title: Web Services for the Internet of Things through CoAP and EXI

abstract: According to the Internet of Things (IoT) vision, everyday objects such as domestic appliances, actuators and embedded systems of any kind in the near future will be connected with each other and with the Internet. These will form a distributed network with sensing capabilities that will allow unprecedented market opportunities, spurring new services, including energy monitoring and control of homes, buildings, industrial processes and so forth. In this paper, we concentrate on the actual implementation of the communication technology, adopting the Representational State Transfer (REST) approach. REST only relies on the HTTP methods such as GET and POST. Embedded communication devices are addressed using Universal Resource Indicators (URI) and data is exchanged through standard XML. We present our TinyOS design and implementation of two components that will play a fundamental role in the communication stack of REST-based devices. First, we focus on the Constrained Application Protocol (CoAP), which allows REST--based communications among applications residing in distributed and networked embedded systems. Second, we present our lightweight implementation of a EXI library: an efficient binary compressor for XML data files. Experimental results are provided in terms of compression, decoding (EXI) and access time (CoAP) performance.


title: Cooling Airflow, Losses, and Temperatures in Large Air-Cooled Synchronous Machines

abstract: At steady-state operation, power losses cause a heating of rotating electrical machines. In air-cooled machines, these losses are evacuated by a forced cooling airflow through the active parts. When designing and optimizing such a machine, the design engineer must be able to get a full picture of the power losses, the cooling airflow, and the temperatures inside the active parts (e.g., core laminations, windings) and the periphery (e.g., winding overhangs). The aim of the designer is to fulfill the customer's requirements regarding the guaranteed temperatures. This paper presents a computation method, where the power loss, airflow, and temperature calculations for the world's largest air-cooled hydrogenerators are coupled in an iterative process. The new contribution of this paper is a calculation software developed by the authors. It includes a state-of-the-art loss computation, an automated airflow network, and a set of linked thermal networks. These computations result in a complete overview of the temperature gradients and allow fine tuning of the cooling airflow and, consequently, optimization of ventilation losses.


Semantic Networks


title: Genetic drift and gene flow in post-famine Ireland

abstract: This study examines the genetic impact of the Great Famine (1846-1851) on the regional genetic structure of Ireland. The Great Famine resulted in a rapid decrease in population size throughout Ireland in a short period of time, increasing the possibility of genetic drift. Our study is based on migration and anthropometric data collected originally in the 1930s from 7211 adult Irish males. These data were subdivided into three time periods defined by year of birth: 1861-1880, 1881-1900, and 1901-1920. Within each time period the data were further subdivided into six geographic regions of Ireland. Estimates of Wright's FST were calculated from parent-offspring migration data and from 17 anthropometric variables (10 head measures, 7 body measures). Over time, the average population size decreased, but average rates of migration increased. The estimates of F st at equilibrium from migration matrix analysis suggest that the net effect of these opposite effects is a reduction in among-group variation. Closer examination shows that within each time period the rate of convergence to equilibrium is slow, meaning that the expected levels of genetic homogeneity revealed from migration matrix analysis are not likely to be seen over short intervals of time. Estimates of FST from anthropometric data show either relatively little change in microdifferentiation or some increase, depending on which variables are analyzed. Investigation of a simple model of demographic and genetic change shows that, given the demographic changes in post-Famine Ireland, FST could in theory increase, decrease, or remain the same over short intervals of time. Overall, the Great Famine appears to have had minimal impact on the genetic structure of Ireland on a regional level. Comparison with studies focusing on local genetic structure shows the opposite. It appears that the level of genetic impact depends strongly on the level of analysis; local populations are affected to a greater extent by demographic shifts than regional populations. We also provide formulas for the standard errors of Fst from metric traits and related statistics. 1 Department of Anthropology, State University of New York, College at Oneonta, Oneonta, NY 13820. 2 Department of Anthropology, University of Kansas, Lawrence, KS 66045. 3 Department of Genetics, Southwest Foundation for Biomedical Research, PO Box 760549, San Antonio, TX 78245. Human Biology, August 1997, v. 69, no. 4, pp. 443^465. Copyright © 1997 Wayne State University Press, Detroit, Michigan 48201-1309


title: Scaling big data mining infrastructure: the twitter experience

abstract: The analytics platform at Twitter has experienced tremendous growth over the past few years in terms of size, complexity, number of users, and variety of use cases. In this paper, we discuss the evolution of our infrastructure and the development of capabilities for data mining on "big data". One important lesson is that successful big data mining in practice is about much more than what most academics would consider data mining: life "in the trenches" is occupied by much preparatory work that precedes the application of data mining algorithms and followed by substantial effort to turn preliminary models into robust solutions. In this context, we discuss two topics: First, schemas play an important role in helping data scientists understand petabyte-scale data stores, but they're insufficient to provide an overall "big picture" of the data available to generate insights. Second, we observe that a major challenge in building data analytics platforms stems from the heterogeneity of the various components that must be integrated together into production workflows---we refer to this as "plumbing". This paper has two goals: For practitioners, we hope to share our experiences to flatten bumps in the road for those who come after us. For academic researchers, we hope to provide a broader context for data mining in production environments, pointing out opportunities for future work.


abstract: Signal processing methods for the automatic transcription of music are developed in this thesis. Music transcription is here understood as the process of analyzing a music signal so as to write down the parameters of the sounds that occur in it. The applied notation can be the traditional musical notation or any symbolic representation which gives sufficient information for performing the piece using the available musical instruments. Recovering the musical notation automatically for a given acoustic signal allows musicians to reproduce and modify the original performance. Another principal application is structured audio coding: a MIDI-like representation is extremely compact yet retains the identifiability and characteristics of a piece of music to an important degree. The scope of this thesis is in the automatic transcription of the harmonic and melodic parts of real-world music signals. Detecting or labeling the sounds of percussive instruments (drums) is not attempted, although the presence of these is allowed in the target signals. Algorithms are proposed that address two distinct subproblems of music transcription. The main part of the thesis is dedicated to multiple fundamental frequency (F0) estimation, that is, estimation of the F0s of several concurrent musical sounds. The other subproblem addressed is musical meter estimation. This has to do with rhythmic aspects of music and refers to the estimation of the regular pattern of strong and weak beats in a piece of music. For multiple-F0 estimation, two different algorithms are proposed. Both methods are based on an iterative approach, where the F0 of the most prominent sound is estimated, the sound is cancelled from the mixture, and the process is repeated for the residual. The first method is derived in a pragmatic manner and is based on the acoustic properties of musical sound mixtures. For the estimation stage, an algorithm is proposed which utilizes the frequency relationships of simultaneous spectral components, without assuming ideal harmonicity. For the cancelling stage, a new processing principle, spectral smoothness, is proposed as an efficient new mechanism for separating the detected sounds from the mixture signal. The other method is derived from known properties of the human auditory system. More specifically, it is assumed that the peripheral parts of hearing can be modelled by a bank of bandpass filters, followed by half-wave rectification and compression of the subband signals. It is shown that this basic structure allows the combined use of time-domain periodicity and frequency-domain periodicity for F0 extraction. In the derived algorithm, the higher-order (unresolved) harmonic partials of a sound are processed collectively, without the need to detect or estimate individual partials. This has the consequence that the method works reasonably accurately for short analysis frames. Computational efficiency of the method is based on calculating a frequency-domain approximation of the summary autocorrelation function, a physiologically-motivated representation of sound. Both of the proposed multiple-F0 estimation methods operate within a single time frame and arrive at approximately the same error rates. However, the auditorily-motivated method is superior in short analysis frames. On the other hand, the pragmatically-oriented method is “complete” in the sense that it includes mechanisms for suppressing additive noise (drums) and for estimating the number of concurrent sounds in the analyzed signal. In musical interval and chord identification tasks, both algorithms outperformed the average of ten trained musicians.


