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检索条件"丛书名=Communications in Computer and Information Science"

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Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition 5th

Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Su...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Dong, Yihang Chen, Xuhang Shen, Yanyan Ng, Michael Kwok-Po Qian, Tao Wang, Shuctiang Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Hong Kong Baptist Univ Dept Math Kowloon Tong Hong Kong Peoples R China Macau Univ Sci & Technol Fac Innovat Engn Cotai Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy of individuals leads to non-negligible natural differences in EEG signals across subjects, posing challenges for cross-subject emotion recognition. While recent studies have attempted to address these issues, they still face limitations in practical effectiveness and model framework unity. Current methods often struggle to capture the complex spatial-temporal dynamics of EEG signals and fail to effectively integrate multi-modal information, resulting in suboptimal performance and limited generalizability across subjects. To overcome these limitations, we develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition that utilizes masked brain signal modeling and interlinked spatial-temporal attention mechanism. The model learns universal latent representations of EEG signals through pre-training on large scale dataset, and employs Interlinked spatial-temporal attention mechanism to process Differential Entropy(DE) features extracted from EEG data. Subsequently, a multi-level fusion layer is proposed to integrate the discriminative features, maximizing the advantages of features across different dimensions and modalities. Extensive experiments on public datasets demonstrate Mood Reader's superior performance in cross-subject emotion recognition tasks, outperforming state-of-the-art methods. Additionally, the model is dissected from attention perspective, providing qualitative analysis of emotion-related brain areas, offering valuable insights for affective research in neural signal processing.

关键词： EEG-based emotion recognition Pre-trained Model spatial-temporal attention masked brain signal modeling

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Assessing Public Value in Co-produced Public Services: The Case of Separate Waste Collection in St. Petersburg, Russia 4th

Assessing Public Value in Co-produced Public Services: The C...

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Bakalets, Daria Golubeva, Anastasia Gilenko, Evgenii St Petersburg State Univ Grad Sch Management 3 Volkhovskiy St Petersburg 199004 Russia

ISBN: (纸本)9783031840777;9783031840784

This paper examines the problem of creating public value for services that involve co-production - cooperation between government organizations and citizens. Expanding on a foundational work by M. Moore, published in 1995, we introduce a modification to the public value creation model to incorporate co-produced public services. Further, we apply this modification using the example of a separate waste collection service (SWC) - a service whose effective implementation critically depends on a tight partnership between government authorities and the population, ultimately aiming to improve environmental conditions and foster sustainable development of the area. Utilizing the collected information on 671 respondents, the developed model is estimated using the partial least squares structural equation modeling (PLS-SEM) approach. We demonstrate that there is a set of factors that, in their interconnection, statistically significantly increase the public value of SWC. These results shed light on most effective measures to enhance public participation in waste separation and thus help to prioritize government spending in this domain.

关键词： Public value Public services Co-production Separate waste collection Partial least squares Structural equation modeling

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Applying Skyline Operator and Taxicab Geometry to Identify Optimal Locations for Establishing Business Properties 37th

Applying Skyline Operator and Taxicab Geometry to Identify O...

引用

37th International Conference on computer Applications in Industry and Engineering

作者： Ghosh, Leena Jana Goto, Takaaki Roy, Subhankar Das, Subhashis Sen, Mainak Ghosh, Partha JC Edutech Naihati W Bengal India Toyo Univ Saitama Japan Acad Technol Adisaptagram W Bengal India Techno India Univ Kolkata W Bengal India

ISBN: (纸本)9783031762727;9783031762734

A proper multi-criteria optimization for any business organization always improvise business intelligence. Handling multiple criteria of customers' through any structured query language is difficult, especially when the criterion are inversely proportional to each other. Handling multi-criteria optimization through skyline operator may be one of the possible best solutions for handling criterion those are inversely proportional. Generally, skyline operator determines an optimal point in the problem domain and ranks other skyline points by computing the shortest distance from that optimal point. But this type of approach is not suitable for applying the skyline operator in the domain of travelling and tourism industry as there may be several physical obstacles upon the shortest distance path. This paper proposes a novel methodology for establishing new business properties or hotels that satisfy all travellers' and property owners' requirements, allowing travellers to visit all their desired locations.

关键词： Multi-criteria optimization Skyline operator Business intelligence Business property. Tour Travel

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Enhancing Automated De-identification of Pathology Text Notes Using Pre-trained Language Models

Enhancing Automated De-identification of Pathology Text Note...

