Client-Side Defense Against Web-Based Identity Theft

Web spoofing is a significant problem involving fraudulent email and web sites that trick unsuspecting users into revealing private information. We discuss some aspects of common attacks and propose a framework for client-side defense: a browser plug-in that examines web pages and warns the user when requests for data may be part of a spoof attack. While the plugin, SpoofGuard, has been tested using actual sites obtained through government agencies concerned about the problem, we expect that web spoofing and other forms of identity theft will be continuing problems in


Computational intelligence in sports: Challenges and opportunities within a new research domain

Computational intelligence is a branch of artificial intelligence that comprises algorithms inspired by nature. The common characteristics of all these algorithms is their collective intelligence and adaptability to a changing environment. Due to their efficiency and simplicity, these algorithms have been employed for problem solving across social and natural sciences. The aim of this paper is to demonstrate that nature-inspired algorithms are also useful within the domain of sport, in particular for obtaining safe and effective training plans targeting various aspects of performance. We outline the benefits and opportunities of applying computational intelligence in sports, and we also comment on the pitfalls and challenges for the future development of this emerging research domain. © 2015 Elsevier Inc. All rights reserved.


Vehicle-to-Vehicle Communication: Fair Transmit Power Control for Safety-Critical Information

Direct radio-based vehicle-to-vehicle communication can help prevent accidents by providing accurate and up-to-date local status and hazard information to the driver. In this paper, we assume that two types of messages are used for traffic safety-related communication: 1) Periodic messages (ldquobeaconsrdquo) that are sent by all vehicles to inform their neighbors about their current status (i.e., position) and 2) event-driven messages that are sent whenever a hazard has been detected. In IEEE 802.11 distributed-coordination-function-based vehicular networks, interferences and packet collisions can lead to the failure of the reception of safety-critical information, in particular when the beaconing load leads to an almost-saturated channel, as it could easily happen in many critical vehicular traffic conditions. In this paper, we demonstrate the importance of transmit power control to avoid saturated channel conditions and ensure the best use of the channel for safety-related purposes. We propose a distributed transmit power control method based on a strict fairness criterion, i.e., distributed fair power adjustment for vehicular environments (D-FPAV), to control the load of periodic messages on the channel. The benefits are twofold: 1) The bandwidth is made available for higher priority data like dissemination of warnings, and 2) beacons from different vehicles are treated with ldquoequal rights,rdquo and therefore, the best possible reception under the available bandwidth constraints is ensured. We formally prove the fairness of the proposed approach. Then, we make use of the ns-2 simulator that was significantly enhanced by realistic highway mobility patterns, improved radio propagation, receiver models, and the IEEE 802.11p specifications to show the beneficial impact of D-FPAV for safety-related communications. We finally put forward a method, i.e., emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages and show that EMDV benefits of the beaconing load control provided by D-FPAV with respect to both probability of reception and latency.


Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering

Word embeddings have become widelyused in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words. In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clustering empirical distributions. By using the Wasserstein distance between distributions, the word-to-word semantic relationship is taken into account in a principled way. The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets. More importantly, the method provides an effective framework for determining when and how much word embeddings contribute to document analysis. Experimental results with multiple embedding models are reported.


Cyberbullying perpetration and victimization among adolescents in Hong Kong

a r t i c l e i n f o Cyberbullying is a growing concern worldwide. Using a sample of 1917 secondary adolescents from seven schools, five psychometric measures (self-efficacy, empathy level, feelings regarding a harmonious school, sense of belonging to the school, and psychosocial wellbeing) and five scales regarding bullying experiences (cyber-and traditional bullying perpetration and victimization; reactions to cyberbullying victimization) were administered to explore the prevalence of cyberbullying in Hong Kong. Findings indicated that male adolescents were more likely than female adolescents to cyberbully others and to be cyber-victimized. Cyberbullying perpe-tration and victimization were found to be negatively associated with the adolescents' psychosocial health and sense of belonging to school. Cyber-and traditional bullying were positively correlated. Multivariate analyses indicated that being male, having a low sense of belonging to school, involvement in traditional bullying perpetra-tion, and experiencing cyber-victimization were associated with an increased propensity to cyberbully others. Technology is advancing rapidly, and peer harassment and aggression are no longer limited to traditional bullying through physical contact. Over the past decade, information and communication technology (ICT) has become increasingly important in the lives of adolescents. In a report by Lenhart, Madden, and Hitlin (2005), it is estimated that close to 90% of American adolescents aged 12 to 17 years surf the Inter-net, with 51% of them using it on a daily basis. Nearly half of the adolescents surveyed have personal mobile phones, and 33% have used a mobile phone to send a text message (Lenhart et al., 2005). Such heavy use of the Internet is not novel among adolescents in Hong Kong. Many empirical studies have been conducted on Hong Kong adolescents on their excessive and/or addictive use of the Internet. Findings of recent studies indicate that a substantially high prevalence rate of internet addiction is reported among Hong Kong adolescents (range In reality, the heavy usage of ICT such as instant mes-saging, e-mail, text messaging, blogs, and social networking sites not only allows adolescents to connect with friends and family, but at the same time also creates the potential to meet and interact with others in harmful ways (Ybarra, Diener-West, & Leaf, 2007). Cyberbullying or online bullying is one such growing concern. Traditional bullying is a widespread problem in both school and community settings, and has long been researched by scholars in general, involves an individual being exposed to negative actions by one or more individuals regularly …


Dynamic Modeling and Performance Analysis of a Grid-Connected Current-Source Inverter-Based Photovoltaic System

Voltage-source inverter (VSI) topology is widely used for grid interfacing of distributed generation (DG) systems. However, when employed as the power conditioning unit in photovoltaic (PV) systems, VSI normally requires another power electronic converter stage to step up the voltage, thus adding to the cost and complexity of the system. To make the proliferation of grid-connected PV systems a successful business option, the cost, performance, and life expectancy of the power electronic interface need to be improved. The current-source inverter (CSI) offers advantages over VSI in terms of inherent boosting and short-circuit protection capabilities, direct output current controllability, and ac-side simpler filter structure. Research on CSI-based DG is still in its infancy. This paper focuses on modeling, control, and steady-state and transient performances of a PV system based on CSI. It also performs a comparative performance evaluation of VSI-based and CSI-based PV systems under transient and fault conditions. Analytical expectations are verified using simulations in the Power System Computer Aided Design/Electromagnetic Transient Including DC (PSCAD/EMTDC) environment, based on a detailed system model.


3D deeply supervised network for automated segmentation of volumetric medical images

While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our proposed 3D DSN.


Targeted Employee Retention : Performance-Based and Job-Related Differences in Reported Reasons for Staying

A content model of 12 retention factors is developed in the context of previous theory and research. Coding of open-ended responses from 24,829 employees in the leisure and hospitality industry lends support to the identified framework and reveals that job satisfaction, extrinsic rewards, constituent attachments, organizational commitment, and organizational prestige were the most frequently mentioned reasons for staying. Advancement opportunities and organizational prestige were more common reasons for staying among high performers and non-hourly workers, and extrinsic rewards was more common among low performers and hourly employees, providing support for ease/desirability of movement and psychological contract rationales. The findings highlight the importance of differentiating human resource management practices when the goal is to retain those employees valued most by the organization.


An Alpha-wave-based binaural beat sound control system using fuzzy logic and autoregressive forecasting model

We are developing a new real-time control system for customizing auditory stimulus (the binaural beat sound) by judging user alpha waves to entrain a userpsilas feeling in the most relaxed way. Since brainwave activity provides the necessary predictive information for arousal states, we use an autoregressive forecasting model to estimate the frequency response series of the alpha frequency bands and the inverted-U concept to determine the userpsilas arousal state. A fuzzy logic controller is also employed to regulate the binaural beat control signal on a forecasting error signal. Our system allows comfortable user self-relaxation. The results of experiments confirm the constructed systempsilas effectiveness and necessity.


A Comparative Study of Stereovision Algorithms

Stereo vision has been and continues to be one of the most researched domains of computer vision, having many applications, among them, allowing the depth extraction of a scene. This paper provides a comparative study of stereo vision and matching algorithms, used to solve the correspondence problem. The study of matching algorithms was followed by experiments on the Middlebury benchmarks. The tests focused on a comparison of 6 stereovision methods. In order to assess the performance, RMS and some statistics related were computed. In order to emphasize the advantages of each stereo algorithm considered, two-frame methods have been employed, both local and global. The experiments conducted have shown that the best results are obtained by Graph Cuts. Unfortunately, this has a higher computational cost. If high quality is not an issue in applications, local methods provide reasonable results within a much lower time-frame and offer the possibility of parallel


Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people’s activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state-of-the-art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph-structure of the citation patterns for the community of experts, and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combing all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state-of-the-art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.


A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images

BACKGROUND AND OBJECTIVES
Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning.

METHODS
We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works.

RESULTS
With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches.

CONCLUSIONS
We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.


Single-Fed Low-Profile High-Gain Circularly Polarized Slotted Cavity Antenna Using a High-Order Mode

In this letter, a single-fed low-profile high-gain circularly polarized slotted cavity antenna using a high-order cavity mode, i.e., TE440 mode, is proposed. The proposed antenna has a simple structure that consists of a radiating linearly polarized slotted cavity antenna and a linear-to-circular polarization converter. An antenna prototype operating at WLAN 5.8-GHz band fabricated with low-cost standard printed circuit board (PCB) process is exemplified to validate the proposed concept. Measured results compared to their simulated counterparts are presented, and a good agreement between simulation and measurement is obtained.


VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation

In this paper, we summarize VAMS 2017 - a workshop on value-aware and multistakeholder recommendation co-located with RecSys 2017. The workshop encouraged forward-thinking papers in this new area of recommender systems research and obtained a diverse set of responses ranging from application results to research overviews.


Articulated pose estimation with flexible mixtures-of-parts

We describe a method for human pose estimation in static images based on a novel representation of part models. Notably, we do not use articulated limb parts, but rather capture orientation with a mixture of templates for each part. We describe a general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations. We show that such relations can capture notions of local rigidity. When co-occurrence and spatial relations are tree-structured, our model can be efficiently optimized with dynamic programming. We present experimental results on standard benchmarks for pose estimation that indicate our approach is the state-of-the-art system for pose estimation, outperforming past work by 50% while being orders of magnitude faster.


Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems

Automatically solving algebra word problems has raised considerable interest recently. Existing state-of-the-art approaches mainly rely on learning from human annotated equations. In this paper, we demonstrate that it is possible to efficiently mine algebra problems and their numerical solutions with little to no manual effort. To leverage the mined dataset, we propose a novel structured-output learning algorithm that aims to learn from both explicit (e.g., equations) and implicit (e.g., solutions) supervision signals jointly. Enabled by this new algorithm, our model gains 4.6% absolute improvement in accuracy on the ALG514 benchmark compared to the one without using implicit supervision. The final model also outperforms the current state-of-the-art approach by 3%.


Fault investigations on die-cast copper rotors

It has been long known to the market that, the use of copper in the rotor in place of aluminium, taking advantage of copper's high conductivity, can be an economic solution for various applications, including electric vehicle, in the development of energy efficient motors. In comparison to aluminium, copper die-casting is extremely difficult to perform due to its high density, viscosity and melting point. Owing to these reasons, the copper die-cast rotors will evince several manufacturing problems. The objective of this paper is to identify the various defects in the manufacturing of copper die-cast rotor, and identify the lamination-coating-condition that sustains copper die-cast pressure and temperature. This paper evaluates the effectiveness of lamination coating on limiting the eddy current loss, and the recoating process to improve, in case the eddy current losses are in excess due to copper die-casting.


The recognition of rice images by UAV based on capsule network

It is important to recognize the rice image captured by unmanned aerial vehicle (UAV) for monitoring the growth of rice and preventing the diseases and pests. Aiming at the image recognition, we use rice images captured by UAV as our data source, the structure of capsule network (CapsNet) is built to recognize rice images in this paper. The images are preprocessed through histogram equalization method into grayscale images and through superpixel algorithm into the superpixel segmentation results. The both results are output into the CapsNet. The function of CapsNet is to perform the reverse analysis of rice images. The CapsNet consists of five layers: an input layer, a convolution layer, a primary capsules layer, a digital capsules layer and an output layer. The CapsNet trains classification and predicts the output vector based on routing-by-agreement protocol. Therefore, the features of rice image by UAV can be precisely and efficiently extracted. The method is more convenient than the traditional artificial recognition. It provides the scientific support and reference for decision-making process of precision agriculture.


A functional taxonomy of wireless sensor network devices

With the availability of cheap computing technology and the standardization of different wireless communication paradigms, wireless sensor networks (WSNs) are becoming a reality. This is visible in the increasing amount of research being done in the WSN area, and the growing number of companies offering commercial WSN solutions. In this regard, we realize that there is a need for an unambiguous classification and definition of WSN devices. Such a classification scheme would benefit the WSN research and industrial community. We consider the spectrum of wireless devices ranging from passive RF devices to gateway devices for the classification. The classification scheme is based on functionality of the devices. This scheme is analogous to an object-oriented classification, where an object is classified on the basis of its functionality and is described by its attributes. Attributes of the wireless devices are grouped into five broad categories: communication, sensing, power, memory, and "other features". Each of these are further classified to provide sufficient details that are required for a typical WSN application


Permittivity and Loss Tangent Characterization for Garment Antennas Based on a New Matrix-Pencil Two-Line Method

The emergence of wearable antennas to be integrated into garments has revealed the need for a careful electromagnetic characterization of textile materials. Therefore, we propose in this paper a new matrix-pencil two-line method that removes perturbations in the calculated effective permittivity and loss tangent which are caused by imperfect deembedding and inhomogeneities of the textile microstrip line structure. The approach has been rigorously validated for high-frequency laminates by comparing measured and simulated data for the resonance frequency of antennas designed using the calculated parameters. The method has been successfully applied to characterize the permittivity and loss tangent of a variety of textile materials and up to a frequency of 10 GHz. Furthermore it is shown that the use of electrotextiles in antenna design influences the effective substrate permittivity.


Mining geographic-temporal-semantic patterns in trajectories for location prediction

In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly treat such predictions as just a type of location recommendation, that is, they predict the next location of a user using location recommenders. However, an user usually visits somewhere for reasons other than interestingness. In this article, we propose a novel mining-based location prediction approach called Geographic-Temporal-Semantic-based Location Prediction (GTS-LP), which takes into account a user's geographic-triggered intentions, temporal-triggered intentions, and semantic-triggered intentions, to estimate the probability of the user in visiting a location. The core idea underlying our proposal is the discovery of trajectory patterns of users, namely GTS patterns, to capture frequent movements triggered by the three kinds of intentions. To achieve this goal, we define a new trajectory pattern to capture the key properties of the behaviors that are motivated by the three kinds of intentions from trajectories of users. In our GTS-LP approach, we propose a series of novel matching strategies to calculate the similarity between the current movement of a user and discovered GTS patterns based on various moving intentions. On the basis of similitude, we make an online prediction as to the location the user intends to visit. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that explores the geographic, temporal, and semantic properties simultaneously. By means of a comprehensive evaluation using various real trajectory datasets, we show that our proposed GTS-LP approach delivers excellent performance and significantly outperforms existing state-of-the-art location prediction methods.


Feature Selection and Kernel Learning for Local Learning-Based Clustering

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.


Using Phase Instead of Optical Flow for Action Recognition

Currently, the most common motion representation for action recognition is optical flow. Optical flow is based on particle tracking which adheres to a Lagrangian perspective on dynamics. In contrast to the Lagrangian perspective, the Eulerian model of dynamics does not track, but describes local changes. For video, an Eulerian phase-based motion representation, using complex steerable filters, has been successfully employed recently for motion magnification and video frame interpolation. Inspired by these previous works, here, we proposes learning Eulerian motion representations in a deep architecture for action recognition. We learn filters in the complex domain in an end-to-end manner. We design these complex filters to resemble complex Gabor filters, typically employed for phase-information extraction. We propose a phaseinformation extraction module, based on these complex filters, that can be used in any network architecture for extracting Eulerian representations. We experimentally analyze the added value of Eulerian motion representations, as extracted by our proposed phase extraction module, and compare with existing motion representations based on optical flow, on the UCF101 dataset.


