title: Critical Factors of Business Intelligence: (Case of an It-Based Company)

abstract: Purpose- This paper aims to identify the effective components of Business Intelligence (BI), in order to facilitate the evaluation of the Business Intelligence in an IT- based Company in Iran. Design/methodology/approach- This paper has identified the effective components of business Intelligence through the case study Company based on a comprehensive review of recent literature. For this purpose, a questionnaire was designed, validated by some BI experts and then analyzed by some statistical methods. The results discussed various perspectives from the business Intelligence point of view to provide some effective and essential components of BI. Findings-The statistical analysis determined five components as effective issues in this case study. These components are "strategy", "Information Technology", "environmental analysis", "Human resource" and " supporting-executive factors" and at least a conceptual pattern of BI is introduced. The overall results from the case study were positive as well, thus reflecting the appropriateness of the experts and managers of the case study Company. Research limitations/implications- The extracted components can act as a guideline for BI adoption in the case study Company. This helps to ensure that the essential issues are covered during design and implementation phase of BI program. For academics, it provides a common language to discuss and study the components crucial for business Intelligence .Originality/value- The paper may present high value to researchers in the business Intelligence field and to practitioners involved with BI program in the case study Company, this paper gives valuable information and guidelines that hopefully will help the leaders to consider the important issues during business Intelligence establishment in the organization. Paper type: Case study


title: Whither motor drives: A case study in switched reluctance motor drives

abstract: Variable speed motor drives and power electronics are at crossroads as their engineering and technology have matured, and products have found wide acceptance in world markets. A debate is going on about the future of both in academic and to a smaller extent in industrial circles with a broad consensus and agreement that there is no more fundamental work possible or breakthrough required in these disciplines. During this time American industries have almost decimated R&D in these disciplines indirectly reinforcing the notion that current technologies are sufficient to address the existing or emerging market needs. This is happening at a time when the energy crisis is at full blast and environmental concern is on upswing. It is illusory to think that these challenges can be faced by incremental work in product development as is the general trend and not by fundamental work. By reviewing the past it is demonstrated that the root cause of present situation and how the challenges of emerging market and societal demands can be met through a case study. This paper presents the rise of power electronics and motor drives from late 1950s to present. The developments in semiconductor devices, power electronic converters and motor drives progressed from fundamental concepts and breakthroughs to tackling secondary problems and that path is briefly traced. In turn, this has contributed to the notion that the engineering in this field has matured. Many of these fundamental developments have come about due to market pressure and demands (a fact usually overlooked in academia) and that correlation is derived. The same market and environmental concerns can and do open up opportunities for fundamental work. A case study of recent work in switched reluctance motor drives is presented highlighting emerging newer directions in motor drives research and development driven primarily by the emerging markets and applications. It is demonstrated that there is no paucity of fundamental problems (but may be a lack of attention to market needs) that can provide the keys to happy kingdom of research and development for a newer generation of engineers, scientists and professors.


title: Toward retail product recognition on grocery shelves

abstract: This paper addresses the problem of retail product recognition on grocery shelf images. We present a technique for accomplishing this task with a low time complexity. We decompose the problem into detection and recognition. The former is achieved by a generic product detection module which is trained on a specific class of products (e.g. tobacco packages). Cascade object detection framework of Viola and Jones [1] is used for this purpose. We further make use of Support Vector Machines (SVMs) to recognize the brand inside each detected region. We extract both shape and color information; and apply feature-level fusion from two separate descriptors computed with the bag of words approach. Furthermore, we introduce a dataset (available on request) that we have collected for similar research purposes. Results are presented on this dataset of more than 5,000 images consisting of 10 tobacco brands. We show that satisfactory detection and classification can be achieved on devices with cheap computational power. Potential applications of the proposed approach include planogram compliance control, inventory management and assisting visually impaired people during shopping.


title: Robust Principal Component Analysis Based on Maximum Correntropy Criterion

abstract: Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced to a quadratic problem that can be efficiently solved by a standard optimization method. The proposed method exhibits the following benefits: 1) it is robust to outliers through the mechanism of MCC which can be more theoretically solid than a heuristic rule based on MSE; 2) it requires no assumption about the zero-mean of data for processing and can estimate data mean during optimization; and 3) its optimal solution consists of principal eigenvectors of a robust covariance matrix corresponding to the largest eigenvalues. In addition, kernel techniques are further introduced in the proposed method to deal with nonlinearly distributed data. Numerical results demonstrate that the proposed method can outperform robust rotational-invariant PCAs based on L(1) norm when outliers occur.


title: The Command Language Grammar: a representation for the user interface of interactive computer systems

abstract: This article introduces and discusses a specific grammatical structure—the Command Language Grammar (CLG)—as a representational framework for describing the user interface aspects of interactive computer systems. CLG partitions a system into a Conceptual Component (tasks and abstract concepts), a Communication Component (command language), and a Physical Component (display, keyboard, etc.), The components are further stratified into distinct Levels—a Task Level, a Semantic Level, a Syntactic Level, and an Interaction Level-each Level being a complete description of the system at its level of abstraction. Each Level's description contains procedures for accomplishing the tasks addressed by the system in terms of the actions available at that Level. That is, the system is described by progressive refinement. An extensive example, a small message-processing system, is described at all Levels in the CLG notation. CLG is discussed from three points of view: the Linguistic View sees CLG as elaborating the structure of the system's user interface and of the communication between the user and the system. The principal goal of CLG in this view is to lay out the space of command language systems. The Psychological View sees CLG as describing the user's mental model of the system. The main concern in this view is with the psychological validity of the CLG description. The Design View sees CLG as a series of representations for specifying the design of a system. CLG proposes a top-down design process in which the conceptual model of the system is first specified and then a command language is created to communicate with it.


title: Image Guidance for All—TilePro Display of 3-Dimensionally Reconstructed Images in Robotic Partial Nephrectomy

abstract: Objective To determine the feasibility of a novel low-barrier-to-entry image guidance system. Methods Initially a randomized crossover study was performed to establish the interface (iPad or 3-dimensional mouse) that minimized both the amount of time required to perform a manual image registration and the error of that registration. A subsequent clinical feasibility study was undertaken on 5 patients undergoing robot-assisted partial nephrectomy. Randomized crossover study primary outcomes were time to task completion, NASA–Task Load Index score, and alignment error (translational and rotational). The Mann-Whitney U test was used to compare groups. Surgeon feedback was sought when assessing the system in a clinical setting. Results In the initial randomized crossover study, the iPad-based system was able to achieve adequate alignment accuracy (Frobenius norm of 0.3; total error of 20.8 mm) in significantly less time (33 seconds; P Conclusion In this study, we have demonstrated the feasibility of a “low-barrier-to-entry” image guidance system in a clinical setting. The system was able to achieve swift and sufficiently accurate alignment, with little impact on the surgical workflow.


title: Changes in Knee Biomechanics After a Hip-Abductor Strengthening Protocol for Runners With Patellofemoral Pain Syndrome

abstract: Context: Very few authors have investigated the relationship between hip-abductor muscle strength and frontal-plane knee mechanics during running. ::: ::: Objective: To investigate this relationship using a 3-week hip-abductor muscle-strengthening program to identify changes in strength, pain, and biomechanics in runners with patellofemoral pain syndrome (PFPS). ::: ::: Design: Cohort study. ::: ::: Setting: University-based clinical research laboratory. ::: ::: Patients or Other Participants: Fifteen individuals (5 men, 10 women) with PFPS and 10 individuals without PFPS (4 men, 6 women) participated. ::: ::: Intervention(s): The patients with PFPS completed a 3-week hip-abductor strengthening protocol; control participants did not. ::: ::: Main Outcome Measure(s): The dependent variables of interest were maximal isometric hip-abductor muscle strength, 2-dimensional peak knee genu valgum angle, and stride-to-stride knee-joint variability. All measures were recorded at baseline and 3 weeks later. Between-groups differences were compared using repeated-measures analyses of variance. ::: ::: Results: At baseline, the PFPS group exhibited reduced strength, no difference in peak genu valgum angle, and increased stride-to-stride knee-joint variability compared with the control group. After the 3-week protocol, the PFPS group demonstrated increased strength, less pain, no change in peak genu valgum angle, and reduced stride-to-stride knee-joint variability compared with baseline. ::: ::: Conclusions: A 3-week hip-abductor muscle-strengthening protocol was effective in increasing muscle strength and decreasing pain and stride-to-stride knee-joint variability in individuals with PFPS. However, concomitant changes in peak knee genu valgum angle were not observed.


title: European integration and environmental policy

abstract: The environment and the European Community - the analysis of political integration, J. Duncan Liefferink, Philip Lowe and Arthur P.J. Mol. Part 1 Globalization, interdependence and EC environmental policy: European environmental policy and global independence - a review of theoretical approaches, Arthur P.J. Mol and J. Duncan Liefferink institutional learning in the European Community - the response to the greenhouse effect, Markus Jachtenfuchs and Michael Huber transnational companies and environmental policy making - the trend of globalization, Wyn Grant "think globally, act locally"? needs, forms and problems of cross-national cooperation among environmental groups, Dieter Rucht. Part 2 Community policy making on the environment: environmental policy making in the European Community - an evaluation of theoretical perspectives, J. Duncan Liefferink and Arthur P.J. Mol EC policy making - an emerging European policy style?, Sonia Mazey and Jeremy Richardson the role of the European Court of Justice, Ida J. Koppen technical regulation and politics - the interplay between economic interests and environmental policy goals in EC car emission legislation, Henning A. Arp. Part 3 National responses to European environmental integration: national responses to the Europeanization of environmental policy - a selective review of comparative research, Henry Buller, Philip Lowe and Andrew Flynn ecological modernization and the integration of European environmental policy, Albert Weale common agenda, Southern rules - European integration and environmental change in the Mediterranean states, Antonio La Spina and Giuseppe Sciortino.


