Multiple Participants Research Articles

Controlled cyclic remote preparation

Multi-party quantum communication has gradually attracted widespread attention. To realize the perfect transmission of quantum states among multiple participants, a novel multi-party controlled cyclic remote preparation protocol for arbitrary single-qubit states with three senders is proposed. With the permission of one controller, each sender can transmit an arbitrary single-qubit state to its neighbor. In addition, we give a universal protocol for multi-party controlled cyclic remote preparation of arbitrary single-qubit states in the case of multiple senders, which can realize deterministic cyclic preparation of multiple quantum states in one direction. The scheme shows that the communication task can be successfully achieved only if all senders cooperate with the controller, and there is no need for the senders to employ information splitting and additional operations before performing measurements. Finally, we discuss the cyclic remote preparation protocol with three senders under five types of noisy environment, and the closeness between the output state and original state is measured by calculating fidelity.

Study on the path toward solutions for NIMBYism in China: A case study based on the qualitative comparative analysis method

With the recent economic and social development of China, there has been a growing awareness among citizens. The concept of Not In My Backyard (NIMBY) has emerged as a highly public issue, capable of sparking conflicts of interest between the public and the government. To assist the government in making well-informed decisions and respecting the interests of local residents, this study comprehensively explores the diverse factors that contribute to the varying outcomes of the NIMBY phenomenon. To achieve this, a qualitative comparative analysis with a fuzzy set (fsQCA) was conducted on 31 cases that occurred in China between 2009 and 2021. The findings reveal seven paths that would lead to the cancellation of NIMBY facilities. Among those paths, government credibility and social media involvement are the two crucial factors in NIMBY problem-solving. To some extent, violence can drive the cancellation of NIMBY projects. Local governments are more likely to compromise on NIMBY in the case of harmful facilities. However, this study emphasizes that constant compromise may not always be the most effective solution for the government. Instead, it proposes three potential approaches that can simultaneously address neighborhood satisfaction and foster local economic development in the context of NIMBYism. These approaches include the following.The construction of a multiple participant system: By involving multiple stakeholders in the decision-making process, such as local residents, government officials, experts, and industry representatives, a more inclusive and transparent approach can be adopted to address NIMBY concerns.The judicious use of online platforms: Leveraging social media and online platforms can facilitate effective communication and engagement between the government, local residents, and other relevant stakeholders. This can help in building trust, disseminating information, and encouraging dialogue to address NIMBY concerns.The popularization of science: Promoting scientific literacy among the public can help address misconceptions and fears associated with NIMBY facilities. By providing accessible and accurate information, this study suggests that the popularization of science can contribute to a better understanding of the benefits and risks associated with such facilities.By implementing these approaches, this study suggests that the government can make well-informed decisions, respect the interests of local residents, and mitigate conflicts arising from the NIMBY phenomenon while fostering local economic development.

Verifiable anonymous quantum communication with authentication based on d-level single-particle states

Anonymous quantum communication (AQC) enables the secure transmission of classical and quantum messages while preserving the anonymity of the sender, receiver, or both. Qudits, quantum states with more advantages than qubits in terms of information transmission rate and eavesdropping detection, offer promising capabilities. In this paper, we propose a verifiable AQC (VAQC) protocol utilizing d-level single-particle states, establishing anonymous entanglement between a public sender and an anonymous receiver. By using quantum teleportation, the public sender can transmit private information to his/her chosen anonymous receiver. The verifiable function of the proposed protocol guarantees the legitimacy of all participants’ identities and verifies the correctness of the anonymous entanglement. Furthermore, we demonstrate that the proposed VAQC protocol satisfies the requirements of correctness, anonymity, and security. The proposed VAQC protocol exhibits extensibility and can be extended to enable private communication between an anonymous sender and an anonymous receiver, as well as anonymous entanglement among multiple participants. This work lays the foundation for practical applications in achieving d-level anonymous entanglement and anonymous transmission of qudits.

