Reviewerly/exHarmony huggingface dataset:
Authoring Moving Parts of Objects in AR, VR and the Desktop

Creating digital twins of real objects is becoming more popular, with smartphones providing 3D scanning capabilities. Adding semantics to the reconstructed virtual objects is important to possibly reproduce training scenarios that otherwise could demand significant resources or happen in dangerous scenarios in some cases. The aim of this work is to evaluate the usability of authoring object component behaviors in immersive and non-immersive approaches. Therefore, we present an evaluation of the perceived ease of use to author moving parts of objects under three different conditions: desktop, augmented reality (AR) and virtual reality (VR). This paper provides insights into the perceived benefits and issues that domain experts might encounter when authoring geometrical component behaviors across each interface. A within-subject study is the major contribution of this work, from which is presented an analysis based on the usability, workload and user interface preferences of participants in the study. To reduce confounding variables in the study, we ensured that the virtual objects and the environment used for the evaluation were digital twins of the real objects and the environment that the experiment took place in. Results show that the desktop interface was perceived as more efficient and easier to use based on usability and workload measures. The desktop was preferred for performing component selection but no difference was found in the preference for defining a behavior and visualizing it. Based on these results, a set of recommendations and future directions are provided to achieve a more usable, immersive authoring experience.
Effortless Polite Telepresence using Intention Recognition

Telepresence technology creates the opportunity for people that were traditionally left out of the workforce to work remotely. In the service industry, a pool of novice remote workers could teleoperate robots to perform short work stints to fill in the gaps left by the dwindling workforce. A hurdle is that consistently talking appropriately and politely imposes a severe mental burden on such novice operators and the quality of the service may suffer. In this study, we propose a teleoperation support system that lets novice remote workers talk freely without considering appropriateness and politeness while maintaining the quality of the service. The proposed system exploits intent recognition to transform casual utterances into predefined appropriate and polite utterances. We conducted a within subject user study where 23 participants played the role of novice remote operators controlling a guardsman robot in charge of monitoring customers’ behaviors. We measured the workload with and without using the proposed support system using NASA task load index questionnaires. The workload was significantly lower ( p &lt;.001) when using the proposed support system ( M = 46.07, SD = 14.36) than when not using it ( M = 62.74, SD = 12.70). The effect size was large (Cohen’s d = 1.23).
Automatic BI-RADS Classification of Breast Magnetic Resonance Medical Records Using Transformer-Based Models for Brazilian Portuguese

This chapter aims to present a classification model for categorizing textual clinical records of breast magnetic resonance imaging, based on lexical, syntactic and semantic analysis of clinical reports according to the Breast Imaging-Reporting and Data System (BI-RADS) classification, using Deep Learning and Natural Language Processing (NLP). The model was developed from transfer learning based on the pre-trained BERTimbau model, BERT model (Bidirectional Encoder Representations from Transformers) trained in Brazilian Portuguese. The dataset is composed of medical reports in Brazilian Portuguese classified into six categories: Inconclusive; Normal or Negative; Certainly Benign Findings; Probably Benign Findings; Suspicious Findings; High Risk of Cancer; Previously Known Malignant Injury. The following models were implemented and compared: Random Forest, SVM, Naïve Bayes, BERTimbau with and without finetuning. The BERTimbau model presented better results, with better performance after finetuning.
Efektivitas Filantropi Indonesia dalam Mendukung Pencapaian Pembangunan Budaya Berkelanjutan Ditinjau dari Aspek Kewarganegaraan

Information and communication technology (ICT) facilitates quick and easy access to a wide range of information and information resources worldwide for students and teachers. Due to the importance of ICT in learning, it is necessary to identify factors that might influence its integration into the educational environment. This study examined the relationship between motivation, self- efficacy and the use of information and communication technology for learning among college students in Kwara State. This study also examined the influence of gender on motivation, self-efficacy and the use of ICT for learning This research is a descriptive design using the cross sectional survey method. The study samples were taken from one federal, one state and one private university (three universities) in Kwara state. The respondents were 296 students (170 boys and 126 girls and 54 arts, 104 social studies and 138 students. As a result it can be recommended that adequate support should be provided to students to obtain ICT resources and applications for the improvement of their studies, also teachers should integrate the use of ICT in their lessons regularly to encourage students to use it.
Examining the Factors That Influence User Information Security Behavior toward COVID-19 Scams

