Multiple Participants Research Articles

A Reuse-Oriented Visual Smart Contract Code Generator for Efficient Development of Complex Multi-Party Interaction Scenarios

Multi-party interaction scenarios usually involve interaction and collaboration between multiple participants, such as business negotiation, teamwork, etc. In multi-party interaction scenarios, trust mechanisms are needed to ensure the use of rights and the fulfillment of obligations. The use of blockchain and smart contracts to establish a decentralized trust mechanism is a solution. The development process of smart contracts may require experts in different fields to design them together, and it is difficult for non-experts to understand smart contract code. The traditional method of smart contract development is for the developer to manually write the smart contract code, which may lead to low development efficiency and the inability of the scene participants to understand the smart contract code. To address this challenge, we propose a reuse-oriented visual smart contract generator to efficiently develop complex multi-party interaction scenarios. This paper proposes a model based on BPMN that uses smart contracts to express the process of multi-party interaction so that the parties in various domains understand the business requirements. Then, a multi-layer reusable library is proposed and used to generate a visual solidity code. We also integrate code compilation, security checking, and deployment into the target blockchain environment for testing. The function of the tool is verified in the case of flight delay insurance, which shows that it can aid the automated development of smart contracts for multi-party interaction scenarios. At the same time, the feasibility of the tool is verified by five cases selected from the literature, which shows that it can aid the development of smart contracts to achieve a high degree of reusability.

Identifying built environment factors influencing the community participation of adults aging with long-term physical disabilities: a qualitative study

This study qualitatively explored how adults aging with long-term physical disabilities perceive the built environment to influence their community participation. A convenience sample of community-dwelling adults with long-term physical disabilities ages 45–65 participated in virtual semi-structured interviews until thematic saturation was reached (n = 20). Data were analyzed using interpretive phenomenological analysis. Five themes were identified: vigilance and sense of safety, accessibility of and access to transportation, community environment accessibility, sense of community, and access to support, spaces, and resources during the COVID-19 pandemic. Multiple participants described their neighborhoods as unsafe and reported not leaving their homes in months due to inaccessible home entrances or pandemic-related concerns. These results enhance our understanding of how this population perceives environmental barriers to community participation, particularly in low-resourced neighborhoods during the COVID-19 pandemic. These results may inform researchers, community organizations, and funding agencies in intervention development and implementation to address built environment barriers.

Genomic analysis of multiplex consanguineous families reveals causes of neurodevelopmental disorders with epilepsy

IntroductionNeurodevelopmental disorders (NDD) are a diverse group of disorders that affect the development of the nervous system. Epilepsy is a common phenotypic aspect of NDD. MethodsWe recruited eight consanguineous families from Pakistan which segregated recessively inherited NDD with epilepsy. Magnetic Resonance imaging (MRI) and Electroencephalogram (EEG) were completed. Exome sequencing was carried out for selected participants from each family. The exome data were analyzed for exonic and splice-site variants that had allele frequencies of less than 0.01 in public databases. ResultsClinical investigations determined that developmental delay, intellectual disability and seizures were manifested by most patients in early childhood. EEG findings were abnormal in the participants of four families. MRI revealed demyelination orcerebral atrophic changes in multiple participants. We identified four novel homozygous variants including nonsense andmissense variants in OCLN, ALDH7A1, IQSEC2 and COL3A1, segregating with the phenotypes in the participants of four families. Previously reported homozygous variants of CNTNAP2, TRIT1 and NARS1 were found in individuals from three families. Clinical utility was observed in directing treatment in case of patients with an ALDH7A1 variant which included pyridoxine administration and enabling accurate counseling about the natural history and recurrence risk. ConclusionOur results add to the clinical and molecular delineation of very rare NDD with epilepsy. The high success rate of exome sequencing is likely attributable to the expectation of homozygous variants in patients of consanguineous families, and in one case, the availability of positional mapping data that greatly aided the variant prioritization.