Centralized assignment of prizes and contestants

We study a contest design problem in which a designer chooses how many Tullock contests to have, how much to award to each contest, and which contestants (of high or low type) should be assigned to which contest. Our main result is that a single grand contest maximizes total effort. We consider three extensions. First, when the designers’ objective changes to maximizing the effort submitted by the winning contestant, we find that the optimal design involves the high-type contestants being assigned to a set of pairwise contests. Second, under multiple participations (a player’s effort is valid in multiple contests, as in several applications), running a contest open to all, along with a parallel contest open only to low types, increases total effort over a single grand contest. Third, tilting the playing field (a player’s effort is multiplied by a tilting factor) in favor of low types increases total effort in a single grand contest, even more than what is possible with multiple participations; thus, in applications, a quota reserved for traditionally disadvantaged categories results in lower total effort than a grand contest that optimally handicaps advantaged categories.

Mass‐based omni‐directional risk indicator (MORI) for multi‐participant traffic

AbstractThis study focuses on the potential of connected and automated vehicles (CAVs) to enhance road traffic safety through the provision of rich physical motion state information. Real‐time risk indicators are crucial for improving driving safety and must be tailored to the specific characteristics of the CAV environment. To this end, this paper introduces the concept of “risk pair” to decompose the joint actions of multiple traffic participants into pairwise interactions. An omnidirectional risk indicator (ORI) is developed to describe the effect of “risk pairs”, and the superposition of ORI based on quality is proposed as the mass‐based ORI (MORI). A comparison between MORI and existing risk indicators shows that MORI has good performance in quantifying one‐dimensional scenarios. In two‐dimensional scenarios involving multiple participating entities, MORI provides two quantitative results: vector summation and scalar summation. The trajectory data from the next generation simulation database is used to validate the MORI model. The results show that both vector summation and scalar summation of MORI demonstrate strong risk quantification capabilities with different directions. Although some limitations of MORI still exist, this approach will provide a scientifically effective means of risk quantification for two‐dimensional complex scenarios.

Music-emotion EEG coupling effects based on representational similarity

BackgroundMusic can evoke intense emotions and music emotion is a complex cognitive process. However, we know little about the cognitive mechanisms underlying these processes, and there are significant individual differences in the emotional responses to the same musical stimuli. New MethodWe used the inter-subject representational similarity analysis (IS-RSA) method to investigate the shared music emotion responses across multiple participants. In addition, we extended IS-RSA to estimate the group cross-frequency coupling effects of music emotion. Based on the cross-frequency coupling IS-RSA, we analyzed the differences in cross-frequency coupling patterns under different music emotions using MI. Comparison of existing methods: most current IS-RSA analyses focus on within-frequency band analysis. However, the cognitive processing of music emotion involves not only activation and brain network connections differences within frequency bands but also information communication between frequency bands. ResultsThe results of the within-frequency band IS-RSA analysis showed that the theta and gamma frequency bands play important roles in the inter-participant consistency of music emotion. The inter-frequency band IS-RSA analysis showed that the theta-beta coupling pattern exhibited stronger inter-participant consistency compared to the theta-gamma coupling pattern, and the theta-beta coupling had significant consistent representation across various music conditions. Through the significant regions of cross-frequency coupling representation similarity analysis, we performed phase-amplitude coupling analysis on FC4-C6 and FC4-Pz connections. For the theta-beta coupling pattern, we found that the MI of these two connections exhibited different coupling patterns under different music conditions, and they showed a significant decrease compared to the baseline period.

Contactless Multi-User Virtual Hair Design Synthesis

This study introduces a virtual reality (VR)-based remote hair design training system that addresses the limitations of physical presence. By leveraging virtual environments and eliminating the need for tangible tools, this system allows hairstylists and learners to collaborate and master hair-design techniques from a distance. In this approach, a section of a user’s hair was derived from their photograph, and during the training, the most compatible 3D hair model was integrated with the user’s 3D avatar using the recommended matching procedure. This aligned hairstyle was subsequently incorporated into a 3D model for hair simulation. Further, VR HMD interaction mapping was used for hair cutting and styling. A collaborative environment for hair design has been crafted, enabling multiple participants to partake in remote hairstyle education. This system offers cost-effective training, enhances cooperative learning, and aligns with the requirements of contact-free education. In essence, this research has transformed remote hair design training with immersive VR technology. To verify the system’s accuracy, tests were conducted, and the results showcased enhancements in the matching procedure.

A two-layer integrated model for cyclist trajectory prediction considering multiple interactions with the environment

Interacting with complex environments is a major challenge for autonomous vehicles (AVs), especially interacting with bicycles, because bicycle trajectories are difficult to predict. Bicycles typically interact with multiple traffic participants simultaneously, resulting in diverse and random trajectories, even sudden-change behavior. Sudden-change behavior of bicycles, which is hazardous for AVs but with sparse data, cannot be well learned by current deep-learning models due to the characteristics of “learning average behavior”. Furthermore, the current domain knowledge of explicit trajectory laws integrated into the deep learning modes cannot be used to describe the implicit decision-making of cyclists which is a major factor affecting cyclists’ sudden change behavior. Based on these observations, this paper develops a two-layer integrated framework for bicycle trajectory prediction. The trajectory generation layer is used to provide a kinematically feasible set of candidate trajectories, and then the trajectory selection layer is performed to select a reasonable trajectory by analyzing the cyclists’ decision-making. In the trajectory generation layer, the kinematic laws are first used to determine a feasible set of endpoints, and then deep learning is utilized to learn and generate each complete trajectory under the known start and endpoints. In the trajectory selection layer, a utility-based model is proposed to analyze the decision-making for cyclists, which is defined as the cyclist’s tradeoff between path efficiency and risk in the multiple interactions of the environment. Finally, the candidate with the maximum utility serves as the predicted trajectory. The reasonableness of the proposed model has been verified using the bicycle trajectory data from an intersection in Shanghai, China. Compared with the state-of-the-art models, the integrated model not only exhibits significantly lower errors but also fewer long-tail samples for the prediction error. In addition, it demonstrates a higher validity of predicting cycling decisions during sudden changes in behaviors.

Joining Forces against Antibiotic Resistance: The One Health Solution.

Antibiotic resistance is a significant global health concern that affects both human and animal populations. The One Health approach acknowledges the interconnectedness of human health, animal health, and the environment. It emphasizes the importance of collaboration and coordination across these sectors to tackle complex health challenges such as antibiotic resistance. In the context of One Health, antibiotic resistance refers to the ability of bacteria to withstand the efficacy of antibiotics, rendering them less effective or completely ineffective in treating infections. The emergence and spread of antibiotic-resistant bacteria pose a threat to human and animal health, as well as to the effectiveness of medical treatments and veterinary interventions. In particular, One Health recognizes that antibiotic use in human medicine, animal agriculture, and the environment are interconnected factors contributing to the development and spread of antibiotic resistance. For example, the misuse and overuse of antibiotics in human healthcare, including inappropriate prescribing and patient non-compliance, can contribute to the selection and spread of resistant bacteria. Similarly, the use of antibiotics in livestock production for growth promotion and disease prevention can contribute to the development of antibiotic resistance in animals and subsequent transmission to humans through the food chain. Addressing antibiotic resistance requires a collaborative One Health approach that involves multiple participants, including healthcare professionals, veterinarians, researchers, and policymakers.

Federated Clique Percolation for Privacy-preserving Overlapping Community Detection

Community structure is a typical characteristic of complex networks. Finding communities in complex networks has many important applications, such as the advertisement and recommendation based on social networks and the discovery of new protein molecules in biological networks, which make it a hot topic in the field of complex network analysis. With the increasing concerns about the leakage of personal privacy, discovering communities spread across the local networks owned by multiple participants accurately while preserving each participant’s privacy has become an emerging challenge in distributed community detection. In this article, we propose a general federated graph learning model for privacy-preserving distributed graph learning and develop two federated clique percolation algorithms (CPAs) based on it to discover overlapping communities distributed across multiple participants’ local networks without disclosing any participant’s network privacy. Homomorphic encryption and hash operation are used in combination to protect the privacy of the vertices and edges of each local network. Furthermore, vertex attributes are involved in the calculation of clique similarity and clique percolation when dealing with attributed networks. The experimental results on real-world and artificial datasets demonstrate that the proposed algorithms achieve identical results to those of their stand-alone counterparts and more than 200% higher accuracy than the simple distributed CPAs without federating learning.

