1,720,981 research outputs found
Federated learning for next generation intelligent applications
Full text linkThe rapid proliferation of smart devices and Internet of Things (IoT) technologies has revolutionised data collection for artificial intelligence (AI)-driven applications, enabling rapid training and near real-time inference. However, the traditional centralised learning approaches require transferring vast amounts of raw data from end devices to a central server. This process leads to substantial network overhead, increased latency, and significant privacy concerns, hindering the scalability and responsiveness of intelligent applications. This thesis exploits federated learning (FL) as a distributed, on-device learning framework that enables collaborative model training without raw data sharing. The distributed architecture of FL offers privacy by design and reduces communication costs by exchanging the model parameters that align with principles of data sovereignty and regulatory compliance. Despite its advantages, FL faces significant challenges in real-world applications, and this thesis aims to address the following three critical challenges: C1) data diversity; C2) robust aggregation ensuring privacy and security in the training process; and finally, C3) energy efficiency. The first contribution introduces the similarity-driven truncated aggregation (SDTA) framework, designed to tackle challenges C1 and C2. SDTA measures the similarity among the model updates to identify and filter the anomalous updates, mitigating the impact of attacks and overfitting without accessing client data. Additionally, it incorporates differential privacy (DP) to strengthen training privacy. The second contribution introduces the semantic-aware federated blockage prediction (SFBP) framework, addressing challenges C1 and C3. Using multi-modal fusion and a lightweight computer vision model for edge-based semantic extraction, the proposed framework reduces communication costs and inference delays while maintaining high prediction accuracy. Additionally, SFBP incorporates a filter mechanism to minimise the effects of noisy or adversarial updates. The third contribution addresses C1 and C3 and develops a hybrid neuromorphic federated learning (HNFL) framework for outdoor human activity recognition (HAR) using wearable sensors. The proposed spiking-long short-term memory (S-LSTM) model combines the energy-efficient spiking neural networks with the sequential data handling strengths of LSTM networks. This approach improves the accuracy while ensuring data privacy and reducing computational costs, making it suitable for deployment on resource-constrained edge devices. Finally, to address challenges C2 and C3, the federated fusion quantisation (FFQ) framework is proposed to improve HAR models in indoor settings. FFQ combines FL with edge-based preprocessing, feature engineering, and model compression to achieve a low false positive rate, essential for applications like fall detection. A customised FedDist algorithm is used for global model aggregation, effectively reducing overfitting in diverse data. Additionally, FFQ applies model compression and quantisation-aware training to lower communication overhead without compromising accuracy. These contributions advance FL by enhancing scalability, robustness, and efficiency, paving the way for next-generation intelligent systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Full text link“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
Full text linkWe 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
SemQNet: Semantic-Aware Quantised Network for mmWave Beam Prediction
Full text linkMillimetre-wave (mmWave) communication systems use large antenna arrays and narrow beams to achieve strong signal power. However, this approach requires extensive beam training, which leads to high overhead. Recently proposed vision-aided beam prediction methods show promising results, reducing this overhead. However, these techniques have considerable computational complexity, hindering practical deployment. To address this issue, we propose a Semantic-Aware Quantised Network (SemQNet) framework that leverages image compression and a lightweight computer vision model to extract semantic information used for training a fully connected neural network (FCNN). Additionally, the proposed SemQNet also uses quantisation-aware training (QAT), which enables low-precision arithmetic operation, reducing the model size in the training process. Our tests on the DeepSense 6G dataset show that SemQNet achieves almost the same top-1 accuracy as existing vision-based methods while reducing the model size by 74.21\%. This smaller model size reduces the communication overhead, making SemQNet a practical and efficient solution for energy-constrained mmWave communication systems
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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