Reviewerly/exHarmony huggingface dataset:
Predicted Stratigraphy: A Case Study from the Sureste Basin (Gulf of Mexico)

Summary Biostratigraphy represents one of the key disciplines of geology by allowing the arrangement of geological formations in space and time based on fossil assemblages. Due to its significance in the oil and gas industry and the fast pace of technological innovations and developments in geosciences, the interpreted biostratigraphical data is prone to become quickly outdated and thus preventing its use in future interpretations. However, the presence of a large amount of available data provides an excellent opportunity for novel studies aiming to update species taxonomies, detect reworking specimens, train machine learning models and test prediction models in order to digitalize biostratigraphic approaches. In this study therefore, we use various data science and machine learning techniques to demonstrate the potentials of an automated biostratigraphic approach. We take advantage of legacy data collected in the Sureste Basin, Gulf of Mexico, where we transformed the original dataset into a final stratigraphic framework. Our inferences indicate that we can get an accurate first insight into the stratigraphy at the studied location within a very short timeframe. Even though inconsistencies were found, our approach proved its potential for future work, which could be improved by increasing the prediction accuracy of biostratigraphic events.
Seismic Tiles, a Data Format to Enable Analytics on Seismic by Digitalization of G&amp;G Logic

Summary Seismic Tiles is a data structure for representing seismic reflectors in tabular form. A tile is a small surface, (reflector segment) that aligns with a seismic reflector. In a similar fashion as how the roads of Google Map are represented by connected road segments, connected tiles will represent seismic reflector surfaces. The value of Seismic Tiles is similar to the value of the Google Map data structure, in that logic now can be applied to seismic reflectors in a straight-forward way. We can now automate interpretation tasks such tasks as prospect identification, 4D anomaly hunting, faults and horizon interpretation) by explicit (and thus explainable) logic. In contrast to the machine learning (ML) way where the machine learns by example, Seismic Tiles allows interpretation logic in "digitalized" form to be applied directly. In contrast to ML approaches, this process does not require any training data, and is fully transparent in its workings. We believe this can be a game changer in the automation of seismic analysis, as Google Maps style technology has been in road navigation.
Formation of research skills of masters of vocational education in the process of professional training

The article considers the problem of formation of research skills of masters of professional education in the process of professional training. It has been found that the professional training of masters of professional education as researchers is possible under the conditions of formation of research skills in them even during their studies in institutions of higher education. It is noted that research activity is aimed at obtaining new knowledge about certain objects, processes or phenomena and has certain stages in its process. A number of components that are used in the process of research activities of future specialists are also defined. The research skills of future specialists are revealed in the independent organization of search work, in the ability to forecast, plan, and organize one’s own work. The professional and pedagogical skills of future vocational education specialists, which ensure their successful research activity, as well as the basic requirements for the organization of research activities of future vocational education specialists in a higher education institution, are considered. The process of forming the research competence of future specialists in a higher education institution includes the completion and protection by the student of a qualification work, as well as the passing of scientific research practice, which is part of a complex multi-component system of forming the research competence of future specialists of professional education. It was found that the research skills of the future professional education specialist are such skills that are formed in the process of learning about research activity and represent a cumulative sequence of actions that allow: to analyze the situation; to detect contradictions; define goals; formulate tasks in accordance with the goals; analyze the results; formulate conclusions. Research skills are skills that are aimed at solving various problems in various fields of activity.
Strengthening data security in Bahrain: leveraging Microsoft Purview to prevent leakage of sensitive information

Sensitive information is a category of classified data that must remain inaccessible to unauthorized users and devices. Traditionally, organizations maintained control over sensitive data within the confines of their corporate perimeters. However, COVID-19 has catalyzed a significant shift, compelling employees to transition from office settings to remote work environments. Thus, sensitive information could be potentially exposed to the internet. Notably, two key categories of sensitive information in Bahrain are Bahraini CPR (Central Population Register) and Bahraini passport numbers, whose unauthorized disclosure could have severe consequences for organizations. This paper explores the capabilities of Microsoft Purview in safeguarding these two critical types of sensitive information from unauthorized leakage outside an organization. Microsoft Purview is a cohesive governance unit that provides the capability to manage and govern your data landscape by mapping the sensitive information with discovery automation[1]. Microsoft Purview harnesses the power of two integral components. The first component is the deployment of Sensitive Information Types (SITs), which rely on regular expression (Regex) based patterns to identify the presence of Bahraini sensitive information. A regex-based pattern can be created to detect and prevent the malicious transfer of social security numbers (SSN) [2]. These SITs are further enhanced through a keyword list that assists in refining the framework's ability to locate sensitive data while minimizing false positives. The second component is the Microsoft Data Loss Prevention (DLP) service, designed to detect the sharing of sensitive data both internally and externally through the Microsoft 365 suite, including e-mails, documents, chats, and endpoints.
PUSAT TERAPI ANAK DOWN SINDROM DI BANJARMASIN

