Intelligent Content Research Articles

Development of a method for determining the indicators of manipulation based on morphological synthesis

Research on the development of methods for identifying signs of hidden manipulation (destructive information and psychological impact) in text messages that are published on Internet sites and distributed among users of social networks is relevant. One of the main problems in the development of these methods is the difficulty of formalizing the process of identifying signs of manipulation in text messages of social network agents. To do this, based on morphological synthesis, it is necessary to determine relevant indicators for analyzing text messages and criteria for making a decision about the presence of signs of manipulation in text messages. Based on morphological synthesis, a method for determining manipulation indicators in text messages was developed, taking into account the achievements of modern technologies of intelligent content analysis of text messages, machine learning methods, fuzzy logic and computational linguistics, which made it possible to reasonably determine a group of indicators for evaluating text messages for signs of manipulation. The stages of the method include evaluating the text message at the level of perception by the indicator of text readability, at the phonetic level by the indicator of emotional impact on the subconscious, at the graphic level by the indicator of text marking intensity, and calculating the integral indicator for making a decision about the presence of manipulation in the text message. Based on the proposed method, specialized software was developed that provided 13 % greater accuracy in evaluating messages for manipulative impact compared to the known method of expert evaluations, which reduced the influence of the subjective factor on the evaluation result

Intelligent Computing Methods for Contextual Driving in Smart Engineering Model Systems

An engineering model system (EMS) is a complex purposeful structure of representation and description type content that is developed during development and application; in other words, it represents the lifecycle of an industrial product. The essential improvements for systems operated that are autonomous, situation controlled, and include cyber physical features of products dramatically changed requirements relative to EMS. Engineering modeling platforms (EMPs) experienced major improvements to cope with new requirements by smart products. These improvements were related to system level representation, autonomous active model features, situation level reaction to changed contexts, system and physical level virtual execution, and the support of situation-level human intervention. The lifecycle support of smart product engineering requires EMS, which behaves as a digital twin of a physically existing products and has an active connection with its contextual world, particularly with cyber units of the cyber physical system that it represents. The work in this paper aimed to develop new methods that can be implemented in EMS under developments in EMP. This is a new area of research that is motivated and at the same time enforced by moving engineering activities into highly integrated and automated complex model spaces. This paper focuses on context driving and the autonomous features of EMS as well as utilization-organized intellectual-property-powered intelligent computing relative to the definition and application of contextually connected object parameters in EMS. It introduces several latest results by the authors as a contribution for EMS to cope with the new requirements. These contributions include a new scenario called the lifecycle representation of contexts (LRC) to enhance integration in EMS, handling contexts in contextually connected autonomous unit (CCAU), organizing the intelligent content of EMS in proposed level-sublevel structure of LRC, and examining findings about situation-level cooperation between human and autonomous process.

The Development of Intelligent Educational Content

The Development of Intelligent Educational Content

The development of emotional intelligence as a decisive factor in the social and professional adaptation of TVE students

The article presents the content and results of research on the development of emotional intelligence in college students. The structure of the article includes substantiation of the relevance of the evolving development of emotional intelligence in flexible social and professional adaptation of students; definition of emotional intelligence as a threshold concept; experimental work, accompanied by the results of diagnostics of the studied competence. The research aims at psychological and pedagogical support for the development of emotional intelligence, correlating with the goals and content of TVE and based on the pedagogy of reconciling the cognitive and emotional processes of development of students in classroom and extracurricular work of the college. Scientific novelty is associated with previously unexplored emotional intelligence as a key competence of interaction in a social or professional group. Thus, the content of the experimental work of the study proves the development of thinking in students of a higher level when they analyze their existing practice of working with emotions and are involved in new and specific experiences of emotional interaction, form ideas for the development of experience and use them to solve problems of social and professional adaptation. Another result of the study is the substantiation of the activities of the tutor and students, aimed not at the content of emotional intelligence, but at the process of mastering it. The article presents an experimentally substantiated technology of activity — from posing questions, problems or situations to identifying and investigating them, when, at the same time, the components of emotional intelligence are gradually developing in conditions of mutual learning, feedback and reflection, constantly fixing areas of improvement.

