Distributed Paradigm Research Articles

A Method for Transforming Non-Convex Optimization Problem to Distributed Form

We propose a novel distributed method for non-convex optimization problems with coupling equality and inequality constraints. This method transforms the optimization problem into a specific form to allow distributed implementation of modified gradient descent and Newton’s methods so that they operate as if they were distributed. We demonstrate that for the proposed distributed method: (i) communications are significantly less time-consuming than oracle calls, (ii) its convergence rate is equivalent to the convergence of Newton’s method concerning oracle calls, and (iii) for the cases when oracle calls are more expensive than communication between agents, the transition from a centralized to a distributed paradigm does not significantly affect computational time. The proposed method is applicable when the objective function is twice differentiable and constraints are differentiable, which holds for a wide range of machine learning methods and optimization setups.

CasCIFF: A cross-domain information fusion framework tailored for cascade prediction in social networks

The explosive growth of the Internet has elevated social networks to a pivotal role in information propagation, reshaping conventional paradigms of information distribution. Utilizing the vast amount of data available online to model and predict this diffusion is of great importance in various fields. Existing approaches for information cascade prediction fall into three main categories: feature-driven methods, point process-based methods, and deep learning-based methods. Among them, deep learning-based methods, characterized by their superior learning and representation capabilities, mitigate the shortcomings inherent in the other methods.However, current deep learning methods still face several persistent challenges. In particular, accurate representation of user attributes remains problematic due to factors such as fake followers and complex network configurations. Previous algorithms that focused on the sequential order of user activations often neglected the rich insights offered by activation timing. Furthermore, these techniques often fail to holistically integrate temporal and structural aspects, thus missing the nuanced propagation trends inherent in information cascades.To address these issues, we propose the Cross-Domain Information Fusion Framework (CasCIFF), which is tailored for information cascade prediction. This framework exploits multi-hop neighborhood information to make user embeddings robust. When embedding cascades, the framework intentionally incorporates timestamps, endowing it with the ability to capture evolving patterns of information diffusion. In particular, CasCIFF seamlessly integrates the tasks of user classification and cascade prediction into a consolidated framework, thereby allowing the extraction of common features that prove useful for all tasks, a strategy anchored in the principles of multi-task learning. After extensive experiments conducted on publicly available datasets, the results demonstrate CasCIFF's superiority over established baseline methods in terms of prediction accuracy.

Dividend Announcements and Share Price Dynamics: Unraveling the Connection

This research paper delves into the dynamic landscape of dividend decisions in 2023, exploring the intricate interplay between market price of select equity shares and dividend announcements. In the ever-evolving global economy, firms face multifaceted challenges and opportunities that shape their dividend policies. Employing a comprehensive analysis of recent data, this study investigates the influence of dividend decisions and their implications on shareholder value. Through meticulous examination of dividend patterns across various industries, this research uncovers novel insights into the shifting paradigms of dividend distribution. The paper strives to establish a connection between market price in taking dividend decisions, shedding light on the delicate balance companies must strike between rewarding shareholders and ensuring sustainable financial growth. Incorporating recent data, this paper contributes to the academic discourse on corporate finance and also offers practical implications for businesses navigating the complex terrain of dividend decisions.

Secondary use of helath data in EU: Exploring the legal challenges of the upcoming European Health Data Space Reguation in light of the findings of the TEHDAS project

