abstract: Yang, B., Rousseau, R., Huang, S., & Yan, S. (2015). Do first rate scientists work at first rate organizations?. Malaysian Journal of Library & Information Science, Vol.20, no. 1: 47-60.In this article we study the relation between first rate scientists and first rate organizations. These notions are operationalized by counting citations (whole counts in the Web of Science) for articles published during the period 2008 to 2011. Care has been taken to obtain clean data by careful institutional name disambiguation. It is found that, especially for large fields, more than 80% of top scientists work at top organizations. This is less the case for smaller fields such as mathematics and computer science. Our research confirms the skewness (elitism) of science, also for the relation between first rate scientists and the organizations to which they belong. We find, moreover, that top institutions have relatively more excellent scientists and relatively fewer researchers with poor performance than the average level of all institutions in a field.


title: Convolutional Neural Networks In Classifying Cancer Through DNA Methylation

abstract: DNA Methylation has been the most extensively studied epigenetic mark. Usually a change in the genotype, DNA sequence, leads to a change in the phenotype, observable characteristics of the individual. But DNA methylation, which happens in the context of CpG (cytosine and guanine bases linked by phosphate backbone) dinucleotides, does not lead to a change in the original DNA sequence but has the potential to change the phenotype. DNA methylation is implicated in various biological processes and diseases including cancer. Hence there is a strong interest in understanding the DNA methylation patterns across various epigenetic related ailments in order to distinguish and diagnose the type of disease in its early stages. In this work, the relationship between methylated versus unmethylated CpG regions and cancer types is explored using Convolutional Neural Networks (CNNs). A CNN based Deep Learning model that can classify the cancer of a new DNA methylation profile based on the learning from publicly available DNA methylation datasets is then proposed.


title: A short review and primer on electrodermal activity in human computer interaction applications

abstract: The application of psychophysiology in human-computer interaction is a growing field with significant potential for future smart personalised systems. Working in this emerging field requires comprehension of an array of physiological signals and analysis techniques. ::: One of the most widely used signals is electrodermal activity, or EDA, also known as galvanic skin response or GSR. This signal is commonly used as a proxy for physiological arousal, but recent advances of interpretation and analysis suggest that traditional approaches should be revised. We present a short review on the application of EDA in human-computer interaction. ::: This paper aims to serve as a primer for the novice, enabling rapid familiarisation with the latest core concepts. We put special emphasis on everyday human-computer interface applications to distinguish from the more common clinical or sports uses of psychophysiology. ::: This paper is an extract from a comprehensive review of the entire field of ambulatory psychophysiology, including 12 similar chapters, plus application guidelines and systematic review. Thus any citation should be made using the following reference: ::: B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O. Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci. The Psychophysiology Primer: a guide to methods and a broad review with a focus on human-computer interaction. Foundations and Trends in Human-Computer Interaction, vol. 9, no. 3-4, pp. 150--307, 2016.


title: A Knowledge-Grounded Neural Conversation Model

abstract: Neural network models are capable of generating extremely natural sounding conversational interactions. Nevertheless, these models have yet to demonstrate that they can incorporate content in the form of factual information or entity-grounded opinion that would enable them to serve in more task-oriented conversational applications. This paper presents a novel, fully data-driven, and knowledge-grounded neural conversation model aimed at producing more contentful responses without slot filling. We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external "facts", allowing the model to be versatile and applicable in an open-domain setting. Our approach yields significant improvements over a competitive Seq2Seq baseline. Human judges found that our outputs are significantly more informative.


title: Study on Network QoS Evaluation Method Based on Nondimensionalization

abstract: For telecom operators, assessing the IP network performance has played an important role in improving the network planning and optimization. Because there are many metrics that can be used in the assessment of the network performance, few multi-metric quality of service (QoS) evaluation methods have been found to compare the QoS in different networks. In order to evalute and measure the performance of the IP netowrk more comprehensively, a new method is proposed, which can nondimensionalize the indices and evalute the efficiency of the end to end links of the IP Network in a integrated manner. The results of experiment indicate that the proposed method can effectively evalute the QoS of the target links and the whole situation of the IP Network.


title: An Induced Susceptibility Response in Soybean Promotes Avirulent Aphis glycines (Hemiptera: Aphididae) Populations on Resistant Soybean

abstract: Observations of virulent Aphis glycines Matsumura populations on resistant soybean in North America occurred prior to the commercial release of Rag genes. Laboratory assays confirmed the presence of four A. glycines biotypes in North America defined by their virulence to the Rag1 and Rag2 genes. Avirulent and virulent biotypes can co-occur and potentially interact on soybean, which may result in induced susceptibility. We conducted a series of experiments to determine if the survival of avirulent biotypes on susceptible and resistant soybean containing the Rag1 or Rag1 + Rag2 genes was affected by the presence of either avirulent or virulent conspecifics. Regardless of virulence to Rag genes, initial feeding by conspecifics increased the survival of subsequent A. glycines populations on both susceptible and resistant soybean. Avirulent populations increased at the same rate as virulent populations if the resistant plants were initially colonized with virulent aphids. These results are the first to demonstrate that virulent A. glycines increase the suitability of resistant soybean for avirulent conspecifics, thus explaining the lack of genetic differentiation observed in North America between A. glycines populations on resistant and susceptible soybean. These results suggest the occurrence of virulence toward Rag genes in North America may be overestimated. In addition this may alter the selection pressure for virulence genes to increase in a population. Therefore, insect resistance management models for A. glycines may need to incorporate induced susceptibility factors to determine the relative durability of resistance genes.


title: A Wideband Voltage-Controlled Oscillator With Gain Linearized Varactor Bank

abstract: A new gain linearized varactor bank suitable for wideband voltage-controlled oscillators (VCOs) is presented in this paper. The VCO tuning gain linearized techniques, namely the gain variation compensation and linear tuning range extension techniques, are used in the proposed varactor bank to achieve a further reduced VCO tuning gain with low variation. The phase noise from Amplitude Modulation to Phase Modulation up conversion is considerably improved thanks to the reduced VCO tuning gain. Fabricated in a 0.18- $\mu{\rm m}$ CMOS technology, a 3-bits VCO prototype employing the proposed varactor bank achieves ${ gain variation at the output frequency from 4.1 to 5 GHz, and exhibits maximum power consumption of 7.2 mW at its peak frequency, 5 GHz.


title: Report CMLA N o 2003-15 To appear in SIAM Journal on Scientific Computing (2005) ANALYSIS OF HALF-QUADRATIC MINIMIZATION METHODS FOR SIGNAL AND IMAGE RECOVERY ∗

abstract: We address the minimization of regularized convex cost functions which are customarily used for edge-preserving restoration and reconstruction of signals and images. In order to accelerate computation, the multiplicative and the additive half-quadratic reformulation of the original cost-function have been pioneered in Geman and Reynolds [IEEE Trans. Pattern Anal. Machine Intelligence, 14 (1992), pp. 367--383] and Geman and Yang IEEE Trans. Image Process., 4 (1995), pp. 932--946]. The alternate minimization of the resultant (augmented) cost-functions has a simple explicit form. The goal of this paper is to provide a systematic analysis of the convergence rate achieved by these methods. For the multiplicative and additive half-quadratic regularizations, we determine their upper bounds for their root-convergence factors. The bound for the multiplicative form is seen to be always smaller than the bound for the additive form. Experiments show that the number of iterations required for convergence for the multiplicative form is always less than that for the additive form. However, the computational cost of each iteration is much higher for the multiplicative form than for the additive form. The global assessment is that minimization using the additive form of half-quadratic regularization is faster than using the multiplicative form. When the additive form is applicable, it is hence recommended. Extensive experiments demonstrate that in our MATLAB implementation, both methods are substantially faster (in terms of computational times) than the standard MATLAB Optimization Toolbox routines used in our comparison study.


title: Success Is All in the Measures: Androgenousness, Curvaceousness, and Starring Frequencies in Adult Media Actresses

abstract: The debate of whether body-mass index (BMI) or waist-to-hip ratio (WHR) is the primary visual cue to female physical attractiveness (FPA) has generated considerable interest. However, experiments addressing this question typically have limited ecological validity and do not capture the ultimate goal of FPA, which is to elicit male sexual arousal. Hence, using an unobtrusive measures design, we retrieved movie and magazine starring frequencies of 125 adult media actresses from a company’s database, operationalized starring frequencies as FPA measures, and tested their relationship to actresses’ anthropometric data. Low BMI was related to frequent movie starring, while WHR, waistto-bust ratio (WBR), and bust size were not. Conversely, low WHR, low WBR, and larger bust size were related to frequent magazine starring, while BMI was not. Visual cues to FPA might be domain-specific, with androgenousness cues salient for attractiveness evaluation of moving bodies and curvaceousness cues salient for posing bodies.


