Miracle-dz/UniFAR-FTUs huggingface dataset:
Anonymization in Social Ne tworks: A Survey on the issues of Data Privacy in Social Network Sites

Recent years have seen unprecedented growth in the popularity of social network systems. Social networks are online applications which allow its users to connect by various link types. Online social networks, such as Facebook, LinkedIn are progressively utilized by many people. It makes digital communication technologies sharpening tools for extending the social circle of people. It has already become an important integral part of our daily lives, enabling us to contact our friends and families on time. As social networks have developed rapidly, recent research has begun to explore social networks to understand their structure, advertising and marketing, and data mining online population, representing 1.2 billion users around the world. More and more social network data has been made publicly available and analyzed in one way or another. But some of the information revealed is meant to be private hence social network data has led to the risk of leakage of confidential information of individuals. However, privacy concerns can be used to prevent these efforts. Privacy pre- serving publishing of social network data becomes a more and more important concern. This leads for privacy-preserving social network data mining, which is the discovery of information and relationships from social network data without violating privacy. Privacy in online social networks data has been of utmost concern. Hence, the research in this field is still in its early years. Privacy preservation is a significant research issue in social networking. Since more personalized information is shared with the public, violating the privacy of a target user become much easier. We argue that the different privacy problems are entangled and that research on privacy in OSNs would benefit from a more holistic approach. In this paper, after an overview on different research subareas of SNSs, we will get more focused on the subarea of privacy protection in social network and we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social net- work data.
Understanding user participation and involvement in ERP use

User participation and involvement are being studied in the context of enterprise resource planning (ERP) systems and the research findings based on the data gathered till date are presented in this paper. Given the enterprise-wide scope of these systems and their high level of complexity, in addition to a different implementation methodology, differences in the nature of user participation and involvement were expected. Using Hartwick and Barki's model based on the theory of reasoned action a revised model was developed and tested empirically. While Hartwick and Barki's model explains user behavior vis-a-vis user participation and involvement, a more parsimonious model demonstrates that the usage dynamics in ERP implementation are indeed different. Given the mandatory nature of ERP usage, the subjective norm influencing behavioral intention concerning use may well be extra-organizational.
Analysis of Data Placement Strategy based on Computing Power of Nodes on Heterogeneous Hadoop Clusters

Hadoop and the term ’Big Data’ go hand in hand. The information explosion caused due to cloud and distributed computing lead to the curiosity to process and analyze massive amount of data. The process and analysis helps to add value to an organization or derive valuable information. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Hadoop relies on its capability to take computation to the nodes rather than migrating the data around the nodes which might cause a significant network overhead. This strategy has its potential benefits on homogeneous environment but it might not be suitable on an heterogeneous environment. The time taken to process the data on a slower node on a heterogeneous environment might be significantly higher than the sum of network overhead and processing time on a faster node. Hence, it is necessary to study the data placement policy where we can distribute the data based on the processing power of a node. The project explores this data placement policy and notes the ramifications of this strategy based on running few benchmark applications.
DTC Scheme for a Four-Switch Inverter-Fed Induction Motor Emulating the Six-Switch Inverter Operation

This paper describes a combination of direct torque control (DTC) and space vector modulation (SVM) for an adjustable speed sensor less induction motor (IM) drive. In this paper we introduced a new strategy i.e., the induction motor drives fed by a four switch inverter emulate the operation of the conventional six switch operation. Therefore, a suitable combination of the four unbalanced voltage vectors intrinsically generated by the FSTPI, leading to the synthesis of the six balanced voltage vectors of the SSTPI. Simulation results have revealed that, the proposed DTC strategy with SVM modulation, FSTPI-fed IM drives exhibit interesting performance
Adaptive system identification using multilayer neural networks and Gaussian potential function networks

This paper deals with the characteristics of multilayer neural networks and radial basis function networks, and provides their hybridization by considering their advantages and disadvantages. The hybrid networks show their effectiveness in system identification as well as alleviate problems of error backpropagation algorithm such as local minima, slow speed, and size of structure by adopting other networks effectively. Potential performance improvement is demonstrated by computer simulation for two general problems of identification: static and dynamical system identification.
Connectionist modelling of phonotactic constraints in word recognition

Connectionist techniques for modeling the temporal statistics of phonemically transcribed spoken discourse as described. The aim is to investigate the limits of modeling psycholinguistic data at this prelexical level. The training data respect the frequency with which phoneme strings occur in conventional speech. The general model proposed uses a backpropagation through time learning procedure to train a network that can predict the identity of the phoneme at the next time step, identify the current one, and confirm the last five, after training on noisy data. The model eschews local representations of words and will have implications for current models of word recognition which use such representations. <<ETX>>
C AS A LOCUS OF CASE/AGREEMENT IN MODERN STANDARD ARABIC

