MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE 1 Running Head : MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE The Cross-National Invariance Properties of a New Scale to Measure 19 Basic Human Values . A Test across Eight Countries

Several studies that measured basic human values across countries with the Portrait Values Questionnaire (PVQ-21) reported violations of measurement invariance. Such violations may hinder meaningful cross-cultural research on human values because value scores may not be comparable. Schwartz et al. (2012) proposed a refined value theory and a new instrument (PVQ-5X) to measure 19 more narrowly defined values. We tested the measurement invariance of this instrument across eight countries. Configural and metric invariance were established for all values across almost all countries. Scalar invariance was supported across nearly all countries for 10 values. The analyses revealed that the cross-country invariance properties of the values measured with the PVQ-5X are substantially better than those measured with the PVQ-21. DOI: https://doi.org/10.1177/0022022114527348 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-94906 Accepted Version Originally published at: Cieciuch, Jan; Davidov, Eldad; Vecchione, Michele; Beierlein, Constanze; Schwartz, Shalom H (2014). The Cross-National Invariance Properties of a New Scale to Measure 19 Basic Human Values: A Test Across Eight Countries. Journal of Cross-Cultural Psychology, 45(5):764-776. DOI: https://doi.org/10.1177/0022022114527348 MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE Jan Cieciuch Institute of Sociology, University of Zurich, Switzerland and University of Finance and Management, Warsaw, Poland Eldad Davidov Institute of Sociology, University of Zurich, Switzerland Michele Vecchione “Sapienza” University of Rome, Italy Constanze Beierlein Leibniz-Institute for the Social Sciences, GESIS, Mannheim, Germany Shalom H. Schwartz The Hebrew University of Jerusalem, Israel and National Research University-Higher School of Economics, Moscow This is a pre-copy-editing, author-produced PDF of an article accepted for publication in the Journal of Cross-Cultural Psychology following peer review. It was first published online in this journal on March 19, 2014. The definitive publisher-authenticated version is available online at: http://jcc.sagepub.com/content/early/2014/03/14/0022022114527348 or under doi: 10.1177/0022022114527348 MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE 1 Running Head: MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE The Cross-National Invariance Properties of a New Scale to Measure 19 Basic Human Values. A Test across Eight Countries Jan Cieciuch jancieciuch@gmail.com Institute of Sociology, University of Zurich, Switzerland and University of Finance and Management, Warsaw, Poland Contact address: Andreasstrasse 15, CH-8050 Zurich, Switzerland Tel: +41 44 635 23 22, Fax: +41 44 635 23 99 Eldad Davidov Institute of Sociology, University of Zurich, Switzerland Michele Vecchione “Sapienza” University of Rome, Italy Constanze Beierlein Leibniz-Institute for the Social Sciences, GESIS, Mannheim, Germany Shalom H. Schwartz The Hebrew University of Jerusalem, Israel and National Research University-Higher School of Economics, Moscow MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE 2 Author Note The work of the first and second authors on this paper was supported by the Scientific Exchange Program (Switzerland) and the Research Priority Program ‘Social Networks’, University of Zurich. The work of the first author was partially supported by Grant DEC2011/01/D/HS6/04077 from the Polish National Science Centre. The work of the fifth author on this paper was partly supported by the Higher School of Economics (HSE) Basic Research Program (International Laboratory of Socio-Cultural Research). The second author would like to thank the EUROLAB, GESIS, Cologne, for their hospitality during work on this paper. The authors would like to thank Lisa Trierweiler for the English proof of the manuscript. MEASUREMENT INVARIANCE OF A REFINED VALUES SCALE 3 Abstract Several studies that measured basic human values across countries with the Portrait Values Questionnaire (PVQ-21) reported violations of measurement invariance. Such violations may hinder meaningful cross-cultural research on human values because value scores may not be comparable. Schwartz et al. (2012) proposed a refined value theory and a new instrument (PVQ-5X) to measure 19 more narrowly defined values. We tested the measurement invariance of this instrument across eight countries. Configural and metric invariance were established for all values across almost all countries. Scalar invariance was supported across nearly all countries for 10 values. The analyses revealed that the cross-country invariance properties of the values measured with the PVQ-5X are substantially better than those measured with the PVQ-21.Several studies that measured basic human values across countries with the Portrait Values Questionnaire (PVQ-21) reported violations of measurement invariance. Such violations may hinder meaningful cross-cultural research on human values because value scores may not be comparable. Schwartz et al. (2012) proposed a refined value theory and a new instrument (PVQ-5X) to measure 19 more narrowly defined values. We tested the measurement invariance of this instrument across eight countries. Configural and metric invariance were established for all values across almost all countries. Scalar invariance was supported across nearly all countries for 10 values. The analyses revealed that the cross-country invariance properties of the values measured with the PVQ-5X are substantially better than those measured with the PVQ-21.


Design and performance analysis of radial flux C-core switched reluctance motor for in-wheel electrical vehicle application

A 12/15 pole dual air-gap single rotor configuration with C-core radial flux switched reluctance motor (RFSRM) is proposed as a potential candidate for in-wheel electrical vehicle application with removable wheel/rotor, without disturbing the stator or stator winding. The proposed topology offers effective stator and rotor contact area without increasing the diameter of electrical motor and has smaller flux path compared to the conventional RFSRM. This arrangement offers good torque with low excitation current. It is easy to wound the winding at stator core compared to toothed or segmented type axial field SRM. In this paper phase inductance and torque are calculated analytically and compared with finite element analysis (FEA) results.


Demand-Side Management via Distributed Energy Generation and Storage Optimization

Demand-side management, together with the integration of distributed energy generation and storage, are considered increasingly essential elements for implementing the smart grid concept and balancing massive energy production from renewable sources. We focus on a smart grid in which the demand-side comprises traditional users as well as users owning some kind of distributed energy sources and/or energy storage devices. By means of a day-ahead optimization process regulated by an independent central unit, the latter users intend to reduce their monetary energy expense by producing or storing energy rather than just purchasing their energy needs from the grid. In this paper, we formulate the resulting grid optimization problem as a noncooperative game and analyze the existence of optimal strategies. Furthermore, we present a distributed algorithm to be run on the users' smart meters, which provides the optimal production and/or storage strategies, while preserving the privacy of the users and minimizing the required signaling with the central unit. Finally, the proposed day-ahead optimization is tested in a realistic situation.


About relationship between business text patterns and financial performance in corporate data

This study uses text and data mining to investigate the relationship between the text patterns of annual reports published by US listed companies and sales performance. Taking previous research a step further, although annual reports show only past and present financial information, analyzing text content can identify sentences or patterns that indicate the future business performance of a company. First, we examine the relation pattern between business risk factors and current business performance. For this purpose, we select companies belonging to two categories of US SIC (Standard Industry Classification) in the IT sector, 7370 and 7373, which include Twitter, Facebook, Google, Yahoo, etc. We manually collect sales and business risk information for a total of 54 companies that submitted an annual report (Form 10-K) for the last three years in these two categories. To establish a correlation between patterns of text and sales performance, four hypotheses were set and tested. To verify the hypotheses, statistical analysis of sales, statistical analysis of text sentences, sentiment analysis of sentences, clustering, dendrogram visualization, keyword extraction, and word-cloud visualization techniques are used. The results show that text length has some correlation with sales performance, and that patterns of frequently appearing words are correlated with the sales performance. However, a sentiment analysis indicates that the positive or negative tone of a report is not related to sales performance.


Graphical models for text mining: knowledge extraction and performance estimation

This thesis would not have been possible unless the constant support of my advisor Professor Fabio Stella. He gave me suggestions, advices and a constant incentive to learn and deeply understand how things works. Special thanks go to Professor Enrico Fagiuoli for its suggestions and its huge knowledge of probabilistic models. internship has been a precious occasion to learn and discuss and to find new friends. Special thanks go my colleagues at DISCo that shared the highs and lows of my Ph.D. In particular, i thank my laboratory colleague Elena Gatti for our discussion and friendship and all the other Ph.D students for the discussion and fun moments. I thanks all the administrative personnel at the department, without them a Ph.D student life is hell. Thanks to all the student who worked with me during this years, part of the work in this thesis has been developed thanks to their contribution. It is a pleasure to thank those who made my Ph.D and thus my thesis possible: Docflow S.p.A. They gave me the possibility to get a sense of business world, their needs and to propose solutions. A special thanks go to all who have tolerated my obsessions and complaints during this years: my family, my friends and Manoela.