title: Prediction of stock price analyzing the online financial news using Naive Bayes classifier and local economic trends

abstract: Market and stock exchange news are special messages containing mainly economical and political information. This paper represents data mining algorithms which has been tested on this available information to learn useful trends about the behaviour of the stock markets. The learned trend holds the key to interpret the present and predict the next stock price. This resented work uses Naive Bayes Algorithm to classify text news related to FTSE100 given on these mentioned websites and the classifier is trained to learn the movement in the stock price (up or down) from the news articles in the web pages of that day. Several heuristics are being used to remove irrelevant parts of the text to get a reasonable performance. This model had demonstrated a statistically significant performance in predicting stock prices compared to other previously developed methods.


title: Developing a standard for TV white space coexistence: technical challenges and solution approaches

abstract: TV white space refers to TV channels that are not used by any licensed services at a particular location and at a particular time. To exploit this unused TVWS spectrum for improved spectrum efficiency, regulatory agencies have begun developing regulations to permit its use this TVWS by unlicensed wireless devices as long as they do not interfere with any licensed services. In the future many heterogeneous, and independently operated, wireless networks may utilize the TVWS. Coexistence between these networks is essential in order to provide a high level of QoS to end users. Consequently, the IEEE 802 LAN/MAN standards committee has approved the P802.19.1 standardization project to specify radio-technology-independent methods for coexistence among dissimilar or independently operated wireless devices and networks. In this article we provide a detailed overview of the regulatory status of TVWS in the United States and Europe, analyze the coexistence problem in TVWS, and summarize existing coexisting mechanisms to improve coexistence in TVWS. The main focus of the article is the IEEE P802.19.1 standardization project, including its requirements and system design, and the major technical challenges ahead.


title: Real-time small obstacle detection on highways using compressive RBM road reconstruction

abstract: Small objects on the road can become hazardous obstacles when driving at high speed. Detecting such obstacles is vital to guaranty the safety of self-driving car users, especially on highways. Such tasks cannot be performed using existing active sensors such as radar or LIDAR due to their limited range and resolution at long distances. In this paper we propose a technique to detect anomalous patches on the road from color images using a Restricted Boltzman Machine neural network specifically trained to reconstruct the appearance of the road. The differences between the observed and reconstructed road patches yield a more relevant segmentation of anomalies than classic image processing techniques. We evaluated our technique on texture-based synthetic datasets as well as on real video footage of anomalous objects on highways.


title: NFaaS: named function as a service

abstract: In the past, the Information-centric networking (ICN) community has focused on issues mainly pertaining to traditional content delivery (e.g., routing and forwarding scalability, congestion control and in-network caching). However, to keep up with future Internet architectural trends the wider area of future Internet paradigms, there is a pressing need to support edge/fog computing environments, where cloud functionality is available more proximate to where the data is generated and needs processing. With this goal in mind, we propose Named Function as a Service (NFaaS), a framework that extends the Named Data Networking architecture to support in-network function execution. In contrast to existing works, NFaaSbuilds on very lightweight VMs and allows for dynamic execution of custom code. Functions can be downloaded and run by any node in the network. Functions can move between nodes according to user demand, making resolution of moving functions a first-class challenge. NFaaSincludes a Kernel Store component, which is responsible not only for storing functions, but also for making decisions on which functions to run locally. NFaaSincludes a routing protocol and a number of forwarding strategies to deploy and dynamically migrate functions within the network. We validate our design through extensive simulations, which show that delay-sensitive functions are deployed closer to the edge, while less delay-sensitive ones closer to the core.


Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network


title: A Wireless Flexible Sensorized Insole for Gait Analysis

abstract: This paper introduces the design and development of a novel pressure-sensitive foot insole for real-time monitoring of plantar pressure distribution during walking. The device consists of a flexible insole with 64 pressure-sensitive elements and an integrated electronic board for high-frequency data acquisition, pre-filtering, and wireless transmission to a remote data computing/storing unit. The pressure-sensitive technology is based on an optoelectronic technology developed at Scuola Superiore Sant'Anna. The insole is a low-cost and low-power battery-powered device. The design and development of the device is presented along with its experimental characterization and validation with healthy subjects performing a task of walking at different speeds, and benchmarked against an instrumented force platform.


title: CCG Supertagging with a Recurrent Neural Network

abstract: Recent work on supertagging using a feedforward neural network achieved significant improvements for CCG supertagging and parsing (Lewis and Steedman, 2014). However, their architecture is limited to considering local contexts and does not naturally model sequences of arbitrary length. In this paper, we show how directly capturing sequence information using a recurrent neural network leads to further accuracy improvements for both supertagging (up to 1.9%) and parsing (up to 1% F1), on CCGBank, Wikipedia and biomedical text.


title: Improving the effectiveness of test suite through mining historical data

abstract: Software regression testing is an integral part of most major software projects. As projects grow larger and the number of tests increases, performing regression testing becomes more costly. If software engineers can identify and run tests that are more likely to detect failures during regression testing, they may be able to better manage their regression testing activities. In this paper, to help identify such test cases, we developed techniques that utilizes various types of information in software repositories. To assess our techniques, we conducted an empirical study using an industrial software product, Microsoft Dynamics AX, which contains real faults. Our results show that the proposed techniques can be effective in identifying test cases that are likely to detect failures.


title: Visual fish tracking: Combining a two-stage graph approach with CNN-features

abstract: Video based underwater monitoring is used by a growing number of marine researchers to observe changes in the ecosystem over time. Especially the analysis of water organisms such as fish gives important information about the health-state of our oceans. Videos of fish communities can be collected in a large scale but the manual analysis by human experts is slow and expensive. Therefore, automatic methods for fish video analysis are required to make use of all collected data. In this paper we investigate computer vision approaches for visual multi-object tracking of fish in their unrestricted natural environment. Our focus is on a two-stage graph approach that uses activations of a convolutional neural network (CNN) to model object appearance. Evaluation results show that the examined two-stage graph tracking approaches have a higher tracking accuracy and are much faster than state of the art in fish tracking.


Quantify customer perception using natural language reviews


title: Structural health monitoring for a wind turbine system: a review of damage detection methods

abstract: Renewable energy sources have gained much attention due to the recent energy crisis and the urge to get clean energy. Among the main options being studied, wind energy is a strong contender because of its reliability due to the maturity of the technology, good infrastructure and relative cost competitiveness. In order to harvest wind energy more efficiently, the size of wind turbines has become physically larger, making maintenance and repair works difficult. In order to improve safety considerations, to minimize down time, to lower the frequency of sudden breakdowns and associated huge maintenance and logistic costs and to provide reliable power generation, the wind turbines must be monitored from time to time to ensure that they are in good condition. Among all the monitoring systems, the structural health monitoring (SHM) system is of primary importance because it is the structure that provides the integrity of the system. SHM systems and the related non-destructive test and evaluation methods are discussed in this review. As many of the methods function on local damage, the types of damage that occur commonly in relation to wind turbines, as well as the damage hot spots, are also included in this review.


title: Learning an Optimal Naive Bayes Classifier

abstract: The naive Bayes classifier is an efficient classification model that is easy to learn and has a high accuracy in many domains. However, it has two main drawbacks: (i) its classification accuracy decreases when the attributes are not independent, and (ii) it can not deal with nonparametric continuous attributes. In this work we propose a method that deals with both problems, and learns an optimal naive Bayes classifier. The method includes two phases, discretization and structural improvement, which are repeated alternately until the classification accuracy can not be improved. Discretization is based on the minimum description length principle. To deal with dependent and irrelevant attributes, we apply a structural improvement method that eliminates and/or joins attributes, based on mutual and conditional information measures. The method has been tested in two different domains with good results.


Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs


title: Multi-Hop Relay-Aided Underlay D2D Communications for Improving Cellular Coverage Quality

abstract: The future wireless networks need to improve spectrum and energy efficiency to satisfy the increasing demand for high data rate. Device-to device (D2D) communications have the ability to address this problem. This paper focuses on the underlay D2D relay function to improve cellular coverage quality. Although there are a few relevant works in this aspect, there is room for further improvement. For example, there is the constraint on the number of relays in a transmission path, which hardly meets the requirement of the cell-edge devices to fully improve their cellular throughput. Also, there is little energy constraint for underlay D2D relay selection, which is difficult to guarantee the service life of underlay D2D relaying links. Furthermore, without careful regulation of transmission power in terms of cellular coverage improvement, it is not conducive to the improvement of spectrum and energy efficiency in this aspect. Therefore, this paper proposes the improved scheme to deal with these problems, which can: 1) improve spectrum efficiency by using underlay spectrum sharing mode and alleviating its weakness (e.g., co-channel interference); 2) enhance comprehensive performance of underlay D2D relaying links by jointly considering multiple Quality-of-Service (QoS) metrics; 3) reduce overhead of relay selection by proposing a greedy algorithm based on a distributed local search; and 4) improve both energy efficiency and convergence time by designing a new power adjustment scheme based on the improved potential game decision algorithm. The theoretical analysis proves the existence of Nash equilibrium, and the simulation results show that the proposed game decision algorithm accelerates convergence and the proposed whole scheme improves cellular coverage quality.


