services approaches to address their unique needs.


title: Adjustable frequency antenna using flexible material for RF energy harvesting application

abstract: A new configuration of an antenna with adjustable frequency is presented. The principle of aperture coupled microstrip antenna is used. The patch antenna is printed on flexible material and can easily be stuck or removed from the outside of a circuit packaging to alter its resonant frequency while the feed network and the connected circuit are unaffected inside. This type of antenna can be used in the application of Radio-frequency (RF) energy harvesting which is strongly dependent on frequency of radiated waves in the vicinity of the antenna. The design for an antenna operating at three frequencies 1800 MHz, 1900 MHz and 2.4 GHz is proposed. Simulated and measured results are both presented, with promising prospects.


title: Internet Of Things Architecture:Current Challenges And Future Direction Of Research

abstract: The Internet of Things (IoT) is a new paradigm that can enable collecting and exchanging data that have never been attainable before.It able to communicate and report user’s information in a more secure way.The reports of Cisco analysts estimate that the IoT will have more than 50 billion of smart sensors and other smart devices or gadgets,all connecting and communicating real time data on the internet by 2020.This will provide deeper insights with data analytics using the IoT paradigm to establish new business,enhance productivity and efficiency,and develop innovative revenue streams.Furthermore,the IoT architecture may combine features and technologies suggested by various methodologies.Since,this architecture is designed where the digital and real worlds are integrating and interacting constantly,various technologies are merged together to form IoT,such as; sensing technologies,pervasive computing,ubiquitous computing,internet protocols,smart objects,embedded parts,etc.When a regular device utilizes intelligent agents,it becomes a smart object.In this way,it is not only used to gather the environment information or interact with the physical world,yet more than that,it must be interconnected with various network devices to exchange and communicate data over the internet. Therefore,the significant measure of available data which is produced by the immense number of interconnected devices will offer opportunities to generate information that will deliver significant benefits to the economy, environment,individuals,and society.In this paper,we present past,current,and future direction of IoT.This paper provides overview and clear examination of the IoT architecture paradigm with the description of its fundamental requirements along with the implementation challenges and future directions.Thus,it will open issues that will face the IoT by new world generation.


title: Context-aware Frame-Semantic Role Labeling

abstract: Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis. Predicting such representations from raw text is, however, a challenging task and corresponding models are typically only trained on a small set of sentence-level annotations. In this paper, we present a semantic role labeling system that takes into account sentence and discourse context. We introduce several new features which we motivate based on linguistic insights and experimentally demonstrate that they lead to significant improvements over the current state-of-the-art in FrameNet-based semantic role labeling.


title: Peer-Supported Storytelling for Grieving Pediatric Oncology Nurses

abstract: Telling stories about deceased patients to supportive peers is frequently mentioned as an activity used for meaning-making in anecdotal reports of clinical practice and the literature addressing nurses' experiences caring for dying children. This study examines peer-supported storytelling for grieving pediatric oncology nurses using a mixed methods single-group descriptive repeated measures design. Participants were 6 registered nurses from a tertiary care pediatric hospital inpatient oncology unit who self-identified as experiencing grief. Participants met in self-selected dyads for 2 storytelling sessions. Questionnaires were completed at baseline, midpoint, and study end. Sessions were audio-recorded. Participants reported (1) receiving and providing support during sessions; (2) that sessions had an impact on their grief; (3) that sessions had an impact on their meaning-making, and the explicit session focus on making sense of and identifying benefit in their experiences was particularly helpful. There...


title: Towards Inverse Sensor Mapping in Agriculture

abstract: In recent years, the drive of the Industry 4.0 initiative has enriched industrial and scientific approaches to build self-driving cars or smart factories. Agricultural applications benefit from both advances, as they are in reality mobile driving factories which process the environment. Therefore, acurate perception of the surrounding is a crucial task as it involves the goods to be processed, in contrast to standard indoor production lines. Environmental processing requires accurate and robust quantification in order to correctly adjust processing parameters and detect hazardous risks during the processing. While today approaches still implement functional elements based on a single particular set of sensors, it may become apparent that a unified representation of the environment compiled from all available information sources would be more versatile, sufficient, and cost effective. The key to this approach is the means of developing a common information language from the data provided. In this paper, we introduce and discuss techniques to build so called inverse sensor models that create a common information language among different, but typically agricultural, information providers. These can be current live sensor data, farm management systems, or long term information generated from previous processing, drones, or satellites. In the context of Industry 4.0, this enables the interoperability of different agricultural systems and allows information transparency.


