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Red deer algorithm to detect the secret key of the monoalphabetic cryptosystem

Soft Computing 2024年第17-18期28卷 10569-10582页

作者： Jain, Ashish Bansal, Sulabh Das, Nripendra Narayan Gupta, Shyam Sunder Department of Information Technology School of Information Security and Data Science Manipal University Jaipur Jaipur303007 India Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Madhya Pradesh Gwalior474005 India

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Tabu search

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of Computer science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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Novel MITM attack scheme based on built-in negotiation for blockchain-based digital twins

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Digital Communications and Networks 2025年第1期11卷 256-267页

作者： Xin Liu Rui Zhou Shohei Shimizu Rui Chong Qingguo Zhou Xiaokang Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China Faculty of Data Science Shiga UniversityHikone 522-8522Japan Beijing Zhongxin Xingkong Network Technology Co Beijing 100005China

As smart contracts,represented by Solidity,become deeply integrated into the manufacturing industry,blockchain-based Digital Twins(DT)has gained momentum in recent *** of the blockchain infrastructures in widespread use today are based on the Proof-of-Work(PoW)mechanism,and the process of creating blocks is known as“mining”.Mining becomes increasingly difficult as the blockchain grows in size and the number of on-chain business systems *** lower the threshold of participation in the mining process,“mining pools”have been *** can cooperate and share the mining rewards according to the hashrate they contributed to the *** is the most widely used communication protocol between miners and mining *** security is essential for the *** this paper,we propose two novel Man-In-The-Middle(MITM)attack schemes against Stratum,which allow attackers to steal miners'hashrate to any mining pool using hijacked TCP *** with existing attacks,our work is more secretive,more suitable for the real-world environment,and more *** Proof-of-Concept(PoC)shows that our schemes work perfectly on most mining softwares and ***,we present a lightweight AI-driven approach based on protocol-level feature analysis to detect Stratum MITM for blockchain-based *** detection model consists of three layers:feature extraction layer,vectorization layer,and detection *** prove that our detection approach can effectively detect Stratum MITM traffic with 98%*** work alerts the communities and provides possible mitigation against these more hidden and profitable attack schemes.

关键词： Digital twins MITM Blockchain Stratum Anomaly detection

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Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks

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Journal of Computer science & technology 2024年第6期39卷 1419-1440页

作者： Pu Wang Jian-Jiang Lu Wei Chen Peng-Peng Zhao Lei Zhao Institute of Artificial Intelligence School of Computer Science and TechnologySoochow UniversitySuzhou 215006China Department of Data Resource and Information Development Soochow UniversitySuzhou 215006China

The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.

关键词： location recommendation popularity prediction spatio-temporal correlation deep learning

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Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

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Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 17610-17621页

作者： Zhou, Wujie Jian, Bitao Liu, Yuanyuan Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310023 China China University of Geosciences School of Computer Science and Technology Wuhan430074 China Nanyang Technological University College of Computing and Data Science 308232 Singapore

The era of smart connectivity spawned by the Internet of Things (IoT) has made the need to achieve environmental perception and understanding of different scenarios increasingly urgent. Among the many scenarios, indoor scene classification has attracted much attention because of its its relevance to the daily lives of people, ranging from comfort regulation in living spaces to the optimal allocation of resources in offices, and a variety of approaches for this task have emerged. However, increasing accuracy remains a crucial objective due to the complexity and disorder of indoor scenes. Therefore, we propose a feature contrast difference and enhanced network for RGB-D indoor scene classification, FCDENet. First, the RGB and Depth images express different information. Therefore, we built a feature contrast difference module for the first two low-level features to extend the receptive fields of the different features, utilizing differential contrast to complement each other. Second, the high-level feature semantic information is abstract. Therefore, we introduced information cluster blocks, which are used to aggregate feature points with similar attributes into compact clusters after being parsed by an initial frequency transform, enabling instantiated representations of the semantic information. Finally, to further enhance the integrated features, we introduced a wavelet transform block in the cross-layer decoding process. In contrast to conventional decoding methods, we employed a wavelet transform for initial denoising cross-layer features and used multiple pooling structures to supplement local information, gradually weighting to achieve higher prediction accuracy. Extensive experiments on two typical indoor datasets, NYUDv2 and SUN RGB-D, show that our results exhibit excellent performance. In addition, to better demonstrate the reliability of the method, we conducted generalizability experiments on other datasets, and the proposed method provides a robust soluti

