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10th International Multidisciplinary Engineering Research Conference on Moratuwa Engineering Research, MERCon 2024

作者： Ayesha, R.L.A. Sanduni Vijayarathna, L.B. Hasini M. Vithanage, N.P.A. Thayasivam, Uthayasanker Alahakoon, Damminda Adikari, Achini Pallewela, Nuwan University of Moratuwa Department of Computer Science and Engineering Katubedda Sri Lanka La Trobe University Research Centre for Data Analytics and Cognition Australia

ISBN: (纸本)9798331529048

Chatbots have become a trending topic with emerging platforms like ChatGPT, Gemini, and Copilot, for conversation assistance. Current chatbots mainly focus on the general public assuming a natural flow of conversation. However, there is a need for a chatbot that supports people with various communication disabilities. This research fills this gap by offering a novel technique for a chatbot that assists people with Aphasia, a condition characterised by difficulties with language. We propose a multi-modal chatbot that is customised and designed to assist users with communication disabilities in navigating a website. Unlike typical chatbots, which rely on one form of communication, our architecture combines multiple modalities to improve comprehension and promote effective communication for people with Aphasia. We focus on gathering multimodal inputs by recognising and combining user intents from diverse sources. The use of Txtai, an all-in-one embeddings database for semantic search improves our chatbot's capacity to process various inputs efficiently. We leverage specialised models like Whisper for audio transcription and MediaPipe Gesture Recognizer for gesture detection to enhance user interactions. Additionally, Rasa Core integration improves conversational experiences for users. We propose that this new approach will make communication more accessible and inclusive for individuals with Aphasia. © 2024 IEEE.

关键词： Gesture recognition

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A Privacy-Preserving E-Voting System Using Blockchain and Linkable Ring Signatures 1

A Privacy-Preserving E-Voting System Using Blockchain and Li...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Berkani, Ahmed-Sami Moumen, Hamouma Benharzallah, Saber Achouri, Youssouf Kechadi, Mohand Tahar Lamie Laboratory University of Batna 2 Department of Computer Science Batna Algeria LaSTIC Laboratory University of Batna 2 Department of Computer Science Batna Algeria School of Computer Science University College Dublin Insight Centre for Data Analytics Belfield Ireland

ISBN: (纸本)9798331541064

In this work we propose a privacy-preserving, blockchain-enabled e-voting system that integrates Linkable Ring Signatures (LRS) to ensure a secure, scalable, and privacypreserving solution for modern e-voting, providing a framework for trusted and transparent electoral processes in the digital age. Our system ensures voter anonymity while preventing double voting through the use of a linkability tag, while blockchain technology is employed to provide an immutable and transparent ledger for vote recording. The InterPlanetary File System (IPFS) is utilized for decentralized and GDPR-compliant storage of public keys. Performance analysis shows scalability challenges for LRS in large elections, but the system remains efficient for small scale use. Future work will focus on optimizing scalability for larger deployments. © 2024 IEEE.

关键词： Differential privacy

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Analysis on DeepFake dataset, Manipulation and Detection Techniques: A Review

Analysis on DeepFake Dataset, Manipulation and Detection Tec...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2022

作者： Rasool, Aale Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Delhi Technological University New Delhi India

ISBN: (纸本)9789819959730

The ability to spot false news is becoming increasingly important in today’s environment. Modern, rapid communication and easy access to all kinds of information were made possible by social networks and internet media. On the other hand, it opens up new avenues for malicious people to exploit vast amounts of publicly available data. Fake images and videos may now be predicted with greater accuracy because of advances in machine learning, particularly deep learning. Many studies have been conducted in recent years to better understand how deepfakes work, and several deep learning-based algorithms to detect deepfake videos or images have been developed. This study provides a comprehensive summary of the most recent manipulation techniques and datasets utilized in this subject. In addition, we reviewed the recent work of other academics on the subject and provided a roadmap for future study to aid scholars in combating the issue of deep fakes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Convolutional neural networks

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Recommendation fairness and where to find it: An empirical study on fairness of user recommender systems

Recommendation fairness and where to find it: An empirical s...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Tommasel, Antonela Assent, Ira Aarhus University Department of Computer Science Digit Aarhus University Centre for Digitalisation Big Data and Data Analytics Denmark Isistan CONICET-UNCPBA Argentina

