title: Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors

abstract: We present differentiable particle filters (DPFs): a differentiable implementation of the particle filter algorithm with learnable motion and measurement models. Since DPFs are end-to-end differentiable, we can efficiently train their models by optimizing end-to-end state estimation performance, rather than proxy objectives such as model accuracy. DPFs encode the structure of recursive state estimation with prediction and measurement update that operate on a probability distribution over states. This structure represents an algorithmic prior that improves learning performance in state estimation problems while enabling explainability of the learned model. Our experiments on simulated and real data show substantial benefits from end-to- end learning with algorithmic priors, e.g. reducing error rates by ~80%. Our experiments also show that, unlike long short-term memory networks, DPFs learn localization in a policy-agnostic way and thus greatly improve generalization. Source code is available at https://github.com/tu-rbo/differentiable-particle-filters .


title: Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

abstract: In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy.


New Transfer Learning Techniques for Disparate Label Sets


title: Anti-programmed-death-receptor-1 treatment with pembrolizumab in ipilimumab-refractory advanced melanoma: a randomised dose-comparison cohort of a phase 1 trial

abstract: BACKGROUND ::: The anti-programmed-death-receptor-1 (PD-1) antibody pembrolizumab has shown potent antitumour activity at different doses and schedules in patients with melanoma. We compared the efficacy and safety of pembrolizumab at doses of 2 mg/kg and 10 mg/kg every 3 weeks in patients with ipilimumab-refractory advanced melanoma. ::: ::: ::: METHODS ::: In an open-label, international, multicentre expansion cohort of a phase 1 trial, patients (aged ≥18 years) with advanced melanoma whose disease had progressed after at least two ipilimumab doses were randomly assigned with a computer-generated allocation schedule (1:1 final ratio) to intravenous pembrolizumab at 2 mg/kg every 3 weeks or 10 mg/kg every 3 weeks until disease progression, intolerable toxicity, or consent withdrawal. Primary endpoint was overall response rate (ORR) assessed with the Response Evaluation Criteria In Solid Tumors (RECIST, version 1.1) by independent central review. Analysis was done on the full-analysis set (all treated patients with measurable disease at baseline). This study is registered with ClinicalTrials.gov, number NCT01295827. ::: ::: ::: FINDINGS ::: 173 patients received pembrolizumab 2 mg/kg (n=89) or 10 mg/kg (n=84). Median follow-up duration was 8 months. ORR was 26% at both doses--21 of 81 patients in the 2 mg/kg group and 20 of 76 in the 10 mg/kg group (difference 0%, 95% CI -14 to 13; p=0·96). Treatment was well tolerated, with similar safety profiles in the 2 mg/kg and 10 mg/kg groups and no drug-related deaths. The most common drug-related adverse events of any grade in the 2 mg/kg and 10 mg/kg groups were fatigue (29 [33%] vs 31 [37%]), pruritus (23 [26%] vs 16 [19%]), and rash (16 [18%] vs 15 [18%]). Grade 3 fatigue, reported in five (3%) patients in the 2 mg/kg pembrolizumab group, was the only drug-related grade 3 to 4 adverse event reported in more than one patient. ::: ::: ::: INTERPRETATION ::: The results suggest that pembrolizumab at a dose of 2 mg/kg or 10 mg/kg every 3 weeks might be an effective treatment in patients for whom there are few effective treatment options. ::: ::: ::: FUNDING ::: Merck Sharp and Dohme.


title: Double layered recommendation algorithm based on fast density clustering: Case study on Yelp social networks dataset

abstract: With the wider applications of mobile devices, network-based recommendation has attracted more attentions than ever. At present, most research of recommender system is on basis of either graph theory or algebraic methods. However almost all of these recommendation algorithms are aiming at static history data, which cannot meet the demand of precise online recommendation for dynamic real-time emerging data. Besides, lots of traditional recommendation algorithms are suffered from high time complexity and parameter tuning difficulty. Here, we propose a personalized recommendation algorithm based on density-distance dynamic clustering model (PRA-DCM). A two layered recommendation algorithm based on fast density clustering is first time put forward. Users and items are clustered respectively into user clusters and item clusters by fast density clustering algorithm. For solving parameter self-adaptive tuning, a novel density-distance clustering center automatic determination method (DCC-ADM) is proposed. Cluster centers could be automatically determined in DCC-ADM for users and items clustering respectively. Moreover, double layered bipartite networks are designed to support precise recommendation. In the first layer bipartite network, user cluster and item cluster are treated as projection nodes. Top n items are recommended for each user node to construct the second layered bipartite network, where edges between user node and item node are calculated on basis of review rates. DCC-ACM algorithm is applied to the second layered bipartite network to realize personalized recommendation for each individual user. Finally a case study on Yelp social network dataset is applied. We compare this algorithm with popular ones, such as non-negative matrix factorization (NMF) algorithm and collaborative filtering algorithm (CF), and find that our algorithm has the advantages of high precision and low complexity.


title: Ranking with multiple hyperplanes

abstract: The central problem for many applications in Information Retrieval is ranking and learning to rank is considered as a promising approach for addressing the issue. Ranking SVM, for example, is a state-of-the-art method for learning to rank and has been empirically demonstrated to be effective. In this paper, we study the issue of learning to rank, particularly the approach of using SVM techniques to perform the task. We point out that although Ranking SVM is advantageous, it still has shortcomings. Ranking SVM employs a single hyperplane in the feature space as the model for ranking, which is too simple to tackle complex ranking problems. Furthermore, the training of Ranking SVM is also computationally costly. In this paper, we look at an alternative approach to Ranking SVM, which we call "Multiple Hyperplane Ranker" (MHR), and make comparisons between the two approaches. MHR takes the divide-and-conquer strategy. It employs multiple hyperplanes to rank instances and finally aggregates the ranking results given by the hyperplanes. MHR contains Ranking SVM as a special case, and MHR can overcome the shortcomings which Ranking SVM suffers from. Experimental results on two information retrieval datasets show that MHR can outperform Ranking SVM in ranking.


title: Nonverbal Social Sensing in Action: Unobtrusive Recording and Extracting of Nonverbal Behavior in Social Interactions Illustrated with a Research Example

abstract: Nonverbal behavior coding is typically conducted by ''hand''. To remedy this time and resource intensive undertaking, we illustrate how nonverbal social sensing, defined as the automated recording and extracting of nonverbal behavior via ubiquitous social sensing platforms, can be achieved. More precisely, we show how and what kind of nonverbal cues can be extracted and to what extent automated extracted nonverbal cues can be validly obtained with an illustrative research example. In a job interview, the applicant's vocal and visual nonverbal immediacy behavior was automatically sensed and extracted. Results show that the applicant's nonverbal behavior can be validly extracted. Moreover, both visual and vocal applicant nonverbal behavior predict recruiter hiring decision, which is in line with previous findings on manually coded applicant nonverbal behavior. Finally, applicant average turn duration, tempo variation, and gazing best predict recruiter hiring decision. Results and implications of such a nonverbal social sensing for future research are discussed.


title: The Incidence and Persistence of Cyclical Job Loss in New Zealand

abstract: In New Zealand, the impact of the 2007–2008 Global Financial Crisis (GFC) was milder than in most other developed countries, though still substantial, with employment declining by 2.5 percent between the fourth quarter of 2008 and the fourth quarter of 2009. There were pronounced declines in job and worker turnover rates, signalling a decline in labour market liquidity and difficulties for new entrants and high-turnover groups of workers (Fabling and Mare, 2012). The current paper documents the extent and composition of employment change between 2000 and 2011, focusing particularly on the 2008–2010 period, when the labour market impacts of the GFC were strongest. As in previous downturns, the incidence of cyclical job loss and unemployment has fallen disproportionately on young and unskilled workers. The paper identifies, for subgroups of workers identified by age, gender and earnings level, the sensitivity of employment growth and labour market flows to aggregate employment fluctuations and also to relative fluctuations across industries and local labour market areas. The rate of job accessions (hiring) is particularly sensitive to the economic cycle and most strongly for young workers. Most of the differences across groups in the size of cyclical employment fluctuations are due to differing responsiveness to common shocks and not to exposure to different industry and local shocks. Finally, the paper traces outcomes for workers whose jobs end, summarising their duration out of work and the wage increases or reductions they experience when they secure employment. Workers who left or lost jobs spent longer out of work after the GFC and settled for lower earnings growth when they did find a job. Both of these effects had partly but not fully abated within 3 years of the onset of the GFC.


