Reviewerly/exHarmony huggingface dataset:
Image Splicing Forgery Detection using Error Level Analysis and CNN

The issue of image forgery through splicing has become increasingly relevant in the current digital era. Splicing involves the manipulation of images by combining parts of two or more different images to create a deceptive composite image. This technique can be employed for various purposes, including the dissemination of false information, damaging someone's reputation, or even creating confusion in specific contexts. Several techniques used to detect splicing involve statistical analysis, color analysis, and texture analysis. Additionally, artificial intelligence developments, such as deep learning, have been applied to enhance detection capabilities. In this study, we employed a Convolutional Neural Network (CNN) model to identify image deviations caused by splicing. Optimization was performed on the convolutional layers of the model to improve CNN performance. The integration of Error Level Analysis (ELA) was also implemented to aid in identifying splicing forgeries, where portions of one image are combined with parts of another. Areas that have undergone splicing may exhibit noticeable differences in error levels. The dataset utilized for this research was sourced from DVMM and CUISDE. The validation accuracy results for our CNN model before incorporating ELA were 61% for DVMM and 74% for CUISDE. After adding ELA, the CNN model demonstrated improved detection accuracy, achieving validation rates of 72% for DVMM and 71% for CUISDE.
Advantages and Disadvantages of Chatgpt in Science Learning: A Systematic Literature Review

Currently many forms of sophisticated technology are used by many people. This of course cannot be separated from the existence of artificial intelligence. One form of sophisticated technology is Chat GPT which was developed by Open AI. Science as a collection of knowledge is the result of human scientific creative activity. The results of scientific creative activities will produce knowledge in the form of facts, concepts, principles, laws, and theories. Science is a human activity characterized by thought processes that take place in the human mind. With developing technology, one of which is Chat GPT, it will make the current science learning process easier. The research aims to examine the advantages and disadvantages of Chatgpt in Science Learning: Systematic Literature Review. The review was conducted based on state-of-the-art methods using the preferred reporting items for reviews and meta-analyses (PRISMA) guidelines. The results of this research explain that. GPT chat has several uses, advantages, and disadvantages in science learning. For this reason, GPT chat must be used as wisely as possible, so that there are no mistakes in its application in science learning or other learning.
Analisis Akuntansi Persediaan Barang Dagang Pada Toko Indomaret Basuki Rahmat Di Kota Jambi

This study aims to describe the recording and valuation of merchandise inventory at Indomaret Basuki Rahmat Jambi City based on Statement of Financial Accounting Standards (PSAK) Number 14 Concerning Inventory and find out the obstacles encountered in carrying out merchandise calculations at Indomaret Basuki Jambi City. This type of research used in this research is descriptive qualitative research. The data collection techniques used are observation, interviews, documents and informants. The research results showrecording and valuation of merchandise inventory at Indomaret Basuki Rahmat City of Jambi is carried out based on the Statement of Financial Accounting Standards (PSAK) Number 14 Concerning Inventory, namely by recording inventory, measuring inventory, costing inventory, recognizing expenses and disclosing inventory. Indomaret Basuki Jambi City uses the FIFO (First In First Out) inventory valuation method. Obstacles encountered in carrying out the calculation of merchandise at Indomaret Basuki Jambi City are calculation errors, layout of goods, supervision that is not optimal, employee negligence and communication, inventory does not match warehouse capacity, and lack of employee cooperation.
the Application of Financial Management Concepts in Cafe Selection Decision Support Systems Using the SAW Method

A Cafe is a place to eat and gather with a more comfortable atmosphere, there are many cafes that exist and are developing in Seruyan district, giving rise to an increase in the economy and the government is expected to be able to help cafes in the Seruyan area, most cafes are managed by young people so that it can have a positive impact if they are in provide support from the government.&#x0D; Therefore the researcher created a decision support system to help related parties to choose the best cafe. This decision support system is designed to provide an objective picture of the cafes in Seruyan.&#x0D; The method used in this study is the SAW (Simple Additive Weighting) method, which is an SPK method used to select various objects that are used as the best alternative from the many existing alternatives, with the criteria used namely location, atmosphere, menu, price, service and cleanliness.&#x0D; With this decision-making system, it is hoped that it can help several parties, such as the tourism office and also the UMKM and industry cooperative services to become a source of reference if you want to develop SMEs and businesses in Seruyan District
Minat Belajar Siswa dalam Mengikuti Pembelajaran Pendidikan Agama Kristen di Kelas VII SMP Negeri 3 Palangka Raya

