Central Entity Research Articles

Optimizing telemetry forwarding for distributed failure recovery in packet-optical networks

Fast network recoverability from hard and soft failures is crucial for network operators to deliver uninterrupted services. Streaming telemetry has been studied as a solution for enabling fast and accurate failure detection in optical networks. However, significant delay is incurred when relying on a centralized entity (e.g., software-defined network controller) to collect, process, and act on telemetry data. Programmable switches (e.g., P4-based) allow telemetry data to be processed at line speed, enabling local on-device (distributed) decisions. These devices can be used to deploy quick and local mitigation to failures while a global solution is being computed on a longer time scale. However, designing network-wide streaming telemetry with distributed decisions remains an open challenge. In this work, we specify the joint optimization of packet-optical networks with on-device failure recovery, considering multiple aspects of the problem. The problem is modeled using linear programming and solved for multiple network realizations. The solutions can be used to program each switch in the network to detect failures and quickly recover the traffic. Results show that the proposed model decreases the required number of register entries to store telemetry data while assuring high recoverability and a minimized number of wavelengths.

An analysis of government financial reporting transparency in developing economies: the extent of compliance with cash-based IPSAS disclosures

This study aims to assess the degree of government compliance with cash-based IPSAS disclosure requirements in developing economies, and to highlight the areas of non-compliance relating to this standard. Based on a self-constructed checklist consisting of 68 disclosure items for a sample of 248 entity-year-observations relating to 62 central government entities from 10 DCs, the study findings reveal significant diversities in compliance levels with cash-based IPSAS disclosure requirements between the selected countries, varying between 33.8 % and 80.8 %, with an overall mean compliance index of 55.1% over the 2020-2023 period. Moreover, the research results demonstrate that, despite the significant improvement in the cash-based IPSAS disclosure level over time, full compliance with this standard has not been achieved by government entities. Thus, a number of non-compliance areas are identified in the present research. This paper contributes to the public sector accounting literature by providing new insights into the degree of government financial transparency in the context of developing countries. This research is among the first to focus exclusively on the analysis of compliance level with cash-based IPSAS and to identify the main areas of non-compliance with this standard.

Auditable and Verifiable Federated Learning Based on Blockchain-Enabled Decentralization.

Auditability and verifiability are critical elements in establishing trustworthiness in federated learning (FL). These principles promote transparency, accountability, and independent validation of FL processes. Incorporating auditability and verifiability is imperative for building trust and ensuring the robustness of FL methodologies. Typical FL architectures rely on a trustworthy central authority to manage the FL process. However, reliance on a central authority could become a single point of failure, making it an attractive target for cyber-attacks and insider frauds. Moreover, the central entity lacks auditability and verifiability, which undermines the privacy and security that FL aims to ensure. This article proposes an auditable and verifiable decentralized FL (DFL) framework. We first develop a smart-contract-based monitoring system for DFL participants. This monitoring system is then deployed to each DFL participant and executed when the local model training is initiated. The monitoring system records necessary information during the local training process for auditing purposes. Afterward, each DFL participant sends the local model and monitoring system to the respective blockchain node. The blockchain nodes representing each DFL participant exchange the local models and use the monitoring system to validate each local model. To ensure an auditable and verifiable decentralized aggregation procedure, we record the aggregation steps taken by each blockchain node in the aggregation contract. Following the aggregation phase, each blockchain node applies a multisignature scheme to the aggregated model, producing a globally verifiable model. Based on the signed global model and the aggregation contract, each blockchain node implements a consensus protocol to store the validated global model in tamper-proof storage. To evaluate the performance of our proposed model, we conducted a series of experiments with different machine learning architectures and datasets, including CIFAR-10, F-MNIST, and MedMNIST. The experimental results indicate a slight increase in time consumption compared with the state-of-the-art, serving as a tradeoff to ensure auditability and verifiability. The proposed blockchain-enabled DFL also saves up to 95% communication costs for the participant side.

A Contribution Evaluation and Reward Framework Based on Shapley Value and Smart Contract

Currently, the advantages of Shapley value in explaining model decisions have made incentive mechanisms Shapley value contribution assessment based become major research focus. This approach ensures the effectiveness and fairness of contribution assessment algorithms. However, there is a significant issue regarding the reliability of the computational results. Additionally, traditional Shapley value-based contribution processes face an issue where computational complexity increases exponentially with the number of participants. Addressing these issues, this paper proposes a contribution assessment and reward framework based on Shapley value and smart contract technology using alliance blockchain. This framework overcomes the reliance on third-party institutions that is characterized by traditional incentive mechanisms, leveraging the decentralized nature of blockchain to eliminate trust issues associated with a single centralized entity. Furthermore, the traceability of blockchain ensures the transparency and traceability of the contribution assessment and reward distribution processes.

