title: A multicamera setup for generating stereo panoramic video

abstract: Traditional visual communication systems convey only two-dimensional (2D) fixed field-of-view video information. The viewer is presented with a series of flat, non-stereoscopic images, which fail to provide a realistic sense of depth. Furthermore, traditional video is restricted to only a small part of the scene, based on the director's discretion and the user is not allowed to 'look around' in an environment. The major goal of our research is to develop new techniques for creating stereo panoramic video sequences. A stereo panoramic video sequence should be able to provide the viewer with stereo vision at any direction (complete 360 degrees field-of-view) at video rates. In this paper we explore the possibility of creating stereo panoramic video using a multi-camera approach, thus creating a high-resolution output. We present a setup that is an extension of a previously known approach, developed for the generation of still stereo panoramas, and demonstrate that it is capable of achieving our goal. We further explore the practical limitations involved in a practical implementation of the setup, namely the limited number of cameras and the non-zero physical size of real cameras. The relevant trade-offs are identified and studied. Experimental results are presented to support our conclusions.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning

abstract: The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network decoding (NND) is currently only feasible for very short block lengths. In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components. Thus, we partition the encoding graph into smaller sub-blocks and train them individually, closely approaching maximum a posteriori (MAP) performance per sub-block. These blocks are then connected via the remaining conventional belief propagation decoding stage(s). The resulting decoding algorithm is non-iterative and inherently enables a high-level of parallelization, while showing a competitive bit error rate (BER) performance. We examine the degradation through partitioning and compare the resulting decoder to state-of-the-art polar decoders such as successive cancellation list and belief propagation decoding.


SQuAD Question Answering Problem : A match-lstm implementation


title: Anomaly detection and monitoring in Internet of Things communication

abstract: The Internet of Things (IoT) presents unique challenges in detecting anomaly and monitoring all connected devices in a network. Moreover, one of the objectives of anonymity in communication is to protect the data traffic of devices. The summary status visualization is indispensable to depict all devices/sensors that are most indicative of a pending failure and a predictive power/energy. Thus, this paper proposes a multi-platform monitoring and anomaly detection system that supports heterogeneous devices. The proposed system addresses the problems of: (i) how to monitor the network to prevent device failures and (ii) how to design a comprehensive feature for the early anomaly detection of IoT communication.


title: Clustering based active learning for biomedical Named Entity Recognition

abstract: The recognition and extraction of biomedical names is an essential task for the biomedical information extraction. However, the preparation of large annotated corpora hinders the training of the Named Entity Recognition (NER) systems. Active learning is reducing the needed manual annotation work in supervised learning task. In this work, we propose a novel clustering based active learning method for the biomedical NER task. We show that the underlying NER system using the proposed method outperforms those with other state of the art active learning methods, including density, Gibbs error and entropy based approaches, as well as the random selection. We compare variations of our proposed method and find the optimal design of the active learning method, which is to use the vector representation of named entities, and to select documents that are `representative' and `informative', as well as to use the Shared Nearest Neighbor (SNN) clustering approach. In particular, the optimal variant of the proposed method achieves a deficiency gain of 36.3% over the random selection.


title: ggRandomForests: Exploring Random Forest Survival

abstract: Random forest (Leo Breiman 2001a) (RF) is a non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF is a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. Random survival forests (RSF) (Ishwaran and Kogalur 2007; Ishwaran et al. 2008) are an extension of Breimans RF techniques allowing efficient nonparametric analysis of time to event data. The randomForestSRC package (Ishwaran and Kogalur 2014) is a unified treatment of Breimans random forest for survival, regression and classification problems. Predictive accuracy makes RF an attractive alternative to parametric models, though complexity and interpretability of the forest hinder wider application of the method. We introduce the ggRandomForests package, tools for visually understand random forest models grown in R (R Core Team 2014) with the randomForestSRC package. The ggRandomForests package is structured to extract intermediate data objects from randomForestSRC objects and generate figures using the ggplot2 (Wickham 2009) graphics package. This document is structured as a tutorial for building random forest for survival with the randomForestSRC package and using the ggRandomForests package for investigating how the forest is constructed. We analyse the Primary Biliary Cirrhosis of the liver data from a clinical trial at the Mayo Clinic (Fleming and Harrington 1991). Our aim is to demonstrate the strength of using Random Forest methods for both prediction and information retrieval, specifically in time to event data settings.


title: A maximum power control scheme based on multi-agent system for distributed flexible network photovoltaic system

abstract: The distributed flexible network PV (photovoltaic) system is a kind of dc micro-grid which is composed of several PV generation systems, a battery bank and loads. Controller of each converter takes local information and control the converter autonomously. However, there are several problems like voltage rise in dc link causing loss of maximum power control by sudden change of circumstances like as the change of irradiance and load. The authors propose introducing MAS (multi-agent system) to solve those problems. The control method is implemented and validated by experiment. Agents are implemented through DSP (digital signal processor) and CAN (controller area network) communication.


title: Analysis and visualization of sentiment and emotion on crisis tweets

abstract: Understanding how people communicate during disasters is important for creating systems to support this communication. Twitter is commonly used to broadcast information and to organize support during times of need. During the 2010 Gulf Oil Spill, Twitter was utilized for spreading information, sharing firsthand observations, and to voice concern about the situation. Through building a series of classifiers to detect emotion and sentiment, the distribution of emotion during the Gulf Oil Spill can be analyzed and its propagation compared against released information and corresponding events. We contribute a series of emotion classifiers and a prototype collaborative visualization of the results and discuss their implications.


title: Natural mapping and intuitive interaction in videogames

abstract: Videogame control interfaces continue to evolve beyond their traditional roots, with devices encouraging more natural forms of interaction growing in number and pervasiveness. Yet little is known about their true potential for intuitive use. This paper proposes methods to leverage existing intuitive interaction theory for games research, specifically by examining different types of naturally mapped control interfaces for videogames using new measures for previous player experience. Three commercial control devices for a racing game were categorised using an existing typology, according to how the interface maps physical control inputs with the virtual gameplay actions. The devices were then used in a within-groups (n=64) experimental design aimed at measuring differences in intuitive use outcomes. Results from mixed design ANOVA are discussed, along with implications for the field.


title: Recognition and localization of ripen tomato based on machine vision.

abstract: Fruits picking by human is a time consuming, tedious and expensive task. For this reason, the automation of fruit harvesting has achieved great popularity in the last decade. Tomato fruits do not ripe simultaneously and one of the main challenges in the design of a tomato harvester robot is its ability in recognition and localization of ripen tomato on the plant. In the current study, a new segmentation algorithm was developed for guidance of a robot arm to pick the ripen tomato using a machine vision system. To reach this aim, a vision system was used to acquire images from tomato plant. The recognition algorithm had to be adaptive to the lighting conditions of greenhouse. Totally 110 color images of tomato were acquired under greenhouse light conditions. The developed algorithm works in two steps: (1) by removing the background in RGB color space and then extract the ripen tomato using combination of RGB, HSI, and YIQ spaces and (2) localizing the ripen tomato using morphological features of image. According to the results, the total accuracy of proposed algorithm was 96.36%.


Designing ranking systems for consumer reviews: The impact of review subjectivity on product sales and review quality


title: Two methods for semi-automatic image segmentation based on fuzzy connectedness and watersheds

abstract: At the present time, one of the best methods for semiautomatic image segmentation seems to be the approach based on the fuzzy connectedness principle. First, we identify some deficiencies of this approach and propose a way to improve it, through the introduction of competitive learning. Second, we propose a different approach, based on watersheds. We show that the competitive fuzzy connectedness-based method outperforms the noncompetitive variant and generally (but not always) outperforms the watershed-based approach. The competitive variant of the fuzzy connectedness-based method can be a good alternative to the watersheds.


