Remote Tasks Research Articles

Exploring the Effect of Viewing Attributes of Mobile AR Interfaces on Remote Collaborative and Competitive Tasks.

Mobile devices have the potential to facilitate remote tasks through Augmented Reality (AR) solutions by integrating digital information into the real world. Although prior studies have explored Mobile Augmented Reality (MAR) for co-located collaboration, none have investigated the impact of various viewing attributes that can influence remote task performance, such as target object viewing angles, synchronization styles, or having a secondary small screen showing other users current view in the MAR environment. In this paper, we explore five techniques considering these attributes, specifically designed for two modes of remote tasks: collaborative and competitive. We conducted a user study employing various combinations of those attributes for both tasks. In both instances, results indicate users' optimal performance and preference for the technique that allows asynchronous viewing of object manipulations on the small screen. Overall, this paper contributes novel techniques for remote tasks in MAR, addressing aspects such as viewing angle and synchronization in object manipulation alongside secondary small-screen interfaces. Additionally, it presents the results of a user study evaluating the effectiveness, usability, and user preference of these techniques in remote settings and offers a set of recommendations for designing and implementing MAR solutions to enhance remote activities.

Exploring the Remote Administration of a Performance-Based Functional Assessment.

Performance-based assessments of instrumental activities of daily living (IADL) can detect subtle functional impairments better than self-reported questionnaires. While most performance-based IADL assessments were developed for in-person administration, remote administration could increase access to vulnerable older adults. This study compared in-person and remote administration of IADL tasks from the Performance Assessment of Self-Care Skills. Community-dwelling older adults completed tasks (shopping, checkbook balancing, and medication management) at baseline (in-person) and follow-up (either in-person or remote, with modifications) two years later. Scores between the two follow-up groups, change in scores from baseline to follow-up, and differential item functioning (DIF) between the two administration methods at follow-up were examined. There were no differences in scores between methods of administration, but remote tasks took longer, and one item had significant DIF (ps < .01). Clinicians found remote administration acceptable and feasible. With minor adaptations, remote administration of the three tasks was supported. Further validation research is needed.

Multi-agent DRL for edge computing: A real-time proportional compute offloading

In the Industrial Internet of Things, devices with limited computing power and energy storage often rely on offloading tasks to edge servers for processing. However, existing methods are plagued by the high cost of device communication and unstable training processes. Consequently, Deep reinforcement learning (DRL) has emerged as a promising solution to tackle the computation offloading problem. In this paper, we propose a framework called multi-agent twin delayed shared deep deterministic policy gradient algorithm (MASTD3) based on DRL. Firstly, we formulate the task offloading conundrum as a long-term optimization problem, which aids in mitigating the challenge of deciding between local or remote task execution by a device, leading to more effective task offloading management. Secondly, we enhance MASTD3 by introducing a priority experience replay buffer mechanism and a model sample replay buffer mechanism, thus improving sample utilization and overcoming the cold-start problem associated with long-term optimization. Moreover, we refine the actor-critic structure, enabling all agents to share the same critic network. This modification accelerates convergence speed during the training process and reduces computational costs during runtime. Finally, experimental results demonstrate that MASTD3 effectively addresses the proportional offloading problem, which is optimized by 44.32%, 29.26%, and 17.47% compared to DDPQN, MADDPG, and FLoadNet.

Asynchronous modeling workflows in CyberWater with on-demand HPC/Cloud access

Workflow management systems (WfMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources. However, existing WfMSs do not simultaneously support local interactive workflow environments and HPC resources. In this paper, we present a mechanism for on-demand access to remote HPC resources from desktop/laptop-based workflow management software to compose, monitor, and analyze scientific workflows in the CyberWater project. CyberWater is an open-data and open-modeling software framework for environmental and water communities. In this work, we extend the open-model, open-data design of CyberWater with on-demand HPC accessing capacity. In particular, we design and implement the LaunchAgent and JobManager, which can be integrated into a local desktop/Laptop environment to allow on-demand usage of remote HPC resources for computational modeling workflows effectively and efficiently. LaunchAgent manages user authentication to remote resources, prepares computation-intensive or data-intensive tasks as batch jobs, submits jobs to remote resources, and monitors quality of services for users. LaunchAgent interacts seamlessly with other components in CyberWater, providing advantages of user-friendly feature-rich desktop software experience and increased computing power through on-demand HPC/Cloud usage. In our evaluations, we demonstrate how a hydrological modeling workflow that consists of both local and remote tasks can be constructed and show that our new on-demand HPC/Cloud usage helps speed up hydrology workflows while allowing asynchronous HPC/Cloud access from workflows using a desktop graphical user interface.

