Natural User Research Articles

INTERACTIVE ARTIFICIAL INTELLIGENCE

Interactive AI is a field of artificial intelligence that allows computers to interact with humans in a natural and intuitive way. This technology uses machine learning algorithms to interpret and respond to voice, facial expressions, and other forms of input. Interactive AI has numerous applications in various fields, including natural language processing, computer vision, robotics, and virtual reality. It can be used to create more natural and intuitive user interfaces and to automate tedious tasks.However, the implementation of interactive AI also comes with challenges and opportunities. One of the primary challenges is the need for significant resources and expertise to develop and deploy this technology. In addition, there are concerns about privacy and security, as well as the potential for bias and discrimination in the algorithms. Despite these challenges, there are also significant opportunities associated with interactive AI. This technology has the potential to revolutionize the way we interact with computers, making them more user- friendly and efficient. It can also lead to the development of new products and services, and improve the overall user experience.To fully unlock the potential of interactive AI, further research is needed to address these challenges and ensure that the technology is developed in an ethical and responsible manner. This includes developing algorithms that are transparent and unbiased, ensuring that user data is protected and secure, and promoting diversity and inclusivity in the development process. Overall, interactive AI has the potential to transform numerous industries and enhance human-computer interactions, making it a field with great promise for the future International Scientific Journal of Engineering and Management ISSN: 2583-6129 Volume: 02 Issue: 04 | April – 2023 DOI: 10.55041/ISJEM00281 www.isjem.com An International Scholarly || Multidisciplinary || Open Access || Indexing in all major Database & Metadata © 2023, ISJEM (All Rights Reserved) | www.isjem.com | Page 2 KEYWORDS: Interactive AI, Artificial Intelligence, Machine Learning, Natural Language Processing, Computer Vision

Multiobjective Gas Distribution Optimization of Natural Gas Pipeline Network Considering User Satisfaction under Shortage Conditions

The management of natural gas user daily designation is taken as the background, the characteristics of users are considered, the distribution mode is integrated, and a multiobjective distribution optimization model of natural gas pipeline network under the insufficient condition is constructed. The decision variable is the actual gas distribution volume of users, aiming to minimize the energy consumption of the compressor station and maximize the user satisfaction under multiple physical constraints. Comparing the solution schemes, when the user satisfaction is the same, scheme 4 solved by the optimization method reduces the compressor energy consumption by 8.6% compared with scheme 1 solved by the manual adjustment method. The three optimization solutions are compared and contrasted, the differences in the operation of the pipeline network are analyzed in different optimization solutions, and the changes of the compressor operating parameters with the increase of user satisfaction are expounded. By analyzing the influence of flow distribution among different types of natural gas users on user satisfaction, it is clarified that the two goals of natural gas user satisfaction and pipeline network operation energy consumption are mutually restrictive.

Application of ecological niche concept in the analysis of nature management conflicts in river basins (on the example of river basins of the North-Western slope of the Crimean Mountains)

The territory of the Crimean Peninsula has a long history of economic development and as a result, in some, especially old-developed, regions of the Crimean Peninsula, there is a problem of the use of the territory by various nature users and, consequently, nature management conflicts are developing. In most cases, qualitative assessment is used to describe nature management conflicts. The paper shows the possibility of quantifying these conflicts using the concept of an ecological niche in a multidimensional factor space. The paper, based on the concept of ecological niche, provides a quantitative assessment of nature management conflicts in the river basins of the northwestern slope of the Crimean Mountains (the basins of the Zapadnii Bulganak, Alma, Kacha, Belbek, and Chernaya rivers). By nature management conflict we mean a situation when, in a multifactorial space, the ecological niches of the main types of nature use intersect and the quantitative measure of this intersection is the measure of the nature management conflict. For the main types of nature use within the river basins of the northwestern slope of the Crimean Mountains (the basins of the Zapadnii Bulganak, Alma, Kacha, Belbek, and Chernaya rivers), their ecological niches are constructed in the space of the factors “Altitude, m” and “Slope, degrees”, as well as “Annual air temperature, °C” and “Annual precipitation, mm”. A quantitative assessment of the manifestation of nature management conflicts is given as a measure of the intersection of their ecological niches.

