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41st International Conference on Machine Learning, ICML 2024

作者： Tian, Hongduan Liu, Feng Liu, Tongliang Du, Bo Cheung, Yiu-Ming Han, Bo TMLR Group Hong Kong Baptist University Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong TMLR Group University of Melbourne Australia Sydney AI Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan Univeristy China

In cross-domain few-shot classification, nearest centroid classifier (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, maximizing optimized kernel dependence (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in Hilbert-Schmidt independence criterion (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: https://***/tmlr-group/MOKD. Copyright 2024 by the author(s)

关键词： Labeled data

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An efficient network operation automation scheme using network status information for local 5G networks 14

An efficient network operation automation scheme using netwo...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Yoon, Mahnsuk Seo, Jihun Cho, Kyucheol Future Mobile Communication Research Center Gumi Korea Republic of Korea Transport Institute Center for Connected & Automated Driving Research Department of the Fourth Industrial Revolution & Transport Sejong Korea Republic of Kyungpook National University School of Computer Secience and Engineering Software Education Center Daegu Korea Republic of

ISBN: (纸本)9798350313277

5G SA communication technology is being recognized as a crucial technology shaping the future of wireless communication. Compared to previous generations of networks, 5G offers ultra-high-speed, low latency, and massive connectivity, presenting both enterprises and individual users with opportunities for new business models and an enhanced quality of life. One of the most significant changes between the conventional mobile communication environment and 5G is the activation of various application cases in B2B services based on the local 5G network. The local 5G network, tailored to specific sites, requires effective management of high-volume data generated in dense areas and necessitates minimizing manual intervention by administrators. The ZSM (Zero-touch Service Management) technology is gaining attention to meet these requirements. ZSM focuses on automated operations for managing large systems like commercial mobile communication networks. It is anticipated that ZSM will also be applied to the local 5G network for efficient management and operation of tailored services. This paper proposes a network management technique that minimizes administrator intervention in the local 5G network environment. The proposed network management technique collects statistical logs from local 5G network base stations and core systems, supports real-time network status checks as well as traffic predictions using machine learning. Additionally, it offers automated operation techniques for the local 5G network by incorporating functions such as disconnecting network access for devices that are factors of malfunction within the specialized network. © 2023 IEEE.

关键词： Automation of operations Local 5G Networks Network Management Zero-touch Service Management

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Primary User-Awareness-Based Energy-Efficient Duty-Cycle Scheme in Cognitive Radio Networks

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computers, Materials & Continua 2022年第3期70卷 5991-6005页

作者： Zilong Jin Chengbo Zhang Kan Yao Dun Cao Seokhoon Kim Yuanfeng Jin School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)Nanjing University of Information Science and Technology Nanjing210044China Jiangsu Institute of Economic and Information Technology Nanjing210003China School of Computer and Communications Engineering Changsha University of Science and TechnologyChangsha410114China Department of Computer Software Engineering Soonchunhyang UniversityAsan31538Rep.of Korea Department of Mathematics Yanbian UniversityYanji133002China

Cognitive radio devices can utilize the licensed channels in an opportunistic manner to solve the spectrum scarcity issue occurring in the unlicensed ***,these cognitive radio devices(secondary users)are greatly affected by the original users(primary users)of licensed *** users have to adjust operation parameters frequently to adapt to the dynamic network environment,which causes extra energy *** consumption can be reduced by predicting the future activity of primary ***,the traditional prediction-based algorithms require large historical data to achieve a satisfying precision accuracy which will consume a lot of time and memory ***,many of these schemes lack methods to deal with the very busy network *** this paper,one semi-supervised learning algorithm,i.e.,tri-training,has been employed to investigate the prediction of primary *** on the prediction results of tri-training,a duty-cycle mechanism and an intermediate node selection approach are proposed to improve the energy *** results show the effectiveness of the proposed algorithm.

关键词： Cognitive radio tri-training duty-cycle intermediate node energy efficiency

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White Box Testing Module for Evaluation of Transformers-Based DNNs

White Box Testing Module for Evaluation of Transformers-Base...

