Reviewerly/exHarmony huggingface dataset:
Convergencia de la trayectoria lingüística en el espacio de estados de un Controlador Difuso aplicado a un Sistema No Lineal

This article presents the design of a fuzzy controller to stability analysis base on the convergence of the linguistic trajectory in the state of space, for an inverted car-pendulum system, the fuzzy controller is of the Mamdani type and, it consists of 25 rules, 3 input variables and each one is composed of five memberships functions. The inverted car-pendulum system is represented by a non-linear model, which is obtained from a linearized equivalent model under the consideration of small oscillations. Results are validated against a PID control base on the trajectory on the phase plane to evaluate the efficiency and effectiveness of the fuzzy controller. The dynamic behavior of the system of both controllers is obtained with a unit impulse input, the simulation of the control system is developed on the MATLAB / SIMULINK software using the FUZZY LOGIC TOOLBOX, which allows to perform test and simulations, and also it shows results of graphic form.
Intelligent Textbooks Based on Image Recognition Technology

In recent years, with the development of science and technology, the education mode has gradually changed from offline teaching to the combination of offline education and online education. Compared with the traditional offline education, the significant advantages of online education are low cost, rich content and flexible use. In this paper, an intelligent textbook based on image recognition technology is designed, which combines online courses with physical textbooks to realize the collaboration of two learning methods. The APP can not only realize the video playback function and other basic functions of the online education platform, but also adopt the image recognition technology, so that users can scan the picture through the APP to watch the explanation video or 3D model projected onto the real picture by AR way. This paper uses SSM framework and OpenCV to build the background, and combined with Objective-C to build an online course APP running on IOS platform. In general, the app not only designs and implements the basic online course learning process, but also realizes the accurate recognition of images by combining the image matching technology based on OpenCV and the perceptual hash algorithm.
An Artificial-Intelligence-Based Method to Automatically Create Interpretable Models from Data Targeting Embedded Control Applications

The development of new automotive drivetrain layouts requires modeling of the involved components to allow for ideal control strategies. The creation of these models is both costly and challenging, specifically because interpretability, accuracy, and computational effort need to be balanced. In this study, a method is put forward which supports experts in the modeling process and in making an educated choice to balance these constraints. The method is based on the artificial intelligence technique of genetic programming. By solving a symbolic regression problem, it automatically identifies equation-based models from data. To address possible system complexities, data-based expressions like curves and maps can additionally be employed for the model identification. The performance of the method is demonstrated based on two examples: 1. Identification of a pure equation based model, demonstrating the benefit of interpretability. 2. Creation of a hybrid-model, combining a base equation with data-based expressions. Possible applications of the method are model creation, system identification, structural optimization, and model reduction. The existing implementation in ETAS ASCMO-MOCA also offers a high efficiency increase by combining and automating the two procedural steps of embedded function engineering and calibration.
Mobile Deployment of Electro Acoustic Compositions and Media Artworks

One of the principal complications in the performance of electro-acoustic music is the need for complex and large sound systems. Working with minimal hardware, P0LARITIES: YOUR REFLECTION IN MYSELF utilizes mobile phone technology and serves as a robust platform for electro-acoustic performance through lightweight and universal hardware installation. The final work resulted in an electroacoustic composition for two percussionists and augmented drums with iOS and LibPD. Hybridizing and augmenting traditional musical instruments provides a compelling path forward but is restricted to either custom instruments or massive computational infrastructure. P0LARITIES: YOUR REFLECTION IN MYSELF acts as a proof of concept for future explorations in the distribution of electro-acoustic music and media artworks by augmenting with mobile phone hardware.
Codes in Rosenbloom-Tsfasman metric: A Survey

Abstract This paper gives a systematic survey of research carried out in the theory of codes equipped with Rosenbloom-Tsfasman metric. In classical coding theory setting, codes are investigated with respect to the Hamming metric which can efficiently address the communication problems arising from channels in which channel noise generates equiprobable errors. But however, not all the real world channels are of that nature, especially, when the possible errors form patterns of a specific shape. Rosenbloom and Tsfasman introduced a non-Hamming metric, called Rosenbloom-Tsfasman metric (RT-metric, in short) that can address the problem of reliable information transmission over parallel noisy channels. Martin, Stinson and Skriganov independently introduced the same metric in the context of the theory of uniform distributions. As this metric happened to be a generalization of the classical Hamming metric, it has attracted so much attention from the coding theory research community and as a result a lot of work has been done in this line of research over the past 3 decades. In this paper we would like to present the key developments in the field of codes with RT-metric.
Analysis of Target Detection and Tracking for Intelligent Vision System