title: Distributed Decentralized Domain Name Service

abstract: We present D3NS, a system to replace the current top level DNS system and certificate authorities, offering increased scalability, security and robustness. D3NS is based on a distributed hash table and utilizes a domain name ownership system based on the Bitcoin blockchain. It addresses previous criticism that a DHT would not suffice as a DNS replacement. D3NS provides solutions to current DNS vulnerabilities such as DDOS attacks, DNS spoofing and censorship by local governments. D3NS eliminates the need for certificate authorities by providing a decentralized authenticated record of domain name ownership. Unlike previous DNS replacement proposals, D3NS is reverse compatible with DNS and allows for incremental implementation within the current system.


title: Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks

abstract: In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal.


title: Answering Complicated Question Intents Expressed in Decomposed Question Sequences

abstract: Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. Existing QA systems face two major problems when evaluated on our dataset: (1) handling questions that contain coreferences to previous questions or answers, and (2) matching words or phrases in a question to corresponding entries in the associated table. We conclude by proposing strategies to handle both of these issues.


title: Looking at Humans in the Age of Self-Driving and Highly Automated Vehicles

abstract: This paper highlights the role of humans in the next generation of driver assistance and intelligent vehicles. Understanding, modeling, and predicting human agents are discussed in three domains where humans and highly automated or self-driving vehicles interact: 1) inside the vehicle cabin, 2) around the vehicle, and 3) inside surrounding vehicles. Efforts within each domain, integrative frameworks across domains, and scientific tools required for future developments are discussed to provide a human-centered perspective on research in intelligent vehicles.


title: Video Frame Synthesis Using Deep Voxel Flow

abstract: We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation). This problem is challenging because video appearance and motion can be highly complex. Traditional optical-flow-based solutions often fail where flow estimation is challenging, while newer neural-network-based methods that hallucinate pixel values directly often produce blurry results. We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow. Our method requires no human supervision, and any video can be used as training data by dropping, and then learning to predict, existing frames. The technique is efficient, and can be applied at any video resolution. We demonstrate that our method produces results that both quantitatively and qualitatively improve upon the state-of-the-art.


title: Digital Pen for Handwritten Digit and GestureRecognition Using Trajectory RecognitionAlgorithm Based On Triaxial Accelerometer-A Review

abstract: In this review paper we are going to discuss a systematic trajectory recognition algorithm framework that can construct effective classifiers for hand writing & gesture identification. Review of Digital Pen for Handwritten Digit and Gesture Recognition using Trajectory Recognition Algorithm based on Accelerometer is discuss for the identification of 2-D handwriting digits and 3-D hand gestures. for this implementation we are going to use triaxial accelerometer, a microcontroller, and an RF wireless transmission module for sensing and collecting accelerations of handwriting and gesture trajectories. The proposed trajectory recognition algorithm composes of the procedures of acceleration acquisition, signal reprocessing, feature generation, feature selection, and feature extraction. The algorithm is capable of translating time-series acceleration signals into important feature vectors. Users can use the pen to write digits or make hand gestures, and the accelerations of hand motions measured by the accelerometer are wirelessly transmitted to a computer for online trajectory recognition. Basically this algorithm is divided in two parts. First part is discussion about the implemented for the Handwritten Digit Recognition while in second part is discussion about the implemented for the Gesture Recognition.


title: New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks

abstract: The studies on the photovoltaic (PV) generation are extensively increasing, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power induced in the photovoltaic modules depends on solar radiation and temperature of the solar cells. Therefore, to maximize the efficiency of the renewable energy system, it is necessary to track the maximum power point of the PV array. In this paper, a maximum power point tracker using fuzzy set theory is presented to improve energy conversion efficiency. A new method is proposed, by using a fuzzy cognitive network, which is in close cooperation with the presented fuzzy controller. The new method gives a very good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The simulation studies show the effectiveness of the proposed algorithm


title: The Liebowitz Social Anxiety Scale: a comparison of the psychometric properties of self-report and clinician-administered formats

abstract: Background. The clinician-administered version of the Liebowitz Social Anxiety Scale (LSAS-CA) is a commonly used assessment device for the evaluation of social anxiety disorder and has been shown to have strong psychometric characteristics. Because of its apparently straightforward rating format and potential savings in time and eort, interest in the use of the LSAS as a self-report (LSAS-SR) measure has increased, and the LSAS-SR has been used in a number of studies. However, the psychometric properties of the LSAS-SR have not been well established. Methods. This study examined the psychometric properties of the LSAS-SR in comparison to the LSAS-CA in a sample of 99 individuals with a primary diagnosis of social anxiety disorder and 53 individuals with no current psychiatric disorder. Results. There was little dierence between the two versions of the LSAS on any scale or subscale score. Both forms were internally consistent and the subscale intercorrelations for the two forms were essentially identical. Correlations of each LSAS-SR index with its LSAS-CA counterpart were all highly significant. Finally, the convergent and discriminant validity of the two forms of the LSAS was shown to be strong. Conclusion. Results of this study suggest that the self-report version of the LSAS compares well to the clinician-administered version and may be validly employed in the assessment of social anxiety disorder.


title: At the intersection of disaster risk and religion: interpretations and responses to the threat of Tsho Rolpa glacial lake

abstract: ABSTRACTOver the last several decades, the scientific community has replaced the ‘acts of god’ explanations for disasters with a view that disasters are actually ‘acts of nature,’ and more recently, ‘acts of human behavior and decision making.’ Despite the secular orientation of contemporary disaster research, religious beliefs still govern people’s interpretations of natural events across many continents and cultures. Using a case study of a rural Sherpa community in Nepal, this paper responds to the challenge of linking religion and disaster risk in the context of a climate change-induced glacial lake outburst flood hazard. Data collection employed ethnographic techniques including participant observation and 53 interviews with community members. Results indicate that the case study community’s religious belief system influences how they interpret and respond to the glacial lake hazard, although their beliefs co-exist with more scientific interpretations of risk. Rather than being immobilised by their b...


EFFECTS OF COLLABORATIVE M-LEARNING AND INDIVIDUAL E-LEARNING ON THE ACADEMIC PERFORMANCE , ATTENTION BENEFIT AND CONSISTENCY OF LEARNING


title: Phase composition and thermal conductivity of zirconia-based thermal barrier coatings

abstract: Atmospheric plasma spraying of powder materials has been used to produce thermal barrier coatings (TBCs) based on ZrO2 stabilized with 7 wt % Y2O3, including coatings doped with neodymium and samarium oxides, for state-of-the-art and next-generation high-temperature gas turbine engines. Doping with neodymium and samarium oxides has been shown to reduce the thermal conductivity of the TBCs by 10–20%. At the same time, changes in the phase composition, crystal structure parameters, and microstructure of the TBCs during heat treatment at the service temperature lead to an increase in the thermal conductivity of all the coatings by 50–70%.


title: THE SOCIAL ORGANIZATION OF CONSPIRACY: ILLEGAL NETWORKS IN THE HEAVY ELECTRICAL EQUIPMENT INDUSTRY*

abstract: We analyze the social organization of three well-known price-fixing conspiracies in the heavy electrical equipment industry. Although aspects of collusion have been studied by industrial organization economists and organizational criminologists, the organization of conspiracies has remained virtually unexplored. Using archival data, we reconstruct the actual communication networks involved in conspiracies in switchgear transformers, and turbines. We find that the structure of illegal networks is driven primarily by the need to maximize concealment, rather than the need to maximize efficiency. However, network structure is also contingent on information-processing requirements imposed by product and market characteristics. Our individual-level model predicts verdict (guilt or innocence), sentence, and fine as functions of personal centrality in the illegal network, network structure, management level, and company size. "People of the same trade seldom meet together but the conversation ends in a conspiracy against the public, or in some diversion to raise prices."


title: Automatic Calibration of a Robot Manipulator and Multi 3D Camera System

abstract: With 3D sensing becoming cheaper, environment-aware and visually-guided robot arms capable of safely working in collaboration with humans will become common. However, a reliable calibration is needed, both for camera internal calibration, as well as Eye-to-Hand calibration, to make sure the whole system functions correctly. We present a framework, using a novel combination of well proven methods, allowing a quick automatic calibration for the integration of systems consisting of the robot and a varying number of 3D cameras by using a standard checkerboard calibration grid. Our approach allows a quick camera-to-robot recalibration after any changes to the setup, for example when cameras or robot have been repositioned. Modular design of the system ensures flexibility regarding a number of sensors used as well as different hardware choices. The framework has been proven to work by practical experiments to analyze the quality of the calibration versus the number of positions of the checkerboard used for each of the calibration procedures.


