Reviewerly/exHarmony huggingface dataset:
MEASURING ARTIFICIAL INTELLIGENCE: METHODOLOGICAL FOUNDATIONS, PRINCIPLES OF CREATION AND INFORMATION TECHNOLOGY BASED ON THE REGULARIZING BAYESIAN APPROACH

The article is devoted to a new direction in the creation of information and measurement systems – measuring artificial intelligence. The paper gives the basic definitions and attributes of measuring artificial intelligence. The field of measurement tasks in which the application of methods and means of intellectualization of measurement processes is necessary is defined. The main types of intelligent measurements are given, their characteristics and specific properties are indicated. It is emphasized that measurement tasks in conditions of information uncertainty caused by inaccuracy, incompleteness, fuzziness of data, their small volumes and uniqueness, incompleteness of models of the measuring object and the environment of its functioning, should be solved on the basis of regularizing methods that ensure the stability of the solutions obtained, as well as with the involvement of knowledge about the object and the environment, which makes it possible to compensate for the lack of completeness data. Therefore, in the article, to solve measurement problems in these conditions, it is proposed to use a regularization approach (RBP) and technologies based on it: Bayesian intelligent measurements (BII) and Bayesian intelligent technologies (BIT).Methodological principles and basic analytical dependencies of BII and BIT are given. The article proposes a methodology for creating a system for evaluating the quality of artificial intelligence (AI) solutions, in particular, evaluating the reliability of solutions, which determines the degree of confidence in the solutions obtained. The methodology is based on scaling of measuring solutions and implemented as a system of metrological support of AI solutions. Within the framework of this methodology and technologies of metrological support, complexes of metrological characteristics are proposed that determine accuracy, reliability (level of errors of the 1-st and 2-nd kind), which ensures traceability and transparency at each stage of obtaining solutions. Practical examples of the implementation of RBP methods and technologies for solving applied problems and creating intelligent measuring systems are given.
SG-Audit: An Efficient and Robust Cloud Auditing Scheme for Smart Grid

Cloud auditing allows users to leverage digital signature evidences to undertake remote data verification and consequently determine the integrity of their data stored in the cloud. While there are many cloud auditing schemes proposed for cloud services, deployments on large scale smart grid (SG) are known to be challenging in practice, for example in terms of inefficiency and lack of robustness. In this paper, we propose an efficient and robust cloud auditing scheme for SG (hereafter referred to as SG-Audit). Specifically, we utilize mobile edge computing (served as proxy signer) to offload the signature computation loads incurred by smart meters (SMs), as well as devising an efficient proxy signer recommendation strategy to ensure each SM obtains high quality service, a scalable index structure to reduce the signature evidence access time during data verification, and a deduplication and sampling based challenge data index generation strategy to narrow down the verification scope. Moreover, we also define three strategic threat scenarios supported by SG-Audit, and further devise a secure cloud auditing protocol to improve robustness. Through rigorous mathematical analysis and extensive experiments, we demonstrate that SG-Audit achieves increased auditing efficiency (by about 42% on average) in comparison to prior work.
Development of Web3 Awareness Scale as the Next Evolution of the Internet

Web3, characterized by its decentralization, user autonomy, and integration of blockchain technologies, introduces a novel ecosystem that is rife with both opportunities and complexities. While these advances are rewriting the conventional norms of digital interaction and data management, they are also ushering in a new set of challenges and learning curves. We introduce the "Web3 Awareness Scale" as a main element through the current study. This carefully designed scale assesses university students' understanding and readiness for the emerging landscape of Web3. Whilst the article provides detailed insights into the technical, real-world applications and challenges of Web3, the inclusion of the scale emphasizes the practical assessment of awareness and readiness among key digital natives - students. As a result of the EFA, 6 items were removed from the scale, and the total explained variance of the two-factor structure consisted of 31. The final version of the scale showed a total explained variation of 64.16%. Specifically, the first factor, Opportunities, accounted for 46.94% of the variance, while the second factor, Risks, accounted for 17.21% of the variance. It is found that the coefficients of all factors indicate a very highly reliable measurement. The interpretation is that both the total scale and each factor have a high level of reliability. The findings from the scale are critical and have the potential to provide invaluable insights. These insights are expected to shape future educational curricula and policy-making, ensuring that the next generation is well-equipped to navigate and exploit the opportunities of Web3.
An Efficient Meta-Platform for Providing Expert Medical Help to Italian and Slovenian Users