A generalized taxonomy of explanations styles for traditional and social recommender systems

Recommender systems usually provide explanations of their recommendations to better help users to choose products, activities or even friends. Up until now, the type of an explanation style was considered in accordance to the recommender system that employed it. This relation was one-to-one, meaning that for each different recommender systems category, there was a different explanation style category. However, this kind of one-to-one correspondence can be considered as over-simplistic and non generalizable. In contrast, we consider three fundamental resources that can be used in an explanation: users, items and features and any combination of them. In this survey, we define (i) the Human style of explanation, which provides explanations based on similar users, (ii) the Item style of explanation, which is based on choices made by a user on similar items and (iii) the Feature style of explanation, which explains the recommendation based on item features rated by the user beforehand. By using any combination of the aforementioned styles we can also define the Hybrid style of explanation. We demonstrate how these styles are put into practice, by presenting recommender systems that employ them. Moreover, since there is inadequate research in the impact of social web in contemporary recommender systems and their explanation styles, we study new emerged social recommender systems i.e. Facebook Connect explanations (HuffPo, Netflix, etc.) and geo-social explanations that combine geographical with social data (Gowalla, Facebook Places, etc.). Finally, we summarize the results of three different user studies, to support that Hybrid is the most effective explanation style, since it incorporates all other styles.


Online Learning for Time Series Prediction

In this paper we address the problem of predicting a time seri e using the ARMA (autoregressive moving average) model, under minimal assumptions on the noi se terms. Using regret minimization techniques, we develop effective online learning algorith ms for the prediction problem, withoutassuming that the noise terms are Gaussian, identically distribu ted or even independent. Furthermore, we show that our algorithm’s performances asymptotically app roaches the performance of the best ARMA model in hindsight.


Logarithmic regret algorithms for online convex optimization

In an online convex optimization problem a decision-maker makes a sequence of decisions, i.e., chooses a sequence of points in Euclidean space, from a fixed feasible set. After each point is chosen, it encounters a sequence of (possibly unrelated) convex cost functions. Zinkevich (ICML 2003) introduced this framework, which models many natural repeated decision-making problems and generalizes many existing problems such as Prediction from Expert Advice and Cover’s Universal Portfolios. Zinkevich showed that a simple online gradient descent algorithm achieves additive regret $O(\sqrt{T})$ , for an arbitrary sequence of T convex cost functions (of bounded gradients), with respect to the best single decision in hindsight. In this paper, we give algorithms that achieve regret O(log (T)) for an arbitrary sequence of strictly convex functions (with bounded first and second derivatives). This mirrors what has been done for the special cases of prediction from expert advice by Kivinen and Warmuth (EuroCOLT 1999), and Universal Portfolios by Cover (Math. Finance 1:1–19, 1991). We propose several algorithms achieving logarithmic regret, which besides being more general are also much more efficient to implement. The main new ideas give rise to an efficient algorithm based on the Newton method for optimization, a new tool in the field. Our analysis shows a surprising connection between the natural follow-the-leader approach and the Newton method. We also analyze other algorithms, which tie together several different previous approaches including follow-the-leader, exponential weighting, Cover’s algorithm and gradient descent.


Averaging Expert Predictions

We consider algorithms for combining advice from a set of experts. In each trial, the algorithm receives the predictions of the experts and produces its own prediction. A loss function is applied to measure the discrepancy between the predictions and actual observations. The algorithm keeps a weight for each expert. At each trial the weights are first used to help produce the prediction and then updated according to the observed outcome. Our starting point is Vovk’s Aggregating Algorithm, in which the weights have a simple form: the weight of an expert decreases exponentially as a function of the loss incurred by the expert. The prediction of the Aggregating Algorithm is typically a nonlinear function of the weights and the experts’ predictions. We analyze here a simplified algorithm in which the weights are as in the original Aggregating Algorithm, but the prediction is simply the weighted average of the experts’ predictions. We show that for a large class of loss functions, even with the simplified prediction rule the additional loss of the algorithm over the loss of the best expert is at most c ln n, where n is the number of experts and c a constant that depends on the loss function. Thus, the bound is of the same form as the known bounds for the Aggregating Algorithm, although the constants here are not quite as good. We use relative entropy to rewrite the bounds in a stronger form and to motivate the update.


Online Convex Programming and Generalized Infinitesimal Gradient Ascent

Convex programming involves a convex set F ⊆ R and a convex function c : F → R. The goal of convex programming is to find a point in F which minimizes c. In this paper, we introduce online convex programming. In online convex programming, the convex set is known in advance, but in each step of some repeated optimization problem, one must select a point in F before seeing the cost function for that step. This can be used to model factory production, farm production, and many other industrial optimization problems where one is unaware of the value of the items produced until they have already been constructed. We introduce an algorithm for this domain, apply it to repeated games, and show that it is really a generalization of infinitesimal gradient ascent, and the results here imply that generalized infinitesimal gradient ascent (GIGA) is universally consistent.


Prediction, learning, and games

Make more knowledge even in less time every day. You may not always spend your time and money to go abroad and get the experience and knowledge by yourself. Reading is a good alternative to do in getting this desirable knowledge and experience. You may gain many things from experiencing directly, but of course it will spend much money. So here, by reading prediction learning and games, you can take more advantages with limited budget.


Air Craft Winglet Design and Performance : Cant Angle Effect

A winglet is a device used to improve the efficiency of aircraft by lowering the lift induced drag caused by wingtip vortices. It is a vertical or angled extension at the tips of each wing. Winglets improve efficiency by diffusing the shed wingtip vortex, which in turn reduces the drag due to lift and improves the wing’s lift over drag ratio Winglets increase the effective aspect ratio of a wing without adding greatly to the structural stress and hence necessary weight of its structure. In this research, a numerical validation procedure (by FLUENT ®, computational fluid dynamics software with The Spalart-Allmaras turbulence model) is described for determination and estimation aerodynamic characteristics of three dimension subsonic rectangular wing (with NACA653218airfoil cross section). It was observed that at the present work a good agreement between the numerical study and the experimental work. This paper describes a CFD 3-dimensional winglets analysis that was performed on a Cessna wing of NACA2412 cross sectional airfoil. The wing has span 13.16 m, root chord 1.857 m, tip chord 0.928 m, sweep angle 11 degree and taper ratio 0.5. The present study shows wing without winglet and wing with winglet at cant angle 0, 30 and 45 degree. A CFD simulation performs by to compare of aerodynamics characteristics of lift coefficient CL, drag coefficient CD and lift to drag ratio, L/D lift, pathlines and pressure contours. The models run at a Mach number of 0.2 at sea level. The pressure and temperature of air at this height are 101.325 kPa and 288.2 K respectively. The results show that the wing with winglet can increase the lift by ratio approximately 12%. The wing with winglet can decrease the drag by ratio approximately 4%. The wing with winglet can increase lift to drag, L/D by about 11% along different phases of flight.


Highly compact 1T-1R architecture (4F2 footprint) involving fully CMOS compatible vertical GAA nano-pillar transistors and oxide-based RRAM cells exhibiting excellent NVM properties and ultra-low power operation

For the first time, nano-meter-scaled 1T-1R non-volatile memory (NVM) architecture comprising of RRAM cells built on vertical GAA nano-pillar transistors, either junction-less or junction-based, is systematically investigated. Transistors are fabricated using fully CMOS compatible technology and RRAM cells are stacked onto the tip of the nano-pillars (with a diameter down to ~37nm) to achieve a compact 4F2 footprint. In addition, through this platform, different RRAM stacks comprising CMOS friendly materials are studied, and it is found that TiN/Ni/HfO2/n+-Si RRAM cells show excellent switching properties in either bipolar or unipolar mode, including (1) ultra-low switching current/power: SET ~20nA/85nW and RESET ~200pA/700pW, (2) multi-level switchability, (3) good endurance, >105, (4) satisfactory retention, 10 years at 85oC; and (5) fast switching speed ~50ns. Moreover, this vertical (gate-all-around) GAA nano-pillar based 1T-1R architecture provides a more direct and flexible test vehicle to verify the scalability and functionality of RRAM candidates with a dimension close to actual application.