Cooperative cache update using multi-agent recurrent deep reinforcement learning for mobile edge networks

Caching the most likely to be requested content at the base stations in a cooperative manner can facilitate direct content delivery without fetching content from the remote content server and thus alleviate the user-perceived latency, reduce the burden on backhaul and minimize the duplicated content transmissions. Content popularity plays a vital role, and it drives caching on edge. In the literature, earlier works considered the content popularity either known earlier or obtained on prediction. However, the content popularity is time-varying and unknown in reality, so the above assumption makes it less practical. Therefore, this paper considers the cooperative cache replacement problem in a realistic scenario where the edge nodes are unaware of the content popularity in mobile edge networks. To address this problem, the main contribution of this paper is to design an intelligent content update mechanism using multi-agent deep reinforcement learning in dynamic environments. With the goal of maximizing the saved delay with deadline and capacity constraints, we formulate the cache replacement problem as Integer linear programming problem. Considering the dynamic nature of the content popularity, high dimensional parameters, and for an intelligent caching decision, we model the problem as a partially observable Markov decision process and present an efficient deep reinforcement learning algorithm by embedding the long short-term memory network (LSTM) into a multi-agent deep deterministic policy gradient formalism. The LSTM inclusion reduces the instability produced by partial observability of the environment. Extensive simulation results demonstrate that the proposed cooperative caching mechanism significantly improves the performance in terms of reward, acceleration ratio and hit ratio compared with existing mechanisms.

Photo2Video: Semantic-Aware Deep Learning-Based Video Generation from Still Content.

Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.

Automatic and intelligent content visualization system based on deep learning and genetic algorithm

Increasing demand in distance education, e-learning, web-based learning, and other digital sectors (e.g., entertainment) has led to excessive amounts of e-content. Learning objects (LOs) are among the most important components of electronic content (e-content) and are preserved in learning object repositories (LORs). LORs produce different types of electronic content. In producing e-content, several visualization techniques are employed to attract users and ensure a better understanding of the provided information. Many of these visualization systems match images with corresponding text using methods such as semantic web, ontologies, natural language processing, statistical techniques, neural networks, and deep neural networks. Unlike these methods, in this study, an automatic and intelligent content visualization system is developed using deep learning and popular artificial intelligence techniques. The proposed system includes subsystems that segment images to panoptic image instances and use these image instances to generate new images using a genetic algorithm, an evolution-based technique that is one of the best-known artificial intelligence methods. This large-scale proposed system was used to test different amounts of LOs for various science fields. The results show that the developed system can be efficiently used to create visually enhanced content for digital use.

On the question of the secret actions of the United States to support the anti-Soviet campaign in the 1970s and 1980s.

Based on the revealed declassified documents, the paper examines previously unknown sources about the attempts of the American government to support various programs of dissent, freedom of speech and mass media in the USSR and countries where the pro-Soviet regime was supported. The subject of the study is the content of foreign intelligence published and declassified documents describing specific US measures to implement covert actions in Afghanistan in order to counteract the influence and strengthen the position of the USSR, as well as support centrifugal trends in the Soviet Union through the organization and financing of programs to support dissent, the development of radio broadcasting, increasing radio coverage areas, assistance in the publication of Samizdat literature, etc. The object of the work is the documentary correspondence of the US President and key figures of the American establishment on the use of resources for the implementation and promotion of anti-Soviet activities. The article reveals previously unknown details of the preparation of options and the implementation of approved programs of covert actions to discredit the Soviet regime as part of the military campaign in Afghanistan, as well as inside the USSR. The sources allow us to highlight the activities of the US Special Coordinating Committee for the preparation of this strategy. The authors draw attention to the complexity of solving financial issues in the American administration, as well as the size of US financial injections to support destructive forces in the Soviet Union. Such evidence from very authoritative sources significantly expands the source base in the scientific coverage of the facts of the implementation of subversive activities of the United States against the USSR in the 1970s and 1980s.

