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arXiv 2025年

作者： Sun, Hongyu Ke, Qiuhong Cheng, Ming Wang, Yongcai Li, Deying Gou, Chenhui Cai, Jianfei Department of Computer Science Renmin University of China China Department of Data Science & AI Monash University Australia

This paper proposes a general solution to enable point cloud recognition models to handle distribution shifts at test time. Unlike prior methods, which rely heavily on training data—often inaccessible during online inference—and are limited to recognizing a fixed set of point cloud classes predefined during training, we explore a more practical and challenging scenario: adapting the model solely based on online test data to recognize both previously seen classes and novel, unseen classes at test time. To this end, we develop Point-Cache, a hierarchical cache model that captures essential clues of online test samples, particularly focusing on the global structure of point clouds and their local-part details. Point-Cache, which serves as a rich 3D knowledge base, is dynamically managed to prioritize the inclusion of high-quality samples. Designed as a plug-and-play module, our method can be flexibly integrated into large multimodal 3D models to support open-vocabulary point cloud recognition. Notably, our solution operates with efficiency comparable to zero-shot inference, as it is entirely training-free. Point-Cache demonstrates substantial gains across 8 challenging benchmarks and 4 representative large 3D models, highlighting its effectiveness. Code is available at https://***/auniquesun/Point-Cache. © 2025, CC BY-NC-SA.

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SimE: A Knowledge Graph Embedding Model to Encode Self-Similar Structures Through Algebraic and Geometric Transformations

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IEEE Access 2025年 13卷 74701-74719页

作者： Amouzouvi, Kossi Chudasama, Yashrajsinh Purohit, Disha Rivas, Ariam Song, Bowen Lehmann, Jens Vahdati, Sahar Vidal, Maria-Esther Technische Universität Dresden ScaDS.AI Dresden/Leipzig Dresden01069 Germany Kwame Nkrumah University of Science and Technology Department of Mathematics Kumasi Ghana Data Science Institute Leibniz University Hannover Hannover30167 Germany TIB Leibniz Information Centre for Science and Technology Hannover30167 Germany L3S Research Center Hannover30167 Germany Chinese University of Geosciences School of Computer Science Wuhan430079 China Amazon Dresden01097 Germany Leipzig04109 Germany

Knowledge Graphs (KGs), with their intricate hierarchies and semantic relationships, present unique challenges for graph representation learning, necessitating tailored approaches to effectively capture and encode their complex structures into useful numerical representations. The fractal-like nature of these graphs, where patterns repeat at various scales and complexities, requires specialized algorithms that can adapt and learn from the multi-level structures inherent in the data. This similarity to fractals requires methods that preserve the recursive detail of knowledge graphs while facilitating efficient learning and extraction of relational patterns. In this study, we explore the integration of similarity group with attention mechanisms to represent knowledge graphs in complex spaces. In our approach, SimE, we make use of the algebraic (bijection) and geometric (similarity) properties of the similarity transformations to enhance the representation of self-similar fractals in KGs. We empirically validate the capability of providing representations of bijections and similarities in benchmark KGs. We also conducted controlled experiments that captured one-to-one, one-to-many, and many-to-many relational patterns and studied the behavior of state-of-the-art models including the proposed SimE model. Because of the lack of benchmark fractal-like KG datasets, we created a set of fractal-like testbeds to assess the subgraph similarity learning ability of models. The observed results suggest that SimE captures the complex geometric structures of KGs whose statements satisfy these algebraic and geometric properties. In particular, SimE is competitive with state-of-the-art KG embedding models and is able to achieve high values of Hits@1. As a result, SimE is capable of effectively predicting correct links and ranking them with the highest ranks. © 2013 IEEE.

关键词： Linear transformations

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Dynamic k-Anonymity for Electronic Health Records: A Topological Framework 19th

Dynamic k-Anonymity for Electronic Health Records: A Topolo...

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19th International Workshop on data Privacy Management, DPM 2024, 8th International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control Systems and of Cyber-Physical Systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in computer Security, ESORICS 2024

作者： Swaminathan, Arjhun Akgün, Mete Medical Data Privacy and Privacy Preserving Machine Learning Department of Computer Science University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen Germany

ISBN: (纸本)9783031823480

With the rapid digitization of Electronic Health Records (EHRs), fast and adaptive data anonymization methods have become increasingly important. While tools from topological data analysis (TDA) have been proposed to anonymize static datasets—allowing the creation of multiple generalizations for different anonymization needs from a single computation—the application to dynamic datasets remains unexplored. To address this, our work adapts existing methodologies to the dynamic setting. We develop an improved version of weighted persistence barcodes that track higher-dimensional holes in data, allowing us to edit persistence information on the fly. Additionally, we introduce filtration trimming, a novel technique designed to update persistence data quickly with minimal computing effort when data is added. Our work represents a significant advancement in healthcare data privacy, offering a refined approach to protecting highly sensitive and evolving patient data through dynamic k-anonymity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Toxic Comment Detection based on Improved Bi Directional Long Short Term Memory 1

