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Further results on permutation pentanomials over finite fields with characteristic two

Designs, Codes and Cryptography 2025年 1-30页

作者： Zhang, Tongliang Kan, Haibin Zheng, Lijing Peng, Jie Zhao, Hanbing Hebei Key Laboratory of Data Science and Application College of Science North China University of Science and Technology Tangshan China College of Computer Science and Artificial Intelligence Fudan University Shanghai China Shanghai Engineering Research Center of Blockchain Shanghai China Shanghai Institute for Mathematics and Interdisciplinary Sciences Shanghai China School of Mathematics and Physics University of South China Hengyang China Mathematics and Science College of Shanghai Normal University Shanghai China

In this paper, we investigate several classes of permutation pentanomials over $${{\mathbb {F}}}_{2^{2m}}$$ of the form $$f(x)=x^t+x^{r_1(q-1)+t}+x^{r_2(q-1)+t}+x^{r_3(q-1)+t}+x^{r_4(q-1)+t}$$ with $$ 1\le r_i\le t$$ for $$i\in [1,4]$$ . A new technique is presented to describe the sufficient condition for f(x) to be a permutation through investigating two kinds of irreducible factors, which are called polynomials of nonzero trace and zero trace, of some certain polynomials over $${{\mathbb {F}}}_{2}$$ . We resolve the open problem the authors left in Zhang et al. (Finite Fields Appl 98:102468, 2024). Numerical results suggest that the results in this paper seem to contain all the permutation pentanomials of that form with $$\textrm{gcd}(x^{r_4}+x^{r_3}+x^{r_2}+x^{r_1}+1,x^t+x^{t-r_1}+x^{t-r_2}+x^{t-r_3}+x^{t-r_4})=1$$ for $$t>23$$ and the conditions presented in Theorems 3.1, 3.7, 3.10 and 3.14 of this paper are also necessary.

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Hadamard Adapter: An Extreme Parameter-Efficient Adapter Tuning Method for Pre-trained Language Models

arXiv

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arXiv 2024年

作者： Chen, Yuyan Fu, Qiang Fan, Ge Du, Lun Lou, Jian-Guang Han, Shi Zhang, Dongmei Li, Zhixu Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Microsoft Beijing China Tencent Shenzhen China Cognitive Intelligence Joint Research Center Shanghai China

Recent years, Pre-trained Language models (PLMs) have swept into various fields of artificial intelligence and achieved great success. However, most PLMs, such as T5 and GPT3, have a huge amount of parameters, fine-tuning them is often expensive and time consuming, and storing them takes up a lot of space. Therefore, it is necessary to adopt a parameter-efficient approach to reduce parameters of PLMs in fine-tuning without compromising their performance in downstream tasks. In this paper, we design a novel adapter which only acts on self-attention outputs in PLMs. This adapter adopts element-wise linear transformation using Hadamard product, hence named as Hadamard adapter, requires the fewest parameters compared to previous parameter-efficient adapters. In addition, we also summarize some tuning patterns for Hadamard adapter shared by various downstream tasks, expecting to provide some guidance for further parameter reduction with shared adapters in future studies. The experiments conducted on the widely-used GLUE benchmark with several SOTA PLMs prove that the Hadamard adapter achieves © 2024, CC BY.

关键词： Linear transformations

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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information leakage

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Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

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Defence Technology（防务技术） 2022年第12期18卷 2150-2159页

作者： Jing-Tao Sun Jing-Ming Li Qiu-Yu Zhang School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China School of Management Science and Engineering Anhui University of Finance and EconomicsBengbuAnhui230030China School of Computer and Communication Lanzhou University of TechnologyLanzhouGansu730050China

Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,***,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of ***,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature ***,the events knowledge graph is constructed by means of sparse representation and l1 *** experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding *** experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.

