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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Beemkumar N Daljeet Pal Singh Zahid Ahmed Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bagalore Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

In this paper, we utilize ANNs to investigate how medicine dosing and management might be improved for the elderly population. The suggested strategy calls for the use of patient-specific data as inputs for the ANNs, such as age, renal and hepatic function, body mass index, and preexisting prescription profiles. The neural network model dynamically changes drug doses using the combination of the Multilayer Perceptron (MLP) algorithm, Backpropagation, and the Stochastic Gradient Descent (SGD) algorithm to maximize therapeutic benefits and reduce the likelihood of adverse drug events. The research highlights the improved accuracy, sensitivity, specificity, precision, and F1 score of the suggested technique compared to numerous conventional methods. As a result, the suggested system has great potential to revolutionize medication management for the elderly population since it is more personalized, flexible, patient-safe, effective, and efficient in its use of resources.

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Effect of the Mixing Structure Parameters of a Self-reflux Burner on Combustion Characteristics and NO_(x)Emission

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Journal of Thermal science 2021年第4期30卷 1224-1236页

作者： XU Qian SHEN Meng SHI Kejian LIU Zhihui AKKURT Nevzat XIONG Yaxuan LIU Lin FENG Junxiao WANG Jiulong School of energy and environmental engineering Shunde Graduate SchoolUniversity of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory of Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Key Laboratory of HVAC Beijing University of Civil Engineering and ArchitectureBeijing 100044China School of Mathematics and Physics University of Science and Technology BeijingBeijing 100083China Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China

To solve the problem of low efficiency of NO_(x)emission reduction in self-reflux burners,this study is based on the concept of coordinated control of self-reflux burner structural and thermal *** completing the structural design and optimization of thermal parameters,we continue to adjust the two key structural parameters:the nozzle axis distance and the length of the cylindrical section,to minimize NO_(x)*** are the two parameters that chiefly affect the mixing of flue gas and fuel *** results show that increasing nozzle axis distance can delay the mixing of gas and air and create a more uniform oxygen concentration field for the combustion *** maximum combustion temperature is reduced from 1973.65 K to 1935.88 K and the volume fraction of NO_(x)in the flue gas is reduced from 188.08×10^(–6)to 143.47×10^(–6).However,compared with the nozzle axis distance,the length of the cylindrical section of the burner has little effect on the mixing of the flow *** different cylindrical section lengths,the maximum combustion temperature does not change more than 3 K,and the volume fraction of NO_(x)in the flue gas changes within 5×10~(–6).

关键词： self-reflux structure structural parameter control thermal parameter combustion characteristic NO_(x)emission

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Saliency Detection From Complex Background with Attention-Based Boundary-Aware Pyramid Pooling Network

SSRN

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SSRN 2022年

作者： Fang, Jinsheng Tao, Yuhao Zhu, Gupei School of Computer Science and Engineering Minnan Normal University Zhangzhou China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou China

Deep convolution network have been widely adopted in saliency detection in recent years and achieved state-of-the-art performance. A central issue for previous methods is the limited capability of detecting objects that are overwhelmed by complex background. Aim to address this problem, we propose a novel attention-based boundary-aware pyramid pooling network, namely ABAPNet, which consists of feature aggregation module, pyramid pooling module and cascading dual attention module. The proposed ABAPNet method transits high-level information to shallow layers to keep transferring important features between layers and obtains more semantic features. Hybrid loss by fusing binary cross entropy (BCE), structural similarity (SSIM) and intersection-over-union (IOU) is employed to reduce the gradient of mixed loss of network output features, which guides the network to better segment boundaries from complex background. Experimental results on 5 public datasets show that the proposed ABAPNet outperforms the competitors. © 2022, The Authors. All rights reserved.

关键词： Object detection

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A Novel Approach to Identify the Anomaly Detection in Electricity usage based on Machine Learning Algorithms and Big Data 3

A Novel Approach to Identify the Anomaly Detection in Electr...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Panneerselvam, M. Neela, K. Rajeshwari, R. Vengadapathiraj, M. Sobitha, S. Mohanavel, V. Sona College of Technology Department of Electrical and Electronics Engineering Salem India Sri Sairam Engineering College Department of Information Technology Chennai India S.A Engineering College Department of Master of Computer Application Chennai India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Comprehension mistakes and frauds may both be detected via the analysis of advanced metering information. By means of on-site examinations or even other techniques, the energy suppliers will be made aware of any suspect usage patterns by this study. The analysis of usage patterns using Machine Learning (ML) techniques may reveal errors, measuring interruptions from assaults, or fine motor control with utility companies. Another of the basic instances of an anomaly detection is fraud detection since it is difficult to categories transactional or utilization information. Additionally, scams vary in type, making it difficult to consistently learn from them. This research study presents an unsupervised ML method to identify out-of-the-ordinary qualities in time series, set a threshold for the proportion of out-of-the-ordinary readings towards the overall measurements, and then classify the moving average as questionable or not. Initially, two different methods are provided to find the differences in time-series based data streaming: Spectral Residual-Convolutional Neural Network and anomaly trained model based on martingales. The properly established information is then subjected to Fisher Linear Discriminant analysis and Two-Class Boosted Decision Tree applications. © 2022 IEEE.