Semi-Supervised Recognition of Sarcasm in Twitter and Amazon


title: The forward osmosis membrane bioreactor: A low fouling alternative to MBR processes

abstract: A novel osmotic membrane bioreactor (OsMBR) is presented. The system utilizes a submerged forward osmosis (FO) membrane module inside a bioreactor. Through osmosis, water is transported from the mixed liquor across a semi-permeable membrane, and into a draw solution (DS) with a higher osmotic pressure. To produce potable water, the diluted DS is treated in a reverse osmosis (RO) unit; the by-product is a reconcentrated DS for reuse in the FO process. Preliminary results from experiments conducted with a flat-sheet cellulose triacetate FO membrane demonstrated high sustainable flux and relatively low reverse transport of solutes from the DS into the mixed liquor. Membrane fouling was controlled with osmotic backwashing. The FO membrane was found to reject 98% of organic carbon and 90% of ammonium-nitrogen; the OsMBR process (bioreactor and FO membrane) was found to remove greater than 99% of organic carbon and 98% of ammonium-nitrogen, respectively; suggesting a better compatibility of the OsMBR with downstream RO systems than conventional membrane bioreactors.


title: A Survey on Concepts, Applications, and Challenges in Cyber-Physical Systems

abstract: The Cyber-Physical System (CPS) is a term describing a broad range of complex, multi-disciplinary, physically-aware next generation engineered system that integrates embedded computing technologies (cyber part) into the physical world. In order to define and understand CPS more precisely, this article presents a detailed survey of the related work, discussing the origin of CPS, the relations to other research fields, prevalent concepts, and practical applications. Further, this article enumerates an extensive set of technical challenges and uses specific applications to elaborate and provide insight into each specific concept. CPS is a very broad research area and therefore has diverse applications spanning different scales. Additionally, the next generation technologies are expected to play an important role on CPS research. All of CPS applications need to be designed considering the cutting-edge technologies, necessary system-level requirements, and overall impact on the real world.


title: USING DATA MINING TO PREDICT SECONDARY SCHOOL STUDENT PERFORMANCE

abstract: Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe’s tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the education domain. The present work intends to approach student achievement in secondary education using BI/DM techniques. Recent real-world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and questionnaires. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level classification and regression tasks. Also, four DM models (i.e. Decision Trees, Random Forest, Neural Networks and Support Vector Machines) and three input selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. number of absences, parent’s job and education, alcohol consumption). As a direct outcome of this research, more efficient student prediction tools can be be developed, improving the quality of education and enhancing school resource management.


title: Digital Nonlinear Self-Interference Cancellation Based on LMS-Volterra Algorithm

abstract: Commonly, the analog self-interference cancellation (ASIC) can not eliminate self-interference completely in the co-time and co-frequency full-duplex (CCFD) system. Therefore, the self-interference cancellation in digital domain is essential to take the CCFD system into practical. A cancellation method based on least mean square Volterra (LMS-Volterra) algorithm is proposed in this paper, which can suppress the residual nonlinear self-interference in digital domain. The digital nonlinear self-interference cancellation model via the Volterra-series and LMS filter is built. Moreover, the detailed formulation of this cancellation procedure is derived. The expression of interference cancellation ratio (ICR) is achieved. This cancellation method can estimate the self-interference channel and suppress the residual nonlinear interference without the pilot sequence. Analysis and simulation results indicate the validity of the proposed digital nonlinear self-interference cancellation algorithm.


title: Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

abstract: In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.


title: FutureMapping: The Computational Structure of Spatial AI Systems

abstract: We discuss and predict the evolution of Simultaneous Localisation and Mapping (SLAM) into a general geometric and semantic `Spatial AI' perception capability for intelligent embodied devices. A big gap remains between the visual perception performance that devices such as augmented reality eyewear or comsumer robots will require and what is possible within the constraints imposed by real products. Co-design of algorithms, processors and sensors will be needed. We explore the computational structure of current and future Spatial AI algorithms and consider this within the landscape of ongoing hardware developments.


title: A concave conjugate approach for nonconvex penalized regression with the MCP penalty

abstract: The minimax concave plus penalty (MCP) has been demonstrated to be effective in nonconvex penalization for feature selection. In this paper we propose a novel construction approach for MCP. In particular, we show that MCP can be derived from a concave conjugate of the Euclidean distance function. This construction approach in turn leads us to an augmented Lagrange multiplier method for solving the penalized regression problem with MCP. In our method each tuning parameter corresponds to a feature, and these tuning parameters can be automatically updated. We also develop a d.c. (difference of convex functions) programming approach for the penalized regression problem. We find that the augmented Lagrange multiplier method degenerates into the d.c. programming method under specific conditions. Experimental analysis is conducted on a set of simulated data. The result is encouraging.


title: Rapid, minimally invasive adult voluntary male circumcision: A randomised trial

abstract: Background. Voluntary medical male circumcision (VMMC) is a priority HIV preventive intervention. To facilitate male circumcision scale- up, the World Health Organization is actively seeking circumcision techniques that are quicker, easier, and safer than open surgical methods. Objective. To compare conventional open surgical circumcision with suturing with a minimally invasive technique using the Gomco circumcision clamp plus tissue adhesive. Methods. We conducted a non-blinded randomised controlled trial comprising 200 male volunteers >18 years of age, seen at the outpatient university teaching clinic of the Catholic University of Mozambique. We compared two interventions – open surgical circumcision with suturing v. Gomco instrument plus tissue adhesive. Our primary outcome was intraoperative time and our secondary outcomes included: ease of performance, post-operative pain, adverse events, time to healing, patient satisfaction and cosmetic result. Results. The intraoperative time was less with the Gomco/tissue adhesive technique (mean 12.8 min v. 22.5 min; p <0.001). Adverse events were similar except that wound disruption was greater in the Gomco/tissue adhesive group, with no difference in wound healing at 4 weeks. Levels of satisfaction were high in both groups. The cosmetic result was superior in the Gomco/tissue adhesive group. Conclusions. This study has important implications for the scale-up of VMMC services. Removing the foreskin with the Gomco instrument and sealing the wound with cyanoacrylate tissue adhesive in adults is quicker, is an easier technique to learn, and is potentially safer than open surgical VMMC. A disposable plastic, Gomco-like device should be produced and evaluated for use in resource-limited settings.


title: Faceted search over RDF-based knowledge graphs

abstract: Knowledge graphs such as Yago and Freebase have become a powerful asset for enhancing search, and are being intensively used in both academia and industry. Many existing knowledge graphs are either available as Linked Open Data, or they can be exported as RDF datasets enhanced with background knowledge in the form of an OWL 2 ontology. Faceted search is the de facto approach for exploratory search in many online applications, and has been recently proposed as a suitable paradigm for querying RDF repositories. In this paper, we provide rigorous theoretical underpinnings for faceted search in the context of RDF-based knowledge graphs enhanced with OWL 2 ontologies. We identify well-defined fragments of SPARQL that can be naturally captured using faceted search as a query paradigm, and establish the computational complexity of answering such queries. We also study the problem of updating faceted interfaces, which is critical for guiding users in the formulation of meaningful queries during exploratory search. We have implemented our approach in a fully-fledged faceted search system, SemFacet , which we have evaluated over the Yago knowledge graph.


title: A Laminated Waveguide Magic-T With Bandpass Filter Response in Multilayer LTCC

abstract: A laminated waveguide magic-T with imbedded Chebyshev filter response is developed in a multilayer low-temperature co-fired ceramic technology by vertically stacked rectangular cavity resonators with highly symmetric coupling structures. The cavities provide even- and odd-symmetric field distributions by simultaneously resonating TE102 and TE201 modes at the center frequency of the magic-T. Thus, the in-phase and out-of-phase responses of the magic-T function are accomplished according to the induced mode. Meanwhile, the filter frequency response is realized by the cascaded cavities with proper coupling strengths according to the filter specification. With the degenerate, but orthogonal cavity modes, a fewer number of cavities are required and the circuit size is further reduced. A third-order bandpass magic-T is designed and fabricated to operate at 24 GHz with 6% fractional bandwidth. The highly symmetric structure of the implemented magic-T provides a low in-band magnitude imbalance ( ±0.25 dB) and phase imbalance (0°-6°). The sum and difference ports also provide an isolation greater than 30 dB in the operation frequency range. Measurement and simulated results are in good agreement, both validating the design concept.


