title: Virtual Reality Social Cognition Training for children with high functioning autism

abstract: Virtual reality appears to be a promising and motivating platform to safely practice and rehearse social skills for children with Autism Spectrum Disorders (ASD). However, the literature to date is subject to limitations in elucidating the effectiveness of these virtual reality interventions. This study investigated the impact of a Virtual Reality Social Cognition Training to enhance social skills in children with ASD. Thirty children between the ages of 7-16 diagnosed with ASD completed 10, 1-h sessions across 5 weeks. Three primary domains were measured pre-post: emotion recognition, social attribution, attention and executive function. Results revealed improvements on measures of emotion recognition, social attribution, and executive function of analogical reasoning. These preliminary findings suggest that the use of a virtual reality platform offers an effective treatment option for improving social impairments commonly found in ASD. The VR-SCT provides an interactive and stimulating approach for children with ASD.The VR-SCT can improve social cognitive skills in children with ASD.Training improved emotion recognition, social attribution, and executive function.The training is a safe and socially non-threatening platform.


Robocup Rescue Robot League Competition


title: Optimal charging strategies in lithium-ion battery

abstract: There is a strong need for advanced control methods in battery management systems, especially in the plug-in hybrid and electric vehicles sector, due to cost and safety issues of new high-power battery packs and high-energy cell design. Limitations in computational speed and available memory require the use of very simple battery models and basic control algorithms, which in turn result in suboptimal utilization of the battery. This work investigates the possible use of optimal control strategies for charging. We focus on the minimum-time charging problem, where different constraints on internal battery states are considered. Based on features of the open-loop optimal charging solution, we propose a simple one-step predictive controller, which is shown to recover the time-optimal solution, while being feasible for real-time computations. We present simulation results suggesting a decrease in charging time by 50% compared to the conventional constant-current / constant-voltage method for lithium-ion batteries.


title: Influencing choice without awareness

abstract: Abstract Forcing occurs when a magician influences the audience’s decisions without their awareness. To investigate the mechanisms behind this effect, we examined several stimulus and personality predictors. In Study 1, a magician flipped through a deck of playing cards while participants were asked to choose one. Although the magician could influence the choice almost every time (98%), relatively few (9%) noticed this influence. In Study 2, participants observed rapid series of cards on a computer, with one target card shown longer than the rest. We expected people would tend to choose this card without noticing that it was shown longest. Both stimulus and personality factors predicted the choice of card, depending on whether the influence was noticed. These results show that combining real-world and laboratory research can be a powerful way to study magic and can provide new methods to study the feeling of free will.


title: The Blockchain as a Decentralized Security Framework [Future Directions]

abstract: The blockchain is emerging as one of the most propitious and ingenious technologies of cybersecurity. In its germinal state, the technology has successfully replaced economic transaction systems in various organizations and has the potential to revamp heterogeneous business models in different industries. Although it promises a secure distributed framework to facilitate sharing, exchanging, and the integration of information across all users and third parties, it is important for the planners and decision makers to analyze it in depth for its suitability in their industry and business applications. The blockchain should be deployed only if it is applicable and provides security with better opportunities for obtaining increased revenue and reductions in cost. This article presents an overview of this technology for the realization of security across distributed parties in an impregnable and transparent way.


title: 39 kDa receptor-associated protein is an ER resident protein and molecular chaperone for LDL receptor-related protein.

abstract: The low density lipoprotein receptor-related protein (LRP) is a multifunctional endocytic receptor with the ability to bind and endocytose several structurally and functionally distinct ligands. A 39 kDa receptor-associated protein (RAP) inhibits all ligand interactions with LRP in vitro. In the present study, we demonstrate that RAP is an endoplasmic reticulum (ER) resident protein. The tetrapepetide sequence HNEL at the C-terminus of RAP is both necessary and sufficient for RAP retention within the ER. Metabolic labeling combined with cross-linking studies show that RAP interacts with LRP in vivo. Pulse-chase analysis reveals that this association is transient early in the secretory pathway and coincides with LRP aggregation and reduced ligand binding activity. Both internal triplicated LRP binding domains on RAP and multiple RAP binding domains on LRP appear to contribute to the aggregation of LRP and RAP. Dissociation of RAP from LRP results from the lower pH encountered later in the secretory pathway and correlates with an increase in LRP ligand binding activity. Taken together, our results thus suggest that RAP functions intracellularly as a molecular chaperone for LRP and regulates its ligand binding activity along the secretory pathway.


title: Dynamic cluster-based over-demand prediction in bike sharing systems

abstract: Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.


title: A Truthful Mechanism for Value-Based Scheduling in Cloud Computing

abstract: We introduce a novel pricing and resource allocation approach for batch jobs on cloud systems. In our economic model, users submit jobs with a value function that specifies willingness to pay as a function of job due dates. The cloud provider in response allocates a subset of these jobs, taking into advantage the flexibility of allocating resources to jobs in the cloud environment. Focusing on social-welfare as the system objective (especially relevant for private or in-house clouds), we construct a resource allocation algorithm which provides a small approximation factor that approaches 2 as the number of servers increases. An appealing property of our scheme is that jobs are allocated non-preemptively, i.e., jobs run in one shot without interruption. This property has practical significance, as it avoids significant network and storage resources for checkpointing. Based on this algorithm, we then design an efficient truthful-in-expectation mechanism, which significantly improves the running complexity of black-box reduction mechanisms that can be applied to the problem, thereby facilitating its implementation in real systems.


title: Side-channel leakage of masked CMOS gates

abstract: There are many articles and patents on the masking of logic gates. However, the existing publications assume that a masked logic gate switches its output no more than once per clock cycle. Unfortunately, this assumption usually does not hold true in practice. ::: ::: In this article, we show that glitches occurring in circuits of masked gates make these circuits susceptible to classical first-order DPA attacks. Besides a thorough theoretical analysis of the DPA-resistance of masked gates in the presence of glitches, we also provide simulation results that confirm the theoretical elaborations. Glitches occur in every CMOS circuit. Consequently, the currently known masking schemes for CMOS gates do not prevent DPA attacks.


Computational Mechanisms For Pun Generation


title: Modeling and Control of the Twisted String Actuation System

abstract: The innovative actuation concept presented in this paper allows the implementation of powerful, simple, compact, and light-weight tendon-based driving systems, using as actuators small-size dc motors characterized by high speed and low torque. Due to its properties, this actuation system is very well suited for implementation in highly integrated robotic devices. The basic working principle of this novel actuation system is introduced, and the constitutive equations of the system are given, together with their experimental validation. Driven by the necessity of controlling the actuation force in the robotic hand, the problem of tracking a desired force profile is tackled. With the aim of guaranteeing a high level of robustness against disturbances, a control algorithm based on a second-order sliding manifold has first been evaluated by means of simulations and then validated by experiments. The results obtained with this simple and compact actuation system demonstrate its suitability for use in robotic devices such as robotic hands.


title: Who Is Responsible for Security and Privacy in the Cloud

abstract: Cloud computing is becoming increasingly attractive both in commercial and government sectors because of the significant cost reduction in their IT operations. The technical benefits of cloud environment (and its service-oriented capabilities) are the availability of computing resources those can quickly be engaged for service execution and released when no longer needed. As the cloud services moving to the mainstream computing, the issues of ownership and the chain of custody of customer data are becoming very critical. In this talk, I will discuss various cloud security issues, and the role and responsibilities of cloud service providers since the secure cloud environment is essential for providing uninterrupted services to customers. This talk will introduce various metrics for cyber security coverage, and demonstrate a tool, called MEGHNAD for estimating security coverage for cloud services. This tool can serve as a specialized Cloud Doctor in prescribing the right combination of security tools for different cloud services and according to the level of security assurance required. It will useful to a cloud provider who wants to incorporate security insurance as part of their Service Level Agreements (SLA).


title: Deep Local Video Feature for Action Recognition

abstract: We investigate the problem of representing an entire video using CNN features for human action recognition. End-to-end learning of CNN/RNNs is currently not possible for whole videos due to GPU memory limitations and so a common practice is to use sampled frames as inputs along with the video labels as supervision. However, the global video labels might not be suitable for all of the temporally local samples as the videos often contain content besides the action of interest. We therefore propose to instead treat the deep networks trained on local inputs as local feature extractors. The local features are then aggregated to form global features which are used to assign video-level labels through a second classification stage. We investigate a number of design choices for this local feature approach. Experimental results on the HMDB51 and UCF101 datasets show that a simple maximum pooling on the sparsely sampled local features leads to significant performance improvement.


