Reviewerly/exHarmony huggingface dataset:
Analisis Sentimen Pengguna Twitter Menggunakan Support Vector Machine Pada Kasus Kenaikan Harga BBM

Twitter is one of the social media with the most active users, which is 24 million active users. Information published on twitter contains comments from users on an object. Sentiment analysis is used to determine whether the data includes negative comments or positive comments because the comments taken on twitter are textual data. The method used in this sentiment analysis is Support Vector Machine (SVM) about public comments on fuel price increases on twitter. The comment data used was 258 data on September 4, 2022 because on that date it was exactly the day after the fuel price increase. First, preprocessing is done to remove unnecessary words or information. Then the data is divided into training data by 80% and testing data by 20%. The accuracy rate is 82.69%, sensitivity is 100%, and specificity is 79.07%. Then from the results of testing 52 data obtained the results of 43 negative comments and 9 positive comments so that it can be concluded that more people disagree with the increase in fuel prices.
Implementation of Hybrid RNN-ANFIS on Forecasting Jakarta Islamic Index

RNN is a type of artificial neural network used to handle problems that require sequential data processing. ANFIS is a method that combines the advantages of fuzzy logic and artificial neural networks to create a system, so can adapt the parameters it uses according to the obtained data so that it can build an automated inference system. In this research, we make combination of RNN in ANFIS, which makes ANFIS able to accept input in the form of time series data so that ANFIS can recognize patterns contained in the time series data and its suitable for forecasting cases in the Jakarta Islamic Index. The membership functions used are three Gaussian functions. The results of the RNN-ANFIS Hybrid model training provide an interpretation that the first membership function represents the trend change indicator value, the second membership function represents the closing price change value in the last eight days, and the third membership function represents the pattern change value in the trend. The model for the Jakarta Islamic Index provides very good predictions with an MSE value of 0.001 and an MAE of 0.246.
Networks for Blockchain-Enabled Applications

With fast evolution of computer and networking technologies, more and more systems and applications are based on interconnecting different devices and systems to form the Internet-of-Something: things, vehicles, and even bodies. Many among those systems require permanent, distributed data storage facilities which are being increasingly implemented using replicated, tamper-proof blockchain ledgers. Most, if not all, of those problems are exacerbated, rather than solved, by blockchain technology. In addition, the use of blockchain brings many challenges that include efficient and reliable data distribution; efficient and collusion-proof consensus mechanisms; efficient coverage of wide geographical areas; and a plethora of existing security- and privacy-related constraints on the networks.
OP123 DAG-informed regression-based modelling is a valid approach to estimating causal effects in compositional data with fixed totals: a simulation study

<h3>Background</h3> Compositional data comprise ‘parts’ of a ‘whole’ (or total), where the parts sum to the whole. In compositional data with fixed totals (e.g. hours within a day), only relative causal effects can be estimated because the effect of increasing one component (e.g. time spent physically active) cannot be distinguished from the effect of decreasing one or more other components (e.g. time spent sedentary). Compositional data are not well understood, but the structure has recently been conceptualised using directed acyclic graphs (DAGs) with deterministic nodes. This work encourages the use of a simple well-established approach, known as the isotemporal (‘leave-one-out’) model, for estimating relative causal effects in compositional data. However, the isotemporal model has been criticised as unsuitable in the presence of non-linear effects. Other, more technically demanding approaches, known as Compositional Data Analyses (CoDA) methods, are promoted instead. This study is the first to investigate the performance of DAG-informed regression-models for estimating causal effects in compositional data with fixed totals in simulated data, where the ground truth is known. <h3>Methods</h3> Using the DagSim package in Python, we simulated compositional data with fixed totals, using the example of physical activity data, in which sleep, sedentary behaviour (SB), light physical activity (LPA), and moderate and vigorous physical activity (MVPA) sum to a fixed total of 24 hours. The time spent in each state was then simulated to contribute to levels of an outcome (fasting plasma glucose, FPG), either in a strictly linear manner, or through non-linear relationships. We assessed the performance of using the DAG-informed isotemporal approach by comparing model estimates to the known (simulated) true relative causal effect of each component on the outcome. <h3>Results</h3> Accurate relative causal effect estimates were obtained using the DAG-informed isotemporal approach, provided the models were parameterised correctly. When the model was not parameterised correctly, e.g. linear terms were used for modelling non-linear relationships, the estimates were biased. In the literature, the isotemporal model is used almost exclusively with linear terms, which might explain some of the previous misconceptions that it is unsuitable for modelling of compositional data. <h3>Conclusion</h3> In compositional data with fixed totals, a simple DAG-informed isotemporal modelling approach recovers the true relative causal effect as long as any non-linear relationships are appropriately parameterised. This method is a viable alternative to the more technically challenging and specialist CoDA methods. The findings cannot be generalised to compositional data with varying totals, which require separate investigation.
PENERAPAN MODEL PEMBELAJARAN TIPE MAKE A MATCH BERBANTUAN MEDIA KONGKRET UNTUK MENINGKATKAN KEMAMPUAN BERPIKIR KRITIS DAN HASIL BELAJAR DI SEKOLAH DASAR

