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Progress in Electromagnetic Research Symposium (PIERS)

作者： Jiangling Dou Chenyang Zhao Jian Song Qingwang Wang Tao Shen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Graduate School Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

As antenna structure grow in complexity, the problem of time-consuming in electromagnetic (EM) simulation have become more common. This problem undoubtedly brings a series of the challenges in raising the effectiveness of antenna optimization. In order to overcome the existing difficulties, an antenna optimization method based on RF-GWO method is proposed. This approach consists of three major components. Firstly, an artificial neural network (ANN) is used to build the dataset. Compared with the traditional sampling method, the data filtered by the artificial neural network (ANN) are closer to the optimization objective. Consequently, it can better responsive to the relationship between the antenna parameters and the optimization objective. Then, the Random Forest method (RF) is utilized to analyze the impact of the ten selected parameters based on the electromagnetic (EM) simulation results on the antenna performance. Random Forest Algorithm (RF) has strong interpretability, allowing for the assessment of the contribution of different parameters to the optimization objective, which can effectively reduce the number of design variables. Finally, the antenna optimization is performed utilizing the Grey Wolf Optimization (GWO) algorithm. Combined with the sensitivity analysis results, the Gray Wolf Algorithm (GWO) can effectively search for improved results in a faster manner. In order to evaluate the algorithm’s performance, the proposed method is employed to optimize a multiband miniaturized microstrip antenna. The result demonstrates that substantial improvement in impedance bandwidth is achieved by the proposed method, with an approximate 29% increase compared to conventional manual optimization methods. This outcome has proved potential and effectiveness of the proposed method in practical implications of antenna optimization.

关键词： Dimensionality reduction Sensitivity analysis Simulation Optimization methods Artificial neural networks Microstrip antennas Bandwidth

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MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation

MIANet: Aggregating Unbiased Instance and General Informatio...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yong Yang Qiong Chen Yuan Feng Tianlin Huang School of Computer Science and Engineering South China University of Technology Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application

Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge is obtained from a few samples in the support set. To address the problem, we propose a multi-information aggregation network (MIANet) that effectively leverages the general knowledge, i.e., semantic word embeddings, and instance information for accurate segmentation. Specifically, in MIANet, a general information module (GIM) is proposed to extract a general class prototype from word embeddings as a supplement to instance information. To this end, we design a triplet loss that treats the general class prototype as an anchor and samples positive-negative pairs from local features in the support set. The calculated triplet loss can transfer semantic similarities among language identities from a word embedding space to a visual representation space. To alleviate the model biasing towards the seen training classes and to obtain multi-scale information, we then introduce a non-parametric hierarchical prior module (HPM) to generate unbiased instance-level information via calculating the pixel-level similarity between the support and query image features. Finally, an information fusion module (IFM) combines the general and instance information to make predictions for the query image. Extensive experiments on PASCAL-5 i and COCO-20 i show that MIANet yields superior performance and set a new state-of-the-art. Code is available at ***/Aldrich2y/MIANet.

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A Multi-objective Antenna Optimization Method Based on Preferred Parameter Spaces

