title: An Overview of the Theory and Applications of Metasurfaces: The Two-Dimensional Equivalents of Metamaterials

abstract: Metamaterials are typically engineered by arranging a set of small scatterers or apertures in a regular array throughout a region of space, thus obtaining some desirable bulk electromagnetic behavior. The desired property is often one that is not normally found naturally (negative refractive index, near-zero index, etc.). Over the past ten years, metamaterials have moved from being simply a theoretical concept to a field with developed and marketed applications. Three-dimensional metamaterials can be extended by arranging electrically small scatterers or holes into a two-dimensional pattern at a surface or interface. This surface version of a metamaterial has been given the name metasurface (the term metafilm has also been employed for certain structures). For many applications, metasurfaces can be used in place of metamaterials. Metasurfaces have the advantage of taking up less physical space than do full three-dimensional metamaterial structures; consequently, metasurfaces offer the possibility of less-lossy structures. In this overview paper, we discuss the theoretical basis by which metasurfaces should be characterized, and discuss their various applications. We will see how metasurfaces are distinguished from conventional frequency-selective surfaces. Metasurfaces have a wide range of potential applications in electromagnetics (ranging from low microwave to optical frequencies), including: (1) controllable “smart” surfaces, (2) miniaturized cavity resonators, (3) novel wave-guiding structures, (4) angular-independent surfaces, (5) absorbers, (6) biomedical devices, (7) terahertz switches, and (8) fluid-tunable frequency-agile materials, to name only a few. In this review, we will see that the development in recent years of such materials and/or surfaces is bringing us closer to realizing the exciting speculations made over one hundred years ago by the work of Lamb, Schuster, and Pocklington, and later by Mandel'shtam and Veselago.


title: Causal Inference with Noisy and Missing Covariates via Matrix Factorization

abstract: Valid causal inference in observational studies often requires controlling for confounders. However, in practice measurements of confounders may be noisy, and can lead to biased estimates of causal effects. We show that we can reduce the bias caused by measurement noise using a large number of noisy measurements of the underlying confounders. We propose the use of matrix factorization to infer the confounders from noisy covariates, a flexible and principled framework that adapts to missing values, accommodates a wide variety of data types, and can augment many causal inference methods. We bound the error for the induced average treatment effect estimator and show it is consistent in a linear regression setting, using Exponential Family Matrix Completion preprocessing. We demonstrate the effectiveness of the proposed procedure in numerical experiments with both synthetic data and real clinical data.


title: Latent Tree Learning with Differentiable Parsers: Shift-Reduce Parsing and Chart Parsing

abstract: Latent tree learning models represent sentences by composing their words according to an induced parse tree, all based on a downstream task. These models often outperform baselines which use (externally provided) syntax trees to drive the composition order. This work contributes (a) a new latent tree learning model based on shift-reduce parsing, with competitive downstream performance and non-trivial induced trees, and (b) an analysis of the trees learned by our shift-reduce model and by a chart-based model.


title: Making Sense of Asynchrony in Interactive Data Visualizations

abstract: Asynchronous interfaces allow users to concurrently issue requests while existing ones are processed. While it is widely used to support non-blocking input when there is latency, it's not clear if people can make use of asynchrony as the data is updating, since the UI updates dynamically and the changes can be hard to interpret. Interactive data visualization presents an interesting context for studying the effects of asynchronous interfaces, since interactions are frequent, task latencies can vary widely, and results often require interpretation. In this paper, we study the effects of introducing asynchrony into interactive visualizations, under different latencies, and with different tasks. We observe that traditional asynchronous interfaces, where results update in place, induce users to wait for the result before interacting, not taking advantage of the asynchronous rendering of the results. However, when results are rendered cumulatively over the recent history, users perform asynchronous interactions and get faster task completion times.


title: A Survey on Wireless Indoor Localization from the Device Perspective

abstract: With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals. This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.


title: Accelerating the Race to Autonomous Cars

abstract: Every automaker is working on driver assistance systems and self-driving cars. Conventional computer vision used for ADAS is reaching its threshold because it is impossible to write code for every possible scenario as a vehicle navigates. In order to develop a truly autonomous car, deep learning and artificial intelligence are required. With deep learning, the vehicle can be trained to have super human levels of perception, driving safer than anyone on the road. An end-to-end artificial intelligence platform based on supercomputers in the cloud and in the vehicle enables cars to get smarter and smarter. Coupled with an extensive software development kit with vision and AI libraries and software modules, automakers, tier 1s, and startups can build scalable systems from ADAS to full autonomy.


title: The Impact of Project Management in Virtual Environment: A Software Industry Perspective

abstract: Virtual team in a project within an Organization could achieve optimize project performance by acquiring appropriate human resources, coordination, communication and regular performance evaluation. According to the literature many ICT tools will collaborate to manage virtual teams, but still most of the projects lead to failure in the software industry. Aim of this research is to discover the most affected factors for virtual project human resource management.


title: Linear and non-linear speech features for detection of Parkinson's disease

abstract: Parkinson's disease (PD) was described by James Parkinson first time and it is now recognized as the second common neurological disorder after Alzheimer. Since most of the people with PD suffer form speech disorder, it is believed that speech analysis can be considered as the easiest way for PD detection. In this research, we try to use extracted features by genetic algorithm and ANFC for classifying between healthy and people with PD. Support vector machines (SVM) is applied as the classifier. Results show higher network accuracy of ANFC features compared to genetic algorithm features.


title: Ontology based ETL process for creation of ontological data warehouse

abstract: Extraction, Transformation and Loading (ETL) is a key process of data warehouse building. It integrates data sources with diverse features and structures. Numerous approaches and implementations of ETL have been introduced. However, they still have the following disadvantages: human-dependence, information integration only in syntactic levels, incomplete the homogeneity solution, difficulty to install and configure, etc. In this paper, we propose an alternative approach to the ETL process by attacking the homogeneity in data sources with an ontology-based methodology. Our approach can overcome the drawbacks of most existing approaches; as it automates the key activities of the process, such as: extraction of metainformation, generation of logical and physical data models, and transformation of information.


title: Security of the Internet of Things: perspectives and challenges

abstract: Internet of Things (IoT) is playing a more and more important role after its showing up, it covers from traditional equipment to general household objects such as WSNs and RFID. With the great potential of IoT, there come all kinds of challenges. This paper focuses on the security problems among all other challenges. As IoT is built on the basis of the Internet, security problems of the Internet will also show up in IoT. And as IoT contains three layers: perception layer, transportation layer and application layer, this paper will analyze the security problems of each layer separately and try to find new problems and solutions. This paper also analyzes the cross-layer heterogeneous integration issues and security issues in detail and discusses the security issues of IoT as a whole and tries to find solutions to them. In the end, this paper compares security issues between IoT and traditional network, and discusses opening security issues of IoT.


title: Global Supply Chain Management: A Reinforcement Learning Approach

abstract: In recent years, researchers and practitioners alike have devoted a great deal of attention to supply chain management (SCM). The main focus of SCM is the need to integrate operations along the supply chain as part of an overall logistic support function. At the same time, the need for globalization requires that the solution of SCM problems be performed in an international context as part of what we refer to as Global Supply Chain Management (GSCM). This paper proposes an approach to study GSCM problems using an artificial intelligence framework called reinforcement learning (RL). The RL framework allows the management of global supply chains under an integration perspective. The RL approach has remarkable similarities to that of an autonomous agent network (AAN); a similarity that we shall discuss. The RL approach is applied to a case example, namely a networked production system that spans several geographic areas and logistics stages. We discuss the results and provide guidelines and implications for ...


title: Improvement of the accuracy of the microstrip-to-waveguide transition model using measured loss tangent at 76.5 GHz

abstract: The accuracy of the microstrip-to-waveguide transition (MWT) simulation model near 76.5 GHz is shown to increase by using the estimated loss tangent of the dielectric substrate. The loss tangent is estimated by measuring the attenuation through two microstrip lines with different lengths connected between a pair of MWT's in a back-to-back configuration. The approach is validated by comparing the simulated and measured results.


title: Trait anxiety and impoverished prefrontal control of attention

abstract: Previous work links hyper-responsive threat detection to anxiety. Using fMRI, this study finds that highly anxious individuals had reduced prefrontal cortex activity and slower target identification during a response conflict task when the task did not fully use up their attentional resources. Trait anxiety is therefore linked to less prefrontal attentional control, even when there are no threatening stimuli.


