Reviewerly/exHarmony huggingface dataset:
Data Security

Data security is a functional area that deals with managing the security of data assets in order to ensure that they are available for the right stakeholder at the right time. This is a heavily risk-driven discipline. This section shows that an analysis of risk events in terms of probability and impact is the basis for selecting measures that help mitigate risk around confidentiality, integrity, and availability of data.
Reference and Master Data Management

In the DAMA DMBOK, reference data management and master data management are lumped together in a single functional area. To stay consistent with the DMBOK, they are also discussed together here. Reference data is loosely defined as ‘data that helps to make sense of other data.’ Typically, it comes in the form of lists or hierarchies of codes with a well-defined meaning. Master data management deals with the challenge of organizing a ‘golden copy’ of data, a source that we believe to be the (single version of the) truth.
CRUD and REST APIs – Pillars of Efficient Data Exchange

In today’s world, the exchange of information and data between systems or devices is really important. As businesses and individuals continue to rely on digital technologies, the need for efficient and secure data transfer protocol becomes a necessity. REST APIs are one way to achieve this goal. REST APIs provide a set of standardized interface that allows systems to communicate with each other in a scalable and flexible manner.
Metadata

Metadata is data about data. It captures everything we need to know about data in order to use or manage it as an asset. We distinguish between business metadata, technical metadata, and operational metadata. This chapter briefly discusses the use of metadata tools and introduces the notions of horizontal lineage and vertical lineage.
Understanding Data

There are many definitions of the term data. Many seem based on a misinterpretation of the datainformation- knowledge-wisdom dichotomy as introduced by Ackoff. Based on the semiotic triangle and the semiotic ladder, it is claimed that data has structure and meaning. The relational model and normalization are introduced as tools for understanding data (rather than for building the best possible database): relations have a well-defined structure and meaning. The relational algebra is introduced as a tool to reason (make logical inferences) about data.
Document and Content Management

The functional area of document and content management deals with what is called unstructured data. This chapter shows that this is a misnomer: Data always has structure and meaning. Documents and other content should also be stored, managed, and retrieved just like ‘normal’ data. The chapter ends with a short description of how structured data and documents/other content can be combined in a database management system.
PENERAPAN STRATEGI PREDICT, ORGANIZE, REHEARSE, PRACTICE AND EVALUATE (PORPE) UNTUK MENINGKATKAN KETERAMPILAN MEMBACA PEMAHAMAN SISWA SEKOLAH DASAR

This research was motivated by the low results of students' comprehension skills on my diversity material in class IV SDN 019 Lubuk Agung. This research method was classroom action research (PTK) which was carried out in two cycles.The subject of this research was class IV students, totaling 20 students. Data collection techniques were in the form of documentation, observation and tests. While technical data analysis uses quantitative and qualitative data analysis. Based on the results of data analysis, it can be seen that the results of students' reading comprehension skills increased after the action, the average completeness of the collaboration results In the pre-action activities, the class average value was 74.75 with classical completeness of 45%, increased in cycle 1 meeting 1 to 75.5 with classical completeness of 50%, increased in cycle 1 meeting 2 to 78 with a classical mastery of 5 5%. The class average score in cycle 2 meeting 1 was 82.25 with classical completeness of 70% and increased in cycle 2 meeting 2 which was 88.5 with classical completeness of 85%. So it can be concluded that the application of the Predict, Organize, Rehearse, Practice And Evaluate (PORPE) strategy can improve the reading comprehension skills of grade IV students at SDN 019 Lubuk Agung.
Development of STEM-Oriented E-Modules to Improve Science Literacy Ability of Elementary School Students

The goal of this project is to create an electronic module, or E-Module, for third grade elementary school kids that is designed using the Book Creator application and integrated with a STEM learning strategy. Development research, also referred to as Research and Development (R&amp;D) employing the ADDIE paradigm, is this kind of study. The subjects of this study are instrument validators, expert validators (material experts, linguists and media experts) and students in class III SDN 07 Kampung Jawa II, students in class III SDN 04 Rawang and students in class III SDN 17 Kampung Baru. The research instrument consisted of validation sheets, questionnaires and test instruments. The data analysis technique used is validity analysis and practicality analysis. The results of the validity by media experts are 91, 68% with very valid qualifications. The results of the validity of the linguists are equal to 88.95% with valid qualifications. The results of the validity of the media experts are equal to 90.41% with very valid qualifications. The percentage of the teacher's score for product feasibility is 92.06%, which is included in the very practical category. The practicality test percentage of students is 90.31%, which is included in the very practical category. Thus, this research succeeded in producing a STEM-based E-module to improve the scientific literacy skills of elementary school students.
Developing an ontology-based system for semantic processing of scientific digital libraries