AN EVALUATION OF AUGMENTED REALITY-BASED USER INTERFACES IN THE DESIGN PROCESS

The aim of this study is to evaluate the user interfaces that reflect different interaction layers in the context of Augmented Reality technology. Depending on the physical characteristics of human interaction with the computer, these layers were examined under four sections: Graphical User Interface (GUI), Tangible User Interface (TUI), Natural User Interface (NUI) and Spatial User Interface (SUI). In this context, a proposed Augmented Reality application interface has been developed to bridge the physical and digital environment. The use of AR-based applications in the design process provided a basis for evaluating the user interface in these interaction layers. In future studies, the interface and experience offered by this application have the potential to be supported by more comprehensive functions and a collaborative working environment.

The network analysis of organizations in watershed management toward sustainability in Northern Iran

Introduction: The integrated water resource management is considered for development planning and water and soil resources management with an emphasis on the socio-economic features of the region for sustainability. The main purpose of study was to identify the organizations involved in the watershed management of Chehelchay of Golestan province in Iran, as well as analyzing the types of communications and interactions between organizations in terms of intensity and type.Methods: Three networks of information exchange, coordination and participation were studied due to network analysis. Thus, the related organizations involved in participatory and comprehensive management of Chehelchay watershed (30 organizations) were introduced during the trips of the research team to the site and using special questionnaire. Then, the other specialized questionnaires were collected from the informed panels of each organization to provide the required data in the network analysis. A total of 84 specialists were interviewed. About half of the respondents (42 individuals) had graduate education and their average work experience was about 15 years. One third of the respondents (28 individuals) were women. The data analysis process was performed with UCINET6.631 and NetDraw.Results and Discussion: The results indicated that the density index in information exchange (31.5%), coordination (27.5%) and participation (10%) networks were less than average. In the participation network, the values of the density index are very low and coherence is the same among the networks (information exchange, coordination, and participation). The results of reciprocity index among the organizational actors in the networks of information exchange (31.10%), coordination (33.52%) and participation (10.13%) were also less than average. Therefore, identifying the key actors with high social power in the network of local natural resources is a requirement of watershed participatory management plan. The runtime and cost of implementing natural resource projects can be significantly decreased by understanding and deploying these organizations and individuals. Social power and comprehensive participatory management is generally the key tools for building trust between local natural resource users. A systematic shift is also required from governmental management toward governance in order to achieve sustainable natural resource and watershed management.

Fake Accounts Identification in Mobile Communication Networks Based on Machine Learning

Fake accounts on online social networks are increasing today with an increase in the number of active social network users. Social media websites allow users to share thoughts, facts, views and re-sharing these into various networks. Social media platforms provide users with enormous valuable information but with this great amount of information in social media, many issues like fake profile, online hacking have also grown. The fake profiles in online social sites create fake news and share unwanted material which contains spam links that affect natural users. The massive issue in social media communication networks is spam and it is necessary to identify fake profiles to stop spam. In this paper, a supervised machine learning algorithm called support vector machine (SVM) is used to identify fake accounts on social media effectively. In order to automatically identify fake online profiles, Random Forest classifier is used with SVM. With this concept, it can be applied online easily to identify millions of accounts that cannot be examined manually. The result of this model is compared with other identification techniques and the results show that the proposed algorithm performs better with high precision and recall. This method efficiently safeguards social media networks from online threats and attacks.