关键词： Large dataset

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An Improved Network Segmentation Performance in Lesion Segmentation based on Mask R-CNN 6

An Improved Network Segmentation Performance in Lesion Segme...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Thivya, T. Kalaivanan, C. Juliet, N. Vengadapathiraj, M. Mary Valantina, G. Mohanavel, V. Velammal Engineering College Department of Information Technology Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India S.A Engineering College Department of Master of Computer Application Chennai India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (纸本)9781665482714

Computational intelligence has grown increasingly common in the analysis of healthcare data in recent years. The deep learning algorithms, in instance, have such a number of fruitful implications in the analysis of medical images. This research study has presented a double awareness deep convolution network-based system for end-to-end healthcare lesion classification. Several fully supervised and poorly supervised extracted features are included in this framework. The issue of the high cost of pixel-level labels with lesions in the medical pictures is alleviated by the weakly supervised segmentation module's use of bounding-box labels of lesion regions, which allows for correct lesion segmentation to be accomplished. In addition to this, a dual attention mechanism has been included for the purpose of improving the network's capacity for visual feature learning. The dual attention mechanism, which consists of both channel and spatial attention, may assist in drawing the network's attention to the extraction of features from significant locations. It is possible for it to considerably minimize the gaps between ground-truth labels and pseudo labels, in comparison to the current standard approach of weakly supervised segmentation using pseudo labels. Final results show that our proposed approach beat the competitors on to an oral disease collection, while our approach was extended to incorporate dermatological disease categorization in the ultimate analysis. © 2022 IEEE.

关键词： Intelligent systems

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ZuantuSet: A Collection of Historical Chinese Visualizations and Illustrations 25

ZuantuSet: A Collection of Historical Chinese Visualizations...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Mei, Xiyao Zhang, Yu Yang, Chaofan Shi, Rui Yuan, Xiaoru Peking University Beijing China University of Oxford Oxford United Kingdom National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Department of Computer Science University of Oxford United Kingdom Center for Research on Ancient Chinese History Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China PKU-WUHAN Institute for Artificial Intelligence China

ISBN: (纸本)9798400713941

Historical visualizations are a valuable resource for studying the history of visualization and inspecting the cultural context where they were created. When investigating historical visualizations, it is essential to consider contributions from different cultural frameworks to gain a comprehensive understanding. While there is extensive research on historical visualizations within the European cultural framework, this work shifts the focus to ancient China, a cultural context that remains underexplored by visualization researchers. To this aim, we propose a semi-automatic pipeline to collect, extract, and label historical Chinese visualizations. Through the pipeline, we curate ZuantuSet, a dataset with over 71K visualizations and 108K illustrations. We analyze distinctive design patterns of historical Chinese visualizations and their potential causes within the context of Chinese history and culture. We illustrate potential usage scenarios for this dataset, summarize the unique challenges and solutions associated with collecting historical Chinese visualizations, and outline future research directions. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： data labeling dataset digital humanities historical visualization

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DarkTrans: A Blockchain-based Covert Communication Scheme with High Channel Capacity and Strong Concealment

DarkTrans: A Blockchain-based Covert Communication Scheme wi...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuwei Xu Zehui Wu Jie Cao Jingdong Xu Guang Cheng School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China College of Computer Science Nankai University Tianjin China

Covert communication technology serves as a crucial tool for safeguarding not only the content of communication but also the identities of the parties involved. In this regard, blockchain emerges as a promising solution due to its decentralized nature, flood propagation of data, and inherent anonymity features. This makes blockchain an ideal candidate for covert communication channels, effectively addressing the weaknesses associated with traditional covert communication methods susceptible to detection, tracing, and interruption. However, the current efforts encounter obstacles like limited practicality, constrained channel capacity, and insufficient concealment capabilities, impeding their broad adoption in real-world scenarios. To address these issues, we propose DarkTrans, a blockchain-based covert communication scheme consisting of an address binary tree and a novel embedding mechanism. The address binary tree as a dynamic label method enables rapid recognition of specific transactions by the recipient, rendering detection by third parties challenging. The embedding mechanism encodes secret messages into transaction values for transmission to augment channel capacity, which can be practically realized within an Ethereum private blockchain. Our experiments with three aspects demonstrate that, compared with the existing scheme, DarkTrans achieves a low embedding time and a high channel capacity. Additionally, Kolmogorov-Smirnov test and sample entropy analysis are conducted to validate the robust concealment of this scheme.

