Client Network Research Articles

SortAlign: a score-based aggregation technique for neural networks in one-round federated learning

In recent years, the growth of data generated on a daily basis in critical domains, such as industrial processes, where data privacy plays a key role, has led to the strong development of Federated Learning. In turn, the need for communication-efficient approaches has given particular importance to One-Round Federated Learning, where a central server coordinates the learning process of a global model using a federated network of clients, or nodes, in a single round of communication. In this study, a novel alignment strategy based on nodes similarity matching for Neural Networks in One-Round Federated Learning is proposed. This method was compared with various federated models and validated using a real-world use case of machining process.

Transfer and Interruptions in Anticorruption Policies in Guatemala (2013–2019)

Guatemala ranks last in the region in terms of the penetration and perception of state corruption ranking 149th out of 180 countries. Therefore it has promoted anti-corruption initiatives such as the International Commission Against Impunity (CICIG 2007–2019). The CICIG was a unique public policy initiative worldwide with international community support and significant progress in reducing corruption. However what were the main failures encountered during the implementation process of the CICIG? This comparative individual case study analyzes two factors: sustainability and capacity building. The explanations for the CICIG’s disruptions are not understood exclusively as the absence of state capacity much less because of its design. My qualitative research delves into endogenous political contexts (client networks resilience). The research identifies some fissures in implementation and draws lessons that can be extrapolated to public policy in Latin America.

Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT Networks

Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks.

Foundation model-driven distributed learning for enhanced retinal age prediction.

The retinal age gap (RAG) is emerging as a potential biomarker for various diseases of the human body, yet its utility depends on machine learning models capable of accurately predicting biological retinal age from fundus images. However, training generalizable models is hindered by potential shortages of diverse training data. To overcome these obstacles, this work develops a novel and computationally efficient distributed learning framework for retinal age prediction. The proposed framework employs a memory-efficient 8-bit quantized version of RETFound, a cutting-edge foundation model for retinal image analysis, to extract features from fundus images. These features are then used to train an efficient linear regression head model for predicting retinal age. The framework explores federated learning (FL) as well as traveling model (TM) approaches for distributed training of the linear regression head. To evaluate this framework, we simulate a client network using fundus image data from the UK Biobank. Additionally, data from patients with type 1 diabetes from the UK Biobank and the Brazilian Multilabel Ophthalmological Dataset (BRSET) were utilized to explore the clinical utility of the developed methods. Our findings reveal that the developed distributed learning framework achieves retinal age prediction performance on par with centralized methods, with FL and TM providing similar performance (mean absolute error of 3.57 ± 0.18 years for centralized learning, 3.60 ± 0.16 years for TM, and 3.63 ± 0.19 years for FL). Notably, the TM was found to converge with fewer local updates than FL. Moreover, patients with type 1 diabetes exhibited significantly higher RAG values than healthy controls in all models, for both the UK Biobank and BRSET datasets (P < .001). The high computational and memory efficiency of the developed distributed learning framework makes it well suited for resource-constrained environments. The capacity of this framework to integrate data from underrepresented populations for training of retinal age prediction models could significantly enhance the accessibility of the RAG as an important disease biomarker.

Branch Networks, Client Networks, and Knowledge Sharing in Audit Firms

SYNOPSIS In large audit firms, interoffice knowledge sharing is embedded in two networks: the branch network, comprising all branch offices, and the client network, formed by similar engagements. Proximity between branch offices may enhance knowledge accessibility, whereas similarity between engagements could impact the relevance of that knowledge. I investigate how the two networks jointly influence the effectiveness of knowledge sharing. I construct client networks based on the similarity of clients’ product offerings and find that clients with greater centrality in the neighboring client network benefit more from knowledge sharing in terms of improved audit quality. I further show that this effect only exists when similar clients in neighboring offices are larger in size and is more salient when there is a gap in expertise between the current auditing office and neighboring offices. By enriching our understanding of interoffice knowledge sharing, these results offer practical insights to auditors, regulators, and audit-report users. Data Availability: Data are available from the data sources indicated in the paper. JEL Classifications: M42.