Large-scale personal assistant technology deployment: the siri experience.


title: Design principles and improvement of cost function based energy aware routing algorithms for wireless sensor networks

abstract: Cost function based routing has been widely studied in wireless sensor networks for energy efficiency improvement and network lifetime elongation. However, due to the complexity of the problem, existing solutions have various limitations. In this paper, we analyze the inherent factors, design principles and evaluation methods for cost function based routing algorithms. Two energy aware cost based routing algorithms named Exponential and Sine Cost Function based Route (ESCFR) and Double Cost Function based Route (DCFR) have been proposed in this paper. For ESCFR, its cost function can map small changes in nodal remaining energy to large changes in the function value. For DCFR, its cost function takes into consideration the end-to-end energy consumption, nodal remaining energy, resulting in a more balanced and efficient energy usage among nodes. The performance of the cost function design is analyzed. Extensive simulations demonstrate the proposed algorithms have significantly better performance than existing competing algorithms.


title: Comparison deep learning method to traditional methods using for network intrusion detection

abstract: Recently, deep learning has gained prominence due to the potential it portends for machine learning. For this reason, deep learning techniques have been applied in many fields, such as recognizing some kinds of patterns or classification. Intrusion detection analyses got data from monitoring security events to get situation assessment of network. Lots of traditional machine learning method has been put forward to intrusion detection, but it is necessary to improvement the detection performance and accuracy. This paper discusses different methods which were used to classify network traffic. We decided to use different methods on open data set and did experiment with these methods to find out a best way to intrusion detection.


Influence of Age and Gender Differences on Mental Health Literacy of Anxiety Disorders


title: Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach

abstract: In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme.


title: Revisiting al-Qaida’s Foundation and Early History

abstract: This article examines the early rise and fall of al-Qaida from its emergence in early 1987 as a splinter group of Maktab al-Khadamat through to its organisational decline following the defeat of the Arab-Afghans at the 1989 Battle of Jalalabad. Drawing from first-hand accounts and primary materials, it contributes a history of al-Qaida’s first stage of development and identifies the factors that drove the organisation’s early growth and decline. The article finds that two factors were crucial to al-Qaida’s early growth: battlefield success and access to combat opportunities for volunteer youths. It determines that al-Qaida’s defeat at Jalalabad and bin Laden’s ordered withdrawal from combat were the cause of its near terminal decline. The article concludes by outlining that the bitter lesson al-Qaida took from its early history was that to attract and retain a significant number of youths, it must appear victorious in battle, and be able to provide access to combat opportunities.


title: Support vector regression model for BigData systems

abstract: Nowadays Big Data are becoming more and more important. Many sectors of our economy are now guided by data-driven decision processes. Big Data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. In such systems, capacity allocation, which is the ability to optimally size minimal resources for achieve a certain level of performance, is a key challenge to enhance performance for MapReduce jobs and minimize cloud resource costs. In order to do so, one of the biggest challenge is to build an accurate performance model to estimate job execution time of MapReduce systems. Previous works applied simulation based models for modeling such systems. Although this approach can accurately describe the behavior of Big Data clusters, it is too computationally expensive and does not scale to large system. We try to overcome these issues by applying machine learning techniques. More precisely we focus on Support Vector Regression (SVR) which is intrinsically more robust w.r.t other techniques, like, e.g., neural networks, and less sensitive to outliers in the training set. To better investigate these benefits, we compare SVR to linear regression.


title: Automated model-based bias field correction of MR images of the brain

abstract: The authors propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method the authors propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. The authors have validated the bias correction algorithm on simulated data and they illustrate its performance on various MR images with important field inhomogeneities. They also relate the proposed algorithm to other bias correction algorithms.


title: Uncertainty and Risk Analysis in Petroleum Exploration and Production

abstract: During the past decades, there have been some significant improvements in uncertainty and risk analysis applied to petroleum exploration and pro- duction. This paper presents a brief overview of the main contributions made in the exploration and production stages, followed by a summary of the main trends in the context of an exhaustible resource. Decisions related to pe- troleum exploration and production are still very complex because of the high number of issues involved in the pro- cess. However, uncertainty and risk analysis are becoming more popular as new hardware and software advances appear, contributing in an important manner to clarify the range and the impacts of new discoveries as well as development and production assets.


title: Fog-Based Crime-Assistance in Smart IoT Transportation System

abstract: Smart transportation safety (STS) envisions improving public safety through a significant paradigm shift for police authority responses on crimes toward a pro-active one. The application of smart surveillance in STS is critical for automatic and accurate identification of events in case of security threats in target environments. Cloud computing reduces costs and high resource consumption of smart surveillance capable STS systems, at the cost of introducing additional latency through far away centralized systems. In this paper, the fog-framework for intelligent public safety in vehicular environment (FISVER) framework applies fog computing in smart video surveillance-based STS to enhance crime assistance in a cost-efficient way. Through fog-FISVER, in-vehicle and fog infrastructures support autonomous and real-time crime detection on public bus services. A fog-FISVER laboratory testbed prototype was created and extensive evaluations in a real testbed were performed. Results show that fog-FISVER delivers outstanding system performance and device survivability behavior over typical STS use cases.


title: Effect of whey protein isolate on strength , body composition and muscle hypertrophy during resistance training

abstract: Purpose of Review: Sarcopenia (skeletal muscle wasting with aging) is thought to underlie a number of serious age-related health issues. While it may be seen as inevitable, decreasing this gradual loss of muscle is vital for healthy aging. Thus, it is imperative to investigate exercise and nutrition-based strategies designed to build a reservoir of muscle mass as early as possible. ::: Recent Findings: Elderly individuals are still able to respond to both resistance training and the anabolic signals provided by protein ingestion, provided specific amino acids, such as leucine, are present. Whey proteins are a rich source of these essential amino acids and rapidly elevate plasma amino acids, thus providing the foundations for preservation of muscle mass. Several studies involving supplementation with whey protein have shown to be effective in augmenting the effects of resistance exercise, in particular when supplementation occurs in the hours surrounding the exercise training. ::: Summary: While further work is required, particularly in elderly people, simple dietary and exercise strategies that may improve the maintenance of skeletal muscle mass will likely result in a decrease in the overall burden of a number of diseases and improve the quality of life as we age.


title: Building the gist of a scene: the role of global image features in recognition

abstract: Humans can recognize the gist of a novel image in a single glance, independent of its complexity. How is this remarkable feat accomplished? On the basis of behavioral and computational evidence, this paper describes a formal approach to the representation and the mechanism of scene gist understanding, based on scene-centered, rather than object-centered primitives. We show that the structure of a scene image can be estimated by the mean of global image features, providing a statistical summary of the spatial layout properties (Spatial Envelope representation) of the scene. Global features are based on configurations of spatial scales and are estimated without invoking segmentation or grouping operations. The scene-centered approach is not an alternative to local image analysis but would serve as a feed-forward and parallel pathway of visual processing, able to quickly constrain local feature analysis and enhance object recognition in cluttered natural scenes.


title: SpectralNet: Spectral Clustering using Deep Neural Networks

abstract: Spectral clustering is a leading and popular technique in unsupervised data analysis. Two of its major limitations are scalability and generalization of the spectral embedding (i.e., out-of-sample-extension). In this paper we introduce a deep learning approach to spectral clustering that overcomes the above shortcomings. Our network, which we call SpectralNet, learns a map that embeds input data points into the eigenspace of their associated graph Laplacian matrix and subsequently clusters them. We train SpectralNet using a procedure that involves constrained stochastic optimization. Stochastic optimization allows it to scale to large datasets, while the constraints, which are implemented using a special-purpose output layer, allow us to keep the network output orthogonal. Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding to unseen data points. To further improve the quality of the clustering, we replace the standard pairwise Gaussian affinities with affinities leaned from unlabeled data using a Siamese network. Additional improvement can be achieved by applying the network to code representations produced, e.g., by standard autoencoders. Our end-to-end learning procedure is fully unsupervised. In addition, we apply VC dimension theory to derive a lower bound on the size of SpectralNet. State-of-the-art clustering results are reported on the Reuters dataset. Our implementation is publicly available at https://github.com/kstant0725/SpectralNet .


title: Maturity Models in Supply Chain Sustainability: A Systematic Literature Review

abstract: A systematic literature review of supply chain maturity models with sustainability concerns is presented. The objective is to give insights into methodological issues related to maturity models, namely the research objectives; the research methods used to develop, validate and test them; the scope; and the main characteristics associated with their design. The literature review was performed based on journal articles and conference papers from 2000 to 2015 using the SCOPUS, Emerald Insight, EBSCO and Web of Science databases. Most of the analysed papers have as main objective the development of maturity models and their validation. The case study is the methodology that is most widely used by researchers to develop and validate maturity models. From the sustainability perspective, the scope of the analysed maturity models is the Triple Bottom Line (TBL) and environmental dimension, focusing on a specific process (eco-design and new product development) and without a broad SC perspective. The dominant characteristics associated with the design of the maturity models are the maturity grids and a continuous representation. In addition, results do not allow identifying a trend for a specific number of maturity levels. The comprehensive review, analysis, and synthesis of the maturity model literature represent an important contribution to the organization of this research area, making possible to clarify some confusion that exists about concepts, approaches and components of maturity models in sustainability. Various aspects associated with the maturity models (i.e., research objectives, research methods, scope and characteristics of the design of models) are explored to contribute to the evolution and significance of this multidimensional area.


