title: LLC Resonant Converter With Matrix Transformer

abstract: In this paper, a high efficiency high power density matrix transformer structure for LLC resonant converters is proposed. Matrix transformer is help to reduce leakage inductance and the AC resistance of windings. Flux cancellation method is utilized to reduce core size and loss. Synchronous Rectifier (SR) devices and output capacitors are integrated into secondary windings to eliminate termination related winding losses, via loss and leakage inductance. A detail design procedure is proposed. A 1MHz 390V /12 V 1kW LLC resonant converter prototype is built.


STEERABLE PARASITIC ARRAY RADIATOR ANTENNA − PRINCIPLE , CONTROL THEORY AND ITS APPLICATIONS −


title: Constipation and Encopresis in Childhood

abstract: 1. Adel Abi-Hanna, MD ::: 2. Alan M. Lake, MD* ::: ::: ::: ::: ::: 1. ::: ::: 2. *Division of Pediatric Gastroenterology and Nutrition, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD. ::: ::: 1. Constipation is a symptom, not a diagnosis, that has multiple etiologies and multiple alternative treatments. ::: ::: 2. Encopresis or fecal soiling has many etiologies, not all of which are associated with fecal retention or responsive to laxatives. ::: ::: 3. Children who have Hirschsprung disease almost never have encopresis because the aganglionic segment maintains tonic contraction due to the absence of inhibitory neurons containing nitric acid and vasoactive intestinal polypeptide. ::: ::: 4. Functional retentive constipation is associated with loss of lower colonic muscle tone and will not resolve until complete repetitive evacuation of the lower colon allows muscle tone to be restored. ::: ::: 5. Successful treatment of constipation and encopresis requires a combination of patient and parent education, medical therapy, nutritional intervention, behavioral modification, and long-term monitoring of compliance. ::: ::: Constipation is a very common frustration for children, parents, and physicians. It is reported to account for nearly 5% of all outpatient visits to pediatric clinics and more than 25% of all referrals to pediatric gastroenterologists. Painful defecation and encopresis (involuntary passage of stool from the anus) usually are the first manifestations noted. Constipation generally is defined by the hard nature of the stool, the pain associated with its passage, or the failure to pass three stools per week. It would be preferable to define constipation as the failure to evacuate the lower colon completely with a bowel movement. This allows the parents and physician to realize that a child who has three small stools daily may not yet have evacuated the colon, while a child who has two large soft stools a week may not be constipated. ::: ::: The frequency of stools in most children decreases from a mean of four per day in the first week of life to 1.7 per day by the age of 2 years. Over this interval, stool volume increases more than tenfold while maintaining …


title: The Semantics of Comparatives and Other Degree Constructions

abstract: (1) It is much more important than you think it is. ::: ::: ::: ::: ::: (1) is an example of an adjectival comparative. In it, the adjective important is flanked by more and a comparative clause headed by than. This article is a survey of recent ideas about the interpretation of comparatives, including (i) the underlying semantics based on the idea of a threshold; (ii) the interpretation of comparative clauses that include quantifiers (brighter than on many other days); (iii) remarks on differentials such as much in (1) above: what they do in the comparative and what they do elsewhere in the language; (iv) the relationship between comparatives and other Degree constructions (e.g. as important, too important); and (v) the types of phrases in which comparatives are found (adjective: tighter versus noun: more water). Given the nature and purpose of this essay, I have tried not to presuppose background in formal semantics and I have departed from standard practice in journal articles by, as much as possible, not interrupting the flow with footnotes and references. There are two appendices. The first provides more analytical detail and there I do rely on formal techniques of natural language semantics. The second covers the sources for the ideas surveyed here.


title: 2$\times$ 2-Slot Element for 60-GHz Planar Array Antenna Realized on Two Doubled-Sided PCBs Using SIW Cavity and EBG-Type Soft Surface fed by Microstrip-Ridge Gap Waveguide

abstract: A wideband 2x2-slot element for a 60-GHz antenna array is designed by making use of two double-sided printed circuit boards (PCBs). The upper PCB contains the four radiating cavity-backed slots, where the cavity is formed in substrate-integrated waveguide (SIW) using metalized via holes. The SIW cavity is excited by a coupling slot. The excitation slot is fed by a microstrip-ridge gap waveguide formed in the air gap between the upper and lower PCBs. The lower PCB contains the microstrip line, being short-circuited to the ground plane of the lower PCB with via holes, and with additional metalized via holes alongside the microstrip line to form a stopband for parallel-platemodes in the air gap. The designed element can be used in large arrays with distribution networks realized in such microstrip-ridge gap waveguide technology. Therefore, the present paper describes a generic study in an infinite array environment, and performance is measured in terms of the active reflection coefficient S11 and the power lost in grating lobes. The study shows that the radiation characteristics of the array antenna is considerably improved by using a soft surface EBG-type SIW corrugation between each 2x2-slot element in E-plane to reduce the mutual coupling. The study is verified by measurements on a 4x4 element array surrounded by dummy elements and including a transition to rectangular waveguide WR15.


title: Model based FMEA for electronic products

abstract: Failure Mode and Effect Analysis (FMEA) has been developed and applied to a variety of electronic products. The FMEA method adopts a reverse thinking of the cause-failure-effect logic, the analyzing of all possible failure modes in an electric product is performed first then their failure causes are searched, which required rich experience in projects of analyst and waste time on those failure modes which unlikely happened. Besides, the environmental factors and structure of modern electronic products is complicated and the traditional FMEA cannot guarantee the adequacy of the failures analysis base on failure mechanism. Starting from the possible loadings of electronic product, a novel model-based FMEA (MB-FMEA) approach with positive thinking is proposed which finding failure mechanisms and modes of the components caused by the loadings and analyzing coupling relationships between them. The relationships between loadings and components, interactions among failure modes can be searched out by a transfer chain model. Application of the proposed approach is shown through the case of a strain test PCB which indicates the method can be used for reliability analysis and PHM of any electronic product.


title: Almost constant-time 3D nearest-neighbor lookup using implicit octrees

abstract: A recurring problem in 3D applications is nearest-neighbor lookups in 3D point clouds. In this work, a novel method for exact and approximate 3D nearest-neighbor lookups is proposed that allows lookup times that are, contrary to previous approaches, nearly independent of the distribution of data and query points, allowing to use the method in real-time scenarios. The lookup times of the proposed method outperform prior art sometimes by several orders of magnitude. This speedup is bought at the price of increased costs for creating the indexing structure, which, however, can typically be done in an offline phase. Additionally, an approximate variant of the method is proposed that significantly reduces the time required for data structure creation and further improves lookup times, outperforming all other methods and yielding almost constant lookup times. The method is based on a recursive spatial subdivision using an octree that uses the underlying Voronoi tessellation as splitting criteria, thus avoiding potentially expensive backtracking. The resulting octree is represented implicitly using a hash table, which allows finding the leaf node a query point belongs to with a runtime that is logarithmic in the tree depth. The method is also trivially extendable to 2D nearest neighbor lookups.


title: Extreme Extraction – Machine Reading in a Week

abstract: We report on empirical results in extreme extraction. It is extreme in that (1) from receipt of the ontology specifying the target concepts and relations, development is limited to one week and that (2) relatively little training data is assumed. We are able to surpass human recall and achieve an F1 of 0.51 on a question-answering task with less than 50 hours of effort using a hybrid approach that mixes active learning, bootstrapping, and limited (5 hours) manual rule writing. We compare the performance of three systems: extraction with handwritten rules, bootstrapped extraction, and a combination. We show that while the recall of the handwritten rules surpasses that of the learned system, the learned system is able to improve the overall recall and F1.


title: Text Summarization Using Sentiment Analysis for DUC Data

abstract: Text Summarization is a way of determining the key concepts that are covered in the given text under consideration. Various techniques have been presented in literature and many are used in commercially available systems. The primary aim of the present work is to find efficacy of using sentiments for text summarization. In this work we present a computationally efficient technique based on sentiments of key words in text for the purpose of summarization. Sentiment analysis is already being used in various domains for analysis of large scale text data interpretation and opinion mining. The present work shows that sentiment analysis can also be used efficiently for the purpose of text summarization. We have tested our results on the standard DUC2002 datasets, and compared our results with different summarization approaches, viz. Random indexing based, LSA based, Graph based and Weighted graph based methods for different percentages of summarization. The proposed scheme is found to be efficient, in particular for 50% summarization.


title: Lexicon Integrated CNN Models with Attention for Sentiment Analysis

abstract: With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentiment analysis that integrates lexicon embeddings and an attention mechanism into Convolutional Neural Networks. Our approach performs separate convolutions for word and lexicon embeddings and provides a global view of the document using attention. Our models are experimented on both the SemEval'16 Task 4 dataset and the Stanford Sentiment Treebank, and show comparative or better results against the existing state-of-the-art systems. Our analysis shows that lexicon embeddings allow to build high-performing models with much smaller word embeddings, and the attention mechanism effectively dims out noisy words for sentiment analysis.


