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Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

Chinese Physics B 2023年第3期32卷 153-163页

作者：叶国栋吴惠山黄小玲 Syh-Yuan Tan Faculty of Mathematics and Computer Science Guangdong Ocean UniversityZhanjiang 524088China Guangdong Provincial Engineering and Technology Research Center of Far Sea Fisheries Management and Fishing of South China Sea Zhanjiang 524088China School of Computing Newcastle UniversityNewcastle NE45TGUK

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map(3D-ILM),an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman(RSA)is presented in this *** the first stage,a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence,Lyapunov exponent,and Shannon *** the second stage,combined with the public key RSA algorithm,a new key acquisition mathematical model(MKA)is constructed to obtain the initial keys for the ***,the key stream can be produced depending on the plain image for a higher ***,a novel process model(NPM)for the input of the 3D-ILM is built,which is built to improve the distribution uniformity of the chaotic *** the third stage,to encrypt the plain image,a pre-process by exclusive OR(XOR)operation with a random matrix is ***,the pre-processed image is performed by a permutation for rows,a downward modulo function for adjacent pixels,a permutation for columns,a forward direction XOR addition-modulo diffusion,and a backward direction XOR addition-modulo diffusion to achieve the final cipher ***,experiments show that the the proposed algorithm has a better ***,the number of pixels change rate(NPCR)is close to ideal case 99.6094%,with the unified average changing intensity(UACI)close to 33.4634%,and the information entropy(IE)close to 8.

关键词： three-dimensional improved logistic chaotic map(3D-ILM) Rivest–Shamir–Adleman(RSA)algorithm image encryption confusion entropy

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Using Hash Learning For a Multi-Model Fashion Recommendation System 5

Using Hash Learning For a Multi-Model Fashion Recommendation...

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5th International Conference on computer Vision and Information technology, CVIT 2024

作者： Shaohua, Du Wei, Zhang Zeyu, He School of Computer Science Beijing Information Science and Technology University Beijing China Beijing Laboratory of National Economic Security Early-warning Engineering Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

ISBN: (纸本)9781510686755

In the fashion recommendation system, with the increase in the number of clothing, the combination of clothing matching is growing exponentially, which leads to the slowdown of training speed and excessive memory usage, and other problems are becoming more and more prominent. Hashing technology can effectively reduce memory usage and improve the recommendation speed. With more and more product images accompanied by text descriptions, multi-modal modeling has become a research hotspot in recommendation systems. Most of the past research adopts the method of processing different modal information separately and then weighting and averaging, but the correlation between the two modalities is not fully utilized. We believe that the visual and textual features of the same product are semantically consistent and have the same aesthetic characteristics. Therefore, we try new modeling approaches to mine the higher-order connections between the two modalities. Another issue is that visual information is much more important than textual information in fashion recommendation systems, so it is not reasonable to equate the two modalities. We model the hash-processed binary representation, optimize the network structure to include visual features in the ranking to compute the loss, and also use textual features to assist the image features for modeling. Tests on two larger datasets from Polyvore show that our model exhibits significant results compared to state-of-the-art models on key evaluation metrics such as user-personalized recommendation and compatibility modeling. © 2024 SPIE.

关键词： Recommender systems

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The influence of culture on creativity in ideation: a review

The influence of culture on creativity in ideation: a review

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2024 International Design Society Conference, Design 2024

作者： Gong, Zhengya Gonçalves, Milene Nanjappan, Vijayakumar Georgiev, Georgi V. Center for Ubiquitous Computing University of Oulu Finland Department of Design Organisation and Strategy Faculty of Industrial Design Engineering Delft University of Technology Netherlands School of Computer Science and Information Technology University College Cork Ireland

The relationship between culture and creativity has sparked the interest of researchers for decades. Although researchers have attempted to establish a connection between culture and creativity, the precise relationship between the two remains ambiguous. The current paper examined extant literature on the subject matter and synthesized the relations between culture and creativity in ideation over the past twenty years. The present study expounds upon the utilized samples, measures implemented to assess creativity and culture, and the study results. © 2024 Proceedings of the Design Society. All rights reserved.

