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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2708-2722页

作者： Xiang, Guodu Wang, Hao Yu, Kui Guo, Xianjie Cao, Fuyuan Song, Yukun Hefei University of Technology Key Laboratory of Knowledge Engineering with the Big Data of Ministry of Education Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Shanxi University School of Computer and Information Technology Taiyuan030006 China

Learning causal structures from observational data is critical for causal discovery and many machine learning tasks. Traditional constraint-based methods first adopt conditional independence (CI) tests to learn a global skeleton layer by layer and then orient the undirected edges to obtain a causal structure. However, the reliability of these statistical tests largely depends on the quality of data samples. In real-life scenarios, the presence of data noise or limited samples often makes many CI tests unreliable at each layer in the skeleton learning phase, leading to an inaccurate skeleton. As the number of layers increases, the inaccurate skeleton will continue to impair the skeleton construction of subsequent layers. Furthermore, an unreliable skeleton hampers the skeleton orientation procedure, resulting in an unsatisfactory causal structure. In this article, we propose a Bootstrap-based layerwise refining (BLR) algorithm for causal structure learning, which includes two new procedures to solve the above problems. First, BLR utilizes a novel layerwise skeleton refining procedure to construct the global skeleton layer by layer based on the bootstrap sampling. Second, BLR employs a collective skeleton orientation procedure that incorporates scoring techniques to collectively orient the global skeleton. The experimental results show that BLR outperforms the state-of-the-art methods on the benchmark Bayesian Network datasets. © 2020 IEEE.

关键词： Refining

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An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

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Research in Astronomy and Astrophysics 2023年第5期23卷 52-65页

作者： Hai-Feng Yang Xiao-Na Yin Jiang-Hui Cai Yu-Qing Yang A-Li Luo Zhong-Rui Bai Li-Chan Zhou Xu-Jun Zhao Ya-Ling Xun Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China School of Computer Science and Technology North University of ChinaTaiyuan 030051China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China

Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio,these spectra often show some anomalous features that do not work well with current *** this paper,a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected,and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame(Spectra Analysis-Frame) is provided to explore their origins from different *** SA-Frame is composed of three parts:NAPC-Spec clustering,characterization and origin ***,NAPC-Spec(Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality,resulting in 13 ***,characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum,where these 13 types are characterized as regular spectra with low S/Ns,splicing problems,suspected galactic emission signals,contamination from city light and un-gregarious type ***,a preliminary analysis of their origins is made from the characteristics of the observational targets,contamination from the sky,and the working status of the *** results would be valuable for improving the overall data quality of large-scale spectral surveys.

关键词： methods data analysis-surveys-techniques spectroscopic-site testing-methods analytical

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Chinese Macro Discourse Parsing on Generative Fusion and Distant Supervision 20th

Chinese Macro Discourse Parsing on Generative Fusion and D...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： He, Longwang Jiang, Feng Bao, Xiaoyi Fan, Yaxin Li, Peifeng Chu, Xiaomin School of Computer Science and Technology Soochow University Suzhou China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9789819970216

Most previous studies on discourse parsing have utilized discriminative models to construct tree structures. However, these models tend to overlook the global perspective of the tree structure as a whole during the step-by-step top-down or bottom-up parsing process. To address this issue, we propose DP-GF, a macro Discourse Parser based on Generative Fusion, which considers discourse parsing from both process-oriented and result-oriented perspectives. Additionally, due to the small size of existing corpora and the difficulty in annotating macro discourse structures, DP-GF addresses the small-sample problems by proposing a distant supervision training method that transforms a relatively large-scale topic structure corpus into a high-quality silver-standard discourse structure corpus. Our experimental results on MCDTB 2.0 demonstrate that our proposed model outperforms the state-of-the-art baselines on discourse tree construction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Trees (mathematics)

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data Analysis with Model Explainability for Air Quality and Pollution Prediction of Urban Areas in Edge Networks 12th

Data Analysis with Model Explainability for Air Quality an...

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12th International Conference on Big data and Artificial Intelligence, BDA 2024

作者： Singh, Akansha Chakraborty, Soumendu Adhikari, Mainak Department of Computer Science Indian Institute of Information Technology Lucknow Lucknow India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram India

ISBN: (纸本)9783031818202

Air quality significantly impacts human health and economic conditions, making precise and timely assessment crucial in urban areas. Existing studies often fail to predict pollution accurately in smaller areas due to varying meteorological conditions. Most existing prototypes rely on standard ML models, trained with generic, publicly available datasets, which often lack local dynamics, adaptability, and synchronization. To address these challenges, in this paper, we propose a new Hybrid Edge-Cloud Air Quality Monitoring System. For initial model training a robust testbed is created by data Fusion of air quality data from diverse sources like Application Programming Interfaces and public datasets. The system trains and refines ML models in the cloud, identifies key features to reduce feature dimensions, and leverages local decision-making using site-specific data collected by sensors on edge devices. Sensors monitor local environmental parameters for real-time analysis and decision-making, ensuring accurate predictions for specific urban locations while optimizing performance and minimizing resource usage on edge devices. Explainable AI techniques improve transparency and confidence in decisions. The model is validated by simulating various ML models on the testbed, with and without feature extraction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Intention Analysis Makes LLMs A Good Jailbreak Defender 31

Intention Analysis Makes LLMs A Good Jailbreak Defender

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yuqi Ding, Liang Zhang, Lefei Tao, Dacheng School of Computer Science Wuhan University China The University of Sydney Australia College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798891761964