When dealing with medical issues, Google and similar search engines can be of great help. A user inputs a couple of words about a particular medical issue and receives a list of replies that hopefully provide also the desired answer. The problem is that it is far from easy and fast to find the most proper answer in the huge list of related web pages, with some of them even misleading or harmful. Experience shows that a large proportion of the population falls for inadequate medical advices, some alternative and some even dangerous. This paper presents a novel meta-platform for electronic and mobile health-related services with its main purpose to provide proper quality information to a user with a medical issue, kind of an expert medical “doctor Google”. The platform is centered around “services” and human help. A “service” is an intermediate medical concept between a user and the expert of related feed-backs which redirects a user to another website, video, or other multimedia item that contains important more detailed information of proper quality for a user. A user can also search for the related medical issues, e.g., further-links through a local search upon services, and also ask a human or an AI chatbot/assistant. The platform is dedicated to Italian and Slovenian users in Italian, Slovenian and English language.
Using Hybrid CNN-LSTM Model for Sentiment Analysis of COVID-19 Tweets

The COVID-19 pandemic has affected many aspects of everyone’s lives, causing problems in health and the economy. People often share their sentiments about the pandemic on social media, particularly on platforms like Twitter. Sentiment analysis involves using various pre-processed tweets and natural language processing (NLP) techniques to classify them as positive, neutral, or negative. It is a demanding task. Platforms with microblogging features have played a crucial role in disseminating information, such as news and emergencies. These data types are rich in sentiments that contribute to the necessary awareness of safety and preparedness. Previous studies have demonstrated that Twitter usage during disasters increases situational awareness and improves disaster response through the tweets of affected users. However, sometimes a single model is insufficient to achieve desired results, necessitating the creation of a hybrid model. This study aimed to develop a sentiment analysis model using a hybrid CNN-LSTM framework to analyze sentiments in tweets during the COVID-19 pandemic. The model utilized a hybrid CNN-LSTM architecture that focused on predicting whether the sentiment was positive or negative. The developed hybrid CNN-LSTM model achieved a testing accuracy of 84.72% and outperformed standalone CNN and LSTM models.
Heuristic Tubular Growth with Adaptive Voxel Filtering for Coronary Artery Segmentation

In 3D coronary computed tomography angiography (CCTA), accurate coronary artery segmentation is crucial for diagnosis and further treatment of cardiovascular diseases. However, for 3D CCTA images, the extremely unbalanced voxel distribution between coronary artery and background voxels brings a huge challenge to data-driven model optimization. To address this problem, we propose a novel coronary artery segmentation framework which consists of two stages: growth and filtering. Specifically, in the growth stage, a local growth algorithm is proposed to segment and connect the targets patch wisely, ignoring the non-target patches. In the filter stage, voxel-level pseudo heart labels are generated from coronary artery ground truths to train a heart segmentation model to exclude irrelevant background voxels. Finally, a point cloud based segmentation model is introduced to remove the false positive voxels within the heart (e.g., veins) and get more accurate segmentation results. Compared with existing methods, our framework could avoid interference from redundant voxels and is more robust to unbalanced class distribution. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art both in quality and quantity (i.e., 6.1 % improvement). Additionally, we will release our full codes and a high-quality annotated coronary artery segmentation dataset (i.e., CORONARY-18) for future research.
Comparison Of Radiograph Results on Antero Posterior Lumbosacral Examination Using a Grid and Without a Grid

The grid is an inspection tool that is used to absorb scattered radiation that is not directional from the object being exposed. Routine projections on lumbosacral examination are posterior (AP) and lateral Antero projections. This study was conducted to determine the comparison of the radiographs of the lumbosacral examination using a grid and without a grid. This type of research is qualitative research with a descriptive approach. The samples taken in this study were 2 samples with lumbosacral examination patients. Data collection was obtained from the method of observation, questionnaires, and documentation. Based on the research that has been done, the lumbosacral examination using the grid produces a better picture than the image that does not use a grid.
Research on Substation Quantum Secure Communication System Based on 5G Private Network Technology

Quantum cryptography is an epoch-making technology with irreplaceable security, which has been widely used in the field of secure communication. This paper first explores the application prospects of quantum communication technology in power systems. Secondly, this paper innovatively proposes a power secure communication method based on quantum keys. At the same time, this paper constructs the quantum key secure communication network architecture and analyzes the application principle and security mechanism of quantum key. Taking the quantum key management system as the core, the author designs and implements the quantum key security storage and business security process application model. Thirdly, this paper uses the local search algorithm to optimize the parameters of the MDI-QKD protocol under symmetric and asymmetric conditions, and gives its optimized key rate. Finally, this paper uses the actual quantum key distribution equipment, quantum VPN and network tester to build an experimental environment. The system uses a comprehensive network tester to simulate the power grid application business information to test and statistically analyze the core performance characteristics of quantum VPN, such as throughput, delay, and encryption algorithm. The experimental results can provide a reference for the popularization and application of the quantum secure communication system in the power grid production control and management information business.
Enhanced Transformers for Radar Jamming Recognition

