Reviewerly/exHarmony huggingface dataset:
Software Design and Artificial Intelligence: A Systematic Mapping Study

Software Design (SD from now on) is a critical process in the Software Development Life Cycle. Like all processes, there is a constant search for the improvement of SD activities. Artificial Intelligence (AI) has proven to be an effective tool to perform activities of any kind, this includes SD assisting. This study presents the state of the art related to the application of AI techniques in SD activities, in order to achieve this goal, we carried out a systematic mapping study consisting of 36 papers. The studies reported the use of 28 AI techniques, such as Neural Networks and Decision Trees; considering 20 SD activities, like diagram generation and design pattern detection. The inclusion of AI techniques in SD is an increasing trend in software development. The collected studies demonstrate the effectiveness of This inclusion, showing successful applications, which in turn may create more interest in the coupling of AI and SD in further studies.
Automatic Translation of Medical Information Based on Text Feature Recognition

In order to improve the effect of the medical information automatic translation system, based on the text feature recognition technology of medical image, this paper constructs an automatic translation system that can recognize medical images. On the basis of the medical information image retrieval based on the combination of text information and visual information, this paper uses automatic image annotation technology and semantic similarity calculation method to extract the text and semantic features of medical information images. Then, this paper uses the inherent multi-information fusion capability of the Bayesian inference network to fuse the text features of medical information images and the semantic features of image content together to realize medical information image retrieval. Finally, this paper designs experiments to test the performance of the medical information automatic translation system. The research shows that the system constructed in this paper has certain effects.
SLAMVis: An Interactive Visualization Approach for Smart Labeling on Multidimensional Data

Multidimensional data is widely applied in various machine learning tasks such as regression, classification and pattern recognition. Their labels are significant, especially in supervised learning tasks. Considering the difficulties encountered in the actual situations including the lack of labeled data, low-quality labeled data, and expensive generation of labels, we propose a novel smart labeling method named SLAMVis, which combines active learning and visual interactive labeling, to obtain effective models and labels through an iterative labeling process. The algorithms are tightly integrated with an interactive visual interface, which is composed of multiple coordinated contextual views. Based on the pattern recognition algorithm combining SOINN and K-means, we also introduce a new query strategy to recommend informative candidate instances. Through quantitative experiments and example usage scenarios, we demonstrate the effectiveness of SLAMVis.
A State-of-the-Art Survey on Decision Trees in the Context of Big Data Analysis and IoT

In the last few decades, the expeditious expansion of the Internet and Internet of things (IoT) resulted in abrupt growth of data in almost every industry, business, and various fields of research. The embedded sensors, actuators, and software in IoT generate data in large volume and greater complexity. Analysis of these heterogeneous data with large volume and diverse dimensionalities is found to be challenging in extracting useful patterns from them and make it beneficial for the organization or society. Specialized tools are desired for analyzing and processing the huge chunk of data named “big data.” Regression and classification are the fundamental tasks for data mining, pattern recognition, and big data analysis in the field of data science. The major sources of data analytics and classification task for big data are generated through embedded sensors in IoT. Soft computing is another area of research where the feature sets of an entity are quantified as some degree of fuzziness rather than exactness. A decision tree (DT) is a rule-based, recursive, and top-down tree structure widely adopted for classification and regression. DT-based algorithms play a vital role in data mining, soft computing, and big data analytics that deal with multiobjective constraints. A number of applications based on DTs have been developed to address the IoT and big data research 100problems. Comparative analysis of various applications of DT can be very helpful in building models with better accuracy and precision. In this work, we perform an extensive state-of-the-art survey on the role of DT in the context of soft computing, big data, and IoT.
Foodwiser: Be Wise with What You Eat

Abstract: With the rise of online food ordering websites, maintaining a proper diet and staying healthy has become an important part of a person's lifestyle. But with the rising work from home trend, maintaining a proper diet and being aligned with the fitness goals is becoming tougher day by day. Also fora person having abnormal food habits, it becomes really tough to maintain a repository of the food intake, manage nutrients and carbs intake, etc. Thus with an aim to solve the above stated problem, we present the application Foodwiser: Be wise with whatyou eat. Keywords: [Deep Learning, CNN, Machine Learning ]
Blockchain-Based Video Copyright Detection