Sanctioned Quotas Versus Information Provisioning for Community Wildlife Conservation in Zimbabwe: A Framed Field Experiment Approach

We investigate the behavioural responses of natural common-pool resource users to three policy interventions—sanctioned quotas, information provisioning, and a combination of both. We focus on situations in which users find utility in multiple resources (pastures and wild animal stocks) that all stem from the same ecosystem with complex dynamics, and management could trigger a regime shift, drastically altering resource regrowth. We performed a framed field experiment with 384 villagers from communities managing common-pool wildlife in Zimbabwe. We find that user groups are likely to manage these natural resources more efficiently when facing a policy intervention (either a sanctioned quota, receiving information about a drastic drop in the stocks’ regrowth below a threshold, or a combination of both), compared to groups facing no intervention. A sanctioned quota is likely to perform better than providing information about the existence of a threshold. However, having information about the threshold also leads to higher efficiency and fewer depletion cases, compared to a situation without any intervention. The main contribution of this study is to provide insights that can inform policymakers and development practitioners about the performance of concrete and feasible policy interventions for community wildlife conservation in Southern Africa.

Thermodynamics-informed neural networks for physically realistic mixed reality

The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the laws of physics. In this paper we present a method for computing the dynamic response of (possibly non-linear and dissipative) deformable objects induced by real-time user interactions in mixed reality using deep learning. The graph-based architecture of the method ensures the thermodynamic consistency of the predictions, whereas the visualization pipeline allows a natural and realistic user experience.Two examples of virtual solids interacting with virtual or physical solids in mixed reality scenarios are provided to prove the performance of the method.

High Speed Emulation in a Vehicle-in-the-Loop Driving Simulator

Rendering accurate multisensory feedback is critical to ensure natural user behavior in driving simulators. In this work, we present a virtual reality (VR)-based Vehicle-in-the-Loop (ViL) simulator that provides visual, vestibular, and haptic feedback to drivers in high speed driving conditions. Designing our simulator around a four-wheel steer-by-wire vehicle enables us to emulate the dynamics of a vehicle traveling significantly faster than the test vehicle and to transmit corresponding haptic steering feedback to the driver. By scaling the speed of the test vehicle through a combination of VR visuals, vehicle dynamics emulation, and steering wheel force feedback, we can safely and immersively run experiments up to highway speeds within a limited driving space. In double lane change and highway weaving experiments, our high speed emulation method tracks yaw motion within human perception limits and provides sensory feedback comparable to the same maneuvers driven manually.

Efficient blockchain addresses classification through cascading ensemble learning approach

Bitcoin is a pseudonymous, decentralised cryptocurrency that has become one of the most widely utilised digital assets to date. Because of its uncontrolled nature and Bitcoin users' inherent anonymity, it has seen a significant surge in its use for illegal operations. This necessitates the use of unique methods for categorising the addresses of Bitcoin users. This research classifies and predicts the portion of users' activities that are lawful and unlawful on the Bitcoin blockchain. The dataset contains almost 27 billion samples that are divided into nine user acts, five of which were unlawful. To predict cross-validation (CV) accuracy, ensemble learning algorithms are trained and tested. With cross-validation accuracy of 68.63% and 49.64%, respectively, gradient boosting emerged as the best ensemble learning algorithm for classification and prediction, while bagging emerged as the worst. To get the best classification and prediction, hyperparameter tuning is used to find the optimal parameters, which helped to enhance the cross-validation accuracy of the bagging algorithm to 67.70%, with moderate improvements in the rest of the learning algorithms.

ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНОМУ ПРОСТОРІ

In today's world, the information and communication space is turning into an extremely dynamic and voluminous area that requires efficient means of processing and analysing information. Traditional methods are no longer always able to meet the challenges of today, so there is a need to introduce artificial intelligence to optimise processes in this area. Information and communication space is a complex concept that covers all aspects related to the dissemination, exchange and perception of information in society. It includes all sources of information, communication technologies, networks, as well as cultural, social, economic and political contexts that influence communication processes. The special importance of the information and communication space lies in the fact that it is a tool for promoting the development of society, democratisation, and the exchange of knowledge and ideas between different groups, countries and peoples. The creation of a unified information and communication space is an important step for Ukraine, which will facilitate its integration into the global information space and ensure active participation in various social processes. The information and communication space is evolving rapidly, and artificial intelligence (AI) is already playing a significant role in its current formation. AI has the potential to revolutionise the way we communicate, access information and make decisions. Artificial intelligence (AI) is a field that develops intelligent machines that can perform tasks that would normally require human intelligence. Artificial intelligence systems are designed to learn from experience, recognise patterns and make decisions based on input data. It is worth noting that algorithms in artificial intelligence systems play a key role in solving complex problems and processing information. In particular, they provide machine learning, where the system analyses huge amounts of data to identify patterns and improves its performance on its own. Deep learning algorithms, such as neural networks, are used to create models that can recognise and solve the semantics of tasks that were previously considered difficult, or even impossible, for computers. Clustering algorithms in the information and communication space help to organise data, making it more understandable and easier to analyse. At the same time, natural language processing algorithms help artificial intelligence systems understand and interact with textual information. This opens up new possibilities for automated text analysis, entity recognition, and natural language user interaction.

