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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Jiang, Liyan Zhang, Yu Shi, Yuanquan School of Information Science and Technology Hainan Normal University Haikou571158 China School of Cybersecurity Guangdong Polytechnic Normal University Guangzhou510665 China School of Computer and Artificial Intelligence Huaihua University Hunan Huaihua418000 China

ISBN: (纸本)9781510666597

The use of fileless technologies in malware continues to grow and fileless malware becomes more dangerous and difficult to detect. To address this challenge, we propose a novel visual method for classifying fileless malware based on few-shot learning. First, we built a fileless malware dataset, which is executed through a local virtual environment to collect malware memory dumps. Secondly, memory dumps are clipped and visualized. We developed a new memory dumps trimming method and a novel binary file visualization technique, which can remove redundant data from memory dumps, significantly compress the file size, and then represent the trimmed memory dumps as RGB images. Finally, we propose a few-shot learning framework, namely MMEL (MAML + Mean_subtraction + Euclidean_normalization + Label_Smothing), to improve the performance of the classification method. Experimental results show that our visualization technique and framework outperform other state-of-the-art few-shot learning methods. © 2023 SPIE.

关键词： Malware

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Locality sensitive hashing-driven multifactorial evolutionary algorithms for multitask optimization

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Applied Soft Computing 2023年 132卷

作者： Yu, Tuo-Bin Zhang, Yu-Hui Gong, Yue-Jiao Li, Yuan School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computer Science and Technology Dongguan University of Technology Dongguan China School of Computer and Information Engineering Henan Normal University Xinxiang China

In recent years, evolutionary multitasking optimization (EMTO) has attracted considerable attention. Different from the traditional algorithms that solve one optimization problem at a time, EMTO aims at solving multiple problems simultaneously, through transferring useful information among individuals. The most well-known algorithm in this area is the multifactorial evolutionary algorithm (MFEA). However, when designing the information sharing mechanism, MFEA and its variants focus solely on the properties of individuals in the objective space, while neglecting the valuable information in the decision space. To make full use of the inherent knowledge of the evolving population, this paper presents a novel EMTO framework by using the locality sensitive hashing (LSH) method to analyze the population distribution in the decision space and then guide the information sharing. Specifically, LSH uses hash functions to map the individuals in the decision space into hash codes, ensuring that similar individuals have a higher probability to be mapped into the same code. Then, based on the matching relationships of the hash codes and skill factors between individuals, different reproduction and evaluation strategies are exerted to satisfy the specific requirements under different conditions. This way, the knowledge from both the decision space and the objective space are utilized for the purpose of better manipulating the individuals. Additionally, an opposition-based learning method is integrated in this algorithm to enhance exploration. The comprehensive experimental studies confirm the efficacy and efficiency of the proposed method on both single-objective multitask benchmarks and multi-objective multitask benchmarks. According to the statistic tests, the proposed single-objective and multi-objective algorithms significantly outperform their best competitors on 14/18 and 12/18 tasks, respectively. © 2022 Elsevier B.V.

关键词： Evolutionary algorithms

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Transforming Diabetes Care: A Review of IoT-Based Mobile Health Systems 10th

Transforming Diabetes Care: A Review of IoT-Based Mobile He...

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10th EAI International Conference on IoT Technologies for Health-Care, HealthyIoT 2023, and 4th EAI International Conference on Wearables in Healthcare, HealthWear 2023

作者： Ashfaq, Fiza Ahad, Abdul Hussain, Mudassar Madeira, Filipe Department of Computer Science University of Management and Technology Sialkot51040 Pakistan School of Software Northwestern Polytechnical University Shaanxi Xian China Istanbul34467 Turkey /Pole of Digital Literacy and Social Inclusion Polytechnic Institute of Santarem Santarem2001-904 Portugal Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

