Search Results (55)

“Coexpressivity” is the property of utterances that marshal multiple linguistic elements and modalities simultaneously to perform the distinct linguistic functions of Jakobson’s classic analysis (1960). This study draws on a longitudinal corpus of natural conversation recorded over six decades with an accomplished “master speaker” of Tsotsil (Mayan), adept at using his language to manage different aspects of social life. The research aims to elaborate the notion of coexpressivity through detailed examples drawn from a range of circumstances. It begins with codified emic speech genres linked to prayer and formal declamation and then ranges through conversational narratives to gossip-laden multiparty interaction, to emphasize coexpressive connections between speech as text and concurrent gesture, gaze, and posture among interlocutors; audible modalities such as sound symbolism, pitch, and speech rate; and finally, specific morphological characteristics and the multifunctional effects of lexical choices themselves. The study thus explores how multiple functions may, in principle, be coexpressed simultaneously or contemporaneously in individual utterances if one takes this range of modalities and expressive resources into account. The notion of “master speaker” relates to coexpressive virtuosity by linking the resources available in speech, body, and interactive environments to accomplishing a wide range of social ends, perhaps with a special flourish although not excluded from humbler, plainer talk. Full article

Teachers with mental illness are vulnerable to work-related triggers that can lead to relapse, affecting their mental health and ability to recover, stay employed, and deliver quality education. This empirical study explored such triggers among public school teachers in Limpopo Province, South Africa. Using Bronfenbrenner’s Ecological Systems Theory, a qualitative phenomenological design was adopted. Semi-structured face-to-face interviews were conducted with 14 participants that were purposively selected across four hospitals. Data were audio-recorded, transcribed verbatim, and analyzed using Tesch’s eight-step open-coding method. Findings revealed being gossiped about by colleagues, excessive workload, limited leadership and parental support, classroom management challenges, high performance expectations without support, and inadequate teacher mental health policies in schools. These triggers can lead to frequent absenteeism and poor teaching outcomes. They will further increase the risk of medication resistance and long-term cognitive decline due to progressive structural brain damage as a result of multiple relapses. The study highlights the urgent need for multi-stakeholder collaboration, including clinicians, academic institutions, union representatives, and the Department of Basic Education, to co-develop effective, context-sensitive strategies to mitigate work-related triggers of mental illness relapse. These strategies are not only essential for enabling long-term workforce participation but also advancing sustainable mental health and well-being. Full article

This study, based on regulatory focus theory and internal locus of control theory, constructs a moderated mediation model to explore how perceived positive workplace gossip indirectly affects employee creativity through promotion-oriented cognitive crafting and risk-taking behavior. Through the analysis of four-wave, two-source survey data from 463 employees, this study found that perceived positive gossip can stimulate promotion-oriented cognitive crafting in the gossiped-about employees, which in turn promotes risk-taking behavior and ultimately enhances creativity. Furthermore, internal locus of control plays a significant moderating role in this mechanism. The gossiped-about employees with a high internal locus of control are more inclined to respond positively when faced with positive gossip, exhibiting higher promotion-oriented cognitive crafting and risk-taking behavior. Overall, this research advances the understanding of positive gossip’s functional consequences and offers practical insights for fostering organizational creativity. Full article

In smart grids, renewable energies play a predominant role, but they produce more and more data, which are volatile by nature. As a result, predicting electrical behaviours has become a real challenge and requires solutions that involve more all microgrid entities in learning processes. This research proposes the design of a coordination model that integrates two decentralised approaches to distributed learning applied to a microgrid: the gossip federated learning approach, which consists of exchanging learning models between neighbouring nodes, and the gossip ensemble learning approach, which consists of exchanging prediction results between neighbouring nodes. The experimentations, based on real data collected in a living laboratory, show that the combination of a coordination model and intelligent digital twins makes it possible to implement and operate these two purely decentralised learning approaches. The results obtained on the predictions confirm that these two implemented approaches can improve the efficiency of learning on the scale of a microgrid, while reducing the congestion caused by data exchanges. In addition, the generic gossip mechanism offers the flexibility to easily define different variants of an aggregation operator, which can help to maximise the performance obtained. Full article

