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Unveiling the social fabric through a temporal, nation-scale social network and its characteristics

Scientific reports 2025年第1期15卷 18383页

作者： Jolien Cremers Benjamin Kohler Benjamin Frank Maier Stine Nymann Eriksen Johanna Einsiedler Frederik Kølby Christensen Sune Lehmann David Dreyer Lassen Laust Hvas Mortensen Andreas Bjerre-Nielsen Methods and Analysis Statistics Denmark 2100 Copenhagen Denmark. Center for Social Data Science (SODAS) University of Copenhagen 1353 Copenhagen Denmark. Center for Law and Economics ETH Zurich 8006 Zurich Switzerland. Department of Applied Mathematics and Computer Science Technical University of Denmark 2800 Kongens Lyngby Denmark. Department of Economics University of Copenhagen 1353 Copenhagen Denmark. Department of Public Health University of Copenhagen 1353 Copenhagen Denmark. ROCKWOOL Foundation 1472 Copenhagen Denmark. Center for Social Data Science (SODAS) University of Copenhagen 1353 Copenhagen Denmark. abn@sodas.ku.dk. Department of Economics University of Copenhagen 1353 Copenhagen Denmark. abn@sodas.ku.dk.

Social networks shape individuals' lives, influencing everything from career paths to health. This paper presents a registry-based, multi-layer and temporal network of the entire Danish population from 2008 to 2021. Our network maps the relationships formed through family, households, neighborhoods, colleagues and classmates for approximately 7.2 million individuals with more than 1.4 billion relations between them over the course of a decade. We outline key properties of this multiplex network, introducing both an individual-focused perspective as well as a bipartite representation. We show how to aggregate and combine the layers, and how to efficiently compute network measures such as shortest paths in large administrative networks. Our analysis reveals how past connections reappear later in other layers, that the number of relationships aggregated over time reflects the position in the income distribution, and that we can recover canonical shortest-path-length distributions when appropriately weighting connections. Along with the network data, we release a Python package that uses the bipartite network representation for efficient analysis.

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Cost-Effective Authenticated Solution (CAS) for 6G-Enabled Artificial Intelligence of Medical Things (AIoMT)

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IEEE Internet of Things Journal 2024年第13期11卷 23977-23984页

作者： Mahmood, Khalid Obaidat, Mohammad S. Shamshad, Salman Alenazi, Mohammed J. F. Kumar, Gulshan Anisi, Mohammad Hossein Conti, Mauro National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Jordan King Abdullah II School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Communication and Computing Engineering Beijing100083 China Amity University Amity School of Engineering and Technology Noida201301 India University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh12311 Saudi Arabia University of Padua Department of Mathematics Padua35131 Italy Lovely Professional University Department of Computer Science and Engineering Phagwara144411 India University of Essex School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

The Internet of Things (IoT) is a network of interconnected objects, which congregate and exchange gigantic amounts of data. Usually, predeployed embedded sensors sense this massive data. Soon, several applications of IoT are anticipated to exploit emerging 6G technology. Healthcare is one of them, where the 6G-inspired paradigm may facilitate the users to exchange information through hundreds of sensors under the assumption of Artificial Intelligence of Things (AIoT). Integration of medical sensors with AIoT is known as Artificial Intelligence of Medical Things (AIoMT). The secure and seamless interactions among 6G-enabled AIoMT users should be the primary challenge. Furthermore, resource-constrained wearable sensing devices, with their inability to execute complex security solutions, provide an ideal attraction for malicious entities to launch diverse attacks. These challenges have motivated us to design a cost-effective authenticated solution (CAS) for 6G-enabled AIoMT healthcare applications. Our CAS protocol not only prevents cyber threats like impersonation session key secrecy but it can also prevent physical threats like hardware tampering. We observe formal and informal security validations to endorse its robustness and effectiveness. Performance comparison reveals that the CAS protocol offers maximum security enrichment. Moreover, CAS is cost-effective as it has achieved 33% and 60% reduction in computation and communication overheads, respectively, compared to contemporary competing related protocols. © 2014 IEEE.

