Reviewerly/exHarmony huggingface dataset:
Opportunities and challenges of artificial intelligence in banking and financial services

AI is a crucial element of the banking and financial industries, providing affordable and reliable banking services. The market for AI in the banking sector is expected to grow at a CAGR of 32.6% from 2021 to 2030, reaching $64.03 billion by 2030. Banks use AI technologies to automate their operational processes, improve customer support, and mitigate potential risks, ultimately increasing efficiency and productivity. AI can help banks identify fraudulent activities and prevent them from occurring. It can also analyze customer data and provide personalized services to customers. Additionally, AI can help banks make better decisions by analyzing large amounts of data. By using AI, banks can reduce costs and increase profits while providing better services to their customers.
Rancang Bangun Sistem Informasi Pengelolaan Inventory Berbasis Website Menggunakan Metode ROP dan EOQ (Studi Kasus: Grosir PA1000 RAJA)

Wholesale PA1000 RAJA is a wholesaler with its address at Batang Kumu II, Rokan Hulu, Riau and has been around for about 9 years. This PA1000 RAJA wholesaler is still recording sales and inventory manually with a book, so that there is often a shortage of goods when ordered by customers or too many goods in the warehouse. Based on the existing problems, a website-based information system was created to manage the existing inventory at the wholesaler. In making this information system applies the ROP method as a form if the inventory is below the minimum limit, then there is a warning so that the wholesaler reorders goods that have been exhausted. After knowing the amount of stock, the Economic Order Quantity (EOQ) method is needed to find out how much the economic quantity of goods that must be ordered. Based on the results of the User Acceptance Test (UAT) which was carried out 3 times, it was stated that all user needs had been met as a whole.
K-Means Hybridization with Enhanced Firefly Algorithm for High-Dimension Automatic Clustering

K-means clustering is not able to select the right number of clusters of data items having high-dimension. So, for determining the ideal number of clusters, we have combined PCA with the Silhouette and Elbow approaches. Additionally, we have a large number of meta-heuristic swarm intelligence algorithms which is influenced by nature and were previously used to solve the automatic data clustering problem. Firefly offers reliable and effective automatic data clustering. The Firefly algorithm automatically divides the entire population into subpopulations, which slows down the convergence and reduces the of capturing local minima in high-dimensional optimization problems. Thus, for automatic clustering, we demonstrated an improved firefly, i.e., we offered a hybridized K-means with an ODFA model. The experimental section displays the results and graphs for the Silhouette, Elbow, and Firefly algorithms.
Digital Education of British Universities in the Post-Pandemic Era

After the COVID-19, online education has basically started at all stages of education worldwide. From a global perspective, the mode of applying digital media for education and teaching has become an irreversible educational trend. The degree of digitalization in education in the UK is leading in the world. Taking the digital teaching practices of three universities in the UK as examples, the paper analyzes the digital teaching achievements of UK universities in the post pandemic era and the current development status of digital education in China and the UK, providing reference and reflection for exploring the current digital teaching in Chinese universities.
HEALTHCARE 4.0 AS AN ESSENTIAL SOLUTION TO HOSPITAL OPERATING MANAGEMENT IN THE 4.0 INDUSTRY ERA

The rapid development of science and technology today has touched various aspects of people's lives, from household chores to working methods and studying, which are made more accessible by technology. This situation is also discussed by innovations developed by the industry to answer challenges or demands from society and new opportunities to conduct in-depth research and technological development to facilitate human work, especially now that the development and sophistication of technology have also entered the medical world when artificial intelligence (AI) plays an essential role in hospital operations or also known as health care 4.0, where the healthcare delivery process becomes a cyber-physical system complete with IoT or internet of things and radio-frequency identification or RFID, wearables, all types of medical equipment, intelligent sensors medical robots, and others that are all integrated with cloud computing, extensive data analysis, and decision support techniques to achieve intelligent and interconnected health care delivery.
Sentiment analysis from textual data using multiple channels deep learning models

Abstract Text sentiment analysis has been of great importance over the last few years. It is being widely used to determine a person’s feelings, opinions and emotions on any topic or for someone. In recent years, convolutional neural networks (CNNs) and long short-term memory (LSTM) have been widely adopted to develop such models. CNN has shown that it can effectively extract local information between consecutive words, but it lacks in extracting contextual semantic information between words. However, LSTM is able to extract some contextual information, where it lacks in extracting local information. To counter such problems, we applied the attention mechanism in our multi-channel CNN with bidirectional LSTM model to give attention to those parts of sentence which have major influence in determining the sentiment of that sentence. Experimental results show that our multi-channel CNN model with bidirectional LSTM and attention mechanism achieved an accuracy of 94.13% which outperforms the traditional CNN, LSTM + CNN and other machine learning algorithms.
Smart library utilization strategy to enhance teachers' reading interest in elementary schools

