Collaborative Scenarios Research Articles

Evolutionary Game and Simulation Analysis of Intelligent Connected Vehicle Industry With Cross-Border Collaborative Innovation

The Made in China 2025 national strategy has prioritized intelligent connected vehicles (ICV) to realize the intelligence and connection transformation and upgrading of the automotive industry, ushering in unprecedented development opportunities. There are two technology paths in the ICV industry: single-vehicle intelligence and vehicle-infrastructure collaboration. Both face problem of low technological innovation efficiency, and key to solving it is breaking down barriers between enterprises and realizing cross-border collaborative innovation. This study offers a new cross-border collaborative innovation development paradigm for the ICV industry, centered on automotive enterprises and technology platform providers. This study examines the impact of changes in key parameters on the evolutionary results using the system dynamics method to analyze the efficiency of cross-border collaborative innovation in the ICV industry. The simulation results showed that cross-border collaborative innovation is inevitable for the ICV industry. Furthermore, compared to the single-vehicle intelligence scenario, the vehicle-infrastructure collaboration scenario shows faster convergence between automotive enterprises and technology platform providers. Finally, the choice of system collaborative innovation strategy is influenced by default cost and the collaborative innovation risk coefficient, whereas the cost-sharing coefficient and network connection fee only have an impact on the cross-border collaborative innovation system’s rate of evolution in the ICV industry.

Structuring ontologies from natural language for collaborative scenario modeling in agri-food systems.

Prospective studies require discussing and collaborating with the stakeholders to create scenarios of the possible evolution of the studied value-chain. However, stakeholders do not always use the same words when referring to one idea. Constructing an ontology and homogenizing vocabularies is thus crucial to identify key variables, which serve in the construction of the needed scenarios. Nevertheless, it is a very complex and time-consuming task. In this paper we present the method we used to manually build ontologies adapted to the needs of two complementary system-analysis models (namely the "Godet" and the "MyChoice" models), starting from interviews of the agri-food system's stakeholders. The objective of the paper is to explore whether and how prospective studies may have to gain from complementing the methodologies used (here Godet) with formal approaches from other disciplines, such as knowledge engineering (here MyChoice), which is usually not the case currently.

A federated learning attack method based on edge collaboration via cloud

AbstractFederated learning (FL) is widely used in edge‐cloud collaborative training due to its distributed architecture and privacy‐preserving properties without sharing local data. FLTrust, the most state‐of‐the‐art FL defense method, is a federated learning defense system with trust guidance. However, we found that FLTrust is not very robust. Therefore, in the edge collaboration scenario, we mainly study the poisoning attack on the FLTrust defense system. Due to the aggregation rule, FLTrust, with trust guidance, the model updates of participants with a significant deviation from the root gradient direction will be eliminated, which makes the poisoning effect on the global model not obvious. To solve this problem, under the premise of not being deleted by the FLTrust aggregation rules, we construct malicious model updates that deviate from the trust gradient to the greatest extent to achieve model poisoning attacks. First, we utilize the rotation of high‐dimensional vectors around axes to construct malicious vectors with fixed orientations. Second, the malicious vector is constructed by the gradient inversion method to achieve an efficient and fast attack. Finally, a method of optimizing random noise is used to construct a malicious vector with a fixed direction. Experimental results show that our attack method reduces the model accuracy by 20%, severely undermining the usability of the model. Attacks are also successful hundreds of times faster than the FLTrust adaptive attack method.

Scenario-Based Programming of Voice-Controlled Medical Robotic Systems

An important issue in medical robotics is communication between physicians and robots. Speech-based communication is of particular advantage in robot-assisted surgery. It frees the surgeon's hands; hence, he can focus on the principal tasks. Man-machine voice communication is the subject of research in various domains (industry, social robotics), but medical robots are very specific. They must precisely synchronize their activities with operators. Voice commands must be possibly short. They must be executed without significant delays. An important factor is the use of a vision system that provides visual information in direct synchronization with surgeon actions. Its functions could be also controlled using speech. The aim of the research presented in this paper was to develop a method facilitating creation of voice-controlled medical robotic systems, fulfilling the mentioned requirements and taking into account possible scenarios of man-machine collaboration in such systems. A robot skill description (RSD) format was proposed in order to facilitate programming of voice control applications. A sample application was developed, and experiments were conducted in order to draw conclusions regarding the usefulness of speech-based interfaces in medical robotics. The results show that a reasonable selection of system functions controlled by voice may lead to significant improvement of man-machine collaboration.

Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control.

In human-robot collaboration scenarios with shared workspaces, a highly desired performance boost is offset by high requirements for human safety, limiting speed and torque of the robot drives to levels which cannot harm the human body. Especially for complex tasks with flexible human behavior, it becomes vital to maintain safe working distances and coordinate tasks efficiently. An established approach in this regard is reactive servo in response to the current human pose. However, such an approach does not exploit expectations of the human's behavior and can therefore fail to react to fast human motions in time. To adapt the robot's behavior as soon as possible, predicting human intention early becomes a factor which is vital but hard to achieve. Here, we employ a recently developed type of brain-computer interface (BCI) which can detect the focus of the human's overt attention as a predictor for impending action. In contrast to other types of BCI, direct projection of stimuli onto the workspace facilitates a seamless integration in workflows. Moreover, we demonstrate how the signal-to-noise ratio of the brain response can be used to adjust the velocity of the robot movements to the vigilance or alertness level of the human. Analyzing this adaptive system with respect to performance and safety margins in a physical robot experiment, we found the proposed method could improve both collaboration efficiency and safety distance.

A framework for robotic arm pose estimation and movement prediction based on deep and extreme learning models

Human-robot collaboration has gained a notable prominence in Industry 4.0, as the use of collaborative robots increases efficiency and productivity in the automation process. However, it is necessary to consider the use of mechanisms that increase security in these environments, as the literature reports that risk situations may exist in the context of human-robot collaboration. One of the strategies that can be adopted is the visual recognition of the collaboration environment using machine learning techniques, which can automatically identify what is happening in the scene and what may happen in the future. In this work, we are proposing a new framework that is capable of detecting robotic arm keypoints commonly used in Industry 4.0. In addition to detecting, the proposed framework is able to predict the future movement of these robotic arms, thus providing relevant information that can be considered in the recognition of the human-robot collaboration scenario. The proposed framework has two main modules. The first one contains a convolutional neural network based on self-calibrated convolutions enabling better discriminative feature extraction and the support of extreme learning machine neural networks with different kernels for predicting robotic arm keypoints. The second module is composed of deep recurrent learning models, such as long short-term memory and gated recurrent unit. These models are able to predict future robotic arm keypoints. All experiments were evaluated using the mean squared error metric. Results show that the proposed framework is capable of detecting and predicting with low error, contributing to the mitigation of risks in human-robot collaboration. In addition, it was possible to verify that the use of convolutional neural networks in conjunction with extreme learning machines can offer a lower detection error in a regression task (e.g., keypoint detection), something that, as far as the authors are aware of, is not yet known, nor had been evaluated previously in the literature.

Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey

Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.

Cone of Vision as a Behavioural Cue for VR Collaboration

Mutual awareness of visual attention is essential for collaborative work. In the field of collaborative virtual environments (CVE), it has been proposed to use Field-of-View (FoV) frustum visualisations as a cue to support mutual awareness during collaboration. Recent studies on FoV frustum visualisations focus on asymmetric collaboration with AR/VR hardware setups and 3D reconstructed environments. In contrast, we focus on the general-purpose CVEs (i.e., VR shared offices), whose popularity is increasing due to the availability of low-cost headsets, and the restrictions imposed by the pandemic. In these CVEs collaboration roles are symmetrical, and the same 2D content available on desktop computers is displayed on 2D surfaces in a 3D space (VR screens). We prototyped one such CVE to evaluate FoV frustrum visualisation within this collaboration scenario. We also implement a FoV visualisation generated from an average fixation map (AFM), therefore directly generated by users' gaze behaviour which we call Cone of Vision (CoV). Our approach to displaying the frustum visualisations is tailored for 2D surfaces in 3D space and allows for self-awareness of this visual cue. We evaluate CoV in the context of a general exploratory data analysis (EDA) with 10 pairs of participants. Our findings indicate that CoV is beneficial during shifts between independent and collaborative work and supports collaborative progression across the visualisation. Self-perception of the CoV improves visual attention coupling, reduces the number of times users watch the collaborator's avatars and offers a consistent representation of the shared reality.

Compact Modeling Technology for the Simulation of Integrated Circuits Based on Graphene Field-Effect Transistors.