ISBN: (纸本)9783031719103

Diabetes, a chronic ailment requiring ongoing care and monitoring, has undergone a healthcare shift thanks to the Internet of Things (IoT). Real-time monitoring, individualized treatment plans, and improved patient-provider communication are all features of IoT-based mobile health solutions. This study explores the components, functionalities, benefits, and disadvantages of IoT-based mobile health systems for the management of diabetes. It investigates wearable sensors, smartphone apps, and linked devices like glucose meter and monitors, placing a focus on their precision, dependability, and usability. The study explores data analytics techniques that allow for customized advice and actions. Patients can effectively control their diabetes with the use of individualized treatments and real-time monitoring. The research also looks into the data analytical methods used in IoT-based mobile health systems for the treatment of diabetes. IoT-based mobile health system issues and implications are also covered. These include issues with data security and privacy, platform and device compatibility, standardization of data formats, and ensuring that all people with diabetes have equal access to technology. Healthcare workers, academics, and policymakers may learn more about the potential of IoT-based mobile health systems to revolutionize diabetes treatment through this thorough assessment. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Electronic health record

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Proportional Feature Pyramid Network for Lane Detection 4

Proportional Feature Pyramid Network for Lane Detection

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4th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Hui, Jiapeng Lian, Guoyun Wu, Jiansheng Ge, Shuting Yang, Jinfeng Shenzhen Polytechnic University Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China University of Science and Technology School of Computer Science and Software Engineering Anshan China

ISBN: (纸本)9798350373233

Lane detection under extreme conditions presents a highly challenging task that requires capturing each crucial pixel to predict the complex topology of lane lines and differentiate the various lane types. Existing methods predominantly rely on deep feature extraction networks with substantial parameters or the fusion of multiple prediction modules, resulting in large model sizes, embedding difficulties, and slow detection speeds. This paper proposes a Proportional Feature Pyramid Network(P-FPN) through fusing the weights into the FPN for lane detection. In our method, the high-level features are explored to predict lanes coarsely while local- detailed features are leveraged to improve localization accuracy. Extensive experiments on the widely used lane detection dataset, CULane dataset, demonstrate that the proposed method achieves the competitive results compared with several state-of-the-art approaches. © 2023 IEEE.

关键词： Feature extraction

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Effect of Cerium Size and Content on Friction Properties of MoS2 Coatings 12th

Effect of Cerium Size and Content on Friction Properties of ...

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12th International Workshop of Advanced Manufacturing and Automation, IWAMA 2022

作者： Wu, Jian Cao, Jiejie Voinov, Nikita School of Mechanical Engineering Changshu Institute of Technology Suzhou China Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University St. Petersburg195251 Russia

ISBN: (纸本)9789811993374

MoS2 coating with cerium additive can increase the friction performance of the substrate. In this paper, the friction of two types of Ce coatings has been studied, with particle sizes of 10 nm and 40 nm, proportions of 1%, 2%, and 4%. Microscopy and a developed tribometer were used to characterize the friction properties of these coatings. The results show Ce particles can make the surface structure of the coating more uniform. The 2% 10 nm Ce and 1% 40 nm Ce particle coatings showed lower friction coefficient values of 0.056, and 0.05, and the wear scars of these two coatings were also uniform. To find the best solid lubricating coating, the 2% 10 nm and 1% 40 nm Ce particle coatings were carried out with other experiments. The 1%, 40 nm coating has a basic coefficient of friction (0.069) showing better friction performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coatings

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Efficiency Assessment of MapReduce Algorithm on a Serverless Platform 3

Efficiency Assessment of MapReduce Algorithm on a Serverless...

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3rd IEEE International Conference on Electronic technology, Communication and Information, ICETCI 2023

作者： Cai, Jingwen Huang, Kai Liao, Zhuoya Henan University International Education College Zhengzhou475000 China Zhuhai College of Science and Technology College of Computer Science Zhuhai519040 China China University of Geosciences School of Mathematics and Physics Wuhan430074 China

ISBN: (纸本)9798350398410

The MapReduce programming approach can divide large-scale data processing tasks into smaller, independent tasks, run those tasks concurrently on server clusters, and then integrate the outputs of those tasks to get the final result. Considering the wide application of MapReduce in massive data processing, this paper applies it to the serverless platform emphasizing ideas and services for realization simulation and applies it to the word count experiment. In the experiment, MapReduce is implemented on Alibaba Cloud and validated using a word count experiment of about 11K words. Its execution time on the platform under different CPU cores, memory configurations, and the number of workers is validated. By changing different platform configurations, it is concluded that compared to the number of workers, the size of CPU core has a huge impact on the response time, and the memory configuration does not affect the execution time of the model to a certain extent. By adjusting the model parameters in the experiment, the serverless implementation has a high efficiency compared with the non-serverless MapReduce model. The relationship between implementation efficiency and platform configuration provides references for optimizing the application of the MapReduce model on the serverless platform. Moreover, it may inspire the configuration of serverless hardware support in the future. At the same time, it further reflects the serverless platform's optimization of resource utilization efficiency in machine learning training. © 2023 IEEE.