Arguably, big data, data processing with advanced ICT solutions and data sharing were accelerated during the end of the 2010s, also fuelled by the COVID-19 pandemic. This latter can be reasonably considered a booster in health care for data-driven digital solutions and regulations, which led to issues concerning both primary and secondary use of digital (electronic) health data. There is an ongoing and comprehensive regulatory effort within the European Union to address the evoked challenges. Regarding the healthcare sector, the European Health Data Space Regulation can be considered a flagship legislation. The main ambition of the European Commission is, on the one hand, to create a common space where natural persons can easily control their electronic health data and, on the other hand, to make it possible for researchers, innovators and policymakers to use these health data sets for common societal purposes in a trusted and secure way based on a specific cross-border digital infrastructure. A European Union project, the Joint Action Towards the European Health Data Space (TEHDAS) has been initiated to support the establishment of the EHDS as regards the secondary use of data. Our analysis identified two important topics: legislative barriers to the secondary use of health data and challenges for citizens’ empowerment, including data altruism. The legal complexity of the ongoing strategic legislative initiatives within the EU poses several challenges for future application. Firstly, there can be a terminological issue based on the lack of a common definition of secondary use of data. Secondly, the regulation of the Member States adjusted to their national health and data utilisation systems can be diverse. Both of these may hinder the desired EU-level harmonisation regarding data-sharing. In this context, the division of regulatory competencies related to health care and the chosen legal basis can be decisive. Overall, the EHDS might be a regulation that can challenge the current legal paradigm of competence distribution among the EU and the Member States regarding digital transition, including data usage in health care. As for the citizens’ empowerment, the goal of an EU-wide ecosystem of health data utilisation requires individuals who are aware of the economic value of their health data and, via practising altruism, want to use these data also for the common good. Specific provisions of the EHDS, such as the right to data portability or the option for opt-out, are definitely for the empowerment of citizens. But, these can also reduce the potential health care-related benefits of data utilisation at both, individual and population level. Altogether, a concert of requirements should be fulfilled for meaningful citizens’ participation, engagement, and practice of data altruism, supported by the complex interplay of the relevant regulations.

Sustainable last-mile distribution with autonomous delivery robots and public transportation

In recent years, the rapid growth of e-commerce and the need to make last-mile logistics more sustainable have stimulated the development of new distribution paradigms, based on air drones and autonomous delivery robots, which are less sensitive to traffic congestion. In this article we deal with a routing problem in which a fleet of autonomous delivery robots can travel not only on the road network but also on the public transportation system (or part of it) to extend their range of action with a given battery capacity. The problem entails building delivery robot routes synchronized with the rides of the public transportation lines, enabling robots to drop on/off public vehicles, where they use dedicated compartments, to reach customers that would be otherwise out of reach. To this purpose, we develop a tailored destroy-and-repair mechanism that, embedded into a neighborhood search algorithm, allows to effectively explore the feasibility region of large-scale instances. Extensive computational results on instances resembling the distribution of drugs to pharmacies in Rome (Italy) show that the proposed algorithmic mechanism allows to obtain a cost reduction up to about 7.5% with respect to a more traditional approach. Moreover, from a managerial point of view, our experiments show that autonomous delivery robots combined with public transportation can provide huge benefits in terms of costs and emissions reduction, when compared to both traditional and electric vans.

ADVANCED METHODS FOR MAINTAINING AND MANAGING THE LIFE CYCLE OF CLOUD ENVIRONMENTS: SURVEY

Resource management is a fundamental concept in cloud computing and virtualization, encompassing the allocation, release, coordination, and monitoring of cloud resources to optimize efficiency. The complexity arises from the virtualized, heterogeneous, and multi-user nature of these resources. Effective governance is challenging due to uncertainty, large-scale infrastructures, and unpredictable user states. This paper presents a comprehensive taxonomy of resource management technologies, offering a detailed analysis of design architecture, virtualization, and cloud deployment models, along with capabilities, objectives, methods, and mechanisms. In a cloud computing environment, deploying application-based resource management techniques necessitates understanding the system architecture and deployment model. This paper explores centralized and distributed resource management system architectures, providing a review of effective resource management techniques for both, accompanied by a comparative analysis. The evolution of cloud computing from a centralized to a distributed paradigm is examined, emphasizing the shift towards distributed cloud architectures to harness the computing power of smart connected devices at the network edge. These architectures address challenges like latency, energy consumption, and security, crucial for IoT-based applications. The literature proposes various methods for distributed resource management, aligning with the distributed nature of these architectures. Resource management in cloud computing involves discovery, provisioning, allocation, and monitoring functions, with sub-functions like mapping and scheduling. Integrated approaches to consolidation and resource management have been explored in numerous studies. This paper summarizes and analyzes existing research on resource management functions, focusing on identification, provisioning, allocation planning, and monitoring, based on their objectives and methods.