关键词： Authentication

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Classification of Customer Complaints: TF-IDF Approaches 2

Classification of Customer Complaints: TF-IDF Approaches

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2nd IEEE International Conference on Intelligent Technologies, CONIT 2022

作者： Alamsyah, Doni Purnama Arifin, Toni Ramdhani, Yudi Hidayat, Febriansyah Achmad Susanti, Leni Bina Nusantara University Entrepreneurship Department Jakarta Indonesia Universitas Adhirajasa Reswara Sanjaya Computer Science Program Bandung Indonesia

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

Bank Rakyat Indonesia (BRI) is an Indonesian state-owned bank;the operations of the bank certainly aim to provide satisfaction to customers. This can be realized by quickly responding to the bank's response to complaints. The number of complaints received by BRI banks to date is large, but the categories of complaints are still mixed so each division of the bank has difficulties and must first filter complaints. This study aims to classify bank complaints using the Text Mining field to make it easier for BRI to provide complaints directly to each division. The classification method used is Neural Network and the data preprocessing method uses the TF -IDF method. The dataset used in the form of text is one million datasets with 5 classes, namely debit cards, credit cards, customer service, mobile banking, and loans. The results of this study indicate that the accuracy of the neural network method is better and becomes a recommendation to be implemented at BRI. This research is useful for banking in Indonesia or the service industry in managing customer relationship management. © 2022 IEEE.

关键词： Customer satisfaction

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Exploiting loop-array dependencies to accelerate the design space exploration with high level synthesis

Exploiting loop-array dependencies to accelerate the design ...

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2015 Design, Automation and Test in Europe Conference and Exhibition, DATE 2015

作者： Pham, Nam Khanh Singh, Amit Kumar Kumar, Akash Khin, Mi Mi Aung DCT Division Data Storage Institute A STAR Singapore ECE Department National University of Singapore Singapore Department of Computer Science University of York United Kingdom

ISBN: (纸本)9783981537048

Recently, the requirement of shortened design cycles has led to rapid development of High Level Synthesis (HLS) tools that convert system level descriptions in a high level language into efficient hardware designs. Due to the high level of abstraction, HLS tools can easily provide multiple hardware designs from the same behavioral description. Therefore, they allow designers to explore various architectural options for different design objectives. However, such exploration has exponential complexity, making it practically impossible to explore the entire design space. The conventional approaches to reduce the design space exploration (DSE) complexity do not analyze the structure of the design space to limit the number of design points. To fill such a gap, we explore the structure of the design space by analyzing the dependencies between loops and arrays. We represent these dependencies as a graph that is used to reduce the dimensions of the design space. Moreover, we also examine the access pattern of the array and utilize it to find the efficient partition of arrays for each loop optimization parameter set. The experimental results show that our approach provides almost the same quality of result as the exhaustive DSE approach while significantly reducing the exploration time with an average of speed-up of 14x. © 2015 EDAA.

关键词： High level synthesis

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DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimizing Radiotherapy Treatment Plans in Oropharyngeal Cancer

DE-ConvGraph 3D UNet: A Novel Deep Learning Model for Optimi...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Murugadoss, Bhuvanashree Amudha, J. Sugumaran, Vijayan Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science and Engineering Karnataka Bengaluru India Oakland University Center for Data Science and Big Data Analytics RochesterMI United States Oakland University Department of Decision and Information Sciences RochesterMI United States

