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16 pages, 447 KiB

Open AccessArticle

How Self-Regulated Learning Is Affected by Feedback Based on Large Language Models: Data-Driven Sustainable Development in Computer Programming Learning

by Di Sun, Pengfei Xu, Jing Zhang, Ruqi Liu and Jun Zhang

Electronics 2025, 14(1), 194; https://doi.org/10.3390/electronics14010194 - 5 Jan 2025

Viewed by 935

Abstract

Self-regulated learning (SRL) is a sustainable development skill that involves learners actively monitoring and adjusting their learning processes, which is essential for lifelong learning. Learning feedback plays a crucial role in SRL by aiding in self-observation and self-judgment. In this context, large language [...] Read more.

Self-regulated learning (SRL) is a sustainable development skill that involves learners actively monitoring and adjusting their learning processes, which is essential for lifelong learning. Learning feedback plays a crucial role in SRL by aiding in self-observation and self-judgment. In this context, large language models (LLMs), with their ability to use human language and continuously interact with learners, not only provide personalized feedback but also offer a data-driven approach to sustainable development in education. By leveraging real-time data, LLMs have the potential to deliver more effective and interactive feedback that enhances both individual learning experiences and scalable, long-term educational strategies. Therefore, this study utilized a quasi-experimental design to examine the effects of LLM-based feedback on learners’ SRL, aiming to explore how this data-driven application could support learners’ sustainable development in computer programming learning. The findings indicate that LLM-based feedback significantly improves learners’ SRL by providing tailored, interactive support that enhances motivation and metacognitive strategies. Additionally, learners receiving LLM-based feedback demonstrated better academic performance, suggesting that these models can effectively support learners’ sustainable development in computer programming learning. However, the study acknowledges limitations, including the short experimental period and the initial unfamiliarity with LLM tools, which may have influenced the results. Future research should focus on refining LLM integration, exploring the impact of different feedback types, and extending the application of these tools to other educational contexts. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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13 pages, 5669 KiB

Open AccessArticle

Optimization of Video Surveillance System Deployment Based on Space Syntax and Deep Reinforcement Learning

by Bingchan Li and Chunguo Li

Electronics 2025, 14(1), 38; https://doi.org/10.3390/electronics14010038 - 26 Dec 2024

Viewed by 343

Abstract

With the widespread deployment of video surveillance devices, a large number of indoor and outdoor places are under the coverage of cameras, which plays a significant role in enhancing regional safety management and hazard detection. However, a vast number of cameras lead to [...] Read more.

With the widespread deployment of video surveillance devices, a large number of indoor and outdoor places are under the coverage of cameras, which plays a significant role in enhancing regional safety management and hazard detection. However, a vast number of cameras lead to high installation, maintenance, and analysis costs. At the same time, low-quality images and potential blind spots in key areas prevent the full utilization of the video system’s effectiveness. This paper proposes an optimization method for video surveillance system deployment based on space syntax analysis and deep reinforcement learning. First, space syntax is used to calculate the connectivity value, control value, depth value, and integration of the surveillance area. Combined with visibility and axial analysis results, a weighted index grid map of the area’s surveillance importance is constructed. This index describes the importance of video coverage at a given point in the area. Based on this index map, a deep reinforcement learning network based on DQN (Deep Q-Network) is proposed to optimize the best placement positions and angles for a given number of cameras in the area. Experiments show that the proposed framework, integrating space syntax and deep reinforcement learning, effectively improves video system coverage efficiency and allows for quick adjustment and refinement of camera placement by manually setting parameters for specific areas. Compared to existing coverage-first or experience-based optimization, the proposed method demonstrates significant performance and efficiency advantages. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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18 pages, 1624 KiB

Open AccessArticle

Self-Supervised, Multi-View, Semantics-Aware Anchor Clustering

by Kaibin Wei, Haifeng Li, Qing Liu and Xiongjian Zhang

Electronics 2024, 13(23), 4782; https://doi.org/10.3390/electronics13234782 - 4 Dec 2024

Viewed by 535

Abstract

Data-driven artificial intelligence systems effectively enhance accuracy and robustness by utilizing multi-view learning to aggregate consistent and complementary information from multi-source data. As one of the most important branches of multi-view learning, multi-view anchor clustering greatly reduces the time complexity via learning similarity [...] Read more.

