Reviewerly/exHarmony huggingface dataset:
Assessing the Ability to Combine Hyperspectral Imaging with TESCAN Integrated Mineral Analyzer to Quickly Classify Pavement Aggregate Minerals

Abstract Asphalt-aggregate interfacial adhesion performance plays an important role in the durability and stability of asphalt mixtures. The determination of the adhesion between the surface minerals of aggregate and the asphalt binder, to quickly and quantitatively evaluate the interface performance between the aggregate and the asphalt binder, has attracted extensive attention. In this article, hyperspectral imaging technology combined with a TESCAN integrated mineral analyzer (TIMA) was proposed to determine the surface mineral compositions of pavement aggregate, and the mineral classification quality and performance of the proposed method were assessed. The test results showed that the endmember spectra of the different minerals could be established based on the TIMA results. The mapping results obtained by the US Geological Survey library were far inferior to those obtained using the proposed method, and the mineral distribution texture of the granite and diabase obtained by the proposed method were in good agreement with those of digital images. The proportion of unclassified minerals of granite was only 3–8 % of the total content, which is an acceptable range for engineering applications, and the three main minerals in diabase could also be effectively identified. The proposed method can be a quick and accurate way to obtain the types and contents of minerals on an aggregate surface from the mineral hyperspectral spectra of aggregates from the same mine or area.
Analysis of financial fraud of listed companies from the perspective of Tripartite game and its countermeasures

Taking evolutionary game theory as the main research tool, on the basis of describing the relationship among listed companies, accounting firms and regulatory departments, this paper analyzes the financial fraud behavior of listed companies by constructing the game model of "listed companies, regulatory departments and accounting firms", and finally uses MATLAB to simulate the game model. The research shows that the financial fraud of listed companies is not only driven by their own interests, but also affected by multiple factors such as the punishment of the regulatory authorities, the regulatory success rate, and the willingness of accounting firms to cooperate. On this basis, from the three perspectives of listed companies, accounting firms and regulatory departments, this paper puts forward countermeasures to curb financial fraud of listed companies.
Node Embedding Approach For Topic Search From Academic Papers

A wide number of studies on embedding large-scale information networks using low-dimensional, community, and neighborhood-aware node representations have recently been published. Although these embedding and classification, models outperformed the traditional methods in graph mining applications. The representation of graphs and their relational structures is very helpful in representing features by applying various standard machine learning techniques and data analysis techniques. A wide range of embedding methods are present in machine learning and these methods are studied in the literature survey for finalizing the two methods for comparison. This paper compares two classifiers-Gradient Boosting and random forest classifier- based on their accuracy score using the Node2vec method on one dataset. The dataset used is arXiv, and the dataset is imported by the use of its API. With the use of the dataset networks created, after the network’s formation, the nodes’ embedding is done as the output of this node embedding will act as an input for the model training step. The model training is used to make the machine learn, and for this Node2vec technique is used in this study. the model is trained by using the classifiers. And the classifiers that are used to achieve the problem of classification for this model are Gradient Boosting and Random Forest Classifiers. This study of the state-of-the-art models shows the efficiency of the models on the graphs. The result shows that the Gradient boosting Classifier performs better than the random forest. The accuracy score of the model by Gradient Boosting is 0.9482 and by Random Forest is 0.8959. This paper will help in picking the best classifier for node classification and model training tasks.
The use of instagram to facilitate middle school mathematical literacy

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Niluh Sulistyani, Cyrenia Novella Krisnamurti, Leonardo Amaris Liaupati, Filipus Kevin Hendar Saputra; The use of instagram to facilitate middle school mathematical literacy. AIP Conf. Proc. 10 January 2023; 2569 (1): 040010. https://doi.org/10.1063/5.0113904 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining Data

