Reviewerly/exHarmony huggingface dataset:
Golay codes and quantum contextuality

It is shown that the codewords of the binary and ternary Golay codes can be converted into rays in ${\mathbb{RP}}^{23}$ and ${\mathbb{RP}}^{11}$ that provide proofs of the Kochen-Specker theorem in real state spaces of dimensions 24 and 12, respectively. Some implications of these results are discussed.
Stereoscopic view synthesis with progressive structure reconstruction and scene constraints

Depth image-based rendering (DIBR) is an important technology in the process of 2D-to-3D conversion. It uses texture images and related depth maps to render virtual views. While there are still some challenging problems in the current DIBR systems, such as disocclusion occurrences. Inpainting methods based on deep learning have recently shown significant improvements and generated plausible images. However, most of these methods may not deal well with the disocclusion holes in the synthesized views, because on the one hand they only treat this issue as generative inpainting after 3D warping, rather than following the full DIBR processing procedures. While on the other hand the distributions of holes on the virtual views are always around the transition regions of foreground and background, which makes them more difficult to distinguish without special constraints. Motivated by these observations, this paper proposes a novel learning-based method for stereoscopic view synthesis, in which the disocclusion regions are restored by a progressive structure reconstruction strategy instead of direct texture inpainting. Additionally, some special cues in the synthesized scenes are further exploited as constraints for the network to alleviate hallucinated structure mixtures among different layers. Extensive empirical evaluations and comparisons validate the strengths of the proposed approach and demonstrate that the model is more suitable for stereoscopic synthesis in the 2D-to-3D conversion applications.
Use of Gerund and Infinitive in Students’ Recount Text

The aim of this research is to find and analyze the use of gerund and infinitive in students‟ recount text. This research used a qualitative approach and the method was descriptive analysis. The research population was the students of school of foreign languages and 60 samples were taken randomly from third to fifth semester. The research design used content analysis because this research employed recount texts as the source of data. It was found that the errors of using gerund for “misformation” was 68.96%, “addition” was 20.68%, “omission” was 10.34% and “misordering” was 3.44%. While, for the infinitives, there were 60% of “misinformation”, 22.22% of “addition”, 11.11% of “misordering”, and 6.66% of “omission”.
Combat Mission Planning for Consumable Unmanned Aerial Vehicles in Complex Threat Environment

Unmanned aerial vehicle (UAV) combat mission planning mainly consists of mission allocation and path planning. Existing mission planning models intend to achieve optimal benefits in the constraint of UAV safety guarantees. However, with the rapid reduction in UAV cost and application of low-cost UAV, some members of the UAV team are allowed to be damaged in realistic mission. Therefore, this work proposes a novel mission planning model with allowed UAV loss rate for reduced earliest arrival time (ETA) in point target surveillance mission. Considering the difficulties caused by the coupling of planned path and UAV damage probability, an approximated algorithm (C-Astar) is proposed by iteratively adding waypoints generated by classical Astar algorithm to the line-of-sight path. Simulation results show that the proposed model and algorithm could achieve lower flight time, and also demonstrate the necessity of considering the allowance of UAV damage, thus feasibility and effectiveness of the proposed work are proved.
SINGLE IMAGE LASER TRIANGULATION SYSTEM FOR 360° EXTERNAL INSPECTION OF CYLINDRICAL SHAPED OBJECTS USING INVERSE TRIANGULATION ALGORITHM

Abstract. This paper proposes a new Laser Triangulation System (LTS) that allows an 360° external inspection for each acquired image using two planar mirrors, one laser line projector, and one camera. The new measurement algorithm is based on the inverse triangulation in which the analysis is made from 3D object space to image space, the opposite of traditional LTS algorithms, with laser peak detection in favourable directions even in an image with multiple reflections of the object. Measurement system principle, calibration and metrological evaluation are described based on simulated experimental data and 3D reconstructions examples. Uncertainty for a 20 mm plain plug gauge measurement was ±0.07 mm for 95.45% confidence.
Determination of Mobile Technology Use in an Interactive Distance Education Classroom Environment

