Towards improving bug tracking systems with game mechanisms

Low bug report quality and human conflicts pose challenges to keep bug tracking systems productive. This work proposes to address these issues by applying game mechanisms to bug tracking systems. We investigate the use of game mechanisms in Stack Overflow, an online community organized to resolve computer programming related problems, for which the improvements we seek for bug tracking systems also turn out to be relevant. The results of our Stack Overflow investigation show that its game mechanisms could be used to address these issues by motivating contributors to increase contribution frequency and quality, by filtering useful contributions, and by creating an agile and dependable moderation system. We proceed by mapping these mechanisms to open-source bug tracking systems, and find that most benefits are applicable. Additionally, our results motivate tailoring a reward and reputation system and summarizing bug reports as future directions for increasing the benefits of game mechanisms in bug tracking systems.


Anti-jamming performance of cognitive radio networks under multiple uncoordinated jammers in fading environment

In this paper, we study both the jamming capability of the cognitive-radio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN), by considering multiple uncooperative jammers and independent Rayleigh flat-fading propagations. A Markov model of CRN transmission is set up for the cross-layer analysis of the anti-jamming performance. The transitional probabilities are derived analytically by considering a smart jamming attack strategy. Average throughput expression is obtained and verified by simulations. The results indicate that CRN communications can be extremely susceptible to smart jamming attacks targeting the CRN spectrum sensing and channel switching procedures.


Usability Analysis of Visual Programming Environments: A 'Cognitive Dimensions' Framework

The cognitive dimensions framework is a broad-brush evaluation technique for interactive devices and for non-interactive notations. It sets out a small vocabulary of terms designed to capture the cognitively-relevant aspects of structure, and shows how they can be traded off against each other. The purpose of this paper is to propose the framework as an evaluation technique for visual programming environments. We apply it to two commercially-available dataflow languages (with further examples from other systems) and conclude that it is effective and insightful; other HCI-based evaluation techniques focus on different aspects and would make good complements. Insofar as the examples we used are representative, current VPLs are successful in achieving a good ‘closeness of match’, but designers need to consider the ‘viscosity’ (resistance to local change) and the ‘secondary notation’ (possibility of conveying extra meaning by choice of layout, colour, etc.).


Customer churn analysis : Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry

Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Using customer transaction and billing data, this study investigates determinants of customer churn in the Korean mobile telecommunications service market. Results indicate that call quality-related factors influence customer churn; however, customers participating in membership card programs are also more likely to churn, which raises questions about program effectiveness. Furthermore, heavy users also tend to churn. In order to analyze partial and total defection, this study defines changes in a customer’s status from active use (using the service on a regular basis) to non-use (deciding not to use it temporarily without having churned yet) or suspended (being suspended by the service provider) as partial defection and from active use to churn as total defection. Thus, mediating effects of a customer’s partial defection on the relationship between the churn determinants and total defection are analyzed and their implications are discussed. Results indicate that some churn determinants influence customer churn, either directly or indirectly through a customer’s status change, or both; therefore, a customer’s status change explains the relationship between churn determinants and the probability of churn. r 2006 Elsevier Ltd. All rights reserved.


Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms


Machine learning applications in genetics and genomics

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.


Real AdaBoost : boosting for credit scorecards and similarity to WOE logistic regression

Adaboost is a machine learning algorithm that builds a series of small decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. We will discuss the AdaBoost methodology and introduce the extension called Real AdaBoost. Real AdaBoost comes from a strong academic pedigree: its authors are pioneers of machine learning and the method has well-established empirical and theoretical support spanning 15 years. Practically speaking, Real AdaBoost is able to produce readable credit scorecards and offers attractive features including variable interaction and adaptive, stage-wise binning. We will contrast Real AdaBoost to the dominant methodology for creating credit scorecards: stepwise weight of evidence logistic regression (SWOELR). Real AdaBoost is remarkably similar to SWOELR and is well positioned to serve as a benchmark for SWOELR models; it may even offer a statistical framework by which we can understand the power of SWOELR. We offer a macro to generate Real AdaBoost models in SAS. INTRODUCTION Financial institutions (FIs) must develop a wide range of models for marketing, fraud detection, loan adjudication, etc. Modeling has undergone a recent renaissance as machine learning has exploded – spurned by the availability of advanced statistical techniques, the ubiquity of powerful computers to execute these techniques, and the well-publicized successes of the companies who have embraced these methods (Parloff 2016). Modeling departments within some FIs face opposing demands: executives want some of the famed value of advanced methods, while government regulators, internal deployment teams and front-line staff want models that are easy to implement, interpret and understand. In this paper we review Real AdaBoost, a machine learning technique that may offer a middle-ground between powerful, but opaque machine learning methods and transparent conventional methods. CONSUMER RISK MODELS One field of modeling where FIs must often strike a balance between power and transparency is consumer risk modeling. Consumer risk modeling involves ranking customers by their credit worthiness (the likelihood they will repay a loan): first by identifying customer characteristics that indicate risk of delinquency, and then combining them mathematically to calculate a relative risk score for each customer (common characteristics include: past loan delinquency, high credit utilization, etc.). CREDIT SCORECARDS In order to keep the consumer risk models as transparent as possible, many FIs require that the final output of the model be in the form of a scorecard (an example is shown in Table 1). Credit scorecards are a popular way to represent customer risk models due to their simplicity, readability, and the ease with which business expertise can be incorporated during the modeling process (Maldonado et al. 2013). A scorecard lists a number of characteristics that indicate risk and each characteristic is subdivided into a small number of bins defined by ranges of values for that characteristic (e.g., credit utilization: 30-80% is a bin for the credit utilization characteristic). Each bin is assigned a number of score points, a value derived from a statistical model and proportional to the risk of that bin (SAS 2012). A customer will fall into one and only one bin per characteristic and the final score of the applicant is the sum of the points assigned by each bin (plus an intercept). This final score is proportional to consumer risk. The procedure for developing scorecards is termed stepwise weight of evidence logistic regression (SWOELR) and is implemented in the Credit Scoring add-on in SAS® Enterprise MinerTM.


Approximation Algorithms for Degree-Constrained Minimum-Cost Network-Design Problems

We study network-design problems with two different design objectives: the total cost of the edges and nodes in the network and the maximum degree of any node in the network. A prototypical example is the degree-constrained node-weighted Steiner tree problem: We are given an undirected graph G(V,E) , with a nonnegative integral function d that specifies an upper bound d(v) on the degree of each vertex v ∈ V in the Steiner tree to be constructed, nonnegative costs on the nodes, and a subset of k nodes called terminals. The goal is to construct a Steiner tree T containing all the terminals such that the degree of any node v in T is at most the specified upper bound d(v) and the total cost of the nodes in T is minimum. Our main result is a bicriteria approximation algorithm whose output is approximate in terms of both the degree and cost criteria—the degree of any node v ∈ V in the output Steiner tree is O(d(v) log  k) and the cost of the tree is O(log  k) times that of a minimum-cost Steiner tree that obeys the degree bound d(v) for each node v . Our result extends to the more general problem of constructing one-connected networks such as generalized Steiner forests. We also consider the special case in which the edge costs obey the triangle inequality and present simple approximation algorithms with better performance guarantees.


Construction and Refinement of Panoramic Mosaics with Global and Local Alignment

This paper presents techniques for constructing full view panoramic mosaics from sequences of images. Our representation associates a rotation matrix (and optionally a focal length) with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimal image mosaic (in the least-squares sense). To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, we develop a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment, we significantly improve the quality of our image mosaics, thereby enabling the creation of full view panoramic mosaics with hand-held cameras.


