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On some properties of the bibasic Humbert hypergeometric functions Ξ_(1) and Ξ_(2)

Applied Mathematics(A Journal of Chinese Universities) 2023年第4期38卷 614-630页

作者： CAI Qing-bo Ghazi S.Khammash Shimaa I.Moustafa Ayman Shehata Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingSchool of Mathematics and Computer ScienceQuanzhou Normal UniversityQuanzhou 362000China Department of Mathematics Al-Aqsa UniversityGazaGaza StripPalestine Department of Mathematics Faculty of ScienceAssiut UniversityAssiut 71516Egypt Department of Mathematics Unaizah College of Science and ArtsUnaizah 56264Qassim UniversityBuraydah 52571QassimSaudi Arabia

The main object of this paper is to deduce the bibasic Humbert functions Ξ_(1) and Ξ_(2)Some interesting results and elementary summations technique that was successfully employed,q-recursion,q-derivatives relations,the q-differential recursion relations,the q-integral representations for Ξ_(1) and Ξ_(2)are *** summation formula derives a list of p-analogues of transformation formulas for bibasic Humbert functions that have been studied,also some hypergeometric functions properties of some new interesting special cases have been given.

关键词： q-calculus bibasic Humbert hypergeometric functions q-derivative

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Automatic Personality Prediction Based on Users’ Chinese Handwriting Change 17th

Automatic Personality Prediction Based on Users’ Chinese H...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Ji, Yu Wu, Wen Hu, Yi He, Xiaofeng Chen, Changzhi He, Liang School of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention School of Computer Science and Technology School of Psychology and Cognitive Science East China Normal University Shanghai China School of Psychology and Cognitive Science East China Normal University Shanghai China Born to Learn Education Technology Sichuan China

ISBN: (纸本)9789819923847

In recent years, personality has been considered as a valuable personal factor being applied to many fields. Although lately some studies have endeavored to implicitly obtain user’s personality from her/his handwriting, they failed to achieve satisfactory prediction performance. Most of the related methods focus on constructing handwriting features, while the handwriting change information is ignored. In fact, user’s handwriting change could reflect her/his physical and mental state more finely, which is helpful for recognizing the user’s personality. Furthermore, the related studies may not fully use Chinese character features to analyze the change of Chinese handwriting. In this paper, we propose an effective Chinese Handwriting Change based Personality Prediction (CHCPP) model to identify users’ personalities. To be specific, we construct the handwritten character sequence based on the writing order. We then extract the Chinese character features and the visual signals of each handwritten character in the sequence to analyze the handwriting change. Meanwhile, we also construct the statistical Chinese character features based on the whole handwritten character set to assist in modeling the change of Chinese handwriting. Lastly, we utilize the handwriting change information and the statistical Chinese character features to acquire the prediction results. The experimental results show that our CHCPP model outperforms the related methods on a real-world dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRESENTATION 11

TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wu, Hongqiu Liu, Yongxiang Shi, Hanwen Zhao, Hai Zhang, Min Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Soochow University Suzhou China

Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. This paper investigates AT from the perspective of the contextualized language representation outputted by PLM encoders. We find the current AT attacks lean to generate sub-optimal adversarial examples that can fool the decoder part but have a minor effect on the encoder. However, we find it necessary to effectively deviate the latter one to allow AT to gain. Based on the observation, we propose simple yet effective Contextualized representation-Adversarial Training (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder. It allows a global optimization of adversarial examples that can fool the entire model. We also find CreAT gives rise to a better direction to optimize the adversarial examples, to let them less sensitive to hyperparameters. Compared to AT, CreAT produces consistent performance gains on a wider range of tasks and is proven to be more effective for language pretraining where only the encoder part is kept for downstream tasks. We achieve the new state-of-the-art performances on a series of challenging benchmarks, e.g. AdvGLUE (59.1 → 61.1), HellaSWAG (93.0 → 94.9), ANLI (68.1 → 69.3). © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Global optimization

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Parallelism or Fairness? How to Be Friendly for SSDs in Cloud Environments

Parallelism or Fairness? How to Be Friendly for SSDs in Clou...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Zhou, Yang Wang, Fang Shi, Zhan Feng, Dan Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350358711

Modern SSDs achieve low latency and high throughput by utilizing multiple levels of SSD parallelism. Fairness is also a critical design consideration in cloud environments and has inspired great interest in recent years to study it. However, we find that excessive striving for SSD parallelism can inadvertently harm the SSD fairness due to the striped data layout. We observe from our experimental analysis that some common access patterns in cloud loads (e.g., high-intensity I/O) are not addressed by SSD parallelism, which can affect both SSD parallelism and fairness. In this paper, we analyze two address mapping policies with orthogonal characteristics from SSD parallelism and fairness perspectives. Based on extensive experiments and observations, we schedule a hybrid address mapping policy to achieve fairness of SSDs in cloud environments by distinguishing different I/O access modes and cold/hot data, while ensuring SSD performance. Experimental results on the latest block-level I/O traces show that our approach significantly improves performance and fairness by up to 77.3% and 62.8%, respectively, over the previous schemes with negligible cost. Meanwhile, it can also reduce the write amplification by 23.5% to 33.5%. © 2024 IEEE.

