Query: Authentication anomaly detection: a case study on a virtual private network

Relevant passages:
Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory <=SEP=> In 1998 and again in 1999, the Lincoln Laboratory of MIT conducted a comparative evaluation of intrusion detection systems (IDSs) developed under DARPA funding. While this evaluation represents a significant and monumental undertaking, there are a number of issues associated with its design and execution that remain unsettled. Some methodologies used in the evaluation are questionable and may have biased its results. One problem is that the evaluators have published relatively little concerning some of the more critical aspects of their work, such as validation of their test data. The appropriateness of the evaluation techniques used needs further investigation. The purpose of this article is to attempt to identify the shortcomings of the Lincoln Lab effort in the hope that future efforts of this kind will be placed on a sounder footing. Some of the problems that the article points out might well be resolved if the evaluators were to publish a detailed description of their procedures and the rationale that led to their adoption, but other problems would clearly remain./par>

Aberrant Behavior Detection in Time Series for Network Monitoring <=SEP=> The open-source software RRDtool and Cricket provide a solution to the problem of collecting, storing, and visualizing service network time series data for the real-time monitoring task. However, simultaneously monitoring all service network time series of interest is an impossible task even for the accomplished network technician. The solution is to integrate a mathematical model for automatic aberrant behavior detection in time series into the monitoring software. While there are many such models one might choose, the primary goal should be a model compatible with real-time monitoring. At WebTV, the solution was to integrate a model based on exponential smoothing and Holt-Winters forecasting into the Cricket/RRDtool architecture. While perhaps not optimal, this solution is flexible, efficient, and effective as a tool for automatic aberrant behavior detection.

Unsupervised Anomaly Detection in Network Intrusion Detection Using Clusters <=SEP=> Most current network intrusion detection systems employ signature-based methods or data mining-based methods which rely on labelled training data. This training data is typically expensive to produce. Moreover, these methods have difficulty in detecting new types of attack. Using unsupervised anomaly detection techniques, however, the system can be trained with unlabelled data and is capable of detecting previously “unseen” attacks. In this paper, we present a new density-based and grid-based clustering algorithm that is suitable for unsupervised anomaly detection. We evaluated our methods using the 1999 KDD Cup data set. Our evaluation shows that the accuracy of our approach is close to that of existing techniques reported in the literature, and has several advantages in terms of computational complexity.

Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications <=SEP=> Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates cluster descriptions in the form of DNF expressions that are minimized for ease of comprehension. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate cluster in large high dimensional datasets.

Data Mining Approaches for Intrusion Detection <=SEP=> In this paper we discuss our research in developing general and systematic methods for intrusion detection. The key ideas are to use data mining techniques to discover consistent and useful patterns of system features that describe program and user behavior, and use the set of relevant system features to compute (inductively learned) classifiers that can recognize anomalies and known intrusions. Using experiments on the s ndmailsystem call data and the network tcpdumpdata, we demonstrate that we can construct concise and accurate classifiers to detect anomalies. We provide an overview on two general data mining algorithms that we have implemented: the association rules algorithm and the frequent episodes algorithm. These algorithms can be used to compute the intraand interaudit record patterns, which are essential in describing program or user behavior. The discovered patterns can guide the audit data gathering process and facilitate feature selection. To meet the challenges of both efficient learning (mining) and real-time detection, we propose an agent-based architecture for intrusion detection systems where the learning agents continuously compute and provide the updated (detection) models to the detection agents.

Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures <=SEP=> Conventional sentiment analysis usually neglects semantic information between (sub-)clauses, as it merely implements so-called bag-of-words approaches, where the sentiment of individual words is aggregated independently of the document structure. Instead, we advance sentiment analysis by the use of rhetoric structure theory (RST), which provides a hierarchical representation of texts at document level. For this purpose, texts are split into elementary discourse units (EDU). These EDUs span a hierarchical structure in the form of a binary tree, where the branches are labeled according to their semantic discourse. Accordingly, this paper proposes a novel combination of weighting and grid search to aggregate sentiment scores from the RST tree, as well as feature engineering for machine learning. We apply our algorithms to the especially hard task of predicting stock returns subsequent to financial disclosures. As a result, machine learning improves the balanced accuracy by 8.6 percent compared to the baseline.