[1] Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Advanced techniques involving the simultaneous processing of multiple SAR acquisitions in time increase the number of locations where a deformation signal can be extracted and reduce associated error. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterer and small baseline methods, which are optimized for different models of scattering. However, the scattering characteristics of real terrains usually lay between these two end-member models. I present here a new method that combines both approaches, to extract the deformation signal at more points and with higher overall signal-to-noise ratio than can either approach alone. I apply the combined method to data acquired over Eyjafjallajökull volcano in Iceland, and detect time-varying ground displacements associated with two intrusion events. Citation: Hooper, A., (2008), A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches, Geophys. Res. Lett., 35, L16302, doi:10.1029/ 2008GL034654.


Machine Learning Methods for Optical Character Recognition

Success of optical character recognition depends on a number of factors, two of which are feature extraction and classi cation algorithms. In this paper we look at the results of the application of a set of classi ers to datasets obtained through various basic feature extraction methods.


Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles

The volume of obstacles encountered in the marine environment is rapidly increasing, which makes the development of collision avoidance systems more challenging. Several fuzzy ontology-based simulators have been proposed to provide a virtual platform for the analysis of maritime missions. However, due to the simulators’ limitations, ontologybased knowledge cannot be utilized to evaluate maritime robot algorithms and to avoid collisions. The existing simulators must be equipped with smart semantic domain knowledge to provide an efficient framework for the decision-making system of AUVs. This article presents type-2 fuzzy ontology-based semantic knowledge (T2FOBSK) and a simulator for marine users that will reduce experimental time and the cost of marine robots and will evaluate algorithms intelligently. The system reformulates the user’s query to extract the positions of AUVs and obstacles and convert them to a proper format for the simulator. The simulator uses semantic knowledge to calculate the degree of collision risk and to avoid obstacles. The available type-1 fuzzy ontology-based approach cannot extract intensively blurred data from the hazy marine environment to offer actual solutions. Therefore, we propose a type-2 fuzzy ontology to provide accurate information about collision risk and the marine environment during real-time marine operations. Moreover, the type-2 fuzzy ontology is designed using Protégé OWL-2 tools. The DL query and SPARQL query are used to evaluate the ontology. The distance to closest point of approach (DCPA), time to closest point of approach (TCPA) and variation of compass degree (VCD) are used to calculate the degree of collision risk between AUVs and obstacles. The experimental and simulation results show that the proposed architecture is highly efficient and highly productive for marine missions and the real-time decision-making system of AUVs. 2014 Elsevier Inc. All rights reserved.


Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.


Gujarati Script Recognition : A Review

This paper is step to locate the researchers, their track in the way for recognizing the characters from various regional scripts in India. This survey paper would provide a path for developing recognition tools for Indian scripts where there is still a scope of recognition accuracy. It provides the reasons for researchers to work for recognition of Indian scripts. In this paper, the various scripts along with their special properties, there feature extraction and recognition techniques are described. Simultaneously a detailed comparison is made on the fronts of techniques used and recognition of Gujarati script.


Survey on security threats of smartphones in Internet of Things

Internet of Things (IoT) is an emerging concept which will interconnect billions of devices (such as smartphones, sensors and other networking devices), to communicate with each other. This interconnection of devices can be beneficial in many ways such as improving location based services and timely coordination. However, this also impose severe security concerns specially on smartphones which contains sensitive data. In this paper, we provide a comprehensive analysis of different security issues in smartphones which are integral part of IoT. We provide an easy and concise overview of different security threats. Our categorization helps in identifying specific security breach and applying relevant countermeasures.


Text Document Clustering based on Phrase Similarity using Affinity Propagation

Affinity propagation (AP) was recently introduced as an un-supervised learning algorithm for exemplar based clustering. In this paper novel text document clustering algorithm has been developed based on vector space model, phrases and affinity propagation clustering algorithm. Proposed algorithm can be called Phrase affinity clustering (PAC). PAC first finds the phrase by ukkonen suffix tree construction algorithm, second finds the vector space model using tf-idf weighting scheme of phrase. Third calculate the similarity matrix form VSD using cosine similarity . In Last affinity propagation algorithm generate the clusters . F-Measure ,Purity and Entropy of Proposed algorithm is better than GAHC ,ST-GAHC and ST-KNN on OHSUMED ,RCV1 and News group data sets.


Development of a credible and integrated electronic voting machine based on contactless IC cards, biometrie fingerprint credentials and POS printer

In recent times there has been a decline in the confidence of common people over electronic voting machines (EVMs). More anticipated than what the reality actually is, today's automated vote casting methods have faced immense controversy related to being vulnerable to hacking and questions have been raised about their transparency and security. This paper proposes the design and development of a novel tamper resistant electronic voting system that aims to mitigate the recurring issues and flaws of existing voting machines of today. To elucidate the system in brief, multiple layered verification process would be carried out on a potential voter by the means of fingerprint recognition and a Near Field Communication (NFC) smart card entry in order to authenticate his or her identity. Subsequently, the person would cast the vote by pressing a button corresponding to a particular candidate which would be recorded in the system providing the vote caster a visual confirmation. The final vote would then be printed out spontaneously onto a ballot box using a POS (Point of Sales) printer for an added level of validation. This system not only prevents multiple vote casts, but also eliminates the discrepancies that commonly arise with a person claiming not to have voted, whereas his or her name is present in the list of vote casters. The project is an incremental extension of a previous research.


LLC resonant converter burst mode control with constant burst time and optimal switching pattern

In this paper, a novel burst mode control solution for LLC resonant converters is proposed for the highest efficiency and small output voltage ripple. The burst time is set to be constant, the off time is modulated by load conditions. During the burst time, three pulse switching pattern is implemented to keep output voltage low frequency ripple minimal. The switch first turned on is controlled to be last turned off to reduce the initial hard switching turn on loss when burst starts. The pulse width of first switch is optimized to settle LLC to the steady-state under the highest efficiency load condition in one pulse. Zero current detection (ZCD) of the resonant current is implemented to achieve first switch partial ZVS, further increasing light load efficiency.


A self-powered ultra-fast DC solid state circuit breaker using a normally-on SiC JFET

This paper introduces a new self-powered solid state circuit breaker (SSCB) concept using a normally-on SiC JFET as the main static switch and a fast-starting isolated DC/DC converter as the protection driver. The new SSCB detects short circuit faults by sensing its drain-source voltage rise, and draws power from the fault condition to turn and hold off the SiC JFET. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or additional wiring. A unique low power isolated DC/DC converter is designed and optimized to provide a fast reaction to a short circuit event. The SSCB prototypes have experimentally demonstrated a fault current interruption capability up to 180 amperes at a DC bus voltage of 400 volts within 0.8 microseconds. DC circuit protection applications provide a unique market opportunity for wide bandgap power semiconductor devices outside the conventional focus on power electronic converter applications.


Noninvasive in vivo glucose sensing on human subjects using mid-infrared light.

Mid-infrared quantum cascade laser spectroscopy is used to noninvasively predict blood glucose concentrations of three healthy human subjects in vivo. We utilize a hollow-core fiber based optical setup for light delivery and collection along with a broadly tunable quantum cascade laser to obtain spectra from human subjects and use standard chemo-metric techniques (namely partial least squares regression) for prediction analysis. Throughout a glucose concentration range of 80-160 mg/dL, we achieve clinically accurate predictions 84% of the time, on average. This work opens a new path to a noninvasive in vivo glucose sensor that would benefit the lives of hundreds of millions of diabetics worldwide.


