Southern Polytechnic State University

Impulsivity is considered a personality trait affecting behavior in many life domains, from recreational activities to important decision making. When extreme, it is associated with mental health problems, such as substance use disorders, as well as with interpersonal and social difficulties, including juvenile delinquency and criminality. Yet, trait impulsivity may not be a unitary construct. We review commonly used self-report measures of personality trait impulsivity and related constructs (e.g., sensation seeking), plus the opposite pole, control or constraint. A meta-analytic principal-components factor analysis demonstrated that these scales comprise 3 distinct factors, each of which aligns with a broad, higher order personality factor-Neuroticism/Negative Emotionality, Disinhibition versus Constraint/Conscientiousness, and Extraversion/Positive Emotionality/Sensation Seeking. Moreover, Disinhibition versus Constraint/Conscientiousness comprise 2 correlated but distinct subfactors: Disinhibition versus Constraint and Conscientiousness/Will versus Resourcelessness. We also review laboratory tasks that purport to measure a construct similar to trait impulsivity. A meta-analytic principal-components factor analysis demonstrated that these tasks constitute 4 factors (Inattention, Inhibition, Impulsive Decision-Making, and Shifting). Although relations between these 2 measurement models are consistently low to very low, relations between both trait scales and laboratory behavioral tasks and daily-life impulsive behaviors are moderate. That is, both independently predict problematic daily-life impulsive behaviors, such as substance use, gambling, and delinquency; their joint use has incremental predictive power over the use of either type of measure alone and furthers our understanding of these important, problematic behaviors. Future use of confirmatory methods should help to ascertain with greater precision the number of and relations between impulsivity-related components.

The paper presents the correlation and correlation coefficient of single-valued neutrosophic sets (SVNSs) based on the extension of the correlation of intuitionistic fuzzy sets and demonstrates that the cosine similarity measure is a special case of the correlation coefficient in SVNS. Then a decision-making method is proposed by the use of the weighted correlation coefficient or the weighted cosine similarity measure of SVNSs, in which the evaluation information for alternatives with respect to criteria is carried out by truth-membership degree, indeterminacy-membership degree, and falsity-membership degree under single-valued neutrosophic environment. We utilize the weighted correlation coefficient or the weighted cosine similarity measure between each alternative and the ideal alternative to rank the alternatives and to determine the best one(s). Finally, an illustrative example demonstrates the application of the proposed decision-making method.

Hyperspectral imaging fully portrays materials through numerous and contiguous spectral bands. It is a very useful technique in various fields, including astronomy, medicine, food safety, forensics, and target detection. However, hyperspectral images include redundant measurements, and most classification studies encountered the Hughes phenomenon. Finding a small subset of effective features to model the characteristics of classes represented in the data for classification is a critical preprocessing step required to render a classifier effective in hyperspectral image classification. In our previous work, an automatic method for selecting the radial basis function (RBF) parameter (i.e., σ) for a support vector machine (SVM) was proposed. A criterion that contains the between-class and within-class information was proposed to measure the separability of the feature space with respect to the RBF kernel. Thereafter, the optimal RBF kernel parameter was obtained by optimizing the criterion. This study proposes a kernel-based feature selection method with a criterion that is an integration of the previous work and the linear combination of features. In this new method, two properties can be achieved according to the magnitudes of the coefficients being calculated: the small subset of features and the ranking of features. Experimental results on both one simulated dataset and two hyperspectral images (the Indian Pine Site dataset and the Pavia University dataset) show that the proposed method improves the classification performance of the SVM.

Calculating and generating optimal motion path automatically is one of the key issues in virtual human motion path planning. To solve the point, the improved A* algorithm has been analyzed and realized in this paper, we modified the traditional A* algorithm by weighted processing of evaluation function, which made the searching steps reduced from 200 to 80 and searching time reduced from 4.359s to 2.823s in the feasible path planning. The artificial searching marker, which can escape from the barrier trap effectively and quickly, is also introduced to avoid searching the invalid region repeatedly, making the algorithm more effective and accurate in finding the feasible path in unknown environments. We solve the issue of virtual human's obstacle avoidance and navigation through optimizing the feasible path to get the shortest path.

