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VSM-RF: A method of relevance feedback in keyword Search over Relational databases

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VSM-RF: A method of relevance feedback in Keyword Search ove...

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Zhao-hui Peng Jun Zhang Shan Wang Chang-liang Wang Li-zhen Cui School of Computer Science and Technology Shandong University Jinan 250101 China School of Computer Science and Technology Dalian Maritime University 116026 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing 100872 China The Hong Kong University of Science and Technology China

In keyword search over relational databases (KSORD), retrieval of user's initial query is often unsatisfying. User has to reformulate his query and execute the new query, which costs much time and effort. In this paper, a method of automatically reformulating user queries by relevance feedback is introduced, which is named VSM-RF. Aimed at the results of KSORD systems, VSM-RF adopts a ranking method based on vector space model to rank KSORD results. After the first time of retrieval, using user feedback or pseudo feedback just as user like, VSM-RF computes expansion terms based on probability and reformulates the new query using query expansion. After KSORD systems executing the new query, more relevant results are produced by the new query in the result list and presented to user. Experimental results verify this method's effectiveness.

关键词： Feedback keyword search Relational databases Computer science Information retrieval Laboratories data engineering knowledge engineering Computer science education Costs

SP3: Enhancing Structured Pruning via PCA Projection

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arXiv 2023年

作者： Hu, Yuxuan Zhang, Jing Zhao, Zhe Zhao, Chen Chen, Xiaodong Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China Tencent AI Lab Tencent Beijing China School of Computer Science and Technology Xi'an Jiaotong University Xi'An China

Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidden dimension (d) in PLMs, a dimension critical to model size and efficiency. This paper introduces a novel structured pruning approach, Structured Pruning with PCA Projection (SP3), targeting the effective reduction of d by projecting features into a space defined by principal components before masking. Extensive experiments on benchmarks (GLUE and SQuAD) show that SP3 can reduce d by 70%, compress 94% of the BERTbase model, and maintain over 96% accuracy and outperform other methods that compress d by 6% in accuracy at the same compression ratio. SP3 has also proven effective with other models, including OPT and Llama. Our data and code are available at ours repo. © 2023, CC BY.

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Detection of ChatGPT Fake Science with the xFakeSci Learning Algorithm

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arXiv 2023年

作者： Hamed, Ahmed Abdeen Wu, Xindong Complex Adaptive Systems and Computational Intelligence Laboratory State University of New York at Binghamton BinghamtonNY13902 United States Sano Centre for Computational Medicine Clinical Data Science Cracow30-072 Poland Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230009 China China Electronics Data Industry Group Shenzhen518057 China

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific articles". In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer’s. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80% to 94%, outperforming common data mining algorithms, which scored F1 values between 38% and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant st

关键词： Learning algorithms

Bring User Feedback into keyword Search over databases

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Bring User Feedback into Keyword Search over Databases

Web Information Systems and Applications Conference (WISA)

作者： Zhaohui Peng Jun Zhang Shan Wang Changliang Wang School of Computer Science & Technology Shandong University Jinan China Key Laboratory of Data Engineering & Knowledge Engineering Ministry of Education Beijing China School of Computer Science & Technology Dalian Maritime University Dalian China Hong Kong University of Science and Technology Hong Kong China

keyword search over relational databases (KSORD) enables casual users to use keyword queries (a set of keywords) to search relational databases just like searching the Web, without any knowledge of the database schema or any need of writing SQL queries. In KSORD, retrieval of user's initial query is often unsatisfying. User has to reformulate his query and execute the new query, which costs much time and effort. A method of automatically reformulating user queries by user feedback aimed at the results of KSORD is introduced in this paper, which is named UFBP (user feedback based on probability). After the first time of retrieval, according to the users' feedback information, UFBP computes terms to be added into the expanded query based on probability and reformulates the new query using query expansion. After KSORD executing the new query automatically, more relevant results are presented to user. Experimental results verify its effectiveness.

关键词： Feedback keyword search Relational databases Tree graphs Information retrieval Computer science Educational technology Writing Information systems Application software

Assessing Extraneous and Intrinsic Cognitive Load in Space Teleoperated Tasks: A Physiological Metrics Study with Cognitive Load Theory

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SSRN

SSRN 2024年

作者： Yan, Zhao Wang, Yahui Chen, Shanguang Yang, Xiaonan Hua, Yaoguang Fang, Haonan Wang, Chaoran School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy Beijing Institute of Technology Jiaxing314019 China Key Laboratory of Industry Knowledge & Data Fusion Technology and Application Ministry of Industry and Information Technology Beijing Institute of Technology Beijing100081 China Astronaut Center of China National Key Laboratory of Human Factors Engineering China

