1,720,964 research outputs found
On the physical layer security of the cooperative rate-splitting aided downlink in UAV networks
No full textUnmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper ( Eve ). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external Eve , we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out Eve without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the Eve , deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA) method together with a bespoke initialization algorithm to avoid any failure due to infeasibility. Our simulation results confirm that the proposed secure transmission scheme outperforms the existing cooperative benchmarkers.</p
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
Digital Healthcare in The Metaverse: Insights into Privacy and Security
No full textIn this article, metaverse healthcare systems are studied from the privacy and security perspectives. We address data communication security for the metaverse, and the privacy and security threats of employing machine learning and artificial intelligence (ML/AI) algorithms for metaverse healthcare. In addition, human-centric data privacy for the social interactions in the metaverse is studied. Our goal is to present new visions and approaches, including physical layer security, semantic metaverse communications, differential privacy, and adversarial machine learning. These approaches have shown promising results in the field of data communications and networking, as well as the computer science domain, showcasing a huge potential to be employed for the metaverse healthcare systems. As a case study, we propose distributed differential privacy for the metaverse healthcare systems, where each virtual clinic perturbs its medical model vector to safeguard privacy against malicious clients and honest-but-curious servers. Through our experiments on the Breast Cancer Wisconsin Dataset, we highlight the privacy-utility trade-off for different adjustable levels of privacy
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
Global Differential Privacy for Distributed Metaverse Healthcare Systems
No full textMetaverse-enabled digital healthcare systems are expected to exploit an unprecedented amount of personal health data, while ensuring that sensitive or private information of individuals are not disclosed. Machine learning and artificial intelligence (ML/AI) techniques can be widely utilized in metaverse healthcare systems, such as virtual clinics and intelligent consultations. In such scenarios, the key challenge is that data privacy laws might not allow virtual clinics to share their medical data with other parties. Moreover, clinical AI/ML models themselves carry extensive information about the medical datasets, such that private attributes can be easily inferred by malicious actors in the metaverse (if not rigorously privatized). In this paper, inspired by the idea of “incognito mode”, which has recently been developed as a promising solution to safeguard metaverse users\u27 privacy, we propose global differential privacy for the distributed metaverse healthcare systems. In our scheme, a randomized mechanism in the format of artificial “mix-up” noise is applied to the federated clinical ML/AI models before sharing with other peers. This way, we provide an adjustable level of distributed privacy against both the malicious actors and honest-but-curious metaverse servers. Our evaluations on breast cancer Wisconsin dataset (BCWD) highlight the privacy-utility trade-off (PUT) in terms of diagnosis accuracy and loss function for different levels of privacy. We also compare our private scheme with the non-private centralized setup in terms of diagnosis accuracy
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
Global Differential Privacy for Distributed Metaverse Healthcare Systems
Get PDFMetaverse-enabled digital healthcare systems are expected to exploit an
unprecedented amount of personal health data, while ensuring that sensitive or
private information of individuals are not disclosed. Machine learning and
artificial intelligence (ML/AI) techniques can be widely utilized in metaverse
healthcare systems, such as virtual clinics and intelligent consultations. In
such scenarios, the key challenge is that data privacy laws might not allow
virtual clinics to share their medical data with other parties. Moreover,
clinical AI/ML models themselves carry extensive information about the medical
datasets, such that private attributes can be easily inferred by malicious
actors in the metaverse (if not rigorously privatized). In this paper, inspired
by the idea of "incognito mode", which has recently been developed as a
promising solution to safeguard metaverse users' privacy, we propose global
differential privacy for the distributed metaverse healthcare systems. In our
scheme, a randomized mechanism in the format of artificial "mix-up" noise is
applied to the federated clinical ML/AI models before sharing with other peers.
This way, we provide an adjustable level of distributed privacy against both
the malicious actors and honest-but curious metaverse servers. Our evaluations
on breast cancer Wisconsin dataset (BCWD) highlight the privacy-utility
trade-off (PUT) in terms of diagnosis accuracy and loss function for different
levels of privacy. We also compare our private scheme with the non-private
centralized setup in terms of diagnosis accuracy
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