title: Quantifying trust in autonomous system under uncertainties

abstract: Over the years, autonomous systems have entered almost all the facets of human life. Gradually, higher levels of autonomy are being incorporated into cyber-physical systems (CPS) and Internet-of-things (IoT) devices. However, safety and security has always been a lurking fear behind adoption of autonomous systems such as self-driving vehicles. To address these issues, we develop a framework for quantifying trust in autonomous system. This framework consist of an estimation method, which considers effect of adversarial attacks on sensor measurements. Our estimation algorithm uses a set-membership method during identification of safe states of the system. An important feature of this algorithm is that it can distinguish between adversarial noise and other disturbances. We also verify the autonomous system by first modeling it as networks of priced timed automata (NPTA) with stochastic semantics and then using statistical probabilistic model checking to verify it against probabilistic specifications. The verification process ensures that the autonomous system behave in accordance to safety specifications within a probabilistic threshold. For quantifying trust on the system, we use confidence results provided by the model checking tool. We have demonstrated our approach by using a case study of adaptive cruise control system under sensor spoofing attacks.


title: Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

abstract: We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold cross-validation. We used class-balanced random sampling within the stochastic gradient descent (SGD) optimization of the CNN to avoid skewed performance in favor of the most represented sleep stages. We achieved high mean F1-score (81%, range 79-83%), mean accuracy across individual sleep stages (82%, range 80-84%) and overall accuracy (74%, range 71-76%) over all subjects. By analyzing and visualizing the filters that our CNN learns, we found that rules learned by the filters correspond to sleep scoring criteria in the American Academy of Sleep Medicine (AASM) manual that human experts follow. Our method's performance is balanced across classes and our results are comparable to state-of-the-art methods with hand-engineered features. We show that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages.


title: Stability of attachment representations: The transition to marriage.

abstract: Attachment theory makes important predictions about stability and change of attachment patterns. Both early and ongoing secure base experiences lead to the development of mental representations. These are expected to be stable enough to guide behavior and yet open to change in light of experience. This study examines the stability of adult attachment representations across the transition to marriage. One hundred fifty-seven couples participating in a longitudinal study of attachment were assessed using the Adult Attachment Interview (AAI), the Current Relationship Interview (CRI), and measures describing relationship functioning and life events three months prior to their weddings and 18 months into their marriages. We assessed whether the adult relationship correlates of the AAI are comparable before and after marriage, by examining the association between classifications and relationship variables at both times. We tested the hypothesis that attachment classifications are stable, and that change is related to experiences in the relationship and/or life events. Seventy-eight percent of the sample received the same primary AAI classification (Secure, Preoccupied, and Dismissing) on both occasions. Change of primary classifications was associated with feelings and cognitions about the relationship. Only 46% of participants initially classified as Unresolved retained the classification. Stability of the Unresolved classification was associated with stressful life events and relationship aggression. Results shed light on the transition to marriage and on the interpretation of the AAI in longitudinal research.


title: Critical success factors for implementing knowledge management in small and medium enterprises

abstract: Purpose – To date, critical success factors (CSFs) for implementing knowledge management (KM) in small and medium enterprises (SMEs) have not been systematically investigated. Existing studies have derived their CSFs from large companies' perspectives and have not considered the needs of smaller businesses. This paper is aimed to bridge this gap.Design/methodology/approach – Existing studies on CSFs were reviewed and their limitations were identified. By integrating insights drawn from these studies as well as adding some new factors, the author proposed a set of 11 CSFs which is believed to be more suitable for SMEs. The importance of the proposed CSFs was theoretically discussed and justified. In addition, an empirical assessment was conducted to evaluate the extent of success of this proposition.Findings – The overall results from the empirical assessment were positive, thus reflecting the appropriateness of the proposed CSFs.Practical implications – The set of CSFs can act as a list of items for SMEs ...


title: An inquiry into anatomy of conflicts

abstract: Conflict analysis and resolution play an important role in business, governmental, political and lawsuit disputes, labor-management negotiations, military operations and others. Many mathematical models of conflict situations have been proposed and investigated. In this paper a novel approach to conflict analysis, based on rough set theory, is outlined. Basic concepts of this approach are defined and analyzed. An illustration of the introduced concepts by the Middle East conflict is presented.


Solving The Travelling Thief Problem with an Evolutionary Algorithm


title: Journal of the Association for Information Systems Validity Issues in the Use of Social Network Analysis with Digital Trace Data

abstract: There is an exciting natural match between social network analysis methods and the growth of data sources produced by social interactions via information technologies, from online communities to corporate information systems. Information Systems researchers have not been slow to embrace this combination of method and data. Such systems increasingly provide “digital trace data” that provide new research opportunities. Yet digital trace data are substantively different from the survey and interview data for which network analysis measures and interpretations were originally developed. This paper examines 10 validity issues associated with the combination of digital trace data and social network analysis methods, with examples from the IS literature, to provide recommendations for improving the validity of future research.


title: An Empirical Examination of the Ex Ante International Interest Rate Transmission

abstract: Using U.S. Treasury bill and Eurodollar futures to proxy for domestic and external interest rates, respectively, this study examines ex ante interest rate transmission across markets for the period 1982-1991. The results indicate that these interest rates are cointegrated and that they Granger-cause each other, implying that both domestic and offshore interest rates move together and that both markets are integrated. Interest rate transmission is found to be more rapid in recent years, a result supporting the idea that the international financial markets are becoming more integrated. Copyright 1995 by MIT Press.


title: Reinforcement Learning based QoS/QoE-aware Service Function Chaining in Software-Driven 5G Slices

abstract: With the ever growing diversity of devices and applications that will be connected to 5G networks, flexible and agile service orchestration with acknowledged QoE that satisfies end-user's functional and QoS requirements is necessary. SDN (Software-Defined Networking) and NFV (Network Function Virtualization) are considered key enabling technologies for 5G core networks. In this regard, this paper proposes a reinforcement learning based QoS/QoE-aware Service Function Chaining (SFC) in SDN/NFV-enabled 5G slices. First, it implements a lightweight QoS information collector based on LLDP, which works in a piggyback fashion on the southbound interface of the SDN controller, to enable QoS-awareness. Then, a DQN (Deep Q Network) based agent framework is designed to support SFC in the context of NFV. The agent takes into account the QoE and QoS as key aspects to formulate the reward so that it is expected to maximize QoE while respecting QoS constraints. The experiment results show that this framework exhibits good performance in QoE provisioning and QoS requirements maintenance for SFC in dynamic network environments.


title: Understanding the Integrated Learning Course Model: Academic Transition to Tertiary Education

abstract: In 1972, the TRIO program leaders at the University of Minnesota (UMN) developed the Integrated Learning (IL) course to meet academic and cultural transition needs of their Upward Bound (UB) secondary school students. These courses were offered during the UB summer bridge program for students who were concurrently enrolled in academically-challenging tertiary courses following graduation from secondary school. Later, use of the IL course shifted from the UB program to the tertiary-level TRIO Student Support Services program. An academically-challenging course like introductory psychology is linked with an IL course. The IL course is customized to use content of its companion class as context for mastering learning strategies and orienting students to the rigor of the tertiary learning environment. The IL course approach has assisted TRIO students improve their academic success in the rigorous academic environment as well as acclimate to the social climate of UMN, one of the largest universities in the United States. The primary purpose of this article is providing an overview of the IL course approach with sufficient information so other institutions could replicate it. Two quasi-experimental studies examined the possible benefits of the IL course. One was in connection with an introductory psychology course. The IL course students earned statistically significantly higher final course grades than nonparticipants. Another study with an introductory biology course replicated results of higher final course grades for IL course students. The IL course fostered higher final course grades and expanded positive study behaviors and their meta cognitive skills necessary for academic success.


title: DeepProposals: Hunting Objects and Actions by Cascading Deep Convolutional Layers

abstract: In this paper, a new method for generating object and action proposals in images and videos is proposed. It builds on activations of different convolutional layers of a pretrained CNN, combining the localization accuracy of the early layers with the high informative-ness (and hence recall) of the later layers. To this end, we build an inverse cascade that, going backward from the later to the earlier convolutional layers of the CNN, selects the most promising locations and refines them in a coarse-to-fine manner. The method is efficient, because i) it re-uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals thanks to the use of the inverse coarse-to-fine cascade. The method is also accurate. We show that our DeepProposals outperform most of the previously proposed object proposal and action proposal approaches and, when plugged into a CNN-based object detector, produce state-of-the-art detection performance.


title: Recognizing Question Entailment for Medical Question Answering.

abstract: With the increasing heterogeneity and specialization of medical texts, automated question answering is becoming more and more challenging. In this context, answering a given medical question by retrieving similar questions that are already answered by human experts seems to be a promising solution. In this paper, we propose a new approach for the detection of similar questions based on Recognizing Question Entailment (RQE). In particular, we consider Frequently Asked Question (FAQs) as a valuable and widespread source of information. Our final goal is to automatically provide an existing answer if FAQ similar to a consumer health question exists. We evaluate our approach using consumer health questions received by the National Library of Medicine and FAQs collected from NIH websites. Our first results are promising and suggest the feasibility of our approach as a valuable complement to classic question answering approaches.