An efficient online/offline heterogeneous proxy signcryption for secure communication in UAV networks

The rapid growth of the Internet of Things (IoT) has led to an increased deployment of unmanned aerial vehicles (UAVs) across various sectors. However, efficiency and security issues are persistently among the primary challenges in UAV networks. In addition, significant communication delays can occur when UAVs perform remote tasks far from a command center (CC); in some cases, they may be unable to communicate with the CC. To address these challenges, in this paper, an efficient online/offline heterogeneous proxy signcryption scheme for secure communication in UAV networks (HOOPSC) is proposed. This scheme enables the CC in a certificateless cryptosystem (CLC) environment to delegate a nearby ground control station (GCS) to act as an agent, and directly send commands to the UAV within an identity-based cryptosystem (IBC) when the UAV undertakes remote tasks far from the CC. The UAV then decrypts and verifies commands for authenticity and confidentiality. In the proposed scheme, the signcryption process is split into offline and online phases, with most of the heavy computations conducted without the availability of the messages during the offline phase. Only light computations are performed in the online phase when a message is available. Moreover, a formal security proof is given in a random oracle model. Finally, a performance analysis reveals that HOOPSC outperforms existing relevant schemes, making it ideal for long-range operations in UAV networks.

Design and Test of Intelligent Farm Machinery Operation Control Platform for Unmanned Farms

The unmanned farm control platform is of great significance in promoting the supervision of farm production with less manpower or autonomous operation of farm machinery and the construction of farm informatization. Addressing the existing control platform for farm location information acquisition is time-consuming, labor-intensive, and lacks the whole process control of multiple types of farm machinery. In this paper, we propose an Internet of Things (IoT) control scheme for intelligent farm machinery operation of unmanned farms and design the access standards for multiple types of farm machinery, as well as realize the remote control of intelligent farm machinery operation by constructing a remote control model. A high-precision map construction method is designed to improve the DeepLabV3+ algorithm to identify fields and roads. The control models of path planning, remote task, remote control, and safety system are built to achieve the remote control of intelligent agricultural machinery operation. The proposed technology is implemented in the platform integration and application tests are carried out. The error of the constructed high-precision map is less than 3 cm, the completeness rate of the automatic boundary extraction rate is 96.71%, and the correctness rate is 95.63%, which can be used to obtain the boundary instead of manual labeling or on-site point picking. The use of the platform for the simultaneous control of three farm machinery operations reduces the number of people in operation and production and reduces the professional requirements of the personnel, which will promote the management of the entire farm by one person or even by no one in the future.

91 Remote Smartphone-Based Assessment Predicts Standard Neuropsychological Test Performance and Cerebral Amyloid Status in Cognitively Normal Older Adults