FCM : THE FUZZY c-MEANS CLUSTERING ALGORITHM

nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.


Relational versus non-relational database systems for data warehousing

Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design has a close relationship with the relational model. Recently, there has been a surge of alternative technologies for large scale analytic processing, most of which are not based on the relational model. Out of these proposals, distributed file systems together with MapReduce have become strong competitors to relational database systems to analyze large data sets, exploiting parallel processing. Moreover, there is progress on using MapReduce to evaluate relational queries. With that motivation in mind, this panel will compare pros and cons of each technology for data warehousing and will identify research issues, considering practical aspects like ease of use, programming flexibility and cost; as well as technical aspects like data modeling, storage, hardware, scalability, query processing, fault tolerance and data mining.


Variance Reduction Methods II 1 Importance Sampling

For this problem, however, the usual approach would be completely inadequate since approximating θ to any reasonable degree of accuracy would require n to be inordinately large. For example, on average we would have to set n ≈ 2.7014× 10 in order to obtain just one non-zero value of I. Clearly this is impractical and a much smaller value of n would have to be used. Using a much smaller value of n, however, would almost inevitably result in an estimate, θ̂n = 0, and an approximate confidence interval [L,U ] = [0, 0]! So the naive approach does not work. We could try to use the variance reduction techniques we have seen in the course so far, but they would provide little, if any, help.


The Effect of Internet Security Breach Announcements on Market Value: Capital Market Reactions for Breached Firms and Internet Security Developers

Assessing the value of information technology (IT) security is challenging because of the difficulty of measuring the cost of security breaches. An event-study analysis, using market valuations, was used to assess the impact of security breaches on the market value of breached firms. The information-transfer effect of security breaches (i.e., their effect on the market value of firms that develop security technology) was also studied. The results show that announcing an Internet security breach is negatively associated with the market value of the announcing firm. The breached firms in the sample lost, on average, 2.1 percent of their market value within two days of the announcement—an average loss in market capitalization of $1.65 billion per breach. Firm type, firm size, and the year the breach occurred help explain the cross-sectional variations in abnormal returns produced by security breaches. The effects of security breaches are not restricted to the breached firms. The market value of security developers is positively associated with the disclosure of security breaches by other firms. The security developers in the sample realized an average abnormal return of 1.36 percent during the two-day period after the announcement—an average gain of $1.06 billion in two days. The study suggests that the cost of poor security is very high for investors.


Explaining and Predicting the Impact of Branding Alliances and Web Site Quality on Initial Consumer Trust of E-Commerce Web Sites

Trust is a crucial factor in e-commerce. However, consumers are less likely to trust unknown Web sites. This study explores how less-familiar e-commerce Web sites can use branding alliances and Web site quality to increase the likelihood of initial consumer trust. We use the associative network model of memory to explain brand knowledge and to show how the mere exposure effect can be leveraged to improve a Web site’s brand image. We also extend information integration theory to explain how branding alliances are able to increase initial trust and transfer positive effects to Web sites. Testing of our model shows that the most important constructs for increasing initial trust in our experimental context are branding and Web site quality. Finally, we discuss future research ideas, limitations, implications, and ideas for practitioners.


A critical analysis of decision support systems research

This paper critically analyses the nature and state of decision support systems (DSS) research. To provide context for the analysis, a history of DSS is presented which focuses on the evolution of a number of sub-groupings of research and practice: personal decision support systems, group support systems, negotiation support systems, intelligent decision support systems, knowledge managementbased DSS, executive information systems/business intelligence, and data warehousing. To understand the state of DSS research an empirical investigation of published DSS research is presented. This investigation is based on the detailed analysis of 1,020 DSS articles published in 14 major journals from 1990 to 2003. The analysis found that DSS publication has been falling steadily since its peak in 1994 and the current publication rate is at early 1990s levels. Other findings include that personal DSS and group support systems dominate research activity and data warehousing is the least published type of DSS. The journal DSS is the major publishing outlet; US ‘Other’ journals dominate DSS publishing and there is very low exposure of DSS in European journals. Around two-thirds of DSS research is empirical, a much higher proportion than general IS research. DSS empirical research is 1 Acknowledgements: We would like to thank Gemma Dodson for research assistance, especially in obtaining articles and coding, and Peter O’Donnell for discussions about DSS history. The research was partially supported by a Monash University Small Grant and a Curtin University Visiting Professorship. Preliminary results from the empirical study were presented at the 2004 IFIP International Conference on Decision Support Systems, Prato, Italy and the 2004 Australasian Conference on Information Systems, Hobart, Australia.


A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research

A review of the literature suggests that few studies use formative indicator measurement models, even though they should. Therefore, the purpose of this research is to (a) discuss the distinction between formative and reflective measurement models, (b) develop a set of conceptual criteria that can be used to determine whether a construct should be modeled as having formative or reflective indicators, (c) review the marketing literature to obtain an estimate of the extent of measurement model misspecification in the field, (d ) estimate the extent to which measurement model misspecification biases estimates of the relationships between constructs using a Monte Carlo simulation, and (e) provide recommendations for modeling formative indicator constructs.


Service quality delivery through Web sites : A critical review of extant know

Evidence exists that service quality delivery through Web sites is an essential strategy to success, possibly more important than low price and Web presence. To deliver superior service quality, managers of companies with Web presences must first understand how customers perceive and evaluate online customer service. Information on this topic is beginning to emerge from both academic and practitioner sources, but this information has not yet been examined as a whole. The goals of this article are to review and synthesize the literature about service quality delivery through Web sites, describe what is known about the topic, and develop an agenda for needed research.


Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE. Keyword: Bi-histogram equalization; Dualistic sub-image; Histogram equalization; Recursive mean-separate; Scalable brightness preservation


Sales Forecasting for Retail Chains

This paper presents a use case of data mining for sales forecasting in retail demand and sales prediction. In particular, the Extreme Gradient Boosting algorithm is used to design a prediction model to accurately estimate probable sales for retail outlets of a major European Pharmacy retailing company. The forecast of potential sales is based on a mixture of temporal and economical features including prior sales data, store promotions, retail competitors, school and state holidays, location and accessibility of the store as well as the time of year. The model building process was guided by common sense reasoning and by analytic knowledge discovered during data analysis and definitive conclusions were drawn. The performances of the XGBoost predictor were compared with those of more traditional regression algorithms like Linear Regression and Random Forest Regression. Findings not only reveal that the XGBoost algorithm outperforms the traditional modeling approaches with regard to prediction accuracy, but it also uncovers new knowledge that is hidden in data which help in building a more robust feature set and strengthen the sales prediction model.


"Applying TAM to E-Services Adoption: The Moderating Role of Perceived Risk"

Consumer adoption of e-services is an important goal for many service providers, however little is known about how different consumer segments perceive and evaluate them for adoption. The Technology Acceptance Model (TAM) explains information systems evaluation and adoption, however the Internetdelivered e-services context presents additional variance that requires supplemental measures to be added to TAM. This research extends TAM to include a perceived usage risk main effect and also tested whether perceived risk moderated several of TAM’s relationships. Results indicate that higher levels of perceived risk deflated ease of use’s effect and inflated subjective norm’s effect on perceived usefulness and adoption intention.


