Reviewerly/exHarmony huggingface dataset:
E-Government Application Service Quality Analysis Using E-Govqual Method and Importance Performance Analysis (IPA)

Application Depok Single Window (DSW) is a combination of various application portal of public services in Depok. To improve the quality of mobile - based e-Government services provided by the Depok City Government to the community, periodic evaluations are needed. But in reality, the Depok City Communication and Information Office has not evaluated the e-Government application service on the DSW application based on the perceptions and expectations of its users, so the application is still experiencing several obstacles, such as problems with the DSW application. Experienced a decrease in users every month which caused at least 0.5% of users of the DSW application from the total population of Depok City. Seeing the problems that exist in the service quality of DSW applications, therefore research is needed. This study aims to determine the quality of service in the DSW application based on user perceptions and expectations, and to obtain suggestions for factors that are the main priority for improvement. The model used in this study is the E-Government Service Quality (e-GovQual) model by adding two variables, namely: User Satisfaction (USAT) and Intent to Reuse (USE). This research uses a quantitative approach, and the data analysis process is carried out with IBM Statistics SPSS version 24 for Importance Performance Analysis (IPA), then SmartPLS version 3.2.9 is also used for analysis of the outer and inner models with the PLS-SEM approach. As a result, all indicators used have a negative gap value with the largest value on the Intention to Use variable with an average r value of −0.64. The factors that become the main priority for improvement are the efficiency (EFI) and Citizen Support (CS4) variables, namely: managers are responsive to user problems, managers have adequate knowledge to answer public questions, managers have the ability to deliver services with trust and confidence and information about adequate service.
Implementation of Support Vector Machine with Lexicon Based for Sentiment Analysis on Twitter

Twitter is one of the social media that is widely used where Indonesia occupies the 6th largest Twitter user in the world. This research is a quantitative study on fine-grained sentiment analysis that extracts sentiment with the topic of the covid vaccine from Twitter with the aim of implementing the Support Vector Machine algorithm. The research flow uses the SEMMA method (Sample, Explore, Modify, Model, and Assess). The collection of data sets in the form of tweets crawled from Twitter by utilizing the Twitter API at the sample stage for further exploration of the attributes of the data set at the explore stage. The modify stage is text preprocessing so that the data set is more structured. After that is the model stage which applies the lexicon based method to assign sentiment classes to the data set. Data sets that have labels will be classified using the Naïve Bayes method and the Support Vector Machine. The final stage of the SEMMA method is to assess the method applied using confusion matrix and k-fold Cross Validation. The accuracy results from the Support Vector Machine method, the best parameter results using the CV Grid Search are the rbf kernel with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{C=100}$</tex> and degree = 0.01 resulting in an accuracy of 85%. The accuracy of the implementation of the Support Vector Machine algorithm produces good scores for the Covid-19 vaccine topic, so that the algorithm can be applied to the classification of sentiment analysis on new data.
Feature Engineering with Word2vec on Text Classification Using The K-Nearest Neighbor Algorithm

Text feature extraction is the process of convering unstructured text data into structured so that machine learning algorithms can process it. One of the commonly used text feature extraction techniques is tf-idf. This technique has the potential to produce high-dimensional data which results in longer computational time and affects accuracy results. This study aims to compare feature extraction between word2vec and TF-IDF. The study uses a data explore 4 step approach with a text classification process whose modeling uses the KNN algorithm. The results showed that the highest accuracy value of TF-IDF with the KNN algorithm was 73% in the 7:3 scenario with 8133 features. The highest accuracy value of Wod2vec with the KNN algorithm was 74% in scenario 9: 1 with 300 features. IDF where word2vec produces data with fewer dimensions. This study can prove that feature extraction with word2vec can be done for machine learning research, not only for deep learning. This study can also be used as a comparison of classification per-formance measurement with different feature extraction which can later be applied in web or mobile apps.
Testing in Blockchain-based Systems: A Systematic Review

The Internet of Things (IoT), insurance, healthcare, government systems, financial services, and other industries have recently adopted the blockchain technology. As blockchain technology evolves, the list will continue to expand. Implementing blockchain technology is becoming more challenging due to various problems such as throughput issues, user identity exposure, and smart contract vulnerability. Therefore, blockchain-based technology should undergo appropriate testing before deploying to a broader range of businesses. This study conducted a comprehensive systematic review on testing the blockchain systems based on three main issues; performance, privacy and smart contract, which aims to identify opportunities for future research. The recently published publications are extracted from two selected digital libraries, IEEE Xplore and Scopus, by applying selected search terms and conditions. This research found that 27.78% of the relevant research is based on blockchain performance testing, 27.78% on blockchain privacy testing, and 44.44% on blockchain smart contract testing. It was observed that more research focuses on smart contract issues than other issues.
The Use of Blockchain for Digital Identity Management in Healthcare

