title: Solving TSP with Shuffled Frog-Leaping Algorithm

abstract: Shuffled frog-leaping algorithm (SFLA) is a new memetic meta-heuristic algorithm with efficient mathematical function and global search capability. Traveling salesman problem (TSP) is a complex combinatorial optimization problem, which is typically used as benchmark for testing the effectiveness as well as the efficiency of a newly proposed optimization algorithm. When applying the shuffled frog-leaping algorithm in TSP, memeplex and submemeplex are built and the evolution of the algorithm, especially the local exploration in submemeplex is carefully adapted based on the prototype SFLA. Experimental results show that the shuffled frog leaping algorithm is efficient for small-scale TSP. Particularly for TSP with 51 cities, the algorithm manages to find six tours which are shorter than the optimal tour provided by TSPLIB. The shortest tour length is 428.87 instead of 429.98 which can be found cited elsewhere.


title: Security attack prediction based on user sentiment analysis of Twitter data

abstract: In recent years, security attacks on the web have been perpetrated by hacker activist organizations that aim to destabilize (using different techniques) web services in a specific context for which they are motivated. Predicting these attacks is an important task that helps to consider what actions should be taken if the attack is latent. Although there are applications to detect security threats on the web, currently there is no system that can predict or forecast whether the attacks can reach consummation. This paper presents a sentiment analysis method on Twitter content to predict future attacks on the web. The method is based on the daily collection of tweets from two sets of users; those who use the platform as a means of expression for views on relevant issues, and those who use it to present contents related to security attacks in the web. Daily information is converted into data that can be analysed statistically to predict whether there is a possibility of an attack. The latter is done by analyzing the collective sentiment of users and groups of hacking activists in response to a global event.


title: iSOAMM: an independent SOA maturity model

abstract: The implementation of an enterprise-wide Service Oriented Architecture (SOA) is a complex task. In most cases, evolutional approaches are used to handle this complexity. Maturity models are a possibility to plan and control such an evolution as they allow evaluating the current maturity and identifying current shortcomings. In order to support an SOA implementation, maturity models should also support in the selection of the most adequate maturity level and the deduction of a roadmap to this level. Existing SOA maturity models provide only weak assistance with the selection of an adequate maturity level.Most of them are developed by vendors of SOA products and often used to promote their products. In this paper, we introduce our independent SOA Maturity Model (iSOAMM), which is independent of the used technologies and products. In addition to the impacts on IT systems, it reflects the implications on organizational structures and governance. Furthermore, the iSOAMM lists the challenges, benefits and risks associated with each maturity level. This enables enterprises to select the most adequate maturity level for them, which is not necessarily the highest one.


title: Learning from Medical Summaries: The University of Michigan at TREC 2015 Clinical Decision Support Track

abstract: The goal of TREC 2015 Clinical Decision Support Track was to retrieve biomedical articles relevant for answering three kinds of generic clinical questions, namely diagnosis, test, and treatment. In order to achieve this purpose, we investigated three approaches to improve the retrieval of relevant articles: modifying queries, improving indexes, and ranking with ensembles. Our final submissions were a combination of several different configurations of these approaches. Our system mainly focused on the summary fields of medical reports. We built two different kinds of indexes – an inverted index on the free text and a second kind of indexes on the Unified Medical Language System (UMLS) concepts within the entire articles that were recognized by MetaMap. We studied the variations of including UMLS concepts at paragraph and sentence level and experimented with different thresholds of MetaMap matching scores to filter UMLS concepts. The query modification process in our system involved automatic query construction, pseudo relevance feedback, and manual inputs from domain experts. Furthermore, we trained a re-ranking sub-system based on the results of TREC 2014 Clinical Decision Support track using Indri’s Learning to Rank package, RankLib. Our experiments showed that the ensemble approach could improve the overall results by boosting the ranking of articles that are near the top of several single ranked lists.


title: A Model for Interactive CSR Campaigns using Storytelling

abstract: Companies today are expected to engage in corporate social responsibility (CSR) and they spend a lot of time, money, and other resources on these tasks. However, in relation to their investment, the gain for most companies is marginal, because their eorts are only perceived by a small number of people. In this paper, our goal is to improve on this situation by involving a greater number of people in CSR campaigns and increasing media attention, while reducing expenses. We propose a model that utilizes storytelling on alternate realities to link social media with CSR tasks. Consumers are engaged in a story through interactive storytelling interfaces, which allow them to contribute to the CSR campaign. The company is always able to monitor and control their running campaign and can prot from social media contributions that spread the campaign goals. We describe the capabilities of the model as well as the problems it faces with storytelling on huge numbers of automatically extracted stories. The main challenge of the kind of storytelling we report is to nd an adequate sto


title: A 16-bit, 125 MS/s, 385 mW, 78.7 dB SNR CMOS Pipeline ADC

abstract: A 16-bit 125 MS/s pipeline analog-to-digital converter (ADC) implemented in a 0.18 ?m CMOS process is presented in this paper. A SHA-less 4-bit front-end is used to achieve low power and minimize the size of the input sampling capacitance in order to ease drivability. The ADC includes foreground factory digital calibration to correct for capacitor mismatches and dithering that can be optionally enabled to improve small-signal linearity. This ADC achieves an SNR of 78.7 dB, an SNDR of 78.6 dB and an SFDR of 96 dB with a 30 MHz input signal, while maintaining an SNR > 76 dB and an SFDR > 85 dB up to 150 MHz input signals. Further, with dithering enabled the worst spur is <-98 dB for inputs below -4 dBFS at 100 MHz IF. The ADC consumes 385 mW from a 1.8 V supply.


title: Improving SDN Scalability With Protocol-Oblivious Source Routing: A System-Level Study

abstract: Software-defined networking (SDN) has been considered as a break-through technology for the next-generation Internet. It enables fine-grained flow control that can make networks more flexible and programmable. However, this might lead to scalability issues due to the possible flow state explosion in SDN switches. SDN-based source routing can reduce the volume of flow-tables significantly by encoding the path information into packet headers. In this paper, we leverage the protocol-oblivious forwarding instruction set to design protocol-oblivious source routing (POSR), which is a protocol-independent, bandwidth-efficient, and flow-table-saving packet forwarding technique. We lay out the packet format for POSR, come up with the packet processing pipelines for realizing unicast, multicast, and link failure recovery, and implement POSR in a protocol-oblivious forwarding-enabled SDN network system. Experiments are then performed in a network testbed, which consists of 14 stand-alone SDN switches, to validate the advantages of POSR. Specifically, we compare POSR with several OpenFlow-based benchmarks for unicast, multicast, and link failure recovery, and confirm that POSR can reduce flow-table utilization effectively, shorten path setup latency and expedite link failure recovery.


title: Variational learning of inducing variables in sparse Gaussian processes

abstract: Sparse Gaussian process methods that use inducing variables require the selection of the inducing inputs and the kernel hyperparameters. We introduce a variational formulation for sparse approximations that jointly infers the inducing inputs and the kernel hyperparameters by maximizing a lower bound of the true log marginal likelihood. The key property of this formulation is that the inducing inputs are defined to be variational parameters which are selected by minimizing the Kullback-Leibler divergence between the variational distribution and the exact posterior distribution over the latent function values. We apply this technique to regression and we compare it with other approaches in the literature.


title: Evaluation of the Main MPPT Techniques for Photovoltaic Applications

abstract: This paper presents evaluations among the most usual maximum power point tracking (MPPT) techniques, doing meaningful comparisons with respect to the amount of energy extracted from the photovoltaic (PV) panel [tracking factor (TF)] in relation to the available power, PV voltage ripple, dynamic response, and use of sensors. Using MatLab/Simulink and dSPACE platforms, a digitally controlled boost dc-dc converter was implemented and connected to an Agilent Solar Array E4350B simulator in order to verify the analytical procedures. The main experimental results are presented for conventional MPPT algorithms and improved MPPT algorithms named IC based on proportional-integral (PI) and perturb and observe based on PI. Moreover, the dynamic response and the TF are also evaluated using a user-friendly interface, which is capable of online program power profiles and computes the TF. Finally, a typical daily insulation is used in order to verify the experimental results for the main PV MPPT methods.


title: Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data

abstract: Topic modeling has been commonly used to discover topics from document collections. However, unsupervised models can generate many incoherent topics. To address this problem, several knowledge-based topic models have been proposed to incorporate prior domain knowledge from the user. This work advances this research much further and shows that without any user input, we can mine the prior knowledge automatically and dynamically from topics already found from a large number of domains. This paper first proposes a novel method to mine such prior knowledge dynamically in the modeling process, and then a new topic model to use the knowledge to guide the model inference. What is also interesting is that this approach offers a novel lifelong learning algorithm for topic discovery, which exploits the big (past) data and knowledge gained from such data for subsequent modeling. Our experimental results using product reviews from 50 domains demonstrate the effectiveness of the proposed approach.


