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关键词： Scalability   Runtime   Static analysis   Costs   Backtracking   Source coding   Skeleton   Supercomputers   Delays   Computer science   Performance analysis   root-cause detection   scalability bottleneck   static analysis  

摘要： It is challenging to scale parallel applications to modern supercomputers because of load imbalance, resource contention, and communications between processes. Profiling and tracing are two main performance analysis approaches for detecting these scalability bottlenecks. Profiling is low-cost but lacks detailed dependence for identifying root causes. Tracing records plentiful information but incurs significant overheads. To address these issues, we present ScalAna, which employs static analysis techniques to combine the benefits of profiling and tracing - it enables tracing's analyzability with overhead similar to profiling. ScalAna uses static analysis to capture program structures and data dependence of parallel applications, and leverages lightweight profiling approaches to record performance data during runtime. Then a parallel performance graph is generated with both static and dynamic data. Based on this graph, we design a backtracking detection approach to automatically pinpoint the root causes of scaling issues. We evaluate the efficacy and efficiency of ScalAna using several real applications with up to 704K lines of code and demonstrate that our approach can effectively pinpoint the root causes of scaling loss with an average overhead of 5.65% for up to 16,384 processes. By fixing the root causes detected by our tool, it achieves up to 33.01% performance improvement.

关键词： Three-dimensional displays   Semantics   Autonomous vehicles   Pipelines   Object detection   Semantic segmentation   Feature extraction   Computer science   Cameras   Training   3D semantic occupancy   autonomous driving   temporal association   contrastive learning  

摘要： 3D semantic occupancy has garnered considerable attention due to its abundant structural information encompassing the entire autonomous driving scene. However, existing 3D occupancy prediction methods are typically tailored for single-frame inputs, resulting in unsatisfactory performance and temporal inconsistencies in real-world continuous scenarios. In this paper, we introduce LinkOcc, a sparse-queries approach incorporating an efficient temporal association mechanism for 3D semantic occupancy prediction. LinkOcc is conceptually built on the prevalent DETR-like framework for 2D segmentation, and we further construct the temporal association mechanism on this basis. Specifically, we propose a near-online training strategy that jointly trains with two adjacent frames, which successfully combines the benefits of both online and off-online methods. Moreover, we introduce a temporal association strategy with contrastive learning to discriminate features for cross-frame semantic-level association. Comprehensive experiments demonstrate that LinkOcc not only surpasses the state-of-the-art methods in 3D occupancy prediction, but also guarantees a promising performance on foreground classes.

关键词： large language models   programming education   plagiarism   integrity  

摘要： The integration of large language models (LLMs) into educational settings represents a significant technological breakthrough, offering substantial opportunities alongside profound ethical challenges. Higher education institutions face the widespread use of these tools by students, requiring them to navigate complex decisions regarding their adoption. This includes determining whether to allow the use of LLMs, defining their appropriate scope, and establishing guidelines for their responsible and ethical application. In the context of computer science education, these challenges are particularly acute. On the one hand, the capabilities of LLMs significantly enhance the tools available to developers and software engineers. On the other hand, students' over-reliance on LLMs risks hindering their development of foundational skills. This study examines these challenges and proposes strategies to regulate the use of LLMs while upholding academic integrity. It focuses on the specific impact of LLMs in programming education, where dependence on AI-generated solutions may erode active learning and essential skill acquisition. Through a comprehensive literature review and drawing on teaching experience and guidelines from global institutions, this study contributes to the broader discourse on the integration of these advanced technologies into educational environments. The goal is to enhance learning outcomes while ensuring the development of competent, ethical software professionals.

关键词： Clustering methods   Computer science   Fuzzy systems   Data mining   Uncertainty   Representation learning   Learning systems   Fuses   Entropy   Data models   Common information   dual anchor graph learning   fuzzy clustering   multiview data   specific information  