title: A CW 20W Ka-band GaN high power MMIC amplifier with a gate pitch designed by using one-finger large signal models

abstract: This paper reports a 20 W Ka-band GaN high power MMIC (Monolithic Microwave Integrated Circuit) amplifier under continuous wave (CW) operation. The one-finger large signal models were made to take account of both the phase difference of RF gate voltage at a gate feeder and thermal effect. By using this model, the gate pitch length of unit cell transistor was optimally designed to obtain maximum output power as MMIC amplifier under CW operation. As a result, 21.7W output power under CW operation was successfully achieved with power added efficiency (PAE) of 19.8% at Ka-band by a single-ended MMIC. To the best of authors' knowledge, this output power is state-of-the-art for GaN MMIC amplifiers under CW operation at Ka-band.


title: IMPROVING THE NEW PRODUCT DEVELOPMENT PROCESS — MAKING TECHNOLOGY PUSH WORK!

abstract: Technology Push Defined What do we mean by “technology push?” Let's first go to Webster for some assistance. Two of the formal definitions that apply to the word push are “to shove” and “to give impetus.” These definitions seem relevant, for what we are dealing with when we talk about technology push is frequently a situation where an organization finds itself with technology that has not been “demanded” by the potential market and therefore, it literally has to push that technology toward its potential markets.


title: Predicting MOOC performance with Week 1 Behavior

abstract: Prior studies on Massive Open Online Courses (MOOCs) suggest that there is a significant decrease in student participation after one week of instruction [8]. This paper uses a combination of students’ Week 1 assignment performance and social interaction within the MOOC to predict their final performance in the course. The study also examines the role external incentives in final MOOC performance. Using logistic regression as a classifier, we are able to predict the probability of students earning certificates for completion of the MOOC, as well as the type of certificate (i.e. Distinction and Normal) earned, with high accuracy.


title: An Inverted Straight Line Mechanism for Augmenting Joint Range of Motion in a Humanoid Robot

abstract: Many robotic joints powered by linear actuators suffer from a loss of torque towards the limits of the range of motion. This paper presents the design of a fully backdriveable, force controllable rotary actuator package employed on the Tactical Hazardous Operations Robot (THOR). The assembly pairs a ball screw-driven linear Series Elastic Actuator (SEA) with a planar straight line mechanism. The mechanism is a novel inversion of a Hoeken’s four-bar linkage, using the ball screw as a linear input to actuate the rotary joint. Link length ratios of the straight line mechanism have been chosen to optimize constant angular velocity, resulting in a nearly constant mechanical advantage and peak torque of 115 [Nm] throughout the 150° range of motion. Robust force control is accomplished through means of a lookup table, which is accurate to within ±0.62% of the nominal torque profile for any load case.Copyright © 2014 by ASME


title: Dynamic Coattention Networks For Question Answering

abstract: Several deep learning models have been proposed for question answering. However, due to their single-pass nature, they have no way to recover from local maxima corresponding to incorrect answers. To address this problem, we introduce the Dynamic Coattention Network (DCN) for question answering. The DCN first fuses co-dependent representations of the question and the document in order to focus on relevant parts of both. Then a dynamic pointing decoder iterates over potential answer spans. This iterative procedure enables the model to recover from initial local maxima corresponding to incorrect answers. On the Stanford question answering dataset, a single DCN model improves the previous state of the art from 71.0% F1 to 75.9%, while a DCN ensemble obtains 80.4% F1.


title: Automatic Recognition of Construction Worker Activities Using Dense Trajectories

abstract: Wide spread monitoring cameras on construction sites provide large amount of information for construction management. The emerging of computer vision and machine learning technologies enables automatic recognition of construction activities from videos. As the executors of construction, the activities of construction workers have strong impact on productivity and progress. Compared to machine work, manual work is more subjective and may differ largely in operation flow and productivity from one worker to another. Hence only a handful of work study on vision based activity recognition of construction workers. Lacking of publicly available datasets is one of the main reasons that currently hinder advancement. The paper studies manual work of construction workers comprehensively, selects 11 common types of activities and establishes a new real world video dataset with 1176 instances. For activity recognition, a cutting-edge video description method, dense trajectories, has been applied. Support vector machines are integrated with a bag-of-features pipeline for activity learning and classification. Performance on multiple types of descriptors (Histograms of Oriented Gradients HOG, Histograms of Optical Flow HOF, Motion Boundary Histogram MBH) and their combination has been evaluated. Experimental results show that the proposed system has achieved a state-of-art performance on the new dataset.


title: A COMPARISON BETWEEN BACKGROUND SUBTRACTION ALGORITHMS USING A CONSUMER DEPTH CAMERA

abstract: Background subtraction is an important preprocessing step in many modern Computer Vision systems. Much work has been done especially in the field of color image based foreground segmentation. But the task is not an easy one so, state of the art background subtraction algorithms are complex both in programming logic and in run time. Depth cameras might offer a compelling alternative to those approaches, because depth information seems to be better suited for the task. But this topic has not been studied much yet, even though the release of Microsoft’s Kinect has brought depth cameras to the public attention. In this paper we strive to fill this gap, by examining some well known background subtraction algorithms for the use with depth images. We propose some necessary adaptions and evaluate them on three different video sequences using ground truth data. The best choice turns out to be a very simple and fast method that we call minimum background.