Sentiment analysis and classification based on textual reviews

Mining is used to help people to extract valuable information from large amount of data. Sentiment analysis focuses on the analysis and understanding of the emotions from the text patterns. It identifies the opinion or attitude that a person has towards a topic or an object and it seeks to identify the viewpoint underlying a text span. Sentiment analysis is useful in social media monitoring to automatically characterize the overall feeling or mood of consumers as reflected in social media toward a specific brand or company and determine whether they are viewed positively or negatively on the web. This new form of analysis has been widely adopted in customer relation management especially in the context of complaint management. For automating the task of classifying a single topic textual review, document-level sentiment classification is used for expressing a positive or negative sentiment. So analyzing sentiment using Multi-theme document is very difficult and the accuracy in the classification is less. The document level classification approximately classifies the sentiment using Bag of words in Support Vector Machine (SVM) algorithm. In proposed work, a new algorithm called Sentiment Fuzzy Classification algorithm with parts of speech tags is used to improve the classification accuracy on the benchmark dataset of Movies reviews dataset.


Categorizing User Sessions at Pinterest

Di erent users can use a given Internet application in many different ways. The ability to record detailed event logs of user inapplication activity allows us to discover ways in which the application is being used. This enables personalization and also leads to important insights with actionable business and product outcomes. Here we study the problem of user session categorization, where the goal is to automatically discover categories/classes of user insession behavior using event logs, and then consistently categorize each user session into the discovered classes. We develop a three stage approach which uses clustering to discover categories of sessions, then builds classi ers to classify new sessions into the discovered categories, and nally performs daily classi cation in a distributed pipeline. An important innovation of our approach is selecting a set of events as long-tail features, and replacing them with a new feature that is less sensitive to product experimentation and logging changes. This allows for robust and stable identi cation of session types even though the underlying application is constantly changing. We deploy the approach to Pinterest and demonstrate its e ectiveness. We discover insights that have consequences for product monetization, growth, and design. Our solution classi es millions of user sessions daily and leads to actionable insights.


A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems

This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.


Fingerprinting Electronic Control Units for Vehicle Intrusion Detection

As more software modules and external interfaces are getting added on vehicles, new attacks and vulnerabilities are emerging. Researchers have demonstrated how to compromise in-vehicle Electronic Control Units (ECUs) and control the vehicle maneuver. To counter these vulnerabilities, various types of defense mechanisms have been proposed, but they have not been able to meet the need of strong protection for safety-critical ECUs against in-vehicle network attacks. To mitigate this deficiency, we propose an anomaly-based intrusion detection system (IDS), called Clock-based IDS (CIDS). It measures and then exploits the intervals of periodic in-vehicle messages for fingerprinting ECUs. The thusderived fingerprints are then used for constructing a baseline of ECUs’ clock behaviors with the Recursive Least Squares (RLS) algorithm. Based on this baseline, CIDS uses Cumulative Sum (CUSUM) to detect any abnormal shifts in the identification errors — a clear sign of intrusion. This allows quick identification of in-vehicle network intrusions with a low false-positive rate of 0.055%. Unlike state-of-the-art IDSs, if an attack is detected, CIDS’s fingerprinting of ECUs also facilitates a rootcause analysis; identifying which ECU mounted the attack. Our experiments on a CAN bus prototype and on real vehicles have shown CIDS to be able to detect a wide range of in-vehicle network attacks.


A3P: adaptive policy prediction for shared images over popular content sharing sites

More and more people go online today and share their personal images using popular web services like Picasa. While enjoying the convenience brought by advanced technology, people also become aware of the privacy issues of data being shared. Recent studies have highlighted that people expect more tools to allow them to regain control over their privacy. In this work, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. In particular, we examine the role of image content and metadata as possible indicators of users' privacy preferences. We propose a two-level image classification framework to obtain image categories which may be associated with similar policies. Then, we develop a policy prediction algorithm to automatically generate a policy for each newly uploaded image. Most importantly, the generated policy will follow the trend of the user's privacy concerns evolved with time. We have conducted an extensive user study and the results demonstrate effectiveness of our system with the prediction accuracy around 90%.


Universal Planning Networks-Long Version + Supplementary

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities.


Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application

Data-driven soft sensors have been widely utilized in industrial processes to estimate the critical quality variables which are intractable to directly measure online through physical devices. Due to the low sampling rate of quality variables, most of the soft sensors are developed on small number of labeled samples and the large number of unlabeled process data is discarded. The loss of information greatly limits the improvement of quality prediction accuracy. One of the main issues of data-driven soft sensor is to furthest exploit the information contained in all available process data. This paper proposes a semisupervised deep learning model for soft sensor development based on the hierarchical extreme learning machine (HELM). First, the deep network structure of autoencoders is implemented for unsupervised feature extraction with all the process samples. Then, extreme learning machine is utilized for regression through appending the quality variable. Meanwhile, the manifold regularization method is introduced for semisupervised model training. The new method can not only deeply extract the information that the data contains, but learn more from the extra unlabeled samples as well. The proposed semisupervised HELM method is applied in a high–low transformer to estimate the carbon monoxide content, which shows a significant improvement of the prediction accuracy, compared to traditional methods.


A 45nm Logic Technology with High-k+Metal Gate Transistors, Strained Silicon, 9 Cu Interconnect Layers, 193nm Dry Patterning, and 100% Pb-free Packaging

A 45 nm logic technology is described that for the first time incorporates high-k + metal gate transistors in a high volume manufacturing process. The transistors feature 1.0 nm EOT high-k gate dielectric, dual band edge workfunction metal gates and third generation strained silicon, resulting in the highest drive currents yet reported for NMOS and PMOS. The technology also features trench contact based local routing, 9 layers of copper interconnect with low-k ILD, low cost 193 nm dry patterning, and 100% Pb-free packaging. Process yield, performance and reliability are demonstrated on 153 Mb SRAM arrays with SRAM cell size of 0.346 mum2, and on multiple microprocessors.


An Intra-Panel Interface With Clock-Embedded Differential Signaling for TFT-LCD Systems

In this paper, an intra-panel interface with a clock embedded differential signaling for TFT-LCD systems is proposed. The proposed interface reduces the number of signal lines between the timing controller and the column drivers in a TFT-LCD panel by adopting the embedded clock scheme. The protocol of the proposed interface provides a delay-locked loop (DLL)-based clock recovery scheme for the receiver. The timing controller and the column driver integrated with the proposed interface are fabricated in 0.13- μm CMOS process technology and 0.18-μm high voltage CMOS process technology, respectively. The proposed interface is verified on a 47-inch Full High-Definition (FHD) (1920RGB×1080) TFT-LCD panel with 8-bit RGB and 120-Hz driving technology. The maximum data rate per differential pair was measured to be as high as 2.0 Gb/s in a wafer test.


Associations among Wine Grape Microbiome, Metabolome, and Fermentation Behavior Suggest Microbial Contribution to Regional Wine Characteristics

UNLABELLED
Regionally distinct wine characteristics (terroir) are an important aspect of wine production and consumer appreciation. Microbial activity is an integral part of wine production, and grape and wine microbiota present regionally defined patterns associated with vineyard and climatic conditions, but the degree to which these microbial patterns associate with the chemical composition of wine is unclear. Through a longitudinal survey of over 200 commercial wine fermentations, we demonstrate that both grape microbiota and wine metabolite profiles distinguish viticultural area designations and individual vineyards within Napa and Sonoma Counties, California. Associations among wine microbiota and fermentation characteristics suggest new links between microbiota, fermentation performance, and wine properties. The bacterial and fungal consortia of wine fermentations, composed from vineyard and winery sources, correlate with the chemical composition of the finished wines and predict metabolite abundances in finished wines using machine learning models. The use of postharvest microbiota as an early predictor of wine chemical composition is unprecedented and potentially poses a new paradigm for quality control of agricultural products. These findings add further evidence that microbial activity is associated with wine terroir

IMPORTANCE
Wine production is a multi-billion-dollar global industry for which microbial control and wine chemical composition are crucial aspects of quality. Terroir is an important feature of consumer appreciation and wine culture, but the many factors that contribute to terroir are nebulous. We show that grape and wine microbiota exhibit regional patterns that correlate with wine chemical composition, suggesting that the grape microbiome may influence terroir In addition to enriching our understanding of how growing region and wine properties interact, this may provide further economic incentive for agricultural and enological practices that maintain regional microbial biodiversity.