Introduction to React


title: Consumer Heterogeneity, Perceived Value, and Repurchase Decision-Making in Online Shopping: The Role of Gender, Age, and Shopping Motives

abstract: 1. IntroductionForrester Research Inc. forecasts that online retailing sales will grow from $263 billion in 2013 to $414 billion in 2018, representing a 9.5 percent compound annual growth rate [Internet Retailer 2014]. By that time, electronic commerce (EC) is expected to account for 11 percent of all retail sales in the U.S. It is believed that the majority of the growth in EC results from existing online shoppers who are spending more time and money in a wider variety of categories [Centre for Retail Research 2014]. As a result, the success of business-to-consumer (B2C) companies relies on their ability to attract customers to revisit the online stores and develop long-term relationships. However, B2C companies often face the fundamental challenge of how to acquire and maintain these consumers [Eid 2011; Lu et al. 2012; Wu et al. 2014].Consumers' perceived value is the core construct and foundation in all relational exchange activities [Wu et al. 2014], and is a critical factor influencing repeat buying action in online shopping contexts [Chiu et al. 2014]. Therefore, it is crucial to identify the factors affecting consumers' perception of value. Wu et al. [2014] have shown that benefits and sacrifice coalesce perceived value. However, the linear relationships between benefits/sacrifice and perceived value might be contingent upon consumer characteristics, such as gender and age. For example, females and males have different need structures and decision models when shopping online [Zhou et al. 2014]; they may react to the same benefits differently resulting in differences in their perceived value, which in turn can result in a different repurchase intention.EC has been accepted by a diverse population of users with heterogeneous backgrounds, in terms of age, gender differences, prior knowledge, cognitive styles, and shopping motives. These human factors are key issues for the development of Web-based applications such as EC, which leads to a significant growth into research in that area over the past decade [Chen & Macredie 2010]. Yet, less attention has been drawn to how these human factors differentially affect perceived value in the context of repeat online shopping. A clear understanding of the effects of these human factors and their interplay would allow online vendors to develop tailored strategies for improving repeat sales.By proposing a conceptual framework based on the means-end chain theory (MEC), this study attempts to explore how two primary consumer characteristics ( gender and age), interact with an important situational variable, i.e. shopping motive, to affect the linear relations between benefits/sacrifice and perceived value from the consumer's perspective. We especially focus on gender and age in the study for the following reasons: (1) the existing literature on gender and age differences related to computer usage found that there are significant impacts of gender and age on attitudes and behaviors related to computers [Yoon & Occena 2015]; (2) gender and age are two of the most widely recognized and investigated individual factors in EC contexts [Lian & Yen 2014]; (3) understanding gender and age differences is practically of importance because these characteristics can be easily identifiable in marketing practice and accessible in consumer segment strategies, and these differences can hold across cultures [Zhou et al. 2014]. Therefore, gender and age should be considered as two significant factors that can influence or moderate the relationships between relational benefits (i.e. product quality and e-service quality) and perceived value in EC.This study contributes to the consumer behavior and e-commerce literature in the following perspectives. First, this research provides a response to scholars' call for in-depth investigations into understanding individual differences in online consumer behavior research [Meyers-Levy & Loken 2015; Zhou et al. 2014]. …


title: Efficient and Effective Higher Order Proximity Modeling

abstract: Bag-of-words retrieval models are widely used, and provide a robust trade-off between efficiency and effectiveness. These models often make simplifying assumptions about relations between query terms, and treat term statistics independently. However, query terms are rarely independent, and previous work has repeatedly shown that term dependencies can be critical to improving the effectiveness of ranked retrieval results. Among all term-dependency models, the Markov Random Field (MRF) [Metzler and Croft, SIGIR, 2005] model has received the most attention in recent years. Despite clear effectiveness improvements, these models are not deployed in performance-critical applications because of the potentially high computational costs. As a result, bigram models are generally considered to be the best compromise between full term dependence, and term-independent models such as BM25. Here we provide further evidence that term-dependency features not captured by bag-of-words models can reliably improve retrieval effectiveness. We also present a new variation on the highly-effective MRF model that relies on a BM25-derived potential. The benefit of this approach is that it is built from feature functions which require no higher-order global statistics. We empirically show that our new model reduces retrieval costs by up to 60%, with no loss in effectiveness compared to previous approaches.


title: Influence of glutathione (GSH) on net uptake of sulphate and sulphate transport in tobacco plants

abstract: Net uptake of sulphate by the roots, and its transport to the shoot was inhibited in intact tobacco plants by 1 h exposure of its roots to 0.1 or 1.0 mM GSH. The relative amount of sulphate taken up that was transported to the shoot was not affected by this treatment. Apparently, sulphate uptake, but not xylem loading of sulphate, was inhibited by GSH. Similar results were obtained when L-cysteine was used instead of GSH. As L-cysteine, mediated inhibition of sulphate, net uptake was counteracted by exposure of the roots to BSO, GSH synthesis seems to be required for inhibition of sulphate uptake by L-cysteine. However, exposure to 0.1 or 1.0 mM GSH did not result in an enhanced GSH level in roots or shoots. Also the cysteine contents remained unchanged


title: Evaluating network intrusion detection systems for high-speed networks

abstract: Network Intrusion Detection Systems (NIDSs) play a crucial role in detecting malicious activities within the networks. Basically, an NIDS monitors network flows and compares it with the pre-defined suspicious patterns. To be effective, different intrusion detection algorithms and packet capturing methods have been implemented. With rapidly increasing network speeds, NIDSs face a challenging problem of monitoring large and diverse traffic volumes; in particular, the high packet drop rate has a significant impact on detection accuracy. In this work, we investigate three popular open-source NIDSs: Snort, Suricata, and Bro along with their comparative performance benchmarks. We investigate key factors (including system resource usage, packet processing speed and packet drop rate) that limit applicability of NIDSs to large-scale networks. Moreover, we also analyse and compare the performance of NIDSs when configurations and traffic volumes are changed.


title: MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment

abstract: This research strives for natural language moment retrieval in long, untrimmed video streams. The problem is not trivial especially when a video contains multiple moments of interests and the language describes complex temporal dependencies, which often happens in real scenarios. We identify two crucial challenges: semantic misalignment and structural misalignment. However, existing approaches treat different moments separately and do not explicitly model complex moment-wise temporal relations. In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network. MAN naturally assigns candidate moment representations aligned with language semantics over different temporal locations and scales. Most importantly, we propose to explicitly model moment-wise temporal relations as a structured graph and devise an iterative graph adjustment network to jointly learn the best structure in an end-to-end manner. We evaluate the proposed approach on two challenging public benchmarks DiDeMo and Charades-STA, where our MAN significantly outperforms the state-of-the-art by a large margin.


title: “Wonder Turner” and “The Amazing Cinemagician” augmented reality and mixed reality art installations

abstract: This paper discusses the approach, evolution and exhibition of the augmented reality art installation Wonder Turner and mixed reality work The Amazing Cinemagician at the Ontario Science Centre Idea Gallery from May 29 through September 6, 2010 in Toronto, Canada, by artist and researcher Helen Papagiannis. Both interactive artworks are featured in the exhibition entitled, The Amazing Cinemagician: New Media Meets Victorian Magic by Helen Papagiannis in association with the Augmented Reality Lab at York University.


title: A Simulative Bionic Intelligent Optimization Algorithm: Artificial Searching Swarm Algorithm and Its Performance Analysis

abstract: In this paper, a novel optimization algorithm - Artificial Searching Swarm Algorithm (ASSA) is presented by analyzing the operating principle and uniform framework of the Bionic Intelligent Optimization Algorithm. ASSA simulates the process of solving optimal design problem to the process of searching optimal goal by searching swarm with the set rules, and finds the optimal solution through the search and cooperation of the searching individual. The algorithm has easy programmable character, and has strong global searching ability. The simulative tests prove the validity of the proposed algorithm.


title: Interlingua based Sanskrit-English machine translation

abstract: This paper is based on the work towards developing a machine translation system for Sanskrit to English. An interlingua based machine translation system in Paninian framework was developed in this work. As lexical functional grammar can handle semantic information along with syntactic analysis, the proposed system uses lexical functional grammar in different levels of analysis phase. In this system the given Sanskrit text is first converted to an intermediate notation called interlingua. This notation is then used for mapping to the target language, English, and generates the translated output. The proposed system works in the translation of simple and complex sentences in the given corpus. The karaka analysis system of Panini is used for the semantic analysis.


title: Friendship prediction and homophily in social media

abstract: Social media have attracted considerable attention because their open-ended nature allows users to create lightweight semantic scaffolding to organize and share content. To date, the interplay of the social and topical components of social media has been only partially explored. Here, we study the presence of homophily in three systems that combine tagging social media with online social networks. We find a substantial level of topical similarity among users who are close to each other in the social network. We introduce a null model that preserves user activity while removing local correlations, allowing us to disentangle the actual local similarity between users from statistical effects due to the assortative mixing of user activity and centrality in the social network. This analysis suggests that users with similar interests are more likely to be friends, and therefore topical similarity measures among users based solely on their annotation metadata should be predictive of social links. We test this hypothesis on several datasets, confirming that social networks constructed from topical similarity capture actual friendship accurately. When combined with topological features, topical similarity achieves a link prediction accuracy of about 92p.


