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关键词： African American high schools   Brown v. Board of Education   culture   curriculum and instruction   Dunbar High School   environment   gifted and advanced students   leadership   relationships   Roosevelt High School   segregation   Sumner High School   Terrell High School  

摘要： This article describes the cultural characteristics of four elite African American high schools, initiated prior to Brown v. Board of Education, that produced outstanding graduates. Positive school culture characteristics examined were leadership, faculty, curriculum and instruction, student qualities, the environment, and internal and external relationships. The authors posed questions investigating how educators and alumni of Gary, Indiana’s Theodore Roosevelt High School viewed the characteristics of its culture and how the culture compared to other elite African American high schools in the extant literature. Using surveys, archival information, and the research literature, the authors collected information describing the high school’s distinctive features. Comparing the results of Roosevelt with the other exemplary high school cultures of Dunbar, Sumner, and Terrell, they reported these insights about their cultural characteristics: (a) high caliber and sustained leadership of individuals who cultivated positive school environments and created institutions that were a distinctive blend of school and community;(b) highly educated and qualified faculty members;(c) a rigorous and challenging curriculum emphasizing the preparation of students for higher education;(d) students who exhibited a strong sense of self-esteem, self-efficacy, and a rejection of the concept of inferiority;(e) a family-like environment that was welcoming, inclusive, and receptive to individuals from varied backgrounds that fostered a strong sense of pride and efficacy;and (f) a family climate experienced by internal and external stakeholders that created a sense of belonging and place. © The Author(s) 2023.

关键词： Algorithms   performance   computational experiments   computer science   guidelines   systematic mapping study   Algorithms   performance   computational experiments   computer science   guidelines   systematic mapping study  

摘要： Computational experiments have been important since the 70s to find solutions to problems using computers, and even more now with the advancement of technology, especially artificial intelligence. The aim of this study is to know the level of interest on the part of researchers toward computational experiments in Computer Science. To find out this information, a Systematic Mapping Study has been carried out in IEEE, DBLP, Scopus, ACM and Web of Science databases. A total of 226 papers were selected as primary studies and 22 types of measurements were found, it was determined that the three most used measurements in computational experiments are performance at 51.33%, efficiency at 15.49%, and effectiveness at 15.04%. A total of 25 frameworks used for the computational experiments were determined, however 74.3% of the reports did not specify any framework used, 11.5% used a custom framework and the rest used a specific framework. It is concluded that performance is the most important measurement in computational experiments and that it is necessary for researchers to rely on a framework so that these experiments can be replicated or refined.

关键词： Autonomous aerial vehicles   Ad hoc networks   Security   Trust management   Peer-to-peer computing   Reliability   Computer science   Energy management   Data processing   FANET   UAV   trust   protocol   energy  

摘要： An efficient data transmission can be established by flying ad hoc networks (FANETs) that emphasized the cooperation and coordination among various unmanned aerial vehicles (UAVs) as a crucial aspect. The FANET's security becomes a crucial area for exploration due to its limited resources. Earlier research on trust-based computation in ad-hoc environments has demonstrated that previous strategies are efficient at safeguarding ad-hoc environments against a wide range of attacks. So, to curb such circumstances, trust-based management systems can be considered the major need of the hour for calculating the reputation score within the network. Trust management systems are aimed at detecting untrustworthy network nodes and keeping track of all the activities being performed by the nodes, for trust score computation. This will aid in segregating the malicious node from the network. In this article, several types of trust mechanisms of FANET have been addressed thoroughly, and several types of trust mechanism calculations are clarified primarily with the major challenges that FANET is encountering in the context of its trust management design, open research problems and solutions are being incorporated with the existing FANET-based protocols.