title: The Application of Signature Character Strengths and Positive Experiences at Work

abstract: We hypothesized that the amount of positive experiences at work (job satisfaction, pleasure, engagement, meaning) is a function of the extent to which the situational circumstances at the workplace allow for the application of an individual’s signature character strengths. For the description of the individual a reliable and valid instrument already exists, but not for the environment. Hence, the newly developed Applicability of Character Strengths Rating Scales (ACS-RS) with information on its reliability and validity were also presented. A sample of 1,111 adults filled in the ACS-RS and measures for possession of character strengths and positive experiences at work. The ACS-RS was reliable by means of internal consistency and inter-rater reliability. It proved to be valid in several ways being sensitive to: (a) the differences in the applicability of trait-relevant behavior in formal versus informal situations by showing higher applicability of the character strengths in the latter; (b) the differences between traits regarding their applicability across situations; (c) people’s disposition to choose situations fitting their dispositions by showing positive relationships between the degree of possession and applicability. Moreover, correlations between applicability of strengths and positive experiences increased with the individual centrality of the strengths. The more signature strengths were applied at the workplace, the higher the positive experiences at work. This study showed that character strengths matter in vocational environments irrespective of their content. Strengths-congruent activities at the workplace are important for positive experiences at work like job satisfaction and experiencing pleasure, engagement, and meaning fostered by one’s job.


title: Key-Value Memory Networks for Directly Reading Documents

abstract: Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed towards using Knowledge Bases (KBs) instead, which has proven effective. Unfortunately KBs often suffer from being too restrictive, as the schema cannot support certain types of answers, and too sparse, e.g. Wikipedia contains much more information than Freebase. In this work we introduce a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation. To compare using KBs, information extraction or Wikipedia documents directly in a single framework we construct an analysis tool, WikiMovies, a QA dataset that contains raw text alongside a preprocessed KB, in the domain of movies. Our method reduces the gap between all three settings. It also achieves state-of-the-art results on the existing WikiQA benchmark.


title: Automatic control for laboratory sterilization process based on arduino hardware

abstract: In this paper It is introduced an automated control system for the sterilization process of biological material. The proposed system is composed of four modules: flame detection, pressure sensor, Gas Control Module and CPU Module. This System is based on Arduino Board. Arduino is an Open Hardware platform that allows a fast prototype development, Arduino microcontroller is programmed on C++. making development and tests not a difficult part of overall work.


title: Forward-flyback mixed ZVS DC-DC converter with non-dissipative LC snubber circuit

abstract: This paper presents a zero-voltage switching (ZVS) forward-flyback DC-DC converter, which is able to process and deliver power efficiently over very wide input voltage variation. The proposed ZVS forward flyback DC/DC converter is part of a Micro-inverter to perform input voltage regulation to achieving maximum power point tracking for Photo-voltaic panel. The converter operates at boundary between current continuous and discontinuous mode to achieve ZVS. Variable frequency with fixed off time is used for reducing core losses of the transformer, achieving high efficiency. In addition, non-dissipative LC snubber circuit is used to get both benefits: 1) the voltage spike is restrained effectively while the switch is turned off with high current in primary side; 2) the main switch still keeps ZVS feature. Finally, experiment results provided from a 200W prototype (30Vdc-50Vdc input, 230Vdc output) validate the feasibility and superior performance of the proposed converter.


title: Optimum Time Coordination of Overcurrent Relays in Distribution System Using BigM (Penalty) Method

abstract: T his paper presents big0M (penalty) method for optimum time coordination of overcurrent (OC) relays. The method is based on the simplex algorithm which is used to find optimum solution of linear programming problem (LPP). The method introduces artificial variables in the objective function to get an initial basic feasible solution (IBFS). Artificial variables are removed using iterative process which also leads to an optimum solution. The OC relays are the major protection devices in a distribution system. To reduce the power outages, mal0 operation of the backup relays should be avoided, and therefore, OC relay time coordination in power distribution network is a major concern of protection engineer. The OC relay time coordination in ring fed distribution networks is a highly constrained optimization problem. The purpose is to find an optimum relay setting to minimize the time of operation of relays and at the same time, to avoid the mal0operation of relays. Big0M simplex technique for optimum time coordination of OC relays in a ring fed distribution system is presented in this paper.


title: Dynamic level of detail 3D occupancy grids for automotive use

abstract: In this paper, a generic approach for three-dimensional environment representation is presented. Scans from range finders are accumulated into a three-dimensional occupancy grid. A probabilistic measurement model is used to represent measurement uncertainties. Free regions are modelled as well and contribute to a precise representation of the environment. In order to acquire a feasible three-dimensional grid, a hierarchical data structure is proposed. As a positive result, the level of detail and respectively the grid resolution is controllable by the application or by the content. As an example application, it will be shown how height information can be derived from a sensor with one horizontal scan plane only.


title: The Effects of Trust Dimensions on Relationship Commitment: the Relative Efficacy and Effect Symmetry

abstract: This study investigates the effects of three trust dimensions on relationship commitment. The results of a survey of 989 customers subscribing to a media service indicate that integrity has the strongest influence on relationship commitment, followed by competence and benevolence. The effect of integrity on relationship commitment is negatively asymmetric both in the early and later stage of the relationship. The effect of competence on relationship commitment is negatively asymmetric in the mature stage of the relationship, while the effect of benevolence on relationship commitment is positively asymmetric in the mature stage of the relationship. That is, benevolence becomes more important in the mature state of the relationship. Managerial implications are discussed.


title: Metal-enhanced PicoGreen fluorescence: application for double-stranded DNA quantification.

abstract: PicoGreen (PG) is a fluorescent probe for both double-stranded DNA (dsDNA) detection and quantification based on its ability to form a luminescent complex with dsDNA as compared with the free dye in solution. To expand the sensitivity of PG detection, we have studied the spectral properties of PG, both free and in complex with DNA in solution, when the fluorophore is in proximity to silver nanoparticles. We show that for a broad range of PG concentrations (20 pM-3.5 microM), it does not form dimers/oligomers and it exists in a monomeric state. On binding to DNA in the absence of silver, PG fluorescence increases approximately 1100-fold. Deposition of PG/DNA complex onto silver island films (SiFs) increases fluorescence approximately 7-fold due to the metal-enhanced fluorescence (MEF) effect, yielding fluorescence enhancement of 7700-fold as compared with the free dye on glass. In contrast to PG in complex with DNA, the free dye on SiFs demonstrates a decrease in brightness approximately 5-fold. Therefore, the total enhancement of PG on binding to DNA on silver reaches a value of approximately 38,000 as compared with free PG on SiFs. Consequently, the metal-enhanced detection of PG fluorescence is likely to find important utility for amplified dsDNA quantification.


title: A new 3D model retrieval approach based on the elevation descriptor

abstract: The advances in 3D data acquisition techniques, graphics hardware, and 3D data modeling and visualizing techniques have led to the proliferation of 3D models. This has made the searching for specific 3D models a vital issue. Techniques for effective and efficient content-based retrieval of 3D models have therefore become an essential research topic. In this paper, a novel feature, called elevation descriptor, is proposed for 3D model retrieval. The elevation descriptor is invariant to translation and scaling of 3D models and it is robust for rotation. First, six elevations are obtained to describe the altitude information of a 3D model from six different views. Each elevation is represented by a gray-level image which is decomposed into several concentric circles. The elevation descriptor is obtained by taking the difference between the altitude sums of two successive concentric circles. An efficient similarity matching method is used to find the best match for an input model. Experimental results show that the proposed method is superior to other descriptors, including spherical harmonics, the MPEG-7 3D shape spectrum descriptor, and D2.


title: A survey on microstrip antennas for portable wireless communication system applications

abstract: In modern wireless communication, microstrip antennas are widely equipped in mobiles, laptops and other portable devices due to their advantages like small size, light weight, low cost and easy integration with planar structures. Moreover, the research evolution in these antennas, which are required for portable wireless communication system applications, is advancing day by day. So, it is essential to notice the research and improvements happening in the area of microstrip antennas for the intended applications. The main objective of this paper is to discuss numerous microstrip antennas for GSM, Wi-Fi and Wi-MAX 2500 applications. The GSM applications cover GSM 900, GSM 1800 (DCS 1800) and GSM 1900 (PCS 1900) bands while the Wi-Fi applications cover 2.4GHz and 5.8GHz bands. Sometimes, these antennas also operate in various bands of GPS, LTE 2300, LTE 2600, UMTS and Wi-MAX 3500 along with the GSM, Wi-Fi and Wi-MAX 2500 bands. This paper initially presents various conventional microstrip antennas that include single, dual, triple and multi band antennas. Then, it reports frequency reconfigurable narrow band antennas and finally multi-port microstrip antennas. It also explains merits and demerits of all antenna categories. Finally, this paper helps to explore and design suitable microstrip antennas for the required applications.


title: Back-to-back wind energy conversion system configuration based on 9-switch dual converter and open-end-winding PMSG

abstract: The following work introduces the use of a dual three-phase converter with reduced number of switches in a back-to-back configuration for its application to an open-end-winding permanent magnet synchronous generator based wind energy conversion systems (WECS). The converter topology consists of a nine-switch dual three-phase voltage source converter capable of controlling two sets of three-phase variables without voltage limitations when operating both three-phase systems at same frequency. This feature is exploited to control both ends of the PMSG and the three phase inputs of a 12 pulse transformer witch connects the grid. The open-end winding generator connection provides more space vectors for control while the 12 pulse grid-connection enables mitigation of switching harmonics.