Optimal Signal Processing for Steady Control of a Robotic Arm Suppressing Hand Tremors for EOD Applications

Teleoperated robotics in recent years has proven to be valuable support in EOD tasks; a remarkable improvement in the systems that control these robots has been the Natural User Interfaces (NUI); however, the research that implements this type of system does not focus on the stability of the robotic arm movements, necessary for this type of applications due to the danger of working with explosives. In this paper, we propose the implementation of an Optimal Signal Processing for a NUI interface based on the Leap Motion (LM) controller. The main objective of this research is to correctly identify the intentional movements of the operator, achieve high stability of the robotic gripper and suppress the physiological tremors from the hand of the operator, considering not to increase the mental workload and not decrease the usability of the system. The signal processing proposed in this paper is composed of three filtering algorithms: Kalman, FIR, and moving average with a threshold. In addition, the obtained results are compared with the most representative processing of recent research using LM for robotic arm control. To evaluate and validate the proposed signal processing, a target path tracking test, a stability analysis of the robotic gripper, and a performance analysis in the execution of Pick and Place tasks, NASA-TLX and SUS questionnaires are developed. Finally, the proposed Optimal Signal processing is implemented in the DOBOT-MAGICIAN and tested by police officers of the EOD Unit-Arequipa (UDEX-AQP); the results indicate a reduction of the average Vibration of 31.61% and the Target Path Tracking error of 67.57%.

УПРАВЛЕНИЕ ОХРАНОЙ ОКРУЖАЮЩЕЙ СРЕДЫ В РОССИИ: ДИНАМИКА ИНТЕНСИВНОСТИ ЭКОЛОГИЧЕСКОГО РЕГУЛИРОВАНИЯ

Environmental governance is one of the practices of the regional authorities' activities to influence various categories of nature users in order to change their behavior. Various methods are used to quantify the process of environmental governance, among which is the assessment of the intensity of environmental regulation, focused on identifying the degree of activity and effectiveness of measures taken by the state to prevent damage to the environment. In this paper, we will attempt to adapt this indicator to the analysis of the environmental activities of the constituent entities of the Russian Federation. Furthermore, we will try to demonstrate that the change in its value is influenced by the degree of development of regional environmental legislation, the effectiveness of the implementation of regional state programs and environmental cooperation between neighboring regions. It is proposed to analyze the intensity of environmental regulation in the Russian context as an assessment of the effectiveness of direct measures of regional authorities to prevent and eliminate environmental pollution.

Quantitative Upper Limb Assessment With Natural User Interface in Children With Hemiparesis

