1,720,963 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Adaptive Load Balancing Model on Edge Gateways to Support IoT Scalability in 5G Networks
Get PDFThe rapid proliferation of Internet of Things (IoT) devices, particularly within the context of smart cities, industrial automation, and connected vehicles, poses significant challenges to network scalability and real-time data processing. With the advent of 5G networks, promising ultra-low latency and massive connectivity, the role of edge computing and specifically edge gateways becomes critical. However, the dynamic and heterogeneous nature of IoT traffic, coupled with varying computational demands, can lead to uneven resource utilization and performance bottlenecks on edge gateways. This paper proposes an adaptive load balancing model designed to optimize resource distribution and enhance IoT scalability in 5G networks. We explore how artificial intelligence (AI) and machine learning (ML) techniques can be leveraged for real-time traffic prediction, dynamic task offloading, and intelligent resource allocation across multiple edge gateways. The proposed model aims to minimize latency, maximize throughput, and ensure high availability for diverse IoT applications. We discuss the architectural components, key adaptive mechanisms, and the integration with 5G network capabilities, alongside outlining persistent challenges and promising future research directions to build more resilient and efficient IoT ecosystems
Analisis Sentimen Publik terkait Migrasi Tenaga Kerja Indonesia di Platform X menggunakan SVM-IndoBERT
Get PDFDiverse public opinions on social and economic issues related to labor migration are often expressed on the social media platform X (Twitter). This research aims to classify public sentiment toward this phenomenon by analyzing tweets containing the hashtag "#KaburAjaDulu". Sentiment classification is performed by comparing two Support Vector Machine (SVM) approaches that utilize indoBERT embeddings, a language model designed to capture the nuances of the Indonesian language. Both SVM models are trained using web crawling data from the X platform, with the main difference lying in the application of hyperparameter tuning on one of the models. The data collected through web crawling from the X platform then undergoes a pre-processing stage that includes text normalization and stopword removal. The results show that the SVM model optimized through hyperparameter tuning achieved an accuracy of 90.5%, higher than the SVM model without tuning which achieved only 77.7%. This finding underscores the importance of hyperparameter tuning in improving the performance of sentiment classification models, especially when utilizing rich feature representations such as indoBERT embeddings to understand deeper language context
Optimization of AI-Powered Edge-IoT Architecture for Real-Time Response in Distributed Smart City Systems
Get PDFSmart city development, increasingly powered by the widespread adoption of Internet of Things (IoT) devices, demands systems capable of processing data in real time and with high reliability. Traditional cloud-based models often fall short due to latency, bandwidth issues, and privacy risks when managing the constant stream of data from distributed IoT sensors. This paper reviews recent advancements and proposes optimization strategies for integrating Artificial Intelligence (AI) into Edge-IoT systems, specifically designed to enhance responsiveness in smart city environments. Key areas include lightweight AI model design, adaptive resource management, efficient data flow, and network enhancements. We highlight technologies such as federated learning, task offloading, and software-defined networking to minimize delays and increase performance. In addition, the paper discusses challenges—scalability, heterogeneity, energy efficiency, and security—while outlining promising directions for future research. This work offers valuable insights for researchers and professionals working to build smart urban systems that are responsive, efficient, and context-aware
A Review and Comparative Analysis of Intrusion Detection Systems for Edge Networks in IoT
Get PDFThis article presents a comprehensive review and comparative analysis of intrusion detection systems (IDS) designed for edge networks in Internet of Things (IoT) environments. The rapid growth of IoT has heightened security vulnerabilities in edge networks, which are the focus of this study. Various IDS approaches, including signature-based, anomaly-based, and hybrid methods, are explored, with an emphasis on the application of machine learning and deep learning techniques. The review includes an analysis of system architectures, algorithms (LSTM, CNN, Transformer), datasets, performance evaluation metrics, and experimental results from prior research. The literature study indicates that deep learning has significant potential to enhance intrusion detection accuracy; however, its effectiveness depends on dataset quality, appropriate data preprocessing, and handling class imbalances. Optimal feature selection, blockchain integration, and ensemble approaches are also critical. In conclusion, a multi-faceted approach combining advanced algorithms, suitable preprocessing techniques, and a deep understanding of IoT attacks is essential. Future research should focus on developing adaptive, efficient, and robust IDS with realistic datasets and comprehensive evaluation methods. This article provides a valuable resource for researchers and practitioners in the field of IoT edge network security
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