Measurement of preoperative and postoperative nasal tip projection and rotation. <=SEP=> OBJECTIVE
To measure the effect of columellar struts and cephalic trim on tip projection and tip rotation using digitized photographs.


METHODS
Using photographs of 62 patients who underwent external rhinoplasty, we retrospectively analyzed nasal tip projection (the Goode method) and rotation (nasolabial angle) before and after surgery. A cartilaginous strut was used in 36 patients, whereas 26 patients did not receive a strut. Patients were categorized into 4 subgroups, depending on the placement of a strut (placement, strut+ vs nonplacement, strut-) and the removal of the cephalic margin (removal, cephalic+ vs nonremoval, cephalic-) of the lateral crus: strut-/cephalic-, n = 17; strut+/cephalic-, n = 23; strut-/cephalic+, n = 9; strut+/cephalic+, n = 12.


RESULTS
Nasal tip projection, measured with the Goode method, increased from 0.58 to 0.60 (P = .02) in the strut+ group; in the strut- group, nasal tip projection did not change significantly. Nasolabial angle increased from 93.96 degrees to 100.92 degrees in the strut+/cephalic- group and from 88.30 degrees to 95.06 degrees in the strut+/cephalic+ group. Removal of the cephalic margin alone (strut-/cephalic+) hardly affected tip rotation (P = .05).


CONCLUSIONS
The external rhinoplasty approach did not lead to a decrease in nasal tip projection. A cartilaginous strut slightly increased nasal tip projection and also increased nasal tip rotation. This effect was accentuated by the removal of the cephalic margin of the lateral crus.

Labeled Faces in the Wild: A Survey <=SEP=> In 2007, Labeled Faces in the Wild was released in an effort to spur research in face recognition, specifically for the problem of face verification with unconstrained images. Since that time, more than 50 papers have been published that improve upon this benchmark in some respect. A remarkably wide variety of innovative methods have been developed to overcome the challenges presented in this database. As performance on some aspects of the benchmark approaches 100% accuracy, it seems appropriate to review this progress, derive what general principles we can from these works, and identify key future challenges in face recognition. In this survey, we review the contributions to LFW for which the authors have provided results to the curators (results found on the LFW results web page). We also review the cross cutting topic of alignment and how it is used in various methods. We end with a brief discussion of recent databases designed to challenge the next generation of face recognition algorithms. Erik Learned-Miller University of Massachusetts, Amherst, Massachusetts, e-mail: elm@cs.umass.edu Gary B. Huang Howard Hughes Medical Institute, Janelia Research Campus, e-mail: gbhuang@cs.umass.edu Aruni RoyChowdhury University of Massachusetts, Amherst, Massachusetts, e-mail: aruni@cs.umass.edu Haoxiang Li Stevens Institute of Technology, Hoboken, New Jersey, e-mail: hli18@stevens.edu Gang Hua Stevens Institute of Technology, Hoboken, New Jersey, e-mail: ganghua@gmail.com

Open production: scientific foundation for co-creative product realization <=SEP=> Globalization and the use of technology call for an adaptation of value creation strategies. As the potential for rationalization and achieving flexibility within companies is to the greatest possible extent exhausted, approaches to the corporate reorganization of value creation are becoming increasingly important. In this process, the spread and further development of information and communication technology often provide the basis for a reorganization of cross-company value nets and lead to a redistribution of roles and tasks between the actors involved in value creation. While cooperative, decentralized and self-organizing value creation processes are in fact being promoted, the associated potential for development and production engineering is being underestimated and hence not implemented sufficiently. This contribution will introduce a value creation taxonomy and then, using its notion and structure, describe the emerging transformations in value creation on the basis of case studies. Finally an adequate framework for analysing and configuring value creation will be presented.

Partisan Media Exposure and Attitudes Toward the Opposition <=SEP=> Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Steerable interfaces for pervasive computing spaces <=SEP=> This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.