Advantages and disadvantages of telerehabilitation for persons with neurological disabilities


title: SOLUTION OF SPARSE RECTANGULAR SYSTEMS USING LSQR AND CRAIG

abstract: We examine two iterative methods for solving rectangular systems of linear equations: LSQR for over-determined systemsAx ≈ b, and Craig's method for under-determined systemsAx = b. By including regularization, we extend Craig's method to incompatible systems, and observe that it solves the same damped least-squares problems as LSQR. The methods may therefore be compared on rectangular systems of arbitrary shape.


title: ClimbSense: Automatic Climbing Route Recognition using Wrist-worn Inertia Measurement Units

abstract: Today, sports and activity trackers are ubiquitous. Especially runners and cyclists have a variety of possibilities to record and analyze their workouts. In contrast, climbing did not find much attention in consumer electronics and human-computer interaction. If quantified data similar to cycling or running data were available for climbing, several applications would be possible, ranging from simple training diaries to virtual coaches or usage analytics for gym operators. This paper introduces a system that automatically recognizes climbed routes using wrist-worn inertia measurement units (IMUs). This is achieved by extracting features of a recorded ascent and use them as training data for the recognition system. To verify the recognition system, cross-validation methods were applied to a set of ascent recordings that were assessed during a user study with eight climbers in a local climbing gym. The evaluation resulted in a high recognition rate, thus proving that our approach is possible and operational.


title: 3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

abstract: With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.


title: Utility of the Mini-BESTest, BESTest, and BESTest sections for balance assessments in individuals with Parkinson disease.

abstract: BACKGROUND AND PURPOSE ::: The Balance Evaluation Systems Test(BESTest) has been shown to be a reliable and valid measure of balance in individuals with Parkinson disease (PD). A less time-consuming assessment may increase clinical utility. We compared the discriminative fall risk ability of the Mini-BESTest with that of the BESTest and determined the reliability and normal distribution of scores for each section of the BESTest and the Mini-BESTest in individuals with PD. ::: ::: ::: METHODS ::: Eighty individuals with idiopathic PD were assessed using the BESTest and Mini-BESTest. A faller was defined as an individual with 2 or more falls in the prior 6 months. Subsets of individuals were used to determine interrater (n = 15) and test-retest reliability (n = 24). ::: ::: ::: RESULTS ::: The Mini-BESTest, total BESTest score, and all sections of the BESTest showed a significant difference between the average scores of fallers and nonfallers. For both the Mini-BESTest and BESTest, interrater (intraclass correlation ICC ≥ 0.91) and test-retest (ICC ≥ 0.88) reliability was high. The Mini-BESTest and BESTest were highly correlated (r = 0.955). Accuracy of identifying a faller was comparable for the Mini-BESTest and BESTest (area under the receiver operating characteristic plots = 0.86 and 0.84, respectively). ::: ::: ::: DISCUSSION ::: No specific section of the BESTest captured the primary balance deficit for individuals with PD. The posttest probabilities for discriminating fallers versus nonfallers were comparable-to-slightly stronger when using the Mini-BESTest. ::: ::: ::: CONCLUSION ::: Although the Mini-BESTest has fewer than half of the items in the BESTest and takes only 15 minutes to complete, it is as reliable as the BESTest and has comparable-to-slightly greater discriminative properties for identifying fallers in individuals with PD.


title: Very High Frequency IVR for Small Portable Electronics With High-Current Multiphase 3-D Integrated Magnetics

abstract: As today's small portable electronics (smartphones, tablets, e-readers, etc.) becomes lighter, thinner, quicker, and smarter, the voltage regulator for the processor is expected to be efficient, miniaturized, integrated, and placed closer to the processor. In this paper, a concept of a very high frequency [tens of megahertz (MHz)] three-dimensional integrated voltage regulator (IVR) for small portable electronics is proposed. The magnetic characterization technique at tens of MHz is investigated, and the issues of and solutions for permeability and loss measurement are demonstrated. The LTCC and NEC flake materials are characterized and compared for the IVR inductor development. Both single-phase and five-phase integrated inductors are designed, fabricated, and experimentally tested at 20 MHz, featuring a simple single-via winding structure, small size, ultralow profile, ultralow DC resistance (DCR), high current-handling ability, air-gap-free magnetics, multiphase integration within one magnetic core, and lateral nonuniform flux distribution.


title: Medical Image Segmentation Using K-Means Clustering and Improved Watershed Algorithm

abstract: We propose a methodology that incorporates k-means and improved watershed segmentation algorithm for medical image segmentation. The use of the conventional watershed algorithm for medical image analysis is widespread because of its advantages, such as always being able to produce a complete division of the image. However, its drawbacks include over-segmentation and sensitivity to false edges. We address the drawbacks of the conventional watershed algorithm when it is applied to medical images by using k-means clustering to produce a primary segmentation of the image before we apply our improved watershed segmentation algorithm to it. The k-means clustering is an unsupervised learning algorithm, while the improved watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. By comparing the number of partitions in the segmentation maps of 50 images, we showed that our proposed methodology produced segmentation maps which have 92% fewer partitions than the segmentation maps produced by the conventional watershed algorithm


title: Differentially Private Online Learning

abstract: In this paper, we consider the problem of preserving privacy in the online learning setting. We study the problem in the online convex programming (OCP) framework---a popular online learning setting with several interesting theoretical and practical implications---while using differential privacy as the formal privacy measure. For this problem, we distill two critical attributes that a private OCP algorithm should have in order to provide reasonable privacy as well as utility guarantees: 1) linearly decreasing sensitivity, i.e., as new data points arrive their effect on the learning model decreases, 2) sub-linear regret bound---regret bound is a popular goodness/utility measure of an online learning algorithm. ::: Given an OCP algorithm that satisfies these two conditions, we provide a general framework to convert the given algorithm into a privacy preserving OCP algorithm with good (sub-linear) regret. We then illustrate our approach by converting two popular online learning algorithms into their differentially private variants while guaranteeing sub-linear regret ($O(\sqrt{T})$). Next, we consider the special case of online linear regression problems, a practically important class of online learning problems, for which we generalize an approach by Dwork et al. to provide a differentially private algorithm with just $O(\log^{1.5} T)$ regret. Finally, we show that our online learning framework can be used to provide differentially private algorithms for offline learning as well. For the offline learning problem, our approach obtains better error bounds as well as can handle larger class of problems than the existing state-of-the-art methods Chaudhuri et al.


Measurement and Analysis of Operating System Fault-tolerance


title: Collective intelligence in law enforcement - The WikiCrimes system

abstract: Collaboration is on the rise, primordially leveraged by the Web 2.0 in which the difference between information producer and consumer decreases significantly. In this paper we describe WikiCrimes, an example of a Web 2.0 application that offers a collaborative environment based on the use and direct manipulation of maps, in order to register and research criminal events. WikiCrimes is driven by three goals: (i) to give more transparency and publicity to criminal information, (ii) to provide means for citizen prevention, and (iii) to reduce the phenomenon of under-reporting (crimes that are not notified to authorities). In the paper we pay particular attention to the fact that in this context, finding the equilibrium between people's participation and information credibility is crucial. Anonymous mass collaboration is the easiest way to receive information; however, the credibility of the received information is depreciated, because the source of information is unknown. We particularly concentrate on the ways we have created to keep the ''participation vs. reliability of information'' trade-off at good level. The reputation model is the main component created with this purpose. The experiences and the obstacles we are facing in the implementation of the project are the subjects of discussion and analyses.


title: An Integrative Model of Predictors of Enjoyment in Console versus Mobile Video Games

abstract: This study tested a model of enjoyment of video games focused on three important predictors; interactivity, realism and spatial presence. In a large, randomized experiment (N = 257), players of traditional console games were compared with their counterparts playing on mobile devices. The hypothesized model included perceived interactivity, perceived reality and spatial presence, with enjoyment as the outcome variable. In the final analysis, the model showed that console games produced higher perceived interactivity and perceived reality. Perceived reality and skill were the most powerful direct predictors of enjoyment. Perceived reality and perceived interactivity indirectly predicted enjoyment via powerful effects on spatial presence. The results suggest that mobile games are highly enjoyable, realistic, and presence-inducing, but console games still exceed them on these variables. The results highlight the importance of studying enjoyment of video games in the context of a model that considers concurrent effects of several game perception variables.


