Multiple Participants Research Articles

Multipartite quantum key agreement against d-dimensional collective-dephasing noise

In this paper, we introduce a novel model for collective-dephasing noise in [Formula: see text]-dimensional space. After that, we construct a multi-particle entangled state to resist the given collective-dephasing noise. Based on the measurement laws of multi-particle entangled state under two different bases, we propose a multipartite quantum key agreement protocol. In this protocol, multiple participants would obtain [Formula: see text]-dimensional secret key even if the quantum channel is influenced by collective-dephasing noise. The security analysis indicates that this protocol can resist both dishonest participant attack and the outsider attacks which include intercept-resend attack and entangle-measure attack. By comparing with the existing QKA protocols, it is clear that our protocol has greater universality from the perspective of dimensionality and number of participants.

Performance Analysis of Gradient Inversion Attack in Federated Learning with Healthcare Systems

Federated learning (FL) is widely applied to healthcare systems with the primary aim of keeping the privacy of patient's data while improving classification quality by using knowledge from multiple participants. However, the training images are believed to be embedded into the shared gradient, which indicates a privacy risk when sharing the gradient with other participants in FL. Therefore, this work aims to design and evaluate an image recovery attack on medical images. More specifically, dummy images are trained to match the dummy gradient to the shared gradient while maintaining the smoothness and naturalness of reconstructed images. On the adversary side, an optimization problem is formulated with variables of dummy images and network parameters treated as constants. We evaluate the gradient attack on two medical datasets and reconstructed images clearly show the details of chest X-ray and MRI images including bone and blood vessels of captured areas. Our work aims to increase the awareness of people on sharing the gradient in FL, especially in healthcare systems.

Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring

Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protection in FL becomes important since unreliable participants may gain access to the jointly trained model. Application of homomorphic encryption (HE) in a secure FL framework prevents the central server from accessing plaintext models. Thus, it is no longer feasible to embed the watermark at the central server using existing watermarking schemes. In this article, we propose a novel client-side FL watermarking scheme to tackle the copyright protection issue in secure FL with HE. To the best of our knowledge, it is the first scheme to embed the watermark to models under a secure FL environment. We design a black-box watermarking scheme based on client-side backdooring to embed a pre-designed trigger set into an FL model by a gradient-enhanced embedding method. Additionally, we propose a trigger set construction mechanism to ensure that the watermark cannot be forged. Experimental results demonstrate that our proposed scheme delivers outstanding protection performance and robustness against various watermark removal attacks and ambiguity attack.

The route to immersion: a conceptual framework for cross-disciplinary immersive theatre and experiences

The trend of immersive theatre, which began in the first decade of the twenty-first century, and has quickly established itself in contemporary mainstream theatre, is a subset of the larger phenomenon of emerging immersive experiences, which has expanded into a variety of fields, including healthcare, marketing, and education. This paper aims to provide a revised conceptual framework from the context of design through technologies with an addition of bodily immersion, or a sensation of bodily transference, creating illusory ownership over a virtual body, and activating praesence, a lived experience of the physical body responding within an imaginative, sensual environment, produced with the help of immersive technology. This concept, taken from the field of theatre and performance, is combined with four other components (systems, spatial, social/empathic, and narrative/sequential) to produce an immersive experience that realises the fullest potential of immersion. The proposed framework serves as a valuable guideline for creating immersive experiences across multiple disciplines, benefiting both creators and participants by enhancing comprehension and setting expectations. Additionally, it can be utilised by academics and students as a criteria for evaluation and analysis.