Fis is a nucleoid-associated protein in Escherichia coli that is abundant during early exponential growth in rich medium but is in short supply during stationary phase. Its role as a transcriptional regulator has been demonstrated for an increasing number of genes. In order to gain insight into the global effects of Fis on E. coli gene expression during different stages of growth in rich medium, DNA microarray analyses were conducted in fis and wild-type strains during early, mid-, late-exponential and stationary growth phases. The results uncovered 231 significantly regulated genes that were distributed over 15 functional categories. Regulatory effects were observed at all growth stages examined. Coordinate upregulation was observed for a number of genes involved in translation, flagellar biosynthesis and motility, nutrient transport, carbon compound metabolism, and energy metabolism at different growth stages. Coordinate down-regulation was also observed for genes involved in stress response, amino acid and nucleotide biosynthesis, energy and intermediary metabolism, and nutrient transport. As cells transitioned from the early to the late-exponential growth phase, different functional categories of genes were regulated, and a gradual shift occurred towards mostly down-regulation. The results demonstrate that the growth phase-dependent Fis expression triggers coordinate regulation of 15 categories of functionally related genes during specific stages of growth of an E. coli culture.

Recently SQL injection attack (SIA) has become a major threat to Web applications. Via carefully crafted user input, attackers can expose or manipulate the back-end database of a Web application. This paper proposes the construction and outlines the design of a static analysis framework (called SAFELI) for identifying SIA vulnerabilities at compile time. SAFELI statically inspects MSIL bytecode of an ASP.NET Web application, using symbolic execution. At each hotspot that submits SQL query, a hybrid constraint solver is used to find out the corresponding user input that could lead to breach of information security. Once completed, SAFELI has the future potential to discover more delicate SQL injection attacks than black-box Web security inspection tools.

This paper develops a robust vision-based mobile manipulation system for wheeled mobile robots (WMRs). In particular, this paper addresses the retention of visual features in the field of view of the camera, which is an important robustness issue in visual servoing. First, the classical approach of image-based visual servoing (IBVS) for fixed-base manipulators is extended to WMRs and a control law with Lyapunov stability is determined. Second, in order to guarantee visibility of visual features, an innovative controller with machine learning using Q-learning is proposed, which can learn its behavior policy and autonomously improve its performance. Third, a hybrid controller for robust mobile manipulation is developed to integrate the IBVS controller and the Q-learning controller through a rule-based arbitrator. This is thought to be the first paper that integrates reinforcement learning or Q-learning with visual servoing to achieve robust operation. Experiments are carried out to validate the approaches developed in this paper. The experimental results show that the new hybrid controller developed here possesses the capabilities of self-learning and fast response, and provides a balanced performance with respect to robustness and accuracy.

This document represents the final report of the Joint Task Force on Computing Curricula - an undertaking of SIGITE (Special Interest Group on Information Technology Education) of the ACM (Association for Computing Machinery), the ACM, and the IEEE Computer Society - for four-year programs in Information Technology. This report dates back to December 2001, as described in Chapter 2.

IL-33 is an IL-1-related cytokine which has been implicated in T(h)2-associated biology and allergic diseases in humans and mice. IL-33 stimulates T(h)2 cells, mast cells, eosinophils, basophils, iNKT cells and circulating CD34(+) stem cells to proliferate and produce pro-allergic cytokines such as IL-5 and IL-13. IL-33 mediates its cytokine effects through a receptor consisting of ST2 and IL-1RAcP. Whereas IL-1RAcP is ubiquitously expressed, ST2 expression is cell-type restricted and determines responsiveness to IL-33. Studies employing ST2-deficient mice have reported variable results on the role of this receptor, and consequently IL-33, with regards to allergic lung inflammation. In this study, we demonstrate that IL-33 is important for allergic lung inflammation. Intra-nasal administration of IL-33 triggered an immediate allergic response in the airways, and more importantly, we show that endogenous IL-33 contributes to airway inflammation and peripheral antigen-specific responses in ovalbumin-induced acute allergic lung inflammation using IL-33-deficient mice. Our results suggest that IL-33 is sufficient and required for severe allergic inflammation in the lung and support the concept of IL-33 as a therapeutic target in allergic lung inflammation.