关键词： Electroencephalography   Feature extraction   Brain modeling   Electronic mail   Transfer learning   Convolutional neural networks   Computer science   Electroencephalography (EEG)   multiview contrastive   neural network   Riemannian neural network   transfer learning   unsupervised domain adaptation (UDA)  

摘要： Domain adaptation has gained significant attention to address the nonstationarity problem in electroencephalography (EEG) data in motor imagery (MI) classification. In MI classification, domain adaptation addresses cross-session variations, enhancing the classifier's generalization capabilities. However, the existing methods have struggled to effectively capture both temporal and spatial features, resulting in limited classification accuracy. To tackle this issue, we propose the multiview adversarial contrastive network (MACNet). The proposed MACNet simultaneously learns spatial and temporal features in two different views: Euclidean and Riemannian. Furthermore, we introduce a multilevel domain mix-up technique to enhance domain alignment at both signal and embedding levels. The proposed MACNet method is evaluated on three public datasets. It achieves an accuracy of 83.79% on the BCI Competition IV dataset, 80.00% on the open source brain-machine interface (OpenBMI) dataset, and 85.83% on the sensorimotor rthythms (SMR) dataset that outperforms previous methods in cross-session transfer learning.

关键词： Prototypes   Training   Semisupervised learning   Perturbation methods   Entropy   Dogs   Computer science   Contrastive learning   intersectional class mismatch   private assignment suppression   prototype learning   semi-supervised learning  

摘要： Traditional Semi-Supervised Learning (SSL) classification methods focus on leveraging unlabeled data to improve the model performance under the setting where labeled set and unlabeled set share the same classes. Nevertheless, the above-mentioned setting is often inconsistent with many real-world circumstances. Practically, both the labeled set and unlabeled set often hold some individual classes, leading to an intersectional class-mismatch setting for SSL. Under this setting, existing SSL methods are often subject to performance degradation attributed to these individual classes. To solve the problem, we propose a Class-wise Contrastive Prototype Learning (CCPL) framework, which can properly utilize the unlabeled data to improve the SSL classification performance. Specifically, we employ a supervised prototype learning strategy and a class-wise contrastive separation strategy to construct a prototype for each known class. To reduce the influence of the individual classes in unlabeled set (i.e., out-of-distribution classes), each unlabeled example can be weighted reasonably based on the prototypes during classifier training, which helps to weaken the negative influence caused by out-of-distribution classes. To reduce the influence of the individual classes in labeled set (i.e., private classes), we present a private assignment suppression strategy to suppress the improper assignments of unlabeled examples to the private classes with the help of the prototypes. Experimental results on four benchmarks and one real-world dataset show that our CCPL has a clear advantage over fourteen representative SSL methods as well as two supervised learning methods under the intersectional class-mismatch setting.

关键词： Adaptation models   Training   Adversarial machine learning   Machine learning   Feature extraction   Data models   Computer science   Transfer learning   Solid modeling   Robustness   Domain adaptation   domain adversarial learning   office31   multitask learning  

摘要： This study presents a technique called Adversarial Multitask Learning (AML) to enhance the effectiveness of domain adaptation methods in practical applications, which are currently highly sought after. The proposed approach addresses the challenges posed by domain shift by effectively managing multiple interconnected tasks through the principles of adversarial training. Utilizing the widely recognized Office31 dataset, we assess the efficacy of our model across different domains. Our approach employs a multitask learning paradigm, focusing on adapting to the target domain while leveraging shared feature representations through classification tasks. This strategy ensures that primary and auxiliary tasks incorporate domain-invariant properties, allowing for robust adaptation to varying domains. The results reveal significant improvements in adaptation performance when compared to conventional domain adaptation techniques. For instance, the accuracy of our AML model in adapting from the webcam domain to the dslr domain reached 88.54%, surpassing 86.46% for models without adversarial training and 78.91% for those lacking categorical training. Furthermore, we conduct a comprehensive examination of how different hyperparameters influence model performance, enhancing our understanding of the fundamental mechanisms underlying Adversarial Multitask Learning for domain adaptability. Overall, this paper contributes significantly to the field of domain adaptation by introducing the AML framework, underscoring the importance of further exploration of multitask learning paradigms and adversarial training to improve domain adaptation in real-world scenarios.

