The problem of peer selection in peer-to-peer (P2P) video content distribution network is significant to solve since it affects the performance and efficiency of the network widely. In this article, a novel framework ...
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The problem of peer selection in peer-to-peer (P2P) video content distribution network is significant to solve since it affects the performance and efficiency of the network widely. In this article, a novel framework is introduced that uses fuzzy linear programming (FLP) to address the inherent uncertainties in peer selection. The primary motivation for the use of FLP lies in its capability to handle the imprecision and vagueness that are characteristic of dynamic P2P environments. Factors such as peer reliability, bandwidth, and proximity are often uncertain in this environment. By using fuzzy logic, the proposed framework models these criteria as fuzzy sets and then integrates uncertainty into the decision-making process. FLP is then applied to optimize peer selection, improving download speed, reducing download time, and enhancing peer reliability. The proposed method is evaluated and analyzed using extensive simulation with SciPy. The result reveals that proposed technique works better compared to some of the traditional methods in terms of download time, download speed and also reliability measure. It also exhibits approximately 20% of increase in download speed as well as a 15% decrease in download time compared to traditional approaches. It leads to faster content retrieval and enhanced the efficiency in content distribution. Also, in selection of reliable peers for content distribution, there is a notable 20% of increase in peer reliability with result of enhanced robustness. The proposed method provides efficient and robust solution to the problem of peer selection. It can be implemented in a broad range of P2P content distribution networks.
Security failures in software arising from failures to practice secure programming are commonplace. Improving this situation requires that practitioners have a clear understanding of the foundational concepts in secur...
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ISBN:
(纸本)9781450394338
Security failures in software arising from failures to practice secure programming are commonplace. Improving this situation requires that practitioners have a clear understanding of the foundational concepts in secure programming to serve as a basis for building new knowledge and responding to new challenges. We developed a Secure Programing Concept Inventory (SPCI) to measure students' understanding of foundational concepts in secure programming. The SPCI consists of thirty-five multiple choice items targeting ten concept areas of secure programming. The SPCI was developed by establishing the content domain of secure programming, developing a pool of test items, multiple rounds of testing and refining the items, and finally testing and inventory reduction to produce the final scale. Scale development began by identifying the core concepts in secure programming. A Delphi study was conducted with thirty practitioners from industry, academia, and government to establish the foundational concepts of secure programming and develop a concept map. To build a set of misconceptions in secure programming, the researchers conducted interviews with students and instructors in the field. These interviews were analyzed using content analysis. This resulted in a taxonomy of misconceptions in secure programming covering ten concept areas. An item pool of multiple-choice questions was developed. The item pool of 225 was administered to a population of 690 students across four institutions. Item discrimination and item difficulty scores were calculated, and the best performing items were mapped to the misconception categories to create subscales for each concept area resulting in a validated 35 item scale.
Based on a current Research to Practice Partnership (RPP) between a southeastern public university and a state virtual public school in the United States, ten high school teachers from a virtual school who teach Compu...
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Based on a current Research to Practice Partnership (RPP) between a southeastern public university and a state virtual public school in the United States, ten high school teachers from a virtual school who teach computerscience (CS) online participated in a summer workshop to collaborate through a participatory action research project regarding design, facilitation, and evaluation strategies to be included in effective professional development. The questions were posed through an online collaborative Jamboard during the summer workshop. The teacher posts were qualitatively analyzed to identify common themes. Recommendations for professional development on design included CS content, how to teach CS, and CS tools and activities. For facilitation, they recommended resources for supplemental instruction and feedback tools for providing feedback in various modalities and a tool repository. For assessment, they recommended content knowledge assessments, including lab assignments, single and pair programming, and coding assessments. Overall recommendations for a professional development course to teach CS online were also offered.
This paper discusses the outcomes and implications of conducting a post-exam review for students' learning of computerprogramming concepts in a collegiate setting. The post-exam review activity is implemented usi...
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ISBN:
(纸本)9798350317107;9798350317114
This paper discusses the outcomes and implications of conducting a post-exam review for students' learning of computerprogramming concepts in a collegiate setting. The post-exam review activity is implemented using a mix of metacognitive techniques including exam wrappers, peer grading, and the principles of self-directed learning. The activity consists of the class going through a document containing the anonymized code snippets that students submitted for an exam's programming problem(s). This document is generated from the Canvas exam using an application developed by the research team with the *** framework. Students are tasked with analyzing the submissions one by one and then determining an appropriate grade by using a majority voting mechanism. With the use of a survey after the activity, the efficacy of the proposed approach was compared to that of classes taken by students which did not incorporate this approach and used a traditional way of grading.
This work-in-progress, innovative-practice, full paper describes an approach to the training of computer scientists and engineers focused on the reading of programs generated by AI-based Large Language Models (LLM). I...
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Recent advancements in generative artificial intelligence are poised to reshape introductory programming education, challenging conventional teaching methodologies. This paper presents a scoping review that explores t...