关键词： Text event mining Knowledge graph of events Mutual information among neighbor domains Sparse representation

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Large-scale coherent Ising machine based on optoelectronic parametric oscillator

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Light(science & Applications) 2022年第12期11卷 2972-2981页

作者： Qizhuang Cen Hao Ding Tengfei Hao Shanhong Guan Zhiqiang Qin Jiaming Lyu Wei Li Ninghua Zhu Kun Xu Yitang Dai Ming Li State Key Laboratory on Integrated Optoelectronics Institute of SemiconductorsChinese Academy of SciencesBeijingChina School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijingChina Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijingChina State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and TelecommunicationsBeijingChina School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghaiChina Peng Cheng Laboratory ShenzhenChina

Ising machines based on analog systems have the potential to accelerate the solution of ubiquitous combinatorial optimization *** some artificial spins to support large-scale Ising machines have been reported,e.g.,superconducting qubits in quantum annealers and short optical pulses in coherent Ising machines,the spin stability is fragile due to the ultra-low equivalent temperature or optical phase *** this paper,we propose to use short microwave pulses generated from an optoelectronic parametric oscillator as the spins to implement a large-scale Ising machine with high *** proposed machine supports 25,600 spins and can operate continuously and stably for ***,the proposed Ising machine is highly compatible with high-speed electronic devices for programmability,paving a low-cost,accurate,and easy-to-implement way toward solving real-world optimization problems.

关键词： stability oscillator coherent

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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DMGNN: Cancer driver gene identification based on mix-moment graph neural network

DMGNN: Cancer driver gene identification based on mix-moment...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Bolin Li, Ziyuan Li, Haodong Li, Junming Kang, Ruiming Shang, Xuequn Bian, Jun Northwestern Polytechnical University School of Computer Science Xian China Northwestern Polytechnical University Key Laboratory of Big Data Storage and Management School of Computer Science China Information Technology Co. Ltd Hangzhou China Xian Jiaotong University Affiliated Childrens Hospital Xian Childrens Hospital Department of General Surgery Xian China

ISBN: (纸本)9798350386226

Cancer development is closely linked to the accumulation of mutations in driver genes. Therefore, identification of driver genes is crucial for understanding the molecular basis of cancer. In various types of methods, approches based on Graph Neural Networks (GNNs) framework are one of the most effective tools to identify driver genes, which can also combine biological networks and multi-omics data to further improve the identification accuracy. However, many GNN frameworks often utilize single-order moment to get neighbourhood information for message passing, which ignores the rich distribution information and gene features of neighbouring genes. Besides, when using GNNs model, it is necessary to stack the hidden layers, which can often lead to both over-smoothing and network degradation problems. To overcome these issues, a new framework called DMGNN was proposed for identifying driver genes. To get rich distribution information and gene features, a mix-moment embedding and attention-based feature selection were utilized among neighbouring genes. To solve the problem posed by hidden layer stacking in the GNN model, a deepwalk method was used to learn remote gene effects for the over-smoothing problem, and the resNet-based hidden layer aggregation was employed to mitigate the network degradation issue. Experimental results demonstrate that the proposed model outperforms many existing methods for identifying cancer driver genes, where the AUROC value was achieved at 0.856 in STRING dataset with at least 2 percentage improvement comparing with other models. The DMGNN is freely available via https://***/lavendar682/DMGNN. © 2024 IEEE.

关键词： Graph neural networks

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ToolEyes: Fine-Grained Evaluation for Tool Learning Capabilities of Large Language Models in Real-world Scenarios 31

ToolEyes: Fine-Grained Evaluation for Tool Learning Capabili...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Ye, Junjie Li, Guanyu Gao, Songyang Huang, Caishuang Wu, Yilong Li, Sixian Fan, Xiaoran Dou, Shihan Ji, Tao Zhang, Qi Gui, Tao Huang, Xuanjing School of Computer Science Fudan University China Institute of Modern Languages and Linguistics Fudan University China Research Institute of Intelligent Complex Systems Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Pengcheng Laboratory China