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Brief Industry Paper: Towards Efficient Task Scheduling for AUTOSAR using Parallel Pruning

Brief Industry Paper: Towards Efficient Task Scheduling for ...

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Real-Time Systems Symposium (RTSS)

作者： Yanxing Yang Nan Zhang Dengke Yan Xian Wei Junlong Zhou Hong Liu Mingsong Chen MoE Engineering Research Center of SW/HW Co-Design Technology and Application East China Normal University School of Computer Science and Engineering Nanjing University of Science and Technology Shanghai Uni-Sentry Intelligent Technology Co. Ltd. China

As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design automation tools, it is difficult to quickly figure out an optimal task scheduling scheme for an AUTOSAR-based system. To address this problem, we introduce a novel task scheduling method that can parallelly search for an optimal solution with the help of our proposed pruning strategy. Experimental results on a real-world AUTOSAR-based autonomous driving system demonstrate that our approach can achieve much better task scheduling solutions than the ones obtained manually and significantly reduce the overall task scheduling time.

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Population-Based Evolutionary Gaming for Unsupervised Person Re-identification

arXiv

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arXiv 2023年

作者： Zhai, Yunpeng Peng, Peixi Jia, Mengxi Li, Shiyong Chen, Weiqiang Gao, Xuesong Tian, Yonghong National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School China Peng Cheng Laboratory China AI Application Research Center Huawei Technologies Co. Ltd China State Key Laboratory of Digital Multimedia Technology Hisense Qingdao China

Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. However, limited by the lack of diversity of discriminant information, a single network has difficulty learning sufficient discrimination ability by itself under unsupervised conditions. To address this limit, we develop a population-based evolutionary gaming (PEG) framework in which a population of diverse neural networks are trained concurrently through selection, reproduction, mutation, and population mutual learning iteratively. Specifically, the selection of networks to preserve is modeled as a cooperative game and solved by the best-response dynamics, then the reproduction and mutation are implemented by cloning and fluctuating hyper-parameters of networks to learn more diversity, and population mutual learning improves the discrimination of networks by knowledge distillation from each other within the population. In addition, we propose a cross-reference scatter (CRS) to approximately evaluate re-ID models without labeled samples and adopt it as the criterion of network selection in PEG. CRS measures a model's performance by indirectly estimating the accuracy of its predicted pseudo-labels according to the cohesion and separation of the feature space. Extensive experiments demonstrate that (1) CRS approximately measures the performance of models without labeled samples;(2) and PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning. © 2023, CC BY.

关键词： Distillation

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S-chain: A Shard-based Blockchain Scheme for Cross-chain Supervision

S-chain: A Shard-based Blockchain Scheme for Cross-chain Sup...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuwei Xu Yuxing Song Junyu Zeng Ran He Jingdong Xu School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China College of Computer Science Nankai University Tianjin China

With the popularization of consortium blockchain, how to supervise numerous business chains has become a problem. Recently, some researchers have proposed the concept of ‘governing chain by chain’, which means that multiple organizations deploy a consortium blockchain to supervise many business chains in the industry. However, as the number of business chains increases, a performance bottleneck occurs on the supervision chain. Sharding is a promising direction to improve scalability, but existing studies focus on public chains. If transplanted to the supervision chain, these solutions will bring two challenges. Firstly, data from multiple business chains will cause storage conflicts and unbalanced sharding. Secondly, sharding makes it difficult for inter-shard transaction validation. Aiming at the challenges, we propose $\mathcal{S}$-chain, a shard-based supervision chain scheme. The contribution of our work lies in three points. Firstly, we design a unified label-based data sharding method that can evenly map data from different business chains to all shards without conflict. Secondly, we propose a mechanism to validate general smart-contract-based inter-shard transactions. At first, we design a smart contract splitting algorithm that splits one inter-shard smart contract into multiple sub-contracts and depicts their dependencies via a Directed Acyclic Graph(DAG). Then, we design an inter-shard transaction validation method by analyzing the dependencies of all sub-contracts. Finally, we implement a prototype system of $\mathcal{S}$-chain and test its performance. The results show that our $\mathcal{S}$-chain outperforms the scheme without sharding on throughput and storage overhead.

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