title: Size matters: evolution of large drug-secreting resin glands in elite pharmaceutical strains of Cannabis sativa (marijuana)

abstract: Most tetrahydrocannabinol (THC) of Cannabis sativa is located in the resin heads of capitate-stalked glandular trichomes. We found that after harvest the resin heads shrink in diameter in exponential decay fashion under ambient room conditions, losing about 15 % in the first month, rising to 24 % over the first year, 32 % by 50 years, and 34 % after a century. An equation accounting for the asymptotic curve descriptive of the progression of shrinkage was determined [original gland head diameter in microns = observed diameter divided by (0.5255 + 0.4745 multiplied by time in days to the power −0.1185)], so that if the age of a specimen is known, the original diameter of the gland heads in the fresh state can be extrapolated. This equation was employed to compare gland head size in samples of different ages. A sample of high-THC medical marijuana strains marketed under license possessed resin head diameters averaging 129 μm, while a sample of low-THC industrial hemp cultivars possessed gland head diameters averaging 80 μm. The mean volume of the resin heads of the narcotic strains was more than four times larger than that of the industrial hemp strains. This is the first documented report of a consistent morphological separator of elite narcotic strains and non-narcotic plants. Most recognized strains of marijuana were bred clandestinely and illicitly during the last half century. The occurrence of large resin gland heads in a sample of officially marketed pharmaceutical strains is an obvious correlate of selection for higher quantity of resin production.


title: Escher sphere construction kit

abstract: M.C. Escher created a myriad of amazing planar tessellations, yet only a few three-dimensional ones such as his wooden fish ball and dodecahedral flower. We have developed an interactive program to design and manufacture “Escher Spheres” sets of tiles that can be assembled into spherical balls. The user chooses from a set of predefined symmetry groups and then deforms the boundaries of the basic domain tile; all corresponding points based on the chosen symmetry class move concurrently, instantly showing the overall result. The interior of the tile can be embellished with a bas-relief. Finally the tile is radially extruded and output as a solid model suitable for free-form fabrication.


title: Big data analytics for supply chain management: A literature review and research agenda

abstract: The main objective of this study is to provide a literature review of big data analytics for supply chain management. A review of articles related to the topics was done within SCOPUS, the largest abstract and citation database of peer-reviewed literature. Our search found 17 articles. The distribution of articles per year of publication, subject area, and affiliation, as well as a summary of each paper are presented. We conclude by highlighting future research directions where the deployment of big data analytics is likely to transform supply chain management practices.


title: A multi-agent planning model for airport ground handling management

abstract: Inefficiency airport ground handing operations is one of the main reasons for flight delays, as it comprises a series of processes and collaborations between various airport's services. Using multi-agent planning (MAP) method, this paper proposes a framework as a management system to improve the airport ground handling management (GHM). With the identification of the services and resources related to GHM, the forward MAP approach is applied to coordinates the tasks and planning in order to reduce both the delays and the operating cost. In this case, the key contribution includes MAP model for airport ground handling operations under a unified framework compatible with the airport collaborative decision making (ACDM) strategy.


title: Hypnos: understanding and treating sleep conflicts in smartphones

abstract: To maximally conserve the critical resource of battery energy, smartphone OSes implement an aggressive system suspend policy that suspends the whole system after a brief period of user inactivity. This burdens developers with the responsibility of keeping the system on, or waking it up, to execute time-sensitive code. Developer mistakes in using the explicit power management unavoidably give rise to energy bugs, which cause significant, unexpected battery drain. In this paper, we study a new class of energy bugs, called sleep conflicts, which can happen in smartphone device drivers. Sleep conflict happens when a component in a high power state is unable to transition back to the base power state because the system is suspended when the device driver code responsible for driving the transition is supposed to execute. We illustrate the root cause of sleep conflicts, develop a classification of the four types of sleep conflicts, and finally present a runtime system that performs sleep conflict avoidance, along with a simple yet effective pre-deployment testing scheme. We have implemented and evaluated our system on two Android smartphones. Our testing scheme detects several sleep conflicts in WiFi and vibrator drivers, and our runtime avoidance scheme effectively prevents sleep conflicts from draining the battery.


title: Corpus-based evaluation of Chinese text normalization

abstract: This paper aims to present a method of developing a corpus consisting of various categories of Non-Standard Words (NSWs) and a representative test set which will be used for the evaluation of the text normalization modules proposed for Standard Mandarin and Taiwanese Mandarin. A total of 191,431 sentences with NSWs are extracted for the Standard Mandarin and a total of 731,524 sentences with NSWs are extracted for Taiwanese Mandarin. In order to make a representative test set, 1,000 sentences for Standard Mandarin and Taiwanese Mandarin are randomly chosen from these sentences, maintaining the same proportion of the source corpus as well as the similar proportion of each category of NSWs.


title: Automatic non-personalized book recommender algorithm for bookstore shelf management

abstract: Generally, the bookstore organizes bookshelf with book's category, writer, recommend, publish date and etc. However, the most important self is the recommend book shelf. The recommend bookshelf often set at the front of the store, because the customers can see the first and obviously. Traditionally the recommend bookshelf was based on the best seller and the newest. The recommend bookshelf was managed by the Non-Personalize recommendation, because the recommendation must affect to the most customers. This paper presented the book recommender algorithm for book recommender shelf. The purchase transaction log and the term in book title was used to predict the next best seller. The ranking of the predicted best seller book was used as the evaluation method, and the result shown that the best seller book was correctly identified up to 43% in the "Cookbooks, Food & Wine" category.


title: Underactuated robot hand for dual-arm manipulation

abstract: In this paper, we propose a novel robot hand called uGRIPP (Underactuated Gripper for Power and Precision Grasp) which has underactuated fingers for dual-arm manipulation tasks. A newly designed underactuated linkage enables the robot hand to achieve not only an adaptive grasp and a pinch grasp, but also manipulation by a palm of the hand. We introduce performance of the proposed linkage by a static analysis. A prototype of the robot hand which has the proposed linkage as fingers was developed. We conducted handling experiments by using the robot hand.


title: A Medium-Voltage Hybrid DC Circuit Breaker, Part I: Solid-State Main Breaker Based on 15 kV SiC Emitter Turn-OFF Thyristor

abstract: This paper discusses the design of a 10 kV and 200 A hybrid dc circuit breaker suitable for the protection of the dc power systems in electric ships. The proposed hybrid dc circuit breaker employs a Thompson coil based ultrafast mechanical switch (MS) with the assistance of two additional solid-state power devices. A low-voltage (80 V) metal-oxide-semiconductor field-effect transistors (MOSFETs)-based commutating switch (CS) is series connected with the MS to realize the zero current turn-OFF of the MS. In this way, the arcing issue with the MS is avoided. A 15 kV SiC emitter turn-OFF thyristor-based main breaker (MB) is parallel connected with the MS and CS branch to interrupt the fault current. A stack of MOVs parallel with the MB are used to clamp the voltage across the hybrid dc circuit breaker during interruption. This paper focuses on the electronic parts of the hybrid dc circuit breaker, and a companion paper will elucidate the principle and operation of the fast acting MS and the overall operation of the hybrid dc circuit breaker. The selection and design of both the high-voltage and low-voltage electronic components in the hybrid dc circuit breaker are presented in this paper. The turn-OFF capability of the MB with and without snubber circuit is experimentally tested, validating its suitability for the hybrid dc circuit breaker application. The CSs' conduction performances are tested up to 200 A, and its current commutating during fault current interruption is also analyzed. Finally, the hybrid dc circuit breaker demonstrated a fast current interruption within 2 ms at 7 kV and 100 A.


title: Approaching a consensus cognitive battery for clinical trials in schizophrenia: The NIMH-MATRICS conference to select cognitive domains and test criteria

abstract: To stimulate the development of new drugs for the cognitive deficits of schizophrenia, the National Institute of Mental Health (NIMH) established the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative. This article presents an overview of decisions from the first MATRICS consensus conference. The goals of the meeting were to 1) identify the cognitive domains that should be represented in a consensus cognitive battery and 2) prioritize key criteria for selection of tests for the battery. Seven cognitive domains were selected based on a review of the literature and input from experts: working memory, attention/vigilance, verbal learning and memory, visual learning and memory, reasoning and problem solving, speed of processing, and social cognition. Based on discussions at this meeting, five criteria were considered essential for test selection: good test–retest reliability, high utility as a repeated measure, relationship to functional outcome, potential response to pharmacologic agents, and practicality/tolerability. The results from this meeting constitute the initial steps for reaching a consensus cognitive battery for clinical trials in schizophrenia.


