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Blockchain based Security Management of Internet of Medical Things (IoMT) using Homomorphic Encryption using Metaheuristics with a Deep Learning Model 2

Blockchain based Security Management of Internet of Medical ...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Santiago, S. Prasanna, B.T. Qureshi, Kashif Subash, K. Trichy India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India Department of Data Science Ai India Department of Data Science Trichy India

ISBN: (纸本)9798350384277

The introduction of the Internet of Medical Things (IoMT), has drastically transformed the worldwide landscape of healthcare delivery. The proliferation of IoMT devices in healthcare systems brings new issues in guaranteeing the security and privacy of sensitive patient data. We must secure a substantial amount of sensitive data from modification since IoMT is a high-volume, continually developing data domain. Since then, blockchain (BC) technology has been recognised as a distributed ledger that can manage a huge number of intelligent transactions and increase data security at the same time. A comprehensive resource management system may be offered by Blockchain-based security mechanisms with specific customisation and additional levels of authentication and verification. It has functioned brilliantly as a crucial component of the Bitcoin cryptocurrency. This work creates a blockchain-based Internet of Medical Things security management system using homomorphic encryption (HE). Key generation shows how the DOA may enhance cryptographic system security and efficacy. The IoMT-based HE-DOA system provided in this study employs blockchain to secure data exchange and diagnostic models. HE-DOA allows safe data transmission and diagnostic models with BC aid in IoMT. The recommended technique includes data collecting, encryption, and categorization. Blockchains (BCs) securely store data, and IoMT devices are crucial to early data collection. For data encryption, the dingo optimisation algorithm (DOA) may generate the key. Performance experiments and comparison with similar protocols show that the recommended procedure may enhance PSNR, accuracy, and computational time by 19.34%, 95.78%, and 1.887ms, respectively. Comparing HE-DOA to prior studies shows its scalability and durability. © 2024 IEEE.

关键词： Distributed ledger

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Leveraging LLMs for Integrated Sentiment and Topic Analysis on African Social Media 23

Leveraging LLMs for Integrated Sentiment and Topic Analysis ...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sibitenda, Harriet Hu, Ruofan Rundensteiner, Elke Diattara, Awa Traore, Assitan Ba, Cheikh University of Gaston Berger Computer Science Department Saint Louis Senegal Data Science Program Worcester Polytechnic Institute Worcester United States Data Science Program Worcester Polytechnic Institute Worcester United States Actroll Data Science Department France

ISBN: (纸本)9798350374889

Leveraging AI to analyze key topics on African social media can enhance public governance. Our study analyzes social media discourse within African society on development concerns by (1) evaluating AI techniques for sentiment, topic, and theme extraction, comparing the accuracy of these methods with human annotations, and (2) extracting key insights from the data to provide policymakers with actionable recommendations for sustainable development. For this study, we utilized a data corpus of 22,036 posts from Twitter and YouTube, all focused on development issues in Africa. We applied topic modeling to extract relevant topics from the corpus and used similarity analysis, powered by Large Language Models, to link these topics to prevalent development themes. Additionally, we leveraged unsupervised models such as VADER and Large Language Models to extract sentiment related to the identified topics. To validate these model-generated sentiments, we conducted a small crowdsourced study to gather human-annotated labels as ground truth. Our sentiment analysis findings show improvements with models like TextBlob, VADER, and Llama. Fine-tuning, partic-ularly with BERT, achieved an impressive Fl score of 0.988. Meanwhile, Llama demonstrated strong precision (0.72) and balanced accuracy (0.55) in capturing contextual sentiment. We identified 304 topics using BERTopic and Llama, with robust coherence (0.81 C-v) and divergence (0.58 IRBO). In theme analysis, the One-vs-Rest classification with ensemble voting performed exceptionally well, with 'Poverty' achieving the highest F1 score of 0.89. Our results suggest that African policymakers prioritize addressing corruption, unemployment, drought, and instability, while closely monitoring the positive impacts of policy interventions. © 2024 IEEE.

