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关键词： Kernel   Linux   Permission   Codes   Access control   Computer science   Computer bugs   Linux kernel   static analysis   permission check   bug detection  

摘要： Permission checks play an essential role in operating system security by providing access control to privileged functionalities. However, it is challenging for kernel developers to scalably verify the soundness of existing checks due to the large codebase and complexity of the kernel. In fact, Linux kernel contains millions of lines of code with hundreds of permission checks, and even worse, its complexity is fast-growing. This paper presents PeX, a static Permission check error detector for LinuX, which takes as input a kernel source code and reports any missing, inconsistent, and redundant permission checks. PeX uses KIRIN (Kernel InteRface based Indirect call aNalysis), a novel, precise, and scalable indirect call analysis technique. Over the interprocedural control flow graph built by KIRIN, PeX automatically identifies permission checks and infers the mappings between permission checks and privileged functions. For each privileged function, PeX examines all possible paths to the function to check if necessary permission checks are correctly enforced. We evaluated PeX on the latest stable Linux kernel v4.18.5 for three types of permission checks: Discretionary Access Controls (DAC), Capabilities, and Linux Security Modules (LSM). PeX reported 45 new permission check errors, 17 of which have been confirmed by the kernel developers.

关键词： Ancestral knowledge systems   Indigenous epistemologies   Decolonizing computing education   Sustainability   Participatory action research   Critical reflexivity  

摘要： This article offers Ancestral Computing for Sustainability (ACS) to dismantle the logics of settler colonialism that affect accessibility, identities, and epistemologies of computer science education (CSE). ACS centers Indigenous epistemologies in researching CSE across four public universities in the United States. This paper describes Ancestral Computing for Sustainability and explores reflections of two students engaging as researchers in ACS inquiry. Drawing on Indigenous methodologies and Participatory Action Research, they share their reflections as co-researchers in ACS through storywork. These critical reflections include their relationship to computing, observations of the interdependent work within ACS, ethics and sustainability, and their experiences within the focus groups. The article ends with recommendations for furthering ACS as a decolonial approach that centers Indigenous epistemologies in CSE. Recommendations for CSE education include Ancestral Knowledge Systems and adding sustainability as a topic within computing education pathways and building student-faculty relationships based on trust is recommended to foster students' academic and personal growth within CSE education and research.

关键词： Computer science   Visualization   Image color analysis   Collaboration   Rendering (computer graphics)   Market research   Biology  

摘要： Aesthetics for the visualization of biomolecular structures have evolved over the years according to technological advances, user needs, and modes of dissemination. In this article, we explore the goals, challenges, and solutions that have shaped the current landscape of biomolecular imagery from the overlapping perspectives of computer science, structural biology, and biomedical illustration. We discuss changing approaches to rendering, color, human-computer interface, and narrative in the development and presentation of biomolecular graphics. With this historical perspective on the evolving styles and trends in each of these areas, we identify opportunities and challenges for future aesthetics in biomolecular graphics that encourage continued collaboration from multiple intersecting fields.

关键词： Proteins   Convolution   Coronaviruses   Predictive models   Hidden Markov models   Databases   Computer science   Binding affinity change   graph neural networks   protein-protein interaction   mutation  

摘要： Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our method shows better results in predicting ACE2 changes, may help in finding favorable antibodies. Code and data availability: https://***/lennylv/DGCddG.

关键词： Quality of service   Probabilistic logic   Throughput   Reliability   Web services   Computer science   Australia   Service recommendation   skyline services   quality of service   space partition  

摘要： Recent developments of Internet technologies have accelerated the growth of Web services (e.g., open APIs). As many services provide similar functionality, service recommendation systems use the Quality of Service (QoS) to help users find optimal services. Space partition attracts significant attention in service recommendation since it improves the diversity of recommendations and accelerates skyline services query. However, existing partition-based service recommendation systems are all implemented on complete QoS. They are not sufficient when some services' QoS values are missing or invalid. To this end, we develop a new partition-based service recommendation method on incomplete QoS (named IQSrec) that combines probabilistic skyline query and space partition. The probabilistic skyline query measures top-$k$k skyline services on incomplete QoS. A dimension-based partition is specially designed for splitting the incomplete QoS service space into $d$d-dimensional partitions with the most representative services. The candidate skyline services are chosen from each partition and merged together for probabilistic skyline computation. IQSrec selects the highest skyline probability services in each partition as recommendations. The experiments on the synthetic and real-world datasets show IQSrec can efficiently recommend skyline services on incomplete QoS. IQSrec has higher accuracy and diversity compared to the state-of-the-art service recommendation approaches.