DR-SISM: A dual reversible secret image sharing mechanism

Secret image sharing (SIS) technology has received wide attention from researchers due to its multi-party management of secret information. Traditional SIS algorithms usually convert secret information into the shares of noise-like images, which may attract the attention of hackers and cannot be effectively managed. Moreover, for communication scenario with multiple user participation, participants hope to embed additional data in their shares, while the secret image and the embedded additional information to be reversibly recovered. However, the embedding capacity, security, and authentication capability are limited in the existing SIS algorithms. Therefore, this paper proposes a dual reversible secret image sharing mechanism, called DR-SISM, which includes reversible hiding stage and reversible sharing stage. This proposed DR-SISM can simultaneously achieve the reversible recovery of secret image and additional data embedded. In the first stage, a reversible data hiding method is constructed that leverages the predictive technique of median edge detection to obtain the layered label map for capturing a higher embedding load and utilizes the bit-plane replacement technique to embed additional information. In the second stage, a novel (k,n) threshold reversible SIS strategy is presented by employing the Chinese remainder theorem (CRT), which can generate visually meaningful shares. The authentication bits produced by employing the hash function are embedded into the share, which are utilized to compare with the authentication bits owned by users for verifying the identities of the participants before the secret image reconstruction. Through the comparison of advanced SIS algorithms, our proposed DR-SISM in this article is preferred over the recent algorithms in respect of embedding capacity, security, and authentication capability.

Aggregation algorithm based on consensus verification

Distributed learning, as the most popular solution for training large-scale data for deep learning, consists of multiple participants collaborating on data training tasks. However, the malicious behavior of some during the training process, like Byzantine participants who would interrupt or control the learning process, will trigger the crisis of data security. Although recent existing defense mechanisms use the variability of Byzantine node gradients to clear Byzantine values, it is still unable to identify and then clear the delicate disturbance/attack. To address this critical issue, we propose an algorithm named consensus aggregation in this paper. This algorithm allows computational nodes to use the information of verification nodes to verify the effectiveness of the gradient in the perturbation attack, reaching a consensus based on the effective verification of the gradient. Then the server node uses the gradient as the valid gradient for gradient aggregation calculation through the consensus reached by other computing nodes. On the MNIST and CIFAR10 datasets, when faced with Drift attacks, the proposed algorithm outperforms common existing aggregation algorithms (Krum, Trimmed Mean, Bulyan), with accuracies of 93.3%, 94.06% (MNIST dataset), and 48.66%, 51.55% (CIFAR10 dataset), respectively. This is an improvement of 3.0%, 3.8% (MNIST dataset), and 19.0%, 26.1% (CIFAR10 dataset) over the current state-of-the-art methods, and successfully defended against other attack methods.

Data provenance - from experimental data to trustworthy simulation models and standards

FAIR (findable, accessible, interoperable and reusable) data usage is one of the main principals that many of the research and funding organizations include in their strategic plans, which means that following the main principals of FAIR data is required in many research projects. The definition of data being FAIR is very general. When implementing that for a specific application or project or even setting a standardized procedure within a working group, a company or a research community, many challenges arise. In this contribution, an overview about our experience with different methods and tools is outlined.We begin with a motivation on potential use cases for the application of FAIR data with increasing complexity starting from a reproducible research paper over collaborative projects with multiple participants such as Round-Robin tests up to data-based models within standardization codes, applications in machine learning or parameter estimation of physics-based simulation models.In a second part, different options for structuring the data (including metadata schema) are discussed. The first one is the openBIS system, which is an open-source lab notebook and PostgreSQL based data management system. A second option is a semantic representation using RDF based on ontologies for the domain of interest.In a third section, requirements for workflow tools to automate data processing are discussed and their integration into reproducible data analysis is presented with an outlook on required information to be stored as metadata in the database.Finally, the presented procedures are exemplarily demonstrated for the calibration of a temperature dependent constitutive model for additively manufactured mortar. A metadata schema for a rheological measurement setup is derived and implemented in an openBIS database. After a short review of a potential numerical model predicting the structural build-up behavior, the automatic workflow to use the stored data for model parameter estimation is demonstrated.