On Local Interpolation of DFT Outputs by


title: Self-Organizing Pervasive Online Communities

abstract: Social software systems have widely been employed in the process of online community forming in order to support collaborative user participation. Nevertheless, the discovery of people with the same interests or a similar context has still to be accomplished manually by the users themselves. Moreover, the inherent dynamic nature of community forming with regards to changing user interests and participation in a pervasive setting increases the needs to automatically adapt communities to the current situation of the user. We introduce a framework for the development of user-centric community platforms supporting the flexible and adaptive management of user communities. For this purpose, we make use of the collaborative synergies of social software and apply advanced features such as recommendation and personalization to realize self-organization of online communities


title: Chemistry and Biological Activities of Ginkgo Biloba

abstract: ABSTRACT: Ginkgo biloba L. is the solo member of the Ginkgoaceae family, and its leaf extract (EGb) has been clinically used for the treatment of cerebral insufficiency. Several experimental studies have demonstrated that EGb has neuroprotective properties against various age-related physiological changes and cerebral injuries induced by ischemia, edema and apoptosis. The major bioactive principles of EGb are recognized to be flavonoids and terpenoids, ginkgolides and bilobalide. Pharmacological actions of EGb and its constituents indude platelet-activating factor antagonism, anti-oxidant and free radical scavenging properties, as well as modulation of neurotransmission, cerebral glucose and lipid metabolism and cerebral membrane fluidity. These effects are involved in the mechanism related to the protective effect of the extract in the central nervous system.


title: Evaluating Digital Forensic Options for the Apple iPad

abstract: The iPod Touch, iPhone and iPad from Apple are among the most popular mobile computing platforms in use today. These devices are of forensic interest because of their high adoption rate and potential for containing digital evidence. The uniformity in their design and underlying operating system (iOS) also allows forensic tools and methods to be shared across product types. This paper analyzes the tools and methods available for conducting forensic examinations of the Apple iPad. These include commercial software products, updated methodologies based on existing jailbreaking processes and the analysis of the device backup contents provided by iTunes. While many of the available commercial tools offer promise, the results of our analysis indicate that most comprehensive examination of the iPad requires jailbreaking to perform forensic duplication and manual analysis of its media content.


title: A review of smart homes - Present state and future challenges

abstract: In the era of information technology, the elderly and disabled can be monitored with numerous intelligent devices. Sensors can be implanted into their home for continuous mobility assistance and non-obtrusive disease prevention. Modern sensor-embedded houses, or smart houses, cannot only assist people with reduced physical functions but help resolve the social isolation they face. They are capable of providing assistance without limiting or disturbing the resident's daily routine, giving him or her greater comfort, pleasure, and well-being. This article presents an international selection of leading smart home projects, as well as the associated technologies of wearable/implantable monitoring systems and assistive robotics. The latter are often designed as components of the larger smart home environment. The paper will conclude by discussing future challenges of the domain.


title: Beyond Bitcoin Enabling Smart Government Using Blockchain Technology

abstract: The new technology Bitcoin has got a lot of attention since it was presented in late 2008 and implemented early 2009. However, the main attention has been to the currency and not so much the underlying blockchain technology. This paper argues that we need to look beyond the currency and investigate the potential use of the blockchain technology to enable smarter governments by utilizing the secure, distributed, open, and inexpensive database technology. The technology is discussed in the perspective of an information infrastructure to investigate its full potential. After a literature review of Bitcoin publications, with a special emphasis on eGovernment literature, the paper presents a relevant use case highlighting the innovation potential of the new technology. The literature review shows that Bitcoin is absent from the e-Government literature. The use case presented shows that Bitcoin could be a promising technology for validating many types of persistent documents in public sector.


title: Applications of agent-based systems in intelligent manufacturing: An updated review

abstract: Abstract Agent technology has been recognized as a promising paradigm for next generation manufacturing systems. Researchers have attempted to apply agent technology to manufacturing enterprise integration, enterprise collaboration (including supply chain management and virtual enterprises), manufacturing process planning and scheduling, shop floor control, and to holonic manufacturing as an implementation methodology. This paper provides an update review on the recent achievements in these areas, and discusses some key issues in implementing agent-based manufacturing systems such as agent encapsulation, agent organization, agent coordination and negotiation, system dynamics, learning, optimization, security and privacy, tools and standards.


title: Positioning Accuracy of Vehicle Trajectories for Road Applications

abstract: Global Navigation Satellite Systems (GNSS) has become a kind of positioning standard due to the high penetration rate of this technology on mass market ITS applications. However, this positioning technique remains a real challenge for very demanding services. This paper reports on a practical and methodological approach for the evaluation of the GNSS positioning and attitude of vehicles in real life conditions. Test scenarios have been set up with several positioning sensors mounted on a vehicle for the collection of raw data on different road sections. The measurement of a high quality reference trajectory allowed to estimate position accuracy under different environmental conditions. We will show in detail the results and identify some typical situations where the quality of GNSS-only positioning is reduced and may impact the level of ITS services, e.g. road user charging or safety applications.


title: An efficient ADMM algorithm for multidimensional anisotropic total variation regularization problems

abstract: Total variation (TV) regularization has important applications in signal processing including image denoising, image deblurring, and image reconstruction. A significant challenge in the practical use of TV regularization lies in the nondifferentiable convex optimization, which is difficult to solve especially for large-scale problems. In this paper, we propose an efficient alternating augmented Lagrangian method (ADMM) to solve total variation regularization problems. The proposed algorithm is applicable for tensors, thus it can solve multidimensional total variation regularization problems. One appealing feature of the proposed algorithm is that it does not need to solve a linear system of equations, which is often the most expensive part in previous ADMM-based methods. In addition, each step of the proposed algorithm involves a set of independent and smaller problems, which can be solved in parallel. Thus, the proposed algorithm scales to large size problems. Furthermore, the global convergence of the proposed algorithm is guaranteed, and the time complexity of the proposed algorithm is O(dN/e) on a d-mode tensor with N entries for achieving an e-optimal solution. Extensive experimental results demonstrate the superior performance of the proposed algorithm in comparison with current state-of-the-art methods.


title: Automatic identification of species with neural networks

abstract: A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification.


title: UAV-Enabled Communication Using NOMA

abstract: Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication. This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwidth constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e., UAV altitude, transmit antenna beamwidth, power allocation, and bandwidth allocation for multiple users. A path-following algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective path-following algorithms are developed to solve them. The numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization.


title: Effective Visualization of Large Multidimensional Datasets

abstract: A new method for assisting with the visualization of large multidimensional datasets is proposed. We classify datasets with more than one million elements as large. Multidimensional data elements are elements with two or more dimensions, each of which is at least binary. Multidimensional data visualization involves representation of multidimensional data elements in a low dimensional environment, such as a computer screen or printed media. Traditional visualization techniques are not well suited to solving this problem. ::: Our data visualization techniques are based in large part on a field of cognitive psychology called preattentive processing. Preattentive processing is the study of visual features that are detected rapidly and with little effort by the human visual system. Examples include hue, orientation, form, intensity, and motion. We studied ways of extending and applying research results from preattentive processing to address our visualization requirements. We used our investigations to build visualization tools that allow a user to very rapidly and accurately perform exploratory analysis tasks. These tasks include searching for target elements, identifying boundaries between groups of common elements, and estimating the number of elements that have a specific visual feature. Our experimental results were positive, suggesting that dynamic sequences of frames can be used to explore large amounts of data in a relatively short period of time. ::: Recent work in both scientific visualization and database systems has started to address the problems inherent in managing large scientific datasets. One promising technique is knowledge discovery, "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data". We hypothesise that knowledge discovery can be used as a filter to reduce the amount of data sent to the visualization tool. Data elements that do not belong to a user-chosen group of interest can be discarded, the dimensionality of individual data elements can be compressed, and previously unknown trends and relationships can be discovered and explored. ::: We illustrate how our techniques can be used by applying them to real-world data and tasks. This includes the visualization of simulated salmon migration results, computerized tomography medical slices, and environmental datasets that track ocean and atmospheric conditions.


title: A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles

abstract: Precise and robust localization in a large-scale outdoor environment is essential for an autonomous vehicle. In order to improve the performance of the fusion of GNSS (Global Navigation Satellite System)/IMU (Inertial Measurement Unit)/DMI (Distance-Measuring Instruments), a multi-constraint fault detection approach is proposed to smooth the vehicle locations in spite of GNSS jumps. Furthermore, the lateral localization error is compensated by the point cloud-based lateral localization method proposed in this paper. Experiment results have verified the algorithms proposed in this paper, which shows that the algorithms proposed in this paper are capable of providing precise and robust vehicle localization.