HyperQA: Hyperbolic Embeddings for Fast and Efficient Ranking of Question Answer Pairs


Visual Dangers and Delights: Nude Photography in East Germany


title: Labia Majora Augmentation with Hyaluronic Acid Filler: Technique and Results

abstract: Background External female genitalia lose elasticity and volume with age. In the literature several techniques address the redundancy of the labia minora, but only few reports describe the augmentation of labia majora with fat grafting. At present, no studies describe the augmentation of the labia majora with hyaluronic acid. ::: ::: Objectives This study aims to present our technique of infiltration of hyaluronic acid filler, analyzing effectiveness, patient satisfaction, and complications. ::: ::: Methods We retrospectively analyzed 54 patients affected by hypotrophy of the labia majora; they were treated with hyaluronic acid filler between November 2010 and December 2014. The Global Aesthetic Improvement Scale (GAIS) filled out by the doctor and the patients was used to evaluate the results 12 months after the infiltration. Complications were recorded. ::: ::: Results A total of 31 patients affected by mild to moderate labia majora hypotrophy were treated with 19 mg/mL HA filler; 23 patients affected by severe labia majora hypotrophy were treated with 21 mg/mL HA filler. Among the first group of patients, one underwent a second infiltration 6 months later with 19 mg/mL HA filler (maximum 1 mL). A significant improvement ( P < .0001) in GAIS score was observed, both in the scores provided by the patients and by the doctor. A greater relative improvement was observed in patients affected by severe hypotrophy. No complications were recorded. ::: ::: Conclusions Hyaluronic acid infiltration of the labia majora is able to provide a significant rejuvenation with a simple outpatient procedure. We achieved significant improvements with one infiltration in all cases. The treatment is repeatable, has virtually no complications and it is reversible. ::: ::: Level of Evidence 4![Graphic][1] Therapeutic ::: ::: [1]: /embed/inline-graphic-1.gif


title: Task matching and scheduling for multiple workers in spatial crowdsourcing

abstract: A new platform, termed spatial crowdsourcing, is emerging which enables a requester to commission workers to physically travel to some specified locations to perform a set of spatial tasks (i.e., tasks related to a geographical location and time). The current approach is to formulate spatial crowdsourcing as a matching problem between tasks and workers; hence the primary objective of the existing solutions is to maximize the number of matched tasks. Our goal is to solve the spatial crowdsourcing problem in the presence of multiple workers where we optimize for both travel cost and the number of completed tasks, while taking the tasks' expiration times into consideration. The challenge is that the solution should be a mixture of task-matching and task-scheduling, which are fundamentally different. In this paper, we show that a baseline approach that performs a task-matching first, and subsequently schedules the tasks assigned per worker in a following phase, does not perform well. Hence, we add a third phase in which we iterate back to the matching phase to improve the assignment per the output of the scheduling phase, and thus further improves the quality of matching and scheduling. Even though this 3-phase approach generates high quality results, it is very slow and does not scale. Hence, to scale our algorithm to large number of workers and tasks, we propose a Bisection-based framework which recursively divides all the workers and tasks into different partitions such that assignment and scheduling can be performed locally in a much smaller and promising space. Our experiments show that this approach is three orders of magnitude faster than the 3-phase approach while it only sacrifices 4% of the results' quality.


Domain Adaptation and Attention-Based Unknown Word Replacement in Chinese-to-Japanese Neural Machine Translation.


title: An empirical assessment of the antecedents of electronic-business implementation and the resulting organizational performance

abstract: – The purpose of this paper is to propose and empirically test a unified framework that captures the antecedents of e-business implementation, that is; organizational factors which is decomposed into organizational capabilities (training availability, technical expertise, knowledge level), knowledge management capabilities (knowledge acquisition, application, and sharing), adhocracy culture, and top management support, e-business implementation, and organizational performance (efficiency, sales performance, customer satisfaction, relationship development). , – Data from a survey of 258 top managers in Saudi Arabian enterprises were collected to empirically test the proposed research model. Additionally, the statistical techniques employed included a confirmatory factor analysis to examine the reliability and validity of the measurement model, and structural equation modeling using AMOS is utilized to test the hypotheses. , – The findings of this study suggest that organizational factors influence e-business implementation. Moreover, e-business implementation affects organizational performance. , – This study did not investigate all organizational factors and knowledge management processes. Future research could assess the influence of additional factors such as technology and environment contexts on e-business implementation. , – Owners/managers considering e-business implementation would be best to focus on internal (organizational) factors and their interaction within and beyond the organization, rather than focussing exclusively on technological considerations. , – This study is significant for at least two reasons: it determines the key antecedents to successful business implementation based on organizational factors and it helps to understand the effects of e-business implementation on organizational performance.


title: A survey on product backlog change management and requirement traceability in agile (Scrum)

abstract: Product backlog comprises the outline of desired functionality in a prioritized order in Scrum. Scrum is an iterative, collaborative, and flexible process to develop a software product. Scrum projects welcome changes at any stage of a project, but such changes significantly affect project management activities and quality, including time and budget overrun. These changes also compromise architectural integrity. The objectives of this research were: 1) to determine how product backlog changes are managed by practitioners in Scrum projects, and 2) to explore the perception of practitioners on the use of requirement traceability for product backlog and project risk management during requirement volatility in Scrum. In this research, we conducted a survey among Scrum practitioners and obtained 89 complete responses through our electronic questionnaire. The practitioners identified the reasons of backlog changes and recorded the changed elements. Majority of practitioners recorded modified requirement as “new requirement,” but they made changes in the original product backlog. The practitioners tended to agree that requirement traceability was helpful in managing product backlog and minimizing project risk. However, majority of the respondents pointed out the lack of specific requirement traceability method for Scrum.


title: Reciprocal Collision Avoidance With Motion Continuity Constraints

abstract: This paper addresses decentralized motion planning among a homogeneous set of feedback-controlled, decision-making agents. It introduces the continuous control obstacle ( Cn-CO), which describes the set of Cn-continuous control sequences (and thus trajectories) that lead to a collision between interacting agents. By selecting a feasible trajectory from Cn-CO's complement, a collision-free motion is obtained. The approach represents an extension to the reciprocal velocity obstacle (RVO, ORCA) collision-avoidance methods so that trajectory segments verify Cn continuity rather than piecewise linearity. This allows the large class of robots capable of tracking Cn-continuous trajectories to employ it for partial motion planning directly-rather than as a mere tool for collision checking. This paper further establishes that both the original velocity obstacle method and several of its recently developed reciprocal extensions (which treat specific robot physiologies only) correspond to particular instances of Cn-CO. In addition to the described extension in trajectory continuity, Cn-CO thus represents a unification of existing RVO theory. Finally, the presented method is validated in simulation-and a parameter study reveals under which environmental and control conditions Cn-CO with admits significantly improved navigation performance compared with inflated approaches based on ORCA.


title: Effect of culture pH on recombinant antibody production by a new human cell line, F2N78, grown in suspension at 33.0 °C and 37.0 °C

abstract: The human host cell line, F2N78, is a new somatic hybrid cell line designed for therapeutic antibody production. To verify its potential as a human host cell line, recombinant F2N78 cells that produce antibody against rabies virus (rF2N78) were cultivated at different culture pH (6.8, 7.0, 7.2, 7.4, and 7.6) and temperatures (33.0 °C and 37.0 °C). Regardless of the culture temperature, the highest specific growth rate was obtained at a pH of 7.0-7.4. Lowering the culture temperature from 37.0 °C to 33.0 °C suppressed cell growth while allowing maintenance of high cell viability for a longer period. However, it did not enhance antibody production because specific antibody productivity did not increase at 33.0 °C. The highest maximum antibody concentration was obtained at 37.0 °C and pH 6.8. The N-linked glycosylation of the antibody was affected by the culture pH rather than the temperature. Nevertheless, G1F was dominant and G2F occupied a larger portion than G0F in all culture conditions. Compared to the same antibody produced from recombinant CHO cells, the antibody produced from rF2N78 cells has more galactose capping and was more similar to human plasma IgG. Taken together, the results obtained here demonstrate the potential of F2N78 as an alternative human host cell line for therapeutic antibody production.


title: A first step towards algorithm plagiarism detection

abstract: In this work, we address the problem of algorithm plagiarism, which occurs when a plagiarist, violating intellectual property rights, steals others' algorithms and covertly implements them. In contrast to software plagiarism, which has been extensively studied, limited attention has been paid to algorithm plagiarism. In this paper, we propose two dynamic value-based approaches, namely N-version and annotation, for algorithm plagiarism detection. Our approaches are motivated by the observation that there exist some critical runtime values which are irreplaceable and uneliminatable for all implementations of the same algorithm. The N-version approach extracts such values by filtering out non-core values. The annotation approach leverages auxiliary information to flag important variables which contain core values. We also propose a value dependence graph based similarity metric in addition to the longest common subsequence based one, in order to address the potential value reordering attack. We have implemented a prototype and evaluated the proposed schemes on various algorithms. The results show that our approaches to algorithm plagiarism detection are practical, effective and resilient to many automatic obfuscation techniques.


title: Challenges and design space of gaze-enabled public displays

abstract: Gaze is an attractive modality for public displays, hence the recent years saw an increase in deployments of gaze-enabled public displays. Although gaze has been thoroughly investigated for desktop scenarios, gaze-enabled public displays present new challenges that are unique to this setup. In contrast to desktop settings, public displays (1) cannot afford requiring eye tracker calibration, (2) expect users to interact from different positions, and (3) expect multiple users to interact simultaneously. In this work we discuss these challenges, and explore the design space of gaze-enabled public displays. We conclude by discussing how the current state of research stands wrt. the identified challenges, and highlight directions for future work.


title: Empirical evaluation of a dynamic and distributed taxi-sharing system

abstract: Modern societies rely on efficient transportation systems for sustainable mobility. In this paper, we perform a large-scale and empirical evaluation of a dynamic and distributed taxi-sharing system. The novel system takes advantage of nowadays widespread availability of communication and computation to convey a cost-efficient, door-to-door and flexible system, offering a quality of service similar to traditional taxis. The shared taxi service is assessed in a real-city scenario using a highly realistic simulation platform. Simulation results have shown the system's advantages for both passengers and taxi drivers, and that trade-offs need to be considered. Compared with the current taxi operation model, results show a increase of 48% on the average occupancy per traveled kilometer with a full deployment of the taxi-sharing system.