CRF-CNN: Modeling Structured Information in Human Pose Estimation

Deep convolutional neural networks (CNN) have achieved great success. On the other hand, modeling structural information has been proved critical in many vision problems. It is of great interest to integrate them effectively. In a classical neural network, there is no message passing between neurons in the same layer. In this paper, we propose a CRF-CNN framework which can simultaneously model structural information in both output and hidden feature layers in a probabilistic way, and it is applied to human pose estimation. A message passing scheme is proposed, so that in various layers each body joint receives messages from all the others in an efficient way. Such message passing can be implemented with convolution between features maps in the same layer, and it is also integrated with feedforward propagation in neural networks. Finally, a neural network implementation of endto-end learning CRF-CNN is provided. Its effectiveness is demonstrated through experiments on two benchmark datasets.


Diagnosis Code Prediction from Electronic Health Records as Multilabel Text Classification : A Survey

This article presents a survey on diagnosis code prediction from various information in Electronic Health Records (EHR): both unstructured free text and structured data. Particularly, our interests are in casting the problem as text classification with multiple sources and using neural network based models. We will first present previous work in this area and describe some simple baseline models for the relevant tasks.


Bandwidth Improvement of Microstrip Patch Antenna Using H- Shaped Patch

Despite the many advantages of microstrip patch antennas, they do have some considerable drawbacks. One of the main limitations with patch antennas is their inherently narrowband performance due to its resonant nature. With bandwidth as low as a few percent; broadband applications using conventional patch designs are limited. So for the antenna miniaturization and bandwidth improvement H-shaped microstrip patch antenna used. In this paper, authors cover two aspects of microstrip antenna designs. The first is the analysis of single element narrowband rectangular microstrip antenna which operates at the central frequency of 3.3 GHz. The second aspect is the analysis and design of slot cut H-shaped microstrip antenna. The simulation process has been done through high frequency structure simulator (HFSS). The properties of antenna such as bandwidth, S parameter, VSWR has been investigated and compared between a single element rectangular and H-shaped microstrip antenna.


A Low-Complexity Full-Duplex Radio Implementation With a Single Antenna

This paper presents a novel low-complexity full-duplex radio design, which only uses a single patch antenna without any duplexer or circulator for passive suppression of self-interference, and a computationally efficient technique for linear digital cancellation. The proposed full-duplex design is tested for IEEE 802.11g Wireless Standard on the WARP (v3) software-defined radio implementation platform. It is shown that this design provides a total suppression of 88 dB, which is sufficient for low-power or short-range full-duplex communication. The dual polarized slot coupled patch antenna used in our design provides an interport isolation as high as 60 dB in 2.4 GHz band. Additionally, the digital domain cancellation utilizes a frequency domain-based estimation and reconstruction approach, which not only offers up to <inline-formula><tex-math notation="LaTeX">$61\%$</tex-math></inline-formula> reduction in the computational complexity but also provides a <inline-formula><tex-math notation="LaTeX">$5-7$ </tex-math></inline-formula> dB better digital cancellation performance in highly selective channel conditions, as compared to the time-domain-based techniques. The proposed full-duplex implementation can be easily applied in OFDM-based wireless systems, such as IEEE 802.11, which is the considered air interface in this paper.


2 Foreword This textbook provides study materials for the participants of the course named Multivariate Analysis of Ecological Data that we teach at our university for the third year. Material provided here should serve both for the introductory and the advanced versions of the course. We admit that some parts of the text would profit from further polishing, they are quite rough but we hope in further improvement of this text. We hope that this book provides an easy-to-read supplement for the more exact and detailed publications like the collection of the Dr. Ter Braak' papers and the Canoco for Windows 4.0 manual. In addition to the scope of these publications, this textbook adds information on the classification methods of the multivariate data analysis and introduces some of the modern regression methods most useful in the ecological research. Wherever we refer to some commercial software products, these are covered by trademarks or registered marks of their respective producers. This publication is far from being final and this is seen on its quality: some issues appear repeatedly through the book, but we hope this provides, at least, an opportunity to the reader to see the same topic expressed in different words.


A route planning method based on improved artificial potential field algorithm

The route planning method based on artificial potential field is an important branch and the hot research focus of assignment planning. The traditional artificial potential field method is easy to fall into the local minimum, and the paper analyzed the causes, and then did some research on the improved artificial potential field method, and then proposed a method modifying the repulsion direction based on the improved artificial potential field to solve the three typical local minimum problem. The simulation results show that the proposed method is effective.


Building a Virtual HPC Cluster with Auto Scaling by the Docker

Solving the software dependency issue under the HPC environment has always been a difficult task for both computing system administrators and application scientists. This work would like to tackle the issue by introducing the modern container technology, the Docker, to be specific. By integrating the auto-scaling feature of service discovery with the light-weight virtualization tool, the Docker, the construction of a virtual cluster on top of physical cluster hardware is attempted. Thus, through the isolation of computing environment, a remedy of software dependency of HPC environment is possible. Keywords—HPC; Docker; auto-scaling; service discovery; MPI; Consul;


GeoClustering: A web service for geospatial clustering

Geospatial clustering is a very important research topic in the field of „Geospatial Knowledge Discovery.‟ Its main purpose is to group similar objects, from large geospatial datasets, into clusters, based on the objects‟ spatial and non-spatial attributes. This capability is very useful for better understanding the patterns and distributions of geographical phenomena. With the emerging availability of large amounts of geospatial data on the Internet, the demand for powerful methods of extracting meaningful information from large amounts of web-based geospatial data is thus also rapidly increasing. In this chapter, we present a geospatial clustering web service called GeoClustering. With this service, users are able to cluster online data sources easily and to then conveniently visualize the clustering results through a friendly map-centric user interface. Since it is web-based, GeoClustering offers a platform that can be accessed by anyone from anywhere at any time. To achieve better interoperability, GeoClustering offers an API and follows the OGC standard data format for spatial data exchange.


Compact Duplexing for a 680-GHz Radar Using a Waveguide Orthomode Transducer

A compact 680-GHz waveguide orthomode transducer (OMT) and circular horn combination has been designed, tested, and characterized in a radar transceiver's duplexer. The duplexing capability is implemented by a hybrid waveguide quasi optical solution, combining a linear polarization OMT and an external grating polarizer. Isolation between the OMT's orthogonal ports' flanges was measured with a vector network analyzer to exceed 33 dB over a >10% bandwidth between 630 and 710 GHz. Calibrated Y-factor measurements using a mixer attached to the OMT ports reveal losses through the transmit and receive paths that sum to an average of 4.7 dB of two-way loss over 660-690 GHz. This is consistent with radar sensitivity measurements comparing the new OMT/horn with a quasi-optical wire grid beam splitter. Moreover, the radar performance assessment validates the OMT as a suitable compact substitute of the wire grid for the JPL's short-range 680-GHz imaging radar.


Feature Extraction Based on Co-occurrence of Adjacent Local Binary Patterns

In this paper, we propose a new image feature based on spatial co-occurrence among micropatterns, where each micropattern is represented by a Local Binary Pattern (LBP). In conventional LBP-based features such as LBP histograms, all the LBPs of micropatterns in the image are packed into a single histogram. Doing so discards important information concerning spatial relations among the LBPs, even though they may contain information about the image’s global structure. To consider such spatial relations, we measure their co-occurrence among multiple LBPs. The proposed feature is robust against variations in illumination, a feature inherited from the original LBP, and simultaneously retains more detail of image. The significant advantage of the proposed method versus conventional LBP-based features is demonstrated through experimental results of face and texture recognition using public databases.


