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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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A Weighted Ensemble Causal Discovery Method for Effective Connectivity Estimation 9

A Weighted Ensemble Causal Discovery Method for Effective Co...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhang, Qiqi Zhang, Yingwei Ding, Yanhui Chen, Yiqiang Song, Shuchao Wu, Shuang Shandong Normal University School of Information Science and Engineering Jinan China Shandong Academy of Intelligent Computing Technology Jinan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China

ISBN: (纸本)9798350319804

Exploring and explaining the effective connectivity (EC) between brain regions can help us understand the mechanisms behind neurodegenerative diseases such as Alzheimer's disease, thus helping us to diagnose patients better. Causal discovery techniques have been widely used in this domain recently, but diverse methods may produce conflicting outcomes because of the differences in underlying principles and search methods. To solve this problem, we integrate the causal relationships generated by different algorithms to obtain high-confidence inferences, resulting in a more reliable weighted ensemble causal discovery (WECD) method with higher accuracy. First of all, WECD defines a distribution metric function to measure the weights of different individual methods. Then, WECD adaptively chooses the critical threshold to obtain the confident edges set based on the generated directed edge confident metric curve. Finally, the directed acyclic graph condition is used to refine the confident edges set and acquire the final causal graph. We validate the effectiveness of WECD with both the simulated dataset and real functional near-infrared spectroscopy (fNIRS) dataset collected on AD patients, MCI patients and healthy controls. By comparing with other existing methods, experimental results demonstrate the potential of WECD as an effective tool for estimating brain EC. © 2023 IEEE.

关键词： Alzheimer's disease causal discovery effective connectivity ensemble learning functional near-infrared spectroscopy

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Context-Driven Index Trimming: A Data Quality Perspective to Enhancing Precision of RALMs

Context-Driven Index Trimming: A Data Quality Perspective to...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Kexin Jin, Ruochun Wang, Haotian Wang, Xi Chen, Huan Tang, Yuhua Wang, Qian Institute for Quantum Information State Key Laboratory of High Performance Computing China College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab Academy of Military Science Beijing China

ISBN: (纸本)9798891761681

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses. However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods. We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts. Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality. Experiments demonstrate average improvement of 3.75% in accuracy on challenging question-answering tasks. Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly. © 2024 Association for Computational Linguistics.

关键词： Data accuracy

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Design of Control Circuit for Active Metasurface Based on Varactor Diode

Design of Control Circuit for Active Metasurface Based on Va...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Zhang, Yue Liu, Yuefei Yang, Guohui Zhang, Kuang Li, Yingsong School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China

ISBN: (纸本)9781733509657

A control circuit for active metasurface based on varactor diode is designed in this paper. By varying the reverse bias voltage on the varactor diode, the designed circuit can regulate the additional phase of the active meta-atoms. The circuit adopts a form of one master and multiple slaves, which can simultaneously control the bias voltage of 960 meta-atoms. Each slave can control up to 64 channels. The control accuracy is 0.04V. The power consumption of a single slave is 160mW. Though experiment verification, the circuit has a good effect in controlling the beam deflection of the metasurface and generating OAM beams. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Timing circuits

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Reinforcement Learning with Token-level Feedback for Controllable Text Generation

Reinforcement Learning with Token-level Feedback for Control...

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Li, Wendi Wei, Wei Xu, Kaihe Xie, Wenfeng Chen, Dangyang Cheng, Yu Cognitive Computing and Intelligent Information Processing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China China Ping An Property & Casualty Insurance Company of China China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761193

To meet the requirements of real-world applications, it is essential to control generations of large language models (LLMs). Prior research has tried to introduce reinforcement learning (RL) into controllable text generation while most existing methods suffer from overfitting issues (finetuning-based methods) or semantic collapse (post-processing methods). However, current RL methods are generally guided by coarse-grained (sentence/paragraph-level) feedback, which may lead to suboptimal performance owing to semantic twists or progressions within sentences. To tackle that, we propose a novel reinforcement learning algorithm named TOLE which formulates TOken-LEvel rewards for controllable text generation, and employs a "first-quantize-then-noise" paradigm to enhance the robustness of the RL algorithm. Furthermore, TOLE can be flexibly extended to multiple constraints with little computational expense. Experimental results show that our algorithm can achieve superior performance on both single-attribute and multi-attribute control tasks. We have released our codes at https://***/WindyLee0822/CTG. © 2024 Association for Computational Linguistics.

关键词： Reinforcement learning

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Improving Fake News Video Detection by Correlating with Neighbors 61

Improving Fake News Video Detection by Correlating with Neig...