Comparing wildlife habitat suitability models based on expert opinion with camera trap detections.

Expert knowledge is used in the development of wildlife habitat suitability models (HSMs) for management and conservation decisions. However, the consistency of such models has been questioned. Focusing on 1 method for elicitation, the analytic hierarchy process, we generated expert-based HSMs for 4 felid species: 2 forest specialists (ocelot [Leopardus pardalis] and margay [Leopardus wiedii]) and 2 habitat generalist species (Pampas cat [Leopardus colocola] and puma [Puma concolor]). Using these HSMs, species detections from camera-trap surveys, and generalized linear models, we assessed the effect of study species and expert attributes on the correspondence between expert models and camera-trap detections. We also examined whether aggregation of participant responses and iterative feedback improved model performance. We ran 160 HSMs and found that models for specialist species showed higher correspondence with camera-trap detections (AUC [area under the receiver operating characteristic curve] >0.7) than those for generalists (AUC<0.7). Model correspondence increased as participant years of experience in the study area increased, but only for the understudied generalist species, Pampas cat (β=0.024 [SE 0.007]). No other participant attribute was associated with model correspondence. Feedback and revision of models improved model correspondence, and aggregating judgments across multiple participants improved correspondence only for specialist species. The average correspondence of aggregated judgments increased as group size increased but leveled off after 5 experts for all species. Our results suggest that correspondence between expert models and empirical surveys increases as habitat specialization increases. We encourage inclusion of participants knowledgeable of the study area and model validation for expert-based modeling of understudied and generalist species.