Internet-of-Things (IoT) devices are interconnected objects embedded with sensors and software, enabling data collection and exchange. These devices encompass a wide range of applications, from household appliances to industrial systems, designed to enhance connectivity and automation. In distributed IoT networks, achieving reliable decision-making necessitates robust consensus mechanisms that allow devices to agree on a shared state of truth without reliance on central authorities. Such mechanisms are critical for ensuring system resilience under diverse operational conditions. Recent research has identified three common limitations in existing consensus mechanisms for IoT environments: dependence on synchronised networks and clocks, reliance on centralised coordinators, and suboptimal performance. To address these challenges, this paper introduces a novel consensus mechanism called Randomised Asynchronous Consensus with Efficient Real-time Sampling (RACER). The RACER framework eliminates the need for synchronised networks and clocks by implementing the Sequenced Probabilistic Double Echo (SPDE) algorithm, which operates asynchronously without timing assumptions. Furthermore, to mitigate the reliance on centralised coordinators, RACER leverages the SPDE gossip protocol, which inherently requires no leaders, combined with a lightweight transaction ordering mechanism optimised for IoT sensor networks. Rather than using a blockchain for transaction ordering, we opted for an eventually consistent transaction ordering mechanism to specifically deal with high churn, asynchronous networks and to allow devices to independently and deterministically order transactions. To enhance the throughput of IoT networks, this paper also proposes a complementary algorithm, Peer-assisted Latency-Aware Traffic Optimisation (PLATO), designed to maximise efficiency within RACER-based systems. The combination of RACER and PLATO is able to maintain a throughput of above 600 mb/s on a 100-node network, significantly outperforming the compared consensus mechanisms in terms of network node size and performance. Full article

Incorrect sensor readings can cause serious problems in Wireless Sensor Networks (WSNs), potentially disrupting the operation of the entire system. As shown in the literature, they can arise from various reasons; therefore, addressing this issue has been a significant challenge for the scientific community over the past few decades. In this paper, we examine the applicability of seven distributed consensus gossip-based algorithms for sensor fusion (namely, the Randomized Gossip algorithm, the Geographic Gossip algorithm, three initial configurations of the Broadcast Gossip algorithm, the Push-Sum protocol, and the Push-Pull protocol) to compensate for incorrect data in WSNs. More specifically, we consider a scenario where the sensor-measured data (measured by a set of independent sensor nodes) are skewed due to Gaussian noise with a various standard deviation $\sigma$, resulting in discrepancies between the measured values and the true value of observed physical quantities. Subsequently, the aforementioned algorithms are employed to mitigate this skewness in order to improve the accuracy of the measured data. In this paper, WSNs are modeled as random geometric graphs with various connectivity, and the performance of the algorithms is evaluated using two metrics (specifically, the mean square error (MSE) and the number of sent messages required for an algorithm to be completed). Based on the presented results, it is identified that all the examined algorithms can significantly suppress incorrect sensor readings (MSE without sensor fusion = −0.42 dB if $\sigma$ = 1, and MSE without sensor fusion = 14.05 dB if $\sigma$ = 5), and the best performance is achieved by PS in dense graphs and by GG in sparse graphs (both algorithms achieve the maximum precision MSE = −24.87 dB if $\sigma$ = 1 and MSE = −21.02 dB if $\sigma$ = 5). Additionally, the performance of the analyzed distributed consensus gossip algorithms is compared to the best deterministic consensus algorithm applied for the same purpose. Full article

Due to its partially decentralized and highly scalable features, the consortium blockchain has currently overtaken other blockchain technologies as the one most frequently used and studied across various industries. However, performance issues such as low transaction efficiency and redundant communication processes continue to hinder the development of consortium blockchains. In the Hyperledger Fabric consortium blockchain system, transaction efficiency is largely influenced by the consensus protocol and broadcast protocol. This paper introduces a novel consortium blockchain network model, DC-SoC, focused on optimizing broadcast protocols. By incorporating the concept of density clustering, a stable propagation structure is established for the blockchain network, thus optimizing data dissemination in the Gossip protocol. Additionally, the concept of social networks is integrated, using trustworthiness scores and economic incentives to evaluate node security. The experimental results demonstrate that when DC-SoC is applied in a large-scale consortium blockchain environment, it significantly improves communication performance between nodes and ensures transmission reliability. Full article