title: A smart wheelchair ecosystem for autonomous navigation in urban environments

abstract: In this paper, we present a system level approach to smart wheelchair system (SWS) navigation in urban environments. The proposed SWS ecosystem has two primary components: a mapping service which generates large-scale landmark maps, and the SWS vehicle itself, which is a client of the mapping service. The SWS prototype integrates 3D LIDAR/imaging systems which provide robust perception in unstructured, outdoor environments. It also leverages these same sensors for map-based localization. In demonstrating the efficacy of the approach, the SWS navigated autonomously over a distance of more than 12 km in a representative urban environment without once losing localization, and without the use of GPS.


title: An efficient watershed algorithm based on connected components

abstract: Abstract In this paper, a formal definition and a new algorithmic technique for the watershed transformation is presented. The novelty of the approach is to adapt the connected component operator to solve the watershed segmentation problem. The resulting algorithm is independent of the number of grey-levels, employs simple data structures, requires less error prone memory management, and issues a lower complexity and a short running time. However, the algorithm does not modify the principle of the watershed segmentation; the output result is the same as that of using any traditional algorithm which does not build watershed lines.


title: Happyinu: exploring how to use games and extrinsic rewards for consistent food tracking behavior

abstract: Consistent food tracking is important for weight loss. Many people struggle with forming and maintaining the habit because it is time consuming and generally un-enjoyable. We describe the design and initial field evaluation of HappyInu, a virtual pet-care game that we developed to promote consistent food tracking. HappyInu uses daily notifications to remind users to track, provides photo-based food tracking to make the process simpler and gives virtual and cash rewards for motivation. In our pilot study, users were excited about the game-based approach and they wanted more in-game tasks and rewards to be offered. Intrinsic rewards were more motivating than cash, particularly pleasure (fun) and the need to contribute to something meaningful. However, users played HappyInu on a casual (non-daily) basis, just as they did with other smartphone games. In this paper, we discuss users' perceptions on the role of games for behavior change and the implications on game design.


title: A graph-based method of newspaper article reconstruction

abstract: The primary information units in a newspaper are the articles. Article reconstruction from newspapers including article aggregation and reading order recovery is known to be a quite challenging task due to the complexity of the multi-article page layout. In this paper, we propose a novel approach for article reconstruction using a bipartite graph framework, which models the complex relationships between text blocks as one-to-one correspondences, and accomplishes the task by finding the optimal match on this graph. During the optimization process, various information sources, including geometric layout, linguistic and semantic content, are deeply mined in the bipartite graph model to deal with the wide range of complex newspaper layouts. Moreover, quite different from the existing methods, we perform the two sub-tasks of article reconstruction in reverse order, that is, we detect the reading orders of the text blocks first and then use the reading order to aggregate blocks belonging to the same articles. Experimental results on 3312 newspaper pages with 23184 articles demonstrate that our method outperforms the state-of-the-art methods for newspaper article reconstruction. In addition, this method has been adopted in several large-scale newspaper digitalization projects.


title: Estimating the Jacobian of the Singular Value Decomposition: Theory and Applications

abstract: The Singular Value Decomposition (SVD) of a matrix is a linear algebra tool that has been successfully applied to a wide variety of domains. The present paper is concerned with the problem of estimating the Jacobian of the SVD components of a matrix with respect to the matrix itself. An exact analytic technique is developed that facilitates the estimation of the Jacobian using calculations based on simple linear algebra. Knowledge of the Jacobian of the SVD is very useful in certain applications involving multivariate regression or the computation of the uncertainty related to estimates obtained through the SVD. The usefulness and generality of the proposed technique is demonstrated by applying it to the estimation of the uncertainty for three different vision problems, namely self-calibration, epipole computation and rigid motion estimation.


title: HOW TO CALCULATE INFORMATION VALUE FOR EFFECTIVE SECURITY RISK ASSESSMENT

abstract: The actual problem of information security (infosec) risk assessment is determining the value of information property or asset. This is particularly manifested through the use of quantitative methodology in which it is necessary to state the information value in quantitative sizes. The aim of this paper is to describe the evaluation possibilities of business information values, and the criteria needed for determining importance of information. For this purpose, the dimensions of information values will be determined and the ways used to present the importance of information contents will be studied. There are two basic approaches that can be used in evaluation: qualitative and quantitative. Often they are combined to determine forms of information content. The proposed criterion is the three-dimension model, which combines the existing experiences (i.e. possible solutions for information value assessment) with our own criteria. An attempt for structuring information value in a business environment will be made as well.


title: An Efficient Regenerative Braking System Based on Battery/Supercapacitor for Electric, Hybrid, and Plug-In Hybrid Electric Vehicles With BLDC Motor

abstract: Complementary features of batteries and supercapacitors can be effectively used in a hybrid energy storage system (HESS). The utilization of the HESS in electric vehicles (EVs) offers many advantages, such as efficient regenerative braking, battery safety, and improved vehicle acceleration. In this paper, a new regenerative braking system (RBS) is proposed for EVs with HESS and driven by brushless DC (BLDC) motor. During regenerative braking, the BLDC acts as a generator. Hence, by using an appropriate switching algorithm, the dc-link voltage is boosted and the energy is transferred to the supercapacitor or the battery through the inverter. The harvested energy can be utilized to improve the vehicle acceleration and/or keep the battery pack from deep discharging while driving uphill. To provide a reliable and smooth brake, braking force distribution is realized through an artificial neural network. Simultaneously, the braking current is adjusted by a PI controller for constant torque braking. To evaluate the performance of the proposed RBS, different simulations and experiments are carried out. The results confirm high capability of the proposed RBS.


Jumping on the Blockchain Bandwagon: Lessons of the Past and Outlook to the Future


title: Information quality benchmarks: product and service performance

abstract: Information quality (IQ) is an inexact science in terms of assessment and benchmarks. Although various aspects of quality and information have been investigated [1, 4, 6, 7, 9, 12], there is still a critical need for a methodology that assesses how well organizations develop information products and deliver information services to consumers. Benchmarks developed from such a methodology can help compare information quality across organizations, and provide a baseline for assessing IQ improvements.


title: Mid-Air Tactile Stimulation Using Indirect Laser Radiation

abstract: In this paper, we demonstrate that a laser irradiated on a thin light-absorbing elastic medium attached on the skin can elicit a tactile sensation of mechanical tap. First, we present simulation results that show laser irradiation to the elastic medium creates inner elastic waves on the basis of thermoelastic effects and these elastic waves trigger the bending deformation of the medium, which then stimulates the skin. Second, we analyze the physical properties of the associated stimulus by measuring its force profile. Third, we identify the perceptual characteristics of the stimulus in comparison to those of mechanical and electrical stimuli by means of a perceptual experiment employing dissimilarity rating. All the evidence indicates that indirect laser radiation provides a sensation of short mechanical tap. Furthermore, little individual difference was observed in the results of the perceptual experiment. To the best of our knowledge, this study is the first in reporting the feasibility of indirect laser radiation for mid-air tactile rendering.


title: Sequence-to-sequence models for punctuated transcription combining lexical and acoustic features

abstract: In this paper we present an extension of our previously described neural machine translation based system for punctuated transcription. This extension allows the system to map from per frame acoustic features to word level representations by replacing the traditional encoder in the encoder-decoder architecture with a hierarchical encoder. Furthermore, we show that a system combining lexical and acoustic features significantly outperforms systems using only a single source of features on all measured punctuation marks. The combination of lexical and acoustic features achieves a significant improvement in F-Measure of 1.5 absolute over the purely lexical neural machine translation based system.


title: Anti-forensics for frame deletion/addition in MPEG video

abstract: Due to the ease with which digital information can be altered, many digital forensic techniques have recently been developed to authenticate multimedia content. One important digital forensic result is that adding or deleting frames from an MPEG video sequence introduces a temporally distributed fingerprint into the video can be used to identify frame deletion or addition. By contrast, very little research exists into anti-forensic operations designed to make digital forgeries undetectable by forensic techniques. In this paper, we propose an anti-forensic technique capable of removing the temporal fingerprint from MPEG videos that have undergone frame addition or deletion. We demonstrate that our proposed anti-forensic technique can effectively remove this fingerprint through a series of experiments.