An Assembly Line Approach to Technical Communication Pedagogy with Intelligent Content Design and Delivery

Topic-based intelligent content design with a better understanding of semantic web schemas could be the new focus for technical communication (TC) pedagogy in academia - a major shift away from designing standalone static documents. A new such TC course focused on the basics of how XML, RDFS, and OWL schema contribute to the semantic web for CCMS and CDP, including the basics of how faceted search works and ontology creation happens for simple e-commerce based, tourism, healthcare, and consumer product information design. The course also attempted to expose students to a basic understanding of iiRDS – the standard that enables dynamic information request and delivery in the era of the Internet of Things and Industry 4.0; and how Microsoft HoloLens - a mixed reality platform caters to intelligent information processing in Toyota and smart city contexts, as case studies. This was an introductory course with conceptual development being the focus, and not programming skills. With this course, we wanted to introduce students to the new industry documentation standards and initiate a process in pedagogy that will ultimately lead to bridging the extreme shortage of qualified job applicants in the industry coming from content strategy as a new field of practice. This paper focused on a pilot study initiating an exercise to explore the extent to which such a course helps teach information management in an English as Foreign Language (EFL) context for computer science majors, within the scope of content language integrated learning (CLIL) and project-based language learning (PBLL).

Optimization of Ice and Snow Sports Industry Chain Structure Based on Sensor Network Communication and Artificial Intelligence

As a new tourism industry model, the ice and snow sports tourism industry has played an increasingly important role in the development of China’s entire tourism industry. This paper analyzes the structural optimization of the ice and snow sports industry chain based on sensor network communication and artificial intelligence and aims to fully apply sensor network communication and artificial intelligence technology to the development of the ice and snow sports industry. Let us look for an optimized method to promote the development of the ice and snow sports industry chain structure. This paper conducts a research on the optimization of the ice and snow sports industry chain structure based on sensor network communication and artificial intelligence. In the method part, this article explains the architecture of sensor nodes and networks, uses the long-term monitoring of wireless sensor networks and the flexibility of the data acquisition structure, combined with the superiority of artificial intelligence in information processing and analysis, to obtain and analyze the experimental data of this research, and introduces the specific content of artificial intelligence and the ice and snow sports industry. In the algorithm, the grey relational analysis method is introduced. In the part of the experimental results, experiments were carried out on the ice and snow city resources, competitive advantages, the quarterly average value of the ice and snow sports enterprise prosperity index, ice and snow sports venues, GDP, and the speed comparison and structure of the development of the sports industry, and simulation experiments were carried out from the perspective of sensors. On the whole, the development curve of China’s ice and snow sports industry basically conforms to the economic development curve. With the slowdown of GDP growth rate, the growth rate of the sports industry slowed down simultaneously. However, despite the continued decline in economic growth, the sports industry has ushered in a peak of development, with a growth rate of 89.45%, a year-on-year increase of 69.3 percentage points. This phenomenon shows that the sports industry has sufficient endogenous development momentum and huge development potential. It is an important driving force and emerging growth point of the economic society and modern services. It has a comprehensive driving role in stimulating urban consumption, promoting economic transformation and upgrading, and driving the development of the service industry.

English Listening Teaching Model in Flipped Classroom Based on Artificial Intelligence Fusion Control Algorithm

In our contemporary life, various information technologies are changing our lives, and the development of information technology has also provided a certain degree of convenience for the development of education. Now, every country attaches great importance to the development of the informatization of education. This article aims to study how to apply artificial intelligence content and control algorithms to the English listening mode of a flipped classroom. The main purpose of the article is how to make the flipped classroom more intelligent, enrich the content of the classroom, and devote itself to improving children's learning efficiency. This article proposes the application of the PSO algorithm in the flipped classroom to help students learn more intelligently and conveniently in this mode of teaching. They can help children improve their academic performance faster and learn more efficiently. The experimental results of this article show that nearly 65.63% of students are familiar with the teaching method of using animation video to train English listening, and nearly 70% of students are very willing to use this teaching method to learn; using more artificial intelligence fusion control algorithms for us, this teaching mode is used for some simplification and assistance. The average test score of the experimental class (82.95 points) is higher than the posttest score of the control class (79.9 points), the excellence rate of the experimental class is 38%, the pass rate is 100%, and the control class has an excellent rate of 32% in the posttest score. The passing rate was 86.7%. Further analysis shows that, in the posttest scores of the experimental class, the excellent rate and the passing rate have increased, respectively. After the test, the scores of the students in the experimental class have improved greatly compared with the control class. The overall performance of the students has improved significantly.

