Reviewerly/exHarmony huggingface dataset:
Instantiation and Implementation of HEAD Metamodel in an Industrial Environment: Non-IoT and IoT Case Studies

Access to resources can take many forms: digital access via an onsite network, through an external site, website, etc., or physical access to labs, machines, information repositories, etc. Whether access to resources is digital or physical, it must be allowed, denied, revoked, or disabled using robust and coherent access control (AC) models. What makes the process of AC more complicated is the emergence of digital transformation technologies and pervasive systems such as the internet of things (IoT) and industry 4.0 systems, especially with the growing demand for transparency in users’ interaction with various applications and services. Controlling access and ensuring security and cybersecurity in IoT and industry 4.0 environments is a challenging task. This is due to the increasing distribution of resources and the massive presence of cyber-threats and cyber-attacks. To ensure the security and privacy of users in industry sectors, we need an advanced AC metamodel that defines all the required components and attributes to derive various instances of AC models and follow the new and increasing demand for AC requirements due to continuous technology upgrades. Due to the several limitations in the existing metamodels and their inability to answer the current AC requirements, we have developed a Hierarchical, Extensible, Advanced, Dynamic (HEAD) AC metamodel with significant features that overcome the existing metamodels’ limitations. In this paper, the HEAD metamodel is employed to specify the needed AC policies for two case studies inspired by the computing environment of Institut Technologique de Maintenance Industrielle (ITMI)-Sept-Îles, QC, Canada; the first is for ITMI’s local (non-IoT) environment and the second for ITMI’s IoT environment. For each case study, the required AC model is derived using the domain-specific language (DSL) of HEAD metamodel, then Xtend notation (an expressive dialect of Java) is utilized to generate the needed Java code which represents the concrete instance of the derived AC model. At the system level, to get the needed AC rules, Cypher statements are generated and then injected into the Neo4j database to represent the Next Generation Access Control (NGAC) policy as a graph. NGAC framework is used as an enforcement point for the rules generated by each case study. The results show that the HEAD metamodel can be adapted and integrated into various local and distributed environments. It can serve as a unified framework, answer current AC requirements and follow policy upgrades. To demonstrate that the HEAD metamodel can be implemented on other platforms, we implement an administrator panel using VB.NET and SQL.
Scalable Algorithms for Densest Subgraph Discovery

As a fundamental problem in graph data mining, Densest Subgraph Discovery (DSD) aims to find the subgraph with the highest density from a graph. It has been studied for several decades and found a large number of real-world applications, such as network community detection, regulatory motif discovery in DNA, graph index construction, and fake follower detection. Although there are many existing DSD algorithms, they are often not scalable or efficient to process large-scale graphs, since most of them are serial algorithms and can only leverage the computing resource of a single CPU core. To tackle these issues, in this paper we propose efficient parallel algorithms for solving the DSD problems on both undirected and directed graphs at scale. Our main idea is to use the k-cores (a kind of dense subgraph) to approximate the densest subgraph in the undirected graphs, and then propose efficient parallel algorithms for computing the cores by optimizing the iterative process and also reducing the number of iterations. We further extend this idea for directed graphs by introducing a novel concept, named w-induced subgraph, to avoid unnecessary enumerations of x or y when searching [x,y]-cores (a kind of directed dense subgraph to approximate the densest). To verify the scalability and efficiency of the proposed algorithms, we have conducted extensive experiments on 12 large real-world graphs, and four of them are billion-scale. The experimental results show that our proposed algorithms outperform the state-of-the-art algorithms on both undirected and directed graphs, in terms of scalability and efficiency.
Active contour model based on hybrid signed pressure force function

Abstract To segment noisy and multi‐target images, an active contour model based on a hybrid signed pressure force function that fuses global and local information of the image is proposed. Firstly, a local signed pressure force function is defined using the local area information of the image. Then, it is combined with the existing global grey density function to construct a new signed pressure force function. Finally, the selective binary and Gaussian filtering regularized level set are modified using the newly defined function. Extensive experiments and comparisons on both synthetic and real images demonstrate that the proposed method is robust to noise and can handle noisy and multi‐target images with high accuracy.
Teachers Curating Virtual Exhibitions for Learning Visual Arts: A Study of Impact and Effectiveness

