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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.

关键词： Public key   Protocols   Elliptic curve cryptography   Smart grids   Smart meters   Data aggregation   Computer science   Certificate-based encryption   data aggregation   identity-based cryptography   smart grid  

摘要： Recent development of smart cities includes advanced and optimized use of modern smart grid (SG) than the traditional power grid. The paradigm of SG has also transformed houses into a home area networks, where several smart devices and appliances are connected to the electricity control centers (ECC). Appliances share their load and consumption related information to ECC through smart meters. This consumption data may be used for supply-demand management, for example, by ramping production up or down as needed. However, security and privacy of the consumer's data are greatly important since fine-grained smart meter data may reveal users presence/absence in their house. To address this issue, several public-key-based or identity-based data aggregation schemes have been proposed in the literature. However, most of such schemes either suffer from the complexity of certificate management or key escrow problem. To eliminate these issues, in this paper, we propose an efficient certificate-based data aggregation (CB-DA) scheme. In the proposed CB-DA scheme, the owner selects a secret key and then use the secret key along with certificates as decryption/signing keys. Our analysis shows that the proposed CB-DA is secure under the random oracle model and more efficient than the existing data aggregation schemes.

关键词： Students  

摘要： This research paper aims to analyze the strengths and weaknesses associated with the utilization of ChatGPT as an educational tool in the context of undergraduate computer science education. ChatGPT's usage in tasks such as solving assignments and exams has the potential to undermine students' learning outcomes and compromise academic integrity. This study adopts a quantitative approach to demonstrate the notable unreliability of ChatGPT in providing accurate answers to a wide range of questions within the field of undergraduate computer science. While the majority of existing research has concentrated on assessing the performance of Large Language Models in handling programming assignments, our study adopts a more comprehensive approach. Specifically, we evaluate various types of questions such as true/false, multi-choice, multi-select, short answer, long answer, design-based, and coding-related questions. Our evaluation highlights the potential consequences of students excessively relying on ChatGPT for the completion of assignments and exams, including self-sabotage. We conclude with a discussion on how can students and instructors constructively use ChatGPT and related tools to enhance the quality of instruction and the overall student experience. Copyright © 2023, The Authors. All rights reserved.

关键词： Complex networks   Computer science  

摘要： Past research has attributed the circulation of online news to two main factors-individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithmmediated information diffusion)-and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called `The Big Sort'). We hypothesized that this latter factor matters for the online spreading of news not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an `hype machine' for news (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects). Rather, news circulation in Reddit worked as a supply-and-demand system: news items scaled linearly with the number of users in each state (with a scaling exponent beta approximate to 1, and a goodness of fit R-2 approximate to 0.95). Furthermore, deviations from such a universal pattern were best explained by state- level personality and cultural factors ( R-2 approximate to {0.12, 0.39}), rather than socioeconomic conditions ( R-2 approximate to {0.15, 0.29}) or political characteristics ( R-2 approximate to {0.06, 0.21}). Higher-than-expected circulation of any type of news was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness). Interestingly, the combination of those factors with low levels of education was then associated with the circulation of a particular type of news, that is, misinformation. These resul