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SSRN 2023年

作者： Guo, Qian Guo, Yi Zhao, Jin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Business Intelligence and Visualization Research Center National Engineering Laboratory for Big Data Distribution and Exchange Technologies Shanghai200436 China Shanghai Engineering Research Center of Big Data & Internet Audience Shanghai200072 China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai200433 China

Knowledge representation is becoming an effective way of information extraction. However, considerable studies ignored its application scenarios in the zero-shot setting. In this paper, we propose a novel framework for prompt language models based on external ontology knowledge called Knowledge-Based Prompt Tuning for the Zero-shot Relation Triplet Extraction (KBPT), which encourages further investigation in low-resource regimes to address the data scarcity problem in Relation Triplet Extraction (RTE). The major task of relation triplet extraction in zero-shot learning is to extract multiple triplets that are consisted of head entities, tail entities, and relation labels from an input sentence where the extracted relation labels do not exist in the training set. The fundamental idea of prompt tuning is to construct a prompt template, then append it behind the input text as the pre-trained language models (PLMs) input, thus, transforming the classification task into a masked language model prediction. However, our proposed model does not involve the mask language model prediction but a well-designed prompt template in a structured text format to generate synthetic training data containing the unseen relation category. Concretely, we utilize the relation labels and incorporate virtual tokens sources from the relation semantics to construct a structured prompt template for generating synthetic training instances. Moreover, to further enrich and supplement prior knowledge, we draw on ontology schema based on external knowledge bases to enhance the capability of semantic representation in the prompt template. To address the problem of knowledge heterogeneity, we synergistically optimize these embedding representations by way of collective training. In addition, we carefully design a Multiple Triplets Decoding (MTD) algorithm to break through the limitation of extracting multiple relation triplets in a sentence, and our proposed model is model-agnostic and can be orthogon

关键词： Ontology

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UAV-assisted Joint Mobile Edge Computing and data Collection via Matching-enabled Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Wang, Boxiong Kang, Hui Li, Jiahui Sun, Geng Sun, Zemin Wang, Jiacheng Niyato, Dusit College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University 639798 Singapore College of Computing and Data Science Nanyang Technological University Singapore

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data collection (DC) have been popular research issues. Different from existing works that consider MEC and DC scenarios separately, this paper investigates a multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint optimization problem to minimize the MEC latency and maximize the collected data volume. This problem can be classified as a non-convex mixed integer programming problem that exhibits long-term optimization and dynamics. Thus, we propose a deep reinforcement learning-based approach that jointly optimizes the UAV movement, user transmit power, and user association in real time to solve the problem efficiently. Specifically, we reformulate the optimization problem into an action space-reduced Markov decision process (MDP) and optimize the user association by using a two-phase matching-based association (TMA) strategy. Subsequently, we propose a soft actor-critic (SAC)-based approach that integrates the proposed TMA strategy (SAC-TMA) to solve the formulated joint optimization problem collaboratively. Simulation results demonstrate that the proposed SAC-TMA is able to coordinate the two subsystems and can effectively reduce the system latency and improve the data collection volume compared with other benchmark algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Generative AI: A Review on Models and Applications

Generative AI: A Review on Models and Applications

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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Kuldeep Singh Kaswan Jagjit Singh Dhatterwal Kiran Malik Anupam Baliyan School of Computing Science and Engineering Galgotias University Greater Noida UP India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Guntur AP India Computer Science and Engineering Matu Ram Institute of Engineering and Management Rohtak Haryana India Department of Computer Science and Engineering Chandigarh University Chandigarh India

Generative Artificial Intelligence (AI) stands as a transformative paradigm in machine learning, enabling the creation of complex and realistic data from latent representations. This review paper comprehensively surveys the landscape of Generative AI, encompassing its foundational concepts, diverse models, training methodologies, applications, challenges, recent advancements, evaluation metrics, and ethical dimensions. The paper begins by introducing Generative AI's significance across various domains, presenting its pivotal role in producing synthetic data with applications spanning image synthesis, text generation, music composition, drug discovery, and more. The objectives lie in elucidating the foundational concepts, delving into model intricacies, unveiling the training procedures, exploring its application landscape, addressing challenges, envisioning future directions, and discussing ethical ramifications. The foundational section elucidates the diverse array of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models, Generative Reinforcement Learning (GRL), and advanced hybrid architectures. Subsequently, evaluation metrics ranging from Inception Score to perceptual similarity metrics and human evaluations are surveyed to assess generative model performance. Finally, ethical considerations underscore the necessity for addressing biases, misuse, intellectual property concerns, and the call for responsible AI development and regulation in the Generative AI landscape.