Childhood Convulsions in the Tropics


title: Towards adaptive health-aware charging of Li-ion batteries: A real-time predictive control approach using first-principles models

abstract: Today's charging strategies of Li-ion batteries are often designed off-line and do not balance optimal fast charging and battery lifetime. By a lack of adaptation during operation, the off-line charging strategies do not allow aging-related changes of the battery properties to be taken into account. As such, to achieve safe operation, batteries and cells are typically operated in a range that is far away from the achievable operational limits. This article presents a nonlinear predictive control strategy that allows real-time optimal health-aware charging based on detailed first-principles models. The approach balances fast charging while improving battery lifetime. By considering the on-line adjustment of battery model parameters, the approach is able to directly take aging- and environment-related changes into account.


title: Psychometric Evaluation of the EMO and the SUS in the Context of a Large-Sample Unmoderated Usability Study

abstract: This article describes the psychometric properties of the Emotional Metric Outcomes (EMO) questionnaire and the System Usability Scale (SUS) using data collected as part of a large-sample unmoderated usability study (n = 471). The EMO is a concise multifactor standardized questionnaire that provides an assessment of transaction-driven personal and relationship emotional outcomes, both positive and negative. The SUS is a well-known standardized usability questionnaire designed to assess perceived usability. In previous research, psychometric evaluation using data from a series of online surveys showed that the EMO and its component scales had high reliability and concurrent validity with loyalty and overall experience metrics but did not find the expected four-factor structure. Previous structural analyses of the SUS have had mixed results. Analysis of the EMO data from the usability study revealed the expected four-factor structure. The factor structure of the SUS appeared to be driven by item tone. The e...


title: Deep Learning towards Mobile Applications

abstract: Recent years have witnessed an explosive growth of mobile devices. Mobile devices are permeating every aspect of our daily lives. With the increasing usage of mobile devices and intelligent applications, there is a soaring demand for mobile applications with machine learning services. Inspired by the tremendous success achieved by deep learning in many machine learning tasks, it becomes a natural trend to push deep learning towards mobile applications. However, there exist many challenges to realize deep learning in mobile applications, including the contradiction between the miniature nature of mobile devices and the resource requirement of deep neural networks, the privacy and security concerns about individuals' data, and so on. To resolve these challenges, during the past few years, great leaps have been made in this area. In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning. The former two aspects cover the primary tasks of deep learning. Then, we go through our two recent applications that apply the data collected by mobile devices to inferring mood disturbance and user identification. Finally, we conclude this paper with the discussion of the future of this area.


title: Route Recommendations for Idle Taxi Drivers: Find Me the Shortest Route to a Customer!

abstract: We study the problem of route recommendation to idle taxi drivers such that the distance between the taxi and an anticipated customer request is minimized. Minimizing the distance to the next anticipated customer leads to more productivity for the taxi driver and less waiting time for the customer. To anticipate when and where future customer requests are likely to come from and accordingly recom- mend routes, we develop a route recommendation engine called MDM: Minimizing Distance through Monte Carlo Tree Search. In contrast to existing techniques, MDM employs a continuous learning platform where the underlying model to predict future customer requests is dynamically updated. Extensive experiments on real taxi data from New York and San Francisco reveal that MDM is up to 70% better than the state of the art and robust to anomalous events such as concerts, sporting events, etc.


title: Dynamic user profiles for web personalisation

abstract: We describe two algorithms for improving the mapping of interests to an ontology.We develop methods for modelling short and long-term user profiles.We introduce methods for adapting user profiles based on ongoing user behaviour.We demonstrate the effectiveness of our approach in a personalised search system. Web personalisation systems are used to enhance the user experience by providing tailor-made services based on the user's interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users' changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalisation system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilises our dynamic user profile to provide a personalised search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours.


title: Big Automotive Data: Leveraging large volumes of data for knowledge-driven product development

abstract: To be successful in the increasingly competitive consumer vehicle market, automotive manufacturers must be highly responsive to customer needs and market trends, while responding to the challenges of climate change and sustainable development. One key to achieving this is to promote knowledge-driven product development through large scale collection of data from connected vehicles, to capture customer needs and to gather performance data, diagnostic data and statistics. Since the volume of data collected from fleets of vehicles using telematics services can be very high, it is important to design the systems and frameworks in a way that is highly scalable and efficient. This can be described as a Big Data challenge in an automotive context. In this paper, we explore the opportunities of leveraging Big Automotive Data for knowledge driven product development, and we present a technological framework for capture and online analysis of data from connected vehicles.


title: Apical secretion of rat submandibular tissue kallikrein continues in the absence of external stimulation: evidence for a constitutive secretory pathway.

abstract: Sequential samples of saliva evoked by low frequency parasympathetic nerve stimulation were collected from cannulated submandibular ducts of anaesthetized rats following periods of varying length without stimulation. After such rest periods the first samples of saliva were found to contain much higher levels of tissue kallikrein (rK1) activity and protein than the ensuing samples from the same stimulation period and the latter contained levels similar to those found previously in parasympathetically evoked saliva rK1 activity in first samples increased with the length of the preceding interval, indicating that a continuous secretion of rK1 occurs from ductal cells into glandular lumina and accumulates there in the absence of stimulation or fluid secretion. The protein secretory pattern following rest pauses was unaffected by alpha-adrenergic receptor blockade and a high percentage of the proteinase activity was resistant to soya bean trypsin inhibitor, showing that the secretion was not caused by exocytosis of storage granules from ductal cells and therefore was likely to be a result of a constitutive secretion of newly synthesized enzyme. The pattern of continuous secretion into lumina detected for total protein in the absence of stimulation, suggests that other secretory proteins may also be secreted similarly but at different rates.


title: Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia

abstract: Real-time functional magnetic resonance imaging (rtfMRI) is a novel technique that has allowed subjects to achieve self-regulation of circumscribed brain regions. Despite its anticipated therapeutic benefits, there is no report on successful application of this technique in psychiatric pop- ulations. The objectives of the present study were to train schizophrenia patients to achieve volitional control of bilateral anterior insula cortex on multiple days, and to explore the effect of learned self- regulation on face emotion recognition (an extensively studied deficit in schizophrenia) and on brain network connectivity. Nine patients with schizophrenia were trained to regulate the hemodynamic response in bilateral anterior insula with contingent rtfMRI neurofeedback, through a 2-weeks train- ing. At the end of the training stage, patients performed a face emotion recognition task to explore behavioral effects of learned self-regulation. A learning effect in self-regulation was found for bilat- eral anterior insula, which persisted through the training. Following successful self-regulation, patients recognized disgust faces more accurately and happy faces less accurately. Improvements in disgust recognition were correlated with levels of self-activation of right insula. RtfMRI training led to an increase in the number of the incoming and outgoing effective connections of the anterior insula. This study shows for the first time that patients with schizophrenia can learn volitional brain regulation by rtfMRI feedback training leading to changes in the perception of emotions and modu- lations of the brain network connectivity. These findings open the door for further studies of rtfMRI


title: FPGA implementation of a binary32 floating point cube root

abstract: This paper presents the implementation of a sequential hardware core to compute a single floating point cube root compliant with the current IEEE 754-2008 standard. The design is based on Newton-Raphson recurrence, reciprocal and cube root units are implemented. Optimal performance requires two iterations for reciprocal and one for cube root units obtaining an accurate approximation of +/− 3 least significant bits. Our proposal is able to be performed up to 149 Mhz over Virtex5. The hardware cost occupies 230 Slices and 12 Dsp48s taking a latency of 19 clock cycles.


title: Automatic ultrasound-MRI registration for neurosurgery using the 2D and 3D LC2 Metric

abstract: Abstract To enable image guided neurosurgery, the alignment of pre-interventional magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is commonly required. We present two automatic image registration algorithms using the similarity measure Linear Correlation of Linear Combination (LC 2 ) to align either freehand US slices or US volumes with MRI images. Both approaches allow an automatic and robust registration, while the three dimensional method yields a significantly improved percentage of optimally aligned registrations for randomly chosen clinically relevant initializations. This study presents a detailed description of the methodology and an extensive evaluation showing an accuracy of 2.51 mm, precision of 0.85 mm and capture range of 15 mm (>95% convergence) using 14 clinical neurosurgical cases.


title: An Efficient and Fine-Grained Big Data Access Control Scheme With Privacy-Preserving Policy

abstract: How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel attribute bloom filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any linear secret-sharing schemes access policy without employing much overhead.


title: Pauling—Wheland resonance theory of benzenoid hydrocarbon

abstract: Abstract The simple resonance-theoretic wavefunction ansatz consisting of an equally weighted combination of Kekule structures is considered in application to benzenoid hydrocarbons. The resultant resonance-theoretic expectation values are (favorably) compared to the logarithm of the number of Kekule structures. Next a simple pictorial scheme for Kekule structure enumeration on catacondensed benzenoids is developed, and extended also to Pauling π-bond orders. Overall Pauling and Wheland's idea that Kekule-structure counts are of use is given some further support, for benzenoids, and the ease of computation is enhanced.