title: Modelling Network Traffic Using Time Series Analysis: A Review

abstract: With the advent of Internet of Things (IoT) technology, the need for tools which facillitate the development and management of network based services has become increasingly important. Issues such as network security and quality of service are no longer just the concern of ISPs and big corporations, but have now become the intimate concern of private users with implications that directly affect the personal lives and businesses of citizens. At the heart of addressing these concerns lies the problem of modelling the networks on which interconnected devices now operate. While the activity of provisioning the networks and responding to threats may still lie in the hands of networking specialists, the ability to at least know when something is amiss remains intrumental to establishing the confidence and peace of mind of IoT users. In this paper we broadly review the historical development of network traffic modelling and trace a path that leads to the use of time series analysis for the said task. A basic introduction to time series analysis is provided in order to facillitate the theoretical discussion regarding the feasibility and suitability of time series analysis techniques for modelling network traffic. The intention is to provide an orientation, for the interested novice, to the domain of time series analysis for network traffic modelling; and to advocate the necessity and utility of further study in these domains for application to IoT concerns.


title: Elasticity in Cloud Computing: State of the Art and Research Challenges

abstract: Elasticity is a fundamental property in cloud computing that has recently witnessed major developments. This article reviews both classical and recent elasticity solutions and provides an overview of containerization, a new technological trend in lightweight virtualization. It also discusses major issues and research challenges related to elasticity in cloud computing. We comprehensively review and analyze the proposals developed in this field. We provide a taxonomy of elasticity mechanisms according to the identified works and key properties. Compared to other works in literature, this article presents a broader and detailed analysis of elasticity approaches and is considered as the first survey addressing the elasticity of containers.


title: Latent Space Policies for Hierarchical Reinforcement Learning

abstract: We address the problem of learning hierarchical deep neural network policies for reinforcement learning. In contrast to methods that explicitly restrict or cripple lower layers of a hierarchy to force them to use higher-level modulating signals, each layer in our framework is trained to directly solve the task, but acquires a range of diverse strategies via a maximum entropy reinforcement learning objective. Each layer is also augmented with latent random variables, which are sampled from a prior distribution during the training of that layer. The maximum entropy objective causes these latent variables to be incorporated into the layer's policy, and the higher level layer can directly control the behavior of the lower layer through this latent space. Furthermore, by constraining the mapping from latent variables to actions to be invertible, higher layers retain full expressivity: neither the higher layers nor the lower layers are constrained in their behavior. Our experimental evaluation demonstrates that we can improve on the performance of single-layer policies on standard benchmark tasks simply by adding additional layers, and that our method can solve more complex sparse-reward tasks by learning higher-level policies on top of high-entropy skills optimized for simple low-level objectives.


title: Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition

abstract: In this paper, we propose using 3D Convolutional Neural Networks for large scale user-independent continuous gesture recognition. We have trained an end-to-end deep network for continuous gesture recognition (jointly learning both the feature representation and the classifier). The network performs three-dimensional (i.e. space-time) convolutions to extract features related to both the appearance and motion from volumes of color frames. Space-time invariance of the extracted features is encoded via pooling layers. The earlier stages of the network are partially initialized using the work of Tran et al. before being adapted to the task of gesture recognition. An earlier version of the proposed method, which was trained for 11,250 iterations, was submitted to ChaLearn 2016 Continuous Gesture Recognition Challenge and ranked 2nd with the Mean Jaccard Index Score of 0.269235. When the proposed method was further trained for 28,750 iterations, it achieved state-of-the-art performance on the same dataset, yielding a 0.314779 Mean Jaccard Index Score.


title: An Improved Zero-Current-Switching Single-Phase Transformerless PV H6 Inverter With Switching Loss-Free

abstract: In this paper, a switching loss-free (SLF) concept for the first six-switches H-bridge inverter (H6-I) topology is proposed. SLF means that its switches are able to operate with soft turn-on and turn-off transitions. In order to implement the SLF goal, a new resonance-trajectory is proposed. Compared with the zero-current-transition H6-I (ZCT-H6-I) topology published in previous literature, the proposed resonance-trajectory can precisely compensate for losses of resonant tanks every switching period. With this intention, an implementing circuit is structured based on the H6-I topology, and its detailed operation principle and performance characteristics are analyzed. As a result, all active switches of the new circuit are switched under zero-current turn-on and zero-current turn-off conditions. Also, the reverse recovery problem of freewheeling diodes is alleviated owing to the zero-current turn-off property of diodes. The SLF target is realized in theory. Finally, experimental results from a 1 kW prototype at 50 kHz switching frequency are provided to verify the effectiveness of the proposed SLF concept in practice. Specifically, the conversion efficiency of the new circuit is over 95% in a wide load range, and there is roughly a 1.5% efficiency improvement compared with the hard-switching H6-I topology.