Toward a Real-Time TCP SYN Flood DDoS Mitigation Using Adaptive Neuro-Fuzzy Classifier and SDN Assistance in Fog Computing

The growth of the Internet of Things (IoT) has recently impacted our daily lives in many ways. As a result, a massive volume of data are generated and need to be processed in a short period of time. Therefore, a combination of computing models such as cloud computing is necessary. The main disadvantage of the cloud platform is its high latency due to the centralized mainframe. Fortunately, a distributed paradigm known as fog computing has emerged to overcome this problem, offering cloud services with low latency and high-access bandwidth to support many IoT application scenarios. However, attacks against fog servers can take many forms, such as distributed denial of service (DDoS) attacks that severely affect the reliability and availability of fog services. To address these challenges, we propose mitigation of fog computing-based SYN Flood DDoS attacks using an adaptive neuro-fuzzy inference system (ANFIS) and software defined networking (SDN) assistance (FASA). The simulation results show that the FASA system outperforms other algorithms in terms of accuracy, precision, recall, and F1-score. This shows how crucial our system is for detecting and mitigating TCP-SYN floods and DDoS attacks.

A new hybrid distribution paradigm: Integrating drones in medicines delivery

A new hybrid distribution paradigm: Integrating drones in medicines delivery

Distributed soft video transmission based on hybrid digital and analog scheme

This study presents a novel distributed soft video delivery scheme using hybrid digital and analog framework, which realizes a relatively lightweight encoder as well as good robustness. Specifically, after applying scalar quantization to the interframes, the scaled analog information (quantization error) and the scaled digital information (quantized source) are superimposed and then transmitted. In this way, the proposed scheme directly delivers pseudo-analog symbols over the orthogonal frequency division multiplexing (OFDM) channels and involves no complicated digital codec. Moreover, we utilize cross-frame correlation to implement a distributed paradigm which further reduces the encoder complexity since the complex motion estimation and compensation algorithms are transferred to the decoder. Accordingly, the power distortion optimization scheme, which resolve the allocation of power and the parameters optimization to achieve minimum transmission distortion, is proposed. To solve it, first, we formulate the power distortion expressions regarding quantization parameters and power allocation coefficients. Subsequently, we divide the problem into two sub-problems based on the fast coordinate descent method and further propose a greedy iterative algorithm to optimize them. We also develop a data-driven optimization algorithm based on deep learning that reduces the additional delay brought by the iterative optimization method. At the receiver, we estimate the quantization output and quantization error by the modified linear least squares estimation with the virtual noise variance. Based on the simulation results, the proposed framework has a better performance in terms of peak signal-to-noise ratio and structural similarity than the relevant cutting-edge schemes while maintaining good robustness.

The open toolbox for behavioral research

In this work, we describe a new open-source MATLAB toolbox for the control of behavioral experiments. The toolbox caters to very different types of experiments in different species, and with different underlying hardware. Typical examples are operant chambers in animals, with or without neurophysiology, behavioral experiments in human subjects, and neurophysiological recordings in humans such as EEG and fMRI. In addition, the toolbox supports communication via Ethernet to either control and monitor one or several experimental setups remotely or to implement distributed paradigms across different computers. This flexibility is possible, since the toolbox supports a wide range of hardware, some of which is custom developments. An example is a fast network-based digital-IO device for the communication with experimental hardware such as feeders or triggers in neurophysiological setups. We also included functions for online video analysis allowing paradigms to be contingent on responses to a screen, the head movement of a bird in an operant chamber, or the physical location of an animal in an open arena. While the toolbox is well tested and many components of it have been in use for many years, we do not see it as a finished product but rather a continuing development with a focus on easy extendibility and customization.