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Findings of the Association for Computational Linguistics, ACL 2023

作者： Qi, Peng Zhao, Yuyang Shen, Yufeng Ji, Wei Cao, Juan Chua, Tat-Seng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China National University of Singapore Singapore

ISBN: (纸本)9781959429623

The prevalence of short video platforms has spawned a lot of fake news videos, which have stronger propagation ability than textual fake news. Thus, automatically detecting fake news videos has been an important countermeasure in practice. Previous works commonly verify each news video individually with multimodal information. Nevertheless, news videos from different perspectives regarding the same event are commonly posted together, which contain complementary or contradictory information and thus can be used to evaluate each other mutually. To this end, we introduce a new and practical paradigm, i.e., cross-sample fake news video detection, and propose a novel framework, Neighbor-Enhanced fakE news video Detection (NEED), which integrates the neighborhood relationship of new videos belonging to the same event. NEED can be readily combined with existing single-sample detectors and further enhance their performances with the proposed graph aggregation (GA) and debunking rectification (DR) modules. Specifically, given the feature representations obtained from single-sample detectors, GA aggregates the neighborhood information with the dynamic graph to enrich the features of independent samples. After that, DR explicitly leverages the relationship between debunking videos and fake news videos to refute the candidate videos via textual and visual consistency. Extensive experiments on the public benchmark demonstrate that NEED greatly improves the performance of both single-modal (up to 8.34% in accuracy) and multimodal (up to 4.97% in accuracy) base detectors. Codes are available in https://***/ICTMCG/NEED. © 2023 Association for Computational Linguistics.

关键词： Benchmarking

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Boosting Meaningful Dependency Mining with Clustering and Covariance Analysis 40

Boosting Meaningful Dependency Mining with Clustering and Co...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Wang, Xi Jin, Ruochun Huang, Wanrong Tang, Yuhua Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology Department of Intelligent Data Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350317152

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDM?. In order to scale with large datasets, FDM ? employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ? is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost. © 2024 IEEE.

关键词： Large datasets

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Retrievable Image Encryption Based on Adaptive Block Compressed Sensing

Retrievable Image Encryption Based on Adaptive Block Compres...

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International Conference on Image, Vision and intelligent Systems, ICIVIS 2023

作者： Fang, Jiuchuan Zhao, Hui Zheng, Mingwen Wang, Zijian Niu, Sijie Gao, Xizhan Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan250022 China School of Mathematics and Statistics Shandong University of Technology Zibo255000 China

ISBN: (纸本)9789819708543

The increasing popularity of the internet and mobile devices, cloud storage has emerged as a popular storage method. However, because data carries a lot of information, untrustworthy servers can access users’ private information. This paper introduces an approach based on adaptive block compressed sensing and hash retrieval. The objective of this paper is to reduce the storage space required by encryption algorithms while simultaneously improving interaction efficiency. Firstly, compress images using block compressed sensing, and use BCS-SPL algorithm to reconstruct. And curves are fitted based on the gradient features and root mean square values of the blocks. Then, the sub-rates are allocated based on the gradient characteristics of the image using a fitting curve, and then the image is compressed. Next, encrypt the image and save it. The features of the queried image are used for retrieval, the results are decrypted, and then reconstructed. Finally, through a series of security analyses, the method is effective. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud storage

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Stable Learning via Triplex Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5267-5276页

作者： Yang, Shuai Jiang, Tingting Dang, Qianlong Gu, Lichuan Wu, Xindong Anhui Agricultural University School of Information and Artificial Intelligence Hefei230036 China Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing Hefei230036 China Northwest A & F University College of Science Yangling712100 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China

Stable learning aims to learn a model that generalizes well to arbitrary unseen target domain by leveraging a single source domain. Recent advances in stable learning have focused on balancing the distribution of confounders for each feature to eliminate spurious correlations. However, previous studies treat all features equally without considering the difficulties of confounder balancing associated with different features, and regard irrelevant features as confounders, deteriorating generalization performance. To tackle these issues, this article proposes a novel triplex learning (TriL) based stable learning algorithm, which performs sample reweighting, causal feature selection, and representation learning to remove spurious correlations. Specifically, first, TriL adaptively assigns weights to the confounder balancing term of each feature in accordance with the difficulties of confounder balancing, and aligns the confounder distribution of each feature by learning a group of sample weights. Second, TriL integrates the sample weights into a weighted cross-entropy model to compute causal effects of features for excluding irrelevant features from the confounder set. Finally, TriL relearns a set of sample weights and uses them to guide a new supervised dual-autoencoder containing two classifiers to learn feature representations. TriL forces the results of two classifiers to remain consistent for removing spurious correlations by using a cross-classifier consistency regularization. Extensive experiments on synthetic and two real-world datasets show the superiority of TriL compared with seven methods. © 2024 IEEE.

关键词： Feature Selection

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IGED:Towards intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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