title: Explaining Gender Differences in Jurors' Reactions to Child Sexual Assault Cases

abstract: In three experiments, we investigated the influence of juror, victim, and case factors on mock jurors' decisions in several types of child sexual assault cases (incest, day care, stranger abduction, and teacher-perpetrated abuse). We also validated and tested the ability of several scales measuring empathy for child victims, children's believability, and opposition to adult/child sex, to mediate the effect of jurors' gender on case judgments. Supporting a theoretical model derived from research on the perceived credibility of adult rape victims, women compared to men were more empathic toward child victims, more opposed to adult/child sex, more pro-women, and more inclined to believe children generally. In turn, women (versus men) made more pro-victim judgments in hypothetical abuse cases; that is, attitudes and empathy generally mediated this juror gender effect that is pervasive in this literature. The experiments also revealed that strength of case evidence is a powerful factor in determining judgments, and that teen victims (14 years old) are blamed more for sexual abuse than are younger children (5 years old), but that perceptions of 5 and 10 year olds are largely similar. Our last experiment illustrated that our findings of mediation generalize to a community member sample. Copyright © 2014 John Wiley & Sons, Ltd. Language: en


title: Extrathymically generated regulatory T cells control mucosal TH2 inflammation

abstract: A balance between pro- and anti-inflammatory mechanisms at mucosal interfaces, which are sites of constitutive exposure to microbes and non-microbial foreign substances, allows for efficient protection against pathogens yet prevents adverse inflammatory responses associated with allergy, asthma and intestinal inflammation. Regulatory T (T(reg)) cells prevent systemic and tissue-specific autoimmunity and inflammatory lesions at mucosal interfaces. These cells are generated in the thymus (tT(reg) cells) and in the periphery (induced (i)T(reg) cells), and their dual origin implies a division of labour between tT(reg) and iT(reg) cells in immune homeostasis. Here we show that a highly selective blockage in differentiation of iT(reg) cells in mice did not lead to unprovoked multi-organ autoimmunity, exacerbation of induced tissue-specific autoimmune pathology, or increased pro-inflammatory responses of T helper 1 (T(H)1) and T(H)17 cells. However, mice deficient in iT(reg) cells spontaneously developed pronounced T(H)2-type pathologies at mucosal sites--in the gastrointestinal tract and lungs--with hallmarks of allergic inflammation and asthma. Furthermore, iT(reg)-cell deficiency altered gut microbial communities. These results suggest that whereas T(reg) cells generated in the thymus appear sufficient for control of systemic and tissue-specific autoimmunity, extrathymic differentiation of T(reg) cells affects commensal microbiota composition and serves a distinct, essential function in restraint of allergic-type inflammation at mucosal interfaces.


title: Toward the Starting Line: A Systems Engineering Approach to Strong AI

abstract: Artificial General Intelligence (AGI) or Strong AI aims to create machines with human-like or human-level intelligence, which is still a very ambitious goal when compared to the existing computing and AI systems. After many hype cycles and lessons from AI history, it is clear that a big conceptual leap is needed for crossing the starting line to kick-start mainstream AGI research. This position paper aims to make a small conceptual contribution toward reaching that starting line. After a broad analysis of the AGI problem from different perspectives, a system-theoretic and engineering-based research approach is introduced, which builds upon the existing mainstream AI and systems foundations. Several promising cross-fertilization opportunities between systems disciplines and AI research are identified. Specific potential research directions are discussed.


title: Chute based automated fish length measurement and water drop detection

abstract: Image processing and analysis techniques have drawn increasing attention since they enable a non-extractive and non-lethal approach to fisheries survey, such as fish size measurement, abundance prediction, catch estimation and compliance, species recognition and population counting. In this work, we present an innovative and effective method for measuring the chute-based fish length based on the morphological midline of the fish. The midline is generated through recursive morphological operations on the segmented fish mask. To conduct reliable measurement, even under harsh environment, we also propose a systematic method for detecting water drop on camera lens. The robust detection, which can be performed either in real-time or in offline processing, is based on a blur measure derived from the gradient of the image and the contour of fish.


title: Autotuning Stencil-Based Computations on GPUs

abstract: Finite-difference, stencil-based discretization approaches are widely used in the solution of partial differential equations describing physical phenomena. Newton-Krylov iterative methods commonly used in stencil-based solutions generate matrices that exhibit diagonal sparsity patterns. To exploit these structures on modern GPUs, we extend the standard diagonal sparse matrix representation and define new matrix and vector data types in the PETSc parallel numerical toolkit. We create tunable CUDA implementations of the operations associated with these types after identifying a number of GPU-specific optimizations and tuning parameters for these operations. We discuss our implementation of GPU auto tuning capabilities in the Orio framework and present performance results for several kernels, comparing them with vendor-tuned library implementations.


title: Web Layout Mining (WLM): A New Paradigm for Intelligent Web Layout Design

abstract: The problem in designing of modern Website projects is to produce contents according to the latest trends and styles. The common Website editors just help to draw the intended layouts but the problem is to design the accurate Web layout according to the demand and latest trends and style. This approach is useful when the user has a specific layout already in mind and is familiar with the Web page layout principles as to what kinds of layouts are possible. It is intrinsically difficult for particularly those who have limited artistic and creative abilities to design good layout from scratch which is acceptable in every respect. An automated system is required that has ability to mine the layouts of the desired type of Websites. The designed system for "Web layout mining (WLM)" helps to mine the most popular web-layouts from the Internet database and design a Web-layout that is near to acceptable and have all the marks and features of modern requirements. The designed system actually bases on a rule based algorithm which helps the user to search out some samples related to his Website category and afterwards the user himself chooses a desired Web-layout and designs its own one with proper implications and variations according to his own requirements.


title: BwE: Flexible, Hierarchical Bandwidth Allocation for WAN Distributed Computing

abstract: WAN bandwidth remains a constrained resource that is economically infeasible to substantially overprovision. Hence, it is important to allocate capacity according to service priority and based on the incremental value of additional allocation. For example, it may be the highest priority for one service to receive 10Gb/s of bandwidth but upon reaching such an allocation, incremental priority may drop sharply favoring allocation to other services. Motivated by the observation that individual flows with fixed priority may not be the ideal basis for bandwidth allocation, we present the design and implementation of Bandwidth Enforcer (BwE), a global, hierarchical bandwidth allocation infrastructure. BwE supports: i) service-level bandwidth allocation following prioritized bandwidth functions where a service can represent an arbitrary collection of flows, ii)independent allocation and delegation policies according to user-defined hierarchy, all accounting for a global view of bandwidth and failure conditions, iii) multi-path forwarding common in traffic-engineered networks, and iv) a central administrative point to override (perhaps faulty) policy during exceptional conditions. BwE has delivered more service efficient bandwidth utilization and simpler management in production for multiple years.


title: Cryptographic Hash Functions: Theory and Practice

abstract: Cryptographic hash functions are an essential building block for security applications. Until 2005, the amount of theoretical research and cryptanalysis invested in this topic was rather limited. From the hundred designs published before 2005, about 80% was cryptanalyzed; this includes widely used hash functions such as MD4 and MD5. Moreover, serious shortcomings have been identified in the theoretical foundations of existing designs. In response to this hash function crisis, a large number of papers has been published with theoretical results and novel designs. In November 2007, NIST announced the start of the SHA-3 competition, with as goal to select a new hash function family by 2012. About half of the 64 submissions were broken within months. We present a brief outline of the state of the art of hash functions half-way the competition and attempt to identify open research issues.


title: Bacterial colony counting by Convolutional Neural Networks

abstract: Counting bacterial colonies on microbiological culture plates is a time-consuming, error-prone, nevertheless fundamental task in microbiology. Computer vision based approaches can increase the efficiency and the reliability of the process, but accurate counting is challenging, due to the high degree of variability of agglomerated colonies. In this paper, we propose a solution which adopts Convolutional Neural Networks (CNN) for counting the number of colonies contained in confluent agglomerates, that scored an overall accuracy of the 92.8% on a large challenging dataset. The proposed CNN-based technique for estimating the cardinality of colony aggregates outperforms traditional image processing approaches, becoming a promising approach to many related applications.


title: [Epidemiology of lung cancer].

abstract: In 2005 approximately 45,000 people contracted lung cancer in Germany and at the same time a total of 40,641 patients died from the disease, making lung carcinoma the most frequent cause of tumor death in men. The peak age for the disease is between 70 and 85 years. The main risk factor of inhaling tobacco smoke is responsible for 85% of the cases of lung cancer. There is a direct correlation between the number of cigarettes smoked and the risk of developing lung cancer. Screening programs, e.g., low-dose computed tomography, are currently undergoing clinical assessment worldwide, but at the present time are not yet being comprehensively established.


title: Anonymizing mobility data using semantic cloaking

abstract: The prevalence of mobile phones has led to an explosion in the amounts of human mobility data stored in the cloud. It has been shown that seemingly anonymized location datasets are highly susceptible for re-identification, and may not provide enough privacy protection. In this paper we quantitatively show how semantic cloaking, the application of semantic labeling to achieve anonymization, can improve the privacy of a mobility dataset for use cases where location coordinates can be replaced by semantic categories. We develop a semantic labeling framework, apply it and evaluate it using the dataset uniqueness ( e ) measure. Our experiments show an improvement in uniqueness ranging between two- and twenty two-fold in comparison to the original, naively anonymized, dataset.


title: Blind Forensics of Median Filtering in Digital Images

abstract: Exposing the processing history of a digital image is an important problem for forensic analyzers and steganalyzers. As the median filter is a popular nonlinear denoising operator, the blind forensics of median filtering is particularly interesting. This paper proposes a novel approach for detecting median filtering in digital images, which can 1) accurately detect median filtering in arbitrary images, even reliably detect median filtering in low-resolution and JPEG compressed images; and 2) reliably detect tampering when part of a median-filtered image is inserted into a nonmedian-filtered image, or vice versa. The effectiveness of the proposed approach is exhaustively evaluated in five different image databases.