The ageing virus hypothesis: Epigenetic ageing beyond the Tree of Life.

A recent thought-provoking theory argues that complex organisms using epigenetic information for their normal development and functioning must irreversibly age as a result of epigenetic signal loss. Importantly, the scope of this theory could be considerably expanded, with scientific benefits, by analyzing epigenetic ageing beyond the borders of the Tree of Life. Viruses that use epigenetic signals for their normal functioning may also age, that is, present an increasing risk of failing to complete their individual life cycle and to disappear with time. As viruses are ancient, abundant, and infect a considerable diversity of hosts, the ageing virus hypothesis, if verified, would have important consequences for many fields of the Life sciences. Uncovering ageing viruses would integrate the most abundant and biologically central entities on Earth into theories of ageing, enhance virology, gerontology, evolutionary biology, molecular ecology, genomics, and possibly medicine through the development of new therapies manipulating viral ageing.

Mapping Financial Connections: Market Integration in Emerging Economies through Graph Theory

In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends.

6G edge-networks and multi-UAV knowledge fusion for urban autonomous vehicles

6G edge-networks and multi-UAV knowledge fusion for urban autonomous vehicles

FedAvg-P: Performance-Based Hierarchical Federated Learning-Based Anomaly Detection System Aggregation Strategy for Advanced Metering Infrastructure.

Advanced metering infrastructures (AMIs) aim to enhance the efficiency, reliability, and stability of electrical systems while offering advanced functionality. However, an AMI collects copious volumes of data and information, making the entire system sensitive and vulnerable to malicious attacks that may cause substantial damage, such as a deficit in national security, a disturbance of public order, or significant economic harm. As a result, it is critical to guarantee a steady and dependable supply of information and electricity. Furthermore, storing massive quantities of data in one central entity leads to compromised data privacy. As such, it is imperative to engineer decentralized, federated learning (FL) solutions. In this context, the performance of participating clients has a significant impact on global performance. Moreover, FL models have the potential for a Single Point of Failure (SPoF). These limitations contribute to system failure and performance degradation. This work aims to develop a performance-based hierarchical federated learning (HFL) anomaly detection system for an AMI through (1) developing a deep learning model that detects attacks against this critical infrastructure; (2) developing a novel aggregation strategy, FedAvg-P, to enhance global performance; and (3) proposing a peer-to-peer architecture guarding against a SPoF. The proposed system was employed in experiments on the CIC-IDS2017 dataset. The experimental results demonstrate that the proposed system can be used to develop a reliable anomaly detection system for AMI networks.

Construction of fuzzy black holes and self-gravitational droplets in exponential f(R) gravity

This paper discusses the construction of non-commutative geometry based self-gravitational droplets or fuzzy black holes (BHs) within the context of exponential-f(R) gravity model 1βeβR−1 , where β is a parameter with units [length 2]. Considering the Einasto density profile, a generalization of the Gaussian profile, we extend the noncommutative-inspired mini BH metric to incorporate dark matter (DM) as a constituent of the BH. We investigate various Einasto DM fuzzy objects filled with anisotropic fluid content, considering different values of the shape parameter and rescaled mass. This study investigates the possibility of stable self-gravitational compact objects with fuzzy mass distributions inspired by a well-known DM profile, the Einasto parametrization. These intriguing astrophysical configurations fall into three categories: fuzzy self-gravitational droplets with no event horizon, and fuzzy black holes featuring either one or two horizons. It is important to highlight that all these astrophysical entities are solutions derived from the exponential-f(R) gravity model. The corresponding effective potentials exhibit a significant resemblance to the effective potential of the classical Schwarzschild Black Hole located at the center of the Galaxy. Specifically, we investigate the potential for these black holes to serve as central galactic entities.