In the present scenario, most of the researchers are highly motivated to do new findings in the field of computer vision because of its tremendous applications such as video surveillance, human-machine interaction, traffic monitoring, human behavioral analysis, the guided missile, and military services, medical applications, and vehicle navigation. A typical video data frame comprises both foregrounds as well as background information. The pixel points that describe the target features in the region of interest are considered foreground information, and the rest of the feature points are treated as background information. Moving object detection plays a pivotal role in any kind of computer vision applications. Moving object detection is the process of identifying the class objects such as people, vehicles, toys, and human faces in the video sequences more precisely without background disturbances. In most cases, the existing moving object detection approaches concentrate only on the foreground information and frequently ignore the background information. As a result, trackers will be deviated away from the target and detect the non-foreground objects. Recently, several contributions have been proposed for moving object detection. However, the robustness and novelty are still challenging to achieve because of the complex environments, including illumination changes, rapid variations in target appearance, similar objects in the background, occlusions, target rotations, scaling, fast and abrupt motion changes, moving soft shadow, flat surface regions, and dynamic backgrounds. This book chapter introduces the concept of an intelligent video surveillance system and the major challenges involved in moving object detection. This chapter also deliberates the related backgrounds and their shortcomings in different perspectives. It also presents potential algorithms for efficient object detection and tracking. Finally, the book chapter is concluded with research opportunities pertaining to object detection and tracking.
An Initial Parameter Search for Rapid Concept Drift Adaptation in Deep Neural Networks

Concept drift is a common issue in data stream mining algorithms that causes prediction models to lose its original performance gradually or abruptly due to the non-stationarity of the data distribution and decision boundaries. To combat concept drifts, prediction models need to be updated periodically or when concept drifts occur to adapt to the current concept. Unfortunately, training deep neural networks often require a large amount of data samples and high computational resource consumption, making adaptation slow when concept drifts occur. This paper proposes an approach by searching for an optimum initial parameter that could be adapted quickly to all possible concept drift situations. The initial parameter search is based on the Reptile [] algorithm, which had been successfully applied in image classification, which allows a neural network model to learn from a few samples and minimal gradient steps. We argue that using an optimum initial parameter allows prior information to be embedded and makes the prediction model less reliant on training exclusively from new data when concept drift occurs. Experimental results show that this approach performs at least as well as current data streaming algorithms but with the lowest computational overhead.
Extracting Requirements and Modeling Information and Controlling Risk

Presents abstracts of papers from the 2020 IEEE Conference on Requirements Engineering and the ACM/ IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS 2020).
Research on the Application of Blockchain Technology in the Management of Financial Transfer Payment

Blockchain technology is considered to be a subversive innovation of computing mode after mainframe, personal computer and Internet. In the financial, medical, supply chain and other industries show great development potential. As an important part of financial management, financial transfer payment system is an important part of modern financial system. This paper combines blockchain technology with the management of financial transfer payment, analyzes the adaptation scenarios of blockchain technology, summarizes the status and development bottleneck of financial transfer payment informatization, tries to establish a financial transfer payment system based on blockchain technology, describes the advantages of blockchain technology in supporting the management of financial transfer payment, and puts forward suggestions on the possible problems.
A Comparative Study of NoSQL and Relational Database

Recently years many researchers have been chosen research area of processing and analysis of huge amounts of data collected by Web-based systems. Everyone is using mobile phones and smart devices to generate large amounts of data on the Internet. NoSQL database technology is based on Dr. Eric Brever's research on distributed systems. Today, countless smartphones and other devices are flooded with news, information, videos, and photos, and everyone is not just a consumer, but a creator. The amount of digital data generated and disseminated has increased dramatically, ushering in an era of unprecedented new approaches and solutions. This article discusses research on the NoSQL database
A Study On Virtual Intelligence

Abstract Virtual Intelligence can be otherwise known as digital intellect. VI is one of the developing technologies of this decade. It is the result of union of two technologies that are swiftly shooting up. They are Virtual Reality and Artificial Intelligence. While Artificial Intelligence will make machines react like an individual, VR will create an imaginary computer universe. This tech intends in transforming machines more like people. Using this VI technology, one can feel virtual world of their desire. This technology is utilized in many fields. Virtual Intelligence will integrate the mode of teaching. This will boost the gaming sector and will play a role in automation.
Human Pose Estimation Based on Multi-resolution Feature Fusion Network