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Innovations in Intelligent Systems and Applications Conference

作者： Gokhan Cetiner Cem Baglum Ugur Yayan Ahmet Yazici Computer Engineering Department Center of Intelligent Systems Applications Research (CISAR) Eskisehir Osmangazi University Eskisehir Turkiye Software Engineering Department Center of Intelligent Systems Applications Research (CISAR) Eskisehir Osmangazi University Eskisehir Turkiye

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Artificial intelligence is actively becoming widespread in every field. The complex structure of deep neural network models, especially those used in real-world applications, may contain faults that lead to unexpected model behavior, even well-trained AI models. To detect these types of faults and model behaviors, it is necessary to perform a white box test of the model's reliability and adequacy by mutating its layers, activation functions, and similar model structures and parameters. White box testing is a method that tests the logical flow of software to detect possible faults and deficiencies in the code. The proposed module performs model-based testing using this testing technique. The module was developed as an extension of the traditional testing tool IM-FIT. It was inspired by IM-FIT's advanced fault detection capacity in traditional software testing, autonomous or customized testing options, and detailed report generation capabilities. The module carries out an advanced scanning and mutation process using its own unique fault library for deep neural networks. The module successfully performed the test by autonomously generating mutants and classifying these mutants. Tests were performed on a Transformer model using the TensorFlow library during the testing process. As a result of the tests performed with 5, 10, and 20 epoch values, a total of 606 mutants were produced. While 414 of these mutants were classified as killed, 192 were classified as survived. This study presents it to the literature as an effective mutation testing tool focusing on deep neural networks.

关键词： software testing Technological innovation Fault detection Focusing Artificial neural networks Transformers Libraries software reliability Intelligent systems Glass box

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AgriLLM:Harnessing Transformers for Framer Queries 3

AgriLLM:Harnessing Transformers for Framer Queries

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3rd Workshop on NLP for Positive Impact, NLP4PI 2024, held in conjunction with the 2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal India Department of Data Science & Computer Applications Manipal Academy of Higher Education Manipal India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

ISBN: (纸本)9798891761759

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. © 2024 Association for Computational Linguistics.

关键词： Distribution transformers

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method 30

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Yuan, Yankun Wang, Lin Xiao, Chonghui Liu, Zijuan Dang, Fan Wang, Xu Zhao, Haitian Miao, Xin Yanshan University School of Information Science and Engineering China National Computer Network Emergency Response Technical Team/Coordination Center of China China Tsinghua University Department of Automation and BNRist China Tsinghua University Global Innovation Exchange China Tsinghua University School of Software and BNRist China

ISBN: (纸本)9798331515966

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications. © 2024 IEEE.

关键词： Adaptive algorithm Anomaly detection Energy conservation IoT LSTM Models QoE

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Retyping of triple-negative breast cancer based on clustering method

Retyping of triple-negative breast cancer based on clusterin...

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作者： Liu, Bo Li, Xingrui Wang, Huina Zhao, Shuangtao Li, Jianqiang Qu, Guangzhi Wang, Fei School of Software Engineering Beijing University of Technology Beijing China Department of Genomic Medicine The University of Texas MD Anderson Cancer Center HoustonTX United States Computer Science and Engineering Department Oakland University RochesterMI United States Healthcare Policy and Research Weill Cornell Medical College New YorkNY United States

Triple-negative breast cancer is the worst prognosis in breast cancer, accounting for 10.0–20.8% of all breast cancers. Considering that triple-negative breast cancer has great heterogeneity and very poor prognosis, clinical medication guidance is in urgent need of a more detailed classification of breast cancer itself. Although many researchers have been dedicated to the clustering of triple-negative breast cancer and have found possible targets based on typing, their results are not closely related to the prognosis. This paper utilizes three clustering methods to retype the patient data with triple-negative breast cancer, and the results show that the triple-negative breast cancer data could be classified into two categories. Eight important genes and three important clinical factors related to the prognosis of two types of triple-negative breast cancer have been obtained. These genes have the following three characteristics: co-expression, differential expression and interaction. In terms of breast cancer control, the prognosis can be controlled as much as possible by regulating gene levels, which provides new directions and ideas for related research on breast cancer prognosis. © 2020 John Wiley & Sons, Ltd.