A Multi-objective Antenna Optimization Method Based on Prefe...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Jiangling Dou Siyu Lin Jian Song Qingwang Wang Tao Shen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Graduate School Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Machine learning has been successfully used in the field of multi-objective antenna design, enabling the enhancement of antenna performance and optimizing efficiency. However, the prediction within an exceptionally vast parameter space imposes significant demands on machine learning algorithms. Moreover, minor adjustments in dimensions can bring about significant alterations in antenna performance, which renders simultaneous optimization for all objectives exceedingly challenging. To solve the multi-objective antenna optimization problem, a multi-objective antenna optimization method based on a preferred parameter space is proposed. Specifically, this method utilizes the KNN algorithm to preliminarily optimize the antenna and acquire a high-quality dataset. Subsequently, the sensitivity of ten selected parameters based on the electromagnetic (EM) simulation results is calculated by the XGBoost algorithm. The value of the parameter sensitivity is acquired by averaging ten calculations. The top four ranked parameters are designated as variables while the rest are fixed as constants. This approach reduces the number of parameters for subsequent optimization, thus greatly improving the subsequent prediction accuracy while ensuring the antenna performance. Finally, the variables derived from XGBoost are input into the fruit-fly optimization algorithm (FOA) to find the near-optimal solution. Hence, the task of parameter optimization under the multi-objectives of the impedance bandwidth and circular polarization characteristics can be effectively accomplished. To further validate its performance, the proposed algorithm is applied to optimize the S-band miniaturized broadband circularly polarized antenna. Compared to the results obtained by electromagnetic simulation only, the optimized results exhibit a 17.95% improvement of impedance bandwidth and a 42.86% enhancement of axial ratio bandwidth. Numerical outcomes validate that the proposed method achieves multi-objective o

关键词： Sensitivity Optimization methods Bandwidth Prediction algorithms Approximation algorithms Broadband communication Broadband antennas

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A Novel Approach to Predicting the Risk of Illegal Activity and Evaluating Law Enforcement Using WideDeep SGRU Model 1

A Novel Approach to Predicting the Risk of Illegal Activity ...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Mishra, Prativa Anusha, P. Kumar, Arvind Pal, Subharun Patra, Jyoti Prasad Chauhan, Amit Computer Science and Application Reva University Karnataka Bangalore India R.M.K. Engineering College Department of ECE Tamilnadu Kavaraipettai India SCA Manav Rachna International Institute of Research and Studies Haryana Faridabad India Jagti India EE And EEE Krupajal Engineering College-KEC Odisha Puri India Department of Life sciences School of Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350371802

The main reaction to the illicit extraction of natural resources in protected areas around the world is law enforcement patrols headed by rangers. On the other hand, research on patrols' efficacy in reducing criminal behavior is lacking. Similarly, tactics to enhance the effectiveness of patrol organization and monitoring have received very little attention. Sequencing is crucial for model training, feature selection, and preprocessing. Preprocessing steps include cleaning, discretizing, duplicating, and normalizing data. Feature selection makes use of genetic algorithms, which are basically search algorithms with an evolutionary bent that factor in natural selection and genetics. Training stacked GRU models necessitates meticulous feature management. Even the most cutting-edge algorithms, GRU and BiGRU, are no match for the suggested technique. An astounding 97.24% accuracy grade was disclosed by the results, showcasing exceptional growth. © 2024 IEEE.

关键词： Genetic algorithms

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Design of P Band Filter Antenna with High Out-of-Band Suppression

Design of P Band Filter Antenna with High Out-of-Band Suppre...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Jiangling Dou Yinsu Yuan Jian Song Qingwang Wang Tao Shen School of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming China Graduate School Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

P-band antennas offer tremendous potential in various fields, including remote sensing, communication, and radar applications. However, It is crucial to enhance their effectiveness in these domains that the current challenges in the P-band, particularly wide bandwidth and exceptional out-of-band suppression be achieved. This paper aims to introduce an efficient design scheme for a high-efficiency, high out-of-band suppression P-band filter antenna. The antenna structure comprises a Y-shaped feeding network, upper and lower parallel dielectric plates, a vertical dielectric plate, a central metal plate, and a pair of microstrip patches. The bottom feeding network employs a Y-shaped feeding network. The coupling of the antenna was effectively controlled and the good bandwidth and impedance matching have been achieved. In order to achieve a wider bandwidth and lower return loss for the antenna, slotting technology was employed on the semi-circular radiation patch. The introduction of a vertical metal plate enhances filtering performance of antenna. Additionally, there are radiation zeros at 0.68 GHz and 1.43 GHz, high-frequency selectivity is imparted and signal reflection losses are effectively reduced. Key performance indicators include a filter antenna center frequency of 0.75 GHz, a −10 dB impedance bandwidth of 32.9% (0.89 GHz–1.24 GHz). In the upper stopband (0.5 GHz–0.8 GHz), the suppression level exceeds 20 dB, while in the lower stopband (1.1 GHz–1.8 GHz), suppression level exceeds 15 dB. Significant suppression characteristics are observed in the 0.5 GHz–0.8 GHz and 1.1 GHz–1.8 GHz frequency ranges. Design elements such as the semi-circular radiating patch, slotting technology, and rectangular slots on the substrate have been employed in the antenna. The suppression level, frequency selectivity had been enhance of antenna. Simulation results indicate a gain of 7.06 dB, overall efficiency beyond 95%, co-pol superior to cross-pol by more than 35 dB, and antenna