Multi-landscapes of Hà Nội in three poetic generations of ethnic minorities

Born with the August Revolution in 1945, the generations of modern Vietnamese ethnic minority writers form their own appearance, contributing to creating unique voices and identities. In this paper, we argue that three generations of ethnic minority poets, which represent three waves of writers, create three types of geographical landscapes of Hanoi. Firstly, the country’s capital in wartime is portrayed vaguely in the dreams of the periphery. It is a central geographical entity that holds the beliefs and aspirations of the entire nation. Secondly, Hanoi is presented through its everyday appearance, which is full of contradictions that make residents leave and dream of an idealised mountainous region. Finally, Hanoi is described as a decentralised metropolis through the gazes of ethnic minority poets who were born in Hanoi. This creates in their poetry the process of deterritorialization, i.e. the process of becoming the Other. It can be said that geographical shifts have created new spaces, thus giving people different concepts of home.

Privacy-Preserving Membership Queries for Federated Anomaly Detection

In this work, we propose a new privacy-preserving membership query protocol that lets a centralized entity privately query datasets held by one or more other parties to check if they contain a given element. This protocol, based on elliptic curve-based ElGamal and oblivious key-value stores, ensures that those 'data-augmenting' parties only have to send their encrypted data to the centralized entity once, making the protocol particularly efficient when the centralized entity repeatedly queries the same sets of data. We apply this protocol to detect anomalies in cross-silo federations. Data anomalies across such cross-silo federations are challenging to detect because (1) the centralized entities have little knowledge of the actual users, (2) the data-augmenting entities do not have a global view of the system, and (3) privacy concerns and regulations prevent pooling all the data. Our protocol allows for anomaly detection even in strongly separated distributed systems while protecting users' privacy. Specifically, we propose a cross-silo federated architecture in which a centralized entity (the backbone) has labeled data to train a machine learning model for detecting anomalous instances. The other entities in the federation are data-augmenting clients (the user-facing entities) who collaborate with the centralized entity to extract feature values to improve the utility of the model. These feature values are computed using our privacy-preserving membership query protocol. The model can be trained with an off-the-shelf machine learning algorithm that provides differential privacy to prevent it from memorizing instances from the training data, thereby providing output privacy. However, it is not straightforward to also efficiently provide input privacy, which ensures that none of the entities in the federation ever see the data of other entities in an unencrypted form. We demonstrate the effectiveness of our approach in the financial domain, motivated by the PETs Prize Challenge, which is a collaborative effort between the US and UK governments to combat international fraudulent transactions. We show that the private queries significantly increase the precision and recall of the otherwise centralized system and argue that this improvement translates to other use cases as well.

A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities

Federated learning (FL) refers to a system of training and stabilizing local machine learning models at the global level by aggregating the learning gradients of the models. It reduces the concern of sharing the private data of participating entities for statistical analysis to be carried out at the server. It allows participating entities called clients or users to infer useful information from their raw data. As a consequence, the need to share their confidential information with any other entity or the central entity called server is eliminated. FL can be clearly interpreted as a privacy-preserving version of traditional machine learning and deep learning algorithms. However, despite this being an efficient distributed training scheme, the client’s sensitive information can still be exposed to various security threats from the shared parameters. Since data has always been a major priority for any user or organization, this article is primarily concerned with discussing the significant problems and issues relevant to the preservation of data privacy and the viability and feasibility of several proposed solutions in the FL context. In this work, we conduct a detailed study on FL, the categorization of FL, the challenges of FL, and various attacks that can be executed to disclose the users’ sensitive data used during learning. In this survey, we review and compare different privacy solutions for FL to prevent data leakage and discuss secret sharing (SS)-based security solutions for FL proposed by various researchers in concise form. We also briefly discuss quantum federated learning (QFL) and privacy-preservation techniques in QFL. In addition to these, a comparison and contrast of several survey works on FL is included in this work. We highlight the major applications based on FL. We discuss certain future directions pertaining to the open issues in the field of FL and finally conclude our work.

Federated Momentum Contrastive Clustering

Self-supervised representation learning and deep clustering are mutually beneficial to learn high-quality representations and cluster data simultaneously in centralized settings. However, it is not always feasible to gather large amounts of data at a central entity, considering data privacy requirements and computational resources. Federated Learning (FL) has been developed successfully to aggregate a global model while training on distributed local data, respecting the data privacy of edge devices. However, most FL research effort focuses on supervised learning algorithms. A fully unsupervised federated clustering scheme has not been considered in the existing literature. We present federated momentum contrastive clustering (FedMCC), a generic federated clustering framework that can not only cluster data automatically but also extract discriminative representations training from distributed local data over multiple users. In FedMCC, we demonstrate a two-stage federated learning paradigm where the first stage aims to learn differentiable instance embeddings and the second stage accounts for clustering data automatically. The experimental results show that FedMCC not only achieves superior clustering performance but also outperforms several existing federated self-supervised methods for linear evaluation and semi-supervised learning tasks. Additionally, FedMCC can easily be adapted to ordinary centralized clustering through what we call momentum contrastive clustering (MCC). We show that MCC achieves state-of-the-art clustering accuracy results in certain datasets such as STL-10 and ImageNet-10. We also present a method to reduce the memory footprint of our clustering schemes.