title: Design and analysis of a rotational leg-type miniature robot with an actuated middle joint and a tail (RoMiRAMT)

abstract: In this paper, a rotational leg-type miniature robot with an actuated middle joint and tail, called RoMiRAMT, is proposed for stable locomotion and improved climbing ability. The robot has four independently actuated rotational legs, giving it advantages of both wheel-type and leg-type locomotion. The design parameters of the rotational legs were determined by 3D simulation within the properly selected candidates. It also has unique characteristics inspired by biological structures: a spine and a tail. An actuated middle joint allows the frontal body to be lifted or lowered, which was inspired by a spine, to climb higher obstacles. Additionally, a multi-functional two degrees of freedom (2-DOF) tail was added; the tail provides instant deceleration, and minimized the likelihood of flipping, while allowing the robot to climb higher obstacles. A microcontroller was embedded in the robot, along with a micro-camera and an inertia measurement unit (IMU) sensor. By controlling the robot using the yaw angle signal measured by the IMU sensor, straight movement was enhanced. The body size of the RoMiRAMT is 155 × 80 mm without the rotational legs, and the weight is 593 g including batteries. The maximum velocity of the robot was 2.58 m/s (16.65 body lengths per second) and the maximum height of an obstacle that the robot can climb was 95 mm, which all were verified by experiments.


title: Performance assessment of a smart road management system for the wireless detection of wildlife road-crossing

abstract: A wireless distributed system for the detection of the wildlife road-crossing events is addressed to prevent the problem of collisions with the approaching vehicles. The system is based on a wireless sensor network architecture deployed along the road sides in order to alert in real-time the drivers about the dangerous presence of deers within a security area determined by Doppler radar modules integrated in the nodes of the wireless network. The system aims at providing the information for the smart management of road signs through their adaptive activation only in presence of real dangers. The performance of the detection has been assessed during a long-term measurement campaign along a road stretch in Trentino, Italy, where the problem of deer road-crossing significantly impacts the driver safety and security.


A Survey on Lung Segmentation Methods


title: Detecting Oriented Text in Natural Images by Linking Segments

abstract: Most state-of-the-art text detection methods are specific to horizontal Latin text and are not fast enough for real-time applications. We introduce Segment Linking (SegLink), an oriented text detection method. The main idea is to decompose text into two locally detectable elements, namely segments and links. A segment is an oriented box covering a part of a word or text line, A link connects two adjacent segments, indicating that they belong to the same word or text line. Both elements are detected densely at multiple scales by an end-to-end trained, fully-convolutional neural network. Final detections are produced by combining segments connected by links. Compared with previous methods, SegLink improves along the dimensions of accuracy, speed, and ease of training. It achieves an f-measure of 75.0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin. It runs at over 20 FPS on 512x512 images. Moreover, without modification, SegLink is able to detect long lines of non-Latin text, such as Chinese.


title: CrowdProbe: Non-invasive Crowd Monitoring with Wi-Fi Probe

abstract: Devices with integrated Wi-Fi chips broadcast beacons for network connection management purposes. Such information can be captured with inexpensive monitors and used to extract user behavior. To understand the behavior of visitors, we deployed our passive monitoring system---CrowdProbe, in a multi-floor museum for six months. We used a Hidden Markov Models (HMM) based trajectory inference algorithm to infer crowd movement using more than 1.7 million opportunistically obtained probe request frames. ::: However, as more devices adopt schemes to randomize their MAC addresses in the passive probe session to protect user privacy, it becomes more difficult to track crowd and understand their behavior. In this paper, we try to make use of historical transition probability to reason about the movement of those randomized devices with spatial and temporal constraints. With CrowdProbe, we are able to achieve sufficient accuracy to understand the movement of visitors carrying devices with randomized MAC addresses.


Fractal Dimension Estimation Methods for Biomedical Images


title: A Policy Search Method For Temporal Logic Specified Reinforcement Learning Tasks

abstract: Reward engineering is an important aspect of reinforcement learning. Whether or not the user's intentions can be correctly encapsulated in the reward function can significantly impact the learning outcome. Current methods rely on manually crafted reward functions that often require parameter tuning to obtain the desired behavior. This operation can be expensive when exploration requires systems to interact with the physical world. In this paper, we explore the use of temporal logic (TL) to specify tasks in reinforcement learning. TL formula can be translated to a real-valued function that measures its level of satisfaction against a trajectory. We take advantage of this function and propose temporal logic policy search (TLPS), a model-free learning technique that finds a policy that satisfies the TL specification. A set of simulated experiments are conducted to evaluate the proposed approach.


title: Asynchronous functional reactive programming for GUIs

abstract: Graphical user interfaces (GUIs) mediate many of our interactions with computers. Functional Reactive Programming (FRP) is a promising approach to GUI design, providing high-level, declarative, compositional abstractions to describe user interactions and time-dependent computations. We present Elm, a practical FRP language focused on easy creation of responsive GUIs. Elm has two major features: simple declarative support for Asynchronous FRP; and purely functional graphical layout. Asynchronous FRP allows the programmer to specify when the global ordering of event processing can be violated, and thus enables efficient concurrent execution of FRP programs; long-running computation can be executed asynchronously and not adversely affect the responsiveness of the user interface. Layout in Elm is achieved using a purely functional declarative framework that makes it simple to create and combine text, images, and video into rich multimedia displays. Together, Elm's two major features simplify the complicated task of creating responsive and usable GUIs.


title: Augur: a decentralized, open-source platform for prediction markets

abstract: Augur is a trustless, decentralized oracle and platform for prediction markets. The outcomes of Augur's prediction markets are chosen by users that hold Augur's native Reputation token, who stake their tokens on the actual observed outcome and, in return, receive settlement fees from the markets. Augur's incentive structure is designed to ensure that honest, accurate reporting of outcomes is always the most profitable option for Reputation token holders. Token holders can post progressively-larger Reputation bonds to dispute proposed market outcomes. If the size of these bonds reaches a certain threshold, Reputation splits into multiple versions, one for each possible outcome of the disputed market; token holders must then exchange their Reputation tokens for one of these versions. Versions of Reputation which do not correspond to the real-world outcome will become worthless, as no one will participate in prediction markets unless they are confident that the markets will resolve correctly. Therefore, token holders will select the only version of Reputation which they know will continue to have value: the version that corresponds to reality.


title: QoE-Based Traffic and Resource Management for Adaptive HTTP Video Delivery in LTE

abstract: There is a growing interest in over-the-top (OTT) dynamic adaptive streaming over Hypertext Transfer Protocol (HTTP) (DASH) services. In mobile DASH, a client controls the streaming rate and the base station in the mobile network decides on the resource allocation. Different from the majority of previous works that focus on client-based rate adaptation mechanisms, this paper investigates the mobile network potential for enhancing the user quality-of-experience (QoE) in multiuser OTT DASH. Specifically, we first present proactive and reactive QoE optimization approaches for adapting the adaptive HTTP video delivery in an long-term evolution network. We then show, using subjective experiments, that by taking a proactive role in determining the transmission and streaming rates, the network operator can provide a better video quality and a fairer QoE across the streaming users. Furthermore, we consider the playout buffer time of the clients and propose a novel playout buffer-dependent approach that determines for each client the streaming rate for future video segments according to its buffer time and the achievable QoE under current radio conditions. In addition, we show that by jointly solving for the streaming and transmission rates, the wireless network resources are more efficiently allocated among the users and substantial gains in the user perceived video quality can be achieved.


title: A Brief survey of Data Mining Techniques Applied to Agricultural Data

abstract: As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy cmeans. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.


title: Entity Identification as Multitasking

abstract: Standard approaches in entity identification hard-code boundary detection and type prediction into labels (e.g., John/B-PER Smith/I-PER) and then perform Viterbi. This has two disadvantages: 1. the runtime complexity grows quadratically in the number of types, and 2. there is no natural segment-level representation. In this paper, we propose a novel neural architecture that addresses these disadvantages. We frame the problem as multitasking, separating boundary detection and type prediction but optimizing them jointly. Despite its simplicity, this architecture performs competitively with fully structured models such as BiLSTM-CRFs while scaling linearly in the number of types. Furthermore, by construction, the model induces type-disambiguating embeddings of predicted mentions.