title: A Supervised Fine-Grained Sentiment Analysis System for Online Reviews

abstract: Sentiment analysis, as a heatedly-discussed research topic in the area of information extraction, has attracted more attention from the beginning of this century. With the rapid development of the Internet, especially the rising popularity of Web2.0 technology, network user has become not only the content maker, but also the receiver of information. Meanwhile, benefiting from the development and maturity of the technology in natural language processing and machine learning, we can widely employ sentiment analysis on subjective texts. In this paper, we propose a supervised learning method on fine-grained sentiment analysis to meet the new challenges by exploring new research ideas and methods to further improve the accuracy and practicability of sentiment analysis. First, this paper presents an improved strength computation method of sentiment word. Second, this paper introduces a sentiment information joint recognition model based on Conditional Random Fields and analyzes the related knowledge of the basi...


title: The Microstructure of Rapidly Solidified Al-Zn-Mg-Cu Alloys with Zr Addition

abstract: The effect of rapid solidification on the microstructure of Al-Zn-Mg-Cu alloys with 8 wt.% Zn-2 wt.% Mg-2.3 wt.% Cu and 0.2 or 0.5 wt.% of Zr additions were investigated using X-ray diffraction measurements (XRD), scanning (SEM) and transmission electron microscopy (TEM) combined with energy dispersive X-ray (EDX) microanalysis. Rapidly solidified ribbons with thickness of 70-100 m were performed by melt spinning technique. The mould cast alloys as well as the melt spun ribbons revealed dendritic microstructure of (Al) solid solution and η Mg(Zn,Cu)2 phase in interdendritic areas. The refinement of the microstructure and reduction of the volume fraction of the η phase up to 1.7%, as compared to 4% in the mould cast alloys was observed in the ribbons. Copper dissolution up to about 20 wt % in the η phase causes a decrease of the lattice parameters. The Al3Zr primary precipitates were observed in the mould cast alloy containing 0.5 wt % of Zr while in the ribbons all zirconium dissolved in the aluminium solid solution.


title: Should we seed the galaxy with life

abstract: If life isn't out there already, we could give it a hand by building spacecraft to spread Earth's microbes far and wide


title: Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages

abstract: We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magnitude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguistic phenomenon of tense. We produce analysis results for more than 1000 languages, conducting - to the best of our knowledge - the largest crosslingual computational study performed to date. We extend existing methodology for leveraging parallel corpora for typological analysis by overcoming a limiting assumption of earlier work: We only require that a linguistic feature is overtly marked in a few of thousands of languages as opposed to requiring that it be marked in all languages under investigation.


title: A simple and flexible volume rendering framework for graphics-hardware-based raycasting

abstract: In this work we present a flexible framework for GPU-based volume rendering. The framework is based on a single pass volume raycasting approach and is easily extensible in terms of new shader functionality. We demonstrate the flexibility of our system by means of a number of high-quality standard and nonstandard volume rendering techniques. Our implementation shows a promising performance in a number of benchmarks while producing images of higher accuracy than obtained by standard pre-integrated slice-based volume rendering.


title: PID CONTROLLER TUNING SCHEME FOR TWIN ROTOR MULTI-INPUT MULTI-OUTPUT SYSTEM BASED PARTICLE SWARM OPTIMIZATION APPROACH

abstract: This paper presents a new intelligent control scheme which utilizes particle swarm optimization (PSO) for off-line tuning of proportionalintegral-derivative (PID) controller for the twin rotor multi-input multioutput system (TRMS). The control objective is to make the beam of the TRMS move quickly and accurately to the desired attitudes. The TRMS exhibits MIMO characteristics, high order non-linearity, significant cross coupling and inaccessibility of some of its states and outputs for measurements. PSO Algorithm is successfully implemented to this problem. Experimental and simulated results of the developed PID controller for a twin rotor system are given to demonstrate its effectiveness. Satisfactory results are anticipated in the experimental as well as in the simulation results. In an attempt to evaluate the performance of the developed controller an experimental and simulated comparative assessments with the conventionally PID tuned method (Ziegler–Nichols method) has been conducted. The results of the PID controller based PSO reveals better performances indices than the other conventional controller. KYWORDS—Evolutionary Computing; Particle swarm algorithm; PID controller; TRMS


title: Threat-related attentional bias in anxious and nonanxious individuals: A metaanalytic study

abstract: This meta-analysis of 172 studies (N = 2,263 anxious, N = 1,768 nonanxious) examined the boundary conditions of threat-related attentional biases in anxiety. Overall, the results show that the bias is reliably demonstrated with different experimental paradigms and under a variety of experimental conditions, but that it is only an effect size of d = 0.45. Although processes requiring conscious perception of threat contribute to the bias, a significant bias is also observed with stimuli outside awareness. The bias is of comparable magnitude across different types of anxious populations (individuals with different clinical disorders, high-anxious nonclinical individuals, anxious children and adults) and is not observed in nonanxious individuals. Empirical and clinical implications as well as future directions for research are discussed.


title: Automatic segmentation of calcified plaques and vessel borders in IVUS images

abstract: Objective ::: Intravascular ultrasound (IVUS) is a diagnostic imaging technique for tomographic visualization of coronary arteries. Automatic analysis of IVUS images is difficult due to speckle noise, artifacts of the catheter, and shadows generated by calcifications. We designed and implemented a system for automated segmentation of coronary artery IVUS images.


title: Finding available parking spaces made easy

abstract: We discuss the problem of predicting the number of available parking spaces in a parking lot. The parking lot is modeled by a continuous-time Markov chain, following Caliskan, Barthels, Scheuermann, and Mauve. The parking lot regularly communicates the number of occupied spaces, capacity, arrival and parking rate through a vehicular ad-hoc network. The navigation system in the vehicle has to compute from this data the probability of an available parking space upon arrival. We derive a structural result that considerably simplifies the computation of the transition probabilities in the navigation system of the vehicle.


title: DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN

abstract: Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task. In particular, we propose to oversample infrequent normal samples - normal samples that occur with small probability, e.g., rare normal events. We show that these samples are responsible for false positives in anomaly detection. However, oversampling of infrequent normal samples is challenging for real-world high-dimensional data with multimodal distributions. To address this challenge, we propose to use a GAN variant known as the adversarial autoencoder (AAE) to transform the high-dimensional multimodal data distributions into low-dimensional unimodal latent distributions with well-defined tail probability. Then, we systematically oversample at the `edge' of the latent distributions to increase the density of infrequent normal samples. We show that our oversampling pipeline is a unified one: it is generally applicable to datasets with different complex data distributions. To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection. We validate our method by demonstrating consistent improvements across several real-world datasets.


title: Stop Band Filter by Using Hybrid Quasi-periodic One Dimensional Photonic Crystal in Microwave Domain

abstract: Hybrid quasi-periodic multilayer structures constructed from high-low indices- contrast (Roger and Air) quarter-wavelength layers are used in novel multilayer systems. The Quasi-periodic hetero-structures, formed by concatenating hybrid quasi-periodic (generalized Fibonacci structures GF (m,n) and generalized Thue-Morse structures GT-M(m,n); m and n are integer) sequences of different order have properties markedly different from classic systems. In this study, the transmission properties of hybrid quasi-periodic photonic crystals with defects are studied by using a Matrix method approach (MM). Results are presented for normal incident wave with TE polarizations. By increasing the parameter n and m of thus structures, the corresponding transmission properties exhibit interesting properties in microwave domain. We propose a narrow stop band polychromatic filter that covers the frequency range of Global System for Mobile Communication (GSM).


title: Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs

abstract: Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used FFT (fast Fourier transform)-based spectrum estimation method.


title: InTml: a description language for VR applications

abstract: We present the Interaction Technique Markup Language (InTml), a profile on top of the core X3D that describes 3D interaction techniques (InTs) and hardware platforms. InTml makes 3D InTs easier to understand, compare, and integrate in complete virtual reality (VR) applications. InTml can be used as a front end for any VR toolkit, so InTml documents that plug together 3D InTs, VR objects, and devices can be fully described and executed.