This study aimed to explore the relationship between physiological performance indicators and both intrinsic and extrinsic cognitive loads during a space robot grasping task, which involves teleoperation requiring high levels of spatial and motor coordination, attention, and decision-making. The hypothesis posited that operational mode would influence extrinsic cognitive load, while task difficulty would impact intrinsic cognitive load. Additionally, a correlation was expected between physiological performance indicators and cognitive load. To test this, a 2x2 factorial experiment was conducted with independent variables being task difficulty (high vs. low) and operational mode (joystick vs. keys), and dependent variables including cognitive load, performance, and learning outcomes. Subjective scales were used to assess intrinsic and extrinsic cognitive load, and physiological data were collected to examine their correlation with cognitive load. The results indicate that operational mode and task difficulty significantly affect cognitive load in space exploration contexts. Specifically, the 'Joystick' mode was associated with higher extrinsic cognitive load, likely due to the challenges it presents in precise control during complex tasks. Performance metrics showed that attention allocation varied by operational mode, with the 'Joystick' mode requiring greater focus on the side view and end-effector, in line with cognitive load theory. Physiological metrics, particularly pupil diameter and EEG data, were crucial in understanding these effects;a decrease in pupil diameter with increased task difficulty reflected heightened intrinsic cognitive load. EEG data further revealed significant variations in brain activity across frontal, prefrontal, and occipital regions, with distinct changes in alpha, theta, and beta frequency bands, highlighting the substantial impact of cognitive load on neural processes. These findings underscore the importance of integrating cognitive

关键词： Space research

Joint-Motion Mutual Learning for Pose Estimation in Videos

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arXiv 2024年

作者： Wu, Sifan Chen, Haipeng Yin, Yifang Hu, Sihao Feng, Runyang Jiao, Yingying Yang, Ziqi Liu, Zhenguang College of Computer Science and Technology Jilin University Changchun China A*STAR Singapore Georgia Institute of Technology Atlanta United States School of Artificial Intelligence Jilin University Changchun China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Institute of Blockchain and Data Security Hangzhou China

Human pose estimation in videos has long been a compelling yet challenging task within the realm of computer vision. Nevertheless, this task remains difficult because of the complex video scenes, such as video defocus and self-occlusion. Recent methods strive to integrate multi-frame visual features generated by a backbone network for pose estimation. However, they often ignore the useful joint information encoded in the initial heatmap, which is a byproduct of the backbone generation. Comparatively, methods that attempt to refine the initial heatmap fail to consider any spatiotemporal motion features. As a result, the performance of existing methods for pose estimation falls short due to the lack of ability to leverage both local joint (heatmap) information and global motion (feature) dynamics. To address this problem, we propose a novel joint-motion mutual learning framework for pose estimation, which effectively concentrates on both local joint dependency and global pixel-level motion dynamics. Specifically, we introduce a context-aware joint learner that adaptively leverages initial heatmaps and motion flow to retrieve robust local joint feature. Given that local joint feature and global motion flow are complementary, we further propose a progressive joint-motion mutual learning that synergistically exchanges information and interactively learns between joint feature and motion flow to improve the capability of the model. More importantly, to capture more diverse joint and motion cues, we theoretically analyze and propose an information orthogonality objective to avoid learning redundant information from multi-cues. Empirical experiments show our method outperforms prior arts on three challenging benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion estimation

SLMP: A Scientific Literature Management Platform Based on Large Language Models

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SLMP: A Scientific Literature Management Platform Based on L...

IEEE International Conference on Big knowledge (ICBK)

作者： Menghao Guo Jinling Jiang Fan Wu Shanxin Sun Chen Zhang Wenhui Li Zeyi Sun Guangyong Chen Xindong Wu Research Center for Life Sciences Computing Zhejiang Lab Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Hefei China

ISBN: (数字)9798331508821

ISBN: (纸本)9798331508838

This paper presents a Scientific Literature Management Platform (SLMP, demo link 1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM 2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge.

关键词： Training Large language models knowledge graphs Information retrieval data models Question answering (information retrieval)

LEGNN: A Label Ensemble Perspective for Training a Label-Noise-Resistant GNN with Reduced Complexity

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arXiv 2024年

作者： Zhao, Rui Shi, Bin Liang, Zhiming Ruan, Jianfei Dong, Bo Lin, Lu School of Computer Science and Technology Xi'an Jiaotong University China Ministry of Education Key Laboratory of Intelligent Networks and Network Security China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering China School of Distance Education Xi'an Jiaotong University China College of Information Sciences and Technology Penn State University United States

Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label noise, that is, a small amount of incorrectly labeled nodes can substantially misguide model training. Mainstream solutions define node classification with label noise (NCLN) as a reliable labeling task, often introducing node similarity with quadratic computational complexity to more accurately assess label reliability. To this end, in this paper, we introduce the Label Ensemble Graph Neural Network (LEGNN), a lower complexity method for robust GNNs training against label noise. LEGNN reframes NCLN as a label ensemble task, gathering informative multiple labels instead of constructing a single reliable label, avoiding high-complexity computations for reliability assessment. Specifically, LEGNN conducts a two-step process: bootstrapping neighboring contexts and robust learning with gathered multiple labels. In the former step, we apply random neighbor masks for each node and gather the predicted labels as a high-probability label set. This mitigates the impact of inaccurately labeled neighbors and diversifies the label set. In the latter step, we utilize a partial label learning based strategy to aggregate the high-probability label information for model training. Additionally, we symmetrically gather a low-probability label set to counteract potential noise from the bootstrapped high-probability label set. Extensive experiments on six datasets demonstrate that LEGNN achieves outstanding performance while ensuring efficiency. Moreover, it exhibits good scalability on dataset with over one hundred thousand nodes and one million edges. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction