Reviewerly/exHarmony huggingface dataset:
Developing an Android-Based E-Textbook to Improve Learning Media Course Outcomes

This research aims to develop an Android-based e-textbook for a learning media course to enhance student learning outcomes. The learning media course was selected because students require an e-textbook to improve their comprehension of the criteria and creation techniques of learning media during online learning. This study utilized the research and development method with the 4D model created by Thiagarajan and Semmels. Six learning media expert validators tested the product’s feasibility, and the product’s effectiveness was tested on students through experiment research. Descriptive statistics and an eligibility criteria table were used to determine the product’s feasibility and usability. In addition, an independent sample t-test was used to determine the product’s effectiveness. According to the expert evaluation results, the product validity falls into the "very good" category (4.50 out of 5, or 90%) in aspects of language, presentation, appearance, and usefulness. The effectiveness test results indicated that the Android-based e-textbook significantly affects student learning outcomes. In addition, students’ perceptions of the usefulness, ease of use, and satisfaction of the Android-based e-textbook are also in the "very good" category (4.27 out of 5, or 85%). According to a qualitative data survey, students mentioned that the most useful aspects of the product were the tutorial videos, quizzes, menus, and page markers. Nevertheless, a few students experienced difficulties with their internet connection and reading limitations.
The Effectiveness of Gamification Elements for the Development of Future Culturologists’ Digital Competence

The aim of the research was to experimentally prove the effectiveness of gamification of learning for the development of future culturologists’ digital competence. The research was based on the information from the monitoring of the subject or professional module, as well as a package of control and measuring materials. It also involved diagnostics of activity component, diagnostics of the level of digital component. Statistical tests — the Anderson-Darling test, the Cramér–von Mises test, Kolmogorov-Smirnov test, the Shapiro-Francia test, the Wilcoxon test, Mann-Whitney U-test, Student’s t-test — were performed to formally verify the consistency of data with the normal distribution law. The greatest differentiation was observed for cognitive, praxiological and reflective criteria. The increase in the cognitive component was promoted by the implementation of strategies for adapting educational content and multiple control of the students’ self-education process. The significant increase in the praxiological component was determined by the intensified student learning activity and, accordingly, the development of the activity component. In all tests, p significantly exceeds the fixed level of significance, so we can conclude that the data of the control and experimental groups were distributed uniformly at the beginning of the experiment. By testing the hypotheses H0 and H1 for EG and CG at the end of the pedagogical experiment, it was found that the statistics for the t-test exceeds the critical value and is in the range of significance. So, the approbation of the research results revealed significant differences in the learning outcomes of CG and EG, which allows us to state the effectiveness of gamification of learning. Based on the research results, we can argue that the gamification of learning has a significant impact on the development of future culturologists’ digital competencies. The study showed a significant positive impact of the introduction of gamification elements in the learning process. Further research should focus on studying ways to improve future culturologists’ digital competencies. It is necessary to study the impact of Internet technologies on the future culturologists’ professional development.
AN OVERVIEW OF PATTERN RECOGNITION

The operation and design of systems that can recognize a pattern in data are the subjects of the Al research field of pattern recognition. Pattern recognition has a wide range of practical uses. These consist of fraud detection, fraud prevention, handwriting recognition, facial recognition, etc. Face recognition uses a person's face to unlock a device rather than a keyboard to enter a password. As a result, there are fewer chances that the passwords will be forgotten or leaked to an unauthorized person. Since their faces are unlikely to resemble the original user's saved face model, this also prohibits other people from using the computer. The usage of handwriting in Al is handwriting recognition, one of the most promising ways to interface with small portable computing devices like personal digital assistants. They suggested visualizing the writing process on regular paper and automatically recovering the pen trajectory from numerical tablet sequences to make this communication technique appear more natural.
A further step towards the practical application of quantum computing in chemistry

Adiabatic state preparation (ASP) represents an efficient way of generating correlated wave functions on quantum computers for subsequent quantum simulation. Here, the author discusses recent work that numerically studied the performance of ASP on strongly correlated molecules and presented several approaches of improving the quality of prepared ground state wave functions.
Deep Hash with Optimal Transport-Based Domain Adaptation for Multisite MRI Retrieval

