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2024 International Conference on Advanced control Systems and Automation Technologies, ACSAT 2024

作者： Sun, Pengyu Zhang, Zhiquan Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798331541668

In today's society, the agricultural logistics and transportation industry faces numerous challenges in meeting the demands for fresh produce preservation and efficient transport. Insufficient environmental monitoring and information management during transportation lead to increased logistics costs and high produce spoilage rates. A high-quality, efficient logistics control system is essential for the agricultural supply chain. This paper presents a logistics control system based on IoT technology, centered on the ESP32 chip, utilizing ESP-NOW and MQTT protocols for communication, integrating multi-sensor data and RFID technology. A sensor module and host gateway were designed, along with a cloud platform visualization dashboard. Through this system, users can view sensor data, track item dynamics, and effectively monitor and control the agricultural logistics process in real-time, enhancing both the efficiency and quality of agricultural logistics. © 2024 IEEE.

关键词： Spoilage

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A New Approach for an Introduction to computer Networking Course: A Case Study 5

A New Approach for an Introduction to Computer Networking Co...

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5th International Conference on Information Technology and Education Technology, ITET 2024

作者： Vogel, Joyce Bouhnik, Dan Jerusalem College of Technology Computer Science and Software Engineering Jerusalem Israel

ISBN: (纸本)9798331540425

The course Introduction to computer Networks (ICN) has become one of the most vital courses in computer Science and software engineering degrees and clearly is an imperative course for a degree in computer networking. However, despite its importance, the course suffers from many issues which lead to a high failure rate and low success. Even students who pass the course have problems understanding key concepts in data communication. One of the key issues is the theoretical nature of the material of the course. Many of the concepts are abstract and dry, which the students find hard to understand and boring. At the Jerusalem college of Technology (JCT), we encountered such problems with our networking course. We therefore decided to suspend the course for a year, research successful networking courses, and apply our findings to a revised networking course. One of the strategic changes that was made was to introduce more practical material into the course. Top-down approach to networking replaced a bottom-up approach, hands-on lab were added to the course, and online quizzes were included. This paper presents some of our findings and changes that were made to the networking course. The problems that were present in our original version of the ICN course are not unique to the JCT but are common to networking courses throughout higher education institutions. We therefore believe that the findings of our research can benefit other faculty who wish to improve their networking courses. © 2024 IEEE.

关键词： Students

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Environment-Tolerant Trust Opportunity Routing Based on Reinforcement Learning for Internet of Underwater Things

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6348-6360页

作者： He, Yu Han, Guangjie Hou, Yun Lin, Chuan Hohai University College of Information Science and Engineering Changzhou213200 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Changzhou213200 China Hohai University College of Computer Science and Software Engineering Nanjing211100 China Northeastern University Software College Shenyang110819 China

The Internet of Underwater Things (IoUT) has garnered significant interest due to its potential applications in monitoring underwater environments. However, the unique characteristics of acoustic communication, such as long propagation delays and high attenuation, present considerable obstacles for achieving efficient and dependable data transmission. Opportunistic routing is a crucial technique for enhancing packet delivery ratios by selecting a set of forwarding nodes and utilizing their cooperative forwarding to boost network throughput. Nevertheless, choosing an excessive number of forwarding nodes can lead to wasteful energy usage and extended communication delays. Moreover, the overlooked trustworthiness of forwarded nodes in most research works can undermine the effectiveness of opportunistic routing. Therefore, this study presents a novel trust opportunistic routing scheme that employs reinforcement learning to achieve resilience in constantly changing underwater settings. The combination of reinforcement learning and trust management enables the proposed opportunistic routing scheme to adapt to the unstable underwater environment and unknown malicious attacks. Initially, a method is introduced for measuring environmental fitness by considering multiple trust factors, including communication success rate, data reliability, and location dynamics. The proposed scheme then uses reinforcement learning to develop a reliable opportunistic routing method based on quantified state information. This component employs the obtained state to formulate action strategies and obtains reward values from environmental inputs. The reward update equation integrates these qualities to optimize the deployment of superior action strategies, finally achieving trust opportunistic routing for underwater data collection. Fundamental experimental results demonstrate that the proposed protocol performs exceptionally well in demanding underwater conditions, outperforming existing method

关键词： Reinforcement learning

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Research on CUDA-based Multidimensional Time Series Motif Mining 5

Research on CUDA-based Multidimensional Time Series Motif Mi...

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5th International Conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Wang, Jimin Wang, Shuaiwei Wang, Junru Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798331533991

Traditional serial motif mining methods struggle to quickly identify motif information in large-scale time series data. A CUDA-based multidimensional motif mining algorithm is proposed to discover motifs in multidimensional time series. The algorithm introduces a multidimensional kNN Matrix Profile (kNN mMP) structure to address the limitations of the multidimensional mMP. Parallel computation of kNN mMP using the CUDA architecture enables rapid motif discovery in massive datasets and facilitates detection of additional motif features, such as strong and weak motifs, as well as identification of anomalous motifs. Experiments on public datasets demonstrate that using CUDA for kNN mMP structure achieves a significant speedup;on NVIDIA GeForce RTX 4060, the kNN-mSTOMP-GPU algorithm exhibits a speedup of over 220 times compared to the STOMP algorithm utilizing only kNN mMP when the sequence length is 200,000. © 2024 IEEE.

关键词： Time series

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A Purified Stacking Ensemble Framework for Cytology Classification 30th

A Purified Stacking Ensemble Framework for Cytology Classif...

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30th International Conference on MultiMedia Modeling, MMM 2024

作者： Qian, Linyi Huang, Qian Chen, Yulin Chen, Junzhou College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031533075

Cancer is one of the fatal threats to human beings. However, early detection and diagnosis can significantly reduce death risk, in which cytology classification is indispensable. Researchers have proposed many deep learning-based methods for automated cancer diagnosis. Nevertheless, due to the similarity of pathological features in cytology images and the scarcity of high-quality datasets, neither the limited accuracy of single networks nor the complex architectures of ensemble methods can meet practical application needs. To address the issue, we propose a purified Stacking ensemble framework, which employs three homogeneous convolutional neural networks (CNNs) as base learners and integrates their outputs to generate a new dataset by a k-fold split and concatenation strategy. Then a distance weighted voting technique is applied to purify the dataset, on which a multinomial logistic regression model with a designed loss function is trained as the meta-learner and performs the final predictions. The method is evaluated on the FNAC, Ascites, and SIPaKMeD datasets, achieving accuracies of 99.85%, 99.24%, and 99.75%, respectively. The experimental results outperform the current state-of-the-art (SOTA) methods, demonstrating its potential for reducing screening workload and helping pathologists detect cancer. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cytology

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Deadline-aware Load Allocation for Coded Computation over Heterogeneous Clusters 5

Deadline-aware Load Allocation for Coded Computation over He...

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5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Cheng, Shiying Lin, Yuxuan Tang, Bin Hohai University College of Computer Science and Software Engineering Nanjing China

ISBN: (纸本)9798331506612

In large-scale distributed systems, the performance of computation tasks is often significantly degraded by straggling nodes. Recently, coded computation has emerged as a promising approach to mitigate the effect of stragglers. However, the performance of coded computation is still significantly affected by node workloads, particularly in heterogeneous clusters. Some load allocation schemes have been proposed to minimize the expected computation latency, but for time-sensitive tasks with strict deadlines, they often result in high failure probabilities. In this paper, we examine the fundamental matrix-vector multiplication tasks and focus on the deadline-aware load allocation problem under two typical runtime models, aiming to minimize the task failure probability. We firstly propose a simple yet effective normal approximation method based on the central limit theorem to approximate the failure probability, transforming the problem into a non-convex multivariate optimization, and then present an efficient iterative load allocation algorithm. Extensive simulations demonstrate the effectiveness of our proposed scheme. © 2024 IEEE.

