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关键词： Ethics   End of life care   Clinical decisions  

摘要： ObjectivesTo outline the jurisprudential position in UK law regarding capital punishment, the death penalty and contrast this with proposed legislation for assisted dying and euthanasia (AD/E). MethodsA historical medico-legal jurisprudential research approach, focusing on investigating the case law which resulted in the eventual cessation of capital punishment and contrasting this with the arguments used in current proposed legislation for AD/E. ResultsAs a society, we are confronted with a similar choice in AD/E as we did in the 1960s with capital punishment, where it has demonstrated, despite a full judiciary process with a jury, that incorrect decisions have been made, resulting in death. In the context of two doctors making irrevocable decisions with prognosis uncertain, diagnostic errors and autonomy being relational-influenced by how others behave towards us-even campaigners for AD/E admit errors are inevitable. Some will have lethal drugs under a wrong diagnosis, an incorrect timespan or coercive pressures;is this cost acceptable? ConclusionIf the abolition of capital punishment is hard won, with the risk set that no one incorrectly should die, is this the standard that should be set for lethal drugs today? Is the right to choose so important that others' lives lost are an acceptable cost to pay? If we do accept a radical individual autonomy ethos, what proportion of incorrect deaths is acceptable?

关键词： social media forensics   digital forensics   artificial intelligence   natural language processing   graph neural networks   Generative Adversarial Networks   computer science   computational social science   data intelligence   network forensics  

摘要： Social media platforms have completely revolutionized human communication and social interactions. Their positive impacts are simply undeniable. What has also become undeniable is the prevalence of harmful antisocial behaviors on these platforms. Cyberbullying, misinformation, hate speech, radicalization, and extremist propaganda have caused significant harms to society and its most vulnerable populations. Thus, the social media forensics field was born to enable investigators and law enforcement agents to better investigate and prosecute these cybercrimes. This paper surveys the latest research works in the field to explore how artificial intelligence (AI) techniques are being utilized in social media forensics investigations. We examine how natural language processing can be used to identify extremist ideologies, detect online bullying, and analyze deceptive profiles. Additionally, we explore the literature on GNNs and how they are applied in social network modeling for forensic purposes. We conclude by discussing the key challenges in the field and suggest future research directions.

关键词： Equity   CS for All  

摘要： Objectives. At the K-12 level, "CS for All" initiatives across the country strive to increase equitable access to and participation in computer science (CS). However, there are many open questions about the implementation and effectiveness of these initiatives, including the extent to which exposing young people to CS early on can shape their longer-term CS interest and engagement. In this article, we examine CS participation among 6th-8th-grade Black girls and assess whether CS participation during middle school shapes CS interest and engagement during their first year of high school. We focus on Black girls in the hopes of developing a more nuanced and rigorous understanding of computing experiences at the intersection of racism and sexism in this field. Participants. The focal group of students in this study are 6th-8th-grade Black girls from New York City. We employ a comparative lens in this article, contextualizing the CS experiences and outcomes of Black girls to Latinas, Asian, and White girls, as well as Black boys. Study Method. We primarily rely on quantitative data for this article, applying a critical lens to our analyses and interpretation. More specifically, we conduct descriptive analyses of course-taking patterns as well as survey data that focus on student attitudes and beliefs about CS. We then carry out inferential analyses of students' administrative records examining how, if at all, middle school CS participation is related to high school outcomes for Black girls. We employ a comparative lens and rely on qualitative data to make sense of our results. Findings. We find that, troublingly, Black girls in the district are disproportionately less likely to receive CS instruction in middle school. Black girls are also less likely than Black boys, Latinas, and White girls to feel that they belong in CS. However, Black girls in CS courses report similar levels of engagement, family, and peer support, as well as value for CS relative to other students i

关键词： Broadening participation in computing   intersectional computing   mentorship  

摘要： Several studies have reported the positive benefits of informal Computer Science learning programs for Black girls, which include staff, mentors, and peers reflective of the girls in the program;however, we do not know enough about what motivates Black women to sign up to teach in such programs, or how representation in mentoring affects future CS learning for Black girls. Here we report the intersectional experiences of 11 Black women who served as instructors and mentors for the virtual INTech Summer Camp. The women in this study confirm that it is imperative to consider the intersectional experiences of Black women in CS as a whole, without explicating their identities. We found that Black women in CS feel that it is their responsibility to give back to Black girls to pay it forward from their own mentorship experiences. The Black women in our study describe seeking to fill a gap in mentorship that they experienced themselves, to share their knowledge and experiences to support the development of others who can relate to that intersection of identifying as Black and a girl or woman. Providing positive representation to encourage young Black girls to pursue tech careers is imperative to diversifying the tech workforce.

