Multiple Participants Research Articles

Development of a Unity–VISSIM Co-Simulation Platform to Study Interactive Driving Behavior

This paper presents the system development of a co-simulation platform aimed at studying driving behavior with multiple participants. The objective of this study was to create an immersive and interactive environment where different driving scenarios could be simulated and driver behavior could be recorded and analyzed. The platform integrated the Unity game engine with the VISSIM microscopic traffic simulator to create a hybrid simulation environment that combined the advantages of both tools. A virtual reality massive multiplayer online (VRMMO) module was developed to capture the interactions of the participants during the simulation experiments. The external control devices of this co-simulation platform were calibrated using the empirical data of a Controller Area Network (CAN-BUS) from actual driving behaviors. The main contributions of this study are the demonstration of the Unity–VISSIM co-simulation platform in simulating interactive driver behavior and the potential for its use in various research areas, such as intelligent transportation systems, human factors, driving education, and traffic safety analyses. The platform could be a valuable tool for evaluating the effectiveness of collective intelligence countermeasures in improving traffic systems, with relatively lower costs and risks.

A lightweight delegated private set intersection cardinality protocol

Secure multiparty computation (MPC) is an important means to realize privacy computing (PC). Private set intersection cardinality (PSI-CA) is a variant of an important problem in MPC research, that is private set intersection. PSI-CA allows multiple participants jointly compute the size of the common intersection of their private sets through interaction without revealing any other information about their respective sets. Delegated PSI-CA delegates a large number of computing in the protocol to some untrusted cloud servers, which makes the protocol efficient. Delegated PSI-CA is considered to be of great significance for the construction of privacy-preserving contact tracing systems recently. However, the existing work is still not ideal for the resource-limited mobile terminal of clients. In this paper, we propose a lightweight delegated PSI-CA protocol based on multi-point oblivious pseudorandom function and collision-resistant hash function. Our protocol does not need to do additional pre-operations, so the computation complexity and the communication complexity on the client side are further reduced compare to the existing works. In addition, we build a privacy-preserving contact tracing system by utilizing our protocol, which can be publicly checked if necessary, named PC-CONTrace. The experimental results show that our system is more practical and advantageous for densely populated areas.

A Microstructural Approach to Self-Organizing: The Emergence of Attention Networks

A recent line of inquiry investigates new forms of organizing as bundles of novel solutions to universal problems of resource allocation and coordination: how to allocate organizational problems to organizational participants and how to integrate participants’ resulting efforts. We contribute to this line of inquiry by reframing organizational attention as the outcome of a concatenation of self-organizing, microstructural mechanisms linking multiple participants to multiple problems, thus giving rise to an emergent attention network. We argue that, when managerial hierarchies are absent and authority is decentralized, observable acts of attention allocation produce interpretable signals that help participants to direct their attention and share information on how to coordinate and integrate their individual efforts. We theorize that the observed structure of an organizational attention network is generated by the concatenation of four interdependent micromechanisms: focusing, reinforcing, mixing, and clustering. In a statistical analysis of organizational problem solving within a large open-source software project, we find support for our hypotheses about the self-organizing dynamics of the observed attention network connecting organizational problems (software bugs) to organizational participants (volunteer contributors). We discuss the implications of attention networks for theory and practice by emphasizing the self-organizing character of organizational problem solving. We discuss the generalizability of our theory to a wider set of organizations in which participants can freely allocate their attention to problems and the outcomes of their allocation are publicly observable without cost. Funding: Financial support for this work was provided by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 100018_150126] (“Relational event modes for bipartite networks with application to collaborative problem solving,” P.I. Alessandro Lomi) and by the Deutsche Forschungsgemeinschaft [Grant 321869138] (“Statistical analysis of time-stamped multi-actor events in social networks,” P.I. Jüergen Lerner). Supplemental Material: The supplemental video containing the dynamic visualization of the data is available at https://zenodo.org/record/7564503 and in the e-companion (available at https://doi.org/10.1287/orsc.2023.1674 ).