Online Footsteps to Purchase: Exploring Consumer Behaviors on Online Shopping Sites

As an important part of the Internet economy, online markets have gained much interest in research community as well as industry. Researchers have studied various aspects of online markets including motivations of consumer behaviors on online markets. However, due to the lack of log data of consumers' online behaviors including their purchase, it has not been thoroughly investigated or validated on what drives consumers to purchase products on online markets. Our research moves forward from prior studies by analyzing consumers' actual online behaviors that lead to actual purchases, and using datasets from multiple online shopping sites that can provide comparisons across different types of online shopping sites. We analyzed consumers' buying process and constructed consumers' behavior trajectory to gain deeper understanding of consumer behaviors on online markets. We find that a substantial portion (24%) of consumers in a general-purpose marketplace (like eBay) discover items from external sources (e.g., price comparison sites), while most (>95%) of consumers in a special-purpose shopping site directly access items from the site itself. We also reveal that item browsing patterns and cart usage patterns are the important predictors of the actual purchases. Using behavioral features identified by our analysis, we developed a prediction model to infer whether a consumer purchases item(s). Our prediction model of purchases achieved over 80% accuracy across four different online shopping sites.


Using Associative Classifiers for Predictive Analysis in Health Care Data Mining

Association rule mining is one of the most important and well researched techniques of data mining for descriptive task, initially used for market basket analysis. It finds all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Classification using Association rule mining is another major Predictive analysis technique that aims to discover a small set of rule in the database that forms an accurate classifier. In this paper, we introduce the combined approach that integrates association rule mining and classification rule mining called Associative Classification (AC). This is new classification approach. The integration is done by focusing on mining a special subset of association rules called classification association rule (CAR). And then classification is being performed using these CAR. Using association rule mining for constructing classification systems is a promising approach. Given the readability of the associative classifiers, they are especially fit to applications were the model may assist domain experts in their decisions. Medical field is a good example was such applications may appear. Consider an example were


Modeling and Optimization of Building Mix and Energy Supply Technology for Urban Districts

Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice, encompassing strategies to both reduce energy consumption and carbon intensity in both energy supply and demand. Most methods focus on one of these two approaches but few integrate decisions for supply and demand simultaneously. This paper presents a novel model that endogenously simulates energy supply and demand at a district scale on an hourly time scale. Demand is specified for a variety of building uses, and losses and municipal loads are calculated from the number of buildings in the district. Energy supply is modeled using technology-specific classes, allowing easy addition of specific equipment or types of energy generation. Standard interfaces allow expansion of the model to include new types of energy supply and demand. The model can be used for analysis of a single design alternative or optimization over a large design space, allowing exploration of various densities, mixes of uses, and energy supply technologies. An example optimization is provided for a community near San Francisco, California. This example uses 21 building types, 32 combined heat and power engines, and 16 chillers. The results demonstrate the ability to compare performance tradeoffs and optimize for three objectives: life cycle cost, annual carbon dioxide emissions, and overall system


Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.


Towards the Success of ERP Systems : Case Study in Two Moroccan Companies

For over a decade, ERP systems in Morocco have shown a significant increase. Companies make large investments in these scale systems in order to anticipate positive impacts on their organization. Measuring ERP systems success becomes an important criterion for the stakeholders in justifying the associated investment. In this paper, we will examine the success of these systems by using a qualitative research method based on a case study approach. We will provide an insight into how two Moroccan organizations perceive the success of their ERP systems. Findings indicated that Information, system quality and net benefits are the three main dimensions of success.


Forward-viewing CMUT arrays for medical imaging

This paper reports the design and testing of forward-viewing annular arrays fabricated using capacitive micromachined ultrasonic transducer (CMUT) technology. Recent research studies have shown that CMUTs have broad frequency bandwidth and high-transduction efficiency. One- and two-dimensional CMUT arrays of various sizes already have been fabricated, and their viability for medical imaging applications has been demonstrated. We fabricated 64-element, forward-viewing annular arrays using the standard CMUT fabrication process and carried out experiments to measure the operating frequency, bandwidth, and transmit/receive efficiency of the array elements. The annular array elements, designed for imaging applications in the 20 MHz range, had a resonance frequency of 13.5 MHz in air. The immersion pulse-echo data collected from a plane reflector showed that the devices operate in the 5-26 MHz range with a fractional bandwidth of 135%. The output pressure at the surface of the transducer was measured to be 24 kPa/V. These values translate into a dynamic range of 131.5 dB for 1-V excitation in 1-Hz bandwidth with a commercial low noise receiving circuitry. The designed, forward-viewing annular CMUT array is suitable for mounting on the front surface of a cylindrical catheter probe and can provide Doppler information for measurement of blood flow and guiding information for navigation through blood vessels in intravascular ultrasound imaging.


Exploration of Virtual and Augmented Reality for Visual Analytics and 3D Volume Rendering of Functional Magnetic Resonance Imaging (fMRI) Data

Statistical analysis of functional magnetic resonance imaging (fMRI), such as independent components analysis, is providing new scientific and clinical insights into the data with capabilities such as characterising traits of schizophrenia. However, with existing approaches to fMRI analysis, there are a number of challenges that prevent it from being fully utilised, including understanding exactly what a 'significant activity' pattern is, which structures are consistent and different between individuals and across the population, and how to deal with imaging artifacts such as noise. Interactive visual analytics has been presented as a step towards solving these challenges by presenting the data to users in a way that illuminates meaning. This includes using circular layouts that represent network connectivity and volume renderings with 'in situ' network diagrams. These visualisations currently rely on traditional 2D 'flat' displays with mouse-and-keyboard input. Due to the constrained screen space and an implied concept of depth, they are limited in presenting a meaningful, uncluttered abstraction of the data without compromising on preserving anatomic context. In this paper, we present our ongoing research on fMRI visualisation and discuss the potential for virtual reality (VR) and augmented reality (AR), coupled with gesture-based inputs to create an immersive environment for visualising fMRI data. We suggest that VR/AR can potentially overcome the identified challenges by allowing for a reduction in visual clutter and by allowing users to navigate the data abstractions in a 'natural' way that lets them keep their focus on the visualisations. We present exploratory research we have performed in creating immersive VR environments for fMRI data.


A scrum-based approach to CMMI maturity level 2 in web development environments

Scrum has become one of the most popular agile methodologies, either alone or combined with other agile practices. Besides, CMMI (Capability Maturity Model Integration) is accepted as a suitable model to measure the maturity of the organizations when developing or acquiring software. Although these two approaches are often considered antagonist, the use of an agile approach to reach certain CMMI maturity levels may result beneficial to organizations that develop Web systems, since they would take the advantages of both approaches. In Web community, this union may be very interesting, because agile approaches fits with the special needs of Web development, and they could be a useful tool for companies getting a certain grade of maturity. This work analyzes the goals of CMMI maturity level 2 and the feasibility of achieving them using the practices proposed by Scrum, trying to assess whether the use of this methodology is suitable for meeting the CMMI generic and specific goals or not. Finally, and based on this analysis, this paper raises a possible extension of Scrum, based on agile techniques, to accommodate the CMMI maturity level 2.


Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types

Non-intrusive appliance load monitoring is the process of disaggregating a household’s total electricity consumption into its contributing appliances. In this paper we propose an approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance’s load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. This paper summarises the contribution described in full in [9].


The hitchhiker's guide to successful residential sensing deployments

Homes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes.


