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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Tao Xiao Zhenyong Zhang Kuan Shao Hao Li State Key Laboratory of Public Big Data School of Computer Science and Engineering Guizhou University Guiyang China

ISBN: (数字)9798331507886

ISBN: (纸本)9798331507893

As convolutional neural networks (CNNs) have shown excellent performance in various inference tasks, it has become increasingly critical to enable Artificial Intelligence of Things ( $A$ IoT) systems to run CNN-based applications. However, using CNN-based applications in many AIoT scenarios raises privacy concerns. Protecting the inference phase can preserve the user's privacy, which is an urgent issue according to worldwide rules and policies. In this paper, we propose SeP 2 CNN, a simple and efficient fully homomorphic encryption (FHE) method to realize the privacy-preserving CNN inference in pervasive AIoT applications. By carefully combining cryptography and deep learning, we compute ReLU with a composite polynomial as the activation function over ciphertexts. To improve the efficiency, we transform the three-dimensional convolution into a two-dimensional convolution to simplify the calculation. Finally, through extensive experiments on multiple datasets, we validate and evaluate the performance of SeP 2 CNN.

关键词： Adaptation models Privacy Convolution Transforms Polynomials Internet of Things Convolutional neural networks Cryptography Artificial intelligence Homomorphic encryption

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A Survey of COVID-19 Diagnosis Based on Deep Learning from X-ray Images and CT-Scan Images 29th

A Survey of COVID-19 Diagnosis Based on Deep Learning from X...

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Wang, Jiaji Chen, Shuwen Zhu, Huisheng School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (纸本)9783031449468

The Coronavirus pandemic has been going on for three years. Undoubtedly, it caused huge damage to people, physically and mentally. The earlier the doctor diagnoses, the sooner the patient will recover In this paper, we review the more advanced applications of deep learning in COVID-19 diagnosis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Marginal Value-Based Edge Resource Pricing and Allocation for Deadline-Sensitive Tasks 42

Marginal Value-Based Edge Resource Pricing and Allocation fo...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wang, Puwei Sun, Zhouxing Zhan, Ying Li, Haoran Du, Xiaoyong Renmin University of China School of Information China Renmin University of China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing100872 China Guizhou University of Finance and Economics Guiyang City550025 China

ISBN: (纸本)9798350334142

In edge computing (EC), resource allocation is to allocate computing, storage and networking resources on the edge nodes (ENs) efficiently and reasonably to tasks generated by users. Due to the resource-limitation of ENs, the tasks often need to compete for the resources. Pricing mechanisms are widely used to deal with the resource allocation problem, and the valuations of tasks play a critical role in the price mechanisms. However, users naturally are not willing to expose the valuations of their tasks due to conflicts of interests. Current research works usually adopt truthful auctions to motivate the users to report honestly the valuations of their tasks. In this paper, we introduce the marginal value to estimate the valuations of tasks, and propose a marginal value-based pricing mechanism using the incentive theory, which motivates the tasks with higher marginal values to actively request more resources. The EC platform sets the resource prices using the price mechanism, and then the users determine their resource requests relying on the resource prices and the valuations of their tasks. After receiving the deadline-sensitive tasks from the users, the resource allocation can be modeled as a knapsack problem with the deadline constraints. Extensive experimental results demonstrate that our approach is computationally efficient and is promising in enhancing the utility of the EC platform and the tasks. © 2023 IEEE.

关键词： Resource allocation

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Hierarchical Scalable data Lake Design for Game Engine 8

Hierarchical Scalable Data Lake Design for Game Engine

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8th International Conference on big data and information Analytics, bigDIA 2022

作者： Yang, Mingbao Zhou, Peng Li, Shaobo Pu, Ruiqiang College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang China School of Mechanical Engineering Guizhou University Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

With the rapid development of information technology, people are more and more inseparable from smart devices, and people and devices produce a large amount of data all the time. How to store and analyze massive data has become a research hotspot of the times. However, researchers mainly study the high reliability, high availability, high fault tolerance, high throughput, and high concurrent processing of big data systems, but there are few studies on designing hierarchical and scalable data lakes. In this study, a layered and extensible design of the data lake is carried out, which reduces the complexity of implementing business indicators during data analysis, and at the same time improves code reuse, scalability, development efficiency and code security. In addition, in order to avoid the problem of data accumulation or even job execution failure due to the skew of ip dimension table data, this paper designs the ip dimension table rowkey. This paper designs a set of real-time and offline big data analysis data lake architecture based on game engine and applies it to the actual production environment. © 2022 IEEE.

关键词： big data

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Optimal Scale Selection in Multi-Scale Interval-Set Decision Tables

Optimal Scale Selection in Multi-Scale Interval-Set Decision...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Xie, Zhen-Huang Wu, Wei-Zhi Wang, Lei-Xi School of Information Engineering Zhejiang Ocean University Zhoushan Zhejiang316022 China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhoushan Zhejiang316022 China

ISBN: (纸本)9798350303780

In traditional interval-set information systems (ISISs), each attribute is single-scale. However, processing and analyzing data at different scales is often necessary for practical applications. This paper introduces the granular information transformation into multi-scale interval-set information systems (MISISs), defines the similarity relation between objects in MISISs, and defines the rough approximation and correlation properties at different scales studied. Then, we propose the multi-scale interval-set decision table (MISDT) concept and discuss the monotonicity of scale variation of the positive region, generalized decision and positive complementarity conditional entropy. Then, we give three criteria for determining the optimal scales and discuss the relationship among the optimal scales of MISDTs. Finally, give heuristic algorithms and examples. © 2023 IEEE.

