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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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LISTENING TO THE AFFECTED: SECURITY IN A HOME AREA NETWORK 67

LISTENING TO THE AFFECTED: SECURITY IN A HOME AREA NETWORK

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67th Annual Meeting of the International Society for the Systems sciences, ISSS 2023

作者： Nshimba, K.T. Goede, Roelien Computer Systems Vaal University of Technology South Africa Unit of Data Science and Computing North-West University South Africa

ISBN: (纸本)9781713893455

With the advent of smart appliances and objects, a smart home has never been this complex. Today's homes are equipped with all kinds of smart objects capable of internet connectivity. Just a few years ago, a Home Area Network (HAN) only had personal computers and mobile devices making up the network. Today the picture is different, fridges, televisions, coffee machines, air conditioners, thermostats, and gate motors, just to name a few, are all capable of communicating with each other and on the Internet. This interconnectivity of various systems introduces a level of complexity to a home area network. For instance, these smart appliances and objects might be using different communication protocols, since each manufacturer may implement security differently. With the lack of standardization when it comes to the Internet of Things (IoT), this complexity opens loopholes in the security of the smart home. By looking at a smart home as a complex system made up of sub-systems that may impact the security of the whole network, Systems Thinking will be a suitable approach to address this problem. Systems thinking was developed to address the complexity created by the interdependency of various systems, both existing and new ones. In this paper, we propose addressing the data privacy issues of smart homes by looking at the problem from a systems thinking perspective. With this perspective in mind, we can address the security problems in a smart home by looking at it as a complex system. We relate the smart home as a system to Churchman's definition of a system as a set of parts coordinated to accomplish a set of goals. In this research, Critical Systems Thinking has been adopted as the preferred methodology for this research. © Copyright, Author(s) (Creative Commons), 2023.

关键词： Internet of things

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Application of meta-learning in cyberspace security:a survey

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Digital Communications and Networks 2023年第1期9卷 67-78页

作者： Aimin Yang Chaomeng Lu Jie Li Xiangdong Huang Tianhao Ji Xichang Li Yichao Sheng Hebei Key Laboratory of Data Science and Application North China University of Science and TechnologyTangshan063000China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and TechnologyTangshan063000China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and TechnologyTangshan063000China

In recent years,machine learning has made great progress in intrusion detection,network protection,anomaly detection,and other issues in ***,these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown *** them,“one-shot learning”,“few-shot learning”,and“zero-shot learning”are challenges that cannot be ignored for traditional machine *** more intractable problem in cyberspace security is the changeable attack *** a new attack mode appears,there are few or even zero samples that can be ***-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when *** purpose is to quickly obtain a model with high accuracy and strong generalization through less data *** article first divides the meta-learning model into five research directions based on different principles of *** are model-based,metric-based,optimization-based,online-learning-based,or stacked ***,the current problems in the field of cyberspace security are categorized into three branches:cyber security,information security,and artificial intelligence security according to different ***,the application research results of various meta-learning models on these three branches are *** the same time,based on the characteristics of strong generalization,evolution,and scalability of meta-learning,we contrast and summarize its advantages in solving ***,the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.

关键词： Meta-learning Cyberspace security Machine learning Few-shot learning

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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Artificial Intelligence Based Diagnosis of Heart Disease Using ECG and Deep Neural Networks

Artificial Intelligence Based Diagnosis of Heart Disease Usi...

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2023 IEEE Conference on Artificial Intelligence, CAI 2023

作者： Gogi, Giovanah Gurung, Santosh Gegov, Alexander Kaymak, Uzay Arabikhan, Farzad University Of Portsmouth School of Computing Portsmouth United Kingdom Eindhoven University of Technology Jheronimus Academy of Data Science Eindhoven Netherlands

ISBN: (纸本)9798350339840

Health is the most important part of life. Unfortunately, people get sick from diseases from time to time which can further lead to deaths. The most common cause of death these days is cardiovascular diseases (CVDs). CVD is a term used for all types of diseases that affect the heart, the blood vessels, and circulatory diseases. This paper focuses on a supervised machine learning technique namely Deep Learning (DL) in diagnosing heart disease efficiently and effectively. © 2023 IEEE.

关键词： Electrocardiograms

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Oil Spill Detection Using 2D Convolution Neural Network and Generative Adversarial Network 4

Oil Spill Detection Using 2D Convolution Neural Network and ...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Madhura, R. Nikkath Bushra, S. Syed Ismail, A. Haleema, P.K. SRM Institute of Science and Technology Department of Computing Technologies KattankulathurTamil Nadu Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Tamil Nadu Chennai India School of Computing and Engineering University of West London RAK Branch Campus United Arab Emirates

ISBN: (纸本)9798350385793

Finding oil spills in the ocean is one of the most crucial tasks in preserving our ecosystem. Using satellite or aerial photographs as input to a deep learning model that makes use of 2D CNN (Convolution Neural Network) and GAN (Generative Adversarial Network). Deep learning assists in automatically detecting oil spills in the ocean. A strong model that may be applied to volumetric data is 2D CNN. To protect the environment and its biodiversity, it is critical to identify and minimize oil spills in delicate maritime environments. Conventional techniques for tracking oil spills in satellite imagery sometimes depend on labor-intensive, human error-prone manual inspection. When deep learning models are taught with a lot of data, they perform well. The Gathering of datasets, however the situation proved to be challenging. As a result, employing Generative Adversarial Networks (GAN) to produce new photos by utilizing preexisting ones. As a result, by giving the model a suitable number of photos to use for training and testing, it improves accuracy. © 2024 IEEE.