关键词： Behavioral research

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xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction 23

xGCN: An Extreme Graph Convolutional Network for Large-scale...

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2023 World Wide Web Conference, WWW 2023

作者： Song, Xiran Lian, Jianxun Huang, Hong Luo, Zihan Zhou, Wei Lin, Xue Wu, Mingqi Li, Chaozhuo Xie, Xing Jin, Hai Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China Microsoft Gaming Redmond United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450394161

Graph neural networks (GNNs) have seen widespread usage across multiple real-world applications, yet in transductive learning, they still face challenges in accuracy, efficiency, and scalability, due to the extensive number of trainable parameters in the embedding table and the paradigm of stacking neighborhood aggregations. This paper presents a novel model called xGCN for large-scale network embedding, which is a practical solution for link predictions. xGCN addresses these issues by encoding graph-structure data in an extreme convolutional manner, and has the potential to push the performance of network embedding-based link predictions to a new record. Specifically, instead of assigning each node with a directly learnable embedding vector, xGCN regards node embeddings as static features. It uses a propagation operation to smooth node embeddings and relies on a Refinement neural Network (RefNet) to transform the coarse embeddings derived from the unsupervised propagation into new ones that optimize a training objective. The output of RefNet, which are well-refined embeddings, will replace the original node embeddings. This process is repeated iteratively until the model converges to a satisfying status. Experiments on three social network datasets with link prediction tasks show that xGCN not only achieves the best accuracy compared with a series of competitive baselines but also is highly efficient and scalable. © 2023 ACM.

关键词： Graph neural networks

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GALAS: Greedy Local-Adjusting Room Allocation Framework 9

GALAS: Greedy Local-Adjusting Room Allocation Framework

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9th IEEE Smart World Congress, SWC 2023

作者： Chi, Lian Xie, Jiajia Wu, Jinxin Chen, Hongyang Zhao, Tianming Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China Hunan University School of Computer Science and Electronic Engineering Changsha China Research Center for Graph Computing Zhejiang Lab Hangzhou China

ISBN: (纸本)9798350319804

In this paper, we study the room allocation problem to optimize room utilization in allocating rooms for courses under all the conflicting constraints, which has been proven NP-complete. We first formulate the problem as an Integer Linear Program (ILP) so it can be solved optimally using an ILP solver. However, solving ILP incurs an exponential run-time complexity, which is impractical for real-world applications. To address this issue, we propose Greedy Algorithm plus Local Adjustment via Swaps (GALAS), a greedy heuristic framework with a reduced run-time that finds near-optimal room allocation solutions. GALAS uses multiple greedy strategies to quickly generate feasible schedules and then optimize them via a sequence of local swap operations. We evaluate the performance of the GALAS on synthetic and real-world datasets. The experimental results show that the GALAS outperforms the state-of-the-art in room utilization and run-time simultaneously. On average, GALAS achieves a near-optimal performance of 98.5% to the optimal solution. © 2023 IEEE.

关键词： Heuristic Local adjustment Room allocation

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Information Gerrymandering in Elections 1

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19th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2023, held in conjunction with the 20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Liu, Xiaoxue Kato, Shohei Ren, Fenghui Su, Guoxin Zhang, Minjie Gu, Wen School of Computing and Information Technology University of Wollongong Wollongong Australia Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan Center for Innovative Distance Education and Research Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (数字)9789819978557

ISBN: (纸本)9789819978540

When making decisions, individuals often seek advice from their family, friends, and social network connections. In this way, individ-uals’ opinions are susceptible to influence from their connections’ viewpoints. For example, in elections, individuals may initially support one party, while social influence may sway their choice to another. A recent study by Stewart et al. introduced a metric, influence assortment, to quantify the phenomenon of information gerrymandering that one party can influence more voters from other parties by strategically distributing their members among the social networks. While this metric correlates strongly with voting outcomes, the finding is only suitable for two-party elections. In this paper, we define the influence assortment that incorporates the level of similarity (homophily) among neighbours in the social network and extend it to multi-party elections. We examine its ability to predict voting outcomes when all parties initially have equal votes and average degrees. Through simulations using a stochastic model of voting behaviour, we demonstrate that the correlation between the influence gap and vote difference is a strong predictor in both small and large scale-free networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Stochastic systems