title: A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines

abstract: Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as the input dimensionality increases, hence the interest in using feature fusion techniques, able to produce feature sets that are more compact and higher level. A plethora of procedures to fuse original variables for producing new ones has been developed in the past decades. The most basic ones use linear combinations of the original variables, such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), while others find manifold embeddings of lower dimensionality based on non-linear combinations, such as Isomap or LLE (Linear Locally Embedding) techniques. More recently, autoencoders (AEs) have emerged as an alternative to manifold learning for conducting nonlinear feature fusion. Dozens of AE models have been proposed lately, each with its own specific traits. Although many of them can be used to generate reduced feature sets through the fusion of the original ones, there also AEs designed with other applications in mind. The goal of this paper is to provide the reader with a broad view of what an AE is, how they are used for feature fusion, a taxonomy gathering a broad range of models, and how they relate to other classical techniques. In addition, a set of didactic guidelines on how to choose the proper AE for a given task is supplied, together with a discussion of the software tools available. Finally, two case studies illustrate the usage of AEs with datasets of handwritten digits and breast cancer.


title: Exploiting Sparsity to Accelerate Fully Connected Layers of CNN-Based Applications on Mobile SoCs

abstract: Convolutional neural networks (CNNs) are widely employed in many image recognition applications. With the proliferation of embedded and mobile devices, such applications are becoming commonplace on mobile devices. Network pruning is a commonly used strategy to reduce the memory and storage footprints of CNNs on mobile devices. In this article, we propose customized versions of the sparse matrix multiplication algorithm to speed up inference on mobile devices and make it more energy efficient. Specifically, we propose a Block Compressed Sparse Column algorithm and a bit-representation-based algorithm (BitsGEMM) that exploit sparsity to accelerate the fully connected layers of a network on the NVIDIA Jetson TK1 platform. We evaluate the proposed algorithms using real-world object classification and object detection applications. Experiments show that performance speedups can be achieved over the original baseline implementation using cuBLAS. On object detection CNNs, an average speedup of 1.82× is obtained over baseline cuBLAS in the fully connected layer of the VGG model, whereas on classification CNNs, an average speedup of 1.51× is achieved for the fully connected layer of the pruned-VGG model. Energy consumption reduction of 43--46% is also observed due to decreased computational and memory bandwidth demands.


title: Physiological and Performance Effects of Generic versus Specific Aerobic Training in Soccer Players

abstract: The aim of this study was to compare the effects of specific (small-sided games) vs. generic (running) aerobic interval training on physical fitness and objective measures of match performance in soccer. Forty junior players were randomly assigned to either generic (n=20) or specific (n=20) interval training consisting of 4 bouts of 4 min at 90-95 % of maximum heart rate with 3 min active rest periods, completed twice a week. The following outcomes were measured at baseline (Pre), after 4 weeks of pre-season training (Mid), and after a further 8 weeks of training during the regular season (Post): maximum oxygen uptake, lactate threshold (Tlac), running economy at Tlac, a soccer-specific endurance test (Ekblom's circuit), and indices of physical performance during soccer matches (total distance and time spent standing, walking, and at low- and high-intensity running speed). Training load, as quantified by heart rate and rating of perceived exertion, was recorded during all training sessions and was similar between groups. There were significant improvements in aerobic fitness and match performance in both groups of soccer players, especially in response to the first 4 weeks of pre-season training. However, no significant differences between specific and generic aerobic interval training were found in any of the measured variables including soccer specific tests. The results of this study showed that both small-sided games and running are equally effective modes of aerobic interval training in junior soccer players.


title: The Self-Manipulation of My Pervasive, Perceived Vital Energy through Product Use: An Introspective-Praxis Perspective

abstract: Consumer researchers as well as others, have overlooked the perceived vital energy dimension of consumer behavior. To deal with this issue, this article uses an introspective-praxis approach to explore as "thickly" as possible the pervasive role perceived vital energy plays in one consumer's everyday use of products namely The author's own. In doing so, it introduces an energy-based model of product use that encompasses the characteristics/states of that vital energy and the product-related operation's employed by the consumer to manipulate those states. After these aspects of energy are considered, several thematic examples concerning its role and significance in his own consumption are discussed. Finally, implications for future research are considered. Copyright 1991 by the University of Chicago.


title: Assigning change requests to software developers

abstract: SUMMARY The paper presents an approach to recommend a ranked list of expert developers to assist in the implementation of software change requests (e.g., bug reports and feature requests). An Information Retrieval (IR)-based concept location technique is first used to locate source code entities, e.g., files and classes, relevant to a given textual description of a change request. The previous commits from version control repositories of these entities are then mined for expert developers. The role of the IR method in selectively reducing the mining space is different from previous approaches that textually index past change requests and/or commits. The approach is evaluated on change requests from three open-source systems: ArgoUML, Eclipse ,a ndKOffice, across a range of accuracy criteria. The results show that the overall accuracies of the correctly recommended developers are between 47 and 96% for bug reports, and between 43 and 60% for feature requests. Moreover, comparison results with two other recommendation alternatives show that the presented approach outperforms them with a substantial margin. Project leads or developers can use this approach in maintenance tasks immediately after the receipt of a change request in a free-form text. Copyright q 2011 John Wiley & Sons, Ltd.


title: A Scalable System for Community Discovery in Twitter During Hurricane Sandy

abstract: The wide use of micro bloggers such as Twitter offers a valuable and reliable source of information during natural disasters. The big volume of Twitter data calls for a scalable data management system whereas the semi-structured data analysis requires full-text searching function. As a result, it becomes challenging yet essential for disaster response agencies to take full advantage of social media data for decision making in a near-real-time fashion. In this work, we use Lucene to empower HBase with full-text searching ability to build a scalable social media data analytics system for observing and analyzing human behaviors during the Hurricane Sandy disaster. Experiments show the scalability and efficiency of the system. Furthermore, the discovery of communities has the benefit of identifying influential users and tracking the topical changes as the disaster unfolds. We develop a novel approach to discover communities in Twitter by applying spectral clustering algorithm to retweet graph. The topics and influential users of each community are also analyzed and demonstrated using Latent Semantic Indexing (LSI).


title: Online and Incremental Appearance-based SLAM in Highly Dynamic Environments

abstract: In this paper we present a novel method for online and incremental appearance-based localization and mapping in a highly dynamic environment. Using position-invariant robust features (PIRFs), the method can achieve a high rate of recall with 100% precision. It can handle both strong perceptual aliasing and dynamic changes of places efficiently. Its performance also extends beyond conventional images; it is applicable to omnidirectional images for which the major portions of scenes are similar for most places. The proposed PIRF-based Navigation method named PIRF-Nav is evaluated by testing it on two standard datasets in a similar manner as in FAB-MAP and on an additional omnidirectional image dataset that we collected. This extra dataset was collected on 2 days with different specific events, i.e. an open-campus event, to present challenges related to illumination variance and strong dynamic changes, and to test assessment of dynamic scene changes. Results show that PIRF-Nav outperforms FAB-MAP; PIRF-Nav at precision-1 yields a recall rate about twice as high (approximately 80% increase) than that of FAB-MAP. Its computation time is sufficiently short for real-time applications. The method is fully incremental, and requires no offline process for dictionary creation. Additional testing using combined datasets proves that PIRF-Nav can function over the long term and can solve the kidnapped robot problem.


title: • WAXMAN ET AL. Solid-State Color Night Vision: Fusion of Low-Light Visible and Thermal Infrared Imagery Solid-State Color Night Vision: Fusion of Low-Light Visible and Thermal Infrared Imagery

abstract: The automatic feed drill includes a spindle mounted therein for rotating in reciprocating movement. The spindle is rotated by spindle rotation gear that is engaged with the main drive gear which is ultimately driven by an air motor or the like. The feed and retraction movement (reciprocation) is caused by a spindle feed gear that is threaded onto the spindle. The spindle feed gear is engaged with a feed gear which, during feed movement, is driven by the main drive gear, and during retraction movement, is shifted into a position wherein the feed gear is prevented from rotating. The spindle also carries a collar or other abutment that engages a shift lever at the end of the feed movement. The shift lever is moved by the collar, and, due to its interconnection with the feed gear, moves the feed gear out of engagement with the main drive gear and into the position wherein it is prevented from rotating. The apparatus also includes a yieldable stop device that holds the feed gear stopped during retraction movement. In the event that excessive torque is encountered, such as by a lower collar on the spindle engaging the housing, the yieldable stop member rotates at the same speed as the spindle feed gear and thus, prevents further retraction of the spindle and avoids damage to the drill.


title: Active Disturbance Rejection Control for Web Tension Regulation

abstract: A new control method is proposed for tension regulation in a web transport system. It is based on a unique active disturbance rejection control (ADRC) strategy, which actively compensates for dynamic changes in the system and unpredictable external disturbances. A simulation of an industrial application is used to provide realism. The results show the effectiveness of the proposed tension controller in coping with large dynamic variations commonly seen in web tension applications. The remarkable disturbance rejection capability of an ADRC is also demonstrated.