title: Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection

abstract: This paper proposes a novel saliency detection method by developing a deeply-supervised recurrent convolutional neural network (DSRCNN), which performs a full image-to-image saliency prediction. For saliency detection, the local, global, and contextual information of salient objects is important to obtain a high quality salient map. To achieve this goal, the DSRCNN is designed based on VGGNet-16. Firstly, the recurrent connections are incorporated into each convolutional layer, which can make the model more powerful for learning the contextual information. Secondly, side-output layers are added to conduct the deeply-supervised operation, which can make the model learn more discriminative and robust features by effecting the intermediate layers. Finally, all of the side-outputs are fused to integrate the local and global information to get the final saliency detection results. Therefore, the DSRCNN combines the advantages of recurrent convolutional neural networks and deeply-supervised nets. The DSRCNN model is tested on five benchmark datasets, and experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art saliency detection approaches on all test datasets.


title: Markerless Motion Capture with unsynchronized moving cameras

abstract: In this work we present an approach for markerless motion capture (MoCap) of articulated objects, which are recorded with multiple unsynchronized moving cameras. Instead of using fixed (and expensive) hardware synchronized cameras, this approach allows us to track people with off-the-shelf handheld video cameras. To prepare a sequence for motion capture, we first reconstruct the static background and the position of each camera using Structure-from-Motion (SfM). Then the cameras are registered to each other using the reconstructed static background geometry. Camera synchronization is achieved via the audio streams recorded by the cameras in parallel. Finally, a markerless MoCap approach is applied to recover positions and joint configurations of subjects. Feature tracks and dense background geometry are further used to stabilize the MoCap. The experiments show examples with highly challenging indoor and outdoor scenes.


title: Cancer Metastasis Detection With Neural Conditional Random Field

abstract: Breast cancer diagnosis often requires accurate detection of metastasis in lymph nodes through Whole-slide Images (WSIs). Recent advances in deep convolutional neural networks (CNNs) have shown significant successes in medical image analysis and particularly in computational histopathology. Because of the outrageous large size of WSIs, most of the methods divide one slide into lots of small image patches and perform classification on each patch independently. However, neighboring patches often share spatial correlations, and ignoring these spatial correlations may result in inconsistent predictions. In this paper, we propose a neural conditional random field (NCRF) deep learning framework to detect cancer metastasis in WSIs. NCRF considers the spatial correlations between neighboring patches through a fully connected CRF which is directly incorporated on top of a CNN feature extractor. The whole deep network can be trained end-to-end with standard back-propagation algorithm with minor computational overhead from the CRF component. The CNN feature extractor can also benefit from considering spatial correlations via the CRF component. Compared to the baseline method without considering spatial correlations, we show that the proposed NCRF framework obtains probability maps of patch predictions with better visual quality. We also demonstrate that our method outperforms the baseline in cancer metastasis detection on the Camelyon16 dataset and achieves an average FROC score of 0.8096 on the test set. NCRF is open sourced at https://github.com/baidu-research/NCRF.


title: Computation of Similarity Measures for Sequential Data using Generalized Suffix Trees

abstract: We propose a generic algorithm for computation of similarity measures for sequential data. The algorithm uses generalized suffix trees for efficient calculation of various kernel, distance and non-metric similarity functions. Its worst-case run-time is linear in the length of sequences and independent of the underlying embedding language, which can cover words, k-grams or all contained subsequences. Experiments with network intrusion detection, DNA analysis and text processing applications demonstrate the utility of distances and similarity coefficients for sequences as alternatives to classical kernel functions.


title: Stock Chatter: Using Stock Sentiment to Predict Price Direction

abstract: This paper examines a popular stock message board and finds slight daily predictability using supervised learning algorithms when combining daily sentiment with historical price information. Additionally, with the profit potential in trading stocks, it is of no surprise that a number of popular financial websites are attempting to capture investor sentiment by providing an aggregate of this negative and positive online emotion. We question if the existence of dishonest posters are capitalizing on the popularity of the boards by writing sentiment in line with their trading goals as a means of influencing others, and therefore undermining the purpose of the boards. We exclude these posters to determine if predictability increases, but find no discernible difference.


title: A novel orthogonal NMF-based belief compression for POMDPs

abstract: High dimensionality of POMDP's belief state space is one major cause that makes the underlying optimal policy computation intractable. Belief compression refers to the methodology that projects the belief state space to a low-dimensional one to alleviate the problem. In this paper, we propose a novel orthogonal non-negative matrix factorization (O-NMF) for the projection. The proposed O-NMF not only factors the belief state space by minimizing the reconstruction error, but also allows the compressed POMDP formulation to be efficiently computed (due to its orthogonality) in a value-directed manner so that the value function will take same values for corresponding belief states in the original and compressed state spaces. We have tested the proposed approach using a number of benchmark problems and the empirical results confirms its effectiveness in achieving substantial computational cost saving in policy computation.


title: From mice to men: What can we learn about personality from animal research?

abstract: The author explores the viability of a comparative approach to personality research. A review of the diverse animal-personality literature suggests that (a) most research uses trait constructs, focuses on variation within (vs. across) species, and uses either behavioral codings or trait ratings; (b) ratings are generally reliable and show some validity (7 parameters that could influence reliability and 4 challenges to validation are discussed); and (c) some dimensions emerge across species, but summaries are hindered by a lack of standard descriptors. Arguments for and against cross-species comparisons are discussed, and research guidelines are suggested. Finally, a research agenda guided by evolutionary and ecological principles is proposed. It is concluded that animal studies provide unique opportunities to examine biological, genetic, and environmental bases of personality and to study personality change, personality-health links, and personality perception.


title: Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images

abstract: In this work, we address the problem of estimating 2d human pose from still images. Articulated body pose estimation is challenging due to the large variation in body poses and appearances of the different body parts. Recent methods that rely on the pictorial structure framework have shown to be very successful in solving this task. They model the body part appearances using discriminatively trained, independent part templates and the spatial relations of the body parts using a tree model. Within such a framework, we address the problem of obtaining better part templates which are able to handle a very high variation in appearance. To this end, we introduce parts dependent body joint regressors which are random forests that operate over two layers. While the first layer acts as an independent body part classifier, the second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This helps to overcome typical ambiguities of tree structures, such as self-similarities of legs and arms. In addition, we introduce a novel data set termed FashionPose that contains over 7,000 images with a challenging variation of body part appearances due to a large variation of dressing styles. In the experiments, we demonstrate that the proposed parts dependent joint regressors outperform independent classifiers or regressors. The method also performs better or similar to the state-of-the-art in terms of accuracy, while running with a couple of frames per second.


title: Nutritional Ecology and Human Health

abstract: In contrast to the spectacular advances in the first half of the twentieth century with micronutrient-related diseases, human nutrition science has failed to stem the more recent rise of obesity and associated cardiometabolic disease (OACD). This failure has triggered debate on the problems and limitations of the field and what change is needed to address these. We briefly review the two broad historical phases of human nutrition science and then provide an overview of the main problems that have been implicated in the poor progress of the field with solving OACD. We next introduce the field of nutritional ecology and show how its ecological-evolutionary foundations can enrich human nutrition science by providing the theory to help address its limitations. We end by introducing a modeling approach from nutritional ecology, termed nutritional geometry, and demonstrate how it can help to implement ecological and evolutionary theory in human nutrition to provide new direction and to better understand and manage OACD.


title: Implications of Hierarchies for RSO Recognition, Identification, and Characterization

abstract: Abstract : In our previous work, we demonstrated that hierarchical (taxonomical) trees can be used to depict hypotheses in a Bayesian object recognition and identification process using Figaro, an open source probabilistic programming language. We assume in this work that we have appropriately defined a satellite taxonomy that allows us to place a given space object (RSO) into a particular class of object without any ambiguity. Such a taxonomy allows one to assess the probability of assignment to a particular class by determining how well the object satisfies the unique criteria of belonging to that class. Furthermore, tree-based taxonomies delineate unique signatures by defining the minimum amount of information required to positively identify a RSO. Because of these properties of taxonomic trees, we can now explore the implications of RSO taxonomic trees for model distance metrics and sensor tasking. In particular, we seek to exploit the fact that taxonomic trees provide a model neighborhood that can be used to initiate a Monte Carlo or Multiple Hypothesis algorithm. We contend this feature of taxonomies will provide a quantifiable metric for model distances and the explicit number of models that should be considered, both of which currently do not exist. Additionally, the discriminating characteristics of taxonomic classes can be used to determine the kind of data and the associated sensor that needs to be tasked to acquire that data. We also discuss the concept of multiple interacting hierarchies that provide deeper insight into how object interact with one another.