Computational Intelligence Models for Insurance Fraud Detection : A Review of a Decade of Research <=SEP=> This paper presents a review of the literature on the application of data mining techniques for the detection of insurance fraud. Academic literature were analyzed and classified into three types of insurance fraud (automobile insurance, crop insurance and healthcare insurance) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that automobile insurance fraud detection have also attracted a great deal of attention in recent years. The main data mining techniques used for insurance fraud detection are logistic models, Decision tree, the Naïve Bayes, and support vector machine.

Fast Optimal Transport Averaging of Neuroimaging Data <=SEP=> Knowing how the Human brain is anatomically and functionally organized at the level of a group of healthy individuals or patients is the primary goal of neuroimaging research. Yet computing an average of brain imaging data defined over a voxel grid or a triangulation remains a challenge. Data are large, the geometry of the brain is complex and the between subjects variability leads to spatially or temporally non-overlapping effects of interest. To address the problem of variability, data are commonly smoothed before performing a linear group averaging. In this work we build on ideas originally introduced by Kantorovich to propose a new algorithm that can average efficiently non-normalized data defined over arbitrary discrete domains using transportation metrics. We show how Kantorovich means can be linked to Wasserstein barycenters in order to take advantage of the entropic smoothing approach used by. It leads to a smooth convex optimization problem and an algorithm with strong convergence guarantees. We illustrate the versatility of this tool and its empirical behavior on functional neuroimaging data, functional MRI and magnetoencephalography (MEG) source estimates, defined on voxel grids and triangulations of the folded cortical surface.

Deep Learning with Edge Computing for Localization of Epileptogenicity Using Multimodal rs-fMRI and EEG Big Data <=SEP=> Epilepsy is a chronic brain disorder characterized by the occurrence of spontaneous seizures of which about 30 percent of patients remain medically intractable and may undergo surgical intervention; despite the latter, some may still fail to attain a seizure-free outcome. Functional changes may precede structural ones in the epileptic brain and may be detectable using existing noninvasive modalities. Functional connectivity analysis through electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI), complemented by diffusion tensor imaging (DTI), has provided such meaningful input in cases of temporal lobe epilepsy (TLE). Recently, the emergence of edge computing has provided competent solutions enabling context-aware and real-time response services for users. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for the monitoring, evaluation and regulation of the epileptic brain, with responsive neurostimulation (RNS; Neuropace). First, an autonomic edge computing framework is proposed for processing of big data as part of a decision support system for surgical candidacy. Second, an optimized model for estimation of the epileptogenic network using independently acquired EEG and rs-fMRI is presented. Third, an unsupervised feature extraction model is developed based on a convolutional deep learning structure for distinguishing interictal epileptic discharge (IED) periods from nonIED periods using electrographic signals from electrocorticography (ECoG). Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods.

Kernel Clustering: Density Biases and Solutions <=SEP=> Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the density mode isolation bias in kernel K-means for a common class of kernels. We call it Breiman’s bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity. We also extend our analysis to other popular kernel clustering methods, e.g., average/normalized cut or dominant sets, where density biases can take different forms. For example, splitting isolated points by cut-based criteria is essentially the sparsest subset bias, which is the opposite of the density mode bias. Our findings suggest that a principled solution for density biases in kernel clustering should directly address data inhomogeneity. We show that density equalization can be implicitly achieved using either locally adaptive weights or locally adaptive kernels. Moreover, density equalization makes many popular kernel clustering objectives equivalent. Our synthetic and real data experiments illustrate density biases and proposed solutions. We anticipate that theoretical understanding of kernel clustering limitations and their principled solutions will be important for a broad spectrum of data analysis applications across the disciplines.

The sounds of silence: language, cognition, and anxiety in selective mutism. <=SEP=> OBJECTIVES
To determine whether oral language, working memory, and social anxiety differentiate children with selective mutism (SM), children with anxiety disorders (ANX), and normal controls (NCs) and explore predictors of mutism severity.


METHOD
Children ages 6 to 10 years with SM (n = 44) were compared with children with ANX (n = 28) and NCs (n = 19) of similar age on standardized measures of language, nonverbal working memory, and social anxiety. Variables correlating with mutism severity were entered in stepwise regressions to determine predictors of mute behavior in SM.