Duty-cycling buildings aggressively: The next frontier in HVAC control

Buildings are known to be the largest consumers of electricity in the United States, and often times the dominant energy consumer is the HVAC system. Despite this fact, in most buildings the HVAC system is run using primitive static control algorithms based on fixed work schedules causing wasted energy during periods of low occupancy. In this paper we present a novel control architecture that uses occupancy sensing to guide the operation of a building HVAC system. We show how we can enable aggressive duty-cycling of building HVAC systems — that is, turn them ON or OFF — to save energy while meeting building performance requirements using inexpensive sensing and control methods. We have deployed our occupancy sensor network across an entire floor of a university building and our data shows several periods of low occupancy with significant opportunities to save energy over normal HVAC schedules. Furthermore, by interfacing with the building Energy Management System (EMS) directly and using real-time occupancy data collected by our occupancy nodes, we measure electrical energy savings of 9.54% to 15.73% and thermal energy savings of 7.59% to 12.85% for the HVAC system by controlling just one floor of our four floor building.


AMPds: A public dataset for load disaggregation and eco-feedback research

A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.


Wefind from four datasets that time expressions are formed by loose structure and the words used to express time information can differentiate time expressions from common text. The findings drive us to design a learning method named TOMN to model time expressions. TOMN defines a constituent-based tagging scheme named TOMN scheme with four tags, namely T, O, M, and N, indicating the constituents of time expression, namely Time token,Modifier,Numeral, and the words Outside time expression. In modeling, TOMN assigns a word with a TOMN tag under conditional random fields with minimal features. Essentially, our constituent-based TOMN scheme overcomes the problem of inconsistent tag assignment that is caused by the conventional position-based tagging schemes (e.g., BIO scheme and BILOU scheme). Experiments show that TOMN is equally or more effective than state-of-the-art methods on various datasets, and much more robust on cross-datasets. Moreover, our analysis can explain many empirical observations in other works about time expression recognition and named entity recognition. ACM Reference Format: Xiaoshi Zhong and Erik Cambria. 2018. Time Expression Recognition Using a Constituent-based Tagging Scheme. In Proceedings of The Web Conference 2018 (WWW’18). ACM, New York, NY, USA, 10 pages. https://doi.org/10. 1145/3178876.3185997


Digital Comics Image Indexing Based on Deep Learning

The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed.


Piecewise 3D Euler spirals

3D Euler spirals are visually pleasing, due to their property of having their curvature and their torsion change linearly with arc-length. This paper presents a novel algorithm for fitting piecewise 3D Euler spirals to 3D curves with G2 continuity and torsion continuity. The algorithm can also handle sharp corners. Our piecewise representation is invariant to similarity transformations and it is close to the input curves up to an error tolerance.


Legged Robots That Balance


Deep Transfer Learning for Person Re-Identification

Person re-identification (Re-ID) poses an inevitable challenge to deep learning: how to learn a robust deep model with millions of parameters on a small training set of few or no labels. In this paper, two deep transfer learning methods are proposed to address the training data sparsity problem, respectively from the supervised and unsupervised settings. First, a two-stepped fine-tuning strategy with proxy classifier learning is developed to transfer knowledge from auxiliary datasets. Second, given an unlabelled Re-Iddataset, an unsupervised deep transfer learning model is proposed based on a co-training strategy. Extensive experiments show that the proposed models achieve a good performance of deep Re-ID models.


3 D Indoor Object Recognition by Holistic Scene Understanding

In this project, we jointly solve the problem of indoor object recognition and scene classification. The joint problem is modeled by a Conditional Random Field (CRF). Specifically, we implement unary potential on scene appearance, unary potential on object appearance, and binary potential on geometry between objects. The two appearance potentials are obtained by training two neural networks respectively and extracting features from them. Binary potential on object-object geometry relationship is inferred by projecting one object onto the other one’s surface and perceiving the overlap. Other potentials include object cuboid geometry, binary potential on scene-object concurrence, and binary potential on object-object concurrence. Experiments on the challenging NYU-RGBD v2 dataset show that the approach of jointly solving object detection and scene classification problems by integrating several feature representations achieves better performance than vanilla classification. Meanwhile, improving the potentials on scene appearance and object-object geometry relationship achieve fairly satisfactory improvements compared with the performance in [12]. We achieve object recognition precision of 0.3859 and scene classification accuracy of 0.6208 among 21 classes of objects and 13 classes of scenes. Both improve obviously comparing to 0.3829 and 0.6086 in baseline [12]. This result is also comparable to the state-of-art method implemented on the identical dataset [18].


CAVE-SOM: Immersive visual data mining using 3D Self-Organizing Maps

Data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. Visual data mining techniques attempt to include a human observer in the loop and leverage human perception for knowledge extraction. This is commonly allowed by performing a dimensionality reduction into a visually easy-to-perceive 2D space, which might result in significant loss of important spatial and topological information. To address this issue, this paper presents the design and implementation of a unique 3D visual data mining framework - CAVE-SOM. The CAVE-SOM system couples the Self-Organizing Map (SOM) algorithm with the immersive Cave Automated Virtual Environment (CAVE). The main advantages of the CAVE-SOM system are: i) utilizing a 3D SOM to perform dimensionality reduction of large multi-dimensional datasets, ii) immersive visualization of the trained 3D SOM, iii) ability to explore and interact with the multi-dimensional data in an intuitive and natural way. The CAVE-SOM system uses multiple visualization modes to guide the visual data mining process, for instance the data histograms, U-matrix, connections, separations, uniqueness and the input space view. The implemented CAVE-SOM framework was validated on several benchmark problems and then successfully applied to analysis of wind-power generation data. The knowledge extracted using the CAVE-SOM system can be used for further informed decision making and machine learning.


Experimental Results of High-Efficiency Resonant Coupling Wireless Power Transfer Using a Variable Coupling Method

We experimentally demonstrate that the efficiency of wireless power transfer utilizing resonant coupling is improved by applying the “matching condition.” The `matching condition' is extracted from an equivalent circuit model of a magnetically coupled wireless power transfer system. This condition is achieved by varying the coupling factor between the source (load) and the internal resonator. Applying this technique results in efficiency improvements of 46.2% and 29.3% at distances of 60 cm and 1 m, respectively. The maximum efficiency is 92.5% at 15 cm. A circuit model based on the extracted parameters of the resonators produces results similar to the experimental data.


Improved Named Entity Translation and Bilingual Named Entity Extraction

Translation of named entities (NE), including proper names, temporal and numerical expressions, is very important in multilingual natural language processing, like crosslingual information retrieval and statistical machine translation. In this paper we present an integrated approach to extract a named entity translation dictionary from a bilingual corpus while at the same time improving the named entity annotation quality.Starting from a bilingual corpus where the named entities are extracted independently for each language, a statistical alignment model is used to align the named entities. An iterative process is applied to extract named entity pairs with higher alignment probability. This leads to a smaller but cleaner named entity translation dictionary and also to a significant improvement of the monolingual named entity annotation quality for both languages. Experimental result shows that the dictionary size is reduced by 51.8% and the annotation quality is improved from70.03 to 78.15 for Chinese and 73.38 to 81.46 in terms of F-score.


Zero-Field Readout Electronics for Planar Fluxgate Sensors Without Compensation Coil

A simple and sensitive readout electronics for planar fluxgate sensors is presented. The system exploits the sense coils to directly generate the compensation static field avoiding the additional coil/s required in standard closed loop configuration, thus providing clear advantages in terms of size and cost. Moreover, feedback configurations are known to provide better linearity and stability of the system. The sensitivity of the developed demonstration system can be easily set from 13.3 to 104.9 mV/μT with nonlinearity ranging from 0.17% to 0.38% of the measuring interval, whereas the corresponding measuring intervals vary from ±301 to ±38 μT. The measuring uncertainty, the noise field spectral density, and the system bandwidth have been estimated in 12.2 nT, ≈ 10 nT/√{Hz}, and ≈1.5 Hz, respectively. The proposed measuring instrument is extremely easy to use and versatile. Moreover, due to the use of commercially available ferromagnetic material and the simple and via-less design, the proposed fluxgate sensor results in a very low cost and reliable device.