Business model of international consulting: main characteristics and elements

Based on the analysis of the essence and modern types of business models of enterprises, the basic professional service business model of the international consulting "Consultant" and reveals its main characteristics is substantiates: the B2B model, the modification of the "Producer" business model, the integration of elements of service and digital business models, customer orientation. Based on the Business Model Canvas, the main components of the basic business model of international consulting are revealed: key partners: partner intra-service and inter-service networks of the consulting firm; key activity: providing professional assistance to business organizations in solving problems of international economic activity; key resources: intellectual human, organizational, market; value proposition: the value services of international consulting for the client; relations with clients: client orientation, client-centricity, positive client experience, cooperation between consultants and clients at all stages of the consulting process; channels: omnichannel, cross-channel communications; customer segments: target audiences, customer networks; cost structure; income streams. Client networks as a component of the basic business model of international consulting are characterized and measures to optimize them are disclosed: forming a loyal attitude of clients to the consulting firm, consultants, and services; accumulation and protection of a network of regular customers; forming a positive customer experience; improving the efficiency of consultant-client relations; development of omnichannel provision of professional assistance to clients; ensuring client orientation of the personnel of the consulting firm; management of the value and quality of consulting services; introduction of new consulting services and technologies based on consulting innovations.

Voices from Periphery: Punjabi Poetry, Resistance and Anti-Colonial Nationalism

This article examines a genre of Punjabi poetry, as a voice from the periphery, a powerful vehicle of the marginalised for the performance of collective expression and resistance against the hegemonic nature of colonial rule, charting a strong relationship between language and articulation of political imagination. The images of rich tribal culture, warriors as sons of the soil, Punjabi valour and mystical symbols were used to portray the punjab’s plural landscape. The socio-economic marginality of disenfranchised individuals, created by a vertically structured patron–client network, operating in a coercive administrative framework, forged a sense of community identity along religious lines. Using religious rhetorics, community identity was converged into Islamic identity to construct nationhood. Anti-colonial nationalism became the language of a meta-project for the attainment of political independence.

Enhancing 802.1X authentication with identity providers using EAP-OAUTH and OAuth 2.0

EAP-OAUTH is a novel Extensible Authentication Protocol (EAP) method that integrates the OAuth 2.0 framework to provide a secure and flexible authentication mechanism for LANs and WLANs that implement the IEEE 802.1X framework. EAP-OAUTH leverages existing, OAuth 2.0-enabled Identity Providers (IdPs) and their single sign-on (SSO) capabilities, thus offering a streamlined authentication experience for both users and organizations. The advantages of EAP-OAUTH for users include an SSO experience and enhanced privacy, while organizations benefit from simplified identity management, reduced operational costs, consistent security policies, and easier compliance. Furthermore, EAP-OAUTH represents a promising solution for addressing the challenges of authentication in modern wireless networks, such as the deployment of various multi-factor or risk-based, adaptive authentication strategies. This article presents an in-depth analysis of the EAP-OAUTH method, its design, implementation, and use cases in enterprise networks and public hotspots. It explores the OAuth 2.0 Device Authorization Grant flow and allows network clients to perform fast re-authentications without resorting to sessions on IdPs or even their SSO features. The implementation of EAP-OAUTH is demonstrated in real-world scenarios, using two IdPs (Google and Auth0), confirming its effectiveness, suitable performance and compatibility with various components of typical Wi-Fi infrastructures.

Building Client Networks and Maximising Your Resources

A report on how an Irish professional dog behavior consultant created a social media based network of past clients who were able to provide opportunities to learn and practice behavior modification skills without the direct supervision of the consultant. The structure of “The Alumni”’ is discussed alongside observations of the beneficial role such a network plays in a particularly challenging semi-rural Irish environment.