RESULTS
Children with SM scored significantly lower on standardized language measures than children with ANX and NCs and showed greater visual memory deficits and social anxiety relative to these two groups. Age and receptive grammar ability predicted less severe mutism, whereas social anxiety predicted more severe mutism. These factors accounted for 38% of the variance in mutism severity.


CONCLUSIONS
Social anxiety and language deficits are evident in SM, may predict mutism severity, and should be evaluated in clinical assessment. Replication is indicated, as are further studies of cognition and of intervention in SM, using large, diverse samples.

Collaborative Filtering Service Recommendation Based on a Novel Similarity Computation Method <=SEP=> Recently, collaborative filtering-based methods are widely used for service recommendation. QoS attribute value-based collaborative filtering service recommendation mainly includes two important steps. One is the similarity computation, and the other is the prediction for QoS attribute value, which the user has not experienced. In previous studies, the performances of some methods need to be improved. In this paper, we propose a ratio-based method to calculate the similarity. We can get the similarity between users or between items by comparing the attribute values directly. Based on our similarity computation method, we propose a new method to predict the unknown value. By comparing the values of a similar service and the current service that are invoked by common users, we can obtain the final prediction result. The performance of the proposed method is evaluated through a large data set of real web services. Experimental results show that our method obtains better prediction precision, lower mean absolute error ( <inline-formula><tex-math notation="LaTeX">$MAE$</tex-math><alternatives> <inline-graphic xlink:href="wu-ieq1-2479228.gif"/></alternatives></inline-formula>) and faster computation time than various reference schemes considered.

TrendLearner: Early Prediction of Popularity Trends of User Generated Content <=SEP=> Accurately predicting the popularity of user generated content (UGC) is very valuable to content providers, online advertisers, as well as social media and social network researchers. However, it is also a challenging task due to the plethora of factors that affect content popularity in social systems. We here focus on the problem of predicting the popularity trend of a piece of UGC (object) as early as possible, as a step towards building more accurate popularity prediction methods. Unlike previous work, we explicitly address the inherent tradeoff between prediction accuracy and remaining interest in the object after prediction, since, to be useful, accurate predictions should be made before interest has exhausted. Moreover, given the heterogeneity in popularity dynamics across objects in most UGC applications, this tradeoff has to be solved on a per-object basis, which makes the prediction task harder. We propose to tackle this problem with a novel two-step learning approach in which we: (1) extract popularity trends from previously uploaded objects, and then (2) predict trends for newly uploaded content. The first step exploits a time series clustering algorithm to represent each trend by a time series centroid. We propose to treat the second step as a classification problem. First, we extract a set of features of the target object corresponding to the distances of its early popularity curve to the previously identified centroids. We then combine these features with content features (e.g., incoming links, category), using them to train classifiers for prediction. Our experimental results for YouTube datasets show that we can achieve Micro and Macro F1 scores between 0.61 and 0.71 (a gain of up to 38% when compared to alternative approaches), with up to 68% of the views still remaining for 50% or 21% of the videos, depending on the dataset. We also show that our approach can be applied to produce predictions of content popularity at a future date that are much more accurate than recently proposed regression-based and state-space based models, with accuracy improvements of at least 33% and 59%, on average.

Relative Physical Knowledge of Actions and Objects <=SEP=> Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., “My house is bigger than me.” However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, “Tyler entered his house” implies that his house is bigger than Tyler. In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.

Graph Databases <=SEP=> Story Slides Slide 1 Graph Databases and the Semantic Web Slide 2 Background Slide 3 Graph Databases: The Definitive Book Slide 4 The Graph Database Space Slide 5 Gartner Magic Quadrant for BI & Analytics Platforms Slide 6 Foreword and Preface Slide 7

Smart cities based on web semantic technologies <=SEP=> A good communication and interaction between citizens and the administration is important and crucial, also can greatly help in improving the quality of urban life of citizen. In this paper, we propose a semantic data model for managing and resolving the problems that exist in cities such as water leak, street faults, broken street lights, and potholes. The main idea is to focus on the best practices of linked open data to describe all issues, and then integrate them in the dataset provided by DBpedia. Hence, our approach is based on the standards of the World Wide Web Consortium.

