Reviewerly/exHarmony huggingface dataset:
Geochemical evaluation of the genetic relationship of Kısacık (Ayvacık, Çanakkale/Türkiye) epithermal gold mineralization using trace and rare earth elements

The Kısacık gold deposit is situated in the Biga Peninsula, Northwest Anatolia, Türkiye. The aim of this study is to investigate the genetic relationship between the Kısacık gold deposit and the surrounding rocks using trace element and rare earth element (REE) geochemistry. The results show that the gold mineralizations in the Kısacık area have a compatible pattern with the Alakeçi-Kısacık Volcanics and the Kuşçayırı plutonic rocks in the near vicinity of the area. The gold mineralizations in the Kısacık area are consistent with the trace and rare earth element (REE) patterns of the upper continental and lower continental crust, as shown by comparison with spider diagrams normalized to different geological settings. The gold enrichments in the listvenites and ultramafic rocks in the area are also consistent with the trace and REE element patterns of these rock types. The results of this study support the conclusion that the gold mineralization in the Kısacık area is related to hydrothermal fluids that leached elements from the Alakeçi-Kısacık Volcanics. In addition, hydrothermal fluids from plutonic rocks were also effective in the mineralization process. These results suggest that the gold mineralization in the Kısacık area is related to hydrothermal fluids that leached elements from the Alakeçi-Kısacık Volcanics.
Recognizing Entity Types via Properties

The mainstream approach to the development of ontologies is merging ontologies encoding different information, where one of the major difficulties is that the heterogeneity motivates the ontology merging but also limits high-quality merging performance. Thus, the entity type (etype) recognition task is proposed to deal with such heterogeneity, aiming to infer the class of entities and etypes by exploiting the information encoded in ontologies. In this paper, we introduce a property-based approach that allows recognizing etypes on the basis of the properties used to define them. From an epistemological point of view, it is in fact properties that characterize entities and etypes, and this definition is independent of the specific labels and hierarchical schemas used to define them. The main contribution consists of a set of property-based metrics for measuring the contextual similarity between etypes and entities, and a machine learning-based etype recognition algorithm exploiting the proposed similarity metrics. Compared with the state-of-the-art, the experimental results show the validity of the similarity metrics and the superiority of the proposed etype recognition algorithm.
Power Efficient Implementation of ECC Using LCSLA Based Dual Field Vedic Multiplier

The Elliptic Curve Cryptographic (ECC) technique is employed for security standards such as Security Key Management (SKM), digital signature, data authentication and so on. The ECC technique is capable of sequential and parallel mode processes through a unified design. It is used for both equally binary fields and prime fields of cryptosystems. The DMM structure has been developed using CSA. This adder requires a greater amount of Full Adder circuits, which occupy more area. To overcome this problem, this paper discusses four different methods, such as DMM-Optimized Carry Look Ahead Adder, DMM-Optimized Carry Bypass Adder, DMM-Look up Table Carry Select Adder, and Dual Field Vedic Multiplier – LCSLA, which is used to increase the performance of the ECC. The parameters like power utilization, time delay information, and hardware area overhead are analyzed and compared with existing methods. Among those four methods, the DVM-LCSLA method gave better results in FPGA and ASIC performances.
WEB APPLICATION DEVELOPMENT TECHNOLOGIES

Abstract. The article discusses the technology of developing a web application, and also reflects the results of the analysis of technologies for implementing web applications for ebusiness. The results of the analysis of technologies for creating web applications are reflected. The main models of interaction between market entities are considered. The article describes the results of a brief analysis of the methods of quantitative analysis of processes.The main sectors of the market and models of interaction of market participants in computer networks are shown. The aspects of the SWOT analysis of the business process are disclosed. consumer sectors of the ecommerce market in Kazakhstan. The article briefly describes the analysis of technologies for creating web applications. Keywords: e-commerce, technology, web application, business process, business process analysis, model.
A Morphological Analysis: A Trend of the English Suffix “-less” in Indonesian Words on X/Twitter

The widespread use of English by people from various parts of the world has led to the emergence of new vocabulary, including the process of affixation between English and Indonesian words. By analyzing samples of common Indonesian words attached with the suffix ‘-less’, this research aims to discuss the use of the suffix ‘-less’ in Indonesian words and how it is used in context. The method used in this research is descriptive qualitative. Data sources were obtained from Indonesian words containing the suffix ‘-less’ that appear in posts, comments, and quotes from X/Twitter users in Indonesia. The data then analyzed based on the theory of word formation as a sub discipline of morphology by Lieber (2009). This research finds that the application of the suffix ‘-less’ is more often found attached to nouns and is rarely found attached to verbs.
Pembuatan Katalog Produk Menggunakan Canva Sebagai Media Promosi Pada Warkop R. Basman Malang