title: The Importance of Being Flexible: The Ability to Both Enhance and Suppress Emotional Expression Predicts Long-Term Adjustment

abstract: Researchers have documented the consequences of both expressing and suppressing emotion using between-subjects designs. It may be argued, however, that successful adaptation depends not so much on any one regulatory process, but on the ability to flexibly enhance or suppress emotional expression in accord with situational demands. We tested this hypothesis among New York City college students in the aftermath of the September 11th terrorist attacks. Subjects' performance in a laboratory task in which they enhanced emotional expression, suppressed emotional expression, and behaved normally on different trials was examined as a prospective predictor of their adjustment across the first two years of college. Results supported the flexibility hypothesis. A regression analysis controlling for initial distress and motivation and cognitive resources found that subjects who were better able to enhance and suppress the expression of emotion evidenced less distress by the end of the second year. Memory deficits wer...


title: Bag-of-visual-words models for adult image classification and filtering

abstract: We present a method to classify images into different categories of pornographic content to create a system for filtering pornographic images from network traffic. Although different systems for this application were presented in the past, most of these systems are based on simple skin colour features and have rather poor performance. Recent advances in the image recognition field in particular for the classification of objects have shown that bag-of-visual-words-approaches are a good method for many image classification problems. The system we present here, is based on this approach, uses a task-specific visual vocabulary and is trained and evaluated on an image database of 8500 images from different categories. It is shown that it clearly outperforms earlier systems on this dataset and further evaluation on two novel web-traffic collections shows the good performance of the proposed system.


Comparative Study of Artificial Neural Networks and Hidden Markov Model for Financial Time Series Prediction


title: AlInGaAs-AlGaAs strained single-quantum-well diode lasers

abstract: Diode lasers with a strained AlInGaAs active layer and AlGaAs confining and cladding layers are reported. Broad-stripe devices were fabricated in graded-index separate-confinement heterostructures grown by organometallic vapor-phase epitaxy on GaAs substrates and containing a single Al/sub y/In/sub x/Ga/sub 1-x-y/As quantum well with 0.12 >


title: Integrated SIW filter and microstrip antenna

abstract: A novel design of integrated Substrate Integrated Waveguide (SIW) filter and microstrip antenna is proposed. In the first step, the methodology consists of synthesising the subsystems combining a filter and an antenna using lumped lowpass prototype networks. To realise the concept, the filtering and radiating functions are combined using multilayer technology on a printed circuit board (PCB). The design is based on the circular cavity structure using TM 010 mode for the filter and TM 110 mode for the antenna. To prove the design technique, modes in the SIW filter and in the microstrip antenna are coupled by capacitive circular iris at the common ground plane. Good antenna characteristics such as gain and radiation pattern illustrate and prove the validity to integrate directly SIW and microstrip technology without the need to use any external matching circuit. This design provides an alternative solution for combining filter and antenna subsystems in the RF front end that is useful for wireless communications systems.


title: Basic considerations and topologies of switched-mode assisted linear power amplifiers

abstract: This paper presents a combined power amplifier system consisting of a linear amplifier unit with a switched-mode (class D) current dumping stage arranged in parallel. With this topology, the fundamental drawback of conventional linear power amplifiers-the high loss-is avoided. Compared to a pure class D (switching) amplifier, the presented system needs no output filter to reduce the switching frequency harmonics. This filter (usually of multistage type) generally deteriorates the transient response of the system and impairs the feedback loop design. Furthermore, the low-frequency distortions of switching amplifiers caused by the interlock delay of their power transistors are avoided with the presented switched-mode assisted linear amplifier system. This can be considered as a master-slave system with a guiding linear amplifier and a supporting class D slave unit. The paper describes the operating principle of the system, analyzes the fundamental relationships for the circuit design, and presents simulation results. Finally, various further topologies of switched-mode assisted linear amplifiers are given.


Satisfiers and Dissatisfiers : A Two-Factor Model for Website Design and Evaluation


title: Age Constraints on Second-Language Acquisition

abstract: This study evaluated the critical period hypothesis for second language (L2) acquisition. The participants were 240 native speakers of Korean who differed according to age of arrival (AOA) in the United States (1 to 23 years), but were all experienced in English (mean length of residence 5 15 years). The native Korean participants’ pronunciation of English was evaluated by having listeners rate their sentences for overall degree of foreign accent; knowledge of English morphosyntax was evaluated using a 144-item grammaticality judgment test. As AOA increased, the foreign accents grew stronger, and the grammaticality judgment test scores decreased steadily. However, unlike the case for the foreign accent ratings, the effect of AOA on the grammaticality judgment test scores became nonsignificant when variables confounded with AOA were controlled. This suggested that the observed decrease in morphosyntax scores was not the result of passing a maturationally defined critical period. Additional analyses showed that the score for sentences testing knowledge of rule based, generalizable aspects of English morphosyntax varied as a function of how much education the Korean participants had received in the United States. The scores for sentences testing lexically based aspects of English morphosyntax, on the other hand, depended on how much the Koreans used English. © 1999 Academic Press


A General Framework For Text Semantic Analysis And Clustering On Yelp Reviews


title: A Core System for the Implementation of Task Sets

abstract: When performing tasks, humans are thought to adopt task sets that configure moment-to-moment data processing. Recently developed mixed blocked/event-related designs allow task set-related signals to be extracted in fMRI experiments, including activity related to cues that signal the beginning of a task block, “set-maintenance” activity sustained for the duration of a task block, and event-related signals for different trial types. Data were conjointly analyzed from mixed design experiments using ten different tasks and 183 subjects. Dorsal anterior cingulate cortex/medial superior frontal cortex (dACC/msFC) and bilateral anterior insula/frontal operculum (aI/fO) showed reliable start-cue and sustained activations across all or nearly all tasks. These regions also carried the most reliable error-related signals in a subset of tasks, suggesting that the regions form a “core” task-set system. Prefrontal regions commonly related to task control carried task-set signals in a smaller subset of tasks and lacked convergence across signal types.


title: Cortical reorganization and phantom phenomena in congenital and traumatic upper-extremity amputees

abstract: Abstract The relationship between phantom limb phenomena and cortical reorganization was examined in five subjects with congenital absence of an upper limb and nine traumatic amputees. Neuromagnetic source imaging revealed minimal reorganization of primary somatosensory cortex in the congenital amputees (M=0.69 cm, SD 0.24) and the traumatic amputees without phantom limb pain (M=0.27 cm, SD 0.25); the amputees with phantom limb pain showed massive cortical reorganization (M=2.22 cm, SD 0.78). Phantom limb pain and nonpainful phantom limb phenomena were absent in the congenital amputees. Whereas phantom limb pain was positively related to cortical reorganization (r=0.87), nonpainful phantom phenomena were not significantly correlated with cortical reorganization (r=0.34). Sensory discrimination was normal and mislocalization (referral of stimulation-induced sensation to a phantom limb) was absent in the congenital amputees. The role of peripheral and central factors in the understanding of phantom limb pain and phantom limb phenomena is discussed in view of these findings.


title: A systematic literature review on Enterprise Architecture Implementation Methodologies

abstract: ContextEnterprise Architecture (EA) is a strategy to align business and Information Technology (IT) within an enterprise. EA is managed, developed, and maintained throughout the EA Implementation Methodology (EAIM). ObjectiveThe aims of this study are to identify the existing effective practices that are used by existing EAIMs, identify the factors that affect the effectiveness of EAIM, identify the current tools that are used by existing EAIMs, and identify the open problems and areas related to EAIM for improvement. MethodA Systematic Literature Review (SLR) was carried out. 669 papers were retrieved by a manual search in 6 databases and 46 primary studies were finally included. ResultFrom these studies 33% were journal articles, 41% were conference papers while 26% were contributions from the studies consisted of book chapters. Consequently, 28 practices, 19 factors, and 15 tools were identified and analysed. ConclusionSeveral rigorous researches have been done in order to provide effective EAIM, however there are still problems in components of EAIM, including: there is lack of tool support for whole part of EA implementation, there are deficiency in addressing the EAIM's practices especially in modeling, management, and maintenance, there is lack of consideration on non-functional requirement in existing EAIM, there is no appropriate consideration on requirement analysis in most existing EAIM. This review provides researchers with some guidelines for future research on this topic. It also provides broad information on EAIM, which could be useful for practitioners.


title: Cyberbullying: Review of an Old Problem Gone Viral

abstract: Purpose: Despite being relatively new, cyberbullying is now well recognized as a serious public health problem affecting children and adolescents. Scientific exploration has lagged media attention, but a synthesis of studies across several disciplines permits an understanding of its epidemiology, phenomenology, mental health dimensions, and management tools. Methods: To assess current knowledge of cyberbullying, we searched the MEDLINE, PubMed and PsycINFO databases for articles on “cyberbullying” and related designations. The Google search engine was used to capture otherwise unpublished legislative, governmental, and community response data and to help identify relevant books and book chapters. Results: A significant proportion of children and adolescents (20%e40%) have been victims of cyberbullying, with females and sexual minorities seemingly at higher risk. Perpetrators are more likely to be male. By nature of the electronic platform, there seems to be an easier path to the bully-victim phenomenon (victims who become bullies or vice versa) than that in traditional bullying. A nonlinear relationship with age is suggested, but demographic data overall are preliminary. Accompanying psychopathology, including an increasingly well-established link to suicidality, is common. Several prevention and management approaches have been proposed to help prevent cyberbullying or mitigate its effects. Discussion: Cyberbullying’s seeming ubiquity, its disproportionate toll on vulnerable populations (e.g., children and sexual minorities), the link with suicidality, and the expected continued rise in Internet penetrance and connectivity make confronting it an urgent matter. A multipronged approach is most likely to succeed and would include: educational media campaigns; school-based programs; parental oversight and involvement; legislative action; and screening and evidencebased interventions by health care providers, especially pediatricians and mental health professionals.