title: Effective smart home system based on flexible cost in Pakistan

abstract: The smart home system is growing up quickly however in Pakistan majority of people are unaware about it. Due to maximum cost of home automation kit, the average person cannot afford to replace their old home appliances into newly built Wi-Fi enabled appliances. This research proposes a cost effective smart home model which can be used devices to make life more convenient, comfortable and home automation be brought within the reach of common public. ESP and NodeMcu components have been proposed to connect home with appliances from various vendors.


title: Automatic Identification of Sarcasm Target: An Introductory Approach

abstract: Past work in computational sarcasm deals primarily with sarcasm detection. In this paper, we introduce a novel, related problem: sarcasm target identification (\textit{i.e.}, extracting the target of ridicule in a sarcastic sentence). We present an introductory approach for sarcasm target identification. Our approach employs two types of extractors: one based on rules, and another consisting of a statistical classifier. To compare our approach, we use two baselines: a na\"ive baseline and another baseline based on work in sentiment target identification. We perform our experiments on book snippets and tweets, and show that our hybrid approach performs better than the two baselines and also, in comparison with using the two extractors individually. Our introductory approach establishes the viability of sarcasm target identification, and will serve as a baseline for future work.


title: Business and Sustainability: An Introduction

abstract: Business increasingly participates in co-regulatory and self-regulatory arrangements along national governments, international organizations, civil society and private-public institutions. These co-regulatory and self-regulatory arrangements span multiple political arenas and jurisdictions from the community level to international relations. Fair trade and energy consumption labels, accounting and transparency standards as well forest certification and emissions trading are well known examples of the increasing role of business in the dynamic regulatory space.


title: Towards A Rigorous Science of Interpretable Machine Learning

abstract: As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learning.


title: Diverse and abundant antibiotic resistance genes in Chinese swine farms

abstract: Antibiotic resistance genes (ARGs) are emerging contaminants posing a potential worldwide human health risk. Intensive animal husbandry is believed to be a major contributor to the increased environmental burden of ARGs. Despite the volume of antibiotics used in China, little information is available regarding the corresponding ARGs associated with animal farms. We assessed type and concentrations of ARGs at three stages of manure processing to land disposal at three large-scale (10,000 animals per year) commercial swine farms in China. In-feed or therapeutic antibiotics used on these farms include all major classes of antibiotics except vancomycins. High-capacity quantitative PCR arrays detected 149 unique resistance genes among all of the farm samples, the top 63 ARGs being enriched 192-fold (median) up to 28,000-fold (maximum) compared with their respective antibiotic-free manure or soil controls. Antibiotics and heavy metals used as feed supplements were elevated in the manures, suggesting the potential for coselection of resistance traits. The potential for horizontal transfer of ARGs because of transposon-specific ARGs is implicated by the enrichment of transposases--the top six alleles being enriched 189-fold (median) up to 90,000-fold in manure--as well as the high correlation (r(2) = 0.96) between ARG and transposase abundance. In addition, abundance of ARGs correlated directly with antibiotic and metal concentrations, indicating their importance in selection of resistance genes. Diverse, abundant, and potentially mobile ARGs in farm samples suggest that unmonitored use of antibiotics and metals is causing the emergence and release of ARGs to the environment.


title: A preliminary assessment of heavy metals in sediments from the Cipero and South Oropouche Rivers in Trinidad, West Indies

abstract: The increasing urbanization and industrial processes in Trinidad within recent years could pose a possible contamination threat to the aquatic environment. The southwestern part of the island houses numerous industrial activities, and the recent sightings of schools of dead fish and other marine organisms in that locality is cause for concern prompting research into this occurrence. Sediment and surface water samples from the Cipero and South Oropouche Rivers in South Trinidad were analyzed for their heavy metal content (Cd, Cr, Cu, Mn, Ni, Pb, and Zn). Another watercourse, the Moruga River, was selected as a control, based on its location away from significant anthropogenic sources, and the levels of heavy metals obtained for this location were considered as background concentrations for both surface waters and sediments. Cadmium, Ni, and Pb were not detected in surface water samples of both rivers. The corresponding order of metals in the Cipero River was Mn > Cr > Zn > Cu, while for the South Oropouche River, the order was Mn > Cr > Cu > Zn. The individual concentrations of metals in sediments found in the Cipero and South Oropouche Rivers varied according to the following orders: Mn > Zn > Cu > Pb > Ni > Cr > Cd and Mn > Zn > Pb > Cu > Ni > Cr > Cd, respectively. Assessments of the pollution status in sediments revealed that the Cipero River was considered polluted with a moderate degree of ecological pollution while the South Oropouche River was also deemed polluted; however, the degree of ecological pollution associated with that river was low. Principal component analysis (PCA) and cluster analysis (CA) confirmed that both anthropogenic and natural sources contributed to heavy metal contamination in sediments of both rivers.


title: A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research

abstract: This survey paper categorizes, compares, and summarizes the data set, algorithm and performance measurement in almost all published technical and review articles in automated accounting fraud detection. Most researches regard fraud companies and non-fraud companies as data subjects, Eigenvalue covers auditor data, company governance data, financial statement data, industries, trading data and other categories. Most data in earlier research were auditor data; Later research establish model by using sharing data and public statement data. Company governance data have been widely used. It is generally believed that ratio data is more effective than accounting data; Seldom research on time Series Data Mining were conducted. The retrieved literature used mining algorithms including statistical test, regression analysis, neural networks, decision tree, Bayesian network, and stack variables etc.. Regression Analysis is widely used on hiding data. Generally the detecting effect and accuracy of NN are superior to regression model. General conclusion is that model detecting is better than auditor detecting rate without assisting. There is a need to introduce other algorithms of no-tag data mining. Owing to the small size of fraud samples, some literature reached conclusion based on training samples and may overestimated the effect of model.


title: Reconfigurable phased array antenna enabling a high gain wide angle beam scanning

abstract: A 2×2 phased array antenna consisting of pattern reconfigurable Square Loop Antennas (SLAs) is presented for high gain wide angle scanning. The single element SLA works in two modes. The first is the tilted beam mode in which it can generate four tilted beams in four quadrants and the second mode is the axial radiation mode. In this paper, it is demonstrated that by combing these two modes, a 2×2 array of SLA is capable of providing high-gain wide-angle beam scanning. It is expected that in comparison with the conventional 2×2 patch array antenna the SLA array can offer a 75% wider scanning angle with gain up to 6 dB higher in the off bore sight direction. This property could be of a huge significance for scanning the wide sky for airborne and marine applications.


Policy Statement — Sexuality , Contraception , and the


title: The stimulus-secretion coupling of glucose-induced insulin release. V. The participation of a microtubular-microfilamentous system.

abstract: The beta cell of the islets of Langerhans has been shown to contain a microtubular-microfilamentous system. The possible role of this system in the insulin secretory process was investigated by examining the effect upon glucoseinduced insulin secretion of mitotic spindle-inhibitors and microtubules-stabilizers. Inhibition of secretion was observed when the pieces of pancreas were pre-incubated for ninety minutes with either colchicine (0.01 to 1.0 mM) or vincristine (0.01 mM) prior to measurement of their secretory response to a high glucose concentration. Significant reduction of glucose-induced secretion was also noticed in thepresence of D 2 O (25 per cent or more, v/v), 2-methyl-2,4-pentanediol (0.5 to 1.0 per cent, v/v) and ethanol (1.0 per cent, v/v). The inhibitory effect of D 2 O was reversible. The stimulant action of glucose upon both calcium uptake and insulin biosynthesis by isolated islets was inhibited by mannoheptulose, but unaffected by either vincristine or D 2 O, suggesting that the latter agents did not interfere with glucose metabolism in the beta cell. These results suggest that the integrity of the microtubular-microfilamentous system is required for glucose to exert its normal stimulant action upon insulin secretion. It is hypothesized that glucose-induced calcium uptake by the beta cell could trigger the release of insulin by causing the contraction of such system, favoring the migration of the secretory granules and their extrusion in the process of emiocytosis.


title: A unified approach to statistical tomography using coordinate descent optimization

abstract: Over the past years there has been considerable interest in statistically optimal reconstruction of cross-sectional images from tomographic data. In particular, a variety of such algorithms have been proposed for maximum a posteriori (MAP) reconstruction from emission tomographic data. While MAP estimation requires the solution of an optimization problem, most existing reconstruction algorithms take an indirect approach based on the expectation maximization (EM) algorithm. We propose a new approach to statistically optimal image reconstruction based on direct optimization of the MAP criterion. The key to this direct optimization approach is greedy pixel-wise computations known as iterative coordinate decent (ICD). We propose a novel method for computing the ICD updates, which we call ICD/Newton-Raphson. We show that ICD/Newton-Raphson requires approximately the same amount of computation per iteration as EM-based approaches, but the new method converges much more rapidly (in our experiments, typically five to ten iterations). Other advantages of the ICD/Newton-Raphson method are that it is easily applied to MAP estimation of transmission tomograms, and typical convex constraints, such as positivity, are easily incorporated.