title: MEBN: A Language for First-Order Bayesian Knowledge Bases

abstract: Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems. Probability is the best-understood and most widely applied formalism for computational scientific reasoning under uncertainty. Increasingly expressive languages are emerging for which the fundamental logical basis is probability. This paper presents Multi-Entity Bayesian Networks (MEBN), a first-order language for specifying probabilistic knowledge bases as parameterized fragments of Bayesian networks. MEBN fragments (MFrags) can be instantiated and combined to form arbitrarily complex graphical probability models. An MFrag represents probabilistic relationships among a conceptually meaningful group of uncertain hypotheses. Thus, MEBN facilitates representation of knowledge at a natural level of granularity. The semantics of MEBN assigns a probability distribution over interpretations of an associated classical first-order theory on a finite or countably infinite domain. Bayesian inference provides both a proof theory for combining prior knowledge with observations, and a learning theory for refining a representation as evidence accrues. A proof is given that MEBN can represent a probability distribution on interpretations of any finitely axiomatizable first-order theory.


title: Minor cognitive disturbances in X-linked spinal and bulbar muscular atrophy, Kennedy's disease

abstract: AbstractSpinal and bulbar muscular atrophy (SBMA), Kennedy's disease, is an adult-onset hereditary neurodegenerative disorder, associated predominantly with a lower motor neuron syndrome and eventually endocrine and sensory disturbances. In contrast to other motor neuron diseases such as amyotrophic lateral sclerosis (ALS), the impairment of cognition in SBMA is not well documented. We conducted a systematic cross-sectional neuropsychological study in order to investigate cognition in SBMA patients more thoroughly. We investigated 20 genetically proven SBMA patients compared to 20 age- and education-matched control subjects using a comprehensive neuropsychological test battery, measuring executive functioning, attention, memory and visuospatial abilities. The SBMA patients performed significantly worse than healthy controls in three sub-tests in the executive and attention domains. This low performance was in the working memory (digit span backward task), verbal fluency category (single letter fluency tas...


title: Forecasting Popularity of Videos Using Social Media

abstract: This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards.


title: Quo vadis, PUF?: Trends and challenges of emerging physical-disorder based security

abstract: The physical unclonable function (PUF) has emerged as a popular and widely studied security primitive based on the randomness of the underlying physical medium. To date, most of the research emphasis has been placed on finding new ways to measure randomness, hardware realization and analysis of a few initially proposed structures, and conventional secret-key based protocols. In this work, we present our subjective analysis of the emerging and future trends in this area that aim to change the scope, widen the application domain, and make a lasting impact. We emphasize on the development of new PUF-based primitives and paradigms, robust protocols, public-key protocols, digital PUFs, new technologies, implementations, metrics and tests for evaluation/validation, as well as relevant attacks and countermeasures.


title: Usage and Perceptions of Agile Software Development in an Industrial Context: An Exploratory Study

abstract: Agile development methodologies have been gaining acceptance in the mainstream software development community. While there are numerous studies of agile development in academic and educational settings, there has been little detailed reporting of the usage, penetration and success of agile methodologies in traditional, professional software development organizations. We report on the results of an empirical study conducted at Microsoft to learn about agile development and its perception by people in development, testing, and management. We found that one-third of the study respondents use agile methodologies to varying degrees, and most view it favorably due to improved communication between team members, quick releases and the increased flexibility of agile designs. The scrum variant of agile methodologies is by far the most popular at Microsoft. Our findings also indicate that developers are most worried about scaling agile to larger projects (greater than twenty members), attending too many meetings and the coordinating agile and non-agile teams.


title: A 2.4-GHz DSSS superregenerative receiver with a simple delay-locked loop

abstract: This letter describes a direct-sequence spread-spectrum superregenerative receiver operating at 2.4 GHz and using a simple PN code synchronization loop. The receiver minimizes overall complexity by using a single signal processing path for data detection and PN code synchronization. An analytical description of the loop dynamics is presented, and the conditions for optimum performance are examined. Experimental results confirm the receiver's ability to perform acquisition and tracking, achieving a sensitivity of -97 dBm and an input dynamic range of 45 dB. The RF part consumes 0.9 mA at a supply voltage of 3 V.


title: Combining sparsity and rotational invariance in EEG/MEG source reconstruction

abstract: We introduce Focal Vector Field Reconstruction (FVR), a novel technique for the inverse imaging of vector fields. The method was designed to simultaneously achieve two goals: a) invariance with respect to the orientation of the coordinate system, and b) a preference for sparsity of the solutions and their spatial derivatives. This was achieved by defining the regulating penalty function, which renders the solutions unique, as a global l(1)-norm of local l(2)-norms. We show that the method can be successfully used for solving the EEG inverse problem. In the joint localization of 2-3 simulated dipoles, FVR always reliably recovers the true sources. The competing methods have limitations in distinguishing close sources because their estimates are either too smooth (LORETA, Minimum l(1)-norm) or too scattered (Minimum l(2)-norm). In both noiseless and noisy simulations, FVR has the smallest localization error according to the Earth Mover's Distance (EMD), which is introduced here as a meaningful measure to compare arbitrary source distributions. We also apply the method to the simultaneous localization of left and right somatosensory N20 generators from real EEG recordings. Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge.


title: Tethered-bead, immune sandwich assay.

abstract: We describe a proof-of-principle, immune sandwich assay in which immune complexes link micron-size beads via DNA tethers to a sensor surface. The number of tethered beads, counted using low-magnification microscopy, provides a measure of the concentration of analyte. The prototype assay was sensitive to pM concentration of analyte. In theory, the assay could be sensitive to sub-fM analyte because beads attached via single-immune complexes and DNA strands form tethers, and tether formation in the absence of analyte is extremely rare. The limiting step at present is binding of streptavidin at the end of DNA to biotin on capture beads. Potential advantages of this type of sensor are discussed.


Speeding Up All-Pairwise Dynamic Time Warping Matrix Calculation


title: The Quest for the Golden Activation Function

abstract: Deep Neural Networks have been shown to be beneficial for a variety of tasks, in particular allowing for end-to-end learning and reducing the requirement for manual design decisions. However, still many parameters have to be chosen in advance, also raising the need to optimize them. One important, but often ignored system parameter is the selection of a proper activation function. Thus, in this paper we target to demonstrate the importance of activation functions in general and show that for different tasks different activation functions might be meaningful. To avoid the manual design or selection of activation functions, we build on the idea of genetic algorithms to learn the best activation function for a given task. In addition, we introduce two new activation functions, ELiSH and HardELiSH, which can easily be incorporated in our framework. In this way, we demonstrate for three different image classification benchmarks that different activation functions are learned, also showing improved results compared to typically used baselines.


title: Max-Pooling Dropout for Regularization of Convolutional Neural Networks

abstract: Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.


title: Coherent optical OFDM: has its time come [Invited

abstract: There has been growing interest in coherent optical orthogonal frequency-division multiplexing (CO-OFDM). We aim to present the case that the time for CO-OFDM has come, in terms of the demand from today's ever-advancing optical networks and the availability of its underlying technologies. We first lay out the signal processing aspect for CO-OFDM and then show a 2×2 multiple-input-multiple-output (MIMO) OFDM representation for the single-mode fiber optical channel. Through numerical simulation and experimental demonstration, we further present and discuss various MIMO-OFDM systems focusing on their polarization-mode dispersion resilience. Among those systems, the 2×1 MIMO-OFDM configuration incorporating polarization-time coding shows promise for optical access networks and broadcast networks. Finally, another class of the frequency-domain equalization techniques, namely, incoherent or coherent optical single-carrier frequency-domain equalization (CO-SCFDE) is discussed and experimentally demonstrated.


title: Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks

abstract: Recent papers have shown that neural networks obtain state-of-the-art performance on several different sequence tagging tasks. One appealing property of such systems is their generality, as excellent performance can be achieved with a unified architecture and without task-specific feature engineering. However, it is unclear if such systems can be used for tasks without large amounts of training data. In this paper we explore the problem of transfer learning for neural sequence taggers, where a source task with plentiful annotations (e.g., POS tagging on Penn Treebank) is used to improve performance on a target task with fewer available annotations (e.g., POS tagging for microblogs). We examine the effects of transfer learning for deep hierarchical recurrent networks across domains, applications, and languages, and show that significant improvement can often be obtained. These improvements lead to improvements over the current state-of-the-art on several well-studied tasks.


title: Innovative Pedagogical Approaches to a Capstone Laboratory Course in Cyber Operations

abstract: This paper provides pedagogical lessons drawn from a capstone hands-on laboratory course in cyber operations. It is taught as a flipped class, where the center piece is a collection of exercises that require teams of students to set up, defend, and attack complex networks. Project designs are presented, including balancing offense and defense to improve course learning outcomes. Lessons on the recruiting and managing of an external "red team" are provided. Grading issues are addressed, as are techniques to manage students of different skills and motivations.


