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关键词： racial democracy   foreign policy   FESTAC'77   Ernesto Geisel   race   civil awakening   decolonisation   military dictatorship   Brazil   Nigeria   Gilberto Freyre   Abdias do Nascimento   Gilberto Gil   national identity   culture   democracia racial   pol & iacute   tica exterior   raza   despertar civil   descolonizaci & oacute   n   dictadura militar   Brasil   identidad nacional   cultura   pol & iacute   tica externa   ra & ccedil   a   descoloniza & ccedil   atilde   o   ditadura militar   Nig & eacute   ria   identidade nacional  

摘要： This article examines the ways in which Brazil's African foreign policy during the Ernesto Geisel administration (1974-9) utilised notions of 'racial democracy' and the nation's Africanity in framing itself as an intrinsic partner to the continent across the Atlantic. It does this through an analysis of Brazil's involvement at the Second World Black and African Festival of Arts and Culture (FESTAC'77, 15 January-12 February 1977), hosted in Lagos, Nigeria. The international event celebrated past and present contributions of Black and African cultures to global civilisation. An assessment of the Brazilian government's delegation to FESTAC'77 shows how the Geisel administration attempted to depict Brazil as a harmoniously integrated society, where, through a historic process of mixing, the nation's racial identity was united into an equitable whole. In contrast, the propagation of these ideas at FESTAC'77 left the regime's racial ideology vulnerable to attack from international and domestic audiences. Este art & iacute;culo examina las formas en que la pol & iacute;tica exterior hacia & Aacute;frica de Brasil durante la administraci & oacute;n de Ernesto Geisel (1974-9) utiliz & oacute;las nociones de 'democracia racial' y la africanidad nacional para ubicarse a s & iacute;mismo como un aliado intr & iacute;nseco del continente al otro lado del Atl & aacute;ntico. El material lo hace a partir del an & aacute;lisis de la participaci & oacute;n brasile & ntilde;a en el Segundo Festival Mundial de Arte y Cultura Negra y Africana (FESTAC'77, 15 de enero-12 de febrero de 1977), en Lagos, Nigeria. El evento internacional celebr & oacute;contribuciones pasadas y presentes de culturas negras y africanas a la civilizaci & oacute;n global. Una evaluaci & oacute;n de la delegaci & oacute;n gubernamental brasile & ntilde;a al FESTAC'77 muestra c & oacute;mo la administraci & oacute;n de Geisel intent & oacute;presentar a Brasil como una sociedad armoniosamente integrada donde, po

关键词： Fake news   Feature extraction   Social networking (online)   Artificial intelligence   Computer science   Taxonomy   Support vector machines   Artificial intelligence (AI) explainability   blockchain-based detection   deceptive content   deep fakes   fake news   misinformation   news propaganda   social bots   social media  

摘要： Fake news is a major threat to democracy (e.g., influencing public opinion), and its impact cannot be understated particularly in our current socially and digitally connected society. Researchers from different disciplines (e.g., computer science, political science, information science, and linguistics) have also studied the dissemination, detection, and mitigation of fake news;however, it remains challenging to detect and prevent the dissemination of fake news in practice. In addition, we emphasize the importance of designing artificial intelligence (AI)-powered systems that are capable of providing detailed, yet user-friendly, explanations of the classification / detection of fake news. Hence, in this article, we systematically survey existing state-of-the-art approaches designed to detect and mitigate the dissemination of fake news, and based on the analysis, we discuss several key challenges and present a potential future research agenda, especially incorporating AI explainable fake news credibility system.

关键词： ChatGPT   academic accreditations   technology programs   computer science   Kingdom of Saudi Arabia   website development   NCAAA   ABET  

摘要： This research aims to utilize information technology to improve education quality, particularly in higher education. A key contribution of this research is the application of generative artificial intelligence, specifically ChatGPT, to validate test questions that meet both international (ABET) and local (NCAAA) academic accreditation standards. The study was conducted within the Information Systems Department's bachelor's program at King Abdulaziz University in the Kingdom of Saudi Arabia, focusing on a website development course. The custom ChatGPT application, named Question Checker, was developed to validate questions generated by instructors. These validation criteria were aligned with the accreditation requirements for technology and computer science programs, ensuring compliance with both ABET and NCAAA standards. The application was tested by validating nine questions related to Student Outcomes, demonstrating its effectiveness in supporting the educational objectives of the program.

关键词： Motion pictures   Scalability   Recommender systems   Feature extraction   Computer science   Automobiles   Testing   Collaborative filtering (CF)   context-aware recommender systems (CARSs)   cross-domain recommender systems (CDRSs)   recommender systems (RSs)   sequential rule mining (SRM)   TopSeq rule  

摘要： Recommender Systems (RSs) are a subclass of information filtering systems. RSs assist users in choosing interesting items from an extensive collection of items. This article addresses two research topics in RS, namely cross-domain RSs (CDRSs) and the context-aware RSs (CARSs). CDRSs were developed to improve the quality of recommendations in a target domain using the source domain information. Moreover, CDRSs look to limit the spread of fake information through RSs. CARSs are designed to utilize contextual information, such as location, time, companions, and others, in the recommendation as user interests change with context. In this work, CDRSs and CARSs are implemented in an integrated manner to construct a more specific RS that offers both these systems' advantages. For including contextual information in data, contextual prefiltering is applied. These approaches recommend items more accurately, overcoming cold start, sparsity, and scalability issues, and provide a more personalized, novel, and diversified recommendation. The developed system, cross-domain recommendation using context-aware sequences (CDRec-CAS), is evaluated in terms of accuracy achieved in recommending preferred item sequences and the next preferred item. In recommending preferred item sequences, it is found that it improves recommendation accuracy that varied from approximately 7.85%-9.74% (considering the single context) and 4.41%-8.17% (considering dual-context) when compared with existing noncontextual RS. In recommending the next preferred item, it is found that it improves recommendation accuracy that varied from approximately 3.81%-9.81% (considering the single context) -2.24%-9.21% (considering dual context) when compared with existing noncontextual RS. The results obtained by implementing CDRec-CAS are compared with existing approaches, proving that recommendations can be enhanced using cross-domain and contextual information.