Amidst the COVID-19 pandemic, cybercriminals have capitalized on the situation and targeted online users. As a result, there has been a surge in the number of complaints related to new types of scams and fraud that have emerged during the pandemic. This study utilizes Protection Motivation Theory (PMT), Cultivation Theory (CT), and other constructs, including fear, attitude, general security awareness, general security orientation, subjective norms, openness, stress, and conscientiousness, to investigate the user information security behavior (ISB) against COVID-19 scams. The study gathered data from 423 internet users through a cross-sectional survey and used Partial Least Square Structural Equation Modeling (PLS-SEM) to test the conceptual framework. The results indicate that stress, government social media, general security awareness, response efficacy, and attitude were all positive determinants of the users’ ISB. The findings also highlight the impact of emotions, such as stress and fear, as well as government social media, on decision-making during a crisis. Additionally, the study found that the combined effects of the two threat appraisal factors on coping behaviors, which are mediated by fear, and the combined effect of the two coping appraisal factors on coping behaviors, which are mediated by attitude, were significant. However, the subjective norms on ISB did not show any variations and surprisingly, the study found that conscientiousness did not moderate the association between attitude and ISB.
Computational issues on the blocked Gibbs algorithm

Nonparametric Bayesian methodology uses information from the data to infer the posterior distribution of the model parameters, without requiring the researcher to specify a prior distribution. This extends the Bayesian framework to scenarios where the underlying probability distributions are not known to have a fixed number of parameters. Instead, nonparametric Bayesian models use infinite-dimensional parameter spaces to allow for greater flexibility. When employing a nonparametric Bayesian hierarchical model with the blocked Gibbs sampler algorithm, estimating the number of latent components is a critical task. This is challenging because it requires using the concept of infinity within the MCMC process to identify a suitable, arbitrary value that effectively characterizes the complexity of the data. In this paper, we present two distinct methodologies including finite approximation and dynamic adjustment for implementing nonparametric Bayesian analysis.
THE IMPACT OF ORGANIZATIONAL CULTURE ON EMPLOYEE PERFORMANCE: THE CASE OF UNIVERSITY OF TECHNOLOGY AND APPLIED SCIENCES-IBRA IN OMAN

Organizational culture is one of the most important factors affecting the performance of employees in various organizations. A system of values and beliefs adopted by members of the organization affects their performance either positively or negatively. This study aims to investigate the factors of organizational culture (decision-making, organizational environment &amp; reward and recognition system) on employee job performance at UTAS- Ibra in Oman, also the study will examine the effect of the organizational culture on employee job performance at UTAS- Ibra. The method used in this study to collect data is a survey questionnaire. For the purpose of this study, 114 completed questionnaires using stratified sampling method from all academic departments and Educational Technology Center at UTAS-Ibra received and analyzed through SPSS program and smart PLS-SEM. Moreover, the study used secondary data from previous studies in different books and journals as additional sources of data. The results indicate that decision-making and the organizational environment factors of organizational culture have a positive relationship with performance. While, reward and recognition system had a negative impact on employee performance. Based on the results of the study, the study recommending that officials in UTAS-Ibra and the university should strengthen the reward and recognition system so that an integrated system is designed to make employees work in a stimulating environment for work, which results in increasing their performance.
Visualization of Metallogenic Belt-Scale Crustal Geometries and Their Implications for Ore Genesis in Northern Chile

Preview this conference paper: Visualization of Metallogenic Belt-Scale Crustal Geometries and Their Implications for Ore Genesis in Northern Chile, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/2214-4609/2023/third-eage-workshop-on-mineral-exploration-in-latin-america-the-role-of-mining-in-the-energy-transition/Template_EAGE_Abstract_Extended_2023-1.gif
RESEARCHER ECONOMY

The Alpha male in "Animal Movie," who hunts wild creatures and creates new chances for civilization, resembles researchers today. Researchers like Alpha males, pioneer new possibilities and inventions, enhancing knowledge and comprehension. The Researchers Economy's slogan emphasizes honoring these pioneers of research and advancement. 9NFTMANIA, a pioneering entity within the blockchain and cryptocurrency space, has introduced a groundbreaking initiative called the Researcher Economy. Recognizing the vital role that researchers play in shaping the understanding and development of specific tokens, non-fungible tokens (NFTs), and crypto currencies, 9NFTMANIA aims to fortify the ecosystem by providing robust support to research scholars. At the core of this mission is the belief that fostering a thriving research community enhances the overall value of digital assets. 9NFTMANIA is actively championing this cause by encouraging and incentivizing research excellence through its Researcher Economy initiative. Through this program, 9NFTMANIA is facilitating research paper publications, creating a knowledge hub, and establishing reward mechanisms to acknowledge and amplify the contributions of researchers who delve into the complexities and innovations within the realm of digital assets. By leading the charge in supporting and empowering research scholars, 9NFTMANIA envisions a future where the understanding and value of tokens, NFTs, and crypto currencies are continually advanced through rigorous academic exploration and collaboration. Moreover this initiative would assist researchers in exploring Metaverse.
Possibilities of didactic engineering in training future specialists