title: Development of a Hindi Lemmatizer

abstract: We live in a translingual society, in order to communicate with people from different parts of the world we need to have an expertise in their respective languages. Learning all these languages is not at all possible; therefore we need a mechanism which can do this task for us. Machine translators have emerged as a tool which can perform this task. In order to develop a machine translator we need to develop several different rules. The very first module that comes in machine translation pipeline is morphological analysis. Stemming and lemmatization comes under morphological analysis. In this paper we have created a lemmatizer which generates rules for removing the affixes along with the addition of rules for creating a proper root word.


title: Adversarial examples in the physical world

abstract: Most existing machine learning classifiers are highly vulnerable to adversarial examples. An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modifications can be so subtle that a human observer does not even notice the modification at all, yet the classifier still makes a mistake. Adversarial examples pose security concerns because they could be used to perform an attack on machine learning systems, even if the adversary has no access to the underlying model. Up to now, all previous work have assumed a threat model in which the adversary can feed data directly into the machine learning classifier. This is not always the case for systems operating in the physical world, for example those which are using signals from cameras and other sensors as an input. This paper shows that even in such physical world scenarios, machine learning systems are vulnerable to adversarial examples. We demonstrate this by feeding adversarial images obtained from cell-phone camera to an ImageNet Inception classifier and measuring the classification accuracy of the system. We find that a large fraction of adversarial examples are classified incorrectly even when perceived through the camera.


title: Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing

abstract: Remote sensing technologies have been widely applied in urban environments’ monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the “salt and pepper” phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.


title: Toward a Wearable Sensor for Eating Detection

abstract: Researchers strive to understand eating behavior as a means to develop diets and interventions that can help people achieve and maintain a healthy weight, recover from eating disorders, or manage their diet and nutrition for personal wellness. A major challenge for eating-behavior research is to understand when, where, what, and how people eat. In this paper, we evaluate sensors and algorithms designed to detect eating activities, more specifically, when people eat. We compare two popular methods for eating recognition (based on acoustic and electromyography (EMG) sensors) individually and combined. We built a data-acquisition system using two off-the-shelf sensors and conducted a study with 20 participants. Our preliminary results show that the system we implemented can detect eating with an accuracy exceeding 90.9% while the crunchiness level of food varies. We are developing a wearable system that can capture, process, and classify sensor data to detect eating in real-time.


title: Emotional speech recognition: Resources, features, and methods

abstract: In this paper we overview emotional speech recognition having in mind three goals. The first goal is to provide an up-to-date record of the available emotional speech data collections. The number of emotional states, the language, the number of speakers, and the kind of speech are briefly addressed. The second goal is to present the most frequent acoustic features used for emotional speech recognition and to assess how the emotion affects them. Typical features are the pitch, the formants, the vocal tract cross-section areas, the mel-frequency cepstral coefficients, the Teager energy operator-based features, the intensity of the speech signal, and the speech rate. The third goal is to review appropriate techniques in order to classify speech into emotional states. We examine separately classification techniques that exploit timing information from which that ignore it. Classification techniques based on hidden Markov models, artificial neural networks, linear discriminant analysis, k-nearest neighbors, support vector machines are reviewed.


title: A Survey on Distributed Hash Table (DHT): Theory, Platforms, and Applications

abstract: Distributed Hash Table (DHT) plays an important role in distributed systems and applications, especially in large-scale distributed environments. In normal Client/Server model (C/S model) since most of the resources is centralized at server, it becomes the most important part as well as the bottleneck and weak point of the system. On the contrary, distributed model (a typical one is peer-to-peer (P2P) model) distributes the resources on the nodes in the system. This model requires that utilizing all the peers’ capability efficiently and providing better robustness. Luckily DHT technology meets the requirements so that it promotes the development of P2P greatly. DHT organizes the distributed resources so well that peers only need to know part of the system they can get resources efficiently. DHT only has two basic operations: get data from DHT and put data into DHT, which is so simple and graceful, but is suitable for a great variety of applications, and provides good robustness and high efficiency, especially in large-scale systems. For decades extensive work has been done for DHT. In academia, researchers have proposed several variants of DHT and improvements, which manage the resources in many kinds of structures, providing abundant choices for the construction of distributed system. Meanwhile, many platforms of DHT is constructed, which can be regarded as a bridge translating DHT from theory to practice and solve many practical problems such as load balance, multiple replicas, consistency, latency and so on. Furthermore, lots of applications based on DHT are proposed such as such as multicast, anycast, distributed file systems, search, storage, content delivery network, file sharing and communication. There are also some surveys about DHT, but most of them only focus on the theory. Plenty of DHT platforms and applications are ignored. The objective of this book is to summarize the development of DHT in both academic and industrial fields. It covers the main theory, platforms and applications of DHT. From this book, readers could learn the principle of several popular DHT structures, many platforms used in both academic and commercial fields, and a wide range of applications DHT can be deployed. Meanwhile, some limitations and week points of DHT are proposed at last. This book includes five chapters. In Chapter 1 some background information about DHT is introduced. Seven variants of DHT are studied and compared in many aspects in Chapter 2, which is the base of the platforms. In Chapter 3 two kinds of platforms (academic and open-source platform and commercial platform) containing fifteen different platforms are analyzed, based on which applications can be constructed. In Chapter 4 eight kinds of applications are introduced, and the advantages of DHT in these applications are analyzed. Chapter 5 summarizes the power of DHT, and analyzes the limitation of DHT.


COMMUNICATIVE CONVENTIONS IN DISCUSSION DISCOURSE


Data Analytics Helps Business Decision Making


title: Origins and Evolution of Antibiotic Resistance

abstract: The massive prescription of antibiotics and their non-regulated and extensive usage has resulted in the development of extensive antibiotic resistance in microorganisms ; this has been of great clinical significance. Antibiotic resistance occurs not only by mutation of microbial genes which code for antibiotic uptake into cells or the binding sites for antibiotics, but mostly by the acquisition of heterologous resistance genes from external sources. The physical characteristics of the microbial community play a major role in gene exchange, but antimicrobial agents provide the selective pressure for the development of resistance and promote the transfer of resistance genes among bacteria. The control of antibiotic usage is essential to prevent the development of resistance to new antibiotics.


title: Smoking Cessation Induces Profound Changes in the Composition of the Intestinal Microbiota in Humans

abstract: Background ::: ::: The human intestinal microbiota is a crucial factor in the pathogenesis of various diseases, such as metabolic syndrome or inflammatory bowel disease (IBD). Yet, knowledge about the role of environmental factors such as smoking (which is known to influence theses aforementioned disease states) on the complex microbial composition is sparse. We aimed to investigate the role of smoking cessation on intestinal microbial composition in 10 healthy smoking subjects undergoing controlled smoking cessation. ::: Methods ::: ::: During the observational period of 9 weeks repetitive stool samples were collected. Based on abundance of 16S rRNA genes bacterial composition was analysed and compared to 10 control subjects (5 continuing smokers and 5 non-smokers) by means of Terminal Restriction Fragment Length Polymorphism analysis and high-throughput sequencing. ::: Results ::: ::: Profound shifts in the microbial composition after smoking cessation were observed with an increase of Firmicutes and Actinobacteria and a lower proportion of Bacteroidetes and Proteobacteria on the phylum level. In addition, after smoking cessation there was an increase in microbial diversity. ::: Conclusions ::: ::: These results indicate that smoking is an environmental factor modulating the composition of human gut microbiota. The observed changes after smoking cessation revealed to be similar to the previously reported differences in obese compared to lean humans and mice respectively, suggesting a potential pathogenetic link between weight gain and smoking cessation. In addition they give rise to a potential association of smoking status and the course of IBD.


title: Comparing Business Intelligence and Big Data Skills - A Text Mining Study Using Job Advertisements

abstract: While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.


