Human-machine Collaboration Research Articles

Ferin Martino: A Small Piano Algorithm and Its Lessons on Creativity, Interaction, and Expression

The process of developing and making music with Ferin Martino, a piano-playing software algorithm has opened up a number of inquiries surrounding modern performance and creativity. Although it is relatively simple program, it is capable of much unique, expressive output that maintains a consistent style. Instead of constructing musical passages note by note, its musical character arises from its structure, independent of content. Recombination of previously-played material ensures that the first randomly generated notes end up yielding musically and stylistically coherent passages without compromising the variety of musical material produced. A flow of pseudorandom numbers is dis- turbed by motion in front of a camera, making the music responsive to its surroundings. Human-machine collaboration with this system has been effective in a variety of applications ranging from background music to computer-aided composition and live performance. A brief explanation of the algorithm is followed by ex- plorations of its resulting character; strengths and challenges; and perspectives it inspires on the relationships between humans and machines and on twenty-first century musicianship.

A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission

In view of the characteristics of the guidance, navigation and control (GNC) system of the lunar orbit rendezvous and docking (RVD), we design an auxiliary safety prediction system based on the human–machine collaboration framework. The system contains two parts, including the construction of the rendezvous and docking safety rule knowledge base by the use of machine learning methods, and the prediction of safety by the use of the base. First, in the ground semi-physical simulation test environment, feature extraction and matching are performed on the images taken by the navigation surveillance camera. Then, the matched features and the rendezvous and docking deviation are used to form training sample pairs, which are further used to construct the safety rule knowledge base by using the decision tree method. Finally, the safety rule knowledge base is used to predict the safety of the subsequent process of the rendezvous and docking based on the current images taken by the surveillance camera, and the probability of success is obtained. Semi-physical experiments on the ground show that the system can improve the level of intelligence in the flight control process and effectively assist ground flight controllers in data monitoring and mission decision-making.

Human–Robot Collaborative Assembly Based on Eye-Hand and a Finite State Machine in a Virtual Environment

With the development of the global economy, the demand for manufacturing is increasing. Accordingly, human–robot collaborative assembly has become a research hotspot. This paper aims to solve the efficiency problems inherent in traditional human-machine collaboration. Based on eye–hand and finite state machines, a collaborative assembly method is proposed. The method determines the human’s intention by collecting posture and eye data, which can control a robot to grasp an object, move it, and perform co-assembly. The robot’s automatic path planning is based on a probabilistic roadmap planner. Virtual reality tests show that the proposed method is more efficient than traditional methods.

Digital Twin-Based Ecogreen Building Design

The high operation cost of green building, insufficient informationization and automation management capability, and the lack of effective operation cost control seriously restrict the development of the industry and the realization of green goals. In order to solve the problem of insufficient capability of green building operation cost management, based on the digital twin technology in the manufacturing field, we analyze the characteristic requirements and theoretical basis of green building operation cost management for system, propose a green building operation cost management system based on digital twin, and refine the design of each structural layer of this system. It is necessary to set up a series of lines, although it takes a certain amount of time. There are four types of applications, namely, the number of types of applications, the completion of the effective number, the comparison of functions, and the implementation capabilities. The study shows that the proposed system framework can improve the efficiency and quality of green building operation cost management through technology upgrade and process optimization. The implementation of digital twin and human-machine collaboration is an advanced stage in the development of digital architecture because virtual things and real things, materials, and numbers are mutually promoting processes. The inspiration of this technological view for architecture is that digital twin and human-machine collaboration not only allow the interaction between virtual and reality and emphasize the feedback of actual construction to virtual simulation but also promote a kind of mutual promotion of human and machine thinking and construction ability.

A Cybersecurity Assessment Model for Small and Medium-Sized Enterprises

The industrial environment is facing important changes from Industry 4.0 and requisite digital transformation. Small and medium-sized enterprises (SMEs) continue to introduce innovative and advanced Industry 4.0 technologies to improve their competitiveness. Industry 4.0 can provide a boost in efficiency, greater production flexibility through human–machine collaboration, and general agility. Yet, new challenges arise from digitalization and connectivity in this environment. This digitalization means greater exposure to possible cyber-attacks from outside. In response to these emergent challenges, we introduce an assessment model for SME cybersecurity; laying the foundation for managerial actions for the SME data security.

A Human-Machine Collaboration Model for Urban Planning in Smart Cities

Smart cities are emerging around the world. In response, government institutions have made efforts to implement technologies that promote citizen welfare. We explore the advances in inclusive and participatory urban planning processes and propose a conceptual model that helps citizens and governments in the decision-making process.