Exploring the benefits of the combination of a software architecture analysis and a usability evaluation of a mobile application

Designing easy to use mobile applications is a difficult task. In order to optimize the development of a usable mobile application, it is necessary to consider the mobile usage context for the design and the evaluation of the user–system interaction of a mobile application. In our research we designed a method that aligns the inspection method “Software ArchitecTure analysis of Usability Requirements realizatioN” SATURN and a mobile usability evaluation in the form of a user test. We propose to use mobile context factors and thus requirements as a common basis for both inspection and user test. After conducting both analysis and user test, the results described as usability problems are mapped and discussed. The mobile context factors identified define and describe the usage context of a mobile application. We oftware architecture analysis


Script independent scene text segmentation using fast stroke width transform and GrabCut

In this paper, a novel script independent scene text segmentation method is presented. The proposed method introduces an approach for character segmentation which combines the advantages of connected component analysis, stroke width analysis and graph cuts. The method was evaluated on a standard dataset, and also on a custom dataset of scene images of text from various Indian scripts. Additionally, a fast implementation of Stroke Width Transform (SWT) is presented here, which improves speed over other implementations by 79%. From our experiments, we have achieved encouraging results on the usefulness of the combination of text/non-text classification with GrabCut. The technique is robust to the language/script of text.


Improving accuracy in E-Textiles as a platform for pervasive sensing

Recently Electronic Textile (E-Textile) technology enables the weaving computation and communication components into the clothes that we wear and objects that we interact with every day. E-Textile enables the design and development of a broad range of pervasive sensing systems such as smart bed sheets for sleep posture monitoring, insoles in medical shoes for monitoring plantar pressure distribution, smart garments and sensing gloves. However the low cost of E-Textiles come at the cost of accuracy. In this work we propose an actuator-based method that increases the accuracy of E-Textiles by means of enabling real-time calibration. Our proposed system increases system accuracy by 38.55% on average (maximum 58.4%).


Imbalance in the cloud: An analysis on Alibaba cluster trace

To improve resource efficiency and design intelligent scheduler for clouds, it is necessary to understand the workload characteristics and machine utilization in large-scale cloud data centers. In this paper, we perform a deep analysis on a newly released trace dataset by Alibaba in September 2017, consists of detail statistics of 11089 online service jobs and 12951 batch jobs co-locating on 1300 machines over 12 hours. To the best of our knowledge, this is one of the first work to analyze the Alibaba public trace. Our analysis reveals several important insights about different types of imbalance in the Alibaba cloud. Such imbalances exacerbate the complexity and challenge of cloud resource management, which might incur severe wastes of resources and low cluster utilization. 1) Spatial Imbalance: heterogeneous resource utilization across machines and workloads. 2) Temporal Imbalance: greatly time-varying resource usages per workload and machine. 3) Imbalanced proportion of multi-dimensional resources (CPU and memory) utilization per workload. 4) Imbalanced resource demands and runtime statistics (duration and task number) between online service and offline batch jobs. We argue accommodating such imbalances during resource allocation is critical to improve cluster efficiency, and will motivate the emergence of new resource managers and schedulers.


Automatic Collocation Suggestion in Academic Writing

In recent years, collocation has been widely acknowledged as an essential characteristic to distinguish native speakers from non-native speakers. Research on academic writing has also shown that collocations are not only common but serve a particularly important discourse function within the academic community. In our study, we propose a machine learning approach to implementing an online collocation writing assistant. We use a data-driven classifier to provide collocation suggestions to improve word choices, based on the result of classification. The system generates and ranks suggestions to assist learners’ collocation usages in their academic writing with satisfactory results. *


Analysis of the MED Oscillation Problem in BGP

The Multi Exit Discriminator (MED) attribute of the Border Gateway Protocol (BGP) is widely used to implement “cold potato routing” between autonomous systems. However, the use of MED in practice has led to BGP persistent oscillation. The MED oscillation problem has been described with example configurations and complicated, step-by-step evaluation of dynamic route computations performed at multiple routers. Our work presents the first rigorous analysis of the MED oscillation problem. We employ the Stable Paths Problem (SPP) formalism that allows a static analysis of the interaction of routing policies. We give a formal definition of MED Induced Routing Anomalies (MIRA) and show that, in general, they can span multiple autonomous systems. However, if we assume that the BGP configurations between ASes follows a common model based on customer/provider and peer/peer relationships, then we show that the scope of any MIRA is always contained within a single autonomous system. Contrary to widely held assumptions, we show that a MIRA can occur even in a fully meshed IBGP configuration. We also show that a stable BGP routing may actually violate the stated semantics of the MED attribute.


Math modeling, neuropsychology, and category learning:: Response to B. Knowlton (1999)

raising the issue of the implications of findings in neuropsychological subjects for category learning [Knowlton, B. (1999) What can neuropsychology tell us about category learning? Trends Cognit. Sci. 3, 123–124]1. Knowlton and Squire2,3 reported various dissociations between categorization and recognition in normal and amnesic subjects to support their view that multiple memory systems mediate these tasks. The multiple-system view is more complex than a single-system approach is to modeling categorization and recognition. On grounds of parsimony, a question of scientific interest is whether a single-system model can suffice to account for these data. In support of this possibility, we demonstrated that an extremely simple version of a single-system exemplar model accounted in quantitative detail for the bulk of the dissociation findings reported by Knowlton and Squire, as long as plausible assumptions were made regarding parameter differences in memory ability between groups4. At the same time, we acknowledged some remaining challenges for the exemplar model, and concluded our article by stating, ‘Whether or not a full account of Knowlton and Squire’s complete set of dissociation findings is available within the exemplar approach remains a subject for future research, but we feel that we have made an important start’ (Ref. 4, p. 255). We also outlined various directions in which the exemplar model could be extended to meet these challenges. In the first part of her commentary1, Knowlton reiterates these acknowledged limitations of the simple exemplar model and argues against the possible lines of extension that we suggested. Her first point pertains to the dissociation exhibited by the severe amnesic E.P., who despite showing zero ability to discriminate between old and new exemplars in a recognition task, displayed normal performance in a classification task5. We had pointed out that stimulus conditions were dramatically different across the particular recognition and classification tasks in question. In the test phase of the recognition task, presentations of the target item were rare and widely spaced between distractor items. By contrast, in the classification test, members of the target category were frequently presented. We noted that if E.P. had even a small residual N o s o f s k y a n d Z a k i – R e s p o n s e Update Comment


An Introduction to Boosting and Leveraging

We provide an introduction to theoretical and practical aspects of Boosting and Ensemble learning, providing a useful reference for researchers in the field of Boosting as well as for those seeking to enter this fascinating area of research. We begin with a short background concerning the necessary learning theoretical foundations of weak learners and their linear combinations. We then point out the useful connection between Boosting and the Theory of Optimization, which facilitates the understanding of Boosting and later on enables us to move on to new Boosting algorithms, applicable to a broad spectrum of problems. In order to increase the relevance of the paper to practitioners, we have added remarks, pseudo code, “tricks of the trade”, and algorithmic considerations where appropriate. Finally, we illustrate the usefulness of Boosting algorithms by giving an overview of some existing applications. The main ideas are illustrated on the problem of binary classification, although several extensions are discussed. 1 A Brief History of Boosting The underlying idea of Boosting is to combine simple “rules” to form an ensemble such that the performance of the single ensemble member is improved, i.e. “boosted”. Let h1, h2, . . . , hT be a set of hypotheses, and consider the composite ensemble hypothesis


High Power Terahertz and Millimeter-Wave Oscillator Design: A Systematic Approach

A systematic approach to designing high frequency and high power oscillators using activity condition is introduced. This method finds the best topology to achieve frequencies close to the fmax of the transistors. It also determines the maximum frequency of oscillation for a fixed circuit topology, considering the quality factor of the passive components. Using this technique, in a 0.13 μm CMOS process, we design and implement 121 GHz and 104 GHz fundamental oscillators with the output power of -3.5 dBm and -2.7 dBm, respectively. Next, we introduce a novel triple-push structure to realize 256 GHz and 482 GHz oscillators. The 256 GHz oscillator was implemented in a 0.13 μm CMOS process and the output power of -17 dBm was measured. The 482 GHz oscillator generates -7.9 dBm (0.16 mW) in a 65 nm CMOS process.


Examining micro-payments for participatory sensing data collections

The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.


Clustering in the Presence of Background Noise

We address the problem of noise management in clustering algorithms. Namely, issues that arise when on top of some cluster structure the data also contains an unstructured set of points. We consider how clustering algorithms can be “robustified” so that they recover the cluster structure in spite of the unstructured part of the input. We introduce some quantitative measures of such robustness that take into account the strength of the embedded cluster structure as well as the mildness of the noise subset. We propose a simple and efficient method to turn any centroid-based clustering algorithm into a noiserobust one, and prove robustness guarantees for our method with respect to these measures. We also prove that more straightforward ways of “robustifying” clustering algorithms fail to achieve similar guarantees.