The critical thinking skills of grade II at SD N Bringin 02 are still low. The low ability to think critically in class II causes low student learning outcomes. This happens because the lack of varied learning models used by teachers makes students bored and shy to ask questions. The weak critical thinking skills of students cause difficulty identifying and understanding in completing the tasks given. The purpose of this study was to apply the Make a Match learning model assisted by concrete media to improve the critical thinking skills and learning outcomes of grade II students at SD Bringin 02. In this class action research there are three stages, namely look, think, act. This research consists of two actions and involves class II which has 18 students. Data collection tools in this study are evaluation tests, observation, skills, documentation, and interviews. The results of research on action I, critical thinking skills showed an average of 64% while action II was 82%. The learning outcomes of action I students get a complete score of 44% and in action II have increased with a complete score of 89%.
Study of SARS-Cov-2 using microscopy: Microscopy and its uses in challenging times

The humanity has witnessed a new virus called SARS-CoV-2 (also called as COVID-19) that emerged in 2019 causing the pandemic
Expert System for Diagnosing Scabies using the Case Based Reasoning Method

Health is very important for humans, people who don't take care of their health can trigger diseases. Currently, diseases that are not given enough attention are skin diseases, one of which is scabies . Scabies is a disease caused by the mite Sarcoptes scabies which is characterized by scabs and itching of the skin. Scabies disease is easily spread either directly or indirectly. At present, people do not really care about scabies because scabies has symptoms that are just ordinary itching, this happens due to the lack of public knowledge about scabies. To obtain a diagnosis of scabies, you can usually consult a doctor, but due to the limited time for consulting a doctor, there are often problems that make it difficult to obtain information on a diagnosis of scabies. Therefore an expert system was designed to find out and get information quickly about the symptoms that are felt associated with scabies. The method used in expert systems is Case Based Reasoning (CBR). The CBR method is to solve problems by recalling the same or similar events (similarities) that have occurred in the past and then using knowledge or information. This expert system can provide solutions for scabies with the diagnosis process being carried out efficiently and saving time. This web-based expert system application was built using the PHP and MySQL programming languages
Affine and projective Lê cycles

Lê cycles of germs of complex analytic functions are analytic cycles that describe, among other things, the topology of the local Milnor fibres: We know from [7,8] that there is a Lê cycle in each dimension, from 0 to that of the singular set, and the multiplicity of the Lê cycles at each point says how many handles of the corresponding dimension we must attach to a ball in order to construct the local Milnor fibre (up to homeomorphism).In [1], José Seade, Roberto Callejas-Bedregal and I defined the global Lê cycles (affine and projective), which are a global extension of the Lê cycles defined by Massey in [7].Here, the relationship between affine and projective Lê cycles will be detailed, this is also mentioned in [2].
Customer Relationship Management in Increasing Customer Loyalty using the K-Means Algorithm

Advances in technology allow humans to more quickly and precisely in carrying out various activities in everyday life. One of the companies engaged in the field of vehicle washing services is Doorsmeer Keluarga Nasution. Doorsmeer. The Nasution Family Doorsmeer, located on Jl. Medan Padang Aek Godang Panyabungan, Mandailing Natal Regency, provides services in the form of car and motorcycle washing. Bookkeeping at the Nasution Family Doorsmeer is still done manually, allowing for loss of important transaction data, as well as a lack of business promotion in the community resulting in slow business development. The purpose of this study is generally to apply Web-based Customer Relationship Management with the K-means algorithm method for marketing/transactions at the Nasution Family Doorsmeer. This research is a combined type, using descriptive qualitative and quantitative methods. From the research results it is known that with the existence of Web-based Customer Relationship Management it can simplify and assist in managing good marketing strategies so that they can increase sales revenue, and by providing the best service will encourage customer loyalty and make it easier to determine loyal customers with the K-means algorithm in order to continue to provide convenience to customers.
Penerapan Tools Fail2Ban Untuk Mencegah Serangan Brute Force Pada Web Server Online Learning Uhamka (OLU)