Knowledge Management in University: Preparedness of Master of Information Sciences Graduates from the Faculty of Humanities and Social Sciences in Osijek for the Labor Market


title: Building Decision Tree Classifier on Private Data

abstract: This paper studies how to build a decision tree classifier under the following scenario: a database is vertically partitioned into two pieces, with one piece owned by Alice and the other piece owned by Bob. Alice and Bob want to build a decision tree classifier based on such a database, but due to the privacy constraints, neither of them wants to disclose their private pieces to the other party or to any third party.We present a protocol that allows Alice and Bob to conduct such a classifier building without having to compromise their privacy. Our protocol uses an untrusted third-party server, and is built upon a useful building block, the scalar product protocol. Our solution to the scalar product protocol is more efficient than any existing solutions.


title: Scalable Forecasting Techniques Applied to Big Electricity Time Series

abstract: This paper presents different scalable methods to predict time series of very long length such as time series with a high sampling frequency. The Apache Spark framework for distributed computing is proposed in order to achieve the scalability of the methods. Namely, the existing MLlib machine learning library from Spark has been used. Since MLlib does not support multivariate regression, the forecasting problem has been split into h forecasting subproblems, where h is the number of future values to predict. Then, representative forecasting methods of different nature have been chosen such as models based on trees, two ensembles techniques (gradient-boosted trees and random forests), and a linear regression as a reference method. Finally, the methodology has been tested on a real-world dataset from the Spanish electricity load data with a ten-minute frequency.


title: Robust visual tracking for non-instrumental augmented reality

abstract: This paper presents a robust and flexible framework for augmented reality which does not require instrumenting either the environment or the workpiece. A model-based visual tracking system is combined with rate gyroscopes to produce a system which can track the rapid camera rotations generated by a head-mounted camera, even if images are substantially degraded by motion blur. This tracking yields estimates of head position at video field rate (50Hz) which are used to align computer-generated graphics on an optical see-through display. Nonlinear optimisation is used for the calibration of display parameters which include a model of optical distortion. Rendered visuals are pre-distorted to correct the optical distortion of the display.


title: Achieving Native GPU Performance for Out-of-Card Large Dense Matrix Multiplication

abstract: In this paper, we illustrate the possibility of developing strategies to carry out matrix computations on heterogeneous platforms which achieve native GPU performance on very large data sizes up to the capacity of the CPU memory. More specifically, we present a dense matrix multiplication strategy on a heterogeneous platform, specifically tailored for the case when the input is too large to fit on the device memory, which achieves near peak GPU performance. Our strategy involves the development of CUDA stream based software pipelines that effectively overlap PCIe data transfers with kernel executions. As a result, we are able to achieve over 1 and 2 TFLOPS performance on a single node using 1 and 2 GPUs respectively.


title: Generalization error of deep neural networks: Role of classification margin and data structure

abstract: Understanding the generalization properties of deep learning models is critical for their successful usage in many applications, especially in the regimes where the number of training samples is limited. We study the generalization properties of deep neural networks (DNNs) via the Jacobian matrix of the network. Our analysis is general to arbitrary network structures, types of non-linearities and pooling operations. We show that bounding the spectral norm of the Jacobian matrix in the network reduces the generalization error. In addition, we tie this error to the invariance in the data and the network. Experiments on the MNIST and ImageNet datasets support these findings. This short paper summarizes our generalization error theorems for DNNs and for general invariant classifiers [1], [2].


Classifying ASR Transcriptions According to Arabic Dialect


title: A Modified Image Steganography Method based on LSB Technique

abstract: Data hiding techniques are considered very important roles with the rapid growth of intensive transfer of multimedia contents and secret communications. On the other hand, steganography is one of the most important information hiding techniques. By using steganography, information is hidden in carriers such as images, audio files, text files, and video files. In this paper, a modified steganography method based on the spatial domain is proposed. Our proposed method represents the message by six binary bits by using LSBraille method (Braille method of reading and writing for blind people) instead of using the ASCII encoding format. In this method, three bits of the message are hidden in a single pixel, and a true image is composed of three layers (Red, Green, and Blue) layer. Two bits are embedded in the Blue layer, and one bit is embedded in the green layer of the same pixel. In the Blue layer, the message is not only embedded in the least significant bit (LSB), but also the second and the third LSB may be changed. However, during each process of embedding, only one bit of the Blue layer is changed. From the experimental results, it is found that the proposed method achieves a very high Maximum Hiding Capacity (MHC), and higher visual quality as indicated by the Peak Signal-toNoise Ratio (PSNR). General Terms Image Steganography, Data hiding in image.


title: A conceptual model for IT Governance: A case study research

abstract: The purpose of this paper is to explore the importance ::: of Information Technology (IT) Governance models for public ::: organizations and presenting an IT Governance model that can ::: be adopted by both practitioners and researchers. A review of the literature in IT Governance has been initiated to shape the intended theoretical background of this study. The systematic literature review formalizes a richer context for the IT Governance concept. An empirical survey, using a questionnaire based on COBIT 4.1 maturity model used to investigate IT Governance practice in multiple case studies from Kingdom of Bahrain. This method enabled the researcher to gain insights to evaluate IT Governance practices.


title: Accurate modeling, wave mechanisms, and design considerations of a substrate integrated waveguide

abstract: A new method of analysis is presented in this paper for the determination of complex propagation constants in substrate integrated waveguides (SIWs). This method makes use of the concept of surface impedance to model the rows of conducting cylinders, and the proposed model is then solved by combining a method of moments and a transverse resonance procedure. The proposed method is further applied to extract results in terms of parametric curves and graphs which demonstrate fundamental and interesting wave guidance and leakage properties of this type of periodic waveguide. Useful design rules are extracted from this analysis, suggesting that appropriate design parameters and regions should be carefully selected for practical applications. In addition, comprehensive review and comparisons with published results are also presented to show the performance and accuracy of the proposed modeling technique. Practical measurements of fabricated samples with different levels of loss have confirmed the accuracy of this new method and validity of design rules


title: Encryption using Dual Key Transformation based on Creation of Multi S- Boxes in AES Algorithm

abstract: The Advanced Encryption Standard (AES) is using in a large scale of applications that need to protect their data and information. A nonlinear substitution operation is the main factor of the AES cipher system strength. The S-Box component that used in AES is fixed, and not changeable. The purpose of the proposed approach is to use dual keys in encryption and decryption processes in SubByte transformation function. The first key generate multi random S-boxes depend on using multi keys that led to generate Sboxes provided that each one has its inverse associated with it, the second key is a random distribution of the S-boxes, the dual keys lead in increasing the complexity degree within the same delay time during the encryption and decryption processes in SubByte function. The results show that the present proposed algorithm has good cryptographic strength, with the added benefit that is resistant to linear and differential cryptanalysis, which require that the S-boxes be known beside the encryption key. General Terms Security, Algorithms, system strength, Plaintext, Ciphertext


title: Simulation of various natural phenomena based on computational fluid dynamics

abstract: Visual simulation of natural phenomena has become one of the most important research topics in computer graphics. Such phenomena include water, fire, smoke, clouds, and so on. Recent methods for the simulation of these phenomena utilize techniques developed in computational fluid dynamics. In this paper, the basic equations (Navier-Stokes equations) for simulating these phenomena are briefly described. These basic equations are used to simulate various natural phenomena. This paper then explains our applications of the equations for simulations of smoke, clouds, and aerodynamic sound.


title: Automatic expressive deformations for stylizing motion

abstract: 3D computer animation often struggles to compete with the flexibility and expressiveness commonly found in traditional animation, particularly when rendered non-photorealistically. We present an animation tool that takes skeleton-driven 3D computer animations and generates expressive deformations to the character geometry. The technique is based upon the cartooning and animation concepts of 'lines of action' and 'lines of motion' and automatically infuses computer animations with some of the expressiveness displayed by traditional animation. Motion and pose-based expressive deformations are generated from the motion data and the character geometry is warped along each limb's individual line of motion. The effect of this subtle, yet significant, warping is twofold: geometric inter-frame consistency is increased which helps create visually smoother animated sequences, and the warped geometry provides a novel solution to the problem of implied motion in non-photorealistic still images.