Filter versus Wrapper Feature Subset Selection in Large Dimensionality Micro array: A Review


title: Multiagent Path Finding With Persistence Conflicts

abstract: Multiagent path finding is the problem of finding paths for a set of agents—one for each agent—in a graph that the agents navigate simultaneously. Agents must navigate from their individual start to goal vertices without any collision. We argue that the prevalent treatment of path conflicts in the literature is incomplete for applications, such as computer games and crowd simulations, and extend the definition of path conflicts to accommodate cases where agents persist in their intermediate locations and even after reaching their goals. We show that an existing algorithm, conflict-based search (CBS), can be extended to handle these cases while preserving its optimality. Experiments show that our variant of CBS generates fewer nodes and runs faster than a competing algorithm.


title: Entity resolution acceleration using the automata processor

abstract: Entity Resolution (ER), the process of finding identical entities across different databases, is critical to many information-integration applications. As sizes of databases explode in the big-data era, it becomes computationally expensive to recognize identical entities among all records with variations allowed across multiple databases. Profiling results show that approximate matching is the primary bottleneck. The Automata Processor (AP), an efficient and scalable semiconductor architecture for parallel automata processing, provides a new opportunity for hardware acceleration for ER. We propose an AP-accelerated ER solution, which accelerates the performance bottleneck of fuzzy matching for similar but potentially inexactly-matched names, and use several different real-world applications to illustrate its effectiveness. We compared the proposed method with several conventional methods and achieved both promising speedups and better accuracy (more correct pairs and less generalized merge distance cost) for different datasets.


title: A Deep Feature based Multi-kernel Learning Approach for Video Emotion Recognition

abstract: In this paper, we describe our proposed approach for participating in the Third Emotion Recognition in the Wild Challenge (EmotiW 2015). We focus on the sub-challenge of Audio-Video Based Emotion Recognition using the AFEW dataset. The AFEW dataset consists of 7 emotion groups corresponding to the 7 basic emotions. Each group includes multiple videos from movie clips with people acting a certain emotion. In our approach, we extract LBP-TOP-based video features, openEAR energy/spectral-based audio features, and CNN (convolutional neural network) based deep image features by fine-tuning a pre-trained model with extra emotion images from the web. For each type of features, we run a SVM grid search to find the best RBF kernel. Then multi-kernel learning is employed to combine the RBF kernels to accomplish the feature fusion and generate a fused RBF kernel. Running multi-class SVM classification, we achieve a 45.23% test accuracy on the AFEW dataset. We then apply a decision optimization method to adjust the label distribution closer to the ground truth, by setting offsets for some of the classifiers' prediction confidence score. By applying this modification, the test accuracy increases to 50.46%, which is a significant improvement comparing to the baseline accuracy 39.33% .


title: The Transporter Classification Database (TCDB): recent advances

abstract: The Transporter Classification Database (TCDB), freely accessible at http://www.tcdb.org, is a relational database containing sequence, structural, functional and evolutionary information about transport systems from a variety of living organisms, based on the International Union of Biochemistry and Molecular Biology-approved transporter classification (TC) system. It is a curated repository for factual information compiled largely from published references. It uses a functional/phylogenetic system of classification, and currently encompasses about 5000 representative transporters and putative transporters in more than 500 families. We here describe novel software designed to support and extend the usefulness of TCDB. Our recent efforts render it more user friendly, incorporate machine learning to input novel data in a semiautomatic fashion, and allow analyses that are more accurate and less time consuming. The availability of these tools has resulted in recognition of distant phylogenetic relationships and tremendous expansion of the information available to TCDB users.


title: Design and test of a PCB Rogowski coil for very high dI/dt detection

abstract: This study proposes the bespoke design of a printed-circuit-board (PCB) integrated Rogowski coil to detect very high dI/dt's in an IGBT non-destructive tester. During reverse bias safe operating area (RBSOA) tests of an IGBT, failures of the transistor or of the freewheeling diode can lead to excessive dI/dt in the circuit. If properly detected, this information can be used to successfully implement non-destructive testing strategies, enabling the energy to be diverted from the device under test (DUT) to a protective switch. Hence, the design of the dI/dt sensing element (i.e., the Rogowski coil, in this case) is a fundamental aspect of the overall system design. Here, the PCB Rogowski coil is designed to detect only high dI/dt neglecting normal operating conditions. Initially the PCB Rogowski is designed according to the anticipated dI/dt values during failure and the required output response. The Rogowski circuit is then modeled and transient analysis was performed to aid the design of the damping output resistance. Finally, the experimental test results are presented.


title: A Survey of Radio Resource Management for Spectrum Aggregation in LTE-Advanced

abstract: In order to satisfy the requirements of future IMT-Advanced mobile systems, the concept of spectrum aggregation is introduced by 3GPP in its new LTE-Advanced (LTE Rel. 10) standards. While spectrum aggregation allows aggregation of carrier components (CCs) dispersed within and across different bands (intra/inter-band) as well as combination of CCs having different bandwidths, spectrum aggregation is expected to provide a powerful boost to the user throughput in LTE-Advanced (LTE-A). However, introduction of spectrum aggregation or carrier aggregation (CA) as referred to in LTE Rel. 10, has required some changes from the baseline LTE Rel. 8 although each CC in LTE-A remains backward compatible with LTE Rel. 8. This article provides a review of spectrum aggregation techniques, followed by requirements on radio resource management (RRM) functionality in support of CA. On-going research on the different RRM aspects and algorithms to support CA in LTE-Advanced are surveyed. Technical challenges for future research on aggregation in LTE-Advanced systems are also outlined.


title: The Move to Artist-Led Online Music Distribution: Explaining Structural Changes in the Digital Music Market

abstract: We propose a model for understanding the transformation of the market structure in the recorded music industry value chain due to new forms of digital distribution. It takes into account the traditional music industry's value chain and distribution network, and the product characteristics of digital music as they relate to consumer value creation. We showcase changes in market structure from various perspectives of the players in the music industry value chain. With this as background, we then present propositions that highlight forces at work in market transformation and how each player's role in the digital music industry value chain is likely to change. We note the new tensions that arise around intellectual property rights for digital music with market structure changes. We support the propositions with illustrative mini-cases.


title: Describing Multimedia Content using Attention-based Encoder--Decoder Networks

abstract: Whereas deep neural networks were first mostly used for classification tasks, they are rapidly expanding in the realm of structured output problems, where the observed target is composed of multiple random variables that have a rich joint distribution, given the input. In this paper we focus on the case where the input also has a rich structure and the input and output structures are somehow related. We describe systems that learn to attend to different places in the input, for each element of the output, for a variety of tasks: machine translation, image caption generation, video clip description, and speech recognition . All these systems are based on a shared set of building blocks: gated recurrent neural networks and convolutional neural networks , along with trained attention mechanisms. We report on experimental results with these systems, showing impressively good performance and the advantage of the attention mechanism.


title: VeTrack: Real Time Vehicle Tracking in Uninstrumented Indoor Environments

abstract: Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return.In this paper, we propose VeTrack, a smartphone-only system that tracks the vehicle's location in real time using the phone's inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel "shadow" tracing method to accurately estimate the vehicle's trajectories despite arbitrary phone/vehicle poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle's real time location with almost negligible latency, with error of 2-4 parking spaces at 80-percentile.


title: Identifying Personal Stories in Millions of Weblog Entries

abstract: Stories of people's everyday experiences have long been the focus of psychology and sociology research, and are increasingly being used in innovative knowledge-based technologies. However, continued research in this area is hindered by the lack of standard corpora of sufficient size and by the costs of creating one from scratch. In this paper, we describe our efforts to develop a standard corpus for researchers in this area by identifying personal stories in the tens of millions of blog posts in the ICWSM 2009 Spinn3r Dataset. Our approach was to employ statistical text classification technology on the content of blog entries, which required the creation of a sufficiently large set of annotated training examples. We describe the development and evaluation of this classification technology and how it was applied to the dataset in order to identify nearly a million


title: Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity

abstract: Global species extinction rates are orders of magnitude above the background rate documented in the fossil record. However, recent data syntheses have found mixed evidence for patterns of net species loss at local spatial scales. For example, two recent data meta-analyses have found that species richness is decreasing in some locations and is increasing in others. When these trends are combined, these papers argued there has been no net change in species richness, and suggested this pattern is globally representative of biodiversity change at local scales. Here we reanalyze results of these data syntheses and outline why this conclusion is unfounded. First, we show the datasets collated for these syntheses are spatially biased and not representative of the spatial distribution of species richness or the distribution of many primary drivers of biodiversity change. This casts doubt that their results are representative of global patterns. Second, we argue that detecting the trend in local species richness is very difficult with short time series and can lead to biased estimates of change. Reanalyses of the data detected a signal of study duration on biodiversity change, indicating net biodiversity loss is most apparent in studies of longer duration. Third, estimates of species richness change can be biased if species gains during post-disturbance recovery are included without also including species losses that occurred during the disturbance. Net species gains or losses should be assessed with respect to common baselines or reference communities. Ultimately, we need a globally coordinated effort to monitor biodiversity so that we can estimate and attribute human impacts as causes of biodiversity change. A combination of technologies will be needed to produce regularly updated global datasets of local biodiversity change to guide future policy. At this time the conclusion that there is no net change in local species richness is not the consensus state of knowledge.


