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Social Media Integration in Public Transportation: A Case Study of Sweden 17

Social Media Integration in Public Transportation: A Case St...

引用

ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Sarkheyli, Azadeh Sarkheyli, Elnaz Halmstad University Department of Informatics Halmstad Sweden Malmö University Department of Urban Studies Malmö Sweden

ISBN: (纸本)9798350351637

In the past twenty years, social media has completely transformed the way businesses interact with their customers. Social media's massive reach and resources have opened new and exciting possibilities for companies to connect with their audience and build strong relationships. This research paper explains how social media is used for public transportation in Sweden. As digital platforms become increasingly important in modern society, transportation systems have sought new ways to engage with passengers and improve their services. Drawing on communication studies and transportation management theories, this study examines how Facebook can be used as a communication channel for public transportation in Sweden. The paper evaluates the strategies employed by the Swedish transportation authority to leverage Facebook to disseminate real-time information, foster community engagement, and address passenger concerns. It reviews existing literature, surveys 106 people in Sweden, and analyzes the most commonly used social media pages. The findings of this study offer insights and highlight opportunities and challenges associated with using social media platforms within public transportation. This research paper provides valuable information for transportation policymakers, practitioners, and researchers who want to improve the efficiency and accessibility of urban mobility systems. © 2024 ieee.

关键词： Facebook Public Transportation Social Media Sweden

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A Prediction Based Resource Reservation Algorithm for Service Handover in Edge computing 10

A Prediction Based Resource Reservation Algorithm for Servic...

引用

10th ieee international conference on Cyber Security and Cloud computing and 9th ieee international conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Guan, Peiyuan Li, Yushuai Taherkordi, Amir University of Oslo Dept of Informatics Oslo Norway

ISBN: (纸本)9798350312461

Handover is a crucial issue in ensuring the continuity of edge services in edge computing (EC) systems. Failure to handle hand-off properly may result in delays, data loss, or service interruption during service switching. Therefore, optimizing the hand-off process to ensure service continuity and satisfactory user experience is a significant challenge in the design of edge computing systems. In this paper, we propose a resource reservation algorithm that reserves a portion of computing resources in each base station to meet quality of service (QoS) requirements during service switching. We use an LSTM model to predict the number of new and existing users at a future time point to provide decision guidance for the resource reservation algorithm. Extensive simulation experiments demonstrate that the proposed algorithm outperforms the benchmark algorithms in a variety of environmental conditions. © 2023 ieee.

关键词： Edge computing

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Deep Learning Scheme for Modulation Classification under Fading Channels 7

Deep Learning Scheme for Modulation Classification under Fad...

引用

7th international conference on Contemporary computing and informatics, IC3I 2024

作者： Ghauri, Sajjad A. Sarfraz, Mubashar Chhabra, Gunjan Kaushik, Keshav Zafar, Asim Khan, Inam Ullah Isra University School of Engineering & Applied Sciences Islamabad Pakistan Numl University Faculty of Engineering and Computing Islamabad Pakistan Graphic Era Hill University Graphic Era Deemed to be University Department of Cse Uttarakhand Dehradun India Amity University Amity School of Engineering and Technology Punjab India National University of Technology Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350350067

The field of automatic modulation classification using deep neural networks has undergone significant development in recent years. In this research, M-QAM and N-PSK modulation formats have been considered for classification. The deep neural network architecture is directly fed with raw signals that are often corrupted with channel noise and fading effects such as Rayleigh and Nakagami. The proposed classifier structure also incorporates normalized cumulants of higher order from the received signal. The proposed DNN architecture's performance is evaluated by comparing its efficiency for raw data and extracted features-based classification. We have validated the classifier efficiency through three different cases. Our proposed classifier demonstrates remarkable capability in efficiently classifying modulation formats. © 2024 ieee.