Hymen : facts and conceptions


title: Are Amazon.com Online Review Helpfulness Ratings Biased or Not

abstract: The helpfulness rating of Amazon product review, a popular vote feature used by Amazon to rank product reviews and display them to online shoppers, has important implications for online shopping decisions. This research investigates how objective those helpfulness ratings are. The general assumption is that the ratings are "representative" views of the shoppers. However, previous studies on product reviews indicate bias may also exist among helpfulness ratings. Using the survey questionnaire, the study found that there were indeed significant differences between the helpfulness ratings displayed at Amazon.com and those from a simulated online shopper population. The survey results also show that there are evidences of rating differences by gender, age, ethnicity, income and mobile device use for shopping. Thus the "true" ratings on online user reviews may well be quite different from what we see at Amazon.com. Implications and limitations of this research are discussed.


title: Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation

abstract: Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse depth data with optional dense RGB, and accomplish depth completion and semantic segmentation changing only the last layer. Our proposal efficiently learns sparse features without the need of an additional validity mask. We show how to ensure network robustness to varying input sparsities. Our method even works with densities as low as 0.8% (8 layer lidar), and outperforms all published state-of-the-art on the Kitti depth completion benchmark.


title: Credit card fraud detection using Hidden Markov Model

abstract: Since past few years there is tremendous advancement in electronic commerce technology, and the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper we present the necessary theory to detect fraud in credit card transaction processing using a Hidden Markov Model (HMM). An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected by using an enhancement to it(Hybrid model).In further sections we compare different methods for fraud detection and prove that why HMM is more preferred method than other methods.


title: Memory Errors in Modern Systems: The Good, The Bad, and The Ugly

abstract: Several recent publications have shown that hardware faults in the memory subsystem are commonplace. These faults are predicted to become more frequent in future systems that contain orders of magnitude more DRAM and SRAM than found in current memory subsystems. These memory subsystems will need to provide resilience techniques to tolerate these faults when deployed in high-performance computing systems and data centers containing tens of thousands of nodes. Therefore, it is critical to understand the efficacy of current hardware resilience techniques to determine whether they will be suitable for future systems. In this paper, we present a study of DRAM and SRAM faults and errors from the field. We use data from two leadership-class high-performance computer systems to analyze the reliability impact of hardware resilience schemes that are deployed in current systems. Our study has several key findings about the efficacy of many currently deployed reliability techniques such as DRAM ECC, DDR address/command parity, and SRAM ECC and parity. We also perform a methodological study, and find that counting errors instead of faults, a common practice among researchers and data center operators, can lead to incorrect conclusions about system reliability. Finally, we use our data to project the needs of future large-scale systems. We find that SRAM faults are unlikely to pose a significantly larger reliability threat in the future, while DRAM faults will be a major concern and stronger DRAM resilience schemes will be needed to maintain acceptable failure rates similar to those found on today's systems.


title: Computer-Assisted Text Analysis for Comparative Politics

abstract: Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in nonEnglish and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.


title: Kynurenine pathway metabolism and the microbiota‐gut‐brain axis

abstract: It has become increasingly clear that the gut microbiota influences not only gastrointestinal physiology but also central nervous system (CNS) function by modulating signalling pathways of the microbiota-gut-brain axis. Understanding the neurobiological mechanisms underpinning the influence exerted by the gut microbiota on brain function and behaviour has become a key research priority. Microbial regulation of tryptophan metabolism has become a focal point in this regard, with dual emphasis on the regulation of serotonin synthesis and the control of kynurenine pathway metabolism. Here, we focus in detail on the latter pathway and begin by outlining the structural and functional dynamics of the gut microbiota and the signalling pathways of the brain-gut axis. We summarise preclinical and clinical investigations demonstrating that the gut microbiota influences CNS physiology, anxiety, depression, social behaviour, cognition and visceral pain. Pertinent studies are drawn from neurogastroenterology demonstrating the importance of tryptophan and its metabolites in CNS and gastrointestinal function. We outline how kynurenine pathway metabolism may be regulated by microbial control of neuroendocrine function and components of the immune system. Finally, preclinical evidence demonstrating direct and indirect mechanisms by which the gut microbiota can regulate tryptophan availability for kynurenine pathway metabolism, with downstream effects on CNS function, is reviewed. Targeting the gut microbiota represents a tractable target to modulate kynurenine pathway metabolism. Efforts to develop this approach will markedly increase our understanding of how the gut microbiota shapes brain and behaviour and provide new insights towards successful translation of microbiota-gut-brain axis research from bench to bedside. This article is part of the Special Issue entitled 'The Kynurenine Pathway in Health and Disease'.


title: Closed-form cauchy-schwarz PDF divergence for mixture of Gaussians

abstract: This paper presents an efficient approach to calculate the difference between two probability density functions (pdfs), each of which is a mixture of Gaussians (MoG). Unlike Kullback-Leibler divergence (D KL ), the authors propose that the Cauchy-Schwarz (CS) pdf divergence measure (D CS ) can give an analytic, closed-form expression for MoG. This property of the D CS makes fast and efficient calculations possible, which is tremendously desired in real-world applications where the dimensionality of the data/features is very high. We show that D CS follows similar trends to D KL , but can be computed much faster, especially when the dimensionality is high. Moreover, the proposed method is shown to significantly outperform D KL in classifying real-world 2D and 3D objects, and static hand posture recognition based on distances alone.


title: Learning for search result diversification

abstract: Search result diversification has gained attention as a way to tackle the ambiguous or multi-faceted information needs of users. Most existing methods on this problem utilize a heuristic predefined ranking function, where limited features can be incorporated and extensive tuning is required for different settings. In this paper, we address search result diversification as a learning problem, and introduce a novel relational learning-to-rank approach to formulate the task. However, the definitions of ranking function and loss function for the diversification problem are challenging. In our work, we firstly show that diverse ranking is in general a sequential selection process from both empirical and theoretical aspects. On this basis, we define ranking function as the combination of relevance score and diversity score between the current document and those previously selected, and loss function as the likelihood loss of ground truth based on Plackett-Luce model, which can naturally model the sequential generation of a diverse ranking list. Stochastic gradient descent is then employed to conduct the unconstrained optimization, and the prediction of a diverse ranking list is provided by a sequential selection process based on the learned ranking function. The experimental results on the public TREC datasets demonstrate the effectiveness and robustness of our approach.


title: Nonlinear Models Using Dirichlet Process Mixtures

abstract: We introduce a new nonlinear model for classification, in which we model the joint distribution of response variable, y, and covariates, x, non-parametrically using Dirichlet process mixtures. We keep the relationship between y and x linear within each component of the mixture. The overall relationship becomes nonlinear if the mixture contains more than one component. We use simulated data to compare the performance of this new approach to a simple multinomial logit (MNL) model, an MNL model with quadratic terms, and a decision tree model. We also evaluate our approach on a protein fold classification problem, and find that our model provides substantial improvement over previous methods, which were based on Neural Networks (NN) and Support Vector Machines (SVM). Folding classes of protein have a hierarchical structure. We extend our method to classification problems where a class hierarchy is available. We find that using the prior information regarding the hierarchical structure of protein folds can result in higher predictive accuracy.


title: Thinking inside the box: Using appearance models and context based on room geometry

abstract: In this paper we show that a geometric representation of an object occurring in indoor scenes, along with rich scene structure can be used to produce a detector for that object in a single image. Using perspective cues from the global scene geometry, we first develop a 3D based object detector. This detector is competitive with an image based detector built using state-of-the-art methods; however, combining the two produces a notably improved detector, because it unifies contextual and geometric information. We then use a probabilistic model that explicitly uses constraints imposed by spatial layout - the locations of walls and floor in the image - to refine the 3D object estimates. We use an existing approach to compute spatial layout [1], and use constraints such as objects are supported by floor and can not stick through the walls. The resulting detector (a) has significantly improved accuracy when compared to the state-of-the-art 2D detectors and (b) gives a 3D interpretation of the location of the object, derived from a 2D image. We evaluate the detector on beds, for which we give extensive quantitative results derived from images of real scenes.


title: Development of MyHomePharmacy: A personalized family medicine management

abstract: There have been many studies which have been conducted on the development of healthcare system by incorporating smartphones such as pharmacy management system. However, the existing systems were limited to storing medicine inventory of pharmacy. Therefore, this paper introduces MyHomePharmacy which is a personalized family medicine management which runs on mobile web platform. Personalized here refers to the function provided where it allows the users to customize the system to manage medicine inventory for their family usage. Interview method has been applied for requirement gathering analysis. All requirements have been classified into functional and non-functional requirements and then depicted in the form of Unified Modelling Language (UML) diagrams for development purposes. Prototype has been developed using Adobe PhoneGap that allows the developed prototype to be accessed via Web platform. Functionality test was conducted where it was found that 60% rated good and 80% functionality success for both tests respectively. All recommendations and improvements about the prototype are recorded for future reference and alteration.


title: Spectral–Spatial Classification of Hyperspectral Data Based on a Stochastic Minimum Spanning Forest Approach

abstract: In this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic minimum spanning forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule in order to build the final classification map. The proposed method is tested on three different data sets of hyperspectral airborne images with different resolutions and contexts. The influences of the number of markers and of the number of realizations M on the results are investigated in experiments. The performance of the proposed method is compared to several classification techniques (both pixelwise and spectral-spatial) using standard quantitative criteria and visual qualitative evaluation.


title: Anxiety Symptoms and Anxiety Disorders: How Are They Related to Premenstrual Disorders?

abstract: Premenstrual symptoms are common among young menstruating women, but the psychiatric disorder premenstrual dysphoric disorder (PMDD) seen only in approximately 3% of this group. The symptom profile of PMDD has been empirically derived from a number of investigations including a large data base from five university centers. The most commonly reported symptoms are depression and mood swings, but a substantial number of women report tension and anxiety. Lifetime psychiatric illness is also common in women with PMDD, and although mood disorders predominate, past histories of anxiety disorders are also common, further suggesting an association between PMDD and anxiety disorders. The strongest data supporting such an association lie with challenge studies that have been used to provoke panic in panic patients and are effective in precipitating panic attacks in women with PMDD. Finally, treatments that are effective for anxiety disorders are also useful in the treatment of PMDD. In this paper, the above outlined relationship between anxiety disorders and PMDD is reviewed.