This article focuses on the study of multi-resolution feature fusion methods for human pose estimation. Most of the existing pose estimation methods are based on high-resolution gradually reducing the resolution to learn advanced semantic features, and then gradually recovering high-resolution features from the low-resolution semantic features to locate keypoints of the person instance. The process without fusion of rich features will inevitably lose more spatial and semantic information. The purpose of the multi-resolution feature fusion method proposed in this paper is to fuse spatial and semantic information on multiple scale feature maps, in addition, our network use the multi-receptive field fusion module on the same scale features to enhance feature extraction. Our methods can utilize more feature information to accurately locate keypoints.
An Efficient Machine Learaning Framework for Speaker Authentication using Voice Input

With the advancements in the hardware industry, an increase in the computation power and development in Artificial Methods, we can think of working on cognitive tasks. We have worked on various speech recognition methods using Natural language processing and Hidden Markov Models. We have done the classification of the users on the basis of their utterances. In this paper, we propose a discrete probability approach. The results which we have got gives us high accuracy results in recognizing the speakers. This helps in concluding that Learning and Uncertain reasoning are important components of Artificial Intelligence that could help in the development of solutions to problems which are interesting and complex.
QoE Prediction of WebRTC Video Calls Using Google Chrome Statistics

Web real-time communication (WebRTC) is an open framework that enables real-time voice, video and text communication among browsers. The WebRTC allows collection of large amounts of statistics through browser-embedded tools which can be used to evaluate quality of experience (QoE). This paper focuses on webrtc-internals as Google Chrome browser-embedded tool for collecting WebRTC statistics. The objective is to consider whether webrtc-internals statistics can be used for QoE prediction of WebRTC video calls. A number of experiments were performed and completed with end-user questionnaire in order to collect webrtc-internals statistics and mean opinion scores (MOS). Multiple linear regression (MLR) was used to quantify the relationship between selected webrtc-internals statistics and QoE in order to propose the QoE prediction model for WebRTC video call.
Pre-college Computer Science Initiative for Augmented and Virtual Reality Development

New curricula initiatives are growing to meet nearfuture, industrial demand for computer science (CS) graduates with Augmented and Virtual Reality (AR/VR) development knowledge. Universities are often at the forefront in developing these curricula to help prepare their students for industry jobs. High schools wanting to offer college aligned CS courses for their students typically work with local universities to adapt courses for their students' needs. This paper presents such an effort along with results from a student survey showing the successful implementation of college-level courses through training of high school teachers. The curricula from this study are available for public use at artncoding.com and may be adapted as needed by educational programs to meet the emerging employment needs of their students in the AR/VR field.
Integrity Mechanism of Artificial Intelligence for Person’s Auto-poiesis

Coming system - informational culture supersedes industrial era and its ergonomics incapable to find time for dynamics of professional requirements sophistication. Inter-disciplinary activity in computer networks leans on education universality necessary for meanings understanding. Subject’s thinking-consciousness exists in strain of discoordination with up-to-date rational noosphere not being trained relatively trans-fundamental speculation ability. Proto-phenomenon of man is endowed by spontaneous integrity and viability allowing adaptation to needed transcendental self-developing processes. Fundamental evolvement of mentality can be achieved in anthropogenic info-sphere of cypher galaxy only in partnership with deep-learned artificial intelligence (DL IA). To accomplish rational auto-poiesis, image of integrity mechanism is to be applied by DL IA to untwist ergo-mind subjective objectization up to functional systems of ego-mind. Cogno-ontological knowledge base and system axiomatic method are to be used. The technology displays innate universal mathematical hardware for ergo-mind speculation.
Network Information System in Economic Management Development in the Big Data Era

One of the important characteristics of the “new economy” era is “big data”. In the era of big data, with the advent of network information systems, companies pay more and more attention to the accumulation of their own knowledge and experience, and to improve work efficiency. The enterprise itself conducts efficient management of information and makes innovative development for the construction of a new model of economic management. In exploring the development of economic management, the big data technology emerging in the information age plays an important role. The use of big data to improve the network information system can enable enterprises to carry out informatized economic management of finance more quickly and efficiently, and make new contributions to the development of economic management of enterprises. The purpose of the research in this article is to analyze and study the application of network information systems in the economic management process in the era of big data, and to make innovations in traditional economic management models. This article starts from the perspective of network information systems, takes the current big data era as the background, and on the relevant realistic background and theoretical basis, takes the innovation and development of economic management development as the specific research object, and makes full use of the current fast-developing big data technology. Make breakthroughs in innovative development of network information systems. The experimental results show that the network information system develops in the process of the traditional economic development model and constructs a new model of economic management development.
Research-led Course Design and Practice of English for Academic Purposes in Chinese-Foreign Cooperatively-run Schools

