title: Testing nonlinear stochastic models on phytoplankton biomass time series

abstract: Abstract Time series of algae mass in German water reservoir lakes being collected for several years in irregular sampling, half weekly on average, are tested in their stationary distribution against several univariate stochastic processes with linear and nonlinear dynamics, with pure additive and with state dependent noise. In each case the parameters of the corresponding Fokker–Planck equations in the stationary state are estimated by maximisation of the log-likelihood. For statistical evaluation the Kolmogorov–Smirnov-test is applied with additional graphical tools, being especially suited for short data sets typical for ecological systems. To obtain the optimally adapted stochastic process, an additional parameter has to be estimated describing the autocorrelation. We use two methods: first, we use a novel method based on the estimation of conditional probabilities directly from the data. The resulting stochastic model with state dependent noise is simulated. Second, we use the Lomb normalised Fourier spectrum for unevenly sampled data, e.g. no measurements during winter time to minimise the overall deviation between the empirical spectrum and spectra obtained from stochastic simulations, the sampling of which follows in detail the one of the empirical data. The parameter value of the first method is used as a starting point for the adaptation of the cumulative spectrum. In this way a stochastic process is obtained which describes the stationary distribution as well as the spectrum of the empirical data both indistinguishable at high statistical confidence.


title: Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition

abstract: We propose a novel context-dependent (CD) model for large-vocabulary speech recognition (LVSR) that leverages recent advances in using deep belief networks for phone recognition. We describe a pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output. The deep belief network pre-training algorithm is a robust and often helpful way to initialize deep neural networks generatively that can aid in optimization and reduce generalization error. We illustrate the key components of our model, describe the procedure for applying CD-DNN-HMMs to LVSR, and analyze the effects of various modeling choices on performance. Experiments on a challenging business search dataset demonstrate that CD-DNN-HMMs can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs, with an absolute sentence accuracy improvement of 5.8% and 9.2% (or relative error reduction of 16.0% and 23.2%) over the CD-GMM-HMMs trained using the minimum phone error rate (MPE) and maximum-likelihood (ML) criteria, respectively.


title: On How to Perform a Gold Standard Based Evaluation of Ontology Learning

abstract: In recent years several measures for the gold standard based evaluation of ontology learning were proposed. They can be distinguished by the layers of an ontology (e.g. lexical term layer and concept hierarchy) they evaluate. Judging those measures with a list of criteria we show that there exist some measures sufficient for evaluating the lexical term layer. However, existing measures for the evaluation of concept hierarchies fail to meet basic criteria. This paper presents a new taxonomic measure which overcomes the problems of current approaches.


title: An architecture for the aggregation and analysis of scholarly usage data

abstract: Although recording of usage data is common in scholarly information services, its exploitation for the creation of value-added services remains limited due to concerns regarding, among others, user privacy, data validity, and the lack of accepted standards for the representation, sharing and aggregation of usage data. This paper presents a technical, standards-based architecture for sharing usage information, which we have designed and implemented. In this architecture, OpenURL-compliant linking servers aggregate usage information of a specific user community as it navigates the distributed information environment that it has access to. This usage information is made OAI-PMH harvestable so that usage information exposed by many linking servers can be aggregated to facilitate the creation of value-added services with a reach beyond that of a single community or a single information service. This paper also discusses issues that were encountered when implementing the proposed approach, and it presents preliminary results obtained from analyzing a usage data set containing about 3,500,000 requests aggregated by a federation of linking servers at the California State University system over a 20 month period.


title: The Association Among Negative Life Events, Perceived Problem Solving Alternatives, Depression, and Suicidal Ideation in Adolescent Psychiatric Patients

abstract: Previous research demonstrated that the combination of a negative life event (NLE) and less constructive perceived problem solving alternatives (PPSAs) was associated with more depression in unselected adolescents. This study investigated these variables and their association with suicidal ideation in 80 adolescent psychiatric patients. Depressed patients reported more negative effects from NLEs, particularly family conflict and academic failure, and were more likely to consider self-destructive PPSAs than their less depressed counterparts. The impacts of NLEs and negative PPSAs were independently associated with both depression and suicidal ideation for this population, in contrast to non-patients.


title: An Introductory Tour of Interactive Rendering

abstract: Given the depth and breadth of the field of interactive rendering, an article summarizing all the significant algorithms and hardware developments in the past few years would become a long yet shallow litany of taxonomies and terms. Instead, the author uses a selection of topics from various areas to illustrate this central theme: the influence of 3D graphics hardware on algorithm design. This article provides an understanding of consumer-level personal graphics processors and a sense of how different algorithms are developed in response to their capabilities.


title: TBXTools: A Free, Fast and Flexible Tool for Automatic Terminology Extraction

abstract: The manual identification of terminology from specialized corpora is a complex task that needs to be addressed by flexible tools, in order to facilitate the construction of multilingual terminologies which are the main resources for computer-assisted translation tools, machine translation or ontologies. The automatic terminology extraction tools developed so far either use a proprietary code or an open source code, that is limited to certain software functionalities. To automatically extract terms from specialized corpora for different purposes such as constructing dictionaries, thesauruses or translation memories, we need open source tools to easily integrate new functionalities to improve term selection. This paper presents TBXTools, a free automatic terminology extraction tool that implements linguistic and statistical methods for multiword term extraction. The tool allows the users to easily identify multiword terms from specialized corpora and also, if needed, translation candidates from parallel corpora. In this paper we present the main features of TBXTools along with evaluation results for term extraction, both using statistical and linguistic methodology, for several corpora.


title: An Integrated Success Model for Evaluating Information System in Public Sectors

abstract: This study modified the dimensions of the TAM and DM the proposed model has been validated by an empirical study based on a questionnaire. The results indicated that information quality has a strong significant influence on IS success (81.9%) followed by Behavioral intention (80.2%); Perceived usefulness (78.8%); whereas the least one is user involvement (70%). The overall results show that the proposed model can be beneficial for decision makers in organizations on evaluating the implementation of information systems. Key wards: DeLone & McLean IS success model, Technology Acceptance Model, Integrated IS Success Model


title: A Broadband LTE/WWAN Antenna Design for Tablet PC

abstract: A broadband yet compact antenna design applicable to tablets and laptops is proposed. The antenna provides an extensive coverage for existing and upcoming mobile communication bands. Several band broadening and antenna miniaturization techniques were employed, including the use of a parasitic element, meandered structures, branched structures and a lump component. The proposed design is planar, compact and can be fabricated via printed circuit board technology. Measurement results exhibits broad resonant bandwidth. Nearly omni-directional patterns and reasonable radiation efficiency are observed.


title: Classification-Based Adaptive Web Scraper

abstract: Web scraping is an important problem in computer science. The problem with the commonly-used position or structure-based web scraping tools is that they need to be manually reconfigured as soon as the structure of the web page changes. In this paper, we try to solve this problem of information extraction for web pages consisting of repetitive blocks. We extract these blocks and their constituent attributes, using a novel classification-based approach. Our approach gives high accuracy when used to extract product-offers from an offer-aggregator website. It is also highly adaptive to the changing structure of a website.


title: The intellectual development of the technology acceptance model: A co-citation analysis

abstract: The goal of this paper is to present a visual mapping of intellectual structure in two-dimensions and to identify the subfields of the technology acceptance model through co-citation analysis. All the citation documents are included in the ISI Web of Knowledge database between 1989 and 2006. By using a sequence of statistical analyses including factor analysis, multidimensional scaling, and cluster analysis, we identified three main trends: task-related systems, e-commerce systems, and hedonic systems. The findings yielded managerial implications for both academic and practical issues.


title: A Narrative Review of the Impact of Disbelief in Chronic Pain

abstract: Abstract Although the experience of being believed is frequently alluded to in chronic pain literature, few studies have specifically explored this phenomenon and even fewer reviews have been offered. This narrative review sought to explore the wider social context in which individuals with chronic pain may experience disbelief toward their pain. Articles were obtained through a search of eight databases and a hand search of the references of full-text papers. Key results within the articles were noted and integrated to form three main themes: stigma, the experience of isolation, and the experience of emotional distress. The experience of stigma can occur in a number of ways. It may be through actual or perceived encounters with others; it can be through the use of psychologic explanations of pain; it can come through a perceived challenge to one’s integrity and subsequently affect an individual’s identity; and such stigma may be influenced by negative female stereotypes. The loss of relationships associated with being disbelieved can lead to the experience of isolation. This may be self-initiated, particularly when an individual has been given a contested diagnosis. Finally, disbelief can lead to emotional distress. This can take the form of guilt, depression, and anger. Throughout the article, implications for health care professionals, working with individuals living with chronic pain, are discussed.


title: A survey on the evolution of privacy enforcement on smartphones and the road ahead

abstract: Abstract With the increasing proliferation of smartphones, enforcing privacy of smartphone users becomes evermore important. Nowadays, one of the major privacy challenges is the tremendous amount of permissions requested by applications, which can significantly invade users’ privacy, often without their knowledge. In this paper, we provide a comprehensive review of approaches that can be used to report on applications’ permission usage, tune permission access, contain sensitive information, and nudge users towards more privacy-conscious behavior. We discuss key shortcomings of privacy enforcement on smartphones so far and identify suitable actions for the future.