Neural networks based on vectorized neurons

As the main research content of artificial intelligence, the artificial neural network has been widely concerned because of its excellent performance in the fields such as computer vision and natural language processing since it was proposed in the 1940s. The neuron model of the traditional neural network was proposed by McCulloch and Pitts in 1943 (MP neurons), But MP neurons is too simple to representing biological neurons. Based on this, this paper studies the attention mechanism and proposes vectorized neuron and its activation function. Firstly, we propose vectorized neurons, then use the attention mechanism to dynamically generate connection weights between vectorized neurons. Nextly, we construct a new type of neural network with vectorized neurons, which we called neural functional group (NFG). Finally, we tested the proposed neural functional group model on two tasks: image classifcation and few-shot learning. The vectorized neuron can be conditionally activated through its activation function. Besides, the vectorized neuron has the potential of representing complex biological neurons, which is difficult for MP neuron. The experimental results show that it can achieve higher accuracy with fewer parameters than convolutional neural networks (CNN) and capsule networks in image classication task; it also competitive to CNN based feature extractor in few-shot learning task.

Analysis on the Application of Artificial Intelligence Technology in Electrical Automation Control

AI technology organically integrates the info and intelligent content of electronics, telecommunications, computer and other fields and disciplines, can achieve accurate control operation, reduce the dependence on human intervention, and greatly improve the intelligent level of these fields, so it has important research value. Based on this, this paper first analyses the AI automation control theory, then studies the electrical automation control based on AI technology, and finally gives the specific application strategy of AI in electrical automation control.

Signal envelope and speech intelligibility differentially impact auditory motion perception

Our acoustic environment contains a plethora of complex sounds that are often in motion. To gauge approaching danger and communicate effectively, listeners need to localize and identify sounds, which includes determining sound motion. This study addresses which acoustic cues impact listeners’ ability to determine sound motion. Signal envelope (ENV) cues are implicated in both sound motion tracking and stimulus intelligibility, suggesting that these processes could be competing for sound processing resources. We created auditory chimaera from speech and noise stimuli and varied the number of frequency bands, effectively manipulating speech intelligibility. Normal-hearing adults were presented with stationary or moving chimaeras and reported perceived sound motion and content. Results show that sensitivity to sound motion is not affected by speech intelligibility, but shows a clear difference for original noise and speech stimuli. Further, acoustic chimaera with speech-like ENVs which had intelligible content induced a strong bias in listeners to report sounds as stationary. Increasing stimulus intelligibility systematically increased that bias and removing intelligible content reduced it, suggesting that sound content may be prioritized over sound motion. These findings suggest that sound motion processing in the auditory system can be biased by acoustic parameters related to speech intelligibility.