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Online Privacy Preservation for Camera-Incremental Person Re-Identification 26

Online Privacy Preservation for Camera-Incremental Person Re...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wu, Sheng Ge, Wenhang Wu, Jiong Meng, Jingke Zhang, Huang School of Computer & Communication Engineering Changsha University of Science & Technology Changsha China China Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia United States School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781643684369

Task-incremental person re-identification aims to train a model with consecutively available cross-camera annotated data in the current task and a small number of saved data in preceding tasks, which may lead to individual privacy disclosure due to data storage and annotation. In this work, we investigate a more realistic online privacy preservation scenario for camera-incremental person re-identification, where data storage in preceding cameras is not allowed, while data in the current camera are intra-camera annotated online by a pedestrian tracking algorithm without cross-camera annotation. In this setup, the missing data of previous cameras not only results in catastrophic forgetting as task-incremental learning, but also makes the cross-camera association infeasible, which further leads to the incapability of person matching across cameras due to the camera-wise domain gap. To solve these problems, we propose an Online Privacy Preservation (OPP) framework based on the generated exemplars of previous cameras by DeepInversion, where generated exemplars used as supplements to alleviate forgetting and enable cross-camera association to be feasible for camera-wise domain shift mitigation, meanwhile further improving the cross-camera matching capability. Specifically, we propose to mine underlying cross-camera positive pairs between samples of the current camera and exemplars of previous cameras by similarity cues. Furthermore, we introduce a mixup learning strategy to handle the domain gap with mixed samples and labels. Finally, intra-camera incremental learning and cross-camera incremental learning are aggregated into the OPP framework. Extensive experiments on Re-ID benchmarks validate the superiority of the OPP framework as compared with state-of-the-art methods. © 2023 The Authors.

关键词： Cameras

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Generative artificial intelligence and its applications in materials science:Current situation and future perspectives

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Journal of Materiomics 2023年第4期9卷 798-816页

作者： Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi School of Computer Engineering and Science Shanghai UniversityShanghai200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai200444China Materials Genome Institute Shanghai UniversityShanghai200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444China Australian Nuclear Science and Technology Organisation Sydney2232Australia School of Chemistry The University of SydneySydney2006Australia

Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required *** the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity ***,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of *** applications of task-specific generative models involving materials inverse design and data augmentation are also *** ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials ***,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding *** work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

关键词： Machine learning Artificial intelligence Generative artificial intelligence Materials science Novel materials discovery Deep learning

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AOCC-FL: Federated Learning with Aligned Overlapping via Calibrated Compensation 42

AOCC-FL: Federated Learning with Aligned Overlapping via Cal...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Haozhao Xu, Wenchao Fan, Yunfeng Li, Ruixuan Zhou, Pan Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350334142

Federated Learning enables collaboratively model training among a number of distributed devices with the coordination of a centralized server, where each device alternatively performs local gradient computation and communication to the server. FL suffers from significant performance degradation due to the excessive communication delay between the server and devices, especially when the network bandwidth of these devices is limited, which is common in edge environments. Existing methods overlap the gradient computation and communication to hide the communication latency to accelerate the FL training. However, the overlapping can also lead to an inevitable gap between the local model in each device and the global model in the server that seriously restricts the convergence rate of learning process. To address this problem, we propose a new overlapping method for FL, AOCC-FL, which aligns the local model with the global model via calibrated compensation such that the communication delay can be hidden without deteriorating the convergence performance. Theoretically, we prove that AOCC-FL admits the same convergence rate as the non-overlapping method. On both simulated and testbed experiments, we show that AOCC-FL achieves a comparable convergence rate relative to the non-overlapping method while outperforming the state-of-the-art overlapping methods. © 2023 IEEE.

关键词： Learning systems

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

SciMRC: Multi-perspective Scientific Machine Reading Compreh...

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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： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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