The aim of this study is to investigate the determination of university students’ use of mobile technology in an interactive distance education classroom environment. The quantitative research method was used in the research. The research was conducted in the fall semester of 2021–2022. A total of 318 university students continuing their education in Kazakhstan voluntarily participated in the research. In the research, a 4-week online training was given to university students. The ‘distance education and mobile technology’ measurement tool developed by the researchers and compiled by experts in the field was used. The measurement tool was delivered and collected by university students via the online method. The analysis of the data was made by using the SPSS programme, frequency analysis, t-test and analysis of variance (ANOVA) test, and the results were added to the research in the form of tables. According to the results obtained from the research, it has been concluded that while university students’ level of knowledge in terms of distance education and mobile technologies is high, the situation of university students not using mobile technologies is quite low, but the rate of using them very often is quite high; and it is used by reflecting on the distance education systems in the course.
Versatile spectrum processing based on a compact diffractive neural network

Spectral processing is of great importance in optical research and applications. However, there are currently no multifunctional spectral processing devices with advanced processing capabilities. In this work, we implement on-demand spectral processing using a compact optical diffraction neural network (D2 NN), which includes only an SLM and a plane mirror. Spectral processing functions can be easily switched by changing the phase diagram on the SLM, which is obtained by computer pre-training of the D<sup>2</sup> NN for a specific task. Tunable single and dual-band spectral filtering and wavelength demultiplexing are achieved.
La trazabilidad del concepto de competencias en información dentro de los marcos de referencia digitales docentes

This article presents the traceability of the concept of information competences in several digital frameworks oriented to teachers. A documentary analysis was carried out based on a bibliographic survey where data from different selected texts were cross-checked. Eight works from international organizations (Unesco, European Union, ISTE) and national educational institutions in Latin America, Spain and the United Kingdom were selected. The different terminologies used by these frameworks in relation to the use of digital technologies in education are discussed, analyzing the concepts of digital competences that they present and identifying the elements of information competences included in them. A table was elaborated with the common aspects found that serve as input for discussion. Although some frameworks do not explicitly include information competences, the following were identified: information search, selection and critical evaluation, and ethical use. These are crossed by the ability to think critically when generating new knowledge.
Classification of Graduation Students of the Faculty of Computers and Multimedia, Universitas Islam Kebangsaan Indonesia Using the Naïve Bayes Classifier Algorithm

The purpose of this study to discuss classification of graduation Students of the Faculty of Computers and Multimedia, Universitas Islam Kebangsaan Indonesia using the naïve bayes classifier algorithm. The methodology used in this study uses a unified modeling language. The results show that the data mining system for classifying graduation Students of the Faculty of Computers and Multimedia, Universitas Islam Kebangsaan Indonesia succeeded in processing datasets, namely training data and sample data to form classes in the classification graduation Students of "Graduated on Time" or "Graduated Late". Processing data mining with the naïve bayes classifier algorithm is able to describe and draw conclusions on large amounts of datasets even though the data is complicated to understand. Research with this web-based system is easy for academics or faculties to use and data processing can be done quickly. Keywords: Classification, Naïve Bayes Classifier, Algorithm
An evolutionary- convolutional neural network for fake image detection

&lt;p&gt;&lt;span lang="EN-US"&gt;The fast development in deep learning techniques, besides the wide spread of social networks, facilitated fabricating and distributing images and videos without prior knowledge. This paper developed an evolutionary learning algorithm to automatically design a convolutional neural network (CNN) architecture for deepfake detection. Genetic algorithm (GA) based on residual network (ResNet) and densely connected convolutional network (DenseNet) as building block units for feature extraction versus multilayer perceptron (MLP), random forest (RF) and support vector machine (SVM) as classifiers generates different CNN structures. A local search mutation operation proposed to optimize three layers: (batch normlization, activation function, and regularizes). This method has the advantage of working on different datasets without preprocessing. Findings using two datasets evidence the efficiency of the suggested approach where the generated models outperform the state-of-art by increasing 1% in the accuracy; this confirms that intuitive design is the new direction for better generalization.&lt;/span&gt;&lt;/p&gt;
Machine learning prediction of video-based learning with technology acceptance model

&lt;span lang="EN-US"&gt;COVID-19 outbreak has significant impacts on education system as almost all countries shift to new way of teaching and learning; online learning. In this new environment, various innovative teaching methods have been created to deliver educational material in ensuring the learning outcomes such as video content. Thus, this research aims to implement machine learning prediction models for video-based learning in higher education institutions. Using survey data from 103 final year accounting students at Malaysian public university, this paper presents the fundamental frameworks of evaluating three machine learning models namely generalized linear model, random forest and decision tree. Besides demography attributes, the performance of each machine learning algorithm on the video-based learning usage has been observed based on the attributes of technology acceptance model namely perceived ease of use, perceived usefulness and attitude. The findings revealed that the perceived ease of use has given the highest weight of contributions to the generalized linear model and random forest while the major effects in decision tree has been given by the attitude variable. However, generalized linear model outperformed the two algorithms in term of the prediction accuracy.&lt;/span&gt;
Content Analysis and Its Application with Dynamic Online Content