关键词： Satellite imagery

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Generative Multimodal data Augmentation for Low-Resource Multimodal Named Entity Recognition 24

Generative Multimodal Data Augmentation for Low-Resource Mul...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ziyan Yu, Jianfei Yang, Jia Wang, Wenya Yang, Li Xia, Rui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computing and Data Science Nanyang Technological University Singapore Wee Kim Wee School of Communication and Information Nanyang Technological University Singapore

ISBN: (纸本)9798400706868

As an important task in multimodal information extraction, Multimodal Named Entity Recognition (MNER) has recently attracted considerable attention. One key challenge of MNER lies in the lack of sufficient fine-grained annotated data, especially in low-resource scenarios. Although data augmentation is a widely used technique to tackle the above issue, it is challenging to simultaneously generate synthetic text-image pairs and their corresponding high-quality entity annotations. In this work, we propose a novel Generative Multimodal data Augmentation (GMDA) framework for MNER, which contains two stages: Multimodal Text Generation and Multimodal Image Generation. Specifically, we first transform each annotated sentence into a linearized labeled sequence, and then train a Label-aware Multimodal Large Language Model (LMLLM) to generate the labeled sequence based on a label-aware prompt and its associated image. We further employ a Stable Diffusion model to generate the synthetic images that are semantically related to these sentences. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed GMDA framework, which consistently boosts the performance of several competitive methods for two subtasks of MNER in both full-supervision and low-resource settings. The low-resource dataset and source code are released at https://***/NUSTM/GMDA. © 2024 ACM.

关键词： data augmentation

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Market Size Estimation Model at the Product Level based on Text Mining Approach

Market Size Estimation Model at the Product Level based on T...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Jung, Ye Lim Yoo, Hyoung Sun Kim, Ji Hui Seoul Korea Republic of University of Science and Technology Data and High Performance Computing Science Daejeon Korea Republic of University of Science and Technology Science and Technology Management Policy Daejeon Korea Republic of

ISBN: (纸本)9798350324457

Information on market size is fundamental for marketing and business decision-making. However, market size information is mostly available at the level of industry or broad categories, which include a large number of product groups. Since many companies require market size information at the segmented product level, especially for their own products, a methodology to estimate market size by specified product group (i.e., item) is required. This study proposes a market size estimation model based on the measurement of string similarity between product names. The market size of the target items is estimated by calculating the sales of individual products matched to the target item according to the measured string similarity. We tested the model by estimating the market size of 1,417 items from 165,056 individual enterprises' product sales data. Eight types of string similarity measuring algorithms were applied, and among them, Cosine, Jaccard, and Jaro-Winkler algorithms showed the highest accuracy in matching the individual product names to the target item names. The market sizes estimated from the model showed a high correlation (Pearson correlation coefficient of 0.93) with those officially surveyed. Our approach can be utilized for intelligent market information systems to dynamically estimate market sizes requested by information users for diverse target products without pre-labeling or predetermined taxonomy on the products. © 2023 IEEE.

关键词： market analysis market size named entity matching string similarity text mining

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Interest Points Analysis for Internet Forum Based on Long-Short Windows Similarity

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Computers, Materials & Continua 2022年第8期72卷 3247-3267页

作者： Xinghai Ju Jicang Lu Xiangyang Luo Gang Zhou Shiyu Wang Shunhang Li Yang Yang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China School of Computing and Information Systems Singapore Management University188065Singapore College of Computer Science and Big Data Fuzhou UniversityFuzhou350116China

For Internet forum Points of Interest(PoI),existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation,which lead to blindness in method *** address this problem,this paper proposed a PoI variation prediction framework based on similarity analysis between long and short *** on the framework,this paper presented 5 PoI analysis algorithms which can be categorized into 2 types,i.e.,the traditional sequence analysis methods such as autoregressive integrated moving average model(ARIMA),support vector regressor(SVR),and the deep learning methods such as convolutional neural network(CNN),long-short term memory network(LSTM),Transformer(TRM).Specifically,this paper firstly divides observed data into long and short windows,and extracts key words as PoI of each ***,the PoI similarities between long and short windows are calculated for training and ***,series of experiments is conducted based on real Internet forum *** results show that,all the 5 algorithms could predict PoI variations well,which indicate effectiveness of the proposed *** the length of long window is small,traditional methods perform better,and SVR is the *** the contrary,the deep learning methods show superiority,and LSTM performs *** results could provide beneficial references for PoI variation analysis and prediction algorithms selection under different parameter configurations.

关键词： Point of interest(PoI)analysis long and short windows sequential analysis deep learning

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Resolution-Adaptive Representation Learning via Eliminating Abnormal Features for Cross-Resolution Person Re-Identification 14

Resolution-Adaptive Representation Learning via Eliminating ...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Lu, Xing Lai, Fengshan Cao, Zhixiang Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350356670

Cross-resolution person re-identification(CR-ReID) seeks to overcome the challenge of retrieving and matching specific person images across cameras with varying resolutions. Numerous existing studies utilize established CNNs and ViTs models to resize captured low-resolution (LR) images and align them with high-resolution (HR) image features. However, these methods overlook the potential feature connections between LR and HR images. Consequently, CNNs or ViTs tend to highlight outliers rather than the intended characteristics in LR image attention maps. In this work, we propose the abnormal feature elimination and reconfiguration transformer (ART) network, a novel network architecture, for robust cross-resolution person re-identification tasks. Our proposed method uses a resolution feature discriminator to learn resolution invariant features and outputs feature matrices of images with different resolutions. We utilize output feature matrices to model LR and HR image interactions through cross-resolution landmark agent attention. We aim to mitigate abnormal image features and prioritize the attention on the target person by learning representations from input images of various resolutions. Extensive evaluations on three real-world datasets confirm the excellent results of our approach. © 2024 IEEE.

关键词： Contrastive Learning

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