Three Essays on Trust Mining in Online Social Networks

This dissertation research consists of three essays on studying trust in online social networks. Trust plays a critical role in online social relationships, because of the high levels of risk and uncertainty involved. Guided by relevant social science and computational graph theories, I develop conceptual and predictive models to gain insights into trusting behaviors in online social relationships. In the first essay, I propose a conceptual model of trust formation in online social networks. This is the first study that integrates the existing graph-based view of trust formation in social networks with socio-psychological theories of trust to provide a richer understanding of trusting behaviors in online social networks. I introduce new behavioral antecedents of trusting behaviors and redefine and integrate existing graph-based concepts to develop the proposed conceptual model. The empirical findings indicate that both socio-psychological and graph-based trust-related factors should be considered in studying trust formation in online social networks. In the second essay, I propose a theory-based predictive model to predict trust and distrust links in online social networks. Previous trust prediction models used limited network structural data to predict future trust/distrust relationships, ignoring the underlying behavioral trust-inducing factors. I identify a comprehensive set of behavioral and structural predictors of trust/distrust links based on related theories, and then build multiple supervised classification models to predict trust/distrust links in online social networks. The empirical results confirm the superior fit and predictive performance of the proposed model over the baselines. In the third essay, I propose a lexicon-based text mining model to mine trust related user-generated content (UGC). This is the first theory-based text mining model to examine important factors in online trusting decisions from UGC. I build domain-specific trustworthiness lexicons for online social networks based on related behavioral foundations and text mining techniques. Next, I propose a lexicon-based text mining model that automatically extracts and classifies trustworthiness characteristics from trust reviews. The empirical evaluations show the superior performance of the proposed text mining system over the baselines

Chemical composition, in vitro digestibility and palatability of nine plant species for dromedary camels in the province of Semnan, Iran

This work studied the chemical composition of plants, and their digestibility and palatability to camels, selecting plants most eaten by camels from the Iranian desert of the province of Semnan. The results indicated that the order of usefulness, from the most useful, was: Salsola arbuscula, Seidlitzia rosmarinus, Suaeda fruticosa, Alhagi camelorum, Haloxylon ammodendron, Halostachys spp., Tamarix tetragyna, Tamarix stricta and Hammada salicornica. No correlation was detected between the organic matterdigestibility in dry matter and chemical composition, and there was no consistent relationship between either of these variables and palatability

Aligning Cybersecurity in Higher Education with Industry Needs

Cybersecurity is among the highest in-demand skills for Information Systems graduates and therefore is critical for the Information Systems curriculum. There is a substantial lack of skilled cybersecurity graduates. It is estimated that there is a global shortage of almost three and a half million cybersecurity professionals in 2022. Organizations are facing difficulties filling security positions. Thus, the Information Systems curriculum must be redesigned to meet business and industry needs and better prepare Information Systems graduates for cybersecurity careers. This study provides a model for designing a cybersecurity course that will align with industry needs to respond to the shortage of cybersecurity professionals. The proposed model is based on backward course design, aligned with the guidelines from the National Institute of Standards and Technology Cybersecurity Framework and The National Initiative for Cybersecurity Education Strategic Plan, and insights from interviews with industry professionals. We applied the model at a higher education institute in the USA, as higher education graduates fill most cybersecurity positions. The designed course was met with high levels of student satisfaction, positive industry feedback, and high levels of student success. Our proposed model can be applied to any educational institute and customized to desired needs of the institute, students, and the industry with minimal cost and time consideration

Thermal Management of E–Motors in Electric Vehicle Application Employing LPTN Model

The electric motor is at the center focus as an alternative to the internal combustion engine for automotive applications since it does not produce greenhouse gas emissions and can contribute significantly to the reduction of fossil fuel consumption globally. As extensive research works are being done on electric vehicles at present, thermal analysis of traction motor is increasingly becoming the key design factor to produce electric motors with high power and torque capabilities in order to satisfy electric vehicle driving requirements. Motor losses cause active heat generation in the motor components and excessive temperature rise affects the electromagnetic performance of the traction motor. High torque and power requirements based on the driving conditions under urban and highway drive conditions demand high capacity motor cooling system in order to keep the temperature within the safe limit. Hence, it is critical to develop and design a temperature prediction tool to dynamically estimate the winding and magnet temperature and regulate cooling to remove excessive heat from the motor. Conventional thermal modeling of motors includes analytical and numerical modeling. Analytical modeling is done by using Lumped Parameter Thermal Network (LPTN) which is analogous to electric circuit and a fast method for predicting temperature. It uses heat transfer equations involving thermal resistances and thermal capacitances to analytically determine temperature at different nodes. Numerical modeling is done in two ways–Finite Element Analysis and Computational Fluid Dynamics. Numerical modeling can produce more accurate results, but it requires more computational time. Since the temperature of motor components has to be predicted very quickly, i.e. during driving, LPTN is more effective because LPTN can quickly predict temperature based on the heat transfer equations. This thesis proposes an LPTN model that predicts motor temperature and regulates the required coolant flow rate simultaneously. Thus, it is able to dynamically predict the temperature. MATLAB Simulink has been used for simulation of the LPTN model for a laboratory PMSM prototype. The thermal resistances in the thermal network model have been obtained from the motor geometrical parameters. The electromagnetic loss data with respect to torque and speed were taken as input, and thus the temperature results of motor components have been found. The future work will be to implement this model into full scale prototype of the motor

A simple powerful bivariate test for two sample location problems in experimental and observational studies