Content analysis is a well-established and widely used research method. In its early form, it was used extensively in the quantitative analysis of newspapers, and its applications later evolved to include electronic media such as radio and television. It has recently been applied to digital media, including the Internet. However, the use of content analysis in analyzing online content has been chiefly applied to static content, such as ‘static’ websites, in the early days of the Internet. Studies that involve its use in analyzing dynamic Internet content—for example, content that resides behind databases—are relatively much less common. This article is not written as a research paper per se. This article will instead discuss reflections on the efficacy of content analysis as a research method when applied to dynamic content such as DRs by using a previous study, which has applied content analysis to the dynamic content of digital repositories (DRs), as a case study. The previous study used as the basis for this article had applied content analysis to several DRs using manual counting by the researcher. In the process, several idiosyncrasies in terms of the way institutions populate their DRs with digital objects and the user metadata to facilitate discoverability of those digital objects were encountered that have introduced some ‘complication.’ This article will focus on how content analysis, as a research method, can be adapted to account for those idiosyncrasies to produce better results. This article will also identify the limitations and challenges of content analysis in dynamic online environments and offer some suggested approaches.
Can the Robot be Considered a Person? The European Perspective

Technological advances are equipping robotic entities with artificial intelligence and endowed with emotional intelligence that gives them a capacity for reflection, analysis, that is closer every day to that of humans. The growing autonomy of robotic entities raises the question of legal responsibility for acts carried out by such entities at the same time as it raises the question of the status of such entities: should they be considered as persons or as things? The European Parliament adopted a resolution in 2017 that assumes that such entities could be granted the status of "electronic person". The question is how this can fit into the legal framework of the Member States of the European Community. This article proposes a first reflection on the thing-person transition of an autonomous robotic entity. The findings show that this transition is not immediate and requires both technological advances and an adjustment of the law.
2022 list of referees for <i>Mineralogical Magazine</i>

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.
Emotional Interaction Design Strategies for Users’ Social Media Usage Motivation

This paper changes the research perspective of social media interaction design, takes McQuail's "media-personal interaction" model as the starting point, combines emotional design, and illustrates the research idea of social design based on the motivation of users' social media use, and proposes four types of social interaction design: instinctive design, information design, communication design, and value design.It also concludes that social interaction design should be designed around the four design types and focus on different levels of emotional design for different users' social media usage motives.This study provides new design ideas and design methods for social design in social media.
An Argumentation and Ontology Based Legal Support System for AI Vehicle Design

As AI products continue to evolve, increasingly legal problems are emerging for the engineers that design them. Current laws are often ambiguous, inconsistent or undefined when it comes to technologies that make use of AI. Engineers would benefit from decision support tools that provide engineer’s with legal advice and guidance on their design decisions. This research aims at exploring a new representation of legal ontology by importing argumentation theory and constructing a trustworthy legal decision system. While the ideas are generally applicable to AI products, our initial focus has been on Autonomous Vehicles (AVs).
Convolutional Neural Network Analysis for Modulation Classification of Wireless Communication Signal

Modulation classification detects the modulation type of received signals to guarantee that the signals can be correctly demodulated and that the transmitted message can be accurately recovered. For the modulation classification of M-PSK, M-QAM and M-APSK modulated signals with similar constellation maps, we analyze waveform characteristics of the signal in the time domain. Based on the waveform characteristics of the signal, we explore the feasibility of using convolutional neural network (CNN) to identify the modulation classification of the signal. We analyze the data input structure and network model required for modulation classification using CNN. Considering the impairment of the signal by additive white Gaussian noise (AWGN), clock offset and Rician multipath fading, a combined channel is simulated in this paper to obtain the impaired data as dataset. The simulation results show that CNN has great potential for modulation classification.
Research on Intelligent Customer Service Q &amp; A Model Based on Deep Neural Network

Logistics industry is one of the important infrastructures for national economic development, and an intelligent question and answer system is the core of all kinds of intelligent robots in the market.In this paper, we propose a dialogue model in the field of logistics to replace human customer service with intelligent customer service to provide users with 24-hour online consultation. In this paper, we propose a dialogue model in the field of logistics to replace human customer service with intelligent customer service to provide users with 24-hour online consultation service. This model first performs data pre-processing by Bert-BiLSTM, uses the Sigmoid function to determine the scenario asked by the user, and then completes named entity recognition by Bert-BiLSTM-CRF. From the experimental results, we can see that the proposed model performs better,and the F1-score evaluation index is improved by 0.96% 3.22% compared with the benchmark model.
Face and Gesture Recognition Based on CNN and CPM Model