Digitalization has occurred in almost all industries, one of them is health industry. Patients” medical records are now easier to be accessed and managed as all related data are stored in data storages or repositories. However, this system is still under development as number of patients still increasing. Lack of standardization might lead to patients losing their right to control their own data. Therefore, implementing private blockchain system with Self-Sovereign Identity (SSI) concept for identity management in health industry is a viable notion. With SSI, the patients will be benefited from having control over their own medical records and stored with higher security protocol. While healthcare providers will benefit in Know You Customer (KYC) process, if they handle new patients, who move from other healthcare providers. It will eliminate and shorten the process of updating patients' medical records from previous healthcare providers. Therefore, we suggest several flows in implementing blockchain for digital identity in healthcare industry to help overcome lack of patient's data control and KYC in current system. Nevertheless, implementing blockchain on health industry requires full attention from surrounding system and stakeholders to be realized.
The Waterfall Model with Agile Scrum as the Hybrid Agile Model for the Software Engineering Team

Hybrid agile is a software development model that combines the agile approach with a non-agile development strategy. A software engineering team chooses the hybrid agile to best fit the development process based on project needs; some projects require a plan-driven approach, and at the same time, agility is a must. However, among many agile methodologies and plan-driven development models, which methodologies or models adopted by a software engineering team lead to hybrid agile. This research aims to identify the software engineering team's approach and model that resulted in the emergence of a hybrid agile model. Data for this research were acquired through a qualitative study using interviews as the data collection instrument and thematic analysis is used to analyze the data and identify the theme. In the context of this paper, we focus on only one (1) theme, which is “models used in a software project”. According to the research findings, the agile Scrum and Waterfall models are the models that have been practised in software projects that have resulted in the hybrid agile model. In the near future, further research will be conducted to determine which process model is employed in certain software process phases of a software project.
Indonesia COVID-19 Online Media News Sentiment Analysis with Lexicon-based Approach and Emotion Detection

Social distancing and isolation are one of the impacts of COVID-19 pandemic, which lead to the increase of internet users across the country especially in suburbs area. Consequently, news regarding COVID-19 reported by the media particularly online media would reach extensive masses. One of the concerns pertaining to this issue is the sentiments and emotions evoked by COVID-19 news, which those sentiments and emotions are crucial in shaping perceptions and attitudes of the public about COVID-19. Therefore, understanding the sentiments and emotions caused by COVID-19 news would help the public more aware in the process of seeking information through online media news. This research used more than 19.000 COVID-19 headlines from known and popular online media starting March 2020 (based on public health authority announcements) to March 2021. NRC Emotion Lexicon used to detect sentiments and emotions from the headlines. The result shown from the analysis stated that 40% of all the headlines evoked negative sentiments. The first seven months were dominated by negative sentiments. Although, at the end of the 2020 positive sentiment started increasing gradually. Sadness, Fear, Trust, and Anticipate were the most dominant emotions evoked by COVID-19 news. The high negative sentiment has no correlation with death-per-million because of COVID-19 in Indonesia.
Enhancing the Usability of Information Systems Internal Quality Assurance Systems: Recently Formed Universities Study

It was found that the implementation of quality education is a reaction to a number of changes in circumstances related to changes in context related to the distribution of student profiles, the internalization of higher education and the job market, the emergence of the workforce and students, dissatisfaction from workers and students, pressure due to limited funds, and demands for accountability to institutions. Sebelas April University has implemented Information Systems Internal Quality Assurance Systems to avoid these problems. The purpose of this study was to analyze and determine the Usability factor in Information Systems Internal Quality Assurance Systems at Sebelas April University, which was recently formed. The measurement model used is the Usability Model from Nielsen's. The results obtained from the evaluation of the Internal Quality Assurance System for Information Systems that refer to the Nielsen Model variables (Learnability, Efficiency, Memorability, Errors, and User Satisfaction) are considered adequate by the user, with a significance level used of 0.1 (10%), total 743 respondents and 88 samples, resulting in a continuum line value for each variable, namely the Learnability variable 1730 (enough), the Efficiency variable 1620 (good), the memory variable 1107 (enough), the Error variable 1380 (enough) and the Satisfaction variable 1441 (enough). The results of this evaluation can be considered to be used as a guide in the framework of the Information System development process.
Game Popularity Level During Covid-19 Pandemic Using Agglomerative Hierarchical Clustering