title: Annealed Sparsity via Adaptive and Dynamic Shrinking

abstract: Sparse learning has received tremendous amount of interest in high-dimensional data analysis due to its model interpretability and the low-computational cost. Among the various techniques, adaptive l1-regularization is an effective framework to improve the convergence behaviour of the LASSO, by using varying strength of regularization across different features. In the meantime, the adaptive structure makes it very powerful in modelling grouped sparsity patterns as well, being particularly useful in high-dimensional multi-task problems. However, choosing an appropriate, global regularization weight is still an open problem. In this paper, inspired by the annealing technique in material science, we propose to achieve "annealed sparsity" by designing a dynamic shrinking scheme that simultaneously optimizes the regularization weights and model coefficients in sparse (multi-task) learning. The dynamic structures of our algorithm are twofold. Feature-wise (spatially), the regularization weights are updated interactively with model coefficients, allowing us to improve the global regularization structure. Iteration-wise (temporally), such interaction is coupled with gradually boosted l1-regularization by adjusting an equality norm-constraint, achieving an annealing effect to further improve model selection. This renders interesting shrinking behaviour in the whole solution path. Our method competes favorably with state-of-the-art methods in sparse (multi-task) learning. We also apply it in expression quantitative trait loci analysis (eQTL), which gives useful biological insights in human cancer (melanoma) study.


title: Mining Frequent Subgraph Patterns from Uncertain Graph Data

abstract: In many real applications, graph data is subject to uncertainties due to incompleteness and imprecision of data. Mining such uncertain graph data is semantically different from and computationally more challenging than mining conventional exact graph data. This paper investigates the problem of mining uncertain graph data and especially focuses on mining frequent subgraph patterns on an uncertain graph database. A novel model of uncertain graphs is presented, and the frequent subgraph pattern mining problem is formalized by introducing a new measure, called expected support. This problem is proved to be NP-hard. An approximate mining algorithm is proposed to find a set of approximately frequent subgraph patterns by allowing an error tolerance on expected supports of discovered subgraph patterns. The algorithm uses efficient methods to determine whether a subgraph pattern can be output or not and a new pruning method to reduce the complexity of examining subgraph patterns. Analytical and experimental results show that the algorithm is very efficient, accurate, and scalable for large uncertain graph databases. To the best of our knowledge, this paper is the first one to investigate the problem of mining frequent subgraph patterns from uncertain graph data.


title: IoT design space challenges: Circuits and systems

abstract: The Internet of Things (IoT) is a rapidly emerging application space, poised to become the largest electronics market for the semiconductor industry. IoT devices are focused on sensing and actuating of our physical environment and have a nearly unlimited breadth of uses. In this paper, we explore the IoT application space and then identify two common challenges that exist across this space: ultra-low power operation and system design using modular, composable components. We survey recent low power techniques and discuss a low power bus that enables modular design. Finally, we conclude with three example ultra-low power, millimeter-scale IoT systems.


title: Social Norms and Energy Conservation

abstract: This paper evaluates a series of programs run by a company called OPOWER to send Home Energy Report letters to residential utility customers comparing their electricity use to that of their neighbors. Using data from randomized natural field experiments at 600,000 treatment and control households across the United States, I estimate that the average program reduces energy consumption by 2.0%. The program provides additional evidence that non-price interventions can substantially and cost effectively change consumer behavior: the effect is equivalent to that of a short-run electricity price increase of 11 to 20%, and the cost effectiveness compares favorably to that of traditional energy conservation programs. Perhaps because the treatment included descriptive social norms, effects are heterogeneous: households in the highest decile of pre-treatment consumption decrease usage by 6.3%, while consumption by the lowest decile decreases by only 0.3%. A regression discontinuity design shows that different categories of “injunctive norms” played an insignificant role in encouraging relatively low users not to increase usage.


title: The particel swarm: Explosion, stability, and convergence in a multidimensional complex space [J

abstract: The particle swarm is an algorithm for finding optimal regions of complex search spaces through the interaction of individuals in a population of particles. This paper analyzes a particle's trajectory as it moves in discrete time (the algebraic view), then progresses to the view of it in continuous time (the analytical view). A five-dimensional depiction is developed, which describes the system completely. These analyses lead to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies. Some results of the particle swarm optimizer, implementing modifications derived from the analysis, suggest methods for altering the original algorithm in ways that eliminate problems and increase the ability of the particle swarm to find optima of some well-studied test functions.


title: How Information Visualization Novices Construct Visualizations

abstract: It remains challenging for information visualization novices to rapidly construct visualizations during exploratory data analysis. We conducted an exploratory laboratory study in which information visualization novices explored fictitious sales data by communicating visualization specifications to a human mediator, who rapidly constructed the visualizations using commercial visualization software. We found that three activities were central to the iterative visualization construction process: data attribute selection, visual template selection, and visual mapping specification. The major barriers faced by the participants were translating questions into data attributes, designing visual mappings, and interpreting the visualizations. Partial specification was common, and the participants used simple heuristics and preferred visualizations they were already familiar with, such as bar, line and pie charts. We derived abstract models from our observations that describe barriers in the data exploration process and uncovered how information visualization novices think about visualization specifications. Our findings support the need for tools that suggest potential visualizations and support iterative refinement, that provide explanations and help with learning, and that are tightly integrated into tool support for the overall visual analytics process.


title: An efficient MapReduce algorithm for counting triangles in a very large graph

abstract: Triangle counting problem is one of the fundamental problem in various domains. The problem can be utilized for computation of clustering coefficient, transitivity, trianglular connectivity, trusses, etc. The problem have been extensively studied in internal memory but the algorithms are not scalable for enormous graphs. In recent years, the MapReduce has emerged as a de facto standard framework for processing large data through parallel computing. A MapReduce algorithm was proposed for the problem based on graph partitioning. However, the algorithm redundantly generates a large number of intermediate data that cause network overload and prolong the processing time. In this paper, we propose a new algorithm based on graph partitioning with a novel idea of triangle classification to count the number of triangles in a graph. The algorithm substantially reduces the duplication by classifying triangles into three types and processing each triangle differently according to its type. In the experiments, we compare the proposed algorithm with recent existing algorithms using both synthetic datasets and real-world datasets that are composed of millions of nodes and billions of edges. The proposed algorithm outperforms other algorithms in most cases. Especially, for a twitter dataset, the proposed algorithm is more than twice as fast as existing MapReduce algorithms. Moreover, the performance gap increases as the graph becomes larger and denser.


title: Personalized Key Frame Recommendation

abstract: Key frames are playing a very important role for many video applications, such as on-line movie preview and video information retrieval. Although a number of key frame selection methods have been proposed in the past, existing technologies mainly focus on how to precisely summarize the video content, but seldom take the user preferences into consideration. However, in real scenarios, people may cast diverse interests on the contents even for the same video, and thus they may be attracted by quite different key frames, which makes the selection of key frames an inherently personalized process. In this paper, we propose and investigate the problem of personalized key frame recommendation to bridge the above gap. To do so, we make use of video images and user time-synchronized comments to design a novel key frame recommender that can simultaneously model visual and textual features in a unified framework. By user personalization based on her/his previously reviewed frames and posted comments, we are able to encode different user interests in a unified multi-modal space, and can thus select key frames in a personalized manner, which, to the best of our knowledge, is the first time in the research field of video content analysis. Experimental results show that our method performs better than its competitors on various measures.


On the Definition of 'Ontology'


A REVIEW OF INTELLIGENT TUTORING SYSTEMS IN E-LEARNING


title: High gain slot-pair substrate-integrated-waveguide antenna for 77 GHz vehicle collision warning radar

abstract: A 77 GHz high gain slot-pair substrate-integrated waveguide (SIW) antenna array has been proposed for automotive radar application. The slot-pair radiation element has been introduced to decrease the guided wavelength, which increases the aperture efficiency. Moreover, the slot-pair element provides the variety to change the antenna performance. Finally, a high gain antenna for forward-looking automotive radar has been fabricated. The measurement gain is 25 dBi and side-lobe level (SLL) is 15 dB, and the aperture efficiency is 62% of the size of 62 mm by 12 mm.