Analyzing Resource Behavior Using Process Mining

It is vital to use accurate models for the analysis, design, and/or control of business processes. Unfortunately, there are often important discrepancies between reality and models. In earlier work, we have shown that simulation models are often based on incorrect assumptions and one example is the speed at which people work. The “Yerkes-Dodson Law of Arousal” suggests that a worker that is under time pressure may become more efficient and thus finish tasks faster. However, if the pressure is too high, then the worker’s performance may degrade. Traditionally, it was difficult to investigate such phenomena and few analysis tools (e.g., simulation packages) support workload-dependent behavior. Fortunately, more and more activities are being recorded and modern process mining techniques provide detailed insights in the way that people really work. This paper uses a new process mining plug-in that has been added to ProM to explore the effect of workload on service times. Based on historic data and by using regression analysis, the relationship between workload and services time is investigated. This information can be used for various types of analysis and decision making, including more realistic forms of simulation.


Heart rate variability and its relation to prefrontal cognitive function: the effects of training and detraining

The aim of the present study was to investigate the relationship between physical fitness, heart rate variability (HRV) and cognitive function in 37 male sailors from the Royal Norwegian Navy. All subjects participated in an 8-week training program, after which the subjects completed the initial cognitive testing (pre-test). The subjects were assigned into a detrained group (DG) and a trained group (TG) based on their application for further duty. The DG withdrew from the training program for 4 weeks after which all subjects then completed the cognitive testing again (post-test). Physical fitness, measured as maximum oxygen consumption (V̇O2max), resting HRV, and cognitive function, measured using a continuous performance task (CPT) and a working memory test (WMT), were recorded during the pre-test and the post-test, and the data presented as the means and standard deviations. The results showed no between-group differences in V̇O2max or HRV at the pre-test. The DG showed a significant decrease in V̇O2max from the pre- to the post-test and a lower resting HRV than the TG on the post-test. Whereas there were no between-group differences on the CPT or WMT at the pre-test, the TG had faster reaction times and more true positive responses on tests of executive function at the post-test compared to the pre-test. The DG showed faster reaction times on non-executive tasks at the post-test compared to the pre-test. The results are discussed within a neurovisceral integration framework linking parasympathetic outflow to the heart to prefrontal neural functions.


Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control.

Power spectrum analysis of heart rate fluctuations provides a quantitative noninvasive means of assessing the functioning of the short-term cardiovascular control systems. We show that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz. Our data therefore provide evidence that the renin-angiotensin system plays a significant role in short-term cardiovascular control in the time scale of seconds to minutes.


Vagal influence on working memory and attention.

The aim of the present study was to investigate the effect of vagal tone on performance during executive and non-executive tasks, using a working memory and a sustained attention test. Reactivity to cognitive tasks was also investigated using heart rate (HR) and heart rate variability (HRV). Fifty-three male sailors from the Royal Norwegian Navy participated in this study. Inter-beat-intervals were recorded continuously for 5 min of baseline, followed by randomized presentation of a working memory test (WMT) based on Baddeley and Hitch's research (1974) and a continuous performance test (CPT). The session ended with a 5-min recovery period. High HRV and low HRV groups were formed based on a median split of the root mean squared successive differences during baseline. The results showed that the high HRV group showed more correct responses than the low HRV group on the WMT. Furthermore, the high HRV group showed faster mean reaction time (mRT), more correct responses and less error, than the low HRV group on the CPT. Follow-up analysis revealed that this was evident only for components of the CPT where executive functions were involved. The analyses of reactivity showed a suppression of HRV and an increase in HR during presentation of cognitive tasks compared to recovery. This was evident for both groups. The present results indicated that high HRV was associated with better performance on tasks involving executive function.


0.9–10GHz low noise amplifier with capacitive cross coupling

This paper presents a 0.9GHz-10GHz Ultra Wideband Low Noise Amplifier (LNA) designed for software-defined-radios (SDR). Capacitive cross coupling (CCC) is used at both input stage and cascade stage for wideband input impedance matching and small noise figure (NF). A combination of inductor peaking load and series inductor between the cascade stages of LNA is employed for a flat gain and enhanced input matching. This LNA is implemented in a standard 0.18µm CMOS technology. For 0.9GHz-10GHz ultra wideband applications, it achieves a maximum gain of 14dB, 3.5dB∼4.1dB NF and +1.8dBm IIP3. It consumes only7.6mW from a 1.8V power supply.


New Direct Torque Control Scheme for BLDC Motor Drives Suitable for EV Applications

This paper proposes a simple and effective scheme for Direct Torque Control (DTC) of Brushless DC Motor. Unlike the traditional DTC where both the torque and flux must to be employed in order to deliver the switching table for the inverter, the proposed DTC scheme utilizes only the torque information. By considering the particular operating principle of the motor, the instantaneous torque can be estimated only by using back EMF ratio in an unified differential equation. The simulation results have shown the good performance of the proposed scheme. It can be seen thus a very suitable technique for EV drives which need fast and precise torque response.


Using the Facebook group as a learning management system: An exploratory study

Facebook is a popular social networking site. It, like many other new technologies, has potential for teaching and learning because of its unique built-in functions that offer pedagogical, social and technological affordances. In this study, the Facebook group was used as a learning management system (LMS) in two courses for putting up announcements, sharing resources, organizing weekly tutorials and conducting online discussions at a teacher education institute in Singapore. This study explores using the Facebook group as an LMS and the students’ perceptions of using it in their courses. Results showed that students were basically satisfied with the affordances of Facebook as the fundamental functions of an LMS could be easily implemented in the Facebook group. However, using the Facebook group as an LMS has certain limitations. It did not support other format files to be uploaded directly, and the discussion was not organized in a threaded structure. Also, the students did not feel safe and comfortable as their privacy might be revealed. Constraints of using the Facebook group as an LMS, implications for practice and limitations of this study are discussed. Practitioner notes What is already known about this topic • Facebook has been popularly used by tertiary students, but many students do not want their teachers to be friends on Facebook • Teacher’s self-disclosure on Facebook can promote classroom atmosphere, teacher’s credibility and student–teacher relationship • Commercial learning management systems (LMSs) have limitations What this paper adds • The Facebook group can be used an LMS as it has certain pedagogical, social and technological affordances • Students are satisfied with the way of using the Facebook group as an LMS British Journal of Educational Technology (2011) doi:10.1111/j.1467-8535.2011.01195.x © 2011 The Authors. British Journal of Educational Technology © 2011 Bera. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. • Younger students are more acceptable with the idea of using the Facebook group as an LMS • Using the Facebook group as an LMS has limitations: it does not support other format files; its discussions are not listed in threads; and it is not perceived as a safe environment Implications for practice and/or policy • The Facebook group can be used an LMS substitute or supplement • Third-party applications are needed to extend the capability of the Facebook group as an LMS • Using Facebook seems to be more appropriate for young learners than adults • Teachers do not have to be students’ friends on Facebook. Introduction Social networking sites (SNSs) are virtual spaces where people of similar interest gather to communicate, share photos and discuss ideas with one another (Boyd & Ellison, 2008; Raacke & Bonds-Raacke, 2008). In recent years, Facebook has become one of the most prominent SNSs. Like any new technology, Facebook seems to offer great potentials for teaching and learning as many students are using Facebook daily. One possible way of using Facebook for teaching and learning is to use its group as an LMS. Research shows that using LMSs possesses numerous benefits for teaching and learning. It enables faculty to shift the focus from content-based learning to process-based learning (Vogel & Klassen, 2001) and helps to “facilitate change from passive to active learning” (Herse & Lee, 2005, p. 51). Using LMSs also has the potential to increase student enrollment (Nunes & McPherson, 2003) and to promote interaction between students and faculty members (Lonn & Teasley, 2009; West, Waddoups & Graham, 2007). Using existing commercial LMSs like Blackboard, however, often has practical constraints (Sanchez-Franco, 2010). For example, LMSs tend to be expensive and that not every school can afford to purchase and maintain them over the long run. Trainee teachers cannot access certain features such as creating a course, enrolling students and setting up student groups as these functions are usually open to instructors or administrators only. The resources in the present LMS are often no longer accessible to trainee teachers after their graduation. Also, the LMS used at school in the future may be different from the one being currently used. They have to shift to a new LMS, and research shows learning a new system is often a painful experience (Black, Beck, Dawson, Jinks & DiPietro, 2007). If the Facebook group can be used as an alternative LMS, it would help to overcome some of the abovementioned constraints. For instance, it would enable a teacher to easily create a new course and enroll students in person if the class size is small. As presented in the following literature review section, many research studies have investigated the usage of Facebook, the effect of teacher’s self-disclosure via Facebook on teacher–student relationship improvement and the academic performance of Facebook users. However, few studies have examined if and how Facebook can be effectively used as an LMS. In this exploratory study, the Facebook group was used as an LMS to put up announcements, share resources, organize weekly tutorial sessions and conduct online discussions. The purpose of this paper is to describe how the Facebook group was used as an LMS in the study and to report students’ perceptions on it. Literature review on Facebook