Suspect tracking based on call logs analysis and visualization

In Thailand, investigator can track and find the suspects by using call logs from suspects' phone numbers and their contacts. In many cases, the suspects changed their phone numbers to avoid tracking. The problem is that the investigators have difficulty to track these suspects from their call logs. Our hypothesis is that each user has a unique calling behavior pattern. The calling pattern is importance for tracking suspect's telephone number. To compare the calling patterns, we consider common contact groups. Thus, the aim of this project is to develop a call logs tracking system which can predict a set of new possible suspect's phone numbers and present their contacts' connection with our network diagram visualization based on Graph database (Neo4j). This system will be very necessary for investigators because it can save investigators' time from analyzing excessive call logs data. The system can predict the possible suspect's phone numbers. Furthermore, our visualization can enhance human's sight ability to connect the relation among related phone numbers. Finally, the experimental results on real call logs demonstrate that our method can track telephone number approximately 69% of single possible suspect phone number's matching while 89% of multiple possible suspect phone numbers' matching.


High-performance complex event processing over streams

In this paper, we present the design, implementation, and evaluation of a system that executes complex event queries over real-time streams of RFID readings encoded as events. These complex event queries filter and correlate events to match specific patterns, and transform the relevant events into new composite events for the use of external monitoring applications. Stream-based execution of these queries enables time-critical actions to be taken in environments such as supply chain management, surveillance and facility management, healthcare, etc. We first propose a complex event language that significantly extends existing event languages to meet the needs of a range of RFID-enabled monitoring applications. We then describe a query plan-based approach to efficiently implementing this language. Our approach uses native operators to efficiently handle query-defined sequences, which are a key component of complex event processing, and pipeline such sequences to subsequent operators that are built by leveraging relational techniques. We also develop a large suite of optimization techniques to address challenges such as large sliding windows and intermediate result sizes. We demonstrate the effectiveness of our approach through a detailed performance analysis of our prototype implementation under a range of data and query workloads as well as through a comparison to a state-of-the-art stream processor.


A 2.4 GHz Interferer-Resilient Wake-Up Receiver Using A Dual-IF Multi-Stage N-Path Architecture

A 2.4 GHz interferer-resilient wake-up receiver for ultra-low power wireless sensor nodes uses an uncertain-IF dualconversion topology, combining a distributed multi-stage N-path filtering technique with an unlocked low-Q resonator-referred local oscillator. This structure provides narrow-band selectivity and strong immunity against interferers, while avoiding expensive external resonant components such as BAW resonators or crystals. The 65 nm CMOS receiver prototype provides a sensitivity of -97 dBm and a carrier-to-interferer ratio better than -27 dB at 5 MHz offset, for a data rate of 10 kb/s at a 10-3 bit error rate, while consuming 99 μW from a 0.5 V voltage supply under continuous operation.


A survey of smoothing techniques for ME models

In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood (ML) training for exponential models, and like other ML methods is prone to overfitting of training data. Several smoothing methods for ME models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of related models. In this work, we survey previous work in ME smoothing and compare the performance of several of these algorithms with conventional techniques for smoothing -gram language models. Because of the mature body of research in -gram model smoothing and the close connection between ME and conventional -gram models, this domain is well-suited to gauge the performance of ME smoothing methods. Over a large number of data sets, we find that fuzzy ME smoothing performs as well as or better than all other algorithms under consideration. We contrast this method with previous -gram smoothing methods to explain its superior performance.


Calcium currents in hair cells isolated from semicircular canals of the frog.

L-type and R-type Ca(2+) currents were detected in frog semicircular canal hair cells. The former was noninactivating and nifedipine-sensitive (5 microM); the latter, partially inactivated, was resistant to omega-conotoxin GVIA (5 microM), omega-conotoxin MVIIC (5 microM), and omega-agatoxin IVA (0.4 microM), but was sensitive to mibefradil (10 microM). Both currents were sensitive to Ni(2+) and Cd(2+) (>10 microM). In some cells the L-type current amplitude increased almost twofold upon repetitive stimulation, whereas the R-type current remained unaffected. Eventually, run-down occurred for both currents, but was prevented by the protease inhibitor calpastatin. The R-type current peak component ran down first, without changing its plateau, suggesting that two channel types generate the R-type current. This peak component appeared at -40 mV, reached a maximal value at -30 mV, and became undetectable for voltages > or =0 mV, suggestive of a novel transient current: its inactivation was indeed reversibly removed when Ba(2+) was the charge carrier. The L-type current and the R-type current plateau were appreciable at -60 mV and peaked at -20 mV: the former current did not reverse for voltages up to +60 mV, the latter reversed between +30 and +60 mV due to an outward Cs(+) current flowing through the same Ca(2+) channel. The physiological role of these currents on hair cell function is discussed.


Increasing pre-kindergarten early literacy skills in children with developmental disabilities and delays.

Two hundred and nine children receiving early childhood special education services for developmental disabilities or delays who also had behavioral, social, or attentional difficulties were included in a study of an intervention to increase school readiness, including early literacy skills. Results showed that the intervention had a significant positive effect on children's literacy skills from baseline to the end of summer before the start of kindergarten (d=.14). The intervention also had significant indirect effects on teacher ratings of children's literacy skills during the fall of their kindergarten year (β=.09). Additionally, when scores were compared to standard benchmarks, a greater percentage of the children who received the intervention moved from being at risk for reading difficulties to having low risk. Overall, this study demonstrates that a school readiness intervention delivered prior to the start of kindergarten may help increase children's early literacy skills.


Deep Learning for Imbalanced Multimedia Data Classification

Classification of imbalanced data is an important research problem as lots of real-world data sets have skewed class distributions in which the majority of data instances (examples) belong to one class and far fewer instances belong to others. While in many applications, the minority instances actually represent the concept of interest (e.g., fraud in banking operations, abnormal cell in medical data, etc.), a classifier induced from an imbalanced data set is more likely to be biased towards the majority class and show very poor classification accuracy on the minority class. Despite extensive research efforts, imbalanced data classification remains one of the most challenging problems in data mining and machine learning, especially for multimedia data. To tackle this challenge, in this paper, we propose an extended deep learning approach to achieve promising performance in classifying skewed multimedia data sets. Specifically, we investigate the integration of bootstrapping methods and a state-of-the-art deep learning approach, Convolutional Neural Networks (CNNs), with extensive empirical studies. Considering the fact that deep learning approaches such as CNNs are usually computationally expensive, we propose to feed low-level features to CNNs and prove its feasibility in achieving promising performance while saving a lot of training time. The experimental results show the effectiveness of our framework in classifying severely imbalanced data in the TRECVID data set.


Clinical performance of porcelain laminate veneers: outcomes of the aesthetic pre-evaluative temporary (APT) technique.

This article evaluates the long-term clinical performance of porcelain laminate veneers bonded to teeth prepared with the use of an additive mock-up and aesthetic pre-evaluative temporary (APT) technique over a 12-year period. Sixty-six patients were restored with 580 porcelain laminate veneers. The technique, used for diagnosis, esthetic design, tooth preparation, and provisional restoration fabrication, was based on the APT protocol. The influence of several factors on the durability of veneers was analyzed according to pre- and postoperative parameters. With utilization of the APT restoration, over 80% of tooth preparations were confined to the dental enamel. Over 12 years, 42 laminate veneers failed, but when the preparations were limited to the enamel, the failure rate resulting from debonding and microleakage decreased to 0%. Porcelain laminate veneers presented a successful clinical performance in terms of marginal adaptation, discoloration, gingival recession, secondary caries, postoperative sensitivity, and satisfaction with restoration shade at the end of 12 years. The APT technique facilitated diagnosis, communication, and preparation, providing predictability for the restorative treatment. Limiting the preparation depth to the enamel surface significantly increases the performance of porcelain laminate veneers.


