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arXiv 2024年

作者： Zhang, Yuxuan Li, Ruizhe Department of Computing Science University of Aberdeen United Kingdom Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University China

Recent advancements in Large Language Models (LLMs) have achieved robust performance across diverse tasks, but fine-tuning these models for specific domains remains resource-intensive. Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) address this challenge by fine-tuning a small subset of parameters. However, existing methods for fusing multiple LoRAs lack dynamic fusion based on contextual inputs and often increase inference time due to token-level operations. We propose DLP-LoRA, a Dynamic Lightweight Plugin that employs a mini-MLP module with only 5M parameters to dynamically fuse multiple LoRAs at the sentence level using top-p sampling strategies. This approach reduces inference time to less than twice that of single LoRA inference by leveraging parallel computation. Evaluations across 26 tasks—including multiple-choice questions and question answering—demonstrate that DLP-LoRA achieves an average accuracy of 92.34% on multiple-choice datasets and significant improvements in BLEU and ROUGE scores on QA datasets, outperforming different LLMs backbones under composite task settings. DLP-LoRA effectively balances performance and efficiency, making it a practical solution for dynamic multi-task adaptation in LLMs. Our code is available at https://***/MeCuping/DLP-LoRA. © 2024, CC BY.

关键词： Problem oriented languages

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A Survey on Privacy-Preserving data Publishing Methods and Models in Relational Electronic Health Records 1

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3rd International Conference on Sustainable Advanced computing, ICSAC 2021

作者： Jayapradha, J. Prakash, M. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu603203 India

ISBN: (数字)9789811690129

ISBN: (纸本)9789811690112

The collections of various digital information from corporations, public sectors, private sectors, and individuals have formed remarkable opportunities for analysis and decision-making. The electronic health record is highly constructive for various study purposes;however, it should be carried in a way that preserves the identity and privacy of the patients. Due to the digital transformation, the flow of data increases that lead to the need for big data. Protecting privacy cannot be straightforward as there are several privacy threats such as membership disclosure, identity disclosure, and attribute disclosure. The privacy methods such as generalization, suppression, pseudonymization, bucketization, slicing are illustrated with examples. The study focused on various privacy models such as k-anonymity, l-diversity, t-closeness, differential privacy, and cryptographic technique. The models are discussed with the sample dataset and workflow. The paper has discussed the security and privacy concerns on data publishing, the need for big data scope, an overview of threats, samples, privacy methods, models, and challenges in privacy-preserving data publishing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Records management

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Scientific Research Transformation Needed for Comprehensive Artificial Intelligence Implementation 17

Scientific Research Transformation Needed for Comprehensive ...

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17th International Conference on Management of Large-Scale System Development, MLSD 2024

作者： Medennikov, Viktor Bogatyreva, Larisa Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia

ISBN: (纸本)9798350375718

The paper deals with complementary changes in scientific research and real economy (exemplified by the farming industry) taking place when using the holistic approach to the industry's digital transformation resulting in the appearance of precise agrarian technologies that require a comprehensive, system-based combination of research and production resources that are able to ensure increased productivity in the industry. We demonstrate that among these new technologies, the artificial intelligence methods have a special significance. They have to, however, go through integration transformations to become standards for the proposed common digital farming management platform. © 2024 IEEE.

关键词： Agricultural economics

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data Augmentation for Cognitive Behavioral Therapy: Leveraging ERNIE Language Models using Artificial Intelligence

Data Augmentation for Cognitive Behavioral Therapy: Leveragi...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Bosubabu Sambana Kondreddygari Archana Suram Indhra Sena Reddy Shaik Meethaigar Jameer Basha Shaik Karishma Department of Computer Science and Engineering (Data Science) School of Computing Mohan Babu University Tirupati India Department of Information Technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Cognitive Behavioral Therapy (CBT) is a proven approach for addressing the irrational thought patterns associated with mental health disorders, but its effectiveness relies on accurately identifying cognitive pathways to provide targeted treatment. In today's digital age, individuals often express negative emotions on social media, where they may reveal cognitive distortions, and in severe cases, exhibit suicidal tendencies. However, there is a significant gap in methodologies designed to analyze these cognitive pathways, which could be critical for psychotherapists aiming to deliver timely and effective interventions in online environments. Cognitive Behavioral Therapy (CBT) framework leveraging acceptance, commitment and data augmentation to categorize and address both textual and visual content as positive or negative. Specifically, the system employs BERT, RoBERTa for Sentiment Analysis and T5, PEGASUS for Text Summarization, mT5 for Text Translation in Multiple Languages focusing on detecting negative emotions and cognitive distortions within social media data. While existing models are primarily designed to identify negative thoughts, the proposed system goes beyond this by predicting additional negative side effects and other potential mental health disorders likes Phobias, Eating Disorders. This enhancement allows for a more comprehensive understanding and intervention strategy, offering psychotherapists a powerful tool for early detection and treatment of various psychological issues.