An empirical investigation of student adoption model toward mobile e-textbook: UTAUT2 and TTF model

Faculty of Economics in Universitas Atma Jaya Yogyakarta (UAJY) has replaced printed textbooks with e-textbooks for its academic activities since 2015. These e-textbooks can be accessed via iPad which is given to each student. Hence, the objective of this study is to test the proposed research model based on integrated UTAUT2 and TTF. Questionnaires were distributed to 326 students in 10 classes. Only junior and sophomore students were eligible to fulfill the questionnaires. The result of the study shows that performance expectancy, effort expectancy, social influence, facilitating condition, and habit have a direct significant relationship on behavioral intention to use mobile e-textbook. The result also shows that both Task technology and technology characteristic positively affect task technology fit. There is also a direct significant relationship between Task technology fit and performance expectancy. We suggest that further studies include experimentation to investigate performance expectancy of mobile e-book. 2) More evaluation should be held in the future in more mature environment. The finding of this study can help policymakers in the Faculty of Economics UAJY to evaluate the policy and formulate better strategy. In addition, these findings can be used by others faculties and universities as mobile e-textbook adoption case reference.


Ethernet-Based Real-Time and Industrial Communications

Despite early attempts to use Ethernet in the industrial context, only recently has it attracted a lot of attention as a support for industrial communication. A number of vendors are offering industrial communication products based on Ethernet and TCP/IP as a means to interconnect field devices to the first level of automation. Others restrict their offer to communication between automation devices such as programmable logic controllers and provide integration means to existing fieldbuses. This paper first details the requirements that an industrial network has to fulfill. It then shows how Ethernet has been enhanced to comply with the real-time requirements in particular in the industrial context. Finally, we show how the requirements that cannot be fulfilled at layer 2 of the OSI model can be addressed in the higher layers adding functionality to existing standard protocols.


Gabor-based kernel PCA with fractional power polynomial models for face recognition

This paper presents a novel Gabor-based kernel principal component analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.


Marker-controlled watershed segmentation of nuclei in H&E stained breast cancer biopsy images

In this paper we present an unsupervised automatic method for segmentation of nuclei in H&E stained breast cancer biopsy images. Colour deconvolution and morphological operations are used to preprocess the images in order to remove irrelevant structures. Candidate nuclei locations, obtained with the fast radial symmetry transform, act as markers for a marker-controlled watershed segmentation. Watershed regions that are unlikely to represent nuclei are removed in the postprocessing stage. The proposed algorithm is evaluated on a number of images that are representative of the diversity in pathology in this type of tissue. The method shows good performance in terms of the number of segmented nuclei and segmentation accuracy.


Empathy decline and its reasons: a systematic review of studies with medical students and residents.

PURPOSE
Empathy is a key element of patient-physician communication; it is relevant to and positively influences patients' health. The authors systematically reviewed the literature to investigate changes in trainee empathy and reasons for those changes during medical school and residency.

METHOD
The authors conducted a systematic search of studies concerning trainee empathy published from January 1990 to January 2010, using manual methods and the PubMed, EMBASE, and PsycINFO databases. They independently reviewed and selected quantitative and qualitative studies for inclusion. Intervention studies, those that evaluated psychometric properties of self-assessment tools, and those with a sample size <30 were excluded.

RESULTS
Eighteen studies met the inclusion criteria: 11 on medical students and 7 on residents. Three longitudinal and six cross-sectional studies of medical students demonstrated a significant decrease in empathy during medical school; one cross-sectional study found a tendency toward a decrease, and another suggested stable scores. The five longitudinal and two cross-sectional studies of residents showed a decrease in empathy during residency. The studies pointed to the clinical practice phase of training and the distress produced by aspects of the "hidden," "formal," and "informal" curricula as main reasons for empathy decline.

CONCLUSIONS
The results of the reviewed studies, especially those with longitudinal data, suggest that empathy decline during medical school and residency compromises striving toward professionalism and may threaten health care quality. Theory-based investigations of the factors that contribute to empathy decline among trainees and improvement of the validity of self-assessment methods are necessary for further research.


Multi-Cell Multiuser Massive MIMO Networks: User Capacity Analysis and Pilot Design

We propose a novel pilot sequence design to mitigate pilot contamination in multi-cell multiuser massive multiple-input multiple-output networks. Our proposed design generates pilot sequences in the multi-cell network and devises power allocation at base stations (BSs) for downlink transmission. The pilot sequences together with the power allocation ensure that the user capacity of the network is achieved and the pre-defined signal-to-interference-plus-noise ratio (SINR) requirements of all users are met. To realize our design, we first derive new closed-form expressions for the user capacity and the user capacity region. Built upon these expressions, we then develop a new algorithm to obtain the required pilot sequences and power allocation. We further determine the minimum number of antennas required at the BSs to achieve certain SINR requirements of all users. The numerical results are presented to corroborate our analysis and to examine the impact of key parameters, such as the pilot sequence length and the total number of users, on the network performance. A pivotal conclusion is reached that our design achieves a larger user capacity region than the existing designs and needs less antennas at the BS to fulfill the pre-defined SINR requirements of all users in the network than the existing designs.


repository copy of Transformational Leadership and Burnout : The Role of Thriving and Followers ' Openness to Experience

eprints@whiterose.ac.uk https://eprints.whiterose.ac.uk/ Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website.


Adaptive scale based entropy-like estimator for robust fitting

In this paper, we propose a novel robust estimator, called ASEE (Adaptive Scale based Entropy-like Estimator) which minimizes the entropy of inliers. This estimator is based on IKOSE (Iterative Kth Ordered Scale Estimator) and LEL (Least Entropy-Like Estimator). Unlike LEL, ASEE only considers inliers' entropy while excluding outliers, which makes it very robust in parametric model estimation. Compared with other robust estimators, ASEE is simple and computationally efficient. From the experiments on both synthetic and real-image data, ASEE is more robust than several state-of-the-art robust estimators, especially in handling extreme outliers.


Home Bias in Online Investments: An Empirical Study of an Online Crowdfunding Market

An extensive literature in economics and finance has documented " home bias, " the tendency that transactions are more likely to occur between parties in the same country or state, rather than outside. Can the Internet help overcome this spatial divide, especially in the context of online financial investments? We address this question in the context of a large online crowd funding marketplace. We analyze detailed transaction data over an extended period of time under typical market conditions, as well as those from a natural experiment, a period in which investors were restricted to only one state due to regulations. We further employ a quasi-experimental design by tracking borrowers who moved across state boundaries, and study how investors' behaviors change when those borrowers moved. Home bias appears to be a robust phenomenon under all three scenarios. This finding has important implications not just for the home bias literature, but also more broadly for the growing research and policy interest on Internet-based crowd funding, especially as a new channel for entrepreneurial financing.


Query optimization techniques for partitioned tables

Table partitioning splits a table into smaller parts that can be accessed, stored, and maintained independent of one another. From their traditional use in improving query performance, partitioning strategies have evolved into a powerful mechanism to improve the overall manageability of database systems. Table partitioning simplifies administrative tasks like data loading, removal, backup, statistics maintenance, and storage provisioning. Query language extensions now enable applications and user queries to specify how their results should be partitioned for further use. However, query optimization techniques have not kept pace with the rapid advances in usage and user control of table partitioning. We address this gap by developing new techniques to generate efficient plans for SQL queries involving multiway joins over partitioned tables. Our techniques are designed for easy incorporation into bottom-up query optimizers that are in wide use today. We have prototyped these techniques in the PostgreSQL optimizer. An extensive evaluation shows that our partition-aware optimization techniques, with low optimization overhead, generate plans that can be an order of magnitude better than plans produced by current optimizers.


Balancing minimum spanning trees and shortest-path trees

We give a simple algorithm to find a spanning tree that simultaneously approximates a shortest-path tree and a minimum spanning tree. The algorithm provides a continuous tradeoff: given the two trees and aγ>0, the algorithm returns a spanning tree in which the distance between any vertex and the root of the shortest-path tree is at most 1+√2γ times the shortest-path distance, and yet the total weight of the tree is at most 1+√2/γ times the weight of a minimum spanning tree. Our algorithm runs in linear time and obtains the best-possible tradeoff. It can be implemented on a CREW PRAM to run a logarithmic time using one processor per vertex.