title: Identifying the Pathways for Meaning Circulation using Text Network Analysis

abstract: In this work we propose a method and algorithm for identifying the pathways for meaning circulation within a text. This is done by visualizing normalized textual data as a graph and deriving the key metrics for the concepts and for the text as a whole using network analysis. The resulting data and graph representation are then used to detect the key concepts, which function as junctions for meaning circulation within a text, contextual clusters comprised of word communities (themes), as well as the most often used pathways for meaning circulation. We then discuss several practical applications of our method ranging from automatic recovery of hidden agendas within a text and intertextual navigation graph-interfaces, to enhancing reading and writing, quick text summarization, as well as group sentiment profiling and text diagramming. We also make a quick overview of the existing computer-assisted text analysis (and, specifically, network text analysis), and text visualization methods in order to position our research in relation to the other available approaches.


title: On the steepest descent algorithm for quadratic functions

abstract: The steepest descent algorithm with exact line searches (Cauchy algorithm) is inefficient, generating oscillating step lengths and a sequence of points converging to the span of the eigenvectors associated with the extreme eigenvalues. The performance becomes very good if a short step is taken at every (say) ten iterations. We show a new method for estimating short steps, and propose a method alternating Cauchy and short steps. Finally, we use the roots of a certain Chebyshev polynomial to further accelerate the methods.


title: Interleaved Boost-Integrated LLC Resonant Converter With Fixed-Frequency PWM Control for Renewable Energy Generation Applications

abstract: This paper proposes a current-fed LLC resonant converter that is able to achieve high efficiency over a wide input voltage range. It is derived by integrating a two-phase interleaved boost circuit and a full-bridge LLC circuit together by virtue of sharing the same full-bridge switching unit. Compared with conventional full-bridge LLC converter, the gain characteristic is improved in terms of both gain range and optimal operation area, fixed-frequency pulsewidth-modulated (PWM) control is employed to achieve output voltage regulation, and the input current ripple is minimized as well. The voltage across the turned-off primary-side switch can be always clamped by the bus voltage, reducing the switch voltage stress. Besides, its other distinct features, such as single-stage configuration, and soft switching for all switches also contribute to high power conversion efficiency. The operation principles are presented, and then the main characteristics regarding gain, input current ripple, and zero-voltage switching (ZVS) considering the nonlinear output capacitance of MOSFET are investigated and compared with conventional solutions. Also, the design procedure for some key parameters is presented, and two kinds of interleaved boost integrated resonant converter topologies are generalized. Finally, experimental results of a converter prototype with 120–240 V input and 24 V/25 A output verify all considerations.


title: 13.6 A 600μW Bluetooth low-energy front-end receiver in 0.13μm CMOS technology

abstract: One of the main goals for the next generation of radios for wireless sensor and body-area networks (WSN and WBAN) is a sub-mW receiver (RX) compliant with energy-harvested supplies. In this direction, the Bluetooth standard has introduced a low-energy operative mode (BLE) with wider channel spacing (2MHz) and relaxed blocker tolerance. The minimum sensitivity required is −70dBm but even with a sensitivity 10dB lower the BLE receiver can have a noise figure close to 19dB [1]. Although linearity and noise specs have been significantly relaxed, the design of a sub-mW solution remains challenging since the power dissipation cannot be simply scaled with the spurious-free-dynamic-range (SFDR). In fact, the ultimate bound is set by the power burned in the voltage-controlled oscillator (VCO), which is used for the generation of the local oscillator (LO) necessary for the signal downconversion. Since, for a targeted phase noise, the current required by the VCO is inversely proportional to the quality factor of the resonator adopted, a straightforward approach is to use a high-Q tank like the FBAR used by Wang et al. [2]. However in low-cost CMOS processes, when high-Q resonators are not present, an alternative strategy is to share the VCO bias current with the other blocks of the RF front-end as in the LMV cell proposed by Tedeschi et al. [3].


title: Task switching in video game players: Benefits of selective attention but not resistance to proactive interference

abstract: Research into the perceptual and cognitive effects of playing video games is an area of increasing interest for many investigators. Over the past decade, expert video game players (VGPs) have been shown to display superior performance compared to non-video game players (nVGPs) on a range of visuospatial and attentional tasks. A benefit of video game expertise has recently been shown for task switching, suggesting that VGPs also have superior cognitive control abilities compared to nVGPs. In two experiments, we examined which aspects of task switching performance this VGP benefit may be localized to. With minimal trial-to-trial interference from minimally overlapping task set rules, VGPs demonstrated a task switching benefit compared to nVGPs. However, this benefit disappeared when proactive interference between tasks was increased, with substantial stimulus and response overlap in task set rules. We suggest that VGPs have no generalized benefit in task switching-related cognitive control processes compared to nVGPs, with switch cost reductions due instead to a specific benefit in controlling selective attention.


title: NoSQL data management systems

abstract: In the last decade, a new class of data management systems collectively called NoSQL systems emerged and are now intensively developed. The main feature of these systems is that they abandon the relational data model and the SQL, do not fully support ACID transactions, and use distributed architecture (even though there are non-distributed NoSQL systems as well). As a result, such systems outperform the conventional SQL-oriented DBMSs in some applications; in addition, such systems are highly scalable under increasing workloads and huge amounts of data, which is important, in particular, for Web applications. Unfortunately, the absence of transactional semantics imposes certain constraints on the class of applications where NoSQL systems can be effectively used and the choice of a particular system significantly depends on the application. In this paper, a review of the main classes of NoSQL data management systems is given and examples of systems and applications where they can be used are discussed.


title: MATLAB - System Generator based Feedback Linearization and PID Control of Aero Thrust Pendulum using FPGA

abstract: This paper presents a model based design and implementation of a closed loop PID controller for Aero Thrust Pendulum, using MATLAB - System Generator Toolbox, to demonstrate the Direct Real Time Simulation and Implementation (DRTSI) scheme. In general, simulation and implementation tasks are carried out in separate frameworks. The act of modelling and simulation are carried out in specialized numerical computing frameworks such as MATLAB/Simulink. Whereas, the system's real time control is implemented through an entirely different software framework, wherein the user is required to code in an entirely new paradigm, which is chip specific. The DRTSI scheme aims at modelling and implementation of a control system in an environment that provides a seamless transition between simulation and practical implementation. Software like d-Space, implements the same but at a reasonably high cost, wherein this scheme bridges the void between simulation and hardware implementation using an integrated system at a steeply lower cost. A method for live tuning of proportional, integral, derivative and system gain is validated and implemented to facilitate robust tuning. This serves to highlight the effect of each component of the PID controller on the response of the system for educational purposes. A conditional anti-reset windup circuit is implemented to counter the constant error integration by the integral controller. Also, Serial communication is established between FPGA and MATLAB/Simulink to facilitate extensive logging of the system’s output and provides visual representation of the system’s response for various gains.


title: Evidential Grid-Based Tracking and Mapping

abstract: Tracking and mapping the local environment form the basis of an autonomous vehicle system. They are often realized separately using occupancy grids, which do not require object or shape assumptions, and model-based object tracking algorithms. Many approaches require a binary classification of the sensor measurements into coming from a static or from a dynamic object, as otherwise inconsistencies between the different representations are likely to occur. This paper presents grid-based tracking and mapping (GTAM), a low-level grid-based approach that simultaneously estimates the static and the dynamic environment, their uncertainties, velocities, as well as information about free space. GTAM works on the level of grid cells, rather than creating object hypotheses. A particle filter is used to obtain continuous cell velocity distributions for all obstacles. Continuous evidences in a Dempster–Shafer model are derived without requiring a binary pre-classification of the sensor measurements. Results and evaluations using a vehicle moving in real dynamic street environments demonstrate the performance of the presented approach.


