Reviewerly/exHarmony huggingface dataset:
Pengembangan Aplikasi E-Voting Untuk Pemilihan RT/RW Menggunakan Metode Waterfall di Lingkungan Masyarakat Daerah Macan Lindungan Bukit

This research focused on RT.003 RW.003 in Macan Lindungan Bukit area. The RT.003.RW.003 area is included in Bukit Baru Village, Ilir Barat I District, Macan Lindungan Region. Bukit Baru Village has existed since February 22, 1996. Initially, Bukit Baru Village was the result of the expansion of Bukit Lama Village. Bukit Baru sub-district, especially the RT.003 area, was initially very quiet and deserted because most of the area was swamp, where the population was still very small. Along with the progress of development and the increase in living standards and the success of government, especially RT.003, Bukit Baru Village, it can be said to be developing well. As an area that is still developing towards a developed society area, the selection of RT.003 and RW.003 in this area is still done manually. Where residents are invited to come to the meeting hall to vote, with paper and pens distributed by the committee to choose the candidate for RT/RW chairman. In this research, we will develop an E-Voting application system, which will be implemented in RT.003 in Macan Lindungan area. E-Voting was developed to make it easier for citizens to vote and to know the results quickly. The method used in software development uses the Waterfall system planning method using Unified Modeling Language (UML) and system testing uses black box testing. With the web-based e-voting application development model, residents no longer have to come to the polling place but residents can simply vote from home. The programming language used in developing this e-voting application is Web-based PHP and a database that uses MYSQL. Residents who are voters in RT.003 area will be given a user name and password in the online voting process, and can see the vision and mission platform of RT.003 chairman candidates in the e-voting application. The e-voting system that was built provides an interface that is easy for users to use and offers a data search process and election report results that can be immediately known, compared to manual processes.
Accountability and the metaverse: unaccounted digital worlds between techwashing mechanisms and new emerging meanings

Purpose This paper aims to understand the main challenges connected with accountability issues across multiple layers of the metaverse, to identify whether and how any techwashing is taking place and to discuss implications for accounting research. Design/methodology/approach To develop the research, the authors refer to a critical dialogic accountability framework, operationalized in the current paper by leveraging the perspectives of accountability as virtues and as mechanisms (Bovens, 2010). The authors discuss who is accountable to whom, for what and in what manner in a relatively unregulated and unaccountable world, through the layers of virtual reality introduced by MacKenzie et al . (2013) and Llewellyn (2007). Methodologically, the study concentrates on 32 start-ups working in the metaverse selected from the Crunchbase database and relies on interviews, direct observation in the field and white paper reports analyzed by means of NVivo coding. Findings The findings show how metaverse creators deal with accountability as a virtue and accountability as a mechanism. Companies who operate metaverses primarily consider accountability in the virtual-physical domain, which focuses on developing the necessary internal and external architecture to enable a particular metaverse to function. Metaverse companies also emphasize the virtual-agential dimension that concentrates on onboarding, engaging with and incentivizing individuals in virtual worlds. There is an emphasis on outlining the virtues or standards that metaverse companies aspire to, but there is very little detail provided. Similarly, there are uneven and limited discussions of the mechanisms that can support accountability in most layers of a virtual world. Research limitations/implications The analysis raises significant questions about the purpose, scope and use of metaverses, which are still a relatively unregulated and unaccountable world. The paper advances the idea that the current creators of metaverses are “techwashing” their projects, providing a utopian ideal of what their universes will look like but obfuscating the realities of their ventures in tech jargon that few people are likely to understand. Therefore, meaning and truth at all levels of the real and virtual worlds remain unaddressed, with implications to be explored in terms of legitimacy and trust of metaverses and the interests that shape them. Originality/value This paper is one of the first to address the issue of accountability in metaverses. It advances an analytical framework to guide future accounting and accountability research into virtual worlds.
Automatic Music Generation