Examining strategic ambidexterity as an antecedent of functional and cross-functional ambidexterity <=SEP=> 

Bitcoin's Academic Pedigree <=SEP=> The concept of cryptocurrencies is built from forgotten ideas in research literature.

A High-Voltage Pulse Generator With Continuously Variable Pulsewidth Based on a Modified PFN <=SEP=> The amount of stored energy is an important issue in the low-droop and high-precision pulse generation. Pulse forming network (PFN) simulating an open-ended transmission line seems to be useful due to its minimum stored energy. The drawbacks of the PFN include low-output pulse quality and constant pulsewidth, which limit their applications. Another limitation of generating rectangular pulses using PFN is that half of the charged voltage appears in the output when delivering the entire stored energy to the matched load. This paper proposes a structure of PFN-based pulse generator that overcomes the above-mentioned limitations. For continuous pulsewidth adjustment, a lossless scheme is presented. By increasing the ratio of the load magnitude to the characteristic impedance, the amplitude of the output pulse voltage would be approximately equal to that of the charged voltage. Therefore, the semiconductor switches can be utilized in the PFN pulse generator easily. In addition, the pulse quality is enhanced and faster rising time is obtained in comparison with the conventional PFNs. In this proposal, the stored energy quantity is limited to three times the energy needed for the output pulse in the case of  $R_{L}=10Z_{0}$ . To verify the proposed structure, a prototype is simulated using PSPICE software, and the experimental results are presented.

Is it a tool suitable for learning? A critical review of the literature on Facebook as a technology-enhanced learning environment <=SEP=> Despite its continuing popularity as the social network site par excellence, the educational value of Facebook has not been fully determined, and results from the mainstream educational paradigms are contradictory, with some scholars emphasizing its pedagogical affordances (e.g., widening context of learning, mixing information and learning resources, hybridization of expertise) and others cautioning against its use for educational purposes. Moreover, systematic reviews about documented educational usage of Facebook as a learning environment are lacking. This article attempts to provide a critical overview of current studies focusing on the use of Facebook as a technology-enhanced learning environment, with the aim of exploring the extent to which its pedagogical potential is actually translated into practice. Only empirical studies published in peer-reviewed academic journals with a specific focus on Facebook as a learning environment have been considered for the review. The authors conducted a comprehensive literature search that identified 23 relevant articles that were subsequently analysed according to a simplified list of guidelines. These articles were further analysed and recoded through a set of emerging categories. The results show that pedagogical affordances of Facebook have only been partially implemented and that there are still many obstacles that may prevent a full adoption of Facebook as a learning environment such as implicit institutional, teacher and student pedagogies, and cultural issues. Finally, a broad observation on the implications of the study is developed with some suggestions for future research.

A Next-Generation Secure Cloud-Based Deep Learning License Plate Recognition for Smart Cities <=SEP=> License Plate Recognition System (LPRS) plays a vital role in smart city initiatives such as traffic control, smart parking, toll management and security. In this article, a cloud-based LPRS is addressed in the context of efficiency where accuracy and speed of processing plays a critical role towards its success. Signature-based features technique as a deep convolutional neural network in a cloud platform is proposed for plate localization, character detection and segmentation. Extracting significant features makes the LPRS to adequately recognize the license plate in a challenging situation such as i) congested traffic with multiple plates in the image ii) plate orientation towards brightness, iii) extra information on the plate, iv) distortion due to wear and tear and v) distortion about captured images in bad weather like as hazy images. Furthermore, the deep learning algorithm computed using bare-metal cloud servers with kernels optimized for NVIDIA GPUs, which speed up the training phase of the CNN LPDS algorithm. The experiments and results show the superiority of the performance in both recall and precision and accuracy in comparison with traditional LP detecting systems.

