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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Vanita G. Kshirsagar Digvijay G. Bhosale Shubhangi Suryawanshi Anita Sachin Mahajan Pramod Patil Jyotsna Vilas Barpute Prashant G. Ahire Rahul A. Patil Automation and Robotics Dr. D. Y. Patil Institute of Technology Pune India Artificial Intelligence and Data Secience Dr. D. Y. Patil Institute of Technology Pune Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pune India Computer Engineering Ajeenkya D. Y. Patil School of Engineering Pune India Computer Engineering Dr. D.Y. Patil Institute of Technology Pune India Computer Science and Engineering Symbiosis Institute of Technology Pune India Computer Engineering Pimpri Chinchwad College of Engineering Pune

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Crime is a dominant concern in contemporary society. It is crucial to prevent it and deliver justice promptly. Forensic science is critical for criminal investigations and maintaining law and order. The traditional tactics utilized in this field are frequently based on manual procedures, which are both time-consuming and prone to error, rendering them ineffectual in providing prompt and dependable justice. Utilizing automation and advanced technologies for forensic science and crime scene investigation is now essential. This paper suggests a portable gadget that utilizes Internet of Things (IOT), Machine Learning and Deep Learning to predict, gain understanding, and possibly recognize offenders. This gadget is designed to reconstruct fingerprints, detect weapons and recognize suspicious activities. It utilizes algorithms and technologies like autoencoders, GANs, SSD and YOLOv8. We talk about the benefits of merging these technologies into one easy-to-use device, which will offer data integration, instant data processing, and improved forensic analysis. The article offers information about automating important areas to improve investigative processes and enhance the criminal justice system's efficiency and effectiveness in the future.

关键词： Cloud computing Automation Forensics Weapons Prevention and mitigation Fingerprint recognition Internet of Things Image reconstruction Testing Videos

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Further results on permutation pentanomials over finite fields with characteristic two

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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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Constant Scaling Asymptotics of Communication Bounds in Covert Channels Against Selective Adversary

Constant Scaling Asymptotics of Communication Bounds in Cove...

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MILCOM, Military Communications Conference

作者： Xinchun Yu Shuangqin Wei Chenhao Ying Xiao-Ping Zhang Information and Data Science Institute Shenzhen International Graduate School Tsinghua University Shenzhen China School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge USA School of Electronic Information and Electrical Engnieering Shanghai Jiao Tong University Shanghai China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

In the existing works on asymptotics of throughput of covert communications, an adversary is typically assumed to run a single binary hypothesis testing to determine the presence of active transmission, which is equivalent to distinguish between an induced mixture distribution by all codewords and the distribution with an inactive transmitter. In this paper, we show that both of the first and second order asymptotics are O(1) when the adversary can afford running parallel simple binary hypothesis testing against detection of each single codeword. The covert constraint in this case is given as an upper-bound δ on the total variation distance (TVD) between the simple distribution induced by each codeword and the one of pure noise. The result is drastically different in a negative way than the commonly known Square Root Law for covert communication over AWGN channels with a more benign adversary.

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PosMLP-Video: Spatial and Temporal Relative Position Encoding for Efficient Video Recognition

arXiv

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arXiv 2024年

作者： Hao, Yanbin Zhou, Diansong Wang, Zhicai Ngo, Chong-Wah Wang, Meng School of Information Science and Technology School of Artificial Intelligence and Data Science University of Science and Technology of China No.96 JinZhai Road Anhui Hefei230026 China School of Computing and Information Systems Singapore Management University 80 Stamford Road Singapore178902 Singapore School of Computer Science and Information Engineering Hefei University of Technology No. 485 Danxia Road Anhui Hefei230601 China

In recent years, vision Transformers and MLPs have demonstrated remarkable performance in image understanding tasks. However, their inherently dense computational operators, such as self-attention and token-mixing layers, pose significant challenges when applied to spatio-temporal video data. To address this gap, we propose PosMLP-Video, a lightweight yet powerful MLP-like backbone for video recognition. Instead of dense operators, we use efficient relative positional encoding (RPE) to build pairwise token relations, leveraging small-sized parameterized relative position biases to obtain each relation score. Specifically, to enable spatio-temporal modeling, we extend the image PosMLP's positional gating unit to temporal, spatial, and spatio-temporal variants, namely PoTGU, PoSGU, and PoSTGU, respectively. These gating units can be feasibly combined into three types of spatio-temporal factorized positional MLP blocks, which not only decrease model complexity but also maintain good performance. Additionally, we enrich relative positional relationships by using channel grouping. Experimental results on three video-related tasks demonstrate that PosMLP-Video achieves competitive speedaccuracy trade-offs compared to the previous state-of-the-art models. In particular, PosMLP-Video pre-trained on ImageNet1K achieves 59.0%/70.3% top-1 accuracy on Something-Something V1/V2 and 82.1% top-1 accuracy on Kinetics-400 while requiring much fewer parameters and FLOPs than other models. The code is released at https://***/zhouds1918/PosMLP Video. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Real-Time Speech Emotion Recognition Using Hybrid Transformer Networks for Mental Health Monitoring