Copyright infringements in the digital age are common, copyright detection plays an effective role in protecting copyright, and the results are often retained as evidence of infringement appeals. In this paper, we proposed a video copyright detection scheme combining on-chain and off-chain based on blockchain technology for the problems of undisclosed digital copyright detection process leading to unreliable and unverifiable results. Firstly, the feature fingerprints of the key frames are extracted from the video content under the blockchain, and then the smart contract was called to automatically perform copyright detection on the blockchain to ensure the originality of the copyrighted work and the verifiability of the detection result. Finally, the video feature value is permanently and immutably stored on the blockchain. Experimental results show that invoking the smart contract on the chain to automatically compare can not only ensure the accuracy rate, but also obtain credible detection results, ensure the transparency and fairness of the results, and also prove the feasibility of the scheme.
A Study on Product Authentication and Authorization

Fake product has become a major concern in the developing countries like Bangladesh.A good number of products found fake or altered now-a-days.Basic information of the product including company-logo, price, manufacturing and expiring date are printed on product-body or packet.Usually, customers believe those information during purchase time.However, these information can be altered in many ways such as generation and distribution of fake product, and changing the tag.Under this circumstances, an idea is generated that can solve the above issues using a cryptographic tool such as authenticated encryption (AE) and Quick Response Code (QR Code).The target point of using AE is to ensure product authentication and authorization.Furthermore, QR code provides user friendly environment.In this paper, encryption is performed based on the product contents using encryption key and can not be altered without key.This encrypted data is used to create a tag.This tag will be based on AES algorithm and converted into QR code.A valid user can get the tag from the QR.In the next step, a valid user will get all information related to that product.
Financial Management Early-Warning Mechanism Construction and Decision Analysis Research Based on Wireless Sensor Network and Data Mining

With the gradual and complete establishment of the current socialist legal market economy management system with Chinese characteristics and the continuous investment in deepening system reform and continuous improvement in the later period, the social financial industry and corporate financial management have gradually increased their risk awareness of corporate financial management. This paper deeply analyzes and studies the statistical methods of financial data-related legal rule interactive mining and proposes a new improved statistical algorithm of financial-related legal rules, which greatly improves the work efficiency of financial data interactive mining. At the same time, a multilevel analysis model based on the concept of corporate financial crisis risk assessment and a corporate financial crisis risk early-warning analysis model for decision-making risk evaluation are proposed. Finally, it can be determined how to choose more internationally representative corporate financial management risk analysis indicators, which have more objectivity and practical application significance than traditional analysis methods. Finally, it is concluded that the accuracy of this model is better than that of other models. The accuracy rate of financial crisis prediction reached 62.35%.
Sistem Informasi Penyewaan Kos Dengan Fitur Google Maps API

OKOS is an online boarding information system that can make it easier for boarding house seekers to choose the boarding house they want, besides that boarding house owners can also register their boarding house business premises. OKOS was created to meet the needs of boarding house seekers from outside the city to be more helpful in finding a boarding house and boarding house owners who can be helped in promoting their boarding house business. The method used to research the creation of the OKOS is the SDLC waterfall which consists of planning, analysis, design, implementation, testing, and maintenance. The conclusion from making the OKOS information system website is that OKOS can meet the needs of boarding house owners and boarding house seekers because OKOS can be an intermediary between boarding house seekers and boarding house owners even though they are in different cities. In addition, OKOS provides complete information so that boarding house seekers can choose a boarding house based on the type of boarding house, facilities, price, room area, maximum occupants per room, to a map of the boarding location. The suggestion that the author can convey is that there is a need for further research on what things are considered for boarding house seekers to choose the desired or needed boarding house so that it can be considered by boarding house owners or application developers like OKOS to attract the attention of boarding house seekers
A Word Selection Method for Producing Interpretable Distributional Semantic Word Vectors

