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A context-aware mobile augmented reality pet interaction model to enhance user experience

A context-aware mobile augmented reality pet interaction mod...

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作者： Luo, Yan Liu, Fang She, Yingying Yang, Baorong School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China College of Computer Engineering Jimei University Xiamen China

Virtual pet applications have been widely developed and applied in various fields. Mobile augmented reality (MAR) provides a new medium for virtual pets, allowing users to have a more immersive interactive experience through MAR pets. However, the issue of the user experience in MAR pets remains uninvestigated and relatively unexploited. Therefore, this article proposes a context-aware MAR pet interaction model (CAPet model) to enhance the user experience in MAR pet systems, which allows the MAR pet makes feedback adaptively corresponding to the dynamic context. In addition, this article presents a user experience pyramid to measure the user experience for MAR pet. According to the proposed CAPet model, a MAR pet-dog application is designed and implemented, based on which user test are conducted. The results of user test indicate the effectiveness of the proposed method on enhancing user experience, which provides a basis for the design of MAR pet applications in the future. © 2022 John Wiley & Sons Ltd.

关键词： Augmented reality

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pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning 39

pFedES: Generalized Proxy Feature Extractor Sharing for Mode...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yi, Liping Yu, Han Ren, Chao Wang, Gang Liu, Xiaoguang Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated learning (FL), as a privacy-preserving collaborative machine learning paradigm, has attracted significant interest from industry and academia. To allow each data owner (FL client) to train a heterogeneous and personalized local model based on its local data distribution, system resources and requirements on model structure, the field of model-heterogeneous personalized federated learning (MHPFL) has emerged. Existing MHPFL approaches either rely on the availability of a public dataset with special characteristics to facilitate knowledge transfer, incur high computational and communication costs, or face potential model leakage risks. To address these limitations, we propose a model-heterogeneous personalized Federated learning approach based on generalized proxy feature Extractor Sharing (pFedES) for supervised image classification tasks. (1) We devise a shared small proxy homogeneous feature extractor before each client’s heterogeneous local model. (2) Clients train them via the proposed iterative learning to enable the exchange of global generalized knowledge and local personalized knowledge. (3) The small proxy local homogeneous extractors produced after local training are uploaded to the server for aggregation to facilitate knowledge fusion across clients. We theoretically prove pFedES converges with a non-convex convergence rate O(1/T). Experiments on 3 benchmark datasets against 9 baselines demonstrate that pFedES performs state-of-the-art model accuracy while maintaining efficient communication and computation. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Vision Meets Language: Multimodal Transformers Elevating Predictive Power in Visual Question Answering

Vision Meets Language: Multimodal Transformers Elevating Pre...

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International Conference on computer and Information Technology (ICCIT)

作者： Sajidul Islam Khandaker Tahmina Talukdar Prima Sarker Md Humaion Kabir Mehedi Ehsanur Rahman Rhythm Annajiat Alim Rasel Department of Computer Science and Engineering (CSE) School of Data and Sciences (SDS) Brac University Dhaka Bangladesh

Visual Question Answering (VQA) is a field where computer vision and natural language processing intersect to develop systems capable of comprehending visual information and answering natural language questions. In visual question answering , algorithms interpret real-world images in response to questions expressed in human language. Our paper presents an extensive experimental study on Visual Question Answering (VQA) using a diverse set of multimodal transformers. The VQA task requires systems to comprehend both visual content and natural language questions. To address this challenge, we explore the performance of various pre-trained transformer architectures for encoding questions, including BERT, RoBERTa, and ALBERT, as well as image transformers, such as ViT, DeiT, and BEiT, for encoding images. Multimodal transformers’ smooth fusion of visual and text data promotes cross-modal understanding and strengthens reasoning skills. On benchmark datasets like the Visual Question Answering (VQA) v2.0 dataset, we rigorously test and fine-tune these models to assess their effectiveness and compare their performance to more conventional VQA methods. The results show that multimodal transformers significantly outperform traditional techniques in terms of performance. Additionally, the models’ attention maps give users insights into how they make decisions, improving interpretability and comprehension. Because of their adaptability, the tested transformer topologies have the potential to be used in a wide range of VQA applications, such as robotics, healthcare, and assistive technology. This study demonstrates the effectiveness and promise of multimodal transformers as a method for improving the effectiveness of visual question-answering systems.