关键词： Bibliographic coupling networks   topic switching behavior   Louvain algorithm   research performance  

摘要： Computer science (CS) plays a crucial role in addressing challenges across various sectors, including education, finance, and healthcare. Investigating scholars' topic switching behavior quantitatively is essential for understanding the driving forces behind technological development in CS. However, the continuous growth of scientific publications poses challenges in accurately tracking scholars' topic switching. To address this, this study utilizes a large-scale dataset from the Microsoft Academic Knowledge Graph (MAKG) to construct bibliographic coupling networks for 16,794 scholars, enabling the detection of their research topics using the Louvain algorithm. Subsequently, this study introduces a novel two-dimensional metric to quantify scholars' topic switching behavior: topic switching degree and topic switching probability. This metric enables the classification of scholars into four distinct types: Type A, Type B, Type C, and Type D. Our analysis reveals that the majority of scholars belong to Type B, characterized by high topic switching probability but low degree, correlating with the highest quantity, quality, and citations of publications. Scholars categorized as Type A and Type D follow, while those falling under Type C, indicating low topic switching probability but high degree, tend to have the lowest publications and citations. Employing these new two-dimensional indicators to measure scholars' topic switching behavior and exploring its relationship with research performance contribute to advancements in technology within the field of computer science.

关键词： Older people   dementia   Parkinson's disease   quality of life   life planning   summative evaluation  

摘要： Objectives In response to the lack of digital support for older people to plan their lives for quality of life, research was undertaken to co-design and then evaluate a new digital tool that combined interactive guidance for life planning with a computerised model of quality of life. Method First, a workshop-based process for co-designing the SCAMPI tool with older people is reported. A first version of this tool was then evaluated over eight consecutive weeks by nine older people living in their own homes. Four of these people were living with Parkinson's disease, one with early-stage dementia, and four without any diagnosed chronic condition. Regular semi-structured interviews were undertaken with each individual older person and, where wanted, their life partner. A more in-depth exit interview was conducted at the end of the period of tool use. Themes arising from analyses of content from these interviews were combined with first-hand data collected from the tool's use to develop a description of how each older person used the tool over the 8 weeks. Results The findings provided the first evidence that the co-designed tool, and in particular the computerised model, could offer some value to older people. Although some struggled to use the tool as it was designed, which led to limited uptake of the tool's suggestions, the older people reported factoring these suggestions into their longer-term planning, as health and/or circumstances might change. Conclusions The article contributes to the evolving discussion about how to deploy such digital technologies to support quality of life more effectively.

关键词： Relays   Wireless sensor networks   Communication system security   Electrical engineering   Wireless networks   Computer science   Q-learning   Cooperative communication   Secrecy capacity   cooperative communication   deep Q-network   outdated channel state information   reinforcement learning   relay selection  

摘要： Cooperative communications is a core research area in wireless vehicular networks (WVNs), thanks to its capability to provide a certain degree of fading mitigation and to improve spectral efficiency. In a cooperative scenario, the intercept probability of the system can be reduced by optimizing the relay selection scheme in order to select the optimal relay from a set of available relays for data transmission. However, due to the mobility of WVNs, the best relay is often selected in practice based on outdated channel state information (CSI), which in turn affects the overall system performance. Therefore, there is a need for a robust relay selection scheme (RSS) that guarantees a satisfactory overall achievable performance in the presence of an outdated CSI. Motivated by this and considering the advantageous features of autoregressive moving average (ARMA), the proposed contribution models a cooperative vehicular communication scenario with relay selection as a Markov decision process (MDP) and proposes two deep Q-networks (DQNs), namely DQN-RSS and DQN-RSS-ARMA. In the proposed framework, two deep reinforcement learning (RL)-based RSS are trained based on the intercept probability, aiming to select the optimal vehicular relay from a set of multiple relays. We then compare the proposed RSS with the conventional methods and evaluate the performance of the network from the security point of view. Simulation results show that DQN-RSS and DQN-RSS-ARMA perform better than conventional approaches, as they reduce intercept probability by approximately 15% and 30%, respectively, compared to the standard ARMA approach.

摘要： 多媒体作为一种教学工具与载体，具有使资源直观化、信息化以及趣味化等特征。《深化教育教学改革全面提高义务教育质量的意见》中明确提出：需要全面发挥与激活现代科技具有的优势特征，为我国教育现代化、长远化发展目标落实提供强有力的支持与帮助。对此，一线教师在初中英语教学中，可以将多媒体技术与学科教学深度融合，直观可视呈现英语文本，引入多元化英语知识内容，引导学生多维度探索学习，逐步提升学生英语学习的有效性，促进英语教育现代化发展。