The progress made toward the definition of a modular compact modeling technology for graphene field-effect transistors (GFETs) that enables the electrical analysis of arbitrary GFET-based integrated circuits is reported. A set of primary models embracing the main physical principles defines the ideal GFET response under DC, transient (time domain), AC (frequency domain), and noise (frequency domain) analysis. Another set of secondary models accounts for the GFET non-idealities, such as extrinsic-, short-channel-, trapping/detrapping-, self-heating-, and non-quasi static-effects, which can have a significant impact under static and/or dynamic operation. At both device and circuit levels, significant consistency is demonstrated between the simulation output and experimental data for relevant operating conditions. Additionally, a perspective of the challenges during the scale up of the GFET modeling technology toward higher technology readiness levels while drawing a collaborative scenario among fabrication technology groups, modeling groups, and circuit designers, is provided.

Fine-Grained Elastic Partitioning for Distributed DNN Towards Mobile Web AR Services in the 5G Era

Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach. The common theme is, under the premise that Quality of Service (QoS) is satisfied, to balance multiple interests by orchestrating computations across heterogeneous computing platforms.

Transforming e-participation: VR-dialogue – building and evaluating an AI-supported framework for next-gen VR-enabled e-participation research

PurposeThe purpose of this study is to explore whether immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles that hinder effective communication and collaboration. Immersive virtual reality (VR) can complement e-participation and help alleviate some major obstacles hindering effective communication and collaboration. VR technologies boost discussion participants' sense of presence and immersion; however, studying emerging VR technologies for their applicability to e-participation is challenging because of the lack of affordable and accessible infrastructures. In this paper, the authors present a novel framework for analyzing serious social VR engagements in the context of e-participation.Design/methodology/approachThe authors propose a novel approach for artificial intelligence (AI)-supported, data-driven analysis of group engagements in immersive VR environments as an enabler for next-gen e-participation research. The authors propose a machine-learning-based VR interactions log analytics infrastructure to identify behavioral patterns. This paper includes features engineering to classify VR collaboration scenarios in four simulated e-participation engagements and a quantitative evaluation of the proposed approach performance.FindingsThe authors link theoretical dimensions of e-participation online interactions with specific user-behavioral patterns in VR engagements. The AI-powered immersive VR analytics infrastructure demonstrated good performance in automatically classifying behavioral scenarios in simulated e-participation engagements and the authors showed novel insights into the importance of specific features to perform this classification. The authors argue that our framework can be extended with more features and can cover additional patterns to enable future e-participation immersive VR research.Research limitations/implicationsThis research emphasizes technical means of supporting future e-participation research with a focus on immersive VR technologies as an enabler. This is the very first use-case for using this AI and data-driven infrastructure for real-time analytics in e-participation, and the authors plan to conduct more comprehensive studies using the same infrastructure.Practical implicationsThe authors’ platform is ready to be used by researchers around the world. The authors have already received interest from researchers in the USA (Harvard University) and Israel and run collaborative online sessions.Social implicationsThe authors enable easy cloud access and simultaneous research session hosting 24/7 anywhere in the world at a very limited cost to e-participation researchers.Originality/valueTo the best of the authors’ knowledge, this is the very first attempt at building a dedicated AI-driven VR analytics infrastructure to study online e-participation engagements.

Clean‐label poisoning attacks on federated learning for IoT

AbstractFederated Learning (FL) is suitable for the application scenarios of distributed edge collaboration of the Internet of Things (IoT). It can provide data security and privacy, which is why it is widely used in the IoT applications such as Industrial IoT (IIoT). Latest research shows that the federated learning framework is vulnerable to poisoning attacks in the case of an active attack by the adversary. However, the existing backdoor attack methods are easy to be detected by the defence methods. To address this challenge, we focus on edge‐cloud synergistic FL clean‐label attacks. Unlike common backdoor attack, to ensure the attack's concealment, we add a small perturbation to realize the clean label attack by judging the cosine similarity between the gradient of the adversarial loss and the gradient of the normal training loss. In order to improve the attack success rate and robustness, the attack is implemented when the global model is about to converge. The experimental results verified that 1% of poisoned data could make an attack successful with a high probability. Our method maintains stealth while performing model poisoning attacks, and the average Peak Signal‐to‐Noise Ratio (PSNR) of poisoning images reaches over 30 dB, and the average Structural SIMilarity (SSIM) is close to 0.93. Most importantly, our attack method can bypass the Byzantine aggregation defence.