title: Delayed perforation: A hazardous complication of endoscopic resection for non-ampullary duodenal neoplasm

abstract: BACKGROUND ::: Perforation is a major complication of endoscopic resection for gastrointestinal neoplasms. However, little is known about delayed perforation after endoscopic resection for non-ampullary duodenal neoplasm. The aim of the present study was to investigate the clinical features of delayed perforation after endoscopic resection for non-ampullary duodenal neoplasm. ::: ::: ::: PATIENTS AND METHODS ::: This was a retrospective cohort study conducted in a referral cancer center. A total of 63 patients (41 with adenomas and 22 with carcinomas) underwent endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) from January 1993 to December 2011. Incidence, outcome, and factors associated with occurrence of delayed perforation were investigated. ::: ::: ::: RESULTS ::: Delayed perforation occurred in four patients (6.3%). All lesions were located distal to Vater's ampulla. Three of four delayed perforations occurred within 36 h after endoscopic resection. All patients developed retroperitonitis, and two also had retroperitoneal abscesses. Although three patients were cured with conservative management, a long hospital stay was required (28-, 80-, and 81-day hospital stay, respectively). One patient required emergency surgery as a result of panperitonitis. There was, fortunately, no mortality in this series. The significant predictors of delayed perforation were location (distal to Vater's ampulla, P = 0.007) and resection method (ESD and piecemeal EMR, P = 0.003). ::: ::: ::: CONCLUSION ::: Endoscopic resection for non-ampullary duodenal neoplasms has a possible risk of morbid complication i.e. delayed perforation, especially in patients with lesions located on the side distal from the ampulla and who are treated with piecemeal EMR or ESD.


title: UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis

abstract: This paper describes our system participating in the aspect-based sentiment analysis task of Semeval 2014. The goal was to identify the aspects of given target entities and the sentiment expressed towards each aspect. We firstly introduce a system based on supervised machine learning, which is strictly constrained and uses the training data as the only source of information. This system is then extended by unsupervised methods for latent semantics discovery (LDA and semantic spaces) as well as the approach based on sentiment vocabularies. The evaluation was done on two domains, restaurants and laptops. We show that our approach leads to very promising results.


title: TarsosDSP, a Real-Time Audio Processing Framework in Java

abstract: This paper presents TarsosDSP, a framework for real-time audio analysis and processing. Most libraries and frameworks offer either audio analysis and feature extraction or audio synthesis and processing. TarsosDSP is one of a only a few frameworks that offers both analysis, processing and feature extraction in real-time, a unique feature in the Java ecosystem. The framework contains practical audio processing algorithms, it can be extended easily, and has no external dependencies. Each algorithm is implemented as simple as possible thanks to a straightforward processing pipeline. TarsosDSP's features include a resampling algorithm, onset detectors, a number of pitch estimation algorithms, a time stretch algorithm, a pitch shifting algorithm, and an algorithm to calculate the Constant-Q. The framework also allows simple audio synthesis, some audio effects, and several filters. The Open Source framework is a valuable contribution to the MIR-Community and ideal fit for interactive MIR-applications on Android.


title: Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling

abstract: Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variablelength text. RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix. And this matrix includes two dimensions: the time-step dimension and the feature vector dimension. Then most existing models usually utilize one-dimensional (1D) max pooling operation or attention-based operation only on the time-step dimension to obtain a fixed-length vector. However, the features on the feature vector dimension are not mutually independent, and simply applying 1D pooling operation over the time-step dimension independently may destroy the structure of the feature representation. On the other hand, applying two-dimensional (2D) pooling operation over the two dimensions may sample more meaningful features for sequence modeling tasks. To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text. This paper also utilizes 2D convolution to sample more meaningful information of the matrix. Experiments are conducted on six text classification tasks, including sentiment analysis, question classification, subjectivity classification and newsgroup classification. Compared with the state-of-the-art models, the proposed models achieve excellent performance on 4 out of 6 tasks. Specifically, one of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks.


title: Impact of stemming on Arabic text summarization

abstract: Stemming is a process of reducing inflected words to their stem or root from a generally written word form. This process is used in many text mining application as a feature selection technique. Moreover, Arabic text summarization has increasingly become an important task in natural language processing area (NLP). Therefore, the aim of this paper is to evaluate the impact of three different Arabic stemmers (i.e. Khoja, Larekey and Alkhalil's stemmer) on the text summarization performance for Arabic language. The evaluation of the proposed system, with the three different stemmers and without stemming, on the dataset used shows that the best performance was achieved by Khoja stemmer in term of recall, precision and F1-measure. The evaluation also shows that the performances of the proposed system are significantly improved by applying the stemming process in the pre-processing stage.


title: Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods

abstract: Neural networks are known to be vulnerable to adversarial examples: inputs that are close to natural inputs but classified incorrectly. In order to better understand the space of adversarial examples, we survey ten recent proposals that are designed for detection and compare their efficacy. We show that all can be defeated by constructing new loss functions. We conclude that adversarial examples are significantly harder to detect than previously appreciated, and the properties believed to be intrinsic to adversarial examples are in fact not. Finally, we propose several simple guidelines for evaluating future proposed defenses.


title: Visual servoing of a five-bar linkage mechanism

abstract: This paper presents a position-based visual servoing system with fixed camera to control the position of a five-bar linkage mechanism. The document records the goals set, the used methods, the obstacles encountered, the proposed solutions to them, and the progress made during the implementation, as well as achievements to reach in the future work. This project is carried out taking into account the growing popularity of this type of control and the advantages it offers compared to others, which could place it as the best method to use.


title: Music training improves verbal but not visual memory: Cross-sectional and longitudinal explorations in children

abstract: The hypothesis that music training can improve verbal memory was tested in children. The results showed that children with music training demonstrated better verbal but not visual memory than did their counterparts without such training. When these children were followed up after a year, those who had begun or continued music training demonstrated significant verbal memory improvement. Students who discontinued the training did not show any improvement. Contrary to the differences in verbal memory between the groups, their changes in visual memory were not significantly different. Consistent with previous findings for adults (A. S. Chan, Y. Ho, & M. Cheung, 1998), the results suggest that music training systematically affects memory processing in accordance with possible neuroanatomical modifications in the left temporal lobe.


K-means Document Clustering Based on Latent Dirichlet Allocation


title: Reduction of lexical ambiguity

abstract: SummaryWe examine various issues faced during the elaboration of lexical disambiguators, e.g. issues related with linguistic analyses underlying disambiguators, and we exemplify these issues with grammatical constraints. We also examine computational problems and show how they are connected with linguistic problems: the influence of the granularity of tagsets, the definition of realistic and useful objectives, and the construction of the data required for the reduction of ambiguity. We show why a formalism is required for automatic ambiguity reduction, we analyse its function and we present a typology of such formalisms.


title: A Robust Flexible Electrochemical Gas Sensor Using Room Temperature Ionic Liquid

abstract: This paper introduces a robust electrochemical gas sensor featuring room temperature ionic liquid (RTIL) as electrolyte and porous polytetrafluoroethylene as a flexible substrate. Using a planar-electrodes-on-permeable-membrane structure, a flexible RTIL sensor with 9.6 mm2 sensing area is microfabricated. The sensor's response to oxygen is measured, achieving sensitivity of 0.48 μA/%O2, a linearity of 0.997 R2 in range of 0%-21% O2, and limit of detection of 0.08% O2. The reported sensor structure and fabrication process enable realization of sensor arrays for multi-gas monitoring in a low power, miniaturized, and wearable system platform.


A Taxonomy of CDNs


title: Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

abstract: This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic texture, or photos to artistic paintings. With adversarial training, we obtain quality comparable to recent neural texture synthesis methods. As no optimization is required at generation time, our run-time performance (0.25 M pixel images at 25 Hz) surpasses previous neural texture synthesizers by a significant margin (at least 500 times faster). We apply this idea to texture synthesis, style transfer, and video stylization.


title: Soft switching three level inverter with passive snubber circuit (S3L inverter)

abstract: Presented here is a three level voltage source inverter for electric drives, uninterruptable power supplies and for mains feeds. Unlike the usual hard switching topologies, it is implemented as completely soft switching. A novel patented snubber circuit is added, which is structured very simply and consists of only a few passive components. This snubber circuit works in principle without any losses; all switching operations in the three level voltage source inverter are in principle free of switching losses. For this reason, very high values of switching frequency up to about 40 kHz, and very high efficiencies up to more than 98%, can be realized, even with utilization of economical standard IGBT's. In particular with utilization of IGCT's, an effective di/dt limitation is to be emphasized, which works without additional inductive voltage stressing of the IGCT's.