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Advancement of Artificial Intelligent and Its Impact on Medicinal Treatment and Pharmaceutical Industries 9

Advancement of Artificial Intelligent and Its Impact on Medi...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Jenifer, A. Ahmed, Syed Imran Sangeetha, G. Chakkaravarthy, A. Prabhu Kumar, Ashok Bakhare, Ruhi Rmk Engineerinh College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Mj College Department of Electronics And Communication Engineering Telangana Hyderabad500053 India R.M.K College of Engineering And Technology Department of Artificial Intelligence And Data Science Tamil Nadu Thiruvallur India Srm Institute of Science And Technology Department of Networking And Communications Tamil Nadu Kattankulathur603203 India Banasthali Vidyapith Department of Computer Science Rajasthan 304022 India Dr. Ambedkar Institute of Management Studies And Research Department of Cse Deekshbhoomi Maharashtra 440010 India

ISBN: (纸本)9798350365092

In an unanticipatedly short period, a patient may seek advice from a computer prior to seeing a doctor, thus establishing the' standard' medical procedure of the future. Thanks to developments in artificial intelligence (AI), the days of misdiagnosing illnesses and treating their symptoms instead of their underlying causes may soon be over. It's possible that something like this will occur soon. To accommodate a complete 3D representation of an organ, imagine how much space would need to be cleared from your laptop or how many years' worth of blood pressure records would need to be eliminated. The image would require both of these things to be made possible. More uses of artificial intelligence and high performance data-driven medicine are made possible by the growing body of data generated in clinics and preserved in electronic medical records as a result of routine testing and medical imaging. These applications have led to the development of methods used by researchers and medical practitioners to address clinical concerns throughout time, and this trend is anticipated to continue. © 2024 IEEE.

关键词： Artificial intelligence

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Real-Time MRI Synthesis from Text Using a Two-Stage Pipeline for Pronunciation Training

Real-Time MRI Synthesis from Text Using a Two-Stage Pipeline...

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engineering Informatics, IEEE

作者： Hrutin Suriya Kotni Saras Chandrika Akkineni V Pranav Kashyap Sai Mandava Nitya Tiwari K S Nataraj Data Science and Artificial Intelligence IIIT Dharwad Karnataka Computer Science and Engineering IIIT Dharwad Karnataka School of Electrical and Computer Sciences IIT Bhubaneswar Odisha Electronics and Communication Engg. IIIT Dharwad Karnataka

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

Providing visual or animated information on how to position and move the tongue and other speech articulators is beneficial for children with hearing impairments to improve their pronunciation. However, since these articulatory movements occur inside the mouth, they are not visible. Real-time MRI of the midsagittal plane of the mouth can be used to extract this information. However, acquiring MRI data for every word is both time-consuming and impractical. This work proposes a method to synthesize MRI sequences for a given text. The approach involves a two-stage pipeline. In the first stage, text-to-speech synthesis is performed to replicate the speaker’s voice characteristics. In the second stage, the synthesized speech is mapped to an MRI sequence using a speech-MRI mapping model trained on the speaker recordings. The synthesized speech and corresponding rtMRI sequences are then integrated to generate a synchronized rtMRI video, providing a simultaneous representation of both articulatory movements and acoustic output. A key advantage of the proposed method is that it eliminates the requirement for phoneme-to-MRI frame alignment during the training of phoneme-to-MRI mapping models, a process that is frequently error-prone when relying on forced-alignment techniques. The Tortoise-TTS system is employed for speech synthesis from the text, ensuring that the generated speech matches the target speaker’s voice characteristics. A bidirectional Long Short-Term Memory network is trained to map speech to the corresponding MRI image sequence. To evaluate the synthesized MRI sequences, both objective measures, such as Frechet Inception Distance, and subjective evaluations are utilized. The mean FID scores ranged from 94.23 to 135.28, comparable to those obtained using other forced alignment-based methods. Subjective evaluations on a 1-to-5 scale yielded an average score of 3.5. The results demonstrate that the generated MRI sequences are realistic and closely resemble natural

关键词： Training Magnetic resonance imaging Pipelines Mouth Auditory system Real-time systems Acoustics Text to speech Recording Synchronization

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CAPLC: Cellular Automata-Powered Lightweight Cipher-Unveiling Secure Communication in Resource-Constrained Environments Using Cellular Automata and Henon Map

CAPLC: Cellular Automata-Powered Lightweight Cipher-Unveilin...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Utsav Upadhyay Dept. of Computational Sciences Brainware University Kolkata West Bengal India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Computer Science & Engineering Sir Padampat Singhania University Udaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

In today's interconnected world, ensuring secure communication in resource-constrained contexts is still challenging. This study offers CAPLC, a novel technique for lightweight encryption that uses Cellular Automata (CA) and Henon maps. Its goal is to improve security in such situations. The method uses 1D Cellular Automata for initial data transformation and incorporates the Henon map to improve encryption strength. We used conventional metrics to evaluate CAPLC's effectiveness on image encryption, and the findings were promising: NPCR of 99.6154%, UACI of 33.88235, and PSNR of 9.1924. These numbers show the method's capacity to dramatically change image while maintaining quality and anonymity. The implications of CAPLC go beyond image encryption, providing a scalable solution for secure communication in circumstances with limited computational resources. Future studies ought to explore CAPLC's real-time application and performance across various kinds of datasets, with a focus on the potential of merging CA with Henon maps to create efficient encryption methods for improving secure communications technology.