Optimal human-machine collaboration for enhanced cost-sensitive biometric authentication

Despite growing interest in human-machine collaboration for enhanced decision-making, little work has been done on the optimal fusion of human and machine decisions for cost-sensitive biometric authentication. An elegant and robust protocol for achieving this objective is proposed. The merits of the protocol is illustrated by simulating a scenario where a workforce of human experts and a score-generating machine are available for the authentication of handwritten signatures on, for example, bank cheques. The authentication of each transaction is determined by its monetary value and the quality of the claimed author’s signature. A database with 765 signatures is considered, and an experiment that involves 24 human volunteers and two different machines is conducted. When a reasonable number of experts are kept in the loop, the average expected cost associated with the workforce-machine hybrid is invariably lower than that of the unaided workforce and that of the unaided machine.

Human–Machine Collaboration for Automated Driving Using an Intelligent Two‐Phase Haptic Interface

Automated Driving In article number 2000229, Chen Lv and co-workers propose a novel human-machine collaboration system based on an intelligent haptic interface for addressing the takeover control problem for automated vehicles. The intelligent haptic torque is applied to the steering wheel and switches its functionality between predictive guidance and haptic assistance according to the varying state and control ability of human drivers. This helps drivers gradually resume manual control during takeover.

A prediction-oriented optimal design for visualisation recommender systems

A good visualisation method can greatly enhance human-machine collaboration in target contexts. To aid the optimal selection of visualisations for users, visualisation recommender systems have been developed to provide the right visualisation method to the right person given specific contexts. A visualisation recommender system often relies on a user study to collect data and conduct analysis to provide personalised recommendations. However, a user study without employing an effective experimental design is typically expensive in terms of time and cost. In this work, we propose a prediction-oriented optimal design to determine the user-task allocation in the user study for the recommendation of visualisation methods. The proposed optimal design will not only encourage the learning of the similarity embedded in the recommendation responses (i.e., users' preference), but also improve the modelling accuracy of the similarities captured by the covariates of contexts (i.e., task attributes). A simulation study and a real-data case study are used to evaluate the proposed optimal design.

A Novel Human-Machine Collaboration Model of an Ankle Joint Rehabilitation Robot Driven by EEG Signals

With the emergence of the phenomenon of social aging, the elderly have frequent physical movement disorders. In particular, the movement disorder of the ankle joint seriously affects the daily life of the elderly. Rehabilitation robots are of great significance for improving the efficiency of rehabilitation, ensuring the quality of rehabilitation, and reducing the labor intensity of workers. As an auxiliary treatment tool, rehabilitation robots should have rich and effective motion modes. The exercise mode should be adaptable for patients with different conditions and different recovery periods. To improve the accuracy of human-computer interaction of ankle joint rehabilitation robots (AJRR), this study proposes a man-machine collaboration model of an EEG-driven AJRR. The model mainly expands from two levels (1) to establish the connection between EEG and intention so as to identify the intention. In the recognition process, first feature extraction is given on the preprocessed EEG. Convolutional neural network (CNN) is selected to extract the deep features of the EEG signal, and support vector machine (SVM) is used for classifying the deep features, thereby realizing intent recognition. (2) The result of intention recognition is input to the human-computer interaction (HCI) system, which controls the movement of the rehabilitation robot after receiving the instruction. This study truly realizes patient-oriented rehabilitation training. Experiments show that the human-machine collaboration model used can show higher accuracy of intention recognition, thereby increasing the satisfaction of using AJRR.

Human-machine collaboration framework for structural health monitoring and resiliency

Post-earthquake damage assessment can be significantly expedited when machine learning (ML) algorithms supported by sensing technologies are used. ML has some limitations when it comes to seismic damage assessment. ML tools require data from each class while training but in the case of seismic structural health monitoring, vibration data are usually available from the undamaged structures. In this paper, a framework called the human-machine collaboration (H-MC) is proposed. The H-MC framework combines the ML tools and human (domain) expertise for damage assessment of real instrumented buildings with only data from undamaged cases. It uses novelty detection as the ML tool and structure-specific analytical model to enable rapid damage detection. Subsequently, the framework is applied to detect damage in real instrumented buildings. The results showed that the H-MC algorithm correctly detected the damaged cases. It also labeled all the undamaged events accurately eliminating false positive detection. Furthermore, it is revealed that the resiliency of a building can be improved when the H-MC method is implemented over traditional field inspection. The proposed framework can be a viable tool for rapid post-earthquake damage assessment which is essential for improving community resiliency.