Phishing on Mobile Devices


title: Sequential model-based optimization for general algorithm configuration

abstract: State-of-the-art algorithms for hard computational problems often expose many parameters that can be modified to improve empirical performance. However, manually exploring the resulting combinatorial space of parameter settings is tedious and tends to lead to unsatisfactory outcomes. Recently, automated approaches for solving this algorithm configuration problem have led to substantial improvements in the state of the art for solving various problems. One promising approach constructs explicit regression models to describe the dependence of target algorithm performance on parameter settings; however, this approach has so far been limited to the optimization of few numerical algorithm parameters on single instances. In this paper, we extend this paradigm for the first time to general algorithm configuration problems, allowing many categorical parameters and optimization for sets of instances. We experimentally validate our new algorithm configuration procedure by optimizing a local search and a tree search solver for the propositional satisfiability problem (SAT), as well as the commercial mixed integer programming (MIP) solver CPLEX. In these experiments, our procedure yielded state-of-the-art performance, and in many cases outperformed the previous best configuration approach.


title: 50 years of RF and microwave sampling

abstract: Measurement of microwave and UHF signals is often done with sampling techniques. In this paper, the techniques and technology of sampling of electrical signals is reviewed from 1950 to the present. It includes both references to the open literature, as well as an extensive review of relevant patents. It also provides an overview of sampling applications and the use of computer technology to compensate and correct for errors in the sampling process.


title: Map API - scalable decentralized map building for robots

abstract: Large scale, long-term, distributed mapping is a core challenge to modern field robotics. Using the sensory output of multiple robots and fusing it in an efficient way enables the creation of globally accurate and consistent metric maps. To combine data from multiple agents into a global map, most existing approaches use a central entity that collects and manages the information from all agents. Often, the raw sensor data of one robot needs to be made available to processing algorithms on other agents due to the lack of computational resources on that robot. Unfortunately, network latency and low bandwidth in the field limit the generality of such an approach and make multi-robot map building a tedious task. In this paper, we present a distributed and decentralized back-end for concurrent and consistent robotic mapping. We propose a set of novel approaches that reduce the bandwidth usage and increase the effectiveness of inter-robot communication for distributed mapping. Instead of locking access to the map during operations, we define a version control system which allows concurrent and consistent access to the map data. Updates to the map are then shared asynchronously with agents which previously registered notifications. A technique for data lookup is provided by state-of-the-art algorithms from distributed computing. We validate our approach on real-world datasets and demonstrate the effectiveness of the proposed algorithms.


title: Video Frame Interpolation via Adaptive Separable Convolution

abstract: Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion, which limits the number of pixels whose kernels can be estimated at once due to the large memory demand. To address this problem, this paper formulates frame interpolation as local separable convolution over input frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D kernels require significantly fewer parameters to be estimated. Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously. Since our method is able to estimate kernels and synthesizes the whole video frame at once, it allows for the incorporation of perceptual loss to train the neural network to produce visually pleasing frames. This deep neural network is trained end-to-end using widely available video data without any human annotation. Both qualitative and quantitative experiments show that our method provides a practical solution to high-quality video frame interpolation.


title: Variations in insecticidal activity and chemical compositions of leaf essential oils from Cryptomeria japonica at different ages

abstract: The larvicidal effects of the essential oils extracted from the leaves of Cryptomeria japonica at different ages (58, 42, and 26 years old) against 2 mosquito species, Aedes aegypti and Aedes albopictus, were studied. The analysis of major constituents of these essential oils was also investigated. Results obtained from the larvicidal tests, using essential oil from the leaves of 58-year-old C. japonica was found to be most effective against both A. aegypti and A. albopictus larvae, indicating tree age has significant influence on mosquito larvicidal activity. In addition, the eleven pure constituents from C. japonica leaf essential oil were also tested individually against the two mosquito larvae. Among them, α-terpinene, γ-terpinene, p-cymene, 3-carene, terpinolene, and β-myrcene shows strong larvicidal effect against the two mosquito larvae. Among these pure constituents, 3-carene exhibits the best larvicidal effect against A. aegypti and terpinolene shows an excellent inhibitory action against A. albopictus larvae. The results of this study show that the leaf essential oil and its effective constituents might be considered as a potent source for the production of fine natural larvicides.


title: Measuring Cross-Category Price Effects with Aggregate Store Data

abstract: Our objective is to understand the cross-category effects of marketing activities using aggregate store-level scanner data. For this, we provide a framework derived from household utility maximizing behavior which assumes that a household chooses the “bundle” of products with the highest utility. We use a second-order Taylor series approximation to an arbitrary utility function to represent bundle utility. Aggregate sales or shares in each category are derived under the assumption that households are heterogeneous in their preferences and in their sensitivities to marketing activities. Our estimation accounts for potential price endogeneity in demand. Using store-level scanner data on four product categories---liquid laundry detergents, powdered laundry detergents, liquid fabric softeners, and sheet fabric softeners---we find evidence for a complementary relationship between liquid softeners and both forms of detergents. We also find that the magnitude of cross-category elasticities are brand specific, i.e., different brands in a category have a different price impact on the demand for a brand in another category. The results have implications for retailers in terms of the potential need for cross-category management, as well as for manufacturers such as Procter & Gamble that participate in all four categories. We compare our model with a log-log regression specification on three criteria---estimated elasticities, hold-out sample predictions, and retailer cross-category pricing. We find that the proposed model produces more reasonable estimates relative to the log-log model; it predicts better and is more useful for pricing purposes. Further, in a simulation study, we show that our proposed model can recover the elasticities from a data-generating process that simulates household-level joint outcomes across categories even after these data have been aggregated to brand-level shares within each category. By contrast, the log-log regression model is unable to do so.


title: Fingerprint Matching using Gabor Filters

abstract: We present a fingerprint matching scheme that utilizes a ridge feature map to match fingerprint images. The technique described here obviates the need for extracting minutiae points to match fingerprint images. The proposed scheme uses a set of 16 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. A circular tessellation of filtered image is then used to construct the ridge feature map. This ridge feature map contains both global and local details in a fingerprint as a compact fixed length feature vector. The fingerprint matching is based on the Euclidean distance between two corresponding feature vectors. The genuine accept rate of the Gabor filter based matcher is observed to be ~ 10% to 15% higher than that of minutiae-based matcher at low false accept rates. Fingerprint feature extraction and matching takes ~ 7.1 seconds on a Pentium IV, 2.4 GHz processor.


title: Maximum power point tracking of photovoltaic water pumping system using fuzzy logic controller

abstract: Diode junction photovoltaic (PV) generators exhibit nonlinear V-I characteristics and the maximum power extractable varies with the intensity of solar radiation, temperature and load conditions. A maximum power point tracking (MPPT) controller is therefore usually employed in PV-generator applications to automatically extract maximum power irrespective of the instantaneous conditions of the PV system. This paper presents a fuzzy logic control (FLC) scheme for extracting the maximum power from a stand-alone PV generator for use in a water pumping system. The PV-generator system comprises a solar panel, DC-DC buck chopper, fuzzy MPP tracker and permanent DC-motor driving a centrifugal pump. The fuzzy controller generates a control signal for the pulse-width-modulation generator which in turn adjusts the duty ratio of the buck chopper to match the load impedance to the PV generator, and consequently maximizes the motor speed and the water discharge rate of a coupled centrifugal pump. The control method has been modelled in Matlab/Simulink and simulation results are presented to confirm its significantly improved power extraction performance under different sunlight conditions, when compared with a directly-connected PV-generator energized pumping system operating.


title: A reputation system for peer-to-peer networks

abstract: We investigate the design of a reputation system for decentralized unstructured P2P networks like Gnutella. Having reliable reputation information about peers can form the basis of an incentive system and can guide peers in their decision making (e.g., who to download a file from). The reputation system uses objective criteria to track each peer's contribution in the system and allows peers to store their reputations locally. Reputation are computed using either of the two schemes, debit-credit reputation computation (DCRC) and credit-only reputation computation (CORC). Using a reputation computation agent (RCA), we design a public key based mechanism that periodically updates the peer reputations in a secure, light-weight, and partially distributed manner. We evaluate using simulations the performance tradeoffs inherent in the design of our system.


title: ARCHANGEL: Trusted Archives of Digital Public Documents

abstract: We present ARCHANGEL; a de-centralised platform for ensuring the long-term integrity of digital documents stored within public archives. Document integrity is fundamental to public trust in archives. Yet currently that trust is built upon institutional reputation --- trust at face value in a centralised authority, like a national government archive or University. ARCHANGEL proposes a shift to a technological underscoring of that trust, using distributed ledger technology (DLT) to cryptographically guarantee the provenance, immutability and so the integrity of archived documents. We describe the ARCHANGEL architecture, and report on a prototype of that architecture build over the Ethereum infrastructure. We report early evaluation and feedback of ARCHANGEL from stakeholders in the research data archives space.