关键词： Measurement Merging Automata Medical services Information processing Parallel processing Real-time systems Encryption Resilience Principal component analysis

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Prediction of Global Sea Water Level using Linear Regression and Gradient Descent

Prediction of Global Sea Water Level using Linear Regression...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Aditya Bhagat Samiksha Thakre Snehlata Dongre Priya Maidamwar Data Science (Centre of Excellence) G H Raisoni College of Engineering Nagpur India Department of Computer Science and Engineering G H Raisoni College of Engineering Nagpur India

A sea level graph was published online in April 2022. "Climate Change: Global Sea Level" seems to be the current name for it. However, it contains little to no anchoring in provable facts. This paper examines a real-world situation. Real worldwide sea level fluctuations range from 0.0 mm/year to +1.0 mm/year on an international scale. The average global sea level has increased by 21 to 24 centimetres (8 to 9 inches) since 1880, according to measurements made from 1880 to 2013. In this study, we are developing models to forecast future seawater levels at the global sea level. In addition, we can observe the significantly rising sea level since the year 2000. The models are developed using JupyterLab Simulation Software, which is combined with the Python programming language to produce a graphic user interface (GUI). The results of the simulation show that the water level of the global sea will keep rising in the years to come. The study’s findings indicate that up until the year 2050, the global sea level will continue to change.

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Explainable AI Framework for Multi-label Classification using Supervised Machine Learning Models

Explainable AI Framework for Multi-label Classification usin...

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2022 Workshop on Advances in Computational Intelligence, its Concepts and Applications, ACI 2022

作者： Singaravel, Siddarth Jaiswal, Dibyanshu Daftari, Jay Shridevi, S. School of Computer Science and Engineering Vellore Institute of Technology India Centre for Advanced Data Science Vellore Institute of Technology India

Water is known as a "universal solvent" as it is extraordinarily frail against contamination. Water quality standards are developed based on logical evidence on the effects of hazardous compounds on a certain quantity of water used. Classification technique of machine learning can be employed to under-stand the water quality status. In this work, supervised machine learning models are being implemented to classify water quality indexes, and the Smote analysis is used to handle the imbalance in the dataset. Artificial neural net-work model is built using the features such as Oxygen, pH, temperature, total suspended sediment, turbidity, nitrogen, and phosphorus as inputs and water quality check as target variable. This target variable is created using Canadian Council of Ministers of the Environment Water Quality Index, and the model works with an accuracy of 87%. The classification is done on XGBoost model as well and it performs with an accuracy of 90%. The explanations for predictions of these models for a data instance were performed using explainable artificial intelligence tools such as LIME and SHAP. The results and interpretations for the predictions seem to be more promising and attractive making the proposed models more interpretable, accurate and efficient. Through our re-search we can benefit our readers by providing them clarity about exactly what features are having more influence on water quality than others from different machine learning algorithms. This will help the developers to gain insights about the significant factors of poor water quality and how to overcome that. © 2020 Copyright for this paper by its authors.

关键词： Water quality

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Real-Time Energy Efficient Communication for Large-Scale Industrial Internet of Things 2nd

Real-Time Energy Efficient Communication for Large-Scale Ind...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khan, Musfira Khan, Muazzam A. Farman, Haleem Malik, Asad W. Ramzan, Muhammad Sher Islamabad Pakistan Department of Computer Science Quaid-I-Azam University Islamabad Pakistan ICESCO Chair for Big Data Analytics and Edge Computing Quaid-I-Azam University Islamabad Pakistan Smart Systems Engineering Lab College of Engineering Prince Sultan University Riyadh Saudi Arabia King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9789819783441

Industrial Internet of Things (IIoTs) comprises large-scale sensors deployed to monitor and control the system. Deploying IoT devices presents challenges due to frequent disconnections and sudden power drainage in intermediate devices responsible for routing data. Consequently, the deployed network becomes sporadic, hindering connectivity with the industrial controller system, especially critical during emergencies. Furthermore, IoT node failures lead to a short network lifetime, which is intolerable in real-time industrial applications. This paper introduces a multi-hop clustering framework designed for reliable communication in industrial environments. The framework dynamically forms clusters, employing cluster heads and backup nodes to stabilize communication in the absence of a cluster head. Each cluster selects a backup node during the initial cluster head selection and later through data piggybacking. The proposed framework provides efficient communication across the IIoT network with the help of a backup and multi-hop clustering mechanism. The proposed framework is compared with two existing protocols i.e. cluster-based coordination routing (CRC) and energy-efficient quality of service routing (EEQSRP) protocols. The results show a considerable decrease in sensor energy consumption and packet delay. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy utilization

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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