Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Chatbot for New Student Registration Information Using Sequence Matcher Algorithm

In an educational institution, the efficient handling of new student enrolment plays an important role in the service of interacting and consulting with prospective pupils or parents who need information. However, Klabat University’s customer service has some barriers such as lack of knowledge about campus information in detail, service according to working hours and so on so that it takes more time to handle it if many questions come in simultaneously. The aim of this research is to address the problems experienced by Klabat University’s customer service. The provision of information is maximized, the researchers aim to create an application to help prospective new students get information from the customer service as a chatbot on mobile devices that contains information about the registration of new students at Klabat University. Customer service can have a support tool in the delivery of information. This research presents the design and implementation of an innovative chatbot. By leveraging the Sequence Matcher algorithm, the chatbot aims to help new students acquire the information they need. The integration of the sequence matcher algorithm allows it to identify the most relevant information and provide the most accurate responses without any human typewriting errors. This research has produced an Android chatbot app and a web application that can be used by the Admin to manage questions and view the chat history of users who are logged in.
Audio-based Indonesia Toxic Language Classification using Bidirectional Long Short Term Memory

Users are able to connect with one another via web-based internet technologies through social media platforms like Twitter. On these platforms, hate speech has become a troubling problem, when people or groups are targeted because of characteristics like color, ethnicity, gender, nationality, religion, or organization. Such information is spreading quickly as a result of the practice of using hate speech to gain fast notoriety. 80% of cybercrime prosecutions in Indonesia involve hate speech and defamation on social media, despite the fact that there are regulations like the UU ITE Law, Chapter 28, Number 2, that forbid inciting hatred or enmity on the basis of race, religion, or ethnicity. This research paper explores a novel approach to audio-based toxic language classification in the Indonesian language, leveraging key techniques including Bidirectional Long Short-Term Memory (BiLSTM), speech recognition, and natural language processing. The study focuses on identifying toxic language using a balanced dataset of 5000 entries, equally divided between toxic and non-toxic categories. This dataset was compiled from diverse sources such as NusaCrowd, Kaggle, and others, and was subsequently cleansed of emojis and symbols to maintain a plain text environment. Utilizing audio-based content analysis, the methodology involved the transcription and classification of audio data, yielding an accuracy of 95.2%, precision of 96.4%, recall of 93.2% and F1 score of 94.8% in the detection of toxic speech. The implications of this research extend beyond the academic sphere of toxic language detection, holding significant real-world application potential in areas such as content moderation for Indonesian social media platforms and toxic language detection in customer service interactions. Thus, this study addresses a critical issue of toxic language proliferation in Indonesian online communities and social media platforms, further contributing to the development of healthier digital environments in the future.
Heuristic Behavior Model for Investor Decision in Cryptocurrency Online Trading

In the era of industry 4.0, cryptocurrency has become an alternative investment instrument besides the capital market, stocks and bonds. Cryptocurrency offers quite tempting benefits even though there are also big risks in it. Wrong decision making can result in investment losses. This is what made us compile research on how heuristic behavior factors influence investors' decision making in investing in cryptocurrency. Research objective is to find factors affecting investor decision in cryptocurrency online trading. Our research uses a quantitative method, we collect data by distributing questionnaires to investors on major cryptocurrency brokerage forums. We gather data from as many as 235 respondents. Our data analysis uses structural equation modeling partial least squares and the software we use is Smart PLS 4. The results of our research find that availability bias, representativeness bias, anchoring and adjustment have a significant effect on investors' decision to invest in cryptocurrency, while overconfidence bias has no significant effect. (Abstract)
Semi-Automated Behavior-Driven Testing for the Web Front-Ends

The required effort and time increase as web applications grow in size and complexity. Regression testing becomes time-consuming and repetitive, and there is a higher chance of human errors and omissions. Therefore, automating tests by recording test scripts and writing test case code (i.e., code for conducting specific test cases) can reduce effort, time, and human errors. However, technical personnel may not always understand and describe user requirements correctly, leading to issues of misaligned requirements. Therefore, integrating Behavior-Driven Development (BDD) into front-end automated testing can help facilitate communication and discussion with users. By collaboratively establishing user stories and scripts for testing, it should be possible to reduce issues of unclear or misaligned requirements. Currently, some project teams combine automated testing with BDD methods, which has been recognized for its effectiveness. However, the manual creation of test cases and the definition of BDD steps increase the workload. Moreover, there still needs to be a systematic workflow and supporting tools for effectively incorporating script recording tools into BDD testing. To address these issues, we propose a systematic process called “Recorder-based BDD Testing for Front-end“ (RBTF), which combines the script recording capabilities of automated testing tools with BDD practices. We also developed a tool called “JUnit to Cucumber Converter“ (JUCC) that facilitates the conversion between BDD specifications and test case codes.
Authentibility Pass: An Accessible Authentication Gateway for People with Reduced Abilities