Abstract: Automatic generation of music is an evolving field that harnesses artificial intelligence and machine learning techniques to create musical compositions. This research paper investigates the application of Long Short-Term Memory (LSTM) networks and Markov chains for automatic music generation. We compare these two distinct models in terms of their capacity to produce creative, coherent, and musically appealing compositions. Our study involves data preprocessing, model training, and comprehensive evaluations to assess the performance of LSTM and Markov chains. The results indicate varying degrees of success and offer valuable insights into the advantages and limitations of each approach. By shedding light on the strengths and weaknesses of these models, we contribute to the ongoing discourse in automatic music generation. This research paves the way for future advancements in the field, ultimately enhancing the world of music composition and fostering creative exploration in AI-generated music.
A Brief Review of Swarm Optimization Algorithms for Electrical Engineering and Computer Science Optimization Challenges

Swarm intelligence (SI), a crucial element in the science of artificial intelligence, is steadily gaining importance as more and more high complexity problems demand solutions that may be imperfect but are nonetheless doable within a reasonable amount of time. Swarm intelligence, which takes most of its inspiration from biological systems, imitates how gregarious groups of animals work together to survive. This paper aims to discuss the key ideas, highlight potential application domains, their variations, and provide a thorough analysis of three SI algorithms, including the Dragonfly algorithm (DA), Grey wolf optimization (GWO), Whale optimization algorithm (WOA), and their applicability to many branches of electrical engineering and computer science. Finally, these methods were applied to the Zakharov, Levy, Sum Squares, and Drop Wave functions to calculate their algorithmic cost. According to the study, WOA outperforms the other two algorithms for the Levy and Zakharov functions, while GWO and DA perform better for the Sum Squares and Drop Wave functions, respectively.
Language, Corpora, and Technology in Applied Linguistics

As culture and society has become more digitalized, especially when computer science and digital technologies have entered a new era in the twenty-first century, translation studies began to utilize a wide range of tools to enhance its reading of texts and contexts, without which translation both as a practice and as a theorization could barely persist. It has become more apparent that two extreme poles between macro and micro visions have formed the diversified terrains of translation studies. On the one hand, technologies like NLP, topic modeling, network analysis and data visualization make distant reading become possible, thus allowing us to have a paradigmatic view of how human’s ideas, beliefs, values, knowledge and even emotions have spread in some patterns across cultural, geographical and language divides in world history. On the other hand, corpus methods, such as the use of keywords, collocates and concordance lines changed the way by which texts were closely read from linear to vertical. With microscope like corpus tools, we could go deeper into the texture for perception of nuanced meaning. While considering a fact that translation is seldom mono modal in conveying meaning, we have to reconceptualize context as a multimodal environment where audio, visual and other resources interact to convey and make meaning. With regard to the fast development of digital technology, translation studies take an active role in gaining an enhanced capability in promoting transformation. Complexity has been favored in terms of theoretical framework and methodology. New questions are asked; old ones revisited with novel tools; but more areas wait to be cultivated and more questions to be approached by combining quantitative and qualitative methods. We could ask if digital technologies would bring new innovation to study of translation history, a heavily-walled land for traditional humanists who tend to repeat “so-what” to question the less significance of data-driven studies. The idea of high-quality machine translation has become so realistic in today’s market that translation educators have to face the shock wave it brought to translation learners and practitioners and rethink the relation between human translators and algorithms. Machine-translation-assisted communication could help remove boundaries for better communication; but at the same time, it also creates conflicts and leads to confrontation. Thus understood, it is imperative to give a concerned attention to digital translation studies, that is, to study translation by resorting to and drawing on the digital technologies. This Research Topic is intended to promote current directions and new developments in cross-disciplinary critical discourse research. We welcome papers which, from a critical-analytical perspective, deal with contemporary social, scientific, political, economic, or professional discourses and genres. Papers addressing the highlighted topics are especially welcome. In giving weight to these topics, we wish to call to attention some of the most pressing problems currently facing the world.
Low-temperature quantum thermometry boosted by coherence generation