关键词： Cryptography   Indexes   Servers   Cloud computing   Privacy   Encryption   Computer science   graph privacy   K-hop reachability query   privacy preserving  

摘要： K-hop Reachability Query (KRQ) is one of fundamental graph queries, which can answer whether a node u can reach a node v within K hops. With the scale of graph data increasing, data owner desires to outsource the local graphs to cloud server. To protect the graph privacy, data owner encrypts graphs before outsourcing them to the cloud server. It imposes a great challenge to KRQ over encrypted graphs. How to realize Privacy-Preserving K-hop Reachability Query (PPKRQ) over encrypted graphs is still an unexplored problem. In this article, we explore this problem and propose a practical scheme. In order to efficiently answer KRQ over encrypted graphs, we construct the encrypted Breadth-First Spanning Tree table and adjacent list D (BFST-D). Based on encrypted BFST table, we can directly judge whether two query nodes are reachable within K hops when they are in one spanning tree. The encrypted adjacent list D can help answer that two query nodes in different spanning trees. To protect the privacy, we utilize the Paillier cryptographic and Order-Revealing Encryption (ORE) to support the comparison and computation over ciphertexts. As a result, our scheme achieves the sensitive information privacy without losing the ability of querying over encrypted graphs. The security analysis shows that our proposed scheme is secure based on semi-honest cloud server. The extensive experiments show the efficiency of our scheme.

关键词： Academic genealogy   Mentorship   Knowledge evolution   Computer science   Knowledge Inheritance   Knowledge mutation  

摘要： Academic genealogy (AG) provides valuable insights into the transmission of knowledge from mentors to mentees, revealing the evolution of knowledge within the academic community. This study explores the intricate dynamics of knowledge evolution within academic genealogies, utilizing on a dataset comprising 16,852 computer science researchers, 613,277 papers, and 11,988 mentorship relationships. By focusing on small-scale knowledge units, our analysis aims to uncover patterns of knowledge inheritance and mutation across different subfields of computer science and highlights several aspects of knowledge evolution in computer science. Firstly, computer science is characterized by strong mentorship ties, indicating the significance of knowledge transmission within the field. Additionally, there is a mix of foundational and developing areas, suggesting a field that is growing and diversifying rather than declining, as indicated by linear regression outcomes. Secondly, our research reveals a surge in collaborative knowledge exchange in computer science since 2000, with fields such as ComputerCommunication Networks and Software Engineering leading in terms of output and impact. Furthermore, areas like Computer Graphics and Artificial Intelligence stand out for their depth and novelty. Thirdly, we categorize researchers into three types: roots, branches, and leaves, reflecting their role in knowledge transmission. Branch researchers tend to innovate, while leaf researchers show a combination of traditional knowledge uptake and new contributions, illustrating the dynamic flow of ideas within the field. Future research endeavors are encouraged to embrace larger datasets and further fortify our understanding of the topic.

关键词： Data models   Computational modeling   Training   Distributed databases   Servers   Data privacy   Computer science   Data elimination   distributed learning   low-quality data   federated unlearning  

摘要： Federated unlearning (FUL) is an emerging distributed machine learning paradigm which enables the removal or unlearning of specific training data effects from trained Federated Learning (FL) models. While current studies mostly focus on client-side FUL to address the "right to be forgotten", and ignore the server's right to remove local models from the global model, particularly when clients are trained with low-quality data. In this paper, we introduce the Server-Initiated Federated Unlearning (SIFU) algorithm, devised to eliminate low-quality data from the global model. SIFU consists of two main components: (i) Identifying low-quality data: we develop a category-based method for quantifying low-quality data for each client and filter out clients containing such data. Datasets are then divided accordingly. (ii) Unlearning low-quality data: we employ gradient ascent training to counteract the adverse effects of low-quality data on local models. To minimize any bias introduced, we concurrently perform several batches of boosting training with good-quality data. SIFU could identify and promptly eliminate the impact of low-quality data on the FL global model while still preserving the benefits of good-quality data. Finally, extensive evaluations are conducted to verify the performance of SIFU with four different kinds of datasets and models. Results show that, compared to retraining from scratch, SIFU accelerates the speed of unlearning by 15x for small datasets (i.e., MNIST and FMNIST) and 20x for large datasets (i.e., CIFAR-10 and CelebA) without any degradation in accuracy, which also outperforms the state of the arts.