The Down syndrome Children's Therapy Center in Banjarmasin is designed to help Down syndrome children get facilities in the process of growth and development. In addition, this design can also be used to introduce Down syndrome to the general public. This design provides facilities in the form of basic therapy and training for down syndrome children, the therapeutic facilities provided consist of motor therapy, sensory therapy, and cognitive therapy. In solving the problems in this design, the Human-Centered design method is used so as to produce concepts that help design. The concept of educational games with the nuances of a zoo is also used as a reference to be able to provide good facilities and in accordance with the needs of Down syndrome children.
GOLF CLUB HOUSE DI SPORT CENTER BANJARBARU

The planning of the golf venue at the Banjarbaru Sport Center by the government that prioritizes golfers in the field of achievement must of course have complete facilities and national standards in order to accommodate when South Kalimantan hosts a golf tournament at the national level, the main facility in a golf venue is the Golf Club House which acting as a place of preparation for golfers before or after playing in the field, this club house must not only accommodate the golf course it serves in order to create a bond between the club house and the golf course and provide a sense of comfort for its visitors. To solve these problems, the functional Architecture method and the User Experience concept are used which pay attention to four points in the design, namely air exchange, lighting, materials and exposure to solar heat
REDESAIN PONDOK PESANTREN DARUL HIJRAH PUTRI MARTAPURA

Darul Hijrah Islamic Boarding School for females is a women's Islamic education institution in Martapura South Kalimantan and has been established since 1995. With a long time of more than 20 years, this boarding school has experienced many developments, especially in terms of development, but the development of Islamic boarding schools in the future will be increasingly complex considering the existence of government business programs that want to cooperate with the world of Islamic Boarding Schools in terms of the creative economy, this is intended to develop the economy and Human Resources (HR) within the boarding school environment. So from that the development of the creative economy as a learning media needs to be included to maintain the existence of this boarding school in the future. The method that was used in this report is a programming method which has two main steps: a) Programming which is a problem-tracking and; b) Schematic Design; a problem solving. The results of the method used finally got the Islamic creative educative place concept which is a combination of the two main variables namely Islamic education and creative education
A Dive into Lexical Simplification with Pre-trained Model

Lexical Simplification (LS) targets the replacement of complex terms with semantically equivalent simpler versions. While traditional LS methods often produce imprecise candidates, contemporary techniques utilize BERT for context-aware replacements. Notably, simplification may require phrasal rather than word-to-word substitutions. Diverging from conventional methods, our approach enables phrase-based replacements, and We improved the masking method of the masked language model to make it more suitable for lexical simplification tasks, finally refined the candidate word ranking. Experimental results show our method exceeds standard benchmarks.
Research on the knowledge embedding vector method based on TF-IDF and Auto-Encoder

At present, most enterprises and organizations attach great importance to the establishment of knowledge base in order to condense and summarize their core competitiveness, so as to inherit knowledge. However, at present, most of the knowledge base is a document library, and the structured information in the knowledge base is very lacking. Based on this, this paper proposes a knowledge embedding vector technology, which combines keyword extraction and text self-coding technology, so as to abstract and represent knowledge semantically in embedded vector space, and achieve the purpose of enriching structured information in knowledge base. Moreover, the knowledge embedding vector technology proposed in this paper can provide theoretical basis for the upper application of knowledge base, such as knowledge graph construction, knowledge search, knowledge recommendation and application.
MCN: Magnitude-Continuity Network for Video Anomaly Detection under Weak Supervision