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ISBN:
(纸本)9798400711770
Recent advancements in generative artificial intelligence are poised to reshape introductory programming education, challenging conventional teaching methodologies. This paper presents a scoping review that explores the current understanding of integrating generative artificial intelligence tools in the learning of introductory programming. Through an analysis of 28 selected studies, this review provides a snapshot of the landscape in mid-2024, presenting benefits, concerns, and recommendations surrounding the use of generative artificial intelligence within programming education. It finds insufficient guidance on how to implement recommended pedagogical strategies, limited consideration of student perceptions and experiences, and a predominance of short study time frames. Additionally, there is a significant research gap in second-level education, particularly in the United Kingdom and Ireland. The paper discusses how these gaps signal a need for more human-centered approaches in the current research. The paper concludes with recommendations for future research, aiming to inspire further inquiry and advance the understanding of generative artificial intelligence's role in programming education from a human-centered perspective.
Many tasks that end users want to accomplish with a computer program are fundamentally data-flow transformations, and both visual and textual programming systems have been created to fill this need, but these are ofte...
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ISBN:
(纸本)9798400707551
Many tasks that end users want to accomplish with a computer program are fundamentally data-flow transformations, and both visual and textual programming systems have been created to fill this need, but these are often inflexible, unapproachable, or cumbersome, satisfying a niche at one stage of the process but limited at others. An approach that suits one part of the program, or one time in its development, may be confounding at another, but the user is stuck with both the constructive and obstructive aspects of a tool's chosen paradigm throughout. Much of this difficulty can be removed by enabling the cohabitation of multiple editing paradigms in a single program for the user to choose how to tackle the current point in the process - and change their mind. We present a new data-flow programming environment where the same program, or parts of the same program, can be viewed and edited as linear text, a node-and-wire graph representation, or a two-dimensional grid layout, and the correspondence between these representations is made clear through a continuous visual identity for each part of the program.
Based on the analyses of existing preference group decision-making(PGDM)methods with intuitionistic fuzzy preference relations(IFPRs),we present a new PGDM framework with incomplete IFPRs.A generalized multiplicative ...
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Based on the analyses of existing preference group decision-making(PGDM)methods with intuitionistic fuzzy preference relations(IFPRs),we present a new PGDM framework with incomplete IFPRs.A generalized multiplicative consistent for IFPRs is defined,and a mathematical programming model is constructed to supplement the missing values in incomplete ***,in this study,another mathematical programming model is constructed to improve the consistency level of unacceptably multiplicative consistent *** group decisionmaking(GDM)with incomplete IFPRs,three reliable sources influencing the weights of experts are ***,a method for determining the weights of experts is developed by simultaneously considering three reliable ***,a targeted consensus process(CPR)is developed in this study with reference to the actual situation of the consensus level of each ***,in response to the proposed multiplicative consistency definition,a novel method for determining the optimal priority weights of alternatives is ***,based on the above theory,a novel GDM method with incomplete IFPRs is developed,and the comparative and sensitivity analysis results demonstrate the utility and superiority of this work.
Computational thinking (CT) and problem-solving skills are increasingly integrated into K-8 school curricula worldwide. Consequently, there is a growing need to develop reliable assessments for measuring students'...
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ISBN:
(纸本)9798400704239
Computational thinking (CT) and problem-solving skills are increasingly integrated into K-8 school curricula worldwide. Consequently, there is a growing need to develop reliable assessments for measuring students' proficiency in these skills. Recent works have proposed tests for assessing these skills across various CT concepts and practices, in particular, based on multi-choice items enabling psychometric validation and usage in large-scale studies. Despite their practical relevance, these tests are limited in how they measure students' computational creativity, a crucial ability when applying CT and problem solving in real-world settings. In our work, we have developed ACE, a novel test focusing on the three higher cognitive levels in Bloom's Taxonomy, i.e., ANALYZING, EVALUATING, and CREATING. ACE comprises a diverse set of 7x3 multi-choice items spanning these three levels, grounded in elementary block-based visual programming. We evaluate the psychometric properties of ACE through a study conducted with 371 students in grades 3-7 from 10 schools. Based on several psychometric analysis frameworks, our results confirm the reliability and validity of ACE. Our study also shows a positive correlation between students' performance on ACE and performance on Hour of Code: Maze Challenge by ***.
In an era of pervasive digitalization, the growing volume and variety of data streams poses a new challenge to the efficient running of data-driven optimization algorithms. Targeting scalable multiobjective evolution ...
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In an era of pervasive digitalization, the growing volume and variety of data streams poses a new challenge to the efficient running of data-driven optimization algorithms. Targeting scalable multiobjective evolution under large-instance data, this article proposes the general idea of using subsampled small-data tasks as helpful minions (i.e., auxiliary source tasks) to quickly optimize for large datasets-via an evolutionary multitasking framework. Within this framework, a novel computational resource allocation strategy is designed to enable the effective utilization of the minions while guarding against harmful negative transfers. To this end, an intertask empirical correlation measure is defined and approximated via Bayes' rule, which is then used to allocate resources online in proportion to the inferred degree of source-target correlation. In the experiments, the performance of the proposed algorithm is verified on: 1) sample average approximations of benchmark multiobjective optimization problems under uncertainty and 2) practical multiobjective hyperparameter tuning of deep neural network models. The results show that the proposed algorithm can obtain up to about 73% speedup relative to existing approaches, demonstrating its ability to efficiently tackle real-world multiobjective optimization involving evaluations on large datasets.
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