Real-Time Speech Emotion Recognition Using Hybrid Transforme...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： P. Janani Durga C. H. Hussaian Basha Nelson Kennedy Babu Christopher S. Senthilkumar N. Rathina Kumar M. Kavitha Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Electrical and Electronics Engineering SR University Hanumakonda Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This study proposes a hybrid method to multi dialect speech recognition using the transfer learning with transformer-based architecture. The primary objective of this method is to enhance the automatic speech recognition (ASR) across the different dialectal variations of specific language. The transfer learning methods are utilized to modify the base model that was trained on high resource language to manage the multiple dialects with limited labelled data for each dialect. This is achieved by using pre trained transformer methods such as Wav2Vec2.0 and Whisper. An approach which employs dialect specific embeddings is presented. It enables the method to capture data and differentiate between prosodic elements and phonetic that are distinctive to a variety of dialects. data augmentation technique are utilized in order to overcome the limited availability of labelled data for low resource dialects. These methods are utilized to increase the efficiency of training set and enhance the robustness of the method. Evaluation of the method is carried out using multi dialect speech datasets which include Arabic MGB-3 and common voice. These datasets represent a variety of dialects that are spoken in various linguistic regions. The result of this study indicates that the transformer-based method is effective in managing difficulties of dialectal variances while the reliability is reduced on different datasets. Different real-world applications are provided by the proposed method for improving voice enabled applications. Furthermore, this method leads the way for the development of ASR method that are more accurate and independent in locations that have a significant linguistic variation.

关键词： Measurement Training Emotion recognition Accuracy Virtual assistants Transfer learning Mental health Transformers Real-time systems Monitoring

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Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

arXiv

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arXiv 2023年

作者： Liu, Yao Cui, Gangfeng Luo, Jiahui Chang, Xiaojun Yao, Lina School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Data 61 CSIRO School of Computer Science and Engineering University of New South Wales SydneyNSW2015 Australia

As a fundamental aspect of human life, two-person interactions contain meaningful information about people's activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this paper, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches in most standard evaluation settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Large datasets

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Incomplete Multi-View Clustering via Multi-Level Contrastive Learning

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IEEE Transactions on Knowledge and data engineering 2025年

作者： Yin, Jun Wang, Pei Sun, Shiliang Zheng, Zhonglong Shanghai Maritime University College of Information Engineering Shanghai201306 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science Shanghai200062 China Zhejiang Normal University School of Computer Science Zhejiang Jinhua321004 China

Although significant progress has been made in multi-view learning over the past few decades, it remains challenging, especially in the context of incomplete multi-view clustering, where modeling complex correlations among different views and handling missing data are key difficulties. In this paper, we propose a novel incomplete multi-view clustering network to address the aforementioned issue, named Incomplete Multi-view Clustering via Multi-level Contrastive Learning (IMC-MCL). Specifically, the proposed model aims to minimize the conditional entropy between views to recover missing data by dual prediction strategy. Moreover, the approach learns multi-level features, including latent, high-level and semantic features, with the goal of satisfying both reconstruction and consistency objectives in distinct feature spaces. Specifically, latent features are utilized to accomplish the reconstruction objective, while high-level features and semantic labels are employed to achieve the two consistency goals through contrastive learning. This framework enables the exploration of shared semantics within high-level features and achieves clustering assignment using semantic features. Extensive experiments have shown that the proposed approach outperforms other state-of-the-art incomplete multi-view clustering methods on seven challenging datasets. © 1989-2012 IEEE.