The Internet of Things has a wide range of applications in the medical field. Due to the heterogeneity of medical data generated by different hospitals, it is very important to analyze and integrate data from different institutions. Functional magnetic resonance imaging (fMRI) is widely used in clinical medicine and cognitive neuroscience, while resting-state fMRI (rs-fMRI) can help reveal functional biomarkers of neurological disorders for computer-assisted clinical diagnosis and prognosis. Recently, how to retrieve similar images or case histories from large-scale medical image repositories acquired from multiple sites has attracted widespread attention in the field of intelligent diagnosis of diseases. Although using multisite data effectively helps increase the sample size, it also inevitably introduces the problem of data heterogeneity across sites. To address this problem, we propose a multisite fMRI retrieval (MSFR) method that uses a deep hashing approach and an optimal transport-based domain adaptation strategy to mitigate multisite data heterogeneity for accurate fMRI search. Specifically, for a given target domain site and multiple source domain sites, our approach uses a deep neural network to map the source and target domain data into the latent feature space and minimize their Wasserstein distance to reduce their distribution differences. We then use the source domain data to learn high-quality hash code through a global similarity metric, thereby improving the performance of cross-site fMRI retrieval. We evaluated our method on the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results show the effectiveness of our method in resting-state fMRI retrieval.
Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention

Automatic image caption generation is an intricate task of describing an image in natural language by gaining insights present in an image. Featuring facial expressions in the conventional image captioning system brings out new prospects to generate pertinent descriptions, revealing the emotional aspects of the image. The proposed work encapsulates the facial emotional features to produce more expressive captions similar to human-annotated ones with the help of Cross Stage Partial Dense Network (CSPDenseNet) and Self-attentive Bidirectional Long Short-Term Memory (BiLSTM) network. The encoding unit captures the facial expressions and dense image features using a Facial Expression Recognition (FER) model and CSPDense neural network, respectively. Further, the word embedding vectors of the ground truth image captions are created and learned using the Word2Vec embedding technique. Then, the extracted image feature vectors and word vectors are fused to form an encoding vector representing the rich image content. The decoding unit employs a self-attention mechanism encompassed with BiLSTM to create more descriptive and relevant captions in natural language. The Flickr11k dataset, a subset of the Flickr30k dataset is used to train, test, and evaluate the present model based on five benchmark image captioning metrics. They are BiLingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Recall-Oriented Understudy for Gisting Evaluation (ROGUE), Consensus-based Image Description Evaluation (CIDEr), and Semantic Propositional Image Caption Evaluation (SPICE). The experimental analysis indicates that the proposed model enhances the quality of captions with 0.6012(BLEU-1), 0.3992(BLEU-2), 0.2703(BLEU-3), 0.1921(BLEU-4), 0.1932(METEOR), 0.2617(CIDEr), 0.4793(ROUGE-L), and 0.1260(SPICE) scores, respectively, using additive emotional characteristics and behavioral components of the objects present in the image.
PENGGUNAAN FITUR MEDIA SOSIAL LIVE DAN ADS DALAM PROMOSI PRODUK BISNIS

Indonesia has a large population of 257 million people and Indonesia also has a considerable social-media users as much as 80 percent. Social Media used is quite diverse so that social media becomes one of the great potential to promote business products. the features used in promoting the business are the Live streaminging feature and the Advertisement manager feature. These features have the benefits of affordable prices, wide reach, market segmentation, increasing sales and measuring success in promoting a product. Success can be measured through awareness, consideration and conversation. This success can be seen through engagement, conversion and return on investment which is the basis of the benefits of a business product promotion.
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models

Pre-trained vision-language models (e.g., CLIP) have shown promising zero-shot generalization in many downstream tasks with properly designed text prompts. Instead of relying on hand-engineered prompts, recent works learn prompts using the training data from downstream tasks. While effective, training on domain-specific data reduces a model's generalization capability to unseen new domains. In this work, we propose test-time prompt tuning (TPT), a method that can learn adaptive prompts on the fly with a single test sample. For image classification, TPT optimizes the prompt by minimizing the entropy with confidence selection so that the model has consistent predictions across different augmented views of each test sample. In evaluating generalization to natural distribution shifts, TPT improves the zero-shot top-1 accuracy of CLIP by 3.6% on average, surpassing previous prompt tuning approaches that require additional task-specific training data. In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data. Project page: https://azshue.github.io/TPT.
Creativity as a Way to Overcome Uncertainty