This paper aims to contribute to the ongoing debate triggered by Chomsky’s (2004a) suggestion that C is a locus of Case/Agreement by investigating the issue in Modern Standard Arabic (MSA). Based on a detailed investigation of Complementizer Agreement (CA) and Case facts related to complementizers in Arabic, it is concluded that the Arabic C does not exhibit CA. However, C bears an Accusative Case feature and enters into an AGREE relation, independently of T, with the embedded subject in [Spec, TP], overwriting a Case feature previously-assigned by T. Thus, the present paper argues for the need to add the functional head C(omplementizer) to the inventory of functional heads that are independently specified for the Case feature and, thus, have the ability to value the Case of a DP in their c-command domain. It also argues, contra Chomsky (2004a, b, 2005), that C and T should have their own set of inflectional features each to initiate an AGREE operation independently accessing the subject of a clause/sentence (cf. Carstens 2003; van Koppen 2005; Haegeman & van Koppen 2009). The argument is based on a detailed investigation of the syntactic behaviour of complementizers in Arabic and an extension of this analysis to a class of elements popularly known in Arabic grammar as inna wa akhwatuha ‘ inna and sisters’, of which the Arabic complementizer anna is a member.
4 Methods for Handling Missing Data

The field of missing data analysis has evolved quickly; outdated strategies have been replaced by missing data procedures that make use of partial data to provide unbiased and efficient parameter estimates. Here, we describe an eight-step process for performing basic missing data procedures (single and multiple imputation) using NORM (Version 2.03; Schafer, 1997) and SAS (Version 9.2; SAS, 2011a). We make use of the automation utility, MIAutomate, to facilitate the analysis of NORM-imputed data with SPSS. We also perform a basic multiple regression to compare the performance of the following five missing data approaches: (1) complete cases analysis, (2) mean substitution, (3) FIML-based Amos, (4) multiple imputation with no auxiliary variables (MI), and (5) multiple imputation with auxiliary variables (MI+). Mean substitution showed clear bias in parameter estimates. Although the complete cases model yielded parameter estimates similar to those obtained with MI and FIML, the standard errors were consistently larger because the estimation was based on fewer cases. The MI and FIML models yielded reasonably unbiased estimates of the b-weights; however, they had noticeably lower t-values compared to the MI+ analysis. Based on prior research and the empirical results shown in this chapter, including auxiliary variables to the missing data model that are highly correlated with variables containing missingness is a good way to reduce the standard errors of estimated parameters. The prescribed missing data procedures will always perform at least as well as more traditional approaches (e.g., complete cases analysis), and in most circumstances, there will be a clear advantage of the prescribed procedures, in terms of estimation bias and/or statistical power. Useful and accessible missing data procedures are now an integral part of many mainstream statistical packages, making it increasingly difficult to justify not using them. Keywords: multiple imputation; SAS Proc MI; Amos; FIML; regression analysis
Hardware-controlled prefetching in directory-based cache coherent systems

Sequential consistency is the popular accepted criterion of correct execution in shared-memory multiprocessors. Typical implementation of sequential consistency requires each access to be delayed until the previous access in the same process completes. This is detrimental to performance. Prefetching is an effective way of overlapping the execution of memory accesses. This paper studies hardware-controlled prefetching in a directory-based cache coherent system, and proposes a new prefetching scheme as an improvement on the normal scheme. Besides, a cycle-by-cycle trace-driven simulation model is built to evaluate these prefetching schemes. Simulation results show that prefetching is effective in improving performance, and the new prefetching scheme we proposed can improve performance further.
Using Bayes Factors to Get the Most out of Linear Regression: A Practical Guide Using R