This study aims to assist the internal team of BPTI UHAMKA in making the Online Learning UHAMKA (OLU) web server so that the login credentials of the UHAMKA academic community are protected from attempted brute force attacks. Fail2Ban is a server security tool where it detects login attempts according to the maximum pre-configured trial value. In its implementation, Fail2Ban which uses the python programming language is installed on the Online Learning UHAMKA (OLU) server that uses the CentOS 7 server operating system. In the way it works, Fail2Ban will block or ban IP addresses that try to log in consecutively within one hour. Based on the comparison before and before its implementation, it can be proven that Fail2Ban can detect brute force attacks that occur and ban the attacker's IP address.
Literature Study of Learning-Based Video Compression

Developments in telecommunications technology today, such as cellular with the fifth generation (5G), the development of IoT prototypes, and the migration of analog TV to digital TV starting in 2022. The development of various research using machine learning. The problem with video format information is that the video file size is quite large, so the transmission process requires a large bandwidth. In addition, sharing services such as Video on Demand (VoD) and Video Broadcasting are sensitive to delay. In comparison, the transmission media has limited capacity, such as terrestrial TV, Ethernet/Fast Ethernet, and wireless cellular data such as 2G, 3G HSPA, 4G, etc. Based on reports from Cisco, the development of internet users has increased by 10% per year, with 80% of total traffic using video. Developments in various video compression standards, such as the most recent H.264 and H.265, produce high-quality, low-bitrate video. Much research has been carried out with various proposed compression methods based on machine learning. Either uses singular block learning based or end-to-end. This research focuses on the literature study of video compression with machine learning.
Synchronic approach to the study of exogenic changes in the contemporary Yorùbá language

This paper provides a focused evaluation of the exogenic changes that occur in the contemporary Yoruba language of West Africa and discusses the reasons and outcomes of such changes to the language. External or exogenic changes have been one of the major sources of linguistics change in the contemporary world. The current evolutions, where different languages come into contact with each other-a phenomenon known as language collision-often create a situation in which one language comes to dominate another. This paper examines the linguistics phenomenon of adoption of foreign words into the Yoruba language, the ways and reasons for the adoptions and the outcome, with particular focus on the massive incorporation of foreign words into the Yoruba language and how that might impact the language’s ability to survive.
Workload Assessment with NASA-TLX and Work Satisfaction as the Basis of Improvement of Work Systems

Employee's workload of PT XYZ is predefined as relatively high if it takes into consideration manpower and the task amount.The exceeded workload can affect the job satisfaction of the employee.Hence, it is needed to do an assessment to measure the workload and its effect on job satisfaction.The workload was measured by NASA-TLX, which consists of mental demand, physical demand, temporal demand, performance, effort, and frustration.At the same time, job satisfaction is measured by a questionnaire that consists of remuneration, promotion, superior's assistance, and colleagues' assistance.The objects of this study are an employee of the Sustainability and Operation Department.The research result in workload is definitely high.Besides that, there is a correlation between workload and job satisfaction.There are two correlation models, positive and negative.A high workload can improve job satisfaction, but otherwise, it can decrease job satisfaction.
DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and Marine resources survey. However, the visibility of the underwater environment and the accuracy of target detection can be affected by complex underwater light changes and unpredictable background noise. To address these issues, we propose an improved underwater target detection algorithm based on YOLOv8n. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region&amp;#039;s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIOU loss function with the Wise-IOU loss function. To conduct our experiments, we create our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on the underwater target detection were 88.6% and 51.8%, respectively, and the improved algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOV8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The improved YOLOV8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The floating-point computation volume of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.
PENGEMBANGAN APLIKASI PERMOHONAN CUTI DENGAN METODE WATERFALL PADA PT AISIN INDONESIA AUTOMOTIVE BERBASIS WEB