关键词： Signal modulation

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Emotional Knowledge Self-Distillation in Dialogue

Emotional Knowledge Self-Distillation in Dialogue

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jian, Zhongquan Wang, Yancheng Wu, Weichao Yao, Junfeng Wang, Meihong Wu, Qingqiang School of Informatics Xiamen University Xiamen China School of Film Xiamen University Xiamen China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia School of Mechanics and Civil Engineering China University of Mining & Technology Beijing China Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen China

ISBN: (纸本)9798350368741

Recognizing emotions in dialogues is vital for effective human-computer interaction, yet remains a challenging task in Natural Language Processing (NLP). Previous studies in Emotion Recognition in Conversation (ERC) have primarily focused on contextual features, while overlooking the importance of emotional features in emotion recognition. To address this gap, we focus on the role of emotional features in ERC and propose a novel method, Emotional Knowledge Self-Distillation (EmoKSD1), to enhance the model's emotional sensitivity. In EmoKSD, utterances are enriched with implicit 〈mask〉 tokens to represent conveyed emotions, allowing the distillation of emotional knowledge from explicit emotional tokens to implicit 〈mask〉 tokens, thereby enhancing the model's ability to perceive subtle emotions within the dialogue. Through thorough evaluations on two public ERC datasets (i.e., IEMOCAP and MELD) using proposed coarse-grained utterance distillation and fine-grained token distillation techniques, EmoKSD demonstrates superior performance compared to existing methods, highlighting the significance of emotional features in ERC. © 2025 ieee.

关键词： Dialogue System Emotion Recognition in Conversation Knowledge Distillation Natural Language Processing

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A Comprehensive Review of Bias Detection and XAI Techniques in NLP 5

A Comprehensive Review of Bias Detection and XAI Techniques ...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Bhavaneetharan, Loganathan Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798331530983

Bias in Natural Language Processing (NLP) models pose urgent ethical and societal challenges by perpetuating stereotypes and inequalities. This review provides a comprehensive overview of state-of-the-art Explainable AI (XAI) techniques - such as LIME, SHAP, and Integrated Gradients - for detecting and mitigating bias. We detail our methodology for selecting relevant studies and analyze key NLP datasets (e.g., StereoSet, CrowS-Pairs) to uncover specific limitations like language coverage and intersectional gaps. Moreover, we highlight emerging challenges in scalability, cultural diversity, and regulatory compliance. By integrating these findings, we propose actionable strategies for fair and transparent NLP systems, positioning this work as a foundation for ongoing improvements in equitable AI solutions. © 2025 ieee.

关键词： Natural language processing systems

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Systematic Review of Contemporary Breast Cancer Detection Techniques Using Machine Learning 7

Systematic Review of Contemporary Breast Cancer Detection Te...

引用

7th international conference on Contemporary computing and informatics, IC3I 2024

作者： Charu Gupta, Kavita University In Computing Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350350067

Breast cancer, the main cause of mortality and disability in women globally, requires novel techniques to early detection, precise diagnosis, and successful treatment. In recent years, the combination of feature selection methodologies with CNN (convolutional neural network) techniques has emerged as a possible strategy to tackling the intricate nature of breast cancer analysis. This systematic review synthesizes the research at the intersection of feature selection and CNN techniques in breast cancer analysis, aiming to provide insights into the synergistic potential of these methodologies in advancing precision oncology. Through a rigorous search and selection process, articles extracted from peer reviewed journals and proceedings of A grade conferences. The review elucidates the diverse modalities of feature selection techniques employed in conjunction with CNN architectures, encompassing filter-based methods, wrapper approaches, embedded feature selection, and hybrid strategies. Presented work showcased the literature, using important parameters like classification of histopathological images, molecular subtype prediction, and treatment response assessment and unveils the transformative potential of feature selection and CNN techniques in augmenting the accuracy, interpretability, and generalizability of breast cancer analysis. Moreover, scrutinize the methodological rigor and translational relevance of the reviewed studies, delineating key challenges, limitations, and avenues for future research. © 2024 ieee.

关键词： Convolutional neural networks

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Hybrid Approaches to Emotion Recognition: A Comprehensive Survey of Audio-Textual Methods and Their Application 4

Hybrid Approaches to Emotion Recognition: A Comprehensive Su...

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4th international conference on Advanced Research in computing, ICARC 2024

作者： Wewelwala, Sahan Hewage Sumanathilaka, T.G.D.K. School of Computing Informatics Institute of Technology 06 Colombo Sri Lanka Swansea University Department of Computer Science Wales Swansea United Kingdom

ISBN: (纸本)9798350384864

This survey study provides a comprehensive analysis of emotion recognition, documenting its progression from conventional techniques to innovative hybrid algorithms that effectively combine textual and audio information. This work distinguishes itself with its thorough examination of the innovative combination of deep learning-based textual sentiment analysis and sophisticated aural signal processing approaches. This unique combination greatly improves the precision of emotion recognition from intricate sources like social media and consumer feedback. The study addresses the difficulties of processing real-time data and reducing bias in varied datasets. It provides new perspectives on the powers and limitations of neural networks and machine learning in this specific scenario. Our research represents a notable advancement in the utilization of these technologies for human-computer interaction, facilitating the creation of digital interfaces that are more empathic and attuned to human emotions. The final statements underscore the pressing requirement for sophisticated multimodal methodologies and the incorporation of developing technology, underscoring our distinctive contribution to the progress of emotion identification systems. This report offers a thorough analysis of the present condition of the subject and outlines a direction for future advancements, highlighting the crucial significance of emotion identification in improving human-computer interaction. © 2024 ieee.