Evaluating long-term profile of demand response under different market designs: A comparison of scarcity pricing and capacity auction

Demand response (DR) is widely acknowledged as a promising solution to maintain the capacity adequacy of the power system. In China, DR is now shifting from government pricing to market-oriented pricing. However, the market pricing mechanism to be adopted is still inconclusive and the long-term influence of different pricing mechanisms on incentivizing DR remains unknown. Moreover, the market contains multiple participants, various strategic behaviors, and non-linear clearing procedures while traditional optimization models cannot capture the complex dynamics. This paper adopts a system dynamic (SD) method to evaluate the long-term profile of DR under different market designs. Firstly, an investment decision model of generation companies is formulated to simulate the growth tendency of capacity supply in a long-term period. Secondly, in the context of unmatured marketization, the growth tendency of DR and retail market users are modeled by Bass diffusion. Finally, scarcity pricing and capacity auction are applied to investigate the incentive influence of different market designs on DR. Through the simulation to 2035, the results show that compared with the energy-only market, scarcity pricing, and capacity auction can raise the capacity of DR from 3.12% to 7.58% and 9.21%, respectively.

DPAUC: Differentially Private AUC Computation in Federated Learning

Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on FL has mostly studied how to protect label privacy during model training. However, model evaluation in FL might also lead to the potential leakage of private label information. In this work, we propose an evaluation algorithm that can accurately compute the widely used AUC (area under the curve) metric when using the label differential privacy (DP) in FL. Through extensive experiments, we show our algorithms can compute accurate AUCs compared to the ground truth. The code is available at https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC

Automated market maker inventory management with deep reinforcement learning

Stock markets are the result of the interaction of multiple participants, and market makers are one of them. Their main goal is to provide liquidity and market depth to the stock market by streaming bids and offers at both sides of the order book, at different price levels. This activity allows the rest of the participants to have more available prices to buy or sell stocks. In the last years, reinforcement learning market maker agents have been able to be profitable. But profit is not the only measure to evaluate the quality of a market maker. Inventory management arises as a risk source that must be under control. In this paper, we focus on inventory risk management designing an adaptive reward function able to control inventory depending on designer preferences. To achieve this, we introduce two control coefficients, AIIF (Alpha Inventory Impact Factor) and DITF (Dynamic Inventory Threshold Factor), which modulate dynamically the behavior of the market maker agent according to its evolving liquidity with good results. In addition, we analyze the impact of these factors in the trading operative, detailing the underlying strategies performed by these intelligent agents in terms of operative, profitability and inventory management. Last, we present a comparison with other existing reward functions to illustrate the robustness of our approach.Graphic

Efficient quantum secret sharing scheme based on monotone span program

How to efficiently share secrets among multiple participants is a very important problem in key management. In this paper, we propose a multi-secret sharing scheme based on the Greenberger Horne Zeilinger (GHZ) state. First, the distributor uses monotone span program to encode the secrets and generate the corresponding secret shares to send to the participants. Then, each participant uses the generalized Pauli operator to embed its own secret share into the transmitted particle. The participant who wants to get the secrets can get multiple secrets at the same time by performing a GHZ-state joint measurement. Furthermore, since this scheme is based on a monotone span program, its access structure is more general than the access structure (t, n) threshold. Compared with other schemes, our proposed scheme is more efficient, less computational cost.

Impact of Advance Care Planning on the Hospitalization-Associated Utilization and Cost of Patients with Alzheimer's Disease-Related Disorders Receiving Primary Care via Telehealth in a Provider Shortage Area: A Quantitative Pre-Study.

Telehealth has been adopted as an alternative to in-person primary care visits. With multiple participants able to join remotely, telehealth can facilitate the discussion and documentation of advance care planning (ACP) for those with Alzheimer's disease-related disorders (ADRDs). We measured hospitalization-associated utilization outcomes, instances of hospitalization and 90-day re-hospitalizations from payors' administrative databases and verified the data via electronic health records. We estimated the hospitalization-associated costs using the Nevada State Inpatient Dataset and compared the estimated costs between ADRD patients with and without ACP documentation in the year 2021. Compared to the ADRD patients without ACP documentation, those with ACP documentation were less likely to be hospitalized (mean: 0.74; standard deviation: 0.31; p < 0.01) and were less likely to be readmitted within 90 days of discharge (mean: 0.16; standard deviation: 0.06; p < 0.01). The hospitalization-associated cost estimate for ADRD patients with ACP documentation (mean: USD 149,722; standard deviation: USD 80,850) was less than that of the patients without ACP documentation (mean: USD 200,148; standard deviation: USD 82,061; p < 0.01). Further geriatrics workforce training is called for to enhance ACP competencies for ADRD patients, especially in areas with provider shortages where telehealth plays a comparatively more important role.