A Practical Multiparty Private Set Intersection Protocol Based on Bloom Filters for Unbalanced Scenarios

Multiparty Private Set Intersection (MPSI) is dedicated to finding the intersection of datasets of multiple participants without disclosing any other information. Although many MPSI protocols have been presented, there are still some important practical scenarios that require in-depth consideration such as an unbalanced scenario, where the server’s dataset is much larger than the clients’ datasets, and in cases where the number of participants is large. This paper proposes a practical MPSI protocol for unbalanced scenarios. The protocol uses the Bloom filter, an efficient data structure, and the ElGamal encryption algorithm to reduce the computation of clients and the server; adopts randomization technology to solve the encryption problem of the 0s in the Bloom filter; and introduces the idea of the Shamir threshold secret-sharing scheme to adapt to multiple environments. A formal security proof and three detailed experiments are given. The results of the experiments showed that the new protocol is very suitable for unbalanced scenarios with a large number of participants, and it has a significant improvement in efficiency compared with the typical related protocol (TIFS 2022).

Institutionalisation of public participation in China's urban regeneration from the perspective of historical institutionalism: Three-stage cases in Guangzhou

Research into public participation in urban regeneration holds a significant position in global urban research, with a considerable body of literature describing the practices of multiple stakeholders' participation in diverse institutional contexts. In the centralised dynamic context for China's urban regeneration, the institutional arrangements and practices of public participation and the evolution processes between them still remain “black boxes”. This paper aims to clarify the institutionalisation process of public participation in China's urban regeneration. Using the historical institutionalism perspective, this research adapts the institutionalisation framework proposed by Burns and Scapens (2000) to the analysis of participatory governance in China's planning context. It analyses three phases of urban regeneration in Guangzhou and provides corresponding case studies for an in-depth analysis. The findings reveal that the success of public participation in urban regeneration varies across phases, mainly due to the degree of government power sharing and the inclusion of non-governmental actors in decision-making through genuinely participative interactions. This research develops an institutionalisation analysis framework and notes that the proposed institutionalisation path only partially applies to public participation in China's urban regeneration. The institutionalisation of public participation from the institutional realm to the realm of action in China's regeneration practices departs from hysteresis, which is the opposite of synchronisation in historical institutionalism. The root cause of this inefficient institutionalisation and its separation from practice is the imbalance in the allocation of power resources. These findings provide institutionalisation insights into the interrelationships between the evolution of policy and practices of public participation.

Gig to the left, algorithms to the right: A case study of the dark sides in the gig economy

In the current wave of digital technology that continues to innovate platform business models, an increasing number of gig economy platforms are deploying algorithms to optimize and reshape legacy transaction processes and create new value for multi-stakeholders. Nevertheless, algorithmic management also leads to many unforeseen dark sides for multiple participants in the practice, compromising their rights and interests (e.g., price discrimination, labor process control, and privacy concerns). Accordingly, this study aims to examine the negative implications of the introduction of digital technology in platform innovation within gig economy platforms, specifically focusing on the dark sides of algorithmic management, from a multi-sided platform perspective. Through a series of interviews with multi-stakeholders of Meituan Takeaway, the largest food-delivery platform in China, and secondary data analysis based on rooting theory, we develop a theoretical framework to deepen the understanding of the dark sides of algorithmic management and provide valuable insights for platforms seeking to optimize their operations management.

Heterogeneous graph neural networks for fraud detection and explanation in supply chain finance

It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers’ transactions in an ongoing business are inspected to support the providers’ decision on whether to lend the money. Considering multiple participants in a supply chain business, the borrowers may use sophisticated tricks to cheat, making fraud detection challenging. In this work, we propose a multitask learning framework, MultiFraud, for complex fraud detection with reasonable explanation. The heterogeneous information from multi-view around the entities is leveraged in the detection framework based on heterogeneous graph neural networks. MultiFraud enables multiple domains to share embeddings and enhance modeling capabilities for fraud detection. The developed explainer provides comprehensive explanations across multiple graphs. Experimental results on five datasets demonstrate the framework’s effectiveness in fraud detection and explanation across domains.

EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party

Federated learning (FL) is a machine learning setting which allows multiple participants collaboratively to train a model under the orchestration of a server without disclosing their local data. Vertical federated learning (VFL) is a special structure in FL. It handles the situation where participants have the same ID space but different feature spaces. In order to guarantee the security and privacy of the local data of each participant, homomorphic encryption (HE) is often used to transmit intermediate parameters or data during the training process. In most VFL frameworks, a trusted third-party server is necessary because the plaintexts of the parameters need to be revealed for the computation. However, it is hard to find such a credible entity in the real world. Existing methods for solving this problem are either communication-intensive or unsuitable for multi-party scenarios. By combining secret sharing (SS) and HE, we propose a novel VFL framework without any trusted third parties called EFMVFL. It allows intermediate parameters to be transmitted among multiple parties without revealing the plaintexts. EFMVFL is applicable to generalized linear models (GLMs) and supports flexible expansion to multiple participants. Extensive experiments under Logistic Regression and Poisson Regression show that our framework is outstanding in communication (reduced by 3.2×– 6.8×) and efficiency (accelerated by 1.6×– 3.1×).

A Model Personalization-based Federated Learning Approach for Heterogeneous Participants with Variability in the Dataset

Federated learning is an emerging paradigm that provides privacy-preserving collaboration among multiple participants for model training without sharing private data. The participants with heterogeneous devices and networking resources decelerate the training and aggregation. The dataset of the participant also possesses a high level of variability, which means the characteristics of the dataset change over time. Moreover, it is a prerequisite to preserve the personalized characteristics of the local dataset on each participant device to achieve better performance. This article proposes a model personalization-based federated learning approach in the presence of variability in the local datasets. The approach involves participants with heterogeneous devices and networking resources. The central server initiates the approach and constructs a base model that executes on most participants. The approach simultaneously learns the personalized model and handles the variability in the datasets. We propose a knowledge distillation-based early-halting approach for devices where the base model does not fit directly. The early halting speeds up the training of the model. We also propose an aperiodic global update approach that helps participants to share their updated parameters aperiodically with server. Finally, we perform a real-world study to evaluate the performance of the approach and compare with state-of-the-art techniques.

Sensortechnology for monitoring challenging behavior in nursing home residents with dementia

Background:Neuropsychiatric symptoms (NPS) are common in affected individuals and can be challenging for (in)formal caregivers. Therefore, they are also referred to as challenging behaviors (CBs). Sensor technology measuring context and behavior can be assistive to effectively manage CBs in an objective fashion. Sensors can help support healthcare professionals, such as nurses, by enabling remote monitoring and alarming on early-stage behavioral changes associated with CBs. This might/ will improve the quality of life (QoL) for both caregivers and clients living in a nursing homes (NH).In the project “MOnitoring Onbegrepen Gedrag bij Dementie met sensortechnologie” (MOOD-Sense), we aim to develop such a monitoring system. Our research focuses on two questions 1) How to develop and implement a monitoring system within the context of nursing homes with parameters on environment, physiology, and behavior, identify and process relevant precursors of challenging behavior with this monitoring system and 2) gain insight in which behaviors are challenging according to nurses and how they are described. This will be represented in an ontology such that sensor data can be translated into the same conceptual information.Methods:The first research question will be examined with a set of experiments in the field (in NH) with an iterative approach. Insights from previous experiments on usability and added value of sensors will be used to improve successive experiments. During each experiment, multiple participants (clients with dementia and CBs) are monitored with both ambient and wearable sensors. For the second research question a qualitative approach is employed, using focus groups (FG) and consensus methods. These FGs will be held amongst nursing staff who are involved in daily care tasks for people with dementia. Subsequently, consensus methods are used to align behavioral descriptors/labels.Results:early findings will be presented at the symposiumDiscussion:Within this project we expect to find precursors of challenging behavior in a personalized fashion based on nurse’s expert knowledge and sensor data. In order to develop a monitoring system that can be embedded within NH’s, real-time alarming, in-situ behavior recognition and trustworthiness are part of our technological requirements. Just-in-time interventions may then be deployed to prevent behavior escalation or the persistence of undesirable situations.