title: Robust enhancement of depth images from depth sensors

abstract: Abstract In recent years, depth cameras (such as Microsoft Kinect and ToF cameras) have gained much popularity in computer graphics, visual computing and virtual reality communities due to their low price and easy availability. While depth cameras (e.g. Microsoft Kinect) provide RGB images along with real-time depth information at high frame rate, the depth images often suffer from several artifacts due to inaccurate depth measurement. These artifacts highly degrade the visual quality of the depth frames. Most of these artifacts originate from two main sources—the missing/invalid depth values and fluctuating valid depth values on the generated contents. In this paper, we propose a new depth image enhancement method, for the contents of depth cameras, which addresses these two main sources of artifacts. We introduce a robust 1D Least Median of Squares (1D LMedS) approach to estimate the depth values of those pixels which have missing/invalid depth values. We use a sequence of frames to look for invalid depth values (considered as outliers), and finally, replace those values with stable and more plausible depth values. By doing so, our approach improves the unstable nature of valid depth values in captured scenes that is perceived as flickering. We use self-recorded and reference datasets along with reference methods to evaluate the performance of our proposed 1D LMedS. Experimental results show improvements both for static and moving parts of a scene.


title: A Neural Click Model for Web Search

abstract: Understanding user browsing behavior in web search is key to improving web search effectiveness. Many click models have been proposed to explain or predict user clicks on search engine results. They are based on the probabilistic graphical model (PGM) framework, in which user behavior is represented as a sequence of observable and hidden events. The PGM framework provides a mathematically solid way to reason about a set of events given some information about other events. But the structure of the dependencies between the events has to be set manually. Different click models use different hand-crafted sets of dependencies. We propose an alternative based on the idea of distributed representations: to represent the user's information need and the information available to the user with a vector state. The components of the vector state are learned to represent concepts that are useful for modeling user behavior. And user behavior is modeled as a sequence of vector states associated with a query session: the vector state is initialized with a query, and then iteratively updated based on information about interactions with the search engine results. This approach allows us to directly understand user browsing behavior from click-through data, i.e., without the need for a predefined set of rules as is customary for PGM-based click models. We illustrate our approach using a set of neural click models. Our experimental results show that the neural click model that uses the same training data as traditional PGM-based click models, has better performance on the click prediction task (i.e., predicting user click on search engine results) and the relevance prediction task (i.e., ranking documents by their relevance to a query). An analysis of the best performing neural click model shows that it learns similar concepts to those used in traditional click models, and that it also learns other concepts that cannot be designed manually.


title: Generative Adversarial Network for Abstractive Text Summarization

abstract: In this paper, we propose an adversarial process for abstractive text summarization, in which we simultaneously train a generative model G and a discriminative model D. In particular, we build the generator G as an agent of reinforcement learning, which takes the raw text as input and predicts the abstractive summarization. We also build a discriminator which attempts to distinguish the generated summary from the ground truth summary. Extensive experiments demonstrate that our model achieves competitive ROUGE scores with the state-of-the-art methods on CNN/Daily Mail dataset. Qualitatively, we show that our model is able to generate more abstractive, readable and diverse summaries.


title: Evaluation of Traditional, Orthogonal, and Radial Tree Diagrams by an Eye Tracking Study

abstract: Node-link diagrams are an effective and popular visualization approach for depicting hierarchical structures and for showing parent-child relationships. In this paper, we present the results of an eye tracking experiment investigating traditional, orthogonal, and radial node-link tree layouts as a piece of empirical basis for choosing between those layouts. Eye tracking was used to identify visual exploration behaviors of participants that were asked to solve a typical hierarchy exploration task by inspecting a static tree diagram: finding the least common ancestor of a given set of marked leaf nodes. To uncover exploration strategies, we examined fixation points, duration, and saccades of participants' gaze trajectories. For the non-radial diagrams, we additionally investigated the effect of diagram orientation by switching the position of the root node to each of the four main orientations. We also recorded and analyzed correctness of answers as well as completion times in addition to the eye movement data. We found out that traditional and orthogonal tree layouts significantly outperform radial tree layouts for the given task. Furthermore, by applying trajectory analysis techniques we uncovered that participants cross-checked their task solution more often in the radial than in the non-radial layouts.


title: A path planning framework for autonomous vehicles

abstract: In this paper we present a framework for path planning of autonomous vehicles in static environments which allows for rapid prototyping and evaluation. We achieve this by decoupling search based path planning into search on the one hand and expansion strategies on the other hand. Thus we are able to combine arbitrary sampling methods to discretize the search space with arbitrary heuristic search algorithms. Sampling methods and searches can be connected at design-time without the need to recompile the code. Other search algorithms and sampling methods that are programmed against our interfaces can be easily added and combined with existing search algorithms and sampling methods. Furthermore, the framework is capable of creating more complex planners, by adding further sampling/search combinations to planning pipelines. We show the strength of our approach by applying our framework to one particular path planning problem and evaluating three different planning pipelines.


title: Monte Mario: platforming with MCTS

abstract: Monte Carlo Tree Search (MCTS) is applied to control the player character in a clone of the popular platform game Super Mario Bros. Standard MCTS is applied through search in state space with the goal of moving the furthest to the right as quickly as possible. Despite parameter tuning, only moderate success is reached. Several modifications to the algorithm are then introduced specifically to deal with the behavioural pathologies that were observed. Two of the modifications are to our best knowledge novel. A combination of these modifications is found to lead to almost perfect play on linear levels. Furthermore, when adding noise to the benchmark, MCTS outperforms the best known algorithm for these levels. The analysis and algorithmic innovations in this paper are likely to be useful when applying MCTS to other video games.


title: End-to-End Comparative Attention Networks for Person Re-identification

abstract: Person re-identification across disjoint camera views has been widely applied in video surveillance yet it is still a challenging problem. One of the major challenges lies in the lack of spatial and temporal cues, which makes it difficult to deal with large variations of lighting conditions, viewing angles, body poses, and occlusions. Recently, several deep-learning-based person re-identification approaches have been proposed and achieved remarkable performance. However, most of those approaches extract discriminative features from the whole frame at one glimpse without differentiating various parts of the persons to identify. It is essentially important to examine multiple highly discriminative local regions of the person images in details through multiple glimpses for dealing with the large appearance variance. In this paper, we propose a new soft attention-based model, i.e. , the end-to-end comparative attention network (CAN), specifically tailored for the task of person re-identification. The end-to-end CAN learns to selectively focus on parts of pairs of person images after taking a few glimpses of them and adaptively comparing their appearance. The CAN model is able to learn which parts of images are relevant for discerning persons and automatically integrates information from different parts to determine whether a pair of images belongs to the same person. In other words, our proposed CAN model simulates the human perception process to verify whether two images are from the same person. Extensive experiments on four benchmark person re-identification data sets, including CUHK01, CHUHK03, Market-1501, and VIPeR, clearly demonstrate that our proposed end-to-end CAN for person re-identification outperforms well established baselines significantly and offer the new state-of-the-art performance.


title: Memories of bug fixes

abstract: The change history of a software project contains a rich collection of code changes that record previous development experience. Changes that fix bugs are especially interesting, since they record both the old buggy code and the new fixed code. This paper presents a bug finding algorithm using bug fix memories: a project-specific bug and fix knowledge base developed by analyzing the history of bug fixes. A bug finding tool, BugMem, implements the algorithm. The approach is different from bug finding tools based on theorem proving or static model checking such as Bandera, ESC/Java, FindBugs, JLint, and PMD. Since these tools use pre-defined common bug patterns to find bugs, they do not aim to identify project-specific bugs. Bug fix memories use a learning process, so the bug patterns are project-specific, and project-specific bugs can be detected. The algorithm and tool are assessed by evaluating if real bugs and fixes in project histories can be found in the bug fix memories. Analysis of five open source projects shows that, for these projects, 19.3%-40.3% of bugs appear repeatedly in the memories, and 7.9%-15.5% of bug and fix pairs are found in memories. The results demonstrate that project-specific bug fix patterns occur frequently enough to be useful as a bug detection technique. Furthermore, for the bug and fix pairs, it is possible to both detect the bug and provide a strong suggestion for the fix. However, there is also a high false positive rate, with 20.8%-32.5% of non-bug containing changes also having patterns found in the memories. A comparison of BugMem with a bug finding tool, PMD, shows that the bug sets identified by both tools are mostly exclusive, indicating that BugMem complements other bug finding tools.