ISBN: (纸本)9798350324457

Recommender systems play a crucial role in how users consume information and establish new social relations. However, different factors (such as the data collection process, the designed recommendation model, or even the interpretation of findings) could make recommenders (unintendedly) prone to biases, favouring certain user groups or items, thus resulting in unfair outcomes. Recommenders also face fairness criticism for inducing filter bubbles, echo chambers, and, more generally, facilitating opinion manipulation. In this work, we study the impact of user recommender systems on fairness. To this end, we carry out a user recommendation task on a politically polarized Twitter data collection. Then, we evaluate how the different politically aligned user groups experience recommendation quality. Finally, we explore causal models to identify data and model-related features that could affect the fairness of recommender outcomes. Our study shows that political alignment is associated with the unfairness of recommenders affecting not only the relevance of recommendations, but also their diversity and the resulting interaction patterns. © 2023 IEEE.

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Transaction fee mechanisms with farsighted miners

Transaction fee mechanisms with farsighted miners

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作者： Leth Hougaard, Jens Pourpouneh, Mohsen Han, Haiting IFRO University of Copenhagen Copenhagen Denmark Department of Computer Science University of Oxford Oxford United Kingdom Department of Data Analytics and Digitalisation Maastricht University Maastricht Netherlands

We investigate the recent fee mechanism EIP1559 of the Ethereum network. Whereas previous studies have focused on myopic miners, we here focus on strategic miners in the sense of miners being able to reason about the future blocks. We derive expressions for optimal miner behavior (in terms of setting block sizes) in the case of two-block foresight and varying degrees of hashing power. Results indicate that a sufficiently large mining pool will have enough hashing power to gain by strategic foresight. We further use a simulation study to examine the impact of both two-block and three-block foresight. In particular, the simulation study indicates that for realistic levels of hashing power, mining pools do not gain from being able to reason more than two blocks ahead. Moreover, even though the presence of strategic miners increases the variation in block sizes and potentially empty blocks, overall system throughput tends to increase slightly compared with myopic mining. We further analyze the effect of varying the base fee updating rule. © 2024 John Wiley & Sons Ltd.

关键词： Blockchain

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HAM: Hybrid Associations Models for Sequential Recommendation (Extended abstract) 39

HAM: Hybrid Associations Models for Sequential Recommendatio...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Peng, Bo Ren, Zhiyun Parthasarathy, Srinivasan Ning, Xia The Ohio State University Department of Computer Science and Engineering United States The Ohio State University Department of Biomedical Informatics United States The Ohio State University Translational Data Analytics Institute Department of Computer Science and Engineering United States The Ohio State University Translational Data Analytics Institute Department of Biomedical Informatics Department of Computer Science and Engineering United States

ISBN: (纸本)9798350322279

Sequential recommendation aims to identify and recommend the next few items of users' interest. It becomes an effective tool to help users select their favorite items from a variety of options. A key challenge in sequential recommendation is to learn the patterns and dynamics, which are most pertinent to inform future interactions of users. With the prosperity of deep learning, many deep models, particularly based on recurrent neural networks [1] and with attention mechanisms [2] , [3] , have been developed for sequential recommendation purposes. However, our analysis demonstrates that, these deep models, particularly those with attention mechanisms, may not always learn meaningful attention weights from the extremely sparse recommendation data, and thus, could degrade the recommendation performance. Therefore, in this study, instead of deep models, we develop novel, effective and efficient hybrid associations models (HAM) to better learn from the sparse and limited recommendation data. This study has been published in IEEE Transactions on Knowledge and data Engineering. Please refer to the full manuscript [4] for more details. © 2023 IEEE.

关键词： Recurrent neural networks

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Addressing the Productivity Paradox in Healthcare with Retrieval Augmented Generative AI Chatbots 25

Addressing the Productivity Paradox in Healthcare with Retri...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Ranasinghe, Sajani De Silva, Daswin Mills, Nishan Alahakoon, Damminda Manic, Milos Lim, Yen Ranasinghe, Weranja Centre For Data Analytics And Cognition La Trobe University Melbourne Australia Virginia Commonwealth University Department Of Computer Science Richmond United States Department Of Urology Melbourne Australia

ISBN: (纸本)9798350340266

Artificial Intelligence (AI) is reshaping the health-care landscape through diverse innovations, personalisations and decision-making capabilities. The human-like intelligence of Generative AI has been fundamental in driving this transformation across the sector. Despite large investments and some early successes, several studies have signalled the emergence of a productivity paradox due to inherent limitations of Generative AI that disintegrate within the complexity of healthcare systems and operations. In this study, we investigate the capabilities of Retrieval Augmented Generation (RAG) and Generative AI chatbots in addressing some of these challenges. We present the design and development of a Retrieval Augmented Generative AI Chatbot framework for consultation summaries, diagnostic insights, and emotional assessments of patients. We further demonstrate the technical value of this framework in service innovation, patient engagement and workflow efficiencies that collectively move to address the productivity paradox of AI in healthcare. © 2024 IEEE.

