Results Of Artificial Intelligence Research Articles

FUTURE OF ANIMATION WITH ARTIFICIAL INTELLIGENCE

Current world is dealing with Artificial Intelligence. Artificial intelligence is as new as Media itself riding on the wave of Artificial Intelligence, the results of Artificial Intelligence is to show the new scope in Indian Media. The term Artificial Intelligence was coined in 1956, but in present time it becomes more popular and it is all because of the technology. A question pops up will Artificial intelligence (AI) impacts the animation industry? Technology is increasing day by day and replaced many jobs that ran to competition within the market, because of Artificial intelligence (AI), will the role of animator remain the same?Animation used to be a very difficult and time-consuming process. Cartoonists and animators had to draw each frame of a movie one by one. Is there a need to adopt new techniques to master the Media Production work with an Artificial Intelligence? Artificial intelligence (AI) is used in almost every field like the media industry, Cinema, cartoons, and animation, and many more. A question arises about how Artificial intelligence (AI) will be used in animation in the future. Whereas Disney is known for using Artificial intelligence (AI) to make storyboard animations just from text. Advancement in Technologies within the animation industry is a continuous process. Therefore, in this research paper researcher has discussed about the future of animation with Artificial Intelligence. Different methods of animations have been covered in this paper. It has been discussed how Artificial intelligence (AI) has affected animation and how modern animation differs from traditional animation. This paper has covered the main function of Artificial intelligence (AI) in animation.

How should the results of artificial intelligence be explained to users? - Research on consumer preferences in user-centered explainable artificial intelligence

Artificial intelligence (AI) has become part of our everyday lives, and its presence and influence are expected to grow exponentially. Regardless of its expanding impact, the perplexing algorithms and processes that drive AI's decision and output can lead to decreased trust, and thus impede the adoption of future AI services. Explainable AI (XAI) in recommender systems has surfaced as a solution that can help users understand how and why an AI recommended a specific product or service. However, there is no standardized explanation method that satisfies users' preferences and needs. Therefore, the main objective of this study is to explore a unified explanation method that centers around human perspective. This study examines the preference for AI interfaces by investigating the components of user-centered explainability, including scope (global and local) and format (text and visualization). A mixed logit model is used to analyze data collected by a conjoint survey. Results show that local explanation and visualization are preferred, and users dislike lengthy textual interfaces. Our findings incorporate the extraction of monetary value from each attribute.

Artificial intelligence in the public sector

The article critically examines the possibilities of using steadily developing artificial intelligence systems in the public sector of foreign countries and Russia. It is noted that despite the prospects of obtaining significant gains, there are a number of technical, economic and socio-ethical limitations associated with the introduction of artificial intelligence, taking into account its features as a general purpose technology. The increasing value of professional judgment, which allows using the results of artificial intelligence, is emphasized. Based on the principles of working with artificial intelligence developed in world practice, as well as the peculiarities of the domestic institutional structure and trust in it by the citizens, a conclusion is made about the need for a cautious approach to the use of artificial intelligence technologies in applications of the Russian public sector. Such practices can not only cause considerable harm to specific individuals in the process of current functioning of domestic institutions, but also hinder their transformation.

Правовое регулирование развития цифровой образовательной среды и коммерциализация результатов научных разработок в образовании

Introduction: one of the national development goals of the Russian Federation until 2030 is digital transformation, which implies the achievement of “digital maturity” of key sectors of the Russian economy and social sphere, including healthcare and education, as well as public administration. The goals, objectives, and measures for the implementation of state policy designed to promote the formation of the digital economy in Russia, the use of science and technology as a key factor in the development of the country, to ensure national interests and the implementation of strategic national priorities are defined by the Strategy for the Development of the Information Society in the Russian Federation for 2017–2030. Purpose: to analyze the main directions of digital transformation of the science and education sector in connection with the intensification of the process of digitalization of all spheres of society, fixed as one of the priorities of Russia’s development at the state level; to study the conditions for effective interaction of universities with industry and business. Methods: the methodological framework for the study is the methods of scientific cognition, among which the main ones are the methods of systematicity, analysis, and comparative law. Results: the necessity of defining a new role of education for the sustainable development of Russia and ensuring structural changes in the country’s economy is revealed; new trends related to the transfer of knowledge and technology from universities to the industry are described, legislative initiatives and other instruments of its stimulation are considered; some problems of the development of science, education, and entrepreneurship in the light of the implementation of the national project “Digital Economy” are identified. Conclusions: a system of educational quality criteria for creating a digital educational environment is defined; it is established that the mechanism of commercialization of the results of scientific applied research and development in the Russian Federation does not function effectively enough; the problems and directions of improving the legal regulation of relations in the field of public-private partnership, the results of artificial intelligence, the development of the digital educational environment are shown.