title: Design of Compact Dual Band High Directive Electromagnetic Bandgap (EBG) Resonator Antenna Using Artificial Magnetic Conductor

abstract: A compact high directive EBG resonator antenna operating in two frequency bands is described. Two major contributions to this compact design are using single layer superstrate and using artificial surface as ground plane. To obtain only the lower operating frequency band using superstrate layer is enough, but to extract the upper operating frequency band both superstrate layer and artificial surface as ground plane are necessary. Therefore, design of a superstrate to work in two frequency bands is very important. Initially, using appropriate frequency selective surface (FSS) structure with square loop elements, we design an optimum superstrate layer for each frequency band separately to achieve maximum directivity. Also, to design an artificial surface to work in the upper frequency band we use a suitable FSS structure over dielectric layer backed by PEC. Next, by using the idea of FSS structure with double square loop elements we propose FSS structure with modified double square loop elements, so that it operates in both of the desired operating frequency bands simultaneously. Finally, the simulation results for two operating frequency bands are shown to have good agreement with measurements.


title: A REVIEW OF POINT CLOUDS SEGMENTATION AND CLASSIFICATION ALGORITHMS

abstract: Abstract. Today 3D models and point clouds are very popular being currently used in several fields, shared through the internet and even accessed on mobile phones. Despite their broad availability, there is still a relevant need of methods, preferably automatic, to provide 3D data with meaningful attributes that characterize and provide significance to the objects represented in 3D. Segmentation is the process of grouping point clouds into multiple homogeneous regions with similar properties whereas classification is the step that labels these regions. The main goal of this paper is to analyse the most popular methodologies and algorithms to segment and classify 3D point clouds. Strong and weak points of the different solutions presented in literature or implemented in commercial software will be listed and shortly explained. For some algorithms, the results of the segmentation and classification is shown using real examples at different scale in the Cultural Heritage field. Finally, open issues and research topics will be discussed.


title: Bad teacher or unruly student: Can deep learning say something in Image Forensics analysis?

abstract: The pervasive availability of the Internet, coupled with the development of increasingly powerful technologies, has led digital images to be the primary source of visual information in nowadays society. However, their reliability as a true representation of reality cannot be taken for granted, due to the affordable powerful graphics editing softwares that can easily alter the original content, leaving no visual trace of any modification on the image making them potentially dangerous. This motivates developing technological solutions able to detect media manipulations without a prior knowledge or extra information regarding the given image. At the same time, the huge amount of available data has also led to tremendous advances of data-hungry learning models, which have already demonstrated in last few years to be successful in image classification. In this work we propose a deep learning approach for tampered image classification. To our best knowledge, this the first attempt to use the deep learning paradigm in an image forensic scenario. In particular, we propose a new blind deep learning approach based on Convolutional Neural Networks (CNN) able to learn invisible discriminative artifacts from manipulated images that can be exploited to automatically discriminate between forged and authentic images. The proposed approach not only detects forged images but it can be extended to localize the tampered regions within the image. This method outperforms the state-of-the-art in terms of accuracy on CASIA TIDE v2.0 dataset. The capability of automatically crafting discriminant features can lead to surprising results. For instance, detecting image compression filters used to create the dataset. This argument is also discussed within this paper.


title: Semi-supervised learning for detecting human trafficking

abstract: Human trafficking is one of the most atrocious crimes and among the challenging problems facing law enforcement which demands attention of global magnitude. In this study, we leverage textual data from the website "Backpage"- used for classified advertisement- to discern potential patterns of human trafficking activities which manifest online and identify advertisements of high interest to law enforcement. Due to the lack of ground truth, we rely on a human analyst from law enforcement, for hand-labeling a small portion of the crawled data. We extend the existing Laplacian SVM and present S3VM-R, by adding a regularization term to exploit exogenous information embedded in our feature space in favor of the task at hand. We train the proposed method using labeled and unlabeled data and evaluate it on a fraction of the unlabeled data, herein referred to as unseen data, with our expert's further verification. Results from comparisons between our method and other semi-supervised and supervised approaches on the labeled data demonstrate that our learner is effective in identifying advertisements of high interest to law enforcement


title: The BIAS map: Behaviors from intergroup affect and stereotypes.

abstract: In the present research, consisting of 2 correlational studies (N = 616) including a representative U.S. sample and 2 experiments (N = 350), the authors investigated how stereotypes and emotions shape behavioral tendencies toward groups, offering convergent support for the behaviors from intergroup affect and stereotypes (BIAS) map framework. Warmth stereotypes determine active behavioral tendencies, attenuating active harm (harassing) and eliciting active facilitation (helping). Competence stereotypes determine passive behavioral tendencies, attenuating passive harm (neglecting) and eliciting passive facilitation (associating). Admired groups (warm, competent) elicit both facilitation tendencies; hated groups (cold, incompetent) elicit both harm tendencies. Envied groups (competent, cold) elicit passive facilitation but active harm; pitied groups (warm, incompetent) elicit active facilitation but passive harm. Emotions predict behavioral tendencies more strongly than stereotypes do and usually mediate stereotype-to-behavioral-tendency links.


title: Greater trochanteric fracture with occult intertrochanteric extension

abstract: Proximal femoral fractures are frequently encountered in the emergency department (ED). Prompt diagnosis is paramount as delay will exacerbate the already poor outcomes associated with these injuries. In cases where radiography is negative but clinical suspicion remains high, magnetic resonance imaging (MRI) is the study of choice as it has the capability to depict fractures which are occult on other imaging modalities. Awareness of a particular subset of proximal femoral fractures, namely greater trochanteric fractures, is vital for both radiologists and clinicians since it has been well documented that they invariably have an intertrochanteric component which may require surgical management. The detection of intertrochanteric or cervical extension of greater trochanteric fractures has been described utilizing MRI but is underestimated with both computed tomography (CT) and bone scan. Therefore, if MRI is unavailable or contraindicated, the diagnosis of an isolated greater trochanteric fracture should be met with caution. The importance of avoiding this potential pitfall is demonstrated in the following case of an elderly woman with hip pain and CT demonstrating an isolated greater trochanteric fracture who subsequently returned to the ED with a displaced intertrochanteric fracture.


title: Measuring Security Risk of Networks Using Attack Graphs

abstract: Today’s computer systems face sophisticated attackers who combine multiple vulnerabilities to penetrate networks with devastating impact. The overall security of a network cannot be determined by simply counting the number of vulnerabilities. To accurately assess the security of networked systems, one must understand how vulnerabilities can be combined to stage an attack. We model such composition of vulnerabilities through attack graphs. By simulating incremental network penetration, and propagating attack likelihoods, we measure the overall security of a networked system. From this, we score risk mitigation options in terms of maximizing security and minimizing cost. We populate our attack graph models from live network scans and databases that have knowledge about properties such as vulnerability likelihood, impact, severity, and ease of exploitation. Our exible model can be used to quantify overall security of networked systems, and to study cost/benet tradeos for analyzing return on security investment.


title: State of the Art in Edge and Trail Bundling Techniques

abstract: Bundling techniques provide a visual simplification of a graph drawing or trail set, by spatially grouping similar graph edges or trails. This way, the structure of the visualization becomes simpler and thereby easier to comprehend in terms of assessing relations that are encoded by such paths, such as finding groups of strongly interrelated nodes in a graph, finding connections between spatial regions on a map linked by a number of vehicle trails, or discerning the motion structure of a set of objects by analyzing their paths. In this state of the art report, we aim to improve the understanding of graph and trail bundling via the following main contributions. First, we propose a data-based taxonomy that organizes bundling methods on the type of data they work on (graphs vs trails, which we refer to as paths). Based on a formal definition of path bundling, we propose a generic framework that describes the typical steps of all bundling algorithms in terms of high-level operations and show how existing method classes implement these steps. Next, we propose a description of tasks that bundling aims to address. Finally, we provide a wide set of example applications of bundling techniques and relate these to the above-mentioned taxonomies. Through these contributions, we aim to help both researchers and users to understand the bundling landscape as well as its technicalities.


title: Effective arabic-english cross-language information retrieval via machine-readable dictionaries and machine translation

abstract: In Cross-Language Information Retrieval (CLIR), queries in one language retrieve relevant documents in other languages Machine-Readable Dictionary (MRD) and Machine Translation (MT) are important resources for query translation in CLIR. We investigate MT and MRD to Arabic-English CLIR. The translation ambiguity associated with these resources is the key problem. We present three methods of query translation using a bilingual dictionary for Arabic-English CLIR. First, we present the Every-Match (EM) method. This method yields ambiguous translations since many extraneous terms are added to the original query. To disambiguate the query translation, we present the First-Match (FM) method that considers the first match in the dictionary as the candidate term. Finally, we present the Two-Phase (TP) method. We show that good retrieval effectiveness can be achieved without complex resources using the Two-Phase method for Arabic-English CLIR. We also empirically evaluate the effectiveness of the MT-based method using short, medium, and long queries from TREC. The effects of the query length on the quality of the MT-based CLIR are investigated.