关键词： Decision making

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Multi-task Learning with Pre-trained Language Models for Mental Illness Screening

Multi-task Learning with Pre-trained Language Models for Men...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Shrestha, Avantika Tlachac, M.L. Flores, Ricardo Hickey, Kevin Rundensteiner, Elke A. Worcester Polytechnic Institute Data Science Department WorcesterMA United States Bryant University Center for Health and Behavioral Sciences Department of Information Systems and Analytics SmithfieldRI United States University of Concepcion Department of Computer Science Biobio Concepcion Chile Worcester Polytechnic Institute Data Science Department Computer Science Department WorcesterMA United States

ISBN: (纸本)9798350362480

Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are mental disorders that reduce quality of life. As they are challenging to detect in a timely manner, recent studies explore the mental illness screening potential of language models on modalities such as transcripts. Such datasets suffer from a limited number of participants. To overcome these challenges, we take a two-pronged approach: (1) we leverage multi-task learning to model multiple illnesses concurrently namely MDD and PTSD screening, and (2) we plug in pre-trained language models as a backbone, namely, Bidirectional Encoder Representations from Transformers (BERT) variants - which are capable of learning the linguistic content of clinical interview transcripts. In particular, we experiment with three multi-task weighting strategies and five BERT variants, applying them to 15 transcript sets extracted from the Distress Analysis Interview Corpus. Our results indicate that leveraging multi-task learning, especially with meta-weighting, increases the screening performance compared to single-task learning. Our multi-task learning model strategies improved the F1 scores for all 15 datasets for both MDD and PTSD screening. Notably, multitask learning improved MDD screening ability by 20 percent with transcripts regarding 'regret' and achieved F1 of 0.89 and 0.82 respectively for MDD and PTSD screening with transcripts regarding 'medical history'. Our findings may help researchers develop more effective mental illness screening models. © 2024 IEEE.

关键词： Multi-task learning

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Design and Implementation for Innovative Tourism Expert System based on Artificial Intelligence 8

Design and Implementation for Innovative Tourism Expert Syst...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Prasanth, R. Anitha, R. Sarathamani, T. Manoj, M. Dr. Sns Rajalakshmi College of Arts and Science Department of Management Tamilnadu Coimbatore India Kolkata India Kpr College of Arts Science and Research Department of Computer Science with Data Analytics Tamilnadu Coimbatore India

ISBN: (纸本)9798350367904

The tourism sector has long been looked at as a vital engine of economic expansion. The most recent developments in the tourism sector have benefitted governments, international organizations, common people and academia. For developing the tourism sector, it is important to analyze and understand tourist visitor patterns. Tourist forecasting helps maximize the tourism-based economy. The international tourist movement has grown at an unprecedented rate in recent decades, and many countries are concentrating on the tourism economy. While tourists visit a tourist place, spend a significant amount of money on their enjoyment, company, and hotel accommodations. ML algorithms are making significant contributions to forecasting system, and several researchers are working to improve the model's accuracy. This research has proposed a machine learning approach for forecasting worldwide tourist arrivals that analyses prior quantitative information about visitors so that the governing body can arrange the necessary facilities for visitors accordingly. The proposed model is applied to a dataset compiled over a decade from all countries around the world. The detailed proposed approach and the various results of experiments are explained in this paper. © 2024 IEEE.

关键词： Artificial Intelligence Classification data Analysis data Mining Tourist Expert System

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EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

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computers, Materials & Continua 2024年第10期81卷 809-825页

作者： Rabia Javed Tanzila Saba Tahani Jaser Alahmadi Sarah Al-Otaibi Bayan AlGhofaily Amjad Rehman Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan Artificial Intelligence&Data Analytics Lab. CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh145111Saudi Arabia

Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to *** cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all *** and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival *** examinations for lung cancer detection,however,frequently fall short of detecting small polyps and *** address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients *** research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological *** proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell *** evaluated the performance of the proposed technique using the histopathological(LC25000)lung *** preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer *** dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training *** model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are *** study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.

关键词： Colon cancer EfficientNetB1 histopathological image processing transfer learning health risks

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