关键词： Cluster analysis

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Text Classification Algorithm for Medical Adverse Events Based on Deep Learning 1

Text Classification Algorithm for Medical Adverse Events Bas...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Yan, Yandong Wang, Hao Zhang, Jiayu Song, Xinyu Lou, Yiwei Dai, Tengyu Wang, Lanhui Lv, Ming School of Software & Microelectronic Peking University Beijing China Chinese Pla General Hospital Health Department Beijing China School of Computer Science Peking University Beijing China Medical Supplies Center of Pla General Hospital Department of Medical Engineering Beijing China

ISBN: (纸本)9798350358780

The management of medical adverse events is an important topic in the field of healthcare. The classification of medical adverse events is the primary link in adverse event management. However, the research on medical adverse event classification technology is still in its early stages, mainly using rules or machine learning methods, and the prediction accuracy is not high enough. To address the above issues, this paper explores the application of deep learning in medical adverse event text classification tasks and proposes a hybrid model that combines deep pyramid convolution and cyclic convolution network structures to achieve category classification of adverse events. This model has two feature extraction modules: deep pyramid convolution and cyclic convolution. These two modules extract features in parallel using BERT word vectors as inputs, and then concatenate the features. The experiment shows that the model has good performance in adverse event classification. In order to further improve the effectiveness of adverse event text classification, this paper proposes two improved models, BRDCN and BRDHW, based on the hybrid model. The BRDCN model abstracts the concatenated features by adding a convolutional network after the feature extraction module to further extract features from the concatenated tensor. The experiment shows that the multi classification performance of adverse event cate-gories in the BRDCN model has improved compared to before improvement. The BRDHW model refers to the gate control structure of the highway network and sets a gate control matrix. This matrix is used to fuse the features extracted by the two modules. The experiment shows that the BRDHW model's multi classification performance of adverse event categories has been further improved. © 2023 IEEE.

关键词： Convolution

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A Case Study of Anomaly Detection in Tinplate Lids: Supervised vs Unsupervised Approaches

A Case Study of Anomaly Detection in Tinplate Lids: Supervis...

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International Conference on Autonomous Robots and Agents, ICARA

作者： Henry O. Velesaca Angel D. Sappa Juan A. Holgado-Terriza ESPOL Polytechnic University FIEC - CIDIS Guayaquil Ecuador Software Engineering Department University of Granada Granada Spain Computer Vision Center Barcelona Spain

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This research presents a comparative study of deep learning-based anomaly detection methods, both supervised and unsupervised, applied to industrial systems for detecting product defects in manufacturing. The study implements the OPC-UA protocol for data communication and algorithm execution using finite state machines, demonstrating its practical application in a tinplate lid system. Integration with OPC-UA ensures realtime data access, interoperability, and scalability across various industrial environments. The experimental results, evaluated using metrics such as Average Precision, Mean AUROC, Mean Pixel AUROC, and Execution Time (CPU and GPU), reveal the strengths and limitations of each approach, providing valuable insights for addressing modern challenges in industrial anomaly detection.

关键词： Deep learning YOLO Visualization Protocols Accuracy Service robots Scalability Automata Security Anomaly detection

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MIMO-Terahertz in 6G Nano-Communications:Channel Modeling and Analysis

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computers, Materials & Continua 2021年第1期66卷 263-274页

作者： Shahid Bashir Mohammed H.Alsharif Imran Khan Mahmoud A.Albreem Aduwati Sali Borhanuddin Mohd Ali Wonjong Noh Business Studies Department Namal InstituteMianwali42250Pakistan Department of Electrical Engineering College of Electronics and Information EngineeringSejong UniversitySeoul05006South Korea Department of Electrical Engineering University of Engineering and Technology PeshawarPeshawar814Pakistan Department of Electronics and Communications Engineering College of EngineeringA’Sharqiyah UniversityIbraOman Wireless and Photonics Networks Research Centre of Excellence Department of Computer and Communication Systems EngineeringUnversiti Putra MalaysiaSelangor43400Malaysia School of Software Hallym UniversityHallymdaehak-gilChunchoen-si24252South Korea

With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by *** wireless mobile communication frequency tends to be saturated,which demands for new ***(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information *** communication has great application prospects in military-civilian integration and coordinated *** China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the ***,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond *** paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel ***,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the *** results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.

关键词： Wireless communication 6G mobile communication terahertz communication MIMO channel modeling

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