This guide is for readers who want to make rich inferences from their data. It does not contain anything new with regard to R code or theoretical development, but it does piece together information in an easy to follow guide. An article was recently published in a journal that is probably not well known by most researchers, Multivariate Behavioral Research, where the authors discuss the theoretical background of Bayesian inference and provide a simple web calculator for linear regression ( Rouder & Morey, 2012; http://pcl.missouri.edu/bf-reg). More recently, R code has been developed (Morey, Rouder , & Jamil, 2015) and compiled to conduct many different types of Bayesian analyses (Morey, 2015; http://bayesfactorpcl.r-forge.r-project.org/ ) . However, this leaves information scattered across websites and journal pages, and so it is not easily taken in by a researcher who doesn't know where to start with either Bayesian theory or R software. This guide has been put together in a way that a researcher who has never used Bayesian inference or R software could conduct, report, and interpret a Bayesian linear regression. It skips over the complicated derivations and mathematics behind the theory, and focuses on why Bayes factors are valuable and desirable to researchers. It then shows step-by-step how R software can be used for Bayesian model comparison as well as parameter estimation in linear regression problems.
International Journal of Advanced Robotic Systems Speech Emotion Recognition Using an Enhanced Kernel Isomap for Human-robot Interaction Regular Paper

which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Speech emotion recognition is currently an active research subject and has attracted extensive interest in the science community due to its vital application to human‐robot interaction. Most speech emotion recognition systems employ high‐dimensional speech features, indicating human emotion expression, to improve emotion recognition performance. To effectively reduce the size of speech features, in this paper, a new nonlinear dimensionality reduction method, called ʹenhanced kernel isometric mappingʹ (EKIsomap), is proposed and applied for speech emotion recognition in human‐robot interaction. The proposed method is used to nonlinearly extract the low‐dimensional discriminating embedded data representations from the original high‐ dimensional speech features with a striking improvement of performance on the speech emotion recognition tasks. Experimental results on the popular Berlin emotional speech corpus demonstrate the effectiveness of the proposed method.
A simple model for a thyristor-driven DC motor considering continuous and discontinuous current modes

A simple model for a thyristor driven DC motor is rigorously established. The derivation is based on the machine equations and on the definition of a pseudoinstantaneous mean-value function. The influence of the converter on the voltage applied to the DC motor is considered. The final model includes all significant effects, has the same structure for continuous and discontinuous current conduction modes, and allows simple control synthesis. The model is compared to the classical model of a DC motor, and the differences are highlighted. It is shown that, with the proposed model, it it easy to understand the influence of a thyristor converter on the dynamic performance of a DC motor, and, therefore, it has a significant educational aspect. A dual-mode adaptive current controller is designed using this model. Experimental and simulation results are compared to validate the work. >
Computational CAM: studying children and media in the age of big data

Abstract New media technologies enable the pervasive and unobtrusive tracking of children’s media use and interaction behaviors, and a host of new opportunities that researchers are only beginning to embrace. The possibilities for unlocking longstanding questions about children and media are thrilling, but the path forward is complicated by questions about epistemology, data, ethics and training. In this essay, I outline the promise and peril of computational social science for children and media research, drawing on advances from related fields to offer suggestions for researchers hoping to make use of big data and computational analytics.
Cascaded Deferred Rendering

A long-standing difficulty with rendering huge distances is depth-fighting; a visual artefact produced when two or more fragments overlap either due to coplanar geometry or insufficient depth precision. This thesis presents two novel methods, Cascaded Deferred Rendering (CDR) and Logarithmic Cascaded Deferred Rendering (LogCDR), as a solution to solve depth-fighting that is due to insufficient depth precision. This thesis also evaluates an existing method, logarithmic depth buffer, comparing it against the standard depth buffer in OpenGL, CDR and LogCDR. The most prominent solution found was logarithmic depth buffer because of performance, no overhead from frustum division and extensive culling, ease of implementation and conveniences such as easier implementation of transparency.
Automated Generation of Schematic Network Maps Adaptive to Display Sizes

Schematic maps have been popularly used for the representation of metro (or underground) lines and nowadays have also been used for the representation of other network maps. This paper presents a strategy for automated generation of schematic network maps for different display sizes, based on a redistribution of line density. This strategy consists of three steps: (a) estimation of spatial density of the network maps, (b) automated redistribution of line density in order to improve map clarity, for different display sizes, and (c) automated generation of schematic representations. An experimental evaluation has been carried out by using two sets of real-life network data. The results indicate that this strategy is able to generate schematic maps with much improved clarity for different display sizes and well-preserved map recognition.
Safety and efficacy of mechanical thrombectomy with Solitaire in patients with acute ischemic stroke