Creativity is seen as a way to overcome uncertainty - an integral feature of the modern world on the way to its knowledge. The attitude towards uncertainty is one of the personality traits, the versatility of the phenomenology of which determines the interdisciplinary status of the problem. Overcoming uncertainty by cognition, the creation of a new one, the courage of creativity become milestones of modernity and need scientific justification and reflection. The results of a study of the peculiarities of the attitude towards uncertainty of people capable of creativity, and people with different levels of intelligence, whose activities are externally stimulated, where cognitive motivation is not leading, are presented. Statistically significant differences were revealed between groups of people with different levels of performance according to the "Creative Field" method in terms of uncertainty tolerance tests. People who have shown the ability to be creative are significantly more tolerant of uncertainty, prefer the new to the familiar, as well as complex tasks compared to people of the stimulus-productive level. They are not limited to the perceived reality, they do not stop at uncertainty and the unknown, but, driven by a cognitive need, they are aimed at an in-depth understanding of the situation, due to which they advance in the knowledge and awareness of reality. People who can only solve the set tasks are helpless in the face of a situation of uncertainty. Achievement motivation limits the development of the thinking process even in the presence of high mental abilities, as well as a low level of intelligence development, i.e. lack of educational resources. This affects the negative perception of the situation of uncertainty and the inability to cope with it. The development of activity on one's own initiative as a unit of creativity is thus the way that allows one to overcome situations of uncertainty. Such people, despite the uncertainty, are able to continue their activities, to delve into the situation. Cognitive motivation helps to follow the path of cognition in new, complex, contradictory and unpredictable situations. The study of creativity as a way to overcome uncertainty reveals the mechanism of pre-adaptation.
Prompt Sensitivity of Language Model for Solving Programming Problems

A popular language model that can solve introductory programming problems, OpenAI’s Codex, has drawn much attention not only in the natural language processing field but also in the software engineering field. It supports programmers by suggesting the next tokens to write, and it can even generate a whole function definition from a document string. We focus on its capability of automatically solving programming problems through code generation from problem descriptions. We investigate the model’s sensitivity to problem descriptions by formatting and modifying them. The experimental results show that the more explicitly formatted problem description enhances the code generation performance from 30.9% (raw) to 39.9% (formatted). Additionally, we observe that code generation relies on information specified in the problem description, such as variable names and constant values, as anonymizing them reduces the performance significantly. Moreover, statistical biases in code generation are identified, such as the generated programs ignoring the problem modification and answering the exact opposite problem. The changes in accuracy across formats suggest that the model does not correctly understand the natural language explaining the problem specification even if the model could solve the programming problems with high accuracy.
RUKYATUL HILAL INSTRUMENT DESIGN BASED ON ARDUINO

Arduino is a Microcontroller platform consisting of a computer chip that is used to control electronic equipment that is flexible and easy to use. In this modern era, the implementation of Rukyatul Hilal can be done using instruments such as Hilal locater, telescope and theodolite. However, these tools are quite expensive and have a fairly heavy load and require long time to prepare. Therefore, this research provides breakthroughs and new innovations in designing the Rukyatul Hilal Instrument based on Arduino. The research uses the Research and Development model type of research, the development model that the researcher will do is to develop an Arduino Microcontroller so that it can be used as a Rukyatul Hilal Instrument. Based on 4 observations of the Moon, The Arduino-based Rukyatul Hilal instrument can successfully show the position of the Moon and verify the data used by the author is correct and the author can also photograph the image of the Moon clearly using a smartphone. However, it should be noted that the position of the Moon when viewed from the Arduino-based Rukyatul Hilal Instrument telescope is not in the middle of the telescope, but sometimes at the bottom, side and top.
"Evaluation of NoSQL Database MongoDB with Respect to JSON Format Data Representation "

The current information technology advancement is attributed to the development of more data-intensive applications. However, due to stringent limitations on data structure, data relations, and other factors, relational databases do not allow us to work with huge data sets or maintain high volume databases. Unstructured data from many industries, including a variety of forms, must be processed and stored in databases. Thus, NoSQL types offer solutions to a number of problems associated with huge data bases. Largely because of the benefits like flexibility and horizontal scalability, NOSQL (Not Only SQL) is in demand. This essay explores many No SQL database types, such as MongoDB, CouchDB, HBase, Cassandra, etc. A document-oriented, cross-platform database called MongoDB offers excellent performance, high availability, and simple scalability. The MongoDB Query Language (MQL), created for ease of use by developers, is used by MongoDB. The documentation contrasts the syntax of MQL versus SQL for typical database operations. When your data is document-centric and doesn't fit well into the schema of a relational database, when you need to support vast size, when you need to quickly prototype, and for a few additional use cases, NoSQL databases like MongoDB are a solid option.The proper encryption methods used to various crucial data fields provide data protection without adversely affecting the database's performance in terms of speed or memory usage. Due to the use of appropriate encryption techniques, this is achievable. Since document-oriented and unstructured data are generally kept in databases, it is frequently used to handle various sorts of data. Examples of such databases include MongoDB, Cassandra, CouchDB, Redis, Hyper-table, and others. Since they are open source, there is a critical necessity to offer good security and protect the user's private information when it is in transit or at rest. There is a need for a single solution that can improve the security of data transfer by providing an improved encryption method that speeds up processes and uses less memory when maintaining databases.
Practical Exploration of English Translation Activity Courses in Colleges and Universities under the Background of Artificial Intelligence