Research on spatial motion safety constraints and cooperative control of robot-assisted craniotomy: Beagle model experiment verification.

Traditional craniotomy depends primarily on the experience of the surgeon. However, the accuracy of manual operation is limited and carries certain surgical risks. The interaction method of current robot-assisted craniotomy is unnatural and inadaptive to the operating style of the surgeon. In this research, we built a hands-on synergistic robotics craniotomy system with human-machine collaboration. Safe isometric surfaces and virtual restraint methods are combined to achieve highly accurate, efficient, minimally invasive and safe craniotomy. Fifteen three-dimensional (3D)-printed beagle skull models were used to evaluate the system accuracy and the related image guidance process. It mainly includes the design of the surgical plan, the adopted strategy based on motion constraint and safe isometric surface, and the impedance control method based on the position inner loop via the human-machine collaboration method. The trajectory tracking experiment was performed by applying human-machine collaboration, and completed an experiment on the 3D-printed beagle skull models involving drilling and milling of the skull performed by the robot, and evaluation of accuracy via computed tomographic (CT) scanning verification after the operation. The 3D-printed beagle skull model experiment shows that the average errors for the top surface and the bottom surface, and the angle error were 0.81±0.15mm, 0.89±0.12mm, and 1.74°±0.16°, respectively. The average milling position errors for the top and bottom surfaces were 0.87±0.19 and 0.93±0.22mm, respectively. The performance of the robot system was evaluated and verified using a 3D-printed beagle model experiment. The proposed collaborative surgical robot system is feasible and can complete a craniotomy, with improved accuracy and surgical safety.

Human–Machine Collaboration for Automated Driving Using an Intelligent Two‐Phase Haptic Interface

Prior to realizing fully autonomous driving, human intervention is periodically required to guarantee vehicle safety. This poses a new challenge in human–machine interaction, particularly during the control authority transition from automated functionality to a human driver. Herein, this challenge is addressed by proposing an intelligent haptic interface based on a newly developed two‐phase human–machine interaction model. The intelligent haptic torque is applied to the steering wheel and switches its functionality between predictive guidance and haptic assistance according to the varying state and control ability of human drivers. This helps drivers gradually resume manual control during takeover. The developed approach is validated by conducting vehicle experiments with 26 participants. The results suggest that the proposed method effectively enhances the driving state recovery and control performance of human drivers during takeover compared with an existing approach. Thus, this new method further improves the safety and smoothness of human–machine interaction in automated vehicles.

Human-machine collaboration in managerial decision making

Although Artificial Intelligence (AI) has become a pervasive organizational phenomenon, it is still unclear if and when people are willing to cooperate with machines. We conducted five empirical studies (total N = 1025 managers). The results show that human managers do not want to exclude machines entirely from managerial decisions, but instead prefer a partnership in which humans have a majority vote. Across our studies, acceptance rates steadily increased up until the point where humans have approximately 70% weight and machines 30% weight in managerial decisions. After this point the curve flattened out, meaning that higher amounts of human involvement no longer increased acceptance. In addition to this overall pattern, we consistently found four classes of managers that reacted differently to different amounts of human versus machine involvement: A first class of managers (about 5%) preferred machines to have the upper hand, a second class of managers (about 15%) preferred an equal partnership between humans and machines, a third class of managers (about 50%) preferred humans to have the upper hand, and a final class of managers (about 30%) preferred humans to have complete control in managerial decisions. Practical implications and directions for future research are discussed.

EFFECTS OF TECHNOLOGICAL DIFFUSION AND ACCESS TO ELECTRICITY ON EMPLOYMENT IN NIGERIA

Demographic transitions and technological advancements may lead to a net loss of 5 million jobs by 2020; hence, about forty percent (1.4 billion) of the global workforce are vulnerable to unemployment. This is because a more significant percentage of tasks that are already being disrupted by automation are repetitive and standardized processes. At the same time, actions/jobs which require empathy, genuine creativity, and critical thinking will be in high demand in the new workforce, thereby achieving a human-machine collaboration. Thus, this study seeks to investigate the influence that technology has on employment in the Nigerian labor market and how Access to electricity and employment are connected using Nigeria as a case study. The unit root test was conducted via Phillip-Perron (PP) statistics and Augmented Dickey-Fuller (ADF) tests. The Auto-Regressive Distributed Lag (ARDL) model was also employed to evaluate the relationship between technology and employment in Nigeria using World Bank data (1960-2017). Results showed that technology and globalization have a long-run statistically significant inverse relationship with employment in Nigeria, which conforms to theory. Policy recommendations promote the acquisition of such skills encompassing critical thinking, empathy, and creativity to enable a better future for the Nigerian labor force.Keywords: Employment; Labor; Technology; Globalization; Nigeria.JEL Classifications: O14, E24, J21DOI: https://doi.org/10.32479/ijeep.10231