A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition


title: Separate hydrolysis and fermentation and simultaneous saccharification and fermentation methods in bioethanol production and formation of volatile by-products from selected corn cultivars

abstract: Abstract Corn (Zea mays L.) is a widely used raw material in the production of bioethanol. Creating new cultivars and applying appropriate fermentation techniques to increase production efficiency is still important for bioethanol producers. The purpose of the study presented here was to evaluate the correlation between differentiators of corn cultivars and fermentation methods in view of the efficiency of bioethanol production. The five selected cultivars differed by FAO (Food and Agriculture Organization) number, type of hybrid cultivar, and type of grain. The tested samples generally showed significant (p


title: Robotic Fingerspelling Hand for the aid of the Deaf and Blind EXECUTIVE SUMMARY

abstract: EXECUTiVE SUMMARY This quarter, work continued on the design and construction of a robotic fingerspelling hand. The hand is being designed to aid in communication for individuals who are both deaf and blind. In the winter quarter, research was centered on determining an effective method of actuation for the robotic hand. This spring 2008 quarter, time was spent designing the mechanisms needed to mimic the size and motions of a human hand. Several methods were used to determine a proper size for the robotic hand, including using the ManneQuinPro human modeling system to approximate the size of an average male human hand and using the golden ratio to approximate the length of bone sections within the hand. After a proper average hand size was determined, a finger mechanism was designed in the SolidWorks design program that could be built and used in the robotic hand.


title: An unsupervised cross-lingual topic model framework for sentiment classification

abstract: Sentiment classification aims to determine the sentiment polarity expressed in a text. In online customer reviews, the sentiment polarities of words are usually dependent on the corresponding aspects. For instance, in mobile phone reviews, we may expect the long battery time but not enjoy the long response time of the operating system. Therefore, it is necessary and appealing to consider aspects when conducting sentiment classification. Probabilistic topic models that jointly detect aspects and sentiments have gained much success recently. However, most of the existing models are designed to work well in a language with rich resources. Directly applying those models on poor-quality corpora often leads to poor results. Consequently, a potential solution is to use the cross-lingual topic model to improve the sentiment classification for a target language by leveraging data and knowledge from a source language. However, the existing cross-lingual topic models are not suitable for sentiment classification because sentiment factors are not considered therein. To solve these problems, we propose for the first time a novel cross-lingual topic model framework which can be easily combined with the state-of-the-art aspect/sentiment models. Extensive experiments in different domains and multiple languages demonstrate that our model can significantly improve the accuracy of sentiment classification in the target language.


title: Medical Image Colorization for Better Visualization and Segmentation

abstract: Medical images contain precious anatomical information for clinical procedures. Improved understanding of medical modality may contribute significantly in arena of medical image analysis. This paper investigates enhancement of monochromatic medical modality into colorized images. Improving the contrast of anatomical structures facilitates precise segmentation. The proposed framework starts with pre-processing to remove noise and improve edge information. Then colour information is embedded to each pixel of a subject image. A resulting image has a potential to portray better anatomical information than a conventional monochromatic image. To evaluate the performance of colorized medical modality, the structural similarity index and the peak signal to noise ratio are computed. Supremacy of proposed colorization is validated by segmentation experiments and compared with greyscale monochromatic images.


title: Achieving IT-Business Strategic Alignment via Enterprise-Wide Implementation of Balanced Scorecards

abstract: IT-business alignment remains one of the top issues of IS managers. This article presents a case study in which four key elements of IT-business alignment-integrated planning, effective communication, active relationship management, and institutionalized culture of alignment-were enhanced by the enterprise-wide implementation of an established strategic management tool, the balanced scorecard.


title: Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery

abstract: Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sum-to-one constraint and the abundance nonnegativity constraint. The first constraint requires the sum of the abundance fractions of materials present in an image pixel to be one and the second imposes a constraint that these abundance fractions be nonnegative. While the first constraint is easy to deal with, the second constraint is difficult to implement since it results in a set of inequalities and can only be solved by numerical methods. Consequently, most LSMA-based methods are unconstrained and produce solutions that do not necessarily reflect the true abundance fractions of materials. In this case, they can only be used for the purposes of material detection, discrimination, and classification, but not for material quantification. The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification. Since no closed form can be derived for this method, an efficient algorithm is developed to yield optimal solutions. In order to further apply the designed algorithm to unknown image scenes, an unsupervised least squares error (LSE)-based method is also proposed to extend the FCLS method in an unsupervised manner.


title: A log-logistic model-based interpretation of TF normalization of BM25

abstract: The effectiveness of BM25 retrieval function is mainly due to its sub-linear term frequency (TF) normalization component, which is controlled by a parameter k1. Although BM25 was derived based on the classic probabilistic retrieval model, it has been so far unclear how to interpret its parameter k1 probabilistically, making it hard to optimize the setting of this parameter. In this paper, we provide a novel probabilistic interpretation of the BM25 TF normalization and its parameter k1 based on a log-logistic model for the probability of seeing a document in the collection with a given level of TF. The proposed interpretation allows us to derive different approaches to estimation of parameter k1 based solely on the current collection without requiring any training data, thus effectively eliminating one free parameter from BM25. Our experiment results show that the proposed approaches can accurately predict the optimal k1 without requiring training data and achieve better or comparable retrieval performance to a well-tuned BM25 where k1 is optimized based on training data.


title: Effect of lithium-pilocarpine-induced status epilepticus on ultrasonic vocalizations in the infant rat pup

abstract: Evidence shows that febrile convulsions induced in rat pups increase ultrasonic vocalizations (USVs); however, the effect of status epilepticus (SE) induced in developing rats on USVs has not been fully investigated. The goal of this study was to analyze USVs following lithium-pilocarpine-induced SE in fourteen-day-old (P14) rat pups. The rat pups were given 3-mEq/kg lithium chloride i.p. on the day before the induction of SE, which was carried out at P14 by subcutaneous injection of 100-mg/kg pilocarpine hydrochloride; control animals were given an equal volume of lithium chloride and saline on P13 and P14, respectively. Ultrasonic vocalizations were monitored at P15, P16, and P21 with a Mini 3 Bat Detector Ultra Sound Advice (15kHz-160kHz) set at 40±4kHz and digitally recorded in WAV format using the Audacity 1.3 beta software. A clear box (60×40×30cm) split down the middle with a holed wall was used; each pup was placed alone in one compartment, whereas its dam was placed on the other cage side at room temperature. Vocalizations were recorded over a 5-minute period, converted to sonograms and spectrograms, and analyzed using the Raven software. Parameters evaluated were as follows: USV frequency, latency to the first USV, and mean USV duration. There was a significant decrease in the latency (35.5±6.9s) and duration (50.8±8.6s) of USVs after SE compared with the control group (81.9±10.8s and 78.1±9.9s, respectively). Status epilepticus affected male and female rats differentially.


title: DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks

abstract: In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is proposed to learn feature representations more suitable for the object detection task and with good generalization capability. By changing the net structures, training strategies, adding and removing some key components in the detection pipeline, a set of models with large diversity are obtained, which significantly improves the effectiveness of model averaging. The proposed approach improves the mean averaged precision obtained by RCNN [1] , which was the state-of-the-art, from $31$ to $50.3$ percent on the ILSVRC2014 detection test set. It also outperforms the winner of ILSVRC2014, GoogLeNet, by 6.1 percent. Detailed component-wise analysis is also provided through extensive experimental evaluation, which provides a global view for people to understand the deep learning object detection pipeline.


title: Multi-modal scene categorization using multi-tasks learning

abstract: The combination of standard visible RGB channels and near infrared (NIR) information has been observed to perform better than RGB-only data in the scene categorization task. However, RGB-NIR data are limited due to the collection difficulties. With limited data, it is hard for most deep networks to learn an effective solution, while humans are able to solve such kind of problem within a little of exercises. It may be because we are able to learn prior knowledge from other tasks or a good mentor and these knowledge is helpful. Inspired by this hypothesis, a new training scheme is proposed in order to introduce the prior knowledge into the intermediate layers of network. First of all, transfer learning is adopted to learn single modal features from related tasks. Then a knowledge distillation method is explored to fuse the RGB and NIR features. As these pre-training stages have been finished, global fine-tuning is employed to jointly train the whole network. The experimental results on the RGB-NIR dataset demonstrate the efficiency of our proposed approach in comparison with the other existing methods.