Quantitative content analysis can enrich research in technical communication by identifying the frequency of thematic or rhetorical patterns and then exploring their relationship through inferential statistics. Over the last decade, the field has published few content analyses, and several of these applications have been qualitative, diluting the method's inherent rigor. This paper describes the versatility of quantitative content analysis and offers a broader application for its use in the field. This discussion frames two original case studies that illustrate the design variability that content analysis offers researchers.

Fuzzy clustering model is an essential tool to find the proper cluster structure of given data sets in pattern and image classification. In this paper, a new weighted fuzzy C-Means (NW-FCM) algorithm is proposed to improve the performance of both FCM and FWCM models for high-dimensional multiclass pattern recognition problems. The methodology used in NW-FCM is the concept of weighted mean from the nonparametric weighted feature extraction (NWFE) and cluster mean from discriminant analysis feature extraction (DAFE). These two concepts are combined in NW-FCM for unsupervised clustering. The main features of NW-FCM, when compared to FCM, are the inclusion of the weighted mean to increase the accuracy, and, when compared to FWCM, the centroid of each cluster is included to increase the stability. The motivation of this work is to meliorate the well-known fuzzy C-Means algorithm (FCM) and a recently proposed fuzzy weighted C-Means algorithm (FWCM). Our finding is that the proposed algorithm gives greater classification accuracy and stability than that of FCM and FWCM. Experimental results on both synthetic and real data demonstrate that the proposed clustering algorithm will generate better clustering results than those of FCM and FWCM algorithms, in particularly for hyperspectral images.

Technology is often touted as the savior of education (Collins & Haverson, 2009). However, is technology the panacea that it is made out to be? This paper is an extended conversation among a group of faculty members at three different universities and their attitudes and beliefs about technology and education. Three professors shared their pro-technology stance and three took a less favorable view. The contents of the conversation were then analyzed by a neutral party to extract the various themes that emerged. What was discovered was that were three major threads to the conversation: technology and educational access, online education, and technology and instructional strategies. While there was little agreement, throughout the evolution of the conversation, both sides began to understand each other a little more.

Analysis of seismic surface waves offers a noninvasive method to estimate the dynamic engineering properties of the near-surface earth. Traditional two-sensor spectral analysis of surface waves (SASW) techniques suffer from several limitations that impede their use in practice, including poor resolution, inability to properly account for multiple modes, and an imprecise understanding of near-field effects. Current multichannel and array analysis of surface waves resolve several of these limitations, but still suffer from a near-field model incompatibility, i.e., estimating plane wave parameters in a cylindrically spreading wave field. This paper introduces cylindrical beamforming, an array processing method to overcome the limitations of plane wave processing methods. To aide in motivating the cylindrical beamformer, the paper discusses the near-field effects and explains some of the underlying physical reasons for previous observations regarding the near-field. The cylindrical beamformer is then introduced to more accurately model the wave field, allowing better estimates of phase velocity from point sources. The results from measured data taken with a linear array on a site in Atlanta are used to compare the methods.

The evolution from transaction marketing to relationship marketing in recent years has resulted in research indicating the need for more rigorous databases and greater utilization of current computerized tracking systems. Relationship selling has been examined and the results stress long‐term perspectives to the dyadic exchange process to enhance sales results. Considering the role of trust and culture in the relationship marketing process would indicate the need to pursue future research into a deeper understanding of the customer. Seeking knowledge of a customer’s personal feelings concerning their comfort level with various communication approaches could enhance the reception of messages crafted for them. The discipline needs to move beyond the numbers to a more abstract analysis of the customer as an individual with specific feelings toward various marketing approaches.