A framework for consciousness

Here we summarize our present approach to the problem of consciousness. After an introduction outlining our general strategy, we describe what is meant by the term 'framework' and set it out under ten headings. This framework offers a coherent scheme for explaining the neural correlates of (visual) consciousness in terms of competing cellular assemblies. Most of the ideas we favor have been suggested before, but their combination is original. We also outline some general experimental approaches to the problem and, finally, acknowledge some relevant aspects of the brain that have been left out of the proposed framework.


Passive returning mechanism for twisted string actuators

The twisted string actuator is an actuator that is gaining popularity in various engineering and robotics and applications. However, the fundamental limitation of actuators of this type is the uni-directional action, meaning that the actuator can contract but requires external power to return to its initial state. This paper proposes 2 novel passive extension mechanisms based on buckling effect to solve the uni-directional issue of the twisted string actuator. The proposed mechanisms are mechanically simple and compact and provide a nearly-constant extension force throughout the operation range. The constant force can fully extend the twisted string actuator with minimal loss of force during contraction. The designed mechanisms are evaluated in a series of practical tests, and their performances are compared and discussed.


Compact two-layer slot array antenna with SIW for 60GHz wireless applications

In a variety of microwave and millimeter-wave applications where high performance antennas are required, waveguide slot arrays have received considerable attention due to their high aperture efficiency, low side lobe levels, and low cross polarization. Resonant slot arrays usually suffer from narrow bandwidth and high cost due to high precision required in manufacturing. Furthermore, because of using standard rectangular waveguides, the antenna array is thick and heavy and is not suitable for monolithic integration with high frequency printed circuits.


Latent Aspect Mining via Exploring Sparsity and Intrinsic Information

We investigate latent aspect mining problem that aims at automatically discovering aspect information from a collection of review texts in a domain in an unsupervised manner. One goal is to discover a set of aspects which are previously unknown for the domain, and predict the user's ratings on each aspect for each review. Another goal is to detect key terms for each aspect. Existing works on predicting aspect ratings fail to handle the aspect sparsity problem in the review texts leading to unreliable prediction. We propose a new generative model to tackle the latent aspect mining problem in an unsupervised manner. By considering the user and item side information of review texts, we introduce two latent variables, namely, user intrinsic aspect interest and item intrinsic aspect quality facilitating better modeling of aspect generation leading to improvement on the accuracy and reliability of predicted aspect ratings. Furthermore, we provide an analytical investigation on the Maximum A Posterior (MAP) optimization problem used in our proposed model and develop a new block coordinate gradient descent algorithm to efficiently solve the optimization with closed-form updating formulas. We also study its convergence analysis. Experimental results on the two real-world product review corpora demonstrate that our proposed model outperforms existing state-of-the-art models.


Self-compassion, body image, and disordered eating: A review of the literature.

Self-compassion, treating oneself as a loved friend might, demonstrates beneficial associations with body image and eating behaviors. In this systematic review, 28 studies supporting the role of self-compassion as a protective factor against poor body image and eating pathology are reviewed. Findings across various study designs consistently linked self-compassion to lower levels of eating pathology, and self-compassion was implicated as a protective factor against poor body image and eating pathology, with a few exceptions. These findings offer preliminary support that self-compassion may protect against eating pathology by: (a) decreasing eating disorder-related outcomes directly; (b) preventing initial occurrence of a risk factor of a maladaptive outcome; (c) interacting with risk factors to interrupt their deleterious effects; and (d) disrupting the mediational chain through which risk factors operate. We conclude with suggestions for future research that may inform intervention development, including the utilization of research designs that better afford causal inference.


Plug-and-Play priors for model based reconstruction

Model-based reconstruction is a powerful framework for solving a variety of inverse problems in imaging. In recent years, enormous progress has been made in the problem of denoising, a special case of an inverse problem where the forward model is an identity operator. Similarly, great progress has been made in improving model-based inversion when the forward model corresponds to complex physical measurements in applications such as X-ray CT, electron-microscopy, MRI, and ultrasound, to name just a few. However, combining state-of-the-art denoising algorithms (i.e., prior models) with state-of-the-art inversion methods (i.e., forward models) has been a challenge for many reasons. In this paper, we propose a flexible framework that allows state-of-the-art forward models of imaging systems to be matched with state-of-the-art priors or denoising models. This framework, which we term as Plug-and-Play priors, has the advantage that it dramatically simplifies software integration, and moreover, it allows state-of-the-art denoising methods that have no known formulation as an optimization problem to be used. We demonstrate with some simple examples how Plug-and-Play priors can be used to mix and match a wide variety of existing denoising models with a tomographic forward model, thus greatly expanding the range of possible problem solutions.


A Practical Algorithm for the Determination of Phase from Image and Diffraction Plane Pictures

An algorithm is presented for the rapid solution of the phase of the complete wave function whose intensity in the diffraction and imaging planes of an imaging system are known. A proof is given showing that a defined error between the estimated function and the correct function must decrease as the algorithm iterates. The problem of uniqueness is discussed and results are presented demonstrating the power of the method.


A Partial Derandomization of PhaseLift using Spherical Designs

The problem of retrieving phase information from amplitude measurements alone has appeared in many scientific disciplines over the last century. PhaseLift is a recently introduced algorithm for phase recovery that is computationally efficient, numerically stable, and comes with rigorous performance guarantees. PhaseLift is optimal in the sense that the number of amplitude measurements required for phase reconstruction scales linearly with the dimension of the signal. However, it specifically demands Gaussian random measurement vectors — a limitation that restricts practical utility and obscures the specific properties of measurement ensembles that enable phase retrieval. Here we present a partial derandomization of PhaseLift that only requires sampling from certain polynomial size vector configurations, called t-designs. Such configurations have been studied in algebraic combinatorics, coding theory, and quantum information. We prove reconstruction guarantees for a number of measurements that depends on the degree t of the design. If the degree is allowed to to grow logarithmically with the dimension, the bounds become tight up to polylog-factors. Beyond the specific case of PhaseLift, this work highlights the utility of spherical designs for the derandomization of data recovery schemes.


Blind Deconvolution Using Convex Programming

We consider the problem of recovering two unknown vectors, w and x, of length L from their circular convolution. We make the structural assumption that the two vectors are members of known subspaces, one with dimension N and the other with dimension K. Although the observed convolution is nonlinear in both w and x, it is linear in the rank-1 matrix formed by their outer product wx*. This observation allows us to recast the deconvolution problem as low-rank matrix recovery problem from linear measurements, whose natural convex relaxation is a nuclear norm minimization program. We prove the effectiveness of this relaxation by showing that, for “generic” signals, the program can deconvolve w and x exactly when the maximum of N and K is almost on the order of L. That is, we show that if x is drawn from a random subspace of dimension N, and w is a vector in a subspace of dimension K whose basis vectors are spread out in the frequency domain, then nuclear norm minimization recovers wx* without error. We discuss this result in the context of blind channel estimation in communications. If we have a message of length N, which we code using a random L x N coding matrix, and the encoded message travels through an unknown linear time-invariant channel of maximum length K, then the receiver can recover both the channel response and the message when L ≳ N + K, to within constant and log factors.


A unified approach to statistical tomography using coordinate descent optimization

Over the past years there has been considerable interest in statistically optimal reconstruction of cross-sectional images from tomographic data. In particular, a variety of such algorithms have been proposed for maximum a posteriori (MAP) reconstruction from emission tomographic data. While MAP estimation requires the solution of an optimization problem, most existing reconstruction algorithms take an indirect approach based on the expectation maximization (EM) algorithm. We propose a new approach to statistically optimal image reconstruction based on direct optimization of the MAP criterion. The key to this direct optimization approach is greedy pixel-wise computations known as iterative coordinate decent (ICD). We propose a novel method for computing the ICD updates, which we call ICD/Newton-Raphson. We show that ICD/Newton-Raphson requires approximately the same amount of computation per iteration as EM-based approaches, but the new method converges much more rapidly (in our experiments, typically five to ten iterations). Other advantages of the ICD/Newton-Raphson method are that it is easily applied to MAP estimation of transmission tomograms, and typical convex constraints, such as positivity, are easily incorporated.