title: Dual Therapy Treatment Strategies for the Management of Patients Infected with HIV: A Systematic Review of Current Evidence in ARV-Naive or ARV-Experienced, Virologically Suppressed Patients

abstract: Objective ::: We reviewed the current literature regarding antiretroviral (ARV)-sparing therapy strategies to determine whether these novel regimens can be considered appropriate alternatives to standard regimens for the initial treatment of ARV-naive patients or as switch therapy for those patients with virologically suppressed HIV infection. ::: ::: ::: Methods ::: A search for studies related to HIV dual therapy published from January 2000 through April 2014 was performed using Biosis, Derwent Drug File, Embase, International Pharmaceutical Abstracts, Medline, Pascal, SciSearch, and TOXNET databases; seven major trial registries, and the abstracts of major conferences. Using predetermined criteria for inclusion, an expert review committee critically reviewed and qualitatively evaluated all identified trials for efficacy and safety results and potential limitations. ::: ::: ::: Results ::: Sixteen studies of dual therapy regimens were critiqued for the ARV-naive population. Studies of a protease inhibitor/ritonavir in combination with the integrase inhibitor raltegravir or the nucleoside reverse transcriptase inhibitor lamivudine provided the most definitive evidence supporting a role for dual therapy. In particular, lopinavir/ritonavir or darunavir/ritonavir combined with raltegravir and lopinavir/ritonavir combined with lamivudine demonstrated noninferiority to standard of care triple therapy after 48 weeks of treatment. Thirteen trials were critiqued in ARV-experienced, virologically suppressed patients. The virologic efficacy outcomes were mixed. Although overall data regarding toxicity are limited, when compared with standard triple therapy, certain dual therapy regimens may offer advantages in renal function, bone mineral density, and limb fat changes; however, some dual combinations may elevate lipid or bilirubin levels. ::: ::: ::: Conclusions ::: The potential benefits of dual therapy regimens include reduced toxicity, improved tolerability and adherence, and reduced cost. Although the data reviewed here provide valuable insights into the effectiveness and tolerability of dual therapy regimens, it remains unclear whether these potential benefits can be maintained long-term. Appropriately powered studies with longer follow-up periods are needed to more definitively assess potential toxicity reduction advantages with dual therapy.


title: Surface and contour-preserving origamic architecture paper pop-ups

abstract: Origamic architecture (OA) is a form of papercraft that involves cutting and folding a single sheet of paper to produce a 3D pop-up, and is commonly used to depict architectural structures. Because of the strict geometric and physical constraints, OA design requires considerable skill and effort. In this paper, we present a method to automatically generate an OA design that closely depicts an input 3D model. Our algorithm is guided by a novel set of geometric conditions to guarantee the foldability and stability of the generated pop-ups. The generality of the conditions allows our algorithm to generate valid pop-up structures that are previously not accounted for by other algorithms. Our method takes a novel image-domain approach to convert the input model to an OA design. It performs surface segmentation of the input model in the image domain, and carefully represents each surface with a set of parallel patches. Patches are then modified to make the entire structure foldable and stable. Visual and quantitative comparisons of results have shown our algorithm to be significantly better than the existing methods in the preservation of contours, surfaces, and volume. The designs have also been shown to more closely resemble those created by real artists.


title: Engagement dynamics and sensitivity analysis of YouTube videos

abstract: YouTube, with millions of content creators, has become the preferred destination for watching videos online. Through the Partner program, YouTube allows content creators to monetize their popular videos. Of significant importance for content creators is which meta-level features (e.g. title, tag, thumbnail) are most sensitive for promoting video popularity. The popularity of videos also depends on the social dynamics, i.e. the interaction of the content creators (or channels) with YouTube users. Using real-world data consisting of about 6 million videos spread over 25 thousand channels, we empirically examine the sensitivity of YouTube meta-level features and social dynamics. The key meta-level features that impact the view counts of a video include: first day view count , number of subscribers, contrast of the video thumbnail, Google hits, number of keywords, video category, title length, and number of upper-case letters in the title respectively and illustrate that these meta-level features can be used to estimate the popularity of a video. In addition, optimizing the meta-level features after a video is posted increases the popularity of videos. In the context of social dynamics, we discover that there is a causal relationship between views to a channel and the associated number of subscribers. Additionally, insights into the effects of scheduling and video playthrough in a channel are also provided. Our findings provide a useful understanding of user engagement in YouTube.


title: Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)

abstract: Bio-medical image segmentation is one of the promising sectors where nuclei segmentation from high-resolution histopathological images enables extraction of very high-quality features for nuclear morphometrics and other analysis metrics in the field of digital pathology. The traditional methods including Otsu thresholding and watershed methods do not work properly in different challenging cases. However, Deep Learning (DL) based approaches are showing tremendous success in different modalities of bio-medical imaging including computation pathology. Recently, the Recurrent Residual U-Net (R2U-Net) has been proposed, which has shown state-of-the-art (SOTA) performance in different modalities (retinal blood vessel, skin cancer, and lung segmentation) in medical image segmentation. However, in this implementation, the R2U-Net is applied to nuclei segmentation for the first time on a publicly available dataset that was collected from the Data Science Bowl Grand Challenge in 2018. The R2U-Net shows around 92.15% segmentation accuracy in terms of the Dice Coefficient (DC) during the testing phase. In addition, the qualitative results show accurate segmentation, which clearly demonstrates the robustness of the R2U-Net model for the nuclei segmentation task.


title: Object Discovery in Videos as Foreground Motion Clustering

abstract: We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the goal is to cluster foreground pixels in videos into different objects. We introduce a novel pixel-trajectory recurrent neural network that learns feature embeddings of foreground pixel trajectories linked across time. By clustering the pixel trajectories using the learned feature embeddings, our method establishes correspondences between foreground object masks across video frames. To demonstrate the effectiveness of our framework for object discovery, we conduct experiments on commonly used datasets for motion segmentation, where we achieve state-of-the-art performance.


title: A Survey of Common Stemming Techniques and Existing Stemmers for Indian Languages

abstract: Stemming is an operation that relates morphological variants of a word. The purpose of stemming is to obtain the stem or radix of those words which are not found in dictionary. If stemmed word is present in dictionary, then that is a genuine word, otherwise it may be proper name or some invalid word. Stemming is the process for reducing inflected or sometimes derived words to their stem, base or root form, generally a written word form. The stem need not be identical to the morphological root of the word, it is usually sufficient that related words map to the same stem, even if this stem is not in itself a valid root. Stemming is used in Information Retrieval systems to improve performance. The design of stemmers is language specific, and requires some to significant linguistic expertise in the language, as well as the understanding of the needs for a spelling checker for that language. A stemmer’s performance and effectiveness in applications such as spelling checker vary across languages. A typical simple stemmer algorithm involves removing suffixes using a list of frequent suffixes, while a more complex one would use morphological knowledge to derive a stem from the words. In this paper a survey of common stemming techniques and existing stemmers for Indian languages have been presented.


title: A low-cost laser scanning solution for flexible robotic cells: spray coating

abstract: In this paper, an adaptive and low-cost robotic coating platform for small production series is presented. This new platform presents a flexible architecture that enables fast/automatic system adaptive behaviour without human intervention. The concept is based on contactless technology, using artificial vision and laser scanning to identify and characterize different workpieces travelling on a conveyor. Using laser triangulation, the workpieces are virtually reconstructed through a simplified cloud of three-dimensional (3D) points. From those reconstructed models, several algorithms are implemented to extract information about workpieces profile (pattern recognition), size, boundary and pose. Such information is then used to on-line adjust the base robot programmes. These robot programmes are off-line generated from a 3D computer-aided design model of each different workpiece profile. Finally, the robotic manipulator executes the coating process after its base programmes have been adjusted. This is a low-cost and fully autonomous system that allows adapting the robots behaviour to different manufacturing situations. It means that the robot is ready to work over any piece at any time, and thus, small production series can be reduced to as much as a one-object series. No skilled workers and large setup times are needed to operate it. Experimental results showed that this solution proved to be efficient and can be applied not only for spray coating purposes but also for many other industrial processes (automatic manipulation, pick-and-place, inspection, etc.).


title: SVM classifier based grape leaf disease detection

abstract: Grape constitutes one of the most widely grown fruit crops in the India. Productivity of grape decreases due to infections caused by various types of diseases on its fruit, stem and leaf. Leaf diseases are mainly caused by bacteria, fungi, virus etc. Diseases are a major factor limiting fruit production and diseases are often difficult to control. Without accurate disease diagnosis, proper control actions cannot be used at the appropriate time. Image Processing is one of the widely used technique is adopted for the plant leaf diseases detection and classification. This paper is intended to aid in the detection and classification leaf diseases of grape using SVM classification technique. First the diseased region is found using segmentation by K-means clustering, then both color and texture features are extracted. Finally classification technique is used to detect the type of leaf disease. The proposed system can successfully detect and classify the examined disease with accuracy of 88.89%.


title: Configurable Business Process Modeling Notation

abstract: In Enterprise Systems of a domain, various Business processes are similar to one another yet differ in some other ways from one organization, project or industry to another. These variations in business process of enterprise systems represent business operations of different organizations. So, there is need of a configurable business process models and a modeling language which could represent all the possible variations of an Enterprise system. Business process are model using Business Process Modeling Notation, BPMN. BPMN is accepted by OMG as a standard. But BPMN is not sufficiently capable to model all aspects of Configurable system. The aim of paper is identify the various dimensions of configurability and then extend the BPMN notation to Configurable BPMN, (C-BPMN) to model the variability exist in process model and construct configurable process model. So the C-BPMN is used to model configurable aspects of business processes. Approach is illustrated by library domain example.