title: Integrity analysis of authenticated encryption based on stream ciphers

abstract: We study the security of authenticated encryption based on a stream cipher and a universal hash function. We consider ChaCha20-Poly1305 and generic constructions proposed by Sarkar, where the generic constructions include 14 AEAD (authenticated encryption with associated data) schemes and 3 DAEAD (deterministic AEAD) schemes. In this paper, we analyze the integrity of these schemes both in the standard INT-CTXT (integrity of ciphertext) notion and in the RUP (releasing unverified plaintext) setting called INT-RUP notion. We present INT-CTXT attacks against 3 out of the 14 AEAD schemes and 1 out of the 3 DAEAD schemes. We then show INT-RUP attacks against 1 out of the 14 AEAD schemes and the 2 remaining DAEAD schemes. Next, we consider ChaCha20-Poly1305 and show that it is provably secure in the INT-RUP notion. Finally, we show that the remaining 10 AEAD schemes are provably secure in the INT-RUP notion.


title: Faster Algorithms for Max-Product Message-Passing

abstract: Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, such as the junction-tree algorithm or loopy belief-propagation. The exact solution to this problem is well-known to be exponential in the size of the maximal cliques of the triangulated model, while approximate inference is typically exponential in the size of the model's factors. In this paper, we take advantage of the fact that many models have maximal cliques that are larger than their constituent factors, and also of the fact that many factors consist only of latent variables (i.e., they do not depend on an observation). This is a common case in a wide variety of applications that deal with grid-, tree-, and ring-structured models. In such cases, we are able to decrease the exponent of complexity for message-passing by 0.5 for both exact and approximate inference. We demonstrate that message-passing operations in such models are equivalent to some variant of matrix multiplication in the tropical semiring, for which we offer an O(N2.5) expected-case solution.


title: A cross-media public sentiment analysis system for microblog

abstract: Since classical public sentiment analysis systems for microblog are based on the text sentiment analysis, it is difficult to determine the sentiment of short text without clear sentiment words in microblog posts. Fortunately, a lot of microblog posts contain images which also represent users' sentiment. To fully understand users' sentiment, we propose a cross-media public sentiment analysis system for microblog. The best advantage of this novel system is the unified cross-media public sentiment analysis framework which fuses the text sentiment and image sentiment not only from sentiment results, but also from sentiment ontology. To enhance presentation effects, this system presents sentiment results from macroscopic view and microscopic view which details the sentiment results in region, topic, microblog content and user diffusion. In our knowledge, this is the first unified cross-media public sentiment analysis system.


title: The Design and Evolution of Fiducials for the reacTIVision System

abstract: The reacTIVision system is software for tracking specially designed fiducials (markers) in a real-time video stream. ReacTIVision was designed to enable expressive gestural control of musical sound, and can track many markers at a high frame rate. The development of reacTIVision involved not only computer vision algorithms, but also the design of a new marker system. We co-designed the computer vision system and markers, applying evolutionary computation to minimise marker size while meeting geometric constraints required to efficiently compute the location and 2D orientation of the markers. The computer vision techniques adopted would not have been practical had the genetic algorithms not been able to solve these constraints. This paper focuses on the design and evolution of the markers, the computer vision aspects of reacTIVision are documented elsewhere.


title: Improving Cross-Resolution Face Matching Using Ensemble-Based Co-Transfer Learning

abstract: Face recognition algorithms are generally trained for matching high-resolution images and they perform well for similar resolution test data. However, the performance of such systems degrades when a low-resolution face image captured in unconstrained settings, such as videos from cameras in a surveillance scenario, are matched with high-resolution gallery images. The primary challenge, here, is to extract discriminating features from limited biometric content in low-resolution images and match it to information rich high-resolution face images. The problem of cross-resolution face matching is further alleviated when there is limited labeled positive data for training face recognition algorithms. In this paper, the problem of cross-resolution face matching is addressed where low-resolution images are matched with high-resolution gallery.A co-transfer learning framework is proposed, which is a cross-pollination of transfer learning and co-training paradigms and is applied for cross-resolution face matching. The transfer learning component transfers the knowledge that is learnt while matching high-resolution face images during training to match low-resolution probe images with high-resolution gallery during testing. On the other hand, co-training component facilitates this transfer of knowledge by assigning pseudolabels to unlabeled probe instances in the target domain. Amalgamation of these two paradigms in the proposed ensemble framework enhances the performance of cross-resolution face recognition. Experiments on multiple face databases show the efficacy of the proposed algorithm and compare with some existing algorithms and a commercial system. In addition, several high profile real-world cases have been used to demonstrate the usefulness of the proposed approach in addressing the tough challenges.


AN IMPROVED CLUSTER HEAD SELECTION TECHNIQUE FOR WIRELESS SENSOR NETWORK USING MODIFIED GENETIC ALGORITHM


title: Profiling and Supporting Adaptive Micro Learning on Open Education Resources

abstract: It is found that learners prefer to use micro learning mode to conduct learning activities through open educational resources (OERs). However, adaptive micro learning is scarcely supported by current OER platforms. In this paper we focus on profiling an effective micro learning process which is central to establish the raw materials and set up rules for the final adaptive process. This work consists of two parts. First, we conducted an educational data mining and learning analysis study to discover the patterns and rules in micro learning through OER. Then based on its findings, we profiled features of both learners and OERs to reveal the full learning story in order to support the decision making process. Incorporating educational data mining and learning analysis, an cloud-based architecture for Micro Learning as a Service (MLaaS) was designed to integrate all necessary procedures together as a complete service for delivering micro OERs. The MLaaS also provides a platform for resource sharing and exchanging in peer-to-peer learning environment. Working principle of a key step, namely the computational decision-making of micro OER adaptation, was also introduced.


title: Boosting ridge regression

abstract: There are several possible approaches to combining ridge regression with boosting techniques. In the simple or naive approach the ridge estimator is used to fit iteratively the current residuals yielding an alternative to the usual ridge estimator. In partial boosting only part of the regression parameters are reestimated within one step of the iterative procedure. The technique allows to distinguish between mandatory variables that are always included in the analysis and optional variables that are chosen only if relevant. The resulting procedure selects optional variables in a similar way as the Lasso, yielding a reduced set of influential variables, while allowing for regularized estimation of the mandatory parameters. The suggested procedures are investigated within the classical framework of continuous response variables as well as in the case of generalized linear models. The performance in terms of prediction and the identification of relevant variables is compared to several competitors as the Lasso and the more recently proposed elastic net. For the evaluation of the identification of relevant variables pseudo ROC curves are introduced.


title: IO characteristics of modern smartphone platform: Android vs. Tizen

abstract: In this work, we examine the IO characteristics of two smartphone platforms: Android and Tizen. We compare how the two platforms differ in using the filesystem, using the database, and accessing the storage. We collected the IOs from select seven different apps that are commonly available on both platforms. By analyzing the collected the IOs on both platforms, a dominant fraction of writes are 4 KB synchronous. On Android and Tizen, respectively, 65% and 45% of all write IO counts 4 KB; 47% and 75% of all writes accesses are SQLite related files such as database, rollback journal, WAL (Write Ahead Log), and etc.; 81% and 86% of all IOs are random accesses; and 90% and 52% of all writes counts are synchronous IO mode. According to our IO analysis, Android IO stack is more sophisticated then Tizen one: (i) disables atime updates, (ii) adopts journal asynchronous commit, and (iii) adopts fdatasync() in SQLite.


title: Cardiovascular changes in the unanaesthetized lingcod (Ophiodon elongatus) during short-term, progressive hypoxia and spontaneous activity

abstract: Cardiac output and arterial blood pressures in the ventral and dorsal aortae (PVAnd PDA) were measured directly and simultaneously in the lingcod. The branchial and systemic conductances to blood flow (Gg and Gs) were calculated. During progressive hypoxia , Gg, Gs, PVA, and PDA were all reduced when the water decreased below 45 mmHg (1 mmHg = 133.322 Pa). Spontaneous activity was characterized by an initial bradycardia and concurrent reduction in , Gg, Gs, PVA, and PDA. Longer periods of activity were subsequently followed by a period of restitution where , Gg, Gs, PVA, and PDA were all elevated.The beat by beat oscillations in flow were usually synchronized with, and in the same direction as, those in vascular conductance during both activity and hypoxia. This synchrony was most apparent between Gs and during the rapid cardiovascular adjustments occurring with activity. It is suggested that the observed relationship between flow and conductance changes was a result of passive changes in conductance when...


title: Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives

abstract: When describing robot motion with dynamic movement primitives (DMPs), goal (trajectory endpoint), shape and temporal scaling parameters are used. In reinforcement learning with DMPs, usually goals and temporal scaling parameters are predefined and only the weights for shaping a DMP are learned. Many tasks, however, exist where the best goal position is not a priori known, requiring to learn it. Thus, here we specifically address the question of how to simultaneously combine goal and shape parameter learning. This is a difficult problem because learning of both parameters could easily interfere in a destructive way. We apply value function approximation techniques for goal learning and direct policy search methods for shape learning. Specifically, we use ''policy improvement with path integrals'' and ''natural actor critic'' for the policy search. We solve a learning-to-pour-liquid task in simulations as well as using a Pa10 robot arm. Results for learning from scratch, learning initialized by human demonstration, as well as for modifying the tool for the learned DMPs are presented. We observe that the combination of goal and shape learning is stable and robust within large parameter regimes. Learning converges quickly even in the presence of disturbances, which makes this combined method suitable for robotic applications.


