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Strategic Integration of Context for Fine-Tuning Topic Model Performance 48

Strategic Integration of Context for Fine-Tuning Topic Model...

引用

48th Annual ieee international Computers, Software, and applications conference (COMPSAC) - Digital Development for a Better Future

作者： Dardouillet, Pierre Salamatian, Kave Verjus, Herve Loukil, Faiza Telisson, David Le Van, Olivier Univ Savoie Mt Blanc LISTIC Annecy France Cegedim Cegedim Business Serv Boulogne Billancourt France

ISBN: (纸本)9798350376975;9798350376968

Issue Tracking systems software serves as an interface between a company and its customers. Customers can report bugs and seek assistance, among other demands. Reported issues include textual description, along with company defined metadata, aim at simplifying issue treatment by experts. In the context of the rapid growth of customer-reported issues, the manual treatment process becomes tedious and time-consuming. As a result, more and more studies focus on automating parts of this process, using semantic extraction and topic modeling approaches to automatically classify issues. To this end, most approaches consider the issue of textual description along with metadata, which can be a source of uncertainty and misleading in many real-world scenarios. Besides, knowledge from the company experts is often neglected. In this paper, we propose a general taxonomy of information incorporation into topic models. This aims to assemble all existing techniques, to further detect literature gaps. In addition, we propose a technique to incorporate expert knowledge into neural topic models. We evaluate our techniques and others in the literature on a real-world dataset coming from the JIRA software of a French HR management company. Results show a significant increase of more than 22% in classification performances when using expert knowledge, in addition to the issue textual description. The results validate our approach's effectiveness in improving the automatic classification of issues.

关键词： Issue Tracking systems Neural Topic Models Information Incorporation

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Evolutionary Optimization of Neuromorphic Architecture for Low- power Cerebellum Prosthetic Instrumentation and Device in Biomedical systems

Evolutionary Optimization of Neuromorphic Architecture for L...

引用

ieee international Instrumentation and Measurement Technology conference (I2MTC) - Rising Above Covid-19

作者： Yang, Shuangming Wang, Haowen Azghadi, Mostafa Rahimi Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China James Cook Univ Coll Sci & Engn Townsville Qld Australia

ISBN: (纸本)9781665453837

Neuromorphic computing is a new generation of technique, which has been used in the neuroprosthetic implementation in biomedical applications. The spike-based computing mechanism enables it with low power consumption and real-time speed. However, there is a lack of optimization strategy for neuromorphic architecture of neuroprosthetic. In this paper, a novel optimization strategy for neuromorphic architecture of neuroprosthetic, named the Evolutionary Neuromorphic Optimization Framework (ENOF), is presented. A HEMA algorithm is proposed for the implementation of ENOF. It can continuously find the optimal mapping scheme and achieve better accuracy by jumping out of local optimization. Experimental results show that the proposed method can cut down the energy consumption and have better stability. Better optimization can be achieved along with the NoC scale increasing. The proposed work is meaningful for the low-power prosthetic instrumentation and device of biomedical systems, and can be applied in healthcare or clinical situations.

关键词： Neuromorphic computing neuroprosthetic spiking neural network biomedical system

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UAV embedded real-time Object Detection by a DCNN Model Trained on Synthetic Dataset

UAV Embedded Real-Time Object Detection by a DCNN Model Trai...

引用

international conference on Unmanned Aircraft systems (ICUAS)

作者： Bernardo, Ricardo Maroquio Batista da Silva, Luis Claudio Ferreira Rosa, Paulo Fernando Inst Fed Educ Ciencia & Tecnol Espirito Santo Cachoeiro De Itapemirim ES Brazil Ctr Fed Educ Tecnol Celso Suckow Fonseca Petropolis RJ Brazil Inst Mil Engn Prog Posgrad Engn Defesa Rio De Janeiro RJ Brazil

ISBN: (纸本)9798350310375

The utilization of unmanned aerial vehicles (UAVs) in civilian and military applications has significantly increased in recent years. A common task associated with these applications is detecting objects of interest in images captured by onboard UAV cameras. The ongoing development of advanced deep convolutional neural network (DCNN) algorithms has substantially improved the accuracy of general image segmentation and classification. However, applying these techniques to images obtained from UAVs requires a representative dataset for enhanced performance. This paper presents a method for DCNN-based object detection, utilizing resources embedded in a 1.5kg quadrotor-type UAV. To address the lack of representative datasets for our target scope, we employed a DCNN model trained on a self-generated synthetic dataset. The proposed method has been validated through real experiments, and the results demonstrate this approach's feasibility for real-time surveillance with fully onboard processing. Furthermore, this offers a stand-alone, portable, and cost-effective solution for surveillance tasks using a small UAV.