关键词： Tweets

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Assistive Large Language Model Agents for Socially-Aware Negotiation Dialogues

Assistive Large Language Model Agents for Socially-Aware Neg...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hua, Yuncheng Qu, Lizhen Haffari, Gholamreza Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761681

We develop assistive agents based on Large Language Models (LLMs) that aid interlocutors in business ***, we simulate business negotiations by letting two LLM-based agents engage in role play.A third LLM acts as a remediator agent to rewrite utterances violating norms for improving negotiation *** introduce a simple tuning-free and label-free In-Context Learning (ICL) method to identify high-quality ICL exemplars for the remediator, where we propose a novel select criteria, called value impact, to measure the quality of the negotiation *** provide rich empirical evidence to demonstrate its effectiveness in negotiations across three different negotiation *** have released our source code and the generated dataset at: https://***/tk1363704/SADAS. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Arewa NLP’s Participation at WMT24 9

Arewa NLP’s Participation at WMT24

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9th Conference on Machine Translation, WMT 2024

作者： Ahmad, Mahmoud Said Khalid, Auwal Abubakar Aliyu, Lukman Jibril Sani, Babangida Abdullahi, Mariya Sunusi Nigeria Arewa Data Science Academy Nigeria Nigeria

ISBN: (纸本)9798891761797

This paper presents the work of our team, "ArewaNLP," for the WMT 2024 shared task. The paper describes the system submitted to the Ninth Conference on Machine Translation (WMT24). We participated in the English-Hausa text-only translation task. We fine-tuned the OPUS-MT-en-ha transformer model and our submission achieved competitive results in this task. We achieve a BLUE score of 27.76, 40.31 and 15.85 on the Development Test, Evaluation Test and Challenge Test respectively. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Einocchio: Efficiently Outsourcing Polynomial Computation with Verifiable Computation and Optimized Newton Interpolation

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1981-1992页

作者： Pei, Xintao Chen, Yuling Long, Yangyang Sang, Haiwei Luo, Yun Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China

Cloud computing, as a promising service platform, has gained significant popularity in addressing emerging data privacy issues in applications such as machine learning and data mining. Researchers have proposed the verifiable computing that allows the cloud users to delegate their computation tasks to the cloud server. Then, the cloud server computes the cryptographic proofs that verify the correctness of the results, a process that is generally faster ompared to local manual computation. However, performing computation tasks or verifying the correctness of encrypted data, such as multivariate polynomial functions, remains a significant challenge. To solve this problem, we propose Einocchio: A verifiable computation scheme that combines the efficient Pinocchio system with homomorphic encryption, which allows the public verification of the computational results on the server side while ensuring data confidentiality and the results. Compared with the existing solutions, Einocchio does not reveal the client's input. Furthermore, we extrapolate Einocchio by optimizing the Pinocchio's quadratic arithmetic program component using a differential optimization method, which reduces the computational workload owing to the conversion from quadratic to linear complexity, thereby increasing the efficiency of the quadratic arithmetic program preprocessing stage. Security analysis demonstrates that Einocchio achieves IND-CPA security. Finally, the performance evaluation confirmed its effectiveness and suitability for cloud computing environments. Compared to the corresponding scheme based on Newton interpolation, Einocchio achieves a threefold greater computational efficiency, with the generation of interpolation polynomials for 50 data inputs occurring in a mere 0.31 ms, while simultaneously reducing the number of computations. © 2005-2012 IEEE.

关键词： Cloud platforms

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A Lower Bound on the Second-Order RDP Function 14

A Lower Bound on the Second-Order RDP Function

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14th IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2024

作者： Nomura, Ryo Waseda University Center for Data Science Tokyo Japan

ISBN: (纸本)9798350366556

The rate-distortion-perception (RDP) problem is investigated in a general context. In recent years, the RDP problem has attracted significant attention from both theoretical and practical standpoints. Various researchers have explored this problem, seeking to better understand its implications and applications. Recently, Nomura [1] explored the self-random number generation (SRNG) problem using f-divergence, which serves as a subproblem within the larger RDP framework. This paper builds upon the work of [1], extending the analysis to the second-order setting and deriving the optimal rate. Furthermore, we apply our findings to the RDP problem, obtaining a lower bound for the second-order RDP function. © 2024 IEEE.

关键词： Random number generation

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Attenuation-Based Attention for Improved Novel View Synthesis 4

Attenuation-Based Attention for Improved Novel View Synthesi...

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Lai, Xunjie Guo, Yongde Liu, Yu City University of Macau Faculty of Data Science China

ISBN: (纸本)9798350361643

Transformer-based novel view synthesis methods aim to partially or fully replace the multilayer perceptron network and the volume rendering equation in Neural Radiance Field (NeRF), thereby achieving superior view synthesis quality result, and acquiring generalization capability. However, directly applying attention mechanism to view synthesis task can lead to detrimental effects. In this paper, we focus on refining the two-stage Transformer architecture to enhance its suitability for view synthesis task. Firstly, we introduce an attenuation coefficient based on the angle and distance between perspectives, which is applied to the features aggregated by the View Transformer. This selective aggregation process mitigates the interference of anomalous features. Secondly, we modify the attention mechanism of the Ray Transformer to align it more closely with the volume rendering procedure. This adjustment improves the regression from the photometric loss, leading to exceptional image synthesis performance. Experimental results demonstrate that our approach effectively prevents erroneous object color synthesis caused by abnormal features in distant epipolar views. Additionally, our model exhibits faster convergence during the initial training stages, surpassing the performance against results of prevailing methods. © 2024 IEEE.