A kinematic and temporal analysis of ergometer rowing and on-water rowing


title: Cellular mechanisms of brain state–dependent gain modulation in visual cortex

abstract: Visual cortical neurons fire at higher rates to visual stimuli during locomotion than during immobility, while maintaining orientation selectivity. The mechanisms underlying this change in gain are not understood. We performed whole-cell recordings from layer 2/3 and layer 4 visual cortical excitatory neurons and from parvalbumin-positive and somatostatin-positive inhibitory neurons in mice that were free to rest or run on a spherical treadmill. We found that the membrane potential of all cell types became more depolarized and (with the exception of somatostatin-positive interneurons) less variable during locomotion. Cholinergic input was essential for maintaining the unimodal membrane potential distribution during immobility, whereas noradrenergic input was necessary for the tonic depolarization associated with locomotion. Our results provide a mechanism for how neuromodulation controls the gain and signal-to-noise ratio of visual cortical neurons during changes in the state of vigilance.


title: Pcg-based game design: creating endless web

abstract: This paper describes the creation of the game Endless Web, a 2D platforming game in which the player's actions determine the ongoing creation of the world she is exploring. Endless Web is an example of a PCG-based game: it uses procedural content generation (PCG) as a mechanic, and its PCG system, Launchpad, greatly influenced the aesthetics of the game. All of the player's strategies for the game revolve around the use of procedural content generation. Many design challenges were encountered in the design and creation of Endless Web, for both the game and modifications that had to be made to Launchpad. These challenges arise largely from a loss of fine-grained control over the player's experience; instead of being able to carefully craft each element the player can interact with, the designer must instead craft algorithms to produce a range of content the player might experience. In this paper we provide a definition of PCG-based game design and describe the challenges faced in creating a PCG-based game. We offer our solutions, which impacted both the game and the underlying level generator, and identify issues which may be particularly important as this area matures.


title: Rich Vehicle Routing Problem: Survey

abstract: The Vehicle Routing Problem (VRP) is a well-known research line in the optimization research community. Its different basic variants have been widely explored in the literature. Even though it has been studied for years, the research around it is still very active. The new tendency is mainly focused on applying this study case to real-life problems. Due to this trend, the Rich VRP arises: combining multiple constraints for tackling realistic problems. Nowadays, some studies have considered specific combinations of real-life constraints to define the emerging Rich VRP scopes. This work surveys the state of the art in the field, summarizing problem combinations, constraints defined, and approaches found.


title: Semantic and syntactic forces in noun phrase production

abstract: Three experiments investigated semantic and syntactic effects in the production of phrases in Dutch. Bilingual participants were presented with English nouns and were asked to produce an adjective noun phrase in Dutch including the translation of the noun. In 2 experiments, the authors blocked items by either semantic category or grammatical gender. Participants performed the task slower when the target nouns were of the same semantic category than when they were from different categories and faster when the target nouns had the same gender than when they had different genders. In a final experiment, both manipulations were crossed. The authors replicated the results of the first 2 experiments, and no interaction was found. These findings suggest a feedforward flow of activation between lexico-semantic and lexico-syntactic information. Speaking involves the retrieval of lexical representations that correspond to our intentions and the development of a syntactically well-formed frame for the to-be-uttered sentence. The development of such a frame is, in part, guided by syntactic information specific to each word: for example, a word’s grammatical category, the subcategorization requirements of verbs, and a noun’s gender (for languages such as Dutch). The focus of this article is on the relationship between retrieving a lexical representation that specifies the meaning the speaker wants to convey and retrieving


title: Rear-Lamp Vehicle Detection and Tracking in Low-Exposure Color Video for Night Conditions

abstract: Automated detection of vehicles in front is an integral component of many advanced driver-assistance systems (ADAS), such as collision mitigation, automatic cruise control (ACC), and automatic headlamp dimming. We present a novel image processing system to detect and track vehicle rear-lamp pairs in forward-facing color video. A standard low-cost camera with a complementary metal-oxide semiconductor (CMOS) sensor and Bayer red-green-blue (RGB) color filter is used and could be utilized for full-color image display or other color image processing applications. The appearance of rear lamps in video and imagery can dramatically change, depending on camera hardware; therefore, we suggest a camera-configuration process that optimizes the appearance of rear lamps for segmentation. Rear-facing lamps are segmented from low-exposure forward-facing color video using a red-color threshold. Unlike previous work in the area, which uses subjective color threshold boundaries, our color threshold is directly derived from automotive regulations and adapted for real-world conditions in the hue-saturation-value (HSV) color space. Lamps are paired using color cross-correlation symmetry analysis and tracked using Kalman filtering. A tracking-based detection stage is introduced to improve robustness and to deal with distortions caused by other light sources and perspective distortion, which are common in automotive environments. Results that demonstrate the system's high detection rates, operating distance, and robustness to different lighting conditions and road environments are presented.


title: Relational Reinforcement Learning

abstract: Relational reinforcement learning is presented, a learning technique that combines reinforcement learning with relational learning or inductive logic programming. Due to the use of a more expressive representation language to represent states, actions and Q-functions, relational reinforcement learning can be potentially applied to a new range of learning tasks. One such task that we investigate is planning in the blocks world, where it is assumed that the effects of the actions are unknown to the agent and the agent has to learn a policy. Within this simple domain we show that relational reinforcement learning solves some existing problems with reinforcement learning. In particular, relational reinforcement learning allows us to employ structural representations, to abstract from specific goals pursued and to exploit the results of previous learning phases when addressing new (more complex) situations.


title: On sample selection bias and its efficient correction via model averaging and unlabeled examples

abstract: Sample selection bias is a common problem encountered when using data mining algorithms for many real-world applications. Traditionally, it is assumed that training and test data are sampled from the same probability distribution, the so called “stationary or non-biased distribution assumption.” However, this assumption is often violated in reality. Typical examples include marketing solicitation, fraud detection, drug testing, loan approval, school enrollment, etc. For these applications the only labeled data available for training is a biased representation, in various ways, of the future data on which the inductive model will predict. Intuitively, some examples sampled frequently into the training data may actually be infrequent in the testing data, and vice versa. When this happens, an inductive model constructed from biased training set may not be as accurate on unbiased testing data if there had not been any selection bias in the training data. In this paper, we first improve and clarify a previously proposed categorization of sample selection bias. In particular, we show that unless under very restricted conditions, sample selection bias is a common problem for many real-world situations. We then analyze various effects of sample selection bias on inductive modeling, in particular, how the “true” conditional probability P (y|x) to be modeled by inductive learners can be misrepresented in the biased training data, that subsequently misleads a learning algorithm. To solve inaccuracy problems due to sample selection bias, we explore how to use model averaging of (1) conditional probabilities P (y|x), (2) feature probabilities P (x), and (3) joint probabilities, P (x, y), to reduce the influence of sample selection bias on model accuracy. In particular, we explore on how to use unlabeled data in a semi-supervised learning framework to improve the accuracy of descriptive models constructed from biased training samples. IBM T.J.Watson Research Center, Hawthorne, NY 10532, weifan@us.ibm.com Department of Computer Science, University at Albany, State University of New York, Albany, NY 12222, davidson@cs.albany.edu


A Survey on Biometrics based Cryptographic Key Generation Schemes


title: Usefulness of 18F-FDG PET/CT to Detect Metastatic Mucinous Adenocarcinoma Within an Inguinal Hernia

abstract: Metastatic mucinous adenocarcinoma in an inguinal hernia is a rare disease and the image findings of (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET)/computed tomography (CT) are little known. Here, we introduce a 57-year-old man with metastatic mucinous adenocarcinoma in an inguinal hernia. On initial (18)F-FDG PET/CT, hypermetabolism was observed in mucinous adenocarcinoma of the cecum, and adenocarcinomas of the transverse and ascending colon, respectively. Follow-up (18)F-FDG PET/CT revealed newly developed multiple hypermetabolism in peritoneal seeding masses and nodules in the pelvic cavity and scrotum. Peritoneal carcinomatosis in the right pelvic side wall was extended to the incarcerated peritoneum and mesentery in the right inguinoscrotal hernia.(18)F-FDG PET/CT was useful to reveal unexpected peritoneal seeding within the inguinal hernia. Also, this case demonstrated that metastatic mucinous adenocarcinomas had variably intense FDG uptake.


title: Vanishing point detection without any a priori information

abstract: Even though vanishing points in digital images result from parallel lines in the 3D scene, most of the proposed detection algorithms are forced to rely heavily either on additional properties (like orthogonality or coplanarity and equal distance) of the underlying 3D lines, or on knowledge of the camera calibration parameters, in order to avoid spurious responses. In this work, we develop a new detection algorithm that relies on the Helmoltz principle recently proposed for computer vision by Desolneux et al (2001; 2003), both at the line detection and line grouping stages. This leads to a vanishing point detector with a low false alarms rate and a high precision level, which does not rely on any a priori information on the image or calibration parameters, and does not require any parameter tuning.


title: Learning under Feature Drifts in Textual Streams

abstract: Huge amounts of textual streams are generated nowadays, especially in social networks like Twitter and Facebook. As the discussion topics and user opinions on those topics change drastically with time, those streams undergo changes in data distribution, leading to changes in the concept to be learned, a phenomenon called concept drift. One particular type of drift, that has not yet attracted a lot of attention is feature drift, i.e., changes in the features that are relevant for the learning task at hand. In this work, we propose an approach for handling feature drifts in textual streams. Our approach integrates i) an ensemble-based mechanism to accurately predict the feature/word values for the next time-point by taking into account the different features might be subject to different temporal trends and ii) a sketch-based feature space maintenance mechanism that allows for a memory-bounded maintenance of the feature space over the stream. Experiments with textual streams from the sentiment analysis, email preference and spam detection demonstrate that our approach achieves significantly better or competitive performance compared to baselines.


title: Software metrics for cooperative scrum based ontology analysis

abstract: One can only control what he can measure. Measuring ontologies in general and inter-organizational ontologies in special is necessary to evaluate ontologies during cooperative development and evolution processes. A software metric is a standard of measuring the degree to which a software system and process possess some property. Metrics help to estimate the progress, quality, and health of a software and its development process. The availability of robust metrics in the early phases of the software development allows the better management of the later steps and a more efficient quality assessment when preventive or corrective actions can easily enhance quality. In this paper, we propose a set of cooperative Scrum based software metrics and architectural metrics that can be applied to cooperative ontology development and evolution process using Scrum framework. These metrics help improve cooperative teams' performance and improve the quality of development and evolution process of inter-organizational ontologies. Furthermore, most of these metrics can be applied easily to any daily ontology activities to ensure ontology reliability, maintainability, and usability.