Design of bipolar pulse generator topology based on Marx supplied by double power

Pulsed power technology has been used for the ablation tumor. Considerable research shows that a high-strength unipolar pulse electric field can induce irreversible electroporation (IRE) on the cell membrane, which can effectively kill cells. But some scholars and doctors have found that muscle contractions occur during treatment, which are associated with the delivery of electric pulses. Confirmed by further studies that bipolar pulses have been proven more advanced in the treatment of tumor because of the elimination of muscle contractions and the effect for ablating non-uniform tissue. So the bipolar pulse generator is needed for the research on the tumor ablation with bipolar pulses. In this paper, a new type of modular bipolar pulsed-power generator base on Marx generator with double power is proposed. The concept of this generator is charging two series of capacitors in parallel by two power sources respectively, and then connecting the capacitors in series through solid-state switches with different control strategy. Utilizing a number of fast solid-state switches, the capacitors can be connected in series with different polarities, so that a positive or negative polarity pulse will be delivered to the load. A laboratory prototype has been implemented in laboratory. The development of this pulse generator can provide the hardware foundation for the research on biological effect without muscle contraction when the tumors are applied with bipolar pulse electric field.


A Data-driven Method for the Detection of Close Submitters in Online Learning Environments

Online learning has become very popular over the last decade. However, there are still many details that remain unknown about the strategies that students follow while studying online. In this study, we focus on the direction of detecting ‘invisible’ collaboration ties between students in online learning environments. Specifically, the paper presents a method developed to detect student ties based on temporal proximity of their assignment submissions. The paper reports on findings of a study that made use of the proposed method to investigate the presence of close submitters in two different massive open online courses. The results show that most of the students (i.e., student user accounts) were grouped as couples, though some bigger communities were also detected. The study also compared the population detected by the algorithm with the rest of user accounts and found that close submitters needed a statistically significant lower amount of activity with the platform to achieve a certificate of completion in a MOOC. These results confirm that the detected close submitters were performing some collaboration or even engaged in unethical behaviors, which facilitates their way into a certificate. However, more work is required in the future to specify various strategies adopted by close submitters and possible associations between the user accounts.


High speed obstacle avoidance using monocular vision and reinforcement learning

We consider the task of driving a remote control car at high speeds through unstructured outdoor environments. We present an approach in which supervised learning is first used to estimate depths from single monocular images. The learning algorithm can be trained either on real camera images labeled with ground-truth distances to the closest obstacles, or on a training set consisting of synthetic graphics images. The resulting algorithm is able to learn monocular vision cues that accurately estimate the relative depths of obstacles in a scene. Reinforcement learning/policy search is then applied within a simulator that renders synthetic scenes. This learns a control policy that selects a steering direction as a function of the vision system's output. We present results evaluating the predictive ability of the algorithm both on held out test data, and in actual autonomous driving experiments.


FLATM: A fuzzy logic approach topic model for medical documents

One of the challenges for text analysis in medical domains is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. The themes in the documents help to retrieve documents on the same topic with and without a query. In this paper, we present a novel approach to topic modeling using fuzzy clustering. To evaluate our model, we experiment with two text datasets of medical documents. The evaluation metrics carried out through document classification and document modeling show that our model produces better performance than LDA, indicating that fuzzy set theory can improve the performance of topic models in medical domains.


Non-Additive Imprecise Image Super-Resolution in a Semi-Blind Context

The most effective superresolution methods proposed in the literature require precise knowledge of the so-called point spread function of the imager, while in practice its accurate estimation is nearly impossible. This paper presents a new superresolution method, whose main feature is its ability to account for the scant knowledge of the imager point spread function. This ability is based on representing this imprecise knowledge via a non-additive neighborhood function. The superresolution reconstruction algorithm transfers this imprecise knowledge to output by producing an imprecise (interval-valued) high-resolution image. We propose some experiments illustrating the robustness of the proposed method with respect to the imager point spread function. These experiments also highlight its high performance compared with very competitive earlier approaches. Finally, we show that the imprecision of the high-resolution interval-valued reconstructed image is a reconstruction error marker.


Class-E LCCL for capacitive power transfer system

This paper presented the design of a capacitive power transfer (CPT) system by implementing a Class-E inverter due to its high efficiency, which in theory can reach close to 100% performance. However, the Class-E inverter is highly sensitive to its circuit parameters under the scenario of having small capacitance at the coupling plate. As a solution, an additional capacitor can be integrated into the Class-E inverter for increased coupling capacitance for a better performance. Both simulation and experimental investigations were carried out to verify the high efficiency CPT system based on Class-E inverter with additional capacitor. The outcome of the investigation exhibited 96% of overall DC-DC power transfer efficiency with 0.97 W output at 1MHz operating frequency.


Low-latency adaptive streaming over tcp

Media streaming over TCP has become increasingly popular because TCP's congestion control provides remarkable stability to the Internet. Streaming over TCP requires adapting to bandwidth availability, but unforunately, TCP can introduce significant latency at the application level, which causes unresponsive and poor adaptation. This article shows that this latency is not inherent in TCP but occurs as a result of throughput-optimized TCP implementations. We show that this latency can be minimized by dynamically tuning TCP's send buffer. Our evaluation shows that this approach leads to better application-level adaptation and it allows supporting interactive and other low-latency applications over TCP.


Graviola: a novel promising natural-derived drug that inhibits tumorigenicity and metastasis of pancreatic cancer cells in vitro and in vivo through altering cell metabolism.

Pancreatic tumors are resistant to conventional chemotherapies. The present study was aimed at evaluating the potential of a novel plant-derived product as a therapeutic agent for pancreatic cancer (PC). The effects of an extract from the tropical tree Annona Muricata, commonly known as Graviola, was evaluated for cytotoxicity, cell metabolism, cancer-associated protein/gene expression, tumorigenicity, and metastatic properties of PC cells. Our experiments revealed that Graviola induced necrosis of PC cells by inhibiting cellular metabolism. The expression of molecules related to hypoxia and glycolysis in PC cells (i.e. HIF-1α, NF-κB, GLUT1, GLUT4, HKII, and LDHA) were downregulated in the presence of the extract. In vitro functional assays further confirmed the inhibition of tumorigenic properties of PC cells. Overall, the compounds that are naturally present in a Graviola extract inhibited multiple signaling pathways that regulate metabolism, cell cycle, survival, and metastatic properties in PC cells. Collectively, alterations in these parameters led to a decrease in tumorigenicity and metastasis of orthotopically implanted pancreatic tumors, indicating promising characteristics of the natural product against this lethal disease.


Hand sign language recognition for Bangla alphabet using Support Vector Machine

The sign language considered as the main language for deaf and dumb people. So, a translator is needed when a normal person wants to talk with a deaf or dumb person. In this paper, we present a framework for recognizing Bangla Sign Language (BSL) using Support Vector Machine. The Bangla hand sign alphabets for both vowels and consonants have been used to train and test the recognition system. Bangla sign alphabets are recognized by analyzing its shape and comparing its features that differentiates each sign. In proposed system, hand signs are first converted to HSV color space from RGB image. Then Gabor filters are used to acquire desired hand sign features. Since feature vector obtained using Gabor filter is in a high dimension, to reduce the dimensionality a nonlinear dimensionality reduction technique that is Kernel PCA has been used. Lastly, Support Vector Machine (SVM) is employed for classification of candidate features. The experimental results show that our proposed method outperforms the existing work on Bengali hand sign recognition.


How Learning Works: Seven Research-Based Principles for Smart Teaching

In this volume, the authors introduce seven general principles of learning, distilled from the research literature as well as from twenty-seven years of experience working one-on-one with college faculty. They have drawn on research from a breadth of perspectives (cognitive, developmental, and social psychology; educational research; anthropology; demographics; and organizational behavior) to identify a set of key principles underlying learningfrom how effective organization enhances retrieval and use of information to what impacts motivation. These principles provide instructors with an understanding of student learning that can help them see why certain teaching approaches are or are not supporting student learning, generate or refine teaching approaches and strategies that more effectively foster student learning in specific contexts, and transfer and apply these principles to new courses.