Analysis of lifestyle factors affecting life satisfaction and depression of the super‐aged in South Korea: Using the Korean Longitudinal Study of Aging (KLoSA)

AbstractBackgroundThe purpose of this study was to confirm the correlation between life satisfaction and depression and lifestyle (physical activity, eating habits, frequency of participation, participation diversity) for the super‐aged in South Korea, and to analyze and confirm lifestyle factors that affect life satisfaction and depression.MethodThe independent variables of this study were life satisfaction and depression, and the dependent variables were analyzed including physical activity, regular eating habits, frequency of participation, and diversity of participation as lifestyle factors. For analysis, correlation analysis and multiple regression analysis were performed through SPSS ver 26.0.ResultAs a result of the correlation analysis, it was confirmed that life satisfaction had a quantitative correlation in the order of participation frequency (r = 0.300, p<0.01), participation diversity (r = 0.215, p<0.01), regular eating habits (r = 0.188, p<0.01), and physical activity (r = 0.122, p<0.05). In the case of depression, it was confirmed that there was a quantitative correlation in the order of life satisfaction (r = 0.507, p<0.01), participation frequency (r = 0.297, p<0.01), regular eating habits (r = 0.218, p<0.01), participation diversity (r = 0.128, p<0.05). As a result of multiple regression analysis, participation frequency (t = 6.262, p<0.001) and regular eating habits (t = 4.627, p<0.001) were found to have statistical significance, and participation frequency (t = 6.540, p<0.001), regular eating habits (t = 4.61, p<0.001).ConclusionThrough this study, lifestyle factors that affect the life satisfaction and depression of the super‐aged in South Korea were identified. Therefore, based on the results of this study, a multi‐faceted intervention program based on lifestyle is needed to improve the life satisfaction and depression of the super‐aged.

The impact of farm household tourism operations on poverty reduction and conservation under the control policies of China’s protected areas

Against the global trend of increasing multiple participants involvement in protected areas management, the Chinese government has implemented a series of stricter policies since 2015. Tourism is an important alternative livelihood for farmers near protected areas. Based on survey data from 1,028 households in six protected areas, this study uses the propensity score matching (PSM) method to empirically examine the change effects of poverty reduction and conservation of farmers engaged in tourism under the influence of protected areas tourism management policies. The PSM method reduces sample self-selection bias and improves the accuracy of research conclusions. The findings reveal that irrespective of whether the areas are subjected to regulatory policies or not, tourism operations are significantly and inversely correlated with households’ multidimensional poverty index, and are notably positively correlated with their comprehensive conservation index. Consequently, tourism operations exhibit substantial poverty alleviation and protective effects. However, the current suite of protected areas control policies has curtailed the sustainable growth of tourism within these areas, resulting in 9.64% decrease in poverty alleviation effects and 10.33% decrease in protective effects derived from tourism operations. Despite their restrictive impacts on tourism, these policies have yielded some positive outcomes, fostering social equity within protected areas and catalyzing the transition from traditional livelihoods. Drawing upon these empirical findings, this study proposes recommendations and directions for fine-tuning China’s existing regulatory policies pertaining to tourism in protected areas.

Educational Simulation Play as a Transcultural Approach to Resilience and Reconciliation

Abstract Educational simulation plays facilitate real experiences in a safe environment and, therefore, are appropriate for gaining a better sense of the dynamic relationships at work in complex environments, for exploring good fits and practical solutions, and for understanding how mistakes occur, mainly when plays, built upon an explicit or implicit model of reality, engage multiple participants and reproduce some of the political, coordination, communication, and coalition-building challenges that often accompany peace and stabilization operations that exist in the real world. Thus, they become an exercise in social science theorizing and a research tool to examine the implications of hypothesized relationships and conflict dynamics in a variety of forms (abstract plays or role-playing activities). By emphasizing needs, interpretations, stereotypes and resources of all participants, simulation plays facilitate strategies for resilience and reconciliation.