Pornographic image detection utilizing deep convolutional neural networks <=SEP=> Many internet users are potential victims of the pornographic images and a large part of them are underage children. Thus, content-based pornographic images detection is an important task in computer vision and multimedia research. Previous solutions usually rely on hand-engineered visual features that are hard to analyze and select. In this paper, to detect pornographic images in any style accurately and efficiently with a single model, a novel scheme utilizing the deep convolutional neural networks (CNN) is proposed. The training data are obtained from internet followed by an improved sliding window method and some novel data augmentation approaches. Then a highly efficient training algorithm is proposed based on two strategies. The first is the pre-trained midlevel representations non-fixed fine-tuning strategy. The second is adjusting the training data at the appropriate time on the basis of the performance of the proposed network on the validation set. Furthermore, we introduce a fast image scanning method which is also based on the sliding window approach in the test. We further propose a fast forward pass method based on the “fixedpoint algorithm”. So our CNN could detect all scale images so fast by one forward pass. The effectiveness of the proposed method is demonstrated in experiments on the proposed dataset and the comparative results show that our ∗Corresponding author Email address: tenglwy@gmail.com (Teng Li) Preprint submitted to Neurocomputing June 9, 2016 method lead to state-of-the-art detection performance.

A Novel DC-Side Zero-Voltage Switching (ZVS) Three-Phase Boost PWM Rectifier Controlled by an Improved SVM Method <=SEP=> A novel active clamping zero-voltage switching three-phase boost pulsewidth modulation (PWM) rectifier is analyzed and a modified minimum-loss space vector modulation (SVM) strategy suitable for the novel zero-voltage switching (ZVS) rectifier is proposed in this paper. The topology of the novel ZVS rectifier only adds one auxiliary active switch, one resonant inductor, and one clamping capacitor to the traditional hard-switched three-phase boost PWM rectifier. With the proposed SVM strategy, the novel ZVS rectifier can achieve ZVS for all the main and auxiliary switches. In addition, the antiparallel diodes can be turned OFF softly, so the reverse recovery current is eliminated. Besides, the voltage stress of all the switches is equal to the dc-link voltage. The operation principle and soft-switching condition of the novel ZVS rectifier are analyzed. The design guidelines of the soft switched circuit parameters are described in detail. A DSP controlled 30 kW prototype is implemented to verify the theory.

Using heuristics to estimate an appropriate number of latent topics in source code analysis <=SEP=> Latent Dirichlet Allocation (LDA) is a data clustering algorithm that performs especially well for text documents. In natural-language applications it automatically finds groups of related words (called “latent topics”) and clusters the documents into sets that are about the same “topic”. LDA has also been applied to source code, where the documents are natural source code units such as methods or classes, and the words are the keywords, operators, and programmer-defined names in the code. The problem of determining a topic count that most appropriately describes a set of source code documents is an open problem. We address this empirically by constructing clusterings with different numbers of topics for a large number of software systems, and then use a pair of measures based on source code locality and topic model similarity to assess how well the topic structure identifies related source code units. Results suggest that the topic count required can be closely approximated using the number of software code fragments in the system. We extend these results to recommend appropriate topic counts for arbitrary software systems based on an analysis of a set of open source systems.

Standard-Compliant Multiview Video Coding and Streaming for Virtual Reality Applications <=SEP=> Virtual reality (VR) systems employ multiview cameras or camera rigs to capture a scene from the entire 360-degree perspective. Due to computational or latency constraints, it might not be possible to stitch multiview videos into a single video sequence prior to encoding. In this paper we investigate the coding and streaming of multiview VR video content. We present a standard-compliant method where we first divide the camera views into two types: Primary views represent a subset of camera views with lower resolution and non-overlapping (minimally overlapping) content which cover the entire 360-degree field-of-view to guarantee immediate monoscopic viewing during very rapid head movements. Auxiliary views consist of remaining camera views with higher resolution which produce overlapping content with the primary views and are additionally used for stereoscopic viewing. Based on this categorization, we propose a coding arrangement in which, the primary views are independently coded in the base layer and the additional auxiliary views are coded as an enhancement layer, using inter-layer prediction from primary views. The proposed system not only meets the low latency requirements of VR systems, but also conforms to the existing multilayer extensions of the High Efficiency Video Coding standard. Simulation results show that the coding and streaming performance of the proposed scheme is significantly improved compared to earlier methods.