title: Services in the Cloud Computing era: A survey

abstract: Cloud Computing is becoming a well-known buzzword nowadays. As a brand new infrastructure to offer services, Cloud Computing systems have many superiorities in comparing to those existed traditional service provisions, such as reduced upfront investment, expected performance, high availability, infinite scalability, tremendous fault-tolerance capability and so on and consequently chased by most of the IT companies, such as Google, Amazon, Microsoft, Salesforce.com. Based on their overwhelming predominance in traditional service provisions and capital accumulation, most of these IT companies have more chance to adapt their services into such a new environment earlier, say Cloud Computing systems. On the other hand, a large number of new companies are spawned with competitive services relayed on those provided Cloud Computing systems. In terms of their provisions, we divide those services into six categories in this paper, say Data as a Service (Daas), Software as a Service (SaaS), Platform as a Service (PaaS), Identity and Policy Management as a Service (IPMaaS), Network as a Service (NaaS), Infrastructure as a Service (IaaS). Detailed analysis to these services are provided, as well as those companies which provide the corresponding service categories.


title: SyncScan: practical fast handoff for 802.11 infrastructure networks

abstract: Wireless access networks scale by replicating base stations geographically and then allowing mobile clients to seamlessly "hand off" from one station to the next as they traverse the network. However, providing the illusion of continuous connectivity requires selecting the right moment to handoff and the right base station to transfer to. Unfortunately, 802.11-based networks only attempt a handoff when a client's service degrades to a point where connectivity is threatened. Worse, the overhead of scanning for nearby base stations is routinely over 250 ms - during which incoming packets are dropped - far longer than what can be tolerated by highly interactive applications such as voice telephony. In this paper we describe SyncScan, a low-cost technique for continuously tracking nearby base stations by synchronizing short listening periods at the client with periodic transmissions from each base station. We have implemented this SyncScan algorithm using commodity 802.11 hardware and we demonstrate that it allows better handoff decisions and over an order of magnitude improvement in handoff delay. Finally, our approach only requires trivial implementation changes, is incrementally deployable and is completely backward compatible with existing 802.11 standards.


title: Embodied Mind Brings Mathematics into Being

abstract: This book is about mathematical ideas, about what mathematics means-and why. Abstract ideas, for the most part, arise via conceptual metaphor-metaphorical ideas projecting from the way we function in the everyday physical world. Where Mathematics Comes From argues that conceptual metaphor plays a central role in mathematical ideas within the cognitive unconscious-from arithmetic and algebra to sets and logic to infinity in all of its forms.


Finite difference time domain methods


title: A wireless IoT system towards gait detection in stroke patients

abstract: Gait monitoring through the Internet of Things (IoT) is able to provide an overall assessment of daily living. All existing systems for predicting abnormality in gait mainly consider the gait related parameters. Their accuracy is limited because consequences due to injuries are significantly affected by different events in the gait. The objective of this study is to present a multisensory system that investigates walking patterns to predict a cautious gait in stroke patient. For this study, a smartphone built-in sensor and an IoT-shoe with a Wi-Fi communication module is used to discreetly monitor insole pressure and accelerations of the patient's motion. To the best of our knowledge, we are the first to use the gait spatiotemporal parameters implemented in smartphones to predict a cautious gait in a stroke patient. The proposed system can warn the user about their abnormal gait and possibly save them from forthcoming injuries from fear of falling.


title: Simultaneous Determination of Isoniazid, Pyrazinamide, Rifampicin and Acetylisoniazid in Human Plasma by High-Performance Liquid Chromatography

abstract: A sensitive and accurate high-performance liquid-chromatography method was developed and validated for the simultaneous determination of pyrazinamide (PZA), isoniazid (INH), rifampicin (RFP) and acetylisoniazid (AcINH) in human plasma. Separation was performed on a Max-RP C12 column using gradient elution and a flow-rate program. The mobile phase was methanol–acetonitrile–buffer (20 mM of heptanesulfonic acid sodium, pH 2.5) with a ratio of 10:8:82 (v/v/v) at the initial phase. All calibration curves had good linearity (r2 > 0.99) between the test ranges. The intra- and inter-day precision was less than 8.8% in good accuracy (<15%). The limit of detection with a signal-to-noise (S/N) of 3 was 0.014, 0.009, 0.023 and 0.054 μg mL−1 for PZA, AcINH, INH and RFP, respectively. The method was selective, sensitive and reliable, and is a good alternative for routine therapeutic drug monitoring of the four compounds during the treatment of tuberculosis patients.


title: Field evaporation behaviour in the γ phase in Ti–Al during analysis in the tomographic atom probe

abstract: Abstract A Ti–48 at% Al alloy has been successfully investigated, using atom probe field ion microscopy and transmission electron microscopy. After a specific heat treatment, this alloy has a (α2+γ) lamellar microstructure. Using the tomographic atom probe (TAP), it has been possible to image the stacking of superlattice planes of γ and to identify titanium as the highest evaporation field element. In addition, the influence of analysis site on atom probe measurements has been estimated for this phase. A TAP analysis has also made it possible to observe an extremely thin step along a γ/γ interface at a near atomic scale.


Flexible pattern matching


title: Applications of CBCT in dental practice: a review of the literature.

abstract: Abstract This article reviews the various clinical applications of cone-beam computed tomography (CBCT). A literature search was conducted via PubMed for publications related to dental applications of CBCT published between January 1998 and June 15, 2010. The search revealed a total of 540 articles, 129 of which were clinically relevant and analyzed in detail. A literature review demonstrated that CBCT has been utilized for oral and maxillofacial surgery, endodontics, implantology, orthodontics, temporomandibular joint dysfunction, periodontics, and restorative and forensic dentistry. This literature review showed that the different indications for CBCT are governed by the needs of the specific dental discipline and the type of procedure performed.


title: Communication Infrastructure Design in Cyber Physical Systems with Applications in Smart Grids: A Hybrid System Framework

abstract: This paper provides a survey on communications in CPS and a tutorial of a unified framework, i.e., hybrid system featured by the co-existence of discrete and continuous system states, for the design of communication infrastructure in cyber physical system (CPS). First, CPS with communication infrastructure is modeled as a hybrid system, in which the discrete system state is the mode of the communication infrastructure while the continuous system state is the state of the physical dynamics. The dynamical system can be considered as being switched among multiple modes corresponding to the discrete system state. Then, the communication infrastructure design is converted to the problems of mode provisioning and scheduling. This framework is applied in the context of smart grid as a special example of CPS and is shown to achieve good performance via numerical simulations.


title: Incivility in nursing education: An intervention.

abstract: Abstract Incivility in nursing education is an unfortunate phenomenon affecting nursing students in all aspects of their educational experience. Students and their instructors are often ill equipped to deal with academic incivility and their lack of ability to handle such behaviors has proven detrimental to the future of the nursing profession. Nursing instructors need tools to help educate nursing students on how to recognize uncivil behaviors within themselves as well as others and ways to combat it. This research project addressed these aspects of academic incivility and implemented an e-learning module that was developed to educate students on incivility. The data was collected through a pre-test, post-test model with resulting statistical analysis using the McNemar's test. Results showed the nursing students obtained increased self-efficacy in regards to their ability to define, detect, and combat academic incivility after viewing the e-learning module. In conclusion, the successful implementation of the e-learning module provides further incentive for schools of nursing to consider implementing incivility education in their curriculums.


title: Enhanced Human Face Recognition Using LBPH Descriptor, Multi-KNN, and Back-Propagation Neural Network

abstract: Face recognition has become a fascinating field for researchers. The motivation behind the enormous interest in the topic is the need to improve the accuracy of many real-time applications. The complexity of the human face and the changes due to different effects make it more challenging to design as well as implement a powerful computational system for human face recognition. In this paper, we presented an enhanced approach to improve human face recognition using a back-propagation neural network (BPNN) and features extraction based on the correlation between the training images. A key contribution of this paper is the generation of a new set called the T-Dataset from the original training data set, which is used to train the BPNN. We generated the T-Dataset using the correlation between the training images without using a common technique of image density. The correlated T-Dataset provides a high distinction layer between the training images, which helps the BPNN to converge faster and achieve better accuracy. Data and features reduction are essential in the face recognition process, and researchers have recently focused on the modern neural network. Therefore, we used a local binary pattern histogram descriptor to prove that there is potential improvement even using traditional methods. We applied five distance measurement algorithms and then combined them to obtain the T-Dataset, which we fed into the BPNN. We achieved higher face recognition accuracy with less computational cost compared with the current approach by using reduced image features. We test the proposed framework on two small data sets, the YALE and AT&T data sets, as the ground truth. We achieved tremendous accuracy. Furthermore, we evaluate our method on one of the state-of-the-art benchmark data sets, Labeled Faces in the Wild (LFW), where we produce a competitive face recognition performance.


title: Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

abstract: Top-$N$ sequential recommendation models each user as a sequence of items interacted in the past and aims to predict top-$N$ ranked items that a user will likely interact in a `near future'. The order of interaction implies that sequential patterns play an important role where more recent items in a sequence have a larger impact on the next item. In this paper, we propose a Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a solution to address this requirement. The idea is to embed a sequence of recent items into an `image' in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters. This approach provides a unified and flexible network structure for capturing both general preferences and sequential patterns. The experiments on public datasets demonstrated that Caser consistently outperforms state-of-the-art sequential recommendation methods on a variety of common evaluation metrics.