Robust Multi‐Party Semi‐Quantum Private Comparison Protocols with Decoherence‐Free States against Collective Noises

AbstractBased on the decoherence‐free states, two multi‐party semi‐quantum private comparison protocols are proposed to counteract collective noises. One can resist the collective‐dephasing noise well, and the other can resist the collective‐rotation noise. With the assistance of a semi‐honest third party (TP), multiple classical participants with restricted quantum abilities can compare their secret information by performing the proposed protocols once. It is manifested that the proposed protocols can resist both external attacks and internal attacks. Additionally, the operations in the multi‐party semi‐quantum private comparison protocols are simulated on the IBM Quantum Experience.

Benefit Sharing of Power Transactions in Distributed Energy Systems with Multiple Participants

With the rapid advancement of China’s power system reform, various provinces have progressively recognized distributed energy systems as autonomous market participants, and their operational paradigm has transitioned from centralized procurement and sales to market competition. This paradigm shift has presented novel demands for the operational efficacy of distributed energy systems within the power market. Presently, China’s distributed energy systems are predominantly managed through collaborative efforts among multiple enterprises. Consequently, during the operational process, it becomes imperative to contemplate how to achieve efficient benefit allocation to ensure the system’s sustainable development. This endeavor also represents a pivotal undertaking in China’s pursuit of its dual-carbon objectives. Therefore, this study endeavors to construct a model for benefit sharing within distributed energy systems, predicated on the distinctive attributes of various stakeholders, in order to facilitate the system’s sustainable progression. Primarily, from the vantage point of the power market and the conduct of system stakeholders, this research scrutinizes key factors that influence benefit allocation, encompassing risk level, profit contribution, and predictive elements. Subsequently, utilizing the CIRTIC anti-entropy weight method Cloud–Shapley methodology, a benefit allocation model is formulated for multiple stakeholders participating in the distributed energy systems market. Finally, the efficacy of the model is substantiated through the simulation and analysis of core stakeholders within the distributed energy system. Simulation results manifest the actual allocation benefits for micro-gas turbines, wind power, and photovoltaics, which amount to CNY 0.941 million, CNY 0.858 million, and CNY 0.881 million, respectively. Moreover, the impacts of risk level, profit contribution, and prediction vary in magnitude concerning the benefit distribution among distinct stakeholders. In future endeavors encompassing post-operational benefit sharing in regional distributed energy systems, it is indispensable to consider the varying influence of different factors on stakeholders, as well as the significance of stakeholders within the system.

Directed Acyclic Graph-Type Distributed Ledgers via Young-Age Preferential Attachment

Distributed ledger technologies provide a mechanism to achieve ordering among transactions that are scattered on multiple participants with no prerequisite trust relations. This mechanism is essentially based on the idea of new transactions referencing older ones in a chain structure. Recently, directed acyclic graph (DAG)-type distributed ledgers that are based on DAGs were proposed to increase the system scalability through sacrificing the total order of transactions. In this paper, we develop a mathematical model to study the process that governs the addition of new transactions to the DAG-type distributed ledger. We propose a simple model for DAG-type distributed ledgers that are obtained from a recursive young-age preferential attachment scheme (i.e., new connections are made preferably to transactions that have not been in the system for very long). We determine the asymptotic degree structure of the resulting graph and show that a forward component of linear size arises if the edge density is chosen sufficiently large in relation to the “young-age preference” that tunes how quickly old transactions become unattractive. Funding: The research of C. Mönch is supported by the Deutsche Forschungsgemeinschaft [Grant 443916008].

Directed Acyclic Graph-Type Distributed Ledgers via Young-Age Preferential Attachment

Distributed ledger technologies provide a mechanism to achieve ordering among transactions that are scattered on multiple participants with no prerequisite trust relations. This mechanism is essentially based on the idea of new transactions referencing older ones in a chain structure. Recently, directed acyclic graph (DAG)-type distributed ledgers that are based on DAGs were proposed to increase the system scalability through sacrificing the total order of transactions. In this paper, we develop a mathematical model to study the process that governs the addition of new transactions to the DAG-type distributed ledger. We propose a simple model for DAG-type distributed ledgers that are obtained from a recursive young-age preferential attachment scheme (i.e., new connections are made preferably to transactions that have not been in the system for very long). We determine the asymptotic degree structure of the resulting graph and show that a forward component of linear size arises if the edge density is chosen sufficiently large in relation to the “young-age preference” that tunes how quickly old transactions become unattractive. Funding: The research of C. Mönch is supported by the Deutsche Forschungsgemeinschaft [Grant 443916008].