A survey on Air Quality forecasting Techniques


title: A dynamic gesture recognition system for the Korean sign language (KSL)

abstract: The sign language is a method of communication for the deaf-mute. Articulated gestures and postures of hands and fingers are commonly used for the sign language. This paper presents a system which recognizes the Korean sign language (KSL) and translates into a normal Korean text. A pair of data-gloves are used as the sensing device for detecting motions of hands and fingers. For efficient recognition of gestures and postures, a technique of efficient classification of motions is proposed and a fuzzy min-max neural network is adopted for on-line pattern recognition.


title: LTH: Semantic Structure Extraction using Nonprojective Dependency Trees

abstract: We describe our contribution to the SemEval task on Frame-Semantic Structure Extraction. Unlike most previous systems described in literature, ours is based on dependency syntax. We also describe a fully automatic method to add words to the FrameNet lexical database, which gives an improvement in the recall of frame detection.


title: Nipah Virus Infection Outbreak with Nosocomial and Corpse-to-Human Transmission, Bangladesh

abstract: Active Nipah virus encephalitis surveillance identified an encephalitis cluster and sporadic cases in Faridpur, Bangladesh, in January 2010. We identified 16 case-patients; 14 of these patients died. For 1 case-patient, the only known exposure was hugging a deceased patient with a probable case, while another case-patient’s exposure involved preparing the same corpse for burial by removing oral secretions and anogenital excreta with a cloth and bare hands. Among 7 persons with confirmed sporadic cases, 6 died, including a physician who had physically examined encephalitis patients without gloves or a mask. Nipah virus–infected patients were more likely than community-based controls to report drinking raw date palm sap and to have had physical contact with an encephalitis patient (29% vs. 4%, matched odds ratio undefined). Efforts to prevent transmission should focus on reducing caregivers’ exposure to infected patients’ bodily secretions during care and traditional burial practices.


title: Large-scale behavioral targeting with a social twist

abstract: Behavioral targeting (BT) is a widely used technique for online advertising. It leverages information collected on an individual's web-browsing behavior, such as page views, search queries and ad clicks, to select the ads most relevant to user to display. With the proliferation of social networks, it is possible to relate the behavior of individuals and their social connections. Although the similarity among connected individuals are well established (i.e., homophily), it is still not clear whether and how we can leverage the activities of one's friends for behavioral targeting; whether forecasts derived from such social information are more accurate than standard behavioral targeting models. In this paper, we strive to answer these questions by evaluating the predictive power of social data across 60 consumer domains on a large online network of over 180 million users in a period of two and a half months. To our best knowledge, this is the most comprehensive study of social data in the context of behavioral targeting on such an unprecedented scale. Our analysis offers interesting insights into the value of social data for developing the next generation of targeting services.


title: Analyzing the Performance of a Blockchain-based Personal Health Record Implementation

abstract: Abstract Background The Personal Health Record (PHR) and Electronic Health Record (EHR) play a key role in more efficient access to health records by health professionals and patients. It is hard, however, to obtain a unified view of health data that is distributed across different health providers. In particular, health records are commonly scattered in multiple places and are not integrated. Objective This article presents the implementation and evaluation of a PHR model that integrates distributed health records using blockchain technology and the openEHR interoperability standard. We thus follow OmniPHR architecture model, which describes an infrastructure that supports the implementation of a distributed and interoperable PHR. Methods Our method involves implementing a prototype and then evaluating the integration and performance of medical records from different production databases. In addition to evaluating the unified view of records, our evaluation criteria also focused on non-functional performance requirements, such as response time, CPU usage, memory occupation, disk, and network usage. Results We evaluated our model implementation using the data set of more than 40 thousand adult patients anonymized from two hospital databases. We tested the distribution and reintegration of the data to compose a single view of health records. Moreover, we profiled the model by evaluating a scenario with 10 superpeers and thousands of competing sessions transacting operations on health records simultaneously, resulting in an average response time below 500 ms. The blockchain implemented in our prototype achieved 98% availability. Conclusion Our performance results indicated that data distributed via a blockchain could be recovered with low average response time and high availability in the scenarios we tested. Our study also demonstrated how OmniPHR model implementation can integrate distributed data into a unified view of health records.


title: Record Setting Software Implementation of DES Using CUDA

abstract: The increase in computational power of off-the-shelf hardware offers more and more advantageous tradeoffs among efficiency, cost and availability, thus enhancing the feasibility of of cryptanalytic attacks aiming to lower the security of widely used cryptosystems. In this paper we illustrate an GPU-based software implementation of the most efficent variant of Data Encryption Standard (DES), showing the performance of a software breaker which effectively exploits the multi-core Nvidia GT200 graphic architecture. The key point is to assess how well the structure of a symmetric key cipher can fit the GPU programming model and the single instruction multiple data architectural parallelism. The proposed breaker outperforms the fastest general purpose CPU-based implementations by an order of magnitude, and, due to the vast availability of GPUs on the market, the speedup translates into a sound improvement in the cost efficiency of the attack. As opposed to solutions based either on application specific or reconfigurable hardware, the proposed implementation does not require any specific technical knowledge from the attacker in order to be successfully built, once our implementation is available. This turns out in a better cost-availability tradeoff and minimizes the required setup time for such an attack to be mounted.


title: LinkedIn skills: large-scale topic extraction and inference

abstract: "Skills and Expertise" is a data-driven feature on LinkedIn, the world's largest professional online social network, which allows members to tag themselves with topics representing their areas of expertise. In this work, we present our experiences developing this large-scale topic extraction pipeline, which includes constructing a folksonomy of skills and expertise and implementing an inference and recommender system for skills. We also discuss a consequent set of applications, such as Endorsements, which allows members to tag themselves with topics representing their areas of expertise and for their connections to provide social proof, via an "endorse" action, of that member's competence in that topic.


Interactive Learning for Sequential Decisions and Predictions


Fifty Years of Classification and Regression Trees 1


title: Knowledge Bases in the Age of Big Data Analytics

abstract: This tutorial gives an overview on state-of-the-art methods for the automatic construction of large knowledge bases and harnessing them for data and text analytics. It covers both big-data methods for building knowledge bases and knowledge bases being assets for big-data applications. The tutorial also points out challenges and research opportunities.


Team SWEEPer: Joint Sentence Extraction and Fact Checking with Pointer Networks


title: Contention-Aware Lock Scheduling for Transactional Databases

abstract: Lock managers are among the most studied components in concurrency control and transactional systems. However, one question seems to have been generally overlooked: "When there are multiple lock requests on the same object, which one(s) should be granted first?" ::: ::: Nearly all existing systems rely on a FIFO (first in, first out) strategy to decide which transaction(s) to grant the lock to. In this paper, however, we show that the lock scheduling choices have significant ramifications on the overall performance of a transactional system. Despite the large body of research on job scheduling outside the database context, lock scheduling presents subtle but challenging requirements that render existing results on scheduling inapt for a transactional database. By carefully studying this problem, we present the concept of contention-aware scheduling, show the hardness of the problem, and propose novel lock scheduling algorithms (LDSF and bLDSF), which guarantee a constant factor approximation of the best scheduling. We conduct extensive experiments using a popular database on both TPC-C and a microbenchmark. Compared to FIFO---the default scheduler in most database systems---our bLDSF algorithm yields up to 300x speedup in overall transaction latency. Alternatively, our LDSF algorithm, which is simpler and achieves comparable performance to bLDSF, has already been adopted by open-source community, and was chosen as the default scheduling strategy in MySQL 8.0.3+


title: The human voice areas: Spatial organization and inter-individual variability in temporal and extra-temporal cortices

abstract: fMRI studies increasingly examine functions and properties of non-primary areas of human auditory cortex. However there is currently no standardized localization procedure to reliably identify specific areas across individuals such as the standard ‘localizers’ available in the visual domain. Here we present an fMRI ‘voice localizer’ scan allowing rapid and reliable localization of the voice-sensitive ‘temporal voice areas’ (TVA) of human auditory cortex. We describe results obtained using this standardized localizer scan in a large cohort of normal adult subjects. Most participants (94%) showed bilateral patches of significantly greater response to vocal than non-vocal sounds along the superior temporal sulcus/gyrus (STS/STG). Individual activation patterns, although reproducible, showed high inter-individual variability in precise anatomical location. Cluster analysis of individual peaks from the large cohort highlighted three bilateral clusters of voice-sensitivity, or “voice patches” along posterior (TVAp), mid (TVAm) and anterior (TVAa) STS/STG, respectively. A series of extra-temporal areas including bilateral inferior prefrontal cortex and amygdalae showed small, but reliable voice-sensitivity as part of a large-scale cerebral voice network. Stimuli for the voice localizer scan and probabilistic maps in MNI space are available for download.


Security Analysis of Elliptic Curve Cryptography and RSA


title: Rib approximation without intercostal nerve compression reduces post-thoracotomy pain: a prospective randomized study.

abstract: OBJECTIVE ::: One of the most important considerations in the care of thoracic surgery patients is the control of pain, which leads to increased morbidity and relevant mortality. ::: ::: ::: METHODS ::: Between February and May 2009, 60 patients undergoing full muscle-sparing posterior minithoracotomy were prospectively randomized into two groups, according to the thoracotomy closure techniques. In the first group (group A), two holes were drilled into the sixth rib using a hand perforator, and sutures were passed through the holes in the sixth rib and were circled from the upper edge of the fifth rib, thereby compressing the intercostal nerve underneath the fifth rib. In the second group (group B), the intercostal muscle underneath the fifth rib was partially dissected along with the intercostal nerve, corresponding to the holes on the sixth rib. Two 1/0 polyglactin (Vicyrl) sutures were passed through the holes in the sixth rib and above the intercostal nerve. ::: ::: ::: RESULTS ::: There were 30 patients in each group. The visual analog score, observer verbal ranking scale (OVRS) scores for pain, and Ramsay sedation scores were used to follow-up on postoperative analgesia and sedation. The von Frey hair test was used to evaluate hyperalgesia of the patients. The patients in group B had lower visual analog scores at rest and during coughing. The patients in group B had lower OVRS scores than group A patients. The groups were not statistically different in terms of the Ramsay sedation scores and von Frey hair tests. ::: ::: ::: CONCLUSIONS ::: Thoracotomy closure by a technique that avoids intercostal nerve compression significantly decreases post-thoracotomy pain.