The Anatomic Basis of Midfacial Aging

Facial aging is a multifactorial, three-dimensional (3D) process with anatomic, biochemical, and genetic correlates. Many exogenous and endogenous factors can signifi cantly impact the perceived age of an individual. Solar exposure [ 1– 3 ] , cigarette smoking [ 1, 2, 4, 5 ] , medications [ 1 ] , alcohol use [ 1 ] , body mass index [ 2 ] , and endocrinologic status [ 1, 6, 7 ] have all been implicated as factors that accelerate cutaneous and subcutaneous aging. These factors act in concert to create a variegated spectrum of facial morphologic aging changes, and thus, Mme. Chanel was partially correct in her statement from the last century. Most of the aging changes that occur in the midface, however, occur predictably in the majority of individuals. Stigmata of midfacial aging typically appear by the middle of the fourth decade. Degenerative changes occur in nearly every anatomic component of the midface and include cranial bone remodeling, tissue descent secondary to gravity, fat atrophy, and deterioration in the condition and appearance of the skin. The lower eyelids and adjacent tissues are often the initial areas of patient concern. This chapter reviews the morphologic changes that occur in the aging midface and discusses the pathogenesis of midfacial aging based upon its anatomic components. An integrated theory of facial aging will be presented. A. E. Wulc , MD, FACS ( ) Associate Clinical Professor of Ophthalmology , University of Pennsylvania


DGASensor: Fast Detection for DGA-Based Malwares

DNS protocol has been used by many malwares for command-and-control (C&C). To improve the resiliency of C&C communication, Domain Generation Algorithm (DGA) has been utilized by recent malwares such as Locky, Conficker and Zeus. Many detection systems have been introduced for DGA-based botnets detection. However, such botnets detection approaches suffer from several limitations, for instance, requiring a group of DGA domains, period behaviors, the presence of multiple bots, and so forth. It is very hard for them to detect an individually running DGA-based malware which leave only a few traces. In this paper, we develop DGASensor to detect DGA-based malwares immediately by identifying a single DGA domain using lexical evidence. First, DGASensor automatically analyzes the lexical patterns of the most popular domains listed in Alexa top 100,000, and then extracts two templates, namely distribution template and structure template. Second, the above two templates, pronounceable attributes, and some frequently used properties like entropy and length, are used to extract features from a single domain. Third, we train our classifier using a non-DGA dataset consisting of domains obtained from Alexa rank and a DGA dataset generated by known DGAs. At last, we provide a short word filter to decrease the false positive rate. We implement a prototype system and evaluate it using the above training dataset with 10-fold cross validation. Moreover, a set of real world DNS traffic collected from a recursive DNS server is used to measure real world performance of our system. The results show that DGASensor detects DGA domains with accuracy 93% in our training dataset and is able to identify a variety of malwares in the real world dataset with an extremely high processing capability.


On the effectiveness of address-space randomization

Address-space randomization is a technique used to fortify systems against buffer overflow attacks. The idea is to introduce artificial diversity by randomizing the memory location of certain system components. This mechanism is available for both Linux (via PaX ASLR) and OpenBSD. We study the effectiveness of address-space randomization and find that its utility on 32-bit architectures is limited by the number of bits available for address randomization. In particular, we demonstrate a <i>derandomization attack</i> that will convert any standard buffer-overflow exploit into an exploit that works against systems protected by address-space randomization. The resulting exploit is as effective as the original exploit, although it takes a little longer to compromise a target machine: on average 216 seconds to compromise Apache running on a Linux PaX ASLR system. The attack does not require running code on the stack.
 We also explore various ways of strengthening address-space randomization and point out weaknesses in each. Surprisingly, increasing the frequency of re-randomizations adds at most 1 bit of security. Furthermore, compile-time randomization appears to be more effective than runtime randomization. We conclude that, on 32-bit architectures, the only benefit of PaX-like address-space randomization is a small slowdown in worm propagation speed. The cost of randomization is extra complexity in system support.


Cool English: a Grammatical Error Correction System Based on Large Learner Corpora

This paper presents a grammatical error correction (GEC) system that provides corrective feedback for essays. We apply the neural sequence-to-sequence model, which is frequently used in machine translation and text summarization, to this GEC task. The model is trained on EF-Cambridge Open Language Database (EFCAMDAT), a large learner corpus annotated with grammatical errors and corrections. Evaluation shows that our system achieves competitive performance on a number of publicly available testsets.


Uses and Gratification Theory – Why Adolescents Use Facebook ?

Due to a dynamic development of the Web 2.0 and new trends in the social media field that change on a daily basis, contemporary media research is shifting its focus to a greater extent on media users, their motivation and behavior in using social network sites in order to explain the extreme popularity of Facebook, Twitter, WhatsApp and other similar SNSs and mobile chat applications among the young. In this paper we wanted to explore the benefits of Facebook use among adolescents as well as which of their needs are gratified thereat. As the theoretical background we used the uses and gratification theory due to its user oriented approach. Furthermore, we wanted to test whether the uses and gratification concept is adequate for analyzing the motivation and behavior of SNSs users as suggested by some previous research. The survey comprising 431 adolescent Facebook users was conducted from October to December 2013 in the City of Zagreb. The results have shown that most adolescents use Facebook for socializing and communicating with their friends, discussing school activities, setting up meetings and dates with friends as well as


Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News

The amount of data in the world and in our lives seems everincreasing and there’s no end to it. The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data pre-processing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up largescale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. Classification is an important data mining technique with broad applications. It classifies data of various kinds. This paper has been carried out to make a performance evaluation of REPTree, Simple Cart and RandomTree classification algorithm. The paper sets out to make comparative evaluation of classifiers REPTree, Simple Cart and RandomTree in the context of dataset of Indian news to maximize true positive rate and minimize false positive rate. For processing Weka API were used. The results in the paper on dataset of Indian news also show that the efficiency and accuracy of RandomTree is good than REPTree, and Simple Cart. Keywords— Simple Cart, RandomTree, REPTree, Weka, WWW


Classification and Regression by randomForest

Recently there has been a lot of interest in “ensemble learning” — methods that generate many classifiers and aggregate their results. Two well-known methods are boosting (see, e.g., Shapire et al., 1998) and bagging Breiman (1996) of classification trees. In boosting, successive trees give extra weight to points incorrectly predicted by earlier predictors. In the end, a weighted vote is taken for prediction. In bagging, successive trees do not depend on earlier trees — each is independently constructed using a bootstrap sample of the data set. In the end, a simple majority vote is taken for prediction. Breiman (2001) proposed random forests, which add an additional layer of randomness to bagging. In addition to constructing each tree using a different bootstrap sample of the data, random forests change how the classification or regression trees are constructed. In standard trees, each node is split using the best split among all variables. In a random forest, each node is split using the best among a subset of predictors randomly chosen at that node. This somewhat counterintuitive strategy turns out to perform very well compared to many other classifiers, including discriminant analysis, support vector machines and neural networks, and is robust against overfitting (Breiman, 2001). In addition, it is very user-friendly in the sense that it has only two parameters (the number of variables in the random subset at each node and the number of trees in the forest), and is usually not very sensitive to their values. The randomForest package provides an R interface to the Fortran programs by Breiman and Cutler (available at http://www.stat.berkeley.edu/ users/breiman/). This article provides a brief introduction to the usage and features of the R functions.


FOR PREDICTING STUDENT PERFORMANCE

Although data mining has been successfully implemented in the business world for some time now, its use in higher education is still relatively new, i.e. its use is intended for identification and extraction of new and potentially valuable knowledge from the data. Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. The success was evaluated with the passing grade at the exam. The impact of students' socio-demographic variables, achieved results from high school and from the entrance exam, and attitudes towards studying which can have an affect on success, were all investigated. In future investigations, with identifying and evaulating variables associated with process of studying, and with the sample increase, it would be possible to produce a model which would stand as a foundation for the development of decision support system in higher education.


A comparative analysis of techniques for predicting academic performance

This paper compares the accuracy of decision tree and Bayesian network algorithms for predicting the academic performance of undergraduate and postgraduate students at two very different academic institutes: Can Tho University (CTU), a large national university in Viet Nam; and the Asian Institute of Technology (AIT), a small international postgraduate institute in Thailand that draws students from 86 different countries. Although the diversity of these two student populations is very different, the data-mining tools were able to achieve similar levels of accuracy for predicting student performance: 73/71% for {fail, fair, good, very good} and 94/93% for {fail, pass} at the CTU/AIT respectively. These predictions are most useful for identifying and assisting failing students at CTU (64% accurate), and for selecting very good students for scholarships at the AIT (82% accurate). In this analysis, the decision tree was consistently 3-12% more accurate than the Bayesian network. The results of these case studies give insight into techniques for accurately predicting student performance, compare the accuracy of data mining algorithms, and demonstrate the maturity of open source tools.