PPVerifier: A Privacy-Preserving and Verifiable Federated Learning Method in Cloud-Edge Collaborative Computing Environment

Federated Learning is a distributed machine learning framework, which mainly adopts cloud-edge collaborative computing mode and supports multiple participants to train models without directly sharing local data. However, participants’ sensitive information may still be leaked through their gradients. Besides, incorrect aggregated results returned by the aggregation server may reduce the effect of joint modeling. This paper proposes a privacy-preserving and verifiable federated learning method called PPVerifier to support privacy protection and verification of aggregated results in cloud-edge collaborative computing environment. By integrating Paillier homomorphic encryption and random number generation technique, all gradients and their ciphertexts can be protected. Meanwhile, an additive secret sharing scheme is introduced to resist potential collusion attacks among the aggregation server, malicious participants, and edge nodes. Moreover, a verification scheme based on discrete logarithm is proposed, which can not only verify the correctness of aggregated results, but also discover lazy aggregation servers, and the verification overhead can be reduced by over half compared with bilinear aggregate signature method. Finally, theoretical analysis and experiments conducted on MNIST dataset prove that our proposed method can achieve gradient protection and correctness verification of the aggregated results with higher efficiency.

Harnessing the Affordances of Action Researchers to Address the Challenges of the COVID-19 Pandemic: Educational Leaders take Action Research Online

The unique affordances of Action Research, including flexibility, playfulness, accessibility, and a focus on practical problem solving provided crucial strategies for generating knowledge and developing solutions to the challenges created by the Covid-19 pandemic. The move to online research settings, in particular, required action researchers to find ways to adapt existing research methods and to devise new approaches. This article describes the work of a group of doctoral students in an Educational Leadership program and their instructor in carrying out action research methods in both synchronous and asynchronous online settings. If the months of the pandemic have taught us nothing else, it is that flexibility and willingness to innovate, which are central to action research, are valuable assets in times of uncertainty. The unique affordances of Action Research include creativity, playfulness, accessibility to multiple participants and audiences, transferability of findings, and a focus on the generation of knowledge designed to be pragmatic and problem-focused. These qualities can be harnessed to address the multiple challenges we have encountered during the pandemic including health equity and access, poverty and unemployment, and the interruption of education for vulnerable student populations. They also offer us hope that action research can continue to contribute to addressing the challenges we are sure to face in the future. As students in an educational leadership doctoral program, we focus on examining problems of practice in our schools and districts through action research. As we adapted to online learning in our own schools, we were able to bring these skills to bear in our doctoral studies by developing strategies for conducting these action research methods in both synchronous and asynchronous online settings. This paper describes some of the approaches we developed in the hope that this will enable other action researchers to implement these methods in their own schools, organizations, and communities. The specific action research methods described in this paper are Future Creating Workshops, Citizens’ Juries, World Café, Nominal Group Technique, and Digital Storytelling.

Tracing IP Address and Details of Unidentified Participants in Webinars

During pandemic the mode of sharing information with the multiple participants in physical mode is reduced, to overcome this the online mode of sharing information and discussions through webinars has been increased globally. In case of any misbehaviors from unidentified participants to stop the webinars, we need to identify the participant who is misusing the opportunity. So, we came with the idea of IP tracking. In this project we provide the option to monitor the unidentified participants and we can access the details of the participant such as IP Address. Through IP Address we can identify the participant and necessary action can be taken from the concerned authorities

Diversity-Based Non-Coherent Signal Detector for Molecular Communication via Reaction-Diffusion