title: A pedagogical model to develop teaching skills. The collaborative learning experience in the Immersive Virtual World TYMMI

abstract: The initial training is a key to improve the learning outcomes of students.This is a descriptive-exploratory research based on quanti-qualitative approach.Immersive Virtual Worlds was used with a pedagogical model to enhance teaching.Pedagogical challenges enhance the performance to teach of pre-service teachers.Students are motivated intellectually and technologically in virtual worlds. The initial training of quality teachers is seen as a key to improving the learning outcomes of students in Chile. The TYMMI project is one of the initiatives being developed to provide a space for simulation for teaching practices in immersive virtual environments in Second Life and Open Sim. Initial Teachers Training belong to the School of Education at the Universidad Catolica de la Santisima Concepcion, participated during 2014 in the implementation of challenges, based on a pedagogical model and teaching strategies such as role play and problem-based learning. Through direct observation and blogs, the results show that participants have an important domain in the pedagogical and technological interaction. Despite the perception of the technical difficulties of using platforms, students emphasize that the experience has been supportive along their teaching practices, and it has allowed them to reinforce subject content, which poses a very motivating intellectual and technological challenge.


title: New potential functions for mobile robot path planning

abstract: The paper first describes the problem of goals unreachable with obstacles nearby when using potential field methods for mobile robot path planning. Then, new repulsive potential functions are presented by taking the relative distance between the robot and the goal into consideration, which ensures that the goal position is the global minimum of the total potential.


title: Event-related brain potentials elicited by syntactic anomaly

abstract: Abstract Event-related brain potentials (ERPs) were recorded from 13 scalp electrodes while subjects read sentences containing syntactic ambiguities. Words which were inconsitent with the “preferred” sentence structure elicited a brain potential (P600) quite distinct from the potential previously observed following contextually inappropriate words (N400). Furthermore, final words in sentences typically judged to be unacceptable elicited an N400-like effect, relative to final words in sentences typically judged to be acceptable. These findings suggest that ERPs are sensitive to syntactic anomaly, including anomaly engendered by disambiguating material following erroneous analysis of a syntactically ambiguous string (the “garden path” effect). We evaluate the speculation that the P600 and N400 effects are elicited as a function of anomaly type (syntactic and semantic, respectively).


title: Scene-Specific Pedestrian Detection Based on Parallel Vision

abstract: As a special type of object detection, pedestrian detection in generic scenes has made a significant progress trained with large amounts of labeled training data manually. While the models trained with generic dataset work bad when they are directly used in specific scenes. With special viewpoints, flow light and backgrounds, datasets from specific scenes are much different from the datasets from generic scenes. In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes. While labeling the data manually spends much time and money, especially for specific scenes, each time with a new specific scene, large amounts of images must be labeled. What's more, the labeling information is not so accurate in the pixels manually and different people make different labeling information. In this paper, we propose an ACP-based method, with augmented reality's help, we build the virtual world of specific scenes, and make people walking in the virtual scenes where it is possible for them to appear to solve this problem of lacking labeled data and the results show that data from virtual world is helpful to adapt generic pedestrian detectors to specific scenes.


title: OCR Post-Processing Error Correction Algorithm using Google Online Spelling Suggestion

abstract: With the advent of digital optical scanners, a lot of paper-based books, textbooks, magazines, articles, and documents are being transformed into an electronic version that can be manipulated by a computer. For this purpose, OCR, short for Optical Character Recognition was developed to translate scanned graphical text into editable computer text. Unfortunately, OCR is still imperfect as it occasionally mis-recognizes letters and falsely identifies scanned text, leading to misspellings and linguistics errors in the OCR output text. This paper proposes a post-processing context-based error correction algorithm for detecting and correcting OCR non-word and real-word errors. The proposed algorithm is based on Google's online spelling suggestion which harnesses an internal database containing a huge collection of terms and word sequences gathered from all over the web, convenient to suggest possible replacements for words that have been misspelled during the OCR process. Experiments carried out revealed a significant improvement in OCR error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized and executed over multiprocessing platforms.


title: Reliable Transition Detection in Videos: A Survey and Practitioner’s Guide

abstract: A large number of shot boundary detection, or equivalently, transition detection techniques have been developed in recent years. They all can be classified based on a few core concepts underlying the different detection schemes. This survey emphasizes those different core concepts underlying the different detection schemes for the three most widely used video transition effects: hard cuts, fades and dissolves. Representative of each concept one or a few very sound and thoroughly tested approaches are present in detail, while others are just listed. Whenever reliable performance numbers could be found in the literature, they are mentioned. Guidelines for practitioners in video processing are also given.


title: Fluorescence nanoscopy in cell biology

abstract: Fluorescence nanoscopy uniquely combines minimally invasive optical access to the internal nanoscale structure and dynamics of cells and tissues with molecular detection specificity. While the basic physical principles of 'super-resolution' imaging were discovered in the 1990s, with initial experimental demonstrations following in 2000, the broad application of super-resolution imaging to address cell-biological questions has only more recently emerged. Nanoscopy approaches have begun to facilitate discoveries in cell biology and to add new knowledge. One current direction for method improvement is the ambition to quantitatively account for each molecule under investigation and assess true molecular colocalization patterns via multi-colour analyses. In pursuing this goal, the labelling of individual molecules to enable their visualization has emerged as a central challenge. Extending nanoscale imaging into (sliced) tissue and whole-animal contexts is a further goal. In this Review we describe the successes to date and discuss current obstacles and possibilities for further development.


title: Why functional programming matters

abstract: As software becomes more and more complex, it is more and more important to structure it well. Well-structured software is easy to write and to debug, and provides a collection of modules that can be reused to reduce future programming costs. In this paper we show that two features of functional languages in particular, higher-order functions and lazy evaluation, can contribute significantly to modularity. As examples, we manipulate lists and trees, program several numerical algorithms, and implement the alpha-beta heuristic (an algorithm from Artificial Intelligence used in game-playing programs). We conclude that since modularity is the key to successful programming, functional programming oers important advantages for software development.


title: Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube

abstract: We give an algorithm for completing an order-$m$ symmetric low-rank tensor from its multilinear entries in time roughly proportional to the number of tensor entries. We apply our tensor completion algorithm to the problem of learning mixtures of product distributions over the hypercube, obtaining new algorithmic results. If the centers of the product distribution are linearly independent, then we recover distributions with as many as $\Omega(n)$ centers in polynomial time and sample complexity. In the general case, we recover distributions with as many as $\tilde\Omega(n)$ centers in quasi-polynomial time, answering an open problem of Feldman et al. (SIAM J. Comp.) for the special case of distributions with incoherent bias vectors. ::: Our main algorithmic tool is the iterated application of a low-rank matrix completion algorithm for matrices with adversarially missing entries.


A Layered Architecture for Office Delivery Robots


title: An Algorithm to Extract Rules from Artificial Neural Networks for Medical Diagnosis Problems

abstract: Artificial neural networks (ANNs) have been successfully applied to solve a variety of classification and function approximation problems. Although ANNs can generally predict better than decision trees for pattern classification problems, ANNs are often regarded as black boxes since their predictions cannot be explained clearly like those of decision trees. This paper presents a new algorithm, called rule extraction from ANNs (REANN), to extract rules from trained ANNs for medical diagnosis problems. A standard three-layer feedforward ANN with four-phase training is the basis of the proposed algorithm. In the first phase, the number of hidden nodes in ANNs is determined automatically by a constructive algorithm. In the second phase, irrelevant connections and input nodes are removed from trained ANNs without sacrificing the predictive accuracy of ANNs. The continuous activation values of the hidden nodes are discretized by using an efficient heuristic clustering algorithm in the third phase. Finally, rules are extracted from compact ANNs by examining the discretized activation values of the hidden nodes. Extensive experimental studies on three benchmark classification problems, i.e. breast cancer, diabetes and lenses, demonstrate that REANN can generate high quality rules from ANNs, which are comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy.


title: Automatically detecting problematic use of smartphones

abstract: Smartphone adoption has increased significantly and, with the increase in smartphone capabilities, this means that users can access the Internet, communicate, and entertain themselves anywhere and anytime. However, there is growing evidence of problematic use of smartphones that impacts both social and heath aspects of users' lives. Currently, assessment of overuse or problematic use depends on one-time, self-reported behavioral information about phone use. Due to the known issues with self-reports in such types of assessments, we explore an automated, objective and repeatable approach for assessing problematic usage. We collect a wide range of phone usage data from smartphones, identify a number of usage features that are relevant to this assessment, and build detection models based on Adaboost with machine learning algorithms automatically detecting problematic use. We found that the number of apps used per day, the ratio of SMSs to calls, the number of event-initiated sessions, the number of apps used per event initiated session, and the length of non-event-initiated sessions are useful for detecting problematic usage. With these, a detection model can identify users with problematic usage with 89.6% accuracy (F-score of .707).