Driver Identification: a Time Series Classification Approach

In the last years, researchers and auto vehicle developers are focusing their attention on the topic of driver identification encouraged by an increasing number of always more sophisticated vehicle sensors able to extract information about the driver. Indeed, driver identification can be very useful to customize and improve driver experience, increase the safety on the road and reduce the global environmental problems. In this work, we propose and explore a set of features (extracted from a car monitoring system) identifying the driver basing on his/her driving behavior. The proposed behavioral features are exploited using a time series classification approach and a multi-layer perceptron (MLP) network is used to evaluate the ability of the proposed features to identify the vehicle driver. The proposed features are tested on a real data set composed of totally 66 observations (each observation consists of a given person driving a given car on a predefined path). The obtained results show that the proposed features have effective driver identification ability.


Color exploitation in hog-based traffic sign detection

We study traffic sign detection on a challenging large-scale real-world dataset of panoramic images. The core processing is based on the Histogram of Oriented Gradients (HOG) algorithm which is extended by incorporating color information in the feature vector. The choice of the color space has a large influence on the performance, where we have found that the CIELab and YCbCr color spaces give the best results. The use of color significantly improves the detection performance. We compare the performance of a specific and HOG algorithm, and show that HOG outperforms the specific algorithm by up to tens of percents in most cases. In addition, we propose a new iterative SVM training paradigm to deal with the large variation in background appearance. This reduces memory consumption and increases utilization of background information.


An Initial Investigation of Test Driven Development in Industry

Test Driven Development (TDD) is a software development practice in which unit test cases are incrementally written prior to code implementation. In our research, we ran a set of structured experiments with 24 professional pair programmers. One group developed code using TDD while the other a waterfall-like approach. Both groups developed a small Java program. We found that the TDD developers produced higher quality code, which passed 18% more functional black box test cases. However, TDD developer pairs took 16% more time for development. A moderate correlation between time spent and the resulting quality was established upon analysis. It is conjectured that the resulting high quality of code written using the TDD practice may be due to the granularity of TDD, which may encourage more frequent and tighter verification and validation. Lastly, the programmers which followed a waterfall-like process often did not write the required automated test cases after completing their code, which might be indicative of the tendency among practitioners toward inadequate testing. This observation supports that TDD has the potential of increasing the level of testing in the industry as testing as an integral part of code development.


Using Nonfinancial Measures to Assess Fraud Risk

This study examines whether auditors can effectively use nonfinancial measures (NFMs) to assess the reasonableness of financial performance and, thereby, help detect financial statement fraud (hereafter, fraud). If auditors or other interested parties (e.g., directors, lenders, investors, or regulators) can identify NFMs (e.g., facilities growth) that are correlated with financial measures (e.g., revenue growth), inconsistent patterns between the NFMs and financial measures can be used to detect firms with high fraud risk. We find that the difference between financial and nonfinancial performance is significantly greater for firms that committed fraud than for their nonfraud competitors. We also find that this difference is a significant fraud indicator when included in a model containing variables that have previously been linked to the likelihood of fraud. Overall, our results provide empirical evidence suggesting that NFMs can be effectively used to assess fraud risk. ∗North Carolina State University; †George Mason University; ‡Brigham Young University. We especially thank Abbie Smith (editor) and the referee for their invaluable guidance. We are grateful for helpful comments from Mark Beasley, Brian Cloyd, Andrew Felo, Glen Gray, Dana Hermanson, Diane Matson, Kevin Melendrez, Mitch Oler, Mark Peecher, and workshop participants at Indiana University, Virginia Tech University, Lehigh University, Utah State University, the 2005 Brigham Young University Research Symposium, the 2006 AAA Annual Meeting, the 2006 International Symposium on Audit Research, the 2007 Auditing Section Midyear Conference, the 2008 Conference on Corporate Governance and Fraud Prevention at George Mason University, the 2008 Corporate Governance and Ethics Symposium at George Mason University, the North Carolina Association of Government Accountants, the North Carolina Office of the State Controller, and First Citizens Bank. Finally, we appreciate the many students who gathered data for our sample of firms. This study was funded by research grants from the Institute of Internal Auditors Research Foundation and the Financial Industry Regulatory Authority Investor Education Foundation.


Edge Detection in Digital Images Using Fuzzy Logic Technique

The fuzzy technique is an operator introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective evaluation. The following paper introduces such operators on hand of computer vision application. In this paper a novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using floating 3x3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily. Keywords—Fuzzy logic, Edge detection, Image processing, computer vision, Mechanical parts, Measurement.


Factors Affecting the Adoption of Electronic Word-of-Mouth in the Tourism Industry

This paper proposes a theoretical framework to explain the factors that influence consumer adoption of electronic word-of-mouth (eWOM) in the decision to purchase travel-related services. Adoption of eWOM is posited to be influenced by the perceived usefulness of eWOM. The perceived usefulness of eWOM, in turn, is expected to be influenced by the nature of eWOM characterized by message basis, positivity, valence, elaborateness, and timeliness; eWOM quantity; source credibility in the form of message forum type, reviewer identity disclosure, expertise, and reputation; and consumers’ prior knowledge of the services being considered. Managerial implications of the framework are also discussed.


Moodle's Ontology Development from UML for Social Learning Network Analysis

The online learning called e-learning is a new learning path that offers to learners to study at their own pace and at the moments that suit them. It is in this perspective that the semantic web has known its emergence in the field of e-learning to offer platforms content more personalized and more adapted to student's and teacher's needs. Since Moodle is the most popular e-learning platform, we propose in this paper to build its OWL ontology by exploring the representative data that we collected from its UML class diagram. The choice of UML class diagram as a basis for data collection for the development of the ontology is justified by the fact that the transition from UML to OWL ontology brings ontology development process closer to the wider software engineering population. The built ontology brings also great benefit in the field of the Social Learning Network Analysis. Because it gives the opportunity to study the behavior of the platform users by giving meaning to their relationships instead of modelling them only as knots and edges.


Dynamic Modeling and Adaptive Control of a Cable-suspended Robot

The level adjustment of cable-driven parallel mechanism is challenging due to the difficulty in obtaining an accurate mathematical model and the fact that different sources of uncertainties exist in the adjustment process. This paper presents application of an adaptive control scheme for a cable suspended robot to handle uncertainties in mass and moments of inertia of end effecter. In section II dynamic equations of motion are derived and the constraints are utilized to obtain the complete required equations. In section III inverse dynamic controller and adaptive controller are presented. Simulations results presented in section IV show the effectiveness of the adaptive controller when there is no enough knowledge about system parameters.


E-Learning’s Impact on the Academic Performance of Student-Teachers: A Curriculum Lens

This study was planned to explore the impact of eLearning on the academic performance of student-teachers. The researchers' concern with e-Learning was sanctioned by the need for a teaching and learning strategy that can help institutions of learning address their problems and improve on their outcome. In this respect, the researchers conducted an experiment to find out whether student-teachers taught using the method of e-Learning (blended learning) perform better than studentteachers taught using the traditional method of teaching and learning. Findings offers new evidence that e-Learning has a significant influence on the performance of students as student-teachers taught using eLearning consistently performed better than student-teachers taught using the traditional method. Based on this result, recommendations are made to training institutions to embrace ICTs and become more flexible by adopting learning approaches that are dynamic and multidimensional as problems in education are becoming more complex.


The state of the art in semantic relatedness: a framework for comparison

Semantic relatedness (SR) is a form of measurement that quantitatively identifies the relationship between two words or concepts based on the similarity or closeness of their meaning. In the recent years, there have been noteworthy efforts to compute SR between pairs of words or concepts by exploiting various knowledge resources such as linguistically structured (e.g. WordNet) and collaboratively developed knowledge bases (e.g. Wikipedia), among others. The existing approaches rely on different methods for utilizing these knowledge resources, for instance, methods that depend on the path between two words, or a vector representation of the word descriptions. The purpose of this paper is to review and present the state of the art in SR research through a hierarchical framework. The dimensions of the proposed framework cover three main aspects of SR approaches including the resources they rely on, the computational methods applied on the resources for developing a relatedness metric, and the evaluation models that are used for measuring their effectiveness. We have selected 14 representative SR approaches to be analyzed using our framework. We compare and critically review each of them through the dimensions of our framework, thus, identifying strengths and weaknesses of each approach. In addition, we provide guidelines for researchers and practitioners on how to select the most relevant SR method for their purpose. Finally, based on the comparative analysis of the reviewed relatedness measures, we identify existing challenges and potentially valuable future research directions in this domain.