Toxic Comment Detection based on Improved Bi Directional Lon...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kamma, Vidya Wbaid, Saef Sudhakar, K. Nisha, A. Saravanan, T. Neil Gogte Institute of Technology Department of Computer Science and Engineering Rangareddy Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, toxic comment detection evolved as important in detecting hatred and harmful content present in user-generated content. However, a few challenges are raised in detecting toxic comments like context sensitivity and missing toxic content. To overcome these issues, improved Bi-directional Long Short-Term Memory (Bi-LSTM) is proposed for toxic comment detection. Initially, data is preprocessed with lemmatization and stop words removal methods for converting the texts to its base form and eliminates the common words respectively. Then, the Word to Vector (Word2Vec) technique converts the words into a dense vector representation by capturing semantic relationships and contextual meanings. Finally, improved Bi-LSTM is employed for detecting the toxic comments by capturing both past as well as future context with its bidirectional architecture and focuses on important words within the context dynamically. From the results, proposed Improved Bi-LSTM model attained better outcomes in terms of accuracy (acc) of 94.32%, precision (pr) of 98.07%, and recall of 95.98% compared to existing Convolutional Neural Networks (CNN)-BiLSTMs. © 2025 IEEE.

关键词： Semantics

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Leveraging LLM in Genetic programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

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6DMA-Aided Cell-Free Massive MIMO Communication

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IEEE Wireless Communications Letters 2025年第5期14卷 1361-1365页

作者： Shi, Xiaoming Shao, Xiaodan Zheng, Beixiong Zhang, Rui School of Science and Engineering Guangdong Shenzhen518172 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada South China University of Technology School of Microelectronics Guangzhou511442 China School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering Lower Kent Ridge Road 117583 Singapore

In this letter, we propose a six-dimensional movable antenna (6DMA)-aided cell-free massive multiple-input multiple-output (MIMO) system to fully exploit its macro spatial diversity, where a set of distributed access points (APs), each equipped with multiple 6DMA surfaces, cooperatively serve all users in a given area. Connected to a central processing unit (CPU) via fronthaul links, 6DMA-APs can optimize their combining vectors for decoding the users' information based on either local channel state information (CSI) or global CSI shared among them. We aim to maximize the average achievable sum-rate via jointly optimizing the rotation angles of all 6DMA surfaces at all APs, based on the users' spatial distribution. Since the formulated problem is non-convex and highly non-linear, we propose a Bayesian optimization-based algorithm to solve it efficiently. Simulation results show that, by enhancing signal power and mitigating interference through reduced channel cross-correlation among users, 6DMA-APs with optimized rotations can significantly improve the average sum-rate, as compared to the conventional cell-free network with fixed-position antennas and that with only a single centralized AP with optimally rotated 6DMAs, especially when the user distribution is more spatially diverse. © 2012 IEEE.

关键词： Convex optimization

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Perspective Chapter: MOOCs in India: Evolution, Innovation, Impact, and Roadmap

arXiv

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arXiv 2025年

作者： Das, Partha Pratim Department of Computer Science Center for Data Science and Analytics Ashoka University Sonepat India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

With the largest population of the world and one of the highest enrolments in higher education, India needs efficient and effective means to educate its learners. India started focusing on open and digital education in 1980's and its efforts were escalated in 2009 through the NMEICT program of the Government of India. A study by the Government and FICCI in 2014 noted that India cannot meet its educational needs just by capacity building in brick and mortar institutions. It was decided that ongoing MOOCs projects under the umbrella of NMEICT will be further strengthened over its second (2017-21) and third (2021-26) phases. NMEICT now steers NPTEL/SWAYAM (India's MOOCs) and several digital learning projects including Virtual Labs, e-Yantra, Spoken Tutorial, FOSSEE, and National Digital Library on India-the largest digital education library in the world. Further, India embraced its new National Education Policy in 2020 to strongly foster online education. In this chapter, we take a deep look into the evolution of MOOCs in India, its innovations, its current status and impact, and the roadmap for the next decade to address its challenges and grow. AI-powered MOOCs is an emerging opportunity for India to lead MOOCs worldwide. © 2025, CC BY-NC-ND.

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Aryan, Adla Mohammed, Abdul Aleem Chabra, Manish Rasheed, Syed Saarib Adnan, Mohammed Raoof, Mohammed Abdul Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Muffakham Jah College of Engineering and Technology Department of Computer Science and Engineering Hyderabad500034 India Methodist College of Engineering and Technology Department of Artificial Intelligence & Data Science Telangana Hyderabad500001 India

ISBN: (纸本)9798331529833

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process. © 2025 IEEE.

关键词： Fruits

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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