Using the Affinity Research Group Model to Involve Undergraduate Students in Computer Science Research*

The Affinity Research Group model is an attractive vehicle for involving undergraduates in research, retaining them, and fostering their interest in higher education. Using this model, students are given opportunities to develop, employ, and integrate knowledge and skills required for research with knowledge and skills required for cooperative work. Potential adopters of the model often inquire about the feasibility of applying the model in a field like computer science, in which it often is the case that a student must have a solid academic foundation in order to be involved in research. This paper addresses this question by illustrating how the model has been applied to computer science research projects that involve students with different skill levels and experience. In particular, the paper presents example structured tasks and related activities that demonstrate how students develop domain expertise, gain an understanding and appreciation of the research process and its practice, and acquire technical, team, communication, problem‐solving, and higher‐level thinking skills.     

Energy-efficient and reliable data delivery in wireless sensor networks

Clustering has been used as one of energy-efficient mechanisms for data routing in wireless sensor networks. In hierarchical routing approaches, cluster heads are responsible for management (e.g. data aggregation, queries dispatch) and transmission of the collected data in the region controlled by them. For efficient data delivery, several researches have proposed various mechanisms for cluster organization and cluster head selection. However, less focus is given in the area of data transmission associated with Base Station (BS). In such a situation, any failure or packet loss may lead to considerable packet loss. For solving this problem, we propose an efficient data routing scheme for controlling data delivery from nodes to BS. In our proposed approach every node is aware about the link quality of all nodes and is able to deliver data to the BS through the most reliable and energy-efficient route.     

The craving to smoke in flight attendants: Relations with smoking deprivation, anticipation of smoking, and actual smoking.

In the study, the authors examined the effects of smoking deprivation, anticipation of smoking, and actual smoking on the craving to smoke. Flight attendants who were light to heavy smokers rated their craving to smoke at predetermined time points during a 2-way short flight (each leg 3–5.5 hr) and a 1-way long flight (8–13 hr). In both short and long flights, craving increased gradually and peaked as landing approached. Craving levels at the end of the 1st leg of the short flights were equal to those at the end of the long flight and were much higher than those at the parallel time point in the long flight. In the short flight, craving levels at the beginning of the 2nd leg dropped relative to the end of the 1st leg, both for participants who smoked during the intermission and for those who did not, though the drop was steeper for the former. The results provide additional evidence for the role of psychological factors in determining the craving to smoke in a naturalistic setting. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Upgrading of dissolution and anti-hypertensive effect of Carvedilol via two combined approaches: self-emulsification and liquisolid techniques

Objective: The aim of the study was to design a self-emulsifying drug delivery system (SEDDS) of the anti-hypertensive Carvedilol in liquid and liquisolid forms as a way to enhance its dissolution profile and anti-hypertensive effect.

Methods: Solubility studies of Carvedilol in various oils, surfactants and co-surfactants were conducted, followed by the construction of pseudo-ternary phase diagrams and other in vitro assessments. The selected SEDDS formulation (S1) was adsorbed onto solid powder excipients and compressed into tablets. The resulting liquisolid tablets were evaluated under British Pharmacopoeia (B.P.) specifications. Pre- and post-compression studies were performed to determine the flow properties and evaluate the liquisolid systems, followed by in vivo studies in hypertensive rats.

Results: Attempts of self-emulsification, droplet size, and thermodynamic stability studies showed acceptable results for the S1 formulation containing Capryol 90, Tween 20, and Transcutol HP (10:53.3:26.2%), respectively. Pre-compression studies showed adequate flowability and compatibility of liquid and solid excipients with Carvedilol. The selected liquisolid tablet (LS7) demonstrated the best disintegration and water absorption ratio in addition to satisfactory friability and hardness. A significantly (p?®. The in vivo study of LS7 formulation revealed a rapid significant (p?®.

Conclusion: Self-emulsifying liquisolid tablets expressed rapid onset of action with enhanced anti-hypertensive effect of Carvedilol.     

Propolis emulgel: a natural remedy for burn and wound

Objectives: To develop an alternative medicine, propolis, in emulgel formulation for burn and wound treatment.

Methods: The effect of two independent variables: emulsifying agent concentration and oil concentration on the cumulative amount of propolis release (dependent variable) from liquid paraffin and isopropyl palmitate emulgels was investigated. Eight formulations were prepared and evaluated for physical appearance, pH, propolis content, viscosity, and in vitro propolis release. The release results were fitted into different kinetic equations and analyzed using contour plot, interaction surface plot and one-way ANOVA. The selected emulgel formulation was investigated for its wound and burn healing activity in rats.

Results: All the prepared emulgels showed acceptable physical properties concerning color, homogeneity, consistency, and pH value. The concentration of emulsifying agent had more pronounced effect on propolis release than oil concentration. Formulations F1–F6 showed “anomalous” drug release, while Formulations F7 and F8 showed zero-order kinetic.

Conclusion: The formulation F7 with 5% isopropyl palmitate, 5% emulsifier mixture, 1% hydroxyl propyl methyl cellulose, 1% oleic acid, and 10% propylene glycol is a promising formula for better management of wound and burn.     