Molecular communication is attractive to the emerging nano-scale communication systems. Traditionally, a detector recovers the information from only the concentration of single messenger molecule, while ignoring the variation of multiple participants in biochemical reaction. In this paper, we propose a non-coherent signal detector, by fully exploiting this ubiquitous biochemical diversity property of multiple reacting molecules. After extracting the channel state information (CSI) independent non-coherent features of received signals, the dynamical transient characteristics of messenger, reactant and product molecules are all utilized to implement the diversity detection, thus formulating a functional single-input multiple-output (SIMO) system via reaction-diffusion communication that has been barely considered before. We design both hard and soft combination strategies to attain the potential diversity gain arise from the dynamical co-variation of participants. Theoretical analysis and numerical simulations are provided to demonstrate the advantages of our detector. Compared with conventional detectors that use only single messenger molecule, the bit error rate (BER) of is substantially reduced. Moreover, the BER performances of our non-coherent detector are even better than coherent maximum a posteriori (MAP) detector that requires accurate CSI estimation, which confirms the dramatic diversity gain provided by our detector. It would have great potentials in reliable nano-scale communications.

NVAS: A non-interactive verifiable federated learning aggregation scheme for COVID-19 based on game theory

The continued spread of COVID-19 seriously endangers the physical and mental health of people in all countries. It is an important method to establish inter agency COVID-19 detection and prevention system based on game theory through wireless communication and artificial intelligence. Federated learning (FL) as a privacy preserving machine learning framework has received extensive attention. From the perspective of game theory, FL can be regarded as a process in which multiple participants play games against each other to maximize their own interests. This requires that the user’s data is not leaked during the training process. However, existing studies have proved that the privacy protection capability of FL is insufficient. In addition, the existing way of realizing privacy protection through multiple rounds of communication between participants increases the burden of wireless communication. To this end, this paper considers the security model of FL based on game theory, and proposes our scheme, NVAS, a non-interactive verifiable privacy-preserving FL aggregation scheme in wireless communication environments. The NVAS can protect user privacy during FL training without unnecessary interaction between participants, which can better motivate more participants to join and provide high-quality training data. Furthermore, we designed a concise and efficient verification algorithm to ensure the correctness of model aggregation. Finally, the security and feasibility of the scheme are analyzed.

Privacy preserving federated learning for full heterogeneity

Federated learning is a novel distribute machine learning paradigm to support cooperative model training among multiple participant clients, where each client keeps its private data locally to protect its data privacy. However, in practical application domains, Federated learning still meets several heterogeneous challenges such data heterogeneity, model heterogeneity, and computation heterogeneity, significantly decreasing its global model performance. To the best of our knowledge, existing solutions only focus on one or two challenges in their heterogeneous settings. In this paper, to address the above challenges simultaneously, we present a novel solution called Full Heterogeneous Federated Learning (FHFL). Firstly, we propose a synthetic data generation approach to mitigate the Non-IID data heterogeneity problem. Secondly, we use knowledge distillation to learn from heterogeneous models of participant clients for model aggregation in the central server. Finally, we produce an opportunistic computation schedule strategy to exploit the idle computation resources for fast-computing clients. Experiment results on different datasets show that our FHFL method can achieve an excellent model training performance. We believe it will serve as a pioneer work for distributed model training among heterogeneous clients in Federated learning.

Recovering the urban forest: The role of trees, tree culture, and place attachment before and after Hurricane Harvey

Trees can be powerful symbols that contribute to the production and consumption of places. Disaster events, such as hurricanes, alter the physical landscape, causing tree damage and loss. In places with strong tree cultures, the reforestation of damaged landscapes becomes an implicit element of recovery plans; however, less is known about the implications of tree loss to community recovery. In 2017, Hurricane Harvey made landfall near the coastal communities of Rockport and Fulton, Texas. Rockport-Fulton, known for its beach tourism, is home to a remnant live oak (Quercus virginiana) forest shaped by coastal onshore winds. Many of Rockport-Fulton’s windswept oaks were damaged or lost along with native and non-native palm trees. Rockport-Fulton’s history is imbued with stories situated around its oak forest. Drawing from multiple sources and participant observations from repeated site visits, we analyzed references to Rockport-Fulton’s trees in news media, organizational communications, and public exhibits before and after Harvey to understand the area’s tree culture and its associated discourses. We also interviewed tourists, business owners, and community members nine months after Harvey to understand their perceptions of recovery efforts. Our findings show that tree narratives pre- and post-Harvey amplify social-ecological systems definitions of resilience and that tree loss was a dominant theme in the recovery process for all participants. Yet we also found that tourists discussed the damage to palm trees, whereas community members focused on the loss of live oaks. Despite these preferences, trees available through replanting efforts contained few live oak and palm species; furthermore, recovery plans did not amplify trees in recovery strategies. Overall, our findings highlight the importance of replanting trees during the disaster recovery process in a way that not only enhances local biodiversity but also reaffirms place characteristics to meet community members’ and visitors’ expectations.