The development of theories, methods, and algorithms for the discovery and formation of new knowledge remains one of the most important tasks for any researcher, especially if they are actively working to create new scientific publications. Yet, there is no universal language to describe full formal concepts (i.e. knowledge) or the systemology of transdisciplinary scientific research. Because of this, researchers face a set of urgent challenges. One such challenge is how to speed up the process of finding information in their own sources. To address this challenge, we created an ontology-related system for processing digital libraries of scientific publications. This system implements the technologies of information retrieval and knowledge discovery in digital libraries with an emphasis on technologies and instruments such as those used in the Semantic Web and cognitive graphics.
A novel classification-based parallel frequent pattern discovery model for decision making and strategic planning in retailing

The exponential growth of retail transactions with a variety of customers having different interests makes the pattern mining problem trivial. Hence this paper proposes a novel model for mining frequent patterns. As per the proposed model the frequent pattern discovery is carried out in three phases. In the first phase, dataset is divided into n partitions based on the time stamp. In the second phase, clustering is performed in each of the partitions parallelly to classify the customers as HIG, MIG, and LIG. In the third phase, proposed algorithm is applied on each of the classified groups to obtain frequent patterns. Finally, the proposed model is validated using a sample dataset and experimental results are presented to explain the capability and usefulness of the proposed model and algorithm. Further, the proposed algorithm is compared with the existing algorithm and it is observed that the proposed algorithm performs better in terms of time complexity.
Private Blockchain-Enabled Security Framework for IoT-Based Healthcare System

The Internet of Things (IoT)-enabled healthcare becomes one of the important technology due to the advances in the information and communications technology (ICT). However, there are several security and privacy issues in an IoT-enabled healthcare system due to security threats and attacks mounted by an adversary. The blockchain technology offers immutability, decentralization, and transparency while the data is stored. In this paper, we design a novel access control scheme, called private blockchain enabled security framework for IoT-based healthcare system (in short, PBESF-IoTHS) which is not only efficient in computation and communication but also offers superior security and more functionality features as compared to other relevant competing schemes. The blockchain-based simulation results also demonstrate the proposed PBESF-IoTHS’s practical usefulness in finding the computational time required for a varied number of blocks mined in a Peer-to-Peer (P2P) blockchain network and a varied number of transactions per block.
Assessing Residential Solar Harvesting Potential Using the National Solar Radiation Database Across Terrestrial Elevation and Household Income

In the current study, the National Solar Radiation Database generated at the National Renewable Energy Laboratory is used to assess solar harvesting potential in Albuquerque, New Mexico. Global Horizontal Irradiance data from 2018 to 2021 is utilized in the study with the objective of investigating possible correlations between solar irradiance, median household income, and terrestrial elevation. Three zones are considered within the city’s limits stretching from east to west, with each zone having an area of two square kilometers. A Python code was developed and used in the irradiance data statistical analysis. The results of the study indicate that solar irradiance values are lower at higher elevation, which is the area of highest household income. At lower elevation, solar harvesting potential is slightly higher, yet income is noticeably lower, which leads to energy justice implications associated with low-income communities having less potential to collect savings from residential solar systems within the considered region. The conclusions from the statistical analysis in combination with the yielded energy justice implications may be helpful in adjusting both renewable energy tax incentives (where applicable) and the real estate valuation process to promote a more equitable energy transition.
A novel UAV target detection method based on the improved YOLOv4-tiny network

Aiming at the poor detection performance of UAV aerial photography based on deep learning on small targets and occlusion targets, the paper proposed an improved UAV aerial photography detection model based on YOLOv4-Tiny. By employing a lightweight YOLOv4-Tiny framework, a multi-scale detection strategy is first introduced to expand the detection scope. Moreover, the hybrid deep and shallow semantic information are utilized to enrich the feature of small and obscured targets. Second, the Efficient channel attention module is added to the enhanced feature extraction network to redistribute channel weights and the secondary screening of feature information, so as to improve target detection ability. To evaluate the proposed model, the paper used a PASCAL VOC data set and the corresponding visualization detection was carried out on the aerial photography data set. The results showed that the proposed model's average accuracy (mAP) is improved by 4.19%, and more importantly, the missing detection rate of both small targets and occlusion targets is effectively reduced. Finally, the proposed model was deployed on the Nvidia Jetson TX2 platform, and the aerial photography experiment showed that the FPS of the model on TX2 reached 30.2, meeting the real-time requirements of UAV aerial photography detection.
Image Steganography in Color Conversion