Interest in learning plays an important role in the student learning process. Students who have a low interest in learning, the quality of learning will decrease and will affect student learning outcomes. The purpose of this research was conducted to describe students' interest in learning Christian education in class VII SMP Negeri 3 Palangka Raya and find out the factors that cause a lack of interest student learning in participating in Christian Religion education lessons in class VII SMP Negeri 3 Palangka Raya. Qualitative research approach with descriptive research. Data collection techniques through observation, questionnaires and documentation. The results showed 1) 5 (39%) students with low learning interest, 5 (38%) students with moderate learning interest and 3 (23%) students with high learning interest. 2) Factors causing the lack of interest in student learning, from internal factors, health, disabilities and internal motivation. External factors include teaching tools and teaching methods.
EFL STUDENTS’ DEMOTIVATION: A PORTRAIT OF TEACHING ENGLISH IN AN ISLAMIC BOARDING SCHOOL

In an English language learning it demands the students to understand vocabulary, grammar, and several language skills. Consequently, the students must have sufficient motivation to concentrate in English class. If not, demotivation emerges and becomes a factor of unsuccessful learning. In relation to demotivation, this research investigates: [1] the factors contributing to the demotivation among students at the Islamic Boarding School of Tremas Pacitan in learning English and [2] the strategies used by students at the Islamic Boarding School of Tremas Pacitan to minimize demotivation in learning English. This research used a descriptive qualitative approach. The subjects of the research were 8B students of the Islamic Boarding School of Tremas Pacitan. The data were collected by conducting observation and doing an interview. The findings revealed that the factors causing learning demotivation that were divided into intrinsic and extrinsic factors. Intrinsic factors had found personal belief and students’ boredom, while extrinsic factors such as teaching methods; classroom conditions; and less adequate facilities. Knowing that, extrinsic factors are more arisen than intrinsic factors. Moreover, students’ strategies to minimize learning demotivation were such as self-studying; asking friends for help; and ignoring their humiliating classmates. It is recommended that English teachers need to improve learning methods and facilities that support learning English.
CyberExpert: Towards an Automated Framework for Cybersecurity Expertise Acquisition and Mastery

The continuously increasing gap in cybersecurity workforce in numbers and in skill level, demands a fundamental shift in how we approach cybersecurity education and training. The complex and ever-evolving cybersecurity industry and threat landscape underscores the need for cybersecurity professionals with versatile skillset, growth mindset and resilient character to support organizations' cyber capabilities, preparedness and resilience. This makes it more challenging to develop and operate educational programs that effectively train cybersecurity talent, which is able to take on and perform in multiple roles and responsibilities, without tailoring the program to be very specific to such roles. Hence, cybersecurity educational programs must train professionals that can stand the test of time, in a fast-paced and quickly changing career. In this paper, we expand on innovative approaches we developed to support experiential learning and expertise acquisition for future cybersecurity professionals. The described approach is implemented and is continuously improved and enhanced as part of the educational framework for students in the Cybersecurity Bachelors offered at the Texas A&M University System - RELLIS Campus <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Work performed while at Texas A&M University - Commerce. We developed a technology platform (virtual and hands-on) that extends the capabilities of existing similar frameworks (cyber-range) to enable higher levels of experiential learning. A cyber-range is a technology solution, used in industry and academia that provides a dedicated environment to enable several cybersecurity use-cases. The core capability of a cyber-range is to provide users with access to safe, isolated, monitored and secured sandbox environment. We propose and continue to develop an integrated cyber-range platform, CyberExpert cyber-range, that is designed and built using open-source and accessible tools. The CyberExpert cyber-range is equipped with data collection and analysis platforms that can enable data-led closed-loop operation to enable automated collection and assessment of users' expertise acquisition and mastery of skills.
Adopting Model-Eliciting Activities in an Undergraduate Software Engineering Course Through Real-World Projects

This innovative practice work in progress paper discusses the integration of Model-Eliciting Activities (MEA) into in software engineering (SE) classes, and the challenges faced in preparing and delivering these activities. The preparation and implementation of MEA in SE classes can be challenging, as it requires creating self-assessable MEA questions that simulate real-world problems, ensuring the integration of MEA in SE course topics, providing feedback, and analyzing learning outcomes. In this research, the authors address these challenges and gain practical experience in designing and implementing MEA in SE classes. The authors conducted experiments over two consecutive semesters in SE courses that cover topics such as requirement solicitation, design and implementation, software testing techniques, secure software, and software quality assurance. They incorporated MEA questions that simulate real-world problems into both face-to-face and online classes, ensuring the integration of MEA in SE course topics, providing feedback, and analyzing the learning gains. This paper contributes to the distribution of MEA application for SE courses. It presents the authors' experiences, challenges, reports evaluations, and findings in implementing MEA in SE courses. Overall, this paper provides insights into the effective integration of MEA into SE courses, and the benefits it can bring to students' learning outcomes.
The Evolution of Team Coordination Commitments in the Context of Computational Projects