Abstract Although visual arts teachers have free access to high quality online artwork images offering them immense teaching resources, making meaningful use of them remains a pedagogical challenge. During the COVID‐19 pandemic, the transition from face‐to‐face to online teaching caused an immediate need for teachers to enhance their digital competencies and technological capability for planning and delivering a blended art curriculum. Herein, I propose a ‘teacher‐curator pedagogy’ via a study in six primary and secondary schools in Hong Kong to assess its effectiveness. The study took place in the academic year of 2021‐22 with 331 student participants. The proposed pedagogy puts visual arts teachers in the role of ‘digital curators’ who create virtual exhibitions for teaching art appreciation and artmaking. This paper addresses the theoretical framework, implementation strategies, and the results of the study highlighting its effectiveness, impact and limitations on teaching when using teacher‐curated virtual exhibitions in both physical and virtual learning contexts. It was found that the technological capacity and confidence of the teacher participants was enhanced through the use of online resources and virtual exhibition tools. Making use of the 3D presentation of exhibits in themes and context through digital content curation, the teachers engaged students with deep interaction through the blending of online and face‐to‐face teaching. However, the proposed pedagogy was limited by the high demand of training and preparation work, hardware and software support, and difficulties in assessing and monitoring learning beyond class.
UPAYA PEMANFAATAN TEKNOLOGI: APLIKASI RPP DIGITAL DALAM MENINGKATKAN KUALITAS PENDIDIKAN DI SEKOLAH INDONESIA KUALA LUMPUR (SIKL) (STUDI KASUS SEKOLAH INDONESIA KUALA LUMPUR (SIKL))

The development of Science and Technology increasingly encourages renewal efforts in the use of technology results in the teaching and learning process. In implementing good learning, we must maximize all learning media in order to achieve learning outcomes. Thus this learning media can be used as a support and coupling for teaching materials in improving the quality of education. The Digital RPP application is a technology use to maximize the increase in learning at the Kuala Lumpur Indonesian School (SIKL). The Digital RPP application aims to facilitate teachers in the teaching and learning process in the current pandemic era. This study uses a descriptive analytical research method, the research approach uses qualitative studies, with an overview that can find out the efforts to use the Digital RPP Application. The results showed that the Digital RPP application is useful for teachers so that it has easy access to interactive and fun learning resources. Thus the Digital RPP application is the main solution for teachers in making lesson plans for that to use technology is highly recommended to improve the quality of education at the Kuala Lumpur Indonesian School (SIKL).
Acceptance of Voice Assistants using Technology Acceptance Model (TAM)

Voice search is becoming increasingly popular with customers as a way to simplify their lives. More individuals than ever are drawn to the technology that seems to give such independence due to the fear of being enslaved by computers. Every year after the holidays, more homes start using voice search. The basic concept is that the user speaks their request into the voice-activated gadget, which streams it through the cloud, transforming it into text. In reality, it is altering the search algorithm. As a result of the opening of Pandora's Box, businesses like Amazon, Apple, and Microsoft are adapting to fulfill the rising demand for voice-enabled gadgets. Voice assistants have created numerous possibilities for capitalizing on the speech bot trend and using this technology in novel and intriguing ways. This study focuses on the use and level of acceptance of voice assistants among various age groups in daily life.
Interpreting the decisions of CNNs via influence functions

An understanding of deep neural network decisions is based on the interpretability of model, which provides explanations that are understandable to human beings and helps avoid biases in model predictions. This study investigates and interprets the model output based on images from the training dataset, i.e., to debug the results of a network model in relation to the training dataset. Our objective was to understand the behavior (specifically, class prediction) of deep learning models through the analysis of perturbations of the loss functions. We calculated influence scores for the VGG16 network at different hidden layers across three types of disturbances in the original images of the ImageNet dataset: texture, style, and background elimination. The global and layer-wise influence scores allowed the identification of the most influential training images for the given testing set. We illustrated our findings using influence scores by highlighting the types of disturbances that bias predictions of the network. According to our results, layer-wise influence analysis pairs well with local interpretability methods such as Shapley values to demonstrate significant differences between disturbed image subgroups. Particularly in an image classification task, our layer-wise interpretability approach plays a pivotal role to identify the classification bias in pre-trained convolutional neural networks, thus, providing useful insights to retrain specific hidden layers.
Adversarial Transfer Learning for Biomedical Named Entity Recognition

Biomedical Named Entity Recognition (BioNER) is one of the basic tasks of biomedical text mining. In reality, the labeled biomedical data is relatively limited, there is a lack of large enough training data to train a strong model, and manual labeling is expensive. To solve this problem, this paper proposes a network model based on deep transfer learning to improve the performance of entity recognition by learning text knowledge in the general domain (source resource) and migrating to the biomedical domain (target resource). In addition, in order to solve the problem of model training bias caused by the imbalance of data volume between the two domains and the large difference between the data, we construct an adversarial neural network model to extract domain-independent features to effectively alleviate the problem of negative migration. Without adding any artificial features, the proposed model is able to learn transferable feature representations better than existing methods and achieve better results on two biomedical field datasets.
Named Entity Recognition Service of Bert-Transformer-CRF Based on Multi-feature Fusion for Chronic Disease Management