title: Interleaved Zero-Current-Transition Buck Converter

abstract: This paper introduces interleaved zero current transition (ZCT) converters where two sets of switches are operating out of phase and share the load power equally. Turn-on transitions at zero current and a significant reduction of the losses associated with diode reverse recovery are accomplished through addition of two small inductors. The paper describes a 30 kW (300 V, 100 A) interleaved ZCT buck converter operating at 32 kHz switching frequency. Losses and efficiency of the experimental prototype compare favorably against the standard hard-switched buck converter, and against previously proposed ZCT approaches.


title: Breast fibroadenomas in the pediatric population: common and uncommon sonographic findings

abstract: BACKGROUND ::: Sonography is usually requested to evaluate palpable pediatric breast lumps, and solid masses are almost always fibroadenomas. Lack of familiarity with the findings of fibroadenomas can lead to diagnostic uncertainty and sometimes unnecessary biopsy and excision. We sought to review the spectrum of sonographic findings in our cases of pathology proven pediatric fibroadenomas. ::: ::: ::: OBJECTIVE ::: The purpose of this retrospective study was to describe the sonographic appearances of pathologically proven pediatric breast fibroadenomas. ::: ::: ::: MATERIALS AND METHODS ::: A query of the Department of Radiology database at our institution was performed for all patients younger than 19 years who underwent breast US from January 2001 to June 2009. A total of 332 patients were identified: 282 girls (85%) and 50 boys (15%). Ninety-one girls and no boys had a solid breast mass based on US findings. Forty-three children had a total of 49 pathologically proven breast masses with the diagnoses of fibroadenoma (44), hamartoma (1), non-Hodgkin lymphoma (1), tubular adenoma (1), pseudoangiomatous stromal hyperplasia (1) and lactation changes (1). Reviews of medical records, histological results and sonographic examinations of all pathology-proven fibroadenomas were performed. US findings were characterized according to location, multiplicity, size, shape, echogenicity and homogeneity, definition of margins, posterior acoustic features and Doppler vascularity. ::: ::: ::: RESULTS ::: The vast majority of solid breast masses in girls are histologically benign. Fibroadenomas accounted for 91% of the pathologically proven solid breast masses. Common findings on US imaging are an oval shape, hypoechoic echo pattern, posterior acoustic enhancement and internal Doppler signal. Lobulations were found in 57% of the masses. Less common findings are absent internal vascular flow and complex echo pattern, while isoechoic echo pattern, posterior shadowing and angular margins are rare or unusual. ::: ::: ::: CONCLUSION ::: Fibroadenomas represent the most common solid mass in the breasts of girls. Sonographic appearances are usually characteristic and do not significantly differ from those found in adults. The radiologist must be aware of common and uncommon sonographic appearances of fibroadenomas in the pediatric age group and should be cautious when recommending histological confirmation based on imaging findings, as breast malignancy is extremely rare.


title: Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers

abstract: Accurate, well-calibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a cost-sensitive decision must be made about examples with example-dependent costs. This paper presents simple but successful methods for obtaining calibrated probability estimates from decision tree and naive Bayesian classifiers. Using the large and challenging KDD’98 contest dataset as a testbed, we report the results of a detailed experimental comparison of ten methods, according to four evaluation measures. We conclude that binning succeeds in significantly improving naive Bayesian probability estimates, while for improving decision tree probability estimates, we recommend smoothing by -estimation and a new variant of pruning that we call curtailment.


title: End-to-End Localization and Ranking for Relative Attributes

abstract: We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute’s features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end-to-end framework also significantly speeds up processing and is much faster than previous methods. We show state-of-the-art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network’s ability to learn meaningful image patches.


title: LBP based recursive averaging for babble noise reduction applied to automatic speech recognition

abstract: Improved automatic speech recognition (ASR) in babble noise conditions continues to pose major challenges. In this paper, we propose a new local binary pattern (LBP) based speech presence indicator (SPI) to distinguish speech and non-speech components. Babble noise is subsequently estimated using recursive averaging. In the speech enhancement system optimally-modified log-spectral amplitude (OMLSA) uses the estimated noise spectrum obtained from the LBP based recursive averaging (LRA). The performance of the LRA speech enhancement system is compared to the conventional improved minima controlled recursive averaging (IMCRA). Segmental SNR improvements and perceptual evaluations of speech quality (PESQ) scores show that LRA offers superior babble noise reduction compared to the IMCRA system. Hidden Markov model (HMM) based word recognition results show a corresponding improvement.


title: NFV enabling network slicing for 5G

abstract: Network slicing is one of the most discussed concepts for designing 5G networks. It is intended to enable operators to slice a single physical network into multiple virtual networks optimized according to specific services and business goals. Network Functions Virtualization (NFV), a technology developed by the European Telecommunication Standards Institute (ETSI), will play a prominent role in implementing this concept. This paper reviews the options that the NFV technology offers to enable network slicing and highlights areas that may require further studies and standardization work.


title: Singing voice detection for karaoke application

abstract: We present a framework to detect the regions of singing voice in musical audio signals. This work is oriented towards the development of a robust transcriber of lyrics for karaoke applications. The technique leverages on a combination of low-level audio features and higher level musical knowledge of rhythm and tonality. Musical knowledge of the key is used to create a song-specific filterbank to attenuate the presence of the pitched musical instruments. This is followed by subband processing of the audio to detect the musical octaves in which the vocals are present. Text processing is employed to approximate the duration of the sung passages using freely available lyrics. This is used to obtain a dynamic threshold for vocal/ non-vocal segmentation. This pairing of audio and text processing helps create a more accurate system. Experimental evaluation on a small database of popular songs shows the validity of the proposed approach. Holistic and per-component evaluation of the system is conducted and various improvements are discussed.


title: Developing learner corpus annotation for Chinese grammatical errors

abstract: This study describes the construction of the TOCFL (Test Of Chinese as a Foreign Language) learner corpus, including the collection and grammatical error annotation of 2,837 essays written by Chinese language learners originating from a total of 46 different mother-tongue languages. We propose hierarchical tagging sets to manually annotate grammatical errors, resulting in 33,835 inappropriate usages. Our built corpus has been provided for the shared tasks on Chinese grammatical error diagnosis. These demonstrate the usability of our learner corpus annotation.


title: Cumulant based automatic modulation classification of QPSK, OQPSK, 8-PSK and 16-PSK

abstract: An automatic modulation classification (AMC) method based on the fourth-order cumulants is proposed to classify QPSK, OQPSK, 8-PSK and 16-PSK. The method consists of two steps: (1) classification of QPSK and OQPSK using the fourth order zero-conjugate cumulant of backward differences of the samples of the received nosiy signal and (2) classification of QPSK, 8-PSK and 16-PSK using the fourth order zero-conjugate cumulant of the received noisy signal and their squares. Step 1 is guided by the observation that the fourth order zero-conjugate cumulants of backward differences are distinct for those two modulation formats. Step 2 is based on the fact that the squaring of an MPSK signal results in another MPSK signal with M/2 phase states. The Monte Carlo simulation results show that the proposed method is able to classify the afore-mentioned modulation schemes at moderately low signal to noise ratios with a small sample size. The proposed method is robust to the presence of carrier phase and frequency estimation errors.


title: A Two-Dimensional Bandwidth Extrapolation Technique for Polarimetric Synthetic Aperture Radar Images

abstract: The resolution of a synthetic aperture radar (SAR) image, in range and azimuth, is determined by the transmitted bandwidth and the synthetic aperture length, respectively. Various superresolution techniques for improving resolution have been proposed, and we have proposed an algorithm that we call polarimetric bandwidth extrapolation (PBWE). To apply PBWE to a radar image, one needs to first apply PBWE in the range direction and then in the azimuth direction, or vice versa . In this paper, PBWE is further extended to the 2-D case. This extended case (2D-PBWE) utilizes a 2-D polarimetric linear prediction model and expands the spatial frequency bandwidth in range and azimuth directions simultaneously. The performance of the 2D-PBWE is shown through a simulated radar image and a real polarimetric SAR image


title: Introduction to Information Retrieval

abstract: Class-tested and coherent, this groundbreaking new textbook teaches web-era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective by three leading experts in the field, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Although originally designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also create a buzz for researchers and professionals alike.