title: Noise-resistant Deep Learning for Object Classification in 3D Point Clouds Using a Point Pair Descriptor

abstract: Object retrieval and classification in point cloud data is challenged by noise, irregular sampling density and occlusion. To address this issue, we propose a point pair descriptor that is robust to noise and occlusion and achieves high retrieval accuracy. We further show how the proposed descriptor can be used in a 4D convolutional neural network for the task of object classification. We propose a novel 4D convolutional layer that is able to learn class-specific clusters in the descriptor histograms. Finally, we provide experimental validation on 3 benchmark datasets, which confirms the superiority of the proposed approach.


title: Geospatial Analysis and the Internet of Things

abstract: As the Internet of Things (IoT) penetrates our everyday lives, being used to address a wide variety of real-life challenges and problems, the location of things becomes an important parameter. The exact location of measuring the physical world through IoT is highly relevant to understand local environmental conditions, or to develop powerful, personalized and context-aware location-based services and applications. This survey paper maps and analyzes the IoT based on its location dimension, categorizing IoT applications and projects according to the geospatial analytical methods performed. The survey investigates the opportunities of location-aware IoT, and examines the potential of geospatial analysis in this research area.


title: UPA 2004 Presentation—Page 1 A Comparison of Questionnaires for Assessing Website Usability

abstract: Five questionnaires for assessing the usability of a website were compared in a study with 123 participants. The questionnaires studied were SUS, QUIS, CSUQ, a variant of Microsoft’s Product Reaction Cards, and one that we have used in our Usability Lab for several years. Each participant performed two tasks on each of two websites: finance.yahoo.com and kiplinger.com. All five questionnaires revealed that one site was significantly preferred over the other. The data were analyzed to determine what the results would have been at different sample sizes from 6 to 14. At a sample size of 6, only 30-40% of the samples would have identified that one of the sites was significantly preferred. Most of the data reach an apparent asymptote at a sample size of 12, where two of the questionnaires (SUS and CSUQ) yielded the same conclusion as the full dataset at least 90% of the time.


title: Joint Recovery of Dense Correspondence and Cosegmentation in Two Images

abstract: We propose a new technique to jointly recover cosegmentation and dense per-pixel correspondence in two images. Our method parameterizes the correspondence field using piecewise similarity transformations and recovers a mapping between the estimated common "foreground" regions in the two images allowing them to be precisely aligned. Our formulation is based on a hierarchical Markov random field model with segmentation and transformation labels. The hierarchical structure uses nested image regions to constrain inference across multiple scales. Unlike prior hierarchical methods which assume that the structure is given, our proposed iterative technique dynamically recovers the structure along with the labeling. This joint inference is performed in an energy minimization framework using iterated graph cuts. We evaluate our method on a new dataset of 400 image pairs with manually obtained ground truth, where it outperforms state-of-the-art methods designed specifically for either cosegmentation or correspondence estimation.


title: The Reversible Residual Network: Backpropagation Without Storing Activations

abstract: Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck, as one needs to store the activations in order to calculate gradients using backpropagation. We present the Reversible Residual Network (RevNet), a variant of ResNets where each layer's activations can be reconstructed exactly from the next layer's. Therefore, the activations for most layers need not be stored in memory during backpropagation. We demonstrate the effectiveness of RevNets on CIFAR-10, CIFAR-100, and ImageNet, establishing nearly identical classification accuracy to equally-sized ResNets, even though the activation storage requirements are independent of depth.


title: Estimating the spatial variability of groundwater recharge in the Sahel using chloride

abstract: Thirteen interstitial water chloride profiles have been obtained from the unsaturated zone of the coastal Quaternary aquifer in Senegal over depth intervals ranging between 7.0 and 35.5 m; three of these profiles reached the water table. A regional study of 119 dug wells was also carried out over an area of some 1600 km2 for the measurement of chloride, as well as other parameters, in samples taken from the water table. ::: ::: These two sets of data have been used, together with rainfall input chemistry collected over a 3 year interval to derive regional estimates of recharge for this area typical of the Sahel margin (mean annual rainfall 1970–1990 around 280 mm). Recharge estimates range from 13 000 to 1100 m3 km−2 year−1. It is concluded that the conjunctive use of interstitial water profiles and regional shallow groundwater chemistry provide an inexpensive technique for recharge estimation which can be widely applied in sedimentary terrains.


title: Putting Continuous Auditing Theory into Practice: Lessons from Two Pilot Implementations

abstract: ABSTRACT: In the almost twenty years since Vasarhelyi and Halper (1991) reported on their pioneering implementation of what has come to be known as Continuous Auditing (CA), the concept has increasingly moved from theory into practice. A 2006 survey by PricewaterhouseCoopers shows that half of all responding firms use some sort of CA techniques, and the majority of the rest plan to do so in the near future. CA not only has an increasing impact on auditing practice, but is also one of the rare instances in which such a significant change was led by the researchers. In this paper we survey the state of CA after two decades of research into continuous auditing theory and practice, and draw out the lessons learned by us in recent pilot CA projects at two major firms, to examine where this unique partnership between academics and auditors will take CA in the future.


3D Sampling Textures for Creative Design and Manufacturing


title: Effective in-class activities for middle school outreach programs

abstract: In the summer of 2007, we established the Utah State University Computer Science Outreach Laboratory as a partnership with a local K-8 school. One major advantage of this outreach program is that we are able to interact with all students in the target grades, not only those who are already interested in computers, as would happen with a computer club or other extracurricular program. This paper presents a sampling of the periodic in-class activities led by University faculty members and volunteer students. Through these activities participants have the opportunity to learn about a variety of computing topics. The core goal of this program is to increase the interest levels in computing among these young students. While the long-term effects are not immediately apparent, surveys of participating students indicate that activities are making a valuable impact. This is supported through our personal observations of, and interactions with, the student participants.


title: A Quranic quote verification algorithm for verses authentication

abstract: The growth of Quranic digital publishing increases the need to develop a better framework to authenticate Quranic quotes with the original source automatically. This paper aims to demonstrate the significance of the quote authentication approach. We propose an approach to verify the e-citation of the Quranic quote as compared with original texts from the Quran. In this paper, we will concentrate mainly on discussing the Algorithm to verify the fundamental text for Quranic quotes.


Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences


A Literature Survey of Early Time Series Classification and Deep Learning


Fire disturbance favours exotic species at Kaituna Wetland, Bay of Plenty


title: Design of waveguide fed broadband circular polarization truncated horn antenna for high power applications

abstract: This paper presents the design of a waveguide fed broadband circular polarization truncated horn antenna for high power applications. It consists of a circular polarization truncated horn antenna and a truncated-square to rectangular waveguide transition. The structure does not have any internal sharp corners and has large inner dimensions in order not to pose much limit on the maximum power-handling capacity. By truncating the corners of a horn, circular polarization modes are generated. The length of the waveguide transition is critical as it affects the axial ratio and return loss of the horn antenna. The antenna has a simulated 3dB axial ratio bandwidth of 23.6% with return loss better than 20dB.


title: Automatic Extrinsic Calibration of Vision and Lidar by Maximizing Mutual Information

abstract: This paper reports on an algorithm for automatic, targetless, extrinsic calibration of a lidar and optical camera system based upon the maximization of mutual information between the sensor-measured surface intensities. The proposed method is completely data-driven and does not require any fiducial calibration targets-making in situ calibration easy. We calculate the Cramer-Rao lower bound CRLB of the estimated calibration parameter variance, and we show experimentally that the sample variance of the estimated parameters empirically approaches the CRLB when the amount of data used for calibration is sufficiently large. Furthermore, we compare the calibration results to independent ground-truth where available and observe that the mean error empirically approaches zero as the amount of data used for calibration is increased, thereby suggesting that the proposed estimator is a minimum variance unbiased estimate of the calibration parameters. Experimental results are presented for three different lidar-camera systems: i a three-dimensional 3D lidar and omnidirectional camera, ii a 3D time-of-flight sensor and monocular camera, and iii a 2D lidar and monocular camera.


title: Web log cleaning for mining of web usage patterns

abstract: Web usage mining (WUM) is a type of web mining, which exploits data mining techniques to extract valuable information from navigation behavior of World Wide Web users. The data should be preprocessed to improve the efficiency and ease of the mining process. So it is important to define before applying data mining techniques to discover user access patterns from web log. The main task of data preprocessing is to prune noisy and irrelevant data, and to reduce data volume for the pattern discovery phase. This paper mainly focus on data preprocessing stage of the first phase of web usage mining with activities like field extraction and data cleaning algorithms. Field extraction algorithm performs the process of separating fields from the single line of the log file. Data cleaning algorithm eliminates inconsistent or unnecessary items in the analyzed data.