Certificates of virginity and reconstruction of the hymen


title: A Comparison of Hybrid Beamforming and Digital Beamforming with Low-Resolution ADCs for Multiple Users and Imperfect CSI

abstract: For 5G, it will be important to leverage the available millimeter wave spectrum. To achieve an approximately omnidirectional coverage with a similar effective antenna aperture compared to state-of-the-art cellular systems, an antenna array is required at both the mobile and base station. Due to the large bandwidth and inefficient amplifiers available in CMOS for mmWave, the analog front end of the receiver with a large number of antennas becomes especially power hungry. Two main solutions exist to reduce the power consumption: hybrid beam forming and digital beam forming with low resolution Analog to digital converters (ADCs). In this paper, we compare the spectral and energy efficiency of both systems under practical system constraints. We consider the effects of channel estimation, transmitter impairments, and multiple simultaneous users for a wideband multipath model. Our power consumption model considers components reported in the literature at 60 GHz. In contrast to many other works, we also consider the correlation of the quantization error, and generalize the modeling of it to nonuniform quantizers and different quantizers at each antenna. The result shows that as the signal-to-noise ratio (SNR) gets larger the ADC resolution achieving the optimal energy efficiency gets also larger. The energy efficiency peaks for 5-b resolution at high SNR, since due to other limiting factors, the achievable rate almost saturates at this resolution. We also show that in the multiuser scenario digital beamforming is in any case more energy efficient than hybrid beamforming. In addition, we show that if mixed ADC resolutions are used, we can achieve any desired tradeoff between power consumption and rate close to those achieved with only one ADC resolution.


title: Tools for Intelligent Search in Collections of Digitized Manuscripts

abstract: The paper presents a methodology for development of tools for semantics oriented search in repositories of digitized manuscripts. This methodology is based on the application of some Semantic Web technologies to existing manuscript collections that may include: electronic catalogue containing marked-up manuscript descriptions, full texts of manuscripts, digital images of manuscript pages. It is directed to the development of software environments that will be able to deal with complex user queries and answer questions using ontological knowledge and dictionaries of synonyms.


title: Processing the Text of the Holy Quran: a Text Mining Study

abstract: The Holy Quran is the reference book for more than 1.6 billion of Muslims all around the world Extracting information and knowledge from the Holy Quran is of high benefit for both specialized people in Islamic studies as well as non-specialized people. This paper initiates a series of research studies that aim to serve the Holy Quran and provide helpful and accurate information and knowledge to the all human beings. Also, the planned research studies aim to lay out a framework that will be used by researchers in the field of Arabic natural language processing by providing a ”Golden Dataset” along with useful techniques and information that will advance this field further. The aim of this paper is to find an approach for analyzing Arabic text and then providing statistical information which might be helpful for the people in this research area. In this paper the holly Quran text is preprocessed and then different text mining operations are applied to it to reveal simple facts about the terms of the holy Quran. The results show a variety of characteristics of the Holy Quran such as its most important words, its wordcloud and chapters with high term frequencies. All these results are based on term frequencies that are calculated using both Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) methods.


title: Network Intrusion Detection System using attack behavior classification

abstract: Intrusion Detection Systems (IDS) have become a necessity in computer security systems because of the increase in unauthorized accesses and attacks. Intrusion Detection is a major component in computer security systems that can be classified as Host-based Intrusion Detection System (HIDS), which protects a certain host or system and Network-based Intrusion detection system (NIDS), which protects a network of hosts and systems. This paper addresses Probes attacks or reconnaissance attacks, which try to collect any possible relevant information in the network. Network probe attacks have two types: Host Sweep and Port Scan attacks. Host Sweep attacks determine the hosts that exist in the network, while port scan attacks determine the available services that exist in the network. This paper uses an intelligent system to maximize the recognition rate of network attacks by embedding the temporal behavior of the attacks into a TDNN neural network structure. The proposed system consists of five modules: packet capture engine, preprocessor, pattern recognition, classification, and monitoring and alert module. We have tested the system in a real environment where it has shown good capability in detecting attacks. In addition, the system has been tested using DARPA 1998 dataset with 100% recognition rate. In fact, our system can recognize attacks in a constant time.


title: The role of nucleus accumbens dopamine in motivated behavior: a unifying interpretation with special reference to reward-seeking

abstract: Abstract Studies addressing behavioral functions of dopamine (DA) in the nucleus accumbens septi (NAS) are reviewed. A role of NAS DA in reward has long been suggested. However, some investigators have questioned the role of NAS DA in rewarding effects because of its role in aversive contexts. As findings supporting the role of NAS DA in mediating aversively motivated behaviors accumulate, it is necessary to accommodate such data for understanding the role of NAS DA in behavior. The aim of the present paper is to provide a unifying interpretation that can account for the functions of NAS DA in a variety of behavioral contexts: (1) its role in appetitive behavioral arousal, (2) its role as a facilitator as well as an inducer of reward processes, and (3) its presently undefined role in aversive contexts. The present analysis suggests that NAS DA plays an important role in sensorimotor integrations that facilitate flexible approach responses. Flexible approach responses are contrasted with fixed instrumental approach responses (habits), which may involve the nigro-striatal DA system more than the meso-accumbens DA system. Functional properties of NAS DA transmission are considered in two stages: unconditioned behavioral invigoration effects and incentive learning effects. (1) When organisms are presented with salient stimuli (e.g., novel stimuli and incentive stimuli), NAS DA is released and invigorates flexible approach responses (invigoration effects). (2) When proximal exteroceptive receptors are stimulated by unconditioned stimuli, NAS DA is released and enables stimulus representations to acquire incentive properties within specific environmental context. It is important to make a distinction that NAS DA is a critical component for the conditional formation of incentive representations but not the retrieval of incentive stimuli or behavioral expressions based on over-learned incentive responses (i.e., habits). Nor is NAS DA essential for the cognitive perception of environmental stimuli. Therefore, even without normal NAS DA transmission, the habit response system still allows animals to perform instrumental responses given that the tasks take place in fixed environment. Such a role of NAS DA as an incentive-property constructor is not limited to appetitive contexts but also aversive contexts. This dual action of NAS DA in invigoration and incentive learning may explain the rewarding effects of NAS DA as well as other effects of NAS DA in a variety of contexts including avoidance and unconditioned/conditioned increases in open-field locomotor activity. Particularly, the present hypothesis offers the following interpretation for the finding that both conditioned and unconditioned aversive stimuli stimulate DA release in the NAS: NAS DA invigorates approach responses toward `safety'. Moreover, NAS DA modulates incentive properties of the environment so that organisms emit approach responses toward `safety' (i.e., avoidance responses) when animals later encounter similar environmental contexts. There may be no obligatory relationship between NAS DA release and positive subjective effects, even though these systems probably interact with other brain systems which can mediate such effects. The present conceptual framework may be valuable in understanding the dynamic interplay of NAS DA neurochemistry and behavior, both normal and pathophysiological.


title: Meta-Analysis of Theory-of-Mind Development: The Truth about False Belief.

abstract: Research on theory of mind increasingly encompasses apparently contradictory findings. In particular, in initial studies, older preschoolers consistently passed false-belief tasks—a so-called “definitive” test of mentalstate understanding—whereas younger children systematically erred. More recent studies, however, have found evidence of false-belief understanding in 3-year-olds or have demonstrated conditions that improve children’s performance. A meta-analysis was conducted ( N � 178 separate studies) to address the empirical inconsistencies and theoretical controversies. When organized into a systematic set of factors that vary across studies, false-belief results cluster systematically with the exception of only a few outliers. A combined model that included age, country of origin, and four task factors (e.g., whether the task objects were transformed in order to deceive the protagonist or not) yielded a multiple R of .74 and an R 2 of .55; thus, the model accounts for 55% of the variance in false-belief performance. Moreover, false-belief performance showed a consistent developmental pattern, even across various countries and various task manipulations: preschoolers went from below-chance performance to above-chance performance. The findings are inconsistent with early competence proposals that claim that developmental changes are due to tasks artifacts, and thus disappear in simpler, revised false-belief tasks; and are, instead, consistent with theoretical accounts that propose that understanding of belief, and, relatedly, understanding of mind, exhibit genuine conceptual change in the preschool years.


title: MMORPG player behavior model based on player action categories

abstract: In this paper we present the modeling of player behavior for Massively Multiplayer Online Role-Playing Games (MMORPGs). We have performed action specific measurements of player sessions in terms of the previously defined action categories for MMORPGs (Trading, Questing, Dungeons, Raiding, and Player versus Player Combat). We explore the hourly trends in user behavior and form models based on observed patterns. We explore the session duration as well as lengths and probability of session segments (i.e., parts of the session consisting of only one category of player actions). Our aim is to create the user behavior model and to combine it with previously established traffic models in order to be able to explain, predict, and generate network traffic of MMORPGs more accurately. As a case study we use World of Warcraft.


title: Outside–in transobturator midurethral sling and the dorsal nerve of the clitoris

abstract: INTRODUCTION AND HYPOTHESIS ::: An anatomical study on fresh cadavers was done to determine the vulnerability of the dorsal nerve of the clitoris to injury during "outside-in" transobturator sling procedures. ::: ::: ::: METHODS ::: The dorsal nerve of the clitoris was identified bilaterally in ten fresh cadavers. Transfixing needles marked the inferomedial border of the obturator foramen. The distance between the foramen border and the nerve was measured. ::: ::: ::: RESULTS ::: The nerve ran medially in close approximation to the ischiopubic ramus and inferior to the obturator foramen in all specimens. In no instance did the nerve follow an aberrant course traversing the obturator foramen. The mean distance between the inferomedial border of the obturator foramen and the nerve was 9.3 mm, range 3-14 mm. ::: ::: ::: CONCLUSIONS ::: When the "outside-in" technique is used, the introducer cannot come into contact with the dorsal nerve of the clitoris because the introducer would have to pass through the ischio-pubic ramus. This is not anatomically possible.