MADAMIRA: A Fast, Comprehensive Tool for Morphological Analysis and Disambiguation of Arabic


title: Secondary Peak Detection of PPG Signal for Continuous Cuffless Arterial Blood Pressure Measurement

abstract: The arterial blood pressure (ABP) is one of the most important physiological parameters for health monitoring. Most of the blood measurement devices in the market determine the ABP through the inflation and the deflation of a cuff controlled by a bladder. This method is very uncomfortable for most of the users and may even cause anxiety, which in turn can affect the blood pressure (BP) (white coat syndrome). This paper investigates a cuffless nonintrusive approach to estimate the BP. The main idea is to measure the pulse transit time (PTT), i.e., the delay between the R-peak of the electrocardiogram (ECG) signal and the following peak of the finger photoplethysmograph (PPG) signal. The main problem of this approach is that when the dicrotic notch of the PPG signal is unobservable, the position and the amplitude of the main peak of the PPG signal will be changed. As a result, the correlation between the BP and the PTT can be affected. To overcome this problem, three types of secondary peak detection methods are designed to reveal the secondary peak from the original PPG signal. Actual ECG, PPG, and the BP measurements extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database that contains clinical signal data reflecting real measurements are used. The results verify that the proposed detection methods improve the correlation relationship between the BP and the PTT, and demonstrate that the adjusted PTT can be used as an indicator of the ABP by removing the dicrotic notch impact on the PPG signal.


title: Toward Optimal Feature Selection

abstract: In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an efficient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm effectively handles datasets with a very large number of features.


title: clMAGMA: high performance dense linear algebra with OpenCL

abstract: This paper presents the design and implementation of several fundamental dense linear algebra (DLA) algorithms in OpenCL. In particular, these are linear system solvers and eigenvalue problem solvers. Further, we give an overview of the clMAGMA library, an open source, high performance OpenCL library that incorporates various optimizations, and in general provides the DLA functionality of the popular LAPACK library on heterogeneous architectures. The LAPACK compliance and use of OpenCL simplify the use of clMAGMA in applications, while providing them with portable performance. High performance is obtained through the use of the high-performance OpenCL BLAS, hardware- and OpenCL-specific tuning, and a hybridization methodology, where we split the algorithm into computational tasks of various granularities. Execution of those tasks is efficiently scheduled over the heterogeneous hardware components by minimizing data movements and mapping algorithmic requirements to the architectural strengths of the various heterogeneous hardware components.


title: IOS Press Mitigating denial of service attacks: A tutorial

abstract: This tutorial describes what Denial of Service (DOS) attacks are. how they can be carried out in IP networks, and how one can defend against them. Distributed DoS (DDoS) attacks are included here as a subset of DoS attacks. A DoS attack has two phases: a deployment and an attack phase. A DoS program must first be deployed on one or more compromised hosts before an attack is possible. Mitigation of DoS attacks requires thus defense mechanisms for both phases. Completely reliable protection against DoS attacks is, however, not possible. There will always be vulnerable hosts in the Internet. and many attack mechanisms are based on ordinary use of protocols, Defense in depth is thus needed to mitigate the effect of DoS attacks. This paper describes shortly many defense mechanisms proposed in the literature. The goal is not to implement all possible defenses. Instead, one should optimize the trade-off between security costs and acquired benefits in handling the most important risks. Mitigation of DoS attacks is thus closely related to risk management.


title: Dynamic Practical Byzantine Fault Tolerance

abstract: This paper describes a novel Byzantine fault tolerant protocol that allows replicas to join and exit dynamically. With the astonishing success of cryptocurrencies, people attach great importance in “blockchain” and robust Byzantine fault tolerant (BFT) protocols for consensus. Among the conventional wisdom, the Practical Byzantine Fault Tolerance (PBFT), proposed by Miguel and Liskov in 1999, occupies an important position. Although PBFT has many advantages, it has fatal disadvantages. Firstly, it works in a completely enclosed environment, where users who want to add or take out any node must stop the whole system. Secondly, although PBFT guarantees liveness and safety if at most $\left\lfloor {\frac{{{\rm{n}} - 1}}{3}} \right\rfloor$ out of a total n replicas are faulty, it takes no measure to deal with these ineffective or malicious replicas, which is harmful to the system and will cause system crash finally. These drawbacks are unbearable in practice. In order to solve them, we present an alternative, Dynamic PBFT.


title: Attentive Long Short-Term Preference Modeling for Personalized Product Search

abstract: E-commerce users may expect different products even for the same query, due to their diverse personal preferences. It is well-known that there are two types of preferences: long-term ones and short-term ones. The former refers to user' inherent purchasing bias and evolves slowly. By contrast, the latter reflects users' purchasing inclination in a relatively short period. They both affect users' current purchasing intentions. However, few research efforts have been dedicated to jointly model them for the personalized product search. To this end, we propose a novel Attentive Long Short-Term Preference model, dubbed as ALSTP, for personalized product search. Our model adopts the neural networks approach to learn and integrate the long- and short-term user preferences with the current query for the personalized product search. In particular, two attention networks are designed to distinguish which factors in the short-term as well as long-term user preferences are more relevant to the current query. This unique design enables our model to capture users' current search intentions more accurately. Our work is the first to apply attention mechanisms to integrate both long- and short-term user preferences with the given query for the personalized search. Extensive experiments over four Amazon product datasets show that our model significantly outperforms several state-of-the-art product search methods in terms of different evaluation metrics.


Ultrafast chemical dynamic behavior in highly epitaxial LaBaCo 2 O 5 + d thin fi lms


title: A 280nW, 100kHz, 1-cycle start-up time, on-chip CMOS relaxation oscillator employing a feedforward period control scheme

abstract: A sub-μW, 1-cycle start-up CMOS relaxation oscillator has been developed with a feedforward period control scheme and a digitally-controlled boost charging technique. The oscillator is implemented in 90nm CMOS and we sucessfuly have demonstrated 100kHz clock generation with ±1%-accuracy and an extremely low power consumption of 280nW.


Path Planning through BAT Algorithm in Complex Environments


title: A novel method for accurate estimation of HRV from smartwatch PPG signals

abstract: Photoplethysmography(PPG) as a non-invasive tool for monitoring various cardiovascular parameters, has become popular due to the ease of wearable integration and pervasive nature. Due to unobtrusive nature of sensor placement at wrist, smartwatches and wrist based fitness bands have gained popularity. However, any movement of the wrist along with frequent loose contacts significantly corrupts the PPG signal. Reliable peak detection from the corrupted PPG signal is essential for any further processing, as many physiological quantities such as heart rate variability (HRV) depends on the peak-to-peak distances in the PPG signal, known as the RR Series. This paper attempts to provide a robust algorithm for peak detection in noise & motion artefact corrupted PPG signals. The algorithm consists of steps to remove the baseline drift in the PPG signal using wavelet filtering and trend removal and subsequent peak detection using autcorrelation for each pseudo-periodic segment of the signal. The validation of the method is done by comparing the PPG peaks detected by the algorithm with RR series extracted from simultaneously captured ECG signal.


title: Using the Implicit Association Test to measure self-esteem and self-concept

abstract: Experiment 1 used the Implicit Association Test (IAT; A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1998) to measure self-esteem by assessing automatic associations of self with positive or negatlve valence. Confirmatory factor analysis (CFA) showed that two IAT measures defined a factor that was distinct from, but weakly correlated with, a factor defined by standard explicit (self-report) measures of self-esteem. Experiment 2 tested known-groups validity of two IAT gender self-concept measures. Compared with well-established explicit measures, the IAT measures revealed triple the difference in measured masculinity-femininity between men and women. Again, CFA revealed construct divergence between implicit and explicit measures. Experiment 3 assessed the self-esteem IAT's validity in predicting cognitive reactions to success and failure. High implicit self-esteem was associated in the predicted fashion with buffering against adverse effects of failure on two of four measures. This research developed from the assumption that distinct implicit and explicit self-esteem constructs require different measurement strategies. In particular, the research pursued implications of Greenwald and Banaji's (1995) definition of implicit self-esteem as "the introspectively unidentified (or inaccurately identified) effect of the self-attitude on evaluation of self-associated and self-dissociated objects" (p. 11 ). Greenwald and Banaji's analysis summarized a widespread recent development of the view that people process social information not only in an explicit (or aware or controlled or reflective or declarative) mode but also in an implicit (i.e., unaware, automatic, intuitive, or procedural) mode (Bargh, Chaiken, Govender, &


title: PALSAR Radiometric and Geometric Calibration

abstract: This paper summarizes the results obtained from geometric and radiometric calibrations of the Phased-Array L-Band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite, which has been in space for three years. All of the imaging modes of the PALSAR, i.e., single, dual, and full polarimetric strip modes and scanning synthetic aperture radar (SCANSAR), were calibrated and validated using a total of 572 calibration points collected worldwide and distributed targets selected primarily from the Amazon forest. Through raw-data characterization, antenna-pattern estimation using the distributed target data, and polarimetric calibration using the Faraday rotation-free area in the Amazon, we performed the PALSAR radiometric and geometric calibrations and confirmed that the geometric accuracy of the strip mode is 9.7-m root mean square (rms), the geometric accuracy of SCANSAR is 70 m, and the radiometric accuracy is 0.76 dB from a corner-reflector analysis and 0.22 dB from the Amazon data analysis (standard deviation). Polarimetric calibration was successful, resulting in a VV/HH amplitude balance of 1.013 (0.0561 dB) with a standard deviation of 0.062 and a phase balance of 0.612deg with a standard deviation of 2.66deg .