Warkop R.Basman promotes with word of mouth brochures and through whatsapp. The purpose of this research is to create promotional media in the form of a product catalog using Canva which displays a design according to the criteria for a good catalog at Warkop R.Basman Malang, to improve promotional media to consumers.&#x0D; This research uses the type of action research involving 15 respondents consisting of 1 owner, 2 marketing experts, 2 design experts, and 10 consumers. This action research was conducted up to cycle II.&#x0D; Based on the EPIC theory (Emphaty, Persuasion, Impact, and Communication) and from the results of distributing questionnaires, the average score is very effective. &#x0D; From the details of the EPIC average score, it can be concluded that the creation of promotional media through product catalogs made using Canva, can be declared very effective as promotional media.
Digital Ecosystems Development in Russian Media Industry as a Result of Their Digital Transformation

In modern world media companies face serious challenges. From one hand, new technological companies enter the media market and displace traditional mass media. From other hand there is an unstable situation in the advertising market in Russia, whereas advertising is the main source of revenue for traditional mass media companies. Authors have an opinion that digital ecosystem creation as a result of digital transformation will help to overcome these difficulties. There are digital ecosystems, which include media services in Russia, however no studies have been found that assess their development. Comparison of media segments was conducted and digital ecosystem development was assessed in this paper. Ecosystems were analyzed by three blocks: ecosystem development as a whole, media segment development in ecosystem and media services development inside media segment. Criteria groups were defined for each block for comparative analysis. Authors make a conclusion, that majority of the biggest traditional media companies in Russia do not have developed digital ecosystem and focus on the development of the main business.
Pemerolehan Tindak Tutur Anak Penyandang Down Syndrom Pada Usia 11 Tahun

Communicating is important for individuals to be able to interact with other humans. Communication makes it easier for humans to convey ideas, ideas, messages, and so on. In communicating, humans create speech acts as a manifestation of the process of communication. In the study of psycholinguistics speech acts are also closely related to a person's psychological state. The surrounding environment and various nearby elements of course affect a person's development in the process of speaking since infancy. One of the obstacles can also be caused by limited physical conditions. For example in people with Down Syndrome. Most of people with this syndrome have difficulty pronouncing words, even speech delays. For this reason, in this discussion the author will discuss the stages of speech act development in children with Down Syndrome in accordance with psycholinguistic studies. This type of research is descriptive qualitative, namely to examine and describe speech acts in children with Down syndrome at the age of 11 years. Researchers obtained data through interviews using the face-to-face note-taking technique (CTS). The data used goes through the following stages: a.) Data processing; b.) results and discussion; c.) Conclusion.
The Solitonic X-Junction as a Photonic Neuron

This chapter describes in detail the physics behind the formation of photorefractive spatial solitons, taking care also of the mathematical aspects. Starting with a description of single soliton formation, the X-Junction solitonic neuron is introduced from both theoretical and experimental perspectives. Experiments on the neuron writing process and selectively erasing the soliton channels of which it is composed are presented. The fabrication of a solitonic neuron in an 8-micron thin film of lithium niobate on insulator, an extremely promising technology nowadays, is also described.
Project Oriented Management (POM) and Logistical Challenges in Maintenance and Network Development Systems

Maintenance, Refurbishment and Network Upgrading Systems occupy a prominent place among service systems, especially due to their dispersed location and large territorial extent. For the efficient opertaion of these systems, it is essential to apply Project Oriented Management (POM), which aims at optimizing logistic processes and meeting project specific requirements.
Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm

Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (dc). The traditional DPC algorithm exhibits noticeable shortcomings in the initial setting of dc when confronted with different datasets, necessitating manual readjustment. To solve this defect, we propose a new algorithm where we integrate DPC with the Black Widow Optimization Algorithm (BWOA), named Black Widow Density Peaks Clustering (BWDPC), to automatically optimize dc for maximizing accuracy, achieving automatic determination of dc. In the experiment, BWDPC is used to compare with three other algorithms on six synthetic data and six University of California Irvine (UCI) datasets. The results demonstrate that the proposed BWDPC algorithm more accurately identifies density peak points (cluster centers). Moreover, BWDPC achieves superior clustering results. Therefore, BWDPC represents an effective improvement over DPC.
Analysis of Machine Reading Comprehension Problem Using Machine Learning Techniques