Improved performance evaluation of speech event detectors.


title: Reactively matched 3-stage C-X-Ku band GaN MMIC power amplifier

abstract: A 3-stage wideband power amplifier (PA) using a 0.15 pm gallium nitride (GaN) monolithic microwave integrated circuit (MMIC) process from NRC is designed, fabricated, and measured. After characterization of the high electron mobility transistor (HEMT), a non-linear model was created from the measured data for use in the design. The reactively matched 3.8 mm × 1.8 mm PA also uses resistive elements for gain compensation and circuit stability. Measurements at 20 dBm source power show 35–38 dBm output power and 10–18% PAE over a 6 to 17 GHz bandwidth. These results demonstrate the highest output power per die area for a 3-stage GaN MMIC PA of this bandwidth in this power range.


title: A visual navigation system for autonomous flight of micro air vehicles

abstract: Many applications of unmanned aerial vehicles (UAVs) require the capability to navigate to some goal and to perform precise and safe landing. In this paper, we present a visual navigation system as an alternative pose estimation method for environments and situations in which GPS is unavailable. The developed visual odometer is an incremental procedure that estimates the vehicle's ego-motion by extracting and tracking visual features, using an onboard camera. For more robustness and accuracy, the visual estimates are fused with measurements from an Inertial Measurement Unit (IMU) and a Pressure Sensor Altimeter (PSA) in order to provide accurate estimates of the vehicle's height, velocity and position relative to a given location. These estimates are then exploited by a nonlinear hierarchical controller for achieving various navigation tasks such as take-off, landing, hovering, target tracking, etc. In addition to the odometer description, the paper presents validation results from autonomous flights using a small quadrotor UAV.


title: Clinical and Functional Outcomes After 2 Years in the Early Detection and Intervention for the Prevention of Psychosis Multisite Effectiveness Trial

abstract: OBJECTIVE ::: To test effectiveness of the Early Detection, Intervention, and Prevention of Psychosis Program in preventing the onset of severe psychosis and improving functioning in a national sample of at-risk youth. ::: ::: ::: METHODS ::: In a risk-based allocation study design, 337 youth (age 12-25) at risk of psychosis were assigned to treatment groups based on severity of positive symptoms. Those at clinically higher risk (CHR) or having an early first episode of psychosis (EFEP) were assigned to receive Family-aided Assertive Community Treatment (FACT); those at clinically lower risk (CLR) were assigned to receive community care. Between-groups differences on outcome variables were adjusted statistically according to regression-discontinuity procedures and evaluated using the Global Test Procedure that combined all symptom and functional measures. ::: ::: ::: RESULTS ::: A total of 337 young people (mean age: 16.6) were assigned to the treatment group (CHR + EFEP, n = 250) or comparison group (CLR, n = 87). On the primary variable, positive symptoms, after 2 years FACT, were superior to community care (2 df, p < .0001) for both CHR (p = .0034) and EFEP (p < .0001) subgroups. Rates of conversion (6.3% CHR vs 2.3% CLR) and first negative event (25% CHR vs 22% CLR) were low but did not differ. FACT was superior in the Global Test (p = .0007; p = .024 for CHR and p = .0002 for EFEP, vs CLR) and in improvement in participation in work and school (p = .025). ::: ::: ::: CONCLUSION ::: FACT is effective in improving positive, negative, disorganized and general symptoms, Global Assessment of Functioning, work and school participation and global outcome in youth at risk for, or experiencing very early, psychosis.


title: Voice operated home automation system based on Kinect sensor

abstract: Control of home appliances using smart technologies which is also known as home automation is a popular (industrial) research area. Many of these systems use remote controlling such as IR sensing, networking, Arduino programing etc. whereas our system uses voice controlling. Proposed system is designed to enable centralize controlling over distant household appliances. In this prototype, we built an interface demonstrating voice control using Kinect V2 as voice receiver and trained a computer system to identify set of voice commands. Then a circuit was constructed using Arduino and light bulbs that mimics actual appliances. We measured accuracy of our system. It is more than 95% when the distance between user and Kinect sensor is 4 m and when there is about 53 dB noise. Hence, the proposed method can be efficiently used with a Kinect V2 for voice controlling.


INVERSE DYNAMICS OF THE SPATIAL 3-RPS PARALLEL ROBOT


title: The role of actively open-minded thinking in information acquisition , accuracy , and calibration

abstract: Errors in estimating and forecasting often result from the failure to collect and consider enough relevant information. We examine whether attributes associated with persistence in information acquisition can predict performance in an estimation task. We focus on actively open-minded thinking (AOT), need for cognition, grit, and the tendency to maximize or satisfice when making decisions. In three studies, participants made estimates and predictions of uncertain quantities, with varying levels of control over the amount of information they could collect before estimating. Only AOT predicted performance. This relationship was mediated by information acquisition: AOT predicted the tendency to collect information, and information acquisition predicted performance. To the extent that available information is predictive of future outcomes, actively open-minded thinkers are more likely than others to make accurate forecasts.


title: Classification, diagnostic imaging, and imaging characterization of a lumbar herniated disk.

abstract: The absence of universal nomenclature standardization with respect to the definition of a disk herniation and its different categories, especially regarding type and location, is still a major problem that will only be overcome when major national or international scientific societies join efforts to support a particular scheme. Meanwhile, it is important to realize that the two models that are currently most used are based on a different [figure: see text] perspective. Trying to straddle the two by opposing, for instance, bulging disk and herniation is doomed to failure because this exercise defies formal logic. MR imaging is currently the most accurate noninvasive imaging modality to diagnose a disk herniation and to determine its exact location. The determination of some pathoanatomic characteristics of herniated disks (type and composition) may require the use of CT, diskography, or CT diskography.


title: Taxonomy of Machine Learning Algorithms to classify real- time Interactive applications

abstract: the needs of Internet applications QoS guarantee increased the demand of internet traffic classification, especially for interactive real time applications. Therefore, several classification methods were developed. Machine Learning (ML) classification is one of the most modern techniques, which solves the problem of traditional port base method. This paper compared experimentally the accuracy of ten ML algorithms, that when it's used to classify interactive applications. The technique applied by collecting of real data from UTM. The result shows that Tree.RandomForest algorithm provided optimal results of 99.8% accuracy, compared with other algorithms.


title: The effect of spin on the flight of a baseball

abstract: New measurements are presented of the lift on a spinning baseball. The experiment utilizes a pitching machine to project the baseball horizontally; a high-speed motion capture system to measure the initial velocity and angular velocity and to track the trajectory over ~5 m of flight; and a ruler to measure the total distance traversed by the ball. Speeds in the range v=50-110 mph and spin rates (topspin or backspin)in the range omega=1500-4500 rpm were utilized, corresponding to Reynold's numbers Re=(1.1-2.4) x 10^5 and spin parameters R*omega/v=0.1-0.6. Standard least-squares fitting procedures were used to extract the initial parameters of the trajectory and to determine the lift and drag coefficients. Comparison is made with previous measurements or parametrizations of the lift and drag coefficients, and implications for the effect of spin on the flight of a long fly ball are discussed.


title: UHF Deployable Helical Antennas for CubeSats

abstract: The design process and the deployment mechanism of a quadrifilar helix antenna (QHA) and a conical log spiral antenna (CLSA) are presented. The two antennas are proposed to operate in the UHF frequency band. They are composed of conductors that are embedded and supported by innovative structural techniques. This allows efficient folding, packaging, and deployment once in space. The conductors in the QHA are composed of beryllium copper and are supported by helical arms of S_2 glass fiber reinforced epoxy. The CLSA, on the other hand, has conductors that are made out of a mesh of phosphor bronze and incorporated inside thin insulators composed of continuous fiber composites. The new aspects of these designs lie in their structures and deployment mechanisms. The deployment mechanisms for both antennas include helical pantograph and origami patterns such as Z-folding configurations. Both antennas are fabricated and tested for both deployment and radiation performance. A comparison is executed between both designs, and their potential deployment possibilities from CubeSats are also investigated.


title: Machine learning approaches to network anomaly detection

abstract: Networks of various kinds often experience anomalous behaviour. Examples include attacks or large data transfers in IP networks, presence of intruders in distributed video surveillance systems, and an automobile accident or an untimely congestion in a road network. Machine learning techniques enable the development of anomaly detection algorithms that are non-parametric, adaptive to changes in the characteristics of normal behaviour in the relevant network, and portable across applications. In this paper we use two different datasets, pictures of a highway in Quebec taken by a network of webcams and IP traffic statistics from the Abilene network, as examples in demonstrating the applicability of two machine learning algorithms to network anomaly detection. We investigate the use of the block-based One-Class Neighbour Machine and the recursive Kernel-based Online Anomaly Detection algorithms.