During the COVID-19 pandemic, various activities of people outside the home were disrupted and made people move more indoors. For some companies take advantage of this pandemic period as their advantage, especially digital game industry companies. Various games have been released and promoted, these games are published on various game platforms. Currently, Steam is one of the biggest gaming platforms. On this platform, there are a lot of games offered by game developers and provide game pages that are currently popular. However, the website does not provide the popularity level of the currently popular games. This causes ambiguity in determining which games have high, medium, or low popularity. This study tries to create a machine learning model to cluster these games into groups using Agglomerative Hierarchical Clusterin. The distance measure used is euclidean, cosine and manhattan/cityblock and uses single, average, complete and ward linkage. Based on the evaluation results, the best cluster results are the silhouette value of 0.639 and the calinski-harabasz value of 90.192.
Waste Classifier using Naive Bayes Algorithm

In people's daily lives, many things happen such as work, school, and business to buy and sell. In carrying out their activities, the community uses the goods they need and later these community activities will produce waste. This waste should be processed according to the type of waste, but people often equate or collect the waste into a container before it goes into the trash or even throws the garbage into a ditch or river, causing garbage to accumulate in one place because it makes it difficult to sort waste or even waste it to cause flooding because it clogs the drainage channel leading to the river. The aim of the research is to calculate the accuracy of the Naïve Bayes algorithm in classifying waste classifiers. The design of this waste classifier using arduino aims to apply the naive Bayes algorithm in classifying organic, inorganic, and hazardous waste, the naive Bayes algorithm is an algorithm for classification with quite a bit of data training. This tool is designed using Arduino uno r3 with capacitive proximity sensor, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16\mathrm{x}2$</tex> lcd, and data table to look for opportunities or data training. From the results of the waste classification test carried out using this waste classifier, the application of the naive Bayes algorithm in the waste classification tool using Arduino got 28% accuracy in the first test, 57% in the second test, and 71% in the third test. With these results, the naive Bayes algorithm can function or work well for classifying waste in the design of a waste classifier using Arduino.
Viola-Jones Algorithm for Face Detection using Wider Face Dataset

Face detection has been one of the most explored problems in computer vision for several years. Using the WIDER FACES data set, this study investigates how the Viola-Jones method can be used to identify faces in 179 photos and how it performs compared to other face detection algorithms. In a previous study for face detection using Viola-jones, the highest accuracy results were obtained at 90.9% for facial images and 75.5% for non-face images. In this study, the Viola-Jones approach had a 100 percent success rate. This approach will be used in the MATLAB algorithm for face identification to get better results than currently available. Experiments using two classes had promising results.
Data Mining on Sales Transaction Data Using the Association Method with Apriori Algorithm

The purpose of this study was to determine consumer buying patterns at CV. XYZ by utilizing one of the data mining methods, namely the association method. This apriori algorithm belongs to the type of data mining rules. In other words, store owners can manage product placement and design marketing campaigns to determine the association rules between item combinations and determine the results of the association rules from consumer purchase analysis. The apriori algorithm is tasked with finding the frequent itemset or the itemset with the most frequent occurrence of all sales transactions so that association rules can be formed with the help of the RapidMiner application. Thus, all the sales transaction data in the company can be reprocessed to obtain critical information. Sales transaction data will be processed using Knowledge Discovery in Database (KDD). The test results with the RapidMiner application get four association rules. The best association rule is that if consumers buy Pants with code 1076, they are also likely to purchase Pants with code 0814 (confidence = 83.3% & lift = 18.5).
A parallel algorithm for maximal cliques enumeration to improve hypergraph construction

Hypergraphs have become a powerful tool in many research fields that benefit from its high-order characteristics, such as network analysis, data mining and deep learning. The construction of hypergraphs plays an important role in subsequent tasks that are based on the hypergraph structure. The k-nearest neighbor method is widely used in hypergraph construction because of its low computational complexity and high data density. However, it only considers the relationship between the central vertex and the k nearest neighbours(or adjacent vertices) and does not consider if the other vertices are adjacent as well. Maximal clique enumeration algorithms can construct hyperedges where all vertices are adjacent, but the time cost is relatively large. In this paper, we introduce a CLIQUES-SEED algorithm, based on CLIQUES algorithm, which reduces the number of recursions required in the search process, and transforms the time complexity into space complexity, reducing the time complexity from O ( 3 n / 3 ) to O ( n 2 / 3 ) in the worst case. We implemented the algorithm on FPGA to realize the parallel algorithm. We also conducted experiments on the hypergraph neural networks using the CLIQUES-SEED algorithm to improve the hypergraphs construction in hypergraph neural networks. The maximum classification accuracy improvement rate was 6.7% compared with the k-nearest neighbor method. The results show that our method can quickly obtain more accurate hypergraphs using maximal clique enumeration algorithm.
Dyadic Obligations: Proofs and Countermodels via Hypersequents