Aligning large language models (LLMs) with human values, particularly when facing complex and stealthy jailbreak attacks, presents a formidable challenge. Unfortunately, existing methods often overlook this intrinsic nature of jailbreaks, which limits their effectiveness in such complex scenarios. In this study, we present a simple yet highly effective defense strategy, i.e., Intention Analysis (IA). IA works by triggering LLMs' inherent self-correct and improve ability through a two-stage process: 1) analyzing the essential intention of the user input, and 2) providing final policy-aligned responses based on the first round conversation. Notably, IA is an inference-only method, thus could enhance LLM safety without compromising their helpfulness. Extensive experiments on varying jailbreak benchmarks across a wide range of LLMs show that IA could consistently and significantly reduce the harmfulness in responses (averagely -48.2% attack success rate). Encouragingly, with our IA, Vicuna-7B even outperforms GPT-3.5 regarding attack success rate. We empirically demonstrate that, to some extent, IA is robust to errors in generated intentions. Further analyses reveal the underlying principle of IA: suppressing LLM's tendency to follow jailbreak prompts, thereby enhancing safety. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Virtual Reality Experiment for 5G Service Convergence Course in Communication Engineering 3

Virtual Reality Experiment for 5G Service Convergence Course...

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3rd International Conference on Educational Technology, ICET 2023

作者： Liu, Dandan Meng, Xin Hu, Shengbo Guizhou Normal University School of Big Data and Computer Science Guiyang Guizhou China

ISBN: (纸本)9798350302332

Virtual Reality (VR) technology is a computer simulation system that allows the creation and experience of virtual worlds. Because of its immersive, interactive, and realistic characteristics, VR is increasingly useful in educational contexts. However, due to the limitation of communication technology, problems such as high latency and vertigo of VR gradually appear in the teaching process, greatly affecting the learning experience of learners. 5G's technical advantages with high speed, low latency, and large capacity can provide the possibility for VR to better serve teaching practice activities. Thus, based on the unique advantages of 5G, this paper describes the virtual reality experiment (VRE) of the 5G service convergence course based on 5G+VR. Combined with our school's training plan for the communication major, this paper proposes the VRE teaching model. According to the results of the learning effect and learning process evaluation, the VRE teaching model can effectively improve students' enthusiasm and initiative in learning, thereby promoting the cultivation of communication professionals. © 2023 IEEE.

关键词： Virtual reality

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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Comparative Analysis of Traditional and Contextual Embedding for Bangla Sarcasm Detection in Natural Language Processing 12

Comparative Analysis of Traditional and Contextual Embedding...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Fahim, Kaji Mehedi Hasan Moontaha, Mithila Rahman, Mashrur Rhythm, Ehsanur Rahman Rasel, Annajiat Alim Brac University School of Data and Sciences Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341102

Sarcasm, a sort of sentiment characterized by a disparity between the apparent and intended meanings of the text, is a key component of sentiment analysis, opinion extraction, and social media analytics. However, sarcasm detection in Bangla has not received sufficient research attention yet. Moreover, there hasn't been a significant amount of study done comparing traditional and contextual word embeddings for the Bengali language. This study aims to address this gap by comparing traditional embedding by using the Bidirectional Gated Recurrent Unit - BiGRU model and contextual embedding by using Bidirectional Encoder Representations from Transformers - BERT for sarcasm detection in Bangla. The collection of the dataset of Bangla text was from social media platforms, containing labelled instances - whether it provides sarcasm or non-sarcasm. Pre-trained word embeddings i.e. GloVe and FastText are used as traditional embedding for this study. By using metrics like precision, recall and F1-score, the performances for both models have been obtained. When the two traditional word embedding approaches are compared, GloVe embedding with Bi-GRU has outperformed FastText embedding with a macro-averaged F1 score of 0.9395. On the other hand, contextual word embedding using BERT has outperformed both the traditional approaches having a better macro-averaged F1 score of 0.9572 and greater class-wise performance as compared with traditional embedding for both non-sarcastic (96%) and sarcastic (96%) text detection. In our findings, contextual word embedding i.e. BERT has performed better as compared with the two traditional word embeddings for this specific Bangla sarcasm detection binary classification task. © 2023 IEEE.

关键词： Embeddings

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Understanding and improving fairness in cognitive diagnosis

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science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation 41

Tackling Non-Stationarity in Reinforcement Learning via Caus...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Wanpeng Li, Yilin Yang, Boyu Lu, Zongqing School of Computer Science Peking University China Center for Statistical Science Peking University China School of Data Science Fudan University China Beijing Academy of Artificial Intelligence China

In real-world scenarios, the application of reinforcement learning is significantly challenged by complex *** existing methods attempt to model changes in the environment explicitly, often requiring impractical prior knowledge of *** this paper, we propose a new perspective, positing that non-stationarity can propagate and accumulate through complex causal relationships during state transitions, thereby compounding its sophistication and affecting policy *** believe that this challenge can be more effectively addressed by implicitly tracing the causal origin of *** this end, we introduce the Causal-Origin REPresentation (COREP) *** primarily employs a guided updating mechanism to learn a stable graph representation for the state, termed as causal-origin *** leveraging this representation, the learned policy exhibits impressive resilience to *** supplement our approach with a theoretical analysis grounded in the causal interpretation for non-stationary reinforcement learning, advocating for the validity of the causal-origin *** results further demonstrate the superior performance of COREP over existing methods in tackling non-stationarity *** code is available at https://***/PKURL/COREP. Copyright 2024 by the author(s)

关键词： Reinforcement learning

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