Leakproofing the Singularity Artificial Intelligence Confinement Problem

This paper attempts to formalize and to address the ‘leakproofing’ of the Singularity problem presented by David Chalmers. The paper begins with the definition of the Artificial Intelligence Confinement Problem. After analysis of existing solutions and their shortcomings, a protocol is proposed aimed at making a more secure confinement environment which might delay potential negative effect from the technological singularity while allowing humanity to benefit from the superintelligence.


Density Based Smart Traffic System with Real Time Data Analysis Using IoT

This paper aims to develop a convenient traffic system that allows a smooth movement of cars which will help build a smarter city. The traffic system currently implemented in many areas is not based on the density of traffic and every road is allotted a preset time. This results in traffic congestion due to large red-light delays and timings allotted for roads in a city that should vary during peak on-off hours, but in reality don't. These traditional systems are not adaptable and fail to support traffic during an unexpected situation or during an accident, and this makes them inefficient. In order to calculate the density of traffic various sensors can be used, each having their merits and demerits. In our proposed system Ultrasound Sensors are used along with Image Processing(using live feed from a camera) that works on a Raspberry Pi platform and calculates the vehicle density and dynamically allots time for different levels of traffic. This in turn allows better signal control and effective management of traffic thereby reducing the probability of a collision. By using Internet Of Things(IoT) real time data from the system can be collected, stored and managed on a cloud. This data can be used to interpret the signal duration in-case any of the sensing equipment fail, and also for future analysis.


Efficient iris segmentation method in unconstrained environments


Does the technology acceptance model predict actual use? A systematic literature review

0950-5849/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.infsof.2009.11.005 * Corresponding author. Tel.: +44 (0) 1782 734090; E-mail address: m.turner@cs.keele.ac.uk (M. Turne Context: The technology acceptance model (TAM) was proposed in 1989 as a means of predicting technology usage. However, it is usually validated by using a measure of behavioural intention to use (BI) rather than actual usage. Objective: This review examines the evidence that the TAM predicts actual usage using both subjective and objective measures of actual usage. Method: We performed a systematic literature review based on a search of six digital libraries, along with vote-counting meta-analysis to analyse the overall results. Results: The search identified 79 relevant empirical studies in 73 articles. The results show that BI is likely to be correlated with actual usage. However, the TAM variables perceived ease of use (PEU) and perceived usefulness (PU) are less likely to be correlated with actual usage. Conclusion: Care should be taken using the TAM outside the context in which it has been validated. 2009 Elsevier B.V. All rights reserved.


A Hierarchical Thread Scheduler and Register File for Energy-Efficient Throughput Processors

Modern graphics processing units (GPUs) employ a large number of hardware threads to hide both function unit and memory access latency. Extreme multithreading requires a complex thread scheduler as well as a large register file, which is expensive to access both in terms of energy and latency. We present two complementary techniques for reducing energy on massively-threaded processors such as GPUs. First, we investigate a two-level thread scheduler that maintains a small set of active threads to hide ALU and local memory access latency and a larger set of pending threads to hide main memory latency. Reducing the number of threads that the scheduler must consider each cycle improves the scheduler’s energy efficiency. Second, we propose replacing the monolithic register file found on modern designs with a hierarchical register file. We explore various trade-offs for the hierarchy including the number of levels in the hierarchy and the number of entries at each level. We consider both a hardware-managed caching scheme and a software-managed scheme, where the compiler is responsible for orchestrating all data movement within the register file hierarchy. Combined with a hierarchical register file, our two-level thread scheduler provides a further reduction in energy by only allocating entries in the upper levels of the register file hierarchy for active threads. Averaging across a variety of real world graphics and compute workloads, the active thread count can be reduced by a factor of 4 with minimal impact on performance and our most efficient three-level software-managed register file hierarchy reduces register file energy by 54%.


Real Time Video Data Mining for Surveillance Video Streams

We extend our previous work [1] of the general framework for video data mining to further address the issue such as how to mine video data. To extract motions, we use an accumulation of quantized pixel differences among all frames in a video segment. As a result, the accumulated motions of segment are represented as a two dimensional matrix. Further, we develop how to capture the location of motions occurring in a segment using the same matrix generated for the calculation of the amount. We study how to cluster those segmented pieces using the features (the amount and the location of motions) we extract by the matrix above. We investigate an algorithm to find whether a segment has normal or abnormal events by clustering and modeling normal events, which occur mostly. In addition to deciding normal or abnormal, the algorithm computes Degree of Abnormality of a segment, which represents to what extent a segment is distant to the existing segments in relation with normal events. Our experimental studies indicate that the proposed techniques are promising.


Multi-patch deep features for power line insulator status classification from aerial images

The status of the insulators in power line can directly affect the reliability of the power transmission systems. Computer vision aided approaches have been widely applied in electric power systems. Inspecting the status of insulators from aerial images has been challenging due to the complex background and rapid view changing under different illumination conditions. In this paper, we propose a novel approach to inspect the insulators with Deep Convolutional Neural Networks (CNN). A CNN model with multi-patch feature extraction method is applied to represent the status of insulators and a Support Vector Machine (SVM) is trained based on these features. A thorough evaluation is conducted on our insulator status dataset of six classes from real inspection videos. The experimental results show that a pre-trained model for classification is more accurate than the shallow features by hand-crafted. Our approach achieves 98.7095% mean Average Precision (mAP) in status classification. We also study the behavior of the neural activations of the convolutional layers. Different results vary with different fully connected layers, and interesting findings are discussed.


Traveling Salesman Problems with Profits

T salesman problems with profits (TSPs with profits) are a generalization of the traveling salesman problem (TSP), where it is not necessary to visit all vertices. A profit is associated with each vertex. The overall goal is the simultaneous optimization of the collected profit and the travel costs. These two optimization criteria appear either in the objective function or as a constraint. In this paper, a classification of TSPs with profits is proposed, and the existing literature is surveyed. Different classes of applications, modeling approaches, and exact or heuristic solution techniques are identified and compared. Conclusions emphasize the interest of this class of problems, with respect to applications as well as theoretical results.


A Review on Position/Speed Sensorless Control for Permanent-Magnet Synchronous Machine-Based Wind Energy Conversion Systems

Owing to the advantages of higher efficiency, greater reliability, and better grid compatibility, the direct-drive permanent-magnet synchronous generator (PMSG)-based variable-speed wind energy conversion systems (WECSs) have drawn the highest attention from both academia and industry in the last few years. Applying mechanical position/speed sensorless control to direct-drive PMSG-based WECSs will further reduce the cost and complexity, while enhancing the reliability and robustness of the WECSs. This paper reviews the state-of-the-art and highly applicable mechanical position/speed sensorless control schemes for PMSG-based variable-speed WECSs. These include wind speed sensorless control schemes, generator rotor position and speed sensorless vector control schemes, and direct torque and direct power control schemes for a variety of direct-drive PMSG-based WECSs.


Smile Esthetics – A Literature

Esthetic dental treatment involves artistic and subjective components design to create the illusion of beauty. An organised systematic approach is required to evaluate, diagnose and resolve esthetic problems predictably. Our ultimate goal is to achieve pleasing composition in the smile by creating an arrangement of various esthetic elements. One of the most important tasks in esthetic dentistry is creating harmonious proportions between the widths of maxillary anterior teeth when restoring or replacing these teeth This review article describes application of the Golden Proportion and Red Proportion in dentistry also the smile design for completely edentulous and for full mouth rehabilitation. Also the future scope for designing smile .


Graph-based Cross Entropy method for solving multi-robot decentralized POMDPs

This paper introduces a probabilistic algorithm for multi-robot decision-making under uncertainty, which can be posed as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP). Dec-POMDPs are inherently synchronous decision-making frameworks which require significant computational resources to be solved, making them infeasible for many real-world robotics applications. The Decentralized Partially Observable Semi-Markov Decision Process (Dec-POSMDP) was recently introduced as an extension of the Dec-POMDP that uses high-level macro-actions to allow large-scale, asynchronous decision-making. However, existing Dec-POSMDP solution methods have limited scalability or perform poorly as the problem size grows. This paper proposes a cross-entropy based Dec-POSMDP algorithm motivated by the combinatorial optimization literature. The algorithm is applied to a constrained package delivery domain, where it significantly outperforms existing Dec-POSMDP solution methods.


