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关键词： Computer science and engineering education   Self-directed learning   Self-management   Self-monitoring   Motivation Online learning  

摘要： There is an increasing need for computer science and engineering (CSE) online education. This study examined CSE online learners' perceptions of self-directed learning (SDL) readiness, strategies, and satisfaction. The researchers surveyed 225 students and conducted semi-structured interviews with 15 online CSE students. The quantitative survey data and the qualitative interview data were analyzed using descriptive statistics and thematic analysis, respectively. The findings were as follows: (1) Both extrinsic motivation strategies (e.g., future career development, building a learning routine) and intrinsic motivation strategies (e.g., interest in learning) were utilized;(2) Diverse metacognition strategies (e.g., assignments, quizzes, and tests, discussing with peers, tracking progress, staying in touch with professors and teaching assistants) and cognitive strategies (e.g., watching recorded lectures, taking notes, reading books, seeking out information) were used to monitor learning;(3) Time -management (e.g., priority, checklist, fixed schedule, time -block for study) and resource management strategies (e.g., focused on lectures, textbook reading) were leveraged;(4) Specific designs or instruction promoted students' SDL skills, such as access to documented learning materials, reminders sent from instructors, availability of the instructor, group interaction, and flexibility, and (5) Student satisfaction depended on the design of the course. The advantages and disadvantages of online learning were identified. The findings indicated that the instruction strategies and online course design are critical for CSE students' SDL.

关键词： Data Access   High Performance Computing   Technical Boundaries   Landscape   Scientific Research   Scalable   Data Management   Computer Science   Cloud Computing   Technical Challenges   Scientific Findings   Rapid Advances   Management Costs   Security Measures   Computational Capabilities   Cloud Environment   Foster Collaboration   Cloud Providers   Software Stack   High Performance Computing Systems   Data Security   Unique System   Central Hub   Application Programming Interface   Cloud Platform   Public Cloud   Comprehensive Training   Containerized   Research Community   Pricing Model  

摘要： At the IEEE/ACM International Conference for High-Performance Computing, Networking, Storage, and Analysis (SC23), held in Denver, experts discussed the convergence of high-performance computing and cloud computing. They explored how this integration could address current scientific computing limitations, enhance computational capabilities, and foster global collaboration while focusing on economic, security, technical, and community challenges and opportunities.

关键词： Blockchains   Engines   Synchronization   Semantics   Query processing   Metadata   Computer science   Blockchain system   Ethereum   key-value (KV) store   log-structure merged tree (LSM-tree)   storage engine  

摘要： Blockchain systems play an important role in distributed ledgers, database systems, etc. As more and more blocks are mined, the storage burden of blockchain system is significantly increased. The current blockchain system uniformly transforms all its data into key-value (KV) items and stores them to the underlying log-structure merged tree (LSM-tree) storage engine ignoring the software semantics. Consequently, it not only aggravates the write amplification effect of the storage engine but also increases the redundancy of data query steps, resulting in the performance bottleneck of blockchain system. In this article, we propose a semantic-integrated LSM-tree-based KV storage engine for blockchain systems, called Block-LSM, which significantly improves the data synchronization and data query efficiency of blockchain system. Specifically, we first design a shared prefix scheme to transform blockchain data into ordered KV pairs to alleviate the key range overlaps of different levels in the underlying LSM-tree-based storage engine. Moreover, we propose to maintain several semantic-orientated memory buffers to isolate different kinds of blockchain data and implement memory buffer space management strategy to further improve memory efficiency. To save space overhead, Block-LSM further aggregates multiple blocks into a group and assigns the same prefix to all KV items from the same block group. We also reduce step redundancy in transaction queries by modifying the body data storage format. Finally, we implement Block-LSM in a real blockchain environment and conduct a series of comparative experiments with the typical blockchain system Ethereum. The evaluation results show that Block-LSM significantly reduces up to 7.56x storage write amplification and increases throughput by 8.64x compared with the original Ethereum design. In terms of data lookups (i.e., transaction and account lookup), Block-LSM improves the throughput by 50% compared to the original Ethereum design.