title: Developer Behavior and Sentiment from Data Mining Open Source Repositories

abstract: Developer sentiment may wax and wane as a project progresses. Open-source projects that attract and retain developers tend to be successful. It may be possible to predict project success, in part, if one can measure developer behavior and sentiment -- projects with active, happy developers are more likely to succeed. We have analyzed GitHub.com projects in an attempt to model these concepts. We have data mined 124 projects from GitHub.com. The projects were automatically mined using sequence mining methods to derive a behavioral model of developer activities. The projects were also mined for developer sentiment. Finally, a regression model shows how sentiment varies with behavioral differences -- a change in behavior is correlated with a change in sentiment. The relationship between sentiment and success is not directly explored, herein. This research project is a preliminary step in a larger research project aimed at understanding and monitoring FLOSS projects using a process modeling approach.


title: N-gram analysis for computer virus detection

abstract: Generic computer virus detection is the need of the hour as most commercial antivirus software fail to detect unknown and new viruses. Motivated by the success of datamining/machine learning techniques in intrusion detection systems, recent research in detecting malicious executables is directed towards devising efficient non-signature-based techniques that can profile the program characteristics from a set of training examples. Byte sequences and byte n-grams are considered to be basis of feature extraction. But as the number of n-grams is going to be very large, several methods of feature selections were proposed in literature. A recent report on use of information gain based feature selection has yielded the best-known result in classifying malicious executables from benign ones. We observe that information gain models the presence of n-gram in one class and its absence in the other. Through a simple example we show that this may lead to erroneous results. In this paper, we describe a new feature selection measure, class-wise document frequency of byte n-grams. We empirically demonstrate that the proposed method is a better method for feature selection. For detection, we combine several classifiers using Dempster Shafer Theory for better classification accuracy instead of using any single classifier. Our experimental results show that such a scheme detects virus program far more efficiently than the earlier known methods.


title: Confounding and Collapsibility in Causal Inference

abstract: Consideration of confounding is fundamental to the design and analysis of studies of causal effects. Yet, apart from confounding in experimental designs, the topic is given little or no discussion in most statistics texts. We here provide an overview of confounding and related concepts based on a counterfactual model for causation. Special attention is given to definitions of confounding, problems in control of confound- ing, the relation of confounding to exchangeability and collapsibility, and the importance of distinguishing confounding from noncollapsibility.


title: Institutional Influences on Manufacturing Organization in Multinational Corporations: The ‘Cherrypicking’ Approach

abstract: Research on the multinational corporation (MNC) is increasingly concerned with the alleged evolution of companies towards a more standardized and rationalized global organization. Only recently, this field has been informed by alternative approaches which generate a more differentiated picture and consider the influence of divergent national institutional contexts on the multinational organization. This paper makes a contribution to this debate from a comparative institutionalist perspective by focusing on manufacturing organization within MNCs. It argues that organization structures and processes in MNCs are sector specific and influenced by national institutional features of the home and host countries. Drawing on data from a specific industrial sector, it identifies the crucial role of home country and host country embeddedness in the (re)organization of manufacturing tasks and work systems. The key question is how actors shape the interaction of these institutional pressures and, hence, manufacturing approaches, location choices and work system designs. Research in British and German subsidiaries of three MNCs suggests that, particularly at subsidiary level, MNCs apply a ‘cherrypicking’ strategy of selected use of work system elements, shaped by the host country business system. It is shown that manufacturing strategies of MNCs originating from highly coordinated business systems are highly context specific and difficult (if not impossible) to transfer elsewhere. Moreover, ‘cherrypicking’ strategies in subsidiaries embedded in such contexts turned out to be highly problematic, especially when managers attempt to combine them with group-wide standardizing work systems.


title: Bayes classifiers for imbalanced traffic accidents datasets.

abstract: Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents.


title: A Distributed Message Delivery Infrastructure for Connected Vehicle Technology Applications

abstract: A complex and vast amount of data will be collected from on-board sensors of operational connected vehicles (CVs), infrastructure data sources such as roadway sensors and traffic signals, mobile data sources such as cell phones, social media sources such as Twitter, and news and weather data services. Unfortunately, these data will create a bottleneck at data centers for processing and retrievals of collected data, and will require the deployment of additional message transfer infrastructure between data producers and consumers to support diverse CV applications. In this paper, we present a strategy for creating an efficient and low-latency distributed message delivery system for CV applications using a distributed message delivery platform. This strategy enables large-scale ingestion, curation, and transformation of unstructured data (roadway traffic-related and roadway non-traffic-related data) into labeled and customized topics for a large number of subscribers or consumers, such as CVs, mobile devices, and data centers. We evaluate the performance of this strategy by developing a prototype infrastructure using Apache Kafka, an open source message delivery system, and compared its performance with the latency requirements of CV applications. We present experimental results of the message delivery infrastructure on two different distributed computing testbeds at Clemson University: the Holocron cluster and the Palmetto cluster. Experiments were performed to measure the latency of the message delivery system for a variety of testing scenarios. These experiments reveal that measured latencies are less than the U.S. Department of Transportation recommended latency requirements for CV applications, which prove the efficacy of the system for CV related data distribution and management tasks.


title: Real time trajectory prediction for collision risk estimation between vehicles

abstract: In this paper, we present our approach for collision risk estimation between vehicles. The vehicles are equipped with GPS receivers and communication devices. Our approach consists on using the knowledge given trough communication tool to predict the trajectories of the surrounding vehicles. Based on these trajectories, we identify the configurations of the collisions between vehicles. The risk is calculated using several indicators that are reflecting not only the possible collisions but also the dangerousness of these collisions. Our algorithm is tested on crossroads using scenarios involving real prototypes producing realistic scenarios.


title: Reproductive pathways of seed development in apomictic guinea grass (Panicum maximum Jacq.) reveal uncoupling of apomixis components

abstract: One hundred and sixty accessions representing global germplasm of guinea grass (Panicum maximum Jacq.), an important apomictic (aposporous) fodder crop, were subjected to study on reproductive diversity in apomictic seed development utilizing ovule clearing and flow cytometric seed screen (FCSS). Single seed FCSS of selected 14 tetraploid and five hexaploid lines demonstrated uncoupling between the three apomixis components, viz. apomeiosis, parthenogenesis and functional endosperm development, in natural as well as experimental populations, though it differed across ploidy levels and genotypes. Reconstruction of reproductive pathways yielded a total of eight different pathways of seed development, arising by uncoupling/recombination between apomixis components. Amongst these, two pathways involving modifications in embryo-sac (ES) (presence of two polar nuclei in aposporous ES that fuse prior to fertilization) and fertilization process (fusion of only one polar nucleus in a sexual ES) have been reported for the first time. Some of the combinations, such as MI (haploids arising from parthenogenetic development of reduced egg cell), were found viable only in hexaploid background. Germplasm lines with higher expression of individual components were also identified. These components (including autonomous endosperm development) were also experimentally partitioned in hexaploid progenies (derived from a tetraploid parent viz. accession IG 04-164) that showed segregation in their reproductive capacities, and are reported for the first time. Occurrence of several apomixis recombinants (phenotypic) in guinea grass lines suggested their hybrid origin, favors a multigene model for apomixis, with their penetrance affected by modifiers and epigenetic mechanisms, in contrast to earlier reports of single locus control. Implications of partitioning components on apomixis research are discussed.


title: SAETA: A Smart Coaching Assistant for Professional Volleyball Training

abstract: This paper introduces a smart assistant for professional volleyball training based on machine-learning techniques (SAETA). SAETA addresses two main aspects of elite sports coaching: 1) technical-tactical effort control, which aims at controlling exercise effort and fatigue levels and 2) exercise quality training, which complements the former by analyzing the execution of player movements. SAETA relies on a sensing infrastructure that monitors both players and their environment, and produces real-time data that is analyzed by different modules of a decision engine. Technical-tactical effort control is based on a dynamic programming model, which selects the best activity and rest durations in interval training, with the goal of maximizing effort while preventing fatigue. Exercise quality control consists of two stages. In the first stage, movements are detected by means of a $k$ -nearest neighbors classifier and in the second stage, movement intensity is classified according to recent statistical data from the player being analyzed. These analyses are reported to coaches and players in real-time. SAETA has been developed in close collaboration with the Universidad Catolica San Antonio de Murcia volleyball team, which competes in the Spanish women’s premier league. Data gathered during training sessions has provided a knowledge base for the algorithms developed, and has been used for the validation of results.


title: Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving

abstract: Risk assessment is a central element for the development and validation of autonomous vehicles. It comprises a combination of occurrence probability and severity of future critical events. Time headway (TH) as well as time-to-contact (TTC) are commonly used risk metrics and have qualitative relations to occurrence probability. However, they lack theoretical derivations and additionally they are designed to only cover special types of traffic scenarios (e.g., longitudinal following between single car pairs). In this paper, we present a probabilistic situation risk model based on survival analysis considerations and extend it to naturally incorporate sensory, temporal, and behavioral uncertainties as they arise in real-world scenarios. The resulting risk spot detector (RSD) is applied and tested on naturalistic driving data of a multilane boulevard with several intersections, enabling the visualization of road criticality maps. Compared to TH and TTC, our approach is more selective and specific in predicting risk. RSD concentrates on driving sections of high vehicle density where large accelerations and decelerations or approaches with high velocity occur.