Discovering Available Drinks Through Natural , Robot-Led , Human-Robot Interaction Between a Waiter and a Bartender


title: Sexual Stigma and Sexual Prejudice in the United States: A Conceptual Framework

abstract: In 1972, psychologist George Weinberg’s book, Society and the Healthy Homosexual , introduced readers to a new term, homophobia , and to the then-novel idea that hostility to homosexuality, rather than homosexuality itself, posed a threat to mental health (Weinberg, 1972 ; see also Herek, 2004) . The following year, the American Psychiatric Association’s Board of Directors declared that homosexuality is not inherently associated with mental illness and voted to remove it from the Diagnostic and Statistical Manual of Mental Disorders , or DSM (Bayer, 1987 ; Minton, 2002) . The American Psychological Association quickly endorsed the psychiatrists’ action and further urged mental health professionals “to take the lead in removing the stigma of mental illness that has long been associated with homosexual orientations” (Conger, 1975 , p. 633). Thus, a major cultural institution renounced its longstanding role in legitimating society’s stigmatization of homosexuality just when the psychological manifestations of such stigma were beginning to be redefined as a social problem. This historic confluence of events provides an appropriate starting point for the present chapter. The term homophobia has gained widespread usage since 1972, even as its limitations have become increasingly apparent. Chief among these is its construction of prejudice as an individual pathology. As I have explained elsewhere (Herek, 2004) , this clinically derived perspective limits our ability to understand hostility toward sexual minorities, both among individuals and in society at large. I have argued instead for the value of framing heterosexuals’ negative responses to sexual minorities in terms of sexual prejudice and of conceptualizing sexual prejudice as the internalization of societal stigma (Herek, 2000a, 2004, 2007 ; Herek, Chopp, & Strohl, 2007) . In the present chapter, I elaborate on these points and provide a more detailed framework than I have previously presented for conceptualizing both societal and individual reactions to homosexuality and sexual minorities in the United States. 1


WEB-BASED EXECUTION OF GRAPHICAL WORKFLOWS : A MODULAR PLATFORM FOR MULTIFUNTIONAL SCIENTIFIC PROCESS AUTOMATION


title: Leader-Follower Consensus for a Class of Nonlinear Multi-Agent Systems

abstract: This paper deals with the leader-follower consensus problem for a class of nonlinear multiagent systems. All agents have identical nonlinear dynamics in the strict feedback form with Lipschitz growth condition. Both full state consensus protocol and dynamic output consensus protocol are provided. It is shown that under a connected undirected information communication topology, the proposed protocols can solve the leader-follower consensus problem. Two consensus protocol design procedures are presented and a numerical example is given to illustrate the proposed protocols.


title: Designing and Implementation of an Online System for Electronic Contract Negotiation Based on Electronic Signature

abstract: The contract is the bridge to transaction, as well as the core of business practice. Accompanying with the development of electronic commerce, contract negotiation under more complicated environment in advantage of internet is becoming increasingly urgent. This paper briefly introduces the status of electronic signature and electronic contract in China, then presents a reference architecture for an online system for electronic contract negotiation and signature in detail. This architecture is systematically designed on the basis of electronic signature technology to realize online contract signing, electronic contract storage on the trusted third party and online or offline verifications. All above ensures electronic contract’s confidentiality, integrity and non-repudiation, makes a breakthrough for electronic signature in business field, and realizes the electronization in the whole process of business negotiation.


title: Vibrations of non-homogeneous rectangular membranes

abstract: Abstract An exact solution for the title problem is obtained for the case where the two subdomains of the non-homogeneous membrane are of rectangular shape. It is shown that the fundamental frequency coefficient obtained by means (a) of the classical Kantorovish method and (b) the optimized Kantorovich method are in very good agreement with the result predicted by the exact method.


title: Interaction atmosphere: blending design style

abstract: Interfaces, created by HCI with functional and ergonomic objectives, due to the proliferation of communication landscape need today new design methodologies to develop standardized solutions. Interaction Design is the discipline that deals with these new design aspects in order to foster interactions simpler and designed for specific contexts. This paper explores the role of creativity and emotions, related to atmosphere interaction in visual interfaces. The research intends to investigate the potential of a blending approach between functionality and creativity that introduces in the user oriented design process some techniques coming from different disciplines. The main goals are: stimulate the creative process, arise inferential aspects about previous positive experience, develop aesthetic awareness of the atmosphere concept as balance of points highlighted in the studies of Cooper, Verplank and Veen. The first aim of this paper is to propose a new blending approach called 'interaction atmosphere' for further application in digital ambient.


A Study On Advertising And Its Impact Of Brand Awareness On Fast Moving Consumer Goods With Reference To Personal Care Products In Chennai


title: Modeling and Analysis of a Dual-Active-Bridge-Isolated Bidirectional DC/DC Converter to Minimize RMS Current With Whole Operating Range

abstract: The triple phase shift (TPS) modulation scheme, which provides three control freedoms, is of great importance for the optimized operation of a dual active bridge (DAB) isolated bidirectional dc/dc converter. First of all, this paper introduces an accurate, universal model to describe the analytic expressions of the DAB converter under TPS control. Based on this, six operating modes of the DAB converter are further discussed. Afterwards, the concept of global optimal condition (GOC) equations is proposed to derive the closed form of analytic expressions of an optimal modulation scheme that makes the DAB converter operate with minimized root-mean-square (rms) current during whole power range with different operating modes. According to the GOC equations, the physical explanation of the proposed modulation scheme is further given in details, and the complex interaction among the control variables, the transferred power, and rms current is revealed. The real-time optimization process of the proposed method is also specified. Finally, the proposed methods are applied to a laboratory prototype. The experimental results confirm the theoretical analysis and practical feasibility of the proposed strategies.


title: Handling cold-start scenarios in point-of-interest recommendations through crowdsourcing

abstract: Point-of-Interest (POI) recommender systems are used to suggest places or venues for a target user based on her preferences. The traditional recommender systems utilise the historical data to learn user preferences and then select places that relate to them. Therefore, knowledge of the features of a POI is important along with the preferences of the target users for a traditional recommender system. This technique faces a serious problem when a new POI emerges in a city. A 'new' POI has no historical data and hence a recommender system fails to learn about its features. This results in absence of the 'new' POIs in the recommended list. Such a scenario is popularly known as the POI cold-start scenario. Online social networks such as Yelp, TripAdvisor, Foursquare, etc. that provide POI recommendations as a service mostly face this particular problem. To address this issue, the proposed work gathers content on the cold-start POIs by crowdsourcing other online social networks and subsequently the dominating features at POIs are identified from the collected review and rating data. These features are utilized to address the cold-start problem. Finally, we develop a POI recommender system that can handle the POI cold-start scenario. We experimented on the real-world data provided by Yelp and the results are found to be significantly better than the state-of-art techniques in handling cold-start scenarios.


title: A Unified Alternating Direction Method of Multipliers by Majorization Minimization

abstract: Accompanied with the rising popularity of compressed sensing, the Alternating Direction Method of Multipliers (ADMM) has become the most widely used solver for linearly constrained convex problems with separable objectives. In this work, we observe that many previous variants of ADMM update the primal variable by minimizing different majorant functions with their convergence proofs given case by case. Inspired by the principle of majorization minimization, we respectively present the unified frameworks and convergence analysis for the Gauss-Seidel ADMMs and Jacobian ADMMs, which use different historical information for the current updating. Our frameworks further generalize previous ADMMs to the ones capable of solving the problems with non-separable objectives by minimizing their separable majorant surrogates. We also show that the bound which measures the convergence speed of ADMMs depends on the tightness of the used majorant function. Then several techniques are introduced to improve the efficiency of ADMMs by tightening the majorant functions. In particular, we propose the Mixed Gauss-Seidel and Jacobian ADMM (M-ADMM) which alleviates the slow convergence issue of Jacobian ADMMs by absorbing merits of the Gauss-Seidel ADMMs. M-ADMM can be further improved by using backtracking, wise variable partition and fully exploiting the structure of the constraint. Beyond the guarantee in theory, numerical experiments on both synthesized and real-world data further demonstrate the superiority of our new ADMMs in practice. Finally, we release a toolbox at this https URL that implements efficient ADMMs for many problems in compressed sensing.


title: 'Beating the news' with EMBERS: forecasting civil unrest using open source indicators

abstract: We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.