关键词： Task analysis   Trajectory   Probabilistic logic   Noise measurement   Training data   Noise   Approximation algorithms   Computer science   formal languages   machine learning   programming   representation learning   robot learning   robot programming  

摘要： Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several well-known limitations: they 1) require large amounts of training data, 2) are hard to interpret, and 3) are hard to refine and adapt. There is an emerging interest in Programmatic Imitation Learning (PIL), which offers significant promise in addressing the above limitations. In PIL, the learned policy is represented in a programming language, making it amenable to interpretation and adaptation to novel settings. However, state-of-the-art PIL algorithms assume access to action labels and struggle to learn from noisy real-world demonstrations. In this paper, we propose Plunder, a novel PIL algorithm that addresses these shortcomings by synthesizing probabilistic programmatic policies that are particularly well-suited for modeling the uncertainties inherent in real-world demonstrations. Our approach leverages an EM loop to simultaneously infer the missing action labels and the most likely probabilistic policy. We benchmark Plunder against several established IL techniques, and demonstrate its superiority across five challenging imitation learning tasks under noise. Plunder policies outperform the next-best baseline by 19% and 17% in matching the given demonstrations and successfully completing the tasks, respectively.

关键词： climate change   educational computer games   game-based learning   motivation   case study  

摘要： Background Teaching climate change is difficult. Its complexity spans many subjects, often taught disjointedly. Many climate change effects are not immediately observable, making it hard for students to connect to it *** This study investigates how we can spark high school students' interest in learning about climate change using educational computer *** We adopted a qualitative case research design to understand how games boost students' drive and their role in motivating them. We selected a high school teacher and her eight students as our subjects, interviewing them in person. We analyzed their responses were using Keller's ARCS Theory of Motivation Model and blending deductive and inductive *** The findings were encouraging: games positively impacted students' interest in climate change. They transformed the learning atmosphere into a concentrated, captivating space where the content was seen as tough yet enjoyable. Moreover, the games helped students make real-world connections, enhancing their understanding and appreciation of the *** Educational games are a powerful tool in motivating students to learn about climate change science. Hence, educators should be ready to harness the games' power to create immersive, fun, and stimulating learning environments.

关键词： Optimization   Statistics   Sociology   Pareto optimization   Computer science   Systematics   History   Archive   archiving methods   environmental selection   evolutionary computation   multiobjective optimization   population update  

摘要： Most multiobjective optimization algorithms maintain an archive explicitly or implicitly during their search. Such an archive can be solely used to store high-quality solutions presented to the decision maker, but in many cases may participate in the search process (e.g., as the population in evolutionary computation). Over the last two decades, archiving, the process of comparing new solutions with previous ones and deciding how to update the archive/population, stands as an important issue in evolutionary multiobjective optimization (EMO). This is evidenced by constant efforts from the community on developing various effective archiving methods, ranging from conventional Pareto-based methods to more recent indicator-based and decomposition-based ones. However, the focus of these efforts is on empirical performance comparison in terms of specific quality indicators;there is lack of systematic study of archiving methods from a general theoretical perspective. In this article, we attempt to conduct a systematic overview of multiobjective archiving, in the hope of paving the way to understand archiving algorithms from a holistic perspective of theory and practice, and more importantly providing a guidance on how to design theoretically desirable and practically useful archiving algorithms. In doing so, we also present that archiving algorithms based on weakly Pareto-compliant indicators (e.g., $\epsilon $ -indicator and IGD+), as long as designed properly, can achieve the same theoretical desirables as archivers based on Pareto-compliant indicators (e.g., hypervolume indicator). Such desirables include the property limit-optimal, the limit form of the possible optimal property that a bounded archiving algorithm can have with respect to the most general form of superiority between solution sets.