It is important that authentication mechanisms are inclusive to all users including those with reduced abilities. This paper describes our work on Authentibility Pass that allows users with physical and cognitive disabilities to communicate their authentication and accessibility requirements to organizations, including e-businesses, thus ensuring that authentication is accessible. In the initial development phase, a Business Model canvas was created for the Authentibility concept, where the key partners, activities, cost structure and revenue streams were established. This contributed to a value proposition canvas to highlight the customers’ tasks, gains, and pains, as well as how Authentibility Pass will mitigate the pains. The second market validation phase is then described consisting of 30 minute interviews (N=9) with key stakeholders, such as higher education institutions, charities and financial institutions. From our findings, it was identified that people with reduced abilities need to repeatedly inform organizations or e-businesses of their requirements. The Authentibility Pass Proof of Concept was subsequently developed comprising of an Android Application, database, web interface and Application Programming Interface (API). User requirements are entered through the Application, including authentication checks for verification. The user’s accessibility and authentication requirements are communicated using token-based authentication and stored in organization databases. The API is designed for organizations and e-businesses with existing database systems. The Business Model and Value Proposition canvases, market validation results, user interface designs and preliminary evaluation results are discussed, including suggestions for future work. Authentibility Pass will increase the awareness of organizations to customer requirements with disabilities, resulting in higher levels of satisfaction.
Design and Build a Sign Language Detection Application with Tensorflow Object Detection and SSD Mobilenet V2

Communicating is a common thing and cannot be separated from human life. Communication is a means of exchanging information between humans. The most relevant communication medium is spoken language. However, on the other hand, some people are unable to use communication language in general because If there is a problem with their hearing organs, one solution for deaf sufferers to communicate is to use sign language, which is a visual-based language but not everyone understands sign language. Along with the development of technology, there is a solution using Deep learning technology, which is one of the fields of artificial intelligence, which will later train the system to recognize sign language patterns, using Tensorflow Object Detection and SSD Mobilenet V2 with the CNN method. The results show that after training using 489 photos, which were divided into 381 training data and 98 test data, the model implemented in the Android application was able to detect sign language with 50% accuracy and correct detection results.
Quality Analysis of Decision Support System Determine Best Campus in Bangka Belitung Province Using Webqual 4.0

The purpose of further study of prospective students is often a separate problem for these prospective students. There are several aspects that affect it, starting from quality of the campus, tuition fees, campus accreditation, the facilities, and the performance. To solve these problems, a web-based decision support system was built that can be used by prospective students to assist decision making. Furthermore, this study aims to analyze the quality of the website. Quality analysis is carried out so that the website built can provide user satisfaction in its use. The analytical method used is Webqual 4.0 which consists of three instruments, namely Usability, Information, and Interaction. There were 31 sample data which were processed using SPSS 26 through validity testing, reliability testing, descriptive analysis and linear regression tests. The results of the test is the instrument used were proved to have a significant role in user satisfaction using the website.
Carbon Credits Storage: A Comparative Multifactor Analysis of On-chain vs Off-chain Approaches

Data management with blockchain technology has significant implications for supply chain management, healthcare systems, finance, and government. Carbon credit systems, often referred to as the emissions reduction mechanism, are one of the areas impacted by blockchain which is a sector that seeks to reduce the levels of greenhouse gas emissions. The transparency, verifiability, and reliability characteristics of blockchain technology make it easier to track and audit information efficiently and reliably. On-chain and off-chain are two terms commonly used in the context of blockchain data management. The term ”onchain” refers to transactions that are explicitly documented on the ledger. Off-chain, on the other hand, refers to transactions that occur outside of the blockchain network. In this paper, we investigate blockchain-based solutions that are related to carbon credit systems and focus on both on-chain and offchain approaches. Moreover, we evaluate their performance and applicability based on the following main metrics: security, scalability, latency, and cost. In addition, we run a comparative analysis to assess these systems, identify their strengths and limitations, and suggest future directions for researchers to follow.
Text Vectorization Techniques for Trending Topic Clustering on Twitter: A Comparative Evaluation of TF-IDF, Doc2Vec, and Sentence-BERT