Traffic Sign Detection and Recognition Using Feature Based and OCR Method


title: The p150-Glued Ssm4p regulates microtubular dynamics and nuclear movement in fission yeast

abstract: During vegetative growth of the fission yeast Schizosaccharomyces pombe, microtubules nucleate from multiple microtubule organising centres (MTOCs) close to the nucleus, polymerising until they reach the end of the cell and then shrinking back to the cell centre. In response to mating pheromone, S. pombe undergoes a morphological switch from a vegetative to a shmooing growth pattern. The switch in growth mode is paralleled by a switch in microtubular dynamics. Microtubules nucleate mostly from a single MTOC and pull on the ends of the cell to move the nucleus back and forth. This movement continues after cellular and nuclear fusion in the zygote and is important to ensure correct chromosome pairing, recombination and segregation during meiosis. Here we show that Ssm4p, a p150-Glued protein, is induced specifically in response to pheromone and is required for this nuclear movement. Ssm4p is associated with the cytoplasmic dynein complex and together with the CLIP-170 homologue Tip1p regulates dynein heavy chain localisation. We also show that Ssm4p collaborates with Tip1p in establishing the shmooing microtubular array.


title: Semi-automated processing and routing within indoor structures for emergency response applications

abstract: In this work, we propose new automation tools to process 2D building geometry data for effective communication ::: and timely response to critical events in commercial buildings. Given the scale and complexity of commercial ::: buildings, robust and visually rich tools are needed during an emergency. Our data processing pipeline consists of ::: three major components, (1) adjacency graph construction, representing spatial relationships within a building ::: (between hallways, offices, stairways, elevators), (2) identification of elements involved in evacuation routes ::: (hallways, stairways), (3) 3D building network construction, by connecting the oor elements via stairways and ::: elevators. We have used these tools to process a cluster of five academic buildings. Our automation tools (despite ::: some needed manual processing) show a significant advantage over manual processing (a few minutes vs. 2-4 ::: hours). Designed as a client-server model, our system supports analytical capabilities to determine dynamic ::: routing within a building under constraints(parts of the building blocked during emergencies, for instance). ::: Visualization capabilities are provided for easy interaction with the system, on both desktop (command post) ::: stations as well as mobile hand-held devices, simulating a command post-responder scenario.


A New Decoding Algorithm for Statistical Machine Translation: Design and Implementation


title: Finding Rising Stars in Co-Author Networks via Weighted Mutual Influence

abstract: Finding rising stars is a challenging and interesting task which is being investigated recently in co-author networks. Rising stars are authors who have a low research profile in the start of their career but may become experts in the future. This paper introduces a new method Weighted Mutual Influence Rank (WMIRank) for finding rising stars. WMIRank exploits influence of co-authors' citations, order of appearance and publication venues. Comprehensive experiments are performed to analyze the performance of WMIRank in comparison to baseline methods, which have ignored weighted mutual influence. AMiner data for years 1995-2000 is used for experiments. List of top 30 authors as per proposed and baseline methods are compared for their average number of papers, average number of citations and achievements. Experimental results provide convincing evidence of the effectiveness of the investigated weighted mutual influence.


title: The social consumption of information and communication technologies (ICTs): insights from research on the appropriation and consumption of new ICTs in the domestic environment

abstract: This paper reviews some of the research on the use and consumption of technology in the domestic space that may not be familiar to members of the usability and design community, especially those traditionally focused on design for the workplace. The paper also introduces original research findings and conclusions from a qualitative study of the adoption and use of new ICTs conducted in a number of households and workplaces in the late 1990s, a time when many new ICT products and services were becoming available, and more traditional media and communication products were becoming widespread in homes. The paper does not make recommendations about the type of products and services to be developed, nor those that may be successful, although it is quite possible to draw this type of conclusion from it. Instead it looks at some rather broader trends in the way that we engage with technologies, rejecting and adopting them, and the importance of our domestic space and social networks to the way we will engage with future technologies aimed at the home space.


title: A hybrid DC-DC converter for sub-microwatt sub-1V implantable applications

abstract: A DC-DC converter for sub-microwatt sub-1V loads is presented for efficient supply regulation of implantable devices powered by thin-film batteries. Using a hybrid, switched-capacitor architecture, it provides 444mV of DC output from a 3.6V DC source. Ultra-low power clock generation, level conversion, and linear regulation, combined with a unique startup circuit, enable a peak efficiency of 56% (4.6X ideal linear regulator) for loads from 5nA to 500nA. Fabricated in 130nm CMOS, it occupies 0.26mm2.


title: Bursty event detection from microblog: a distributed and incremental approach

abstract: As a new form of social media, microblogs e.g., Twitter and Weibo are playing an important role in people's daily life. With the rise in popularity and size of microblogs, there is a need for distributed approaches that can detect bursty event with low latency from the short-text data stream. In this paper, we propose a distributed and incremental temporal topic model for microblogs called Bursty Event dEtection BEE+. BEE+ is able to detect bursty events from short-text dataset and model the temporal information. And BEE+ processes the post-stream incrementally to track the topic drifting of events over time. Therefore, the latent semantic indices are preserved from one time period to the next. In order to achieve real-time processing, we design a distributed execution framework based on Spark engine. To verify its ability to detect bursty event, we conduct experiments on a Weibo dataset of 6,360,125 posts. The results show that BEE+ can outperform the baselines for detecting the meaningful bursty events and track the topic drifting. Copyright © 2015 John Wiley & Sons, Ltd.


The Unexpected Consequences of Asymmetric Competition . An Application to the Pharmaceutical Industry ∗


title: The Effects of Anti-Vaccine Conspiracy Theories on Vaccination Intentions

abstract: The current studies investigated the potential impact of anti-vaccine conspiracy beliefs, and exposure to anti-vaccine conspiracy theories, on vaccination intentions. In Study 1, British parents completed a questionnaire measuring beliefs in anti-vaccine conspiracy theories and the likelihood that they would have a fictitious child vaccinated. Results revealed a significant negative relationship between anti-vaccine conspiracy beliefs and vaccination intentions. This effect was mediated by the perceived dangers of vaccines, and feelings of powerlessness, disillusionment and mistrust in authorities. In Study 2, participants were exposed to information that either supported or refuted anti-vaccine conspiracy theories, or a control condition. Results revealed that participants who had been exposed to material supporting anti-vaccine conspiracy theories showed less intention to vaccinate than those in the anti-conspiracy condition or controls. This effect was mediated by the same variables as in Study 1. These findings point to the potentially detrimental consequences of anti-vaccine conspiracy theories, and highlight their potential role in shaping health-related behaviors.


title: A Variational Observation Model of 3D Object for Probabilistic Semantic SLAM

abstract: We present a Bayesian object observation model for complete probabilistic semantic SLAM. Recent studies on object detection and feature extraction have become important for scene understanding and 3D mapping. However, 3D shape of the object is too complex to formulate the probabilistic observation model; therefore, performing the Bayesian inference of the object-oriented features as well as their pose is less considered. Besides, when the robot equipped with an RGB mono camera only observes the projected single view of an object, a significant amount of the 3D shape information is abandoned. Due to these limitations, semantic SLAM and viewpoint-independent loop closure using volumetric 3D object shape is challenging. In order to enable the complete formulation of probabilistic semantic SLAM, we approximate the observation model of a 3D object with a tractable distribution. We also estimate the variational likelihood from the 2D image of the object to exploit its observed single view. In order to evaluate the proposed method, we perform pose and feature estimation, and demonstrate that the automatic loop closure works seamlessly without additional loop detector in various environments.


title: High-Precision Depth Estimation with the 3D LiDAR and Stereo Fusion

abstract: We present a deep convolutional neural network (CNN) architecture for high-precision depth estimation by jointly utilizing sparse 3D LiDAR and dense stereo depth information. In this network, the complementary characteristics of sparse 3D LiDAR and dense stereo depth are simultaneously encoded in a boosting manner. Tailored to the LiDAR and stereo fusion problem, the proposed network differs from previous CNNs in the incorporation of a compact convolution module, which can be deployed with the constraints of mobile devices. As training data for the LiDAR and stereo fusion is rather limited, we introduce a simple yet effective approach for reproducing the raw KITTI dataset. The raw LiDAR scans are augmented by adapting an off-the-shelf stereo algorithm and a confidence measure. We evaluate the proposed network on the KITTI benchmark and data collected by our multi-sensor acquisition system. Experiments demonstrate that the proposed network generalizes across datasets and is significantly more accurate than various baseline approaches.


title: Rule Induction and Reasoning over Knowledge Graphs

abstract: Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from KGs is a crucial task for KG completion, cleaning and curation. This tutorial presents state-of-the-art rule induction methods, recent advances, research opportunities as well as open challenges along this avenue. We put a particular emphasis on the problems of learning exception-enriched rules from highly biased and incomplete data. Finally, we discuss possible extensions of classical rule induction techniques to account for unstructured resources (e.g., text) along with the structured ones.