Data-driven artificial intelligence systems effectively enhance accuracy and robustness by utilizing multi-view learning to aggregate consistent and complementary information from multi-source data. As one of the most important branches of multi-view learning, multi-view anchor clustering greatly reduces the time complexity via learning similarity graphs between anchors and data instead of data-to-data similarities, which has gained widespread attention in data-driven artificial intelligence domains. However, two issues still exist in current methods: (1) They commonly utilize orthogonal regularization to enhance anchor diversity, which may lead to a distorted anchor distribution, e.g., some clusters might have few or even no corresponding anchors. (2) They only utilize view-sharing anchors to aggregate complementary and consistent information between views, which may fail to ensure anchor robustness due to the heterogeneity gap between views. To this end, self-supervised, multi-view, semantics-aware anchor clustering (SMA2C) is proposed, containing multi-view representation alignment, adaptive anchor selection, and global spectral optimization. Specifically, SMA2C devises dual-level contrastive learning on representations and clustering partitioning between views within a deep encoding–decoding architecture to achieve multi-granularity alignment of views with the heterogeneity gap. Meanwhile, SMA2C introduces adaptive anchor selection via filtering outliers and refining clusters to enhance correlations between clusters and anchors, which ensures the diversity and discriminability of anchors. Finally, extensive evaluations across four real-world datasets confirm that SMA2C establishes a new benchmark for multi-view anchor clustering. In particular, SMA2C achieves a 6.75% improvement in accuracy over the second-best result on the HW dataset. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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18 pages, 397 KiB

Open AccessArticle

Enhancing Peer Fairness via Data-Driven Analysis for Outlier Detection

by Zhengkun Di, Jinqiannan Zhang, Weixing Tan and Xiaoqi Sun

Electronics 2024, 13(23), 4735; https://doi.org/10.3390/electronics13234735 - 29 Nov 2024

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Abstract

Fairness in peer review is of vital importance in academic activities. Current peer review systems focus on matching suitable experts with proposals but often ignore the existence of outliers. Previous research has shown that outlier scores in reviews could decrease the fairness of [...] Read more.

Fairness in peer review is of vital importance in academic activities. Current peer review systems focus on matching suitable experts with proposals but often ignore the existence of outliers. Previous research has shown that outlier scores in reviews could decrease the fairness of these systems. Therefore, outlier detection in peer review systems is essential for maintaining fairness. In this paper, we introduce a novel method that employs data-crossing analysis to detect outlier scores, aiming to improve the reliability of peer review processes. We utilize a confidential dataset from a review organization. Due to the inability to access ground truth scores, we systematically devise data-driven deviations from an estimated ground truth through data-crossing analysis. These deviations reveal inconsistencies and abnormal scoring behaviors of different reviewers. Subsequently, the review process is strengthened by providing a structured mechanism to identify and mitigate biases. Extensive experiments demonstrate its effectiveness in improving the accuracy and fairness of academic assessments, contributing to the broader application of AI-driven methodologies to achieve more reliable and equitable outcomes. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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15 pages, 443 KiB

Open AccessArticle

Leveraging Large Language Models for Efficient Alert Aggregation in AIOPs

by Junjie Zha, Xinwen Shan, Jiaxin Lu, Jiajia Zhu and Zihan Liu

Electronics 2024, 13(22), 4425; https://doi.org/10.3390/electronics13224425 - 12 Nov 2024

Viewed by 781

Abstract

Alerts are an essential tool for the detection of anomalies and ensuring the smooth operation of online service systems by promptly notifying engineers of potential issues. However, the increasing scale and complexity of IT infrastructure often result in “alert storms” during system failures, [...] Read more.

Alerts are an essential tool for the detection of anomalies and ensuring the smooth operation of online service systems by promptly notifying engineers of potential issues. However, the increasing scale and complexity of IT infrastructure often result in “alert storms” during system failures, overwhelming engineers with a deluge of often correlated alerts. Therefore, effective alert aggregation is crucial in isolating root causes and accelerating failure resolution. Existing approaches typically rely on either semantic similarity or statistical methods, both of which have significant limitations, such as ignoring causal relationships or struggling to handle infrequent alerts. To overcome these drawbacks, we propose a novel two-phase alert aggregation approach. We employ temporal–spatial clustering to group alerts based on their temporal proximity and spatial attributes. In the second phase, we utilize large language models to trace the cascading effects of service failures and aggregate alerts that share the same root cause. Experimental evaluations on datasets from real-world cloud platforms demonstrate the effectiveness of our method, achieving superior performance compared to traditional aggregation techniques. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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20 pages, 521 KiB

Open AccessArticle

SafeMD: Ownership-Based Safe Memory Deallocation for C Programs

by Xiaohua Yin, Zhiqiu Huang, Shuanglong Kan and Guohua Shen

Electronics 2024, 13(21), 4307; https://doi.org/10.3390/electronics13214307 - 1 Nov 2024

Viewed by 581

Abstract

Rust is a relatively new programming language that aims to provide memory safety at compile time. It introduces a novel ownership system that enforces the automatic deallocation of unused resources without using a garbage collector. In light of Rust’s promise of safety, a [...] Read more.