Distributed Learning Mechanisms for Anomaly Detection in Privacy-Aware Energy Grid Management Systems

Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-physical attacks, issues related to the security and privacy of grid systems are receiving much attention from researchers. In this paper, privacy-aware energy grid management systems with anomaly detection networks and distributed learning mechanisms are proposed. The anomaly detection network consists of a server and a client learning network, which collaboratively learn patterns without sharing data, and periodically train and exchange knowledge. We also develop learning mechanisms with federated, distributed, and split learning to improve privacy and use Q-learning for decision-making to facilitate interpretability. To demonstrate the effectiveness and robustness of the proposed schemes, extensive simulations are conducted in different energy grid environments with different target distributions, ORRs, and attack scenarios. The experimental results show that the proposed schemes not only improve management performance but also enhance privacy and security levels. We also compare the management performance and privacy level of the different learning machines and provide usage recommendations.

Live 360° Video Streaming to Heterogeneous Clients in 5G Networks

Live 360° Video Streaming to Heterogeneous Clients in 5G Networks

The Impact of Contractor–Client Network Structure on Building Project Quality

The Impact of Contractor–Client Network Structure on Building Project Quality

FELIX: First operational experience with the new ATLAS readout system and perspectives for HL-LHC

The Front-End Link eXchange (FELIX) readout system is deployed as the new interface connecting detector front-end electronics with the data acquisition and Timing, Trigger and Control (TTC) systems. FELIX functions as a router between custom serial links from front-end ASICs and FPGAs to data collection and processing components via a commodity switched network. FELIX uses commodity server technology in combination with FPGA-based PCIe I/O cards. FELIX servers run a software routing platform serving data to network clients performing a number of data preparation, monitoring and control functions. This paper covers the design of FELIX as well as the first operational experience gained during the Run 3 start, including the challenges faced commissioning the system for each ATLAS sub-detector. Over the next decade, the ATLAS detector will be required to operate in an increasingly harsh collision environment and challenging data taking conditions. To maintain physics performance, the detector will undergo a series of upgrades. In particular, the readout system capabilities will be improved. The planned evolution of FELIX for High-Luminosity LHC will be described, including architectural changes and status of early integration with detector development projects.