The article discusses the possibilities of didactic engineering in preparing future specialists for independent professional activities. Didactic engineering is a methodology (theory, practice, diagnostics) of organizing activities to solve didactic problems using engineering methods (metric approach, modeling, numerical analysis, use of information and communication technologies). Within the framework of didactic engineering, the tasks of creating new effective didactic systems, teaching aids and diagnosing the quality of training of specialists working in a virtualreal environment are solved. This allows us to outline the path for rapid sustainable professional development of the student, establish key invariant abilities, determine the role of knowledge in solving professional problems, and generally reveal the reasons why some people solve professional problems more successfully than others. The authors, based on the theoretical and practical results of the study, highlight problematic issues in the adaptation of future specialists to independent professional activities and the need to implement innovative approaches. Based on the conducted research, a set of interrelated conditions aimed at increasing the level of readiness of future specialists to carry out innovative professional activities was put forward for discussion. The process of training and education of a student must be organized based on his personal professional interest, personal growth through the construction of a professional development trajectory. Consequently, in systems built within the framework of didactic engineering, it is necessary to introduce the category “complexity of problems”, since in the future this makes it possible to objectively assess the professional potential of a future specialist.
Development of Folklore Text Teaching Materials (Hikayat) in the Form of Interactive Digital Multimedia in High Schools (SMA)

The rapid development of the times requires teachers to have digital literacy skills. Teachers must master several technological sciences that can be applied in the learning process. An innovative and interactive learning process is one of the determining factors for the success of learning. The
Decision-Making Support in Identifying Extremist Content on the Information and Telecommunications Network of the Internet

The issues of automatic detection of extremist content on the Internet are considered. A conceptual model of decision support for extremist content detection based on machine learning is presented. An algorithm for detecting signs of extremism based on a probabilistic thematic model is proposed. It allows to determine the unlawful content of texts by the set of words included in them (based on the probability of categorizing the words as extremist content). Studies confirming the effectiveness of the proposed algorithm in identifying extremist themes in texts have been conducted.
Application of Single View 3D Reconstruction Based on Target Detection and Segmentation in Autonomous Driving

With the rapid development of the field of autonomous driving, more and more image processing technologies are applied to autonomous vehicles. The single-view 3D reconstruction technology based on deep learning has broad application prospects in the field of autonomous driving due to its advantages of simple data acquisition, low computing cost, and strong applicability. However, the current mainstream single-view reconstruction algorithm cannot achieve simultaneous reconstruction of multiple objects, and the real scene images captured by the equipment on the self-driving vehicle often contain many entities, such as vehicles, pedestrians, and traffic signs. In this paper, an image processing module is designed, combined with a single-view reconstruction algorithm, to realize the 3D model reconstruction of multiple vehicle targets in the real shooting image in the autonomous driving scene. Experiments show that this module can accurately segment vehicles and improve the accuracy of reconstruction. It can provide some help for the environmental perception of automated driving.
Mechanism of semantic processing of lexicalized and novel compound words: An eye movement study.

The integration of semantic information of compound words with context is a crucial aspect of reading comprehension. In two eye-tracking experiments, we used two-character and four-character Chinese lexicalized and novel compound words to investigate how Chinese readers integrate semantic information of compound words with contexts in the present study. By manipulating the temporary plausibility of the first constituent through varying the preceding verb, we aimed to investigate how readers process semantic information of compound words during normal reading. A significant plausibility effect pattern in the first constituent region was observed for the four-character novel words, but not for the lexicalized compound words and two-character novel compound words. However, for both two-character and four-character novel compound words, a reverse plausibility effect was found in the second constituent region. This was not the case for lexicalized compound words. These results indicate that novel compound words are integrated with the context in a decompositional manner, while lexicalized compound words are integrated holistically. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Manipulation Mask Generator: High-Quality Image Manipulation Mask Generation Method Based on Modified Total Variation Noise Reduction