abstract: Hematoxylin and Eosin (H&E) stained Whole Slide Images (WSIs) are utilized for biopsy visualization-based diagnostic and prognostic assessment of diseases. Variation in the H&E staining process across different lab sites can lead to significant variations in biopsy image appearance. These variations introduce an undesirable bias when the slides are examined by pathologists or used for training deep learning models. To reduce this bias, slides need to be translated to a common domain of stain appearance before analysis. We propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain. This unsupervised generative adversarial approach includes self-attention mechanism for synthesizing images with finer detail while preserving the structural consistency of the biopsy features during translation. SAASN demonstrates consistent and superior performance compared to other popular stain normalization techniques on H&E stained duodenal biopsy image data.


title: A compact dual-band meander-line antenna for biomedical applications

abstract: This paper presents the design of a low profile dual-band (on- / off-body modes) planar antenna in meandered shape with microstrip line feed for biomedical applications. The antenna is designed to operate in the ZigBee dual band 868-928 MHz and 2.4-2.5 GHz respectively. While the proposed antenna is simulated on direct surface of human skin model, it shows a good performance with the broad bandwidth for |S11| less than -10 dB. The antenna is designed with 50 Ω input port and on LTCC substrate (er = 7.1, tanδ = 0.005) with miniaturized volume of 16.5 mm × 17 mm × 0.8 mm.


title: Design, simulation and manufacturing of a Tracked Surveillance unmanned ground vehicle

abstract: This paper describes design, Simulation and manufacturing procedures of HIRAD - a teleoperated Tracked Surveillance UGV for military, Rescue and other civilian missions in various hazardous environments. A Double Stabilizer Flipper mechanism mounted on front pulleys enables the Robot to have good performance in travelling over uneven terrains and climbing stairs. Using this Stabilizer flipper mechanism reduces energy consumption while climbing the stairs or crossing over obstacles. The locomotion system mechanical design is also described in detail. The CAD geometry 3D-model has been produced by CATIA software. To analyze the system mobility, a virtual model was developed with ADAMS Software. This simulation included different mobility maneuvers such as stair climbing, gap crossing and travelling over steep slopes. The simulations enabled us to define motor torque requirements. We performed many experiments with manufactured prototype under various terrain conditions Such as stair climbing, gap crossing and slope elevation. In experiments, HIRAD shows good overcoming ability for the tested terrain conditions.


title: Path planning and control of multiple aerial manipulators for a cooperative transportation

abstract: This paper presents planning and control of multiple aerial manipulators for cooperative transportation. Individual aerial manipulators which consist of a hexacopter and 2-DOF robotic arm are controlled by an augmented adaptive sliding mode controller based on a closed-chain robot dynamics. The desired path for each aerial manipulator is obtained by using RRT* to transport an object to the desired position. It also considers the constraints about the grasping point at the end effector. To validate the proposed planning and control algorithm, an experimental result with multiple custom-made aerial manipulators is presented, which involves two aerial manipulators tracking the user-guided command and planned trajectory.


title: Animation of trees in wind using sparse motion capture data

abstract: We present a novel approach to animate an entire tree from the motion of a few branches. Animating medium sized trees in wind presents an arduous situation because it is difficult to create the motion of flexible branches in turbulent wind flows. The animation of trees in wind is important in biology and ecology where understanding how trees move can inform us about the cultivation of trees and forests designed to withstand strong winds. Animation of trees in wind is also useful in the production of films and games. We use passive optical motion capture to record the motion of retro-reflective markers placed on some of the tree branch tips. These motion data are processed to remove noise, create motion traces, and add missing data. Given the processed motion data, we solve for wind velocity in a discrete formulation of aerodynamic drag and tree branch dynamics. This sparse collection of wind velocity samples is interpolated over the volume of the crown and enriched with a turbulence model to create a wind field that drives the animation of an entire tree crown. The wind field can also be used to drive the animation of other similar trees. In this process, 300 KB of data recorded from 10 s of tree motion can be processed and replayed in 16 s of computation time, not including rendering.


title: Perceptions of Hong Kong physical education teachers on the inclusion of students with disabilities

abstract: Abstract Based on Lev Vygotsky’s social constructivism theory, this study examined the perceptions of Hong Kong physical education (PE) teachers regarding the inclusion of students with disabilities in general PE programmes. Eight secondary PE teachers (female = 5, male = 3) were recruited for individual semi-structured interviews. Data gathered from the interviews were audio-recorded and transcribed. Interview transcripts were analysed and presented as descriptive summaries, as well as underwent content analysis. Three themes emerged based on the content analysis of the teachers’ interview data: (1) favourable, but with concerns, (2) hope to be professional, and (3) lack of connection. Results indicated that the teachers acknowledged the benefits of inclusion in PE; however, the teachers also expressed concern regarding the instructional and environmental barriers to the inclusion of students with disabilities in general PE programmes. The findings demonstrated the need for highly frequent and efficient communication, involvement, and collaboration among stakeholders involved in inclusive PE.


title: Blaming the consumer – once again: the social and material contexts of everyday food waste practices in some English households

abstract: In public debates about the volume of food that is currently wasted by UK households, there exists a tendency to blame the consumer or individualise responsibilities for affecting change. Drawing on ethnographic examples, this article explores the dynamics of domestic food practices and considers their consequences in terms of waste. Discussions are structured around the following themes: (1) feeding the family; (2) eating ‘properly’; (3) the materiality of ‘proper’ food and its intersections with the socio-temporal demands of everyday life and (4) anxieties surrounding food safety and storage. Particular attention is paid to the role of public health interventions in shaping the contexts through which food is at risk of wastage. Taken together, I argue that household food waste cannot be conceptualised as a problem of individual consumer behaviour and suggest that policies and interventions might usefully be targeted at the social and material conditions in which food is provisioned.


title: Survey of recent progress in semantic image segmentation with CNNs

abstract: In recent years, convolutional neural networks (CNNs) are leading the way in many computer vision tasks, such as image classification, object detection, and face recognition. In order to produce more refined semantic image segmentation, we survey the powerful CNNs and novel elaborate layers, structures and strategies, especially including those that have achieved the state-of-the-art results on the Pascal VOC 2012 semantic segmentation challenge. Moreover, we discuss their different working stages and various mechanisms to utilize the structural and contextual information in the image and feature spaces. Finally, combining some popular underlying referential methods in homologous problems, we propose several possible directions and approaches to incorporate existing effective methods as components to enhance CNNs for the segmentation of specific semantic objects.


title: Understanding the role of motives in smartphone addiction

abstract: Smartphone has become highly prevalent in recent years. It demonstrates as a useful technology that brings productivity and convenience in our daily life. However, smartphone usage can also drive psychological and behavioral maladaptive dependency, which may result in negative problems. Given that research on smartphone addiction is still limited in the information systems literature, this paper aims to propose a framework of motives from the functionalist perspective and investigate why users will be addicted to smartphones. Four categories of motives for smartphone usage are proposed in this study. They include enhancement motives (i.e., information seeking and perceived enjoyment), social motives (i.e., social relationship), coping motives (i.e., mood regulation and pastime), and conformity motives (i.e., conformity). We empirically test our research model using online survey method. The findings illustrate that perceived enjoyment, mood regulation, pastime, and conformity positively affect smartphone addiction, whereas information seeking and social relationship have no significant effects. Implications for both research and practice are provided.


title: Color-based hands tracking system for sign language recognition

abstract: The paper describes a real-time system which tracks the uncovered/unmarked hands of a person performing sign language. It extracts the face and hand regions using their skin colors, computes blobs and then tracks the location of each hand using a Kalman filter. The system has been tested for hand tracking using actual sign-language motion by native signers. The experimental results indicate that the system is capable of tracking hands even while they are overlapping the face.


title: Stochastic modified equations and adaptive stochastic gradient algorithms

abstract: We develop the method of stochastic modified equations (SME), in which stochastic gradient algorithms are approximated in the weak sense by continuous-time stochastic differential equations. We exploit the continuous formulation together with optimal control theory to derive novel adaptive hyper-parameter adjustment policies. Our algorithms have competitive performance with the added benefit of being robust to varying models and datasets. This provides a general methodology for the analysis and design of stochastic gradient algorithms.


title: A Low-Noise Amplifier With Tunable Interference Rejection for 3.1- to 10.6-GHz UWB Systems

abstract: An ultrawideband common-gate low noise amplifier with tunable interference rejection is presented. The proposed LNA embeds a tunable active notch filter to eliminate interferer at 5-GHz WLAN and employs a common-gate input stage and dual-resonant loads for wideband implementation. This LNA has been fabricated in a 0.18-?m CMOS process. The measured maximum power gain is 13.2 dB and noise figure is 4.5-6.2 dB with bandwidth of 3.1-10.6 GHz. The interferer rejection is 8.2 dB compared to the maximum gain and 7.6 dB noise figure at 5.2 GHz , respectively. The measured input P1dB is -11 dBm at 10.3 GHz. It consumes 12.8 mA from 1.8-V supply voltage.