title: Training support for pouring task in casting process using stereoscopic video see-through display - Presentation of molten metal flow simulation based on captured task motion

abstract: The work support technology using augmented reality has been researched in the field of manufacturing. It is the technology that can present appropriate instructions to a worker. Thereby, the worker performs a task requiring an advanced skill even if she/he is not an expert. In this technology, most of work instructions are presented while the worker is working. However, there are many manufacturing tasks that is difficult to be retried when it fails. In order to deal with such tasks, a training support technology that a worker can train tasks beforehand using augmented reality is proposed. This technology can present simulated task results according to the motion of the worker. As a simulation result, for example, the worker can see internal state of the work object. And she/he can train as many times as she/he wants. Therefore, her/his skills can be improved through trial and error. In this paper, a concept of task training support technology is introduced. To realize the concept, the pouring task in casting process is selected as a target task and the prototype system is constructed. Then, the evaluation results of pouring task training using the prototype system are reported.


title: A Load-Balancing Self-Organizing Incremental Neural Network

abstract: Clustering is widely used in machine learning, feature extraction, pattern recognition, image analysis, information retrieval, and bioinformatics. Online unsupervised incremental learning is an important branch of data clustering. However, accurately separating high-density overlapped areas in a network has a direct impact on the performance of the clustering algorithm. In this paper, we propose a load-balancing self-organizing incremental neural network (LB-SOINN) to achieve good clustering results and demonstrate that it is more stable than an enhanced SOINN (E-SOINN). LB-SOINN has all the advantages of E-SOINN, such as robustness to noise and online unsupervised incremental learning. It overcomes the shortcomings of the topology structure generated by E-SOINN, such as dependence on the sequence of the input data, and avoids the turbulence that occurs when separating a composite class into subclasses. Furthermore, we also introduce a distance combination framework to obtain good performance for high-dimensional space-clustering tasks. Experiments involving both artificial and real world data sets indicate that LB-SOINN has superior performance in comparison with E-SOINN and other methods.


title: Value of Endothelial Dysfunction in the Pathogenesis ofPortal Hypertension

abstract: Background: Until now it was thought that the main cause of portal hypertension is a mechanical obstruction of blood flow in the portal vein due to proliferation of connective tissue in the liver parenchyma (irreversible factor). The role of reversible factors remained underestimated. These include the following components: tone of the blood vessels, blood rheology, micro thrombosis, swelling of the liver parenchyma. The tone of hepatic blood vessels is provided by a number of factors, among which the most important is nitrogen monoxide (NO) – natural vasorelaxsing substance, produced by vascular endothelium. The role of endotheliumdependent factors in the pathogenesis of many chronic diseases is extensively studied for today, but the impact of endothelial dysfunction on liver hemodynamics and forming of portal hypertension is not clear yet. ::: Aim & Objectives: The aim of our research was to examine the condition of hepatic blood flow in patients with portal hypertension, which developed on the background of chronic alcoholic steatohepatitis (ASH) and liver cirrhosis (LC), depending on the degree of endothelial dysfunction in these patients. ::: Methods: Research was carried out at the Gastroenterological Department of Emergency Hospital in Chernivtsi. The study involved 63 patients with ASH, having signs of initial stage of portal hypertension (study group 1). Also 31 patients with LC with expressed portal hypertension were examined (study group 2). As controls 62 ASH patients without any signs of portal hypertension were examined (control group). Exclusion criteria in both groups were: old age, the presence of cardiac failure, kidney failure and other diseases in decompensate stage. ::: Results: Summarizing the results obtained in the research process, it should be noted that in all examined patients with chronic alcoholic hepatitis EGD and Doppler signs of preclinical stages of hyperkinetic type of portal hypertension were observed. Absence of cirrhotic changes of liver tissue in these patients is confirmed by histopathological study of liver biopsies. Blood tests have revealed the decreasing of NO level (endothelial vasorelaxsing factor) and depression of endothelium-dependent fibrinolytic activity of plasma. Doppler examination of the brachial artery patients of the 1-st group have revealed decreased endothelium-dependent vasodilation (?DVD) - 7,3±0,18% (against 12,9±0,22% in controls (?<0,05)). Amongst 82 % patients of the 2nd group ?DVD was only 5,4±0,15%, what is significantly less, than in 1-st and control groups (?<0,05). Amongst 18 % patients of 2nd group during conducting of test for reactive hyperemia it was registered the paradoxical vasoconstrictive reaction, indicating a more expressed endothelial dysfunction. The analysis of the data determined the availability of reverse correlation between the degree of portal hypertension and the level of NO in blood (r=0.87) and between the degree of portal hypertension. and EDVD index (r=0,54) with a high degree of probability, indicating the important pathogenetic role of endothelial dysfunction in the development and progression of portal hypertension. ::: Conclusion: There are manifestations of endothelial dysfunction in patients with portal hypertension, which have been developed on the ASH background. The gradual increasing severity of portal hypertension is observed in the same moment with the deterioration of the endothelium functional condition. The imbalance of endothelium-dependent vasoactive substances is an important part of potentiating of hepatic hemodynamic failure and formation of high pressure in the portal vein system in such patients.. Prospects for further investigations is the search for medications to correct endothelial dysfunction in order to improve results of treatment of patients with portal hypertension on the chronic alcoholic steatohepatitis background.


title: A low noise, 1.28µA quiescent regulator with broadband high PSR for micropower sensors

abstract: An ultra-low power, low-noise, high power supply rejection (PSR) op-ampless regulator is presented in this paper. It uses a native composite power transistor plus a sandwich on-chip native capacitor in the pre-regulator to enhance broadband high PSR performance. Besides, a pseudo-resistor based low-pass filter is used to reduce the Brokaw's voltage regulator circuit noise. Realized using GlobalFoundries 1.8V/3.3V CMOS 0.18µm process, the regulator gives a mean output 0.992V for 8 samples while occupying a silicon area of 0.095mm2. It consumes only 1.28µA and drives a maximum output current of 2mA. The measured output noise is 80.1nV/vHz at 1kHz and 14.2nV/vHz at 100kHz while the integrated output noise over 10Hz–100kHz bandwidth is 13.88µVrms. The measured PSR is −79.1dB @ 10Hz, −55.3dB @ 1MHz and −41.6dB @ 10MHz. The proposed regulator is useful for sensor circuit applications using energy harvesting power source.


title: Sodium Benzoate, a Metabolite of Cinnamon and a Food Additive, Upregulates Neuroprotective Parkinson Disease Protein DJ-1 in Astrocytes and Neurons

abstract: DJ-1 (PARK7) is a neuroprotective protein that protects cells from oxidative stress. Accordingly, loss-of-function DJ-1 mutations have been linked with a familial form of early onset Parkinson disease. Mechanisms by which DJ-1 level could be enriched in the CNS are poorly understood. Recently we have discovered anti-inflammatory activity of sodium benzoate (NaB), a metabolite of cinnamon and a widely-used food additive. Here we delineate that NaB is also capable of increasing the level of DJ-1 in primary mouse and human astrocytes and human neurons highlighting another novel neuroprotective effect of this compound. Reversal of DJ-1-inducing effect of NaB by mevalonate, farnesyl phosphate, but not cholesterol and ubiquinone, suggests that depletion of intermediates, but not end products, of the mevalonate pathway is involved in the induction of DJ-1 by NaB. Accordingly, either an inhibitor of p21(ras) farnesyl protein transferase (FPTI) or a dominant-negative mutant of p21(ras) alone was also able to increase the expression of DJ-1 in astrocytes suggesting an involvement of p21(ras) in DJ-1 expression. However, an inhibitor of geranyl geranyl transferase (GGTI) and a dominant-negative mutant of p21(rac) had no effect on the expression of DJ-1, indicating the specificity of the effect. Similarly lipopolysaccharide (LPS), an activator of small G proteins, also inhibited the expression of DJ-1, and NaB and FPTI, but not GGTI, abrogated LPS-mediated inhibition. Together, these results suggest that NaB upregulates DJ-1 via modulation of mevalonate metabolites and that p21(ras), but not p21(rac), is involved in the regulation of DJ-1.


title: ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation

abstract: We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the state-of-the-art semantic segmentation network PSPNet, while its category-wise accuracy is only 8% less. We evaluated ESPNet on a variety of semantic segmentation datasets including Cityscapes, PASCAL VOC, and a breast biopsy whole slide image dataset. Under the same constraints on memory and computation, ESPNet outperforms all the current efficient CNN networks such as MobileNet, ShuffleNet, and ENet on both standard metrics and our newly introduced performance metrics that measure efficiency on edge devices. Our network can process high resolution images at a rate of 112 and 9 frames per second on a standard GPU and edge device, respectively.