Artificial Intelligence Marketing: A Research Review and Prospects

The development of artificial intelligence provides new opportunities and solutions for marketing and promotes the transformation of marketing to intelligence. Many companies have begun to deploy artificial intelligence marketing. As an emerging research topic, artificial intelligence marketing has also attracted more and more researchers’ attention. However, the current research on artificial intelligence marketing is mostly based on a specific marketing scenario, and the lack of a systematic review of artificial intelligence marketing is not conducive to researchers and practitioners to systematically understand artificial intelligence marketing. In view of this, this paper combs the current research results of artificial intelligence marketing from three aspects: connotation, corporate practice, and user response.The study identifies 72 relevant literatures through the steps of identifying keywords, searching literatures, preliminarily screening, supplementing important literatures, and intensively reading literatures. Through combing the literature, this paper proposes the definition of artificial intelligence marketing, discusses its foundation, characteristics and purpose; then, it summarizes the practical application of artificial intelligence in user insight, content management, interactive delivery, and monitoring and evaluation; finally, the psychological and behavioral responses of users to data collection, delivery and recommendation, and human-computer interaction in the artificial intelligence marketing process are further sorted out. This paper summarizes the research framework of artificial intelligence marketing and discusses future research directions.The study finds that the current definition of artificial intelligence marketing has not yet formed a unified definition. Big data and artificial intelligence are its technical foundation, and intelligence is its important feature, which is mainly manifested in intelligent processing, intelligent decision-making and intelligent execution. The purpose is to realize the value co-creation of enterprises and users. In the practical application of enterprises, artificial intelligence can help enterprises insight and predict users, complete automated and intelligent content management, realize real-time interaction with users and intelligent content delivery and recommendation, and perform real-time and abnormal monitoring to help enterprises accurately grasp the marketing effect. For data collection, delivery and recommendation, and human-computer interaction in the artificial intelligence marketing process, users will have different feelings and reactions such as being served or used, understood or manipulated, and their acceptance of artificial intelligence customer service is low.The main contributions of this paper are that: First, it systematically sorts out the context of artificial intelligence marketing for the first time, which helps researchers and practitioners to strengthen their deep understanding of artificial intelligence marketing. Second, it summarizes the research framework and future research directions of artificial intelligence marketing, which can provide a certain reference for future research. Third, by exploring the practical application of artificial intelligence marketing in enterprises and the psychological and behavioral responses of users, it helps practitioners to better carry out artificial intelligence marketing.

The assessment of emotional intelligence in social care and healthcare student selection: a qualitative descriptive study

ABSTRACT Background Effective student selection methods are needed to identify applicants who are expected to complete their studies and succeed professionally. The assessment of emotional intelligence has recently been identified as an important element of student selection for nursing studies. Purpose This small-scale study, conducted in Finland, sought to capture the content of emotional intelligence that is considered relevant to social care and healthcare student selection from the perspectives of social care and healthcare educators and professionals. Methods Five semi-structured focus group interviews (n = 30) were conducted with the educators and professionals. The data were analysed qualitatively using both deductive and inductive content analyses. Findings The analysis of the data identified participants’ perspectives on: perception of emotions, understanding emotions, accepting emotions, emotional management, emotional expression, utilising emotions and emotional awareness in social contexts. The participating educators and professionals indicated that applicants should demonstrate basic abilities across all these aspects of emotional intelligence in order to cope with the demands of social care and healthcare studies. Conclusions Findings support the notion of the comprehensive assessment of emotional intelligence in student selection contexts. By ascertaining whether students have adequate basic emotional intelligence abilities, the risk of emotional exhaustion during clinical practice could be reduced; higher education institutions may better be able to select applicants who are likely to complete their studies and who are willing and able to work as social care and healthcare professionals.

