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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Vu, Doan Nam Long Igamberdiev, Timour Habernal, Ivan Trustworthy Human Language Technologies Germany Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

ISBN: (纸本)9798891761681

Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice of granularity at which DP is applied is often neglected. For example, neural machine translation (NMT) typically operates on the sentence-level granularity. From the perspective of DP, this setup assumes that each sentence belongs to a single person and any two sentences in the training dataset are independent. This assumption is however violated in many real-world NMT datasets, e.g., those including dialogues. For proper application of DP we thus must shift from sentences to entire documents. In this paper, we investigate NMT at both the sentence and document levels, analyzing the privacy/utility trade-off for both scenarios, and evaluating the risks of not using the appropriate privacy granularity in terms of leaking personally identifiable information (PII). Our findings indicate that the document-level NMT system is more resistant to membership inference attacks, emphasizing the significance of using the appropriate granularity when working with DP. © 2024 Association for Computational Linguistics.

关键词： Differential privacy

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Spatio-Temporal Heterogeneous Graph Neural Network With Multi-view Learning For Traffic Prediction 27th

Spatio-Temporal Heterogeneous Graph Neural Network With Mult...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Song, Liting Ren, Qianqian Zhou, Ying Department of Computer Science and Technology Heilongjiang University Harbin150080 China Heilongjiang Network Space Research Center Harbin150090 China

ISBN: (纸本)9783031781827

Among various traffic data modeling and predicting methods, graph learning-based models attract more attention, because of their powerful representation ability for modeling spatial and temporal dependencies with graph neural networks. Despite their promising performance, several key problems have not been well addressed: 1) Sensed data are often noisy in many real transportation scenarios. 2) The spatio-temporal correlations of traffic data are complex and dynamic, especially for long-term modeling and predicting. In such cases, existing methods may not lead to satisfactory prediction results. In this paper, we aim at the above problems by exploring a Spatio-Temporal Heterogeneous Graph Neural Network With Multi-View Learning Framework(MVJGL) for traffic prediction. In particular, we first model different types of traffic features and construct multiple graph structures. Then, we design two parallel heterogeneous gated temporal convolution modules to extract long and short-term temporal dependencies from different traffic features, respectively. Moreover, we introduce parallel graph convolutions to cross-characterize the time-varying spatial dependencies of each view. Extensive experiment results on four real traffic datasets show the superior performance grain obtained by the proposed model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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On the design of an AI-driven secure communication scheme for internet of medical things environment

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Digital Communications and Networks 2023年第5期9卷 1080-1089页

作者： Neha Garg Rajat Petwal Mohammad Wazid D.P.Singh Ashok Kumar Das Joel J.P.C.Rodrigues Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Center for Security Theory and Algorithmic ResearchInternational Institute of Information TechnologyHyderabad500032India College of Computer Science and Technology China University of Petroleum(East China)Qingdao266555China Instituto de Telecomunicaçoes 6201-001CovilhaPortugal

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the *** is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare *** other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare ***,this system is prone to securityand privacy-related attacks on healthcare ***,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is *** mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for *** discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to ***,we provide the security analysis of the proposed scheme to show its security against different possible ***,a comparative study of the proposed scheme with other similar schemes is *** results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality ***,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

关键词： Internet of Medical Things(IoMT) Security Authentication and key agreement Artificial Intelligence(AI) Big data analytics

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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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2024 IEEE International Conference on Data and Software Engineering, ICoDSE 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Kurnianingrum, Dian Ratnapuri, Chyntia Ika Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Entrepreneurship Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331506407

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus. © 2024 IEEE.

关键词： Decoding

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ConvNeXt Model for Breast Cancer Image Classification 6

ConvNeXt Model for Breast Cancer Image Classification

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Setiawan, Devin Karnyoto, Andrea Stevens Intan, Indo Pardamean, Bens Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83. © 2024 IEEE.