Modern instrumental and orchestral art of the Bukovyna region

Statement of the problem. The modern instrumental and orchestral art of Bukovyna, which has deep historical roots dating back to Proto-Slavic times, stands out for its bright originality. The creation of a musical-instrumental collective involves the search for a creative personality with the appropriate professional level, musical and organizational and communication skills, and the ability to communicate tolerantly with all members of the ensemble. That is why there is a need to highlight the artistic activities of both the instrumental groups themselves and their leaders. Because it is thanks to their creative restlessness that today Bukovyna is proud of a significant number of highly professional ensembles and orchestras of instrumental music. Objectives, scientific novelty, and methods of the research. The purpose of the study is to highlight the process of developing the instrumental collectives, whose activities contributed to the shaping of the modern Bukovyna musical culture. The special task of the research and its innovative component is to consider the role of outstanding figures of the leaders of these collectives in the cultural development of Bukovina. Historical, biographical, musicological and cultural approaches were chosen to reveal the stated topic, analytical and generalizing methods were used in the processing of reference sources and scientific literature. Results of the research. Bukovyna is historically multinational region, where the Ukrainian, in particular Hutsul, Romanian, Polish, Hungarian influences is felt. So, the creative activity and repertoire of the folk groups and academic instrumental ensembles are very diverse. The music art of instrumental ensembles has been always gaining interest and respect from the listeners, due to its distinguished professionalism and ease of perception. The artistic and educational activities of such instrumental ensembles as «Triple musicians», «Plai», Chamber and Academic Symphonic orchestra of the Chernivtsi Regional Philharmonic hall, folk music ensemble «Bukovyna» and the Brass Orchestra «Dixie-Band» of the Central Palace of Culture, the orchestra group of the Honoured Academic Bukovynyan song and dance ensemble named after A. Kushnirenko was highlighted. The active educational position of the leaders of these groups as an outstanding extraordinary personalities, their importance in the formation and development of cultural and educational processes in Bukovyna, popularization of the folk music of the Bukovyna region were emphasized. Such leaders are: People’s Artists of Ukraine – Andrii Kushnirenko, Yurii Gina, Pavlo Chebotov; Honored Artists of Ukraine – Mykola Hakman, Yuriy Bleshchuk, Yevhen Tarnavskyi, Oleksandr Zhukov; Honored Art Workers of Ukraine – Viktor Kostryzh, Yosyp Sozanskyi; Honored Cultural Workers of Ukraine – Illia Miskyi, Heorhii Novakovskyi. A significant number of famous vocalists from Bukovyna have worked with the orchestras at different times, such as Honoured Artists of Ukraine Mariia Melnychuk, Nina Kapliienko, Vasyl Pyndyk, Oksana Savchuk, Iryna Styts-Kulikovska, Honoured Worker of Culture of Ukraine Vasyl Fedoriuk, a poet-songwriter Mykola Bakai and others. Instrumental ensembles are regular participants of regional art festivals “Bukovynska vesna” (“Bukovyna Spring”), “Vizerunky Bukovyny” (“Patterns of Bukovyna”), “Bukovynski zustritchi” (“Bukovyna meetings”), “Chervona Ruta” (“Red rue”), “Bukovyna Suvenir”, and others. In the best traditions of brass music, brass band marches and brass concerts are constantly held in Chernivtsi, which turn into real festivals of folk music art. Conclusion. In this context, we consider the active concerts and competition activities of these groups to be important, because the instrumental art of the Bukovyna musicians are known far beyond the borders of Ukraine. The orchestras are multiple time participants of AllUkrainian and International competitions, and showcased their art to the audience of Europe, Canada, Australia and other parts of the world contributing in the constant growth of authority and world recognition of Ukrainian music.

Circular economy strategies and practices: A case study with multiple participants in the packaging sector in Brazil

This research analyses the leading strategies and practices for transforming Brazil's Circular Economy of packaging. In-depth semi-structured interviews (n=53) were conducted with stakeholders active in the Brazilian packaging sector and triangulated with document analysis. The main strategies identified were environmental education programs, MSW diversion practices, environmental crimes (regeneration) inspection, efficient use of products, and waste reduction (optimisation). As for sharing, the inequality resulting from regional differences in logistics infrastructure was highlighted. The paper has theoretical contributions, such as applying the ReSOLVE method and practices for generating social, environmental and economic benefits.

Exploring tacit knowledge based on an expert nurse's practice for stroke patients.

This study explored tacit knowledge based on an expert nurse's practice who cares for stroke patients by using the hermeneutic phenomenological approach. The participant ('Ms. A') was a nursing researcher and college faculty member involved in the education of advanced practice nurses; her specialty was stroke rehabilitation nursing. She was asked to describe the meaning and value she gained from her memorable nursing experiences. Four interviews-approximately 1 heach-were conducted, and the associated data were interpreted together with the participant based on the method of interpretive circulation. Notably, the analysis was ended when a fusion of horizons was recognized. The participant recalled her nursing experiences based on six model cases. During the analysis, the following five elements were extracted: [belief in the ability of vulnerable people to survive]; [being together]; [respect for human dignity]; [preparedness to respond to and bear suffering together]; and [theoretical knowledge base approaches true understanding of patient experience]. Further, the nursing model-the Roy Adaptation Model-utilized by Ms. A in the process of recognizing humans as whole beings was deeply interpreted and implemented as a guideline for her implicit advanced practice. Moreover, her deep understanding and utilization of theoretical knowledge base also built the foundation for her implicit advanced practice. In conclusion, Ms. A's tacit knowledge and the elements support the process of tacit knowledge acquisition. Her narratives, hermeneutic attitude as an interpreter, and learning attitude throughout interaction with others will strongly help her knowledge development. We intend to continue the study with multiple participants and explore the structure of tacit knowledge possessed by advanced practitioners. Future endeavours will include the development of a tacit knowledge learning strategy.