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud. Full article

Autonomous vehicles are revolutionizing the future of intelligent transportation systems by integrating smart and intelligent onboard units (OBUs) that minimize human intervention. These vehicles can communicate with their environment and one another, sharing critical information such as emergency alerts or media content. However, this communication infrastructure is susceptible to cyber-attacks, necessitating robust mechanisms for detection and defense. Among these, the most critical threat is the denial-of-service (DoS) attack, which can target any entity within the system that communicates with autonomous vehicles, including roadside units (RSUs), or other autonomous vehicles. Such attacks can lead to devastating consequences, including the disruption or complete cessation of service provision by the infrastructure or the autonomous vehicle itself. In this paper, we propose a system capable of detecting DoS attacks in autonomous vehicles across two scenarios: an infrastructure-based scenario and an infrastructureless scenario, corresponding to vehicle-to-everything communication (V2X) Mode 3 and Mode 4, respectively. For Mode 3, we propose an ensemble learning (EL) approach, while for the Mode 4 environment, we introduce a gossip learning (GL)-based approach. The gossip and ensemble learning approaches demonstrate remarkable achievements in detecting DoS attacks on the UNSW-NB15 dataset, with efficiencies of 98.82% and 99.16%, respectively. Moreover, these methods exhibit superior performance compared to existing schemes. Full article

The development of artificial intelligence (AI) and self-driving technology is expected to enhance intelligent transportation systems (ITSs) by improving road safety and mobility, increasing traffic flow, and reducing vehicle emissions in the near future. In an ITS, each autonomous vehicle acts as a node with its own local machine learning models, which can be updated using locally collected data. However, for autonomous vehicles to learn effective models, they must be able to learn from data sources provided by other vehicles and infrastructure, utilizing innovative learning methods to adapt to various autonomous driving scenarios. Distributed learning plays a crucial role in implementing these learning tasks in an ITS. This review provides a systematic overview of distributed learning in the field of ITSs. Within an ITS, vehicles can engage in distributed learning by interacting with peers through opportunistic encounters and clustering. This study examines the challenges associated with distributed learning, focusing on issues related to privacy and security in data intelligence sharing, communication quality and speed, and trust. Through a thorough analysis of these challenges, this study presents potential research avenues to address these issues, including the utilization of incentive mechanisms that rely on reputation, the adoption of rapid convergence techniques, and the integration of opportunistic federated learning with blockchain technology. Full article

Healthcare management faces significant challenges related to upward communication. Sharing information in healthcare is crucial to the improvement of person-centered, safe, and effective patient care. An adverse event (AE) is an unintended or unexpected incident that causes harm to a patient and may lead to temporary or permanent disability. Learning from adverse events in healthcare is crucial to the improvement of patient safety and quality of care. Informal communication channels represent an untapped resource with regard to gathering data about the development of AEs. In this viewpoint paper, we start by identifying how informal communication played a key factor in some high-profile adverse events. Then, we present three Critical Challenge points that examine the role of informal communication in adverse events by (1) understanding how the prevailing trends in healthcare will make informal communication more important, (2) explaining how informal communication is part of the group-level sensemaking process, and (3) highlighting the potential role of informal communication in “breaking the silence” around critical and adverse events. Gossip, as one of the most important sources of informal communication, was examined in depth. Delineating the role of informal communication and adverse events within the healthcare context is pivotal to understanding and improving team and upward communication in healthcare organizations. For clinical leaders, the challenge is to cultivate a climate of communication safety, whereby informal communication channels can be used to collect soft intelligence that are paths to improving the quality of care and patient safety. Full article

The detection of fake news has emerged as a crucial area of research due to its potential impact on society. In this study, we propose a robust methodology for identifying fake news by leveraging diverse aspects of language representation and incorporating auxiliary information. Our approach is based on the utilisation of Bidirectional Encoder Representations from Transformers (BERT) to capture contextualised semantic knowledge. Additionally, we employ a multichannel Convolutional Neural Network (mCNN) integrated with stacked Bidirectional Gated Recurrent Units (sBiGRU) to jointly learn multi-aspect language representations. This enables our model to effectively identify valuable clues from news content while simultaneously incorporating content- and context-based cues, such as user posting behaviour, to enhance the detection of fake news. Through extensive experimentation on four widely used real-world datasets, our proposed framework demonstrates superior performance (↑3.59% (PolitiFact), ↑6.8% (GossipCop), ↑2.96% (FA-KES), and ↑12.51% (LIAR), considering both content-based features and additional auxiliary information) compared to existing state-of-the-art approaches, establishing its effectiveness in the challenging task of fake news detection. Full article