关键词： Sentiment analysis Social networking (online) Mood Anxiety disorders Medical treatment Mental health data augmentation Distortion Classification algorithms Artificial intelligence

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High efficient net gain and low noise figure based vertical cavity semiconductor optical amplifiers for wavelength division multiplexing applications

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Journal of Optical Communications 2025年第2期46卷 247-256页

作者： Prabu, Ramachandran Thandaiah Balasubramanian, Arunsundar Balakrishnan, Nithyasundari Jebanazer, Jeneetha Kandasamy, Mohana Sundaram Govindaswamy, Nishanthi Mahmoud, Rashida Maher Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha University Tamilnadu Chennai India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chennai93104 India Department of ECE S.A. Engineering College Tamilnadu Chennai India Department of ECE Panimalar Engineering College Tamilnadu Chennai India Department of CSE R.M.D. Engineering College Kavarapettai Tamilnadu Chennai India Department of CSE R.M.K. Engineering College Kavarapettai Tamilnadu Chennai India Sohag Institute of Technology Sohag Egypt

This paper has demonstrated the high efficient net gain and low noise figure based vertical cavity semiconductor light amplifiers for wavelength division multiplexing applications. Previous study on the chip reflective gain variations versus SOA current under temperature effects is clarified. We have transferred light semiconductor amplifiers for wavelength multiplexing schemes applications. Amplifier output power is demonstrated with bias current and amplifier active layer region length variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The amplifier output noise variations are clarified against the bias current and amplifier active layer region width variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The output OSNR variations are studied clearly and deeply against the bias current and amplifier active layer region length variations based input signal power of 3, 6.5, 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. The signal loss is demonstrated versus both active layer width/length and temperature based optimum 30 % gallium core dopant ratio and 28 % arsenide cladding dopant ratio at optimum input signal power of 10 dBm, 1550 nm wavelength and optimum amplifier confinement factor of 0.45. SOA amplifier output power can be enhance with the management of both bias current and input signal power and the reduction of active amplifier length. The amplifier output noise can be enhance with the management of both bias current and input signal power and the reduction of active amplifier width. Output OSNR system can be improved with the management of both bias current and input signal power and the reduction of active amplifier length. The SOA amplifier gain can be improved with the management of both bias current and input signal power and the reduction of active amplifier length. The SOA amplifie

关键词： Noise figure

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HealthGuardian: Artificial Intelligence System for Early Diagnosis and Clinical Support

HealthGuardian: Artificial Intelligence System for Early Dia...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： V Radhamani D. Sudha Devi E Chandra Blessie P J Isabella Priya R Tanushree Department of Computing (Decision and Computing Sciences) Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computing (Data Science) Coimbatore Institute of Technology Coimbatore Tamil Nadu India Department of Computing (Artificial Intelligence and Machine Learning) Coimbatore Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

In today's fast-paced world, access to timely and accurate healthcare information is critical, yet many individuals face challenges in assessing their symptoms, finding appropriate care, and understanding potential treatments. To address these issues, we propose HealthGuardian, an AI-powered clinical decision support system designed to assist users with symptom checking, personalized treatment recommendations, healthcare provider suggestions, online consultations, and early-stage diabetes prediction. HealthGuardian allows users to input their symptoms through natural language processing (NLP) and a recur-rent neural network (RNN) model, providing possible diagnoses and advising when to seek medical attention. The system offers personalized treatment plans based on medical history, suggests nearby healthcare providers using the Overpass API, and facilitates online consultations with doctors. Additionally, it features a predictive tool for early-stage diabetes risk assessment using various machine learning algorithms. The system leverages machine learning algorithms such as logistic regression, decision tree classifier, Naive Bayes, and random forest classifier to enhance diagnostic accuracy and prediction reliability. Our results demonstrate that HealthGuardian improves early diagnosis accuracy, provides actionable treatment guidance, and increases healthcare accessibility, helping users make informed decisions about their health.