The recognition of jamming type is a key step among advanced anti-jamming techniques. Currently, deep convolutional neural network (CNN)-based and recurrent neural network (RNN)-based methods have been used for radar jamming recognition. However, they both have disadvantage for modeling the long-range dependencies of radar jamming signals. Fortunately, the transformer model is good at capture relationships of radar jamming signals in a long distance utilizing self-attention mechanism. Hence, three transformer-based models are developed for radar jamming recognition. Firstly, a well-designed Transformer (i.e., RadarTR) is proposed. But the recognition accuracy will decrease with limited training samples. Thus, based on RadarTR, two enhanced Transformers are proposed to alleviate the overfitting problem in terms of data augmentation and model structure, respectively. Specifically, RadarTR is combined with soft augmentation (i.e., RadarTR-SA), which exchanges regions between samples of different classes to generate new training samples and thus learns better features from the training samples. Besides, RadarTR is combined with auxiliary recognizer (i.e., RadarTR-AR), which additionally utilizes the distinguishing features in middle Transformer blocks and increases the gradient signal, and thus it achieves better generalization capability. The experimental results show that the proposed methods obtain good recognition performances in terms of accuracy compared with the state-of-the-art methods.
Improved Neural Collaborative Filtering Recommendation Method based on Multi-Head Attention and Feature Fusion

Recommender systems play an important role in personalized recommendations, and neural collaborative filtering models have achieved some success as an effective recommendation method. However, neural collaborative filtering has limitations in feature representation capabilities and interaction relationship modeling, which limit their recommendation accuracy and personalization capability. In order to address this problem, this paper proposes an improved neural collaborative filtering method, called MHAN-MF. In this method, we propose two improvements: (1) by concatenating the user embedding vector, item embedding vector, and the element-wise product of the user and item embedding vector, the concatenated vector is used as the input feature of the Multi-Layer Perceptron(MLP), thereby enriching the input features of the model and improving its expression ability; (2) the introduction of multi-head attention enables the model to focus on different parts of the input features from different perspectives, to better capture relevant features in the data, and to improve the ability to model complex interactions. We experimentally validated the MHANMF method on publicly accessible datasets. The experimental results show that the MHAN-MF method significantly outperforms other methods in terms of recommendation effectiveness, which verifies the effectiveness of our proposed method.
AN ANALYSIS OF IMPLEMENTATION CHALLENGES FOR ENGLISH FOR SPECIFIC PURPOSES (ESP) FORMATIVE ASSESSMENT VIA BLENDED LEARNING MODE AT CHINESE VOCATIONAL POLYTECHNICS

In the midst of the current digital learning era, traditional assessment has gradually shifted over prioritizing summative assessment towards equally emphasizing formative assessment within blended learning mode. However, there are still limited studies that investigate the implementation of formative assessment via blended learning mode in China particularly looking at the situation at Chinese vocational polytechnics. To explore actual formative assessment practices, a qualitative case study was conducted at two vocational polytechnics and experienced ESP teachers were selected as participants. Rich data were collected through rounds of semi-structured interviews and document analysis and emerging themes from the elicited and analysed data show five categories of challenges. The findings revealed that though most ESP teachers perceived formative assessment as an effective and fair way to monitor students’ learning process, there are still challenges that must be mitigated especially when implemented via online platforms. The challenges come in the form of difficulty in following ESP assessment principles, limited training and absence of guidelines, large class size, and students’ lack of motivation influence their assessment practices. This study justifies the need to develop explicit guidelines for best practices in ESP formative assessment for teachers’ reference.
Improving Masked Autoencoders by Learning Where to Mask

Masked image modeling is a promising self-supervised learning method for visual data. It is typically built upon image patches with random masks, which largely ignores the variation of information density between them. The question is: Is there a better masking strategy than random sampling and how can we learn it? We empirically study this problem and initially find that introducing object-centric priors in mask sampling can significantly improve the learned representations. Inspired by this observation, we present AutoMAE, a fully differentiable framework that uses Gumbel-Softmax to interlink an adversarially-trained mask generator and a mask-guided image modeling process. In this way, our approach can adaptively find patches with higher information density for different images, and further strike a balance between the information gain obtained from image reconstruction and its practical training difficulty. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.
Learning Domain-Invariant Representations from Text for Domain Generalization