Time Series Data Cleaning under Multi-Speed Constraints

As the basis of data management and analysis, data quality issues have increasingly become a research hotspot in related fields, which contributes to optimization of big data and artificial intelligence technology. Generally, physical failures or technical defects in data collectors and recorders cause anomalies in collected data. These anomalies will strongly impact on subsequent data analysis and artificial intelligence processes; thus, data should be processed and cleaned accordingly before application. Existing repairing methods based on smoothing will cause a large number of originally correct data points being over-repaired into wrong values. The constraint-based methods such as sequential dependency and SCREEN cannot accurately repair data under complex conditions since the constraints are relatively simple. A time series data repairing method under multi-speed constraints is further proposed based on the principle of minimum repairing. Then, dynamic programming is used to solve the problem of data anomalies with optimal repairing. Specifically, multiple speed intervals are set to constrain time series data, and a series of candidate repairing points are formed for each data point according to the speed constraints. Next, the optimal repair solution is selected from these candidates based on the dynamic programming method. With regard to the feasibility study of this method, an artificial dataset, two real datasets, and another real dataset with real anomalies are employed for experiments in case of different rates of anomalies and data sizes. Experimental results demonstrate that, compared with the existing methods based on smoothing or constraints, the proposed method has better performance in terms of RMS errors and time cost. In addition, the investigation of clustering and classification accuracy with several datasets reveals the impact of data quality on subsequent data analysis and artificial intelligence. The proposed method can improve the quality of data analysis and artificial intelligence results.

Personalized Reform of Computer Education Based on Network Teaching Mode

The current education and teaching reforms are constantly creating a “student-centered, teacherled” teaching model, and promote the development of education and teaching in a personalized direction. Therefore, cultivating and improving the learning ability of college students through personalized network teaching mode has become one of the hot issues in the current education and teaching reform and development. The purpose of this paper is to study the personalized reform of computer education based on the network teaching model, based on the constructivist learning theory, combined with the research results of artificial intelligence, to the knowledge representation, student personality evaluation and teaching strategy decisionmaking in the personalized computer-assisted teaching system research and exploration have been carried out on other issues, forming a network teaching system with certain intelligence. In computer teaching, “Configuring Computer” is used as the learning theme to compare the advantages and disadvantages of personalized network teaching and traditional network teaching. A detailed analysis of the two inquiry learning and a more in-depth reflection were carried out. The experimental results show that the students’ inquiry cooperation ability and the quality of works in personalized online teaching have been improved to varying degrees. On the whole, the ratio of excellent to good in learning evaluation increased, and the ratio of medium to poor decreased, and the quality of multimedia works of more than 13% of students improved to excellent. The two teachings were carried out smoothly and basically achieved the expected goals proposed in this article.

Predicción de la Resistencia de las Conexiones Tipo Canal en Estructuras Compuestas de Acero y Hormigón

This article deals with channel connections in steel and concrete composite structures, with the main objective of obtaining a new formulation to determine the resistance of this widely used type of connection in the American region. Data from channel connections tests are collected from reliable experimental and numerical sources, and a valid database is created to make predictions by means of different combinations of artificial intelligence techniques, to obtain the most effective cases. Finally, a new formulation is proposed to calculate the resistance of channel connections in solid slab, applying statistical techniques to a database that incorporates experimental, numerical and artificial intelligence results, demonstrating its superiority with respect to the existing formulations in the international literature, by means of statistical measures appropriately applied to the studied case. The proposed methodology is novel and effective, and it can be expanded to other types of structural elements. Keywords: composite structures, connections, resistance, prediction.

Learning in Artificial Intelligence: Does Bloom's Taxonomy Apply?

From the early days of science and philosophy, humans have wondered exactly what it is that makes us intelligent beings. Plato felt that a good education should include instruction in music, gymnastics, and dialectic, and pondered and how we learned the things we did. Humans also been interested in building machines that mimic their abilities. The original motivation of building mechanical machines was to free us from mundane tasks and to allow fewer people to do particular difficult jobs. Today we build electronic machines that not only do our chores and to assist us physically, but also to help us intellectually.A side affect of building intelligent, electronic machines is that we come to know how humans work a little bit better, and we also learn to better appreciate our amazing ability to learn. There is a danger that we may take the results of artificial intelligence too seriously, and to extend them in ways that are not appropriate. The issues surrounding the mechanisms of artificial intelligence and machine learning may or may not apply to the way that people learn. They are simply the way that we imagine we may learn, a model that needs to be tested against reality. On the other hand, if it makes a computer appear intelligent then it is sufficient, then it is sufficient for our purposes whether it is the way people do it or not. The difference is in intent: are we attempting to automate some difficult process, or are we trying to determine how the human brain acquires new information?

Modelling and combining emotions, visual speech and gestures in virtual head models

This paper describes the generation and realistic blending of emotional facial expressions, visual speech, facial poses and other non-emotional secondary facial expressions on 3D computer graphical head models. The generation and blending of these expressions is done by means of the mathematical formulation of a psychological theory of facial expression generation. In total, 23 emotional expressions, 21 emotion blends, 19 visemes, 342 viseme blends and 37 secondary expressions and postures have been modelled, which can result in an infinite number of realistic facial expressions, due to the blending of these entities at different intensities that may vary continuously with time. The blending algorithm enables animators to script their animations at higher, more user-friendly levels or to use the results of artificial intelligence and computational psychological methods to generate and manage expressive, autonomous or near-autonomous virtual characters, without having to rely on performance-based methods.