Objective:Routine cognitive screening in the elderly may facilitate earlier diagnosis of neurodegenerative diseases and access to care and resources for patients and families. However, despite growing rates of Alzheimer's and related disorders (ADRD), the availability and implementation of cognitive screening for older adults in the US remains quite limited. Remote cognitive assessment via smartphone app may reduce several barriers to more widespread screening. We examined the validity of a remote app-based cognitive screening protocol in healthy older adults by examining remote task convergence with standard-person assessments and cerebral amyloid (Aß) status as an AD biomarker.Participants and Methods:Participants (N =117) were cognitively unimpaired adults aged 60-80 years (67.5% female, 88% White, 75% education &gt; 16 years). A portion had Aß PET imaging results available from prior research participation [(Aß positive (Aß+) n =26, and Aß negative (Aß-) n = 44]. A modified Telephone Interview for Cognitive Status (TICSm) cutoff score of &gt;34 was used to establish unimpaired cognition. Participants completed 8 consecutive assessment days using Mobile Monitoring of Cognitive Change (M2C2), a smartphone app-based testing platform developed as part of the National Institute of Aging's Mobile Toolbox initiative. Brief (i.e., 3-4 minute) M2C2 sessions were assigned daily within morning, afternoon, and evening time windows. Tasks included measures of visual working memory (WM), processing speed (PS), and episodic memory (EM) (see Thompson et al., 2022). Participants then completed a battery of standard neuropsychological assessments in-person at a follow-up visit.Results:Participants completed 22.6 (SD = 2.6) out of 24 assigned sessions (3 sessions x 8 days) on average. Performance on all M2C2 tasks decreased significantly with age. Women performed significantly better on WM and EM tasks relative to men. There were no detectable significant differences in performance by race or education. Shorter mean reaction time on M2C2 PS trials predicted faster Trails A and B completion (ß = .26, p &lt; .01, 95% CI [3.8, 23.3] and ß = .20, p &lt; .05, 95% CI [.23, 6.8], respectively). Greater mean M2C2 WM accuracy predicted longer maximum backward digital span (ß = .24, p = .01, 95% CI [.02, .16]). Greater mean M2C2 EM accuracy predicted stronger Logical Memory delayed recall (ß = .33, p &lt; .001, 95% CI [.004, .012]) and total immediate recall on the Free and Cued Selective Reminding Test (ß = .19, p &lt; .05, 95% CI [.000, .006]). Moreover, EM significantly distinguished Aß- and Aß+ individuals (t (68) = 3.0, p &lt; .01) with fair accuracy (AUC = .72).Conclusions:Mean performance across 8-days on each M2C2 task predicted same-domain cognitive task performance on a standard assessment battery, with medium effect sizes. Performance on the EM task was also sensitive to cerebral Aß status, consistent with subtle memory changes implicated in the preclinical stage of AD. These findings support the validity of this remote testing protocol in healthy older adults, with implications for future efforts to facilitate accessible and sensitive cognitive screening for early detection of ADRD. Limitations include the restricted generalizability of this primarily white and college educated sample.

The influence of temporal (a)synchrony and linguistic complexity on audiovisual speech perception

Audiovisual (AV) cues often lead to improved speech understanding, especially when the auditory input is impoverished (e.g., in background noise, for people with hearing loss). This AV improvement occurs when both the auditory and visual information can be integrated together effectively (e.g., through temporal synchrony). However, whether the complexity of speech signals influences this integrated percept and how this contributes to speech understanding is still unknown. This study investigates whether individual differences in perception of temporal synchrony of the auditory and visual speech signal explain individual variability in AV speech recognition, and whether this differs across different levels of linguistic processing. By implementing five different levels of linguistic complexity (i.e., varying phoneme-viseme connections, vocabulary knowledge and/or linguistic context) and introducing temporal asynchrony in a remote task with English speaking adults we ask the following questions: (1) Is temporal synchrony needed to observe AV speech perception benefits? (2) Does this vary by linguistic level? (3) Does linguistic complexity influence synchrony perception? (4) And does synchrony perception explain individual variability in AV speech perception? These findings add value in deciding how AV synchronization should be balanced in hearing aid designs as well as in augmented / virtual reality devices.

How to define the correct guidelines for enhanced telepresence and task embodiment in remote palpation.

Teleoperated robots have been widely accepted in several fields of medical practice, enhancing human abilities and allowing remote operation. However, such technology has not been able yet to permeate areas such as primary care and physical examination. Such applications strongly rely on the quality of the interaction between doctor and patient, and on its multimodal nature. In order to achieve remote physical examination is thus mandatory to have a good doctor-robot interface, but what does good mean? Ultimately, the goal is for the user to achieve task embodiment, making the remote task feel like the in-person one. Several research groups have proposed a wide variety of interfaces, showcasing largely different methods of control and feedback, because of the absence of design guidelines. In this work, we argue that the ideal interface for a remote task should resemble as close as possible the experience provided by the in-person equivalent, keeping in consideration the nature of the target users. To support our claims, we analyze many remote interfaces and compare them with the respective in-person task. This analysis is not limited to the medical sector, with examples such as remote abdominal surgery, but it expands to all forms of teleoperation, up to nuclear waste handling and avionics.

A novel video game for remote studies of motor adaptation in children.

Here we designed a motor adaptation video game that could be played remotely (at home) through a web browser. This required the child to adapt to a visuomotor rotation between their hand movement and a ball displayed in the game. The task had several novel features, specifically designed to allow the study of the developmental trajectory of adaptation across a wide range of ages. We test the concurrent validity by comparing children's performance on our remote task to the same task performed in the laboratory. All participants remained engaged and completed the task. We quantified feedforward and feedback control during this task. Feedforward control, a key measure of adaptation, was similar at home and in the laboratory. All children could successfully use feedback control to guide the ball to a target. Traditionally, motor learning studies are performed in a laboratory to obtain high quality kinematic data. However, here we demonstrate concurrent validity of kinematic behavior when conducted at home. Our online platform provides the flexibility and ease of collecting data that will enable future studies with large sample sizes, longitudinal experiments, and the study of children with rare diseases.