title: A circularly polarized planar antenna modified for passive UHF RFID

abstract: The majority of RFID tags are linearly polarized dipole antennas, but a few use a planar, dual-dipole antenna that facilitates circular polarization, but requires a three-terminal IC. In this paper, we present a novel way to achieve circular polarization with a planar antenna using a two-terminal IC. We present an intuitive methodology for design, and perform experiments that validate circular polarization. The results show that the tag exhibits strong circular polarization, but the precise axial ratio of the tag remains uncertain due to lack of precision in the experimental system.


title: Adverse reactions following injection with a permanent facial filler polyacrylamide hydrogel (Aquamid): causes and treatment

abstract: Background ::: Polyacrylamide hydrogel (Aquamid), an atoxic non-immunogenic gel of the non-resorbable type, has gained widespread popularity as an injectable filler for facial augmentation. However, adverse events (AEs) have occurred, the nature of which seems obscure because of negative findings on culture and a pattern of foreign-body response on microscopy.


title: EEG-Based BCI Control Schemes for Lower-Limb Assistive-Robots

abstract: Over recent years, brain-computer interface (BCI) has emerged as an alternative communication system between the human brain and an output device. Deciphered intents, after detecting electrical signals from the human scalp, are translated into control commands used to operate external devices, computer displays and virtual objects in the real-time. BCI provides an augmentative communication by creating a muscle-free channel between the brain and the output devices, primarily for subjects having neuromotor disorders, or trauma to nervous system, notably spinal cord injuries (SCI), and subjects with unaffected sensorimotor functions but disarticulated or amputated residual limbs. This review identifies the potentials of electroencephalography (EEG) based BCI applications for locomotion and mobility rehabilitation. Patients could benefit from its advancements such as, wearable lower-limb (LL) exoskeletons, orthosis, prosthesis, wheelchairs, and assistive-robot devices. The EEG communication signals employed by the aforementioned applications that also provide feasibility for future development in the field are sensorimotor rhythms (SMR), event-related potentials (ERP) and visual evoked potentials (VEP). The review is an effort to progress the development of user’s mental task related to LL for BCI reliability and confidence measures. As a novel contribution, the reviewed BCI control paradigms for wearable LL and assistive-robots are presented by a general control framework fitting in hierarchical layers. It reflects informatic interactions, between the user, the BCI operator, the shared controller, the robotic device and the environment. Each sub layer of the BCI operator is discussed in detail, highlighting the feature extraction, classification and execution methods employed by the various systems. All applications’ key features and their interaction with the environment are reviewed for the EEG-based activity mode recognition, and presented in form of a table. It is suggested to structure EEG-BCI controlled LL assistive devices within the presented framework, for future generation of intent-based multifunctional controllers. Despite the development of controllers, for BCI-based wearable or assistive devices that can seamlessly integrate user intent, practical challenges associated with such systems exist and have been discerned, which can be constructive for future developments in the field.


title: CPA-Wrist: Compliant Pneumatic Actuation for Antagonistic Tendon Driven Wrists

abstract: In this letter, we propose a new compliant robotics wrist design concept. The system is tendon driven, pneumatically actuated and impedance controlled. We show simulative and experimental results for orientation-based impedance control, human robot interaction with deflection, and collision as well as zero gravity control. Large range point-to-point movements can be performed within 0.5 s and the system stabilizes itself within 0.4 s after external large deflective contacts. Maximum achieved task stiffness are 15 N<inline-formula><tex-math notation="LaTeX">$\cdot$ </tex-math></inline-formula>m/rad and 0.5 N<inline-formula><tex-math notation="LaTeX">$\cdot$</tex-math> </inline-formula>m/rad in simulation and experiment, respectively.


title: A Survey of Medicare Data Processing and Integration for Fraud Detection

abstract: Healthcare is an important aspect in everyday life, with quality and affordable care being essential for a population's well-being and life expectancy. Even so, associated costs for medical services continue to rise. One aspect contributing to increased costs in healthcare is waste and fraud. In particular, with the rapidly rising elderly population in the United States, programs like Medicare are subject to high losses due to fraud. Therefore, fraud detection approaches are critical in lessening these losses. Even so, many studies using Medicare data do not provide sufficient details regarding data processing and/or integration making it potentially more difficult to understand the experimental results and challenging to reproduce the experiments. In this paper, we present current research using Medicare data to detect fraud, focusing on data processing and/or integration, and assess any gaps in the provided data-related details. We then present discussions on important details to look for when processing and merging different Medicare datasets indicating opportunities for future work.


title: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving

abstract: For autonomous driving, vehicle detection is the prerequisite for many tasks like collision avoidance and path planning. In this letter, we present a real-time three-dimensional (RT3D) vehicle detection method that utilizes pure LiDAR point cloud to predict the location, orientation, and size of vehicles. In contrast to previous 3-D object detection methods, we used a pre-RoIpooling convolution technique that moves a majority of the convolution operations to ahead of the RoI pooling, leaving just a small part behind, so that significantly boosts the computation efficiency. We also propose a pose-sensitive feature map design which can be strongly activated by the relative poses of vehicles, leading to a high regression accuracy on the location, orientation, and size of vehicles. Experiments on the KITTI benchmark dataset show that the RT3D is not only able to deliver competitive detection accuracy against state-of-the-art methods, but also the first LiDAR-based 3-D vehicle detection work that completes detection within 0.09 s which is even shorter than the scan period of mainstream LiDAR sensors.


A Primer on Autonomous Aerial Vehicle Design


A Short Executive Function Training Program Improves Preschoolers’ Working Memory


title: Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

abstract: Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway.


title: Book recommendation system for digital library based on user profiles by using association rule

abstract: Due to wide application of management system, information data grows rapidly. On one hand, people have a large number of information resources. On the other hand, the time cost and difficulty of people finding the proper information increases. To tackle the problems, book recommendation is one of the solutions for university libraries which possess huge volumes of books and reading-intensive users. This paper proposes a library book recommendation system based on user profile loaning and apply association rule to create model. The result shows that new association rule algorithm suitable to apply for recommender book in library.


title: The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits

abstract: We present Epoch-Greedy, an algorithm for contextual multi-armed bandits (also known as bandits with side information). Epoch-Greedy has the following properties: ::: ::: 1. No knowledge of a time horizon T is necessary. ::: ::: 2. The regret incurred by Epoch-Greedy is controlled by a sample complexity bound for a hypothesis class. ::: ::: 3. The regret scales as O(T2/3S1/3) or better (sometimes, much better). Here S is the complexity term in a sample complexity bound for standard supervised learning.


title: Sentiment analysis of Twitter data: Case study on digital India

abstract: Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of Twitter data and lot needs to be done. In our work we are trying to perform sentiment analysis of the Twitter data set that expresses opinion about Modi ji's Digital India Campaign. In my work, I have collected these sentiments and classified polarity of sentiments in these opinions w.r.t. Positive, Negative or Neutral. Twitter data is collected for analysis using Twitter API. Out of the two widely used approaches used for sentiment analysis, Machine Learning & Dictionary Based approach, we are using Dictionary Based approach to analyze data posted by different users. Then polarity classification of this data is done. In this paper we discuss sentiment analysis of Twitter data, existing tools available for sentiment analysis, related work, framework used, case study to demonstrate the work followed by the results section. Results clearly demonstrate that the 50% of the collected opinions are positive, 20% are Negative and rests 30% are neutral.


title: The Smart Vision Local Navigation Aid for Blind and Visually Impaired

abstract: The SmartVision prototype is a small, cheap and easily wearable navigation aid for blind and visually impaired persons. Its functionality addresses global navigation for guiding the user to some destiny, and local navigation for negotiating paths, sidewalks and corridors, with avoidance of static as well as moving obstacles. Local navigation applies to both inand outdoor situations. In this article we focus on local navigation: the detection of path borders and obstacles in front of the user and just beyond the reach of the white cane, such that the user can be assisted in centering on the path and alerted to looming hazards. Using a stereo camera worn at chest height, a portable computer in a shoulder-strapped pouch or pocket and only one earphone or small speaker, the system is inconspicuous, it is no hindrence while walking with the cane, and it does not block normal surround sounds. The vision algorithms are optimised such that the system can work at a few frames per second.


title: Binocular Camera Calibration Using Rectification Error

abstract: Reprojection error is a commonly used measure for comparing the quality of different camera calibrations, for example when choosing the best calibration from a set. While this measure is suitable for single cameras, we show that we can improve calibrations in a binocular or multi-camera setup by calibrating the cameras in pairs using a rectification error. The rectification error determines the mismatch in epipolar constraints between a pair of cameras, and it can be used to calibrate binocular camera setups more accurately than using the reprojection error. We provide a quantitative comparison of the reprojection and rectification errors, and also demonstrate our result with examples of binocular stereo reconstruction.