This study aims to explore the team coordination commitments reported by undergraduate students pursuing biomedical and agricultural engineering majors after completing three team-based computational modeling assignments throughout the course of a semester. In each post-assignment reflection, the teams were asked to reflect on what aspects could be done better for the next project in terms of team coordination to address the issues that didn't work. Team-based reflections were analyzed using thematic analysis. We identified students' recognition of the importance of scheduling and coordinating meetings, task management and delegation, collaboration and teamwork, and time management towards teams coordination. Furthermore, the results of this study provide the evolution of teams' commitments for three teams, indicating that teams had a shift in their coordination priorities for the different projects.
Agile Teaching: Automated Student Support and Feedback Generation

The software engineering industry prioritizes efficiency through the use of tools and processes such as version control and Agile methodologies. Automation has allowed developers to be more productive and deliver superior results. However, automation is not as widespread in software engineering education. To improve educational outcomes and provide more effective feedback to students, this research implements software engineering methods and automation techniques in software engineering education and evaluates their effectiveness. Our focus is on establishing a connection between source code and natural language, allowing us to generate a natural language description from a given source code sample. To generate feedback, we employ deep learning models to learn code representations and gain a deeper understanding of code. However, the complexity of code makes it challenging to learn its representation accurately. After learning about code, we compare students' code with the instructor-provided solution based on configurable thresholds and generate comments to provide guidance. This work extends the Transformer model, GraphCodeBERT, which is a pre-trained model for programming languages that incorporates the inherent structure of code. We utilize both syntax-level information, such as abstract syntax trees, and semantic-level information, such as data flow, during pre-training. The data flow graph has nodes representing variables and edges indicating the “where-the-value-comes-from” relationship between variables. Our model is based on the Transformer neural architecture and uses a gated graph neural network model to learn code embeddings. This function incorporates code structure, a copy mechanism, and relative position representations, allowing the model to better understand the semantics of code. We evaluated our approach on the Java dataset in terms of BLEU, METEOR, and ROUGE-L metrics and compared it with the state-of-the-art code comment generation model, GTrans. Our model demonstrated improvements in two metrics of METEOR and ROUGE-L.
AR-Classroom: Augmented Reality Technology for Learning 3D Spatial Transformations and Their Matrix Representation

Project AR-Classroom aims to enhance undergraduate students learning spatial transformations and their mathematical representations. Understanding closely allied spatial and mathematical concepts significantly contributes to STEM learning in fields of computer graphics, computer-aided design, computer vision, robotics, and many more. The technology and learning innovations of this research include novel AR features and their implications for learning. In AR-Classroom, a student can hold and manipulate a 3D physical model (a LEGO space shuttle as an example) while simultaneously interacting with AR visualization of 3D rotations. Two usability tests with 24 participants total have been conducted for AR-Classroom leading to promising results and recommendations for improvements. The project contributes to advancing our knowledge in (1) the role of interplay between physical and virtual manipulatives to engage students in embodied learning and (2) the features of AR to make difficult, invisible concepts visible for supporting an intuitive and formal understanding of spatial reasoning and mathematical formulation.
Classification of Solo Batik patterns using deep learning convolutional neural networks algorithm

The ideology of the Solo Batik pattern has not been conveyed to the public. In addition, a lot of people are unaware that batik contains particular patterns that are also used for particular activities. This study uses a convolutional neural network model to categorize 9 different Solo Batik patterns according to their use of elaborate geometric shapes, complicated symbols, patterns, dots, and natural designs. With 1 to 4 hidden layers, we aim to select the number of hidden layers that yields the highest accuracy. A 100×100 pixel image is used as the input. The feature extraction process then makes use of 3×3 feature maps from three convolution layers. The dropout regularization is then added, with settings ranging from 0.1 to 0.9. The Adam algorithm is also used in this model to perform optimization. The 3-layered convolutional neural networks (CNN) with a dropout value of 0.2, run in 20 epochs, produced accuracy results of 97.77%, which was the highest. Additionally, it can be inferred that applying a certain number of hidden layers and adding right dropout regularization values has an impact on raising the accuracy score.
Analisis Semiotika Makna Nasionalisme melalui Text Mining pada Media Sosial Twitter di Kejuaraan AFF Tahun 2020

This study visualises semiotic analysis of the meaning of nationalism in the 2020 ASEAN Football Federation championship using the constructivist paradigm. The primary data were collected through non-participant observation using Text Mining techniques with the help of a rapid miner. The method of this research consists of several stages including text processing and the use of the K-Means clustering technique to obtain data groupings that have similarities. Furthermore, the results of clustering were analyzed using the theory of semiotic triangle meaning Charles S. Peirce. The results showed that the sign includes texts that have the full meaning of nationalism or the teachings of love for the homeland. Sentences tweeted on Twitter social media are divided into state symbols represented by words such as national team as many as 22653 words, PSSI as many as 5097 words, and garuda with 1170 words. The attitude of nationalism is represented by words such as enthusiasm as many as 1893 words, fighting as many as 982 words, supporting as many as 11747 words, proud as many as 612 words, and hope as many as 506 words. Meanwhile, the cluster evaluation shows that 17 of the 28 available clusters have interpretations of nationalism values that emphasize texts that contain an attitude of national spirit through the 2020 AFF Cup final match.
Integrating TPACK in Pre-Service Teachers’ EFL Course: Impacts on Perception, Knowledge, and Practices