title: A Strategic Empty Container Logistics Optimization in a Major Shipping Company

abstract: In this paper, we present a system that Compania Sud Americana de Vapores (CSAV), one of the world's largest shipping companies, developed to support its decisions for repositioning and stocking empty containers. CSAV's main business is shipping cargo in containers to clients worldwide. It uses a fleet of about 700,000 TEU containers of different types, which are carried by both CSAV-owned and third-party ships. Managing the container fleet is complex; CSAV must make thousands of decisions each day. In particular, imbalances exist among the regions. For example, China often has a deficit of empty containers and is a net importer; Saudi Arabia often has a surplus and is a net exporter. CSAV and researchers from the University of Chile developed the Empty Container Logistics Optimization System (ECO) to manage this imbalance. ECO's multicommodity, multiperiod model manages the repositioning problem, whereas an inventory model determines the safety stock required at each location. CSAV uses safety stock to ensure high service levels despite uncertainties, particularly in the demand for containers. A hybrid forecasting system supports both the inventory and the multicommodity network flow model. Major improvements in data gathering, real-time communications, and automation of data handling were needed as input to the models. A collaborative Web-based optimization framework allows agents from different zones to interact in decision making. The use of ECO led to direct savings of $81 million for CSAV, a reduction in inventory stock of 50 percent, and an increase in container turnover of 60 percent. Moreover, the system helped CSAV to become more efficient and to overcome the 2008 economic crisis.


title: STS-k: a multilevel sparse triangular solution scheme for NUMA multicores

abstract: We consider techniques to improve the performance of parallel sparse triangular solution on non-uniform memory architecture multicores by extending earlier coloring and level set schemes for single-core multiprocessors. We develop STS-k, where k represents a small number of transformations for latency reduction from increased spatial and temporal locality of data accesses. We propose a graph model of data reuse to inform the development of STS-k and to prove that computing an optimal cost schedule is NP-complete. We observe significant speed-ups with STS-3 on 32-core Intel Westmere-Ex and 24-core AMD `MagnyCours' processors. Incremental gains solely from the 3-level transformations in STS-3 for a fixed ordering, correspond to reductions in execution times by factors of 1.4(Intel) and 1.5(AMD) for level sets and 2(Intel) and 2.2(AMD) for coloring. On average, execution times are reduced by a factor of 6(Intel) and 4(AMD) for STS-3 with coloring compared to a reference implementation using level sets.


title: Comparing alice, greenfoot & scratch

abstract: This panel will showcase and compare three leading Initial Learning Environments (ILE): Alice, Greenfoot and Scratch.


title: Geiger: monitoring the buffer cache in a virtual machine environment

abstract: Virtualization is increasingly being used to address server management and administration issues like flexible resource allocation, service isolation and workload migration. In a virtualized environment, the virtual machine monitor (VMM) is the primary resource manager and is an attractive target for implementing system features like scheduling, caching, and monitoring. However, the lackof runtime information within the VMM about guest operating systems, sometimes called the semantic gap, is a significant obstacle to efficiently implementing some kinds of services.In this paper we explore techniques that can be used by a VMM to passively infer useful information about a guest operating system's unified buffer cache and virtual memory system. We have created a prototype implementation of these techniques inside the Xen VMM called Geiger and show that it can accurately infer when pages are inserted into and evicted from a system's buffer cache. We explore several nuances involved in passively implementing eviction detection that have not previously been addressed, such as the importance of tracking disk block liveness, the effect of file system journaling, and the importance of accounting for the unified caches found in modern operating systems.Using case studies we show that the information provided by Geiger enables a VMM to implement useful VMM-level services. We implement a novel working set size estimator which allows the VMM to make more informed memory allocation decisions. We also show that a VMM can be used to drastically improve the hit rate in remote storage caches by using eviction-based cache placement without modifying the application or operating system storage interface. Both case studies hint at a future where inference techniques enable a broad new class of VMM-level functionality.


title: Classroom Discipline and Student Responsibility: The Students' View.

abstract: Abstract There is, internationally, increasing interest in the quality of children's character. This paper examines, in 21 elementary and 21 secondary schools, the role of classroom discipline in promoting student responsibility for the protection of learning and safety rights in the classroom. The results indicate that teachers are seen by students to react to classroom misbehavior by increasing their use of coercive discipline, which inhibits the development of responsibility in students and distracts them from their schoolwork. Unfortunately, teachers fail to increase their use of more productive techniques, such as discussions, rewards for good behavior and involvement in decision-making. Implications of these findings are discussed.