A web browsing interface for people with severe speech and motor impairment

Purpose The Web has become an indispensable medium used by people across the world for education, information, entertainment, social interaction as well as for various daily activities involving shopping and employment-related tasks. It is therefore becoming increasingly essential that the Web must be accessible to all people to provide equal access and equal opportunity. This is specifically more important for people with various kind of disabilities. Several initiatives such as development of Web accessibility guidelines, tools and technologies have been undertaken to make the Web usable for people with different disabilities. However, only a handful of them are aimed at people with Severe Speech and Motor Impairment (SSMI). This paper aims to present a Web browsing interface for people with severe speech and motor impairment. Design/methodology/approach The browser allows easy dissemination of information through World Wide Web for people with SSMI. The browser is augmented with both automatic as well as manual scanning mechanisms through which a motor disorder person can access the browser graphical user interface (GUI). Further, the browser provides an intelligent content scanning mechanism through which the Web contents can be accessed with less time and cognitive effort. Along with the desktop version, WebSanyog is successfully ported on Android-based tablets to make the system portable. Findings The system has been exhaustively field tested by people with SSMI. The browser has been deployed at the Indian Institute of Cerebral Palsy (IICP), Kolkata. The performance of the browser has been measured in terms of three parameters: The Task execution time (TET); Error rates analysis (ER); and Overall usability score by the subject. The evaluation results suggests that the proposed Web browsing interface is effective in terms of task execution time, cognitive effort and overall user satisfaction. Originality/value The browser GUI is integrated with an automatic scanning mechanism as an alternate way to access and navigate through Web pages, instead of using keyboard and mouse. The browser provides novel content access mechanisms that makes navigating through Web page contents like links, images and embedded videos easier and faster. To facilitate text entry, the browser provides two different options, namely, the predictive virtual scanning keyboard and a novel icon-based query entry scheme that allows generating search queries through the selection of multiple icons.

Attentional Markov Model for Human Mobility Prediction

Accurate human mobility prediction is important for many applications in wireless networks, including intelligent content caching and prefetching, network optimization, etc. However, modeling mobility patterns has been a challenging problem due to the complicated human mobility patterns influenced by the long-term correlation with historical trajectories and context information, and the long time interval between consecutive mobility records. In this paper, we integrate the novel attention technique into the Markov model to predict future locations. This model allows us to consider the time context of users' mobility behavior, which overcomes the disadvantage of the traditional Markov model in modeling the periodicity of trajectories. We conducted extensive evaluations using two different mobility datasets, which involve over 20,000 users. Our evaluations show that our proposed solution outperforms the state-of-the-art algorithms by over 6.6% on average. In addition, compared with the method based on deep recurrent neural networks achieving the same performance, our proposed model runs significantly faster, i.e., reducing the computation time by over 170 times, demonstrating the effeteness of our proposed model.

An intelligent content provider based on students learning style to increase their engagement level and performance

ABSTRACT Learning trend has been shifted from a conventional way to a digital way in the form of E-learning, but it faces a high dropout ratio. Lack of engagement is one of the primary factors reported for this issue as the same type of course content is presented to learners despite their different background, knowledge and learning styles. Different researchers used adaptive learning techniques to increase students’ performance and engagement, but it has not affected too much for reducing the dropout ratio. One of the major problems with these adaptive techniques is to focus on one thing. To target this issue we have proposed the framework that provides adaptive content to each student based on his learning dimensions and knowledge background as per Bloom’s Taxonomy. The results of the experiment show a significant increase in students’ engagement and performance.

Speech stream segregation to control an ERP-based auditory BCI

Objective. The use of natural sounds in auditory brain–computer interfaces (BCI) has been shown to improve classification results and usability. Some auditory BCIs are based on stream segregation, in which the subjects must attend one audio stream and ignore the other(s); these streams include some kind of stimuli to be detected. In this work we focus on event-related potentials (ERP) and study whether providing intelligible content to each audio stream could help the users to better concentrate on the desired stream and so to better attend the target stimuli and to ignore the non-target ones. Approach. In addition to a control condition, two experimental conditions, based on the selective attention and the cocktail party effect, were tested using two simultaneous and spatialized audio streams: (a) the condition A2 consisted of an overlap of auditory stimuli (single syllables) on a background consisting of natural speech for each stream, (b) in condition A3, brief alterations of the natural flow of each speech were used as stimuli. Main results. The two experimental proposals improved the results of the control condition (single words as stimuli without a speech background) both in a cross validation analysis of the calibration part and in the online test. The analysis of the ERP responses also presented better discriminability for the two proposals in comparison to the control condition. The results of subjective questionnaires support the better usability of the first experimental condition. Significance. The use of natural speech as background improves the stream segregation in an ERP-based auditory BCI (with significant results in the performance metrics, the ERP waveforms, and in the preference parameter in subjective questionnaires). Future work in the field of ERP-based stream segregation should study the use of natural speech in combination with easily perceived but not distracting stimuli.