title: Neurobiology of emotion perception I: the neural basis of normal emotion perception

abstract: There is at present limited understanding of the neurobiological basis of the different processes underlying emotion perception. We have aimed to identify potential neural correlates of three processes suggested by appraisalist theories as important for emotion perception: 1) the identification of the emotional significance of a stimulus; 2) the production of an affective state in response to 1; and 3) the regulation of the affective state. In a critical review, we have examined findings from recent animal, human lesion, and functional neuroimaging studies. Findings from these studies indicate that these processes may be dependent upon the functioning of two neural systems: a ventral system, including the amygdala, insula, ventral striatum, and ventral regions of the anterior cingulate gyrus and prefrontal cortex, predominantly important for processes 1 and 2 and automatic regulation of emotional responses; and a dorsal system, including the hippocampus and dorsal regions of anterior cingulate gyrus and prefrontal cortex, predominantly important for process 3. We suggest that the extent to which a stimulus is identified as emotive and is associated with the production of an affective state may be dependent upon levels of activity within these two neural systems.


title: Entrepreneurship, Saving and Social Mobility

abstract: This paper examines entrepreneurship in order to analyze, first, the degree to which the opportunity to start or own a business affects the household's saving behavior and the implication of this behavior for the distribution of wealth and, second, the relationship between the extent of entrepreneurship in the economy and socioeconomic mobility, that is, the movement of families across wealth classes over time. First, a number of stylized facts based on data from the Panel Study of Income Dynamics (PSID) and the Survey of Consumer Finances (SCF) are outlined. They show relevant differences in asset holdings and wealth mobility between entrepreneurs - economic agents that own a business - and workers. Second, a dynamic general equilibrium model of income and wealth distribution with an explicit entrepreneurial choice is developed. The model is calibrated to match the key features of the data, and it is then used to obtain an estimate of the quantitative importance for capital accumulation and wealth concentration of households that undertake entrepreneurial activities, via their different microeconomic behavior. Through the modeling of the entrepreneurial activities, the model economy developed in this study generates a stationary distribution of wealth with a degree of concentration that accounts for the inequality observed in the U. S. economy. The model also successfully replicates the main patterns of socioeconomic mobility in which entrepreneurs experience higher upward mobility than workers.


title: Brain Painting: First Evaluation of a New Brain–Computer Interface Application with ALS-Patients and Healthy Volunteers

abstract: Brain-computer interfaces (BCI) enable paralyzed patients to communicate; however, up to date, no creative expression was possible. The current study investigated the accuracy and user friendliness of P300-Brain Painting, a new BCI-application developed to paint pictures using brain activity only. Two different versions of the P300-Brain Painting application were tested: A coloured matrix tested by a group of ALS-patients (n = 3) and healthy participants (n = 10), and a black & white matrix tested by healthy participants (n = 10). The three ALS-patients achieved high accuracies; two of them reaching above 89% accuracy. In healthy subjects, a comparison between the P300-Brain Painting application (coloured matrix) and the P300-Spelling application revealed significantly lower accuracy and P300 amplitudes for the P300-Brain Painting application. This drop in accuracy and P300 amplitudes was not found when comparing the P300-Spelling application to an adapted, black & white matrix of the P300-Brain Painting application. By employing a black and white matrix, the accuracy of the P300-Brain Painting application was significantly enhanced and reached the accuracy of the P300-Spelling application. ALS patients greatly enjoyed P300-Brain Painting and were able to use the application with the same accuracy as healthy subjects. P300-Brain Painting enables paralyzed patients to express themselves creatively and to participate in the prolific society through exhibitions.


title: A Perspective on Regional and Global Strategies of Multinational Enterprises

abstract: Multinational enterprises (MNEs) are the key drivers of globalization, as they foster increased economic interdependence among national markets. The ultimate test to assess whether these MNEs are global themselves is their actual penetration level of markets across the globe, especially in the broad ‘triad’ markets of NAFTA, the European Union and Asia. Yet, data on the activities of the 500 largest MNEs reveal that very few are successful globally. For 320 of the 380 firms for which geographic sales data are available, an average of 80.3% of total sales are in their home region of the triad. This means that many of the world’s largest firms are not global but regionally based, in terms of breadth and depth of market coverage. Globalization, in terms of a balanced geographic distribution of sales across the triad, thus reflects a special, and rather unusual, outcome of doing international business (IB). The regional concentration of sales has important implications for various strands of mainstream IB research, as well as for the broader managerial debate on the design of optimal strategies and governance structures for MNEs. Journal of International Business Studies (2004) 35, 3–18. doi:10.1057/palgrave. jibs.8400073


title: Does the use of Fibonacci numbers in Planning Poker affect effort estimates?

abstract: Background: The estimation technique Planning Poker is common in agile software development. The cards used to propose an estimate in Planning Poker do not include all numbers, but for example only the numbers 0, ½, 1, 2, 3, 5, 8, 13, 20, 40 and 100. We denote this, somewhat inaccurately, a Fibonacci scale in this paper. In spite of the widespread use of the Fibonacci scale in agile estimation, we do not know much about how this scale influences the estimation process. Aim: Better understanding of the effect of going from a linear scale to a Fibonacci scale in effort estimation. Method: We conducted two empirical studies. In the first study, we gave computer science students the same estimation task. Half of the students estimated the task using the Fibonacci scale and the other half a linear scale. The second study included four estimation teams, each composed of four software professionals, estimating the effort to complete the same ten tasks. Two of the teams estimated the first five tasks using the Fibonacci scale and the last five using the linear scale. The two other teams used the scales in the opposite sequence. Results: We found a median decrease in the effort estimates of 60% (first study) and 26% (second study) when using a Fibonacci scale instead of the traditional linear scale. The scale difference in the effort estimates decreased as the developers' skill increased. Conclusion: The use of a Fibonacci scale, and possibly other non-linear scales, is likely to affect the effort estimates towards lower values compared to linear scales. A possible explanation for this scale-induced effect is that people tend to be biased towards toward the middle of the provided scale, especially when the uncertainty is substantial. The middle value is likely to be perceived as lower for the Fibonacci than for the line


Meta-reasoning: What can we learn from meta-memory?


title: Application of Requirement-oriented Data Quality Evaluation Method

abstract: The data quality directly determines the data further analysis and other applications. With the important of data quality, data is a major asset in software applications and information systems. Getting and ensuring high quality data is critical to the software and related business operations. At present, there are many methods and techniques of data quality assessment or evaluation. Based on the evaluation criteria of data quality, the paper mainly starts from the needs of application software and uses the requirements as a guide in the evaluation process. Define evaluation criteria based on user-defined requirements. In this paper, the method proposed in a practical case has been applied, and achieved good practical results.


title: LSTM Based Multiple Squashing Functions Deep Learning Model for Advanced Traffic Management System Attack Detection

abstract: Advanced Traffic Management System is started as a standalone system and now with the developed technology, it has turned to more complex networks through which many types of attacks are occurring on the system. These attacks are difficult to identify and to resolve them. In this paper, we are focusing on detecting these attacks present in the Advanced Traffic Management systems through the booming concept “Deep learning model”. The model proposed in the paper is the extension of Long Short-Term Memory(LSTM) recurrent model. The basics of Recurrent Neural Networks(RNN) model is taken due to its feedback behavior. The model is applied on a dataset that is collected from the online website and validated the model by coding it in python. By the end of the code execution, the model gives us whether the given test data has any attack in it or not. The model checks for more number of features for attack detection than the LSTM


title: Fingerprint image enhancement using filtering techniques

abstract: Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. In this work we propose two methods for fingerprint image enhancement. The first one is carried out using local histogram equalization, Wiener filtering, and image binarization. The second method use a unique anisotropic filter for direct grayscale enhancement. The results achieved are compared with those obtained through some other methods. Both methods show some improvement in the minutiae detection process in terms of either efficiency or time required.


title: Clustering Time Series Using Unsupervised-Shapelets

abstract: Time series clustering has become an increasingly important research topic over the past decade. Most existing methods for time series clustering rely on distances calculated from the entire raw data using the Euclidean distance or Dynamic Time Warping distance as the distance measure. However, the presence of significant noise, dropouts, or extraneous data can greatly limit the accuracy of clustering in this domain. Moreover, for most real world problems, we cannot expect objects from the same class to be equal in length. As a consequence, most work on time series clustering only considers the clustering of individual time series "behaviors," e.g., individual heart beats or individual gait cycles, and contrives the time series in some way to make them all equal in length. However, contriving the data in such a way is often a harder problem than the clustering itself. In this work, we show that by using only some local patterns and deliberately ignoring the rest of the data, we can mitigate the above problems and cluster time series of different lengths, i.e., cluster one heartbeat with multiple heartbeats. To achieve this we exploit and extend a recently introduced concept in time series data mining called shapelets. Unlike existing work, our work demonstrates for the first time the unintuitive fact that shapelets can be learned from unlabeled time series. We show, with extensive empirical evaluation in diverse domains, that our method is more accurate than existing methods. Moreover, in addition to accurate clustering results, we show that our work also has the potential to give insights into the domains to which it is applied.


title: Quinoxaline-2,3-diones: potential d-amino acid oxidase (DAAO) inhibitors

abstract: A family of quinoxaline-2,3-diones was prepared and evaluated to exhibit d-amino acid oxidase (DAAO) inhibitory activities with IC50 values ranging from 0.6 to 15 μM in vitro. SAR analysis revealed their binding modes with DAAO and provided an important clue for further design of highly potent DAAO inhibitors with novel structures.