Distributional semantic models represent the meaning of words as vectors. We introduce a selection method to learn a vector space that each of its dimensions is a natural word. The selection method starts from the most frequent words and selects a subset, which has the best performance. The method produces a vector space that each of its dimensions is a word. This is the main advantage of the method compared to fusion methods such as NMF, and neural embedding models. We apply the method to the ukWaC corpus and train a vector space of N=1500 basis words. We report tests results on word similarity tasks for MEN, RG-65, SimLex-999, and WordSim353 gold datasets. Also, results show that reducing the number of basis vectors from 5000 to 1500 reduces accuracy by about 1.5-2%. So, we achieve good interpretability without a large penalty. Interpretability evaluation results indicate that the word vectors obtained by the proposed method using N=1500 are more interpretable than word embedding models, and the baseline method. We report the top 15 words of 1500 selected basis words in this paper.
Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed.An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target.The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted.The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.
Supplemental Material: A carbon-rich lithospheric mantle as a source for the large CO&lt;sub&gt;2&lt;/sub&gt; emissions of Etna volcano (Italy)

Figures S1 and S2, Tables S1 and S2, analytical methods, and references for the literature data reported in the figures.&lt;br&gt;
Image and Video Processing Fundamentals

This chapter will focus on image and video processing fundamentals including required mathematics, standard algorithms, implementation options, and implementing a practical example into a C++ application.
Utilizing Analog Circuits by Neural-Network based Multi-Layer-Perceptron

This manuscript presents an artificial-neural-network based programmable-neuron for implementation of analog circuits with multi-layer-perceptron. The proposed programmable-neuron can estimate linear, hyperbolic, tangent and sigmoid functions which are used to activate the analog circuits. With this, a neural-network-designer can utilize maximum number of controller-bits to select an activation-function kind with no actual change. For this neuron, 0.18-µm CMOS-technology is used for simulations and demonstrates a good estimation in peak error with ideal sigmoid and hyperbolic tangent function by 7.3% and 29.34% respectively. To assess the usefulness of the neuron, a Multi-Layer-Perceptron-neural-network (MLP-NN) is used. The MLP-NN is trained to carry out XOR-logic gate for handling signals in frequency-range from 3mHz to 60MHz. The correctness of the proposed-neuron is over 99.9%. These results shows that there is a decrease of 49% in power consumption with related to previous works.
ONLINE LEARNING IN THE SYSTEM OF SOCIAL RESPONSIBILITY DEVELOPMENT OF STUDENTS

The article discusses some of the features of online learning in the system of studying social responsibility. A new understanding of the main pedagogical categories is being clarified - educational activity, learning environment, educational tasks in the conditions of modern technologized education. A scientific and theoretical review of the literature showed the versatility of studying the problem of social responsibility in various fields of sciences: philosophy (Platon, Aristotle, T. Hobbes, J. Locke, I. Kant, etc.), jurisprudence (A.R. Kornilov, A.R. Lavrent'ev, S.N. Kozhevnikov and others), psychology of personality development (S.L. Serebryakova, L.M. Arkhangelsky, A.A. Guseinov and others), pedagogy (I.Yu. Novichkova, S.P. Akunina, V.N. Lukin and others). Several scientific works on corporate social responsibility contributed to the development of this study (F. Rosati, R. Costa, A. Calabrese, J. Lee, M. Cho, etc.). The work of foreign scientists (S.L. Davis, L.M. Rives, S. Ruiz-de-Maya) "Personal social responsibility: developing and testing the scale" is of scientific and methodological value, which determines the need to develop a concept of social responsibility, which includes the individual human behavior of a person as a modern the citizen as a whole. The results of studies by J.C.R. Sousa, E.S. Siqueira, E. Binotto, L.H.N. Nobre on the perception of subjects of the educational process of universities of social responsibility, depending on the degree of discussion and the level of socialization of students, are significant. According to C. Roofe, the problem of social responsibility is not given special attention, which leads to a constant decline in the moral and spiritual component of education in the country. The diagnostic tool was a questionnaire to identify the personal attitude of teachers to the problem of social responsibility of students and the quality of work to develop the desired personality characteristics at the university. The author's questionnaire "Ideas of social responsibility" has passed the validation confirmation procedure (Alpha-Cronbach: 660927 and Standards. Alpha: 669767) by specialists of the Biostatistics sector of the Research Management Department of WKMU named after Marat Ospanov. The result of the research is the highlighted relationship of creativity with the formed cultural and social experience of the individual, which is the basis of socially responsible behavior. Highlighted as a result of observation as an initial method of empirical cognition, psychological, pedagogical, methodological features of online education will allow teachers and students to timely adjust their activities in order to achieve their goals in the system of studying students' social responsibility.
Digitising (Romanian) Cyrillic using Transkribus: new perspectives