The basic system $$\textbf{E}$$ of dyadic deontic logic proposed by Åqvist offers a simple solution to contrary-to-duty paradoxes and allows to represent norms with exceptions. We investigate $$\textbf{E}$$ from a proof-theoretical viewpoint. We propose a hypersequent calculus with good properties, the most important of which is cut-elimination, and the consequent subformula property. The calculus is refined to obtain a decision procedure for $$\textbf{E}$$ and an effective countermodel computation in case of failure of proof search. By means of the refined calculus, we prove that validity in $$\textbf{E}$$ is Co-NP and countermodels have polynomial size.
Structure-based graph convolutional networks with frequency filter

Message-passing neural networks (MPNNs) have attracted a lot interesting in academia and industry and have been applied to various graph analytical problems of real-world applications and achieved prominent successes. For the most of current works on MPNNs, they mainly focus on two categories: spectral-based and spatial-based methods. The former seeks to distill useful information (e.g. low-pass and high-pass signals) and the later designs structural schemes. However, it is not enough to only utilize one of them. We tackle this drawback by proposing an efficient and elegant method of taking advantage of the structural graph neural network (GNN) for advanced spectral-based information filtering. Through the learnable frequency components and global virtual neighbor nodes, the proposed scheme is elaborated to obtain informative and structured messages of neighbor nodes and latent space correlational nodes. Experimental results show that our method outperforms the state-of-the-art performance in a series of datasets of graph tasks.
Review on Watermarking Techniques Aiming Authentication of Digital Image Artistic Works Minted as NFTs into Blockchains

The recent creation of Non Fungible Tokens (NFTs) has enabled a multibillionaire market for digital artistic works including images or sequence of images, videos, and animated gifs. With this new trend issues regarding fraud, stolen works, authenticity, and copyright came along. The goal of this chapter is to provide an overview of the watermarking techniques that can be employed to mitigate those issues. We will discuss transparency, robustness, and payload of watermarking techniques aiming to educate the artists, researchers, and developers about the many approaches that watermarking techniques provide and the resulting trade-offs. We focus on fragile watermarking techniques due to their high transparency for embedding into artistic works. We discuss the spread spectrum and Least Significant Bit techniques. We describe the usual process of NFT minting into a blockchain and propose a more secure certification protocol with watermarking which employs the same usual NFT minting offered by current marketplaces. The proposed certification protocol mints a checksum string into a blockchain, ensuring the validity of the watermark and the information embedded into this watermark. This proposed protocol validates the date of creation and author identification which are transparently embedded in the artistic work, thus, increasing the security and confidence of markets for artistic works transactions.
Evaluation of a Semantic Search Approach based on AMR for Information Retrieval in Image Exploitation

As part of the unprecedented wealth of data available nowadays, semi-formal reports in the domain of remote sensing can convey information important for decision making in structured and unstructured text parts. For such reports, often kept in large data management systems, targeted information retrieval remains difficult, e.g., the extraction of texts parts relevant to a question posed via natural language. The work presented in this paper therefore aims at finding the relevant documents in data management systems and extracting their relevant content parts based on natural language questions. For this purpose, an approach for semantic information retrieval based on Abstract Meaning Representation (AMR) is adapted, extended and evaluated for the considered domain of remote sensing and image exploitation. In detail, two different metrics used in AMR, Smatch and SemBleu, are compared for their suitability in an AMR-based search. The first results presented in this paper are promising. In addition, more detailed experiments regarding the performance of the metrics under differently formulated yet semantically equivalent questions reveal interesting insights into their ability for semantic comparison.
Machine Learning to Determine Upper Extremity Motion from Inertial Measurement Unit Signals

Within the medical field and more specialized focus on the therapeutic part, the anatomical analysis of human limbs is a highly important issue, to improve the way in which ailments are treated at the movement level. For these reasons, part of the technological studies has focused on the detection of human movements and activities, to obtain an accurate interpretation within a computational system. The present project has a technological approach in the detection of characteristic movements of the human upper extremity with the use of positioning sensors; and with the information obtained from these, could make use of machine learning to detect such movements. Finally, these information and results from the machine learning will be displayed through the use of a graphical interface.