Cabrio Teams, Primary Care for Homeless People In Belgium- Limburg

Cabrio aims to prevent homelessness with a cross-sector, multidisciplinary and autonomous team that establishes contact with homeless people in their living environment in an outreach manner, offers customized support and connects to social facilities. The team aims to help homeless people in finding sustainable housing. In the Belgian province of Limburg there are 3 Cabrio teams that serve 5 local authorities. The welfare organisation CAW Limburg coordinates the 3 Cabrio teams.&#x0D; Cabrio is active within the framework that is offered by the Flemish Action Plan to combat homelessness 2020-2024.&#x0D; https://www.departementwvg.be/welzijn-en-samenleving/welzijnswerk/dak-en-thuisloosheid &#x0D; Objectives: &#x0D; •Making contact in the social environment of homeless people and building a close relationship with homeless people.&#x0D; •Offer support in various life domains and realize a connection with services (network). Guarantee continuity in care.&#x0D; •Strengthening the personal network of clients.&#x0D; •Working on sustainable housing with social and other housing services.&#x0D; •Working on connection through small-scale projects with a high level of participation of the target group. This is part of the socialization of care.&#x0D; •Signalling structural barriers to local policy makers. &#x0D; Methodical framework: Cabrio works in an outreaching and multidisciplinary way and uses methods from addiction care, counselling for people with psychological vulnerability and welfare work. Cabrio works from a shared vision of homelessness.&#x0D; Outreach work: Counsellors contact clients in the public space, at their home or in the semi-public space (e.g. institution, facility) with a participative basic attitude. They focus on making a connection with 'distrustful' clients&#x0D; Multidisciplinary work: To aim for care continuity the Cabrio team brings together different disciplines: general social work (CAW Limburg), addiction care (ZorGGroepZin), psychiatric home care (Beschut Wonen, Mobiel Herstelteam Psychiatrie), special youth care (CANO Pieter Simenon), outreach work (straathoekwerk).&#x0D; •Expertise of supervisors is shared in the Cabrio team.&#x0D; •Different perspectives and different expertise provides customized guidance.&#x0D; •The collaboration and sharing of expertise strengthens the communication and trust between the client and the team and improves the follow-up of specific cases.&#x0D; •Supervisors acquire new insights and methods in guiding homeless people to their own work setting.&#x0D; Special methods: Harm reduction (or harm-limiting work), motivational interviewing, circle of behaviour change Prochaska and Diclemente, making quarters, guideline for dealing with ethical dilemmas, Housing First.&#x0D; 1.4.Registration by the Local Homeless Consultation&#x0D; The local authorities organize a Local Homeless Consultation (LDO) in which various welfare organizations participate that have contacts with homeless people through their daily practice. The LDO has 3 tasks: &#x0D; •Making an inventory of homeless people in the work area.&#x0D; •Coordinating which service/counsellor takes care of the specific client.&#x0D; •Tackling signals or structural barriers.&#x0D; The LDO refers to the Cabrio team if the client:&#x0D; •Does not have stable housing (see Ethos).&#x0D; •Scores bad on several life domains and struggles with special problems (e.g. addiction, psychological vulnerability, medical problems, ...)&#x0D; •Has a disrupted personal network or does not have or has a few contacts with care services (also called care avoiders).&#x0D;

A heuristic approach for the integrated production–transportation problem with process flexibility

We study an integrated multi-item production and distribution problem considering a network of multiple plants and clients, who are geographically dispersed, with direct shipments from the plants to the clients. In addition to the decisions on production and distribution, a decision needs to be taken on the level of process flexibility in the network, i.e., which items can be produced in which plants. There is a clear trade-off between these decisions. On the one hand, a network with total flexibility where each plant can produce all types of items allows for lower transportation costs, but requires large investments in flexibility and frequent setups. On the other hand, a network with a limited amount of flexibility where each plant produces only a few items, will increase the transportation costs, but requires a lower investment in flexibility. We model this problem as an extension of the capacitated lot-sizing problem. We limit the investment in flexibility by a budget constraint and minimize the operational costs. Varying this budget allows us to analyze different levels of flexibility. In this paper, we propose mathematical models and a hybrid solution method that combines a mixed integer programming-based approach and a kernel search heuristic. Our computational results using data sets from the literature show that the proposed hybrid method produces on average better solutions with significantly lower computational times when compared with the results produced by a state-of-the-art optimization software. Additional computational results are presented by varying key parameters and analyzing their impact on the value of flexibility. These computational experiments indicate that some of the main managerial insights which were derived in the literature for the case without transportation costs are no longer valid when we consider transportation costs.

Organized Crime in the Corruption of Garuda Indonesia's Former Director (Analysis of the Jakarta District Court Decision)

This article delves into the complex world of organized crime and traces the historical roots of its development and impact on society. Focusing on corruption cases involving PT Garuda Indonesia and Rolls Royce the transnational dimensions of organized crime are examined. A criminological lens helps dissect the structural elements of organized crime to uncover networks of clients who seek to exploit legal loopholes. International legal mechanisms such as the UN Convention against Transnational Organized Crime aim to standardize responses to this widespread threat. Examining corruption within this framework highlights the global reach of organized crime and the need for collective action. Lastly as society grows the challenges of organized crime also increase. Global understanding combined with international cooperation guided by legal frameworks is essential to combat the complex and widespread nature of organized crime. This case study highlights the importance of criminology in identifying and combating the problem of organized crime in an interconnected world by underscoring the urgent need to address not only individual crimes but also their interconnectedness.