Video anomaly detection is a complex task that combines anomaly detection and video image processing. Video anomaly detection aims to identify abnormal behavior in video and is widely used in the real world. In existing works, Feature-Magnitude have shown good performance in weakly supervised video anomaly detection. However, Feature Magnitude-based methods is easily affected by the environment, resulting in error detection. Motivated by the fact that the abnormal events in real video are often continuous, we propose a Magnitude-Contiuity Network. In addition, in order to further improve the robustness of existing Feature Magnitude-based methods, the dynamic Feature-Magnitude learning loss function is used. Experimental results on UCF-Crime and ShanghaiTech datasets show that the robustness and performance of the proposed method are significantly improved.
Research on path planning algorithm of partially observable penetration test based on reinforcement learning

In this paper, a new deep reinforcement learning algorithm DEU-DRQNper is proposed for penetration test path planning under partially observable conditions. The algorithm is improved on the basis of DRQN algorithm, including the experience playback of priority sequence, the action exploration and selection strategy combining ɛ greedy algorithm and UCB algorithm, and the way of Double DQN calculating the target Q value. Priority sequence experience playback mechanism selects more valuable experience sequences according to the probability distribution corresponding to the priority of experience sequences during sampling to accelerate learning. The hybrid exploration strategy of ɛ greedy algorithm and UCB algorithm gives consideration to exploration and utilization, thus improving efficiency. The method of Double DQN calculating the target q value effectively solves the problem of overestimation of q value and makes learning more stable. Experiments in several simulation environments show that DEU-DRQNper achieves better performance in penetration test scenarios with incomplete state observation, which provides an effective solution for applying deep reinforcement learning to penetration test path planning with partial observability and sequence dependence. This study expands the application of deep reinforcement learning in the field of penetration testing.
Comprehensive Teacher Teaching Posture Evaluation Model: Based on OpenPose Posture Recognition in the Context of Digitalization and Intelligence Integration

In the contemporary landscape, the assessment of teaching postures among educators traditionally relies on manual techniques, such as micro-teaching sessions and peer evaluations. However, these methods are often characterized by their inefficiency. Consequently, there is a pressing demand for automated approaches to evaluate and intervene in teaching postures. In the age of data-driven intelligence, this paper introduces the development of an intelligent teaching posture evaluation model. This model is constructed upon the foundation of the OpenPose posture recognition framework, which transforms unstructured video data into structured, analyzable data. Subsequent analysis of this structured data yields valuable metrics for the assessment of teaching postures. Ultimately, through the application of a fuzzy comprehensive evaluation method, this model enables quantitative assessments of teaching postures for both pre-service and in-service educators. The experimental results demonstrate that the evaluations generated by this model closely align with traditional manual assessments. This finding holds substantial implications for enhancing the efficiency and precision of teacher skill evaluations. Additionally, it offers innovative insights and methodologies for supporting educators in improving their teaching postures.
CREATION OF A NATIONAL DATABASE OF GENOMIC INFORMATION IN UZBEKISTAN

The article highlights current issues in the history of the creation and use of genomic information in the leading countries of the world. Considerable attention is paid to a comparative analysis of the regulatory framework of forensic genetic databases of foreign countries, especially the United Kingdom and the United States. In the context of improving the regulatory framework for the activities of the forensic service in Uzbekistan, in the implementation of the Law “On Genomic Registration” establishing the legal basis for obtaining, recording and storing biological samples, the article highlights and describes the characteristic features and prospects for the development of the database used in automated information system designed to store genomic and personal information. Taking into account the geographical and administrative structure of Uzbekistan, the authors proposed a centralized architecture for the formation of a national database, according to which information on state registration stored in a single database of genomic information should be developed as an information retrieval system, specially protected from external connections. The authors come to the conclusion that the national database of genomic information being formed will improve the efficiency of detection and investigation of a wide range of crimes, while simultaneously solving issues of searching for missing persons. Keywords: genomic information, database, DNA, CODIS, forensics, centralized database, identification.
The Influence of Smart Factory Implementation Factors on Positive Coginition and Acceptance Intention

This study attempted to suggest a significant direction for smart factory introduction by adopting a research model on the cognition and acceptance intention of the technology, organizational, and environmental factors needed for such introduction. It also aimed to help businesses enhance their manufacturing efficiency and competitiveness. To analyze the objectives of this study,, a self-report questionnaire survey was performed against the employees from companies with a smart factory system from March 27 to April 26, 2022. Specifically, an online survey using Google Forms and a face-to-face questionnaire survey were conducted. Among a total of 466 questionnaires (240: Google Forms survey, 226: face-to-face survey) collected, 440 copies, excluding 26 poorly answered ones, were used for final analysis. The collected data were analyzed using IBM SPSS 25.0 and AMOS 25.0.
Optimization of credit scoring models combination based on quantum annealing