We consider information dissemination over a network of gossiping agents. In this model, a source keeps the most up-to-date information about a time-varying binary state of the world, and n receiver nodes want to follow the information at the source as accurately as possible. When the information at the source changes, the source first sends updates to a subset of $m\le n$ nodes. Then, the nodes share their local information during the gossiping period, to disseminate the information further. The nodes then estimate the information at the source, using the majority rule at the end of the gossiping period. To analyze the information dissemination, we introduce a new error metric to find the average percentage of nodes that can accurately obtain the most up-to-date information at the source. We characterize the equations necessary to obtain the steady-state distribution for the average error and then analyze the system behavior under both high and low gossip rates. We develop an adaptive policy that the source can use to determine its current transmission capacity m based on its past transmission rates and the accuracy of the information at the nodes. Finally, we implement a clustered gossiping network model, to further improve the information dissemination. Full article

The e-book services we use today have a serious drawback in that we will no longer be able to read the books we have purchased when the service is terminated. One way to solve this problem is to build a decentralized system that does not depend on a specific company or organization by combining smart contracts running on the Ethereum blockchain and distributed storage such as an IPFS. However, a simple combination of existing technologies does not make the stored e-book data persistent, so the risk of purchased e-books becoming unreadable remains. In this paper, we propose a decentralized distributed storage called d-book-repository, which has both access management function and data durability for purchased e-books. This system uses NFTs as access rights to realize strict access control by preventing clients who do not have NFTs from downloading e-book data. In addition, e-book data stored on storage nodes in the distributed storage is divided into shards using Reed–Solomon codes, and each storage node stores only a single shard, thereby preventing the creation of nodes that can restore the entire content from locally stored data. The storage of each shard is not handled by a single node but by a group of nodes, and the shard is propagated to all nodes in the group using the gossip protocol, where erasure codes are utilized to increase the resilience against node departure. Furthermore, an incentive mechanism to encourage participation as a storage node is implemented using smart contracts. We built a prototype of the proposed system on AWS and evaluated its performance. The results showed that both downloading and uploading 100 MB of e-book data (equivalent to one comic book) were completed within 10 s using an instance type of m5.xlarge. This value is only 1.3 s longer for downloading and 2.2 s longer for uploading than the time required for a simple download/upload without access control, confirming that the overhead associated with the proposed method is sufficiently small. Full article

Linear networks (sometimes called chain-type networks) occur frequently in Internet of Things (IoT) applications, where sensors or actuators are deployed along pipelines, roads, railways, mines, and international borders. LoRa, short for Long Range, is an increasingly important technology for the IoT with great potential for linear networking. Despite its potential, limited research has explored LoRa’s implementation in such networks. In this paper, we addressed two important issues related to LoRa linear networks. The first is contention, when multiple nodes attempt to access a shared channel. Although originally designed to deal with interference, LoRa’s technique of synchronisation with a transmission node permits a novel approach to contention, which we explored. The second issue revolves around routing, where linear networks permit simpler strategies, in contrast to the common routing complexities of mesh networks. We present gossip routing as a very lightweight approach to routing. All our evaluations were carried out using real equipment by developing real networks. We constructed networks of up to three hops in length and up to three nodes in width. We carried out experiments looking at contention and routing. We demonstrate using the novel approach that we could achieve up to 98% throughput. We compared its performance considering collocated scenarios that achieved 84% and 89% throughputby using relay widths of two and three at each hop, respectively. Lastly, we demonstrate the effectiveness of gossip routing by using various transmission probabilities. We noticed high performance up to 98% throughputat $T_{prob}$ = 0.90 and $T_{prob}$ = 0.80 by employing two and three active relay nodes, respectively. The experimental result showed that, at $T_{prob}$ = 0.40, it achieved an average performance of 62.8% and 73.77% by using two and three active relay nodes, respectively. We concluded that LoRa is an excellent technology for Internet of Things applications where sensors and actuators are deployed in an approximately linear fashion. Full article