A robust and verifiable cryptographically secure election scheme


title: A Rule-Based System to Extract Financial Information

abstract: Extracting up-to-date information from financial documents can be important in making investment decisions. However, the unstructured nature and enormity of the volume of such data makes manual analysis tedious and time consuming. Information extraction technology can be applied to automatically extract the most relevant and precise financial information. This paper introduces a rule-based information extraction methodology for the extraction of highly accurate financial information to aid investment decisions. The methodology includes a rule-based symbolic learning model trained by the Greedy Search algorithm and a similar model trained by the Tabu Search algorithm. The methodology has been found very effective in extracting financial information from NASDAQ-listed companies. Also, the Tabu Search based model performed better than some well-known systems. The simple rule structure makes the system portable and it should make parallel processing implementations easier.


abstract: A novel approach for image enhancement by using contrast limited adaptive histogram equalization method will produces a good contrast images such as medical images. In this paper, we propose a new method for image enhancement by using contrast limited adaptive histogram equalization method. We propose a general framework with a adaptive histogram equalization method. We are going to prove its effectiveness in comparison to other contrast enhancement method.


title: High-Performance Volume Rendering on the Ubiquitous WebGL Platform

abstract: We have invented a high-performance GPU based volume rendering algorithm on the ubiquitous WebGL platform. As WebGL is emerging as a standard platform for web applications on mobile devices, a remarkable advantage of our approach is that the advanced volume rendering algorithms can run directly on mobile devices such as smart phones and tablets in which GPU is embedded, anywhere and anytime. Technically, we deployed a novel single-pass rendering pipeline, in contrast to the other WebGL volume rendering systems using the multi-pass approach. We extended the pseudo-color shading and ray function blending by using a custom transfer function widget in the web browser, which enables interactive feature enhancement in rendering. And lastly, to get rid of the limitation in the loop iteration for dynamic loops on the current mobile platforms, we devised a WebGL compliant 3D texture slicer as an alternative solution to rendering of large datasets. Extensive experimental results and performance assessments are reported in the paper.


title: The Role of Perfectionism and Emotional Regulation in Explaining Decision-Making Styles

abstract: Individuals differ in the manner they approach decision making, namely their decision-making styles. While some people typically make all decisions fast and without hesitation, others invest more effort into deciding even about small things and evaluate their decisions with much more scrutiny. The goal of the present study was to explore the relationship between decision-making styles, perfectionism and emotional processing in more detail. Specifically, 300 college students majoring in social studies and humanities completed instruments designed for assessing maximizing, decision commitment, perfectionism, as well as emotional regulation and control. The obtained results indicate that maximizing is primarily related to one dimension of perfectionism, namely the concern over mistakes and doubts, as well as emotional regulation and control. Furthermore, together with the concern over mistakes and doubts, maximizing was revealed as a significant predictor of individuals' decision commitment. The obtained findings extend previous reports regarding the association between maximizing and perfectionism and provide relevant insights into their relationship with emotional regulation and control. They also suggest a need to further explore these constructs that are, despite their complex interdependence, typically investigated in separate contexts and domains.


title: A Framework for Robot Manipulation: Skill Formalism, Meta Learning and Adaptive Control

abstract: In this paper we introduce a novel framework for expressing and learning force-sensitive robot manipulation skills. It is based on a formalism that extends our previous work on adaptive impedance control with meta parameter learning and compatible skill specifications. This way the system is also able to make use of abstract expert knowledge by incorporating process descriptions and quality evaluation metrics. We evaluate various state-of-the-art schemes for meta parameter learning and experimentally compare selected ones. Our results clearly indicate that the combination of our adaptive impedance controller with a carefully defined skill formalism significantly reduces the complexity of manipulation tasks even for learning peg-in-hole with submillimeter industrial tolerances. Overall, the considered system is able to learn variations of this skill in under 20 minutes. In fact, experimentally the system was able to perform the learned tasks without visual feedback faster than humans, leading to the first learning-based solution of complex assembly at such real-world performance.


title: Real-Time Energy Management in Microgrids

abstract: Energy management in microgrids is typically formulated as an offline optimization problem for day-ahead scheduling by previous studies. Most of these offline approaches assume perfect forecasting of the renewables, the demands, and the market, which is difficult to achieve in practice. Existing online algorithms, on the other hand, oversimplify the microgrid model by only considering the aggregate supply-demand balance while omitting the underlying power distribution network and the associated power flow and system operational constraints. Consequently, such approaches may result in control decisions that violate the real-world constraints. This paper focuses on developing an online energy management strategy (EMS) for real-time operation of microgrids that takes into account the power flow and system operational constraints on a distribution network. We model the online energy management as a stochastic optimal power flow problem and propose an online EMS based on Lyapunov optimization. The proposed online EMS is subsequently applied to a real-microgrid system. The simulation results demonstrate that the performance of the proposed EMS exceeds a greedy algorithm and is close to an optimal offline algorithm. Lastly, the effect of the underlying network structure on energy management is observed and analyzed.


title: The Airport Ground Movement Problem: Past and Current Research and Future Directions

abstract: Determining efficient airport operations is an impor- tant and critical problem for airports, airlines, passengers and other stakeholders. Moreover, it is likely to become even more so given the traffic increases which are expected over the next few years. The ground movement problem forms the link between other airside problems, such as arrival sequencing, departure sequencing and gate/stand allocation. This paper provides an overview, categorisation and critical examination of the previous research for ground movement and highlights various important open areas of research. Of particular importance is the ques- tion of the integration of various airport operations and their relationships which are considered in this paper. Index Terms —Airside airport operations, ground movement, taxiing, survey, future work, integration of airport operations.


title: Online calibration and synchronization of cellphone camera and gyroscope

abstract: Gyroscope is playing a key role in helping estimate camera rotation during mobile video capture. The fusion of gyroscope and visual measurements needs the knowledge of camera projection parameters, the gyroscope bias and the relative orientation between gyroscope and camera. Moreover, the timestamps of gyroscope and video frames are usually not well synchronized. In this paper, we propose an online method that estimates all the necessary parameters while capturing videos. Our contributions are (1) simultaneous online camera self-calibration and camera-gyroscope calibration based on an implicit extended Kalman filter, and (2) generalization of coplanarity constraint of camera rotation in a rolling shutter camera model for cellphones. The proposed method is able to accurately estimate the needed parameters online with all kinds of camera motion, and can be embedded in gyro-aided applications such as video stabilization and feature tracking.


title: Studies on the effects of probiotic Lactobacillus mixture given orally on vaginal and rectal colonization and on parameters of vaginal health in women with intermediate vaginal flora.

abstract: OBJECTIVES ::: The vaginal microflora is composed of many bacterial species and plays a major role in maintaining the balance of this complex environment. This study was conducted in order to assess the degree and persistence of the colonization of vaginal epithelium by strains from an orally administered mixture of lactobacilli, containing Lactobacillus fermentum 57A, Lactobacillus plantarum 57B and Lactobacillus gasseri 57C. We also monitored its effects on parameters of vaginal health, especially total lactobacilli counts, vaginal pH and Nugent score. ::: ::: ::: STUDY DESIGN ::: The patient group in this open study consisted of clinically healthy women with intermediate vaginal flora. Altogether 37 women were included in the study; 25 finished the full cycle consisting of 8 visits during 70 days. Lactobacillus mixture was administered as 1×10(8) c.f.u. once a day for 60 days. Lactobacillus isolates collected from vaginal and rectal samples from studied women during all visits were typed using molecular methods (PFGE for L. fermentum and L. gasseri and MLST for L. plantarum). Total lactobacilli counts, vaginal pH and Nugent score were also determined during the visits. ::: ::: ::: RESULTS ::: We confirmed that the ingested strains were able to reach and colonize both sites within the third and eighth visits, i.e. between the 20th and 70th days of the study. Maximal colonization was recorded between the fifth and seventh visits (31st-60th days). Moreover, ingestion of the Lactobacillus mixture was related to normalization of vaginal parameters (within 28-60 days after the initiation of the treatment). This was demonstrated by a decrease of vaginal pH and Nugent score together with an increase of total numbers of lactobacilli in the vagina and rectum. No adverse events were noted during the course of the study. ::: ::: ::: CONCLUSIONS ::: Oral application of the combination of the three probiotic strains derived from vaginal microbiota of healthy woman with high adherence abilities to both vaginal and colonic epithelium in vitro shows that both individual strains and their mixture can colonize vagina for some weeks, the effect of which is correlated with significant improvement of such parameters like pH and Nugent score values and total numbers of vaginal lactobacilli. This indicates that the mixture may be a good candidate for the planned double-blind, placebo-controlled randomized studies involving larger numbers of women.


title: Performance Analysis of Rayleigh and Rician Fading Channel Models using Matlab Simulation

abstract: An effort has been made to illustrate the performance comparison of the Rayleigh and Rician fading channel models by using MATLAB simulation in terms of source velocity and outage probability. We have developed algorithms for the Rayleigh and Rician fading channels, which computes the envelop and outage probability. The parameters such as source velocity and outage probability play very important role in the performance analysis and design of the digital communication systems over the multipath fading environment.


title: User Preference Learning with Multiple Information Fusion for Restaurant Recommendation.