关键词： Sorting

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Digital Twin Empowered Task Offloading for Mobile Edge Computing in 6G Internet of Vehicles

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IEEE Internet of Things Journal 2025年

作者： Zhou, Xiongjie Peng, Yu Lan, Tian Zhang, Zhaogong Tang, Bin Guan, Xin Heilongjiang University School of Computer and Big Data Harbin 150080 China State Grid Heilongjiang Electric Power Company Limited Harbin 150080 China Guangdong Unversity of Science and Technology School of Computer Science Dongguan 523000 China Qingdao Innovation and Development Center of Harbin Engineering University Qingdao 266000 China

The rapid development of the Internet of Vehicles (IoV) and sixth-generation (6G) technology has made traditional cloud computing architectures inadequate for vehicular networks. With ultra-low latency, high bandwidth, and massive connectivity, 6G networks provide essential support for task offloading in mobile edge computing. Task offloading transfers computational tasks from vehicles to edge base stations or cloud servers. However, traditional methods struggle to adapt to diverse user tasks and dynamic network environments. These limitations increase offloading delays. Digital twin (DT) offers real-time simulation to reflect system dynamics and address these challenges. In this paper, we propose a task offloading framework that combines DTs with deep reinforcement learning. We propose a multi-layer dynamic framework with an error-reward feedback mechanism to handle complexity, dynamic changes, and errors. This approach enables the conclusion of task offloading strategies that minimize latency. The framework includes a threshold-based warning mechanism to effectively manage edge base station loads. By monitoring load in real time, the system evaluates base station status and adjusts task allocation to maintain stability. We propose a multi-dimensional state encoder architecture, comprising environment encoders, base station state encoders, and task state encoders to effectively extract critical features. The proposed architecture enables the conclusion of task offloading strategies that minimize latency. Experimental results demonstrate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize energy consumption and latency. © 2014 IEEE.

关键词： 6G Deep Reinforcement Learning Digital Twin Internet of Vehicles Mobile Edge Computing Task Offloading

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Logging Multi-Component Supply Chain Production in Blockchain 2021

Logging Multi-Component Supply Chain Production in Blockchai...

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4th International Conference on computers in Management and Business, ICCMB 2021

作者： Madhwal, Yash Chistiakov, Ivan Yanovich, Yury Faculty of Computer Science National Research University Higher School of Economics Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Inst. of Sci. and Technol. and Lab. of Data Mining and Predictive Modelling Inst. for Info. Transmiss. Prob. Russia

ISBN: (纸本)9781450388610

The supply chain is a thriving industry where numerous parties have different interests. Subsequently, the immense volume of data produced is difficult to audit. Some information can be lost or intentionally distorted in the process. Blockchain as an open, public, borderless, neutral, and censorship-resistant architecture can significantly complement supply chains. A new supply chain architecture is proposed in this work, where the tokenized directed acyclic hypergraph (DAG) represents real-world production processes. An anti-aerosol respirator manufacturing is used as an illustration example. By tokenizing all parts of multi-component products, supply chain data is automatically timestamped and secured. Moreover, the DAG design allows one to trace-back all the elements of the final product to their origin. Blockchain can formally audit the entire supply chain without the need to go from place to place. A single incorruptible operations log creates an enabling environment for an unbiased reputation system to emerge. © 2021 ACM.

关键词： Smart contract

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Regularity-Based Evolutionary Multi-Objective Optimization Review

SSRN

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SSRN 2024年

作者： Lu, Yulan Guo, Xueyi Geng, Jinggai Wang, Shuai Wang, Zhengkun Zhou, Aimin Sun, Jianyong Si, Xinhui Sun, Xin Zhang, Hu School of Mathematics and Physics University of Science and Technology Beijing Beijing100083 China School of the Aliyun School of Big Data School of Materials Science & Engineering Changzhou University Jiangsu Changzhou213164 China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China School of Computer Science and Technology East China Normal University Shanghai200062 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China CASIC Research Institute of Intelligent Decision Engineering Beijing100074 China

Under mild conditions, the Pareto optimal solutions of a continuous m-dimensional multi-objective optimization problem (MOP) form a piece-wise (m-1)-dimensional manifold structure, which is known as regularity property. As a domain knowledge of MOP, this regularity property has been widely incorporated into the design of multiobjective evolutionary algorithms in the past two decade years. In this paper, we conduct a comprehensive review on the developments of regularity-based evolutionary multi-objective optimization (REMO) approaches, analyzing the current research landscape, summarizing key directions, and anticipating future trends. The aim is to provide some valuable insights for upcoming research endeavors. © 2024, The Authors. All rights reserved.

关键词： Optimization algorithms

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