Reading is a crucial tool for unlocking the gateway to knowledge and plays a vital role in personal development. Engaging in reading allows individuals to refine comprehension skills from various angles, preventing engagement in risky behaviors due to a lack of knowledge. In educational settings, students look to their teachers for guidance and inspiration, including setting an example through reading books. Despite quality libraries and literacy programs, student interest in reading may not reach its full potential if teachers fail to act as reading role models. Unfortunately, enthusiasm for reading among teachers in Indonesia remains low. This research aims to uncover the availability of reading media in elementary schools utilizing the e-Library application (Smart Library). It focuses on the design of a reading media utilization program and the strategic implementation to enhance teachers' reading interest. Additionally, the research evaluates the impact of this media and program on teachers. A descriptive qualitative approach is employed, with research subjects from elementary schools in Bandung using Smart Library. Data collection techniques include observation, interviews, and documentation. Findings reveal the Smart Library Utilization Strategy Model to Increase Teachers' Reading Interest in Elementary Schools, comprising four steps: (1) Schools should provide comfortable reading media meeting teachers' needs, digital and physical; (2) School principals must design an innovative and diverse reading media utilization program to effectively enhance teachers' reading interest; (3) Implementation should proceed through strategic steps, from socialization to appreciation; and (4) Regular evaluation of teachers' reading interest improvement is essential to assess quality and identify factors influencing it.
SENTIMENT ANALYSIS USING LONG TERM MEMORY (LSTM) BOOK CASE STUDY: UNSOLICITED ADVICE FOR MURDERERS BY VERA WONG'S

Reading books is one of the most effective ways to reduce stress. In today's digital era, access to finding and buying books is getting easier, so reader reviews are important in choosing books that match interests. However, with a large number of reviews, Natural Language Processing (NLP) with the Long Short-Term Memory (LSTM) method is used to help analyze positive and negative sentiments from many book reviews. This sentiment analysis is useful for readers to evaluate the quality of books, as well as for authors and sellers to find out the opinions of readers and improve the quality of their work. In this study, the book review dataset "Vera Wong's Unsolicited Advice for Murderers" from the Goodreads website is used, which is then divided into training data and validation data with a ratio of 75%: 25%. The Long Short-Term Memory (LSTM) method is used to analyze the sentiment of the reviews. The model architecture built consists of Embedding Layer, LSTM Layer with 128 neuron units, 3 Dense Layer with ReLU activation function, 3 Dropout Layer, and Fully Connected Layer with and Sigmoid activation function, Binary Cross Entropy loss function, and RMSprop optimizer. The model training process was conducted with 30 epochs. The evaluation results show that the model achieved an accuracy of 90%, indicating the model performs relatively well in correctly classifying positive sentiments.
IMPLEMENTATION OF THE ROUGH SET METHOD WITH A DEEP LEARNING APPROACH IN THE PROCESS OF DIAGNOSING OTITIS DISEASE

Otitis is a common health problem in the human ear and often requires a quick and accurate diagnosis. Deep learning is a method in the computer field to provide performance in classification. This research aims to develop an analysis model using a Deep Learning (DL) approach in diagnosing otitis. The method used in this development involves the performance of the Rough Set (RS) and Artificial Neural Network (ANN) methods to provide optimal analysis output. The research dataset refers to the clinical diagnosis of otitis patients which consists of 3 types, namely acute, effusion, and chronic. The test results of the analysis model developed using the DL approach were able to provide quite good output with an accuracy level of 99%. These results are based on the analysis pattern obtained based on the performance of the RS method. Based on these results, it can be concluded that the analytical model developed provides maximum and better results compared to the previous model based on the output and presentation of a systematic process in classifying otitis disease.
Design and Build Web-Based Freelance Applications

In today's modern era, many people have switched to looking for freelance work. One of the interesting things is freelance work because there is no work attachment to employee status, so that the person can have several jobs at one time. But until now, to find freelance work is not easy because usually many job search applications provide services only for permanent workers. For companies that are looking for freelance labor is also not easy, because freelance job search provider services are also not much. The purpose of designing this Lancer application is to make it easier for people who want to find freelance jobs. This website is designed using UML (Unified MarkUp Language). The method used in building this website uses SDLC (Software Development Life Cycle) which includes the stages of analysis, design, creation, and testing. This website is created using Laravel and MySQL frameworks.
Analisis Kesulitan Dalam Menyelesaikan Persamaan Nilai Mutlak Menggunakan Model Pembelajaran Discovery Learning Untuk Siswa Kelas X SMK Negeri II Kota Tambolaka