title: Wideband monofilar helical antenna for wireless communication in remote area

abstract: This paper presents the wideband monofilar helical antenna which is aimed to increase low level signal reception commonly occurs in the remote area such as highlands, ridge, and borderlands. The use of helical antenna type is due to its ability in producing circularly polarized wave useful to support wireless communication frequently surrounded by some obstructions. The proposed antenna is expected to operate at wideband frequency range including cellular communication and connectable to the GSM modem associated with a laptop or PC device. The antenna is designed by using thin copper wire with spiral turn number of 8 and fed through a 50Ω female N-connector type. The proposed monofilar helical antenna is then realized based on the optimum design for experimental characterization. The measurement result shows that the realized antenna has working frequency from 1200 MHz to 2250 MHz and has the gain up to 14.5 dBi.


title: Social Network Analysis, Large-Scale.

abstract: In data logging applications, a method and apparatus are disclosed for storing an increasing number of consecutive real-time data points, such as those descriptive of AC (alternating current) power line parameters, in a fixed amount of memory by periodically applying a novel compression technique. The compression technique preserves time relationships in the data while maximizing the level of detail consistent with a limited amount of memory. When the memory is full each data point stored is paired with another data point and one value is calculated that will represent the pair of data points, reducing the necessary memory to half and allowing more data points to be stored.


title: A Layered Fabric 3D Printer for Soft Interactive Objects

abstract: We present a new type of 3D printer that can form precise, but soft and deformable 3D objects from layers of off-the-shelf fabric. Our printer employs an approach where a sheet of fabric forms each layer of a 3D object. The printer cuts this sheet along the 2D contour of the layer using a laser cutter and then bonds it to previously printed layers using a heat sensitive adhesive. Surrounding fabric in each layer is temporarily retained to provide a removable support structure for layers printed above it. This process is repeated to build up a 3D object layer by layer. Our printer is capable of automatically feeding two separate fabric types into a single print. This allows specially cut layers of conductive fabric to be embedded in our soft prints. Using this capability we demonstrate 3D models with touch sensing capability built into a soft print in one complete printing process, and a simple LED display making use of a conductive fabric coil for wireless power reception.


title: Why We Need New Evaluation Metrics for NLG

abstract: The majority of NLG evaluation relies on automatic metrics, such as BLEU . In this paper, we motivate the need for novel, system- and data-independent automatic evaluation methods: We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG. We also show that metric performance is data- and system-specific. Nevertheless, our results also suggest that automatic metrics perform reliably at system-level and can support system development by finding cases where a system performs poorly.


title: On the Limits of LoRaWAN Channel Access

abstract: The rising tide of the Internet of Things has brought to the surface numerous low-power, long-range and low-bitrate wireless network technologies. One of them, LoRaWAN, is being intensely popularized as a solution for sensor networks, however, its potential and limitations are unclear, because there is still neither accurate study nor massive LoRaWAN deployment. This paper surveys and analyzes LoRaWAN operation, focusing on performance evaluation of its channel access as the most crucial component for massive machine type communication. We reveal and point out weaknesses of the LoRaWAN specification and propose solutions to improve LoRaWAN performance.


title: Performance evaluation of subthreshold Schmitt trigger using body bias techniques

abstract: The technique of body biasing has achieved great success in modern IC design as in it, is possible to alter the threshold voltage of MOS transistor so that device leakage and timing performance can be improved. In this paper a Schmitt trigger circuit has been designed using four types of body bias techniques. The proposed circuits were simulated in SPICE 180nm CMOS technology parameters. A comparative study of all the proposed Schmitt trigger circuits in terms of lower and upper threshold voltages, noise margins and power dissipation has been done and specific applications for each circuit has been suggested.


title: Static Handwritten Signature Recognition Using Discrete Random Transform and Combined Projection Based Technique

abstract: In this paper, we proposed Discrete Radon Transform (DRT) technique for feature extraction of static signature recognition to identify forgeries. Median filter has been introduced for noise cancellation of handwritten signature. This paper describes static signature verification techniques where signature samples of each person was collected and cropped by automatic cropping system. Projection based global features are extracted like Horizontal, Vertical and combination of both the projections, these all are one dimensional feature vectors to recognize the handwritten static signature. The distance between two corresponding vectors can be measured with Dynamic Time Warping algorithm (DTW) and using only six genuine signatures samples of each person has been employed here in order to train our system. In the proposed system process time required for training our system for each person is between 1.5 to 4.2 seconds and requires less memory for storage. The optimal performance of the system was found using proposed technique for Combined projection features and it gives FAR of 5.60%, FRR of 8.49% and EER 7.60%, which illustrates such new approach to be quite effective and reliable.


title: Cell-Phone Addiction: A Review

abstract: We present a review of the studies that have been published about addiction to cell phones. We analyse the concept of cell phone addiction as well as its prevalence, study methodologies, psychological features and associated psychiatric comorbidities. Research in this field has generally evolved from a global view of the cell phone as a device to its analysis via applications and contents. The diversity of criteria and methodological approaches that have been used is notable, as is a certain lack of conceptual delimitation that has resulted in a broad spread of prevalent data. There is a consensus about the existence of cell phone addiction, but the delimitation and criteria used by various researchers vary. Cell phone addiction shows a distinct user profile that differentiates it from Internet addiction. Without evidence pointing to the influence of cultural level and socioeconomic status, the pattern of abuse is greatest among young people, primarily females. Intercultural and geographical differences have not been sufficiently studied. The problematic use of cell phones has been associated with personality variables such as extraversion, neuroticism, self-esteem, impulsivity, self-identity and self-image. Similarly, sleep disturbance, anxiety, stress, and, to a lesser extent, depression, which are also associated with Internet abuse, have been associated with problematic cell phone use. In addition, the present review reveals the coexistence relationship between problematic cell phone use and substance use such as tobacco and alcohol.


title: A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling

abstract: Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem of constructing such models from randomized experiments data is known as Uplift Modeling in the literature. Many algorithms have been proposed for uplift modeling and some have generated promising results on various data sets. Yet little is known about the theoretical properties of these algorithms. In this paper, we propose a new tree-based ensemble algorithm for uplift modeling. Experiments show that our algorithm can achieve competitive results on both synthetic and industry-provided data. In addition, by properly tuning the "node size" parameter, our algorithm is proved to be consistent under mild regularity conditions. This is the first consistent algorithm for uplift modeling that we are aware of.


title: Transverse Flux Machines with rotary transformer concept for wide speed operations without using Permanent Magnet material

abstract: This paper presents a new concept of Transverse Flux Machine (TFM) design that uses rotary transformers to replace the Permanent Magnet (PM) field excitation in the rotor. The rotary transformer has an inductive interface that allows for contactless transfer of energy to the field windings embedded in the rotor core. In order to achieve high magnetic coupling, a highly permeable ferrite core is selected for the transformer which is excited with high electrical frequency of 100 kHz. A field power converter is used to regulate the transformer secondary winding voltage to maintain constant DC field in the rotor. The proposed TFM has a modular structure that is free of PM, which makes it cost-effective, without sacrificing its peak torque and power when compared to a similar sized PM based TFM.


title: Transport mechanism in sub 100°C processed high mobility polycrystalline ZnO transparent thin film transistors

abstract: We demonstrate high performance ZnO TFTs with record field effect mobility 229 cm2/V.s, on/off ratio exceeding 107 (limited only by our simple device structure) and sub threshold swing (S) <150 mV/dec, surpassing the performance of many reported amorphous Indium Gallium Zinc Oxide (a-IGZO) thin film transistors.1,2 The tail state distribution of the density of states (DOS) in ZnO extracted via 2D numerical simulations matched to experiment, demonstrates unequivocally a similar mobility mechanism that underpins all Transparent Conducting Oxides (TCOs)-whether amorphous or not. The characteristic Temperature of ZnO is found to be ∼463 K and the tail state density of states (DOS) is ∼1.3 ×1020cm−3eV−1.


title: An Eye-Tracking Study of Java Programmers and Application to Source Code Summarization

abstract: Source Code Summarization is an emerging technology for automatically generating brief descriptions of code. Current summarization techniques work by selecting a subset of the statements and keywords from the code, and then including information from those statements and keywords in the summary. The quality of the summary depends heavily on the process of selecting the subset: a high-quality selection would contain the same statements and keywords that a programmer would choose. Unfortunately, little evidence exists about the statements and keywords that programmers view as important when they summarize source code. In this paper, we present an eye-tracking study of 10 professional Java programmers in which the programmers read Java methods and wrote English summaries of those methods. We apply the findings to build a novel summarization tool. Then, we evaluate this tool. Finally, we further analyze the programmers’ method summaries to explore specific keyword usage and provide evidence to support the development of source code summarization systems.


title: A hybrid cryptography algorithm for cloud computing security

abstract: In the present scenario, the Cloud Computing is very famous and flexible technology. It facilitates customers with effortlessness, quickness, competence etc in their work area through the services. Cloud provides huge data centre to handle the large amount of information. The Cloud Computing benefits the organizations to handle their large volume of information. The main issue in cloud computing is data security, because number of customers are sharing same cloud. This study aimed at designing a new security method by using a hybrid cryptosystem, for data security in the cloud. The need for the current investigation is to protect data from unauthorized access or hackers in cloud at the time of data transmission by encrypting the user data. Cloud computing constitutes several security issues including data access control, identity management, auditing, integrity control and risk management therefore, this hybrid cryptosystem is designed and comprises of both symmetric and asymmetric cryptography algorithm in which Blowfish symmetric algorithm deals with data confidentiality whereas, RSA asymmetric algorithm deals with an authentication. This method also includes the Secure Hash Algorithm −2 for data integrity. The present study concluded that the proposed method provides high security on data transmission over the internet and proper network access on demand to a shared tank of constructive computing resources, mainly net, server, and storage application.