title: An improved and very efficient MPPT controller for PV systems subjected to rapidly varying atmospheric conditions and partial shading

abstract: Maximum power point tracking (MPPT) is a very important necessity in a system of energy conversion from a renewable energy source. Many research papers have been produced with various schemes over past decades for the MPPT in photovoltaic (PV) system. This research paper inspires its motivation from the fact that the keen study of these existing techniques reveals that there is still quite a need for an absolutely generic and yet very simple MPPT controller which should have all the following traits: total independence from system's parameters, ability to reach the global maxima in minimal possible steps, the correct sense of tracking direction despite the abrupt atmospheric or parametrical changes, and finally having a very cost-effective and energy efficient hardware with the complexity no more than that of a minimal MPPT algorithm like Perturb and Observe (P&O). The MPPT controller presented in this paper is a successful attempt to fulfil all these requirements. It extends the MPPT techniques found in the recent research papers with some innovations in the control algorithm and a simplistic hardware. The simulation results confirm that the proposed MPPT controller is very fast, very efficient, very simple and low cost as compared to the contemporary ones.


title: Object-oriented application development with MeVisLab and Python

abstract: MeVisLab is a research and rapid prototyping platform for medical image processing developed by MeVis Medical Solutions AG and Fraunhofer MEVIS. We present an object-oriented approach for developing applications using MeVisLab and Python. Our approach simplifies the development of scripting intensive and complex applications for medical image processing. The program flow becomes clearer and the resulting modules are easier to maintain compared to the common functional approach thus making development less error prone.


title: Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification

abstract: In this paper, two supervised methods for enhancing the classification accuracy of the Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the NMF algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The first method employs discriminant analysis in the features derived from NMF. In this way, a two-phase discriminant feature extraction procedure is implemented, namely NMF plus Linear Discriminant Analysis (LDA). The second method incorporates the discriminant constraints inside the NMF decomposition. Thus, a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived. The introduced methods have been applied to the problem of frontal face verification using the well-known XM2VTS database. Both methods greatly enhance the performance of NMF for frontal face verification


title: An Interactive Microarray Call-Graph Visualization

abstract: In this paper we present an interactive call-graph visualization tool for viewing large programs. Our space-filling grid-based visualization shows the functions of a programs call-graph. The grid view provides an overview of all of the methods, allowing the user to investigate and view subsets of functions, and finally jump to source code for more details on demand. Our tool assists programmers by reducing large call graphs into smaller subgraphs with function relationships that matter for program comprehension. In our benchmarks, we view and explore code relationships in programs with 18,720 functions at interactive frame rates. We provide two use cases with several findings on investigating profile-guided optimizations in C++ and critical sections in concurrent Java programs. Our software visualization tool is Java based and portable across multiple platforms.


title: A CORDIC processor for FFT computation and its implementation using gallium arsenide technology

abstract: In this paper, the architecture and the implementation of a complex fast Fourier transform (CFFT) processor using 0.6 /spl mu/m gallium arsenide (GaAs) technology are presented. This processor computes a 1024-point FFT of 16 bit complex data in less than 8 /spl mu/s, working at a frequency beyond 700 MHz, with a power consumption of 12.5 W. The architecture of the processor is based on the COordinate Rotation DIgital Computer (CORDIC) algorithm, which avoids the use of conventional multiplication-and-accumulation (MAC) units, but evaluates the trigonometric functions using only add and shift operations, Improvements to the basic CORDIC architecture are introduced in order to reduce the area and power of the processor. This together with the use of pipelining and carry save adders produces a very regular and fast processor, The CORDIC units were fabricated and tested in order to anticipate the final performance of the processor. This work also demonstrates the maturity of GaAs technology for implementing ultrahigh-performance signal processors.


title: Intention-Aware Motion Planning

abstract: As robots venture into new application domains as autonomous vehicles on the road or as domestic helpers at home, they must recognize human intentions and behaviors in order to operate effectively. This paper investigates a new class of motion planning problems with uncertainty in human intention. We propose a method for constructing a practical model by assuming a finite set of unknown intentions. We first construct a motion model for each intention in the set and then combine these models together into a single Mixed Observability Markov Decision Process (MOMDP), which is a structured variant of the more common Partially Observable Markov Decision Process (POMDP). By leveraging the latest advances in POMDP/MOMDP approximation algorithms, we can construct and solve moderately complex models for interesting robotic tasks. Experiments in simulation and with an autonomous vehicle show that the proposed method outperforms common alternatives because of its ability in recognizing intentions and using the information effectively for decision making.