关键词： Volume rendering

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A Time Series Sales data Forecasting Model Based on TCN and Its Three Variants 5

A Time Series Sales Data Forecasting Model Based on TCN and ...

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5th International Conference on Industrial IoT, Big data and Supply Chain, IIoTBDSC 2024

作者： Chen, Sian Department of Data Science and Big Data Technology Brunel London School North China University of Technology Beijing China

ISBN: (纸本)9798331539818

The forecasting of sales data is a widely studied issue in the fields of artificial intelligence and time series forecasting. TCN (Temporal Convolutional Network) is the most adept at managing time series data among all classical models due to its distinctive architecture. In this paper, we design a TCN-based model for time series forecasting of sales data and propose three variants: a combined model of TCN with RNN, LSTM and GRU to enhance the performance of time series forecasting. Experimental results show that these models perform well in handling the sales data forecasting task, especially the combination of TCN with LSTM performs best in several metrics. © 2024 IEEE.

关键词： Spatio-temporal data

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Evolutionary Expert Model Merging with Task-Adaptive Iterative Self-Improvement Process for Large Language Modeling on Aspect-Based Sentiment Analysis

Evolutionary Expert Model Merging with Task-Adaptive Iterati...

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2024 IEEE International Conference on Internet of Things and Intelligence Systems, IoTaIS 2024

作者： Inoshita, Keito Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9798331521219

In recent years, natural language processing (NLP) has achieved notable advancements, particularly with large language models (LLMs) enhancing tasks such as sentiment analysis. Aspect-based sentiment analysis (ABSA), which involves identifying sentiment related to specific aspects within text, poses a more complex challenge compared to simple sentiment classification. To address this complexity, integrating multiple expert models has emerged as a promising approach. In this study, we propose the Evolutionary Expert Model merging with Task-adaptive Iterative Self-improvement Process (EEM-TISP) to improve ABSA task performance. EEM leverages evolutionary algorithms to merge expert models, optimizing task-specific outputs, while TISP iteratively refines these outputs, enhancing accuracy. Our results demonstrate that EEM significantly improves performance in simpler tasks, such as Aspect Term Sentiment Analysis (ATSA) and Aspect Category Sentiment Analysis (ACSA), surpassing individual expert models. TISP further boosts accuracy in these simpler tasks, though performance declines were observed in more complex ones. These findings indicate that while TISP is effective for simpler tasks, further optimization is required to tackle complex ABSA challenges, calling for future research into flexible prompt design and task-specific approaches. ©2024 IEEE.

关键词： Aspect-Based Sentiment Analysis Evolutionary Expert Model Merging Iterative Self-Improvement Large Language Model Task Adaptation

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Towards Probing Speech-Specific Risks in Large Multimodal Models: A Taxonomy, Benchmark, and Insights

Towards Probing Speech-Specific Risks in Large Multimodal Mo...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yang, Hao Qu, Lizhen Shareghi, Ehsan Haffari, Gholamreza Department of Data Science & AI Monash University Australia

ISBN: (纸本)9798891761643

Large Multimodal Models (LMMs) have achieved great success recently, demonstrating a strong capability to understand multimodal information and to interact with human users. Despite the progress made, the challenge of detecting high-risk interactions in multimodal settings, and in particular in speech modality, remains largely unexplored. Conventional research on risk for speech modality primarily emphasises the content (e.g., what is captured as transcription). However, in speech-based interactions, paralinguistic cues in audio can significantly alter the intended meaning behind utterances. In this work, we propose a speech-specific risk taxonomy, covering 8 risk categories under hostility (malicious sarcasm and threats), malicious imitation (age, gender, ethnicity), and stereotypical biases (age, gender, ethnicity). Based on the taxonomy, we create a small-scale dataset for evaluating current LMMs capability in detecting these categories of risk. We observe even the latest models remain ineffective to detect various paralinguistic-specific risks in speech (e.g., Gemini 1.5 Pro is performing only slightly above random baseline). © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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