In this digital era, where technology is rapidly advancing, social media has become a primary platform for obtaining and disseminating information. Knowing what is being widely discussed and trending on social media is crucial for important aspects such as politics, economic, social and cultural issues. The objective of this research is to perform clustering on texts or sentences, and within each cluster, identify the most influential keywords that can serve as parameters to determine the topics being discussed in each cluster. Twitter was chosen as the social media platform to be analyzed in this research due to its text-based nature. The clustering method used is DBSCAN, considering that the number of clusters is unknown, and three text embedding techniques to be compared, namely TF-IDF, Doc2Vec, and Sentence-BERT. The performance of clustering with different text embedding techniques were evaluated using the silhouette coefficient. Hyperparameter tuning has been done to find the best-performing hyperparameters. From the best-performing technique, topic finding within the resulting clusters was conducted using Latent Dirichlet allocation (LDA). The results of this research indicated that clustering with DBSCAN and TF-IDF, with the highest silhouette coefficient, namely -0.00001, produced one cluster and 3342 outliers. DBSCAN and Doc2Vec, with the highest silhouette coefficient, namely 0.71590, produced one cluster and one outlier. DBSCAN and Sentence-BERT, with the highest silhouette coefficient, namely -0.02425, produced two clusters and two outliers. Based on the research findings, smaller silhouette scores tend to have a more varied number of clusters. DBSCAN with each tested text embeddings showed that the topic for every cluster, except for the first cluster of DBSCAN that use Sentence-BERT, were COVID-19 related topic. The DBSCAN and Sentence-BERT model, despite having a lower silhouette score, successfully identifies two separate clusters with distinct topics, whereas the other models only identify a single cluster.
Why Johnny was allowed to send this email: How to better provide transparency in email security towards the recipient

Email security is not transparent at all today. It depends on opportunistic transport encryption: Mail Transfer Agents (MTAs) try to establish a TLS-secured connection before transmitting an email, but if it is not possible, the email is simply transmitted unencrypted. First approaches exist that provide transparency towards the sender of an email before the actual transmission of an email with potentially sensitive content is done. The sender decides whether the connection is ’’good enough’’ for the specific content to be sent via email. However, recipients of emails still do not have any form of transparency whether incoming emails were properly secured during transport. This leads to situations where recipients complain about received emails (with sensitive content) at data protection authorities (DPAs). In this paper, we come up with an approach to close the transparency gap and inform the recipient about the confidentiality level of an email and that the security mechanisms to protect the data were appropriate.
Dynamic Numerical Studies of a Rotorcraft Entering and Exiting Rectangular Tunnel

In recent years, the utilization of multi-rotor drones has seen a steady rise. Unmanned aerial vehicles (UAV) have found diverse applications, including aerial photography, transportation, military operations, and agriculture. The advent of these compact flying machines offers humanity a range of convenient and versatile choices across various sectors. Although unmanned aerial vehicles hold significant practical value, their stable flight is contingent on favorable weather conditions. Consequently, it becomes crucial to investigate the influence of environmental airflow on aircraft stability. This is particularly important for small machines like rotary-wing aircraft operating in confined spaces, where understanding the aerodynamic effects of the flow field is crucial to avert potential crashes. This study delves into the variations in the flow field during the entry and exit of a single rotor into a tunnel. Utilizing dynamic simulations, it examines how tunnels impact the rotor’s flow field and analyzes thrust curves to understand dynamic rotor behavior. The study also explores the effect of vortex generation on the rotor and employs a simple square tunnel to assess how tunnel size and flight speed influence the rotor’s flow field during flight. The findings hold potential as reference points to enhance flight stability during tunnel flights.
A Declarative Specification for Authoring Metrics Dashboards

Despite their ubiquity, authoring dashboards for metrics reporting in modern data analysis tools remains a manual, time-consuming process. Rather than focusing on interesting combinations of their data, users have to spend time creating each chart in a dashboard one by one. This makes dashboard creation slow and tedious. We conducted a review of production metrics dashboards and found that many dashboards contain a common structure: breaking down one or more metrics by different dimensions. In response, we developed a high-level specification for describing dashboards as sections of metrics repeated across the same dimensions and a graphical interface, Quick Dashboard, for authoring dashboards based on this specification. We present several usage examples that demonstrate the flexibility of this specification to create various kinds of dashboards and support a data-first approach to dashboard authoring.
Implementing a Secure Blockchain-Based Wallet System with Multi-Factor Authentication