Integrating symbolic images into a multimedia database system using classification and abstraction approaches

Symbolic images are composed of a finite set of symbols that have a semantic meaning. Examples of symbolic images include maps (where the semantic meaning of the symbols is given in the legend), engineering drawings, and floor plans. Two approaches for supporting queries on symbolic-image databases that are based on image content are studied. The classification approach preprocesses all symbolic images and attaches a semantic classification and an associated certainty factor to each object that it finds in the image. The abstraction approach describes each object in the symbolic image by using a vector consisting of the values of some of its features (e.g., shape, genus, etc.). The approaches differ in the way in which responses to queries are computed. In the classification approach, images are retrieved on the basis of whether or not they contain objects that have the same classification as the objects in the query. On the other hand, in the abstraction approach, retrieval is on the basis of similarity of feature vector values of these objects. Methods of integrating these two approaches into a relational multimedia database management system so that symbolic images can be stored and retrieved based on their content are described. Schema definitions and indices that support query specifications involving spatial as well as contextual constraints are presented. Spatial constraints may be based on both locational information (e.g., distance) and relational information (e.g., north of). Different strategies for image retrieval for a number of typical queries using these approaches are described. Estimated costs are derived for these strategies. Results are reported of a comparative study of the two approaches in terms of image insertion time, storage space, retrieval accuracy, and retrieval time. Received June 12, 1998 / Accepted October 13, 1998     

An adaptive automated method for identity verification with performance guarantees

Businesses often rely on customers that access an automated resource to receive information, service or products. We present an adaptive method for automated authentication with performance guarantee of probabilistic error bounds, referred to as identity verification. A database includes a record of identifiers for each identity. The identifiers are partitioned into groups, where identifiers in the same group are correlated while identifiers in different groups are independent. A claimant requesting access into the system is probed with a sequence of identifiers. The response to an identifier can be a match, no-match, or ambiguous. Each identifier is characterized by prior response probabilities for a legitimate claimant and for an impostor. Impostor’s response probabilities are updated during a session. The method guarantees that the probabilities of accepting an impostor or rejecting a legitimate claimant do not exceed specified parameters. Once a given number of identifiers have been probed and the claimant has been neither accepted nor rejected, the session terminates with an inconclusive decision and the claimant is referred to further manual interrogation. The method computes various performance characteristics such as the probability that a session of a legitimate claimant terminates inconclusively. These characteristics are valuable in the design of an effective record of identifiers for each of the claimants.     

Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

Presently, smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping, e-learning, e-healthcare, etc. Despite the benefits of advanced technologies, issues are also existed from the transformation of the physical word into digital word, particularly in online social networks (OSN). Cyberbullying (CB) is a major problem in OSN which needs to be addressed by the use of automated natural language processing (NLP) and machine learning (ML) approaches. This article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks, named SRO-MLCOSN model. The presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking sites. The SRO-MLCOSN model initially employs Glove technique for word embedding process. Besides, a multiclass-weighted kernel extreme learning machine (M-WKELM) model is utilized for effectual identification and categorization of CB. Finally, Search and Rescue Optimization (SRO) algorithm is exploited to fine tune the parameters involved in the M-WKELM model. The experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision, recall, and F1-score of 96.24%, 98.71%, and 97.46% respectively.     

Enhanced Adaptive Brain-Computer Interface Approach for Intelligent Assistance to Disabled Peoples

Assistive devices for disabled people with the help of Brain-Computer Interaction (BCI) technology are becoming vital bio-medical engineering. People with physical disabilities need some assistive devices to perform their daily tasks. In these devices, higher latency factors need to be addressed appropriately. Therefore, the main goal of this research is to implement a real-time BCI architecture with minimum latency for command actuation. The proposed architecture is capable to communicate between different modules of the system by adopting an automotive, intelligent data processing and classification approach. Neuro-sky mind wave device has been used to transfer the data to our implemented server for command propulsion. Think-Net Convolutional Neural Network (TN-CNN) architecture has been proposed to recognize the brain signals and classify them into six primary mental states for data classification. Data collection and processing are the responsibility of the central integrated server for system load minimization. Testing of implemented architecture and deep learning model shows excellent results. The proposed system integrity level was the minimum data loss and the accurate commands processing mechanism. The training and testing results are 99% and 93% for custom model implementation based on TN-CNN. The proposed real-time architecture is capable of intelligent data processing unit with fewer errors, and it will benefit assistive devices working on the local server and cloud server.     

Cumulative voting consensus method for partitions with variable number of clusters

Over the past few years, there has been a renewed interest in the consensus clustering problem. Several new methods have been proposed for finding a consensus partition for a set of n data objects that optimally summarizes an ensemble. In this paper, we propose new consensus clustering algorithms with linear computational complexity in n. We consider clusterings generated with random number of clusters, which we describe by categorical random variables. We introduce the idea of cumulative voting as a solution for the problem of cluster label alignment, where, unlike the common one-to-one voting scheme, a probabilistic mapping is computed. We seek a first summary of the ensemble that minimizes the average squared distance between the mapped partitions and the optimal representation of the ensemble, where the selection criterion of the reference clustering is defined based on maximizing the information content as measured by the entropy. We describe cumulative vote weighting schemes and corresponding algorithms to compute an empirical probability distribution summarizing the ensemble. Given the arbitrary number of clusters of the input partitions, we formulate the problem of extracting the optimal consensus as that of finding a compressed summary of the estimated distribution that preserves maximum relevant information. An efficient solution is obtained using an agglomerative algorithm that minimizes the average generalized Jensen-Shannon divergence within the cluster. The empirical study demonstrates significant gains in accuracy and superior performance compared to several recent consensus clustering algorithms.