关键词： Load management   Switches   Delays   Receivers   Load modeling   Computer science   Transport protocols   Datacenter   lossless networks   load balancing  

摘要： Various datacenter network (DCN) load balancing schemes have been proposed in the past decade. Unfortunately, most of these solutions designed for lossy DCNs do not work well for Priority Flow Control (PFC) enabled lossless DCNs, primarily due to the reason that the individual congestion signals used in these solutions, e.g., link load, queue length, Round Trip Time (RTT) and Explicit Congestion Notification (ECN), may not be able to correctly or timely reflect the hop-by-hop PFC pausing. This paper first reveals the above problems via extensive experiments, and then based on the insights learned, we present Proteus, a PFC-aware load balancing scheme that is resilient to PFC pausing by exploring a combination of multi-level congestion signals. At its heart, Proteus leverages RTT-level signals (i.e., RTT and link utilization) to detect path status for initial routing decision, and exploits sub-RTT level signal (i.e., cumulative sojourn time) to reflect instantaneous PFC pausing and make timely rerouting choices based on the idea of better-late-than-never. We have implemented Proteus in the hardware programmable switch. Our testbed experiments as well as large-scale simulations show that Proteus can effectively handle PFC pausing under realistic workloads and achieve up to 35%, 31%, 28%, 22% and 46%, 42%, 34%, 29% better average FCT and 99(th) percentile FCT than CONGA, DRILL, Hermes and MP-RDMA, respectively.

关键词： Task analysis   Representation learning   Semantics   Self-supervised learning   Training   Mutual information   Computer science   Data mining   graph neural networks   graph representation learning   machine learning  

摘要： Contrastive learning has been successfully applied in unsupervised representation learning. However, the generalization ability of representation learning is limited by the fact that the loss of downstream tasks (e.g., classification) is rarely taken into account while designing contrastive methods. In this article, we propose a new contrastive-based unsupervised graph representation learning (UGRL) framework by 1) maximizing the mutual information (MI) between the semantic information and the structural information of the data and 2) designing three constraints to simultaneously consider the downstream tasks and the representation learning. As a result, our proposed method outputs robust low-dimensional representations. Experimental results on 11 public datasets demonstrate that our proposed method is superior over recent state-of-the-art methods in terms of different downstream tasks. Our code is available at https://***/LarryUESTC/GRLC.

关键词： Adaptive learning   Computer education   Learning styles   Scoping review  

摘要： Adaptive learning is a teaching approach aiming to personalize the learning experience for each student. In Computer Science Education (CSE), Adaptive Learning Systems (ALS) can provide students with customized lessons, exercises, and assessments based on their previous knowledge, strengths, and weaknesses. Some literature reviews focus on adaptive learning techniques, but none specifically analyze the methods used in adaptive systems in CSE. Our overall objective is to identify the computing techniques that are implemented within ALS to provide adaptive computer education in higher education. We conducted a scoping review (SR) based on the Guidelines for performing Systematic Literature Reviews in Software Engineering, defining six research questions and a search string. We performed searches in four databases, retrieving 512 documents. After applying the selection criteria, we selected 29 works. The results present quantitative and qualitative data from those 29 studies. Most research has used adaptive learning in programming education (12 articles) and databases (9 papers). Nine articles used learning styles as the core of adaptation, with the Felder-Silverman model being used by seven of them. The most commonly used approach was the rule-based system approach built by experts within the proposal, which was used in 17.24% of the articles. Clustering, collaborative filtering, fuzzy logic, and K-NN were used in 10.34% of the proposals. The positive aspect of "delivering adaptive content" was pointed out in seven articles. As for negative aspects, the problem of lack of student engagement was mentioned in four articles. This study discusses the application of dynamic and static models based on learning styles (LS) and the challenges involved in their use. Additionally, another aspect pointed out is that it is crucial to carefully consider pedagogical aspects in learning systems and have mixed development teams. The study also discusses student motivation and the use