Deeper Inside PageRank

This paper serves as a companion or extension to the “Inside PageRank” paper by Bianchini et al. [19]. It is a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, suggested alternatives to the traditional solution methods, sensitivity and conditioning, and finally the updating problem. We introduce a few new results, provide an extensive reference list, and speculate about exciting areas of future research.


Practical Design and Implementation of Metamaterial-Enhanced Magnetic Induction Communication

The wireless communications in complex environments, such as underground and underwater, can enable various applications in the environmental, industrial, homeland security, law enforcement, and military fields. However, conventional electromagnetic wave-based techniques do not work due to the lossy media and complicated structures. Magnetic induction (MI) has been proved to achieve reliable communication in such environments. However, due to the small antenna size, the communication range of MI is still very limited, especially for the portable mobile devices. To this end, Metamaterial-enhanced MI (M2I) communication has been proposed, where the theoretical results predict that it can significantly increase the data rate and range. Nevertheless, there exists a significant gap between the theoretical prediction and the practical realization of M2I; the theoretical model relies on an ideal spherical metamaterial, while it does not exist in nature. In this paper, a practical design is proposed by leveraging a spherical coil array to realize M2I communication. The full-wave simulation is conducted to validate the design objectives. By using the spherical coil array-based M2I communication, the communication range can be significantly extended, exactly as we predicted in the ideal M2I model. Finally, the proposed M2I communication is implemented and tested in various environments.


The somatic marker hypothesis: A critical evaluation

The somatic marker hypothesis (SMH; [Damasio, A. R., Tranel, D., Damasio, H., 1991. Somatic markers and the guidance of behaviour: theory and preliminary testing. In Levin, H.S., Eisenberg, H.M., Benton, A.L. (Eds.), Frontal Lobe Function and Dysfunction. Oxford University Press, New York, pp. 217-229]) proposes that emotion-based biasing signals arising from the body are integrated in higher brain regions, in particular the ventromedial prefrontal cortex (VMPFC), to regulate decision-making in situations of complexity. Evidence for the SMH is largely based on performance on the Iowa Gambling Task (IGT; [Bechara, A., Tranel, D., Damasio, H., Damasio, A.R., 1996. Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex. Cerebral Cortex 6 (2), 215-225]), linking anticipatory skin conductance responses (SCRs) to successful performance on a decision-making paradigm in healthy participants. These 'marker' signals were absent in patients with VMPFC lesions and were associated with poorer IGT performance. The current article reviews the IGT findings, arguing that their interpretation is undermined by the cognitive penetrability of the reward/punishment schedule, ambiguity surrounding interpretation of the psychophysiological data, and a shortage of causal evidence linking peripheral feedback to IGT performance. Further, there are other well-specified and parsimonious explanations that can equally well account for the IGT data. Next, lesion, neuroimaging, and psychopharmacology data evaluating the proposed neural substrate underpinning the SMH are reviewed. Finally, conceptual reservations about the novelty, parsimony and specification of the SMH are raised. It is concluded that while presenting an elegant theory of how emotion influences decision-making, the SMH requires additional empirical support to remain tenable.


Crowdsourced Comprehension: Predicting Prerequisite Structure in Wikipedia

The growth of open-access technical publications and other open-domain textual information sources means that there is an increasing amount of online technical material that is in principle available to all, but in practice, incomprehensible to most. We propose to address the task of helping readers comprehend complex technical material, by using statistical methods to model the “prerequisite structure” of a corpus — i.e., the semantic impact of documents on an individual reader’s state of knowledge. Experimental results using Wikipedia as the corpus suggest that this task can be approached by crowdsourcing the production of ground-truth labels regarding prerequisite structure, and then generalizing these labels using a learned classifier which combines signals of various sorts. The features that we consider relate pairs of pages by analyzing not only textual features of the pages, but also how the containing corpora is connected and created.


The Advanced Health and Disaster Aid Network: A Light-Weight Wireless Medical System for Triage

Advances in semiconductor technology have resulted in the creation of miniature medical embedded systems that can wirelessly monitor the vital signs of patients. These lightweight medical systems can aid providers in large disasters who become overwhelmed with the large number of patients, limited resources, and insufficient information. In a mass casualty incident, small embedded medical systems facilitate patient care, resource allocation, and real-time communication in the advanced health and disaster aid network (AID-N). We present the design of electronic triage tags on lightweight, embedded systems with limited memory and computational power. These electronic triage tags use noninvasive, biomedical sensors (pulse oximeter, electrocardiogram, and blood pressure cuff) to continuously monitor the vital signs of a patient and deliver pertinent information to first responders. This electronic triage system facilitates the seamless collection and dissemination of data from the incident site to key members of the distributed emergency response community. The real-time collection of data through a mesh network in a mass casualty drill was shown to approximately triple the number of times patients that were triaged compared with the traditional paper triage system.


Cache-aware Roofline model: Upgrading the loft

The Roofline model graphically represents the attainable upper bound performance of a computer architecture. This paper analyzes the original Roofline model and proposes a novel approach to provide a more insightful performance modeling of modern architectures by introducing cache-awareness, thus significantly improving the guidelines for application optimization. The proposed model was experimentally verified for different architectures by taking advantage of built-in hardware counters with a curve fitness above 90%.


Deception Detection using Real-life Trial Data

Hearings of witnesses and defendants play a crucial role when reaching court trial decisions. Given the high-stake nature of trial outcomes, implementing accurate and effective computational methods to evaluate the honesty of court testimonies can offer valuable support during the decision making process. In this paper, we address the identification of deception in real-life trial data. We introduce a novel dataset consisting of videos collected from public court trials. We explore the use of verbal and non-verbal modalities to build a multimodal deception detection system that aims to discriminate between truthful and deceptive statements provided by defendants and witnesses. We achieve classification accuracies in the range of 60-75% when using a model that extracts and fuses features from the linguistic and gesture modalities. In addition, we present a human deception detection study where we evaluate the human capability of detecting deception in trial hearings. The results show that our system outperforms the human capability of identifying deceit.


TUTORIAL ON AGENT-BASED MODELING AND SIMULATION

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agentbased applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.


Human activity recognition with wearable sensors

This thesis investigates the use of wearable sensors to recognize human activity. The activity of the user is one example of context information – others include the user’s location or the state of his environment – which can help computer applications to adapt to the user depending on the situation. In this thesis we use wearable sensors – mainly accelerometers – to record, model and recognize human activities. Using wearable sensors allows continuous recording of activities across different locations and independent from external infrastructure. There are many possible applications for activity recognition with wearable sensors, for instance in the areas of healthcare, elderly care, personal fitness, entertainment, or performing arts. In this thesis we focus on two particular research challenges in activity recognition, namely the need for less supervision, and the recognition of high-level activities. We make several contributions towards addressing these challenges. Our first contribution is an analysis of features for activity recognition. Using a data set of activities such as walking, standing, sitting, or hopping, we analyze the performance of commonly used features and window lengths over which the features are computed. Our results indicate that different features perform well for different activities, and that in order to achieve best recognition performance, features and window lengths should be chosen specific for each activity. In order to reduce the need for labeled training data, we propose an unsupervised algorithm which can discover structure in unlabeled recordings of activities. The approach identifies correlated subsets in feature space, and represents these subsets with low-dimensional models. We show that the discovered subsets often correspond to distinct activities, and that the resulting models can be used for recognition of activities in unknown data. In a separate study, we show that the approach can be effectively deployed in a semi-supervised learning framework. More specifically, we combine the approach with a discriminant classifier, and show that this scheme allows high recognition rates even when using only a small amount of labeled training data. Recognition of higher-level activities such as shopping, doing housework, or commuting is challenging, as these activities are composed of changing sub-activities and vary strongly across individuals. We present one study in which we recorded 10h of three different high-level activities, investigating to which extent methods for low-level activities can be scaled to the recognition of high-level activities. Our results indicate that for settings as ours, traditional supervised approaches in combination with data from wearable accelerometers can achieve recognition rates of more than 90%. While unsupervised techniques are desirable for short-term activities, they become crucial for long-term activities, for which annotation is often impractical or impossible. To this end we propose an unsupervised approach based on topic models that allows to discover high-level structure in human activity data. The discovered activity patterns correlate with daily routines such as commuting, office work, or lunch routine, and they can be used to recognize such routines in unknown data.