关键词： Logistic regression Accuracy Machine learning algorithms Neural networks Medical services Diabetes Reliability Risk management Medical diagnostic imaging Random forests

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Multi-View Oriented GPLVM: Expressiveness and Efficiency

arXiv

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arXiv 2025年

作者： Yang, Zi Li, Ying Lin, Zhidi Zhang, Michael Minyi Olmos, Pablo M. School of Computing and Data Science City University of Hong Kong Hong Kong Department of Statistics and Data Science National University of Singapore Singapore Department of Signal Theory and Communications Universidad Carlos III de Madrid Madrid Spain

The multi-view Gaussian process latent variable model (MV-GPLVM) aims to learn a unified representation from multi-view data but is hindered by challenges such as limited kernel expressiveness and low computational efficiency. To overcome these issues, we first introduce a new duality between the spectral density and the kernel function. By modeling the spectral density with a bivariate Gaussian mixture, we then derive a generic and expressive kernel termed Next-Gen Spectral Mixture (NG-SM) for MV-GPLVMs. To address the inherent computational inefficiency of the NG-SM kernel, we propose a random Fourier feature approximation. Combined with a tailored reparameterization trick, this approximation enables scalable variational inference for both the model and the unified latent representations. Numerical evaluations across a diverse range of multi-view datasets demonstrate that our proposed method consistently outperforms state-of-the-art models in learning meaningful latent representations. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

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Construction of Anti-Terrorism Intelligence Graph Based on DIKW Framework 25

Construction of Anti-Terrorism Intelligence Graph Based on D...

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25th IEEE International Conferences on High Performance computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Gao, Peng Yuan, Haoran Liu, Yanfei Mao, Bowen Duan, Yucong Wang, Zhenhua Gansu Police Vocational College Gansu China Chongqing City Science and Technology College Artificial Intelligence and Big Data Academy Chongqing China Tianjin University Chongqing Police College Department of Intelligence and Computing Department of Infor Security Chongqing China Tianjin University Department of Intelligence and Computing Tianjin China School of Computer Science and Technology Hainan University Haikou China Chongqing Police College Department of Tactics Chongqing China

ISBN: (纸本)9798350330014

'Construction of anti-Terrorism Intelligence Graph Based on the DIKW Framework' simplifies the process of handling decentralized and diverse sources of intelligence in counter-terrorism. It uses the DIKW model to consolidate and structure data for better efficiency. It creates a data graph to illustrate relationships, making data more accessible. This data is then used to form an information graph, connecting data with relevant knowledge. From this, intelligent models are extracted, aiding in counter-terrorism prevention and prediction. This approach emphasizes transforming data into intelligence and knowledge hierarchically to improve counter-terrorism efforts. © 2023 IEEE.

关键词： Terrorism

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CAPSULE NETWORK PROJECTORS ARE EQUIVARIANT AND INVARIANT LEARNERS

arXiv

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arXiv 2024年

作者： Everett, Miles Durrant, Aiden Zhong, Mingjun Leontidis, Georgios Department of Computing Science University of Aberdeen United Kingdom Interdisciplinary Centre for Data and AI University of Aberdeen United Kingdom

Learning invariant representations has been the longstanding approach to self-supervised learning. However, recently progress has been made in preserving equivariant properties in representations, yet do so with highly prescribed architectures. In this work, we propose an invariant-equivariant self-supervised architecture that employs Capsule Networks (CapsNets) which have been shown to capture equivariance with respect to novel viewpoints. We demonstrate that the use of CapsNets in equivariant self-supervised architectures achieves improved downstream performance on equivariant tasks with higher efficiency and fewer network parameters. To accommodate the architectural changes of CapsNets, we introduce a new objective function based on entropy minimisation. This approach which we name CapsIE (Capsule Invariant Equivariant Network) achieves state-of-the-art performance on the equivariant rotation tasks on the 3DIEBench dataset compared to prior equivariant SSL methods, while performing competitively against supervised counterparts. Our results demonstrate the ability of CapsNets to learn complex and generalised representations for large-scale, multi-task datasets compared to previous CapsNet benchmarks. Code is available at https://***/AberdeenML/CapsIE. © 2024, CC BY.

关键词： Self-supervised learning

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Instantons in ϕ4 theories: Transseries, virial theorems, and numerical aspects

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Physical Review D 2024年第3期110卷 036003-036003页

作者： Ludovico T. Giorgini Ulrich D. Jentschura Enrico M. Malatesta Tommaso Rizzo Jean Zinn-Justin Department of Physics Department of Computing Sciences Institute for Data Science and Analytics Dipartimento di Fisica Rome unit Piazzale Aldo Moro 5 00185 Rome Italy /

We discuss numerical aspects of instantons in two- and three-dimensional ϕ4 theories with an internal O(N) symmetry group, the so-called N-vector model. By combining asymptotic transseries expansions for large arguments with convergence acceleration techniques, we obtain high-precision values for certain integrals of the instanton that naturally occur in loop corrections around instanton configurations. Knowledge of these numerical properties is necessary in order to evaluate corrections to the large-order factorial growth of perturbation theory in ϕ4 theories. The results contribute to the understanding of the mathematical structures underlying the instanton configurations.

关键词： Feynman diagrams Particle interactions Path integrals Resummation methods Quantum field theory Spontaneous symmetry breaking

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