116 IEEE 802.11 Wireless Local Area Networks


title: Centralized Synchronization Using Synchrophasors and Dynamic Delay Compensation

abstract: Synchronizing a generator to a power system and synchronizing two islanded systems together are critical yet delicate problems that have existed ever since alternating current was adopted as a means of transmitting electrical power. Mis-synchronization can damage equipment (e.g., generators, transformers and breakers). This article describes a centralized synchronization control based on synchrophasors. This control automatically determines running and incoming voltage sources for each breaker using a topological method whose inputs are the states of the breakers and disconnecting switches. A synchronism check (25) is performed on each breaker. In addition, an automatic synchronization (25A) may be performed according to the breaker selected by the operator. Dynamic compensation of the variable delays due to the use of synchrophasors allows the required level of accuracy to be achieved. The control has been implemented on an IEC 61131 platform and tested on a modified IEEE 3-machine 9-bus system simulated in real-time using Hypersim. The overall architecture is fully redundant and it is 61850 compliant.


title: Diffusion MRI visualization

abstract: Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.


title: Intelligent Refrigerator Based on Internet of Things

abstract: Based on the technology of radio frequency identification (RFID), combined with Internet and information processing technology, intelligent refrigerator for food management is developed, food records the within the refrigerator is achieved. These data will be automatically uploaded to the production of intelligent refrigerator cloud data service platform, easy to be viewed by the user. And according to the food inside the refrigerator it can provide recipes. Wireless communication module is used to achieve the reception of Internet information. A week before shelf life of the food, a warning will be sent to the user. When the food over the shelf life, there will be usage warning to the user. When the food is reduced or not available through the information on the Internet, different businesses will be compared to choose the best food. At the same time the wireless communication module can also be used to connect intelligent terminal and the refrigerator, the situation inside the refrigerator is checked.


title: A New Single-Phase Switched-Coupled-Inductor DC–AC Inverter for Photovoltaic Systems

abstract: This paper presents a new single-phase switched-coupled-inductor dc–ac inverter featuring higher voltage gain than the existing single-phase qZ-source and semi-Z-source inverters. Similar to the single-phase qZ-source and semi-Z-source inverters, the proposed inverter also has common grounds between the dc input and ac output voltages, which is beneficial especially for photovoltaic inverter systems. The inverter volume and maximum current flowing can be reduced significantly through the coupling of all inductors. A theoretical analysis of the proposed inverter is described and a 280-W experimental prototype is built to verify the performance of the inverter.


title: Actor-critic algorithms

abstract: In this article, we propose and analyze a class of actor-critic algorithms. These are two-time-scale algorithms in which the critic uses temporal difference learning with a linearly parameterized approximation architecture, and the actor is updated in an approximate gradient direction, based on information provided by the critic. We show that the features for the critic should ideally span a subspace prescribed by the choice of parameterization of the actor. We study actor-critic algorithms for Markov decision processes with Polish state and action spaces. We state and prove two results regarding their convergence.


title: Characterization of Printed Moisture Sensors in Packaging Surveillance Applications

abstract: This work presents a study on the performance of printed low-cost moisture sensors fabricated using conductive ink on paper substrates. The sensors are intended to add value to the surveillance of packages. Two different kinds of sensors are evaluated and characterized. The two sensors have similar geometrical shapes, but different measuring principles are employed. The first sensor is measuring the moisture content in cellulose-based substrates, while the second is measuring high levels of relative humidity in the surroundings. The sensors have been developed so that they can be integrated into radio frequency identification (RFID) systems for surveillance in logistic chains. A laboratory setup of a RFID tag with sensor capability based on an ordinary passive RFID tag has been shown.


title: Understanding WeChat users' liking behavior: An empirical study in China

abstract: Abstract Liking behavior in social media is growing in popularity. Yet there has been a lack of research and understanding of the factors that motivate users to click “like” in social media. In this study, based on the uses and gratifications theory, a research model is developed to examine what factors affecting users' liking behavior in WeChat. And gender is considered as the moderating factor in the model. Using 215 valid data from the WeChat users in China, the model is empirically assessed by PLS-SEM. Results show that three types of gratifications positively affect WeChat users' liking behavior: hedonic gratification (enjoyment), social gratification (social support) and utilitarian gratification (information seeking). In particular, enjoyment plays the most important role in determining the liking behavior, followed by social support and information seeking. In addition, gender acts as the moderator on the relationship between each gratification and liking behavior.


title: Segmental stabilizing exercises and low back pain. What is the evidence? A systematic review of randomized controlled trials

abstract: Study design: A systematic review of randomized controlled trials.Objectives: To evaluate the effectiveness of segmental stabilizing exercises for acute, subacute and chronic low back pain with regard to pain, recurrence of pain, disability and return to work.Methods: MEDLINE, EMBASE, CINAHL, Cochrane Controlled Trials Register, PEDro and article reference lists were searched from 1988 onward. Randomized controlled trials with segmental stabilizing exercises for adult low back pain patients were included. Four comparisons were foreseen: (1) effectiveness of segmental stabilizing exercises versus treatment by general practitioner (GP); (2) effectiveness of segmental stabilizing exercises versus other physiotherapy treatment; (3) effectiveness of segmental stabilizing exercises combined with other physiotherapy treatment versus treatment by GP and (4) effectiveness of segmental stabilizing exercises combined with other physiotherapy treatment versus other physiotherapy treatment.Results: Seven trials were i...


Rolling Bearing Reliability Estimation Based on Logistic Regression Model


title: Cloud software development platforms: A comparative overview

abstract: Doing things “in the cloud” has become ubiquitous, and the cloud has become a rich environment for the use of technology anywhere and anytime to solve problems, connect people, and improve lives. Software engineering paradigms have been shifting during the last decade from “Software-as-a-Product (SaaP)” to “Software-as-a-Service (SaaS)” provided “in the cloud”. The SaaS software paradigm comes with new capabilities and technical challenges for cloud software development, as compared to mobile and stand-alone software development. Distributed multitiered architecting, design, and programming for cloud software development require new strategies specifically motivated by the SaaS paradigm. This paper aims to compare and contrast cloud software development platforms and frameworks, namely, Java platform, Enterprise Edition (Java EE); Microsoft platform, .NET framework; Ruby on Rails framework; Zend framework; Node.js framework; and Django (Python) framework. A comparative overview is presented to help cloud software engineers select an appropriate platform / framework to solve a complex problem.


title: KnowItNow: Fast, Scalable Information Extraction From The Web

abstract: Numerous NLP applications rely on search-engine queries, both to extract information from and to compute statistics over the Web corpus. But search engines often limit the number of available queries. As a result, query-intensive NLP applications such as Information Extraction (IE) distribute their query load over several days, making IE a slow, offline process.This paper introduces a novel architecture for IE that obviates queries to commercial search engines. The architecture is embodied in a system called KnowItNow that performs high-precision IE in minutes instead of days. We compare KnowItNow experimentally with the previously-published KnowItAll system, and quantify the tradeoff between recall and speed. KnowItNow's extraction rate is two to three orders of magnitude higher than KnowItAll's.


title: Data clustering based on correlation analysis applied to highly variable domains

abstract: Clustering of traffic data based on correlation analysis is an important element of several network management objectives including traffic shaping and quality of service control. Existing correlation-based clustering algorithms are affected by poor results when applied to highly variable time series characterizing most network traffic data. This paper proposes a new similarity measure for computing clusters of highly variable data on the basis of their correlation. Experimental evaluations on several synthetic and real datasets show the accuracy and robustness of the proposed solution that improves existing clustering methods based on statistical correlations.