关键词： Wavelet decomposition

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Attack based on data: a novel perspective to attack sensitive points directly

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Cybersecurity 2024年第3期7卷 111-123页

作者： Yuyao Ge Zhongguo Yang Lizhe Chen Yiming Wang Chengyang Li School of Information Science and Technology North China University of TechnologyBeijingChina Bejing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of TechnologyBeijingChina School of Computer Science Peking UniversityBeijingChina

Adversarial attack for time-series classification model is widely explored and many attack methods are *** there is not a method of attack based on the data *** this paper,we innovatively proposed a black-box sparse attack method based on data *** method directly attack the sensitive points in the time-series data accord-ing to statistical features extract from the *** frst,we have validated the transferability of sensitive points among DNNs with different ***,we use the statistical features extract from the dataset and the sensi-tive rate of each point as the training set to train the predictive ***,predicting the sensitive rate of test set by predictive ***,perturbing according to the sensitive *** attack is limited by constraining the LO norm to achieve one-point *** conduct experiments on several datasets to validate the effectiveness of this method.

关键词： Black-box adversarial attack Time series classification data mining

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A data representation method using distance correlation

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Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Efficient Plug-and-Play Module for Transformer-Based 3D Human Pose Estimation 5

Efficient Plug-and-Play Module for Transformer-Based 3D Huma...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Qiu, Yaru Zhang, Jianfang Wu, Guoxia Sun, Yuanyuan School of Data Science Qingdao University of Science and Technology Qingdao China School of Information Science and Technology Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9798331528911

Video pose transformers (VPTs) have demonstrated remarkable performance in 3D human pose prediction. However, transformer-based architectures are often computationally intensive, leading to prolonged training times. To address this issue, we propose an efficient and plug-and-play module named the Adaptive Token Management (ATM), to prune and recover pose tokens, enabling high efficiency and accurate predictions within the VPTs framework. ATM consists of two key modules: the Dynamic Token Pruning (DTP) and the Lightweight Token Recovery (LTR). The DTP is presented for dynamic tokens pruning, where it determines the optimal number of representative tokens based on feature distribution and selects the representative tokens through a scoring mechanism. The LTR module, an optional lightweight module, is designed for tasks requiring the recovery of full-length tokens. It selects an appropriate full-length token as a learnable initialization token, utilizing attention mechanisms to restore spatio-temporal information. We conducted extensive experiments on the Human3.6M and MPI-INF-3DHP datasets, demonstrating that our approach can enhance the efficiency of mainstream VPTs while maintaining prediction accuracy. The results show that, when using MixSTE as the underlying framework on the Human3.6M dataset, ATM accelerated training time per epoch, reduced FLOPs by over 40%, and decreased prediction error by 0.2. © 2024 IEEE.

关键词： Distribution transformers

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WheatNGP: Neural Radiation Field Optimization for Wheat Flocks Under Boundless Free Trajectories 5

WheatNGP: Neural Radiation Field Optimization for Wheat Floc...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Tian, Liming Qiu, Yaru Wu, Guoxia Sun, Yuanyuan School of Data Science Qingdao University of Science and Technology Qingdao China School of Information Science and Technology Qingdao University of Science and Technology Qingdao China

ISBN: (纸本)9798331528911

Accurate NeRF synthesis can help plant breeders to effectively analyze and identify plant traits to improve crop yield. Most of the existing mesh-based NeRF new perspective synthesis methods are targeted at bounded scenes, such as Instant-NGP, Plenoxels, DVGO, and tensorf, but wheat is usually grown in outdoor field environments, which belongs to non-boundary scenes. At present, the widely used method is to rely on space warping, but it can only deal with forward trajectory and 360° center surround trajectory, which is a special trajectory, and can't deal with arbitrary trajectory, but there are more free trajectories in the daily shooting process. In order to solve this problem, we propose a new warping function called Variable warping. In this study, we use cell phone to shoot wheat experimental field data, and use the improved Variable warping algorithm to synthesize the new perspective of wheat under free trajectory. The experimental results show that it allows us to reconstruct the new perspective of wheat with high quality under arbitrary camera trajectory. The experimental results show that allows us to perform high-quality reconstruction of wheat under arbitrary camera trajectories with new perspective synthesis. © 2024 IEEE.

关键词： Borderless component Free Trajectories Novel view synthesis of wheat

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