Efficient Keyword-Aware Representative Travel Route Recommendation

With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities, or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users’ historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.


Sudoku as a SAT Problem

Sudoku is a very simple and well-known puzzle that has achieved international popularity in the recent past. This paper addresses the problem of encoding Sudoku puzzles into conjunctive normal form (CNF), and subsequently solving them using polynomial-time propositional satisfiability (SAT) inference techniques. We introduce two straightforward SAT encodings for Sudoku: the minimal encoding and the extended encoding. The minimal encoding suffices to characterize Sudoku puzzles, whereas the extended encoding adds redundant clauses to the minimal encoding. Experimental results demonstrate that, for thousands of very hard puzzles, inference techniques struggle to solve these puzzles when using the minimal encoding. However, using the extended encoding, unit propagation is able to solve about half of our set of puzzles. Nonetheless, for some puzzles more sophisticated inference techniques are required.


Variational Inference using Implicit Distributions

Generative adversarial networks (GANs) have given us a great tool to fit implicit generative models to data. Implicit distributions are ones we can sample from easily, and take derivatives of samples with respect to model parameters. These models are highly expressive and we argue they can prove just as useful for variational inference (VI) as they are for generative modelling. Several papers have proposed GAN-like algorithms for inference, however, connections to the theory of VI are not always well understood. This paper provides a unifying review of existing algorithms establishing connections between variational autoencoders, adversarially learned inference, operator VI, GAN-based image reconstruction, and more. Secondly, the paper provides a framework for building new algorithms: depending on the way the variational bound is expressed we introduce prior-contrastive and jointcontrastive methods, and show practical inference algorithms based on either density ratio estimation or denoising.


Hand3D: Hand Pose Estimation using 3D Neural Network

We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image. Different from previous works that mostly run on 2D depth image domain and require intermediate or post process to bring in the supervision from 3D space, we convert the depth map to a 3D volumetric representation, and feed it into a 3D convolutional neural network(CNN) to directly produce the pose in 3D requiring no further process. Our system does not require the ground truth reference point for initialization, and our network architecture naturally integrates both local feature and global context in 3D space. To increase the coverage of the hand pose space of the training data, we render synthetic depth image by transferring hand pose from existing real image datasets. We evaluation our algorithm on two public benchmarks and achieve the state-ofthe-art performance. The synthetic hand pose dataset will be available.


"If It Matters, I Can Explain It": Social Desirability of Knowledge Increases the Illusion of Explanatory Depth

This paper explores whether social desirability affects the illusion of explanatory depth (IEOD) by comparing the magnitude of this illusion in topics with different levels of social desirability within several domains. This question was chosen because prior literature shows that social expectations about how much a person should know about a certain topic affect the magnitude of the IOED. Previous research shows also that social desirability has an effect on a similar illusion related to argumentation, and that the IOED is affected by the way a person thinks knowledge is distributed in his or her social group. In order to do so, 184 participants were assigned randomly to three knowledge domains (history, economics, and devices) and in each domain they rated their understanding of a high-desirability and a low-desirability topic following a standard IOED procedure. Results show that social desirability has an effect on the IOED magnitude and that overestimation of understanding varies among domains. Particularly, participants tend to overestimate their understanding of high desirability topics only. This effect was stronger in the historical domain.


Behavioral oscillation in priming: competing perceptual predictions conveyed in alternating theta-band rhythms.

The brain constantly creates perceptual predictions about forthcoming stimuli to guide perception efficiently. Abundant studies have demonstrated that perceptual predictions modulate sensory activities depending on whether the actual inputs are consistent with one particular prediction. In real-life contexts, however, multiple and even conflicting predictions might concurrently exist to be tested, requiring a multiprediction coordination process. It remains largely unknown how multiple hypotheses are conveyed and harmonized to guide moment-by-moment perception. Based on recent findings revealing that multiple locations are sampled alternatively in various phase of attentional rhythms, we hypothesize that this oscillation-based temporal organization mechanism may also underlie the multiprediction coordination process. To address the issue, we used well established priming paradigms in combination with a time-resolved behavioral approach to investigate the fine temporal dynamics of the multiprediction harmonization course in human subjects. We first replicate classical priming effects in slowly developing trends of priming time courses. Second, after removing the typical priming patterns, we reveal a new theta-band (∼4 Hz) oscillatory component in the priming behavioral data regardless of whether the prime was masked. Third, we show that these theta-band priming oscillations triggered by congruent and incongruent primes are in an out-of-phase relationship. These findings suggest that perceptual predictions return to low-sensory areas not continuously but recurrently in a theta-band rhythm (every 200-300 ms) and that multiple predictions are dynamically coordinated in time by being conveyed in different phases of the theta-band oscillations to achieve dissociated but temporally organized neural representations.


The Impact of Experimental Setup in Prepaid Churn Prediction for Mobile Telecommunications: What to Predict, for Whom and Does the Customer Experience Matter?

Prepaid customers in mobile telecommunications are not bound by a contract and can therefore change operators (‘churn’) at their convenience and without notification. This makes the task of predicting prepaid churn both challenging and financially rewarding. This paper presents an explorative, real world study of prepaid churn modeling by varying the experimental setup on three dimensions: data, outcome definition and population sample. Firstly, we add Customer Experience Management (CEM) data to data traditionally used for prepaid churn prediction. Secondly, we vary the outcome definition of prepaid churn. Thirdly, we alter the sample of the customers included, based on their inactivity period at the time of recording. While adding CEM parameters did not influence the predictability of churn, one variation on the sample and especially a particular change in the outcome definition had a substantial


A Comparative Study of Staff Removal Algorithms

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respect to these metrics, and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation, our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal but applicable to other segmentation problems as well.


Real-Time Object Detection and Recognition System Using OpenCV via SURF Algorithm in Emgu CV for Robotic Handling in Libraries


PUSHOVER ANALYSIS OF A MULTISTORIED BUILDING

To model the advanced behaviour of reinforced concrete analytically in its non-linear zone is difficult. This has led engineers in the past to believe heavily on empirical formulas that were derived from numerous experiments for the design of reinforced concrete structures. For structural design and assessment of reinforced concrete members, the nonlinear analysis has become a vital tool. The method can be used to study the behaviour of reinforced concrete structures as well as force redistribution. This analysis of the nonlinear response of RC structures to be distributed out in an exceedingly routine fashion. It helps within the investigation of the behaviour of the structure below completely different loading conditions and also the cracks pattern. In the present study, the non-linear response of RCC frame using the loading has been carried out with the intention to investigate the relative importance of many factors in the non-linear analysis of RCC frames. The structure was modelled and analyzed in STAAD Pro V8i, SAP2000 and designed manually. The description of the reinforcement was done in AutoCAD 2010. Keywordsmulti-storied building, STAAD PRO, Floor plan, SAP 2000, Push over analysis.