title: Factors Affecting Chick Provisioning by Caspian Terns Nesting in the Columbia River Estuary

abstract: Abstract We investigated factors affecting chick provisioning by radio-tagged Caspian Terns (Sterna caspia) nesting in a large colony on East Sand Island in the Columbia River estuary during 2001. Caspian Tern predation on juvenile salmonids (Oncorhynchus spp.) in the estuary prompted resource managers to relocate ca. 9,000 pairs of terns nesting on Rice Island (river km 34) to East Sand Island (river km 8), where terns were expected to consume fewer salmonids in favor of marine forage fishes. This study investigated factors influencing foraging success, diet composition, and overall reproductive success at the managed Caspian Tern colony. Our results indicated that daytime colony attendance by nesting terns averaged 64% and decreased throughout the chick-rearing period, while duration of foraging trips averaged 47 min and increased during the same period; these seasonal changes were more strongly related to date than chick age. Average meal delivery rates to 2-chick broods (0.88 meals h-1) were 2.6 times...


title: Effects of Intraligand Electron Delocalization, Steric Tuning, and Excited-State Vibronic Coupling on the Photophysics of Aryl-Substituted Bipyridyl Complexes of Ru(II)

abstract: The synthesis and photophysical characterization of a series of aryl-substituted 2,2‘-bipyridyl complexes of RuII are reported. The static and time-resolved emission properties of [Ru(dpb)3](PF6)2, where dpb is 4,4‘-diphenyl-2,2‘-bipyridine, have been examined and are contrasted with those of [Ru(dmb)3](PF6)2 (dmb = 4,4‘-dimethyl-2,2‘-bipyridine). It is shown through analysis of electrochemical data and detailed fitting of the emission spectrum that the unusually large radiative quantum yield for [Ru(dpb)3](PF6)2 in CH3CN solution at room temperature is due to reduction of the degree of geometric distortion along primarily ring-stretch acceptor mode coordinates relative to other molecules in this class. It is proposed that the 3MLCT excited state of [Ru(dpb)3]2+ is characterized by a ligand conformation in which the 4,4‘-phenyl substituents are coplanar with the bipyridyl fragment, leading to extended intraligand electron delocalization and a smaller average change in the C−C bond length upon formation of...


title: Cultures and Selves: A Cycle of Mutual Constitution

abstract: The study of culture and self casts psychology’s understanding of the self, identity, or agency as central to the analysis and interpretation of behavior and demonstrates that cultures and selves define and build upon each other in an ongoing cycle of mutual constitution. In a selective review of theoretical and empirical work, we define self and what the self does, define culture and how it constitutes the self (and vice versa), define independence and interdependence and determine how they shape psychological functioning, and examine the continuing challenges and controversies in the study of culture and self. We propose that a self is the ‘‘me’’ at the center of experience—a continually developing sense of awareness and agency that guides actions and takes shape as the individual, both brain and body, becomes attuned to various environments. Selves incorporate the patterning of their various environments and thus confer particular and culture-specific form and function to the psychological processes they organize (e.g., attention, perception, cognition, emotion, motivation, interpersonal relationship, group). In turn, as selves engage with their sociocultural contexts, they reinforce and sometimes change the ideas, practices, and institutions of these environments.


title: "This Post Will Just Get Taken Down": Characterizing Removed Pro-Eating Disorder Social Media Content

abstract: Social media sites like Facebook and Instagram remove content that is against community guidelines or is perceived to be deviant behavior. Users also delete their own content that they feel is not appropriate within personal or community norms. In this paper, we examine characteristics of over 30,000 pro-eating disorder (pro-ED) posts that were at one point public on Instagram but have since been removed. Our work shows that straightforward signals can be found in deleted content that distinguish them from other posts, and that the implications of such classification are immense. We build a classifier that compares public pro-ED posts with this removed content that achieves moderate accuracy of 69%. We also analyze the characteristics in content in each of these post categories and find that removed content reflects more dangerous actions, self-harm tendencies, and vulnerability than posts that remain public. Our work provides early insights into content removal in a sensitive community and addresses the future research implications of the findings.


title: Computer aided diagnostic for cancer detection using MRI images of brain (Brain tumor detection and classification system)

abstract: The brain is one of the vital organ of the body where it is the custodian of the involuntary and voluntary actions like walking, vision, memory. Now a days the most common brain disorders are Alzheimer's disease, Epilepsy (paralysis or stroke), tumors, brain tumors. Early diagnosis and proper treatment of brain tumors is required. The Computer Aided Diagnostic tools (CAD) can be used by the doctor for 2nd opinion to analyze the brain tumors and it is witnessing a rapid development in last decade. The CAD tools finds its major application in Medical (Macro and micro) imaging. The proposed diagnosis techniques is consists of three parts. Firstly a technique for segmenting the tumor, secondly optimize the data set and use Gray level Co-occurrence Matrix (GLCM) for texture analysis is proposed and lastly classify the tumor using Support vector machine classifier into Benign or malignant one is proposed. The classifier accuracy is obtained by using 3 kernels method that are Radial Basis Function, Linear and Polynomial kernel. In this paper the proposed algorithm and the GUI of CAD tools for Brain tumor detection and classification is designed and simulated in MATLAB.


title: Graphene Reflectarray Metasurface for Terahertz Beam Steering and Phase Modulation

abstract: We report a THz reflectarray metasurface which uses graphene as active element to achieve beam steering, shaping and broadband phase modulation. This is based on the creation of a voltage controlled reconfigurable phase hologram, which can impart different reflection angles and phases to an incident beam, replacing bulky and fragile rotating mirrors used for terahertz imaging. This can also find applications in other regions of the electromagnetic spectrum, paving the way to versatile optical devices including light radars, adaptive optics, electro-optical modulators and screens.


title: Design Optimization Of N-shaped Roof Trusses

abstract: Abstract Design optimization of a class of plane trusses called the N-shaped truss (NST) is addressed. The parametric model of NST presented is intended for real-world application, avoiding simplifications of the design details that compromise the applicability. The model, which includes 27 discrete variables concerning topology, configuration and sizing of the truss, presents a challenging optimization problem. Aspects of such challenge include large search space dimensionality, absence of a closed-form objective function (OF) and constraints, multimodal objective function and costly CPU time per objective function evaluation. Three implementations of general-purpose genetic algorithms (GAs) are tested for this problem, along with a version of taboo search called reactive taboo search (RTS). In this study, the raw version of RTS exhibited better performance than the tested versions of GA but lacks some of the GA capabilities to span the search space. A modification of RTS that uses a population-based exploitation of the search history is proposed. The optimization results show that the introduced modification can further improve the performance of RTS.


title: Neuroscience: Viable Applications in Education?

abstract: As a relatively young science, neuroscience is still finding its feet in potential collaborations with other disciplines. One such discipline is education, with the field of neuroeducation being on the horizon since the 1960s. However, although its achievements are now growing, the partnership has not been as successful as first hopes suggested it should be. Here the authors discuss the theoretical barriers and potential solutions to this, which have been suggested previously, with particular focus on levels of research in neuroscience and their applicability to education. Moreover, they propose that these theoretical barriers are driven and maintained by practical barriers surrounding common language and research literacy. They propose that by overcoming these practical barriers through appropriate training and shared experience, neuroeducation can reach its full potential.


title: Towards enabling deep learning techniques for adaptive dynamic programming

abstract: Human-level control through deep learning and deep reinforcement learning have revealed the unique and powerful potentials through a very complex Go game. The AlphaGo, developed by Google DeepMind, has beat the top Go game player early this year. The scientific and technological advancement behind the success of AlphaGo attracted researchers from multiple areas, including machine learning, artificial intelligence, computational intelligence and so on. Adaptive dynamic programming (ADP) methods have the similar fundamental principle with reinforcement learning, and show strong performance for continuous time and continuous state systems. Deep learning techniques are also possible to be integrated for ADP designs. In this paper, we discuss the key techniques and components in deep reinforcement learning and then present the successful applications for computer games and maze navigation. Future opportunities for deep learning enabled ADP will be discussed at the end.


2 Maturity Models in Business Process Management


title: Understanding continued information technology usage behavior: a comparison of three models in the context of mobile internet

abstract: This study examines the utility of three prospective models for understanding the continued IT usage behavior. The three models include: Expectation-Confirmation Model in IT Domain (ECM-IT), Technology Acceptance Model (TAM), and a hybrid model integrating TAM and ECM-IT (extended ECM-IT). Based on a survey of 1826 mobile Internet users, the LISREL analysis shows that all three models meet the various goodness-of-fit criteria. When compared using special indices for differentiating among alternative good models, TAM has the best fit to the data followed by ECM-IT, and the extended ECM-IT. In terms of variance explained for intention to continue IT usage, the extended ECM-IT has the highest R2 (67%) followed by TAM (63%), and ECM-IT (50%). We conclude that TAM is the most parsimonious and generic model that can be used to study both initial and continued IT adoption; the extended ECM-IT explains continued IT usage behavior as well as TAM; and both the ECM-IT and extended ECM-IT models provide additional information to increase our understanding of continued IT usage.


title: MICROSTRIP ARRAY ANTENNA WITH NEW 2D-ELECTROMAGNETIC BAND GAP STRUCTURE SHAPES TO REDUCE HARMONICS AND MUTUAL COUPLING

abstract: This paper presents microstrip array antenna integrated with novel shapes of 2D-electromagnetic band-gap structure (2D- EBG). Three difierent shapes of 2D-EBG are used for harmonic suppression, optimizing the current distribution on the patches and decreasing the mutual coupling between array elements. As a result, the performance of the antenna array is improved. The three novel shapes of 2D-EBG presented are star, H shaped and I shaped slots. Simulated and measured results verify the improved performance of the array antenna compared to the array antenna without EBG as well as antenna array with conventional EBG shapes. The harmonic suppression and re∞ection coe-cients are improved by about 18dB. Minimum mutual coupling is less than i20dB, and the antenna size is reduced by 15% compared to the original size.