title: Characterizing logging practices in open-source software

abstract: Software logging is a conventional programming practice. While its efficacy is often important for users and developers to understand what have happened in the production run, yet software logging is often done in an arbitrary manner. So far, there have been little study for understanding logging practices in real world software. This paper makes the first attempt (to the best of our knowledge) to provide a quantitative characteristic study of the current log messages within four pieces of large open-source software. First, we quantitatively show that software logging is pervasive. By examining developers' own modifications to the logging code in the revision history, we find that they often do not make the log messages right in their first attempts, and thus need to spend a significant amount of efforts to modify the log messages as after-thoughts. Our study further provides several interesting findings on where developers spend most of their efforts in modifying the log messages, which can give insights for programmers, tool developers, and language and compiler designers to improve the current logging practice. To demonstrate the benefit of our study, we built a simple checker based on one of our findings and effectively detected 138 pieces of new problematic logging code from studied software (24 of them are already confirmed and fixed by developers).


title: Serretti A: Mindfulness-based stress reduction for stress management in healthy people: a review and meta-analysis

abstract: Abstract Background: Mindfulness-based stress reduction (MBSR) is a clinically standardized meditation that has shown consistent efficacy for many mental and physical disorders. Less attention has been given to the possible benefits that it may have in healthy subjects. The aim of the present review and meta-analysis is to better investigate current evidence about the efficacy of MBSR in healthy subjects, with a particular focus on its benefits for stress reduction. Materials and methods: A literature search was conducted using MEDLINE (PubMed), the ISI Web of Knowledge, the Cochrane database, and the references of retrieved articles. The search included articles written in English published prior to September 2008, and identified ten, mainly low-quality, studies. Cohen's d effect size between meditators and controls on stress reduction and spirituality enhancement values were calculated. Results: MBSR showed a nonspecific effect on stress reduction in comparison to an inactive control, both in reducing s...


title: Support Vector Machine-recursive feature elimination for the diagnosis of Parkinson disease based on speech analysis

abstract: Parkinson disease has become a serious problem in the old people. There is no precise method to diagnosis Parkinson disease now. Considering the significance and difficulty of recognizing the Parkinson disease, the measurement of samples' voices is regard as one of the best non-invasive ways to find the real patient. Support Vector Machine is one of the most effective tools to classify in machine learning, and it has been applied successfully in many areas. In this paper, we implement the SVM-recursive feature elimination which has not been used before for selecting the subset including the most important features for classification from the original features. We also implement SVM with PCA for selecting the principle components for diagnosis PD set with 22 features in order to compare. At last, we discuss the relationship between SVM-RFE and SVM with PCA specially in the experiment. The experiment illustrates that the SVM-RFE has the better performance than other methods in general.


title: A Theoretical Framework for Conversational Search

abstract: This paper studies conversational approaches to information retrieval, presenting a theory and model of information interaction in a chat setting. In particular, we consider the question of what properties would be desirable for a conversational information retrieval system so that the system can allow users to answer a variety of information needs in a natural and efficient manner. We study past work on human conversations, and propose a small set of properties that taken together could measure the extent to which a system is conversational. Following this, we present a theoretical model of a conversational system that implements the properties. We describe how this system could be implemented, making the action space of an conversational search agent explicit. Our analysis of this model shows that while theoretical, the model could be practically implemented to satisfy the desirable properties presented. In doing so, we show that the properties are also feasible.


title: Recurrent Autoregressive Networks for Online Multi-object Tracking

abstract: The main challenge of online multi-object tracking is to reliably associate object trajectories with detections in each video frame based on their tracking history. In this work, we propose the Recurrent Autoregressive Network (RAN), a temporal generative modeling framework to characterize the appearance and motion dynamics of multiple objects over time. The RAN couples an external memory and an internal memory. The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory. We conduct experiments on the MOT 2015 and 2016 datasets to demonstrate the robustness of our tracking method in highly crowded and occluded scenes. Our method achieves top-ranked results on the two benchmarks.


title: The Emerging "Big Dimensionality"

abstract: The world continues to generate quintillion bytes of data daily, leading to the pressing needs for new efforts in dealing with the grand challenges brought by Big Data. Today, there is a growing consensus among the computational intelligence communities that data volume presents an immediate challenge pertaining to the scalability issue. However, when addressing volume in Big Data analytics, researchers in the data analytics community have largely taken a one-sided study of volume, which is the "Big Instance Size" factor of the data. The flip side of volume which is the dimensionality factor of Big Data, on the other hand, has received much lesser attention. This article thus represents an attempt to fill in this gap and places special focus on this relatively under-explored topic of "Big Dimensionality", wherein the explosion of features (variables) brings about new challenges to computational intelligence. We begin with an analysis on the origins of Big Dimensionality. The evolution of feature dimensionality in the last two decades is then studied using popular data repositories considered in the data analytics and computational intelligence research communities. Subsequently, the state-of-the-art feature selection schemes reported in the field of computational intelligence are reviewed to reveal the inadequacies of existing approaches in keeping pace with the emerging phenomenon of Big Dimensionality. Last but not least, the "curse and blessing of Big Dimensionality" are delineated and deliberated.


title: A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws

abstract: As ball screw has complex structure and long range of distribution, single signal collected by one sensor is difficult to express its condition fully and accurately. Multi-sensor data fusion usually has a better effect compared with single signal. Multi-sensor data fusion based on neural network(BP) is a commonly used multi-sensor data fusion method, but its application is limited by local optimum problem. Aiming at this problem, a multi-sensor data fusion method based on deep learning for ball screw is proposed in this paper. Deep learning, which consists of unsupervised learning and supervised learning, is the development and evolution of traditional neural network. It can effectively alleviate the optimization difficulty. Parallel superposition on frequency spectra of signals is directly done in the proposed deep learning-based multi-sensor data fusion method, and deep belief networks(DBN) are established by using fused data to adaptively mine available fault characteristics and automatically identify the degradation condition of ball screw. Test is designed to collect vibration signals of ball screw in 7 different degradation conditions by using 5 acceleration sensors installed on different places. The proposed fusion method is applied in identifying the degradation degree of ball screw in the test to demonstrate its efficacy. Finally, the multi-sensor data fusion based on neural network is also applied in degradation degree monitoring. The monitoring accuracy of deep learning-based multi-sensor data fusion is higher compared with that of neural network-based multi-sensor data fusion, which means the proposed method has more superiority.


title: A mobile robot exploration algorithm

abstract: An algorithm for path planning to a goal with a mobile robot in an unknown environment is presented. The robot maps the environment only to the extent necessary to achieve the goal. Mapping is achieved using tactile sensing while the robot is executing a path to the specified goal. Paths are generated by treating unknown regions in the environment as free space. As obstacles are encountered en route to a goal, the model of the environment is updated and a new path to the goal is planned and executed. Initially the paths to the goal generated by this algorithm will be negotiable paths. However, as the robot acquires more knowledge about the environment, the length of the planned paths will be optimized. The optimization criteria can be modified to favor or avoid unexplored regions in the environment. The algorithm makes use of the quadtree data structure to model the environment and uses the distance transform methodology to generate paths for the robot to execute. >


title: Change of direction speed in soccer: how much braking is enough?

abstract: The aims of the present study were to examine: 1) the validity and reliability of a new timing system to assess running kinematics during change of direction (COD), and 2) the determinants of COD-speed. Twelve young soccer players performed three 20-m sprints, either in straight line or with one 45o- or 90oCOD. Sprints were monitored using timing gates and two synchronized 100-Hz laser guns, to track players’ velocities before, during and after the COD. The validity analysis revealed trivial-to-small biases and smallto-moderate typical errors of the estimate with the lasers compared with the timing gates. The reliability was variable-dependent, with trivial- (distance at peak speed) to-large (distance at peak deceleration) typical errors. Kinematic variables were angle-dependent, with likely lower peak speed, almost-certainly slower minimum speed during the COD and almost-certainly greater deceleration reached for 90o-COD vs. 45oCOD sprints. The minimum speed during the COD was largely correlated with sprint performance for both sprint angles. Correlations with most of the other independent variables were unclear. The new timing system showed acceptable levels of validity and reliability to assess some of the selected running kinematics during COD sprints. The ability to maintain a high speed during the COD may be the determinant of COD-speed.


title: Healthy convenience: nudging students toward healthier choices in the lunchroom

abstract: Many students cite lack of time as one of the major constraints determining what they eat for lunch at school. In some schools, students receive as little as 15–20 minutes to purchase and eat their...