Automated Irrigation System-IoT Based Approach

Agriculture is a major source of earning of Indians and agriculture has made a big impact on India's economy. The development of crops for a better yield and quality deliver is exceptionally required. So suitable conditions and suitable moisture in beds of crop can play a major role for production.. Mostly irrigation is done by tradition methods of stream flows from one end to other. Such supply may leave varied moisture levels in filed. The administration of the water system can be enhanced utilizing programmed watering framework This paper proposes a programmed water system with framework for the terrains which will reduce manual labour and optimizing water usage increasing productivity of crops. For formulating the setup, Arduino kit is used with moisture sensor with Wi-Fi module. Our experimental setup is connected with cloud framework and data is acquisition is done. Then data is analysed by cloud services and appropriate recommendations are given.


Self-Autonomous Wireless Sensor Nodes With Wind Energy Harvesting for Remote Sensing of Wind-Driven Wildfire Spread

The satellite-based remote sensing technique has been widely used in monitoring wildfire spread. There are two prominent drawbacks with this approach of using satellites located in space: (1) very low sampling rate (temporal resolution problem) and (2) lack of accuracy (spatial and spectral resolution problem). To address these challenges, a wireless sensor network deployed at ground level with high-fidelity and low-altitude atmospheric sensing for wind speed of local wildfire spread has been used. An indirect approach in sensing wind speed has been proposed in this paper as an alternative to the bulky conventional wind anemometer to save cost and space. The wind speed is sensed by measuring the equivalent electrical output voltage of the wind turbine generator (WTG). The percentage error in the wind speed measurement using the proposed indirect method is measured to be well within the ±4% limit with respect to wind anemometer accuracy. The same WTG also functions as a wind energy harvesting (WEH) system to convert the available wind energy into electrical energy to sustain the operation of the wireless sensor node. The experimental results show that the designed WEH system is able to harvest an average electrical power of 7.7 mW at an average wind speed of 3.62 m/s for powering the operation of the wireless sensor node that consumes 3.5 mW for predicting the wildfire spread. Based on the sensed wind speed information, the fire control management system determines the spreading condition of the wildfire, and an adequate fire suppression action can be performed by the fire-fighting experts.


Real-parameter optimization with differential evolution

This study reports how the differential evolution (DE) algorithm performed on the test bed developed for the CEC05 contest for real parameter optimization. The test bed includes 25 scalable functions, many of which are both non-separable and highly multi-modal. Results include DE's performance on the 10 and 30-dimensional versions of each function


Opposition-based Magnetic Optimization Algorithm with parameter adaptation strategy

Magnetic Optimization Algorithm (MOA) has emerged as a promising optimization algorithm that is inspired by the principles of magnetic field theory. In this paper we improve the performance of the algorithm in two aspects. First an Opposition-Based Learning (OBL) approach is proposed for the algorithm which is applied to the movement operator of the algorithm. Second, by learning from the algorithm’s past experience, an adaptive parameter control strategy which dynamically sets the parameters of the algorithm during the optimization is proposed. To show the significance of the proposed parameter adaptation strategy, we compare the algorithm with two well-known parameter setting techniques on a number of benchmark problems. The results indicate that although the proposed algorithm with the adaptation strategy does not require to set the parameters of the algorithm prior to the optimization process, it outperforms MOA with other parameter setting strategies in most large-scale optimization problems. We also study the algorithm while employing the OBL by comparing it with the original version of MOA. Furthermore, the proposed algorithm is tested and compared with seven traditional population-based algorithms and eight state-of-theart optimization algorithms. The comparisons demonstrate that the proposed algorithm outperforms the traditional algorithms in most benchmark problems, and its results is comparative to those obtained by the state-of-the-art algorithms.


Lightweight IPS for port scan in OpenFlow SDN networks

Security has been one of the major concerns for the computer network community due to resource abuse and malicious flows intrusion. Before a network or a system is attacked, a port scan is typically performed to discover vulnerabilities, like open ports, which may be used to access and control them. Several studies have addressed Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) methods for detecting malicious activities, based on received flows or packet data analysis. However, those methods lead to an increase in switching latency, due to the need to analyze flows or packets before routing them. This may also increase network overhead when flows or packets are duplicated to be parsed by an external IDS. On the one hand, an IDS/IPS may be a bottleneck on the network and may not be useful. On the other hand, the new paradigm called Software Defined Networking (SDN) and the OpenFlow protocol provide some statistical information about the network that may be used for detecting malicious activities. Hence, this work presents a new port scan IPS for SDN based on the OpenFlow switch counters data. A non-intrusive and lightweight method was developed and implemented, with low network overhead, and low memory and processing power consumption. The results showed that our method is effective on detecting and preventing port scan attacks.


Improved Option Pricing Using Artificial Neural Networks and Bootstrap Methods

A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.


High quality & low thermal resistance eutectic flip chip LED bonding

Die attach process plays a key role in the thermal management of high power LED packages by governing the thermal resistance of the solder interfaces between the LED chips and the substrate materials. In this paper, comparison was made between the GaN based flip chip LED packages fabricated by (1) heated collet and (2) flux reflow eutectic die attach process. Thermal transient characteristics of the samples had been investigated based on the evaluation of the differential structure function and chip temperature measurement by infrared microscopy. It was shown that LEDs with higher void content would result in higher thermal resistance and chip temperature. Under high power operation at 6W, it was estimated that the junction temperature would exceed 80°C with void content 40%, which was at least 10°C higher comparing to that with void content 20%. Based on the result, LEDs samples bonded by heated collet die attach process had an average void content of 8.8% with 0.9% standard deviation, which were smaller comparing to the samples bonded by flux reflow die attach process (40% void content with standard deviation 20.5%). Due to the advantage of small void content control, it is possible to fabricate high power LED package with low thermal resistance, consistent and reliable die attach quality by using heated collet die attach process.


Unconstrained Pose-Invariant Face Recognition Using 3D Generic Elastic Models

Classical face recognition techniques have been successful at operating under well-controlled conditions; however, they have difficulty in robustly performing recognition in uncontrolled real-world scenarios where variations in pose, illumination, and expression are encountered. In this paper, we propose a new method for real-world unconstrained pose-invariant face recognition. We first construct a 3D model for each subject in our database using only a single 2D image by applying the 3D Generic Elastic Model (3D GEM) approach. These 3D models comprise an intermediate gallery database from which novel 2D pose views are synthesized for matching. Before matching, an initial estimate of the pose of the test query is obtained using a linear regression approach based on automatic facial landmark annotation. Each 3D model is subsequently rendered at different poses within a limited search space about the estimated pose, and the resulting images are matched against the test query. Finally, we compute the distances between the synthesized images and test query by using a simple normalized correlation matcher to show the effectiveness of our pose synthesis method to real-world data. We present convincing results on challenging data sets and video sequences demonstrating high recognition accuracy under controlled as well as unseen, uncontrolled real-world scenarios using a fast implementation.


Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge

We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and nally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.


Multiple templates auto exposure control based on luminance histogram for onboard camera

In order to adjust the exposure of high resolution and frequency images from an on-board camera in real time, a simple exposure control approach based on luminance histogram analysis is presented to realize light control precisely and rapidly. The algorithm divides the initial image into nine blocks and calculates the mean brightness of every part. It uses the luminance histogram to analyze the degree of exposure of both: the global image and every part, especially interest areas. According to the area size of the neighborhood on the two edges of the histogram, the different weight matrix can be decided to quickly calculate the optimal brightness for the next frame, and then the optimal exposure time can be determined accurately. The algorithm does not need to determine an expensive quadratic function to decide the weight of every part so as to fast perform auto exposure control. The experimental results show that the algorithm can correctly and fast adjust the exposure time of the on-board camera on an autonomous robot, and has strong adaptability for various shootings outdoor.