关键词： Lung cancer

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Blockchain-based privacy and security preserving in electronic health: a systematic review

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Multimedia Tools and Applications 2023年第18期82卷 28493-28519页

作者： Kiania, Kianoush Jameii, Seyed Mahdi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Shahr-e-Qods Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

In today’s world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange information between medical stakeholders (patients, physicians, insurance companies, pharmaceuticals, medical researchers, etc.) suffer weaknesses in terms of security and privacy due to having centralized architecture. Blockchain technology ensures the privacy and security of EHR systems thanks to the use of encryption. Moreover, due to its decentralized nature, this technology prevents central failure and central attack points. In this paper, a systematic literature review (SLR) is proposed to analyze the existing Blockchain-based approaches for improving privacy and security in electronic health systems. The research methodology, paper selection process, and the search query are explained. 51 papers returned from our search criteria published between 2018 and Dec 2022 are reviewed. The main ideas, type of Blockchain, evaluation metrics, and used tools of each selected paper are discussed in detail. Finally, future research directions, open challenges, and some issues are discussed. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Smart contract

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems Engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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Elevating Localization Accuracy in Wireless Sensor Networks:A Refined DV-Hop Approach

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computers, Materials & Continua 2024年第10期81卷 1511-1528页

作者： Muhammad Aamer Ejaz Kamalrulnizam Abu Bakar Ismail Fauzi Bin Isnin Babangida Isyaku Taiseer Abdalla Elfadil Eisa Abdelzahir Abdelmaboud Asma Abbas Hassan Elnour Department of Computer Science Faculty of ComputingUniversiti Technologi MalaysiaJohor Bahru81310Malaysia Department of Computer Science Faculty of Computing and Information TechnologySule Lamido UniversityP.M.B.048Kafin Hausa741103Nigeria Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Humanities Research Center Sultan Qaboos UniversityMuscat123Oman

Localization is crucial in wireless sensor networks for various applications,such as tracking objects in outdoor environments where GPS(Global Positioning System)or prior installed infrastructure is ***,traditional techniques involve many anchor nodes,increasing costs and reducing *** solutions do not address the selection of appropriate anchor nodes and selecting localized nodes as assistant anchor nodes for the localization process,which is a critical element in the localization ***,an inaccurate average hop distance significantly affects localization *** propose an improved DV-Hop algorithm based on anchor sets(AS-IDV-Hop)to improve the localization *** simulation analysis,we validated that the ASIDV-Hop proposed algorithm is more efficient in minimizing localization errors than existing *** ASIDV-Hop algorithm provides an efficient and cost-effective solution for localization in Wireless Sensor *** strategically selecting anchor and assistant anchor nodes and rectifying the average hop distance,AS-IDV-Hop demonstrated superior performance,achieving a mean accuracy of approximately 1.59,which represents about 25.44%,38.28%,and 73.00%improvement over other algorithms,*** estimated localization error is approximately 0.345,highlighting AS-IDV-Hop’s *** substantial reduction in localization error underscores the advantages of implementing AS-IDV-Hop,particularly in complex scenarios requiring precise node localization.

关键词： Localization anchor nodes collinearity connectivity hop based

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Machine Learning by Symbolic Regression for Automatic Synthesis of Universal Stabilization System of Motion 32

Machine Learning by Symbolic Regression for Automatic Synthe...

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32nd Mediterranean Conference on Control and Automation, MED 2024

作者： Diveev, Askhat Barabash, Artem Federal Research Center 'Computer Science and Control' The Russian Academy of Sciences Department Control of Robotic Devices Moscow119333 Russia

ISBN: (纸本)9798350395440

This work describes automatization of creation process of automatic control systems. This needs not only for faster construction of control systems, but also to attract the machine to the solving the control problems, in order to computer found some solutions itself and offered them to developers. Here the optimal control problem in the extended statement is considered, when its solution can be realized directly in a real object. Here, for it purpose it is proposed to synthesize the universal stabilization system of a control object motion along any trajectory from enough wide class. The model of control object with stabilization system and with reference model for generation of a trajectory is called extended model of control object. The optimal control problem with the extended model of control object for realization of its solution it is enough to solve in classical form and to find a control function as a function of time. Universal stabilization system will provide quality motion a control object along obtained trajectory. For synthesis of a universal stabilization system machine learning control by symbolic regression is used. A computational example of application considered approach to a control of spatial motion of a quadcopter group is presented. © 2024 IEEE.

关键词： Trajectories

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