Dynamic Path Planning Based on Service Level of Road Network

Vehicle–road coordination is an important application scenario in the sustainable development of urban transportation. In this scenario, by navigating the vehicles in the road network, the vehicles can run more smoothly in the city, reduce unnecessary detours and parking, and realize energy savings and emission reductions. Although vehicle–road coordination in a large area has not been fully realized, people’s travel is increasingly dependent on navigation. If the trips of most vehicles follow the same navigation suggestion in a short period of time, some sections in the given route of the navigation will bear excessive traffic load. In order to solve this potential problem, this paper relies on the vehicle–road collaboration scenario and combines the service level of the road network factors between vehicles to plan the travel path of the vehicle. This keeps the traffic load of each road section in the path at a reasonable level. Within the scope, considering the overall utilization of road resources and the efficiency of road network traffic, we established the road network evaluation index through the simulation comparison with the Dijkstra algorithm. Under the path planning method proposed in this paper, the total travel time of the vehicle is reduced by 23.4%, and the road network operation efficiency is improved by 6.6%, which proves that the method can be used. This method can effectively alleviate the load of the road network, improve operation efficiency, and finally achieve the purpose of energy saving and emission reduction.

A consistency method for digital twin model of human-robot collaboration

Human-robot collaboration, which strives to use the best skills of humans and robots, shows board application prospects in meeting efficiency and economy requirements in various fields. As an effective method to realize physical-virtual interaction, digital twin plays a critical role in enhancing the perception and interaction in human-robot collaboration. Based on real-time interaction, digital twin reflects the physical states and further makes dynamic decisions. However, there are still some difficulties in the dynamic implementation of digital twin in human-robot collaboration. A vital issue is how to build the digital twin model to describe the physical elements in the collaborative scenario and the collaboration relationship. Moreover, how to keep the model consistency is also an important problem to be solved. In order to tackle the above problems, the four-tuple digital twin model of human-robot collaboration is firstly introduced in this paper, which includes the robot model, human model, collaborative environment model and collaboration relation model. Then, a model consistency method is further proposed, which considers two types of inconsistencies caused by model structure and model parameter, and the implementation of the above two aspects is illustrated in detail. Finally, a case study is carried out to show the feasibility of the proposed method.

Influence of task decision autonomy on physical ergonomics and robot performances in an industrial human-robot collaboration scenario.

Adoption of human–robot collaboration is hindered by barriers in collaborative task design. A new approach for solving these problems is to empower operators in the design of their tasks. However, how this approach may affect user welfare or performance in industrial scenarios has not yet been studied. Therefore, in this research, the results of an experiment designed to identify the influences of the operator’s self-designed task on physical ergonomics and task performance are presented. At first, a collaborative framework able to accept operator task definition via parts’ locations and monitor the operator’s posture is presented. Second, the framework is used to tailor a collaborative experience favoring decision autonomy using the SHOP4CF architecture. Finally, the framework is used to investigate how this personalization influences collaboration through a user study with untrained personnel on physical ergonomics. The results from this study are twofold. On one hand, a high degree of decision autonomy was felt by the operators when they were allowed to allocate the parts. On the other hand, high decision autonomy was not found to vary task efficiency nor the MSD risk level. Therefore, this study emphasizes that allowing operators to choose the position of the parts may help task acceptance and does not vary operators’ physical ergonomics or task efficiency. Unfortunately, the test was limited to 16 participants and the measured risk level was medium. Therefore, this study also stresses that operators should be allowed to choose their own work parameters, but some guidelines should be followed to further reduce MSD risk levels.

A Methodology to Design and Evaluate HRI Teaming Tasks in Robotic Competitions

As social robots become more prominent in our lives, their interaction with humans takes an increasing role, and new collaborative scenarios emerge. This development brings the need to realize robust test methods enabling the design and evaluation of Human-Robot Interaction (HRI) teaming tasks to prove functionality and promote adoption. In this article, we present a general-purpose and repeatable methodology for conducting studies in collaborative HRI in the range of robotic competitions. The methodology includes a step-by-step approach to design HRI teaming tasks tailored to be enacted in a robotic competition and to evaluate the performance of social robots to execute the designed tasks, exploring the relationship between robots’ performance and user perceptions based on the feedback of the users participating to such tasks. We assess the feasibility of the methodology to design and evaluate an HRI teaming task in the context of “Smart CIties RObotics Challenges” (SciRoc) competition, which targets at investigating the impact of social of robots in smart cities.