title: Informed consent does not mean rational consent: Cognitive limitations on decision‐making

abstract: If all decisions by the patient could be made on an intelligent basis; if all patients had sufficient scientific background and sufficient knowledge of the human body; if the decisions of all patients could be sufficiently free of the fear of the unknown, of superstition and other extraneous influences upon the decision‐making processes; if all patients were able to understand the physician and communicate with him; if the physician were not faced with these and various other impediments, doctor‐patient accord would be a problem of manageable proportions.1


Bootstrapping Dialog Systems with Word Embeddings


title: Rapid Distance-Based Outlier Detection via Sampling

abstract: Distance-based approaches to outlier detection are popular in data mining, as they do not require to model the underlying probability distribution, which is particularly challenging for high-dimensional data. We present an empirical comparison of various approaches to distance-based outlier detection across a large number of datasets. We report the surprising observation that a simple, sampling-based scheme outperforms state-of-the-art techniques in terms of both efficiency and effectiveness. To better understand this phenomenon, we provide a theoretical analysis why the sampling-based approach outperforms alternative methods based on k-nearest neighbor search.


title: New Designs of Bandpass Diplexer and Switchplexer Based on Parallel-Coupled Bandpass Filters

abstract: In this paper, new designs of a bandpass diplexer and switchplexer are presented. Based on the proposed design method, they can be implemented by the direct connection of two parallel-coupled bandpass filters. As a result, the circuit size and mismatch losses can be reduced compared with conventional designs. In addition, based on the proposed filter prototypes, multiple functional blocks in the RF front end can be integrated into a single component. As an example, a balun-integrated bandpass switchplexer that effectively integrates all the functional blocks between the antenna and the TX/RX amplifiers in a single-band RF transceiver is demonstrated. In this way, not only can the level of integration of the RF front end be further improved, but the circuit size and power losses can also be reduced.


title: Lips: An IDE for model driven engineering based on natural language processing

abstract: Combining both, state-of-the art natural language processing (NLP) algorithms and semantic information offered by a variety of ontologies and databases, efficient methods have been proposed that assist system designers in automatically translating text-based specifications into formal models. But due to ambiguities in natural language, these approaches usually require user interaction. Following these achievements, we consider natural language as a further input language that is used in the design flow for systems and software. Consequently, concepts from integrated development environments (IDE) as they can be found for programming languages such as Java need to be made available for natural language specifications as well. In this paper, we propose lips, an integrated development environment that is seamlessly implemented on top of Eclipse. It contains recent NLP algorithms that extract formal models suited for the Eclipse Modeling Framework and therefore provide a starting point for an ongoing implementation. Whenever user interaction is required, lips makes use of well-known IDE concepts such as markers and quick fixes thereby enabling a holistic user experience.


title: Intermediate view interpolation based on multiview video plus depth for advanced 3D video systems

abstract: A system for video on multiscopic 3D displays is considered where the data representation consists of multiview video plus scene depth. At most, 3 multiview video signals are being transmitted and used together with the depth data to generate intermediate views at the receiver. The paper presents an approach to such an intermediate view interpolation that separates unreliable image regions along depth discontinuities from reliable image regions. These image regions are processed with different algorithms and then fused to obtain the final interpolated view. In contrast to previous layered approaches, two boundary layers and one reliable layer is used. Moreover, the presented technique does not rely on 3D graphics support but uses image-based 3D warping instead. For enhanced quality intermediate view generation, hole-filling and filtering methods are described. As a result, high quality intermediate views for an existing 9-view auto-stereoscopic display are presented, which prove the suitability of the approach for advanced 3D video (3DV) systems.


title: Transfer Printing Techniques for Materials Assembly and Micro/Nanodevice Fabrication

abstract: Transfer printing represents a set of techniques for deterministic assembly of micro-and nanomaterials into spatially organized, functional arrangements with two and three-dimensional layouts. Such processes provide versatile routes not only to test structures and vehicles for scientific studies but also to high-performance, heterogeneously integrated functional systems, including those in flexible electronics, three-dimensional and/or curvilinear optoelectronics, and bio-integrated sensing and therapeutic devices. This article summarizes recent advances in a variety of transfer printing techniques, ranging from the mechanics and materials aspects that govern their operation to engineering features of their use in systems with varying levels of complexity. A concluding section presents perspectives on opportunities for basic and applied research, and on emerging use of these methods in high throughput, industrial-scale manufacturing.


title: Cellular chirality arising from the self-organization of the actin cytoskeleton

abstract: Cellular mechanisms underlying the development of left-right asymmetry in tissues and embryos remain obscure. Here, the development of a chiral pattern of actomyosin was revealed by studying actin cytoskeleton self-organization in cells with isotropic circular shape. A radially symmetrical system of actin bundles consisting of α-actinin-enriched radial fibres (RFs) and myosin-IIA-enriched transverse fibres (TFs) evolved spontaneously into the chiral system as a result of the unidirectional tilting of all RFs, which was accompanied by a tangential shift in the retrograde movement of TFs. We showed that myosin-IIA-dependent contractile stresses within TFs drive their movement along RFs, which grow centripetally in a formin-dependent fashion. The handedness of the chiral pattern was shown to be regulated by α-actinin-1. Computational modelling demonstrated that the dynamics of the RF-TF system can explain the pattern transition from radial to chiral. Thus, actin cytoskeleton self-organization provides built-in machinery that potentially allows cells to develop left-right asymmetry.


Augmented Reality using a Neural Network


title: Resistance to TMV in transgenic plants results from interference with an early event in infection.

abstract: Constitutive expression of the tobacco mosaic virus (TMV) coat protein (CP) gene in transgenic tobacco plants results in inhibition of disease symptom development following inoculation with TMV. Evidence is presented here that this protection is also observed in leaf mesophyll protoplasts isolated from these plants. Protoplasts were resistant to infection by TMV at concentrations of 10 microgram/ml to 1 mg/ml when introduced by either electroporation or polyethylene glycol-mediated inoculation. There was little protection against infection by TMV RNA and the protection was lost as the concentration of TMV RNA in the inoculum increased. When virus was incubated briefly at pH 8.0 prior to inoculation, protection broke down in a manner similar to that observed following RNA inoculation. Analogous results were obtained in experiments with whole plants. Because virus treated in this manner has presumably lost little or no CP, these results suggest that expression of the TMV CP gene in transgenic plant cells prevents TMV from uncoating. A model is presented for the mechanism of this blockage which relates these results to early events in TMV infection.


title: No-Regret Learning in Extensive-Form Games with Imperfect Recall

abstract: Counterfactual Regret Minimization (CFR) is an efficient no-regret learning algorithm for decision problems modeled as extensive games. CFR's regret bounds depend on the requirement of perfect recall: players always remember information that was revealed to them and the order in which it was revealed. In games without perfect recall, however, CFR's guarantees do not apply. In this paper, we present the first regret bound for CFR when applied to a general class of games with imperfect recall. We also show that CFR applied to any abstraction belonging to our class results in a regret bound not just for the abstract game, but for the full game as well. We verify our theory and show how imperfect recall can be used to trade a small increase in regret for a significant reduction in memory in three domains: die-roll poker, phantom tic-tac-toe, and Bluff.


title: Anomaly detection in surveillance video using motion direction statistics

abstract: A novel approach for detecting anomaly in visual surveillance system is proposed in this paper. It is composed of three parts:(a) a dense motion field and motion statistics method, (b) one-class SVM for one-class classification, (c) motion directional PCA for feature dimensionality reduction. Experiments demonstrate the effectiveness of proposed algorithm in detecting abnormal events in surveillance video, while keeping a low false alarm rate. Moreover, it works well in complicated situation where the common tracking or detection module won't work.


title: Transcript Imaging with cDNA-AFLP: A Step-by-Step Protocol

abstract: The method of cDNA-AFLP allows detection of differentially expressed transcripts using PCR. This report provides a detailed and updated protocol for the cDNA-AFLP procedure and an analysis of interactions between its various parameters. We studied the effects of PCR cycle number and template dilution level on the number of transcript derived fragments (TDFs). We also examined the use of magnetic beads to synthesise cDNA and the effect of MgCl2 concentration during amplification. Finally, we determined the detection level of the cDNA-AFLP method using TDFs of various sizes and composition. We could detect TDFs corresponding to a single copy per cell of a specific transcript in a cDNA-AFLP pattern, indicating high sensitivity of the method. Also, there was no correlation between concentration of detectable TDF and the fragment size, stressing the high stringency of the amplification reactions. Theoretical considerations and specific applications of the method are discussed.


title: S-curve profile switching method using fuzzy system for position control of DC motor under uncertain load

abstract: In the paper, we consider the motion profile for position control of DC motor. Usually, a motion profile is created only once, before DC motor is driven, by using parameters such as distance to move, maximal jerk, maximal acceleration and maximal velocity. If the uncertain load is exists, the maximal velocity can be reduced and the system may not track the desired velocity and large vibration can be occurred. We propose fuzzy s-curve profile switching method that replaces profile with more suitable profile for precise driving of the system. Immediately after the motor begin to drive, new maximal velocity is predicted based on undershoot of velocity by fuzzy system. The new profile based on the new maximal velocity is created. By applying the new profile to DC motor, as a result, we can achieve the precise position control. S-curve profile is used as a type of profile in this paper.