abstract: If properly analyzed, the multi-aspect rating data could be a source of rich intelligence for providing personalized restaurant recommendations. Indeed, while recommender systems have been studied for various applications and many recommendation techniques have been developed for general or specific recommendation tasks, there are few studies for restaurant recommendation by addressing the unique challenges of the multiaspect restaurant reviews. As we know, traditional collaborative filtering methods are typically developed for single aspect ratings. However, multi-aspect ratings are often collected from the restaurant customers. These ratings can reflect multiple aspects of the service quality of the restaurant. Also, geographic factors play an important role in restaurant recommendation. To this end, in this paper, we develop a generative probabilistic model to exploit the multi-aspect ratings of restaurants for restaurant recommendation. Also, the geographic proximity is integrated into the probabilistic model to capture the geographic influence. Moreover, the profile information, which contains customer/restaurantindependent features and the shared features, is also integrated into the model. Finally, we conduct a comprehensive experimental study on a real-world data set. The experimental results clearly demonstrate the benefit of exploiting multi-aspect ratings and the improvement of the developed generative probabilistic model.


title: Analyzing cloning evolution in the Linux kernel

abstract: Abstract Identifying code duplication in large multi-platform software systems is a challenging problem. This is due to a variety of reasons including the presence of high-level programming languages and structures interleaved with hardware-dependent low-level resources and assembler code, the use of GUI-based configuration scripts generating commands to compile the system, and the extremely high number of possible different configurations. This paper studies the extent and the evolution of code duplications in the Linux kernel. Linux is a large, multi-platform software system; it is based on the Open Source concept, and so there are no obstacles in discussing its implementation. In addition, it is decidedly too large to be examined manually: the current Linux kernel release (2.4.18) is about three million LOCs. Nineteen releases, from 2.4.0 to 2.4.18, were processed and analyzed, identifying code duplication among Linux subsystems by means of a metric-based approach. The obtained results support the hypothesis that the Linux system does not contain a relevant fraction of code duplication. Furthermore, code duplication tends to remain stable across releases, thus suggesting a fairly stable structure, evolving smoothly without any evidence of degradation.


title: Sample-based informationl-theoretic stochastic optimal control

abstract: Many Stochastic Optimal Control (SOC) approaches ::: rely on samples to either obtain an estimate of the ::: value function or a linearisation of the underlying system model. ::: However, these approaches typically neglect the fact that the ::: accuracy of the policy update depends on the closeness of the ::: resulting trajectory distribution to these samples. The greedy ::: operator does not consider such closeness constraint to the ::: samples. Hence, the greedy operator can lead to oscillations ::: or even instabilities in the policy updates. Such undesired ::: behaviour is likely to result in an inferior performance of the ::: estimated policy. We reuse inspiration from the reinforcement ::: learning community and relax the greedy operator used in SOC ::: with an information theoretic bound that limits the ‘distance’ of ::: two subsequent trajectory distributions in a policy update. The ::: introduced bound ensures a smooth and stable policy update. ::: Our method is also well suited for model-based reinforcement ::: learning, where we estimate the system dynamics model from ::: data. As this model is likely to be inaccurate, it might be ::: dangerous to exploit the model greedily. Instead, our bound ::: ensures that we generate new data in the vicinity of the current ::: data, such that we can improve our estimate of the system ::: dynamics model. We show that our approach outperforms ::: several state of the art approaches on challenging simulated ::: robot control tasks.


title: DC and RF breakdown voltage characteristics of SiGe HBTs for WiFi PA applications

abstract: Breakdown voltage and RF characteristics relevant for RF power amplifiers (PA) are presented in this paper. Typically, DC collector-to-emitter breakdown voltage with base open (BV CEO ) or DC collector-to-base breakdown with emitter open (BV CBO ) has been presented as the metric for voltage limit of PA devices. In practical PA circuits, the RF envelope voltage can swing well beyond BV CEO without causing a failure. An analysis of output power swing limitations and DC breakdown is presented with attention to biasing and temperature.


title: Dynamic models of large-scale brain activity

abstract: Cognitive activity requires the collective behavior of cortical, thalamic and spinal neurons across large-scale systems of the CNS. This paper provides an illustrated introduction to dynamic models of large-scale brain activity, from the tenets of the underlying theory to challenges, controversies and recent breakthroughs.


title: Anticipation of increasing monetary reward selectively recruits nucleus accumbens

abstract: Comparative studies have implicated the nucleus accumbens (NAcc) in the anticipation of incentives, but the relative responsiveness of this neural substrate during anticipation of rewards versus punishments remains unclear. Using event-related functional magnetic resonance imaging, we investigated whether the anticipation of increasing monetary rewards and punishments would increase NAcc blood oxygen level-dependent contrast (hereafter, “activation”) in eight healthy volunteers. Whereas anticipation of increasing rewards elicited both increasing self-reported happiness and NAcc activation, anticipation of increasing punishment elicited neither. However, anticipation of both rewards and punishments activated a different striatal region (the medial caudate). At the highest reward level ($5.00), NAcc activation was correlated with individual differences in self-reported happiness elicited by the reward cues. These findings suggest that whereas other striatal areas may code for expected incentive magnitude, a region in the NAcc codes for expected positive incentive value.


title: Clustered TDB: A Clustered Triple Store for Jena

abstract: This paper describes the design of Clustered TDB, a clustered triple store designed to store and query very large quantities of Resource Description Framework (RDF) data. It presents an evaluation of an initial prototype, showing that Clustered TDB offers excellent scaling characteristics with respect to load times and query throughput. Design decisions are justified in the context of a literature review on Database Management System (DBMS) and RDF store clustering, and it is shown that many techniques created during the course of DBMS research are applicable to the problem of storing RDF data.


title: Monolithic DC-DC boost converter with current-mode hysteretic control

abstract: A monolithic DC-DC boost converter with current-mode hysteretic control is designed and simulated in 0.18-μm CMOS technology. The system is simple, robust, and has a fast response to external changes. It does not require an external clock, and the output is regulated by voltage feedback in addition to limiting the inductor current by sensing it. A non-overlapping clock is internally generated to drive the power switches using buffers designed to minimize power dissipation. For conversion specifications of 1.8 V to 3.3 V at 150 mA, overall efficiency of 94.5% is achieved. Line regulation is 17.5 mV/V, load regulation is 0.33% for a 100 mA current step, while the output voltage ripple is below 30 mV for nominal conditions.


title: Attentive Pooling Networks

abstract: In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training. In the context of pair-wise ranking or classification with neural networks, AP enables the pooling layer to be aware of the current input pair, in a way that information from the two input items can directly influence the computation of each other's representations. Along with such representations of the paired inputs, AP jointly learns a similarity measure over projected segments (e.g. trigrams) of the pair, and subsequently, derives the corresponding attention vector for each input to guide the pooling. Our two-way attention mechanism is a general framework independent of the underlying representation learning, and it has been applied to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in our studies. The empirical results, from three very different benchmark tasks of question answering/answer selection, demonstrate that our proposed models outperform a variety of strong baselines and achieve state-of-the-art performance in all the benchmarks.


title: The design synthesis of multiband artificial magnetic conductors using high impedance frequency selective surfaces

abstract: This paper introduces several different design methodologies for multiband artificial magnetic conducting (AMC) surfaces. The paper begins by investigating the multiband properties exhibited by a conventional electromagnetic bandgap (EBG) AMC that consists of a frequency selective surface (FSS) on top of a thin dielectric substrate with a PEC back plane. The higher-order resonances associated with these surfaces have not been discussed in detail to date, as previous research has been concerned only with exploiting the primary resonant frequency. However, it will be shown that by understanding and making appropriate use of these higher order resonances, it is possible to design multiband AMC surfaces that work for nearly any desired combination of operating frequencies. The first multiband AMC design approach that will be considered is based on the introduction of FSS screens that have fractal or nearly fractal unit cell geometries. This is followed by a more general and robust genetic algorithm (GA) technique for the synthesis of optimal multiband AMC surfaces. In this case, a GA is used to evolve multiband AMC surface designs by simultaneously optimizing the geometry and size of the FSS unit cell as well as the thickness and dielectric constant of the substrate material. Finally, several examples of multiband AMC surfaces are presented, including some practical dual-band and tri-band designs genetically evolved for operation at GPS and cellular frequencies, as well as an example illustrating the success in creating a multiband AMC surface with angular stability.


title: Food microbiology: Bacteriocins: developing innate immunity for food

abstract: Bacteriocins are bacterially produced antimicrobial peptides with narrow or broad host ranges. Many bacteriocins are produced by food-grade lactic acid bacteria, a phenomenon which offers food scientists the possibility of directing or preventing the development of specific bacterial species in food. This can be particularly useful in preservation or food safety applications, but also has implications for the development of desirable flora in fermented food. In this sense, bacteriocins can be used to confer a rudimentary form of innate immunity to foodstuffs, helping processors extend their control over the food flora long after manufacture.


title: Artificial Intelligence Based Malware Analysis

abstract: Artificial intelligence methods have often been applied to perform specific functions or tasks in the cyber-defense realm. However, as adversary methods become more complex and difficult to divine, piecemeal efforts to understand cyber-attacks, and malware-based attacks in particular, are not providing sufficient means for malware analysts to understand the past, present and future characteristics of malware. ::: In this paper, we present the Malware Analysis and Attributed using Genetic Information (MAAGI) system. The underlying idea behind the MAAGI system is that there are strong similarities between malware behavior and biological organism behavior, and applying biologically inspired methods to corpora of malware can help analysts better understand the ecosystem of malware attacks. Due to the sophistication of the malware and the analysis, the MAAGI system relies heavily on artificial intelligence techniques to provide this capability. It has already yielded promising results over its development life, and will hopefully inspire more integration between the artificial intelligence and cyber--defense communities.