Machine reading Comprehension is a significant challenge in the field of natural language programming. In this problem, the objective is to read and grasp a given text passage before responding to questions that are dependent on the material. The most modern machine reading comprehension systems have accuracy levels that are superior to those of humans. On the other hand, when domains are switched, the majority of machine reading comprehension systems see a considerable drop in performance. However, certain machine reading comprehension systems have previously outperformed humans on a range of standard datasets, despite the evident and vast disparity among them. This is the case even though MRC models are not designed to read like humans. This demonstrates the need for enhancing the currently available datasets, assessment criteria, and models to progress the machine reading comprehension models toward "actual" comprehension. In this work, the analysis of the machine reading comprehension problem performed by using logistic regression, K- nearest neighbor, and random forest. This strategy will include perspectives that are topic-oriented, concept-oriented, and time-oriented, and it will provide support for the summary of multilingual texts with the assistance of several machine reading comprehension models that are currently in development.
National Exam Try Out Application Design At SMP Negeri 35 Makassar

The problem with the current system is that Try Out for the National Examination at SMP Negeri 35 Makassar is still done manually, therefore I provide a solution for designing the Try Out application, which produces an application that can help students in preparation for the National Examination. This research aims to design an effective and efficient national exam try-out application at SMP Negeri 35 Makassar, implementing the Dynamic System Development Method (DSDM) at SMP Negeri 35 Makassar. This data was obtained through literature studies and interviews. Data analysis uses PHP programming scripts and MySQL data. The results of this research show that the Try Out application is user-friendly, has an attractive appearance, and has complete features that can make things easier for students, especially in carrying out Try Outs, so that the process of processing test results is more effective and efficient, the Try Out Application is quite good, useful for users and so needed that it was declared feasible based on the tests carried out, with an average score of 86.00% from 44 respondents.
IDENTIFICATION OF SOFTWARE CLONE FILES USING MACHINE LEARNING

One technique to reduce the likelihood of introducing a bug is to identify code clone files. Refactoring, often known as removal, is the process of making software more understandable and maintainable. When additional clones are discovered in software programs, we need a method to help developers with refactored code and to enhance software quality. This method must tell developers which clone files require reworking. To provide developers with recommendations on which files require code refactoring, our research proposes a novel learning technique that automatically extracts features from the identified code clones and trains models to present a novel technique to enhance classification performance by transforming outliers into Unknown clone sets. The Eclipse dataset, which comprises 213 Java software files, was utilized for this project. Support Vector Machine (SVM), Random Forest, Bagging, and K-Nearest Neighbors (KNN) are the algorithms that are employed. We compare these four categorization models and recommend the model with the highest accuracy. Our tool can be developed and applied to reduce system bugs. By recognizing software clone files and eliminating duplication code, our study can enhance clone maintenance. Also, the possibility of bad design for a system, difficulty in a system improvement or modification, and introduction of a new bug can be decreased by identifying and refactoring clones. KEYWORDS: Cloning, Refactoring, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision trees.
The professional group development monitoring platform of double height plan based on Echarts+Vue+Element-UI

Aiming at the management problems in the construction plan of high-level higher vocational schools and majors with Chinese characteristics, we have designed and developed a monitoring platform for professional group development. The platform includes several main modules: the school big screen visual data analysis module, the professional big screen visual data analysis module, the data management module, and the account management module. By utilizing the API provided by the back-end server to acquire relevant data, and integrating the use of ECharts, Vue, Element UI, Axios, and other front-end libraries and plug-ins, we successfully developed each module in the monitoring platform for professional group development. Through the implementation and utilization of the project, the platform demonstrates characteristics of simplicity, universality, practicality, and effective operation. However, it is important to provide further explanation and background information, ensuring the readers' full understanding of the design and development of the monitoring platform. Additionally, clearer explanations of the functionalities and roles of each module in the platform can be provided to improve coherence and logic in the article.
Multi-view robust adversarial attack: a method based on channel attention weighted feature similarity constraint

Although deep learning has developed rapidly in recent years, the existence of adversarial examples casts a shadow over its future. Deep neural networks (DNNs) can be unreliable when facing these carefully crafted adversarial examples, limiting their real-world applications. While adversarial examples usually lack robustness and tend to lose effect when the viewing angle is changed. Moreover, it is impossible to control the angle at which adversarial examples are seen by the airborne detector. Therefore, we propose a multi-view robust adversarial attack by introducing perspective transformation and feature similarity loss to mitigate the negative effect that viewing angle changes bring to the features. We evaluated our adversarial patch on our multi-view test dataset and the results show that our proposed method reduced the mAP by 73.4%. Furthermore, compared to other methods, our approach performs best in the data with low depression angles, demonstrating its excellent multi-view robustness.
Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study