Quantitative, efficient, and accurate measurement of upper limb motor performance is relevant for monitoring upper limb progress in daily activities of people with cerebral palsy, which is helpful to define an appropriate rehabilitation procedure. Currently, motion capture sensors using a Natural User Interface (NUI) are the most feasible solutions due to the low-cost implementation, portability, and effectiveness for upper limb motion analysis. A quantitatively assessment study of the upper limb motor performance for children with spastic hemiparesis using the NUI Kinect v2 sensor is described in this paper. The study participants were eighteen children with an average age of 8.28 ± 2.32 years having a Manual Ability Classification System level I and II. The assessment was done for each participant before and after the application of a Modified Constrained-Induced Movement Therapy (mCIMT) applied five days a week along twelve weeks. According to obtained results significant differences (p<0.05) in the Fugl-Meyer Assessment (FMA) score for motor performance in abduction movement of upper limb between the first and last sessions were quantitatively detected. The described NUI assessment method helped to quantitatively show that the applied therapy sessions were effective to improve upper extremity motor performance. Additionally, the upper limb movements motorically limited after the rehabilitation therapy were also identified. The main contribution of the described NUI assessment method is its potential use as a quantitative measurement tool, which might be used by specialists to objectively diagnose and consequently define an appropriate rehabilitation therapy for patients with hemiparesis.

Фіскально-регулююча роль рентної плати в Україні

Resource rent taxation is an instrument of state regulation designed to stimulate natural resource users to minimize the loss of natural resources during their extraction and involvement in economic circulation, to ensure their rational use. Together with the performance of the regulatory function, resource rent taxation should be a significant source of income for the state budget. The purpose of the article is to highlight the current problems of resource rent taxation in Ukraine from the standpoint of its fiscal and regulatory role, to formulate proposals of an applied nature regarding the improvement of taxation of natural resources in the conditions of the intensification of European integration and martial law. The following general scientific and special methods were used in the research process: logical analysis, synthesis, expert evaluation, description, comparison, theoretical generalization and abstract-logical. The current state of distribution of resource rent taxation to state and local budgets is highlighted. It is also revealed that the State Budget of Ukraine accumulates the main part of resource rent taxation. The fiscal role of the resource rent taxation in the revenues of the Consolidated Budget of Ukraine in general and in terms of individual payments, in particular, is analyzed. It has been proven that the resource rent taxation remains one of the few mandatory payments that increase revenues and ensure the fulfillment of planned indicators. The main changes in the mechanism of accrual and payment of resource rent taxation in Ukraine are highlighted. It was established that resource rent taxation does not ensure rational use of natural resources. It was concluded that the reform of resource rent taxation should be based on the differentiation of rates depending on the objects of taxation, taking into account objective assessments of the productivity of sources of natural resources. At the same time, it is necessary to focus on ensuring optimal profitability indicators of economic entities that use natural resources, especially in strategically important areas, such as gas or oil production.

Clean heating and air pollution: Evidence from Northern China

In response to the severe air pollution caused by winter heating, the Chinese government has proposed a clean heating policy since 2017. This paper first uses air quality monitoring and household micro-survey data in northern cities to investigate the impact of this policy on air pollution through the DID approach. The empirical results show that the air quality index of the pilot cities has decreased by 12.3 units, which means that the clean heating policy has significantly reduced air pollution. The finding still holds through a series of robustness analyses such as controlling for confounding policies and Goodman-Bacon decomposition. Besides, we explore the mechanism at the urban and household levels. It shows that this policy has significantly increased the consumption of natural gas and the number of natural gas users in urban central heating and also prompted more households to use natural gas and electricity instead of solar energy to replace coal for heating. The heterogeneity analysis shows that this policy has a greater impact on both low-urbanization cities and developed cities. Furthermore, we analyze price sensitivity and the costs and benefits of the policy, and find that the increase in natural gas prices significantly inhibits the policy effect, while the price of coal has no significant effect. In addition, the policy significantly reduces the incidence of lung disease among middle-aged and elderly people and leads to a significant increase in household heating costs in winter, which indicates that the clean heating policy has generated environmental and health benefits despite increasing government and household expenditures.

Revisiting natural user interaction in virtual world.

Natural user interaction in virtual environment is a prominent factor in any mixed reality applications. In this paper, we revisit the assessment of natural user interaction via a case study of a virtual aquarium. Viewers with the wearable headsets are able to interact with virtual objects via head orientation, gaze, gesture, and visual markers. The virtual environment is operated on both Google Cardboard and HoloLens, the two popular wireless head-mounted displays. Evaluation results reveal the preferences of users over different natural user interaction methods.