<p>Abstract</p> <p>Background</p> <p>In many areas of medical research, a bivariate analysis is desirable because it simultaneously tests two response variables that are of equal interest and importance in two populations. Several parametric and nonparametric bivariate procedures are available for the location problem but each of them requires a series of stringent assumptions such as specific distribution, affine-invariance or elliptical symmetry.</p> <p>The aim of this study is to propose a powerful test statistic that requires none of the aforementioned assumptions. We have reduced the bivariate problem to the univariate problem of sum or subtraction of measurements. A simple bivariate test for the difference in location between two populations is proposed.</p> <p>Method</p> <p>In this study the proposed test is compared with Hotelling's <it>T</it>²test, two sample Rank test, Cramer test for multivariate two sample problem and Mathur's test using Monte Carlo simulation techniques. The power study shows that the proposed test performs better than any of its competitors for most of the populations considered and is equivalent to the Rank test in specific distributions.</p> <p>Conclusions</p> <p>Using simulation studies, we show that the proposed test will perform much better under different conditions of underlying population distribution such as normality or non-normality, skewed or symmetric, medium tailed or heavy tailed. The test is therefore recommended for practical applications because it is more powerful than any of the alternatives compared in this paper for almost all the shifts in location and in any direction.</p

Unsupervised brand name extraction using domain adaptation

Business intelligence and analytics is an area of research that analyzes the existing business data to extract the insights needed for a successful business planning. Textual data derived from tweets, forum posts, and blogs are from different business domains, and contain useful information for the organizations. This thesis proposes a method for extracting brand and product names from text; brand names as a subset of named entities can give a great deal of information about the whole document. In this thesis, a context window is defined to capture the context of a word in a sentence. In addition, a word embedding model is locally trained to have a domain specific model and finally, a domain adaptation technique is employed to transfer the knowledge from one domain with labeled data to a new domain. The results indicate a significant improvement in recall measure for extracting brand names from a new domain

Aligning Information Systems Security in Higher Educaiton With Industry Needs

Organizations have become especially vulnerable to security threats to their most important asset, information. As a result, information security (ISec) has become one of the most demanded skills of Information System (IS) graduates and therefore is of critical importance for ISec curriculum. However, there is still a big shortage in skilled ISec graduates that meet industry needs. Organizations are facing difficulties filling security analyst positions, and it is predicted there will be a global shortage of two million cyber security professionals by 2019. Previous research stresses that IS curriculum need to be redesigned to meet the business and industry needs and better prepare IS graduates for future careers (Lee and Han 2008; Tan et al 2018). This study provides a framework for how to use backwards course design to develop an Information Systems Security course that will align with industry needs. The proposed framework uses the three main stages of backwards course design including, identifying desired results, determining acceptable evidence, and planning learning experiences and instruction. In stage one, course educational outcomes and learning goals are redesigned to align with the industry needs. In stage two, all the course evaluations criteria and assessment methods are designed to support the updated learning objectives, and in stage three, instructional methods and learning activities are redesigned. We use the theoretical framework to redesign an IS security course at a medium sized business school in the southeastern United States to align with industry needs by incorporating the current and future security needs of US companies. To address the security industry needs, we research current security trends and needs, future security plans and needs, and required and preferred qualifications of job candidates by US security companies. Multiple interviews are done to survey IS security experts to examine the current and future industry needs. We will compile our research findings to create the outcomes and learning goals. Then we will develop our assessments and course actives to support the learning goals and outcomes. The final redesigned course along practical implications are presented

The positive edge pricing rule for the dual simplex

International audienceIn this article, we develop the two-dimensional positive edge criterion for the dual simplex. This work extends a similar pricing rule implemented by Towhidi et al. [24] to reduce the negative effects of degeneracy in the primal simplex. In the dual simplex, degeneracy occurs when nonbasic variables have a zero reduced cost, and it may lead to pivots that do not improve the objective value. We analyze dual degeneracy to characterize a particular set of dual compatible variables such that if any of them is selected to leave the basis the pivot will be nondegenerate. The dual positive edge rule can be used to modify any pivot selection rule so as to prioritize compatible variables. The expected effect is to reduce the number of pivots during the solution of degenerate problems with the dual simplex. For the experiments, we implement the positive edge rule within the dual simplex of the COIN-OR LP solver, and combine it with both the dual Dantzig and the dual steepest edge criteria. We test our implementation on 62 instances from four well-known benchmarks for linear programming. The results show that the dual positive edge rule significantly improves on the classical pricing rules

Ranking the Cobalt Coating Nanostructures, Produced by Direct current Through the Analytic Hierarchy Process (AHP)

In recent years, cobalt coating has been known as an alternative material instead of chromium in corrosion and erosion resistant behavior. Extensive research has been carried out on a variety of electroplated cobalt coatings. In this study, for the first time, the relative priority of the cobalt coating has been calculated and ranked theoretically by the Analytic Hierarchy Process (AHP). For this purpose, through the AHP and the Expert Choice software, benefiting from expert opinions, the relative weights of the effective parameters on achieving nanostructure coating have been calculated. Then, by using the weights obtained, the relative priority of five available Co coatings was calculated and the quality of them was ranked. Among available Co coatings, the coating with 5 mA/ current density, pH 3, electrolyte saccharin of 0.25 grams per liter and a temperature of 45 °C during 30 minutes, in comparing with others had more favorable conditions for achieving nano-grain size. This shows that before experimental tests, the best alternatives to achieve the ultimate goal could be anticipated. This anticipation leads to reduce in trial and error and the multiplicity of the tests in investigations. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3488