The development of credit scoring models holds significant importance in banking for crediting operations. Combining multiple credit scoring models using a stacking generalization approach often yields improved results. However, selecting the optimal combination of credit scoring models is NP-Hard. In recent years, emerging quantum annealing (QA) techniques have shown promise in the field of combinatorial optimization and are considered potential solutions for NP-Hard problems. Hence, we propose an approach that employs QA for solving the credit scoring model combination optimization problem using a stacking generalization approach. By employing both auxiliary variable substitution and polynomial reduction methods, two distinct Quadratic Unconstrained Binary Optimization (QUBO) models suitable for quantum annealing are formulated. Experimental results demonstrate that the hybrid quantum annealing solver outperforms simulated annealing and simulated quantum annealing in terms of optimization capability across various problem sizes. The polynomial reduction and auxiliary variable substitution methods each exhibit their own advantages in terms of solution quality and computational efficiency.
Interactive Home Product Design Method Using Augmented Reality Technology

Augmented Reality (AR) is currently a hot research direction in the world, but its main research objects are computer vision and image processing. However, the application of AR technology in virtual reality systems is still in its infancy. Based on this, the article adopts a home interactive product design scheme based on AR technology. This article uses 3D modeling and rendering technology to realize the generation of 3D stereoscopic images of household products. On this basis, this article uses the augmented reality method to combine virtual scenes with real scenes, allowing users to view and control virtual items in real time through mobile phones and other terminal devices. Through this interactive method, users can personally experience the use of household products, and then understand the characteristics and scope of application of the products. Experimental results show that the design efficiency of the design method using augmented reality technology reaches a maximum of 85.1%, which proves that the method proposed in the article can effectively improve the efficiency of home product design, and also provides designers with a new design tool and idea.
Frequency-based Adversarial Privacy Protection Strategy Against Face Recognition Neural NetworksFrequency-based Adversarial Privacy Protection Strategy

Over the past few years, countless videos and images have been uploaded to social media platforms every day, which presents a challenge in how to protect the private data of large numbers of users. At the same time, face recognition algorithms have brought a lot of convenience to society in many fields, but if these face recognition methods with high accuracy are used for the wrong purpose, it will cause great threats and harm to personal privacy. Previous work on protecting personal privacy against deep neural networks mainly focus on increasing cyberspace security and adding digital watermarking to videos and images, which can not prevent attackers using face recognition algorithms to collect massive personal portraits of certain targets from social media platforms. To solve this problem, in this paper, we propose a frequency-based protection framework, which utilize invisible adversarial texture within high-frequency area, against face recognition neural networks to prevent them from obtaining correct identities, thus protect privacy portraits being used for further misuse.
A component search method by feature Mixing based Continual Learning

Component platforms are widely used in developing large web and mobile applications to improve development efficiency and test coverage. Searching for the appropriate component in the component platform and introducing it into the project for use makes development efficient and reduces redundant code. Machine learning methods are now widely used in large component platforms to enhance automatic retrieval or identification. However, current machine learning models are less accurate in identifying components or icons with differences due to the difficulty of collecting a large amount of label data in online environments that can help learning. To address this problem, this paper improves the feature mixing method in active learning and embeds it into a classical continuous learning model to solve the problem of accuracy degradation when the model handles discrepant tasks and tasks with limited training data. The model proposed in this paper ensures that the accuracy and performance of the model do not degrade significantly when facing a series of tasks with less data in the dataset after training on the first few tasks. Compared with large neural network recognition tasks, the improved continuous learning model is more lightweight and can learn multiple tasks using a single model. Easy to use when combined with front-end engineering and can be added to projects through dependency libraries. Compared to various classical benchmark models in continual learning, the accuracy of the proposed model can be improved by about fifteen percent.
GUFAD: A Graph-based Unsupervised Fraud Account Detection Framework

The detection of fraud accounts at scenarios of registration and login is a critical task for Internet enterprises, which can help to avoid economic losses at a very early stage. In industry, most companies tend to deploy supervised models such as rule-based models. However, these methods are significantly restricted in scalability since they highly rely on domain knowledge and manual annotations. Therefore, we designed a novel account graph analysis approach for uncovering fraudulent patterns.