关键词： Cloud platforms

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A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

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computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer Science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management Center and Institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

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A new network intrusion detection system based on auto-encoding and variance analysis 5

A new network intrusion detection system based on auto-encod...

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5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Chen, Dequ Zhang, Ming Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Computer Science and Mathematics Fujian Fuzhou China

ISBN: (纸本)9798350310603

In today's big data era, ensuring the network security of information technology (IT) communication facilities is one of the most challenging issues. In fact, with progress in technology, hackers have developed increasingly complex and risky network attacks, making malicious access identification a very tired task. Under various threats, the existing analysis methods encounter many challenges in detecting and mitigating these illegal accesses. This work presents a new network intrusion detection system (IDS) which combines deep learning method and mathematical strategy. Indeed, our IDS makes full use of data analysis, mathematical modeling, and deep learning to select and optimize features that are more relevant for classification. We use NSL-KDD dataset to test the performance of the IDS. The experimental results show that our proposed method has better performance compared with traditional deep learning, machine learning and recently proposed advanced methods. © 2023 IEEE.

关键词： Network security

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Unveiling Vulnerability of Self-Attention 30

Unveiling Vulnerability of Self-Attention

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liong, Khai Jiet Wu, Hongqiu Zhao, Hai Xiaoshan, Li Minjie, Bian Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Technology Innovation Department Shanghai Data Group Co. Ltd. China Shanghai Data Group Co. Ltd. China

ISBN: (纸本)9782493814104

Pre-trained language models (PLMs) are shown to be vulnerable to minor word changes, which poses a big threat to real-world systems. While previous studies directly focus on manipulating word inputs, they are limited by their means of generating adversarial samples, lacking generalization to versatile real-world attack. This paper studies the basic structure of transformer-based PLMs, the self-attention (SA) mechanism. (1) We propose a powerful perturbation technique HackAttend, which perturbs the attention scores within the SA matrices via meticulously crafted attention masks. We show that state-of-the-art PLMs fall into heavy vulnerability that minor attention perturbations (1%) can produce a very high attack success rate (98%). Our paper expands the conventional text attack of word perturbations to more general structural perturbations. (2) We introduce S-Attend, a novel smoothing technique that effectively makes SA robust via structural perturbations. We empirically demonstrate that this simple yet effective technique achieves robust performance on par with adversarial training when facing various text attackers. Code is publicly available at ***/liongkj/HackAttend. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Mid-infrared Photons Released by NAD^(+)Reduction in the Tricarboxylic Acid Cycle of Myelinated Neuron

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Neuroscience Bulletin 2023年第7期39卷 1146-1150页

作者： Daoling Peng Wenpeng Qi Feng Zhang Bo Song Key Laboratory of Theoretical Chemistry of Environment Ministry of EducationSchool of EnvironmentSouth China Normal UniversityGuangzhou 510006China Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China Key Laboratory of Optical Technology and Instruments for Medicine Ministry of EducationShanghai Key Lab of Modern Optical SystemsSchool of Optical-Electrical Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Dear Editor,Life can utilize energy with high efficiency,especially with the help of aerobic respiration,where the tricarboxylic acid(TCA)cycle acts as an important metabolic engine for mitochondria to produce the cell energy currency of adenosine triphosphate(ATP),and nicotinamide adenine dinucleotide(NAD)plays a particular role of energy management molecule in the TCA cycle[1].

关键词： carboxylic utilize cycle

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GCFuzz: An Intelligent Method for Generating IoT Protocols Test Cases Using GAN with CVAE 7th

GCFuzz: An Intelligent Method for Generating IoT Protocols ...

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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Zhong, Ming Zeng, Zisheng Guo, Yijia Zhao, Dandan Zhang, Bo Li, Shenghong Peng, Hao Ding, Zhiguo School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua China Key Laboratory of Intelligent Educational Technology and Application in Zhejiang Normal University Jinhua321004 China School of Cyber Science and Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031855924

The importance of Internet of Things (IoT) systems security cannot be ignored, particularly in the realm of communication. IoT protocols serve as standards for communication and interaction among devices in IoT environments. This paper enhances IoT security by identifying and exposing protocol vulnerabilities through fuzzing, a crucial method for discovering security flaws. Traditional generation-based fuzzers require reverse engineering to understand protocol grammar and generate test cases. In this paper, we propose an intelligent method called GCFuzz, which generates high-quality test cases without prior knowledge of the protocol. To understand different classes of protocol grammar, GCFuzz applies Generative Adversarial Networks (GAN) with Conditional Variational Autoencoders (CVAE) to train a generative model on various classes of protocol messages. After training, the model generates fake but credible messages. Additionally, to accurately cluster real protocol messages, we devise a novel clustering method based on a keyword. Results show that, compared to GANFuzz, Peach, BooFuzz, and Sulley, the test cases generated by GCFuzz have stronger targeting and less redundancy, while GCFuzz has higher accurate testing efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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