Graph Trilateration for Indoor Localization in Sparsely Distributed Edge Computing Devices in Complex Environments Using Bluetooth Technology

Spatial navigation patterns in indoor space usage can reveal important cues about the cognitive health of participants. In this work, we present a low-cost, scalable, open-source edge computing system using Bluetooth low energy (BLE) beacons for tracking indoor movements in a large, 1700 m2 facility used to carry out therapeutic activities for participants with mild cognitive impairment (MCI). The facility is instrumented with 39 edge computing systems, along with an on-premise fog server. The participants carry a BLE beacon, in which BLE signals are received and analyzed by the edge computing systems. Edge computing systems are sparsely distributed in the wide, complex indoor space, challenging the standard trilateration technique for localizing subjects, which assumes a dense installation of BLE beacons. We propose a graph trilateration approach that considers the temporal density of hits from the BLE beacon to surrounding edge devices to handle the inconsistent coverage of edge devices. This proposed method helps us tackle the varying signal strength, which leads to intermittent detection of beacons. The proposed method can pinpoint the positions of multiple participants with an average error of 4.4 m and over 85% accuracy in region-level localization across the entire study area. Our experimental results, evaluated in a clinical environment, suggest that an ordinary medical facility can be transformed into a smart space that enables automatic assessment of individuals’ movements, which may reflect health status or response to treatment.

Charting the electric vehicle battery reuse and recycling network in North America

As electric vehicle (EV) sales grow across the world, a common question arises: “what happens to the batteries?” Using expert elicitation, this study identifies the current pathways for retired EV batteries in the United States and Canada and anticipates how the network might evolve in the future. The majority of end-of-life (EOL) EVs are currently managed within the manufacturer and dealership network, but more will enter the independent afterlife market as growing volumes reach EOL out-of-warranty. The interviews indicate that safety, transportation, and accessible information about battery composition and remaining capacity are critical issues across sectors. Participants demonstrated a strong commitment to creating a closed-loop value chain, motivating novel partnerships between recyclers and producers. At the same time, the value of EOL batteries as a material supply source may create competition between recycling and repurposing in the short term. State and federal governments are implementing policies to facilitate access to information and incentivize domestic manufacturing, but compared to other countries, the US lacks a mechanism to ensure that batteries will be collected and recycled. In addition, there is no national tracking system that would provide more robust data on LIB management. Multiple participants noted that the network handles the majority of EOL batteries without significant policy intervention. However, at present, the system depends the economics of reuse and recycling when accounting for the cost of collection and processing, which creates a risk of stranded batteries and/or wasted materials for packs that are lower-value or difficult to access.

Co-design of a Mobile App for Engaging Breast Cancer Patients in Reporting Health Experiences: Qualitative Case Study.

The World Health Organization recommends incorporating patient-reported experience measures and patient-reported outcome measures to ensure care processes. New technologies, such as mobile apps, could help report and monitor patients' adverse effects and doubts during treatment. However, engaging patients in the daily use of mobile apps is a challenge that must be addressed in accordance with the needs of people. We present a qualitative case study documenting the process of identifying the information needs of breast cancer patients and health care professionals during the treatment process in a Chilean cancer institution. The study aims to identify patients' information requirements for integration into a mobile app that accompanies patients throughout their treatment while also providing features for reporting adverse symptoms. We conducted focus groups with breast cancer patients who were undergoing chemotherapy (n=3) or who completed chemotherapy between 3 months and 1 year (n=1). We also surveyed health care professionals (n=9) who were involved in patient care and who belonged to the oncology committee of the cancer center where the study took place. Content analysis was applied to the responses to categorize the information needs and the means to satisfy them. A user interface was designed according to the findings of the focus groups and was assessed by 3 trained information system and user interaction design experts from 2 countries, using heuristic evaluation guidelines for mobile apps. Patients' information needs were classified into 4 areas: an overview of the disease, information on treatment and day-to-day affairs, assistance on the normality and abnormality of symptoms during treatment, and symptoms relevant to report. Health care professionals required patients to be provided with information on the administrative and financial process. We noted that the active involvement of the following 4 main actors is required to satisfy the information needs: patients, caregivers, social network moderators, and health professionals. Seven usability guidelines were extracted from the heuristic evaluation recommendations. A mobile app that seeks to accompany breast cancer patients to report symptoms requires the involvement of multiple participants to handle the reports and day-to-day information needs. User interfaces must be designed with consideration of the patient's social conventions and the emotional load of the disease information.