P 2 P Overlay , Internet Underlay and Their Mutual Impact


title: A psychologically-based taxonomy of misdirection

abstract: Magicians use misdirection to prevent you from realizing the methods used to create a magical effect, thereby allowing you to experience an apparently impossible event. Magicians have acquired much knowledge about misdirection, and have suggested several taxonomies of misdirection. These describe many of the fundamental principles in misdirection, focusing on how misdirection is achieved by magicians. In this article we review the strengths and weaknesses of past taxonomies, and argue that a more natural way of making sense of misdirection is to focus on the perceptual and cognitive mechanisms involved. Our psychologically-based taxonomy has three basic categories, corresponding to the types of psychological mechanisms affected: perception, memory, and reasoning. Each of these categories is then divided into subcategories based on the mechanisms that control these effects. This new taxonomy can help organize the magicians’ knowledge of misdirection in a meaningful way, and facilitate the dialogue between magicians and scientists.


title: Distributed Control of PEV Charging Based on Energy Demand Forecast

abstract: This paper presents a new distributed smart charging strategy for grid integration of plug-in electric vehicles (PEVs). The main goal is to smooth the daily grid load profile while ensuring that each PEV has a desired state of charge level at the time of departure. Communication and computational overhead, and PEV user privacy are also considered during the development of the proposed strategy. It consists of two stages: 1) an offline process to estimate a reference operating power level based on the forecasted mobility energy demand and base loading profile, and 2) a real-time process to determine the charging power for each PEV so that the aggregated load tracks the reference loading level. Tests are carried out both on primary and secondary distribution networks for different heuristic charging scenarios and PEV penetration levels. Results are compared to that of the optimal solution and other state-of-the-art techniques in terms of variance and peak values, and shown to be competitive. Finally, a real vehicle test implementation is done using a commercial-of-the-shelf charging station and an electric vehicle.


title: Can Leaders Step Outside of the Gender Box? An Examination of Leadership and Gender Role Stereotypes

abstract: This study examined gender stereotypes for leaders using a more indirect method than is typical in stereotype research. Rather than reveal the leader's gender, this study used vignettes in which the leader's gender was unknown. Consistent with their hypothesis, the authors found that participants were more likely to infer a male (female) gender identity than a female (male) gender identity when presented with a leader using a masculine (feminine) style. They also hypothesized that a leader using a gender-consistent leadership style would be viewed more positively than a leader using a gender-inconsistent style. Contrary to this hypothesis, results revealed that men using a gender-inconsistent (feminine) style were actually evaluated more positively than men using a gender-consistent style. It is interesting that a perceived female leader who used a gender-inconsistent (masculine) style was evaluated more positively than a perceived male leader who used a masculine style, but only by female participants. P...


COMPARATIVE STUDY OF HAND GESTURE RECOGNITION SYSTEM


Novel Hybrid Approaches For Real Coded Genetic Algorithm To Compute The Optimal Control Of A Single Stage Hybrid Manufacturing Systems


title: HURI – A Novel Algorithm for Mining High Utility Rare Itemsets

abstract: In Data mining field, the primary task is to mine frequent itemsets from a transaction database using Association Rule Mining (ARM). Utility Mining aims to identify itemsets with high utilities by considering profit, quantity, cost or other user preferences. In market basket analysis, high consideration should be given to utility of item in a transaction, since items having low selling frequencies may have high profits. As a result, High Utility Itemset Mining emerged as a revolutionary field in Data Mining. Rare itemsets provide useful information in different decision-making domains. High Utility Rare Itemset Mining, HURI algorithm proposed in [12], generate high utility rare itemsets of users’ interest. HURI is a two-phase algorithm, phase 1 generates rare itemsets and phase 2 generates high utility rare itemsets, according to users’ interest. In this paper, performance evaluation and complexity analysis of HURI algorithm, based on different parameters have been discussed which indicates the efficiency of HURI.


title: Overview of total least-squares methods

abstract: We review the development and extensions of the classical total least-squares method and describe algorithms for its generalization to weighted and structured approximation problems. In the generic case, the classical total least-squares problem has a unique solution, which is given in analytic form in terms of the singular value decomposition of the data matrix. The weighted and structured total least-squares problems have no such analytic solution and are currently solved numerically by local optimization methods. We explain how special structure of the weight matrix and the data matrix can be exploited for efficient cost function and first derivative computation. This allows to obtain computationally efficient solution methods. The total least-squares family of methods has a wide range of applications in system theory, signal processing, and computer algebra. We describe the applications for deconvolution, linear prediction, and errors-in-variables system identification.


title: Multiclass From Binary: Expanding One-Versus-All, One-Versus-One and ECOC-Based Approaches

abstract: Recently, there has been a lot of success in the development of effective binary classifiers. Although many statistical classification techniques have natural multiclass extensions, some, such as the support vector machines, do not. The existing techniques for mapping multiclass problems onto a set of simpler binary classification problems run into serious efficiency problems when there are hundreds or even thousands of classes, and these are the scenarios where this paper's contributions shine. We introduce the concept of correlation and joint probability of base binary learners. We learn these properties during the training stage, group the binary leaner's based on their independence and, with a Bayesian approach, combine the results to predict the class of a new instance. Finally, we also discuss two additional strategies: one to reduce the number of required base learners in the multiclass classification, and another to find new base learners that might best complement the existing set. We use these two new procedures iteratively to complement the initial solution and improve the overall performance. This paper has two goals: finding the most discriminative binary classifiers to solve a multiclass problem and keeping up the efficiency, i.e., small number of base learners. We validate and compare the method with a diverse set of methods of the literature in several public available datasets that range from small (10 to 26 classes) to large multiclass problems (1000 classes) always using simple reproducible scenarios.


title: Stoppage pattern analysis of public bus GPS traces in developing regions

abstract: Identification of Public Bus Stops and characterising their waiting time is of significance in the domain of Intelligent Transportation System and public interest. Surveys suggest that the existing systems and algorithms cannot be merely replicated in the context of developing regions, because of the kind of heterogeneity and chaotic situation prevalent in their transport system, in contrast to the orderly and much regulated system of the developed world. Moreover a significant fraction of population residing here is not so tech-savvy, hence crowd-sourced based strategies renders useless. Either novel algorithms/techniques are required or the existing ones have to undergo suitable adaptation and innovation to suit the contrasting characteristics of developing regions. Using customized hardware we present the stoppage pattern analysis of public bus GPS traces of more than 2000 km for a 20 km route of Durgapur, a suburban city (West Bengal, India). We have revealed that 53.03 % of the travel time for the selected route has higher predictability with Standard deviation of 30.73%.


title: Automatic Transfer Function Generation Using Contour Tree Controlled Residue Flow Model and Color Harmonics

abstract: Transfer functions facilitate the volumetric data visualization by assigning optical properties to various data features and scalar values. Automation of transfer function specifications still remains a challenge in volume rendering. This paper presents an approach for automating transfer function generations by utilizing topological attributes derived from the contour tree of a volume. The contour tree acts as a visual index to volume segments, and captures associated topological attributes involved in volumetric data. A residue flow model based on Darcy's law is employed to control distributions of opacity between branches of the contour tree. Topological attributes are also used to control color selection in a perceptual color space and create harmonic color transfer functions. The generated transfer functions can depict inclusion relationship between structures and maximize opacity and color differences between them. The proposed approach allows efficient automation of transfer function generations, and exploration on the data to be carried out based on controlling of opacity residue flow rate instead of complex low-level transfer function parameter adjustments. Experiments on various data sets demonstrate the practical use of our approach in transfer function generations.


title: Towards latent context-aware recommendation systems

abstract: The emergence and penetration of smart mobile devices has given rise to the development of context-aware systems that utilize sensors to collect available data about users in order to improve various user services. Recently, the use of context-aware recommender systems (CARS) aimed at recommending items to users has expanded, particularly those that consider user context. Adding context to recommendation systems is challenging, because the addition of various environmental contexts to the recommendation process results in the expansion of its dimensionality, and thus increases sparsity. Therefore, existing CARS tend to incorporate a small set of pre-defined explicit contexts which do not necessary represent user context or reflect the optimal set of features for the recommendation process. We suggest a novel approach centered on representing environmental features as low dimensional unsupervised latent contexts. We extract data from a rich set of mobile sensors in order to infer unexplored user contexts in an unsupervised manner. The latent contexts are hidden context patterns modeled as numeric vectors which are efficiently extracted from raw sensor data. The latent contexts are automatically learned for each user utilizing unsupervised deep learning techniques and PCA on the data collected from the user's mobile phone. Integrating the data extracted from high dimensional sensors into a new latent context-aware recommendation algorithm results in up to a 20% increase in recommendation accuracy.