引用

2024 International Workshop on Deidentification of Electronic Medical Record Notes

作者： Huang, Yong-Zhen Peng, Tzu-Cheng Lin, Heng-Yu Sy, Eugene Chang, Yung-Chun Taipei Med Univ Coll Management Grad Inst Data Sci Taipei Taiwan Natl Taiwan Univ Dept Nursing Canc Ctr Taipei Taiwan Taipei Med Univ Hosp Clin Big Data Res Ctr Taipei Taiwan

ISBN: (纸本)9789819779659;9789819779666

With the recent advancements in artificial intelligence (AI), particularly the surge in large language models (LLMs) led by industry giants such as OpenAI, Microsoft, and Google, a new era in clinical medicine and digital health has been ushered in. These advancements, transformative as they are, introduce significant privacy concerns, particularly in the healthcare sector, which heavily relies on Electronic Health Records notes (EHRs) text notes. Our research directly addresses these privacy challenges by enhancing the T5-Efficient-BASE-DL2 model for the de-identification of patient data. We fine-tuned the T5 model with specific hyperparameter adjustments and implemented exact match evaluations to significantly reduce false positives and improve the accuracy of entity recognition, particularly for sensitive information processing. This was achieved using regular expressions and the Pycountry library. Our enhancements resulted in a notable improvement in the model's performance, with a 14% increase in the macro-average F1-score for Task 1, rising from 0.7787 to 0.9187, and a 30% improvement for Task 2, from 0.4871 to 0.79. These advancements not only enhance the security and efficiency of healthcare information management but also uphold the integrity of individual privacy and facilitate the safe sharing of data for research purposes without compromising confidentiality. Our research lays the groundwork for future developments in improving rule-based methodologies and expanding datasets, aiming to further integrate machine learning with linguistic rules for more effective data privacy solutions in healthcare.

关键词： Large Language Models Electronic Health Records notes Sensitive Health information

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Joint UAV Trajectory Optimization and Task Offloading in Integrated Air-Ground Networks 7th

Joint UAV Trajectory Optimization and Task Offloading in Int...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Zhang, Yuanwei Shuai, Jiaqi Wen, Weichang Cui, Haixia South China Normal Univ Sch Elect & Informat Engn Foshan 528200 Peoples R China

ISBN: (纸本)9789819600540;9789819600557

As an essential component of the integrated air-ground-space networks, the unmanned aerial vehicle (UAV) is indispensable for the mobile edge computing (MEC) processing. In this paper, considering the highly dynamic nature of information exchange, we apply the multiagent reinforcement learning (MARL) for the UAV-based communication networks and introduce a low-complexity K-means algorithm for the precluster/partition users to improve the system performance. We devise a general reward and punishment strategy within the multi-agent reinforcement learning process to generate the optimal trajectories and promote the coverage maximization. Furthermore, we utilize NOMA technology to provide stable data rate gains and allocate more power to weaker users under optimizing energy consumption so as to maximize the overall system throughput. Simulation results demonstrate that the proposed joint optimization algorithm achieves excellent convergence performance in UAV based integrated air-ground-space networks.

关键词： UAV K-means algorithm multi-agent reinforcement learning

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Integrating IoT and Machine Learning for Human Fall Detection and Activity Monitoring 4th

Integrating IoT and Machine Learning for Human Fall Detectio...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Bittal, Vijaylaxmi Jain, Mayur Patil, Shruti Patil, Harshada Chaudhari, Soham SVKMs IOT Dept Comp Engn Dhule India

ISBN: (纸本)9783031837890;9783031837906

Elderly falls mostly refer to critical incidents, which imply a critical need for detection systems that can accurately detect falls. This paper presents an IoT-based fall-detection and activity monitoring system by the integration of supervised machine learning algorithms with MPU6050 motion sensors. The performance of six machine learning algorithms has been tested on collected motion data to detect and classify activities such as sitting, standing, walking, and falling. Results showed the Random Forest model yielded the highest accuracy (96%) and F1-score (95.5%) for detecting falls compared to SVM and outperformed it in classification performance as well as robustness to data variations. The proposed system is guaranteed to be a reliable, user-friendly solution in elderly care due to its real-time functionality and low false alarm rates.

关键词： Fall Detection Machine Learning Activity Monitoring

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Temporal Knowledge Graph Link Prediction Using Synergized Large Language Models and Temporal Knowledge Graphs 5th

Temporal Knowledge Graph Link Prediction Using Synergized La...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Chen, Yao Shen, Yuming Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Although large language models and temporal knowledge graphs each have significant advantages in the field of artificial intelligence, they also face certain challenges. However, through collaboration, large language models and temporal knowledge graphs can complement each other, addressing their respective shortcomings. This collaborative approach aims to harness the potential feasibility and practical effectiveness of large language models as external knowledge bases for temporal knowledge graph reasoning tasks. In our research, we have meticulously designed a synergized model that leverages the knowledge from the graph as prompts. The answers generated by the large language model undergo careful processing before being seamlessly incorporated into the training dataset. The ultimate goal is to significantly enhance the reasoning capabilities of temporal knowledge graphs. Experimental results underscore the positive impact of this synergized model on the completion tasks of temporal knowledge graphs, showcasing its potential to address gaps in knowledge and improve overall performance. While its influence on prediction tasks is relatively weak, the collaborative synergy demonstrates promising avenues for further exploration and development in the realm of AI research.