Health Monitoring Using Smart Home Technologies: Scoping Review.

The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring. This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring. Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed. Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy. This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps-in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector.

Putative transmission of extended-spectrum β-lactamase-producing Escherichia coli among men who have sex with men in Amsterdam, the Netherlands

ObjectivesA previous study showed higher prevalence of Escherichia coli (E. coli) expressing extended-spectrum β-lactamases (ESBL-Ec) among men who have sex with men (MSM) in Amsterdam, the Netherlands, compared with the general Dutch population. This study genetically characterised the ESBL-Ec isolates and investigated whether the increased prevalence could be explained by transmission between participants. MethodsWhole-genome sequences were obtained from 93 unique ESBL-Ec isolates that were cultured from rectal swabs of 79 participants. Isolates were typed according to the Achtman MLST scheme and ESBL and virulence genes were identified. Pairwise SNP distances were determined between isolates. Isolate pairs with ≤ 25 SNPs were considered part of a putative transmission event, and events between multiple participants formed putative transmission clusters. To investigate whether putatively transmitted isolates belonged to globally expanded lineages, the level of hierarchical clustering with international isolates was assessed using core genome MLST (cgMLST) implemented on the Enterobase platform. ResultsThe most frequently detected E. coli types were ST131:blaCTX-M-15 (16 of 117, 13.5%), ST131:blaCTX-M-27, ST3075:blaCTX-M-15 and ST14:blaSHV-12 (all six of 117, 6.5%). Fourteen putative transmission events were identified, forming four putative transmission clusters. The largest putative transmission cluster contained ST131 isolates, which clustered with multiple international isolates in SNP and cgMLST analysis. One other transmission cluster (ST14:blaSHV-12) and two transmission events (ST14:blaSHV-12 and ST394:blaCTX-M-15) contained very rarely reported strains. ConclusionsThe identification of unique ESBL-Ec strains involved in putative transmission and carried by multiple participants demonstrated a high probability of ESBL-Ec transmission between MSM in Amsterdam; therefore, ESBL-Ec infection should be considered in cases of sexually active MSM having associated symptoms.

Cloud-Assisted Private Set Intersection via Multi-Key Fully Homomorphic Encryption

With the development of cloud computing and big data, secure multi-party computation, which can collaborate with multiple parties to deal with a large number of transactions, plays an important role in protecting privacy. Private set intersection (PSI), a form of multi-party secure computation, is a formidable cryptographic technique that allows the sender and the receiver to calculate their intersection and not reveal any more information. As the data volume increases and more application scenarios emerge, PSI with multiple participants is increasingly needed. Homomorphic encryption is an encryption algorithm designed to perform a mathematical-style operation on encrypted data, where the decryption result of the operation is the same as the result calculated using unencrypted data. In this paper, we present a cloud-assisted multi-key PSI (CMPSI) system that uses fully homomorphic encryption over the torus (TFHE) encryption scheme to encrypt the data of the participants and that uses a cloud server to assist the computation. Specifically, we design some TFHE-based secure computation protocols and build a single cloud server-based private set intersection system that can support multiple users. Moreover, security analysis and performance evaluation show that our system is feasible. The scheme has a smaller communication overhead compared to existing schemes.