Why are depictives different from resultatives


title: An overview of emerging results in cooperative UAV control

abstract: Inexpensive fixed wing UAV are increasingly useful in remote sensing operations. They are a cheaper alternative to manned vehicles, and are ideally suited for dangerous or monotonous missions that would be inadvisable for a human pilot. Groups of UAV are of special interest for their abilities to coordinate simultaneous coverage of large areas, or cooperate to achieve goals such as mapping. Cooperation and coordination in UAV groups also allows increasingly large numbers of aircraft to be operated by a single user. Specific applications under consideration for groups of cooperating UAV are border patrol, search and rescue, surveillance, communications relaying, and mapping of hostile territory. The capabilities of small UAV continue to grow with advances in wireless communications and computing power. Accordingly, research topics in cooperative UAV control include efficient computer vision for real-time navigation and networked computing and communication strategies for distributed control, as well as traditional aircraft-related topics such as collision avoidance and formation flight. Emerging results in cooperative UAV control are presented via discussion of these topics, including particular requirements, challenges, and some promising strategies relating to each area. Case studies from a variety of programs highlight specific solutions and recent results, ranging from pure simulation to control of multiple UAV. This wide range of case studies serves as an overview of current problems of Interest, and does not present every relevant result.


title: Dynamic Precedence Effect Modeling for Source Separation in Reverberant Environments

abstract: Reverberation continues to present a major problem for sound source separation algorithms. However, humans demonstrate a remarkable robustness to reverberation and many psychophysical and perceptual mechanisms are well documented. The precedence effect is one of these mechanisms; it aids our ability to localize sounds in reverberation. Despite this, relatively little work has been done on incorporating the precedence effect into automated source separation. Furthermore, no work has been carried out on adapting a precedence model to the acoustic conditions under test and it is unclear whether such adaptation, analogous to the perceptual Clifton effect, is even necessary. Hence, this study tests a previously proposed binaural separation/precedence model in real rooms with a range of reverberant conditions. The precedence model inhibitory time constant and inhibitory gain are varied in each room in order to establish the necessity for adaptation to the acoustic conditions. The paper concludes that adaptation is necessary and can yield significant gains in separation performance. Furthermore, it is shown that the initial time delay gap and the direct-to-reverberant ratio are important factors when considering this adaptation.


title: Multi-material design and 3D printing method of lower limb prosthetic sockets

abstract: The paper shows a research activity aimed at integrating low cost industrial technologies in the design, test and manufacture of medical devices. This work focuses on lower limb prosthesis and in particular on the custom-fit component interacting with the residual limb, i.e. the socket. The process going from the 3D reconstruction of patients' limb to the manufacture of the socket by means of a 3D printer has been designed. Moreover, this must be as automatic as possible and should not require the presence of a design and simulation expert. This implied a deep involvement of physicians and orthopaedic technicians in order to embed rules and procedures in the system. The process is divided in three steps: design, test and manufacture. For each step some details are shown and at last some conclusions are drawn mainly concerning the challenge of multi-material 3D printing of the socket.


Polarity Contrast for the Detection of Verbal Irony


title: Testbed for a LiFi system integrated in streetlights

abstract: In this paper, a functional LiFi real-time testbed implemented on FPGAs is presented. The setup evaluates the performance of our design in a downlink scenario where the transmitter is embedded on the streetlights and a mobile phone's camera is used as receiver, therefore achieving the goal of lighting and communicating simultaneously. To validate the design, simulations of the whole system are performed. where simulations of the channel between streetlight and mobile device show the scope of the reflection in an outdoor environment. The measurements are carried out to characterize the modulator, in particular the FPGA resources and latency due to the encoder blocks. In addition the feasibility of the communication function in presence of the ambient light is verified.


title: Issues and approaches of database integration

abstract: In many large companies the widespread usage of computers has led a number of different application-specific databases to be installed. As company structures evolve, boundaries between departments move, creating new business units. Their new applications will use existing data from various data stores, rather than new data entering the organization. Henceforth, the ability to make data stores interoperable becomes a crucial factor for the development of new information systems. Data interoperability may come in various degrees. At the lowest level, commercial gateways connect specific pairs of database management systems (DBMSs). Software providing facilities for defining persistent views over different databases [6] simplifies access to distant data but does not support automatic enforcement of consistency constraints among different databases. Full interoperability is achieved by distributed or federated database systems, which support integration of existing data into virtual databases (i.e. databases which are logically defined but not physically materialized). The latter allow existing databases to remain under control of their respective owners, thus supporting a harmonious coexistence of scalable data integration and site autonomy requirements [9]. Federated systems are very popular today. However, before they become marketable, many issues remain to be solved. Design issues focus on either human-centered aspects (cooperative work, including autonomy issues and negotiation procedures) or database-centered aspects (data integration, schema/database evolution). Operational issues investigate system interoperability mainly in terms of support of new transaction types, new query processing algorithms, security concerns, etc. General overviews may be found elsewhere [4, 9]. This paper is devoted to database integration, possibly the most critical issue. Simply stated, database integration is the process which takes as input a set of databases, and produces as output a single unified description of the input schemas (the integrated schema) and the associated mapping information supporting integrated access to existing data through the integrated schema. As such, database integration is also used in the process of re-engineering an exist i ng l egacy system. Database integration has attracted many diverse and diverging contributions. The purpose, and the main intended contribution of this article is to provide a clear picture of what are the approaches and the current solutions and what remains to be achieved.


title: Generating event storylines from microblogs

abstract: Microblogging service has emerged to be a dominant web medium for billions of individuals sharing and spreading instant news and information, therefore monitoring the event evolution on microblog sphere is crucial for providing both better user experience and deeper understanding on real-time events. In this paper we explore the problem of generating storylines from microblogs for user input queries. This problem is challenging due to the sparse, dynamic and social nature of microblogs. Given a query of an ongoing event, we propose to sketch the real-time storyline of the event by a two-level solution. We first propose a language model with dynamic pseudo relevance feedback to obtain relevant tweets, and then generate storylines via graph optimization. Comprehensive experiments on Twitter data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.


title: Biominerals and Fossils Through Time

abstract: Preface 1. The concept of microstructural sequence, as exemplified by mollusc shells and coral skeletons 2. Compositional data on mollusc shells and coral skeletons 3. Origin of microstructural diversity 4. Diversity of structural and growth-mode patterns in skeletal Ca-carbonate of some plants and animals 5. Connecting the layered growth and crystallization model to the chemical and physiological approaches 6. Micro-crystallized and amorphous biominerals in bones, teeth and siliceous structures 7. More information from the fossil records through a better understanding of their diagenetic behavior and fossilization status of biominerals References Index.


title: A DNA-Based Archival Storage System

abstract: Demand for data storage is growing exponentially, but the capacity of existing storage media is not keeping up. Using DNA to archive data is an attractive possibility because it is extremely dense, with a raw limit of 1 exabyte/mm3 (109 GB/mm3), and long-lasting, with observed half-life of over 500 years. This paper presents an architecture for a DNA-based archival storage system. It is structured as a key-value store, and leverages common biochemical techniques to provide random access. We also propose a new encoding scheme that offers controllable redundancy, trading off reliability for density. We demonstrate feasibility, random access, and robustness of the proposed encoding with wet lab experiments involving 151 kB of synthesized DNA and a 42 kB random-access subset, and simulation experiments of larger sets calibrated to the wet lab experiments. Finally, we highlight trends in biotechnology that indicate the impending practicality of DNA storage for much larger datasets.


title: A Survey and Tutorial of RFID Anti-Collision Protocols

abstract: RFID technologies have revolutionized the asset tracking industry, with applications ranging from automated checkout to monitoring the medication intakes of elderlies. In all these applications, fast, and in some cases energy efficient, tag reading is desirable, especially with increasing tag numbers. In practice, tag reading protocols face many problems. A key one being tag collision, which occurs when multiple tags reply simultaneously to a reader. As a result, an RFID reader experiences low tag reading performance, and wastes valuable energy. Therefore, it is important that RFID application developers are aware of current tag reading protocols. To this end, this paper surveys, classifies, and compares state-of-the-art tag reading protocols. Moreover, it presents research directions for existing and future tag reading protocols.