title: Real-time tone mapping on GPU and FPGA

abstract: Low-level computer vision algorithms have high computational requirements. In this study, we present two real-time architectures using resource constrained FPGA and GPU devices for the computation of a new algorithm which performs tone mapping, contrast enhancement, and glare mitigation. Our goal is to implement this operator in a portable and battery-operated device, in order to obtain a low vision aid specially aimed at visually impaired people who struggle to manage themselves in environments where illumination is not uniform or changes rapidly. This aid device processes in real-time, with minimum latency, the input of a camera and shows the enhanced image on a head mounted display (HMD). Therefore, the proposed operator has been implemented on battery-operated platforms, one based on the GPU NVIDIA ION2 and another on the FPGA Spartan III, which perform at rates of 30 and 60 frames per second, respectively, when working with VGA resolution images (640 × 480).


title: Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective

abstract: Many Internet-based services have already been ported to the mobile-based environment, embracing the new services is therefore critical to deriving revenue for services providers. Based on a valence framework and trust transfer theory, we developed a trust-based customer decision-making model of the non-independent, third-party mobile payment services context. We empirically investigated whether a customer's established trust in Internet payment services is likely to influence his or her initial trust in mobile payment services. We also examined how these trust beliefs might interact with both positive and negative valence factors and affect a customer's adoption of mobile payment services. Our SEM analysis indicated that trust indeed had a substantial impact on the cross-environment relationship and, further, that trust in combination with the positive and negative valence determinants directly and indirectly influenced behavioral intention. In addition, the magnitudes of these effects on workers and students were significantly different from each other.


An Ensemble Model of Clinical Information Extraction and Information Retrieval for Clinical Decision Support


title: Low QRS voltage and its causes.

abstract: Electrocardiographic low QRS voltage (LQRSV) has many causes, which can be differentiated into those due to the heart's generated potentials (cardiac) and those due to influences of the passive body volume conductor (extracardiac). Peripheral edema of any conceivable etiology induces reversible LQRSV, reduces the amplitude of the P waves and T waves, decreases the duration of P waves, QRS complexes, and QT intervals, and alters in turn the measurements of the signal-averaged electrocardiogram and T wave alternans, all with enormous clinical implications.


title: Maximizing Network Topology Lifetime Using Mobile Node Rotation

abstract: One of the key challenges facing wireless sensor networks (WSNs) is extending network lifetime due to sensor nodes having limited power supplies. Extending WSN lifetime is complicated because nodes often experience differential power consumption. For example, nodes closer to the sink in a given routing topology transmit more data and thus consume power more rapidly than nodes farther from the sink. Inspired by the huddling behavior of emperor penguins where the penguins take turns on the cold extremities of a penguin “huddle”, we propose mobile node rotation, a new method for using low-cost mobile sensor nodes to address differential power consumption and extend WSN lifetime. Specifically, we propose to rotate the nodes through the high power consumption locations. We propose efficient algorithms for single and multiple rounds of rotations. Our extensive simulations show that mobile node rotation can extend WSN topology lifetime by more than eight times on average which is significantly better than existing alternatives.


title: A mathematical stiffness matrix for characterising mechanical performance of the Orthofix DAF.

abstract: This study investigates a method for characterising the 3D support to a bone fracture site that is provided by a fracture stabilising device. The method is demonstrated with the Orthofix DAF unilateral external fixator, for which a mathematical stiffness matrix is defined using experimental measurements in six degrees of freedom. Single forces or bending moments are applied to a model fracture stabilised by the fixator, and 3D inter fragmentary displacements are measured by an instrumented spatial linkage. The 6x6 stiffness matrix for the fracture site is calculated from the product of the vector of forces and moments and the inverse of the vector of displacements. A transformation matrix is used to determine the stiffness matrix for a range of anatomical angles (between the plane of the fixator frame and the sagittal plane). Comparison between measured displacements (for an angle of 30 degrees) and the corresponding calculated displacements showed agreement to within 8%. The method enables fracture site stiffness for a fixation device to be characterised comprehensively, and its properties to be identified in comparison with other devices. It also provides the means of analysing inter fragmentary motion that may arise from physiological loading, regardless of complexity.


title: Group-Based Trajectory Modeling in Clinical Research

abstract: Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.


title: Discovering Denial Constraints

abstract: Integrity constraints (ICs) provide a valuable tool for enforcing correct application semantics. However, designing ICs requires experts and time. Proposals for automatic discovery have been made for some formalisms, such as functional dependencies and their extension conditional functional dependencies. Unfortunately, these dependencies cannot express many common business rules. For example, an American citizen cannot have lower salary and higher tax rate than another citizen in the same state. In this paper, we tackle the challenges of discovering dependencies in a more expressive integrity constraint language, namely Denial Constraints (DCs). DCs are expressive enough to overcome the limits of previous languages and, at the same time, have enough structure to allow efficient discovery and application in several scenarios. We lay out theoretical and practical foundations for DCs, including a set of sound inference rules and a linear algorithm for implication testing. We then develop an efficient instance-driven DC discovery algorithm and propose a novel scoring function to rank DCs for user validation. Using real-world and synthetic datasets, we experimentally evaluate scalability and effectiveness of our solution.


title: Big Data: state-of-the-art concepts, techniques, technologies, modeling approaches and research challenges

abstract: This work has been supported by COMPETE: POCI-01-0145- FEDER-007043 and FCT – Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013, and the SusCity project, MITP-TB/CS/0026/2013.


A Countermeasure against Spoofing and DoS Attacks based on Message Sequence and Temporary ID in CAN


title: Cascade-based attack vulnerability on the US power grid

abstract: The vulnerability of real-life networks subject to intentional attacks has been one of the outstanding challenges in the study of the network safety. Applying the real data of the US power grid, we compare the effects of two different attacks for the network robustness against cascading failures, i.e., removal by either the descending or ascending orders of the loads. Adopting the initial load of a node j to be Lj !" kj# Rm2Cj km$% a with kj and Cj being the degree of the node j and the set of its neighboring nodes, respectively, where a is a tunable parameter and governs the strength of the initial load of a node, we investigate the response of the US power grid under two attacks during the cascading propagation. In the case of a < 0:7, our investigation by the numerical simulations leads to a counterintuitive finding on the US power grid that the attack on the nodes with the lowest loads is more harmful than the attack on the ones with the highest loads. In addition, the almost same effect of two attacks in the case ofa ! 0:7 may be useful in furthering studies on the control and defense of cascading failures in the US power grid.


title: Predictive translation memory: a mixed-initiative system for human language translation

abstract: The standard approach to computer-aided language translation is post-editing: a machine generates a single translation that a human translator corrects. Recent studies have shown this simple technique to be surprisingly effective, yet it underutilizes the complementary strengths of precision-oriented humans and recall-oriented machines. We present Predictive Translation Memory, an interactive, mixed-initiative system for human language translation. Translators build translations incrementally by considering machine suggestions that update according to the user's current partial translation. In a large-scale study, we find that professional translators are slightly slower in the interactive mode yet produce slightly higher quality translations despite significant prior experience with the baseline post-editing condition. Our analysis identifies significant predictors of time and quality, and also characterizes interactive aid usage. Subjects entered over 99% of characters via interactive aids, a significantly higher fraction than that shown in previous work.


title: Implementation of a Cost-effective V2X hardware and software platform

abstract: Vehicular ad-hoc networks (VANET) aim at exchanging traffic safety and/or general purpose data by vehicles with fixed devices and other vehicles. The IEEE 802.11p standard defines the physical and medium access control layers for VANETs. High costs of commercial devices make practical experiments quite expensive. This work proposes a low cost, customizable device that supports different VANET standards for 5.9 GHz and 700 MHz frequency bands.


title: Model-Based Learning Using a Mixture of Mixtures of Gaussian and Uniform Distributions

abstract: We introduce a mixture model whereby each mixture component is itself a mixture of a multivariate Gaussian distribution and a multivariate uniform distribution. Although this model could be used for model-based clustering (model-based unsupervised learning) or model-based classification (model-based semi-supervised learning), we focus on the more general model-based classification framework. In this setting, we fit our mixture models to data where some of the observations have known group memberships and the goal is to predict the memberships of observations with unknown labels. We also present a density estimation example. A generalized expectation-maximization algorithm is used to estimate the parameters and thereby give classifications in this mixture of mixtures model. To simplify the model and the associated parameter estimation, we suggest holding some parameters fixed-this leads to the introduction of more parsimonious models. A simulation study is performed to illustrate how the model allows for bursts of probability and locally higher tails. Two further simulation studies illustrate how the model performs on data simulated from multivariate Gaussian distributions and on data from multivariate t-distributions. This novel approach is also applied to real data and the performance of our approach under the various restrictions is discussed.