Inter-individual single-trial classification of MEG data using M-CCA

Neuroscientific studies often involve some form of group analysis over multiple participants. This requires alignment of recordings across participants. A naive solution is to assume that participants’ recordings can be aligned anatomically in sensor space. However, this assumption is likely violated due to anatomical and functional differences between individual brains. In magnetoencephalography (MEG) recordings the problem of inter-subject alignment is exacerbated by the susceptibility of MEG to individual cortical folding patterns as well as the inter-subject variability of sensor locations over the brain due to the use of a fixed helmet. Hence, an approach to combine MEG data over individual brains should relax the assumptions that a) brain anatomy and function are tightly linked and b) that the same sensors capture functionally comparable brain activation across individuals. Here we use multiset canonical correlation analysis (M-CCA) to find a common representation of MEG activations recorded from 15 participants performing a grasping task. The M-CCA algorithm was applied to transform the data of a set of multiple participants into a common space with maximum correlation between participants. Importantly, we derive a method to transform data from a new, previously unseen participant into this common representation. This makes it useful for applications that require transfer of models derived from a group of individuals to new individuals. We demonstrate the usefulness and superiority of the approach with respect to previously used approaches. Finally, we show that our approach requires only a small number of labeled data from the new participant. The proposed method demonstrates that functionally motivated common spaces have potential applications in reducing training time of online brain-computer interfaces, where models can be pre-trained on previous participants/sessions. Moreover, inter-subject alignment via M-CCA has the potential for combining data of different participants and could become helpful in future endeavors on large open datasets.

Severity and worsening of fatigue among individuals with multiple sclerosis.

Fatigue is associated with reduced quality of life and social participation, and poor employment outcomes. However, most studies examining fatigue are limited by small sample sizes or short follow-up periods. To characterize the natural history of fatigue. The North American Research Committee on Multiple Sclerosis Registry participants with ≥7 years of longitudinal data between 2004 and 2019 and a relapsing disease course were included. A subset of participants enrolled within 5 years of diagnosis was identified. The Fatigue Performance Scale assessed fatigue and ≥1-point increase in Fatigue Performance Scale sustained at the next survey defined fatigue worsening. Of 3057 participants with longitudinal data, 944 were within 5 years of multiple sclerosis diagnosis. Most participants (52%) reported fatigue worsening during follow-up. Median time to fatigue worsening ranged from 3.5 to 5 years at lower levels of index fatigue. Fatigue worsening was associated with lower annual income, increasing disability, lower initial fatigue level, taking injectable disease-modifying therapies and increasing depression levels in the relapsing multiple sclerosis participants. Most multiple sclerosis participants early in their disease suffer from fatigue and at least half reported fatigue worsening over time. Understanding factors associated with fatigue may help to identify populations most at risk of fatigue worsening will be informative for the overall management of patients with multiple sclerosis.

Research on The Application of Blockchain-Based Data Security in Healthcare

This study focuses on the use of blockchain data security technology and its application in pharmaceutical companies, providing a theoretical foundation for achieving digitalization, efficient traceability, and ensuring the quality and safety of drugs in the rapidly advancing technological landscape. The research is based on a thorough review of the literature, supplemented by case studies, and provides insights into the data security scheme required for pharmaceutical companies' traceability processes. It also highlights the various steps involved in the traceability procedures, which have the potential to produce data and involve multiple participants. Effective technical security measures should be employed to manage and use the data to ensure its security and provide tools for maintaining drug quality. Also, the study highlights the technical foundation required to implement data security management, including blockchain and data security technology in the various data states analyzed, provides valuable management insights for medical enterprises or institutions, and explores the realization principles of various data security technologies in conjunction with blockchain technology. The research provides a comprehensive application of literature and cases and serves as a guide for enterprises' actual data security management, providing resources for pharma companies undergoing digital transformation to make strategic decisions about information management. Overall, the study offers valuable insights for learning lessons and generating new ideas in pharmaceutical traceability.