关键词： Slot antennas Filtering Simulation Bandwidth Microstrip antennas Directive antennas Dielectrics

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Leveraging AI to Unlock the Potential of Data science

Leveraging AI to Unlock the Potential of Data Science

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rakesh Kumar Yadav Gadug Sudhamsu Manish Kumar Goyal S. Yuvaraj Leena A. Deshpande Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance.

关键词： Industries Finance Organizations Medical services computer architecture Data science Artificial intelligence Information technology

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From a Social Cognitive Perspective: Context-aware Visual Social Relationship Recognition

arXiv

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arXiv 2024年

作者： Wu, Shiwei Zhang, Chao Chen, Joya Xu, Tong Wu, Likang Hu, Yao Chen, Enhong The Anhui Province Key Lab of Big Data Analysis and Application School of Data Science and School of Computer Science University of Science and Technology of China China The College of Design and Engineering National University of Singapore Singapore The Xiaohongshu.Inc. China

People's social relationships are often manifested through their surroundings, with certain objects or interactions acting as symbols for specific relationships, e.g., wedding rings, roses, hugs, or holding hands. This brings unique challenges to recognizing social relationships, requiring understanding and capturing the essence of these contexts from visual appearances. However, current methods of social relationship understanding rely on the basic classification paradigm of detected persons and objects, which fails to understand the comprehensive context and often overlooks decisive social factors, especially subtle visual cues. To highlight the social-aware context and intricate details, we propose a novel approach that recognizes Contextual Social Relationships (ConSoR) from a social cognitive perspective. Specifically, to incorporate social-aware semantics, we build a lightweight adapter upon the frozen CLIP to learn social concepts via our novel multi-modal side adapter tuning mechanism. Further, we construct social-aware descriptive language prompts (e.g., scene, activity, objects, emotions) with social relationships for each image, and then compel ConSoR to concentrate more intensively on the decisive visual social factors via visual-linguistic contrasting. Impressively, ConSoR outperforms previous methods with a 12.2% gain on the People-in-Social-Context (PISC) dataset and a 9.8% increase on the People-in-Photo-Album (PIPA) benchmark. Furthermore, we observe that ConSoR excels at finding critical visual evidence to reveal social relationships. © 2024, CC BY.

关键词： Modal analysis

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External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection

arXiv

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arXiv 2025年

作者： Cao, Biwei Wu, Qihang Cao, Jiuxin Liu, Bo Gui, Jie School of Cyber Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Ministry of Education of China Nanjing211189 China Purple Mountain Laboratories Nanjing210000 China School of Computer Science and Engineering Southeast University Nanjing211189 China Engineering Research Center of Blockchain Application Supervision And Management Southeast University Ministry of Education China