ISBN: (纸本)9798891761964

Existing evaluations of tool learning primarily focus on validating the alignment of selected tools (e.g., various APIs) for large language models (LLMs) with expected outcomes. However, these approaches rely on a limited set of scenarios where answers can be pre-determined. Furthermore, a sole emphasis on outcomes disregards the complex capabilities required for LLMs to effectively use tools. To tackle this issue, we propose ToolEyes, a fine-grained system tailored for the evaluation of the LLMs' tool learning capabilities in authentic scenarios. The system meticulously examines seven real-world scenarios, analyzing five dimensions crucial to LLMs in tool learning: format alignment, intent comprehension, behavior planning, tool selection, and answer organization. Additionally, ToolEyes incorporates a tool library boasting approximately 600 tools, serving as an intermediary between LLMs and the physical world. Evaluations involving ten LLMs across three categories reveal a preference for specific scenarios and limited cognitive abilities in tool learning. Intriguingly, expanding the model size even exacerbates the hindrance to tool learning. The code and data are available at https://***/Junjie-Ye/ToolEyes. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Side-Channel Masking with Common Shares

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2022年第3期2022卷 290-329页

作者： Wang, Weijia Guo, Chun Yu, Yu Ji, Fanjie Su, Yang School of Cyber Science and Technology Shandong University Qingdao China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Qingdao China Quan Cheng Shandong Laboratory Jinan China Shandong Research Institute of Industrial Technology Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Qi Zhi Institute Shanghai China Shanghai Key Laboratory of Privacy-Preserving Computation Shanghai China

To counter side-channel attacks, a masking scheme randomly encodes key-dependent variables into several shares, and transforms operations into the masked correspondence (called gadget) operating on shares. This provably achieves the de facto standard notion of probing security. We continue the long line of works seeking to reduce the overhead of masking. Our main contribution is a new masking scheme over finite fields in which shares of different variables have a part in common. This enables the reuse of randomness/variables across different gadgets, and reduces the total cost of masked implementation. For security order d and circuit size , the randomness requirement and computational complexity of our scheme areÕ(d2) andÕ(d2) respectively, strictly improving upon the state-of-the-artÕ(d2) andÕ(d3) of Coron et al. at Eurocrypt 2020. A notable feature of our scheme is that it enables a new paradigm in which many intermediates can be precomputed before executing the masked function. The precomputation consumesÕ(d2) and produces Õ(d) variables to be stored in RAM. The cost of subsequent (online) computation is reduced to Õ(d), effectively speeding up e.g., challenge-response authentication protocols. We showcase our method on the AES on ARM Cortex M architecture and perform a T-test evaluation. Our results show a speed-up during the online phase compared with state-of-the-art implementations, at the cost of acceptable RAM consumption and precomputation time. To prove security for our scheme, we propose a new security notion intrinsically supporting randomness/variables reusing across gadgets, and bridging the security of paral lel compositions of gadgets to general compositions, which may be of independent interest. © 2022, Ruhr-University of Bochum. All rights reserved.

关键词： Side channel attack

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A genetic programming approach with adaptive region detection to skin cancer image classification

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Journal of Automation and Intelligence 2024年第4期3卷 240-249页

作者： Kunjie Yu Jintao Lian Ying Bi Jing Liang Bing Xue Mengjie Zhang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471000China Longmen Laboratory Luoyang471000China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang453000China School of Engineering and Computer Science&Centre for Data Science and Artificial Intelligence Victoria University of WellingtonWellington6201New Zealand

Dermatologists typically require extensive experience to accurately classify skin *** recent years,the development of computer vision and machine learning has provided new methods for assisted *** skin cancer image classification methods have certain limitations,such as poor interpretability,the requirement of domain knowledge for feature extraction,and the neglect of lesion area information in skin *** paper proposes a new genetic programming(GP)approach to automatically learn global and/or local features from skin images for *** achieve this,a new function set and a new terminal set have been *** proposed GP method can automatically and flexibly extract effective local/global features from different types of input images,thus providing a comprehensive description of skin images.A new region detection function has been developed to select the lesion areas from skin images for feature *** performance of this approach is evaluated on three skin cancer image classification tasks,and compared with three GP methods and six non-GP *** experimental results show that the new approach achieves significantly better or similar performance in most *** analysis validates the effectiveness of our parameter settings,visualizes the multiple region detection functions used in the individual evolved by the proposed approach,and demonstrates its good convergence ability.

关键词： Genetic programming Skin cancer image classification Region detection Feature extraction

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