title: Spatial-Temporal Event Detection from Geo-Tagged Tweets

abstract: As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA) algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events.


title: A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices

abstract: In a number of disciplines, the data (e.g., graphs, manifolds) to be analyzed are non-Euclidean in nature. Geometric deep learning corresponds to techniques that generalize deep neural network models to such non-Euclidean spaces. Several recent papers have shown how convolutional neural networks (CNNs) can be extended to learn with graph-based data. In this work, we study the setting where the data (or measurements) are ordered, longitudinal or temporal in nature and live on a Riemannian manifold -- this setting is common in a variety of problems in statistical machine learning, vision and medical imaging. We show how recurrent statistical recurrent network models can be defined in such spaces. We give an efficient algorithm and conduct a rigorous analysis of its statistical properties. We perform extensive numerical experiments demonstrating competitive performance with state of the art methods but with significantly less number of parameters. We also show applications to a statistical analysis task in brain imaging, a regime where deep neural network models have only been utilized in limited ways.


title: Phase calibrated ring oscillator PUF design and implementation on FPGAs

abstract: A ring oscillator physical unclonable function (RO PUF) is an application-constrained hardware security primitive that can be used for authentication and key generation. PUFs depend on variability during the fabrication process to produce random outputs that are nevertheless stable across multiple measurements. Unfortunately, RO PUFs are known to be unstable especially when implemented on an Field Programmable Gate Array (FPGA). In this work, we comprehensively evaluate the RO PUF's stability on FPGAs, and we propose a phase calibration process to improve the stability of RO PUFs. The results show that the bit errors in our PUFs are reduced to less than 1%.


title: Learning to recommend with social trust ensemble

abstract: As an indispensable technique in the field of Information Filtering, Recommender System has been well studied and developed both in academia and in industry recently. However, most of current recommender systems suffer the following problems: (1) The large-scale and sparse data of the user-item matrix seriously affect the recommendation quality. As a result, most of the recommender systems cannot easily deal with users who have made very few ratings. (2) The traditional recommender systems assume that all the users are independent and identically distributed; this assumption ignores the connections among users, which is not consistent with the real world recommendations. Aiming at modeling recommender systems more accurately and realistically, we propose a novel probabilistic factor analysis framework, which naturally fuses the users' tastes and their trusted friends' favors together. In this framework, we coin the term Social Trust Ensemble to represent the formulation of the social trust restrictions on the recommender systems. The complexity analysis indicates that our approach can be applied to very large datasets since it scales linearly with the number of observations, while the experimental results show that our method performs better than the state-of-the-art approaches.


title: Vehicle trajectory prediction for adaptive cruise control

abstract: In this paper, we propose a new vehicle trajectory prediction algorithm for adaptive cruise control (ACC). When vehicle trajectory prediction is not precise enough, it is possible for a neighboring vehicle to be detected as a target. Thus, we propose a new method using both yaw rate and curvature rate to precisely predict vehicle trajectory and to resolve an undesirable case in ACC system. The proposed method was validated via CarSim and MATLAB/Simulink. Also, we validated the proposed method via experimental results with a test vehicle on highway system for the practicality.


title: DC ultra-fast solid state circuit breaker using the voltage inductor to activate short circuit protection

abstract: This paper introduces a new DC solid-state circuit breaker (SSCB) concept using a small inductor, which is similar to the inductor used in electro-mechanical breakers. The new SSCB scheme uses the voltage inductor to activate a short circuit protection, in this way the circuit starts to open in the moment of fault condition to prevent the over load current.


title: Design of Dual-Band Microstrip Patch Antenna with Defected Ground Plane for Modern Wireless Applications

abstract: In this paper, we design and analyze a dual-band rectangular microstrip patch antenna with defected ground plane for 2.5 GHz and 3.5 GHz communications. The design concept is to have two slots etched out from the ground plane of a microstrip patch antenna designed for 2.5 GHz operation to enable second frequency band (3.5 GHz) operation. The positions of etched slots are determined using parametric analysis to meet the objectives of large impedance bandwidth and high gains in the desired bands and mismatching in the undesired bands. The proposed antenna provides wide impedance bandwidths of 13.56% (2.3-2.7 GHz) and 10.36% (3.3-3.7 GHz) at center frequencies of 2.5 GHz and 3.5 GHz, respectively. The gains in E-Plane are 6.7 dB and 5.1 dB, and the gains in H-Plane are 6.5 dB and 4.88 dB, for 2.5 GHz and 3.5 GHz, respectively. The proposed dual-band antenna shows monopole-like radiation patterns with higher gains as compared to monopole antennas.


title: Adaptive energy-aware free viewpoint video transmission over wireless networks

abstract: As consumer-level cameras are becoming cheaper and cheaper, the scene of interest can now be captured by multiple cameras simultaneously from different viewpoints. By transmitting texture and depth maps of the views captured using two adjacent cameras, users can synthesize any view in between using depth image based rendering (DIBR) technique such as 3D-warping. This provides users with continuous view angles and is key technique for Free Viewpoint TV (FTV), etc. In this scenario, multiple views must be delivered through bandwidth-limited wireless networks, how to adaptive reduce the source coding rate to fit the bandwidth becomes an important issue. The standard multi-view video codec H.264 MVC explores the inter-view and intra-view correlation to reduce source encoding rate. But all views are required at the client side for decoding any single view, which is apparently not transmission efficient. A better solution is using H.264 to differentially encode the two adjacent views separately, but still this requires double-folder bandwidth comparing to the traditional video streaming. Multiple description coding (MDC) encodes one view into multiple descriptions for robust transmission. This inspires another option: sending only part of each view. This can greatly reduce the source encoding rate, but interpolating skipped frames before synthesizing the middle view consumes mobile devices' precious battery life. Whether to send at a lower source encoding rate but with extra energy consumption or send at a high source encoding rate is nontrivial, and has not been studied in any formal way. In this paper, we investigated both source encoding rate reduction and energy consumption with the bandwidth constraint. We formulated the problem into an optimization problem, i.e., maximizing the system utility, which is defined as the received video quality minus the weighted energy consumption. Simulation results showed that our proposed scheme provides a significant advantage over competing schemes in typical network conditions.


title: A Tutorial on Dual Decomposition and Lagrangian Relaxation for Inference in Natural Language Processing

abstract: Dual decomposition, and more generally Lagrangian relaxation, is a classical method for combinatorial optimization; it has recently been applied to several inference problems in natural language processing (NLP). This tutorial gives an overview of the technique. We describe example algorithms, describe formal guarantees for the method, and describe practical issues in implementing the algorithms. While our examples are predominantly drawn from the NLP literature, the material should be of general relevance to inference problems in machine learning. A central theme of this tutorial is that Lagrangian relaxation is naturally applied in conjunction with a broad class of combinatorial algorithms, allowing inference in models that go significantly beyond previous work on Lagrangian relaxation for inference in graphical models.


title: Efficient Event-Driven Simulation of Large Networks of Spiking Neurons and Dynamical Synapses

abstract: A simulation procedure is described for making feasible large-scale simulations of recurrent neural networks of spiking neurons and plastic synapses. The procedure is applicable if the dynamic variables of both neurons and synapses evolve deterministically between any two successive spikes. Spikes introduce jumps in these variables, and since spike trains are typically noisy, spikes introduce stochasticity into both dynamics. Since all events in the simulation are guided by the arrival of spikes, at neurons or synapses, we name this procedure event-driven.The procedure is described in detail, and its logic and performance are compared with conventional (synchronous) simulations. The main impact of the new approach is a drastic reduction of the computational load incurred upon introduction of dynamic synaptic efficacies, which vary organically as a function of the activities of the pre- and postsynaptic neurons. In fact, the computational load per neuron in the presence of the synaptic dynamics grows linearly with the number of neurons and is only about 6% more than the load with fixed synapses. Even the latter is handled quite efficiently by the algorithm.We illustrate the operation of the algorithm in a specific case with integrate-and-fire neurons and specific spike-driven synaptic dynamics. Both dynamical elements have been found to be naturally implementable in VLSI. This network is simulated to show the effects on the synaptic structure of the presentation of stimuli, as well as the stability of the generated matrix to the neural activity it induces.


title: People Identification Using Gait Via Floor Pressure Sensing and Analysis

abstract: This paper presents an approach to people identification using gait based on floor pressure data. By using a large area high resolution pressure sensing floor, we were able to obtain 3D trajectories of the center of foot pressures over a footstep which contain both the 1D pressure profile and 2D position trajectories of the COP. Based on the 3D COP trajectories a set of features are then extracted and used for people identification together with other features such as stride length and cadence. The Fisher linear discriminant is used as the classifier. Encouraging results have been obtained using the proposed method with an average recognition rate of 94% and false alarm rate of 3% using pair-wise footstep data from 10 subjects.