Minor element deficiencies noted in commercial tomato crops in Ireland during 1961


title: Adaptive Cruise Control System: Comparing Gain-Scheduling PI and LQ Controllers

abstract: Abstract Over the recent years, a considerable growth in the number of vehicles on the road has been observed. This increases importance of vehicle safety and minimization of fuel consumption, subsequently prompting manufacturers to equip cars, with more advanced features such as adaptive cruise control (ACC)or collision avoidance and collision warning system (CWS). This paper investigates two control applications design namely the gain scheduling proportional-integral (GSPI) control and gain scheduling Linear Quadratic (GSLQ)control for ACC, covering a high range speed. The control system consist of two loops in cascade, with the inner loop controlling the vehicle speed and the outer loop switching between the cruise control (CC) and the ACC mode and calculating the reference speed. A nonlinear dynamic model of the vehicle is constructed and then a set of operating points is determined and then a of linear models is extracted in operating point. For each operating point, PI and LQ controllers are obtained off-line. An integrated Simulink model including the nonlinear dynamic vehicle model and the ACC controller (either PI or LQ) was used to test the controllers in various traffic scenarios. Comparison results between the two controllers applications is provided to show the validity of the design.


title: Programmatic gold: targeted and scalable quality assurance in crowdsourcing

abstract: Crowdsourcing is an effective tool for scalable data annotation in both research and enterprise contexts. Due to crowdsourcing's open participation model, quality assurance is critical to the success of any project. Present methods rely on EM-style post-processing or manual annotation of large gold standard sets. In this paper we present an automated quality assurance process that is inexpensive and scalable. Our novel process relies on programmatic gold creation to provide targeted training feedback to workers and to prevent common scamming scenarios. We find that it decreases the amount of manual work required to manage crowdsourced labor while improving the overall quality of the results.


Fingerprinting Electronic Control Units for Vehicle Intrusion Detection


title: Controllable Abstractive Summarization

abstract: Current models for document summarization ignore user preferences such as the desired length, style or entities that the user has a preference for. We present a neural summarization model that enables users to specify such high level attributes in order to control the shape of the final summaries to better suit their needs. With user input, we show that our system can produce high quality summaries that are true to user preference. Without user input, we can set the control variables automatically and outperform comparable state of the art summarization systems despite the relative simplicity of our model.


title: Less sickness with more motion and/or mental distraction

abstract: BACKGROUND: Motion sickness may reduce passenger comfort and crew performance. Countermeasures are dominated by medication with specific and often undesirable side effects. OBJECTIVE: To shown that sickness due to motion can be reduced by adding an inherent non-sickening vibration and by mental distraction. METHODS: Eighteen blindfolded subjects were exposed to 20 minutes of off-vertical axis rotation (OVAR). Vibration was added by means of a head rest. Effects of OVAR and vibration were tested separately and in combination, while the subjects were performing an audio letter memorising task. This task was absent to test the effect of mental distraction. Effects were quantified by rating subjective misery and objective task performance. RESULTS: Sixteen subjects completed the experiment and showed in mutual comparable conditions that head vibration reduced the amount of sickness by 25%, the mental distraction did so by 19%, and the combined effect resulted in a reduction of 39%. Their task performance decreased with increasing sickness. CONCLUSION: Sickness due to low-frequency motion can be reduced by adding a high-frequency vibration and by mental distraction. The effect of vibration can be understood by assuming an internal model used by the central nervous system to optimise the control of body motion. © 2015 - IOS Press and the authors. All rights reserved.


title: Dual-Band Circularly Polarized Rotated Patch Antenna With a Parasitic Circular Patch Loading

abstract: A dual-band circularly polarized (CP) rotated patch antenna is proposed in this letter. Dual-band CP operation of the proposed antenna is achieved by adopting a circular patch below the rotated rectangular patch. The extra circular patch not only provides a higher band, but is also employed to tune the operating frequency of the lower band, which is generated by the rotated rectangular patch. The rectangular patch and the circular patch can be arranged on the front and back of a thin substrate and fed by a single slot. The dual CP operating bands can be tuned separately with a small frequency ratio of 1.18. A prototype of the proposed antenna is built to validate the design strategy. The measured 3-dB axial-ratio (AR) bandwidth is 2.1% (2.238-2.285 GHz) for the lower band and 2.0% (2.645-2.695 GHz) for the higher band, with the reflection coefficient being lower than -10 dB. The radiation pattern and gain are also measured and compared to the simulated ones.


title: Intestinal Wound Healing Requires a Wnt Balancing Act

abstract: Inflammatory bowel disease (IBD) encompasses a group of disorders of the colon and small intestine including Crohn's disease and ulcerative colitis. It affects roughly 396 per 100,000 persons worldwide ( 1 ) and in the United States is responsible for more than $1.7 billion in overall health care costs. The chronic or recurrent inflammation associated with this disease causes severe damage to the epithelial lining of the intestine. On page 108 of this issue, Miyoshi et al. ( 2 ) present a model for wound healing in the intestinal tract that may have clinical relevance to mucosal repair in disorders of intestinal ulceration.


A SURVEY ON NON ADAPTIVE IMAGE INTERPOLATION ALGORITHMS BASED ON QUANTITATIVE MEASURES


title: AntMonitor: System and Applications

abstract: Author(s): Shuba, Anastasia; Le, Anh; Alimpertis, Emmanouil; Gjoka, Minas; Markopoulou, Athina


title: Game design workshop to develop computational thinking skills in teenagers with Autism Spectrum Disorders

abstract: Computational Thinking (CT) has been described as the use of abstraction, automation and analysis skills for problem solving, thus becoming a set of fundamental skills for the 21st century. Many initiatives have emerged to promote early development of CT skills. However, to date, there are no studies in which CT is developed in minority groups, such as individuals with Autism Spectrum Disorders. In this paper we present the design and evaluation of a Game Design Workshop aimed at teenagers with Autism Spectrum Disorders. An analysis of the artifacts produced by the participants of a pilot offering of the workshop was performed using a rubric for CT skills acquisition. The results showed that the participants, all of them with high-functioning autism and no previous experience with programming, acquired intermediate level skills related to CT.


title: Dynamic Trees as Search Trees via Euler Tours, Applied to the Network Simplex Algorithm

abstract: Thedynamic tree is an abstract data type that allows the maintenance of a collection of trees subject to joining by adding edges (linking) and splitting by deleting edges (cutting), while at the same time allowing reporting of certain combinations of vertex or edge values. For many applications of dynamic trees, values must be combined along paths. For other applications, values must be combined over entire trees. For the latter situation, an idea used originally in parallel graph algorithms, to represent trees by Euler tours, leads to a simple implementation with a time of O(logn) per tree operation, wheren is the number of tree vertices. We apply this representation to the implementation of two versions of the network simplex algorithm, resulting in a time of O(logn) per pivot, wheren is the number of vertices in the problem network.


title: An Evaluation of Domain-Specific Language Technologies for Code Generation

abstract: Software systems are becoming increasingly complex, requiring a deep knowledge to work and program with them. This is especially true for simulation frameworks used by scientists and engineers, but also applies to completely different domains such as mobile or web applications. To ease working with these systems, domain-specific languages (DSLs) are a convenient way to enable domain experts describe settings and problems they want to solve using terms familiar to them. Building upon this specification in the DSL, a compiler transform this to the target software framework, e.,g., runnable program code. To write such a compiler, a solid implementation framework is needed. In this paper, we propose criteria for the evaluation of textual programming language implementation frameworks to which we accordingly evaluate four technologies, namely Spoofax/IMP, Rascal MPL, a custom approach using C++ and a custom approach using Scala.


title: Are There Different Types of Female Orgasm?

abstract: In attempt to identify and validate different types of orgasms which females have during sex with a partner, data collected by Mah and Binik (2002) on the dimensional phenomenology of female orgasm were subjected to a typological analysis. A total of 503 women provided adjectival descriptions of orgasms experienced either with a partner (n = 276) or while alone (n = 227). Latent-class analysis revealed four orgasm types which varied systematically in terms of pleasure and sensations engendered. Two types, collectively labelled “good-sex orgasms,” received higher pleasure and sensation ratings than solitary-masturbatory ones, whereas two other types, collectively labelled “not-as-good-sex orgasms,” received lower ratings. These two higher-order groupings differed on a number of psychological, physical and relationship factors examined for purposes of validating the typology. Evolutionary thinking regarding the function of female orgasm informed discussion of the findings. Future research directions were outlined, especially the need to examine whether the same individual experiences different types of orgasms with partners with different characteristics, as evolutionary theorizing predicts should be the case.