A System for Online News Recommendations in Real-Time with Apache Mahout


title: An efficient compression technique for ITU-T group 3 coded images using variable length codes with reduced average length

abstract: In this paper a set of variable Length codes are designed for source coding of data. The designed codes satisfy the properties of being uniquely decodable, prefix free codes and satisfy Kraft McMillan inequality. The designed prefix codes are used for compressing binary Image for Facsimile Transmission. The proposed Codewords have achieved reduced average length and increase in coding efficiency when applied to the set of standard CCITT images. The proposed codes have average length of 7.7 which is less compared to 8.63 of standard Modified Huffman codes used for CCITT Group3 compression. An improvement of up to 10.4% in coding efficiency and reduced average length of 1.24 bits/symbol is achieved for a particular CCITT image. The proposed scheme has less number of Codewords 71 compared to that of CCITT Group 3 standard codes which are 183 in number. The proposed Codewords requires less memory for encoding and decoding using lookup tables and as well as less CPU time for processing.


title: Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor

abstract: In this paper, we present a real-time algorithm for automatic recognition of not only physical activities, but also, in some cases, their intensities, using five triaxial wireless accelerometers and a wireless heart rate monitor. The algorithm has been evaluated using datasets consisting of 30 physical gymnasium activities collected from a total of 21 people at two different labs. On these activities, we have obtained a recognition accuracy performance of 94.6% using subject-dependent training and 56.3% using subject-independent training. The addition of heart rate data improves subject-dependent recognition accuracy only by 1.2% and subject-independent recognition only by 2.1%. When recognizing activity type without differentiating intensity levels, we obtain a subject-independent performance of 80.6%. We discuss why heart rate data has such little discriminatory power.


title: Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction

abstract: Deep learning algorithms excel at extracting patterns from raw data, and with large datasets, they have been very successful in computer vision and natural language applications. However, in other domains, large datasets on which to learn representations from may not exist. In this work, we develop a novel multimodal CNN-MLP neural network architecture that utilizes both domain-specific feature engineering as well as learned representations from raw data. We illustrate the effectiveness of such network designs in the chemical sciences, for predicting biodegradability. DeepBioD, a multimodal CNN-MLP network is more accurate than either standalone network designs, and achieves an error classification rate of 0.125 that is 27% lower than the current state-of-the-art. Thus, our work indicates that combining traditional feature engineering with representation learning can be effective, particularly in situations where labeled data is limited.


title: Visual Reasoning with Multi-hop Feature Modulation

abstract: Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue. For such tasks, one successful approach is to condition image-based convolutional network computation on language via Feature-wise Linear Modulation (FiLM) layers, i.e., per-channel scaling and shifting. We propose to generate the parameters of FiLM layers going up the hierarchy of a convolutional network in a multi-hop fashion rather than all at once, as in prior work. By alternating between attending to the language input and generating FiLM layer parameters, this approach is better able to scale to settings with longer input sequences such as dialogue. We demonstrate that multi-hop FiLM generation achieves state-of-the-art for the short input sequence task ReferIt --- on-par with single-hop FiLM generation --- while also significantly outperforming prior state-of-the-art and single-hop FiLM generation on the GuessWhat?! visual dialogue task.


title: Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification

abstract: Convolutional Neural Networks (CNNs) have gained remarkable success in computer vision, which is mostly owe to their ability that enables learning rich image representations from large-scale annotated data. In the field of medical image analysis, large amounts of annotated data may be not always available. The number of acquired ground-truth data is sometimes insufficient to train the CNNs without overfitting and convergence issues from scratch. Hence application of the deep CNNs is a challenge in medical imaging domain. However, transfer learning techniques are shown to provide solutions for this challenge. In this paper, our target task is to implement diabetic retinopathy fundus image classification using CNNs based transfer learning. Experiments are performed on 1014 and 1200 fundus images from two publicly available DR1 and MESSIDOR datasets. In order to complete the target task, we carry out experiments using three different methods: 1) fine-tuning all network layers of each of different pre-trained CNN models; 2) fine-tuning a pre-trained CNN model in a layer-wise manner; 3) using pre-trained CNN models to extract features from fundus images, and then training support vector machines using these features. Experimental results show that convolutional neural networks based transfer learning can achieve better classification results in our task with small datasets (target domain), by taking advantage of knowledge learned from other related tasks with larger datasets (source domain). Transfer learning is a promising technique that promotes the use of deep CNNs in medical field with limited amounts of data.


title: A shadow removal method for tesseract text recognition

abstract: For shadowed text images, the character recognition performance of Tesseract drops significantly. In this paper, we propose a new method to process the shadowed text images for the Tesseract's optical character recognition engine. First, a local adaptive threshold algorithm is used to transform the grayscale image into a binary image to capture the contours of texts. Next, to delete the salt-and-pepper noise in the shadow areas we propose a double-filtering algorithm, in which a projection method is used to remove the noise between texts and the median filter is used to remove the noise within characters. Finally, the processed binary image is fed into the Tesseract's optical character recognition engine. Experimental results show that the proposed method can achieve a better character recognition performance.


title: Center Frequency and Bandwidth Controllable Microstrip Bandpass Filter Design Using Loop-Shaped Dual-Mode Resonator

abstract: A design approach for developing microwave bandpass filters (BPFs) with continuous control of the center frequency and bandwidth is presented. The proposed approach exploits a simple loop-shaped dual-mode resonator that is tapped and perturbed with varactor diodes to realize center frequency tunability and passband reconfigurabilty. The even- and odd-mode resonances of the resonator can be predominately controlled via the incorporated varactors, and the passband response reconfiguration is obtained with the proposed tunable external coupling mechanism, which resolves the return losses degradation attributed to conventional fixed external coupling mechanisms. The demonstrated approach leads to a relatively simple synthesis of the microwave BPF with an up to 33% center frequency tuning range, an excellent bandwidth tuning capability, as well as a high filter response reconfigurability, including an all-reject response.


title: Big omics data experience

abstract: As personalized medicine becomes more integrated into healthcare, the rate at which human genomes are being sequenced is rising quickly together with a concomitant acceleration in compute and storage requirements. To achieve the most effective solution for genomic workloads without re-architecting the industry-standard software, we performed a rigorous analysis of usage statistics, benchmarks and available technologies to design a system for maximum throughput. We share our experiences designing a system optimized for the "Genome Analysis ToolKit (GATK) Best Practices" whole genome DNA and RNA pipeline based on an evaluation of compute, workload and I/O characteristics. The characteristics of genomic-based workloads are vastly different from those of traditional HPC workloads, requiring different configurations of the scheduler and the I/O subsystem to achieve reliability, performance and scalability. By understanding how our researchers and clinicians work, we were able to employ techniques not only to speed up their workflow yielding improved and repeatable performance, but also to make more efficient use of storage and compute resources.


title: Chronic ulcerative stomatitis: a distinct clinical entity?

abstract: Chronic ulcerative stomatitis (CUS) is a mucocutaneous disorder which is characterised by persistent oral mucosal ulceration. The clinical appearance is often reminiscent of oral lichen planus (OLP) leading to erroneous diagnoses. The immune mediated inhibition of the CUS protein (CUSP) is implicated in the pathogenesis of CUS. CUSP acts as an anti-apoptotic protein and when its action is prevented it may result in significant epithelial injury. The objective or this article is to present the first documented case of CUS in South Africa, with relevant reference to current international literature. CUS should be considered in patients previously diagnosed with OLP but who are unresponsive to glucocorticosteroid therapy. The condition can be successfully managed using hydroxychloroquine.


COLOR ENHANCEMENT OF UNDERWATER CORAL REEF IMAGES USING CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION ( CLAHE ) WITH RAYLEIGH DISTRIBUTION


title: Telemanipulation Assistance Based on Motion Intention Recognition

abstract: In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user’s motion intention. In this paper, user’s motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture is applied. If the task is aligning the end-effector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user’s motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.


title: When Employees Do Bad Things for Good Reasons: Examining Unethical Pro-Organizational Behaviors

abstract: We propose that employees sometimes engage in unethical acts with the intent to benefit their organization, its members, or both---a construct we term unethical pro-organizational behavior. We suggest that positive social exchange relationships and organizational identification may lead to unethical pro-organizational behavior indirectly via neutralization, the process by which the moral content of unethical actions is overlooked. We incorporate situational and individual-level constructs as moderators of these relationships and consider managerial implications and future research.


title: The HFB Face Database for Heterogeneous Face Biometrics research

abstract: A face database, composed of visual (VIS), near infrared (NIR) and three-dimensional (3D) face images, is collected. Called the HFB face database, it is released now to promote research and development of heterogeneous face biometrics (HFB). This release of version 1 contains a total of 992 images from 100 subjects; there are 4 VIS, 4 NIR, and 1 or 2 3D face images per subject. In this paper, we describe the apparatuses, environments and procedure of the data collection and present baseline performances of the standard PCA and LDA methods on the database.


title: Multilevel Marketing: Pyramid-Shaped Schemes or Exploitative Scams?

abstract: We identify the conditions on the tendency of agents to spread information by word of mouth, under which a principal can design a pyramid scam to exploit a network of boundedly rational agents whose beliefs are coarse. Our main result is that a pyramid scam is sustainable only if its underlying reward scheme compensates the participants based on multiple levels of their downlines (e.g., for recruiting new members to the pyramid and for recruitments made by these new members). Motivated by the growing discussion on the legitimacy of multilevel marketing schemes and their resemblance to pyramid scams, we use our model to compare the two phenomena based on their underlying compensation structure.


Making Life Work


title: Evolutionary game dynamics

abstract: Evolutionary game dynamics is the application of population dynamical methods to game theory. It has been introduced by evolutionary biologists, anticipated in part by classical game theorists. In this survey, we present an overview of the many brands of deterministic dynamical systems motivated by evolutionary game theory, including ordinary differential equations (and, in particular, the replicator equation), differential inclusions (the best response dynamics), difference equations (as, for instance, fictitious play) and reaction-diffusion systems. A recurrent theme (the so-called `folk theorem of evolutionary game theory') is the close connection of the dynamical approach with the Nash equilibrium, but we show that a static, equilibrium-based viewpoint is, on principle, unable to always account for the long-term behaviour of players adjusting their behaviour to maximise their payoff.