Rust is a relatively new programming language that aims to provide memory safety at compile time. It introduces a novel ownership system that enforces the automatic deallocation of unused resources without using a garbage collector. In light of Rust’s promise of safety, a natural question arises about the possible benefits of exploiting ownership to ensure the memory safety of C programs. In our previous work, we developed a formal ownership checker to verify whether a C program satisfies exclusive ownership constraints. In this paper, we further propose an ownership-based safe memory deallocation approach, named SafeMD, to fix memory leaks in the C programs that satisfy exclusive ownership defined in the prior formal ownership checker. Benefiting from the C programs satisfying exclusive ownership, SafeMD obviates alias and inter-procedural analysis. Also, the patches generated by SafeMD make the input C programs still satisfy exclusive ownership. Usually, a C program that satisfies the exclusive ownership constraints is safer than its normal version. Our evaluation shows that SafeMD is effective in fixing memory leaks of C programs that satisfy exclusive ownership. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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19 pages, 1568 KiB

Open AccessArticle

CPEQA: A Large Language Model Based Knowledge Base Retrieval System for Chinese Confidentiality Knowledge Question Answering

by Jian Cao and Jiuxin Cao

Electronics 2024, 13(21), 4195; https://doi.org/10.3390/electronics13214195 - 25 Oct 2024

Viewed by 1099

Abstract

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usually rely on NLP through the retrieval of corpus and answering databases to [...] Read more.

Large language models (LLMs) have exhibited remarkable performance on various natural language processing (NLP) tasks, particularly in the construction of the intelligent question-answering system. These systems, especially in specialized fields, usually rely on NLP through the retrieval of corpus and answering databases to efficiently provide accurate and concise answers. This paper focuses on the national confidentiality publicity and education field, aiming to address the dilemma of inaccurate knowledge retrieval in this field. Therefore, we design an intelligent confidentiality question-answering system CPEQA by comprehensively utilizing the LLMs platform and information retrieval technique. CPEQA is capable of providing professional answers to questions about Chinese confidentiality publicity and education raised by users. Additionally, we also integrate the conventional database retrieval technique and LLMs into the database query construction, enabling CPEQA to perform real-time queries and data analysis for both single-table and multi-table querying tasks. Through extensive experiments with generated query sentences, we show both methodological comparisons and empirical evaluations of CPEQA’s performance. Experimental results indicate that CPEQA has achieved competitive results on answering precision, recall rate and other metrics. Finally, we explore the challenges of the CPEQA system associated with these techniques and outline potential avenues for future research in this emerging field. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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14 pages, 6262 KiB

Open AccessArticle

Degradation-Guided Multi-Modal Fusion Network for Depth Map Super-Resolution

by Lu Han, Xinghu Wang, Fuhui Zhou and Diansheng Wu

Electronics 2024, 13(20), 4020; https://doi.org/10.3390/electronics13204020 - 12 Oct 2024

Viewed by 622

Abstract

Depth map super-resolution (DSR) is a technique aimed at restoring high-resolution (HR) depth maps from low-resolution (LR) depth maps. In this process, color images are commonly used as guidance to enhance the restoration procedure. However, the intricate degradation of LR depth poses a [...] Read more.

Depth map super-resolution (DSR) is a technique aimed at restoring high-resolution (HR) depth maps from low-resolution (LR) depth maps. In this process, color images are commonly used as guidance to enhance the restoration procedure. However, the intricate degradation of LR depth poses a challenge, and previous image-guided DSR approaches, which implicitly model the degradation in the spatial domain, often fall short of producing satisfactory results. To address this challenge, we propose a novel approach called the Degradation-Guided Multi-modal Fusion Network (DMFNet). DMFNet explicitly characterizes the degradation and incorporates multi-modal fusion in both spatial and frequency domains to improve the depth quality. Specifically, we first introduce the deep degradation regularization loss function, which enables the model to learn the explicit degradation from the LR depth maps. Simultaneously, DMFNet converts the color images and depth maps into spectrum representations to provide comprehensive multi-domain guidance. Consequently, we present the multi-modal fusion block to restore the depth maps by leveraging both the RGB-D spectrum representations and the depth degradation. Extensive experiments demonstrate that DMFNet achieves state-of-the-art (SoTA) performance on four benchmarks, namely the NYU-v2, Middlebury, Lu, and RGB-D-D datasets. Full article

(This article belongs to the Special Issue Advances in Data-Driven Artificial Intelligence)

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