Abstract The definition of architecture is a crucial task in software development, where the architect is responsible for making the right decisions to meet specific functional and quality requirements. These architectural design decisions form the foundation that shapes the arrangement of elements within a system. Unfortunately, these decisions are often poorly documented, implicit in various artifacts, or inadequately updated, leading to negative consequences on the maintainability of a system and resulting in rework and cost overruns. The objective of this systematic mapping study is to comprehend the current state regarding approaches for traceability of architectural design decisions and how these decisions are linked with the different artifacts used in software development. To achieve this, an information extraction protocol is followed, utilizing databases with search strings, inclusion, and exclusion criteria. The findings demonstrate that this knowledge is highly relevant; however, it is rarely explicitly documented. As a result, most works propose diverse approaches to extract this knowledge from existing technical documentation, commonly used tools, and other sources of product and process information. In contrast, it is evident that there is no standard for documenting design decisions, leading each author to present a subjective version of what is important and where to trace these decisions. This suggests that there is still a significant amount of research to be conducted regarding the traceability of these architectural design decisions and their connection with software artifacts. Such research could lead to intriguing new proposals for investigation.
A GOLD MINE OF TEXT TEXT MINING VERSUS MANUAL CODING METHODS REGARDING QUALITY OF CARE IN LONG-TERM CARE FOR OLDER ADULTS

Abstract In long-term care for older adults, large amounts of text are collected that cover the quality of care, such as transcribed interviews, medical records, and text in electronic health records. Researchers currently analyze textual data manually to gain insights, which is a time-consuming process. Text mining (TM) could provide a solution, as this methodology can analyze large amounts of text automatically. Therefore, this study aims to compare TM to the gold standard of manual coding regarding sentiment analysis and thematic content analysis. Data was collected from interviews with residents (n=21), family (n=20) and care professionals (n=20). TM models were developed and compared to the manual approach. The results of the manual and TM approach were evaluated in three ways: accuracy, consistency and expert feedback. Accuracy showed how similar the approaches are. Consistency showed if an individual approach finds the same themes in similar text segments. Expert feedback was used to show the perceived correctness of the TM approach. The accuracy analysis showed that 81.8% of text segments receive the same sentiment code in both approaches and 83.7% of text segments receive the same thematic codes. Interviews coded by TM had a higher consistency compared to those coded manually. Expert feedback showed that the TM had a limited understanding of the context. However, it also showed certain inconsistencies of manual coding. The current study has shown that TM can be an effective tool for quickly and accurately identifying sentiment and thematic content in large amounts of textual data.
Analysis a large data by using Weyl function

Analyze large amounts of data using Weyl functions. "Frequency variables" have been shown to provide important information that is lost in other techniques. This method applies to mood studies of information in the media and on the Internet. Presented is a quantitative analysis of information on the Internet regarding his BP oil spill in the United States. Rapid analysis of information from the social network (Twitter) on current affairs is important for government and political commentators. etc.
BSGAT: A Graph Attention Network for Binary Code Similarity Detection

Binary Code Similarity Detection (BCSD), which calculates the similarity between binary code snippets, plays a vital role in various security fields. Since binary functions have complete semantics, the main research objects in BCSD are binary functions. Current approaches face challenges in effectively capturing the semantic information of assembly instructions and the structural information of control flow graphs (CFG) in binary functions. This paper proposes a graph attention network (GAT) for BCSD, called BSGAT, to detect similarity between binary functions. Our contribution is twofold: first, we propose a strategy to generate rich representations of basic blocks in CFG; second, we introduce GAT, which assigns different weights to basic blocks in CFG, enabling the generation of more discriminative embeddings for target binary functions. We conduct experiments on a binary function similarity detection task and a real vulnerability detection task. The results show that our proposed model BSGAT outperforms existing models in both tasks. In the binary function similarity detection task, BSGAT achieved the highest average AUC value of 0.872. In the real vulnerability detection task, BSGAT achieves the highest average recall@10 0.378, surpassing the best-performing model Gemini (0.337) in the comparison models, with a significant improvement of 12.2%. Our code is available at https://github.com/quchao777/BSGAT.git.