In an increasingly advanced digital era, the use of information technology has become an important need for industries and companies, including automotive manufacturing companies such as PT Aisin Indonesia Automotive. Currently, the application for leave of employees in such companies is still done verbally or through manual submission, which can take a long time and lead to clerical errors. Therefore, the development of a web-based leave application application is expected to increase the efficiency of the employee leave application process, facilitate access and approval, and reduce the potential for administrative errors. By using the Waterfall method, it is expected that each stage of application development will be carried out in a structured and systematic manner, so that it can provide real benefits for PT Aisin Indonesia Automotive in managing employee leave and applying modern information technology.
SUREL+: Moving from Walks to Sets for Scalable Subgraph-Based Graph Representation Learning

Subgraph-based graph representation learning (SGRL) has recently emerged as a powerful tool in many prediction tasks on graphs due to its advantages in model expressiveness and generalization ability. Most previous SGRL models face computational issues related to the high cost of extracting subgraphs for each training or testing query. Recently, SUREL was proposed to accelerate SGRL, which samples random walks offline and joins these walks online as a proxy of subgraphs for prediction. Thanks to the reusability of sampled walks across different queries, SUREL achieves state-of-the-art performance in terms of scalability and prediction accuracy. However, SUREL still suffers from high computational overhead caused by node redundancy in sampled walks. In this work, we propose a novel framework SUREL+ that upgrades SUREL by using node sets instead of walks to represent subgraphs. By definition, such set-based representations avoid repeated nodes, but node sets can be irregular in size. To solve this issue, we design a dedicated sparse data structure to efficiently store and access node sets, and provide a specialized operator to join them in parallel batches. SUREL+ is modularized to support multiple types of set samplers, structural features, and neural encoders to complement the loss of structural information after the reduction from walks to sets. Extensive experiments have been performed to verify the effectiveness of SUREL+ in the prediction tasks of links, relation types, and higher-order patterns. SUREL+ achieves 3--11× speedups of SUREL while maintaining comparable or even better prediction performance; compared to other SGRL baselines, SUREL+ achieves ~20× speedups and significantly improves the prediction accuracy.
Implementation of Management Information Systems in Educational Institutions Public Vocational School 8 Samarinda

Management Information System is a system designed to provide information to support decision-making on management activities (planning, implementing, organising and controlling) within an organisation. Understanding management information systems based on information technology, including understanding the concept of information systems, information use, and the value of information. System boundaries have been determined by encouraging the quality of the system's work. The work process in each system produces a continuous result (output) and an evaluation system through feedback treatment. The sense is that if the output contains information contrary to the organisation's achievements, it will become input in the system. If the system has received the results of the feedback, the system will make the necessary revisions and adjustments so that the output is consistent with its original purpose. Then with the rapid advancement of technology, The ability of an educator must be able to tame technology so that they can become slaves or tools, not enslaved by technology. This research aims to identify Islamic education management information systems and their concepts and implementation processes in Islamic educational institutions. The research results are: Implementation of existing information systems in schools, and the community feels the output of the implementation of the information system. Data collection techniques used in this study were observation and interviews with related educational institutions.
Qual poderia ser a relação entre o diabetes e a COVID-19? Ciência tenta interpretar essa relação

According to the Brazilian Society of Diabetes, there are currently about 15,7 million people living with this disease in Brazil.This number representes 6,9% of the Brazilian population.Diabetes is considered a chronic disease, characterized by a disorder in the organismo in which the body can not properly produce or use insulin (an essencial hormone for the functioning of the body).In general, there are 2 types of diabetes, and accorgind to some authors, both can be considered comorbidities for the worsening of COVID-19, an acute respiratory disease caused by a vírus, knowed by fever, dry cough and shortness of breath, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019.Reserachers reveal that there is a bidirectional relationship between COVID-19 and diabetes.Thus, it may be that diabetes has direct implications between glucose impairment and the loss of activity of imune cells Conversando sobre Meio Ambiente e Saúde: Uma Abordagem Popular Rafael Castoldi (Org.)
RePaLM: A Data-Driven AI Assistant for Making Stronger Pattern Choices

Security mechanisms based on patterns, such as Pattern Lock, are commonly used to prevent unauthorized access. They introduce several benefits, such as ease of use, an additional layer of security, convenience, and versatility. However, many users tend to create simple and easily predictable patterns. To address this issue, we propose a data-driven real-time assistant approach called RePaLM. RePaLM is a neural network-based assistant that provides users with information about less commonly used pattern points, aiming to help users to make stronger, less predictable pattern choices. Our user study shows that RePaLM can effectively nudge users towards using less predictable patterns without compromising memorability. Overall, RePaLM is a promising solution for enhancing the security of pattern-based authentication systems.