CHAPTER 13 Psychological Issues in Support of Multiple Activities


title: Control of a Hybrid AC/DC Microgrid Involving Energy Storage and Pulsed Loads

abstract: This paper presents a real-time coordinated control of the hybrid ac/dc microgrids involving energy storage and pulsed loads. Grid-isolated hybrid microgrid applications require special considerations due to the intermittent generation, online energy storage control, and pulsed loads. In this study, we introduce a comprehensive frequency and voltage control scheme for a hybrid ac/dc microgrid consisting of a synchronous generator, solar generation emulator, and bidirectional (ac/dc and dc/dc) converters. A bidirectional controlled ac/dc converter with an active and reactive power decoupling technique is used to link the ac bus with the dc bus, while regulating the system voltage and frequency. A dc/dc boost converter with a maximum power point tracking function is implemented to maximize the intermittent energy generation from solar generators. Current-controlled bidirectional dc/dc converters are applied to connect each lithium-ion battery bank to the dc bus. Lithium-ion battery banks act as energy storage devices that serve to increase the system resiliency by absorbing or injecting power. Experimental results are presented for verification of the introduced hybrid ac/dc power flow control scheme.


title: CS-HOG: Color similarity-based HOG

abstract: Conventional object detection methods often use local features based on object shape, of which the HOG feature is typical. In recent years, Color Self-Similarity (CSS) has been proposed as a local feature that uses color information. CSS involves computing color similarity as a basis for deciding the sameness of objects, and thus represent a feature that is effective for object detection. It has also been reported that detection performance can be improved by using the CSS feature together with the HOG feature or other shape-based feature. We propose a Color Similarity-based HOG (CS-HOG) feature that is based on color similarity for object detecting shapes. The CS-HOG feature enables clarification of the object shape by using color similarity to calculate the degree of object sameness, thus achieving highly-accurate object detection. Evaluation experiments show that the CS-HOG feature improves performance from 22.5% and 27.2% compared to the HOG feature and the CSS feature, and by 4.2% compared to the HOG feature and the CSS feature used together.


title: Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding

abstract: The convolutional sparse model has recently gained increasing attention in the signal and image processing communities, and several methods have been proposed for solving the pursuit problem emerging from it -- in particular its convex relaxation, Basis Pursuit. In the first of this two-part work, we have provided a theoretical back-bone for this model, providing guarantees for the uniqueness of the sparsest solution and for the success of pursuit algorithms by introducing the notion of stripe sparsity and other related measures. Herein, we extend the analysis to a noisy regime, thereby considering signal perturbations and model deviations. We address questions of stability of the sparsest solutions and the success of pursuit algorithms, both greedy and convex. Classical definitions such as the RIP are generalized to the convolutional model, and existing notions such as the ERC are connected to our setting. On the algorithmic side, we demonstrate how to solve the global pursuit problem by using simple local processing, thus offering a first of its kind bridge between global modeling of signals and their patch-based local treatment.


title: A novel background-weighted histogram scheme based on foreground saliency for mean-shift tracking

abstract: Effective appearance models are one critical factor for robust object tracking. In this paper, we introduce foreground feature saliency concept into the background modelling, and put forward a novel foreground saliency-based background-weighted histogram scheme (FSBWH) for target representation and tracking, which exploits salient features from both foreground and background. We think that background and foreground salient features are both crucial for target representation and tracking. Experimental results show that the proposed FSBWH scheme can improve the robustness and performance of tracker significantly especially in complex occlusions and similar background scenes.


title: Modeling, analysis, and implementation of a photovoltaic grid-tie inverter system

abstract: Modeling of a complete photovoltaic (PV) inverter system poses a significant challenge because it involves different time-scaled dynamics including filter elements, fast current loop, voltage loop, maximum power point tracking (MPPT) loop, and response of the energy source. This paper intends to utilize the state-space modeling approach for stability analysis for a grid-tie PV inverter system by eigenvalue evaluation with different irradiation levels, PV output voltages, voltage-loop gains and current-loop gains. The stability analysis can not only show more insight of the two-loop interaction with current-loop gain variation, but also show the stability trend due to the change of both the source voltage and the source impedance with the input PV voltage change, which are quite difficult to detect with conventional single-variable transfer function analysis. The controller based on state-space modeling and analysis has been designed and implemented. Experimental results are used to verify the stability trend predicted in the model.


title: Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

abstract: In the quest for deciphering disease-associated biomarkers, high-performing tools for multiplexed protein expression profiling of crude clinical samples will be crucial. Affinity proteomics, mainly represented by antibody-based microarrays, have during recent years been established as a proteomic tool providing unique opportunities for parallelized protein expression profiling. But despite the progress, several main technical features and assay procedures remains to be (fully) resolved. Among these issues, the handling of protein microarray data, i.e. the biostatistics parts, is one of the key features to solve. In this study, we have therefore further optimized, validated, and standardized our in-house designed recombinant antibody microarray technology platform. To this end, we addressed the main remaining technical issues (e.g. antibody quality, array production, sample labelling, and selected assay conditions) and most importantly key biostatistics subjects (e.g. array data pre-processing and biomarker panel condensation). This represents one of the first antibody array studies in which these key biostatistics subjects have been studied in detail. Here, we thus present the next generation of the recombinant antibody microarray technology platform designed for clinical immunoproteomics.


title: AVEC 2018 Workshop and Challenge: Bipolar Disorder and Cross-Cultural Affect Recognition

abstract: The Audio/Visual Emotion Challenge and Workshop (AVEC 2018) "Bipolar disorder, and cross-cultural affect recognition'' is the eighth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: bipolar disorder classification, cross-cultural dimensional emotion recognition, and emotional label generation from individual ratings, respectively.


title: PreCo: A Large-scale Dataset in Preschool Vocabulary for Coreference Resolution

abstract: We introduce PreCo, a large-scale English dataset for coreference resolution. The dataset is designed to embody the core challenges in coreference, such as entity representation, by alleviating the challenge of low overlap between training and test sets and enabling separated analysis of mention detection and mention clustering. To strengthen the training-test overlap, we collect a large corpus of about 38K documents and 12.4M words which are mostly from the vocabulary of English-speaking preschoolers. Experiments show that with higher training-test overlap, error analysis on PreCo is more efficient than the one on OntoNotes, a popular existing dataset. Furthermore, we annotate singleton mentions making it possible for the first time to quantify the influence that a mention detector makes on coreference resolution performance. The dataset is freely available at this https URL.


title: Jalview Version 2—a multiple sequence alignment editor and analysis workbench

abstract: Summary: Jalview Version 2 is a system for interactive WYSIWYG editing, analysis and annotation of multiple sequence alignments. Core features include keyboard and mouse-based editing, multiple views and alignment overviews, and linked structure display with Jmol. Jalview 2 is available in two forms: a lightweight Java applet for use in web applications, and a powerful desktop application that employs web services for sequence alignment, secondary structure prediction and the retrieval of alignments, sequences, annotation and structures from public databases and any DAS 1.53 compliant sequence or annotation server. ::: ::: Availability: The Jalview 2 Desktop application and JalviewLite applet are made freely available under the GPL, and can be downloaded from www.jalview.org ::: ::: Contact: g.j.barton@dundee.ac.uk


title: Disseminating Self-Help: Positive Psychology Exercises in an Online Trial

abstract: Background: The recent growth of positive psychology has led to a proliferation in exercises to increase positive thoughts, behaviors, and emotions. Preliminary evidence suggests that these exercises hold promise as an approach for reducing depressive symptoms. These exercises are typically researched in isolation as single exercises. The current study examined the acceptability of several multi-exercise packages using online dissemination. Objective: The purpose of this study was to investigate methods of dissemination that could increase the acceptability and effectiveness of positive psychology exercises. To achieve this goal, we compared the use of positive psychology exercises when delivered in packages of 2, 4, or 6 exercises. Methods: Self-help–seeking participants enrolled in this study by visiting an online research portal. Consenting participants were randomly assigned to receive 2, 4, or 6 positive psychology exercises (or assessments only) over a 6-week period. These exercises drew from the content of group positive psychotherapy. Participants visited an automated website that distributed exercise instructions, provided email reminders, and contained the baseline and follow-up assessments. Following each exercise, participants rated their enjoyment of the exercise, answered how often they had used each technique, and completed outcome measures. Results: In total, 1364 individuals consented to participate. Attrition rates across the 2-, 4-, and 6-exercise conditions were similar at 55.5% (181/326), 55.8% (203/364), and 52.7% (168/319) respectively but were significantly greater than the attrition rate of 42.5% (151/355) for the control condition (χ 2 3 = 16.40, P < .001). Participants in the 6-exercise condition were significant more likely than participants in the 4-exercise condition to use both the third (F 1,312 = 5.61, P = .02) and fourth (F 1,313 = 6.03, P = .02) exercises. For 5 of the 6 exercises, enjoyment was related to continued use of the exercise at 6-week follow-up (r’s = .12 to .39). All conditions produced significant reductions in depressive symptoms (F1,656 = 94.71, P < .001); however, a significant condition by time interaction (F 3,656 = 4.77, P = .003) indicated that this reduction was larger in the groups that received 2 or 4 exercises compared with the 6-exercise or control condition. Conclusion: Increasing the number of exercises presented to participants increased the use of the techniques and did not increase dropout. Participants may be more likely to use these skills when presented with a variety of options. Increasing the number of exercises delivered to participants produced a curvilinear relationship with those in the 2- and 4-exercise conditions reporting larger decreases in depressive symptoms than participants in the 6-exercise or control conditions. Although research generally offers a single exercise to test isolate effects, this study supports that studying variability in dissemination can produce important findings.