title: Awan: Locality-Aware Resource Manager for Geo-Distributed Data-Intensive Applications

abstract: Today, many organizations need to operate on datathat is distributed around the globe. This is inevitable due to thenature of data that is generated in different locations such as videofeeds from distributed cameras, log files from distributed servers, and many others. Although centralized cloud platforms havebeen widely used for data-intensive applications, such systemsare not suitable for processing geo-distributed data due to highdata transfer overheads. An alternative approach is to use anEdge Cloud which reduces the network cost of transferringdata by distributing its computations globally. While the EdgeCloud is attractive for geo-distributed data-intensive applications, extending existing cluster computing frameworks to a wide-areaenvironment must account for locality. We propose Awan: anew locality-aware resource manager for geo-distributed dataintensiveapplications. Awan allows resource sharing betweenmultiple computing frameworks while enabling high localityscheduling within each framework. Our experiments with theNebula Edge Cloud on PlanetLab show that Awan achieves up toa 28% increase in locality scheduling which reduces the averagejob turnaround time by approximately 18% compared to existingcluster management mechanisms.


title: Soft-Switching Zeta–Flyback Converter With a Buck–Boost Type of Active Clamp

abstract: This paper presents the system analysis, design consideration, and implementation of a soft-switching zeta-flyback converter to achieve zero-voltage switching (ZVS). In the proposed converter, the zeta and flyback topologies are adopted in the output side to achieve the following features: to share the power components in the transformer primary side, to achieve the partial magnetizing flux reset, and to share the output power. The buck-boost type of active clamp is connected in parallel with the primary side of the isolation transformer to recycle the energy stored in the leakage inductor of isolated transformer and to limit the peak voltage stress of switching devices due to the transformer leakage inductor when the main switch is turned off. The active clamp circuit can make the switching devices to turn on at ZVS. Experimental results taken from a laboratory prototype rated at 240 W, input voltage of 150 V, output voltage of 12 V, and switching frequency of 150 kHz are presented to demonstrate the converter performance. Based on the experimental results, the circuit efficiency is about 90.5% at rated output power, and the output voltage variation is about 1%.


title: Communication intelligence (COMINT) signal processor: A physical realization

abstract: This paper focuses on the hardware implementation of communication intelligence (COMINT) signal processor. The Processor is capable of intercepting at most 3 communication signals and extracting their parameters such as center frequency, bandwidth, modulation type and symbol rate. COMINT signal processor can demodulate BPSK and 8PSK signal among the intercepted signals. Computationally intensive algorithms are implemented in DSP processor, where parallelism is achieved by using FPGA. A Graphical user interface (GUI) provides the capability of controlling the functionality of COMINT signal processor and displays and records the data obtained from processor.


title: Transfer Learning and Deep Feature Extraction for Planktonic Image Data Sets

abstract: Studying marine plankton is critical to assessing the health of the world's oceans. To sample these important populations, oceanographers are increasingly using specially engineered in situ digital imaging systems that produce very large data sets. Most automated annotation efforts have considered data from individual systems in isolation. This is predicated on the assumption that the images from each system are so different that there would be little benefit to considering out-of-domain data. Meanwhile, in the computer vision community, much effort has been dedicated to understanding how using out-of-domain images can improve the performance of machine classifiers. In this paper, we leverage these advances to evaluate how well weights transfer between Convolutional Neural Networks (CNNs) trained on data from two radically different plankton imaging systems. We also examine the utility of CNNs as feature extractors on a third unique plankton data set. Our results indicate that these data sets are perhaps more similar in the eyes of a machine classifier than previously assumed. Further, these tests underscore the value of using the rich feature representations learned by CNNs to classify data in vastly different domains.


title: A Linear Programming Approach for the Three-Dimensional Bin-Packing Problem

abstract: Abstract In this paper we consider the three-dimensional bin packing problem (3D-BPP), when the bins are identical with the aim of minimizing the number of the used bins. We introduce a mixed-integer linear programming formulation (MILP1). Some special valid inequalities will be presented in order to improve the relaxed lower bound of MILP1. A large set of experimental tests has been carried out. The obtained results show that our method provides a satisfactory performance.


title: FatTire: declarative fault tolerance for software-defined networks

abstract: This paper presents FatTire, a new language for writing fault-tolerant network programs. The central feature of this language is a new programming construct based on regular expressions that allows developers to specify the set of paths that packets may take through the network as well as the degree of fault tolerance required. This construct is implemented by a compiler that targets the in-network fast-failover mechanisms provided in recent versions of the Open- Flow standard, and facilitates simple reasoning about network programs even in the presence of failures. We describe the design of FatTire, present algorithms for compiling FatTire programs to OpenFlow switch configurations, describe our prototype FatTire implementation, and demonstrate its use on simple examples.


title: On the LLR criterion based shortening design for LDPC codes

abstract: In shortening based coding design, a certain amount of known information bits (for instance, all 0 bits) will be inserted into each information packet for encoding and be removed before transmission. Since they are known, the decoder can assume a complete codeword in decoding, even those known bits are not delivered at all. Basically, shortening provides a framework to generate a set of more powerful codewords with shorter size and lower rate from one mother LDPC code. In this paper, a novel log-likelihood ratio (LLR) criterion is proposed to identify those shortening information indices for LDPC codes. Firstly, the LLR criterion is introduced to show how to determine the LLR reliability metric for each individual information bit via a simple noise-free decoding step. Then it is proposed to determine the shortened information indices based on the LLR ordering. The relationship between the proposed shortening scheme and the known approaches are highlighted as well. Finally, the irregular QC-LDPC codes in IEEE 802.11ac standard is utilized as an example to verify the proposed LLR criterion based shortening design. And our analysis unveils that the proposed shortening scheme provides an efficient shortening scheme for LDPC codes with lower computational complexity but better reliability performance.


title: Multiview Supervised Dictionary Learning in Speech Emotion Recognition

abstract: Recently, a supervised dictionary learning (SDL) approach based on the Hilbert-Schmidt independence criterion (HSIC) has been proposed that learns the dictionary and the corresponding sparse coefficients in a space where the dependency between the data and the corresponding labels is maximized. In this paper, two multiview dictionary learning techniques are proposed based on this HSIC-based SDL. While one of these two techniques learns one dictionary and the corresponding coefficients in the space of fused features in all views, the other learns one dictionary in each view and subsequently fuses the sparse coefficients in the spaces of learned dictionaries. The effectiveness of the proposed multiview learning techniques in using the complementary information of single views is demonstrated in the application of speech emotion recognition (SER). The fully-continuous sub-challenge (FCSC) of the AVEC 2012 dataset is used in two different views: baseline and spectral energy distribution (SED) feature sets. Four dimensional affects, i.e., arousal, expectation, power, and valence are predicted using the proposed multiview methods as the continuous response variables. The results are compared with the single views, AVEC 2012 baseline system, and also other supervised and unsupervised multiview learning approaches in the literature. Using correlation coefficient as the performance measure in predicting the continuous dimensional affects, it is shown that the proposed approach achieves the highest performance among the rivals. The relative performance of the two proposed multiview techniques and their relationship are also discussed. Particularly, it is shown that by providing an additional constraint on the dictionary of one of these approaches, it becomes the same as the other.


title: A Neural Clickbait Detection Engine

abstract: In an age where people are becoming increasing likely to trust information found through online media, journalists have begun employing techniques to lure readers to articles by using catchy headlines, called clickbait. These headlines entice the user into clicking through the article whilst not providing information relevant to the headline itself. Previous methods of detecting clickbait have explored techniques heavily dependent on feature engineering, with little experimentation having been tried with neural network architectures. We introduce a novel model combining recurrent neural networks, attention layers and image embeddings. Our model uses a combination of distributed word embeddings derived from unannotated corpora, character level embeddings calculated through Convolutional Neural Networks. These representations are passed through a bidirectional LSTM with an attention layer. The image embeddings are also learnt from large data using CNNs. Experimental results show that our model achieves an F1 score of 65.37% beating the previous benchmark of 55.21%.


title: Trust in School: A Pathway to Inhibit Teacher Burnout?.

abstract: Purpose – The purpose of this paper is to consider trust as an important relational source in schools by exploring whether trust lowers teacher burnout. The authors examine how trust relationships with different school parties such as the principal relate to distinct dimensions of teacher burnout. The authors further analyze whether school-level trust additionally influences burnout. In doing this, the authors account for other teacher and school characteristics. Design/methodology/approach – The authors use quantitative data gathered during the 2008-2009 school year from 673 teachers across 58 elementary schools in Flanders (i.e. the northern Dutch-speaking region of Belgium). Because teacher and school characteristics are simultaneously related to burnout, multilevel modeling is applied. Findings – Trust can act as a buffer against teacher burnout. Teachers’ trust in students demonstrates the strongest association with burnout compared to trust in principals or colleagues. Exploring relationships of tru...