In chronic disease management, there are many kinds of chronic diseases, the professional terms are complex and diseases have their own characterised disease descriptions, making it difficult for some existing service recommendation methods to make accurate and personalised recommendations for patients with chronic diseases. Therefore, the paper proposes a service recommendation method based on the BERT-Transformer-CRF named entity recognition technology (BTC-SR) to achieve more accurate recommendation services. Firstly, the input disease text data is identified by a BERT (Bidirectional Encoder Representations from Transformers)-Transformer-CRF (Conditional Random Fields) model incorporating radical and pinyin features for named entities, then the relationships between the entities are extracted, and finally an implicit representation of the user is combined to present a recommendation list for the user. Experiments show that the proposed model achieves an F1 value of 60.15 for entity recognition on the CMeEE dataset, which provides better recognition results and lays the foundation for more accurate service recommendations.
Denying Evolution Resampling: An Improved Method for Feature Selection on Imbalanced Data

Imbalanced data classification is an important problem in the field of computer science. Traditional classification algorithms often experience a decrease in accuracy when the data distribution is uneven. Therefore, measures need to be taken to improve the balance of the dataset and enhance the classification accuracy of the model. We have designed a data resampling method to improve the accuracy of classification detection. This method relies on the negative selection process to constrain the data evolution process. By combining the CRITIC method with regression coefficients, we establish crossover selection probabilities for elite genes to achieve an evolutionary resampling process. Based on independent weights, the feature analysis improves by 3%. We evaluated the resampled results on publicly available datasets using traditional logistic regression with cross-validation. Compared to the other resampling models, the F1 score performance of the logistic regression five-fold cross-validation is more stable than the other methods using the two sampling results of the proposed method. The effectiveness of the proposed method is verified based on F1 score evaluation results.
Semantic segmentation in autonomous driving—an example of FCN

With the continuous development of artificial intelligence, autonomous driving technology is becoming more and more mature. The accuracy and real-time judgment of road conditions in autonomous driving technology are very important. In autonomous driving, road condition information is first collected, then the collected road condition information is analyzed, and finally instructions are given. When analyzing the road condition information, it is important to be able to accurately determine which category each part of the image containing the current road condition information belongs to, and use it as the basis for giving instructions to the autonomous vehicle. The purpose of semantic segmentation is to segment the image into regions belonging to different semantic categories, which can recognize and understand the content of each pixel in the image, i.e., it can classify the pixel points in the image so that the content of the image can be segmented. By using semantic segmentation techniques to process images containing road information, it is possible to classify the content that appears in the road information and feed it to the autonomous vehicle control. The main technology of semantic segmentation is neural network, and this paper will use semantic segmentation technology to process road condition information, using FCN as the network and cityscapes dataset as the test set. Considering the high requirements of autonomous driving for both safety and real-time, this paper also considers both correctness and processing speed when using semantic segmentation.
Uncertainty: qualitative and quantitative aspects

The analysis the concept of uncertainty, its origin, interpretation in various scientific fields, sources of uncertainty, qualitative and quantitative aspects of this concept, as well as approaches to quantifying uncertainty are presented. Methods of historical and comparative analysis were used in the study. The main conclusion is the necessity of taking into account the presence of uncertainty and quantification of uncertainty of the results of mathematical modeling of objects and phenomena of the surrounding world in the same way as we do when assessing measurement uncertainty of experimental data. In is shown that the interval approach to estimating uncertainty of modeling results currently seems to be the most promising. The concept of uncertainty first proposed at the beginning of the last century refers to epistemic situations involving imperfect or unknown information. This concept had only a qualitative aspect for a long time. In the second half of the twentieth century, almost simultaneously with the development of a risk-based approach in the field of technological security, there was an interest in understanding uncertainty, its origin and typification. We are indebted to metrology for giving uncertainty a quantitative aspect, in which, instead of the measurement error paradigm, a measurement uncertainty paradigm was developed, and approaches (partly controversial) to its quantitative assessment were proposed. Uncertainty is an attribute of any data obtained both experimentally or theoretically (currently, usually by mathematical modeling). In the field of experimental research, specifying the uncertainty interval of the result has long been a scientific standard and routine. The time has come to make it mandatory for the results of theoretical research. To date, three alternative methods of quantitative estimation of uncertainty have been developed: probabilistic, fuzzy and interval methods. Methods for leveling the negative features of its initial «naive» version have been proposed in modern interval analysis. It seems to be the most promising method of quantifying uncertainty of the results of mathematical modeling today.
PENGENALAN JURUSAN ILMU KELAUTAN PADA SISWA SMAN N 1 (SMANSA) PONTIANAK MELALUI KEGIATAN PROGRAM PARENT TEACHING DAY (PTD)