title: Object tracking by mean shift and radial basis function neural networks

abstract: In this paper, a tracker based on mean shift and radial basis function neural networks called MS-RBF is addressed. As its name implies, two independent trackers have been combined and linked together. The mean shift algorithm estimates the target’s location within only two iterations. The scale and orientation of target are computed by exploiting 2-D correlation coefficient between reference and target candidate histograms instead of using Bhattacharyya coefficient. A code optimization strategy, named multiply–add–accumulate (MAC), is proposed to remove useless memory occupation and programmatic operations. MAC implementation has reduced computational load and made overall tracking process faster. The second tracker “RBFNN” has an input feature vector that contains variables such as local contrast, color histogram, gradient, intensity, and spatial frequency. The neural network learns the color and texture features from the target and background. Then, this information is used to detect and track the object in other frames. The neural network employs Epanechnikov activation functions. The features extracted in any frame are clustered by Fuzzy C-Means clustering which produces the means and variances of the clusters. The experimental results show that the proposed tracker can resist to different types of occlusions, sudden movement, and shape deformations.


title: Incremental LSTM-based dialog state tracker

abstract: A dialog state tracker is an important component in modern spoken dialog systems. We present an incremental dialog state tracker, based on LSTM networks. It directly uses automatic speech recognition hypotheses to track the state. We also present the key non-standard aspects of the model that bring its performance close to the state-of-the-art and experimentally analyze their contribution: including the ASR confidence scores, abstracting scarcely represented values, including transcriptions in the training data, and model averaging.


title: Musical Motivation: Towards a model synthesising the research

abstract: This article explores motivation to participate in music making activities. It examines historical and current theories of motivation and sets out a model describing the way that the characteristics of the individual including, personality, self-concept and personal goals interact with the environment to influence motivation and subsequent behaviour. The environment may include culture and sub-cultures, society, time, place, institutions, family and peers and the rewards and punishments that these offer. The article then explores research relating to motivation to actively participate in music. This supports the notion that musical motivation is determined by complex interactions between the individual and the environment within which they find themselves. The article concludes with a consideration of directions for future research.


title: Technical Section: Automatic pose-independent 3D garment fitting

abstract: Given a virtual garment model on a reference human model, we propose an automated 3D garment fitting system that fits the garment model to a target human model. The proposed method can transfer garment models between human models without any user guidance even when the reference and target human models have different poses. Our goal is not to resize or deform the original garment model according to the target human model but to yield realistic fitting results of the given garment on the target human models. Using pose-independent segmentation and cloth simulation, we achieve realistic and automatic fitting results in reasonable running time. Our method can replace the time-consuming manual fitting process that is necessary for many applications that use virtual garments, such as games, animations, CAD tools and online clothing stores.


Study on advantages and disadvantages of Cloud Computing – the advantages of Telemetry Applications in the Cloud


title: Implementation of SSVEP based BCI with Emotiv EPOC

abstract: In recent years, steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) has received much attentions. However, most SSVEP based BCI devices are not portable and have high price, which are not suitable to be used for clinical and commercial purpose. Thanks to the low cost and portable Emotiv EPOC, it brings BCI into daily life. In this paper, SSVEP based BCI through Emotiv EPOC is implemented. BCI 2000 is employed to connect Emotiv EPOC and Matlab to implement the online system. The online experiments have the accuracy of 95.83±3.59 %, information transfer rate (ITR) with 22.85±1.85 bits/min and detection duration of 5.25±2.14 sec.


title: Social signal processing: state-of-the-art and future perspectives of an emerging domain

abstract: The ability to understand and manage social signals of a person we are communicating with is the core of social intelligence. Social intelligence is a facet of human intelligence that has been argued to be indispensable and perhaps the most important for success in life. This paper argues that next-generation computing needs to include the essence of social intelligence - the ability to recognize human social signals and social behaviours like politeness, and disagreement - in order to become more effective and more efficient. Although each one of us understands the importance of social signals in everyday life situations, and in spite of recent advances in machine analysis of relevant behavioural cues like blinks, smiles, crossed arms, laughter, and similar, design and development of automated systems for Social Signal Processing (SSP) are rather difficult. This paper surveys the past efforts in solving these problems by a computer, it summarizes the relevant findings in social psychology, and it proposes a set of recommendations for enabling the development of the next generation of socially-aware computing.


title: Tinea Capitis and Tinea Corporis with a Severe Inflammatory Response due to Trichophyton tonsurans

abstract: Trichophyton tonsurans is an anthropophilic dermatophyte, with a worldwide distribution, although its prevalence varies considerably between different geographical regions. Whereas in North America infections due to this fungus are exceptionally common, on the European continent they appear relatively seldom. Although T. tonsurans is primarily associated with tinea capitis, it can also be the cause of tinea corporis and tinea unguium. The course of infection is usually only mildly symptomatic. We describe here two cases of urease-positive T. tonsurans infections with atypically extensive cutaneous lesions and severe inflammatory responses. .


title: The Self-Paced Graz Brain-Computer Interface: Methods and Applications

abstract: We present the self-paced 3-class Graz brain-computer interface (BCI) which is based on the detection of sensorimotor electroencephalogram (EEG) rhythms induced by motor imagery. Self-paced operation means that the BCI is able to determine whether the ongoing brain activity is intended as control signal (intentional control) or not (non-control state). The presented system is able to automatically reduce electrooculogram (EOG) artifacts, to detect electromyographic (EMG) activity, and uses only three bipolar EEG channels. Two applications are presented: the freeSpace virtual environment (VE) and the Brainloop interface. The freeSpace is a computer-game-like application where subjects have to navigate through the environment and collect coins by autonomously selecting navigation commands. Three subjects participated in these feedback experiments and each learned to navigate through the VE and collect coins. Two out of the three succeeded in collecting all three coins. The Brainloop interface provides an interface between the Graz-BCI and Google Earth.


title: An Analysis of the Relative Hardness of Reuters-21578 Subsets

abstract: The existence, public availability, and widespread acceptance of a standard benchmark for a given information retrieval (IR) task are beneficial to research on this task, because they allow different researchers to experimentally compare their own systems by comparing the results they have obtained on this benchmark.The Reuters-21578 test collection, together with its earlier variants, has been such a standard benchmark for the text categorization (TC) task throughout the last 10 years.However, the benefits that this has brought about have somehow been limited by the fact that different researchers have “carved” different subsets out of this collection and tested their systems on one of these subsets only; systems that have been tested on different Reuters-21578 subsets are thus not readily comparable.In this article, we present a systematic, comparative experimental study of the three subsets of Reuters-21578 that have been most popular among TC researchers.The results we obtain allow us to determine the relative hardness of these subsets, thus establishing an indirect means for comparing TC systems that have, or will be, tested on these different subsets.


title: Generating custom code for efficient query execution on heterogeneous processors

abstract: Processor manufacturers build increasingly specialized processors to mitigate the effects of the power wall in order to deliver improved performance. Currently, database engines have to be manually optimized for each processor which is a costly and error- prone process. In this paper, we propose concepts to adapt to and to exploit the performance enhancements of modern processors automatically. Our core idea is to create processor-specific code variants and to learn a well-performing code variant for each processor. These code variants leverage various parallelization strategies and apply both generic- and processor-specific code transformations. Our experimental results show that the performance of code variants may diverge up to two orders of magnitude. In order to achieve peak performance, we generate custom code for each processor. We show that our approach finds an efficient custom code variant for multi-core CPUs, GPUs, and MICs.


title: Multilevel single-phase inverter based on TNPC-type

abstract: A new single-phase 7-level pulse-width modulation (PWM) inverter using T-type Neutral-point-clamped (TNPC) celIs is presented to obtain high quality output voltage minimizing the number of switching devices. It includes two TNPC cells series-connected together and connected to with different voltage levels. To avoid inherent problems caused by voltage unbalance, modified Buck-boost converters are added at DC-link side to make DC voltage sources for the proposed inverter. Operational principles and the switching pattern are analyzed in this paper. To verify the performance of the proposed inverter, PSIM simulation and experimental results are also shown in this paper.


title: Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

abstract: Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as “mapped features” in feature nodes and the structure is expanded in wide sense in the “enhancement nodes.” The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.


title: The impact of online video lecture recordings and automated feedback on student performance

abstract: To what extent a blended learning configuration of face-to-face lectures, online on-demand video recordings of the face-to-face lectures and the offering of online quizzes with appropriate feedback has an additional positive impact on the performance of these students compared to the traditional face-to-face course approach? In a between-subjects design in which students were randomly assigned to a group having access to the online lectures including multiple choice quizzes and appropriate feedback or to a group having access to the online lectures only, 474 students (161 men and 313 women) of a course on European Law agreed to participate in the experiment. By using regression analysis we found that the course grade of the students was predicted by their grade point average, their study discipline, their grade goal for the course, the expected difficulty-level of the course, the number of online lectures they viewed, the number of lectures the students attended in person and the interaction between the lectures they viewed online and attended in person. Students who attended few lectures had more benefit from viewing online lectures than students who attended many lectures. In contrast to our expectations, the regression analysis did not show a significant effect of automated feedback on student performance. Offering recordings of face-to-face lectures is an easy extension of a traditional course and is of practical importance, because it enables students who are often absent from the regular face-to-face lectures to be able to improve their course grade by viewing the lectures online.