Multiple per-claim reinsurance based on maximizing the Lundberg exponent

In this paper, we consider the optimal per-claim reinsurance problem for an insurer who designs a reinsurance contract with multiple reinsurance participants. In contrast to using the value-at-risk as a short-term risk measure, we take the Lundberg exponent in risk theory as a risk measure for the insurer over a long-term horizon because the Lundberg upper bound performs better in measuring the infinite-time ruin probability. To reflect various risk preferences of the reinsurance participants, we adopt a type of combined premium principle in which the expected premium principle, variance premium principle, and exponential premium principle are all special cases. Based on maximization of the insurer's Lundberg exponent, the optimal reinsurance is formulated within a static setting, and we derive optimal multiple reinsurance strategies within a general admissible policies set. In general, these optimal strategies are shown to have non-piecewise linear structures, differing from conventional reinsurance strategies such as quota-share, excess-of-loss, or linear layer reinsurance arrangements. In some special cases, the optimal reinsurance strategies reduce to classical results.

Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques

BackgroundPresentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task.MethodsEEG single trials are decomposed with discrete wavelet transform (DWT) up to the 4{th} level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.ResultsThe proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60pm {6.5}, sensitivities 93.55pm {4.5}, specificities 94.85pm {4.2}, precisions 92.50pm {5.5}, and area under the curve (AUC) 0.93pm {0.3} using SVM and k-NN machine learning classifiers.ConclusionThe proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

Representing and Determining Argumentative Relevance in Online Discussions: A General Approach

Understanding an online argumentative discussion is essential for understanding users' opinions on a topic and their underlying reasoning. A key challenge in determining completeness and persuasiveness of argumentative discussions is to assess how arguments under a topic are connected in a logical and coherent manner. Online argumentative discussions, in contrast to essays or face-to-face communication, challenge techniques for judging argument relevance because online discussions involve multiple participants and often exhibit incoherence in reasoning and inconsistencies in writing style. We define relevance as the logical and topical connections between small texts representing argument fragments in online discussions. We provide a corpus comprising pairs of sentences, labeled with argumentative relevance between the sentences in each pair. We propose a computational approach relying on content reduction and a Siamese neural network architecture for modeling argumentative connections and determining argumentative relevance between texts. Experimental results indicate that our approach is effective in measuring relevance between arguments, and outperforms strong and well-adopted baselines. Further analysis demonstrates the benefit of using our argumentative relevance encoding on a downstream task, predicting how impactful an online comment is to certain topic, comparing to encoding that does not consider logical connection.

Smart Thermometer–Based Participatory Surveillance to Discern the Role of Children in Household Viral Transmission During the COVID-19 Pandemic

Children's role in spreading virus during the COVID-19 pandemic is yet to be elucidated, and measuring household transmission traditionally requires contact tracing. To discern children's role in household viral transmission during the pandemic when enveloped viruses were at historic lows and the predominance of viral illnesses were attributed to COVID-19. This cohort study of a voluntary US cohort tracked data from participatory surveillance using commercially available thermometers with a companion smartphone app from October 2019 to October 2022. Eligible participants were individuals with temperature measurements in households with multiple members between October 2019 and October 2022 who opted into data sharing. Proportion of household transmissions with a pediatric index case and changes in transmissions during school breaks were assessed using app and thermometer data. A total of 862 577 individuals from 320 073 households with multiple participants (462 000 female [53.6%] and 463 368 adults [53.7%]) were included. The number of febrile episodes forecast new COVID-19 cases. Within-household transmission was inferred in 54 506 (15.4%) febrile episodes and increased from the fourth pandemic period, March to July 2021 (3263 of 32 294 [10.1%]) to the Omicron BA.1/BA.2 wave (16 516 of 94 316 [17.5%]; P < .001). Among 38 787 transmissions in 166 170 households with adults and children, a median (IQR) 70.4% (61.4%-77.6%) had a pediatric index case; proportions fluctuated weekly from 36.9% to 84.6%. A pediatric index case was 0.6 to 0.8 times less frequent during typical school breaks. The winter break decrease was from 68.4% (95% CI, 57.1%-77.8%) to 41.7% (95% CI, 34.3%-49.5%) at the end of 2020 (P < .001). At the beginning of 2022, it dropped from 80.3% (95% CI, 75.1%-84.6%) to 54.5% (95% CI, 51.3%-57.7%) (P < .001). During summer breaks, rates dropped from 81.4% (95% CI, 74.0%-87.1%) to 62.5% (95% CI, 56.3%-68.3%) by August 2021 (P = .02) and from 83.8% (95% CI, 79.2%-87.5) to 62.8% (95% CI, 57.1%-68.1%) by July 2022 (P < .001). These patterns persisted over 2 school years. In this cohort study using participatory surveillance to measure within-household transmission at a national scale, we discerned an important role for children in the spread of viral infection within households during the COVID-19 pandemic, heightened when schools were in session, supporting a role for school attendance in COVID-19 spread.