Game-Combined Multi-Agent DRL for Tasks Offloading in Wireless Powered MEC Networks

Wireless powered mobile edge computing (MEC) networks, where wireless devices (WDs) are allowed to offload parts of computation-intensive tasks to remote servers and charge the built-in batteries over the air, have been envisaged as a promising technology to ensure the ultra-low-power requirement and enhance the continuous work capacity of WDs. However, when multiple WDs coexist in the network, it is non-trivial to minimize the total tasks delay since the optimization variables are intrinsically coupled. Even more, channels are dynamically varying from time to time and the tasks are unpredictable, which aggravates the difficulty to obtain the closed-form solution. Although reinforcement learning (RL) has been proved to be effective for such complex optimization problems, there is still the challenge that the training of neural networks is time-consuming. This paper considers a challenging hybrid tasks offloading scenario, where offloading tasks can be partially executed locally and remotely in parallel, and each WD is endowed to take both the active RF-transmission and passive backscatter communication (BackCom) for remote tasks offloading. Furthermore, a game-combined multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to minimize the total tasks delay with the fairness consideration of multiple WDs, i.e., potential game for offloading decision and MADDPG for time scheduling and harvested energy splitting. The introduction of potential game which can be proved to converge with finite iterations, helps to accelerate the training and reduce the computation complexity. Equipped with the feature of ‘centralized training with decentralized execution,’ once well trained, each agent in MADDPG can figure out the proper time scheduling and harvested energy splitting independently without sharing information with others. Besides the unilateral contention among WDs for the offloading decision by potential game, a fully decentralized framework is finally designed for the proposed algorithm. Numerical results demonstrate that the game-combined MADDPG algorithm can achieve the near-optimal performance compared with existing centralized approaches, and reduce the convergence time compared with other no-game learning approaches.

The influence of remote work on ergonomics and work satisfaction

Purpose: The purpose of the article is to present the impact of remote work on ergonomics and job satisfaction. The purpose of the study is understood from the perspective of the impact of remote work on corporate employees. Design/methodology/approach: Data for the study was collected from employees working at a corporation in Bydgoszcz, Poland. The study used the PLS (partial least squares) analysis method to understand the relationship between remote work and job satisfaction, and between work ergonomics and remote task performance. Findings: The results of the study indicate that ergonomics closely influences job satisfaction. In addition, the study confirmed the hypothesis that remote work has a significant impact on task performance. Research limitations/implications: The study is limited to employees of corporations in a specific region of Poland. Therefore, future research could focus more on the impact of remote work on corporate culture in other parts of Poland. Practical implications: The study provides directions for human resource management to implement effective practices for improving remote work. Social implications: This study offers support for corporate employees, providing insights into how the work environment can be improved, thereby enhancing work and employee satisfaction. Originality/value: This is a study that attempts to provide insight into how ergonomics is important in corporate work, especially when working remotely. The findings provide important implications for improving ergonomics at work and increasing employee satisfaction. Keywords: management, human resources, remote work, ergonomics, job satisfaction. Category of the paper: research paper.

USE OF INNOVATIVE LEARNING TECHNOLOGIES IN HIGHER EDUCATIONAL IN THE CONDITIONS OF MIXED EDUCATION

The article is devoted to one of the urgent pedagogical problems of domestic science, which reveals the importance of introducing innovative technologies into the higher educational process in conditions of mixed learning. The article clarified difference between the concepts of “pedagogical technology”, “innovative technology”, “innovative teaching method”, “mixed, synchronous and asynchronous learning”, “contact and non-contact hours”. The publication presents the features of the use innovative technologies in the educational process as: information and communication technologies (video conferences in Zoom, Google Meet, Webex Meet; chats, forums, websites, multimedia, “Kahoot!”), cloud technologies (Skydrive, Sway, Prezi, Google services, Moodle), Mind Maps, Scribe and interactive technologies (Case studies, business and role-playing games). The article analyzes different approaches of scientists to the classification of innovative learning technologies. Attention is focused on the recommendations of the Ministry of Education and Science of Ukraine regarding the introduction of a mixed form of education organization into the educational process of a higher school in accordance with today’s requirements. The role of digitization of higher education and its impact on various forms of knowledge acquisition and formation of students’ abilities and skills, conducting scientific research and creative activities, forms of control, in particular the performance of individual tasks, control and independent works of students, testing, defense of qualification and course projects are described. The use of innovative learning technologies contributes to the formation of search, research, communicative, analytical abilities and skills in students of higher education, the realization of their abilities by each participant in the educational process, an individual approach to learning, the development of cognitive interests, the activation of students’ creative abilities, a successful combination of classroom and remote tasks work.