An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective


title: Integral Images for Block Matching

abstract: The integral image representation is a remarkable idea that permits to evaluate the sum of image values over rectangular regions of the image with four operations, regardless of the size of the region. It was first proposed under the name of summed area table in the computer graphics community by Crow’84, in order to efficiently filter texture maps. Itwas later popularized in the computer vision community by Viola & Jones’04 with its use in their real-time object detection framework. In this article we describe the integral image algorithm and study its application in the context of block matching. We investigate tradeoffs and the limits of the performance gain with respect to exhaustive block matching. Source Code The source code and the online demo are accessible from the IPOL web page of this article 1 .


title: Using Bayes to get the most out of non-significant results

abstract: No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors.


title: Support Vector Echo-State Machine for Chaotic Time-Series Prediction

abstract: A novel chaotic time-series prediction method based on support vector machines (SVMs) and echo-state mechanisms is proposed. The basic idea is replacing "kernel trick" with "reservoir trick" in dealing with nonlinearity, that is, performing linear support vector regression (SVR) in the high-dimension "reservoir" state space, and the solution benefits from the advantages from structural risk minimization principle, and we call it support vector echo-state machines (SVESMs). SVESMs belong to a special kind of recurrent neural networks (RNNs) with convex objective function, and their solution is global, optimal, and unique. SVESMs are especially efficient in dealing with real life nonlinear time series, and its generalization ability and robustness are obtained by regularization operator and robust loss function. The method is tested on the benchmark prediction problem of Mackey-Glass time series and applied to some real life time series such as monthly sunspots time series and runoff time series of the Yellow River, and the prediction results are promising


title: Preemptive RANSAC for live structure and motion estimation

abstract: A system capable of performing robust live ego-motion estimation for perspective cameras is presented. The system is powered by random sample consensus with preemptive scoring of the motion hypotheses. A general statement of the problem of efficient preemptive scoring is given. Then a theoretical investigation of preemptive scoring under a simple inlier–outlier model is performed. A practical preemption scheme is proposed and it is shown that the preemption is powerful enough to enable robust live structure and motion estimation.


title: A memory efficient reachability data structure through bit vector compression

abstract: When answering many reachability queries on a large graph, the principal challenge is to represent the transitive closure of the graph compactly, while still allowing fast membership tests on that transitive closure. Recent attempts to address this problem are complex data structures and algorithms such as Path-Tree and 3-HOP. We propose a simple alternative based on a novel form of bit-vector compression. Our starting point is the observation that when computing the transitive closure, reachable vertices tend to cluster together. We adapt the well-known scheme of word-aligned hybrid compression (WAH) to work more efficiently by introducing word partitions. We prove that the resulting scheme leads to a more compact data structure than its closest competitor, namely interval lists. In extensive and detailed experiments, this is confirmed in practice. We also demonstrate that the new technique can handle much larger graphs than alternative algorithms.


title: River Networks for Instant Procedural Planets

abstract: Realistic terrain models are required in many applications, especially in computer games. Commonly, procedural models are applied to generate the corresponding models and let users experience a wide variety of new environments. Existing algorithms generate landscapes immediately with view-dependent resolution and without preprocessing. Unfortunately, landscapes generated by such algorithms lack river networks and therefore appear unnatural. Algorithms that integrate realistic river networks are computationally expensive and cannot be used to generate a locally adaptive high resolution landscape during a fly-through. In this paper, we propose a novel algorithm to generate realistic river networks. Our procedural algorithm creates complete planets and landscapes with realistic river networks within seconds. It starts with a coarse base geometry of a planet without further preprocessing and user intervention. By exploiting current graphics hardware, the proposed algorithm is able to generate adaptively refined landscape geometry during fly-throughs.


IdentiDroid: Android can finally Wear its Anonymous Suit


title: Sleep-dependent memory consolidation

abstract: The concept of ‘sleeping on a problem’ is familiar to most of us. But with myriad stages of sleep, forms of memory and processes of memory encoding and consolidation, sorting out how sleep contributes to memory has been anything but straightforward. Nevertheless, converging evidence, from the molecular to the phenomenological, leaves little doubt that offline memory reprocessing during sleep is an important component of how our memories are formed and ultimately shaped.


title: Craniosacral Therapy for the Treatment of Chronic Neck Pain

abstract: Neck pain is a significant public health problem with 1 in 2 people experiencing neck pain at least once in their lifetime.1 Neck pain is often recurrent, of nonspecific nature, and associated with disability in both social and occupational life.2–4 For the treatment of chronic courses, evidence is still limited, as only therapeutic exercises, acupuncture, and manual therapies were recommended in recent clinical practice guidelines.5–7 On asking manual therapists about their perception and use of complementary and alternative medicine for the treatment of chronic pain conditions, one repeatedly mentioned treatment was craniosacral therapy (CST).8 ::: ::: CST is thought to be a noninvasive, mindfulness-based treatment approach using gentle manual palpation techniques to release fascial restrictions between the cranium and the sacrum.9 The craniosacral system anatomically encompasses the structures of the central nervous system including the skull, the cranial sutures, the cerebrospinal fluid, and the membranes of the brain and the spinal cord. It is influenced by and linked to the musculoskeletal system,10 and presumably to the vascular and endocrine system as well as to the sympathetic and parasympathetic nervous system.11 In the craniosacral theory, fascial restrictions within the craniosacral system lead to abnormal, arrhythmic motion of the cerebrospinal fluid. This craniosacral rhythm is assessable by palpation and quantifiable by encephalogram, myelogram, and magnetic resonance imaging.12 There is also growing evidence for fascial involvement in pain chronification. Studies have shown increased activity of fascial nociceptors within restricted connective tissue, which can contribute to remodeling processes of inflammation and fibrosis, increased tissue stiffness, muscle tension, and chronic pain.13,14 However, research on craniosacral diagnostic and treatment mechanisms revealed very heterogenous results,11,12 with only preliminary evidence supporting inherent processes of peripheral and descending pain inhibition due to gentle fascial palpation techniques.11,15,16 ::: ::: The effectiveness of craniosacral treatment on health outcomes has been shown for a number of chronic pain syndromes, but it is limited to observational designs and randomized controlled trials with low to moderate methodological quality.17–19 Efficacy studies and studies on musculoskeletal pain have not been conducted to date,20 although neck and back pain were the most frequent symptoms for which CST was requested.21 Therefore, this study aimed at investigating the efficacy of CST in chronic nonspecific neck pain in comparison with a manual sham control intervention.


ISSN:2229-6093 Micro strip Patch Antenna and its Applications: a Survey


title: Unsupervised clustering and epigenetic classification of single cells

abstract: Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.


title: The causal impact of media in financial markets

abstract: It is challenging to disentangle the causal impact of media reporting from the impact of the events being reported. We solve this problem by comparing the behaviors of investors with access to different media coverage of the same information event. We use zip codes to identify 19 mutually exclusive trading regions, corresponding to 19 large U.S. cities and local newspapers (e.g., the Houston Chronicle). For all earnings announcements of S&P 500 Index firms, we find that local media coverage strongly predicts local trading, after controlling for characteristics of the earnings surprise, firm, local investors, and reporting newspaper(s). Moreover, the local trading-local coverage effect: 1) depends precisely on the specific timing of local reporting (e.g., one day after the earnings announcement, two days afterward, etc.) and 2) disappears entirely during extreme weather events, which leaves media content unchanged, but disrupts transmission to investors.


title: The use of electromyography and magnetic resonance imaging to evaluate a core strengthening exercise programme

abstract: BACKGROUND ::: The question that was asked in the study was whether a training routine based on curl-up exercises with a load provided by body mass of the person increases local muscle strength or local muscle endurance. ::: ::: ::: OBJECTIVE ::: The aim of this study was to evaluate the effect of 4 weeks training based on a small load and low movement velocity on electrical activity (EMG), cross-sectional area (CSA) of core stabilisers. ::: ::: ::: METHODS ::: The EMG activity was measured in the rectus abdominis (RA), obliquus abdominis externus and erector spinae (ES) muscles. CSA of the muscles: RA, anterolateral abdominal, psoas major, quadratus lumborum, ES, and multifidus at the level of L3-L4 were measured too. ::: ::: ::: RESULTS ::: The training increased the CSA and thickness in most of the muscles studied. Statistically significant correlation was found only for the ES circumference (left side) and EMG activity for the right side (r= 0.627, p= 0.022) and left side (r= 0.624, p= 0.023). ::: ::: ::: CONCLUSIONS ::: The training programme resulted in a increase in the number of curl-up repetitions revealing an endurance increase in abdominal muscles. Furthermore, there was a increase in the EMG activity of the RA. An increase of the CSA of all tested muscles showed an increase of muscle active force.