Deep hybrid models for daily natural gas consumption forecasting and complexity measuring

AbstractAccurately forecasting daily natural gas consumption (NGC) is challenging due to its complex time‐varying high‐dimensional features. Therefore, this paper proposes a novel hybrid model for daily NGC forecasting consisting of two stages. In Phase I, the parallel local outlier factor‐isolation forest‐based data preprocessing algorithm is applied to denoise input data and preserve valuable features. In Phase II, the convolutional neural network (CNN)–stacked long short‐term memory (SLSTM) can extract and model spatio‐temporal features and is a hybrid deep learning forecaster. The influencing mechanism of different factors on the daily NGC prediction accuracy has not been mathematically and experimentally revealed. Thus, a hybrid complexity measure model () is developed by combining the variation coefficient analysis, local fluctuation coefficient analysis, and kurtosis analysis. The historical data sets of three representative cities were collected in the case studies, and six advanced models were selected for comparison. The results reveal that high‐quality input data preprocessed in Phase I enables better prediction performance for all models. The CNN‐SLSTM outperforms other algorithms with an average prediction improvement of about 26%–49%. Prediction models perform better in low NGC periods and cities with predominantly industrial natural gas users due to their lower data complexity.

Current theoretical issues of ecological legal order in the Republic of Kazakhstan

This article discusses current theoretical issues of ecological legal order in the RK. The authors claims that the rule of law is a particularly effective mechanism for increasing the level of environmental awareness of citizens, forcing nature users to comply with the established level of ecological legal order. The scientific novelty of this study is determined by the fact that the paper clarifies and details certain understudied aspects of the concept “ecological legal order”, and also considers ecological legal order as the main ecological and legal institution. The article analyzes that legal order of rational nature management is established under the influence of legislative and other requirements of environmental law. The authors also note that ecological legal order is established on the basis of legal liability. The general trend in developing ecological legal order is to increase social significance of environmental legislation, further modernization of material norms and improving protective forms of their implementation. The purpose of the work is to create a general theoretical and practical concept of ecological legal order in modern Kazakhstan. The authors sets the task to determine the ecological legal order, analyze its place in the field of law, consider the relationship of environmental law and legal responsibility, determine the relationship of environmental legal awareness, education, culture and law and order. The preparation of the work required analysis of prominent research papers, which significantly contributed to development of environmental and legal science. Keywords: ecological legal order, environmental law, legal awareness, norm, environmental legislation, legal relations, law, responsibility

Направления оптимизации налогового и бюджетного законодательства РФ в контексте достижения целей устойчивого развития

The article analyzes the current tax and budget legislation of the Russian Federation regarding taxes and fees paid by economic agents in connection with the natural economic activities, in the context of achievement of sustainable development goals established by the strategic planning documents of the Russian Federation. The shortcomings of the current fiscal mechanism were identified. They are in particular as follows: lack of financial resources at the regional and local levels to the elimination of environmental damage accumulated as a result of environmental activities of economic entities at the micro- and mesoeconomic levels; long term, as well as the complicated character of administrative procedures related to the analysis of applications from local governments and state authorities of the constituent entities of the Russian Federation for the allocation of funds from the federal budget for the implementation of environmental projects and programs. The author’s suggestions for mechanism improvement of the distribution of environmental taxes and fees between the levels of the budgetary system of the Russian Federation, tax legislation improvement of the Russian Federation, taking into account the requirements of sustainable development are substantiated. The forecasted social, environmental and economic effect is as follows. A toolkit is being developed to encourage nature users for the practical implementation of innovative resource-saving technical and technological solutions in environmental management practice that can reduce the anthropogenic impact on ecosystems. Federal entities of the Russian Federation, municipalities receive additional financial resources that allow them to implement environmental projects and programs at their own expense. The managerial burden on the federal administrative apparatus is reduced. The quality of life of citizens is obviously improved. The results of the study can find practical application in government authorities of the Russian Federation, federal entities of the Russian Federation, local governments in the development of the main directions of tax and budget policy, environmental projects and programs.