Challenges of using homomorphic encryption to secure cloud computing

With the emergence of cloud computing, the concept of information security has become a major issue. Indeed, the security of such a system is the greatest concern of computer scientists, providers of cloud and organizations who want to adopt and benefit from these services. Cloud computing providers must implement concepts ensuring network security, hardware, data storage and strategies of control and access to services. All these elements help to preserve data security and ensuring the availability of services associated with the Cloud, to better satisfy clients and acquire and build their trust. However, even if the data storage security in cloud servers is assured, reluctance remain when it comes to process the confidential data. Indeed, the fear that sensitive data is being used is a major obstacle in the adoption of cloud services by enterprises. To overcome this obstacle, the use of methods that can perform operations on encrypted data without knowing the secret key, seems to be an effective way to strengthen the confidentiality of information. In this paper we will examine the challenges facing Homomorphic Encryption methods to allow suppliers of cloud to perform operations on encrypted data, and provide the same results after treatment, as if they were performing calculations on raw data.


Compact LTCC Bandpass Filter With Wide Stopband Using Discriminating Coupling

This paper presents a novel compact low-temperature cofired ceramic (LTCC) bandpass filter (BPF) with wide stopband and high selectivity. The proposed circuit consists of two coupled λg/4 transmission-line resonators. A special coupling region is selected to realize a novel discriminating coupling scheme for generating a transmission zero (TZ) at the third harmonic frequency. The mechanism is analyzed and the design guideline is described. The source-load coupling is introduced to generate two TZs near the passband and one in the stopband. Thus, wide stopband can be obtained without extra circuits. Due to the LTCC multilayer structures, the filter size is 0.058 λg×0.058 λg×0.011 λg, or 2.63 mm × 2.61 mm × 0.5 mm. The simulated and measured results of the demonstrated LTCC BPF are presented to validate the proposed design.


Cluster-based Web Summarization

We propose a novel approach to abstractive Web summarization based on the observation that summaries for similar URLs tend to be similar in both content and structure. We leverage existing URL clusters and construct per-cluster word graphs that combine known summaries while abstracting out URL-specific attributes. The resulting topology, conditioned on URL features, allows us to cast the summarization problem as a structured learning task using a lowest cost path search as the decoding step. Early experimental results on a large number of URL clusters show that this approach is able to outperform previously proposed Web summarizers.


Very-Heavy Sled Training for Improving Horizontal-Force Output in Soccer Players.

BACKGROUND
Sprint running acceleration is a key feature of physical performance in team sports, and recent literature shows that the ability to generate large magnitudes of horizontal ground-reaction force and mechanical effectiveness of force application are paramount. The authors tested the hypothesis that very-heavy loaded sled sprint training would induce an improvement in horizontal-force production, via an increased effectiveness of application.

METHODS
Training-induced changes in sprint performance and mechanical outputs were computed using a field method based on velocity-time data, before and after an 8-wk protocol (16 sessions of 10- × 20-m sprints). Sixteen male amateur soccer players were assigned to either a very-heavy sled (80% body mass sled load) or a control group (unresisted sprints).

RESULTS
The main outcome of this pilot study is that very-heavy sled-resisted sprint training, using much greater loads than traditionally recommended, clearly increased maximal horizontal-force production compared with standard unloaded sprint training (effect size of 0.80 vs 0.20 for controls, unclear between-groups difference) and mechanical effectiveness (ie, more horizontally applied force; effect size of 0.95 vs -0.11, moderate between-groups difference). In addition, 5-m and 20-m sprint performance improvements were moderate and small for the very-heavy sled group and small and trivial for the control group, respectively. Practical Applications: This brief report highlights the usefulness of very-heavy sled (80% body mass) training, which may suggest value for practical improvement of mechanical effectiveness and maximal horizontal-force capabilities in soccer players and other team-sport athletes.

RESULTS
This study may encourage further research to confirm the usefulness of very-heavy sled in this context.


Analysis of malicious input issues on intelligent systems

Intelligent systems can facilitate decision making and have been widely applied to various domains. The output of intelligent systems relies on the users’ input. However, with the development of Web-Based Interface, users can easily provide dishonest input. Therefore, the accuracy of the generated decision will be affected. This dissertation presents three essays to discuss the defense solutions for malicious input into three types of intelligent systems: expert systems, recommender systems, and rating systems. Different methods are proposed in each domain based on the nature of each problem. The first essay addresses the input distortion issue in expert systems. It develops four methods to distinguish liars from truth-tellers, and redesign the expert systems to control the impact of input distortion by liars. Experimental results show that the proposed methods could lead to the better accuracy or the lower misclassification cost. The second essay addresses the shilling attack issue in recommender systems. It proposes an integrated Value-based Neighbor Selection (VNS) approach, which aims to select proper neighbors for recommendation systems that maximize the e-retailer’s profit while protecting the system from shilling attacks. Simulations are conducted to demonstrate the effectiveness of the proposed method. The third essay addresses the rating fraud issue in rating systems. It designs a twophase procedure for rating fraud detection based on the temporal analysis on the rating series.


Why it can matter more than IQ

Goleman highlights the many reports of the disintegration of civility and safety, and the onslaught of mean-spirited impulse running amok and claims that these only reflect back to us, on a larger scale, a creeping sense of emotions out of control in our own lives, and in the lives of those people living around us. He seeks to make sense of the senselessness and bring about change which will help our children fare better in life.


Learning decision trees from dynamic data streams

This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental algorithm, that works online, processing each example in constant time, and performing a single scan over the training examples. It uses analytical techniques to choose the splitting criteria, and the information gain to estimate the merit of each possible splitting-test. For multi-class problems the algorithm grows a binary tree for each possible pair of classes, leading to a forest of trees. Decision nodes and leaves contain naive-Bayes classifiers playing different roles during the induction process. Naive-Bayes in leaves are used to classify test examples, naive-Bayes in inner nodes can be used as multivariate splitting-tests if chosen by the splitting criteria, and used to detect drift in the distribution of the examples that traverse the node. When a drift is detected, all the sub-tree rooted at that node will be pruned. The use of naive-Bayes classifiers at leaves to classify test examples, the use of splitting-tests based on the outcome of naive-Bayes, and the use of naive-Bayes classifiers at decision nodes to detect drift are directly obtained from the sufficient statistics required to compute the splitting criteria, without no additional computations. This aspect is a main advantage in the context of high-speed data streams. This methodology was tested with artificial and real-world data sets. The experimental results show a very good performance in comparison to a batch decision tree learner, and high capacity to detect and react to drift.


Hockey Action Recognition via Integrated Stacked Hourglass Network

A convolutional neural network (CNN) has been designed to interpret player actions in ice hockey video. The hourglass network is employed as the base to generate player pose estimation and layers are added to this network to produce action recognition. As such, the unified architecture is referred to as action recognition hourglass network, or ARHN. ARHN has three components. The first component is the latent pose estimator, the second transforms latent features to a common frame of reference, and the third performs action recognition. Since no benchmark dataset for pose estimation or action recognition is available for hockey players, we generate such an annotated dataset. Experimental results show action recognition accuracy of 65% for four types of actions in hockey. When similar poses are merged to three and two classes, the accuracy rate increases to 71% and 78%, proving the efficacy of the methodology for automated action recognition in hockey.


A qualitative exploration of individuals' motivators for seeking substance user treatment.

Substance abuse continues to greatly impact the US population. The purpose of this study is to improve understanding of the reasons why individuals seek substance user treatment. Qualitative interviews were conducted with 50 men (n = 25) and women (n = 25) across eight substance user treatment programs in Durham, North Carolina, in 2010. We identify six motivating themes regarding reasons for seeking treatment and explore how these themes vary by the participants' mode of entry into treatment. Findings provide a holistic understanding of how and why individuals come into treatment that can enhance the quality and impact of substance user services.


Learning Context for Text Categorization

This paper describes our work which is based on discovering context for text document categorization. The document categorization approach is derived from a combination of a learning paradigm known as relation extraction and an technique known as context discovery. We demonstrate the effectiveness of our categorization approach using reuters 21578 dataset and synthetic real world data from sports domain. Our experimental results indicate that the learned context greatly improves the categorization performance as compared to traditional categorization approaches.


β-VAE : L EARNING B ASIC

Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β > 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.


Exploration of Large Networks with Covariates via Fast and Universal Latent Space Model Fitting

Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing likelihood for a special class of inner-product models while working simultaneously for a wide range of different latent space models, such as distance models, which allow latent vectors to affect edge formation in flexible ways. These fitting methods, especially the one based on projected gradient descent, are fast and scalable to large networks. We obtain their rates of convergence for both inner-product models and beyond. The effectiveness of the modeling approach and fitting algorithms is demonstrated on five real world network datasets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.