For image steganography, no matter how ingenious the distortion function is designed, the embedding operation will lead to changes in the distribution of cover image, which causes security risks. In this paper, an image steganography based on decolorization and colorization is proposed, which can accomplish the embedding and extraction task of secret information during the process of color translation. Unlike traditional image steganography, the proposed steganography counteracts the embedding influence of secret information through decolorization network. In addition, a robust embedding algorithm is utilized to embed secret image into cover image by considering the de-colorization and colorization as an attack. Moreover, different training datasets are created to analysis their impact on the reconstruction performance of the colorization network. Finally, simulation results are given to verify the feasibility of the proposed steganography.
Weakly Supervised Medical Image Segmentation with Soft Labels and Noise Robust Loss

Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-labeled annotation is not only expensive but also is subjective, error-prone, and inter-/intra- observer variability introduces noise to labels. This is particularly a problem when using deep learning models for segmenting medical images due to the ambiguous anatomical boundaries. Image-based medical diagnosis tools using deep learning models trained with incorrect segmentation labels can lead to false diagnoses and treatment suggestions. Multi-rater annotations might be better suited to train deep learning models with small training sets compared to single-rater annotations. The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a “noise-tolerant loss” function. The model was evaluated by comparing it to binary ground truth for 17 knees MRI scans for clinical segmentation and detection of bone marrow lesions (BML). The proposed method successfully improved precision 14, recall 22, and Dice score 8 percent compared to a binary cross-entropy loss function. Overall, the results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels.
Conversational Ontologies for Human–Machine Interaction: Application for Cultural Heritage

This work discusses ontology-based dialogue systems, highlighting their advantages in managing human–machine interactions through dialogues. The paper systematically reviews research contributions from 2003 to 2022 that combine studies and analyses from different research areas on dialogue systems and ontologies. In the present review, the discussed “conversational” ontologies have been sorted according to their purposes into (a) conversational ontologies oriented to user preferences and interests; (b) ontologies to improve spoken language’s understanding; (c) task-oriented conversational ontologies; (d) general, domain-independent conversational ontologies and (e) use cases of conversational ontologies into specific domains and/or applications. Additionally, considerations are made on the need to develop conversational ontologies in the context of tourism and cultural heritage.
A Study on Analysis of Items in Cultural Section of 『Chinese I』 in High School: Focusing on the College Scholastic Ability Test and Mock Test

The purpose of this study is to examine the scope of instruction in the cultural section taught at school through high school 『Chinese I』 and the degree of reflection in the questions of the CSAT, and the organic connection between learning and evaluation and the systematicity.BR To this end, the entire cultural domain covered in 11 high school 『Chinese I』 textbooks currently used in high schools was organized, divided into 92 categories, and organized into tables.BR Most of the questions introduced cultural items, which are the core topics of textbooks, and made them the subject of evaluation, but there are still quite a few questions that go beyond the scope of culture every year.BR Finally, through suggestions on the structure of textbooks and curricula, the future direction of Chinese cultural education in secondary education was presented.
Analysis of Terrorism Big Data Using Machine Learning

The purpose of this study is to analyze terrorism-related structured and unstructured big data using machine learning techniques for big data analysis to confirm the predictability of terrorism and derive implications. To this end, big data on terrorism was obtained from research institutes specializing in terrorism, articles and documents posted on various portals and SNS were collected, put into machine learning, and analyzed and learned, and the results were confirmed. The analysis results are as follows. First, as a result of conducting K-Means Clustering, a representative unsupervised learning among machine learning techniques, eight regional clusters of terrorism occurred around the world in 2021 (near Afghanistan, Democratic Republic of the Congo, India, Iraq, Myanmar, Nigeria, Pakistan, and Somalia). Next, as a result of clustering by type of terrorism, assassination using weapons excluding explosives, kidnapping or kidnapping targeting key facilities were identified. In addition, as a result of clustering around the means used in terrorism, it was confirmed that there were many explosives ahead, but there were differences according to the cluster. Finally, clusters of military, police, government personnel, civilians, and private property were identified as a result of clustering centered on targets of terrorism. Second, as a result of sentiment analysis by setting unstructured texts on terrorism events as research subjects, it was confirmed that there were differences in views on terrorism between terrorism researchers and the general public. Based on these research results, policy suggestions were made on ways to prevent terrorism.
Few-Shot Named Entity Recognition via Label-Attention Mechanism

