Human-Centricity in Tax Administration:Practice of the Federal Tax Service of Russia

In recent years,the Federal Tax Service(FTS)of Russia has been steadi-ly evolving from service-oriented to human-centric tax administration.Taxpayers,who invest in the country’s development,could not be treated as the customers in a narrow business context.One of the top priorities of the Government of the Russian Federation is to re-focus public administration from a service-oriented approach to a client-centric model,where public services are convenient and integrated into the daily lives of the citizens.The large-scale changes require transformation of organizational culture in the tax administration.The commitment of the FTS leadership is the key to the transformation.

A Study on the Challenges of Human-Centric Cyber-Security and the Guarantee of Information Quality

Information security and quality management are often considered two different fields. However, organizations must be mindful of how software security may affect quality control. This paper examines and promotes methods through which secure software development processes can be integrated into the Systems Software Development Life-cycle (SDLC) to improve system quality. Cyber-security and quality assurance are both involved in reducing risk. Software security teams work to reduce security risks, whereas quality assurance teams work to decrease risks to quality. There is a need for clear standards, frameworks, processes, and procedures to be followed by organizations to ensure high-level quality while reducing security risks. This research uses a survey of industry professionals to help identify best practices for developing software with fewer defects from the early stages of the SDLC to improve both the quality and security of software. Results show that there is a need for better security awareness among all members of software development teams.

From Digital Human Modeling to Human Digital Twin: Framework and Perspectives in Human Factors

The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.

Evaluating Methods of Visual Assistance for Workers to Improve Quality and Usability in Individualized Kitchen Cabinet Assembly

In times of digitalisation, visual assistance systems in assembly are increasingly important. The design of these assembly systems needs to be highly complex to meet the requirements. Due to the increasing number of variants in production processes, as well as shorter innovation and product life cycles, assistance systems should improve quality and reduce complexity of assembly processes. However, many large kitchen manufacturers still assemble kitchen cabinets manually, due to the high variety of components, such as rails and fittings. This paper focuses on the analysis and evaluation of virtual assistance systems to improve quality and usability in individualised kitchen cabinet assembly processes at a large German manufacturer. A solution is identified and detailed.

A Novel Data Schema Integration Framework for the Human-Centric Services in Smart City

Human-centric service is an important domain in smart city and includes rich applications that help residents with shopping, dining, transportation, entertainment, and other daily activities. These applications have generated a massive amount of hierarchical data with different schemas. In order to manage and analyze the city-wide and cross-application data in a unified way, data schema integration is necessary. However, data from human-centric services has some distinct characteristics, such as lack of support for semantic, matching, large number of schemas, and incompleteness of schema element labels. These make the schema integra- tion difficult using existing approaches. We propose a novel framework for the data schema integration of the human-centric services in smart city. The framework uses both schema metadata and instance data to do schema matching, and introduces human intervention based on a similarity entropy criteria to balance precision and efficiency. Moreover, the framework works in an incremental manner to reduce computation workload. We conduct an experiment with real-world dataset collected from multiple estate sale application systems. The results show that our approach can produce high-quality mediated schema with relatively less human in- terventions compared to the baseline method.

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Face recognition has been rapidly developed and widely used.However,there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding.Emerging challenges for face recognition are resulted from information loss.This study aims to tackle these challenges with a broad learning system(BLS).We integrated two models,IR3C with BLS and IR3C with a triplet loss,to control the learning process.In our experiments,we used different strategies to generate more challenging datasets and analyzed the competitiveness,sensitivity,and practicability of the proposed two models.In the model of IR3C with BLS,the recognition rates for the four challenging strategies are all 100%.In the model of IR3C with a triplet loss,the recognition rates are 94.61%,94.61%,96.95%,96.23%,respectively.The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.

Quantum Inspired Differential Evolution with Explainable Artificial Intelligence-Based COVID-19 Detection

Recent advancements in the Internet of Things(Io),5G networks,and cloud computing(CC)have led to the development of Human-centric IoT(HIoT)applications that transform human physical monitoring based on machine monitoring.The HIoT systems find use in several applications such as smart cities,healthcare,transportation,etc.Besides,the HIoT system and explainable artificial intelligence(XAI)tools can be deployed in the healthcare sector for effective decision-making.The COVID-19 pandemic has become a global health issue that necessitates automated and effective diagnostic tools to detect the disease at the initial stage.This article presents a new quantum-inspired differential evolution with explainable artificial intelligence based COVID-19 Detection and Classification(QIDEXAI-CDC)model for HIoT systems.The QIDEXAI-CDC model aims to identify the occurrence of COVID-19 using the XAI tools on HIoT systems.The QIDEXAI-CDC model primarily uses bilateral filtering(BF)as a preprocessing tool to eradicate the noise.In addition,RetinaNet is applied for the generation of useful feature vectors from radiological images.For COVID-19 detection and classification,quantum-inspired differential evolution(QIDE)with kernel extreme learning machine(KELM)model is utilized.The utilization of the QIDE algorithm helps to appropriately choose the weight and bias values of the KELM model.In order to report the enhanced COVID-19 detection outcomes of the QIDEXAI-CDC model,a wide range of simulations was carried out.Extensive comparative studies reported the supremacy of the QIDEXAI-CDC model over the recent approaches.