College English teaching includes five aspects, which are cultivating students’ English listening ability, speaking ability, reading ability, reading ability, and reading ability. These five aspects complement each other and are indispensable. Cultivating students’ English reading ability can enhance students’ cross-cultural awareness and English application ability. Among these five English abilities, only English translation can cultivate students’ Chinese ability and English ability at the same time. The college stage is an important turning stage on the way of students’ learning and growth. During this period, the main teaching goal of teachers is not to help students take exams, but more importantly, students could adapt to the society. Under the background of artificial intelligence, teachers need to find new and effective teaching concepts to improve the practical effect of translation teaching, which has attracted the attention and attention of college teachers in recent years. Correct use of artificial intelligence in college English translation education can greatly stimulate students’ enthusiasm for learning, help students to think innovatively, and encourage students to actively participate in English translation learning.
Rancang Bangun Sistem Informasi Data Penjualan Motor Menggunakan Metode Object Oriented Analysis And Design Dengan UML Modelling

Human needs for technology in everyday life can not be released, almost all aspects of life can not be separated from technology. Many people use technology to facilitate their activities. This research was conducted at CV. Agus Motor which is a business engaged in the sale of used motorcycles. The system that runs on the CV. Agus Motor has several approved problems, the first is the amount of motorcycle data, customer data, and sales data that is processed using manual data such as using records on paper media or lost or scattered records. The second problem, there is no proper storage media to store these data. Problem three, there is no purchase report every month issued by employees having difficulty in providing sales results. This system is designed using HTML, PHP, CSS, and using MySQL database. The method in this study uses object-oriented analysis and design with uml modeling. system that can help in the sales process to facilitate the company such as processing data into consumer data and motorcycle sales transactions that can generate payment reports from consumers. In this payment system employees are expected to be able to input customer data, motor data, and monthly sales report scores
Towards a connected and dynamic scholarly record of updates, corrections, and retractions

The scholarly record is evolving. As a community, we are very good at convening and acting on new research objects, especially container outputs—preprints, data, software, and reviews, to name a few. We’re less good at linking those objects together. And we usually focus on the act of citation, with many excellent community-created guides for citing different types of objects. If the objects are now largely very well identifiable and citeable, Crossref, with its members and integrators, now needs to turn our collective attention to building the evidence trail. This is made up of the relationships between objects and the acts that are performed on them over time, which could be by the original creators or those reusing and building on the work in later generations. It’s all about metadata and relationships, and we call it the Research Nexus. Together with our governing board—comprising members that include publishers, funders, institutions, and tool-makers—we are exploring Crossref’s role in maintaining trust in the scholarly record. With this discussion paper, we seek advice on better managing a particular and critical area of relationship metadata: corrections, retractions, and errata (CRE metadata). Crossmark is a set of relationship metadata and a button on article pages and PDFs that alerts readers to the currency and accuracy of the object they’re looking at. Responding to the recent workshops and initiatives in the community, we discuss possible strategies for the future of Crossmark, present adoption trends and usage data, and set out some ideas and dilemmas for community advice.
Spatio-Temporal Model for EUS Video Detection of Pancreatic Anatomy Structures

Current 3D imaging techniques (computed tomography scan, magnetic resonance imaging) offer poor detection of early-stage pancreatic cancers, which in turn leads to high mortality rates. Endoscopic ultrasound (EUS) is a proven alternative to increase early diagnosis and identify potentially curable surgery candidates. However, mastering EUS requires a lot of practice to properly navigate and interpret video flow. Real time computer assisted localization of anatomical structures could improve lesion detection while easing the overall procedure by pointing out anatomical landmarks otherwise complex to identify. For this purpose, we propose a novel architecture built on top of object detection literature by combining spatial attention and temporal information. In parallel, we have created EUS-D50, a representative EUS dataset constituted of 50 EUS pancreas videos with spatial annotations including pancreas parenchyma and lesions. On EUS-D50, our work achieve an mAP@50 of $$58.36\%$$ for pancreatic parenchyma and lesion.