title: Teaching computer organization/architecture with limited resources using simulators

abstract: As the complexity and variety of computer system hardware increases, its suitability as a pedagogical tool in computer organization/architecture courses diminishes. As a consequence, many instructors are turning to simulators as teaching aids, often using valuable teaching/research time to construct them. Many of these simulators have been made freely available on the Internet, providing a useful and time-saving resource for other instructors. However, finding the right simulator for a particular course or topic can itself be a time-consuming process. The goal of this paper is to provide an easy-to-use survey of free and Internet-accessible computer system simulators as a resource for all instructors of computer organization and computer architecture courses.


title: QEMU, a Fast and Portable Dynamic Translator

abstract: We present the internals of QEMU, a fast machine emulator using an original portable dynamic translator. It emulates several CPUs (x86, PowerPC, ARM and Sparc) on several hosts (x86, PowerPC, ARM, Sparc, Alpha and MIPS). QEMU supports full system emulation in which a complete and unmodified operating system is run in a virtual machine and Linux user mode emulation where a Linux process compiled for one target CPU can be run on another CPU.


title: Learning and Testing Causal Models with Interventions

abstract: We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network $\mathcal{M}$ on a graph with $n$ discrete variables and bounded in-degree and bounded `confounded components', we show that $O(\log n)$ interventions on an unknown causal Bayesian network $\mathcal{X}$ on the same graph, and $\tilde{O}(n/\epsilon^2)$ samples per intervention, suffice to efficiently distinguish whether $\mathcal{X}=\mathcal{M}$ or whether there exists some intervention under which $\mathcal{X}$ and $\mathcal{M}$ are farther than $\epsilon$ in total variation distance. We also obtain sample/time/intervention efficient algorithms for: (i) testing the identity of two unknown causal Bayesian networks on the same graph; and (ii) learning a causal Bayesian network on a given graph. Although our algorithms are non-adaptive, we show that adaptivity does not help in general: $\Omega(\log n)$ interventions are necessary for testing the identity of two unknown causal Bayesian networks on the same graph, even adaptively. Our algorithms are enabled by a new subadditivity inequality for the squared Hellinger distance between two causal Bayesian networks.


title: Studies on the Thermal Cycling Reliability of BGA System-in-Package (SiP) With an Embedded Die

abstract: Nowadays, major trends in the design of electronic products are toward multifunction and miniaturization. To meet these trends, system-in-package (SiP) has been adapted as one of the core packaging technologies for many product applications. Among the various types of SiPs, SiP with embedded dies has become important due to the smaller size achieved through embedded dies and better electrical performance by the shorter interconnection length. However, reliability data of the SiP with embedded dies have not been reported yet. Therefore, it is necessary to investigate the reliability of the SiPs with embedded dies and the effect of the embedded die on the SiP reliability. Of the several reliability tests, a detailed thermal cycling test (T/C test) was performed on board-level packaged samples. A finite element method (FEM) simulation was also performed to find out the stress and strain distribution of ball grid array (BGA) solder positions and to predict the potential failure sites under the T/C test. Through this paper, it was found that the failure position of the BGA changed from the corner solder ball position of the conventional BGA package, where the largest distance from neutral point was, to the inner BGA solder ball positions, where the edge of embedded die was located, due to the complicated structure of embedded die SiPs. Furthermore, FEM results showed that the inner site of the BGA is more vulnerable than the corner BGAs. This was well matched with experimental results.


title: A Greedy Data Association Technique for Multiple Object Tracking

abstract: With recent advancements in complex image analysis algorithms and global optimization techniques, the qualitative performance of Multiple Object Tracking (MOT) has improved significantly, at the cost of slow processing speed. With a focus on high-speed performance, in this paper, we propose a fast data association technique for tracking multiple objects by using Tracking-by-Detection paradigm. Followed by a pre-processing stage of creating reliable tracklets from given detection responses, we propose a threshold-based greedy algorithm that iteratively finds a locally optimum solution with significantly low computational overhead. Experiments conducted on two benchmark datasets show that our method is able to achieve qualitative results comparable to the existing state-of-the-art algorithms with an advantage of 50-600 times faster processing speed.


title: Using CMMI together with agile software development: A systematic review

abstract: Abstract Background The search for adherence to maturity levels by using lightweight processes that require low levels of effort is regarded as a challenge for software development organizations. Objective This study seeks to evaluate, synthesize, and present results on the use of the Capability Maturity Model Integration (CMMI) in combination with agile software development, and thereafter to give an overview of the topics researched, which includes a discussion of their benefits and limitations, the strength of the findings, and the implications for research and practice. Methods The method applied was a Systematic Literature Review on studies published up to (and including) 2011. Results The search strategy identified 3193 results, of which 81 included studies on the use of CMMI together with agile methodologies. The benefits found were grouped into two main categories: those related to the organization in general and those related to the development process, and were organized into subcategories, according to the area to which they refer. The limitations were also grouped into these categories. Using the criteria defined, the strength of the evidence found was considered low. The implications of the results for research and practice are discussed. Conclusion Agile methodologies can be used by companies to reduce efforts in getting to levels 2 and 3 of CMMI, there even being reports of applying agile practices that led to achieving level 5. However, agile methodologies alone, according to the studies, were not sufficient to obtain a rating at a given level, it being necessary to resort to additional practices to do so.


title: Breaking the Chain: Liberation from the Temporal Markov Assumption for Tracking Human Poses

abstract: We present an approach to multi-target tracking that has expressive potential beyond the capabilities of chain-shaped hidden Markov models, yet has significantly reduced complexity. Our framework, which we call tracking-by-selection, is similar to tracking-by-detection in that it separates the tasks of detection and tracking, but it shifts temporal reasoning from the tracking stage to the detection stage. The core feature of tracking-by-selection is that it reasons about path hypotheses that traverse the entire video instead of a chain of single-frame object hypotheses. A traditional chain-shaped tracking-by-detection model is only able to promote consistency between one frame and the next. In tracking-by-selection, path hypotheses exist across time, and encouraging long-term temporal consistency is as simple as rewarding path hypotheses with consistent image features. One additional advantage of tracking-by-selection is that it results in a dramatically simplified model that can be solved exactly. We adapt an existing tracking-by-detection model to the tracking-by-selection framework, and show improved performance on a challenging dataset.


title: NN-Noxim: High-Level Cycle-Accurate NoC-based Neural Networks Simulator

abstract: Numerous advances have been made in developing intelligent system. The artificial neural network (ANN) was proposed, inspired by biological neural network, to solve the problems of pattern recognition, prediction and control optimization. While ANN provides the most advanced and accurate algorithms for many AI applications, it will consume much computing time and computing power. Hence, it is necessary to design a hardware efficient ANN accelerator for the future complex computing operation. However, due to the intensive computation and communication among each ANN neuron, the interconnection between each ANN neuron become complicated as the ANN size is scaling up. On the other hand, the network-on-chip (NoC) interconnection is proven as an efficient way to solve the problems of complicated multicore interconnection. Therefore, the NoC-based ANN design is an attractive way for the ANN hardware accelerator design. To facilitate the NoC-based ANN evaluation in the system level, we present a cycle-accurate NoC-based neural network simulator, NN-Noxim, in this paper. The classification precision of the simulation output is verified by the commercial high-level neural network simulator. Consequently, the proposed simulator can be used for the NoC-based neural network design, neural network computing design, and other related researches in the future.


title: Development of a behavioral test of visuospatial neglect.

abstract: In a method and system for controlling high voltage direct current power transmission plants, the delay angle for the each converter is calculated directly by a calculating arrangement, which is provided for controlling all converters totally, from data set by an operator and data transmitted by suitable sending-receiving apparatus from each converter. Each converter receives the delay angle signal by suitable sending-receiving apparatus from the calculating arrangement and operates in accordance with the received delay angle value. The delay angle value is given the following equation. where alpha is a delay angle value. E is either a measured or calculated A.C. voltage, and if it is a measured voltage, it is the voltage from an A.C. system to which each converter is connected, V is either a set or calculated D.C. voltage, Id is either a set or calculated D.C. current and X is a converter commutation reactance.