title: Marrow Stromal Cells as Stem Cells for Nonhematopoietic Tissues

abstract: Marrow stromal cells can be isolated from other cells in marrow by their tendency to adhere to tissue culture plastic. The cells have many of the characteristics of stem cells for tissues that can roughly be defined as mesenchymal, because they can be differentiated in culture into osteoblasts, chondrocytes, adipocytes, and even myoblasts. Therefore, marrow stromal cells present an intriguing model for examining the differentiation of stem cells. Also, they have several characteristics that make them potentially useful for cell and gene therapy.


title: The EPFL jumpglider: A hybrid jumping and gliding robot with rigid or folding wings

abstract: Recent work suggests that wings can be used to prolong the jumps of miniature jumping robots. However, no functional miniature jumping robot has been presented so far that can successfully apply this hybrid locomotion principle. In this publication, we present the development and characterization of the ‘EPFL jumpglider’, a miniature robot that can prolong its jumps using steered hybrid jumping and gliding locomotion over varied terrain. For example, it can safely descend from elevated positions such as stairs and buildings and propagate on ground with small jumps. The publication presents a systematic evaluation of three biologically inspired wing folding mechanisms and a rigid wing design. Based on this evaluation, two wing designs are implemented and compared1.


title: Mobile robot navigation and scene modeling using stereo fish-eye lens system

abstract: We present an autonomous mobile robot navigation system using stereo fish-eye lenses for navigation in an indoor structured environment and for generating a model of the imaged scene. The system estimates the three-dimensional (3D) position of significant features in the scene, and by estimating its relative position to the features, navigates through narrow passages and makes turns at corridor ends. Fish-eye lenses are used to provide a large field of view, which images objects close to the robot and helps in making smooth transitions in the direction of motion. Calibration is performed for the lens-camera setup and the distortion is corrected to obtain accurate quantitative measurements. A vision-based algorithm that uses the vanishing points of extracted segments from a scene in a few 3D orientations provides an accurate estimate of the robot orientation. This is used, in addition to 3D recovery via stereo correspondence, to maintain the robot motion in a purely translational path, as well as to remove the effects of any drifts from this path from each acquired image. Horizontal segments are used as a qualitative estimate of change in the motion direction and correspondence of vertical segment provides precise 3D information about objects close to the robot. Assuming detected linear edges in the scene as boundaries of planar surfaces, the 3D model of the scene is generated. The robot system is implemented and tested in a structured environment at our research center. Results from the robot navigation in real environments are presented and discussed.


title: Generalized video deblurring for dynamic scenes

abstract: Several state-of-the-art video deblurring methods are based on a strong assumption that the captured scenes are static. These methods fail to deblur blurry videos in dynamic scenes. We propose a video deblurring method to deal with general blurs inherent in dynamic scenes, contrary to other methods. To handle locally varying and general blurs caused by various sources, such as camera shake, moving objects, and depth variation in a scene, we approximate pixel-wise kernel with bidirectional optical flows. Therefore, we propose a single energy model that simultaneously estimates optical flows and latent frames to solve our deblurring problem. We also provide a framework and efficient solvers to optimize the energy model. By minimizing the proposed energy function, we achieve significant improvements in removing blurs and estimating accurate optical flows in blurry frames. Extensive experimental results demonstrate the superiority of the proposed method in real and challenging videos that state-of-the-art methods fail in either deblurring or optical flow estimation.


title: Foot Design for a Large Walking Delta Robot

abstract: A large Delta robot has been modified to pick up and place a triangular shaped foot. The robot pushes the foot down to lift the body and the three associated fixed support legs. It then moves the foot sideways so that the body moves in the opposite direction to a new position where the fixed legs are lowered to the ground and the foot lifted ready for a new placement. In this way the robot is able to move in any direction with a tripod gait. The use of a combined foot and gripper for moving heavy objects is investigated. In the implementation reported, the gearboxes used are found to limit the operation and spring assistance is investigated as a trade off with payload. The small prototype, stands as tall as a person, weighs a third of a ton, and has a payload capability of near 100%. It is envisaged as a prototype for a much larger walking robot capable of general-purpose heavy lifting.


title: [Distribution of haplotypes for four Y-sTR loci and validation in forensic science by using a double-fluorescent multiplex PCR system].

abstract: Objective We focus on developing a multiplex PCR system for Y-STR loci that can be detected by double fluorescent system and assessing their usefulness in forensic mixture samples. Methods The primers of four Y-STR loci (DYS-GATA-A10,DYS531,DYS557 and DYS448) amplified by multiplex PCR technique were labeled with fluorescence, then the PCR products of these Y-STRs loci were detecting and typing by ABI PRISM310 Genetic Analyzer. Results When 120 unrelated individuals from the Han population in Chengdu were detected by the system, Y-GATA-A10,DYS531,DYS557 and DYS448 showed 5, 5, 8, 7 alleles, respectively. A total of 78 different haplotypes was identified and the genetic diversity reached 0.9881.To the three cases of mixture stains failed by using conventional autosomal STR analysis, our multiplex system drew conforming conclusion comparing to the suspect′s Y-STRs genotypes. Conclusion Our results show that the multiplex system of four Y-STR will be very powerful for Y-STR database establishing, the paternity testing and mixture stains identifying.


title: Appearance-and-Relation Networks for Video Classification

abstract: Spatiotemporal feature learning in videos is a fundamental problem in computer vision. This paper presents a new architecture, termed as Appearance-and-Relation Network (ARTNet), to learn video representation in an end-to-end manner. ARTNets are constructed by stacking multiple generic building blocks, called as SMART, whose goal is to simultaneously model appearance and relation from RGB input in a separate and explicit manner. Specifically, SMART blocks decouple the spatiotemporal learning module into an appearance branch for spatial modeling and a relation branch for temporal modeling. The appearance branch is implemented based on the linear combination of pixels or filter responses in each frame, while the relation branch is designed based on the multiplicative interactions between pixels or filter responses across multiple frames. We perform experiments on three action recognition benchmarks: Kinetics, UCF101, and HMDB51, demonstrating that SMART blocks obtain an evident improvement over 3D convolutions for spatiotemporal feature learning. Under the same training setting, ARTNets achieve superior performance on these three datasets to the existing state-of-the-art methods.


title: Detection and classification of pests from crop images using Support Vector Machine

abstract: Agriculture is the mother of all culture. Economy and prosperity of a country depends on agriculture production. Agriculture provides food as well as raw material for industry. Agriculture production is inversely affected by pest infestation and plant diseases. Early pest identification and disease detection will help to minimize the loss of production. Naked eye observation is a common using method to identify the pest. But it is time consuming process. In this paper we proposed an automatic pest identification system using image processing techniques. Color feature is used to train the SVM to classify the pest pixels and leaf pixels. Morphological operations are used to remove the unwanted elements in the classified image.


FMEA and Fault Tree based Software Safety Analysis of a Railroad Crossing Critical System


title: Effects of Attention and Emotion on Face Processing in the Human Brain An Event-Related fMRI Study

abstract: We used event-related fMRI to assess whether brain responses to fearful versus neutral faces are modulated by spatial attention. Subjects performed a demanding matching task for pairs of stimuli at prespecified locations, in the presence of task-irrelevant stimuli at other locations. Faces or houses unpredictably appeared at the relevant or irrelevant locations, while the faces had either fearful or neutral expressions. Activation of fusiform gyri by faces was strongly affected by attentional condition, but the left amygdala response to fearful faces was not. Right fusiform activity was greater for fearful than neutral faces, independently of the attention effect on this region. These results reveal differential influences on face processing from attention and emotion, with the amygdala response to threat-related expressions unaffected by a manipulation of attention that strongly modulates the fusiform response to faces.


title: Background subtraction based on Gaussian mixture models using color and depth information

abstract: In this paper, we propose a background subtraction (BGS) method based on the Gaussian mixture models using color and depth information. For combining color and depth information, we used the probabilistic model based on Gaussian distribution. In particular, we focused on solving color camouflage problem and depth denoising. For evaluating our method, we built a new dataset containing normal, color camouflage and depth camouflage situations. The dataset files consist of color, depth and ground truth image sequences. With these files, we compared the proposed algorithm with the conventional color-based BGS techniques in terms of precision, recall and F-measure. As a result, our method showed the best performance. Thus, this technique will help to robustly detect regions of interest as pre-processing in high-level image processing stages.