title: Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey

abstract: Visualization and visual analysis play important roles in exploring, analyzing, and presenting scientific data. In many disciplines, data and model scenarios are becoming multifaceted: data are often spatiotemporal and multivariate; they stem from different data sources (multimodal data), from multiple simulation runs (multirun/ensemble data), or from multiphysics simulations of interacting phenomena (multimodel data resulting from coupled simulation models). Also, data can be of different dimensionality or structured on various types of grids that need to be related or fused in the visualization. This heterogeneity of data characteristics presents new opportunities as well as technical challenges for visualization research. Visualization and interaction techniques are thus often combined with computational analysis. In this survey, we study existing methods for visualization and interactive visual analysis of multifaceted scientific data. Based on a thorough literature review, a categorization of approaches is proposed. We cover a wide range of fields and discuss to which degree the different challenges are matched with existing solutions for visualization and visual analysis. This leads to conclusions with respect to promising research directions, for instance, to pursue new solutions for multirun and multimodel data as well as techniques that support a multitude of facets.


title: Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses

abstract: This paper presents an online detection-based two-stage multi-object tracking method in dense visual surveillances scenarios with a single camera. In the local stage, a particle filter with observer selection that could deal with partial object occlusion is used to generate a set of reliable tracklets. In the global stage, the detection responses are collected from a temporal sliding window to deal with ambiguity caused by full object occlusion to generate a set of potential tracklets. The reliable tracklets generated in the local stage and the potential tracklets generated within the temporal sliding window are associated by Hungarian algorithm on a modified pairwise tracklets association cost matrix to get the global optimal association. This method is applied to the pedestrian class and evaluated on two challenging datasets. The experimental results prove the effectiveness of our method.


Stacked RNNs for Encoder-Decoder Networks : Accurate Machine Understanding of Images


title: Limiting cache-based side-channel in multi-tenant cloud using dynamic page coloring

abstract: Multi-tenant cloud, which features utility-like computing resources to tenants in a “pay-as-you-go” style, has been commercially popular for years. As one of the sole purposes of such a cloud is maximizing resource usages to increase its revenue, it usually uses virtualization to consolidate VMs from different and even mutually-malicious tenants atop a powerful physical machine. This, however, also enables a malicious tenant to steal security-critical information such as crypto keys from victims, due to the shared physical resources such as caches. In this paper, we show that stealing crypto keys in a virtualized cloud may be a real threat by evaluating a cache-based side-channel attack against an encryption process. To mitigate such attacks while not notably degrading performance, we propose an approach that leverages dynamic cache coloring: when an application is doing security-sensitive operations, the VMM is notified to swap the associated data to a safe and isolated cache line. This approach may eliminate cache-based side-channel for security-critical operations, yet ensure efficient resource sharing during normal operations. We demonstrate the applicability by illustrating a preliminary implementation based on Xen and its performance overhead.


An Efficient LDA Algorithm for Face Recognition


title: Designing ETL processes using semantic web technologies

abstract: One of the most important tasks performed in the early stages of a data warehouse project is the analysis of the structure and content of the existing data sources and their intentional mapping to a common data model. Establishing the appropriate mappings between the attributes of the data sources and the attributes of the data warehouse tables is critical in specifying the required transformations in an ETL workflow. The selected data model should besuitable for facilitating the redefinition and revision efforts, typically occurring during the early phases of a data warehouse project, and serve as the means of communication between the involved parties. In this paper, we argue that ontologies constitute a very suitable model for this purpose and show how the usage of ontologies can enable a high degree of automation regarding the construction of an ETL design.


title: Evolving Culture vs Local Minima

abstract: We propose a theory that relates difficulty of learning in deep architectures to culture and language. It is articulated around the following hypotheses: (1) learning in an individual human brain is hampered by the presence of effective local minima; (2) this optimization difficulty is particularly important when it comes to learning higher-level abstractions, i.e., concepts that cover a vast and highly-nonlinear span of sensory configurations; (3) such high-level abstractions are best represented in brains by the composition of many levels of representation, i.e., by deep architectures; (4) a human brain can learn such high-level abstractions if guided by the signals produced by other humans, which act as hints or indirect supervision for these high-level abstractions; and (5), language and the recombination and optimization of mental concepts provide an efficient evolutionary recombination operator, and this gives rise to rapid search in the space of communicable ideas that help humans build up better high-level internal representations of their world. These hypotheses put together imply that human culture and the evolution of ideas have been crucial to counter an optimization difficulty: this optimization difficulty would otherwise make it very difficult for human brains to capture high-level knowledge of the world. The theory is grounded in experimental observations of the difficulties of training deep artificial neural networks. Plausible consequences of this theory for the efficiency of cultural evolutions are sketched.