In this paper we discuss the application of the software platform Transkribus (transkribus.eu), an AI-assisted tool for Handwritten Text Recognition (HTR), to 16th century Romanian manuscript and printed sources using Cyrillic scripts. After an overview of the basic functionality of the HTR technology and Transkribus, we discuss the Romanian and bilingual Slavonic-Romanian sources we used, give an insight on training specific and generic as well as smart (i.e. transliterating from Cyrillic into Latin script) models, evaluate their performance and discuss implications of HTR for philological research in the Digital Age. We conclude with an outlook on future research perspectives.
Stateful Library Analysis and Migration System (SLAM)

Interoperability between research management systems, especially digital libraries or repositories, has been a central theme in the community for the past years, with the discussion focused on means of enriching, linking, and disseminating outputs. This paper considers a frequently overlooked aspect, namely the migration of records across systems, by introducing the Stateful Library Analysis and Migration system (SLAM) and presenting practical experiences with migrating records from DSpace and Digital Commons repositories to Figshare.
The conditions for the young teachers’ professional development in the space of a pedagogical university

Contemporary challenges of the education system cause the growth of professional difficulties for the young teachers, reduce the effectiveness of their adaptation and consolidation in the educational institutions. The monitoring of professional deficient conditions of the young teachers allows these difficulties to be leveled on time, thereby improving the quality of their adaptation, consolidation, and professional self-identification. The theory and methodology of minimizing professional difficulties have been studied by scientists, but each region has its own specifics of the development of the education system and the peculiarity of supporting the professional development of young specialists. This makes it possible to conceptually define a methodology for identifying professional difficulties of the young teachers of Altai Krai and develop a mechanism for implementing individual educational routes. The purpose of this work is to identify the conditions for the young teachers’ professional development in the space of a pedagogical university. Methods. The methodological basis consists of a systematic approach as the basis for diagnosing professional difficulties of the young teachers; a competence-based approach as the basis for structuring professional difficulties. The research uses the method of collecting primary information, the method of analysis, generalization, information processing, and the design method. The results of the study are the following. The technology of the annual career guidance event «The Job Fair» with the involvement of employers as active participants is described. Questionnaires are developed for employers and young specialists to identify professional difficulties of a group of young teachers. The monitoring of the young teachers’ deficient conditions was carried out. Twelve additional professional training programs are developed for the young teachers of Altai Krai. A mechanism for building and implementing of a professional development individual trajectory has been made. The results of employers’ diagnostics and a list of professional development programs are presented.
Investigation of solar thermal tides using model data

The study of wave motions in the atmosphere, in particular, atmospheric solar tides is considered in this research. Using the middle and upper atmosphere model (MUAM) data, the latitudinal-high cross-sections of the amplitude and phase of individual tidal components, as well as their temporal variability, were shown. The most suitable periods for the complex Morlet wavelet transform of data were specified for the further use. The results obtained were used to study nonlinear interactions both between atmospheric tides and the mean flow and with each other.
FOREX ROBOT DEVELOPMENT IN THE PERSPECTIVE OF LAW ENFORCEMENT INTELLIGENCE AND INTELLIGENCE STRATEGY

In the development of technology in the financial world, we are also now familiar with trading robots. This robot works to make it easier to trade in cyberspace. Trading robots do not offer a solution to generate instant profitable transactions. Profitable long-term forex trading is much more than just tactical analysis of forex trading and the use of trading robots. Have a special program that does all the work to look tempting. But this is how trading robot’s work. Trading robot sellers who prefer to see a decent trading robot. Such a system requires constant human adaptation and supervision. The general public who wants to use trading robots well must know the risks and systems of the trading robot and there is no guarantee that using a trading robot will be 100% profitable. It should be emphasized again that the trading robot will make decisions based on the conditions that have occurred and the right decisions at that time. Thus, no foreign trade organizer dares to give absolute guarantees.