title: Knowledge Management and Organizational Performance: An Exploratory Survey

abstract: Ninety organizations were surveyed in an exploratory investigation of the organizational impact of knowledge management (KM). A search of the literature revealed 12 KM practices. Results indicated that these KM practices were directly related to organizational performance which, in turn, was directly related to financial performance. In addition, a different set of KM practices were associated with specific value disciplines (i.e., customer intimacy, product development and operational excellence). Interestingly, a significant gap exists between the KM practices that firms believe to be important and those that turned out to be directly related to organizational performance. The implications of this study are significant for both practitioners and academics. Suggestions are offered for future work in this area.


title: Color constancy using CNNs

abstract: In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods. The network consists of one convolutional layer with max pooling, one fully connected layer and three output nodes. Within the network structure, feature learning and regression are integrated into one optimization process, which leads to a more effective model for estimating scene illumination. This approach achieves state-of-the-art performance on a standard dataset of RAW images. Preliminary experiments on images with spatially varying illumination demonstrate the stability of the local illuminant estimation ability of our CNN.


title: Adversarial Neural Machine Translation

abstract: In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimize the distinction between human translation and the translation given by an NMT model. To achieve this goal, inspired by the recent success of generative adversarial networks (GANs), we employ an adversarial training architecture and name it as Adversarial-NMT. In Adversarial-NMT, the training of the NMT model is assisted by an adversary, which is an elaborately designed Convolutional Neural Network (CNN). The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human. The goal of the NMT model is to produce high quality translations so as to cheat the adversary. A policy gradient method is leveraged to co-train the NMT model and the adversary. Experimental results on English$\rightarrow$French and German$\rightarrow$English translation tasks show that Adversarial-NMT can achieve significantly better translation quality than several strong baselines.


title: CFD optimization of an immersed particle heat exchanger

abstract: An innovative immersed particle heat exchanger has been recently proposed by the authors: it makes use of very small solid particles as intermediate medium to perform heat transfer between two gas flows at different temperature. The potential of such heat exchanger has been already demonstrated by the authors, who have developed a 1D model for the computation of the pipe length that ensures a prescribed heat exchange and for the evaluation of the influence of particle characteristics. As completion of the heat exchanger design procedure, this paper provides a numerical procedure for the design optimization of the other geometric parameters defining the heat exchanger, such as diameters and angles of inlet and outlet pipes and particle injection mode. The objective of this numerical optimization is to maximize the heat exchanger efficiency by maximizing the dispersion of the particles falling in countercurrent within the flow. Optimized configurations have been obtained using two different optimization algorithms: these results demonstrate that a numerical optimization based on 3D thermo-fluid-dynamic simulations is needed to complete the design of the heat exchanger: such approach, which incorporates 3D flow paths and particle trajectories, allows to limit the undesired horizontal shifts of the falling particles near elbows and thus to retrieve a thermal efficiency closer to the theoretical value calculated by the 1D model. The experimental validation of the employed 3D model has been performed on the initially-guessed configuration, where the 3D features cited above are more evident.


title: On Generalized Super-Coherent States

abstract: A set of operators, the so-called k-fermion operators, that interpolate between boson and fermion operators are introduced through the consideration of an algebra arising from two non-commuting quon algebras. The deformation parameters q and 1/q for these quon algebras are roots of unity with q = exp(2πi/k) and k ∈ N\{0, 1}. The case k = 2 corresponds to fermions and the limiting case k → ∞ to bosons. Generalized coherent states (connected to k-fermionic states) and super-coherent states (involving a k-fermionic sector and a purely bosonic sector) are investigated. Paper to appear in the Proceedings of the VIII International Conference on Symmetry Methods in Physics (JINR, Dubna, Russia, 28 July – 2 August 1997). The Proceedings of the Conference will be published in the Russian Journal of Nuclear Physics (Yadernaya Fizika).


title: Big data and its epistemology

abstract: The article considers whether Big Data, in the form of data-driven science, will enable the discovery, or appraisal, of universal scientific theories, instrumentalist tools, or inductive inferences. It points out, initially, that such aspirations are similar to the now-discredited inductivist approach to science. On the positive side, Big Data may permit larger sample sizes, cheaper and more extensive testing of theories, and the continuous assessment of theories. On the negative side, data-driven science encourages passive data collection, as opposed to experimentation and testing, and hornswoggling "unsound statistical fiddling". The roles of theory and data in inductive algorithms, statistical modeling, and scientific discoveries are analyzed, and it is argued that theory is needed at every turn. Data-driven science is a chimera.


title: Quantitative estimation of electro-osmosis force on charged particles inside a borosilicate resistive-pulse sensor

abstract: Nano and micron-scale pore sensors have been widely used for biomolecular sensing application due to its sensitive, label-free and potentially cost-effective criteria. Electrophoretic and electroosmosis are major forces which play significant roles on the sensor's performance. In this work, we have developed a mathematical model based on experimental and simulation results of negatively charged particles passing through a 2μm diameter solid-state borosilicate pore under a constant applied electric field. The mathematical model has estimated the ratio of electroosmosis force to electrophoretic force on particles to be 77.5%.


title: Medical 3D Printing for the Radiologist

abstract: The number and complexity of 3D-printed models from DICOM images acquired and interpreted by radiologists is growing exponentially, and radiologists will benefit from a fundamental understanding of the principles and applications of 3D printing.


title: A cluster-based resampling method for pseudo-relevance feedback

abstract: Typical pseudo-relevance feedback methods assume the top-retrieved documents are relevant and use these pseudo-relevant documents to expand terms. The initial retrieval set can, however, contain a great deal of noise. In this paper, we present a cluster-based resampling method to select better pseudo-relevant documents based on the relevance model. The main idea is to use document clusters to find dominant documents for the initial retrieval set, and to repeatedly feed the documents to emphasize the core topics of a query. Experimental results on large-scale web TREC collections show significant improvements over the relevance model. For justification of the resampling approach, we examine relevance density of feedback documents. A higher relevance density will result in greater retrieval accuracy, ultimately approaching true relevance feedback. The resampling approach shows higher relevance density than the baseline relevance model on all collections, resulting in better retrieval accuracy in pseudo-relevance feedback. This result indicates that the proposed method is effective for pseudo-relevance feedback.


title: Determination of 3D object pose in point cloud with CAD model

abstract: This paper introduces improvements to estimate 3D object pose from point clouds. We use point-pair feature for matching instead of traditional approaches using local feature descriptors. In order to obtain high accuracy estimation, a discriminative descriptor is introduced for point-pair features. The object model is a set of point pair descriptors computed from CAD model. The voting process is performed on a local area of each key-point to boost the performance. Due to the simplicity of descriptor, a matching threshold is defined to enable the robustness of the algorithm. A clustering algorithm is defined for grouping similar poses together. Best pose candidates will be selected for refining and final verification will be performed. The robustness and accuracy of our approach are demonstrated through experiments. Our approach can be compared to state-of-the-art algorithms in terms of recognition rates. These high accurate poses especially useful for robot in manipulating objects in the factory. Since our approach does not use color feature, it is independent to light conditions. The system give accurate pose estimation even when there is no light in the area.


title: A Neural Approach to Source Dependence Based Context Model for Statistical Machine Translation

abstract: In statistical machine translation, translation prediction considers not only the aligned source word itself but also its source contextual information. Learning context representation is a promising method for improving translation results, particularly through neural networks. Most of the existing methods process context words sequentially and neglect source long-distance dependencies. In this paper, we propose a novel neural approach to source dependence-based context representation for translation prediction. The proposed model is capable of not only encoding source long-distance dependencies but also capturing functional similarities to better predict translations (i.e., word form translations and ambiguous word translations). To verify our method, the proposed mode is incorporated into phrase-based and hierarchical phrase-based translation models, respectively. Experiments on large-scale Chinese-to-English and English-to-German translation tasks show that the proposed approach achieves significant improvement over the baseline systems and outperforms several existing context-enhanced methods.


title: Representations of recent and remote autobiographical memories in hippocampal subfields

abstract: The hippocampus has long been implicated in supporting autobiographical memories, but little is known about how they are instantiated in hippocampal subfields. Using high-resolution functional magnetic resonance imaging (fMRI) combined with multivoxel pattern analysis we found that it was possible to detect representations of specific autobiographical memories in individual hippocampal subfields. Moreover, while subfields in the anterior hippocampus contained information about both recent (2 weeks old) and remote (10 years old) autobiographical memories, posterior CA3 and DG only contained information about the remote memories. Thus, the hippocampal subfields are differentially involved in the representation of recent and remote autobiographical memories during vivid recall. © 2013 The Authors. Hippocampus Published by Wiley Periodicals, Inc.