A Blockchain-Based Federated-Learning Framework for Defense against Backdoor Attacks

Federated learning (FL) is a technique that involves multiple participants who update their local models with private data and aggregate these models using a central server. Unfortunately, central servers are prone to single-point failures during the aggregation process, which leads to data leakage and other problems. Although many studies have shown that a blockchain can solve the single-point failure of servers, blockchains cannot identify or mitigate the effect of backdoor attacks. Therefore, this paper proposes a blockchain-based FL framework for defense against backdoor attacks. The framework utilizes blockchains to record transactions in an immutable distributed ledger network and enables decentralized FL. Furthermore, by incorporating the reverse layer-wise relevance (RLR) aggregation strategy into the participant’s aggregation algorithm and adding gradient noise to limit the effectiveness of backdoor attacks, the accuracy of backdoor attacks is substantially reduced. Furthermore, we designed a new proof-of-stake mechanism that considers the historical stakes of participants and the accuracy for selecting the miners of the local model, thereby reducing the stake rewards of malicious participants and motivating them to upload honest model parameters. Our simulation results confirm that, for 10% of malicious participants, the success rate of backdoor injection is reduced by nearly 90% compared to Vanilla FL, and the stake income of malicious devices is the lowest.

Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real‐World Environments

AbstractThe distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in real‐world environments. Our method is based on a space‐time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think‐aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.

Visual Gaze Labeling for Augmented Reality Studies

AbstractAugmented Reality (AR) provides new ways for situated visualization and human‐computer interaction in physical environments. Current evaluation procedures for AR applications rely primarily on questionnaires and interviews, providing qualitative means to assess usability and task solution strategies. Eye tracking extends these existing evaluation methodologies by providing indicators for visual attention to virtual and real elements in the environment. However, the analysis of viewing behavior, especially the comparison of multiple participants, is difficult to achieve in AR. Specifically, the definition of areas of interest (AOIs), which is often a prerequisite for such analysis, is cumbersome and tedious with existing approaches. To address this issue, we present a new visualization approach to define AOIs, label fixations, and investigate the resulting annotated scanpaths. Our approach utilizes automatic annotation of gaze on virtual objects and an image‐based approach that also considers spatial context for the manual annotation of objects in the real world. Our results show, that with our approach, eye tracking data from AR scenes can be annotated and analyzed flexibly with respect to data aspects and annotation strategies.

Application of Minecraft in the study of evacuation dynamics under fire emergency conditions

Fire emergencies pose a great threat to human health and cause numerous deaths in modern buildings. Minecraft has proved to be a useful tool in children’s education and artificial intelligence studies, but no research has used Minecraft for evacuation experiments under fire emergency. In this study, we conducted a series of evacuation experiments under both normal and fire-emergency conditions in Minecraft, which can provide 3D visual effects and allows for the simultaneous participation of multiple participants. A new software program, MCTrack, was developed to accurately recognise pedestrian locations from the Minecraft database and then automatically draw the trajectories and fundamental diagrams. The results showed that in the fire emergency scenarios, the pedestrians exhibited a greater detour angle and avoided the fire in advance, rather than detouring only when they were close to the fire. The times to reach maximum density and velocity in the fire emergency scenario in a square room were delayed compared with the normal-condition scenario because of the longer travel distance caused by the detour, and the maximum velocity was slightly increased owing to the pushing behaviour. Fire evacuations in a corridor-like room were characterised by the formation of a self-organised queue and sequential passage through the exit, which led to a narrower and more concentrated distribution in the fundamental diagrams. Finally, the commercial egress simulators Pathfinder and AnyLogic were used to simulate the same scenarios to evaluate the experimental results of Minecraft. The results indicated that Minecraft has feasibility in providing realistic pedestrian behaviour and crowd flow characteristics during evacuations.