Challenges and Opportunities for Multi-Device Management in Classrooms

Orchestrating digital devices in classrooms is challenging. We conducted an observational study to understand how teachers manage multi-device classrooms involving tablets, computers, and video-projectors. Two categories of device management tasks stand out: content sharing and remote control. Based on our observations, we identify 17 multi-device management tasks that are important to teachers in this specific device configuration. We conducted a second study to investigate how teachers could perform these tasks with the help of a smartwatch. For remote control tasks, participants preferred smartwatches to interact from a distance, whereas for content-sharing tasks, they preferred to interact directly with the devices displaying the content. We outline design opportunities for multi-device management controls in learning environments, and challenges for HCI research on classroom orchestration.

Adaptive nonlinear motion parameters estimation algorithm for digital twin of multi-link mechanism motion trajectory synthesis

Monitoring systems of automated production lines requires high accuracy and processing speed of acquired data. Nowadays digital twin technologies are rapidly developing as part of Industry 4.0. Digital twin is a virtual representation of a physical system that mimics the behaviour of a real object, and is used in real-time control, monitoring and disturbance prediction, without influence on a real object. Digital twin is being used in many fields of application such as: healthcare, manufacturing, education, city development, etc. Nonetheless, despite the rising popularity of digital twin researches, there is no basic approach to digital twin synthesis. Mostly, in state of the art articles, systems with known parameters are considered. In this paper, approach to development of digital twin, based on internal model for multi-link mechanisms with unknown motion parameters, is presented. Described approach intended to ease the task of production line monitoring and extend digital twin technology application field. In this article adaptive controller design, based on internal model, is presented, theoretical explanations of design process are provided, and application of the control algorithm in the task of multi-link mechanism motion trajectory parameters estimation is described. In this work the experimental synthesis of digital twin for Kuka youBot manipulator based on presented approach in real time is described. Adaptive motion parameters estimation was made for two links of the manipulator for the sake of confirmation of presented approach usage in the task of digital twin for systems with unknown parameters. Motion trajectory parameters of the robot links were defined manually with the use of chaotic generators to estimate the accuracy of the adaptive control system. This approach was used in order to calculate the error of reference tracking by comparing the control signal for digital twin with the reference exogenous input for manipulator links. As the results of the experiment, the graphs of the error reference tracking for both links are presented. As the goal of the experiment, the convergence of reference tracking error to the field [–0.005, 0.005] radian was set, and as can be seen from graphs this goal was achieved. There is a small disturbance of the error convergence on the graphs within the desired field which was caused by noised measurements and delays of the chosen modelling environment real-time simulations. In the conclusion of this paper, further work to reduce the influence of mentioned causes is described. The results of this paper can be used in further digital twin technology researches. Presented approach can be implemented in robots remote control tasks, production lines technical condition monitoring and in general motion trajectory parameters estimation tasks for multi-link mechanisms

From QoS to QoE plus QoT in Beyond 5G Networks

In the Beyond 5G (B5G) era, a paradigm shift from technical Quality of Service (QoS) oriented networks to user-centric Quality of Experience (QoE) centred network architectures is expected to occur. With this development, the infusion of QoE user requirements into B5G networks will be critical to the emergence of ultra-reliable and ultra-low latency haptic-enabled Internet applications of the future. One such application which will signify the emergence of a tele-haptic Internet will be the mission-critical use case of remote robotic surgical task performance, precipitating a transition from content-based to skillset delivery networks for an augmented user experience. In extending network QoS to user focused QoE and with it, Quality of Task (QoT) dimensions, human users in a global control loop (such as robotic surgeons) will be capable of true-to-life immersive remote task performance through the manipulation of objects in real-time and across large geographical distances. In this paper, we discuss the linkages between network and user-centric QoS and QoE (with QoT) perspectives. Further, we explain the emergence of a future B5G network and haptic-enabled Internet of Skills (IoS), and draw on the example of an architecture applied to the task-sensitive mission-critical use case of tele-haptic surgery. In doing so, we conceptualise and present Task-Technology Fit (TTF) theoretical and predictive modelling that can be empirically applied to this futuristic use case for a novel QoE/QoT perspective of future B5G communication networks.