The purpose of this study was to determine the difficulty in solving the absolute value equation using the Discovery Learning learning model for grade X students of SMK Negeri 2 Tambolaka City. This type of research is a descriptive qualitative method, which means that the data in the study is in the form of sentence structure obtained from test results, interviews and ducumentation. The subjects of the study were 10 grade X students of SMK Negeri 2 Tambolaka City and three people were randomly selected to be interviewed. Data analysis techniques are Data obtained in this study in the form of data on absolute value test results and interview results. After the data is collected, data reduction is carried out which aims to focus on the things to be researched, namely analyzing the answers of students who have been selected as research subjects. The results showed that the overall percentage of student test results contained 3 types of difficulties carried out by students, namely concept difficulty 80%, principle difficulty 50%, verbal difficulty 50%. The impact of these difficulties is that students do not relearn the material that has been given during learning and do not pay attention to researchers delivering the material
WORK STRESS AND BURNOUT : SISTEMATIC LITERATUR REVIEW (SLR) DAN ANALISA BIBLIOMETRIK

This Systematic Literature Review (SLR) employs the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) methodology and VosViewer bibliometric analysis to investigate work stress and burnout. Data were extracted from Sciendirect, covering the period from 2021 to 2023. Initially identifying 118 journals, stringent inclusion and exclusion criteria were applied, resulting in a focused set of 31 journals for comprehensive review. The review reveals a prevalent experience of work stress and burnout among employees. This underscores the urgency of implementing measures to minimize these issues, as their persistence could lead to detrimental consequences for companies. Consequently, the study emphasizes the critical importance of proactive strategies to mitigate work-related stress and burnout, not only for the well-being of employees but also to safeguard overall organizational health and performance. Recognizing the significance of addressing these challenges, organizations are urged to prioritize interventions and initiatives that foster a healthier work environment and contribute to sustained employee satisfaction and productivity.
A Corpus Analysis of Hate Speech in Pakistani Political Discourse on Twitter

This paper is a corpus-based quantitative study on language change that has arisen during national political events, such as the no-confidence movement against Imran Khan, the Prime Minister of Pakistan. This event, including many others, has given rise to the hate speech in Pakistani community online. In this paper, language change is discussed in terms of hate speech, collocation patterns, frequency of words (Nouns, Adjectives), N-Grams and Urdu hate speech words. A certain political time period is selected to make a comparison of the use of language and explore whether any variations have transpired during the political upheavals. These dynamics are explored because the frequency of hate speech words may suggest that how much aggressive the online political discourse has become. Moreover, it may also suggest that what kind of image is presented to the rest of the world through such usage of words. To find out the case, a data of 50,000 tweets was gathered using Tweetarchivist.com. This data was analyzed through sketch engine. The major findings from the research, reported in this paper, show that many of the tweets, through their collocations and frequency, contain hate speech elements which convey a negative image of the country across the globe.
IMPROVING STUDENTS' LANGUAGE SKILLS THROUGH GROUP LEARNING

The study aims to find out how to improve students' language skills by using a group learning model. The development of language skills can be done with a variety of techniques, one of which is the application of group learning techniques. The province of group learning is viewed from two paradigms, namely psychological and social paradigms. In this group learning process, the teacher acts as a motivator, facilitator, mediator, and evaluator. The stages used in this group learning are the group formation stage and the presentation stage. The presentation stage includes preparation and preparation of learning units (Planungsphase), implementation stage (Durchfuehrungsphase), and summary and feedback stage (Anwendungsphase). The application of group learning techniques was carried out on 3rd semester students in the German language education study program in the Sprechfertigkeit course using the Netzwerk B1 textbook. Based on the results of the study, it shows that the group learning model can improve students' language skills.
A Teaching Supervision Platform Based on Deep Learning

The integration of artificial intelligence technology with modern network communication technology in an educational quantification system holds significant importance for enhancing the quality of classroom learning for students. In many vocational school education systems, teachers often act as knowledge transmitters. In traditional classrooms, it is often challenging for teachers to efficiently obtain the learning progress of each student. Due to the structure of the curriculum, students' classroom learning situations typically have to be assessed through a combination of assignments and end-of-term exams. This makes it difficult for teachers to promptly correct students' erroneous learning methods. These issues render many students who are trained through vocational education less adaptable to modernized societal production. This article takes the Shanghai Science and Technology Management School as a typical case and, based on classroom teaching theory, proposes a design and implementation method for an instructional platform that integrates artificial intelligence technology and network communication technology. The system design utilizes artificial intelligence technology for behavior and facial expression-based classroom teaching supervision and combines it with an automated assignment grading system to generate accurate analytical reports on students' classroom learning situations. Research indicates that using this system accurately analyzes students' learning situations during assignment completion, effectively enhances teachers' understanding of students' learning quality, and reduces teachers' burdens in classroom teaching.
BAZAAR: Anonymous Resource Sharing