title: Speed Control of DC Motor using Fuzzy Logic based on

abstract: Speed control of DC Motor is vital in many applications. In this paper, an effort has been made to control the speed of the DC motor using fuzzy logic control (FLC) based on LabVIEW (Laboratory Virtual Instrument Engineering Workbench) program. LabVIEW provides a graphical programming environment suited for high-level or system-level design. The fuzzy logic controller designed to apply the required control voltage that sent to dc motor based on fuzzy rule base of motor speed error (e) and change of speed error (Ce). In this paper results of FLC, PI and PID Controller are compared. The simulation results demonstrate that the response of DC motor with FLC show a satisfactory well damped control performance.


title: Polyphenol-enriched oolong tea increases fecal lipid excretion

abstract: OBJECTIVE ::: To assess possibility of polyphenol-enriched oolong tea to reduce dietary lipid absorption in humans. ::: ::: ::: DESIGN ::: Twelve healthy adult subjects, three males and nine females, aged (mean+/-s.d.) 22.0+/-1.8 years, respectively, were randomly divided into two groups. The participants were followed a double-blind placebo-controlled crossover design, including 7-day washout periods and 10-day treatment periods. During the treatment periods, subjects were given about 38 g of lipids from potato chips (19 g each within 30 min after lunch and dinner) and total 750 ml beverages (placebo- or polyphenol-enriched oolong tea) at three meals. Blood samples were collected for biochemical examination at days 8, 18, 25 and 35 of the study period. On the last 3 days of each treatment period, feces were collected to measure the excretion of lipids. ::: ::: ::: RESULTS ::: Lipid excretion into feces was significantly higher in the polyphenol-enriched oolong tea period (19.3+/-12.9 g/3 day) than in the placebo period (9.4+/-7.3 g/3 day) (P < 0.01). Cholesterol excretion tended to increase in polyphenol-enriched oolong tea period (1.8+/-1.2 g/3 day) compared with that of placebo (1.2+/-0.6 g/3 day) (P = 0.056). ::: ::: ::: CONCLUSIONS ::: The results of this study indicated that polyphenol-enriched oolong tea could increase lipid excretion into feces when subjects took high-lipid diet.


title: Iterative Copy-Move Forgery Detection Based on a New Interest Point Detector

abstract: In this paper, we propose a novel copy-move forgery detection scheme that can accurately localize duplicated regions with a reasonable computational cost. To this end, a new interest point detector is proposed utilizing the advantages of both block-based and traditional keypoint-based methods. The detected keypoints adaptively cover the entire image, even low contrast regions, based on a uniqueness metric. Moreover, a new filtering algorithm is employed, which can effectively prune the falsely matched regions. Considering the new interest point detector, an iterative improvement strategy is proposed. The whole procedure is iterated along with adjusting the keypoints density based on the achieved information. The experimental results demonstrate that the proposed scheme outperforms the state-of-the-art methods using two public benchmark databases.


title: Vection and cybersickness generated by head-and-display motion in the Oculus Rift

abstract: Cybersickness is often experienced when viewing virtual environments through head-mounted displays (HMDs). This study examined whether vection (i.e., illusory self-motion) and mismatches between perceived and physical head motions contribute to such adverse experiences. Observers made oscillatory yaw head rotations while viewing stereoscopic optic flow through an Oculus Rift HMD. Vection and cybersickness were measured under 3 conditions of visual compensation for physical head movements: “compensated”, “uncompensated”, and “inversely compensated”. When a nearer aperture was simulated by the HMD, vection was found to be strongest in the “compensated” condition and weakest in the “inversely compensated” condition. However, vection was similar for all 3 conditions during full-field exposures. Cybersickness was most severe for the “inversely compensated” condition, but was not different for the other two conditions. We conclude that mismatches between perceived and physical head-movements can contribute strongly to cybersickness. The relationship between vection and cybersickness is weaker and appears complex.


title: Control and Experiment of Pulsewidth-Modulated Modular Multilevel Converters

abstract: A modular multilevel converter (MMC) is one of the next-generation multilevel converters intended for high- or medium-voltage power conversion without transformers. The MMC is based on cascade connection of multiple bidirectional chopper-cells per leg, thus requiring voltage-balancing control of the multiple floating DC capacitors. However, no paper has made an explicit discussion on voltage-balancing control with theoretical and experimental verifications. This paper deals with two types of pulsewidth-modulated modular multilevel converters (PWM- MMCs) with focus on their circuit configurations and voltage-balancing control. Combination of averaging and balancing controls enables the PWM-MMCs to achieve voltage balancing without any external circuit. The viability of the PWM-MMCs, as well as the effectiveness of the voltage-balancing control, is confirmed by simulation and experiment.


title: Mutual-Structure for Joint Filtering

abstract: Previous joint/guided filters directly transfer structural information from the reference to the target image. In this paper, we analyze the major drawback--that is, there may be completely different edges in the two images. Simply considering all patterns could introduce significant errors. To address this issue, we propose the concept of mutual-structure, which refers to the structural information that is contained in both images and thus can be safely enhanced by joint filtering. We also use an untraditional objective function that can be efficiently optimized to yield mutual structure. Our method results in important edge preserving property, which greatly benefits depth completion, optical flow estimation, image enhancement, stereo matching, to name a few.


title: SCHEDULING IN CLOUD COMPUTING

abstract: Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism works as a vital role in the cloud computing. Thus my protocol is designed to minimize the switching time, improve the resource utilization and also improve the server performance and throughput. This method or protocol is based on scheduling the jobs in the cloud and to solve the drawbacks in the existing protocols. Here we assign the priority to the job which gives better performance to the computer and try my best to minimize the waiting time and switching time. Best effort has been made to manage the scheduling of jobs for solving drawbacks of existing protocols and also improvise the efficiency and throughput of the server.


title: AFA-PredNet: The Action Modulation Within Predictive Coding

abstract: The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action. We propose a neural architecture in which such regularities of active inference are encoded hierarchically. We further suggest that this encoding emerges during the embodied learning process when the appropriate action is selected to minimize the prediction error in perception. Therefore, this predictive stream in the sensorimotor loop is generated in a top-down manner. Specifically, it is constantly modulated by the motor actions and is updated by the bottom-up prediction error signals. In this way, the top-down prediction originally comes from the prior experience from both perception and action representing the higher levels of this hierarchical cognition. In our proposed embodied model, we extend the PredNet Network, a hierarchical predictive coding network, with the motor action units implemented by a multi-layer perceptron network (MLP) to modulate the network top-down prediction. Two experiments, a minimalistic world experiment, and a mobile robot experiment are conducted to evaluate the proposed model in a qualitative way. In the neural representation, it can be observed that the causal inference of predictive percept from motor actions can be also observed while the agent is interacting with the environment.


title: Analysis on credit card fraud detection methods

abstract: Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.


title: Detecting finger gestures with a wrist worn piezoelectric sensor array

abstract: In this paper, we demonstrate a novel approach for the use of piezoelectric pressure sensors on a wearable wrist band to detect the occurrence of individual finger gestures. The system is designed to be wearer independent and require no training for the wearer or the system to perform accurate gesture detection. We used continuous signal windowing to identify bulk changes of several signal features before, during, and after a gesture was made by the wearer. For a range of window lengths and two filtering choices, a thresholding method was applied to the signals to determine the occurrence of a gesture. The error rate for missed and incorrectly labelled gestures were calculated for unfiltered and filtered data, as well as for various window lengths and thresholds. We found that longer window lengths resulted in fewer errors, that the maximum value within a window was a good indicator of whether an event occurred, and that thresholds needed to be sufficiently greater than the signal average, but too large a threshold value leads to missed events.


title: Computer Vision and Image Analysis based Techniques for Automatic Characterization of Fruits – a Review

abstract: paper presents the recent developments and application of image analysis and computer vision system in an automatic fruit recognition system. It is essential to throw light on basic concepts and technologies associated with computer vision system, a tool used in image analysis of fruit characterization. In India, in view of the ever-increasing population, losses in handling and processing and the increased expectation of food products of high quality and safety standards, there is a need for the growth of accurate, fast and objective quality determination of fruits. A number of challenges had to be overcome to enable the system to perform automatic recognition of the kind of fruit or fruit variety using the images from the camera. Several types of fruits are subject to significant variation in color and texture, depending on how ripe they are. There are many processes in agriculture where decisions are made based on the appearance of the product. Applications for grading the fruit by its quality, size or ripeness are based on its appearance, as well as a decision on whether it is healthy or diseased. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on an automatic fruit characterization system.


title: Neural Photo Editing with Introspective Adversarial Networks

abstract: The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation. We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images. To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN. Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method. We validate our contributions on CelebA, SVHN, and CIFAR-100, and produce samples and reconstructions with high visual fidelity.