LR-BA: Backdoor attack against vertical federated learning using local latent representations

In vertical federated learning (VFL), multiple participants can collaborate in training a model with distributed data features and labels managed by one of them. The cooperation provides opportunities for a malicious participant to conduct a backdoor attack. However, the attack is challenging when the adversary does not own labels with the mitigation of other participants. In this paper, we discover that an adversary can exploit local latent representations output in the inference stage to inject a backdoor in VFL, even without access to labels. With little auxiliary labeled data, the adversary fine-tunes its bottom model to make it output specific latent representation for backdoor input instances, which induces the federated model to predict the attacker-specified label regardless of benign participants. Our experiments show that the proposed attack can achieve a high attack success rate with little loss of main task accuracy and outperform existing backdoor attacks. We also explore possible defenses against the attack. Our research demonstrates the potential security threat to VFL.

Institutional investor inattention bias in auctioned IPOs

We investigate a unique dataset that includes detailed bidding information of 783 Chinese IPOs. First, we find that institutional investors who participate in multiple IPOs on a single day suffer from limited attention and submit less precise bids. Second, we form several proxies for investor attention capacity and show that investors with larger attention capacities are less affected by the inattention bias. Furthermore, we show that investors are more affected by the inattention bias in IPOs that are more difficult to price. Moreover, we find that the allocation rule change from pro-rata to lottery leads to a reduction in investors’ multiple participations, and improves their bidding precision. Third, we show that investors are more likely to submit lower bids when participating in multiple IPOs. Consequently, the investors affected by the inattention bias are allocated fewer shares and earn less profit. Finally, we find that aggregate investor inattention at the IPO level significantly reduces the IPO's pricing efficiency.

Association of physical activity, smoking, and socioeconomic factors on health checkup participation in community-dwelling stroke survivors aged 50 years or older

BackgroundWe investigated the sociodemographic and health-related factors associated with health checkup participation in community-dwelling stroke survivors.MethodsAmong participants of the Korea National Health and Nutrition Examination Survey, 642 stroke survivors were included. We investigated the sociodemographic, medical, and health-related quality of life factors—evaluated by the EuroQol 5-Dimension Questionnaire (EQ-5D)—associated with participation in any type of health checkup. To explore the associations between multiple variables and health checkup participation, a multivariable complex-sample logistic regression model was used.ResultsOne-third of the community-dwelling stroke survivors did not receive a health checkup in the past two years. Insufficient physical activity (OR: 0.5, 95% CI: 0.3–0.9), current smoking (OR: 0.4, 95% CI: 0.2–0.8), low education level (OR: 0.5, 95% CI: 0.3–0.9), living alone (OR: 0.5, 95% CI: 0.3–0.998), and no occupation (OR: 0.5, 95% CI: 0.3–0.9) showed independent negative associations with health checkup participation. Among the five EQ-5D dimensions, mobility, self-care, usual activities, and pain/discomfort dimensions were associated with health checkup participation rate.ConclusionPolicies and further research are needed to promote health checkups for stroke survivors who are physically inactive, currently smoking, living alone, unemployed, less educated, or having extreme problems in their daily lives.

Monte Carlo-Based Microsimulation Approach for Estimating the Collision Probability of Real Traffic Conflicts

During driving in real traffic, often conflict situations arise which do not lead to an accident; however, they offer a good indication of traffic (un)safety. There are many methods and indicators for classifying whether a current driving situation can be considered as a conflict or not (e.g., time to collision, post-encroachment time). However, not many approaches exist to predict how such conflicts will evolve once they have been identified and what the collision probability would be given the conditions of the conflict. Currently available methods make strong assumptions about driver behavior during the conflict, usually do not consider all participants involved in the conflict (only two vehicles), and have limited applicability to various types of real conflicts. In this paper, a Monte Carlo-based microsimulation approach is proposed to estimate the probability of collision for a conflict of any type. Unlike previous approaches, the proposed method can be used to simulate any type of conflict with an arbitrary initial conflict condition and an arbitrary number of vehicles in conflict. The method was developed based on real-world video data of a complex signalized intersection, from which conflicts were identified with the DOCTOR method. As proof of concept, the proposed method was applied to a real conflict involving four vehicles which was extracted from these video data and validated with a real traffic accident. The results show that our approach is capable of simulating real conflicts of various types with multiple participants and predicting their probability of collision.