A trust distributed learning (D‐NWDAF) against poisoning and byzantine attacks in B5G networks

AbstractBeyond 5G networks, (B5G) introduce the emergence of a smart service and an automate management archetype of running network slices, such as the network data analytics function (NWDAF). These new architectures provide a large usage of advanced machine learning algorithms, in order to dynamically build efficient decisions. In this perspective, the Distributed Learning (D‐), known as A Distributed NWDAF Architecture (D‐NWDAF) has proved its efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Notwithstanding, FL is vulnerable to different attacks, where FL nodes (Leaf NWDAF: AI‐VNFs*) may upload malicious updates to the FL central (root NWDAF) entity so that it can cause a construction failure of the FL global model and affect the global performance. Moreover, attacks detection of FL‐based solutions give “black‐box” decisions about the performance of running network slices and the attacks detected. In other words, detection solutions do not provide any details about why and how such ML attacks decisions were made. Thus, such decisions cannot be properly trusted and comprehended by B5G slice managers. To resolve this issue, we leverage Dimensional Reduction (DR) and Explainable Artificial Intelligence (XAI) algorithms which aim to detect attacks and improve the transparency of black‐box FL attacks detection‐making process. In the present article, we design a novel DR‐XAI‐powered framework to detect attacks and explain the FL‐based attacks detection. We first build a deep learning model in a federated way, to predict key performance indicators (KPI) of network slices. Then, we try to detect the malicious FL nodes, using DR and several XAI models, such as RuleFit, in order to enhance the level of trustiness, transparency, and the explanation of the FL‐based attacks detection, while adhering the data privacy, to different B5G network stakeholders.

AN ASSESSMENT OF THE EFFECTIVENESS OF THE INTERNAL AUDIT AT THE UNIVERSITY OF SIERRA LEONE

<p>Internal auditing is crucial for evaluating and improving internal control processes within government entities. Its primary objective is to assess the effectiveness and efficiency of internal controls, risk management, and governance processes. By identifying weaknesses and suggesting improvements, internal auditors contribute to better resource utilization and accountability. Internal audits can be conducted at various levels within the government, including central agencies, ministries, departments, or individual entities. This allows for a comprehensive examination of different aspects of government operations. Supreme Audit Institutions (SAIs) are independent governmental bodies responsible for conducting external audits of public sector entities. They assess the effectiveness and efficiency of financial management, compliance with laws and regulations, and the overall stewardship of public resources. While internal auditors focus on assessing internal controls and ensuring the reliability of financial information, SAIs provide an external perspective on the quality and impact of internal auditing within government entities. This collaboration helps promote good governance, transparency, and accountability in the use of public resources. A robust control system, which includes internal control, risk management, and audit functions, is essential for good governance. It helps safeguard public funds and maintain public trust. Adopting a risk-based approach to integrity and implementing targeted controls can enhance efficiency and effectiveness in resource utilization. Collaboration with citizens, the media, and non-governmental organizations further strengthens the functioning of the control system. This engagement enhances transparency and accountability and ensures that the government is responsive to the needs and concerns of the public. Internal and external auditing by SAIs are vital components of the public financial management system. They work together to assess and improve government operations, enhance accountability, and ensure the efficient use of public resources, ultimately contributing to good governance. In Sierra Leone, SAIs is likened to Audit Service Sierra Leone (ASSL). The concurrent triangulation approach is a specific mixed methods model used in this study. The research findings reveal that the internal audit department of the University of Sierra Leone is not adequately resourced to perform its functions effectively. However, segregation of duties and access to information is maintained but requires a functional audit committee, an increased number of competent auditors, and auditing software.</p><p><strong>JEL:</strong> H83, M42, H11, H72, H76, K42, O17</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0771/a.php" alt="Hit counter" /></p>

Subchannel assignment for social-assisted UAV cellular networks using dynamic hypergraph coloring

Subchannel assignment for social-assisted UAV cellular networks using dynamic hypergraph coloring

Re-visioning and critically emphasizing the need for psychosocial intervention in addressing body image distress in head and neck cancer patients: A review of literature