关键词： computer science   diversity and inclusion   gender equity   higher education   science education   women in science  

摘要： The gender imbalance in computer science (CS) is one of the most challenging issues in American education. CS is the only science, technology, engineering, and mathematics (STEM) field in which women's representation has steadily declined in recent decades. In this study, we explored one potential approach that could be effective in increasing college women's participation in CS: participation in Women-in-Computing (WiC) groups. Through participant observation and individual interviews in a WiC group at a major research university, we investigated how students engage in WiC, the impacts of the WiC on identity and belonging, and the challenge of sustainability. The results were coded using a hybrid of grounded and deductive coding and indicate that WiC groups offer various programs and events that enable women in CS to fully participate, learn, and grow. WiC represents an identity, a community, a safe space, and a journey. The results also suggest that the WiC has had positive impacts on students' identity and belonging, as evidenced by increased self-efficacy, reduced imposter syndrome, and enhanced sense of belonging and community. Furthermore, we outline three strategies employed by the WiC to ensure the group's sustainability. Our study sheds light on how WiC can encourage women to enter and persist in CS, and on some of the characteristics of a successful WiC. We demonstrate that WiC may be potentially effective in diversifying CS through identity-based participation. Moreover, student leaders design both the structure of the group and the leadership continuity process to ensure sustainability.

关键词： Curricula  

摘要： Seven years ago (2016), we began integrating Robotics into our Computer Science curriculum. This paper explores the mission, initial goals and objectives, specific choices we made along the way, and why and outcomes. Of course, we were not the first to do so. Our contribution in this paper is to describe a seven-year experience in the hope that others going down this road will benefit, perhaps avoiding some missteps and dead-ends. We offer our answers to many questions that anyone undertaking bootstrapping a new robotics program may have to deal with. At the end of the paper, we discuss a set of lessons learned, including striking the right balance between depth and breadth in syllabus design and material organization, the significance of utilizing physical robots and criteria for selecting a suitable robotics platform, insights into the scope and design of a robotics lab, the necessity of standardizing hardware and software configurations, along with implementation methods, and strategies for preparing students for the steep learning curve. © 2024, CC BY-NC-ND.

摘要： We summarize national-scale data for Ph.D. earners in engineering or computer science from 2015 to 2019 whose post-graduate school employment is known, highlighting outcomes for biological/biomedical/biosystems engineering students. We use NSF’s Survey of Earned Doctorates (SED), which has collected information from Ph.D. recipients in the USA since 1957. The data are collected at the time of degree completion and constitute a greater than 90% response rate. Compared to all engineering and computer science disciplines, biological/biomedical/biosystems engineering has a higher proportion going to 4yr/med/research institutions (52% vs. 33%) and non-profit (3.6% vs. 2.9%) and lower proportion going to industry (33% vs. 48%), government (4.3% vs. 8.4%), and is similar for non-US positions (6.1% vs. 5.7%). Compared to 2010–2014 biological/biomedical/biosystems engineering Ph.D. recipients, more 2015–2019 recipients are going to industry (25% to 33%) and fewer to 4yr/med/research institutions (59% to 52%) and governmet (5.3% to 4.3%). Across all engineering and computer science disciplines, a smaller proportion of females entered industry (43%) compared to males (49%), while a larger proportion of females entered 4yr/med/research institutions (37%) compared to males (32%). Over half of Asian doctoral recipients entered industry, as compared to 38% of Hispanic doctoral recipients. In contrast, a higher proportion of Hispanic individuals (37%) entered 4yr/med/research institutions after their doctoral programs, as compared to 31% of Asian doctoral recipients. Black doctoral recipients had the highest proportion enter positions in government (14%) and non-profit (4%) sectors. Our results are situated in the broader literature focused on postdoctoral career, training, and employment sectors and trends in STEM. We discuss implications for graduate programs, policymakers, and researchers.