Collaborative scenario building: Engaging stakeholders to unravel opportunities for urban adaptation planning

Uncertainties arising from the climate crisis and socioeconomic challenges require decision makers to advance climate change adaptation planning and practice. In response, many cities have adopted climate change adaptation strategies, which is also the case in the Czech Republic. Although adaptation planning should be prospective, requiring long-term, future-oriented approaches, the use of future scenarios have not been fully acknowledged yet. In this methodological paper, we introduce a combined approach for delivering spatially explicit future land use/land cover (LULC) scenarios developed as supporting tools for adaptation planning in three major Czech cities—Prague, Brno, and Ostrava. The methodological approach consists of (1) Shared Socioeconomic Pathways (SSPs) downscaled to city level, to capture underpinning social, environmental, and economic dynamics; (2) collaborative processes with stakeholders; and (3) computer modelling of LULC scenarios. While collaborative process collects the perceptions of local needs, challenges, and opportunities, spatially explicit modelling of LULC changes allows for an easy-to-read visualization of the plausible futures. Nesting the scenarios in SSPs makes them consistent with global socioeconomic drivers. The introduced methodological approach can support adaptation planning by engaging stakeholders directly in the scenario building process, to elaborate on plausible future urban developments while considering an array of different stakes and challenges.

Research on optimization and application of Spark decision tree algorithm under cloud‐edge collaboration

This paper optimizes and parallelizes the C4.5 algorithm on the Spark platform under cloud-edge collaboration. First, the information entropy calculation in the definition of boundary points and Fayyad's theorem is improved by the Gini index, which reduces the number of information entropy calculations for selecting segmentation points in the traditional C4.5 algorithm continuous attribute discretization operation and simplifies the calculation formula, thus reducing the execution time of the algorithm. On this basis, the CFS algorithm is introduced to optimize the calculation of the information gain ratio, to facilitate the selection of better attributes for decision tree partitioning. The improved C4.5 algorithm is then parallelized in Spark platform. In this paper, we choose the “intelligent door guard” of cloud-edge collaboration under the epidemic prevention and control management context as the application scenario for experimental verification and make timely risk assessment for those who want to enter the place and give them a response whether they are allowed to enter or not. Experiments show that the improved parallel C4.5 algorithm reduces the running time and raises the accuracy of the algorithm, compared with the traditional C4.5 algorithm and previous improvements to it. In the application scenario of cloud-edge collaboration, the improved algorithm in this paper decreases the time delay and increases the inspection efficiency.

Analysis and assessment software for multi-user collaborative cognitive radio networks

<span>Computer simulations are without a doubt a useful methodology that allows to explore research queries and develop prototypes at lower costs and timeframes than those required in hardware processes. The simulation tools used in cognitive radio networks (CRN) are undergoing an active process. Currently, there is no stable simulator that enables to characterize every element of the cognitive cycle and the available tools are a framework for discrete-event software. This work presents the spectral mobility simulator in CRN called “App MultiColl-DCRN”, developed with MATLAB’s app designer. In contrast with other frameworks, the simulator uses real spectral occupancy data and simultaneously analyzes features regarding spectral mobility, decision-making, multi-user access, collaborative scenarios and decentralized architectures. Performance metrics include bandwidth, throughput level, number of failed handoffs, number of total handoffs, number of handoffs with interference, number of anticipated handoffs and number of perfect handoffs. The assessment of the simulator involves three scenarios: the first and second scenarios present a collaborative structure using the multi-criteria optimization and compromise solution (VIKOR) decision-making model and the naïve Bayes prediction technique respectively. The third scenario presents a multi-user structure and uses simple additive weighting (SAW) as a decision-making technique. The present development represents a contribution in the cognitive radio network field since there is currently no software with the same features.</span>

Elaborating Team Roles for Artificial Intelligence-based Teammates in Human-AI Collaboration

The increasing importance of artificial intelligence (AI) in everyday work also means that new insights into team collaboration must be gained. It is important to research how changes in team composition affect joint work, as previous theories and insights on teams are based on the knowledge of pure human teams. Especially, when AI-based systems act as coequal partners in collaboration scenarios, their role within the team needs to be defined. With a multi-method approach including a quantitative and a qualitative study, we constructed four team roles for AI-based teammates. In our quantitative survey based on existing team role concepts (n = 1.358), we used exploratory and confirmatory factor analysis to construct possible roles that AI-based teammates can fulfill in teams. With nine expert interviews, we discussed and further extended our initially identified team roles, to construct consistent team roles for AI-based teammates. The results show four consistent team roles: the coordinator, creator, perfectionist and doer. The new team roles including their skills and behaviors can help to better design hybrid human-AI teams and to better understand team dynamics and processes.