title: Determinants of satisfaction and continuance intention towards self‐service technologies

abstract: Purpose – Self‐service technologies (SSTs) allow customers to offer their own service encounters via the interaction of electronic service interfaces or machines rather than by interacting with a firm's service personnel. This paper aims to develop an integrated model designed to predict and explain an individual's continuous use of SSTs based on the concepts of technology readiness (TR), technology acceptance model (TAM), and theory of planned behavior (TPB).Design/methodology/approach – The participants are from 481 SST users as the sample finally. Structural equation modeling is applied to demonstrate the stability of the proposed model and the results of hypotheses testing.Findings – This paper finds that consumers' satisfaction significantly influences continuance intention, while the perceived usefulness, perceived ease of use, subjective norm (SN), and perceived behavioral control (PBC) simultaneously influence satisfaction. Optimism and innovativeness are also the significant motivators of satisfa...


title: Selective Dissolution of Surface Nickel Close to Platinum in PtNi Nanocatalyst toward Oxygen Reduction Reaction

abstract: We report new insights in dissolution mechanisms of nickel in PtNi bimetallic nanoparticles (NPs) to develop active and durable oxygen reduction catalysts for fuel cells. Leaching out nickel by using acidic aqueous solution has been regarded as one of the most efficient chemical treatments to obtain a platinum-rich surface, which has shown both increased activity and stability during oxygen reduction reaction. In this work, we introduce a new approach using hydroquinone dissolved in ethanol to leach out nickel from PtNi NPs. The degree of alloying level is followed by X-ray photoelectron and absorption spectroscopies. Electrochemical measurements including potential cycling under oxygen reduction conditions allow us to investigate the dissolution behavior of nickel, depending on the chemical systems, and assess the relationship with electrochemical activity and stability. From comparative studies regarding the traditional acid treatment and the hydroquinone method introduced in this article, it is reveale...


title: From early virtual garment simulation to interactive fashion design

abstract: Virtual garment design and simulation involves a combination of a large range of techniques, involving mechanical simulation, collision detection, and user interface techniques for creating garments. Here, we perform an extensive review of the evolution of these techniques made in the last decade to bring virtual garments to the reach of computer applications not only aimed at graphics, but also at CAD techniques for the garment industry. As a result of the advances in the developments of virtual garment simulation technologies, we then detail a framework which fits the needs of the garment industry of virtual garment design and prototyping, concentrating on interactive design, simulation and visualization features. The framework integrates innovative tools aimed towards efficiency and quality in the process of garment design and prototyping, taking advantage of state-of-the-art algorithms from the field of mechanical simulation, animation and rendering.


title: An interference suppression strategy for satellite-borne AIS

abstract: This paper focuses on the interference suppression for satellite-borne Marine Automatic Identification System (AIS). Based on the analysis for the two kinds of interferences, Doppler frequency diversity method is proposed to suppress the interference by making use of known satellite orbit. Besides, an iterative reception method is proposed to overcome the Doppler frequency ambiguity existing in some special cases for the first conflict. Both the analysis and simulation show proposed joint interference suppression strategy is powerful for suppressing interference and achieves higher packet detection probability, increasing by 10%–20% compared to the traditional method.


title: Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

abstract: In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition. Specifically, we design a new model called Deep Reconstruction-Classification Network (DRCN), which jointly learns a shared encoding representation for two tasks: (i) supervised classification of labeled source data, and (ii) unsupervised reconstruction of unlabeled target data. In this way, the learnt representation not only preserves discriminability, but also encodes useful information from the target domain. Our new DRCN model can be optimized by using backpropagation similarly as the standard neural networks.


title: Graph-based submodular selection for extractive summarization

abstract: We propose a novel approach for unsupervised extractive summarization. Our approach builds a semantic graph for the document to be summarized. Summary extraction is then formulated as optimizing submodular functions defined on the semantic graph. The optimization is theoretically guaranteed to be near-optimal under the framework of submodularity. Extensive experiments on the ICSI meeting summarization task on both human transcripts and automatic speech recognition (ASR) outputs show that the graph-based submodular selection approach consistently outperforms the maximum marginal relevance (MMR) approach, a concept-based approach using integer linear programming (ILP), and a recursive graph-based ranking algorithm using Google's PageRank.


title: H-Tree: An efficient index structure for event matching in publish/subscribe systems

abstract: Content-based publish/subscribe systems have been employed to deal with complex distributed information flows in many applications. It is well recognized that event matching is a fundamental component of such large-scale systems. Event matching is to search in a space which is composed of all subscriptions. As the scale and complexity of a system grow, the efficiencies of event matching become more critical to the system performance. Most existing methods suffer performance degradation problem when a system has both large number of subscriptions and large number of constraints. In this paper, we present H-Tree (Hash Tree), a highly efficient index structure for event matching. H-Tree is a hash table in nature which is a combination of hash lists and hash chaining. A hash list is realized on an indexed attribute by dividing the attribute's value domain into cells. Multiple hash lists are chained into a hash tree. The basic idea behind H-Tree is that matching efficiencies are improved when the search space is substantially reduced by pruning most of the impossible subscriptions. We have implemented H-Tree and conducted extensive experiments in different settings. Experimental results show that H-Tree outperforms its counterparts to a large degree. In particular, the matching time is faster by three order of magnitude than its counterparts when both the number of subscriptions and the number of constraints are large.


title: A New Framework for Distributed Submodular Maximization

abstract: A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algorithms for these problems. However, these results suffer from high number of rounds, suboptimal approximation ratios, or both. We develop a framework for bringing existing algorithms in the sequential setting to the distributed setting, achieving near optimal approximation ratios for many settings in only a constant number of MapReduce rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint.


title: A Patch-Based Saliency Detection Method for Assessing the Visual Privacy Levels of Objects in Photos

abstract: Photo privacy protection has recently received increasing attention from the public. However, the overprotection of photo privacy by hiding too much visual information can make photos meaningless. To avoid this, visual information with different degrees of privacy sensitivity can be filtered out using various image-processing techniques. Objects in a photo usually contain visual information that can potentially reveal private information; this potential depends on both the visual saliency of the objects and on the specific categories to which the objects belong. In this paper, we aim to quantitatively evaluate the influence of visual saliency information on privacy and objectively evaluate the levels of visual privacy that objects contain. Meeting this objective faces two challenges: 1) determining a method of effectively detecting generic objects in a photo for the extraction of saliency information and 2) determining a scientific method for assessing the visual private information contained in objects. To cope with these challenges, we first propose a hierarchical saliency detection method that combines a patch-based saliency detection strategy with an objectness estimation strategy to effectively locate salient objects and obtain the saliency information of each object. The proposed method results in a small set of class-independent locations with high quality and a mean average best overlap score of 0.627 at 1150 locations, which is superior to the score of other saliency detection methods. Second, we build a computational privacy assessment system to scientifically calculate and rank the privacy risks of objects in a photo by creating an improved risk matrix and using the Borda count method. The proposed computational privacy assessment method matches human evaluations to a relatively high degree.


title: A collaborative cyber incident management system for European interconnected critical infrastructures

abstract: Abstract Today's Industrial Control Systems (ICSs) operating in critical infrastructures (CIs) are becoming increasingly complex; moreover, they are extensively interconnected with corporate information systems for cost-efficient monitoring, management and maintenance. This exposes ICSs to modern advanced cyber threats. Existing security solutions try to prevent, detect, and react to cyber threats by employing security measures that typically do not cross the organization's boundaries. However, novel targeted multi-stage attacks such as Advanced Persistent Threats (APTs) take advantage of the interdependency between organizations. By exploiting vulnerabilities of various systems, APT campaigns intrude several organizations using them as stepping stones to reach the target infrastructure. A coordinated effort to timely reveal such attacks, and promptly deploy mitigation measures is therefore required. Organizations need to cooperatively exchange security-relevant information to obtain a broader knowledge on the current cyber threat landscape and subsequently obtain new insight into their infrastructures and timely react if necessary. Cyber security operation centers (SOCs), as proposed by the European NIS directive, are being established worldwide to achieve this goal. CI providers are asked to report to the responsible SOCs about security issues revealed in their networks. National SOCs correlate all the gathered data, analyze it and eventually provide support and mitigation strategies to the affiliated organizations. Although many of these tasks can be automated, human involvement is still necessary to enable SOCs to adequately take decisions on occurring incidents and quickly implement counteractions. In this paper we present a collaborative approach to cyber incident information management for gaining situational awareness on interconnected European CIs. We provide a scenario and an illustrative use-case for our approach; we propose a system architecture for a National SOC, defining the functional components and interfaces it comprises. We further describe the functionalities provided by the different system components to support SOC operators in performing incident management tasks.


title: A Combined Data Mining Approach for DDoS Attack Detection

abstract: Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not provide effective defense against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. It is necessary to analyze the fundamental features of DDoS attacks because these attacks can easily vary the used port/protocol, or operation method. In this paper, we propose a combined data mining approach for modeling the traffic pattern of normal and diverse attacks. This approach uses the automatic feature selection mechanism for selecting the important attributes. And the classifier is built with the theoretically selected attribute through the neural network. And then, our experimental results show that our approach can provide the best performance on the real network, in comparison with that by heuristic feature selection and any other single data mining approaches.