关键词： Decision tables

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A Part-of-Speech Tagging Model Employing Word Clustering and Syntactic Parsing

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Chinese Journal of Electronics 2025年第1期23卷 109-114页

作者： Lichi Yuan School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model, by employing word clustering and syntactic parsing model. Firstly, In order to overcome the defects of the classical HMM, Markov family model (MFM), a new statistical model was introduced. Secondly, to solve the problem of data sparseness, we propose a bottom-to-up hierarchical word clustering algorithm. Then we combine syntactic parsing with part-of-speech tagging. The Part-of-Speech tagging experiments show that the improved Part-Of-Speech tagging model has higher performance than Hidden Markov models (HMMs) under the same testing conditions, the precision is enhanced from 94.642% to 97.235%.

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A method for correcting characteristic X-ray net peak count from drifted shadow peak

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Nuclear Science and Techniques 2023年第11期34卷 155-167页

作者： Lin Tang Xing‑Ke Ma Kai‑Bo Shi Yeng‑Chai Soh Hong‑Tao Shen College of Electronic Information and Electrical Engineering Chengdu UniversityChengdu 610106China Guangxi Key Laboratory of Nuclear Physics and Nuclear Technology Guangxi Normal UniversityGuilin 541004China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei 230039China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore College of Nuclear Technology and Automation Engineering Chengdu University of TechnologyChengdu 610059China

To correct spectral peak drift and obtain more reliable net counts,this study proposes a long short-term memory(LSTM)model fused with a convolutional neural network(CNN)to accurately estimate the relevant parameters of a nuclear pulse signal by learning of samples.A predefined mathematical model was used to train the CNN-LSTM model and generate a dataset composed of distorted pulse *** trained model was validated using simulated *** relative errors in the amplitude estimation of pulse sequences with different degrees of distortion were obtained using triangular shaping,CNN-LSTM,and LSTM *** a result,for severely distorted pulses,the relative error of the CNN-LSTM model in estimating the pulse parameters was reduced by 14.35%compared with that of the triangular shaping *** slightly distorted pulses,the relative error of the CNN-LSTM model was reduced by 0.33%compared with that of the triangular shaping *** model was then evaluated considering two performance indicators,the correction ratio and the efficiency ratio,which represent the proportion of the increase in peak area of the two characteristic peak regions of interest(ROIs)to the peak area of the corrected characteristic peak ROI and the proportion of the increase in peak area of the two characteristic peak ROIs to the peak areas of the two shadow peak ROI,*** measurement results of the iron ore samples indicate that approximately 86.27%of the decreased peak area of the shadow peak ROI was corrected to the characteristic peak ROI,and the proportion of the corrected peak area to the peak area of the characteristic peak ROI was approximately 1.72%.The proposed CNN-LSTM model can be applied to X-ray energy spectrum correction,which is of great significance for X-ray spectroscopy and elemental content analyses.

关键词： Peak correction Triangular shaping Deep learning Long short-term memory Convolutional neural network X-ray fluorescence spectroscopy Silicon drift detector

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Convolutional Neural Network with Attention Mechanism for Image-Based Smoke Detection 4

Convolutional Neural Network with Attention Mechanism for Im...

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4th International Conference on Advances in computer Technology, information Science and Communications, CTISC 2022

作者： Chen, Hanyang Ji, Qingge Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing School of Computer Science and Engineering Guangzhou510006 China

ISBN: (数字)9781665458726

ISBN: (纸本)9781665458726

Smoke detection based on computer vision plays a key role in fire prevention. With the development of deep learning, convolutional neural network frameworks are increasingly used in this field. However, due to the large span of smoke's scale and the variety of its color as well as its texture, the features are even more complex. In addition, the sampling of features by the existing neural networks is limited to the size of the convolution kernel. It is difficult to extract the global features. So, detecting smoke from pictures is still a problem. In this work, we proposed a feature-fusion method based on local and global attention mechanisms. It is able to better fuse the low-level and high-level features. In order to solve the scale problem, we proposed a mechanism for selection and offset of the receptive field. In addition, we combine the transformer based on self-attention mechanism and convolution operation to make the model aware of global information. Finally, we optimize the sampling of feature maps by the detection head of the one-stage network based on the smoke color features. Experiments show that the proposed model outperforms state-of-the-art relevant competitors by 4.6% on AP.5:.95. © 2022 IEEE.

关键词： Smoke

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Multilingual knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

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Dynamic Scaling of Unit Tests for Code Reward Modeling

arXiv

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arXiv 2025年

作者： Ma, Zeyao Zhang, Xiaokang Zhang, Jing Yu, Jifan Luo, Sijia Tang, Jie School of Information Renmin University of China China Tsinghua University China Key Laboratory of Data Engineering and Knowledge Engineering Beijing China

Current large language models (LLMs) often struggle to produce accurate solutions on the first attempt for code generation. Prior research tackles this challenge by generating multiple candidate solutions and validating them with LLM-generated unit tests. The execution results of unit tests serve as reward signals to identify correct solutions. As LLMs always confidently make mistakes, these unit tests are not reliable, thereby diminishing the quality of reward signals. Motivated by the observation that scaling the number of solutions improves LLM performance, we explore the impact of scaling unit tests to enhance reward signal quality. Our pioneer experiment reveals a positive correlation between the number of unit tests and reward signal quality, with greater benefits observed in more challenging problems. Based on these insights, we propose CodeRM-8B, a lightweight yet effective unit test generator that enables efficient and high-quality unit test scaling. Additionally, we implement a dynamic scaling mechanism that adapts the number of unit tests based on problem difficulty, further improving efficiency. Experimental results show that our approach significantly improves performance across various models on three benchmarks (e.g., with gains of 18.43% for Llama3-8B and 3.42% for GPT-4o-mini on HumanEval Plus). © 2025, CC BY-SA.

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