关键词： Scientific production   Publication growth   Hyperprolific authors   Computer science   DBLP  

摘要： In this article we study and characterize the phenomenon of the hyperprolific authors, who are the most productive researchers according to a given repository in a specific period of time. Particularly, we are interested in investigating and characterizing a subset of such hyperprolific authors who present a sudden growth in the number of published articles and coauthors, as well as concentrate their publications in a few specific journals, what can be seen as an anomalous behavior. Using data collected from the DBLP repository and covering the last 10 years, we propose a set of discriminative dimensions (features) aimed at characterizing the behavior of hyperprolific authors, ultimately helping to identify anomalous ones. Moreover, using a strategy based on ranking aggregation to identify the most prominent anomalous authors, we demonstrate that the best dimensions to characterize such anomalous behaviors may vary significantly among authors, but it is possible to identify a clear subset of them who present such behavior. Our results show that the top-ranked (most anomalous) authors manifest a distinct behavior from the middle-ranked ones. Indeed, each one of the five most anomalous authors published more than 48 journal articles in 2021 while collaborating with more than 1,000 coauthors in their careers. Specifically, one of such authors published more than 140 articles in just a single journal.

关键词： Computer science education   E-learning platform   Automatic corrector   Automatic plagiarism detector   Auto-monitoring   Near real-time feedback  

摘要： In this article, we present an educational reform we implemented a few years ago to respond to a marked drop in the success of students entering higher education in computer science. The main objective of our reform is to adapt the teaching methods of Generation X-Y to Generation Y-Z or more. To do this, we propose two approaches. First, to make learning more active, stimulating and empowering. Second, to make learning more individualized in a context of a large group of students with an optimization of the teacher's time. We first present an analysis of the probable reasons for the lower level of students and the specific issues encountered by both students and teachers. Then, we detail how we implemented these solutions in the form of an original e-learning platform based on two back-end tools able to manage large number of students: an efficient real-time auto-corrector of source codes and a very robust anti-plagiarism software based on computer distance information theory. Finally, we present the results obtained after an experimentation of 8 years. These results are put into perspective by an analysis of 15 years of qualitative and quantitative indicators carried out on hundreds of students each year. The analysis shows that the technical skills and the involvement of students are improved even in groups of several hundreds of students. Computer science teachers wishing to quickly and concretely develop the involvement of their students can rely on these tried and tested key levers and solutions presented.

关键词： Task analysis   Sensors   Path planning   Particle swarm optimization   Computer science   Resource management   Planning   Beetle swarm optimization   mobile crowd sensing   path planning   participant executing time  

摘要： Mobile crowd sensing (MCS) is a popular sensing paradigm that recruits participants carrying sensing devices to collect data for specific tasks in a sensing area. In an MCS system, when a participant executes limited multiple location-based tasks according to different task accessing sequences, its executing time which depends on its travelling path is different. Therefore, how to perform an efficient path planning to find the optimal executing time for each participant is of vital importance. In this paper, we revisit the path planning problem in MCS and propose a novel path planning method based on the beetle swarm optimization (BSO) algorithm. First, a participant executing time is defined as the sum of the moving time along its travelling path and the sensing time for executing all tasks on this path. Then, to minimize it, an improved BSO algorithm is utilized to find the optimal path for each participant. In particular, a largest-order-value (LOV) rule is introduced to convert each position of beetles from a multi-dimensional vector into a travelling path for the participant. The simulation results verify that the proposed method is superior to other baselines in terms of the participant executing time and shows a faster convergence speed.

摘要： Reaching out across computer science research communities.

关键词： Task analysis   Crowdsourcing   Optimization   Technological innovation   Heuristic algorithms   Data models   Computer science   Spatio-temporal crowdsourcing   online bilateral assignment   priority queue   bilateral online reassignment   two-stage task assignment  

摘要： With the advent of intelligent technology, the users of spatio-temporal crowdsourcing and their participation in the crowdsourcing tasks continue to increase exponentially. This poses new challenges to the crowdsourcing field. One of the core research areas of spatio-temporal crowdsourcing is task assignment. Most of the existing research on task assignment is focused on offline optimal task assignment, where, the platform has already learned all the information about workers and tasks beforehand. However, these studies cannot obtain good results in real-world situations. At the same time, online task assignment problems often result in local optimal assignment. To solve these problems, more attention needs to be paid to online task assignments and the arrival time of workers. This paper proposes an Online Bilateral Assignment (OBA) problem based on the online assignment model. The competitive ratio of the Greedy algorithm is analyzed according to the OBA problem model. Also, another solution to the OBA problem according to the Greedy algorithm, the Improved-Baseline algorithm, is proposed. Additionally, a Bilateral Online Priority Reassignment algorithm (BOPR) is proposed. The BOPR algorithm realizes real-time task/worker assignment through the bilateral assignment as a solution for online task assignment. In order to guarantee the number of matching tasks, a priority queue is designed in the BOPR algorithm. Considering the waiting time deadlines of tasks and workers and the error rate for priority ranking, it avoids tasks and workers waiting too long and assigns each task to the best possible extent. On this basis, a two-stage assignment strategy is designed for unsuccessful tasks, which could minimize the error rate of the task and significantly improve the efficiency of task assignment. Finally, through experiments on real data sets, the algorithm's performance in terms of global utility value and the number of matches is evaluated.