Best of Both Worlds: Making Word Sense Embeddings Interpretable.


title: Sky Detection in Hazy Image

abstract: Sky detection plays an essential role in various computer vision applications. Most existing sky detection approaches, being trained on ideal dataset, may lose efficacy when facing unfavorable conditions like the effects of weather and lighting conditions. In this paper, a novel algorithm for sky detection in hazy images is proposed from the perspective of probing the density of haze. We address the problem by an image segmentation and a region-level classification. To characterize the sky of hazy scenes, we unprecedentedly introduce several haze-relevant features that reflect the perceptual hazy density and the scene depth. Based on these features, the sky is separated by two imbalance SVM classifiers and a similarity measurement. Moreover, a sky dataset (named HazySky) with 500 annotated hazy images is built for model training and performance evaluation. To evaluate the performance of our method, we conducted extensive experiments both on our HazySky dataset and the SkyFinder dataset. The results demonstrate that our method performs better on the detection accuracy than previous methods, not only under hazy scenes, but also under other weather conditions.


title: What Motivates Consumers to Write Online Travel Reviews?

abstract: The Web provides a fertile ground for word-of-mouth communication and more and more consumers write about and share product-related experiences online. Given the experiential nature of tourism, such first-hand knowledge communicated by other travelers is especially useful for travel decision making. However, very little is known about what motivates consumers to write online travel reviews. A Web-based survey using an online consumer panel was conducted to investigate consumers' motivations to write online travel reviews. Measurement scales to gauge the motivations to contribute online travel reviews were developed and tested. The results indicate that online travel review writers are mostly motivated by helping a travel service provider, concerns for other consumers, and needs for enjoyment/positive self-enhancement. Venting negative feelings through postings is clearly not seen as an important motive. Motivational differences were found for gender and income level. Implications of the findings for online travel communities and tourism marketers are discussed.


title: Euclidean Distance Mapping

abstract: Based on a two-component descriptor, a distance label for each point, it is shown that Euclidean distance maps can be generated by effective sequential algorithms. The map indicates, for each pixel in the objects (or the background) of the originally binary picture, the shortest distance to the nearest pixel in the background (or the objects). A map with negligible errors can be produced in two picture scans which has to include forward and backward movement for each line. Thus, for expanding/shrinking purposes it may compete very successfully with iterative parallel propagation in the binary picture itself. It is shown that skeletons can be produced by simple procedures and since these are based on Euclidean distances it is assumed that they are superior to skeletons based on d4−, d8−, and even octagonal metrics.


title: IoT Forensic: Bridging the Challenges in Digital Forensic and the Internet of Things

abstract: The smart devices have been used in the most major domain like the healthcare, transportation, smart home, smart city and more. However, this technology has been exposed to many vulnerabilities, which may lead to cybercrime through the devices. With the IoT constraints and low-security mechanisms applied, the device could be easily been attacked, treated and exploited by cyber criminals where the smart devices could provide wrong data where it can lead to wrong interpretation and actuation to the legitimate users. To comply with the IoT characteristics, two approaches towards of having the investigation for IoT forensic is proposed by emphasizing the pre-investigation phase and implementing the real-time investigation to ensure the data and potential evidence is collected and preserved throughout the investigation.


title: Deconstructing Mindfulness and Constructing Mental Health: Understanding Mindfulness and its Mechanisms of Action

abstract: Research on mindfulness indicates that it is associated with improved mental health, but the use of multiple different definitions of mindfulness prevents a clear understanding of the construct. In particular, the boundaries between different conceptualizations of mindfulness and emotion regulation are unclear. Furthermore, the mechanisms by which any of these conceptualizations of mindfulness might influence mental health are not well-understood. The two studies presented here addressed these questions using correlational, self-report data from a non-clinical sample of undergraduate students. The first study used a combination of exploratory and confirmatory factor analyses to better understand the factor structure of mindfulness and emotion regulation measures. Results indicated that these measures assess heterogeneous and overlapping constructs, and may be most accurately thought of as measuring four factors: present-centered attention, acceptance of experience, clarity about one’s internal experience, and the ability to manage negative emotions. A path analysis supported the hypothesis that mindfulness (defined by a two-factor construct including present-centered attention and acceptance of experience) contributed to clarity about one’s experience, which improved the ability to manage negative emotions. The second study developed these findings by exploring the mediating roles of clarity about one’s internal life, the ability to manage negative emotions, non-attachment (or the extent to which one’s happiness is independent of specific outcomes and events), and rumination in the relationship between mindfulness and two aspects of mental health, psychological distress and flourishing mental health. Results confirmed the importance of these mediators in the relationship between mindfulness and mental health.


title: A Self-Training Method for Semi-Supervised GANs

abstract: Since the creation of Generative Adversarial Networks (GANs), much work has been done to improve their training stability, their generated image quality, their range of application but nearly none of them explored their self-training potential. Self-training has been used before the advent of deep learning in order to allow training on limited labelled training data and has shown impressive results in semi-supervised learning. In this work, we combine these two ideas and make GANs self-trainable for semi-supervised learning tasks by exploiting their infinite data generation potential. Results show that using even the simplest form of self-training yields an improvement. We also show results for a more complex self-training scheme that performs at least as well as the basic self-training scheme but with significantly less data augmentation.


title: SkewTune: mitigating skew in mapreduce applications

abstract: We present an automatic skew mitigation approach for user-defined MapReduce programs and present SkewTune, a system that implements this approach as a drop-in replacement for an existing MapReduce implementation. There are three key challenges: (a) require no extra input from the user yet work for all MapReduce applications, (b) be completely transparent, and (c) impose minimal overhead if there is no skew. The SkewTune approach addresses these challenges and works as follows: When a node in the cluster becomes idle, SkewTune identifies the task with the greatest expected remaining processing time. The unprocessed input data of this straggling task is then proactively repartitioned in a way that fully utilizes the nodes in the cluster and preserves the ordering of the input data so that the original output can be reconstructed by concatenation. We implement SkewTune as an extension to Hadoop and evaluate its effectiveness using several real applications. The results show that SkewTune can significantly reduce job runtime in the presence of skew and adds little to no overhead in the absence of skew.


title: Fast and Effective Geometric K-Nearest Neighbors Multi-frame Super-Resolution

abstract: Multi-frame super-resolution is possible when there is motion and non-redundant information from a sequence of low-resolution input images. Remote sensors, surveillance videos and modern mobile phones are examples of devices able to easily gather multiple images of a same scene. However, combining a large number of frames into a higher resolution image may not be computationally feasible by complex super-resolution techniques. We discuss herein a set of simple and effective high-performance algorithms to fastly super-resolve several low-resolution images in an always-on low-power environment, with possible applications in mobile computing, forensics, and biometrics. The algorithms rely on geometric k-nearest neighbors to decide which information to consider in each high-resolution pixel, have a low memory footprint and run in linear time as we increase the number of low-resolution input images. Finally, we suggest a minimum number of input images for multi-frame super-resolution, considering that we expect a good response as fast as possible.


Fast Logistic Regression for Data Mining, Text Classification and Link Detection


title: Qualitative Cleaning of Uncertain Data

abstract: We propose a new view on data cleaning: Not data itself but the degrees of uncertainty attributed to data are dirty. Applying possibility theory, tuples are assigned degrees of possibility with which they occur, and constraints are assigned degrees of certainty that say to which tuples they apply. Classical data cleaning modifies some minimal set of tuples. Instead, we marginally reduce their degrees of possibility. This reduction leads to a new qualitative version of the vertex cover problem. Qualitative vertex cover can be mapped to a linear-weighted constraint satisfaction problem. However, any off-the-shelf solver cannot solve the problem more efficiently than classical vertex cover. Instead, we utilize the degrees of possibility and certainty to develop a dedicated algorithm that is fixed parameter tractable in the size of the qualitative vertex cover. Experiments show that our algorithm is faster than solvers for the classical vertex cover problem by several orders of magnitude, and performance improves with higher numbers of uncertainty degrees.


title: Combining Text and Linguistic Document Representations for Authorship Attribution

abstract: In this paper, we provide several alternatives to the classical Bag-Of-Words model for automatic authorship attribution. To this end, we consider linguistic and writing style information such as grammatical structures to construct different document representations. Furthermore we describe two techniques to combine the obtained representations: combination vectors and ensemble based meta classification. Our experiments show the viability of our approach.


title: Green Scale Research Tool for Multi-Criteria and Multi-Metric Energy Analysis Performed During the Architectural Design Process

abstract: Prevailing computational tools available to and used by architecture and engineering professionals purport to gather and present thorough and accurate perspectives of the environmental impacts associated with their contributions to the built environment. The presented research of building modeling and analysis software used by the Architecture, Engineering, Construction, and Operations (AECO) industry reveals that many of the most heavily relied-upon industry tools are isolated in functionality, utilize incomplete models and data, and are disruptive to normative design and building optimization workflows. This paper describes the current models and tools, their primary functions and limitations, and presents our concurrent research to develop more advanced models to assess lifetime building energy consumption alongside operating energy use. A series of case studies describes the current state-of-the-art in tools and building energy analysis followed by the research models and novel design and analysis Tool that the Green Scale Research Group has developed in response. A fundamental goal of this effort is to increase the use and efficacy of building impact studies conducted by architects, engineers, and building owners and operators during the building design process.


title: Time-varying Feedback Stabilization of Car-like Wheeled Mobile Robots

abstract: Many nonholonomic mechanical systems, such as common wheeled mobile robots, are controllable but cannot be stabilized to given positions and orientations bv using smooth pure-state feedback control. However, as shown in Samson (1990), such systems may still be stabilized by using smooth time-varying feedbacks,—i.e., feedbacks that explicitly depend on the time variable. This possibility is here applied to the stabilization of a class of nonlinear systems whose equations encompass simple car models. A set of stabilizing smooth time-varying feedbacks is derived, and simulation results are given.