Community Health Centers (CHCs) provide family-oriented healthcare services for people living in rural and urban medically underserved communities; they are an important part of the government's plan to make healthcare more affordable. An optimization model is developed to determine the best location and number of new CHCs in a geographical network, as well as what services each CHC should offer at which capacity level. The weighted demand coverage of the needy population is maximized subject to budget and capacity constraints, where costs are fixed and variable. Statistical methods are applied to national health databases to determine important predictors of healthcare need and disease weights, and these methods are applied to census data to obtain county-based estimates of demand. Using several performance metrics such as the number of encounters, service of uninsured persons, and coverage of rural counties, the results of the system approach to location are analyzed using the state of Georgia as a prototype. It is demonstrated that optimizing the overall network can result in improvements of 20% in several measures. The proposed model is used to analyze policy questions such as how to serve the uninsured.

Research frameworks outline key aspects of STEM (science, technology, engineering, mathematics) integration for teachers, but translating this research into productive changes in teachers’ classroom practices remains a challenge, particularly in schools without an emphasis on STEM integration. In this article, we detail how a STEM education descriptive framework was used to design and enact a year-long professional development with eight secondary teachers at non-STEM focused schools in Southeast USA. Using teacher self-efficacy scores on pre- and post-tests, self-reported STEM integration efforts, and other feedback, we saw productive changes in teachers’ classroom practices and self-efficacy. We conclude with how this STEM education descriptive framework can be helpful in designing effective professional development for teachers at non-STEM focused schools.

Security assessment is largely ad hoc today due to its inherent complexity. The existing methods are typically experimental in nature highly dependent of the assessor's experience, and the security metrics are usually qualitative. We propose to address the dual problems of experimental analysis and qualitative metrics by developing two complementary approaches for security assessment: (1) analytical modeling, and (2) metrics-based assessment. To avoid experimental evaluation, we put forward a formal model that permits the accurate and scientific analysis of different security attributes and security flaws. To avoid qualitative metrics leading to ambiguous conclusions, we put forward a collection of mathematical formulas based on which quantitative metrics can be derived. The vulnerability analysis model responses to the need for a theoretical foundation for modeling information security, and security metrics are the cornerstone of risk analysis and security management. In addition to the security analysis approach, we discuss security testing methods as well. A Relative Complete Coverage (RCC) principle is proposed along with an example of applying the RCC principle. The innovative ideas proposed in this paper include a hierarchical multi-level modeling approach to modeling vulnerability using model composition and refinement techniques, a data-centric, quantitative metrics mechanism, and multidimensional assessment capturing both process and product elements in a formalized framework.

In order to reach the goals of the Information Security Automation Program (ISAP) [1], we propose an ontological approach to capturing and utilizing the fundamental concepts in information security and their relationship, retrieving vulnerability data and reasoning about the cause and impact of vulnerabilities. Our ontology for vulnerability management (OVM) has been populated with all vulnerabilities in NVD [2] with additional inference rules, knowledge representation, and data-mining mechanisms. With the seamless integration of common vulnerabilities and their related concepts such as attacks and countermeasures, OVM provides a promising pathway to making ISAP successful.

Common practice holds that 80% of usability findings are discovered after five participants. Recent findings from web testing indicate that a much larger number of participants is required to get results and that independent teams testing the same web-based product do not replicate results. How many users are enough for web testing?

Game jams are events that allow game designers to develop innovative games in a time-constrained environment, typically within a 48-hour period during a weekend. Jams provide participants an opportunity to improve their skills, collaborate with their peers, and advance research and creativity in the field of game design. Having coordinated numerous jams locally and as one of the largest venues in the world for GGJ 2011, the authors present learned lessons on how to make these events into amazing collaborative opportunities and their results from research in surveying game jam participants before and after the authors’ most recent jam weekend.