title: The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance

abstract: Power spectral analysis of the beat-to-beat variations of heart rate or the heart period (R–R interval) has become widely used to quantify cardiac autonomic regulation (Appel et al., 1989; Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996; Berntson et al., 1997; Denver et al., 2007; Thayler et al., 2010; Billman, 2011). This technique partitions the total variance (the “power”) of a continuous series of beats into its frequency components, typically identifying two or three main peaks: Very Low Frequency (VLF) <0.04 Hz, Low Frequency (LF), 0.04–0.15 Hz, and High Frequency (HF) 0.15–0.4 Hz. It should be noted that the HF peak is shifted to a higher range (typically 0.24–1.04 Hz) in infants and during exercise (Berntson et al., 1997). The HF peak is widely believed to reflect cardiac parasympathetic nerve activity while the LF, although more complex, is often assumed to have a dominant sympathetic component (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996; Berntson et al., 1997; Billman, 2011). Based upon these assumptions, Pagani and co-workers proposed that the ratio of LF to HF (LF/HF) could be used to quantify the changing relationship between sympathetic and parasympathetic nerve activities (i.e., the sympatho-vagal balance) (Pagani et al., 1984, 1986; Malliani et al., 1991) in both health and disease. However, this concept has been challenged (Kingwell et al., 1994; Koh et al., 1994; Hopf et al., 1995; Eckberg, 1997; Houle and Billman, 1999; Billman, 2011). Despite serious and largely under-appreciated limitations, the LF/HF ratio has gained wide acceptance as a tool to assess cardiovascular autonomic regulation where increases in LF/HF are assumed to reflect a shift to “sympathetic dominance” and decreases in this index correspond to a “parasympathetic dominance.” Therefore, it is vital to provide a critical assessment of the assumptions upon which this concept is based.


title: The LiteOS Operating System: Towards Unix-Like Abstractions for Wireless Sensor Networks

abstract: This paper presents LiteOS, a multi-threaded operating system that provides Unix-like abstractions for wireless sensor networks. Aiming to be an easy-to-use platform, LiteOS offers a number of novel features, including: (1) a hierarchical file system and a wireless shell interface for user interaction using UNIX-like commands; (2) kernel support for dynamic loading and native execution of multithreaded applications; and (3) online debugging, dynamic memory, and file system assisted communication stacks. LiteOS also supports software updates through a separation between the kernel and user applications, which are bridged through a suite of system calls. Besides the features that have been implemented, we also describe our perspective on LiteOS as an enabling platform. We evaluate the platform experimentally by measuring the performance of common tasks, and demonstrate its programmability through twenty-one example applications.


title: New LTPS Pixel Circuit With AC Driving Method to Reduce OLED Degradation for 3D AMOLED Displays

abstract: This work presents a new low-temperature polysilicon (LTPS) thin-film transistor pixel circuit with a novel driving scheme that is based on the simultaneous emission and reverse-biased methods. During high-speed 3D operation at 240 Hz, the proposed circuit can successfully compensate for the TFT threshold voltage variation and improve the IR drop in the power line. Simulation and experimental results confirm the stability of the OLED current and the amelioration of the OLED lifetime. Furthermore, over the entire range of data voltages, the nonuniformity of OLED current is less than 5%.


title: Novel Small Molecule Inhibitors of Cancer Stem Cell Signaling Pathways

abstract: The main aim of oncologists worldwide is to understand and then intervene in the primary tumor initiation and propagation mechanisms. This is essential to allow targeted elimination of cancer cells without altering normal mitotic cells. Currently, there are two main rival theories describing the process of tumorigenesis. According to the Stochastic Model, potentially any cell, once defunct, is capable of initiating carcinogenesis. Alternatively the Cancer Stem Cell (CSC) Model posits that only a small fraction of undifferentiated tumor cells are capable of triggering carcinogenesis. Like healthy stem cells, CSCs are also characterized by a capacity for self-renewal and the ability to generate differentiated progeny, possibly mediating treatment resistance, thus leading to tumor recurrence and metastasis. Moreover, molecular signaling profiles are similar between CSCs and normal stem cells, including Wnt, Notch and Hedgehog pathways. Therefore, development of novel chemotherapeutic agents and proteins (e.g., enzymes and antibodies) specifically targeting CSCs are attractive pharmaceutical candidates. This article describes small molecule inhibitors of stem cell pathways Wnt, Notch and Hedgehog, and their recent chemotherapy clinical trials.


title: Measuring endorsement of the new ecological paradigm: a revised NEP scale

abstract: Dunlap and Van Liere's New Environmental Paradigm (NEP) Scale, published in 1978, has become a widely used measure of proenvironmental orientation. This article develops a revised NEP Scale designed to improve upon the original one in several respects: (1) It taps a wider range of facets of an ecological worldview, (2) It offers a balanced set of pro- and anti-NEP items, and (3) It avoids outmoded terminology. The new scale, termed the New Ecological Paradigm Scale, consists of 15 items. Results of a 1990 Washington State survey suggest that the items can be treated as an internally consistent summated ratingscale and also indicate a modest growth in pro-NEP responses among Washington residents over the 14 years since the original study.