In multi axis NC machining, there is a certain security risk in wireless data transmission. A security risk prediction model for wireless data transmission in multi axis NC machining is designed. Through frequent pattern mining, multi axis NC machining data wireless transmission security risk data mining is implemented. Based on machine learning, the security risk prediction model of data wireless transmission is constructed. The model is mainly divided into four parts: data collection and processing module, grey synthesis and Markov prediction module, Bayesian network and evidence theory module and risk quantification calculation module. AHP method is used to analyze the weight of model indexes. Test the prediction performance of the model. The test results show that the accuracy of risk prediction of the model in different scenarios is high, higher than 88%, and the prediction time is short, less than 6 s, which proves the prediction performance of the model.
Forensic Network Research Special Interest Group Annual Conference

Lindsey Gilling Mcintosh, Acting Forensic Network Manager, shares a review of last year’s Forensic Network Annual Research Conference and details of how to get involved in this year!
Usability Improvement Through User Interface Design with Human Centered Design (HCD) Method on Junior High School Websites

Menes Mathla'ul Anwar Central Junior High School is one of the schools that is trying to take advantage of technology in the world of education by having a profile website that is used for school purposes. Based on observations and interviews, the school still feels dissatisfied with the User Interface and User Experience of the existing website due to poor navigation naming and some things that still do not meet the needs of the school. In the initial design evaluation using the System Usability Scale (SUS) method, it shows that the Menes Mathla'ul Anwar Central Junior High School website has an average usability score of 35.42 with an acceptance rate that is in the not acceptable category. This study intends to evaluate usability and provide user interface recommendations in the form of a prototype on the Menes Mathla'ul Anwar Central Junior High School website. Data collection begins with an observation then a literature study, and interviews. For the design, Human Centered Design (HCD) is used, which consists of four stages of the Problem, namely analysis of the context of use, analysis of user needs, making of solution designs, and evaluation of solution designs. The results of the evaluation of the solution design using the Heuristic Evaluation (HE) and System Usability Scale (SUS) methods indicate that the designed solution design is considered good enough and users can interact effectively and easily. The HE method shows that the solution design only has 4 problems, 3 of which fall into the low-priority category, and 1 of them falls into the category that does not affect the user. Meanwhile, in the SUS method, the average score for the website design solution usability is 70,93 with the acceptance rate in the acceptable category
Random Forest for Code Smell Detection in JavaScript

JavaScript has become one of the most widely used programming languages. JavaScript is a dynamic, interpreted, and weakly-typed scripting language especially suited for the development of web applications. While these characteristics allow the language to offer high levels of flexibility, they also can make JavaScript code more challenging to write, maintain and evolve. One of the risks that JavaScript and other programming languages are prone to is the presence of code smells. Code smells result from poor programming choices during source code development that negatively influence source code comprehension and maintainability in the long term. This work reports the result of an approach that uses the Random Forest algorithm to detect a set of 11 code smells based on software metrics extracted from JavaScript source code. It also reports the construction of two datasets, one for code smells that affect functions/methods, and another for code smells related to classes, both containing at least 200 labeled positive instances of each code smell and both extracted from a set of 25 open-source JavaScript projects.
An In-Depth Analysis of Cooperative Multi-Robot Hierarchical Reinforcement Learning

In most cases, it is known that Multi-Robot System (MRS) performs more effectively than a Single-Robot System (SRS) in complex environments. Therefore, Deep Reinforcement Learning (DRL) has been gaining interest in training robots to solve tasks in complex environments cooperatively. However, Deep Reinforcement Learning continues to be hampered by challenges in complex environments. Recently, the Multi-Agent Hierarchical Deep Deterministic Policy Gradient (MH-DDPG) has been proposed for implementing hierarchical learning for a group of robots engaged in collaboratively problem-solving. This study provides a more in-depth investigation of the performance of the MH-DDPG algorithm by examining the influence of variations in the number of robots, the sensitivity settings, and the hierarchical levels. The experimental results reveal performance tradeoffs regarding these parameters, which should be considered following the task and environment complexity.
Touching The future

UK researchers have developed a robot fingertip that creates signals similar to the brain activity associated with human touch. Science fiction author, Gareth L. Powell, explores the possible implications of this technology.
Race to the Top

With their current facility no longer fit for a 21st century outfit, the Aston Martin Formula One team has broken ground on the sport’s first smart factory
The Value Provided by a Scientific Explanation