Few-shot named entity recognition aims to identify specific words with the support of very few labeled entities. Existing transfer-learning-based methods learn the semantic features of words in the source domain and migrate them to the target domain but ignore the different label-specific information. We propose a novel Label-Attention Mechanism (LAM) to utilize the overlooked label-specific information. LAM can separate label information from semantic features and learn how to obtain label information from a few samples through the meta-learning strategy. When transferring to the target domain, LAM replaces the source label information with the knowledge extracted from the target domain, thus improving the migration ability of the model. We conducted extensive experiments on multiple datasets, including OntoNotes, CoNLL’03, WNUT’17, GUM, and Few-Nerd, with two experimental settings. The results show that LAM is 7% better than the state-of-the-art baseline models by the absolute F1 scores.
Path Planning of Substation Inspection Robot Based on Improved Ant Colony Algorithm

At present, most of the substation inspection robots use ant colony optimization (ACO) to realize path planning, but the traditional ACO has deficiencies such as long planning path, slow convergence speed and blind search, therefore, an improved ACO is proposed in this paper. First, an initial path is planned using the A* algorithm; Second, introducing the cost of the target node in the heuristic function in order to speed up the algorithm's search efficiency; Finally, the pheromone update rule is improved by conditioning factors to make the ants converge to the optimal path faster. Simulation experiments show that the proposed improved ACO has better merit-seeking ability and faster convergence speed.
MLGAL: Multi-level Label Graph Adaptive Learning for node clustering in the attributed graph

Node clustering aims to divide nodes into disjoint groups. Recently, a considerable amount of research leverages Graph Neural Networks (GNNs) to learn compact node embeddings, which are then used as input of the traditional clustering methods to get better clustering results. While in most of these methods the node representation learning and the clustering are performed separately, a few works have further coupled them in a self-supervised learning manner. The coupling, however, should be carefully designed to avoid potential noises in the pseudo labels generated automatically during the training process. To address the above problems, in this article, we propose Multi-level Label Graph Adaptive Learning (MLGAL), a novel unsupervised learning algorithm for the node clustering problem. We first design a graph filter to smooth the node features. Then, we iteratively choose the similar and the dissimilar node pairs to perform the adaptive learning with the multi-level label, i.e., the node-level label and the cluster-level label generated automatically by our model. We conduct extensive experiments on four benchmark datasets to evaluate the performance of our model. The results demonstrate that our model outperforms the state-of-the-art baselines on four benchmark datasets.
Changes and Meaning of Generation Z College Students' Lifestyle in the COVID-19

Objectives The purpose of this study is to explore the changed lifestyle and meaning experienced by Generation Z college students in the COVID-19 situation. Methods A case study was conducted using in-depth interviews with eight students attending a two-year university in the Seoul metropolitan area who experienced remote classes due to COVID-19. In order to secure validity and reliability, not only did it go through discussions and deliberations among researchers, but it also conducted triangular verification and member check through in-depth interviews with researchers. Results The changes in the lives of Generation Z college students due to COVID-19 were categorized as first, daily life stopped in time, second, opportunities to spread deprived social relationships, online, a new but already familiar online learning method, and fifth, relaxed physical, mental, and economic stress. Regarding these experiences, research participants showed negative perceptions in part, but overall, they were found to have positive meanings. They reinterpreted previously recognized authority by looking at universities and professors who could not adapt quickly to rapidly changing non-face-to-face situations and perceived it as an opportunity to create their own lives autonomously and independently even in lethargic situations. In addition, it was recognized as an opportunity to reorganize one's social relationship and redefine one's perspective by reviewing the way of human relations and communication that one has had. Conclusions In the future, it is believed that it will provide an important basic understanding in seeking a new educational field in higher education or lifelong education for the early adult learners.
Software Implementation of Modular Reduction by Pseudo-mersenne Primes

Modern cryptosystems allow the use of operation in prime fields with special kind of modules that can speed up the prime field operation: multiplication, squaring, exponentiation. The authors took into account in the optimizations: the CPU architecture and the multiplicity of the degree of the modulus in relation to the machine word width. As example, shown adopted module reduction algorithms hard-coded for modern CPU in special form of pseudo-Mersenne prime used in MAC algorithm Poly1305, - in electronic signature algorithm EdDSA and - in short message encryption algorithm DSTU 9041. These algorithms have been software implemented on both 32-bit and 64-bit platforms and compared with Barrett modular reduction algorithm for different pseudo-Mersenne and generalized-Mersenne modules. Timings for proposed and Barrett algorithms for different modules are presented and discussed.