ISBN: (纸本)9798350359527

Automated radiotherapy treatment planning aims to improve treatment accuracy and efficiency. However, the prevalent Knowledge-Based Planning (KBP) method faces issues like lengthy manual problem formulation and challenges in accurately modeling human anatomy and processing high-dimensional data. This work focuses on treatment plan optimization and considers deep learning as a potential solution. Deep learning algorithms, by virtue of their ability to learn from large quantities of data and model complex relationships, can automate the formulation of the optimization problem in KBP, saving significant time and effort. However, despite these compelling advantages, it should be noted that the current convolution-based encoder-decoder models used for radiotherapy treatment plan optimization have a limited capability in capturing long-range dependencies between distant voxels. This work aims to introduce 'DE-ConvGraph 3D UNet', a novel deep learning model, to address these limitations and optimize radiotherapy treatment plans for oropharyngeal cancer. The proposed 'DE-ConvGraph 3D UNet' model includes a Graph Convolutional Network (GCN) component to capture long-range dependencies between distant voxels. Furthermore, the dual-encoder structure of the model combines the strengths of GCN and 3D Convolutional U-Net, enabling global relationships and local pattern capturing in a 3D patient volume. Experiments were conducted using the benchmark dataset-OpenKBP-Opt. The proposed model shows improvements in performance in comparison to state-of-The-Art U-Net variants in terms of mean squared voxel-wise error, dose volume histogram points and clinical criteria satisfaction. © 2023 IEEE.

关键词： Learning algorithms

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Implementation of computer Vision of Jakarta Weather Image Categorization Using ResNet

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Procedia computer science 2023年 227卷 813-822页

作者： Kartika Purwandari Join W.C. Sigalingging Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

At the moment, weather data is crucial for supporting neighborhood activities. The economy and trade are both centered in Jakarta, which is also Indonesia's capital. Therefore, it is crucial to have access to weather information so that these activities don't get disrupted, which would then hinder commercial and trade activity. Social media has been a very popular tool for spreading information recently. Particularly on Instagram, where users favor taking images and sharing the information they encounter. @jktinfo is the Instagram account that posts information about the situation in Jakarta and the area, including the current weather. The @jktinfo account is utilized in this project to gather data. Utilizing a variety of techniques, the collected photographs of sunny, cloudy, and wet situations were.

关键词： computer vision weather image categorization data augmentation deep transfer learning residual network

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EmoSync: Facial Emotion Detection for Adaptive Music Playback 3

EmoSync: Facial Emotion Detection for Adaptive Music Playbac...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Devarajan, Devikanniga Rajkumar, K. GITAM Deemed to be University Computer Science and Engineering Nagadenahalli Doddabalpur Karnataka Bangalore561203 India SRM Institute of Science and Technology Department of Data Science and Business Systems School of Computing Tamil Nadu Kattankulathur603 203 India

ISBN: (纸本)9798350372120

This work investigates using Convolutional Neural Networks (CNN) and VGGNet architecture, for realtime emotion detection. Raw data from the FER-2013 dataset is transformed from CSV format into organized image directories, segmented by distinct emotions. The resulting model, equipped with convolutional, pooling, and dense layers, effectively classifies emotions from 48x48 grayscale images. In application, the model overlays detected emotions onto a webcam stream. An integrated music player module synchronizes music with the subject's emotions. Overall, this research explores a comprehensive interactive system focused on recognizing and responding to human emotions. Without further training data, our model, to the best of our understanding, achieves modern single-network accuracy of 74.57% on FER2013 © 2024 IEEE.

关键词： Python

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Towards Target-Dependent Sentiment Classification in News Articles 16th

Towards Target-Dependent Sentiment Classification in News Ar...

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16th International Conference on Diversity, Divergence, Dialogue, iConference 2021

作者： Hamborg, Felix Donnay, Karsten Gipp, Bela Department of Computer Science University of Konstanz Konstanz Germany Heidelberg Academy of Sciences and Humanities Heidelberg Germany Department of Political Science University of Zurich Zurich Switzerland Data and Knowledge Engineering University of Wuppertal Wuppertal Germany

ISBN: (纸本)9783030713041

Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news as an essential information source in individual and societal decision making. This article introduces NewsTSC, a manually annotated dataset to explore TSC on news articles. Investigating characteristics of sentiment in news and contrasting them to popular TSC domains, we find that sentiment in the news is expressed less explicitly, is more dependent on context and readership, and requires a greater degree of interpretation. In an extensive evaluation, we find that the current state-of-the-art in TSC performs worse on news articles than on other domains (average recall AvgRec= 69.8 on NewsTSC compared to AvgRev= [75.6, 82.2 ] on established TSC datasets). Reasons include incorrectly resolved relation of target and sentiment-bearing phrases and off-context dependence. As a major improvement over previous news TSC, we find that BERT’s natural language understanding capabilities capture the less explicit sentiment used in news articles. © 2021, Springer Nature Switzerland AG.