In this essay we investigate how deductive and probabilistic explanations add value to the theory providing the explanation. We offer an analysis of how, e.g., an explanation in the form of an abduction differs from a mere prediction in this regard. We apply this analysis to respond to a challenge posed by Glymour (Theory and Evidence. Princeton University Press, Princeton, 1980) regarding Bayesian confirmation of new theories using old data. Last, we consider additional criteria involving subjunctive conditionals and counterfactual conditionals for distinguishing explanations from mere predictions, which help illuminate why an explanation carries different cognitive value than does a mere prediction.
An Idea of Japanese Language Learning for Indonesians via Mobile Application Development

Almost every aspect of the world has shifted online, including language learning. People's lifestyle of learning a new language has altered from traditional offline courses to learning a new language online. In learning the Japanese Language, it is a bit harder to learn it entirely online. This research used netnography and interview with the experts to get the insight about the Japanese language learning with the head of Japanese Language department from two universities in Indonesia. There are a lot of things that can be missed out, for example, making a mistake in pronouncing words or phrases and continuously making that mistake because there are no teachers to correct it, or learners lose motivation to keep learning. This paper proposed innovation to help solve that problem and create a more balanced way to learn the Japanese Language fully online.
ON THE ISSUE OF MEDIATIZATION OF EDUCATION

The purpose of the article is to study the theoretical aspects of the problem of mediatization of education. The article discusses the content and various interpretations of the concepts of “media” and “mediatization”. The main attention is paid to the peculiarities of the process of mediatization, both in the school education system and in the field of higher education. Mediatization in school education is considered in the context of all elements of the educational process. First of all, this is expressed in the organization of the educational process in remote mode. In addition, mediatization makes it possible to conduct lessons using information technology, students perform creative projects, gain access to information sources and collections of various libraries. The mediatization of education expands opportunities for obtaining and correspondence education in the mode of distance communication, as well as through various online courses for obtaining professional education. The article also highlights the main tasks of mediatization of education and methods of organizing the educational process using mass communication media. Among these tasks are the possibility of obtaining information, using the potential of mediatization of education for moral and ethical education and personal development, familiarization with the achievements of the development of mass media, explaining their role and significance for the educational process, practical training in media technologies, aesthetic education of students. The evolutionary possibilities and trends in the development of education in the context of its mediatization, as well as the risks and negative aspects of this process are shown. The increasing role of the mediatization of education is explained by the continuous nature of education, total digitalization, the demand and availability of online courses for a wide audience. On the other hand, the mediatization of education will also have negative consequences. Firstly, the process of socialization of students will be difficult, and secondly, the educational process will acquire a formal character, which will affect its quality. Comparing the positive and negative aspects of the mediatization of education, it seems that there should be no abandonment of educational technologies that have been tested for decades, and here the task is to find the necessary balance between old technologies and new ones, which mutually complement each other, will contribute to the improvement of the educational system so that it meets the challenges and requirements of modernity. Methodologically, the article is based on the theoretical developments of foreign and Russian researchers. The article is based on general philosophical and socio-humanitarian methods of analysis.
Easy Rider

An all-electric premium motorcycle from Indian developer Ultraviolette aims to put its ICE rivals in the shade
Thales to Keep Viper in Contact with Earth

The EngineerVol. 300, No. 7920 NEWSThales to Keep Viper in Contact with EarthJason FordJason FordSearch for more papers by this authorJason FordPublished Online:4 Oct 2022https://doi.org/10.12968/S0013-7758(22)90586-0AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 September 2020Volume 300Issue 7920ISSN (print): 0013-7758ISSN (online): 2753-7145 Metrics History Published online 4 October 2022 Published in print 1 September 2020 Information© MA Business Ltd.PDF download
EMEC goes with the flow in tidal battery first

The EngineerVol. 301, No. 7923 NEWSEMEC goes with the flow in tidal battery firstANDREW WADEANDREW WADESearch for more papers by this authorANDREW WADEPublished Online:30 Sep 2022https://doi.org/10.12968/S0013-7758(22)90136-9AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 January 2021Volume 301Issue 7923ISSN (print): 0013-7758ISSN (online): 2753-7145 Metrics History Published online 30 September 2022 Published in print 1 January 2021 Information© MA Business Ltd.PDF download
Convergence of the Phenomenon of Specialization in the Field of Digital Technology of Education