关键词： Neurons   Task analysis   Mathematical models   Training   Three-dimensional displays   Firing   Computer science   Action recognition   ANN-SNN conversion   dual threshold   spiking neural networks (SNNs)   ultralow latency  

摘要： Spiking neural networks (SNNs) have garnered significant attention for their potential in ultralow-power event-driven neuromorphic hardware implementations. One effective strategy for obtaining SNNs involves the conversion of artificial neural networks (ANNs) to SNNs. However, existing research on ANN-SNN conversion has predominantly focused on image classification task, leaving the exploration of action recognition task limited. In this article, we investigate the performance degradation of SNNs on action recognition task. Through in-depth analysis, we propose a framework called scalable dual threshold mapping (SDM) that effectively overcomes three types of conversion errors. By effectively mitigating these conversion errors, we are able to reduce the time required for the spike firing rate of SNNs to align with the activation values of ANNs. Consequently, our method enables the generation of accurate and ultralow-latency SNNs. We conduct extensive evaluations on multiple action recognition datasets, including University of Central Florida (UCF)-101 and Human Motion DataBase (HMDB)-51. Through rigorous experiments and analysis, we demonstrate the effectiveness of our approach. Notably, SDM achieves a remarkable Top-1 accuracy of 92.94% on UCF-101 while requiring ultralow latency (four time steps), highlighting its high performance with reduced computational requirements.

关键词： computer science   persistence   retention   underserved students   computer science identity   sense of belonging   Tinto  

摘要： Female, Black, Latinx, Native American, low-income, and rural students remain underrepresented among computer science undergraduate degree recipients. Along with student, family, and secondary school characteristics, college organizational climate, curricula, and instructional practices shape undergraduates' experiences that foster persistence until graduation. Our quasi-experimental project, Improving the Persistence and Success of Students from Underrepresented Populations in Computer Science (I-PASS), is designed to augment students' persistence until they earn their computer science degree. Drawing on prior research, including Tinto's model of effective institutional actions for retention, I-PASS Scholars-all low-income, female and/or members of underserved demographics groups- receive a four-year scholarship;mentoring, tutoring, advising;and opportunities to integrate into the academic and social life of the campus. Students' written reflections and attitude surveys suggest I-PASS's components foster their retention by, among other mechanisms, enhancing their computer science identity development and sense of belonging in the major.

关键词： Blockchains   Encryption   Authentication   Servers   Organizations   Computer science   Access control   Cross-domain   data access   certificate   blockchain   key negotiation  

摘要： In the current hypergrowth phase of the Internet of Things, cross-domain data access becomes more and more frequently. Whereas, the lack of trust between domains makes cross-domain data access extremely hard. Traditional schemes typically depend on a third party to establish trust between data accessing entities, which can easily result in single point of failure. To conquer the aforementioned challenge, this paper proposes BSCDA, a blockchain-based cross-domain data access scheme designed to enable secure data transmission across domains. The decentralization, transparency, and anti-tampering features of blockchain perfectly solve the issue of single point of failure and foster trust among various domains. Specifically, a certificate management method is developed to address the certificate storage issue by leveraging a mapping table to store the revocation certificate index on the blockchain. This method not only ensures the verifiability of the certificate but also reduces the storage overhead. Additionally, a four-party key agreement mechanism is designed to guarantee the secure data transmission during the process of cross-domain data access. Security analysis prove the feasibility of our proposed scheme. Extensive experiments demonstrate the superiority of our scheme in cross-domain data access.

关键词： Aircraft final assembly   Worker allocation   Human-machine collaboration   Production optimization  

摘要： The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human - machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

关键词： Collaboration   Measurement   Engineering profession   Education   Social networking (online)   Productivity   Computer science   Centrality metric   collaborative research   overlapping community   social network   women-only institutes  

摘要： Nowadays, the relevance of women-only educational institutes is questionable due to various reasons that include social uplifting, ample opportunities, and a free mindset among the population. Though women colleges and universities bring many female students to the purview of the traditional education system every year, other activities like research works and collaborative projects are still a concern for the educationists. In the current manuscript, the role of leading women-only universities and institutions is analyzed throughout India, to scrutinize its impact and opportunities in the research domain. All types of educational institutions and collaborative research work among them are represented as a social network. Different centrality metrics, such as closeness, betweenness, and eigenvector, are utilized to examine the influence of the individual institutes in the collaborative research network. An overlapping community detection method is proposed to perceive the posture of gender-biased universities in the mixed institutional model. The supremacy of the proposed approach is presented over the real-life benchmark datasets. The analytical results exhibit notable improvement over the state-of-the-art approaches and unfold a contemporary framework for strategic analysis in the higher education sector.