关键词： Object detection

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5G Advanced: A Step Towards 6G

5G Advanced: A Step Towards 6G

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2024 ieee international conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Routray, Sudhir K. Singh, Monika Samal, Laxmipriya Samal, Debashisa Sarangi, Susanta K. Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India SoA University Department of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350390759

This article explores the features and proximity of 5G advanced (well known as 5.5G) to 6G cellular communication system, emphasizing its speed, connectivity, and intelligence. Expected benefits of 5.5G include an enhanced user experience, industrial revolution, and support for the Internet of Things (IoT). The motivation behind 5.5G arises from addressing 5G limitations, accommodating IoT growth, enabling real-time applications, providing personalized experiences, and driving economic growth. Anticipated applications span from enhanced mobile broadband to ultra-reliable low latency communication and IoT-based massive machine-type communication, extending to personalized experiences, next-gen gaming, connected healthcare, and advanced education. Despite being in development, 5.5G holds immense significance for bridging the physical and digital world, fostering innovation, and unlocking the potential of the digital age. © 2024 ieee.

关键词： 5G mobile communication systems

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A Process Reduction Method for Spatial Information in real-time AR Snow Visualization systems 47

A Process Reduction Method for Spatial Information in Real-T...

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47th ieee-Computer-Society Annual international conference on Computers, Software, and applications (COMPSAC)

作者： Kobayashi, Yasuaki Kawakami, Tomoya Matsumoto, Satoru Yoshihisa, Tomoki Teranishi, Yuuichi Univ Fukui Grad Sch Engn Fukui Japan Osaka Univ Cybermedia Ctr Osaka Japan Shiga Univ Fac Data Sci Hikone Shiga Japan Natl Inst Informat & Commun Technol Tokyo Japan

ISBN: (纸本)9798350326970

In this paper, the authors propose a real-time AR visualization system that enables the user to visually grasp the future snow-covered situation at the current location, aiming to support residents and visitors in heavy snow areas. The proposed system generates snow-covered spatial information that reflects the snow-covered situation on spatial information in the real world sensed by 3D LiDAR, and composes it on the video image captured by a camera. This paper describes a lightweight method of spatial information processing by reducing the amount of spatial data transmission.

关键词： Augmented reality 3D LiDAR snow-covered polygon mesh process offloading

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PulseSync BP: A Neural Network Framework for Non-Contact Blood Pressure Estimation

PulseSync BP: A Neural Network Framework for Non-Contact Blo...

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2024 international conference on Advances in Data Engineering and Intelligent computing systems, ADICS 2024

作者： Ghosh, Tanumay Singal, Omisha Lodh, Namit Suchitra, S. School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350364828

Using a cutting-edge neural network framework, 'PulseSync BP' estimates blood pressure without contact. Using photoplethysmogram (PPG) signals and other physiological data, this novel model uses advanced signal processing. The suggested framework changes blood pressure monitoring by eliminating cuff-based measures. PulseSync BP's RNN architecture is carefully designed for optimal prediction accuracy. The algorithm effectively estimates blood pressure through extensive experimentation, demonstrating its potential for continuous healthcare applications. This framework's capacity to use various physiological cues shows its adaptability and versatility. PulseSync BP allows real-time, non-intrusive blood pressure monitoring, advancing individualized health tracking solutions. Cuffless blood pressure estimate is advanced here, demonstrating how artificial intelligence could improve healthcare accessibility and patient-centric monitoring. PulseSync BP and other revolutionary technologies can transform blood pressure monitoring in healthcare. © 2024 ieee.

关键词： Blood pressure

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Innovative Data Replication Techniques for Effective Latency Reduction in Mobile Edge computing and IoT 4

Innovative Data Replication Techniques for Effective Latency...