An Automated Method for Extracting and Analyzing Railway Infrastructure Cost Data

The capability of extracting information and analyzing it so that it is in a common format is essential for performing predictions, comparing projects through cost benchmarking, and having a deeper understanding of the project costs. However, the lack of standardization and the manual inclusion of data make this process very time-consuming, unreliable, and inefficient. To tackle this problem, a novel approach with a big impact is presented combining the benefits of data mining, statistics, and machine learning to extract and analyze the information related to railway infrastructure cost data. To validate the suggested approach, data from 23 real historical projects from the client network rail were extracted, allowing their costs to be comparable. Finally, some machine learning and data analytics methods were implemented to identify the most relevant factors allowing cost benchmarking to be performed. The presented method proves the benefits of data extraction for gathering, analyzing, and benchmarking each project in an efficient manner, and to develop a deeper understanding of the relationships and the relevant factors that matter in infrastructure costs.

Implementation Of The Dude Application As A Network Management System

The development of information technology and especially networks is very rapid. Therefore, there is a demand for a sophisticated computer network system. Where the admin cannot know the condition of each user's computer and whether the existing service system is on or off. Display and sound are technologies for detecting network damage on company computers. Implementation of a computer network client monitoring system using display and sound allows the system to send reports when a client computer or service shuts down automatically to the admin without involving the user. For this reason, researchers are trying to develop The Dudemonitoring network application in the form of display and sound. The results obtained from using this network monitoring application are monitoring the services carried out by each host or IP address. If a network error is correctly detected, a notification will be sent to the network administrator, then the network administrator will be able to repair network damage..

FedBully: A Cross-Device Federated Approach for Privacy Enabled Cyber Bullying Detection using Sentence Encoders

Cyberbullying has become one of the most pressing concerns for online platforms, putting individuals at risk and raising severe public concerns. Recent studies have shown a significant correlation between declining mental health and cyberbullying. Automated detection offers a great solution to this problem; however, the sensitivity of client-data becomes a concern during data collection, and as such, access may be restricted. This paper demonstrates FedBully, a federated approach for cyberbullying detection using sentence encoders for feature extraction. This paper introduces concepts of secure aggregation to ensure client privacy in a cross-device learning system. Optimal hyper-parameters were studied through comprehensive experiments, and a computationally and communicationally inexpensive network is proposed. Experiments reveal promising results with up to 93% classification AUC (Area Under the Curve) using only dense networks to fine-tune sentence embeddings on IID datasets and 91% AUC on non-IID datasets, where IID refers to Independent and Identically Distributed data. The analysis also shows that data independence profoundly impacts network performance, with AUC decreasing by a mean of 5.1% between Non-IID and IID. A rich and extensive study has also been performed on client network size and secure aggregation protocols, which prove the robustness and practicality of the proposed model. The novel approach presented offers an efficient and practical solution to training a cross-device cyberbullying detector while ensuring client-privacy.

Servitization and construction industry: ideas and insights from the commercial building sector

Servitization, which can be defined as the combination of products and services in a single integrated solution, has been a topic attracting attention from scholars and managers all around the globe. Despite this, very few studies have proposed to analyse this phenomenon on the construction industry. Thus, this research objective is to fill this gap by evaluating how the construction companies from the commercial building sector are applying servitization to their business offers and what are the impacts of this application on their immediate network of suppliers and clients. With this objective in sight, it was performed a case study of three cases where servitization was being applied. The study analysed three networks (two dyads and on triad) of suppliers and clients of servitization. From the findings, it can be said that servitization brought results similar than found in other industries, such as differentiation and new streams of revenue. It also changed the cost structure of the offer, allowing for transferring part of the CAPEX to OPEX, and reducing of total costs involved. This work helps to understand how servitization is impacting the commercial building sector and, consequently, part of the construction industry as a whole.