A good education system will produce superior human resources who can be relied upon in the progress of the nation and the welfare of the Indonesian people in the future. The role of teachers and parents, facilities and infrastructure in the implementation of the learning process greatly determines the quality of learning outcomes. Parent Teaching Day (PTD) is one of the programs from SMAN No. 1 which provides opportunities for parents to teach at school, related to various kinds of knowledge or competencies possessed by their respective parents. One of the PTD materials carried out by SMANSA class XI D is an introduction to the Department of Marine Science, Tanjungpura University. The purpose of this activity is to introduce the marine science department which includes knowledge about the marine environment by one of the student's parents as well as an attempt to increase student motivation. The activity was carried out using the lecture and demonstration method about 3 fields of study in the marine science department, attended by 36 students of class XI D and their homeroom teacher, carried out smoothly and the participants responded very enthusiastically. The evaluation of the introduction to the marine science department was carried out through a verbal questionnaire by the presenters, showing an increase in the understanding of class XI D SMANSA students as participants in the activity. Keywords: system, PTD, SMANSA, marine science.
Planning and Furnishing of School Facilities to Support Learning Transformation

Recently Japanese schools are returning to hands-on experiences, project-based learning, and authentic educational opportunities for children while cultivating environments that encourage students to be innovative and critically thinking through science, technology, engineering, arts, and mathematics (STEAM) education. Given that schools are one of the key learning media, it is crucial to consider the construction and design of the school space in response to the learning transformation in Japan. This paper outlines the preconditions for nurturing a ubiquitous environmental strategy that permits learning at anytime, anywhere, by anybody, and about anything.
THE IMPLEMENTATION OF VIRTUAL REALITY (VR) TECHNOLOGY IN REAL ESTATE INDUSTRY

COVID-19 pandemic impact has accelerated the real estate industry to adopt technology evolution quickly. Due to the normalisation changes from the pandemic, all the physical real estate selling processes, including housing tours, sales, and purchase agreements, could not be effectively done as before. In this situation, Virtual Reality (VR) has become a prominent approach for real estate agents to improve their marketing strategies. This prospective study was designed to investigate the potential activities needed in VR implementation and to explore the importance of VR in influencing real estate marketing. Primary data was gathered from 60 real estate respondents using purposive sampling and descriptively analysed using SPSS software. The findings indicate that VR tours of the property, 360-degree visualisations, and visualisations of room furniture are the most potential activities needed in VR implementation. While VR is important because it indicates detailed information about the property, provides high-quality photos, and is a good investment opportunity. Overall, this study provides insights into how VR technology can help realtors survive in the evolving real estate industry.
Analysis of Key Technologies and Applications of Distributed Cooperative Countermeasure in Communication Countermeasure

As collaborative interfere with the operational needs, from the aspects of distributed jamming and cognitive EW expounds the current situation of the development of collaborative interference, analyzes the distributed collaborative jamming communication application, formed on the basis of distributed collaborative, with “cognitive target reconnaissance recognition” and “multiple machine collaborative jamming decision” as the support, levels using task coordination, parameter coordination, such as signal level coordination way, The structure model to realize cooperative jamming between equipment is presented, and the key technologies to realize cooperative jamming are expounded, which lays a foundation for further research.
IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR STATE – OWNED ASSETS FORECASTING OF ROOM RENTAL PRICES IN INDONESIA

Leasing is a state-owned assets utilization scheme that needs to be optimize because of its easy to find objects and large potential for non-tax revenue. In the city of Yogyakarta, the economy grows above the national average, this is supported by the mobility of tourists, overseas students, and businessman. The characteristics of the regional economy are suitable for the optimization of state-owned assets through leasing scheme in the form of lodging room. The author tries to develop a state-owned assets leasing price forecasting model for lodging room using an Artificial Neural Network to capture the potential state revenue. By using market data for lodging room rental from the OYO website, author create a model architecture with the backpropagation algorithm. Analysis results of this study indicate that the obtained network model achieves an accuracy of 97.5%. There are 25 state-owned assets buildings that can be projected as objects of lodging space rental utilization with a predicted rental value of IDR 108,570.00 to IDR 122,669.00 per day.