title: Fingerprint liveness detection based on quality measures

abstract: A new fingerprint parameterization for liveness detection based on quality measures is presented. The novel feature set is used in a complete liveness detection system and tested on the development set of the LivDET competition, comprising over 4,500 real and fake images acquired with three different optical sensors. The proposed solution proves to be robust to the multi-sensor scenario, and presents an overall rate of 93% of correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake.


title: Surface Management System Field Trial Results

abstract: NASA Ames Research Center, in cooperation with the FAA, has completed research and development of a proof-of-concept Surface Management System (SMS). This paper reports on two recent SMS field tests as well as final per-formance and benefits analyses. Field tests and analysis support the conclusion that substantial portions of SMS technology are ready for transfer to the FAA and deployment throughout the National Airspace System (NAS). Other SMS capabilities were accepted in concept but require additional refinement for inclusion in subsequent de-velopment spirals. SMS is a decision support tool that helps operational specialists at Air Traffic Control (ATC) and NAS user facilities to collaboratively manage the movements of aircraft on the surface of busy airports, thereby im-proving capacity, efficiency, and flexibility. SMS provides accurate predictions of the future demand and how that demand will affect airport resources – information that is not currently available. The resulting shared awareness enables the Air Traffic Control Tower (ATCT), Terminal Radar Approach Control (TRACON), Air Route Traffic Control Center (ARTCC), and air carriers to coordinate traffic management decisions. Furthermore, SMS uses its ability to predict how future demand will play out on the surface to evaluate the effect of various traffic management decisions in advance of implementing them, to plan and advise surface operations. The SMS concept, displays, and algorithms were evaluated through a series of field tests at Memphis International Airport (MEM). An operational trial in September, 2003 evaluated SMS traffic management components, such as runway configuration change planning; shadow testing in January, 2004 tested tactical components (e.g., Approval Request (APREQ) coordina-tion, sequencing for departure, and Expected Departure Clearance Time (EDCT) compliance). Participants in these evaluations rated the SMS concept and many of the traffic management displays very positively. Local and Ground controller displays will require integration with other automation systems. Feedback from FAA and NAS user par-ticipants support the conclusion that SMS algorithms currently provide information that has acceptable and benefi-cial accuracy for traffic management applications. Performance analysis results document the current accuracy of SMS algorithms. Benefits/cost analysis of delay cost reduction due to SMS provides the business case for SMS deployment.


title: Optical music recognition: state-of-the-art and open issues

abstract: For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones.


title: Preprint ANL/MCS-P1588-0309. On-Line Economic Optimization of Energy Systems Using Weather Forecast Information

abstract: We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.


title: Classification Active Learning Based on Mutual Information

abstract: Selecting a subset of samples to label from a large pool of unlabeled data points, such that a sufficiently accurate classifier is obtained using a reasonably small training set is a challenging, yet critical problem. Challenging, since solving this problem includes cumbersome combinatorial computations, and critical, due to the fact that labeling is an expensive and time-consuming task, hence we always aim to minimize the number of required labels. While information theoretical objectives, such as mutual information (MI) between the labels, have been successfully used in sequential querying, it is not straightforward to generalize these objectives to batch mode. This is because evaluation and optimization of functions which are trivial in individual querying settings become intractable for many objectives when we are to select multiple queries. In this paper, we develop a framework, where we propose efficient ways of evaluating and maximizing the MI between labels as an objective for batch mode active learning. Our proposed framework efficiently reduces the computational complexity from an order proportional to the batch size, when no approximation is applied, to the linear cost. The performance of this framework is evaluated using data sets from several fields showing that the proposed framework leads to efficient active learning for most of the data sets.


title: Using the k-Nearest Neighbor Graph for Proximity Searching in Metric Spaces ⋆

abstract: Proximity searching consists in retrieving from a database, objects that are close to a query. For this type of searching problem, the most general model is the metric space, where proximity is defined in terms of a distance function. A solution for this problem consists in building an offline index to quickly satisfy online queries. The ultimate goal is to use as few distance computations as possible to satisfy queries, since the distance is considered expensive to compute. Proximity searching is central to several applications, ranging from multimedia indexing and querying to data compression and clustering. ::: ::: In this paper we present a new approach to solve the proximity searching problem. Our solution is based on indexing the database with the k-nearest neighbor graph (knng), which is a directed graph connecting each element to its k closest neighbors. ::: ::: We present two search algorithms for both range and nearest neighbor queries which use navigational and metrical features of the knng graph. We show that our approach is competitive against current ones. For instance, in the document metric space our nearest neighbor search algorithms perform 30% more distance evaluations than AESA using only a 0.25% of its space requirement. In the same space, the pivot-based technique is completely useless.


title: Deep-Learning-Based Classification for DTM Extraction from ALS Point Cloud

abstract: Airborne laser scanning (ALS) point cloud data are suitable for digital terrain model (DTM) extraction given its high accuracy in elevation. Existing filtering algorithms that eliminate non-ground points mostly depend on terrain feature assumptions or representations; these assumptions result in errors when the scene is complex. This paper proposes a new method for ground point extraction based on deep learning using deep convolutional neural networks (CNN). For every point with spatial context, the neighboring points within a window are extracted and transformed into an image. Then, the classification of a point can be treated as the classification of an image; the point-to-image transformation is carefully crafted by considering the height information in the neighborhood area. After being trained on approximately 17 million labeled ALS points, the deep CNN model can learn how a human operator recognizes a point as a ground point or not. The model performs better than typical existing algorithms in terms of error rate, indicating the significant potential of deep-learning-based methods in feature extraction from a point cloud.


title: Maternal and child undernutrition and overweight in low-income and middle-income countries

abstract: Summary Maternal and child malnutrition in low-income and middle-income countries encompasses both undernutrition and a growing problem with overweight and obesity. Low body-mass index, indicative of maternal undernutrition, has declined somewhat in the past two decades but continues to be prevalent in Asia and Africa. Prevalence of maternal overweight has had a steady increase since 1980 and exceeds that of underweight in all regions. Prevalence of stunting of linear growth of children younger than 5 years has decreased during the past two decades, but is higher in south Asia and sub-Saharan Africa than elsewhere and globally affected at least 165 million children in 2011; wasting affected at least 52 million children. Deficiencies of vitamin A and zinc result in deaths; deficiencies of iodine and iron, together with stunting, can contribute to children not reaching their developmental potential. Maternal undernutrition contributes to fetal growth restriction, which increases the risk of neonatal deaths and, for survivors, of stunting by 2 years of age. Suboptimum breastfeeding results in an increased risk for mortality in the first 2 years of life. We estimate that undernutrition in the aggregate—including fetal growth restriction, stunting, wasting, and deficiencies of vitamin A and zinc along with suboptimum breastfeeding—is a cause of 3·1 million child deaths annually or 45% of all child deaths in 2011. Maternal overweight and obesity result in increased maternal morbidity and infant mortality. Childhood overweight is becoming an increasingly important contributor to adult obesity, diabetes, and non-communicable diseases. The high present and future disease burden caused by malnutrition in women of reproductive age, pregnancy, and children in the first 2 years of life should lead to interventions focused on these groups.


title: Big data analytics for network anomaly detection from netflow data

abstract: Cyber-attacks wasorganized in a simple and random way in the past. However attacks are carried out systematically and long term nowadays. In addition, the high calculation volume and continuous changes in network data distribution have made it more difficult to analyze data and detect abnormal behaviors within. For this reason, big data solutions have become essential. In this paper, firstly network anomaly and attack detection studies on big data has been reviewed. Then, a public big network data was analyzed with a new unsupervised anomaly detection approach on Apache Spark cluster in Azure HDInsight. Finally, the results obtained from a case study were evaluated, %96 accuracy was achieved. The results were visualized after dimension reduction using Principal Component Analysis (PCA). The identified anomalies may provide usable outputs to understand the behavior of the network, distinguishing the attacks, providing better cyber security, and protecting critical infrastructures.


title: Sizing up visualizations: effects of display size in focus+context, overview+detail, and zooming interfaces

abstract: Whereas the literature is clear on the benefits of large displays and visualizations, little is known about their combination, that is, how display size affect the usability of visualizations. We describe a controlled experiment where 19 participants used focus+context, overview+detail, and zooming techniques with varying display sizes (13.8, 1.5, and 0.17 megapixels). Participants navigated geographical maps to find specific locations, compare items, and follow routes. Results show that for multi-scale navigation, classic interactive visualization techniques did not benefit from being scaled to a large display: In contrast to the literature we find similar performance on medium and large displays. Across display sizes, overview+detail works the best, in particular for comparing items. Focus+context is relatively more difficult to use at a small display size. We explain these findings and discuss the design of interactive visualization techniques for large displays.