In areas such as manufacturing or logistics, it is beneficial for everyone to share access capacity with others. Increased efficiency increases profits, lowers prices for consumers, and reduces environmental impact. However, in order to share a resource such as manufacturing capacity, suitable partners must be found. Ideally, a centralized exchange is used to find partners, but this comes with privacy risks. Since participants in the exchange are competitors, they can use information about someone else's capacity to their disadvantage, e.g., by undercutting the prices of an already poorly performing competitor to drive it out of business. In this paper, we show that such an exchange can be set up without compromising the privacy of its participants. We formalize privacy goals in the context of resource sharing via an indistinguishability game. We also propose Bazaar, a protocol that allows participants to find suitable matches while satisfying our formal privacy goals.
Optimizing HE operations via Level-aware Key-switching Framework

In the state-of-the-art Homomorphic Encryption (HE) schemes, the key-switching procedure is commonly used as a building block of non-linear operations, but also a major performance bottleneck. Its complexity is primarily determined by the corresponding gadget decomposition, which transforms a ciphertext entry into a vector of small elements to reduce the noise growth from the multiplication with an evaluation key. Prior works such as Cheon et al. (SAC 2018) and Halevi et al. (CT-RSA 2019) fixed a decomposition function in the setup phase which is applied across all ciphertext levels, thus yielding suboptimal performance.
Small Timescaled Data for Covid-19 Prediction with RNN-LSTM in Tangerang Regency

Throughout the pandemic, many people have become familiarised with the new type of virus that has been spreading throughout the world, called the Coronavirus. On the 2nd of March, the year 2020, the Indonesian government had announced the identification of first Covid-19 case in Indonesia. With the arrival of Covid-19, and its spreading across all the provinces of Indonesia, the number of positive cases keeps growing even in the present day. Tangerang Regency is one of the areas that has opaqued citizens in the Banten Province. The purpose of this research is to discuss how to predict the sum of Covid-19 cases in the Tangerang Regency using the RNN-LSTM method. Although this method is very eloquent if used to perform a sequential task, its complexity and loss of gradient can make this model difficult to be trained, hence resulting in the use of the Long Short-Term Memory (LSTM) to reduce these weaknesses and help the RNN to look back on past data. This research uses Python as the programming language and Jupyter Notebook for the visualization of the results of the prediction. Therefore, the prediction model has been evaluated using various computational methods, such as RMSE with its error percentage of 0.05, and MSE and MAE with the same error percentage of 0.03 with the loss of their models being 9.6793e-04.
AutoDL: Enriching PowerBI AutoML Features with Deep Learning

PowerBI, a graphical analytics software developed by Microsoft, has incorporated various AI and ML tools to provide accurate predictions and insights for the data feeded into the software. PowerBI is equipped with the AutoML tool which automates the process of applying machine learning to obtain data driven insights. In recent years, the explosion of data has been phenomenal, and due to the rise in interconnectivity and the age of IoT it has led to the emergence of big data. Though AutoML, which uses machine learning, isn't equipped to handle big data. To tackle the presented challenge of volume and scalability, this study aims to provide a solution, AutoDL, with the usage of deep learning models and algorithms. In this study, we attempt to create a base automated deep learning pipeline providing better results on big data. The pipeline was tested on 2 big data sets and we received affirmative results.
Smart Monitoring of Digital Media Communication at Social Platforms for fixing the Issues with fake content

Today, more than 4 billion people use social media globally now, and this trend is growing year over year with significant implications for privacy. Verification and authentication are highly difficult tasks because of extensive usage of social media-based global news. Several researches have introduced the use of Distributed ledger technology (Blockchain) in social media to address these limitations. The most well-known decentralized method used today is blockchain technology, which has been taken into consideration when creating the next generation of decentralized social platforms. This article also performs a systematic study and provides a summary of how fake news spreads in the real world, how it spreads in a decentralized system, and how the rumour block strategy functions. This article also covers a number of difficulties in implementing Blockchain in social media fake environments and suggests alternate approaches for implementation and further study.