关键词： Decision making

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Fededrated Learning: Research, Challenges, and Future Directions 5

Fededrated Learning: Research, Challenges, and Future Direct...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kaswan, Kuldeep Singh Dhatterwal, Jagjit Singh Yadav, Rishika Malik, Kiran Tripathi, Aanjey Mani Galgotias University School of Computing Science and Engineering UP Greater Noida India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation AP Guntur India Graphic Era Hills University Department of Computer Science and Engineering Uttarakhand Dehradun India Mriem Department of Computer Science and Engineering Rohtak India Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350383522

Federated Learning, a disruptive and novel aspect of machine learning, is at the forefront of decentralized, privacy-conscious data processing. This in-depth review study navigates the complex environment of Federated Learning Ecosystems, attempting to extract the core of the topic while shining light on its research horizons, pressing difficulties, and promising approaches. Our trip begins with a look at the fundamental concepts of Federated Learning, emphasizing its importance in the landscape of distributed machine learning paradigms. We track its development via historical milestones and key contributions, offering context for the field's increasing research. This study focuses on the many features of Federated Learning research, including privacy-preserving techniques, communication protocols, aggregation approaches, and real-world applications. We aggregate major results and advances within these domains, highlighting notable authors and their contributions. However, the route to Federated Learning's full fulfillment is plagued with difficulties, including questions about privacy, communication efficiency, and scalability. In the future, we will enlighten the growing paths of Federated Learning research, forecasting trends, providing insights into overcoming present problems, and visualizing its integration into diverse fields. To summarize, this review article serves as a guidepost for scholars, practitioners, and enthusiasts wanting to understand the vast domain of Federated Learning. It captures the core of this dynamic discipline, offers strategic counsel for resolving its obstacles, and encourages optimism about its bright future within the domain of distributed machine learning paradigms. © 2024 IEEE.

关键词： data Analysis Distributed Environments Edge Computing Federated Learning Machine Learning

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Automated Machine Learning in Critical Energy Infrastructure for Net Zero Carbon Emissions 32

Automated Machine Learning in Critical Energy Infrastructure...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Moraliyage, Harsha Haputhanthri, Dilantha Samarajeewa, Chamod Mills, Nishan De Silva, Daswin Manic, Milos Jennings, Andrew La Trobe University Centre for Data Analytics and Cognition Melbourne Australia Virginia Commonwealth University Department of Computer Science Richmond United States

ISBN: (纸本)9798350399714

Increasing energy consumption and diversified energy generation have led to the proliferation of energy management (EM) systems for optimized grid operations and net zero carbon emissions. Artificial Intelligence (AI) is centric to EM systems to process large volumes of high velocity data for anomalies, predictions, optimization and other actionable insights that unravel the complexities of grid operations. Managing Energy AI capabilities itself is becoming an increasingly complex task that requires extensive resourcing and expertise. In this paper, we aim to address this gap by formalizing the role of Automated Machine Learning (AutoML) by proposing a novel framework for its key functionalities in critical energy infrastructure. This framework provides a generic and cohesive abstraction to assist with integrating and managing the complexities of AI capabilities. The framework is empirically evaluated in the microgrid setting of a multi-campus, multi-functional tertiary education institution. Results from these experiments confirm the performance contributions of the proposed framework in addressing the complexities of AI capabilities of EM systems as they transition towards microgrid energy optimisation and net zero carbon emissions. © 2023 IEEE.

关键词： Cloud computing architecture

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