title: An Evaluation Of Women's Attitudes Towards Anger In Other Women And The Impact Of Such On Their Own Anger Expression Style

abstract: AN EVALUATION OF WOMEN’S ATTITUDES TOWARDS ANGER IN OTHER WOMEN AND THE IMPACT OF SUCH ON THEIR OWN ANGER EXPRESSION STYLE by NANCY PRAILL May 2010 Advisor: Dr. Gonzalez-Prendes Major: Social Work Degree: Master’s of Social Work Research suggests that gender socialization contributes to how women perceive anger in general and anger in other women, and to how women express anger themselves. The purpose of this study was to evaluate whether there is a relationship between how women perceive anger in other women and how they express anger themselves. Women age 18-75 (N=228) completed a vignette based on the vignettes, and theory of anger diversion in Cox, Van Velsor & Hulgus (2004), as well as the STAXI-2. Spearman Rho correlations found a relationship between how women express anger and how likely they feel other women would react in a similar manner. Comparisons were made and correlations were also found between how appropriate women gauge different anger responses, how likely they felt other women would engage in a similar manner, and women’s own anger expression style. Implications for practice and future research are included.


title: A 1.8 V LDO voltage regulator with foldback current limit and thermal protection

abstract: This paper introduces the design of a l.8 V low dropout voltage regulator (LDO) and a foldback current limit circuit which limits the output current to 3 mA when load over-current occurs. The LDO was implemented in a 0.18 μm CMOS technology. The measured result reveals that the LDO's power supply rejection (PSR) is about −58 dB and −54 dB at 20 Hz and 1 kHz respectively, the response time is 4 μs and the quiescent current is 20 μA. The designed LDO regulator can work with a supply voltage down to 2.0 V with a drop-out voltage of 200 mV at a maximum load current of 240 mA.


title: Image Annotation and Two Paths to Text Illustration

abstract: This paper describes our participation to the ImageCLEF 2016 scalable concept image annotation main task and Text Illustration teaser. Regarding image annotation, we focused on better localizing the detected features. For this, we identified the saliency of the image to collect a list of potential interesting places into the image. We also added a specific human attribute detector that boosted the results of the best performing team in 2015. For the text illustration, we proposed two complementary approaches. The first one relies on semantic signatures that give a textual description of an image. This description is further matched to the textual query. The second approach learns a common latent space, in which visual and textual features are directly comparable. We propose a robust description, as well as the use of an auxiliary dataset to improve retrieval. While the first approach only uses external data, the second one was mainly learned from the provided training dataset.


title: Style in the long tail: discovering unique interests with latent variable models in large scale social E-commerce

abstract: Purchasing decisions in many product categories are heavily influenced by the shopper's aesthetic preferences. It's insufficient to simply match a shopper with popular items from the category in question; a successful shopping experience also identifies products that match those aesthetics. The challenge of capturing shoppers' styles becomes more difficult as the size and diversity of the marketplace increases. At Etsy, an online marketplace for handmade and vintage goods with over 30 million diverse listings, the problem of capturing taste is particularly important -- users come to the site specifically to find items that match their eclectic styles. In this paper, we describe our methods and experiments for deploying two new style-based recommender systems on the Etsy site. We use Latent Dirichlet Allocation (LDA) to discover trending categories and styles on Etsy, which are then used to describe a user's "interest" profile. We also explore hashing methods to perform fast nearest neighbor search on a map-reduce framework, in order to efficiently obtain recommendations. These techniques have been implemented successfully at very large scale, substantially improving many key business metrics.


title: Disability Pattern in Chronic Migraine With Medication Overuse: A Comparison With Migraine Without Aura

abstract: OBJECTIVES ::: To assess headache-related disability in chronic migraine with medication overuse (CMO), as defined by Silberstein and Lipton (1996, 2001) in comparison to that caused by migraine without aura, investigating similarities and differences in disability patterns in these two conditions. ::: ::: ::: BACKGROUND ::: It is well known that migraine has a marked impact on daily activities. Less information is available on the functional consequences of chronic migraine. ::: ::: ::: METHODS ::: This was a prospective study on 247 patients with CMO and 264 with migraine without aura. All completed the validated Italian version of the Migraine Disability Assessment questionnaire (MIDAS). The MIDAS total score and scores of each question (investigating total or partial inability to perform different daily activities) were evaluated, and compared in the two groups using the Mann-Whitney rank sum test. We also evaluated the number of missed days (question 1) and days with significantly reduced effectiveness at work (question 2) and in nonwork activities (questions 3, 4, and 5), using descriptive statistics. ::: ::: ::: RESULTS ::: MIDAS total score in the CMO group was rather high (mean 84.1, median 70) and significantly higher than in the migraine group (mean 23.4, median 19). CMO patients also reported higher scores than migraine patients in MIDAS questions investigating disability in different domains, the differences being highly significant for questions 3, 4, and 5. In both CMO and migraine the number of days with total or significant disability in nonwork activities was higher than the number of days with total or significant disability in work activities, and days spent at work with significantly reduced effectiveness were more than work days missed. ::: ::: ::: CONCLUSIONS ::: Our findings show that CMO has a profound effect on patients' daily functioning. They suggest that differences between CMO and migraine are mainly quantitative, while the two groups are similar in terms of disability pattern. These results are consistent with the fact that CMO may be an evolution of episodic migraine, characterized by increase in headache frequency and more evident disability in performing different daily duties.


Quality circles and institutional culture: a proposal


title: Leveraging multi-aspect time-related influence in location recommendation

abstract: Point-Of-Interest (POI) recommendation aims to mine a user’s visiting history and find her/his potentially preferred places. Although location recommendation methods have been studied and improved pervasively, the challenges w.r.t employing various influences including temporal aspect still remain unresolved. Inspired by the fact that time includes numerous granular slots (e.g. minute, hour, day, week and etc.), in this paper, we define a new problem to perform recommendation through exploiting all diversified temporal factors. In particular, we argue that most existing methods only focus on a limited number of time-related features and neglect others. Furthermore, considering a specific granularity (e.g. time of a day) in recommendation cannot always apply to each user or each dataset. To address the challenges, we propose a probabilistic generative model, named after Multi-aspect Time-related Influence (MATI) to promote the effectiveness of the location (POI) recommendation task. We also develop an effective optimization algorithm based on Expectation Maximization (EM). Our MATI model firstly detects a user’s temporal multivariate orientation using her check-in log in Location-based Social Networks(LBSNs). It then performs recommendation using temporal correlations between the user and proposed locations. Our method is applicable to various types of the recommendation models and can work efficiently in multiple time-scales. Extensive experimental results on two large-scale LBSN datasets verify the effectiveness of our method over other competitors. Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval]: Information filtering; H.2.8 [Database Applications]: Data mining; J.4 [Computer Applications]: Social and Behavior Sciences


title: Semi-supervised Spectral Clustering for Classification

abstract: We propose a Classification Via Clustering (CVC) algorithm which enables existing clustering methods to be efficiently employed in classification problems. In CVC, training and test data are co-clustered and class-cluster distributions are used to find the label of the test data. To determine an efficient number of clusters, a Semi-supervised Hierarchical Clustering (SHC) algorithm is proposed. Clusters are obtained by hierarchically applying two-way NCut by using signs of the Fiedler vector of the normalized graph Laplacian. To this end, a Direct Fiedler Vector Computation algorithm is proposed. The graph cut is based on the data structure and does not consider labels. Labels are used only to define the stopping criterion for graph cut. We propose clustering to be performed on the Grassmannian manifolds facilitating the formation of spectral ensembles. The proposed algorithm outperformed state-of-the-art image-set classification algorithms on five standard datasets.


title: The Statistical Analysis of fMRI Data

abstract: In recent years there has been explosive growth in the number of neuroimaging studies performed using functional Magnetic Resonance Imaging (fMRI). The field that has grown around the acquisition and analysis of fMRI data is intrinsically interdisciplinary in nature and involves contributions from researchers in neuroscience, psychology, physics and statistics, among others. A standard fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. Statistics plays a crucial role in understanding the nature of the data and obtaining relevant results that can be used and interpreted by neuroscientists. In this paper we discuss the analysis of fMRI data, from the initial acquisition of the raw data to its use in locating brain activity, making inference about brain connectivity and predictions about psychological or disease states. Along the way, we illustrate interesting and important issues where statistics already plays a crucial role. We also seek to illustrate areas where statistics has perhaps been underutilized and will have an increased role in the future.


title: Virtual reality: A brief survey

abstract: Virtual Reality is based on the notion of immersion i.e. a new technological advancement in the field of human-machine interaction bringing it closer to real life. It is a technology for simulation of a real or virtual world in which one can immerse, touch, & sense the objects with the virtual presence in that 3-D world. This paper reviews the ideas & concepts behind the architectural representation, supporting software & hardware implementations, various categorized languages & modelling tools etc. This paper also studies current research objectives, comparison with other virtualized environments, development trends of VR, & modeling methods. Based on the analysis of structure & functioning of virtual reality environment, this paper presents various applications of virtual reality & different categories of issues related to it.