Domain generalization (DG) aims to transfer the knowledge learned in the source domain to the unseen target domain. Most DG methods focus on studying how to learn domain-invariant representations that remain invariant across different domains. For humans, we tend to use the same word or text to describe images from different domains but of the same category. Therefore, text can be considered a natural domain-invariant representation. Inspired by this, this paper studies how to introduce text representations into domain generalization tasks. Specifically, the text representations generated by CLIP text encoder are used to guide the image representation learning of the visual model. To alleviate domain bias and weak discriminability caused by CLIP representations, a joint loss is proposed by combining the text representation regularization loss with standard image-level supervised loss. The proposed method is simple yet efficient, and can achieve competitive performance compared with the existing state-of-the-art methods on five standard DG datasets.
Remote Sensing and Radiometric studies of Gabal kadabora – Gabal Umm Ghieg and its surrounding area, Central Eastern Desert, Egypt

Gabal Kadabora - Gabal Umm Gheig and its surrounding area is located in Central Eastern Desert (CED). This region is dominated by igneous, metamorphic, and sedimentary rocks ranging in age from the Late Proterozoic to the Quaternary. Based on the published geological map, Digital elevation model (DEM) and Radiometric maps three structural trends; NW-SE, NE-SW and N-S directions are detected. Spectrometric data vary from 1.86 to 184.58 μR/h for the total count (T.C), from 0.08 to 4.07 % for (K), from 0.11 to 11.25 ppm for eU, and from 0.4 to 29.33 ppm for eTh . Identification of radiometric anomalies was performed by calculating the factor (X + 2σ) for eU, eU/eTh and eU/K measurements for all single sites in each rock units. Radiometric analysis indicates the presence of many regions for uranium exploration, where thirteen relatively high uraniferous zones are suggested for further exploration. Correlation between uraniferous zones and lineaments distribution shows that some highly uraniferous parts exist where high lineaments density prevails. Moreover, some uraniferous parts exist at the boundaries between the rock units.
PERANCANGAN ARSITEKTUR SISTEM INFORMASI MANAJEMEN DAN AKADEMIK SDN BABADAN 01 DENGAN ZACHMAN FRAMEWORK

Industry 4.0 develops in Indonesia and the Covid-19 Pandemic, all business process activities are required to be able to adapt by digizing service. For SDN Babadan 01 itself, the business process is still using the old method which requires all processes to be carried out directly face to face such as registration of new students and is not yet effective. Therefore the design of Management and Academic Information System is needed to support the business processes that are carried out. With the creation of a Management Information System and Academic Information System, it is hoped that it can help facilitate all academic and non-academic matters at SDN Babadan 01. For data collection , interviews and literature studies were used. This study uses the Zachman Framework which consist of 6 columns and 6 rows. Based on the research that has been done, the design of SIAKAD and SIM at SDN Babadan 01 has resulted. This design can be used as a reference for application developers.
Research on the Transmission Relationship of Grid Science and Technology Innovation Indicators under the Smart Grid

In recent years, the power industry is committed to clean and low-carbon transformation and development, continues to increase investment in research and development, and innovation capacity has been significantly enhanced, which has strongly supported national energy security and protection. The digital and intelligent upgrading of the industry has become an inevitable trend for the future development of the electric power industry. This paper starts from the grid science and technology innovation index system under the smart grid, based on the improved TrustRank-ISM model to study the conduction relationship and correlation between science and technology innovation indexes, and better explores the relationship between science and technology innovation and smart grid construction from specific indexes, which is conducive to the grid to further improve the science and technology innovation index system and increase the application of key core technologies, to promote the smart grid construction and This will contribute to the low-carbon and green development of energy.
Development of Android-Based Learning Media: a Literature Review

The development of information and communication technology such as android has not been widely used to support the learning process. Supposedly technological advances such as android can be utilized in the learning process. This study aims to discover some of the inventions of Android-based learning media that are currently being developed to support an efficient and effective learning process. The research design was in the form of a review of journals published in the Sinta database with the keywords "development of learning media" and "android-based learning media". The data used is in the form of journals related to the development of Android-based learning media in the 2016-2022 period. Based on the analysis that has been done, Android-based learning media that has been developed are in the form of video, PowerPoint, and applications. This Android-based learning media has proven successful in increasing student understanding, student motivation, and student cognitive learning outcomes towards a material as well as student skills in mastering technology.