Limits of artificial intelligence in controlling and the ways forward: a call for future accounting research

PurposeLooking at the limits of artificial intelligence (AI) and controlling based on complexity and system-theoretical deliberations, the authors aimed to derive a future outlook of the possible applications and provide insights into a future complementary of human–machine information processing. Derived from these examples, the authors propose a research agenda in five areas to further the field.Design/methodology/approachThis article is conceptual in its nature, yet a theoretically informed semi-systematic literature review from various disciplines together with empirically validated future research questions provides the background of the overall narration.FindingsAI is found to be severely limited in its application to controlling and is discussed from the perspectives of complexity and cybernetics. A total of three such limits, namely the Bremermann limit, the problems with a partial detectability and controllability of complex systems and the inherent biases in the complementarity of human and machine information processing, are presented as salient and representative examples. The authors then go on and carefully illustrate how a human–machine collaboration could look like depending on the specifics of the task and the environment. With this, the authors propose different angles on future research that could revolutionise the application of AI in accounting leadership.Research limitations/implicationsFuture research on the value promises of AI in controlling needs to take into account physical and computational effects and may embrace a complexity lens.Practical implicationsAI may have severe limits in its application for accounting and controlling because of the vast amount of information in complex systems.Originality/valueThe research agenda consists of five areas that are derived from the previous discussion. These areas are as follows: organisational transformation, human–machine collaboration, regulation, technological innovation and ethical considerations. For each of these areas, the research questions, potential theoretical underpinnings as well as methodological considerations are provided.

Social Production System: A Three-Layer Smart Framework for Implementing Autonomous Human-Machine Collaborations in a Shop Floor

The continuous fast-changing of production requirements and the development of advanced information technology have promoted the development of the production system in a more knowledge-intensive, resources-decentralized, and autonomous collaborative manner. In order to provide guidelines for the smart transformation of the production shop floor, this paper proposes a smart production system with a three-layer framework defined as the social production system (Social-PS) by integrating cyber-physical system (CPS), knowledge graph technology, and production-event driven model. The concept of smart resources (include smart operator, smart machine, smart workpiece, and smart gateway), social sensor, the unified information model is clarified to construct the physical carrier of Social-PS. The unified knowledge repository’s overall construction method based on the knowledge graph is elaborated to provide task-resource-associated heterogeneous production data sharing and management for human-machine collaboration. Subsequently, to support the production dynamic monitoring and interaction, a production-event and active knowledge indexing model is proposed. Furthermore, to validate the proposed Social-PS framework, a software and hardware integrated prototype system is implemented to demonstrate the knowledge-driven human-machine collaboration of the production process, which provides a basis for realizing smart and autonomous production collaboration.

Interface Design for Human-Machine Collaborations in Drone Management

Unmanned aerial vehicles (UAVs) operate with large degrees of autonomy when executing tasks from a ground-based controller. In this work, we design an (immersive) interface for drone management. Our interface supports operators in mission execution of one or several (automated) UAVs within a fleet. This allows operators to assign tasks to UAVs, while safe operations are ensured through in-flight conflict detection and prevention. Our interface fulfills both functional (e.g., reliability) and non-functional requirements (e.g., perceived cognitive load during use). A key design challenge is the latency in information exchanges, which introduces information asymmetries between the humans and UAVs. We then validate the effectiveness of our interface design across several experiments. We find that our design reduces the perceived cognitive load and improves task performance. Altogether, this work has implications for designing interfaces in human-machine collaboration, so humans can effectively control, interact, or collaborate with automated machines, such as UAVs.

Discussion on Data Features and Construction Models of Translation Corpus in the Era of Big Data

With the rapid development of artificial intelligence in the current era of big data, the construction of translation corpus has become a key factor in effectively achieving a highly intelligent translation. In the era of big data, the data sources and data types of translation corpus are becoming more and more diversified, which will inevitably bring about a new revolution in the construction of translation corpus. The construction of the translation corpus in the era of big data can fully rely on third-party open source data, crowd-sourcing translation, machine closed-loop, human-machine collaboration and other multiple modes to comprehensively improve the quality of translation corpus construction to better serve translation practice.

Long-Term Person Tracking for Unmanned Aerial Vehicle Based on Human-Machine Collaboration

Long-Term Person Tracking for Unmanned Aerial Vehicle Based on Human-Machine Collaboration