title: Long range audio and audio-visual event detection using a laser Doppler vibrometer

abstract: Association of audio events with video events presents a challenge to a typical camera-microphone approach in order to ::: capture AV signals from a large distance. Setting up a long range microphone array and performing geo-calibration of ::: both audio and video sensors is difficult. In this work, in addition to a geo-calibrated electro-optical camera, we propose ::: to use a novel optical sensor - a Laser Doppler Vibrometer (LDV) for real-time audio sensing, which allows us to ::: capture acoustic signals from a large distance, and to use the same geo-calibration for both the camera and the audio (via ::: LDV). We have promising preliminary results on association of the audio recording of speech with the video of the ::: human speaker.


title: Mobile Netw Appl DOI 10.1007/s11036-008-0139-0 Unsupervised Learning for Solving RSS Hardware Variance Problem in WiFi Localization

abstract: Hardware variance can significantly degrade the positional accuracy of RSS-based WiFi localization systems. Although manual adjustment can reduce positional error, this solution is not scalable as the number of new WiFi devices increases. We propose an unsupervised learning method to automatically solve the hardware variance problem in WiFi localization. This method was designed and implemented in a working WiFi positioning system and evaluated using different WiFi devices with diverse RSS signal patterns. Experimental results demonstrate that the proposed learning method improves positional accuracy within 100 s of learning time.


title: Approximate analytical solution for mathematical models of thermal ignition and non-isothermal catalytic zero order reaction in a spherical geometry

abstract: Abstract In this paper an approximate analytical solution for the Frank-Kamenetskii equation modeling thermal ignition without the depletion of the combustibles in a spherical annulus and non-isothermal zero order reaction in spherical catalyst particle is presented. The approximate solution is compared with the numerical solution and is in good agreement with the numerical solution. The approximate solution obtained is valid for all values of the distance parameter. Multiple solutions occur for some range of Frank-Kamenetskii parameter ( λ ). The multiplicity is infinite for the case of a solid sphere and λ = 2 . Interesting relation is obtained for λ at the turning points. For the non-isothermal zero order reaction in a spherical catalyst particle the effectiveness factor was obtained using the approximate solution. The values of the effectiveness factor obtained from the approximate solution are accurate compared with the exact values obtained from numerical computations.


title: Programming moves: Design and evaluation of applying embodied interaction in virtual environments to enhance computational thinking in middle school students

abstract: We detail the design, implementation, and an initial evaluation of a virtual reality education and entertainment (edutainment) application called Virtual Environment Interactions (VEnvI). VEnvI is an application in which students learn computer science concepts through the process of choreographing movement for a virtual character using a fun and intuitive interface. In this exploratory study, 54 participants as part of a summer camp geared towards promoting participation of women in science and engineering programmatically crafted a dance performance for a virtual human. A subset of those students (16) participated in an immersive embodied interaction metaphor in VEnvI. In creating this metaphor that provides first-person, embodied experiences using self-avatars, we seek to facilitate engagement, excitement and interest in computational thinking. We qualitatively and quantitatively evaluated the extent to which the activities of the summer camp, programming the dance moves, and the embodied interaction within VEnvI facilitated students' edutainment, presence, interest, excitement, and engagement in computing, and the potential to alter their perceptions of computing and computer scientists. Results indicate that students enjoyed the experience and successfully engaged the virtual character in the immersive embodied interaction, thus exhibiting high telepresence and social presence. Students also showed increased interest and excitement regarding the computing field at the end of their summer camp experience using VEnvI.


title: Fingerprint attendance system for classroom needs

abstract: Fingerprint attendance system aims to automate the attendance procedure of an educational institution using biometric technology. This will save time wasted on calling out names and it gives a fool-proof method of attendance marking. A hand-held device is used to mark the attendance without the intervention of teacher. The device can be passed and students can mark attendance during the lecture time. Students would be made to place their finger over the sensor so as to mark their presence in the class. It can communicate with a host computer using its USB interface. This device operates from a rechargeable battery. GUI application in host computer helps the teacher to manage the device and attendance.


title: Scalable training of L1-regularized log-linear models

abstract: The L-BFGS limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L 2 regularization, but it cannot be used for an L 1 -regularized loss due to its non-differentiability whenever some parameter is zero. Efficient algorithms have been proposed for this task, but they are impractical when the number of parameters is very large. We present an algorithm Orthant-Wise Limited-memory Quasi-Newton (OWL-QN), based on L-BFGS, that can efficiently optimize the L 1 -regularized log-likelihood of log-linear models with millions of parameters. In our experiments on a parse reranking task, our algorithm was several orders of magnitude faster than an alternative algorithm, and substantially faster than L-BFGS on the analogous L 2 -regularized problem. We also present a proof that OWL-QN is guaranteed to converge to a globally optimal parameter vector.


title: Video Saliency Detection via Spatial-Temporal Fusion and Low-Rank Coherency Diffusion

abstract: This paper advocates a novel video saliency detection method based on the spatial-temporal saliency fusion and low-rank coherency guided saliency diffusion. In sharp contrast to the conventional methods, which conduct saliency detection locally in a frame-by-frame way and could easily give rise to incorrect low-level saliency map, in order to overcome the existing difficulties, this paper proposes to fuse the color saliency based on global motion clues in a batch-wise fashion. And we also propose low-rank coherency guided spatial-temporal saliency diffusion to guarantee the temporal smoothness of saliency maps. Meanwhile, a series of saliency boosting strategies are designed to further improve the saliency accuracy. First, the original long-term video sequence is equally segmented into many short-term frame batches, and the motion clues of the individual video batch are integrated and diffused temporally to facilitate the computation of color saliency. Then, based on the obtained saliency clues, inter-batch saliency priors are modeled to guide the low-level saliency fusion. After that, both the raw color information and the fused low-level saliency are regarded as the low-rank coherency clues, which are employed to guide the spatial-temporal saliency diffusion with the help of an additional permutation matrix serving as the alternative rank selection strategy. Thus, it could guarantee the robustness of the saliency map’s temporal consistence, and further boost the accuracy of the computed saliency map. Moreover, we conduct extensive experiments on five public available benchmarks, and make comprehensive, quantitative evaluations between our method and 16 state-of-the-art techniques. All the results demonstrate the superiority of our method in accuracy, reliability, robustness, and versatility.


title: A comparative gender based evaluation of e-commerce website: A hybrid MCDM approach

abstract: Past few years have seen an escalated addition in ecommerce websites along with the users, with significant additions from both male and female populations. With the proliferated competition knowing your customer base becomes important. The disquisition focuses on the parallel evaluation of these websites for both genders. The factors individuate itself for males and females, which has been considered in the comparative evaluation. This segregation has been done through calculation of weights using fuzzy AHP which is inputted to TOPSIS for the culminating evaluation. The differentiation of the factor behaviour for males and females provides the industry with the improvement area to enhance their customer base in both the bifurcations, with the findings concreting facts and needs of both the genders.


title: Comparative Analysis and Framework Evaluating Mimicry-Resistant and Invisible Web Authentication Schemes

abstract: In web authentication, the many password alternatives proposed over the years, despite having different designs and objectives, all predominantly rely on an element of secrecy. This motivates us, herein, to provide the first detailed exploration of the integration of a fundamentally different element of defense into the design of web authentication schemes: a mimicry resistance dimension. We analyze web authentication mechanisms with respect to new properties related to mimicry-resistance, and in particular evaluate invisible techniques that provide some mimicry-resistance (unlike those relying solely on static secrets), including device fingerprinting schemes, PUFs (physically unclonable functions), and a subset of Internet geolocation mechanisms.


title: Crowdturfers, Campaigns, and Social Media: Tracking and Revealing Crowdsourced Manipulation of Social Media

abstract: Crowdturfing has recently been identified as a sinister counterpart to the enormous positive opportunities of crowdsourcing. Crowdturfers leverage human-powered crowdsourcing platforms to spread malicious URLs in social media, form "astroturf" campaigns, and manipulate search engines, ultimately degrading the quality of online information and threatening the usefulness of these systems. In this paper we present a framework for "pulling back the curtain" on crowdturfers to reveal their underlying ecosystem. Concretely, we analyze the types of malicious tasks and the properties of requesters and workers in crowdsourcing sites such as Microworkers.com, ShortTask.com and Rapidworkers.com, and link these tasks (and their associated workers) on crowdsourcing sites to social media, by monitoring the activities of social media participants. Based on this linkage, we identify the relationship structure connecting these workers in social media, which can reveal the implicit power structure of crowdturfers identified on crowdsourcing sites. We identify three classes of crowdturfers -- professional workers, casual workers, and middlemen -- and we develop statistical user models to automatically differentiate these workers and regular social media users.