关键词： Convex optimization

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Reducing Age of Collection with Dynamic-Frame Time Division Multiple Access 100

Reducing Age of Collection with Dynamic-Frame Time Division ...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Lai, Yurong Han, Xinhui Wang, Xueer Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798331517786

This paper introduces a dynamic-frame time division multiple access (DF-TDMA) scheme aimed at decreasing the age of collection (AoC) in collaborative monitoring scenarios. Unlike the conventional age of information (AoI) metric, AoC decreases only when partial information from multiple sources is aggregated to form a complete observation. Previous studies on AoC predominantly assumed that complete observations are derived from aggregating information from all sources. However, this assumption does not hold in scenarios where sources are correlated, and a complete observation can be achieved with partial information from only a subset of sources. To address this, new channel access protocols are necessary to achieve a low network-wide average peak AoC. DF-TDMA is proposed as a solution, featuring dynamically adjustable TDMA frame sizes to minimize time wastage and thereby reduce AoC. While the dynamic frame size adjustment complicates AoC analysis, we theoretically derive the average peak AoC of DF-TDMA. Simulations show that DF-TDMA maintains a stable average peak AoC across varying numbers of sources N, and notably reduces the average peak AoC compared to a fixed-frame TDMA scheme, particularly under conditions with a large N. © 2024 IEEE.

关键词： Time division multiple access

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Application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

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computers, Materials & Continua 2023年第4期75卷 1901-1918页

作者： Ahmad O.Aseeri Sajjad Hussain Chauhdary Mohammed Saeed Alkatheiri Mohammed A.Alqarni Yu Zhuang Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science Texas Tech UniversityLubbockUSA

IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped *** paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT *** has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device *** IoT network gathers information of interest from multiple cluster members selected by the proposed *** addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT *** analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance *** enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

关键词： Cyber-physical systems security data aggregation Internet of Things physical unclonable function swarm intelligences

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A Semi-Supervised image classification algorithm inspired by the Primacy Effect 4

A Semi-Supervised image classification algorithm inspired by...

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4th International Conference on Digital Signal and computer Communications, DSCC 2024

作者： Zhao, Dianqing Li, Chaofan Zhu, Anmin College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781510681538

Semi-supervised-Learning(SSL) providing a solution to leverage vast amounts of unlabeled data. In cognitive psychology, the Primacy-effect refers to the phenomenon where the initial information encountered tends to leave a deeper impression in human cognitive processes, serving as the basis for subsequent judgments. Inspired by the Primacy-effect, this paper proposes a novel semi-supervised image classification algorithm. The core idea of this algorithm is to mimic the beneficial effects of the Primacy effect on human cognitive processes, simulate similar phenomenon on artificial neural networks. In this paper, the initially labeled data is referred to as exemplar. The algorithm includes an exemplar prediction module, whose main function is to accurately identify examples, ensuring that the model forms a "deep impression" of them. We found that due to the scarcity of examples, it is easy to cause model overfitting. Therefore, we proposes the Weighted-Gradient-Chain technique. Additionally, Pseudo-labeling technique was employed, but during model training, we found that generated erroneous Pseudo-labels could introduce errors. To enhance the quality of Pseudo-label generation, this paper proposes a Pre-Pseudo-labeling method. A series of experiments were conducted on multiple datasets. The results indicate that the proposed model performed well. © 2024 SPIE.

关键词： Image classification

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Active self-training for weakly supervised 3D scene semantic segmentation

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Computational Visual Media 2024年第3期10卷 425-438页

作者： Gengxin Liu Oliver van Kaick Hui Huang Ruizhen Hu College of Computer Science&Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Carleton UniversityOttawa K1S 5B6Canada

Since the preparation of labeled datafor training semantic segmentation networks of pointclouds is a time-consuming process, weakly supervisedapproaches have been introduced to learn fromonly a small fraction of data. These methods aretypically based on learning with contrastive losses whileautomatically deriving per-point pseudo-labels from asparse set of user-annotated labels. In this paper, ourkey observation is that the selection of which samplesto annotate is as important as how these samplesare used for training. Thus, we introduce a methodfor weakly supervised segmentation of 3D scenes thatcombines self-training with active learning. Activelearning selects points for annotation that are likelyto result in improvements to the trained model, whileself-training makes efficient use of the user-providedlabels for learning the model. We demonstrate thatour approach leads to an effective method that providesimprovements in scene segmentation over previouswork and baselines, while requiring only a few userannotations.

关键词： semantic segmentation weakly supervised self-training active learning

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