In the past few decades, many Chinese-foreign cooperatively-run schools have gained the approval of the People's Republic of China and developed rapidly. In these schools, traditional English teaching mode emphasizing the knowledge instruction can no longer meet the requirements of international education. The research-led teaching and learning model refers to the learning process that starts from a problem or phenomenon and helps students to actively explore knowledge about the problem and solve the problem through independent research or group cooperation under the teacher's guidance. Research-led teaching and learning is a teaching model that can effectively stimulate students' interests in learning, train their critical thinking and integrating knowledge, and improve their ability to learn independently and solve practical problems. Some highly specialized Chinese-foreign cooperatively-run schools have higher requirements for academic English and the students' English level in listening, speaking, reading and writing. This article starts with the necessity of research-led EAP teaching mode in Chinese-foreign cooperatively-run schools, summarizes the course design and practice of Chinese-foreign cooperation in running schools between the University of Southampton and Dalian Polytechnic University, exemplifies some specific research-led projects to explain the EAP course design and practice in this Chinese-foreign cooperatively-run schools, and provide some experience for other similar education institutions.
Judicial technical expertise methods for investigation of cybercrimes

Abstract Computer technical expertise is rather new sort of the expertise which are carried out within criminal, civil and arbitration proceedings. And though separate expertise of computer means was carried out and earlier, “computer crimes” laid the foundation for all-round development of judicial computer technical expertise as independent sort of examination. Its emergence is caused by growth of use of computer and information systems in our country in recent years.
Emotions and Monoamines: New Approach to the Emotional Text Classification in Sentiment Analysis

The paper presents the classification of Internet-texts in Russian according to their monoamine status. We consider the levels of serotonin, noradrenaline and dopamine supposedly present in the blood of the text producer. The procedure of the non-discrete annotation of emotional tonality was used for the corpora data to identify the verbal markers for low-serotonergic, high-serotonergic, low-noradrenergic, high-noradrenergic, low-dopaminergic and high-dopaminergic texts. It is based on the Lövheim Cube model which includes eight emotional classes. The assessment results were mapped to a particular point in the Cube’s 3D-space. We identified six subcorpora from the assessed data according to their monoamine status. The parameters of verbal structures proper to the corpora-antipodes provided by Sketch Engine gave us potential serotonergic features such as absolutist words, adjective-noun combinations, numerals, noradrenergic features – indefinite and demonstrative pronouns, quantifier words – and dopaminergic features – combinatorics of кaк, etc. The obtained results give a perspective for the sentiment analysis which relates not only the linguistic representation with its corresponding emotional status but an individual’s biochemical response as well, thereby representing the wider mechanism of emotion generation.
Accuracy Improvement of Solar Power Estimation Using Real-Time Degradation Computation of PV Panels

Many applications including power trading and planning require the accurate estimation of solar power in real time. As the power output of the solar panels degrades over the time period, so its real-time estimation is tough without the degradation parameter. In the proposed method, the effect of degradation in terms of performance ratio is incorporated along with other meteorological parameters. The degradation is calculated in real time using the clustering-based technique without physical inspection on site. Initially, the power is estimated using Support Vector Regression (SVR) model with the meteorological parameters. The estimation is further fine-tuned in sync with the degradation rate. The model is validated on the real data (Meteorological parameters and Solar power) procured from the solar plant. After refinement, the estimation results show significant improvement in terms of statistical measures. Now, the estimation accuracy in terms of coefficient of determination R 2 is 92% and the error metrics normalized root mean square error (NMRSE), mean absolute percentage error (MAPE), root mean square error (RMSE) are 7.13, 5.92 and 14.54, respectively.
On Universal Colexifications.

Colexification occurs when two distinct concepts are lexified by the same word. The term covers both polysemy and homonymy. We posit and investigate the hypothesis that no pair of concepts are colexified in every language. We test our hypothesis by analyzing colexification data from BabelNet, Open Multilingual WordNet, and CLICS. The results show that our hypothesis is supported by over 99.9% of colexified concept pairs in these three lexical resources.