title: 3D Vision Guided Robotic Charging Station for Electric and Plug-in Hybrid Vehicles

abstract: Electric vehicles (EVs) and plug-in hybrid vehicles (PHEVs) are rapidly gaining popularity on our roads. Besides a comparatively high purchasing price, the main two problems limiting their use are the short driving range and inconvenient charging process. In this paper we address the following by presenting an automatic robot-based charging station with 3D vision guidance for plugging and unplugging the charger. First of all, the whole system concept consisting of a 3D vision system, an UR10 robot and a charging station is presented. Then we show the shape-based matching methods used to successfully identify and get the exact pose of the charging port. The same approach is used to calibrate the camera-robot system by using just known structure of the connector plug and no additional markers. Finally, a three-step robot motion planning procedure for plug-in is presented and functionality is demonstrated in a series of successful experiments.


title: A PWM buck-boost AC chopper solving the commutation problem

abstract: A novel pulsewidth modulation buck-boost AC chopper using regenerative DC snubbers is proposed and analyzed. Compared to the previous buck-boost AC choppers, AC snubbers causing power loss are eliminated using regenerative DC snubbers. Experimental results show that the proposed scheme gives good steady-state performance of the AC chopper, which coincides with the theoretical results.


title: Prospects for inferring very large phylogenies by using the neighbor-joining method

abstract: Current efforts to reconstruct the tree of life and histories of multigene families demand the inference of phylogenies consisting of thousands of gene sequences. However, for such large data sets even a moderate exploration of the tree space needed to identify the optimal tree is virtually impossible. For these cases the neighbor-joining (NJ) method is frequently used because of its demonstrated accuracy for smaller data sets and its computational speed. As data sets grow, however, the fraction of the tree space examined by the NJ algorithm becomes minuscule. Here, we report the results of our computer simulation for examining the accuracy of NJ trees for inferring very large phylogenies. First we present a likelihood method for the simultaneous estimation of all pairwise distances by using biologically realistic models of nucleotide substitution. Use of this method corrects up to 60% of NJ tree errors. Our simulation results show that the accuracy of NJ trees decline only by ≈5% when the number of sequences used increases from 32 to 4,096 (128 times) even in the presence of extensive variation in the evolutionary rate among lineages or significant biases in the nucleotide composition and transition/transversion ratio. Our results encourage the use of complex models of nucleotide substitution for estimating evolutionary distances and hint at bright prospects for the application of the NJ and related methods in inferring large phylogenies.


title: Sensor Based PUF IoT Authentication Model for a Smart Home with Private Blockchain

abstract: With ubiquitous adoption of connected sensors, actuators and smart devices are finding inroads into daily life. Internet of Things (IoT) authentication is rapidly transforming from classical cryptographic user-centric knowledge based approaches to device signature based automated methodologies to corroborate identity between claimant and a verifier. Physical Unclonable Function (PUF) based IoT authentication mechanisms are gaining widespread interest as users are required to access IoT devices in real time while also expecting execution of sensitive (even physical) IoT actions immediately. This paper, delineates combination of BlockChain and Sensor based PUF authentication mechanism for solving real-time but non-repudiable access to IoT devices in a Smart Home by utilizing a mining less consensus mechanism for the provision of immutable assurance to users' and IoT devices' transactions i.e. commands, status alerts, actions etc.


title: Fostering Scale-up and ecosystem for entrepreneurship growth in Sub-Saharan Africa

abstract: Over the last few years entrepreneurship agenda has been promoted by governments and international development agencies as one solution to innovation in economic development in Africa and across the world. The concept of an entrepreneurial ecosystem refers to the collective and systemic nature of entrepreneurship that include a conducive culture, facilitating policies and leadership, availability of dedicated finance, relevant human capital and a wide set of institutional and infrastructural supports. These factors can determine the rate of business start-ups, failures and survival in a given environment. The Global Entrepreneurship Monitor (GEM) proposed that entrepreneurship dynamics can be linked to conditions that enhance (or hinder) new business creation. In the GEM´s methodology these conditions are known as Entrepreneurial Framework Conditions (EFCs). From a geographic perspective, African economies have the lowest scores in almost all EFCs considered. The drivers of economic growth in what is called the New Economy have become an important policy question. This paper employs GEM and World Bank – Doing Business and Enterprise Survey data to explore these issues in a more systematic fashion and link the conceptual issues outlined above to five Sub-Saharan African economies – Nigeria, Angola, Burkina Faso, Uganda and South Africa.


title: Procedural Generation of 3D Cave Models with Stalactites and

abstract: Summary The increasing popularity of computer graphics applications in video games and movie production has resulted in a growing demand for the development of virtual environments with rich visual scene content. As such, the use of procedural content generation techniques is an attractive solution that can avoid the manual effort involved in the creation of highly complex scenes, by automating the generation of scene content. However, while there is much research on procedural content generation techniques, the procedural generation of 3D cave models is relatively unexplored. The focus of our research is on procedural cave generation, and this paper presents a method of producing a smooth triangle mesh of procedurally generated interior cave walls with visually plausible stalactites and stalagmites.


title: Challenges in Discriminating Profanity from Hate Speech

abstract: In this study we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes n-grams, skip-grams and clustering-based word representations. We apply approaches based on single classifiers as well as more advanced ensemble classifiers and stacked generalization, achieving the best result of 80% accuracy for this 3-class classification task. Analysis of the results reveals that discriminating hate speech and profanity is not a simple task, which may require features that capture a deeper understanding of the text not always possible with surface n-grams. The variability of gold labels in the annotated data, due to differences in the subjective adjudications of the annotators, is also an issue. Other directions for future work are discussed.


title: Text-based emotion classification using emotion cause extraction

abstract: In recent years, increasing impact of social networks on people's opinions and decision making has attracted lots of attention. Microblogging, one of the most popular social network applications that allows people to share ideas and discuss over various topics, is taken as a rich resource of opinion and emotion data. In this paper, we propose and implement a novel method for identifying emotions in microblog posts. Unlike traditional approaches which are mostly based on statistical methods, we try to infer and extract the reasons of emotions by importing knowledge and theories from other fields such as Sociology. Based on the theory that a triggering cause event is an integral part of emotion, the technique of emotion cause extraction is used as a crucial step to improve the quality of selected features. First, after thorough analysis on sample data we constructed an automatic rule-based system to detect and extract the cause event of each emotional post. We build an emotion corpus with Chinese microblog posts labeled by human annotators. Then a classifier is trained to classify emotions in microblog posts based on extracted cause events. The overall performance of our system is very promising. The experiment results show that our approach is effective in selecting informative features. Our system outperformed the baseline noticeably in most cases, suggesting its great potential. This exploration should provide a new way to look at the emotion classification task and lay the ground for future research on textual emotion processing.


title: #HashtagWars: Learning a Sense of Humor

abstract: In this work, we present a new dataset for computational humor, specifically comparative humor ranking, which attempts to eschew the ubiquitous binary approach to humor detection. The dataset consists of tweets that are humorous responses to a given hashtag. We describe the motivation for this new dataset, as well as the collection process, which includes a description of our semi-automated system for data collection. We also present initial experiments for this dataset using both unsupervised and supervised approaches. Our best supervised system achieved 63.7% accuracy, suggesting that this task is much more difficult than comparable humor detection tasks. Initial experiments indicate that a character-level model is more suitable for this task than a token-level model, likely due to a large amount of puns that can be captured by a character-level model.


title: NGMN View on 5G Architecture

abstract: The fifth generation of mobile technology (5G) will address the socio-economic demands and business contexts of 2020 and beyond. Leading operators from around the globe have defined their vision for 5G, considering customer and business contexts as well as potential technologies and migration issues in an initiative set up by the NGMN Alliance. While their concrete vision is described in the NGMN 5G whitepaper, this paper describes the key points, such as 5G design principles, 5G components, network slicing, 5G radio access technology and 5G interfacing options that have implications to the 5G architecture design.


title: A Personalised Tourist Trip Design Algorithm for Mobile Tourist Guides

abstract: Mobile tourist guides evolve towards automated personalized tour planning devices. The contribution of this article is to put forward a combined artificial intelligence and metaheuristic approach to solve tourist trip design problems (TTDP). The approach enables fast decision support for tourists on small footprint mobile devices. The orienteering problem, which originates in the operational research literature, is used as a starting point for modelling the TTDP. The problem involves a set of possible locations having a score and the objective is to maximize the total score of the visited locations, while keeping the total time (or distance) below the available time budget. The score of a location represents the interest of a tourist in that location. Scores are calculated using the vector space model, which is a well-known technique from the field of information retrieval. The TTDP is solved using a guided local search metaheuristic. In order to compare the performance of this approach with an algorithm that appeared in the literature, both are applied to a real data set from the city of Ghent. A collection of tourist points of interest with descriptions was indexed and subsequently queried with popular interests, which resulted in a test set of TTDPs. The approach presented in this article turns out to be faster and produces solutions of better quality.