Most research that has explored integrating the Technological Pedagogical Content Knowledge (TPACK) framework into teacher education programs primarily utilized context-unspecific self-report instruments. This mixed-methods study explored the efficacy of integrating four domains of the TPACK framework in an EFL course on the perception and knowledge of (30) pre-service teachers in an Egyptian university. The study also investigated the effect of the TPACK-based course on participants’ lesson plans and teaching practices. Data collection instruments included a test, a questionnaire, observation checklists, lesson plans, and semi-structured interviews. Quantitative analysis showed that pre-service teachers’ knowledge and perception of PCK, TCK, TPK, and TPCK have significantly improved after the course. Qualitative analysis of participants’ lesson plans, observation, and interviews revealed that participants moderately integrated technologies in their instruction and PCK was the most dominant in their teaching. The study provided implications for implementing TPACK in teachers’ education programs.
Corelation of Mammography with Histopathology in Breast Lump

Introduction: In females breast cancer is the second most common cancer in India with an incidence of 25.8 per 1,00,000 individuals. Most of the patients present with breast lump. Many due to lack of awareness present very late with advanced stage. Hence it is very important to diagnose the condition early with a proper screening modality. Materials and Methods: A prospective study was conducted on 100 women presenting with breast lump. All patients underwent mammography followed by histopathological assessment of the lump. Results: Irregular shape with ill defined borders on mammography was found to be significant marker of malignancy. Calcification in malignancy was 55.22 times more common than benign. The sensitivity of mammography was 84.62% and specificity of 80% with diagnostic accuracy of 83%. Conclusion: Mammography was found to be a reliable modality in diagnosing breast malignancy and hence concluding that it can be used as a diagnostic tool in breast lumps.
Diagnostic Accuracy of Mammography and FNAC in Comparison to Gold Standard Histopathological Examination in Carcinoma Breast

Context: Screening and diagnostic efforts for breast cancer are critical because the disease has a high rate of successful outcomes with early identification and treatment. The mammography (MG) and fine needle aspiration cytology (FNAC) are individually effective diagnostic modalities for detection of breast pathologies. Aim: To compare the efficacy of mammography and FNAC in Cancer Breast. Setting &amp; Design: The prospective study conducted on patients admitted for surgery for Breast lump or symptoms of breast malignancy in surgical wards of JLN Medical College, Ajmer and undergoing surgery for same with an aim to evaluate breast lesions using mammography and FNAC independently and in correlation with gold standard histopathology. Results: A total number of 100 cases of breast lump studied. The mammography, FNAC and histopathology results were collected and analysed. In present study, Mammography shows Sensitivity – 93.846, Specificity – 82.857, Positive Predictive value – 91.045, Negative Predictive Value – 87.87and Accuracy – 90. FNAC shows Sensitivity – 93.8, Specificity – 97.14, Positive Predictive value – 98.38, Negative Predictive Value – 89.474 and Accuracy - 95 Conclusion: This study confirms that the Mammography and FNAC have significantly higher sensitivity and NPV in detecting the both benign and malignant lesions of the breast. In the present study, FNAC has more accuracy in predicting breast cancer than mammogram. Association between FNAC and Mammography result was calculated to be.717 Key words: Breast lesions; fine needle aspiration cytology; mammography; histopathological examination. Key message: for successful treatment outcome of carcinoma breast early diagnosis is must.
STUDENTS’ ERROR ANALYSIS IN CONSTRUCTING NEGATIVE AND INTEROGATIVE SENTENCE IN SIMPLE PRESENT TENSE

Abstract&#x0D; The aim of this research is to analyze and to identify kinds of errors made by students of Junior High School in constructing negative and interrogative sentences in simple present tense. This research used comprehensive literature review from various source. By the result, it is informed the proportion of students’ simple present error in constructing interrogative sentence based on surface strategy taxonomy, the percentage omission is 80.9%, addition is 0.20%, misformation is 0,47%, and misordering is 0.62%. so that the most common error committed by the students in constructing negative and interrogative sentence is omission error. The finding of this research provide a literature for English teachers and also for students, should pay more attention to writing teaching and learning process and also stress on the material in which difficult for the students. They may use a new method in teaching learning process especially in teaching grammar about simple present tense .