title: Improved edge-coloring with three colors

abstract: We show an O(1.344^n)=O(2^0^.^4^2^7^n) algorithm for edge-coloring an n-vertex graph using three colors. Our algorithm uses polynomial space. This improves over the previous O(2^n^/^2) algorithm of Beigel and Eppstein [R. Beigel, D. Eppstein, 3-coloring in time O(1.3289n), J. Algorithms 54 (2) (2005) 168-204.]. We apply a very natural approach of generating inclusion-maximal matchings of the graph. The time complexity of our algorithm is estimated using the ''measure and conquer'' technique.


title: Guided Alignment Training for Topic-Aware Neural Machine Translation

abstract: In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio. We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network. With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3%. Even larger MT quality gains are obtained through domain adaptation of a general domain NMT system to e-commerce data. The developed NMT system also performs well on the IWSLT speech translation task, where an ensemble of four variant systems outperforms the phrase-based baseline by 2.1% BLEU absolute.


title: Learners' acceptance of e-learning in South Korea: Theories and results

abstract: One of the most significant changes in the field of education in this information age is the paradigm shift from teacher-centered to learner-centered education. Along with this paradigm shift, understanding of students' e-learning adoption behavior among various countries is urgently needed. South Korea's dense student population and high educational standards made investment in e-learning very cost-effective. However, despite the fact that South Korea is one of the fastest growing countries in e-learning, not much of the research results have been known to the globalized world. By investigating critical factors on e-learning adoption in South Korea, our study attempts to fill a gap in the individual country-level e-learning research. Based on the extensive literature review on flow theory, service quality, and the Technology Acceptance Model, our study proposes a research model which consists of four independent variables (instructor characteristics, teaching materials, design of learning contents, and playfulness), two belief variables (perceived usefulness and perceived ease of use), and one dependent variable (intention to use e-learning). Results of regression analyses are presented. Managerial implications of the findings and future research directions are also discussed.


Cross Layer Security Frameworks for Wireless Sensor Networks


title: Improved online/offline signature schemes

abstract: The notion of on-line/off-line signature schemes was introduced in 1990 by Even, Goldreich and Micali. They presented a general method for converting any signature scheme into an on-line/off-line signature scheme, but their method is not very practical as it increases the length of each signature by a quadratic factor. In this paper we use the recently introduced notion of a trapdoor hash function to develop a new paradigm called hash-sign-switch, which can convert any signature scheme into a highly efficient on-line/off-line signature scheme: In its recommended implementation, the on-line complexity is equivalent to about 0.1 modular multiplications, and the size of each signature increases only by a factor of two. In addition, the new paradigm enhances the security of the original signature scheme since it is only used to sign random strings chosen off-line by the signer. This makes the converted scheme secure against adaptive chosen message attacks even if the original scheme is secure only against generic chosen message attacks or against random message attacks.


title: Brain state decoding for rapid image retrieval

abstract: Human visual perception is able to recognize a wide range of targets under challenging conditions, but has limited throughput. Machine vision and automatic content analytics can process images at a high speed, but suffers from inadequate recognition accuracy for general target classes. In this paper, we propose a new paradigm to explore and combine the strengths of both systems. A single trial EEG-based brain machine interface (BCI) subsystem is used to detect objects of interest of arbitrary classes from an initial subset of images. The EEG detection outcomes are used as input to a graph-based pattern mining subsystem to identify, refine, and propagate the labels to retrieve relevant images from a much larger pool. The combined strategy is unique in its generality, robustness, and high throughput. It has great potential for advancing the state of the art in media retrieval applications. We have evaluated and demonstrated significant performance gains of the proposed system with multiple and diverse image classes over several data sets, including those from Internet (Caltech 101) and remote sensing images. In this paper, we will also present insights learned from the experiments and discuss future research directions.


title: Interactive surface segmentation for medical images

abstract: Active contours, known as snakes, have been widely used for medical images. In this paper a new algorithm for 3D segmentation is proposed based on the snake technique, which can include both gradient and textural features as image forces and a new external force to improve the sensitivity of a normal snake against image noise. After the contour has been well defined on one slice of volume data by a 2D segmentation method, the contours of the subsequent slices can be obtained through our modified snake method. Experiments on medical MRI volume images have proved the validity of the algorithm.


title: Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review

abstract: The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks represent an important special case of these conditions, though weight sharing is not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.


title: Word Root Finder: a Morphological Segmentor Based on CRF

abstract: Morphological segmentation of words is a subproblem of many natural language tasks, including handling out-of-vocabulary (OOV) words in machine translation, more effective information retrieval, and computer assisted vocabulary learning. Previous work typically relies on extensive statistical and semantic analyses to induce legitimate stems and affixes. We introduce a new learning based method and a prototype implementation of a knowledge light system for learning to segment a given word into word parts, including prefixes, suffixes, stems, and even roots. The method is based on the Conditional Random Fields (CRF) model. Evaluation results show that our method with a small set of seed training data and readily available resources can produce fine-grained morphological segmentation results that rival previous work and systems.


Handwritten Signature Verification using Neural Network