关键词： MapReduce

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Latency-Optimal Pyramid-based Joint Communication and Computation Scheduling for Distributed Edge Computing 42

Latency-Optimal Pyramid-based Joint Communication and Comput...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Chen, Quan Wang, Kaijia Guo, Song Shi, Tuo Li, Jing Cai, Zhipeng Zomaya, Albert Guangdong University of Technology School of Computers Guangzhou China Hong Kong Polytechnic University Department of Computing Hong Kong Tianjin University College of Intelligence and Computing Tianjin China Georgia State University Department of Computer Science Atlanta United States University of Sydney School of Computer Science Sydney Australia

ISBN: (纸本)9798350334142

By combing edge computing and parallel computing, distributed edge computing has emerged as a new paradigm to accelerate computation at the edge. Considering the parallelism of both computation and communication, the problem of Minimum Latency joint Communication and Computation Scheduling (MLCCS) is studied recently. However, existing works have rigid assumptions that the communication time of each device is fixed and the workload can be split arbitrarily small. Aiming at making the work more practical and general, the MLCCS problem without the above assumptions is studied in this paper. Firstly, the MLCCS problem under a general model is formulated and proved to be NP-hard. Secondly, a pyramid-based computing model is proposed to consider the parallelism of communication and computation jointly, which has an approximation ratio of 1 + δ, where δ is related to devices' communication rates. An interesting property under such computing model is identified and proved, i.e., the optimal latency can be obtained under arbitrary scheduling order when all the devices share the same communication rate. When the devices own different communication rates, the optimal scheduling order is also obtained. Additionally, when the workload cannot be split arbitrarily, an approximation algorithm with ratio of at most 2 (1 + δ) is proposed. Finally, the theoretical analysis and simulation results verify that the proposed algorithm has high performance in terms of latency. Two testbed experiments are also conducted, which show that the proposed method outperforms the existing methods, reducing the latency by up to 29.2% in real-world applications. © 2023 IEEE.

关键词： Edge computing

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Efficient Privacy-Preserving Multi-Functional Data Aggregation Scheme for Multi-Tier IoT System 28

Efficient Privacy-Preserving Multi-Functional Data Aggregati...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Tao, Yunting Kong, Fanyu Shi, Yuliang Yu, Jia Zhang, Hanlin Liu, Huiyi School of Software Shandong University China College of Computer Science and Technology Qingdao University China College of Information Engineering Binzhou Polytechnic China Shandong Sansec Information Technology Co. Ltd China

ISBN: (纸本)9798350300482

The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient privacy-preserving multi-functional data aggregation scheme for multi-tier IoT architecture. The scheme supports privacy-preserving calculation of mean, variance, and anomaly proportion. The scheme uses the Paillier cryptosystem and the BLS algorithm for encryption and signature, and uses blinding techniques to keep the size of the IoT system secret. In order to make the Paillier algorithm more suitable for the IoT scenario, we also improve its efficiency of encryption and decryption. The performance evaluation shows that the scheme improves encryption efficiency by 43.7% and decryption efficiency by 45% compared to the existing scheme. © 2023 IEEE.

关键词： Internet of things

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Study on the Dispersion Characteristics of Coal Complex Resistivity Under the Effect of Liquid Nitrogen Cyclic Freeze-Thaw and Evaluation of Permeability Enhancement

SSRN

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SSRN 2024年

作者： Li, Jian Xu, Xiaokai Zhang, Yugui Hou, Haihai Wang, Kunpeng Tian, Lin Jian, Kuo Xue, Zhengzheng Zhang, Zehua School of Safety Science and Engineering Henan Polytechnic University Henan Jiaozuo454003 China School of Energy Science and Engineering Henan Polytechnic University Henan Jiaozuo454003 China College of Mining Liaoning Technical University Liaoning Fuxin123000 China College of Geophysics Chengdu University of Technology Sichuan Chengdu610059 China School of Resources and Environment Henan Polytechnic University Henan Jiaozuo454003 China College of Energy and Materials Engineering Taiyuan University of Science and Technology Shanxi Taiyuan030024 China