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4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Garg, Mohan Aswini, E. Gupta, Mohit Premalatha, K.P. Habbu, Shraddha Kuppuraj, T. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Eee Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore641021 India

ISBN: (纸本)9798331518981

applications like smart cities, driverless vehicles, healthcare, and industrial automation are made possible by the revolutionary grouping of the IoT and MEC. Problems with data management, especially with lowering latency for real-time applications, plague these systems. System efficiency, operational dependability, and user experience can all take a hit when data transmission delays occur. Copying and distributing data over numerous servers or edge nodes enables faster access to information, and novel data replication techniques are being developed to meet this. Because of resource limitations, unpredictable user requests, and changing network circumstances, traditional data replication approaches struggle in the distributed and dynamic settings of MEC and the Internet of Things (IoT). Intelligent data placement algorithms, network-aware replication, and demand-based dynamic replication are examples of advanced approaches. By decreasing latency and avoiding the need for repeated calls to the central cloud, these strategies guarantee that data is always available at the proper location and time. Particularly for latency-sensitive applications like augmented reality, autonomous vehicles, and telemedicine, this paper assesses these methods using simulations and case studies from real-world MEC and IoT deployments, showing that they can considerably decrease latency, improve system responsiveness, and increase data availability. Results show that MEC and IoT data management may be enhanced in terms of performance, scalability, and user happiness, which advances the state of the art. © 2024 ieee.

关键词： Mobile edge computing

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Privacy-Preserving Human Activity Recognition in Smart Homes using Deep Learning and Edge computing for real-time Processing 5

Privacy-Preserving Human Activity Recognition in Smart Homes...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 ieee.

关键词： Smart homes

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Efficient time Series Classification using Spiking Reservoir

Efficient Time Series Classification using Spiking Reservoir

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ieee international conference on Fuzzy systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / international Joint conference on Neural Networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Dey, Sounak Banerjee, Dighanchal George, Arun M. Mukherjee, Arijit Pal, Arpan TCS Res Embedded Devices & Intelligent Syst Kolkata India

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

In recent times, in many industrial domains, the need for in-situ processing of time series data from sensors have grown extensively, to ensure low-latency real-time responses, making embedded edge AI an important area of work. Most current techniques, being computationally intensive, are not suited for edge implementation;and in real world scenarios, connectivity to the cloud for processing sensed data leads to higher latency and often less reliability. Neuromorphic systems, coupled with spiking neural networks (SNN) offer a solution to such problems. This paper explores this evolving paradigm to address the need for low-footprint efficient time series classifiers implementable on edge, and targeted for predictive maintenance scenarios. A reservoir-based SNN architecture is designed and tried for classification of different vibration time series datasets. While the system is found to obtain at par classification accuracy for each of the datasets compared to prior arts, it is also observed to be more efficient in terms of synaptic operations per timestep (13% to 38%) using Gaussian temporal spike encoding scheme compared to Poisson rate encoding. Moreover, the system is found to be robust with respect to learning with reduction in training data (upto 20%).

关键词： Spiking Neural Network Reservoir computing time series classification Neuromorphic computing

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A Digital Twin Framework for applications in Ophthalmology 24

A Digital Twin Framework for Applications in Ophthalmology

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international conference on Automation, Quality and Testing, Robotics (AQTR)

作者： Iliuta, Miruna-Elena Pop, Eugen Moisescu, Mihnea-Alexandru Mitulescu, Traian-Costin Univ Politehn Bucuresti Fac Automat Control & Comp Bucharest Romania Univ Med & Pharm Carol Davila Bucharest Romania

ISBN: (纸本)9798350361940;9798350361933;9798350361919

A Digital Twin represents a physical entity through simulation of it's data, behavior, and communication within the physical environment. Digital Twin constitute a digital replica of the corresponding object or process, being able to provide their real-time assessment and monitoring. Digital Twin founds many industrial applications, but they are also increasingly used in healthcare, where they already have a relevant utility and impact. In healthcare systems, Digital Twins is considered as an important progress factor which revolutionizes the patient care providing mode. In this paper, a Digital Twin framework for healthcare application, focusing on ophthalmology is presented. Given the high level of complexity in creating a Digital Twin associated with the eye due to the optical properties of the visual system, a neural network has been trained to identify glaucoma based on a dataset. Glaucoma detection was performed by processing images and extracting the cup/disc ratio, which, at the time of pathology detection, has values ranging from 0.6-0.8. A prediction was realized using linear regression to assess the progression of the patient's pathology over the next 5 years.

关键词： Digital Twin healthcare neural networks glaucoma ophthalmology applications

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