关键词： Large language models Temporal knowledge graphs Synergetic pattern Completion task Prediction task

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A Gender Inclusivity Framework for the Cybersecurity Profession in Africa 4th

A Gender Inclusivity Framework for the Cybersecurity Profess...

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： da Veiga, Adele Bongiovanni, Ivano Univ South Africa UNISA Coll Sci Engn & Technol Sch Comp Florida Campus Johannesburg South Africa Univ Queensland Brisbane Brisbane Qld Australia

ISBN: (纸本)9783031840777;9783031840784

There is a lack of skilled workers in cybersecurity with a global under-representation of women in this profession. In Africa, women represent only nine per cent of the cybersecurity profession, facing various challenges and barriers to entering and progressing as cybersecurity professionals. This study applied a scoping literature review to identify and consolidate the challenges women face entering and working through the ranks of the cybersecurity profession in Africa. A Gender Inclusivity Framework for the Cybersecurity Profession in Africa is proposed in this study, incorporating the requisite organisational theory and the findings from the literature review. By advocating change that needs to happen at a resource, societal, educational, organisational, and government level, the research offers a holistic framework to transform the cybersecurity profession in Africa into a more equitable field, emphasizing the empowerment of African women.

关键词： Cybersecurity cybersecurity profession women gender inclusivity equity diversity Africa

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"Pre-diagnosis and Referral of Patients to a Specialist Medical Team Through Facial Biomarker Detection" 1

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Tomas Munoz, Jose Villaroel, Benjamin Taramasco, Carla Gatica, Gustavo Univ Andres Bello Santiago Chile

ISBN: (数字)9783031832079

ISBN: (纸本)9783031832062;9783031832079

Diseases can show facial characteristics and deformities, which can serve as diagnostic markers for various conditions like endocrine and metabolic syndromes, genetic disorders, and facial neuromuscular diseases. While facial recognition technology has been around for over fifty years, its use in clinical medicine has surged in the past decade. AI-based facial recognition has shown superior performance in diagnosing diseases. The research aims to establish a framework and methodology for identifying optimal algorithms and techniques for early diagnosis of neurodegenerative diseases. Through a comprehensive review and modeling of existing algorithms in the literature. The proposed development focuses on the early detection of neurodegenerative diseases to facilitate timely treatment and disease management. The implementation of the machine learning algorithms which are derived of convolutional neural networks (CNN), histogram of gradients (HOG), OpenFace 2.0 and supporting vector machine (SVM) in the proof-of-concept simulation achieve an accuracy of 87,5% and an F1 Score 83,3%. These results and its approach show a superior risk detection accuracy than that of the human physicians and thus is a viable diagnostic support for identifying neurodegenerative illness and referring patients to the appropriate medical specialist.

关键词： Machine Learning Artificial Intelligence computer Medicine Facial Landmarks Neurodegenerative Diseases

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Outdoor Biophotovoltaic LED Luminaire for Sustainable Energy Solutions

Outdoor Biophotovoltaic LED Luminaire for Sustainable Energy...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Campoverde, Christian Vega, Andy Merino, Pabel Rodriguez, Luis Medina, Santiago Vargas, Alba Azuero, Luis Univ Nacl Loja Loja Ecuador

ISBN: (纸本)9783031834318;9783031834325

This research focuses on designing and constructing a biophotovoltaic (BPV) lamp to explore the feasibility and efficiency of utilizing energy produced by plants and microorganisms for lighting LED lamps. The prototype primarily utilizes clay-based photosynthetic microbial fuel cells (PMFC) equipped with carbon, graphite, and aluminum electrodes. Additionally, it incorporates advanced technologies such as supercapacitors with ultra-fast charging times and surface-mount LED (SMD) lights that offer harmonious illumination with minimal energy consumption. A significant challenge in BPV is the low output current, as the energy is derived from the oxidation of biomass (exudates) rather than directly from the photosystems. This latter method is more complex and often directly harmful to the photosynthetic species, representing a nascent area of research. The prototype achieved a peak power output sufficient to sustain LED illumination for 9 h per day. The integration of supercapacitors substantially enhanced energy storage and delivery efficiency, thus minimizing energy loss. Although currently constrained by low output currents, the findings suggest that BPV systems have the potential to serve as a sustainable energy solution. Further development of electrode materials and configurations could improve energy harvesting capabilities, paving the way for practical applications in renewable energy. This study's results are expected to contribute to the development of innovative BPV energy harvesting techniques based on the redox effect, membrane types, electrode positioning, and electrode materials, thereby advancing the field of renewable and sustainable energy.

关键词： Biophotovoltaics BPV Electrodes PMFC Redox

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