A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field

The head-mounted eye-tracking technology is often used to manipulate the motion of servo platform in remote tasks, so as to achieve visual aiming of servo platform, which is a highly integrated human-computer interaction effect. However, it is difficult to achieve accurate manipulation for the uncertain meanings of gaze points in eye-tracking. To solve this problem, a method of classifying gaze points based on a conditional random field is proposed. It first describes the features of gaze points and gaze images, according to the eye visual characteristic. An LSTM model is then introduced to merge these two features. Afterwards, the merge features are learned by CRF model to obtain the classified gaze points. Finally, the meaning of gaze point is classified for target, in order to accurately manipulate the servo platform. The experimental results show that the proposed method can classify more accurate target gaze points for 100 images, the average evaluation values Precision = 86.81%, Recall = 86.79%, We = 86.79%, these are better than relevant methods. In addition, the isolated gaze points can be eliminated, and the meanings of gaze points can be classified to achieve the accuracy of servo platform visual aiming.

Effects of the presence of a cell phone and exposure to natural environments on remote associates task performance

In today’s advanced information society, creativity in work is highly valued, and there is growing interest in the kinds of work environments that produce more creative outcomes. Recent researchers have demonstrated that when environmental factors change a worker’s attentional state to a diffused state, the worker has access to more information than usual, which can contribute to creativity. Here, we examined whether manipulating environmental factors (the presence of a cell phone and exposure to natural environment) that could affect such attention states would improve performance on the Remote Associates Task, a measure of creativity. Our results showed that the presence of a cell phone increased creative performance regardless of immersion in natural environment. In contrast, exposure to nature did not facilitate creative performance; instead, feelings of pleasure increased, and frustration decreased. These results suggest that the presence of a cell phone can enhance creativity by influencing workers’ attentional states. The current study provides a meaningful approach to enhancing creativity by modulating attentional states through environmental factors. It also highlights the essential features of environmental factors that can moderate creative abilities.

Secure and intelligent slice resource allocation in vehicles-assisted cyber physical systems

Cyber physical systems (CPSs) are multi-dimensional complex systems integrated with computing, networking and physical modules, enabling to realize complex computing and communication tasks. Vehicles are composed of many electrical and electronic hardware modules. With integrating mobile vehicles, CPSs can be extended to realize more remote tasks. Network slicing (NS) is recognized as the key enabler towards the agile enabler of vehicles-assisted CPSs. In NS, one crucial technical issue is the slice resource allocation. Intelligent approaches, such as deep learning (DP), emerge and can be adopted to allocate slice resource allocation. In addition, due to the heterogeneity nature of physical elements in CPSs, security is strongly required to be addressed. On these basis, secure and intelligent slice resource allocation in vehicles-assisted CPSs is researched in this paper. At first, system models of vehicles-assisted CPSs with NS scheme and slices are described. Then, the intelligent resource allocation framework, abbreviated as Sec-Slice-Intell, is proposed and described. The goal of Sec-Slice-Intell framework is to achieve secure resource allocation per slice for NS-enabled CPSs. In order to validate the merits of the proposed framework, evaluation work is conducted and illustrated, in the form of simulation.

Quantum steering with vector vortex photon states with the detection loophole closed

Violating a nonlocality inequality enables the most powerful remote quantum information tasks and fundamental tests of quantum physics. Loophole-free photonic verification of nonlocality has been achieved with polarization-entangled photon pairs, but not with states entangled in other degrees of freedom. Here we demonstrate completion of the quantum steering nonlocality task, with the detection loophole closed, when entanglement is distributed by transmitting a photon in an optical vector vortex state, formed by optical orbital angular momentum (OAM) and polarization. As well as opening up a high-efficiency encoding beyond polarization, the critically important demonstration of vector vortex steering opens the door to new free-space and satellite-based secure quantum communication devices and device-independent protocols.