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： P. Manju Bala S. Priyadharshini S. Usharani A. Balachandar Department of Artificial Intelligence and Data Science IFET College of Engineering (An Autonomous Institution) India Department of Computer Science and Engineering IFET College of Engineering (An Autonomous Institution) India Department of Artificial Intelligence and Machine Learning IFET College of Engineering (An Autonomous Institution) India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques.

关键词： Ultrasonic imaging Recurrent neural networks Accuracy Three-dimensional displays Breast cancer Spatial databases Planning

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FAT: Tilted Federated Learning with Alternating Direction Method of Multipliers

FAT: Tilted Federated Learning with Alternating Direction Me...

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International Conference on computer Supported Cooperative Work in Design

作者： Bo Cui Zhen Yang College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

While federated learning (FL) has made significant strides in addressing data privacy concerns, the challenges of heterogeneous data and unfair performance among participants remain substantial. Existing solutions confront challenges such as high computational costs, difficulty in balancing performance with fairness, and poor convergence in partially data heterogeneous environments. The alternating direction method of multipliers (ADMM) is a highly promising approach that effectively addresses issues related to data heterogeneity by imposing constraints on local client updates through dual variables. In this paper, we propose a novel FL framework, named FAT (tilted FL with ADMM), designed to address the issue of data heterogeneity while reducing bias and unfair treatment towards different clients, and it provides a better trade-off between accuracy and fairness. We conducted experiments on two real-world datasets, and the results demonstrate that, compared to existing methods, FAT significantly improves fairness while maintaining accuracy. Our experiments demonstrate that FAT significantly outperforms existing state-of-the-art methods in both accuracy and fairness, offering a superior trade-off between these crucial aspects.

关键词： Measurement data privacy Accuracy Federated learning Convex functions Fats Computational efficiency

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Analysis of Skin Lesions for Identifying of Skin Cancer Melanoma using Customized CNN Model with Transfer Learning

Analysis of Skin Lesions for Identifying of Skin Cancer Mela...

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Global Conference for Advancement in Technology (GCAT)

作者： Ns Sinchana K Jeevitha S Kumar Swamy Ashwini Kodipalli Trupthi Rao Department of Computer Science and Engineering Global Academy of Technology Bangalore Karnataka India Department of Artificial Intelligence and Data Science and Engineering Global Academy of Technology Bangalore Karnataka India

Melanoma is a type of cancer known to be the most dangerous and is called the life threatening disease. Every year, there is rise in number of people being diagnosed with melanoma which is more frequent among the males than the females. This type of skin cancer has an uncontrollable growth of cells that can grow really faster, if not treated it can spread all over the body. Hence this disease needs to be detected at the early stages so that the patients can be treated properly. Machine learning approaches were applied to detect skin cancer but the drawbacks were that the performance to identify the cancer was limited. Therefore, we constructed a model by utilizing a Convolution Neural Network of the most advanced deep learning where we conduct investigations for discovering the melanoma skin cancer on the basis of the skin lesion. In this research, using transfer learning techniques ResNet and VGG based models have been brought together for melanoma skin detection. The procedures were applied to train these models using optimizers like RMSprop, Adam, Stochastic Gradient Descent (SGD) and findings were presented with the support of accuracy, confusion matrix, sensitivity, specificity and precision.

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OIDS: A Smart Framework for Intrusion Detection in 5G WSN’s by using Ontology

OIDS: A Smart Framework for Intrusion Detection in 5G WSN’s...

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Advances in Computation, Communication and Information Technology (ICAICCIT), International Conference on

作者： Sover Singh Bisht Anuj Singh Rajput Sanjeev Kumar Mandal Department of Data Science Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula’s Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

The increasing depth and applications of 5G wireless sensor networks also raise the possibility of network intrusions. In this research, a network intrusion detection system based on the ontology notion is proposed. As it creates ontologies and maintains the relationships among sensor nodes in a network, it is referred upon as a smart system. This proposed system finding is able to secure data in 5G networks. The current detection technique’s parameters such as patrol nodes, live nodes, hop, sensor nodes, and energy (in joules) are used to gauge how well the suggested system performs. Attack estimation rate (AER) and precision are the types of metrics in use.