In artificial intelligence, any model that wants to achieve a good result is inseparable from a large number of high-quality data. It is especially true in the field of tamper detection. This paper proposes a modified total variation noise reduction method to acquire high-quality tampered images. We automatically crawl original and tampered images from the Baidu PS Bar. Baidu PS Bar is a website where net friends post countless tampered images. Subtracting the original image with the tampered image can highlight the tampered area. However, there is also substantial noise on the final print, so these images can’t be directly used in the deep learning model. Our modified total variation noise reduction method is aimed at solving this problem. Because a lot of text is slender, it is easy to lose text information after the opening and closing operation. We use MSER (Maximally Stable Extremal Regions) and NMS (Non-maximum Suppression) technology to extract text information. And then use the modified total variation noise reduction technology to process the subtracted image. Finally, we can obtain an image with little noise by adding the image and text information. And the idea also largely retains the text information. Datasets generated in this way can be used in deep learning models, and they will help the model achieve better results.
Some Results on Self-Complementary Linear Codes

Binary codes have a special place in coding theory since they are one of the most commonly used in practice. There are classes of codes specific only to the binary case. One such class is self-complementary codes. Self-complementary linear codes are binary codes that, together with any vector, contain its complement as well. This paper is about binary linear self-complementary codes. A natural goal in coding theory is to find a linear code with a given length n and dimension k such that the minimum distance d is maximal. Codes with these properties are called optimal. Another important issue is classifying the optimal codes, i.e., finding exactly one representative of each equivalence class. In some sense, the classification problem is more general than the minimum distance bounds problem. In this work, we summarize the classification results for self-complementary codes with the maximum possible minimum distance and a length of up to 20. For the classification, we developed a new algorithm that is much more efficient compared to existing ones in some cases.
Towards a cryptography encyclopedia: a survey on attribute-based encryption

Access control, a key feature of digital business models, such as streaming, relies on the implementation of encryption schemes. The diverse use of encryption schemes has led to the development of schemes with a variety of properties. This variety and a lack of comprehensive overview make it difficult for developers to select an appropriate scheme. To bridge this gap, we envision a cryptography encyclopedia. In this survey, we create a sub-encyclopedia for attribute-based encryption (ABE) schemes. More specifically, we provide an overview of relevant features and performance metrics and a taxonomy for ABE schemes. We also perform a performance and feature evaluation of 42 ABE schemes and apply our proposed topology to these approaches. %The content related to Section in the body of this document is not displayed in the PDF. Please confirm the addition of Section numbers or paragraph names.
CBS: A Deep Learning Approach for Encrypted Traffic Classification with Mixed Spatio-Temporal and Statistical Features

With the rapid advancement of the internet and online applications, traffic classification has become an increasingly significant topic in computer networks. Managing network resources, improving service quality, and enhancing cybersecurity are critical. Due to traffic encryption techniques, traditional traffic classification approaches have become ineffective and inaccurate. Therefore, the scientific community considers deep learning a high-performance approach for classifying encrypted traffic. This paper proposes an encrypted traffic classification approach, CBS, based on a deep learning technique. CBS can classify encrypted traffic at two levels using 1D-CNN, attention-based Bi-LSTM, and SAE deep network models. The proposed model classifies traffic types and applications based on a comprehensive set of session and packet-level features. CBS accurately distinguishes traffic classes using spatial, temporal, and statistical features extracted from packet content relationships, temporal relationships between packets in a session, and statistical characteristics of a work session. A traffic data augmentation technique based on a GAN network is employed to mitigate the impact of data imbalance on traffic classes. The proposed platform’s performance is evaluated on the public ISCX VPN-Non VPN 2016 dataset. The results demonstrate that the platform accurately and efficiently identifies applications and classifies encrypted traffic. Compared to state-of-the-art methods, the proposed traffic classification model improves precision by 21.3%, accuracy by 13.1%, recall by 18.11%, and F1 score by 19.79%.
Model-Based Design of User Story Using Named Entity Recognition (NER)

The needs analysis method to determine system specifications is generally represented as a user story. A user story is built to achieve the goals and values from the user’s point of view as the party requesting and will use the system. In this study, Named-Entity Recognition (NER) has been applied to build a model-based design for analysis techniques to identify, find and categorize user stories. Therefore, it can help system analysts and software development teams understand and improve user stories’ quality. The design model using spaCy’s library has an evaluation of the ten datasets. The order to evaluate the accuracy value is known for precision value is 97.45%, the recall value reached 99.67%, and the F1-Score was 84.61%. The experimental results show that the number of sentences does not affect the recall value and f1-score. However, the amount of data does not determine the accuracy of the label prediction.