An optimal multi-objective demand side management of a smart Microgrid consists of various building loads, considering relativity index and uncertainty

This paper addresses microgrid energy management in the presence of distributed generation and active loads while accounting for operational, economic, environmental, and reliability factors. The objective is to minimize the cost of shutdown during N-1 contingencies, projected emission-related expenses, and the overall operational cost of microgrids integrated with distributed generations. A stochastic scheduling technique that optimizes short-term microgrid operation, reducing costs and pollution through renewable resources is introduced. Utilizing demand response programs among residential, commercial, and industrial participants is proposed to counter the uncertainty of renewable resource-generated power. The demand response program implementation suggests considering financial incentives as bid prices, along with energy packages managed by demand response providers. Simulation results demonstrate three strategies to decrease operating costs and pollutants, contrasting scenarios with and without responsive load engagement. Equations governing AC power flow, microgrid operations, reliability, distributed generation, and, demand response programs and batteries, form the analytical core. Stochastic programming addresses uncertainties tied to demand, energy prices, renewable distributed generation production, and microgrid equipment availability. To attain dependable, optimal solutions with dynamic responsiveness, the combined ant lion optimization and crow search algorithm are employed. The proposed method is tested on a sample smart microgrid for validation. Numerical findings unequivocally underscore demand-side management potency in reducing power generation uncertainties from wind turbines and photovoltaics. This paper offers insights into microgrid energy management complexities, paving the way for resilient, cost-effective, and environmentally conscious energy distribution paradigms.

A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

The Energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional Intelligent Transportation Systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among Electric Vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system’s security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme’s robustness is solicited through the widely-accepted formal Random or Real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94% and 28.08% in comparison to the existing competitive schemes.

Privet: A Privacy-Preserving Vertical Federated Learning Service for Gradient Boosted Decision Tables

Vertical federate learning (VFL) has recently emerged as an appealing distributed paradigm empowering multi-party collaboration for training high-quality models over vertically partitioned datasets. Gradient boosting has been popularly adopted in VFL, which builds an ensemble of weak learners (typically decision trees) to achieve promising prediction performance. Recently there have been growing interests in using decision table as an intriguing alternative weak learner in gradient boosting, due to its simpler structure, good interpretability, and promising performance. In the literature, there have been works on privacy-preserving VFL for gradient boosted decision trees, but no prior work has been devoted to the emerging case of decision tables. Training and inference on decision tables are different from that the case of generic decision trees, not to mention gradient boosting with decision tables in VFL. In light of this, we design, implement, and evaluate Privet, the first system framework enabling privacy-preserving VFL service for gradient boosted decision tables. Privet delicately builds on lightweight cryptography and allows an arbitrary number of participants holding vertically partitioned datasets to securely train gradient boosted decision tables. Extensive experiments over several real-world datasets and synthetic datasets demonstrate that Privet achieves promising performance, with utility comparable to plaintext centralized learning.

Trans(gender) journeys: rights and the (non-)recognition of "human".

A human right paradigm has been challenging the biomedical perspectives that tend to be normalized in the Western context concerning the lives of trans people. The aim of this study is to understand how trans people in Portugal and Brazil perceive the (non-)recognition of their socio-cultural, economic and political rights. Specifically, the study intends to know in what extent these perceptions influence the processes of identity (de)construction. For this purpose, 35 semi-structured interviews were conducted with people self-identified as trans, transsexuals and transvestites in Brazil and Portugal. The narratives of the participants were analyzed according to the thematic analysis method and the following six main themes emerged: (i) Who are the rights for; (ii) Types of rights; (iii) Paradigm of distribution of rights; (iv) Local or global rights; (v) Non-recognition of the "human"; and, (vi) Transphobias (and cissexism). The results allowed the knowledge of rights and the non-recognition of the "human" which is the central organizer of the analysis. Among the main conclusions of this study, we emphasize the circumscription of rights to certain international, regional and/or national contexts; the existence of local instead of global rights, since they are influenced by regional and international law, but they depend on the legislation in force in each country; and the way human rights can also be understood as a platform of invisibility and exclusion of other people. Based on a commitment to social transformation, this article also contributes to rethinking the violence that is exercised on trans people as a continuum, whether through 'normalizing devices' by medical contexts, family contexts, public space, or even through internalized transphobia. Social structures produce and sustain transphobias and, simultaneously, are responsible for fighting them by changing the paradigm about the conception of transsexualities.