title: Organizational Strategy, Structure, and Process

abstract: Organizational adaptation is a topic that has received only limited and fragmented theoretical treatment. Any attempt to examine organizational adaptation is difficult, since the process is highly complex and changeable. The proposed theoretical framework deals with alternative ways in which organizations define their product-market domains (strategy) and construct mechanisms (structures and processes) to pursue these strategies. The framework is based on interpretation of existing literature and continuing studies in four industries (college textbook publishing, electronics, food processing, and health care).


title: Fully automatic calibration of LIDAR and video streams from a vehicle

abstract: This work describes a fully automatic technique to calibrate a geometric mapping between lidar and video feeds on a mobile ground-based platform. This data association is a crucial first step for any multi-modal scene understanding system which aims to leverage the complementary information of the two sensors. While several systems have been previously described which use hand-calibration or specific scenery to achieve this goal, the system described here is fully automatic and generates an accurate association without user intervention or calibration objects. The estimated parameters include the 7 classical camera parameters for a linear pinhole model, i.e., rotation, position, and focal length parameters, as well as an estimation of the radial distortion. The system uses a multi stage process to bootstrap difficult parameters based on robust estimates of easier ones. The calibration algorithm is tested empirically using free online data supplied as part of the DARPA Urban Challenge autonomous vehicle competition [14]. Experiments are performed to illustrate the stability, and computation cost of the algorithm.


title: Remain seated: towards fully-immersive desktop VR

abstract: In this work we describe the scenario of fully-immersive desktop VR, which serves the overall goal to seamlessly integrate with existing workflows and workplaces of data analysts and researchers, such that they can benefit from the gain in productivity when immersed in their data-spaces. Furthermore, we provide a literature review showing the status quo of techniques and methods available for realizing this scenario under the raised restrictions. Finally, we propose a concept of an analysis framework and the decisions made and the decisions still to be taken, to outline how the described scenario and the collected methods are feasible in a real use case.


title: MultiLabel Classification on Tree- and DAG-Structured Hierarchies

abstract: Many real-world applications involve multi-label classification, in which the labels are organized in the form of a tree or directed acyclic graph (DAG). However, current research efforts typically ignore the label dependencies or can only exploit the dependencies in tree-structured hierarchies. In this paper, we present a novel hierarchical multi-label classification algorithm which can be used on both tree- and DAG-structured hierarchies. The key idea is to formulate the search for the optimal consistent multi-label as the finding of the best subgraph in a tree/DAG. Using a simple greedy strategy, the proposed algorithm is computationally efficient, easy to implement, does not suffer from the problem of insufficient/skewed training data in classifier training, and can be readily used on large hierarchies. Theoretical results guarantee the optimality of the obtained solution. Experiments are performed on a large number of functional genomics data sets. The proposed method consistently outperforms the state-of-the-art method on both tree- and DAG-structured hierarchies.


title: Nirikshan: mining bug report history for discovering process maps, inefficiencies and inconsistencies

abstract: Issue tracking systems such as Bugzilla, Mantis and JIRA are Process Aware Information Systems to support business process of issue (defect and feature enhancement) reporting and resolution. The process of issue reporting to resolution consists of several steps or activities performed by various roles (bug reporter, bug triager, bug fixer, developers, and quality assurance manager) within the software maintenance team. Project teams define a workflow or a business process (design time process model and guidelines) to streamline and structure the issue management activities. However, the runtime process (reality) may not conform to the design time model and can have imperfections or inefficiencies. We apply business process mining tools and techniques to analyze the event log data (bug report history) generated by an issue tracking system with the objective of discovering runtime process maps, inefficiencies and inconsistencies. We conduct a case-study on data extracted from Bugzilla issue tracking system of the popular open-source Firefox browser project. We present and implement a process mining framework, Nirikshan, consisting of various steps: data extraction, data transformation, process discovery, performance analysis and conformance checking. We conduct a series of process mining experiments to study self-loops, back-and-forth, issue reopen, unique traces, event frequency, activity frequency, bottlenecks and present an algorithm and metrics to compute the degree of conformance between the design time and the runtime process.


title: Stereo panorama with a single camera

abstract: Full panoramic images, covering 360 degrees, can be created either by using panoramic cameras or by mosaicing together many regular images. Creating panoramic views in stereo, where one panorama is generated for the left eye, and another panorama is generated for the right eye is more problematic. Earlier attempts to mosaic images from a rotating pair of stereo cameras faced severe problems of parallax and of scale changes. A new family of multiple viewpoint image projections, the Circular Projections, is developed. Two panoramic images taken using such projections can serve as a panoramic stereo pair. A system is described to generates a stereo panoramic image using circular projections from images or video taken by a single rotating camera. The system works in real-time on a PC. It should be noted that the stereo images are created without computation of 3D structure, and the depth effects are created only in the viewer's brain.


title: Predictive collaborative filtering with side information

abstract: Recommender systems have been widely studied in the literature as they have real world impacts in many E-commerce platforms and social networks. Most previous systems are based on the user-item recommendation matrix, which contains users' history recommendation activities on items. In this paper, we propose a novel predictive collaborative filtering approach that exploits both the partially observed user-item recommendation matrix and the item-based side information to produce top-N recommender systems. The proposed approach automatically identifies the most interesting items for each user from his or her non-recommended item pool by aggregating over his or her recommended items via a low-rank coefficient matrix. Moreover, it also simultaneously builds linear regression models from the item-based side information such as item reviews to predict the item recommendation scores for the users. The proposed approach is formulated as a rank constrained joint minimization problem with integrated least squares losses, for which an efficient analytical solution can be derived. To evaluate the proposed learning technique, empirical evaluations on five recommendation tasks are conducted. The experimental results demonstrate the efficacy of the proposed approach comparing to the competing methods.


Braille to English Converter


title: Atomic Decomposition by Basis Pursuit

abstract: The time-frequency and time-scale communities have recently developed a large number of overcomplete waveform dictionaries---stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for decomposition have been proposed, including the method of frames (MOF), matching pursuit (MP), and, for special dictionaries, the best orthogonal basis (BOB). ::: Basis pursuit (BP) is a principle for decomposing a signal into an "optimal"' superposition of dictionary elements, where optimal means having the smallest l1 norm of coefficients among all such decompositions. We give examples exhibiting several advantages over MOF, MP, and BOB, including better sparsity and superresolution. BP has interesting relations to ideas in areas as diverse as ill-posed problems, abstract harmonic analysis, total variation denoising, and multiscale edge denoising. ::: BP in highly overcomplete dictionaries leads to large-scale optimization problems. With signals of length 8192 and a wavelet packet dictionary, one gets an equivalent linear program of size 8192 by 212,992. Such problems can be attacked successfully only because of recent advances in linear and quadratic programming by interior-point methods. We obtain reasonable success with a primal-dual logarithmic barrier method and conjugate-gradient solver.


title: JackIn space: designing a seamless transition between first and third person view for effective telepresence collaborations

abstract: Traditional telepresence systems only supported first person view and users had difficulty in recognizing the surrounding situation of their remote workspace. JackIn Space is a telepresence system that solves this problem by seamlessly integrating first person view with third person view. With a head-mounted first person camera and multiple depth sensors installed in the environment, the surrogate user's first person view smoothly changes to the out-of-body third person view, and the user connected to the surrogate user can virtually look around the environment. The user can also dive into other surrogate users to gain different perspectives. Our evaluation supports that the concept and the function of JackIn Space was quite well accepted and our prototype system provides a more natural viewpoint selection. Overall, JackIn Space supports better remote collaborations.


title: Comparison of static ambulance location models

abstract: Over the years, several ambulance location models have been discussed in the literature. Most of these models have been further developed to take more complicated situations into account. However, the existing standard models have never been compared computationally according to the criteria used in practice. In this paper, we compare several ambulance location models on coverage and response time criteria. In addition to four standard ambulance location models from the literature, we also present two models that focus on average and expected response times. The computational results show that the Maximum Expected Covering Location Problem (MEXCLP) and the Expected Response Time Model (ERTM) perform the best over all considered criteria. However, as the computation times for ERTM are long, we advice to use the MEXCLP except when response times are more important than coverage.


Rich Annotation Guided Learning


title: Topic Detection and Tracking Pilot Study Final Report

abstract: Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream.


title: Assisted Lead Sheet Composition Using FlowComposer

abstract: We present FlowComposer, a web application that helps users compose musical lead sheets, i.e. melodies with chord labels. FlowComposer integrates a constraint-based lead sheet generation tool in which the user retains full control over the generation process. Users specify the style of the lead sheet by selecting a corpus of existing lead sheets. The system then produces a complete lead sheet in that style, either from scratch, or from a partial lead sheet entered by the user. The generation algorithm is based on a graphical model that combines two Markov chains enriched by Regular constraints, representing the melody and its related chord sequence. The model is sampled using our recent result in efficient sampling of the Regular constraint. The paper reports on the design and deployment of FlowComposer as a web-service, part of an ecosystem of online tools for the creation of lead sheets. FlowComposer is currently used in professional musical productions, from which we collect and show a number of representative examples.