title: MarioQA: Answering Questions by Watching Gameplay Videos

abstract: We present a new benchmark dataset for video question answering (VideoQA) designed to evaluate algorithms' capability of spatio-temporal event understanding. Existing datasets either require very high-level reasoning from multi-modal information to find answers, or is mostly composed of the questions that can be answered by watching a single frame. Therefore, they are not suitable to evaluate models' real capacity and flexibility for VideoQA. To overcome such critical limitations, we focus on event-centric questions that require understanding temporal relation between multiple events in videos. An interesting idea in dataset construction process is that question-answer pairs are automatically generated from Super Mario video gameplays given a set of question templates. We also tackle VideoQA problem in the new dataset, referred to as MarioQA, by proposing spatio-temporal attention models based on deep neural networks. Our experiments show that the proposed deep neural network models with attention have meaningful performance improvement over several baselines.


title: Put that where? voice and gesture at the graphics interface

abstract: A person stands in front of a large projection screen on which is shown a checked floor. They say, "Make a table," and a wooden table appears in the middle of the floor."On the table, place a vase," they gesture using a fist relative to palm of their other hand to show the relative location of the vase on the table. A vase appears at the correct location."Next to the table place a chair." A chair appears to the right of the table."Rotate it like this," while rotating their hand causes the chair to turn towards the table."View the scene from this direction," they say while pointing one hand towards the palm of the other. The scene rotates to match their hand orientation.In a matter of moments, a simple scene has been created using natural speech and gesture. The interface of the future? Not at all; Koons, Thorisson and Bolt demonstrated this work in 1992 [23]. Although research such as this has shown the value of combining speech and gesture at the interface, most computer graphics are still being developed with tools no more intuitive than a mouse and keyboard. This need not be the case. Current speech and gesture technologies make multimodal interfaces with combined voice and gesture input easily achievable. There are several commercial versions of continuous dictation software currently available, while tablets and pens are widely supported in graphics applications. However, having this capability doesn't mean that voice and gesture should be added to every modeling package in a haphazard manner. There are numerous issues that must be addressed in order to develop an intuitive interface that uses the strengths of both input modalities.In this article we describe motivations for adding voice and gesture to graphical applications, review previous work showing different ways these modalities may be used and outline some general interface guidelines. Finally, we give an overview of promising areas for future research. Our motivation for writing this is to spur developers to build compelling interfaces that will make speech and gesture as common on the desktop as the keyboard and mouse.


title: Active Learning for Data Streams Under Concept Drift and Concept Evolution

abstract: Data streams classification is an important problem however, poses many challenges. Since the length of the data is theoretically infinite, it is impractical to store and ::: process all the historical data. Data streams also experience change of its underlying dis-tribution (concept drift), thus the classifier must adapt. Another challenge of data stream classification is the possible emergence and disappearance of classes which is known as (concept evolution) problem. On the top of these challenges, acquiring labels with such large data is expensive. In this paper, we propose a stream-based active learning (AL) strategy (SAL) that handles the aforementioned challenges. SAL aims at querying the labels of samples which results in optimizing the expected future error. It handles ::: concept drift and concept evolution by adapting to the change in the stream. Furthermore, as a part of the error reduction process, SAL handles the sampling bias problem and queries the samples that caused the change i.e., drifted samples or samples coming from new classes. To tackle the lack of prior knowledge about the streaming data, non-parametric Bayesian modelling is adopted namely the two representations of Dirichlet process; Dirichlet mixture models and stick breaking process. Empirical results obtained on real-world benchmarks show the high performance of the proposed SAL method compared to the state-of-the-art methods.


title: Supervised Hash Coding With Deep Neural Network for Environment Perception of Intelligent Vehicles

abstract: Image content analysis is an important surround perception modality of intelligent vehicles. In order to efficiently recognize the on-road environment based on image content analysis from the large-scale scene database, relevant images retrieval becomes one of the fundamental problems. To improve the efficiency of calculating similarities between images, hashing techniques have received increasing attentions. For most existing hash methods, the suboptimal binary codes are generated, as the hand-crafted feature representation is not optimally compatible with the binary codes. In this paper, a one-stage supervised deep hashing framework (SDHP) is proposed to learn high-quality binary codes. A deep convolutional neural network is implemented, and we enforce the learned codes to meet the following criterions: 1) similar images should be encoded into similar binary codes, and vice versa; 2) the quantization loss from Euclidean space to Hamming space should be minimized; and 3) the learned codes should be evenly distributed. The method is further extended into SDHP+ to improve the discriminative power of binary codes. Extensive experimental comparisons with state-of-the-art hashing algorithms are conducted on CIFAR-10 and NUS-WIDE, the MAP of SDHP reaches to 87.67% and 77.48% with 48 b, respectively, and the MAP of SDHP+ reaches to 91.16%, 81.08% with 12 b, 48 b on CIFAR-10 and NUS-WIDE, respectively. It illustrates that the proposed method can obviously improve the search accuracy.


title: The molecular landscape of head and neck cancer

abstract: Head and neck squamous cell carcinomas (HNSCCs) arise in the mucosal linings of the upper aerodigestive tract and are unexpectedly heterogeneous in nature. Classical risk factors are smoking and excessive alcohol consumption, and in recent years, the role of human papillomavirus (HPV) has emerged, particularly in oropharyngeal tumours. HPV-induced oropharyngeal tumours are considered a separate disease entity, which recently has manifested in an adapted prognostic staging system while the results of de-intensified treatment trials are awaited. Carcinogenesis caused by HPV in the mucosal linings of the upper aerodigestive tract remains an enigma, but with some recent observations, a model can be proposed. In 2015, The Cancer Genome Atlas (TCGA) consortium published a comprehensive molecular catalogue on HNSCC. Frequent mutations of novel druggable oncogenes were not demonstrated, but the existence of a subgroup of genetically distinct HPV-negative head and neck tumours with favourable prognoses was confirmed. Tumours can be further subclassified based on genomic profiling. However, the amount of molecular data is currently overwhelming and requires detailed biological interpretation. It also became apparent that HNSCC is a disease characterized by frequent mutations that create neoantigens, indicating that immunotherapies might be effective. In 2016, the first results of immunotherapy trials with immune checkpoint inhibitors were published, and these may be considered as a paradigm shift in head and neck oncology.


title: Toward a coherent theory of environmentally significant behavior

abstract: This article develops a conceptual framework for advancing theories of environmentally significant individual behavior and reports on the attempts of the author’s research group and others to develop such a theory. It discusses definitions of environmentally significant behavior; classifies the behaviors and their causes; assesses theories of environmentalism, focusing especially on value-belief-norm theory; evaluates the relationship between environmental concern and behavior; and summarizes evidence on the factors that determine environmentally significant behaviors and that can effectively alter them. The article concludes by presenting some major propositions supported by available research and some principles for guiding future research and informing the design of behavioral programs for environmental protection. Recent developments in theory and research give hope for building the understanding needed to effectively alter human behaviors that contribute to environmental problems. This article develops a conceptual framework for the theory of environmentally significant individual behavior, reports on developments toward such a theory, and addresses five issues critical to building a theory that can inform efforts to promote proenvironmental behavior.


title: Throwing Away the Key: Has the Adam Walsh Act Lowered the Threshold for Sexually Violent Predator Commitments Too Far?

abstract: The Adam Walsh Act allows a person to be committed indefinitely as a sexually violent predator (SVP) who has never been convicted of, or even charged with, a sex crime. The government need only convince a judge by clear and convincing evidence that the person committed, or attempted, certain offenses. I consider the question US v. Comstock explicitly left open: Does the Act violate due process? I ask whether persons subject to SVP commitment deserve procedural protection. Contrary to belief, the rate of sex offender recidivism is quite small, which makes a robust due process regime necessary. I then contrast the due process rights of SVPs with those of other individuals whose mental state is at issue and demonstrate that SVPs have fewer protections. I discuss the Supreme Court’s justification for reduced SVP due process. I next compare state SVP protections with the constitutional minima afforded by the Court. This data provide a map to SVP legislation that should prove of practical importance to judges and practitioners. One might suspect that states would have afforded only the minimum, but most have provided more. Finally, I evaluate the Act from a public policy and constitutional perspective and conclude that it violates common sense and due process.


title: Attribute-based people search in surveillance environments

abstract: We propose a novel framework for searching for people in surveillance environments. Rather than relying on face recognition technology, which is known to be sensitive to typical surveillance conditions such as lighting changes, face pose variation, and low-resolution imagery, we approach the problem in a different way: we search for people based on a parsing of human parts and their attributes, including facial hair, eyewear, clothing color, etc. These attributes can be extracted using detectors learned from large amounts of training data. A complete system that implements our framework is presented. At the interface, the user can specify a set of personal characteristics, and the system then retrieves events that match the provided description. For example, a possible query is “show me the bald people who entered a given building last Saturday wearing a red shirt and sunglasses.” This capability is useful in several applications, such as finding suspects or missing people. To evaluate the performance of our approach, we present extensive experiments on a set of images collected from the Internet, on infrared imagery, and on two-and-a-half months of video from a real surveillance environment. We are not aware of any similar surveillance system capable of automatically finding people in video based on their fine-grained body parts and attributes.