The effect of liquid nitrogen freeze-thaw fracturing on coal seams can be potentially evaluated by the complex resistivity method. The real part (Reρ) and the imaginary part (Imρ) of the complex resistivity and permeability of coal were determined under different cycle times and different bedding directions. The reason for permeability enhancement was discussed, and the dispersion mechanism of complex resistivity during cyclic freeze-thaw fracturing was analysed. The results indicated that: (1) With an increase in cycle time, the Reρ and |Imρ| increase, the fully polarized frequency (fp) of Reρ and the characteristic frequency (fc) of Imρ decrease, and the dispersion degree (α) of the curve increases. (2) The Reρ and |Imρ| in the vertical bedding direction are greater compared to the parallel bedding direction. The fp and fc of the former are smaller than those of the latter;the former has a greater variation in Reρ and |Imρ| between adjacent fracturing cycles and a higher α. Moreover, the difference in electrical properties of the bedding structure continuously decreases with the increase in cycle time. (3) Under the effect of liquid nitrogen cyclic freeze-thaw, a complex network of fractures is formed, the anisotropic characteristics of coal are weakened, and effective conductive channels are damaged. The peak frost heave force decreases exponentially with the increase in cycle time, leading to a gradual decrease in the conductivity of coal, and the difference in bedding electrical properties disappears. (4) Comparing the inversion degree of measured data with three conductive models, ρ0 and τ are selected as the optimum parameters for evaluating the effect of liquid nitrogen cyclic freeze-thaw. Then, a logarithmic evaluation model is constructed based on the ρ0 and τ. This work provides a new perspective based on electrical detection for evaluating the permeability enhancement of coal during the liquid nitrogen cyclic freeze-thaw. © 2024, The Authors. All rights

关键词： Coal

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Global and local complementary multi-path feature fusion network for the classification of crop remote sensing images 4

Global and local complementary multi-path feature fusion net...

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2024 4th International Conference on Artificial Intelligence and Industrial technology Applications, AIITA 2024

作者： Zhang, Keqian Cui, Tengfei Wu, Wei Zhang, Yichi Zhang, Ziqian Si, Qiliang Song, Xiangyang Cheng, Gang Henan Polytechnic University College of Surveying and Land Information Engineering 2001 Century Road Jiaozuo Henan China Boston University Metropolitan College 1010 Commonwealth Avenue BostonMA United States Guilin University of Technology College of Geomatics and Geoinformation No. 319 Yanshan Street Guangxi Guilin China Hohai University School of Artificial Intelligence and Automation No. 8 Focheng West Road Jiangning District Jiangsu Nanjing China Henan University of Science and Technology School of Software No. 48 Xiyuan Road Jianxi District Henan Luoyang China

The accuracy and efficiency of crop distribution information extraction are pivotal in ensuring global food security. In long-time-series optical satellite data, most existing methods focus on extracting spatial features using Convolutional Neural Networks (CNNs), which do not adequately mine and model the spatial-temporal information. The development of the attention mechanism allows for the extraction of global features in remote-sensing images of long temporal sequences. To extract global attentional features with complementary features in crop remote sensing images, we propose a Global and Local Complementary Multi-path Feature Fusion Network (GLMP), which is capable of extracting global features from remote sensing images of long temporal sequence, that enhances the local characteristics of crop images derived from CNNs, thus obtaining more effective multi-scale complementary features. This extraction of features enhances the comprehension of crop images, thereby boosting the performance of associated tasks. Within GLMP, we introduce two pivotal modules: the Hybrid Attention and Convolutional Paths Module (HACM) and the Multi-path Feature Fusion Module (MPFM). These modules synergistically converge multi-path features, yielding more discriminative feature information. Experimental results on the ZueriCrop dataset show that the proposed GLMP technique is effective;it performs promisingly having a total accuracy of 90.2% and an F1 value of 62.5%. Furthermore, the ablation study verifies the substantial improvement in classification accuracy for remote sensing crop images of long-time series in nature, specifically attributed to the HACM and MPFM modules. © Published under licence by IOP Publishing Ltd.

关键词： Convolutional neural networks

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