Abstract Background Stroke is the third most common cause of death. Ischemic stroke accounts for 87% of all strokes. The standard treatment for acute ischemic stroke (AIS) is IV recombinant tissue plasminogen activator for patients who can reach the hospital with in 4.5 h. A rapid and complete recanalization is a major factor in good clinical outcomes in the treatment of acute cerebral ischemia. Stent-based recanalization techniques are increasingly used in stroke caused by large vessel occlusion, with promising results. Objective To evaluate the safety and efficacy of Solitaire stent retriever in recanalizing cerebral vessels in patients with AIS. Materials and Methods We treated 16 patients of AIS with a mean age of 52.25 years, ranging from 16 to 76 years with Solitaire clot retrieval system from September 2011 to March 2013 at our stroke center. The anterior circulation strokes were treated within 6 h of stroke onset and the posterior circulation strokes were treated up to 24 h of stroke onset. Patients with intracerebral bleed and established large acute infarcts were excluded. Results Out of the 16 patients of AIS treated with Solitaire clot retrieval system, good recanalization (thrombolysis in cerebral infarction 2 [TICI 2] and TICI 3) was achieved in 14/16 patients (87.5%) and no recanalization achieved in 2/16 (12.5%) patients. Clinical outcome was good (modified Rankin score 2 or less) in 10 out of 16 patients (62.5%). Conclusion Mechanical clot retrieval with Solitaire appears to be safe and effective in acutely occluded large intracranial vessels.
The Soft Robotics Toolkit: Shared Resources for Research and Design

Abstract This article describes the development of the Soft Robotics Toolkit, a set of open access resources to support the design, fabrication, modeling, characterization, and control of soft robotic devices. The ultimate aim of the toolkit is to support researchers in building upon each other's work, and thereby advance the field of soft robotics. An additional aim is to support educators and encourage students to pursue careers in engineering and science by making the resources as accessible as possible. The toolkit was developed and refined through a series of pilot studies and user tests. Specifically, the resources were used by students in a project-based medical device design course; volunteers from a variety of backgrounds tested the toolkit and provided feedback, and soft robotics researchers used the collection of resources and contributed to its development. Throughout all user studies, qualitative data were collected and used to guide improvements to the toolkit. This process of testing and re.
Advances in instantaneous and dynamic localization in indoor environments

OF THE DISSERTATION Advances in instantaneous and dynamic localization in indoor environments by Begümhan Turgut Dissertation Director: Richard P. Martin The knowledge of the exact location of an object or person is an important enabler for a wide variety of applications. Localization, itself, has a long and distinguished history from sextants to the geodesic tools of modern civil engineers. The recent general availability of global positioning systems has enabled the development of new applications from car navigation and emergency assistance systems to location targeted advertisements. However these systems are largely limited to outdoor spaces preventing the accurate determination of location inside of a building. In this dissertation, we consider the issues of both the instantaneous and dynamic localization of people in an indoor environment where GPS signals are not available. We begin by studying instantaneous localization and the variety of new techniques that share the common approach of utilizing a number of fixed reference points with known locations in order to determine the location of the target. Since indoor environments usually prevent a direct line of sight from the target to a known point we need to infer the targets location from secondary measurements with varying reliability. We then outline a new method for instantaneous localization whose output not only provides the location of the target but also the confidence of the localization based on the environmental variability.
MEDICAL IMAGE ANALYSIS AND PROCESSING USING A DUAL TRANSFORM

The demand for images in medical field has increased drastically over the years. The need for reducing the storage space has resulted in image compression. This paper presents a dual transform for medical image compression algorithm. The experimental results determines how the compression ratio (CR), peak signal to noise ratio (PSNR) and SNR (signal to noise ratio) of different compression algorithms responds to dual transform algorithm.
Experts Ranking in Online Communities Using Combination of Text and Link Analysis

Online communities are a question answering environment where individuals can express their opinions freely. Quality of the information generated in the online community is dependent on the individual expertise level. If the individual has higher level of expertise, shared knowledge in online community is valuable and reliable. Experts ranking methods used for determining expertise level of individuals and evaluating the accuracy of shared knowledge. In this study, a novel hybrid method for ranking experts in online communities is presented. This approach incorporates users' relationship and content of users' answers to finding expert users in an online community. This method is applicable to all online communities and only corpus in the field of online community is needed to accomplish that. We evaluated our proposed method on Java online community and Cryptography section of StackExchange online community. Correlation between scores of our method and scores of expert users introduced in both online communities exceeds 0.8, which is highly a reasonable value.
Designing task allocations and cognitive support

This paper summarizes a method for Cognitive Task Analysis that guides the early stages of software development, aiming at an optimal cognitive load for operators of process control systems. The method is based on a practical theory of cognitive task load and support, which can be used to establish adequate task (re-)allocations and to design cognitive support. Recent test cases showed that application of the method resulted in improved task allocations and the design of effective cognitive support. One example is the design of user-interface support for handling high-demand situations on a ships bridge: an information handler, a rule provider, a diagnosis guide and a task scheduler.