title: Progress in Silicon Single-Photon Avalanche Diodes

abstract: Silicon single-photon avalanche diodes (SPADs) are nowadays a solid-state alternative to photomultiplier tubes (PMTs) in single-photon counting (SPC) and time-correlated single-photon counting (TCSPC) over the visible spectral range up to 1-mum wavelength. SPADs implemented in planar technology compatible with CMOS circuits offer typical advantages of microelectronic devices (small size, ruggedness, low voltage, low power, etc.). Furthermore, they have inherently higher photon detection efficiency, since they do not rely on electron emission in vacuum from a photocathode as do PMTs, but instead on the internal photoelectric effect. However, PMTs offer much wider sensitive area, which greatly simplifies the design of optical systems; they also attain remarkable performance at high counting rate, and offer picosecond timing resolution with microchannel plate models. In order to make SPAD detectors more competitive in a broader range of SPC and TCSPC applications, it is necessary to face several issues in the semiconductor device design and technology. Such issues will be discussed in the context of the two possible approaches to such a challenge: employing a standard industrial high-voltage CMOS technology or developing a dedicated CMOS-compatible technology. Advances recently attained in the development of SPAD detectors will be outlined and discussed with reference to both single-element detectors and integrated detector arrays.


title: Simulation of radiation pattern reconfigurable microstrip patch antenna using feeding delay line technique

abstract: This paper proposes radiation reconfigurable patch antenna using feeding delay line technique. By using simulation and optimization from Computer Simulation Technology (CST) the 2 × 2 antenna array is designed and investigated. The direction of radiation pattern is controlled by interchanging two different lengths of corporate feeding network using OPEN and SHORT states of feeding line network. Working at 7.5 GHz for meteorology application, three directions of radiation pattern are obtained from the proposed reconfigurable antenna according to the states of feeding line network, −30 degree, 0 degree and +30 degree. Good results from the simulation and optimization analysis promise the feasibility of the proposed technique.


title: Do medical marijuana laws reduce addictions and deaths related to pain killers?

abstract: Recent work finds that medical marijuana laws reduce the daily doses filled for opioid analgesics among Medicare Part-D and Medicaid enrollees, as well as population-wide opioid overdose deaths. We replicate the result for opioid overdose deaths and explore the potential mechanism. The key feature of a medical marijuana law that facilitates a reduction in overdose death rates is a relatively liberal allowance for dispensaries. As states have become more stringent in their regulation of dispensaries, the protective value generally has fallen. These findings suggest that broader access to medical marijuana facilitates substitution of marijuana for powerful and addictive opioids.


title: Our Twitter Profiles, Our Selves: Predicting Personality with Twitter

abstract: Psychological personality has been shown to affect a variety of aspects: preferences for interaction styles in the digital world and for music genres, for example. Consequently, the design of personalized user interfaces and music recommender systems might benefit from understanding the relationship between personality and use of social media. Since there has not been a study between personality and use of Twitter at large, we set out to analyze the relationship between personality and different types of Twitter users, including popular users and influentials. For 335 users, we gather personality data, analyze it, and find that both popular users and influentials are extroverts and emotionally stable (low in the trait of Neuroticism). Interestingly, we also find that popular users are `imaginative' (high in Openness), while influentials tend to be `organized' (high in Conscientiousness). We then show a way of accurately predicting a user's personality simply based on three counts publicly available on profiles: following, followers, and listed counts. Knowing these three quantities about an active user, one can predict the user's five personality traits with a root-mean-squared error below 0.88 on a $[1,5]$ scale. Based on these promising results, we argue that being able to predict user personality goes well beyond our initial goal of informing the design of new personalized applications as it, for example, expands current studies on privacy in social media.


title: Intrarater and Interrater Reliability of the Flexicurve Index, Flexicurve Angle, and Manual Inclinometer for the Measurement of Thoracic Kyphosis

abstract: Objective. This study aimed to describe the interrater and intrarater reliability of the flexicurve index, flexicurve angle, and manual inclinometer in swimmers. A secondary objective was to determine the level of agreement between the inclinometer angle and the flexicurve angle and to provide an equation to approximate one angle from the other. Methods. Thirty swimmers participated. Thoracic kyphosis was measured using the flexicurve and the manual inclinometer. Intraclass correlation coefficient, 95% confidence interval, and standard error of measurement were computed. Results. The flexicurve angle and index showed excellent intrarater (ICC = 0.94) and good interrater (ICC = 0.86) reliability. The inclinometer demonstrated excellent intrarater (ICC = 0.92) and interrater (ICC = 0.90) reliability. The flexicurve angle was systematically smaller and correlated poorly with the inclinometer angle (R2 = 0.384). The following equations can be used for approximate conversions: flexicurve angle = (0.275 × inclinometer angle) + 8.478; inclinometer angle = (1.396 × flexicurve angle) + 8.694. Conclusion. The inclinometer and flexicurve are both reliable instruments for thoracic kyphosis measurement in swimmers. Although the flexicurve and inclinometer angles are not directly comparable, the approximate conversion factors provided will permit translation of flexicurve angle to inclinometer angle and vice versa.


On the Semantic Mapping of Schema-agnostic Queries : A Preliminary Study


title: Evidence for Sub-Haplogroup H5 of Mitochondrial DNA as a Risk Factor for Late Onset Alzheimer's Disease

abstract: Background ::: Alzheimer's Disease (AD) is the most common neurodegenerative disease and the leading cause of dementia among senile subjects. It has been proposed that AD can be caused by defects in mitochondrial oxidative phosphorylation. Given the fundamental contribution of the mitochondrial genome (mtDNA) for the respiratory chain, there have been a number of studies investigating the association between mtDNA inherited variants and multifactorial diseases, however no general consensus has been reached yet on the correlation between mtDNA haplogroups and AD.


title: A Survey on Botnet Architectures, Detection and Defences

abstract: Botnets are known to be one of the most serious Internet security threats. In this survey, we review botnet architectures and their controlling mechanisms. Botnet infection behavior is explained. Then, known botnet models are outlined to study botnet design. Furthermore, Fast-Flux Service Networks (FFSN) are discussed in great details as they play an important role in facilitating botnet traffic. We classify botnets based on their architecture. Our classification criterion relies on the underlying CC C&C traffic, bot generated traffic, and DNS traffic, (2) botmaster traceback detection, and (3) botnet detection using virtual machines. Finally, we summarize botnet defence measures that should be taken after detecting a botnet.


title: Embodied interface for levitation and navigation in a 3D large space

abstract: We propose an embodied interface that allows both physical and virtual displacement within an Immersive Virtual Environment (IVE). It consists of a modular wearable used to control a mechanical motion base from which a user is suspended. The motion base is able to navigate in 3D through seven wires whose length is adjusted via a parallel link manipulator. Furthermore, an IMU-based body posture detection enables users to "fly" within the IVE at their own will, providing hands-free navigation, and facilitating other tasks' interactions. To assess the usability of this embodied interface, we compared it with a Joystick-based navigation control. The results showed that this interface allows effective navigation towards several targets located in 3D space. Even though the efficiency of target reach of the Joystick-based interaction is higher, a subjective assessment shows that the interface is comparable to the Joystick in hedonic qualities and attractiveness. Further analysis showed that with more practice, participants might navigate with a performance comparable with the Joystick. Finally, we analyzed the embodied behavior during 3D space navigation. This sheds light on requirements for further 3D navigation design.


title: Informal learning in work environments: training with the Social Web in the workplace

abstract: The Internet and its increasing usage has changed informal learning in depth. This change has affected young and older adults in both the workplace and in higher education. But, in spite of this, formal and non-formal course-based approaches have not taken full advantage of these new informal learning scenarios and technologies. The Web 2.0 is a new way for people to communicate across the Internet. Communication is a means of transformation and knowledge exchange. These are the facts that cannot be obviated by the organisations in their training programmes and knowledge management. This special issue is devoted to investigating how informal learning changes or influences online information in Social Web and training strategies in institutions. In order to do so, five papers will present different approaches of informal learning in the workplace regarding Web 2.0 capabilities.


title: Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

abstract: ABSTRACT This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin’s car-like robot.


title: Active harmonic elimination for multilevel converters

abstract: This paper presents an active harmonic elimination method to eliminate any number of specific higher order harmonics of multilevel converters with equal or unequal dc voltages. First, resultant theory is applied to transcendental equations characterizing the harmonic content to eliminate low order harmonics and to determine switching angles for the fundamental frequency switching scheme and a unipolar switching scheme. Next, the residual higher order harmonics are computed and subtracted from the original voltage waveform to eliminate them. The simulation results show that the method can effectively eliminate the specific harmonics, and a low total harmonic distortion (THD) near sine wave is produced. An experimental 11-level H-bridge multilevel converter with a field programmable gate array controller is employed to implement the method. The experimental results show that the method does effectively eliminate any number of specific harmonics, and the output voltage waveform has low THD.