In the autonomous motion control of unmanned equipment, in order to make the control more precise and efficient, in addition to the automatic operation of unmanned system, it is also necessary to introduce a certain amount of human-computer interaction. In this paper, by calculating the control requirements of unmanned equipment in specific cases and comparing with existing technologies, a gesture recognition pro-gram is established. The results show that it is feasible to control the movement of unmanned equipment through gesture recognition technology. Moreover, gesture control has the advantages of high accuracy, simplicity and efficiency, and can control multiple unmanned equipment in real time.
Health Assessment of Higher Education System Based on 3D Baseline Cube Model

Educational ecology is a branch of pedagogy that uses ecosystem theory to study the law between education and the external ecological environment. In this paper, We use ecology research methods to evaluate the health and sustainability of the education system. Based on Educational stability, carrying capacity and teaching quality, we build the three-dimensional baseline cube model (called the Tri-E model) for comprehensive measurement. In terms of educational stability, we build educational pyramids according to different academic qualifications and calculate Gini coefficient according to geographical distribution and the Educational Stability Index (ESI) is summarized. For the carrying capacity and quality of education, the weight of each factor is determined by the AHP. The Education Carrying Capacity Index (ECCI) and Education Quality Index (EQI) are calculated according to the relevant indicators of the countries studied. These indicators will determine a vector in the 3D Baseline cube to obtain a measure of the health status of higher education Tri-E Index (TEI). We analyzed some representative countries (UK, India, and China) with TEI of 0.741, 0.521, and 0.665, respectively. Referencing China’s education reform, we put forward the current problems facing the Indian education system and how to reform it. After a quantitative analysis of the actual resistance index, the conclusion is that it is difficult for the reform to be completely successful.
HDGFI: Hierarchical Dual-Level Graph Feature Interaction Model for Personalized Recommendation

Under the background of information overload, the recommendation system has attracted wide attention as one of the most important means for this problem. Feature interaction considers not only the impact of each feature but also the combination of two or more features, which has become an important research field in recommendation systems. There are two essential problems in current feature interaction research. One is that not all feature interactions can generate positive gains, and some may lead to an increase in noise. The other is that the process of feature interactions is implicit and uninterpretable. In this paper, a Hierarchical Dual-level Graph Feature Interaction (HDGFI) model is proposed to solve these problems in the recommendation system. The model regards features as nodes and edges as interactions between features in the graph structure. Interaction noise is filtered by beneficial interaction selection based on a hierarchical edge selection module. At the same time, the importance of interaction between nodes is modeled in two perspectives in order to learn the representation of feature nodes at a finer granularity. Experimental results show that the proposed HDGFI model has higher accuracy than the existing models.
THE FUTURE IN CENTRAL BANKS ACTIVITY – CENTRAL BANK DIGITAL CURRENCY –

Developments in recent years in the technologies that underpin money transfers and financial investments have challenged banking in general and central banking in particular. On the one hand, with the onset and manifestation of the 2008 crisis, the banking system is being called into question as to whether it can be trusted, and on the other hand, also in the same historical context, blockchain technology is emerging and is seen as a threat by banks. Lately, there has been interest in Central Bank Digital Currencies (CBDCs) which are a digital replacement for banknotes and coins in physical form. In this paper we will highlight the features of CBDCs and the proposed Ethereum-based central bank money technology.
IFLA Standards, UNIMARC and COBISS System: The Importance of Authority Data

Bibliographic control is one of the few internationally highly standardized activities. The generally accepted IFLA Library Reference Model (IFLA LRM) has influenced the modification of numerous other standards in the field of bibliographic control. In this context, also on UNIMARC and ISBD. Moreover, at the IFLA WLIC 2022 Congress, the working group for the revision of international cataloguing principles announced that it was analyzing the introduction of the term “work with entities”. In the COBISS system, developments in the field of bibliographic control and standards related to it are closely monitored. The transition to the new millennium shed a new light on the organization of bibliographic information, placing in the focus of interest authority data as the basic building block of a new approach to managing the sources of the bibliographic universe. Therefore, first of all, the state of authority data in the COBISS.net network should be analyzed, which can serve as a starting point for development and cooperation between the member countries of the COBISS.net network also on this field.