title: Distributed quantum programming

abstract: In this paper we explore the structure and applicability of the Distributed Measurement Calculus (DMC), an assembly language for distributed measurement-based quantum computations. We describe the formal language’s syntax and semantics, both operational and denotational, and state several properties that are crucial to the practical usability of our language, such as equivalence of our semantics, as well as compositionality and context-freeness of DMC programs. We show how to put these properties to use by constructing a composite program that implements distributed controlled operations, in the knowledge that the semantics of this program does not change under the various composition operations. Our formal model is the basis of a quantum virtual machine construction for distributed quantum computations, which we elaborate upon in the latter part of this work. This virtual machine embodies the formal semantics of DMC such that programming execution no longer needs to be analysed by hand. Far from a literal translation, it requires a substantial concretisation of the formal model at the level of data structures, naming conventions and abstraction mechanisms. At the same time we provide automatisation techniques for program specification where possible to obtain an expressive and user-friendly programming environment.


title: LCL filter design and inductor ripple analysis for 3-level NPC grid interface converter

abstract: The harmonic filter for a 3-level neutral point clamped (NPC) grid interface converter is designed in this paper with good filtering performance and small component size. LCL topology is selected because of the attenuation and size tradeoff. The design for inverter side inductor L1 is emphasized due to its cost. A detailed inductor current ripple analysis is given based on the space vector modulation (SVM). The analysis derives the inductor volt-second and the maximum current ripple equation in line cycle. It also reveals the switching cycle current ripple distribution over line cycle, with the consideration of power factor and other factors. The total system loss is calculated with different ripple current. Inductor L1 is determined by the loss and size tradeoff. Also the capacitor and grid side inductor L2 is designed based on attenuation requirement. A damping circuit suppresses the LCL resonant peak. The filter design is verified by both simulation and a 200kVA 3-level NPC converter hardware.


title: Unified Hypersphere Embedding for Speaker Recognition

abstract: Incremental improvements in accuracy of Convolutional Neural Networks are usually achieved through use of deeper and more complex models trained on larger datasets. However, enlarging dataset and models increases the computation and storage costs and cannot be done indefinitely. In this work, we seek to improve the identification and verification accuracy of a text-independent speaker recognition system without use of extra data or deeper and more complex models by augmenting the training and testing data, finding the optimal dimensionality of embedding space and use of more discriminative loss functions. Results of experiments on VoxCeleb dataset suggest that: (i) Simple repetition and random time-reversion of utterances can reduce prediction errors by up to 18%. (ii) Lower dimensional embeddings are more suitable for verification. (iii) Use of proposed logistic margin loss function leads to unified embeddings with state-of-the-art identification and competitive verification accuracies.


title: Cross-lingual Argumentation Mining: Machine Translation (and a bit of Projection) is All You Need!

abstract: Argumentation mining (AM) requires the identification of complex discourse structures and has lately been applied with success monolingually. In this work, we show that the existing resources are, however, not adequate for assessing cross-lingual AM, due to their heterogeneity or lack of complexity. We therefore create suitable parallel corpora by (human and machine) translating a popular AM dataset consisting of persuasive student essays into German, French, Spanish, and Chinese. We then compare (i) annotation projection and (ii) bilingual word embeddings based direct transfer strategies for cross-lingual AM, finding that the former performs considerably better and almost eliminates the loss from cross-lingual transfer. Moreover, we find that annotation projection works equally well when using either costly human or cheap machine translations. Our code and data are available at \url{http://github.com/UKPLab/coling2018-xling_argument_mining}.


title: Real-Time Remote Health-Monitoring Systems in a Medical Centre: A Review of the Provision of Healthcare Services-Based Body Sensor Information, Open Challenges and Methodological Aspects

abstract: Promoting patient care is a priority for all healthcare providers with the overall purpose of realising a high degree of patient satisfaction. A medical centre server is a remote computer that enables hospitals and physicians to analyse data in real time and offer appropriate services to patients. The server can also manage, organise and support professionals in telemedicine. Therefore, a remote medical centre server plays a crucial role in sustainably delivering quality healthcare services in telemedicine. This article presents a comprehensive review of the provision of healthcare services in telemedicine applications, especially in the medical centre server. Moreover, it highlights the open issues and challenges related to providing healthcare services in the medical centre server within telemedicine. Methodological aspects to control and manage the process of healthcare service provision and three distinct and successive phases are presented. The first phase presents the identification process to propose a decision matrix (DM) on the basis of a crossover of ‘multi-healthcare services’ and ‘hospital list’ within intelligent data and service management centre (Tier 4). The second phase discusses the development of a DM for hospital selection on the basis of integrated VIKOR-Analytic Hierarchy Process (AHP) methods. Finally, the last phase examines the validation process for the proposed framework.


title: AUDIO TRANSFORMATION TECHNOLOGIES APPLIED TO VIDEO GAMES

abstract: In this paper we present a set of spectral domain audio transformation technologies and some existing and proposed applications of these technologies in video games. The technologies include real-time voice transformation and tempo/pitch transformation for music. Voice Transformation is mainly used to create new character's voices modifying existing recorded material but also for transforming the player's voice in real-time in a singing game. Tempo/Pitch transformations are more focused in transforming background music to create different sound atmosphere for different scenes in a video-game. Audio Transformation techniques are very useful to reduce sound creation/recording costs but also for reducing the size of the game without compromising the quality of the sound. Moreover, these real-time techniques can offer new attractive sound features to games.


title: On Latent Fingerprint Image Quality

abstract: Latent fingerprints which are lifted from surfaces of objects at crime scenes play a very important role in identifying suspects in the crime scene investigations. Due to poor quality of latent fingerprints, automatic processing of latents can be extremely challenging. For this reason, latent examiners need to be involved in latent identification. To expedite the latent identification and alleviate subjectivity and inconsistency in latent examiners’ feature markups and decisions, there is a need to develop latent fingerprint identification systems that can operate in the “lights-out” mode. One of the most important steps in “lights-out” systems is to determine the quality of a given latent to predict the probability that the latent can be identified in a fully automatic manner. In this paper, we (i) propose a definition of latent value determination as a way of establishing the quality of latents based on a specific matcher’s identification performance, (ii) define a set of features based on ridge clarity and minutiae and evaluate them based on their capability to determine if a latent is of value for individualization or not, and (iii) propose a latent fingerprint image quality (LFIQ) that can be useful to reject the latents which cannot be successfully identified in the “lights-out” mode. Experimental results show that the most salient latent features include the average ridge clarity and the number of minutiae. The proposed latent quality measure improves the rank-100 identification rate from 69 % to 86 % by rejecting 50 % of latents deemed as poor quality. In addition, the rank-100 identification is 80 % when rejecting 80 % of the latents in the databases assessed as ‘NFIQ = 5’; however, the same identification rate can be achieved by rejecting only 21 % of the latents with low LFIQ.


title: PixelGAN Autoencoders

abstract: In this paper, we describe the "PixelGAN autoencoder", a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels (PixelCNN) that is conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code. We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder. For example, by imposing a Gaussian distribution as the prior, we can achieve a global vs. local decomposition, or by imposing a categorical distribution as the prior, we can disentangle the style and content information of images in an unsupervised fashion. We further show how the PixelGAN autoencoder with a categorical prior can be directly used in semi-supervised settings and achieve competitive semi-supervised classification results on the MNIST, SVHN and NORB datasets.


title: Sexual Orientation and Gender Identity Data Collection in Clinical Settings and in Electronic Health Records: A Key to Ending LGBT Health Disparities

abstract: Abstract The Institute of Medicine's (IOM's) 2011 report on the health of LGBT people pointed out that there are limited health data on these populations and that we need more research. It also described what we do know about LGBT health disparities, including lower rates of cervical cancer screening among lesbians, and mental health issues related to minority stress. Patient disclosure of LGBT identity enables provider–patient conversations about risk factors and can help us reduce and better understand disparities. It is essential to the success of Healthy People 2020's goal of eliminating LGBT health disparities. This is why the IOM's report recommended data collection in clinical settings and on electronic health records (EHRs). The Center for Medicare and Medicaid Services and the Office of the National Coordinator of Health Information Technology rejected including sexual orientation and gender identity (SOGI) questions in meaningful use guidelines for EHRs in 2012 but are considering this issue aga...


title: Cloud-based Data Mining Framework: A Model to Improve Maternal Healthcare

abstract: The foundation of quality health care depends upon the presence of competent health personnel working in a situation where prescriptions and health supplies are accessible when required and in sufficient quantity and of guaranteed quality. This paper conduces to propound a decision support framework model for the Department of Health (DOH) which able to innovate the acquisition and allocation management of medicines and health supplies with the aim of improving the maternal healthcare in the Philippines. In-depth interviews were conducted to DOH officials and facility managers of Rural Health Units (RHU) and in the 3rd district of Albay, Bicol Philippines. Data triangulation and literature review are employed to design the framework. Finally to assess its applicability, a simulative-evaluation is conveyed. Respondents reported on the unreliability of obtaining healthcare supplies for RHU's, which results untimely and suboptimal rendering of healthcare services. Also, insufficient provision of medicines from the government and lack of accountability within the supply system due of inadequate and incoherent terminal reports were revealed to contribute to the current situation. To address the mentioned challenges, this study recommends the consideration of the proposed framework model employing cloud computing and data mining to remarkably improve the administration on the provision of medicines and health supplies, guaranteeing its auspicious accessibility for the benefit of Filipino pregnant women ensuring their health as carriers of the lives to be born as the future of the nation.