title: Passive data link layer 802.11 wireless device driver fingerprinting

abstract: Motivated by the proliferation of wireless-enabled devices and the suspect nature of device driver code, we develop a passive fingerprinting technique that identifies the wireless device driver running on an IEEE 802.11 compliant device. This technique is valuable to an attacker wishing to conduct reconnaissance against a potential target so that he may launch a driver-specific exploit. ::: ::: In particular, we develop a unique fingerprinting technique that accurately and efficiently identifies the wireless driver without modification to or cooperation from a wireless device. We perform an evaluation of this fingerprinting technique that shows it both quickly and accurately fingerprints wireless device drivers in real world wireless network conditions. Finally, we discuss ways to prevent fingerprinting that will aid in improving the security of wireless communication for devices that employ 802.11 networking.


title: Development of advanced driver assistance systems with vehicle hardware-inthe-loop simulations ∗

abstract: This paper presents a new method for the design and validation of advanced driver assistance systems (ADASs). With vehicle hardware-in-the-loop (VEHIL) simulations, the development process, and more specifically the validation phase, of intelligent vehicles is carried out safer, cheaper, and is more manageable. In the VEHIL laboratory, a full-scale ADAS-equipped vehicle is set up in a hardware-in-the-loop simulation environment, where a chassis dynamometer is used to emulate the road interaction and robot vehicles to represent other traffic. In this controlled environment, the performance and dependability of an ADAS is tested to great accuracy and reliability. The working principle and the added value of VEHIL are demonstrated with test results of an adaptive cruise control and a forward collision warning system. On the basis of the 'V' diagram, the position of VEHIL in the development process of ADASs is illustrated.


title: Discovering Internet marketing intelligence through online analytical web usage mining

abstract: This article describes a novel way of combining data mining techniques on Internet data in order to discover actionable marketing intelligence in electronic commerce scenarios. The data that is considered not only covers various types of server and web meta information, but also marketing data and knowledge. Furthermore, heterogeneity resolution thereof and Internet- and electronic commerce-specific pre-processing activities are embedded. A generic web log data hypercube is formally defined and schematic designs for analytical and predictive activities are given. From these materialised views, various online analytical web usage data mining techniques are shown, which include marketing expertise as domain knowledge and are specifically designed for electronic commerce purposes.


title: A Data Generator for Cloud-Scale Benchmarking

abstract: In many fields of research and business data sizes are breaking the petabyte barrier. This imposes new problems and research possibilities for the database community. Usually, data of this size is stored in large clusters or clouds. Although clouds have become very popular in recent years, there is only little work on benchmarking cloud applications. In this paper we present a data generator for cloud sized applications. Its architecture makes the data generator easy to extend and to configure. A key feature is the high degree of parallelism that allows linear scaling for arbitrary numbers of nodes. We show how distributions, relationships and dependencies in data can be computed in parallel with linear speed up.


title: Query rewriting for horn-SHIQ plus rules

abstract: Query answering over Description Logic (DL) ontologies has become a vibrant field of research. Efficient realizations often exploit database technology and rewrite a given query to an equivalent SQL or Datalog query over a database associated with the ontology. This approach has been intensively studied for conjunctive query answering in the DL-Lite and EL families, but is much less explored for more expressive DLs and queries. We present a rewriting-based algorithm for conjunctive query answering over Horn-SHIQ ontologies, possibly extended with recursive rules under limited recursion as in DL+log. This setting not only subsumes both DL-Lite and EL, but also yields an algorithm for answering (limited) recursive queries over Horn-SHIQ ontologies (an undecidable problem for full recursive queries). A prototype implementation shows its potential for applications, as experiments exhibit efficient query answering over full Horn-SHIQ ontologies and benign downscaling to DL-Lite, where it is competitive with comparable state of the art systems.


title: Robust model predictive control for an uncertain smart thermal grid

abstract: The focus of this paper is on modeling and control of Smart Thermal Grids (STGs) in which the uncertainties in the demand and/or supply are included. We solve the corresponding robust model predictive control (MPC) optimization problem using mixed-integer-linear programming techniques to provide a day-ahead prediction for the heat production in the grid. In an example, we compare the robust MPC approach with the robust optimal control approach, in which the day-ahead production plan is obtained by optimizing the objective function for entire day at once. There, we show that the robust MPC approach successfully keeps the supply-demand balance in the STG while satisfying the constraints of the production units in the presence of uncertainties in the heat demand. Moreover, we see that despite the longer computation time, the performance of the robust MPC controller is considerably better than the one of the robust optimal controller.


title: Personality-Based Recommendations: Evidence from Amazon.com

abstract: In this paper, we evaluate the accuracy of personality-based recommendations using a real-world data set from Amazon.com. We automatically infer the personality traits, needs, and values of users based on unstructured user-generated content in social media, rather than administering questionnaires or explicitly asking the users to self-report their characteristics. We nd that personality characteristics signicantly increase the performance of recommender systems, in general, while dierent personality models exhibit statistically signicant dierences in predictive performance.


title: Revisiting Android reuse studies in the context of code obfuscation and library usages

abstract: In the recent years, studies of design and programming practices in mobile development are gaining more attention from researchers. Several such empirical studies used Android applications (paid, free, and open source) to analyze factors such as size, quality, dependencies, reuse, and cloning. Most of the studies use executable files of the apps (APK files), instead of source code because of availability issues (most of free apps available at the Android official market are not open-source, but still can be downloaded and analyzed in APK format). However, using only APK files in empirical studies comes with some threats to the validity of the results. In this paper, we analyze some of these pertinent threats. In particular, we analyzed the impact of third-party libraries and code obfuscation practices on estimating the amount of reuse by class cloning in Android apps. When including and excluding third-party libraries from the analysis, we found statistically significant differences in the amount of class cloning 24,379 free Android apps. Also, we found some evidence that obfuscation is responsible for increasing a number of false positives when detecting class clones. Finally, based on our findings, we provide a list of actionable guidelines for mining and analyzing large repositories of Android applications and minimizing these threats to validity


title: Image resizing via non-homogeneous warping

abstract: Image resizing aims to adapt images to displays with different sizes and aspect ratios. In this paper, we provide a new image resizing approach for efficiently determining the non-homogeneous warp that better preserves the global image configuration and concentrates the distortion in regions of the image where they are least-likely to be noticed. Considering the different properties of large displays and small displays, we design different strategies for upsizing and downsizing. We define a variety of quadratic metrics to measure image distortion. We introduce a patch-linking scheme that can better preserve the global image configuration. We formulate image resizing as a quadratic minimization problem, which can be efficiently solved. We experiment with our method on a variety category of images and compare our results to the state of the art.


title: Spatio-temporal proximity and social distance: a confirmation framework for social reporting

abstract: Social reporting is based on the idea that the members of a location-based social network observe real-world events and publish reports about their observations. Application scenarios include crisis management, bird watching or even some sorts of mobile games. A major issue in social reporting is the quality of the reports. We propose an approach to the quality problem that is based on the reciprocal confirmation of reports by other reports. This contrasts with approaches that require users to verify reports, that is, to explicitly evaluate their veridicality. We propose to use spatio-termporal proximity as a first criterion for confirmation and social distance as a second one. By combining these two measures we construct a graph containing the reports as nodes connected by confirmation edges that can adopt positive as well as negative values. This graph builds the basis for the computation of confirmation values for individual reports by different aggregation measures. By applying our approach to two use cases, we show the importance of a weighted combination, since the meaningfulness of the constituent measures varies between different contexts.


title: Arm Motor Recovery Using a Virtual Reality Intervention in Chronic Stroke

abstract: Introduction. Despite interest in virtual environments (VEs) for poststroke arm motor rehabilitation, advantages over physical environment (PE) training have not been established. Objective. The authors compared kinematic and clinical outcomes of dose-matched upper-limb training between a 3D VE and a PE in chronic stroke. Methods: Participants (n = 32) were randomized to a 3D VE or PE for training. They pointed to 6 workspace targets (72 trials, 12 trials/target, randomized) for 12 sessions over 4 weeks with similar feedback on precision, movement speed, and trunk displacement. Primary (kinematics, clinical arm motor impairment) and secondary (activity level, arm use) outcomes were compared by time (PRE, POST, and follow-up, RET), training environment, and impairment severity (mild, moderate-to-severe) using mixed-model analyses of variance (ANOVAs). Results. Endpoint speed, overall performance on a reach-to-grasp task, and activity levels increased in both groups. Only participants in the VE group improv...


title: Accuracy evaluation of a credit card fraud detection system on Hadoop MapReduce

abstract: In digitalization era, credit card fraud detection is of high significance to financial organizations. This paper discussed about credit card fraud detection by parallelizing of Negative Selection Algorithm on the Cloud computing platform. We present performance evaluation of running the algorithm on the cloud by MapReduce framework and show it's dramatically results on real world financial data. We argue that, for the fraud detection rate, False Negative rate, fraud catching rate (True Positive rate) and false alarm rate (False Positive rate), Cost and Hit rate that are the best metrics for a desirable credit card fraud detection system.