With the increased use of blockchain technology, the significance of digital currencies has grown, making it crucial to ensure the security of blockchain wallets. A blockchain wallets private key serves as proof of digital asset ownership, thus, it must be protected at all times. Security threats such as phishing attacks can result in severe digital asset losses. Therefore, implementing rigorous authentication protocols is of vital importance to prevent unauthorized access. This paper proposes a secure implementation of a blockchain wallet website by integrating four different multifactor authentication settings to enhance cryptographic keys and digital asset management. We developed a comprehensive system model to visualize the system interactions and provide an adequate understanding of its architecture. Furthermore, the solution was implemented using Python and Ethereum libraries, followed by extensive evaluation and testing. The results show that system models enhance and facilitate the implementation and validation of blockchain wallets, thereby contributing to the development of robust security measures.
Goal-Oriented Prioritized Non-Functional Testing with Stakeholders' Priorities

Non-functional testing is crucial in software product line engineering to ensure high-quality end products. However, software complexity, limited testing resources, and time constraints pose challenges in conducting comprehensive testing. Additionally, non-functional testing can be seen as a multi-criteria decision problem since it involves multiple quality attribute requirements. Stakeholders often prioritize certain quality attributes over others based on their specific needs, goals, interests, and the value they perceive each quality aspect holds for them. Therefore, incorporating these priorities during test planning is essential for guiding decision-making and aligning the testing process with their needs. This paper presents a methodology for addressing non-functional testing in goal-driven software product lines. The methodology employs a semi-automated process for non-functional testing that utilizes the domain goal model as a testing basis to identify areas (features) within the application within the test space of quality attribute of the application and employs a multi-criteria decision method for prioritizing those features based on stakeholders' assigned priority values. Our goal-oriented methodology optimizes testing efforts by focusing on critical areas, aligning priorities with stakeholders' expectations to enhance software quality and reliability. The methodology supports three key testing activities: capturing of testable non-functional requirements and use case definition for early testing, effective testing scopes design with stakeholders' priorities, and creation of standardized test cases using pre-designed templates. A prototype toolkit was implemented to support the proposed methodology.
Token-Based Identity Model Using OpenID Connect For Unmanaged Systems

According to recent advancements in the cloud and identity domains, there is a drive to remove outdated technologies like Virtual Private Networks (VPNs) and promote Zero Trust Paradigms (ZTP). However, there is a lot of resistance to implementing ZTP among businesses. VPN presents challenges when the user base expands across the boundary, the applications are on-premises, and SaaS requires a hybrid model. Also, when we look deeply into the currently available technologies in Zero Trust, a tight bonding of the user to the device is missing. That is, the enterprise prefers to access its SaaS applications and on-premises applications through the systems that have been issued to it. However, the reality is that there are cases where enterprises would like their employees to bring their own devices (BYOD). In such cases, the challenge for the administrator is how to trust this new device. This paper discusses the issues associated with some of the current VPN techniques. And in the interim, it suggests special ways to incorporate tokens and device parameters to truly believe that the user is a member of an enterprise and grant exclusive access to enterprise applications in the hybrid model
Deploying Blockchains to Simplify AI Algorithm Auditing

Artificial Intelligence has largely occupied various sectors in the world. A huge number of business companies have incorporated several machine learning algorithms for day-to-day decision making. With increasing applications of AI algorithms, the concerns regarding its outcomes have also increased due to bias. In AI algorithms, bias occurs due to multiple reasons including incomplete data, skewed data, human error and so on. These algorithms have the tendency to amplify partially and discrimination in the results instead of benefiting them. This makes it compulsory for the algorithms to be audited. Currently, AI algorithm auditing processes have several challenges including tendency of biases to be deeply ingrained into the system, making these difficult to mitigate; lack of transparency in decision making and many more. This study presents the emerging technology of blockchains to be a viable solution to the existing problem. It comprehensively discusses the suitability of blockchains for transparency in the process of algorithm auditing which is bound to easily capture the issue and the layer consisting it. Consequently, the process of algorithm auditing will be more convenient and more productive. Moreover, this review also discusses some potential challenges that need to be addressed and some future recommendations for this integration.