With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impacts on cyberspace. Currently, the information presentation formats have evolved gradually, with the news formats shifting from texts to multimodal contents. As a result, detecting multimodal fake news has become one of the research hotspots. However, multimodal fake news detection research field still faces two main challenges: the inability to fully and effectively utilize multimodal information for detection, and the low credibility or static nature of the introduced external information, which limits dynamic updates. To bridge the gaps, we propose ERIC-FND, an external reliable information-enhanced multimodal contrastive learning framework for fake news detection. ERIC-FND strengthens the representation of news contents by entity-enriched external information enhancement method. It also enriches the multimodal news information via multimodal semantic interaction method where the multimodal constrative learning is employed to make different modality representations learn from each other. Moreover, an adaptive fusion method is taken to integrate the news representations from different dimensions for the eventual classification. Experiments are done on two commonly used datasets in different languages, X (Twitter) and Weibo. Experiment results demonstrate that our proposed model ERIC-FND outperforms existing state-of-the-art fake news detection methods under the same settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Emerging Trends of Language Models and Their Significance in Digital Era

Emerging Trends of Language Models and Their Significance in...

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International Conference on Advances in Information Technology (ICAIT)

作者： Rishi Kumar Praveen Gujjar J Guru Prasad M S Raghavendra M Devadas Lubna Ambreen Rahul Kumar Pal Department of Computer Science and Engg. Graphic Era (Deemed to be University) Dehradun India Faculty of Management Studies JAIN (Deemed-to-be University) Bengaluru India Department of Computer Science and Engineering Gitam School of Technology Gitam (Deemed-to-be University) Bengaluru India Department of Computer Application Graphic Era (Deemed to be University) Dehradun India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This research investigates the rapidly evolving landscape of language models and their emerging trends. Language models, powered by innovative techniques in machine learning and natural language processing, have witnessed significant advancements in recent years. Language models have seen remarkable advancements in recent years, with emerging trends shaping the landscape of natural language processing (NLP). This research explores the latest developments in language models, such as transformer architectures and deep learning techniques, and assesses their significance in various applications. This research tries to understand the impact of these trends on tasks like text generation, sentiment analysis, and machine translation. This research also explores these advancements' potential societal and ethical implications, highlighting the need for responsible AI development. This study underscores the importance of staying updated on emerging trends in language models to harness their full potential while ensuring responsible and ethical usage in the ever-evolving field of NLP.

关键词： Training Ethics Data privacy Sentiment analysis Medical services Market research Transformers Natural language processing Data models Machine translation

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External Reliable Information-enhanced Multimodal Contrastive Learning for Fake News Detection 39

External Reliable Information-enhanced Multimodal Contrastiv...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Cao, Biwei Wu, Qihang Cao, Jiuxin Liu, Bo Gui, Jie School of Cyber Science and Engineering Southeast University Nanjing 211189 China Key Laboratory of Computer Network and Information of Ministry of Education of China Nanjing 211189 China Purple Mountain Laboratories Nanjing 210000 China School of Computer Science and Engineering Southeast University Nanjing 211189 China Engineering Research Center of Blockchain Application Supervision And Management Southeast University Ministry of Education China

ISBN: (纸本)157735897X

With the rapid development of the Internet, the information dissemination paradigm has changed and the efficiency has been improved greatly. While this also brings the quick spread of fake news and leads to negative impacts on cyberspace. Currently, the information presentation formats have evolved gradually, with the news formats shifting from texts to multimodal contents. As a result, detecting multimodal fake news has become one of the research hotspots. However, multimodal fake news detection research field still faces two main challenges: the inability to fully and effectively utilize multimodal information for detection, and the low credibility or static nature of the introduced external information, which limits dynamic updates. To bridge the gaps, we propose ERIC-FND, an external reliable information-enhanced multimodal contrastive learning framework for fake news detection. ERIC-FND strengthens the representation of news contents by entity-enriched external information enhancement method. It also enriches the multimodal news information via multimodal semantic interaction method where the multimodal constrative learning is employed to make different modality representations learn from each other. Moreover, an adaptive fusion method is taken to integrate the news representations from different dimensions for the eventual classification. Experiments are done on two commonly used datasets in different languages, X (Twitter) and Weibo. Experiment results demonstrate that our proposed model ERIC-FND outperforms existing state-of-the-art fake news detection methods under the same settings. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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