title: Constructing Datasets for Multi-hop Reading Comprehension Across Documents

abstract: Most Reading Comprehension methods limit themselves to queries which can be answered using a single sentence, paragraph, or document. Enabling models to combine disjoint pieces of textual evidence would extend the scope of machine comprehension methods, but currently there exist no resources to train and test this capability. We propose a novel task to encourage the development of models for text understanding across multiple documents and to investigate the limits of existing methods. In our task, a model learns to seek and combine evidence - effectively performing multi-hop (alias multi-step) inference. We devise a methodology to produce datasets for this task, given a collection of query-answer pairs and thematically linked documents. Two datasets from different domains are induced, and we identify potential pitfalls and devise circumvention strategies. We evaluate two previously proposed competitive models and find that one can integrate information across documents. However, both models struggle to select relevant information, as providing documents guaranteed to be relevant greatly improves their performance. While the models outperform several strong baselines, their best accuracy reaches 42.9% compared to human performance at 74.0% - leaving ample room for improvement.


title: Time-Interleaved Analog-to-Digital Conversion With Online Adaptive Equalization

abstract: Online adaptive equalization is proposed to calibrate channel mismatch errors in time-interleaved (TI) analog-to-digital converter (ADC) arrays with a linear reference path. Code-domain filters are shown to be effective in removing interchannel as well as intrachannel nonlinearity and mismatch errors, including offset, gain, and differential nonlinearity (DNL) and integral nonlinearity (INL) mismatches. An 8-bit, 600-MS/s, 0.13- μm CMOS prototype measured a signal-to-noise plus distortion ratio (SNDR) of 31.0/47.4 dB, a spurious-free dynamic range (SFDR) of 32.8/63.6 dB, a DNL of 1.30/0.30 LSBs, and an INL of 1.70/0.23 LSBs in lab testing without/with equalization, respectively.


title: Mapping the lateral extent of human cadaver decomposition with soil chemistry.

abstract: Soil below decomposing cadavers may have a different lateral spatial extent depending upon whether scavengers have access to the human cadaver or not. We examined the lateral spatial extent of decomposition products to a depth of 7cm of soils beneath two decomposing corpses, one in which the subject was autopsied, unclothed and placed under a wire cage to restrict scavenger access and one in which the subject was not autopsied, unclothed and exposed to scavengers. The two bodies had accumulated degree days (ADD) of 5799 and 5469 and post mortem interval (PMI) of 288 and 248d, respectively. The spatial extent for dissolved organic carbon (DOC) and organic nitrogen (DON) for both bodies was large but similar suggesting some movement off site for both compounds. Mean DOC was 1087±727 and 1484±1236μgg(-1) dry soil under the two corpses relative to 150±68μgg(-1) in upslope control soils. Sulfate tended to have 'hot spots' of lower values relative to the control soils indicative of anaerobic respiration. pH was lower and electrical conductivity was higher in the soil under both decomposing cadavers relative to control soils. Some of the nutrients examined downslope of the human remains were significantly higher than control soils upslope suggesting movement of decomposition products off-site which could be an important factor when using human remains detector dogs.


title: A Software Architecture for RGB-D People Tracking Based on ROS Framework for a Mobile Robot

abstract: This paper describes the software architecture of a distributed multi-people tracking algorithm for mobile platforms equipped with a RGB-D sensor. Our approach features an efficient point cloud depth-based clustering, an HOG-like classification to robustly initialize a person tracking and a person classifier with online learning to drive data association. We explain in details how ROS functionalities and tools play an important role in the possibility of the software to be real time, distributed and easy to configure and debug.


title: Certificateless Remote Anonymous Authentication Schemes for WirelessBody Area Networks

abstract: Wireless body area network (WBAN) has been recognized as one of the promising wireless sensor technologies for improving healthcare service, thanks to its capability of seamlessly and continuously exchanging medical information in real time. However, the lack of a clear in-depth defense line in such a new networking paradigm would make its potential users worry about the leakage of their private information, especially to those unauthenticated or even malicious adversaries. In this paper, we present a pair of efficient and light-weight authentication protocols to enable remote WBAN users to anonymously enjoy healthcare service. In particular, our authentication protocols are rooted with a novel certificateless signature (CLS) scheme, which is computational, efficient, and provably secure against existential forgery on adaptively chosen message attack in the random oracle model. Also, our designs ensure that application or service providers have no privilege to disclose the real identities of users. Even the network manager, which serves as private key generator in the authentication protocols, is prevented from impersonating legitimate users. The performance of our designs is evaluated through both theoretic analysis and experimental simulations, and the comparative studies demonstrate that they outperform the existing schemes in terms of better trade-off between desirable security properties and computational overhead, nicely meeting the needs of WBANs.


title: A short review on data security and privacy issues in cloud computing

abstract: Cloud computing has gained substantial research interest, owing to its vast range of services. The major issues in cloud computing are its security and privacy. The term security has multiple facets such as confidentiality, availability and integrity. A perfect security solution must ensure all the security parameters effectively. This paper focuses on the security of data alone. This paper intends to review various security and privacy related issues and the possible solutions in literature. The challenges associated with the security and privacy related issues are also discussed.


title: Virtual Machine Allocation in P2P-cloud Live Video Straming

abstract: Using of content delivery network (CDN) is one solution for video streaming but it is costly. Hybrid CDN-P2P video streaming over the Internet is a developing technology that recently has gained attention by network and content providers. One of the main limitations of such systems is inadequate resources, such as upload bandwidth, that leads to reduce the quality experienced by the users. Due to the churn issue in P2P networks, renting fixed number of servers from a CDN provider can be costly. With emerging and developing of cloud services by public cloud providers, it seems dynamically renting the streaming servers is more efficient from cost and performance point of view. In this paper, we have proposed layered upload bandwidth Cloud-P2P architecture and an algorithm for live video streaming in which the rented virtual machines (VM) are dynamically added and removed to/from the P2P overlay network to provide the required level of Quality of Service (QoS) in a cost efficient manner. Our simulation evaluation shows the efficiency of the proposed allocation algorithm.


title: Finding Influential Users in Social Media Using Association Rule Learning

abstract: Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods.


title: Query-by-example keyword spotting using long short-term memory networks

abstract: We present a novel approach to query-by-example keyword spotting (KWS) using a long short-term memory (LSTM) recurrent neural network-based feature extractor. In our approach, we represent each keyword using a fixed-length feature vector obtained by running the keyword audio through a word-based LSTM acoustic model. We use the activations prior to the softmax layer of the LSTM as our keyword-vector. At runtime, we detect the keyword by extracting the same feature vector from a sliding window and computing a simple similarity score between this test vector and the keyword vector. With clean speech, we achieve 86% relative false rejection rate reduction at 0.5% false alarm rate when compared to a competitive phoneme posteriorgram with dynamic time warping KWS system, while the reduction in the presence of babble noise is 67%. Our system has a small memory footprint, low computational cost, and high precision, making it suitable for on-device applications.


title: Extracting situational awareness from microblogs during disaster events

abstract: Microblogging sites such as Twitter and Weibo are increasingly being used to enhance situational awareness during various natural and man-made disaster events such as floods, earthquakes, and bomb blasts. During any such event, thousands of microblogs (tweets) are posted in short intervals of time. Typically, only a small fraction of these tweets contribute to situational awareness, while the majority merely reflect the sentiment or opinion of people. Real-time extraction of tweets that contribute to situational awareness is especially important for relief operations when time is critical. However, automatically differentiating such tweets from those that reflect opinion / sentiment is a non-trivial challenge, mainly because of the very small size of tweets and the informal way in which tweets are written (frequent use of emoticons, abbreviations, and so on). This study applies Natural Language Processing (NLP) techniques to address this challenge. We extract low-level syntactic features from the text of tweets, such as the presence of specific types of words and parts-of-speech, to develop a classifier to distinguish between tweets which contribute to situational awareness and tweets which do not. Experiments over tweets related to four diverse disaster events show that the proposed features identify situational awareness tweets with significantly higher accuracy than classifiers based on standard bag-of-words models.


title: Context-Sensitive Microlearning of Foreign Language Vocabulary on a Mobile Device

abstract: We explore the use of ubiquitous sensing in the home for contextsensitive microlearning. To assess how users would respond to frequent and brief learning interactions tied to context, a sensor-triggered mobile phone application was developed, with foreign language vocabulary as the learning domain. A married couple used the system in a home environment, during the course of everyday activities, for a four-week study period. Built-in and stick-on multi-modal sensors detected the participants' interactions with hundreds of objects, furniture, and appliances. Sensor activations triggered the audio presentation of English and Spanish phrases associated with object use. Phrases were presented on average 57 times an hour; this intense interaction was found to be acceptable even after extended use. Based on interview feedback, we consider design attributes that may have reduced the interruption burden and helped sustain user interest, and which may be applicable to other context-sensitive, always-on systems.