The importance of classification in reducing the congestion price in peerto-peer energy trading

Peer-to-peer energy trading is a model in which energy can be exchanged directly betweenenergy producers and consumers. This model offers an innovative approach to energy markets. Intraditional energy trading models, energy producers collect energy at a single point and then distribute it toconsumption points. However, with peer-to-peer energy trading, energy producers can offer energy directlyto consumers, thus reducing the costs of intermediaries and distribution networks. [1], [12]The importance of the peer-to-peer energy trading model comes from several aspects: Energy Efficiency,Renewable Energy Incentives, Flexibility and Reliability etc.Peer-to-peer energy trading allows the energy system to become more decentralized and more sustainable.However, an appropriate classification and regulation is necessary for peer-to-peer energy trading to workeffectively. [2]The most important of these classifications is the distance category. Distance and peer-to-peer energytrading offers a new paradigm for efficient and sustainable energy distribution. Compared to traditionalenergy distribution models, this new approach allows energy consumers to exchange energy amongthemselves. This aims to provide a more flexible, efficient, and environmentally friendly energydistribution. [3], [13]In this study, I made simulations to see how the distance could have an effect on the energy trade betweenpeers, and as a result of the simulations, the distance and arrangement of consumers from each other wasexamined. As a result, the regulation of consumers has proven to have an effect on trade, and theclassification of consumers shows that it has a large effect on lowering the congestion price.

Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture

Artificial intelligence (AI) research and market have grown rapidly in the last few years, and this trend is expected to continue with many potential advancements and innovations in this field. One of the emerging AI research directions is Distributed Artificial Intelligence (DAI). It has been motivated by technological advances in communication, networking, and hardware, together with the nature of data being generated from connected, distributed, and diverse objects. DAI is expected to create a fertile environment for innovative, advanced, robust, and scalable approaches for AI supporting the vision of smart societies. In this paper, we explore state of the art on DAI and identify the opportunities and challenges of provisioning distributed AI as a service (DAIaaS). We provide a taxonomy and a comprehensive review covering the literature from 2016 to 2022. It comprises various aspects of DAI, including AI workflow, distribution paradigms, supporting infrastructure, management techniques, and applications. Based on the gained insights from the conducted review, we propose Imtidad, a framework for provisioning DAIaaS over the cloud, fog, and edge layers. We refine this framework and propose the Imtidad software Reference Architecture (RA) for designing and deploying DAI services. In addition, we extended the framework and developed a future networking infrastructure transformation framework, as it is one of the main enablers for DAI. This framework and RA can be used as guidance facilitating the transition to the future DAI, where DAI is decoupled from the design and development of smart applications. This paper, including the proposed framework, RA, taxonomy, and detailed review, is expected to have an extensive impact on DAI research and accelerate innovations in this area.

Authentic omnichannel: Providing consumers with a seamless brand experience through authenticity

AbstractOmnichannel represents a customer‐oriented distribution paradigm through which retailers can deliver a seamless customer experience and create an authentic brand narrative that is communicated to customers across diverse touchpoints. Despite the increasing relevance of the omnichannel approach, research on how omnichannel can affect the customer experience remains scant. This research consists of a qualitative study and three experimental studies. Drawing from signaling theory, we contend that the signal congruency established by omnichannel—where all the channels are aligned and convey a consistent message to customers—can enhance consumers' purchase intention and perceptions of brand authenticity. We further investigate the role of brand authenticity as a mediator of the relationship between multichannel customer experience (seamless vs. nonseamless) and purchase intention, as well as of brand untrustworthiness as a moderator of the relationship between multichannel customer experience and brand authenticity. The results show that a seamless multichannel customer experience has a significant main effect on purchase intention and that participants in the seamless multichannel customer experience condition perceive the brand as more authentic than those in the nonseamless multichannel customer experience condition. Both the mediation and moderation hypotheses are supported. These findings enhance the literature on signaling theory and omnichannel. They also provide insightful implications for retailers in terms of managing the omnichannel customer experience. Overall, this study integrates the research areas of brand authenticity and omnichannel and provides valuable insights by indicating how seamlessness can boost consumers' perception of brand authenticity. Furthermore, the study advances our knowledge by investigating the impact of brand authenticity as both a result of the omnichannel customer experience and a predictor of purchase intention.