摘要： Multiview anchor graph clustering has been a prominent research area in recent years, leading to the development of several effective and efficient methods. However, three challenges are faced by current multiview anchor graph clustering methods. First, real-world data often exhibit uncertainty and poor discriminability, leading to suboptimal anchor graphs when directly extracted from the original data. Second, most existing methods assume the presence of common information between views and primarily explore it for clustering, thus neglecting view-specific information. Third, further exploration and exploitation of the learned anchor graph to enhance clustering performance remains an open research question. To address these issues, a novel dual anchor graph fuzzy clustering method is proposed in this article. First, a novel matrix factorization-based dual anchor graph learning method is proposed to address the first two issues by extracting highly discriminative hidden representations for each view and subsequently deriving both common and specific anchor graphs from these hidden representations. Then, to address the third issue, a novel anchor graph fuzzy clustering method is developed with cooperative learning to exploit and utilize the common and specific anchor graphs fully. Meanwhile, a fuzzy membership structure preservation mechanism with dual anchor graphs is constructed to enhance clustering performance. Finally, negative Shannon entropy is further introduced to adaptively adjust the view weighing. Extensive experiments on several datasets demonstrate the effectiveness of the proposed method.

关键词： Data visualization   Bars   Computer science   Strain   Sensitivity   Encoding   Visualization   Magnitude   axis manipulation   cognition   bias   framing effects  

摘要： When visualising data, chart designers have the freedom to choose the upper and lower limits of numerical axes. Axis limits can determine the physical characteristics of plotted values, such as the physical position of data points in dot plots. In two experiments (total N=300), we demonstrate that axis limits affect viewers' interpretations of the magnitudes of plotted values. Participants did not simply associate values presented at higher vertical positions with greater magnitudes. Instead, participants considered the relative positions of data points within the axis limits. Data points were considered to represent larger values when they were closer to the end of the axis associated with greater values, even when they were presented at the bottom of a chart. This provides further evidence of framing effects in the display of data, and offers insight into the cognitive mechanisms involved in assessing magnitude in data visualisations.

关键词： HI   IT-BiLSTM   rotating machinery   RUL  

摘要： Rotating machinery, as a key component of mechanical equipment, plays a crucial role in ensuring the reliability and safety of the equipment. This article proposes a RUL prediction framework based on interactive bidirectional long short-term memory (IT-BiLSTM) and function fitting, aiming to improve the accuracy of RUL prediction in rotating machinery. Firstly, to unify the failure threshold under different working conditions, a data interception method based on root mean square is proposed, and empirical mode decomposition is used to reconstruct the intercepted signals;then, 16 features are extracted, and health indicator (HI) is constructed through IT-BiLSTM, which is mapped to the [0,1] interval;finally, the time when HI is 0 is found through function fitting, which is the current RUL. The accuracy of the proposed method is verified using two datasets. The results show that the proposed IT-BiLSTM can more accurately predict the RUL of rotating machinery.

关键词： Computer science   Machine learning   Technology  

摘要： Developers are racing to find new ways to train large language models, after sucking the Internet dry of usable information.

摘要： Computer science (CS) is increasingly becoming part of the curricula of K-12 education in different countries. However, there are few K-12 CS teachers, and tools to offer K-12 CS education are often limited. Massive Open Online Courses (MOOCs) might help to temporarily address these challenges, and enable more schools to offer CS education. The goal of this systematic review is to give an overview of how CS MOOCs have been used in K-12 education. Nineteen papers from 2014 to May 2024 were included, describing thirteen different MOOCs. This review summarizes the research performed with these MOOCs and discusses directions for future research. Our findings show that most CS MOOCs target only part of the CS curriculum. When using a MOOC, a classroom teacher has an important role in supporting and managing students as they work in the MOOC. Research evaluating MOOCs is diverse, both in aims and in methods. In conclusion, MOOCs can play a valuable role in K-12 CS education, although additional teacher training to support students might be required. Moreover, additional learning material is needed to cover the full curriculum, as most MOOCs focus on programming and computational thinking. © 2025, CC BY-SA.

关键词： Machine learning   Computer science   Authorship   Databases  

摘要： In a year that will see many AI achievements and battles, let’s not forget that not all AI research makes the front pages.

关键词： Survival analysis   Computer science education   Supplemental instruction   Diversity in STEM  

摘要： This study examines the success of undergraduate students in computer science supplementary courses offered by a non-profit organization in partnership with colleges and universities across the U.S. Using a novel dataset from the nonprofit organization, we present one of the first descriptive overviews of students enrolled in supplemental computer science programs. Moreover, we conduct a survival analysis finding that racial and gender disparities in traditional computer science programs exist in these supplemental courses. However, the study finds that when supplemental courses are taken for credit, students are much more likely to complete the course, offering an important insight into program design and student motivation.