title: The Metaphorical Structure of the Human Conceptual System

abstract: If anything is central to Cognitive Science, it is the nature of the human conceptual system. We have found that that system is fundamentally metaphorical in character. That is, it contains metaphorical as well as nonmetaphorical concepts, and the metaphorical structure is extremely rich and complex. Nonmetaphorical concepts are those that emerge directly from our experience and are defined in their own terms. These include at least (1) spatial orientations (e.g., UpDOWN, IN-OUT, NEAR-FAR, FRONT-BACK), (2) ontological concepts arising in physical experience (e.g., ENTITY, SUBSTANCE, CONTAINER, PERSON), and (3) structured experiences and activities (e .g . , EATING, MOVING, TRANSFERRING OBJECTS FROM PLACE TO PLACE, etc.). Metaphorical concepts are those which are understood and structured not merely on their own terms, but rather in terms of other concepts. This involves conceptualizing one kind of object or experience in terms of a difSerent kind of object or experience. ,Paralleling the kinds of nonmetaphorical concepts, there are roughly three types of metaphorical concepts, which are realized by a vast number of linguistic expressions:


title: Article Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction

abstract: Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (i.e., sensor coverage and performance), persistence (i.e., time lag between subsequent observations) and data sources (i.e., ground-based and space-based receivers).


title: Removal of total ammonia nitrogen (TAN), nitrate and total organic carbon (TOC) from aquaculture wastewater using electrochemical technology: A review

abstract: article i nfo Protein rich wastes from aquaculture systems result in total ammonia nitrogen (TAN), total organic carbon (TOC) and biochemical oxygen demand (BOD). A number of conventional approaches have been adopted for the removal of these wastes in aquaculture ponds and hatcheries with varying degrees of success but they face critical problems such as membrane fouling, high cost or the generation of toxic by-products. To overcome such issues, electrochemical technology is commonly employed. The advantages of electrochemi- cal treatment include high efficiency, ambient operating conditions, small equipment sizes, minimal sludge generation and rapid start-up. An even better system involves bio-electrochemical reactors (BERs), which have the potential to generate energy from wastewater (by means of microbial fuel cells) or a valuable prod- uct such as hydrogen (using microbial electrolysis cells). Mechanisms of cathodic nitrate reduction and anod- ic oxidation in electrochemical and bio-electrochemical technology are reported in this review. Also some work on the simultaneous removal of nitrate and organic matter by Electro-Fenton and microbial fuel cells are elaborated upon. It is apparent that BERs can remove contaminants at high efficiencies (≈99%) whilst giving least impact upon the environment.


title: Research on water-saving irrigation automatic control system based on internet of things

abstract: To improve irrigation water use efficiency and reduce cost of irrigation water,this paper discussed the design of wireless sensor network and Internet technology of farmland automatic irrigation control method.Emphasis on An analysis of the routing protocol of sensor network nodes is emphasised to achieve the system hardware and software design,it will constitute a variety of sensors intelligent sensor network to enhance the overall automation system and monitoring levels.The final analysis of the network in the Internet based on the agricultural plants of farmland water-saving irrigation system integrated approach.Users use mobile phones or wireless PDA can easily soil moisture content of online monitoring and control to realize the irrigation automation.Application results show that system through the embedded control technology complete intelligent irrigation,improve the agricultural irrigation water use efficiency and irrigation system automatization is generally low status,can well realize water saving.


title: A case study on propagating and updating provenance information using the CIDOC CRM

abstract: Provenance information of digital objects maintained by digital libraries and archives is crucial for authenticity assessment, reproducibility and accountability. Such information is commonly stored on metadata placed in various Metadata Repositories (MRs) or Knowledge Bases (KBs). Nevertheless, in various settings it is prohibitive to store the provenance of each digital object due to the high storage space requirements that are needed for having complete provenance. In this paper, we introduce provenance-based inference rules as a means to complete the provenance information, to reduce the amount of provenance information that has to be stored, and to ease quality control (e.g., corrections). Roughly, we show how provenance information can be propagated by identifying a number of basic inference rules over a core conceptual model for representing provenance. The propagation of provenance concerns fundamental modelling concepts such as actors, activities, events, devices and information objects, and their associations. However, since a MR/KB is not static but changes over time due to several factors, the question that arises is how we can satisfy update requests while still supporting the aforementioned inference rules. Towards this end, we elaborate on the specification of the required add/delete operations, consider two different semantics for deletion of information, and provide the corresponding update algorithms. Finally, we report extensive comparative results for different repository policies regarding the derivation of new knowledge, in datasets containing up to one million RDF triples. The results allow us to understand the tradeoffs related to the use of inference rules on storage space and performance of queries and updates.


title: Detecting overlapping protein complexes in protein-protein interaction networks

abstract: ClusterONE detects overlapping protein complexes from large-scale weighted and unweighted protein-interaction networks.


title: The nature of clinical depression: Symptoms, syndromes, and behavior analysis

abstract: In this article we discuss the traditional behavioral models of depression and some of the challenges analyzing a phenomenon with such complex and varied features. We present the traditional model and suggest that it does not capture the complexity of the phenomenon, nor do syndromal models of depression that dominate the mainstream conceptualization of depression. Instead, we emphasize ideographic analysis and present depression as a maladaptive dysregulation of an ultimately adaptive elicited emotional response. We emphasize environmental factors, specifically aversive control and private verbal events, in terms of relational frame theory, that may transform an adaptive response into a maladaptive disorder. We consider the role of negative thought processes and rumination, common and debilitating aspects of depression that have traditionally been neglected by behavior analysts.


title: Being Immersed in Social Networking Environment: Facebook Groups, Uses and Gratifications, and Social Outcomes

abstract: Abstract A Web survey of 1,715 college students was conducted to examine Facebook Groups users' gratifications and the relationship between users' gratifications and their political and civic participation offline. A factor analysis revealed four primary needs for participating in groups within Facebook: socializing, entertainment, self-status seeking, and information. These gratifications vary depending on user demographics such as gender, hometown, and year in school. The analysis of the relationship between users' needs and civic and political participation indicated that, as predicted, informational uses were more correlated to civic and political action than to recreational uses.


title: Minimal broker overlay design for content-based publish/subscribe systems

abstract: Mission-critical distributed applications, such as Internet advertising platforms, increasingly utilize distributed publish/subscribe systems as a messaging substrate for information dissemination. These applications require low latency performance from the substrate, as the timely delivery of messages can have a direct impact on revenue. The cost of managing and operating distributed publish/subscribe systems, however, can be prohibitive due to system size and scale. It is, therefore, critical to derive low latency message delivery from a minimal set of system resources. ::: ::: To this end, this paper presents a solution for designing low latency, minimal-broker overlay networks for content-based publish/subscribe systems. The solution is developed in two parts. First, a framework is developed to quantify the similarity of entities in content-based publish/subscribe systems. Second, algorithms are presented for designing overlays that utilize a minimal number of brokers in order to provide low latency performance at reduced cost.


KNOWLEDGE INTENSIVE BUSINESS SERVICES AS CO-PRODUCERS OF INNOVATION Pim den Hertog*


title: A non-blocking internal binary search tree

abstract: Recent work on concurrent search trees has yielded solutions which either rely on locking parts of the data structure or exhibit suboptimal memory use. Trees are typically non-trivial to parallelise due to having multiple mutable fields per node but their average search time relative to simpler structures like linked-lists makes them desirable. We present a parallel binary search tree algorithm built using single-word reads, writes, and compare-and-swap. In this algorithm, operations will only contend if concurrent updates affect the same node(s). Updates are non-blocking as threads can complete each other's operations if necessary and each operation is linearisable. Experimental evidence shows it to be fast when compared with alternative solutions and scalable to large numbers of concurrently executing threads. It outperforms concurrent skip lists in the majority of scenarios tested; showing 65% more throughput when the performance difference of every experiment is averaged, and its memory footprint is significantly smaller than that of the other structures tested.


title: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition

abstract: We describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. affine (wavelet) frames. We propose a modification to the matching pursuit algorithm of Mallat and Zhang (1992) that maintains full backward orthogonality of the residual (error) at every step and thereby leads to improved convergence. We refer to this modified algorithm as orthogonal matching pursuit (OMP). It is shown that all additional computation required for the OMP algorithm may be performed recursively. >


title: Towards Collusive Fraud Detection in Online Reviews

abstract: Online review fraud has evolved in sophistication by launching intelligent campaigns where a group of coordinated participants work together to deliver deceptive reviews for the designated targets. Such collusive fraud is considered much harder to defend against as these campaign participants are capable of evading detection by shaping their behaviors collectively so as not to appear suspicious. The present work complements existing studies by exploring more subtle behavioral trails connected with collusive review fraud. A novel statistical model is proposed to further characterize, recognize, and forecast collusive fraud in online reviews. The proposed model is completely unsupervised, which bypasses the difficulty of manual annotation required for supervised modeling. It is also highly flexible to incorporate collusion characteristics available for better modeling and prediction. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method and the improvements in learning and predictive abilities.