Filtering Out High Noise Data for Distributed Deep Neural Networks

Artificial intelligence-based cyber-physical systems (CPS) applications have been spread across various fields such as smart cities, medical services, and industrial controls. When CPS devices are connected to a cloud server, big data streams generated by CPS devices impose enormous bandwidth pressure and exert excessive compute loads to the cloud server. Due to unpredictable environments and uncertainty in reality, these issues are mainly attributed to a large amount of high noise data captured and uploaded by CPS devices. To overcome these issues, this paper proposes a cyber-physical-cloud based framework for distributed deep neural networks (DDNNs) to prevent high noise data from being uploaded to the cloud. The proposed framework features a lightweight data filtering module enabled by depthwise separable convolutions to identify and filter out the high noise data that the cloud cannot recognize. Extensive experimental results demonstrate that the proposed data filtering module can achieve an accuracy of up to 83.72% in identifying high noise data and the proposed framework can effectively save bandwidth of up to 63.42% as compared to benchmarking methods. Note to Practitioners—This paper is motivated by the problems of enormous bandwidth pressure and excessive cloud compute loads in cyber-physical-cloud distributed computing paradigms. These problems are mainly caused by high noise data generated by CPS devices, because CPS devices often work in disturbing and unstable environments and there are uncontrollable uncertainties in reality. Especially for the emerging artificial intelligence-driven cyber-physical-cloud distributed paradigms, there is no existing research to solve the unnecessary transmission and cloud compute loads caused by high noise data. To tackle the challenge, this paper develops a novel cyber-physical-cloud distributed framework with data filtering capabilities to prevent high noise data from being uploaded. The proposed framework supports two popular loosely coupled and closely coupled distributed computing paradigms. Extensive experiments confirm that the proposed cyber-physical-cloud distributed framework can efficiently filter out high noise data and alleviate unnecessary transmission and needless cloud compute loads introduced by high noise data.

Blockchain and Digital Assets: A New Paradigm for Copyright and Patent Distribution through Micropayments

Blockchain and Digital Assets: A New Paradigm for Copyright and Patent Distribution through Micropayments

Few-Shot GAN: Improving the Performance of Intelligent Fault Diagnosis in Severe Data Imbalance

In severe data imbalance scenarios, fault samples are generally scarce, challenging the health management of industrial machinery significantly. Generative adversarial network, a promising solution to solve the data imbalance problem, suffers from a negative overfitting issue when trained with few samples. To tackle challenges, this paper proposes a Few-shot GAN which uses a sample-rich class to provide a sample distribution paradigm for the sample-poor class. More specifically, the GAN is first pre-trained using a sample-rich class. Then, a fine-tuning strategy based on anchor samples is developed, which on the one hand keeps the generated samples close to the real samples and on the other hand preserves the learned complex sample distributions as much as possible. Experiments demonstrate that the overfitting problem of the GAN with few samples trained is well solved and the diversity of the generated samples is improved. In addition, to avoid the offset of features extracted by the fault diagnosis model due to the addition of numerous generated samples in severe data imbalance scenarios, large-margin learning is introduced to constrain the similarities between the features of the generated samples and the real samples. The performance of the fault diagnosis model is significantly improved when numerous generated samples are added, benefiting the predictive maintenance-based decision and avoiding unexpected economic loss.