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Age Detection in a Surveillance Video Using Deep Learning Technique

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SN computer science 2021年第4期2卷 1-11页

作者： Vasavi, S. Vineela, P. Raman, S. Venkat Department of Computer Science and Engineering VR Siddhartha Engineering College Vijayawada India Department of Space Advanced Data Research Institute Hyderabad India

The video streams that are collected from CCTV surveillance camera can be used in many applications such as crowd analysis, forensic, self-profile analysis, and social network user’s analysis. Soft biometrics such as age, gender, height, skin color can be used for human analysis. This requires object detection and feature analysis. Works reported for face recognition and age detection have poor performance with real-world profile images. It may be because of incomplete description of the human object. Also, they rely on traditional image processing algorithms that extract hand-crafted features. Deep learning workflow transforms the identified patterns into mathematical modeling that can be used for subsequent prediction. Residual networks can skip connections and can address vanishing gradient problem with improved accuracy. Wide ResNet 34-based system is proposed in this paper that automatically predicts age of human object in video images. Modified Wide ResNet is used for feature extraction that learns facial keypoints, image reconstruction using Simultaneous algebraic reconstruction technique for up sampling, 101 number of classes (101-way classification) ranging from 0 to 100. Proposed system accuracy is evaluated using mean absolute error and with Pearson correlation coefficient that finds correlation between actual age and predicted age. Experimental results proved that data augmented Wide ResNet out performs the existing age prediction methods with 5% increase in accuracy. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Age prediction data augmentation Feature extraction Image reconstruction Wide ResNet

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The Emergent Aging Model: Aging as an Emergent Property of Biological Systems

arXiv

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arXiv 2024年

作者： Qin, Hong Department of Computer Science and Engineering Department of Biology Geology and Environmental Sciences University of Tennessee at Chattanooga ChattanoogaTN United States Department of Computer Science School of Data Sciences Old Dominion University NorfolkVA United States

Based on the study of cellular aging using the single cell model organism of budding yeast and corroborated by other studies, we propose the Emergent Aging Model (EAM). EAM hypothesizes that aging is an emergent property of complex biological systems, exemplified by biological networks such as gene networks. An emergent property refers to traits that a system has at the system level but which its low-level components do not. EAM is based on a quantitative definition of aging using the mortality rate. A biological entity with a constant mortality rate is considered non-aging which is equivalent to a first-order chemical reaction. Aging can be quantitatively defined an increasing mortality rate over time, corresponding to an organism’s increasing chance of dying over time. EAM posits that biological aging can arise at the system level of an organism, even if the system is composed of only non-aging components. EAM is consistent with the observation that aging is largely stochastic, influenced by numerous genes and epigenetic factors, with no single gene or factor known as the bone fide cause of aging. A parsimonious version of EAM can predict the Gompertz model of biological aging, the Strehler-Mildvan correlation, and the trade-off between initial reproductive fitness (asexual reproductive fitness) and late-life survival. EAM has been applied to experimental results of the replicative lifespan of the budding yeast and can potentially offer new insights into the aging process of other biological species. © 2024, CC BY.

关键词： Population statistics

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Augmented Reality and Artificial Intelligence in Sign Language Expression

Augmented Reality and Artificial Intelligence in Sign Langua...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Kaviyaraj R P. Sathya P. Nandhini Vinoth Kumar P Sureshkumar Chelliah Ezhilmathi S Department of computational intelligence SRM institute of science and technology Kattankulathur India Department of Computer Science and Engineering Sri Shanmuga Collage of Engineering and Technology Sankari India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology OMR Chennai

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Sign language, the vibrant tapestry of hand gestures and facial expressions, is the lifeblood of Deaf and hardof-hearing communities. For millions of signers, American Sign Language (ASL) runs deeper than communication, fundamental to identity, expression, and belonging. And yet an unshakeable communication gap leaves users of ASL frequently marooned away from the hearing world, kept from education, healthcare, or employment, or from basic, everyday transactions. By posing this new unsolved challenge to the power and promise of Artificial Intelligence (AI), this work leads the way towards closing that chasm by real-time recognition and translation of full ASL. Our approach employs a novel variant of Random Forest and utilizes cutting-edge video processing techniques to identify and understand the nuanced, often exquisitely delicate detail of ASL signing, at unprecedented levels of accuracy, and at speed. Another layer of innovation that characterizes our work is our integration of augmented reality (AR). By embedding AR along with our translator of artificial intelligence tech, we intend to completely change the way American Sign Language (ASL) is conveyed by directly engraining our already robust Random Forest model and advanced video processing techniques to project the ASL translation directly into your visual field in real time. The goal to demystify this complex and vivid language and, in doing so, to remove the communication barriers that persist between the Deaf community and the rest of the world, thus, fostering inclusion.

关键词： Sign language Computational modeling Signal processing algorithms Assistive technologies Predictive models Streaming media Feature extraction

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