title: A two-output series-resonant inverter for induction-heating cooking appliances

abstract: Multiple-burner induction-heating cooking appliances are suitable for using multiple-output inverters. Some common approaches use several single-output inverters or a single-output inverter multiplexing the loads along the time periodically. By specifying a two-output series-resonant high-frequency inverter, a new inverter is obtained fulfilling the requirements. The synthesized converter can be considered as a two-output extension of a full-bridge topology. It allows the control of the two outputs, simultaneously and independently, up to their rated powers saving component count compared with the two-converter solution and providing a higher utilization of electronics. To verify theoretical predictions, the proposed converter is designed and tested experimentally in an induction-heating appliance prototype. A fixed-frequency control strategy is digitally implemented with good final performances for the application, including ZVS operation for active devices and a quick heating function. Although the work is focused on low-power induction heating, it can be probably useful for other power electronic applications.


title: Ultrastructural analysis of 3 hyaluronic acid soft-tissue fillers using scanning electron microscopy.

abstract: BACKGROUND ::: Although hyaluronic acid (HA) specifications such as molecular weight and particle size are fairly well characterized, little information about HA ultrastructural and morphologic characteristics has been reported in clinical literature. ::: ::: ::: OBJECTIVE ::: To examine uniformity of HA structure, the effects of extrusion, and lidocaine dilution of 3 commercially available HA soft-tissue fillers. ::: ::: ::: MATERIALS AND METHODS ::: Using scanning electron microscopy and energy-dispersive x-ray analysis, investigators examined the soft-tissue fillers at various magnifications for ultrastructural detail and elemental distributions. ::: ::: ::: RESULTS ::: All HAs contained oxygen, carbon, and sodium, but with uneven distributions. Irregular particulate matter was present in RES but BEL and JUV were largely particle free. Spacing was more uniform in BEL than JUV and JUV was more uniform than RES. Lidocaine had no apparent effect on morphology; extrusion through a 30-G needle had no effect on ultrastructure. ::: ::: ::: CONCLUSION ::: Descriptions of the ultrastructural compositions and nature of BEL, JUV, and RES are helpful for matching the areas to be treated with the HA soft-tissue filler architecture. Lidocaine and extrusion through a 30-G needle exerted no influence on HA structure. Belotero Balance shows consistency throughout the syringe and across manufactured lots.


title: Incorporating Luminance, Depth and Color Information by a Fusion-Based Network for Semantic Segmentation

abstract: Semantic segmentation has made encouraging progress due to the success of deep convolutional networks in recent years. Meanwhile, depth sensors become prevalent nowadays; thus, depth maps can be acquired more easily. However, there are few studies that focus on the RGB-D semantic segmentation task. Exploiting the depth information effectiveness to improve performance is a challenge. In this paper, we propose a novel solution named LDFNet, which incorporates Luminance, Depth and Color information by a fusion-based network. It includes a sub-network to process depth maps and employs luminance images to assist the depth information in processes. LDFNet outperforms the other state-of-art systems on the Cityscapes dataset, and its inference speed is faster than most of the existing networks. The experimental results show the effectiveness of the proposed multi-modal fusion network and its potential for practical applications.


title: Teachers’ Performance Evaluation in Higher Educational Institution using Data Mining Technique

abstract: Educational Data Mining (EDM) is an evolving field exploring pedagogical data by applying different machine learning techniques/tools. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education. The main objective of any educational institution is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge that predicts teachers’ performance. This study presents an efficient system model for evaluation and prediction of teachers’ performance in higher institutions of learning using data mining technologies. To achieve the objectives of this work, a two-layered classifier system was designed; it consists of an Artificial Neural Network (ANN) and Decision Tree. The classifier system was tested successfully using case study data from a Nigerian University in the South West of Nigeria. The data consists of academic qualifications for teachers as well as their experiences and grades of students in courses they taught among others. The attribute selected were evaluated using two feature selection methods in order to get a subset of the attributes that would make for a compact and accurate predictive model. The WEKA machine learning tool was used for the mining. The results show that, among the six attributes used, Working Experience, and Rank are rated the best two attributes that contributed mostly to the performance of teachers in this study. Also, considering the time taken to build the models and performance accuracy level, C4.5 decision tree outperformed the other two algorithms (ID3 and MLP) with good performance of 83.5% accuracy level and acceptable kappa statistics of 0.743. It does mean that C4.5 decision tree is best algorithm suitable for predicting teachers’ performance in relation to the other two algorithms in this work. General Terms Data Mining


title: Nonlinear Control of a Quarter Car Active Suspension

abstract: In this paper, a quarter-car suspension system with a hydraulic actuator, acting under the effects of coulomb friction, is examined. Nonlinear control laws will be developed and compared with both a passive system and a system acting under linear control. An observer-based nonlinear controller and an adaptive nonlinear controller will also be presented. Experimental results will be compared with both theoretical and simulation results.


title: Explicit design of transfer functions for volume-rendered images by combining histograms, thumbnails, and sketch-based interaction

abstract: Visual quality of volume rendering for medical imagery strongly depends on the underlying transfer function. Conventional Windows–Icons–Menus–Pointer interfaces typically refer the user to browse a lengthy catalog of predefined transfer functions or to pain-staking refine the transfer function by clicking and dragging several independent handles. To turn the standard design process less difficult and tedious, this paper proposes novel interactions on a sketch-based interface that supports the design of 1D transfer functions via touch gestures to directly control voxel opacity and easily assign colors. User can select different types of transfer function shapes including ramp function, free hand curve drawing, and slider bars similar to those of a mixing table. An assorted array of thumbnails provides an overview of the data when editing the transfer function. User performance is evaluated by comparing the time and effort necessary to complete a number of tests with sketch-based and conventional interfaces. Users were able to more rapidly explore and understand volume data using the sketch-based interface, as the number of design iterations necessary to obtain a desirable transfer function was reduced. In addition, informal evaluation sessions carried out with professionals (two senior radiologists, a general surgeon and two scientific illustrators) provided valuable feedback on how suitable the sketch-based interface is for illustration, patient communication and medical education.


title: Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation

abstract: Learning semantic segmentation models under image-level supervision is far more challenging than under fully supervised setting. Without knowing the exact pixel-label correspondence, most weakly-supervised methods rely on external models to infer pseudo pixel-level labels for training semantic segmentation models. In this paper, we aim to develop a single neural network without resorting to any external models. We propose a novel self-guided strategy to fully utilize features learned across multiple levels to progressively generate the dense pseudo labels. First, we use high-level features as class-specific localization maps to roughly locate the classes. Next, we propose an affinity-guided method to encourage each localization map to be consistent with their intermediate level features. Third, we adopt the training image itself as guidance and propose a self-guided refinement to further transfer the image's inherent structure into the maps. Finally, we derive pseudo pixel-level labels from these localization maps and use the pseudo labels as ground truth to train the semantic segmentation model. Our proposed self-guided strategy is a unified framework, which is built on a single network and alternatively updates the feature representation and refines localization maps during the training procedure. Experimental results on PASCAL VOC 2012 segmentation benchmark demonstrate that our method outperforms other weakly-supervised methods under the same setting.


title: A minimal convolutional neural network for handwritten digit recognition

abstract: The contribution of this paper is to bridge the gap on understanding the mathematical structure and the computational implementation of a convolutional neural network using a minimal model. The proposed minimal convolutional neural network is presented using a layering approach. This approach provides a clear understanding of the main mathematical operations in a convolutional neural network. Hence, it benefits beginners and non-mathematical prolific researchers to understand the operation of a convolutional neural network without having an intimidating experience. A handwritten digit recognition using MNIST handwritten digit dataset is used to experiment the performance of the proposed minimal convolutional neural network.


title: Bridging the appearance gap: Multi-experience localization for long-term visual teach and repeat

abstract: Vision-based, route-following algorithms enable autonomous robots to repeat manually taught paths over long distances using inexpensive vision sensors. However, these methods struggle with long-term, outdoor operation due to the challenges of environmental appearance change caused by lighting, weather, and seasons. While techniques exist to address appearance change by using multiple experiences over different environmental conditions, they either provide topological-only localization, require several manually taught experiences in different conditions, or require extensive offline mapping to produce metric localization. For real-world use, we would like to localize metrically to a single manually taught route and gather additional visual experiences during autonomous operations. Accordingly, we propose a novel multi-experience localization (MEL) algorithm developed specifically for route-following applications; it provides continuous, six-degree-of-freedom (6DoF) localization with relative uncertainty to a privileged (manually taught) path using several experiences simultaneously. We validate our algorithm through two experiments: i) an offline performance analysis on a 9km subset of a challenging 27km route-traversal dataset and ii) an online field trial where we demonstrate autonomy on a small 250m loop over the course of a sunny day. Both exhibit significant appearance change due to lighting variation. Through these experiments we show that safe localization can be achieved by bridging the appearance gap.