Joint Motion Control of a Powered Lower Limb Orthosis for Rehabilitation

Many patients with spinal injures are confined to wheelchairs, leading to a sedentary lifestyle with secondary pathologies and increased dependence on a carer. Increasing evidence has shown that locomotor training reduces the incidence of these secondary pathologies, but the physical effort involved in this training is such that there is poor compliance. This paper reports on the design and control of a new ＂human friendly＂ orthosis （exoskeleton）, powered by high power pneumatic Muscle Actuators （pMAs）. The combination of a highly compliant actuation system, with an intelligent embedded control mechanism which senses hip, knee, and ankle positions, velocity, acceleration and force, produces powerful yet inherently safe operation for paraplegic patients. This paper analyzes the motion of ankle, knee, and hip joints under zero loading, and loads which simulate human limb mass, showing that the use of ＂soft＂ actuators can provide a smooth user friendly motion. The application of this technology will greatly improve the rehabilitative protocols for paraplegic patients.

Interaction design for paediatric emergency VR training

Background Virtual reality(VR)in healthcare training has increased adoption and support,but efforts are still required to mitigate usability concerns.Methods This study conducted a usability study of an in-use emergency medicine VR training application,available on commercially available VR hardware and with a standard interaction design.Nine users without prior VR experience but with relevant medical expertise completed two simulation scenarios for a total of 18 recorded sessions.They completed NASA Task Load Index and System Usability Scale questionnaires after each session,and their performance was recorded for the tracking of user errors.Results and Conclusions Our results showed a medium(and potentially optimal)Workload and an above average System Usability Score.There was significant improvement in several factors between users'first and second sessions,notably increased Performance evaluation.User errors with the strongest correlation to usability were not directly tied to interaction design,however,but to a limited'possibility space'.Suggestions for closing this'gulf of execution'were presented,including'voice control'and'hand-tracking',which are only feasible for this commercial product now with the availability of the Oculus Quest headset.Moreover,wider implications for VR medical training were outlined,and potential next steps towards a standardized design identified.

Digital ID framework for human-centric monitoring and control of smart buildings

Smart offices can help employers attract and retain talented people and can positively impact well-being and productivity.Thanks to emerging technologies and increased computational power,smart buildings with a specific focus on personal experience are gaining attraction.Real-time monitoring and estimation of the human states are key to achieving individual satisfaction.Although some studies have incorporated real-time data into the buildings to predict occupants’indoor experience(e.g.,thermal comfort and work engagement),a detailed framework to integrate personal prediction models with building systems has not been well studied.Therefore,this paper proposes a framework to predict and track the real-time states of each individual and assist with decision-making(e.g.,room assignment and indoor environment control).The core idea of the framework is to distinguish individuals by a new concept of Digital ID(DID),which is then integrated with recognition,prediction,recommendation,visualization,and feedback systems.The establishment of the DID database is discussed and a systematic prediction methodology to determine occupants’indoor comfort is developed.Based on the prediction results,the Comfort Score Index(CSI)is proposed to give recommendations regarding the best-fit rooms for each individual.In addition,a visualization platform is developed for real-time monitoring of the indoor environment.To demonstrate the framework,a case study is presented.The thermal sensation is considered the reference for the room allocation,and two groups of people are used to demonstrate the framework in different scenarios.For one group of people,it is assumed that they are existing occupants with personal DID databases.People in another group are considered the new occupants without any personal database,and the public database is used to give initial guesses about their thermal sensations.The results show that the recommended rooms can provide better thermal environments for the occupants compared to the randomly assigned rooms.Furthermore,the recommendations regarding the indoor setpoints(temperature and lighting level)are illustrated using a work engagement prediction model.However,although specific indoor metrics are used in the case study to demonstrate the framework,it is scalable and can be integrated with any other algorithms and techniques,which can serve as a fundamental framework for future smart buildings.

A semi-transparent selective undo algorithm for multi-user collaborative editors

Multi-user collaborative editors are useful computer-aided tools to support human-to-human collaboration.For multi-user collaborative editors,selective undo is an essential utility enabling users to undo any editing operations at any time.Collaborative editors usually adopt operational transformation(OT)to address concurrency and consistency issues.However,it is still a great challenge to design an efficient and correct OT algorithm capable of handling both normal do operations and user-initiated undo operations because these two kinds of operations can interfere with each other in various forms.In this paper,we propose a semi-transparent selective undo algorithm that handles both do and undo in a unified framework,which separates the processing part of do operations from the processing part of undo operations.Formal proofs are provided to prove the proposed algorithm under the well-established criteria.Theoretical analysis and experimental evaluation are conducted to show that the proposed algorithm outperforms the prior OT-based selective undo algorithms.

Integrated Framework for Vehicle Interior Design Using Digital Human Model

Evaluating the human friendliness of vehicles is essential for designing new vehicles since large numbers of human-machine interactions occur frequently inside vehicles. In this research, we develop an integrated framework for vehicle interior design using a digital human model （DHM）. In this framework, the knowledge-based parametric modelling function of vehicles is implemented using a commercial computer-aided design （CAD） system. The combination of the DHM and the CAD system enables designers into carry out ergonomic evaluations of various human-vehicle interactions and understand the effects of modifications of vehicle design parameters on occupants during designing. Further, the information on human-vehicle interaction obtained using this system can be transmitted to dedicated biomechanical analysis software. By analysing human motions inside vehicles using such software, we can obtain optimized interior design parameters.