Analyzes the problems of convergence of the phenomenon of socialization in interactive methods of online learning. The advantages and some disadvantages of digital technology in comparison with traditional teaching technologies are listed. New educational technologies define a convergent model of an intellectual educational environment and point to the convergence of educational programs and standards.
Criminal liability for the sale of narcotic drugs or psychotropic substances using electronic or information and telecommunication networks

Subject of research: the norms of the Russian criminal law regulating responsibility for the sale of narcotic drugs, psychotropic substances and their analogues using mass media or electronic or information and telecommunication networks (including the Internet), as well as the practice of their application and scientific works in this field.&#x0D; Purpose of research: to develop scientifically sound proposals for improving the legislative regulation of the qualifying attribute provided for in paragraph b of Part 2 of Article 228.1 of the Criminal Code of the Russian Federation, and the practice of its application.&#x0D; Methods and objects of research: the methodological basis of the research is formed by the universal dialectical method of cognition, the formal legal method, as well as methods of analysis, synthesis and modeling. The object of the study is the social relations that develop regarding the criminal law regulation and application of the norms on liability for the sale of narcotic drugs, psychotropic substances and their analogues using mass media or electronic or information and telecommunication networks (including the Internet).&#x0D; Main results of research: author comes to the conclusion that it is impossible to commit actions that constitute the sale of narcotic drugs, psychotropic substances or their analogues exclusively using information and telecommunication networks. The key factors influencing the increase in the public danger of the sale of narcotic drugs, psychotropic substances and their analogues are identified, to which the author refers: the use of information and telecommunication networks for the transmission of information (communication) during marketing, as well as the preservation of the confidentiality of communication (anonymity or the use of pseudonyms). The author proposes to abandon the use of the term electronic networks and to change the wording of the qualifying feature under consideration.
Comparative analysis of the Light-CNN and FaceNet methods for identifying and maintaining human faces

Maintaining the identity while synthesizing the frontal view image is the most critical step in developing a "recognition via generation" framework. To this end, this paper investigates, tests and compares the performance of two deep learning architectures: Light-CNN and FaceNet. The Light-CNN is used to learn a robust feature for face verification tasks that produces a high-level facial identity accuracy over many traditional deep learning models. FaceNet, on the other hand, is a model to maps face images into a compact Euclidean space where distances directly represent a measure of face similarity. In our comparison, we use the TP-GAN model to perform several pre-processing stages. The face features are then extracted from the synthesized face images using Light-CNN and FaceNet as 256- and 128-dimensional representations, respectively. We evaluate the accuracy performances of Light-CNN and FaceNet architectures on Multi-PIE and FEI datasets.
Emotional Speaker Verification Using Novel Modified Capsule Neural Network

Capsule Neural Network (CapsNet) models are regarded as efficient substitutes for convolutional neural networks (CNN) due to their powerful hierarchical representation capability. Nevertheless, CNN endure their inability of recording spatial information in spectrograms. The main constraint of CapsNet is related to the compression method which can be implemented in CNN models but cannot be directly employed in CapsNet. As a result, we propose a novel architecture based on dual-channel long short-term memory compressed CapsNet (DC-LSTM–COMP CapsNet) for speaker verification in emotional as well as stressful talking environments. The proposed approach is perceived as a modified Capsule network that attempts to overcome the limitations that exist within the original CapsNet, as well as in CNN while enhancing the verification performance. The proposed architecture is assessed on four distinct databases. The experimental analysis reveals that the average speaker verification performance is improved in comparison with CNN, the original CapsNet, as well as the conventional classifiers. The proposed algorithm notably achieves the best verification accuracy across the four speech databases. For example, using the Emirati dataset, the average percentage equal error rates (EERs) obtained is 10.50%, based on the proposed architecture which outperforms other deep and classical models.