title: An Efficient Conjunctive Keyword and Phase Search Scheme for Encrypted Cloud Storage Systems

abstract: There have been increasing interest in the area of privacy-protected searching as industries continue to adopt cloud technologies. Much of the recent efforts have been towards incorporating more advanced searching techniques. Although many have proposed solutions for conjunctive keyword search, it is only recently that researchers began exploring phrase search over encrypted data. In this paper, we present a scheme that incorporates both functionalities. Our solution makes use of symmetric encryption, which provides computational and storage efficiency over schemes based on public key encryption. By considering the statistical properties of natural languages, we were able to design indexes that significantly reduce storage cost when compared to existing solutions. Our solution allows for simple ranking of results and requires a low storage cost while providing document and keyword security. By using both the index and the encrypted documents to performs searches, our scheme is also currently the only phrase search scheme capable of searching for non-indexed keywords.


title: Extended VIKOR method in comparison with outranking methods

abstract: The VIKOR method was developed to solve MCDM problems with conflicting and noncommensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria, and on proposing compromise solution (one or more). The VIKOR method is extended with a stability analysis determining the weight stability intervals and with trade-offs analysis. The extended VIKOR method is compared with three multicriteria decision making methods: TOPSIS, PROMETHEE, and ELECTRE. A numerical example illustrates an application of the VIKOR method, and the results by all four considered methods are compared. � 2006 Elsevier B.V. All rights reserved.


title: If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts

abstract: Spam in online social networks (OSNs) is a systemic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors, as well as negatively affecting users' engagement. As spammers continuously keep creating newer accounts and evasive techniques upon being caught, a deeper understanding of their spamming strategies is vital to the design of future social media defense mechanisms. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics. Our analysis includes over 100 million messages collected from Twitter over the course of 1 month. We show that there exist two behaviorally distinct categories of spammers and that they employ different spamming strategies. Then, we illustrate how users in these two categories demonstrate different individual properties as well as social interaction patterns. Finally, we analyze the detectability of spam accounts with respect to three categories of features, namely content attributes, social interactions, and profile properties.


title: Teleoperated robot writing using EMG signals

abstract: In this paper, we have developed a method to tele-control a robot arm to imitate human writing skills using electromyography (EMG) signals. The control method is implemented on the Baxter® robot with a brush attached on the endpoint of its arm to imitate human writing skills, and a MYO sensor is used to measure the surface electromyographic (sEMG) signals generated by contractions of human muscles involved in writing tasks. A haptic device Sensable® Omni is used to control the motion of the Baxter® robot arm, and V-Rep® is employed to simulate the movement of the arm and to calculate the inverse kinematic of Baxter® robot arm. All the communications for Baxter® robot, V-Rep simulator, Omni device and MYO are integrated in MATLAB®/Simulink®. The main test is to make the Baxter® arm following the movement of a human subject when writing with Omni stylus, and the EMG signals are processed using low pass filter and moving average technique to extract the smoothed envelope which is utilised to control the variation of position instead of variation of force along the vertical Z axis, so when the operator writes with force variations, the Baxter® can draw lines with variable thickness. This imitation system is successfully tested to make the Baxter® arm to follow the writing movement of the operator's hand and would have potential applications in more complicated human robot teleoperation tasks such as tele-rehabilitation and tele-surgery.


title: Abstract Coherent Hierarchical Culling: Hardware Occlusion Queries Made Useful

abstract: We present a simple but powerful algorithm for optimizing the usage of hardware occlusion queries in arbitrary complex scenes. Our method minimizes the number of issued queries and reduces the delays due to the latency of query results. We reuse the results of occlusion queries from the last frame in order to initiate and schedule the queries in the next frame. This is done by processing nodes of a spatial hierarchy in a front-to-back order and interleaving occlusion queries with rendering of certain previously visible nodes. The proposed scheduling of the queries makes use of spatial and temporal coherence of visibility. Despite its simplicity, the algorithm achieves good culling efciency for scenes of various types. The implementation of the algorithm is straightforward and it can be easily integrated in existing real-time rendering packages based on common hierarchical data structures.


title: Mining the web to predict future events

abstract: We describe and evaluate methods for learning to forecast forthcoming events of interest from a corpus containing 22 years of news stories. We consider the examples of identifying significant increases in the likelihood of disease outbreaks, deaths, and riots in advance of the occurrence of these events in the world. We provide details of methods and studies, including the automated extraction and generalization of sequences of events from news corpora and multiple web resources. We evaluate the predictive power of the approach on real-world events withheld from the system.


title: HOW TO GAMIFY INFORMATION SYSTEMS - ADAPTING GAMIFICATION TO INDIVIDUAL PREFERENCES

abstract: Gamification refers to the use of game elements in a non-entertainment-based context, and is a well- known approach to motivate users of information systems. Despite the positive effects of games in daily life, more gamification projects fail. A possible explanation for this observation is that game elements are often designed without considering the needs of different groups of users. Consequently, we aim to develop a gamification approach that allows us to adapt the gamification elements to the individual motivation structures of information system users. To achieve our goal, we rely on self- determination theory to design different specifications of game elements. We exemplarily developed four different specifications for each of the nine gamification elements we found in the literature. We are currently in the process of conducting a discrete choice experiment allowing us to match the motivation structures of system users and their preferences regarding the specifications of game elements. Based on our results, we expect to better understand how gamification can motivate users to use systems more regularly. In a subsequent step, we plan to rely on our results to design different gamification configurations for an information system, allowing us to adapt the gamification elements to individual preferences of the users.


title: Fuzzy controller based on PLC S7-1200: Application to a servomotor

abstract: This paper presents the design and validation of a fuzzy logic controller implemented with an industrial programmable logic controller (PLC). The chosen device belongs to the S7-1200 series of Siemens, whereas the code has been developed in Ladder Diagram language using the software TIA Portal. The fuzzy controller is of Mamdani type and is applied to control the speed of a servomotor. A comparison with a Simulink/Matlab fuzzy controller is done to validate the developed software module and to show the feasibility of the PLC to manage this kind of control algorithm.


title: A NOVEL APPROACH TO THE DESIGN OF DUAL-BAND POWER DIVIDER WITH VARIABLE POWER DIVIDING RATIO BASED ON COUPLED-LINES

abstract: This paper presents an approach to the design of a novel dual-band power divider with variable power dividing ratio. To achieve dual-band operation, a novel dual-band quarter-wave length transformer based on coupled-lines is proposed, which is used to replace the quarter-wave length transformer in Wilkinson power divider. The proposed dual-band power divider features a simple compact planar structure with wide bandwidth performance for small frequency ratio. Closed-form design equations with one degree of design freedom are derived using even- and odd-mode analysis and transmission line theory. For veriflcation purpose, power dividers operating at 2.4/3.8GHz with dividing ratios of 2:1 and 1:1 are designed, simulated and measured. The simulated and measured results are in good agreement.


title: Color-mood analysis of films based on syntactic and psychological models

abstract: The emergence of peer-to-peer networking and the increase of home PC storage capacity are necessitating efficient scaleable methods for video clustering, recommending and browsing. Based on film theories and psychological models, color-mood is an important factor affecting user emotional preferences. We propose a compact set of features for color-mood analysis and subgenre discrimination. We introduce two color representations for scenes and full films in order to extract the essential moods from the films: a global measure for the color palette and a discriminative measure for the transitions of the moods in the movie. We captured the dominant color ratio and the pace of the movie. Despite the simplicity and efficiency of the features, the classification accuracy was surprisingly good, about 80%, possibly thanks to the prevalence of the color-mood association in feature films


title: Activities of Garlic Oil, Garlic Powder, and Their Diallyl Constituents against Helicobacter pylori

abstract: Chronic Helicobacter pylori disease is reduced with Allium vegetable intake. This study was designed to assess the in vivo anti- H. pylori potential of a variety of garlic substances. The garlic materials all showed substantial but widely differing anti- H. pylori effects against all strains and isolates tested. The MICs (range, 8 to 32 μg/ml) and minimum bactericidal concentrations (MBCs) (range, 16 to 32 μg/ml) of undiluted garlic oil (GO) were smaller than those of garlic powder (GP) (MIC range, 250 to 500 μg/ml; MBC range, 250 to 500 μg/ml) but greater than the MIC of allicin (4.0 μg/ml) (Table 2) present in GP. Allicin (MIC, 6 μg/ml; MBC, 6 μg/ml) was more potent than diallyl disulfide (MIC range, 100 to 200 μg/ml; MBC range, 100 to 200 μg/ml), its corresponding sulfide, but of a strength similar to that of diallyl tetrasulfide (MIC range, 3 to 6 μg/ml; MBC range, 3 to 6 μg/ml). Antimicrobial activity of the diallyl sulfides increased with the number of sulfur atoms. Time course viability studies and microscopy showed dose-dependent anti- H. pylori effects with undiluted GO, GP, allicin, and diallyl trisulfide after a lag phase of ca. 1 to 2 h. Substantial in vitro anti- H. pylori effects of pure GO and GP and their diallyl sulfur components exist, suggesting their potential for in vivo clinical use against H. pylori infections.


abstract: Surveying a suite of algorithms that offer a solution to managing large document archives.