关键词： Emotion Recognition

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Single Camera-enabled Reinforcement Learning Traffic Signal Control System supporting Life-long Assessment 9

Single Camera-enabled Reinforcement Learning Traffic Signal ...

引用

ieee international conference on Smart computing (SMARTCOMP)

作者： Tran, Toan V. Sartipi, Mina Univ Tennessee Ctr Urban Informat & Progress Chattanooga TN 37401 USA

ISBN: (纸本)9798350322811

Traffic signals, also known as traffic lights, are an essential tool for managing intersections. Improving traffic signal control can enhance the overall performance of transportation systems. Recently, reinforcement learning has shown promise in optimizing traffic signal control by utilizing high-resolution, real-time data from advanced traffic monitoring systems. However, creating a reinforcement learning-based control that is robust to all scenarios is challenging because the real world is a diverse, non-stationarity and open-ended environment. Therefore, a system that can continually monitor and evaluate the traffic signal control's operation is necessary for deploying such these controls. In this paper, we proposed a novel reinforcement learning-based traffic signal control system that supports near real-time performance assessment.

关键词： intelligent transportation system traffic signal control reinforcement learning

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Bridging the Gap with Sensor Networks and Cloud computing in the Geospatial Web for Optimized Data Services 7

Bridging the Gap with Sensor Networks and Cloud Computing in...

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7th international conference on Contemporary computing and informatics, IC3I 2024

作者： Veeresh, G. Sreevani, N. Lakhanpal, Sorabh Sharma, Saurabh Reddy, B.Ravali Al-Rubaye, Taqi Mohammed Khattab New Horizon College of Engineering Bangalore India Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technologies Department of Medical Laboratory Technology Najaf Iraq

ISBN: (纸本)9798350350067

Implementing cloud computing and data networks for local internet services is a priority. This research addresses the issue by introducing a novel approach to enhance the quality of regional internet data services. The proposed technique, Geographical Data Fusion with Dynamic Cloud Resource Allocation (GDF-DCRA), enhances map analytics by combining geographical data fusion with dynamic cloud resource allocation. GDF-DCRA enhances the speed of geospatial analytics by reducing sensor data, efficiently allocating cloud resources, and integrating spatial and temporal data. It consistently outperforms six industry benchmarks in terms of speed, latency, scalability, data accuracy, bandwidth efficiency, fault tolerance, and resource utilisation. Despite the increasing difficulty of working with real-time location data, the proposed approach has the potential to address these challenges. © 2024 ieee.

关键词： Spatio-temporal data

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An Event-Triggered Task Migration Strategy for Mobile Edge computing Based on CTMDPs 10

An Event-Triggered Task Migration Strategy for Mobile Edge C...

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10th international conference on Systems and informatics, ICSAI 2024

作者： Ma, Zhuo Jiang, Qi Zhou, Zhiwei Yang, Huan School of Electrical Engineering and Automation Hefei University of Technology Hefei China

ISBN: (纸本)9798331510138

Collaboration of edge nodes through task migration offers a promising solution for mobile edge computing to meet the varied computation-intensive demands with constrained resources. However, most existing task migration strategies perform in discrete-time paradigm, which affects modeling accuracy and control responsiveness. To bridge this gap, this paper presents an event-triggered task migration strategy based on continuous-time Markov decision processes (CTMDPs), which precisely captures the users behaviors, task characters and migration processing. By normalizing the CTMDPs into an equivalent discrete-time formulation, a relative value iteration (RVI) algorithm with long-run average criterion is employed to obtain the optimal solution. To alleviate the computational overhead, an event-based Q-learning approach is also presented to derive the approximate optimal policies. Extensive simulations are conducted to validate the effectiveness of both algorithms. © 2024 ieee.

关键词： Mobile edge computing

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