The Anthropocene Obscene: Poetic inquiry and evocative evidence of inequality

AbstractPoetic inquiry is used to highlight contrasting lived experiences of vulnerability and worsening socio‐ecological outcomes among Australia's fastest growing coastal communities. Our approach interweaves multiple participant voices across local and national scales to juxtapose the contrasts of inequality, enmesh social and ecological experiences, and ask reflexive questions of audiences. We offer an evocative portrayal of inequality to the growing body of work demonstrating that unequal and intensifying vulnerabilities are created and sustained through complicated, non‐adaptive and hierarchical social systems. We demonstrate that poetic inquiry can interrogate complex system phenomena and broad concepts, such as the Anthropocene, to distil critical and systemic issues while retaining undeniable connections with the deeply personal implications of socio‐ecological change. Hence, poetic inquiry can serve analytical and descriptive purposes towards an emotional and political aesthetic providing a compelling reorientation from more conventional modes of inquiry and representation. In this study, the misuse of power and privilege in the Anthropocene is reduced and revealed as the Obscene.

Insights and Inspirations from Attempts of the EU to “Rebuild the Tower of Babel”

Based on the historical characteristics of Europe’s linguistic diversity, the EU has implemented a pluralistic language policy, aiming to “Rebuild the Tower of Babel” within its organization. However, the language governance in the process of the EU integration has not been smooth. The conflicts between linguistic legislation and linguistic practice, between efforts to preserve multilingualism and the reality of English monolingualism, and between the multiple participants in language governance and their varying interests are still nagging at the EU language policy-makers. The underlying reasons are those difficulties to reconcile administrative efficiency with multilingual equality, the pragmatic instrumental value of one language with its national symbolic value, and the interests of many countries behind languages race. Insights into the EU language governance provide good inspirations for China’s language policy and planning, which will help China better present Chinese language and culture to the world, telling China’s stories well and making China’s voice heard.

Development and Functional Validation Method of the Scenario-in-the-Loop Simulation Control Model Using Co-Simulation Techniques

With the facilitated development of highly automated driving functions and automated vehicles, the need for advanced testing techniques also arose. With a near-infinite number of potential traffic scenarios, vehicles have to drive an increased number of test kilometers during development, which would be very difficult to achieve with currently utilized conventional testing methods. State-of-the-Art testing technologies such as Vehicle-in-the-Loop (ViL) or Scenario-in-the-Loop (SciL) can provide a long-term solution; however, validation of these complex systems should also be addressed. ViL and SciL technologies provide real-time control and measurement with multiple participants; however, they require enormous computational capacity and low-latency communication to provide comparable results with real-world testing. 5G (fifth-generation wireless) communication and Edge computing can aid in fulfilling these needs, although appropriate implementation should also be tested. In the current paper, a realized control model based on the SciL architecture was presented that was developed with real-world testing data and validated utilizing co-simulation and digital twin techniques. The model was established in Simcenter Prescan© connected to MATLAB Simulink® and validated using IPG CarMaker®, which was used to feed the simulation with the necessary input data to replace the real-world testing data. The aim of the current paper was to introduce steps of the development process, to present the results of the validation procedure, and to provide an outlook of potential future implementations into the state of the art in proving ground ecosystems.