Head and neck cancers (HNCs) patients undergo extensive debilitating surgeries on the part of their body which is considered as a unique characteristic feature and a central entity to communication and perception, instilling a sense of self: The face. Hereby, alterations and discontinuity in this sense of self lead to fragmentation of body image, thereby shattering the sense of identity and making it difficult for patients to recognize selves and return back to the precancerous identity. Body image dissatisfaction is associated with varied psychosocial adverse outcomes such as anxiety, depression, decreased quality of life affecting social dynamics, romantic relationship distress relating to sexual and intimate health, and excessive efforts spent on appearance-fixing behaviors and the need for constant reassurance. As body image goes way beyond just mere appreciation of self and is a multifaceted, intricate web crucially affecting an individual’s identity and existence, it is of utmost importance that medical health professionals aim at making oncologic treatment a more holistic model which considers a patient’s mental, emotional and physical well-being. Present literature offers a wide scope for studies focused on psychological distress in breast cancer patients; however, limited empirical research has been conducted on body image concerns and the need for psychosocial interventions in HNC patients. A need for an in-depth understanding into this aspect of psycho-oncology will help oncologic intervention to be more patient-centric, providing the benefits of both biomedical and psychosocial skills.

A Secure Storage For Medical Information Scheme Using Blockchain

Nowadays, many companies, organizations, hospitals and individuals have adopted centralized data storage systems to store and share data. However, these systems create a single point of failure and involve a centralized entity or third party, which can cause concern for users. Decentralized storage systems are therefore needed to overcome the drawbacks of the traditional approach. However, in the face of centralization issues, this paper proposes a combination of Hyperledger Fabric, InterPlanetary File System (IPFS), Attribute-Based Access Control (ABAC), and proxy re-encryption to enhance the security and transparency features of decentralized storage systems. Thus, the proposed scheme provides a secure decentralized system storage of medical information using a consortium blockchain

Publication patterns of posters and oral presentations at the Annual Meeting of the Joint AANS/CNS Cerebrovascular Section.

The American Association of Neurological Surgeons (AANS) and Congress of Neurological Surgeons (CNS) Joint Cerebrovascular (CV) Section serves as a centralized entity for the dissemination of information related to CV neurosurgery. The quality of scientific conferences, such as the CV Section's Society of NeuroInterventional Surgery Annual Meeting, can be gauged by the number of poster and oral presentations that are published in peer-reviewed journals. However, publication rates from the CV Section's meetings are unknown. The objective of this study was to assess the rate at which abstracts presented at the AANS/CNS CV Section Annual Meeting from 2014 to 2018 were subsequently published in peer-reviewed journals. The abstract titles for all accepted poster and oral podium presentation abstracts from the 2014-2018 Annual Joint AANS/CNS CV Section Meetings were searched using PubMed. A match was defined as sufficient similarity between the abstract and its corresponding journal publication with regard to title, authors, methods, and results. Five-year impact factors (IFs) from Journal Citation Reports (JCR), the country of the corresponding author, and the number of citations in the Scopus database were obtained using the articles' digital object identifier when available, or the exact article title, journal, and year of publication. Of the 607 total poster and oral presentations from the 2014-2018 Annual Meetings of the AANS/CNS Joint CV Section, 46.29% (n = 281) have been published. Published articles received 3233 total citations for an average number of citations per article (± SD) of 10.89 ± 16.37. The average 5-year JCR IF of published studies was 4.64 ± 3.13. Additionally, 98.22% of published abstracts were in publication within 4 years from the time the abstract was presented. The most common peer-reviewed neurosurgical journals featuring these publications were the Journal of Neurosurgery, World Neurosurgery, the Journal of NeuroInterventional Surgery, Neurosurgery, and the Journal of Clinical Neuroscience. Nearly half of all poster and oral presentations at the annual meetings of the AANS/CNS Joint CV Section from 2014 to 2018 have been published in PubMed-indexed, peer-reviewed journals. The average number of citations per publication (10.89 ± 16.37) reflects the high quality of abstracts accepted for presentation. It is important to continuously assess the quality of research presented at national conferences to ensure that standards are being maintained for the advancement of clinical practice in a given area of medicine. Conference abstract publication rates in peer-reviewed journals represent a way in which research quality can be gauged, and the authors encourage others to conduct similar investigations in their subspecialty area of interest and/or practice.

Anomaly detection in log-event sequences: A federated deep learning approach and open challenges

Anomaly detection in log-event sequences: A federated deep learning approach and open challenges