Health-related quality of life of children and adolescents with one vs. multiple disabilities – Are there differences from the parents’ perspective?

ABSTRACT Background Multiple disabilities represent separate entities that cannot be reduced to the sum of disabilities or the main disability. This study compared the health-related quality of life (HRQoL) of children and adolescents with multiple disabilities and those with one developmental disability. We further assessed how comorbidities and guardianship type affected HRQoL. Method On a sample of 190 children and adolescents with developmental disabilities aged 2–18 years (mean age 11.19 years, SD 3.76), we evaluated HRQoL using the PedsQL 4.0 Generic Core Scale (parent proxy-reports). Results Participants with multiple disabilities and participants with one developmental disability statistically significantly differed in all domains of HRQoL, except for emotional functioning. Multiple disabilities resulted in a lower HRQoL. Comorbidities like epilepsy diminished participants’ HRQoL. Guardianship type did not affect HRQoL. Conclusions Disability complexity affects HRQoL. Since each impairment increases difficulties in daily functioning, all existing disorders need to be considered when planning services for children with disabilities.

Rematching on-the-fly: Sequential matched randomization and a case for covariate-adjusted randomization.

Covariate-adjusted randomization (CAR) can reduce the risk of covariate imbalance and, when accounted for in analysis, increase the power of a trial. Despite CAR advances, stratified randomization remains the most common CAR method. Matched randomization (MR) randomizes treatment assignment within optimally identified matched pairs based on covariates and a distance matrix. When participants enroll sequentially, sequentially matched randomization (SMR) randomizes within matches found "on-the-fly" to meet a pre-specified matching threshold. However, pre-specifying the ideal threshold can be challenging and SMR yields less-optimal matches than MR. We extend SMR to allow multiple participants to be randomized simultaneously, to use a dynamic threshold, and to allow matches to break and rematch if a better match later enrolls (sequential rematched randomization; SRR). In simplified settings and a real-world application, we assess whether these extensions improve covariate balance, estimator/study efficiency, and optimality of matches. We investigate whether adjusting for more covariates can be detrimental upon covariate balance and efficiency as is the case of traditional stratified randomization. As secondary objectives, we use the case study to assess how SMR schemes compare side-by-side with common and related CAR schemes and whether adjusting for covariates in the design can be as powerful as adjusting for covariates in a parametric model. We find each SMR extension, individually and collectively, to improve covariate balance, estimator efficiency, study power, and quality of matches. We provide a case-study where CAR schemes with randomization-based inference can be as and more powerful than non-CAR schemes with parametric adjustment for covariates.

Dependable federated learning for IoT intrusion detection against poisoning attacks

Network intrusion detection methods based on federated learning (FL) and edge computing have great potential for protecting the cybersecurity of the Internet of Things. It overcomes the disadvantages of the traditional centralized method, such as high latency, overloaded network, and privacy leakage. At the same time, it can combine private data from multiple participants to train models, and the rich data can train more effective models. However, the inherent security vulnerabilities of the FL framework do not ensure the robustness of the global models trained collaboratively. Towards FL, each participant has access to model parameters and training data, and malicious participants can affect the global model by tampering with data or weights. This paper studies label-flipping attacks in FL-based IoT intrusion detection. We propose a lightweight detection mechanism to mitigate the impact of poisoning attacks on FL-based intrusion detection methods in IoT networks. The detection mechanism on a central server filters anomalous participants and excludes their uploaded models from the global model aggregation. Specifically, we propose a scoring mechanism for evaluating participants based on the loss of the local model and the training dataset size. Afterwards, the Manhattan similarity between each participant will be calculated according to the scores. Finally, the anomalous participants will be found by clustering algorithm for similarity cluster analysis. The experimental results show that our proposed detection method can defend against label-flipping attacks in FL. On the CIC-IDS-2017 dataset, our method can improve the accuracy of the intrusion detection model trained based on FL from 84.3% to 97.1%, while enhancing the protection of IoT network security.