The precise measurement of low temperatures is significant for both the fundamental understanding of physical processes and technological applications. In this work, we present a method for low-temperature measurement that improves thermal range and sensitivity by generating quantum coherence in a thermometer probe. Typically, in temperature measurements, the probes thermalize with the sample being measured. However, we use a two-level quantum system, or qubit, as our probe and prevent direct probe access to the sample by introducing a set of ancilla qubits as an interface. We describe the open system dynamics of the probe using a global master equation and demonstrate that while the ancilla-probe system thermalizes with the sample, the probe per se evolves into a nonthermal steady state due to nonlocal dissipation channels. The populations and coherences of this steady state depend on the sample temperature, allowing for precise and wide-range low-temperature estimation. We characterize the thermometric performance of the method using quantum Fisher information and show that the quantum Fisher information can exhibit multiple and higher peaks at different low temperatures with increasing quantum coherence and the number of ancilla qubits. Our analysis reveals that the proposed approach, using a nonthermal qubit thermometer probe with temperature-dependent quantum coherence generated by a multiple qubit interface between a thermal sample and the probe qubit, can enhance the sensitivity of temperature estimation and broaden the measurable low-temperature range.
Undergraduate students’ reading preference in the extensive reading program: Diary method

Although the Extensive Reading (ER) program has multiple benefits for language learners, the long nature of this program can make it daunting, especially for those with limited book selection. Thus, ensuring a book collection that fits the learners’ preferences is crucial, not only to maintain their reading enjoyment during the program but also to save costs. This research then investigated learners' reading preferences during an ER program, particularly by looking at the reading genres that were frequently chosen by the learners. This research was conducted on 30 university students who took English as a mandatory course. The course used ER as part of the curriculum. This research recorded the learners’ reading preferences through a reading log. This log must be filled in after reading a book or text. Overall, the research generated 373 entries. The results of this research indicated participants’ high interest in books with the themes of drama (slice of life), tales, and comedy. The current research also discovered a preference difference between gender. In conclusion, this research believes ER practitioners must consider learners’ preferences to provide suitable book collections for students. Thus, the learners can read more comfortably and maintain their motivation.
An Analysis of Immersive Communication in VR Documentary: A Case Study of Planet Earth II

The rapid development of Virtual Reality (VR) technology has brought significant changes to the field of cultural media. Among them, VR documentaries, as an emerging form of communication, provide viewers with a brand-new sensory experience through their powerful immersive capabilities. This article will analyze the immersive communication features of VR documentaries, using the classic VR documentary Planet Earth II as an example, and explore its impact on viewers based on existing literature and data. By using VR headsets and 360-degree video capture technology, documentary filmmakers are able to immerse viewers in wildlife habitats and interact with the natural environment. This immersive experience enhances the audiences emotional empathy and engagement, giving them a deeper understanding and appreciation of the beauty and fragility of the natural world. In addition, as VR technology continues to evolve, it can be expected that future documentary productions will add elements of interactivity and personalization. Viewers may be able to choose what interests them, follow their favorite characters to explore, and interact with objects in the documentary. This personalized experience will make viewers more engaged and further drive innovation in documentary production.
Effect of Foreign Direct Investment on Economic Growth in Rwanda

The research study aimed to examine the effects of foreign direct investment (FDI) on the economic growth of Rwanda, with a specific focus on Rwanda. The study sought to explore the relationship between FDI and key economic indicators such as the inflation rate, exchange rate fluctuations, and balance of trade, to gain insights into the overall impact of FDI on Rwanda's economic development. The research adopted a mixed-methods approach, combining quantitative and qualitative data collection and analysis. Data were collected through questionnaires and documentary research. Documentary research involved analyzing archival data from the BNR to obtain historical information and supporting evidence on FDI and its impact on economic growth in Rwanda. Quantitative data were analyzed using python 3, employing statistical techniques, including descriptive statistics, correlation analysis, and regression analysis. These analyses explored the relationships between FDI and economic growth indicators, such as the inflation rate, exchange rate fluctuations, and balance of trade. Qualitative data obtained from documentary research were thematically analyzed to gain a deeper understanding of the stakeholders' perspectives on the consequences of FDI in Rwanda. The research findings confirm that Rwanda Both inflation and economic growth rates in Rwanda have shown upward trends. Inflation started relatively low at 2.3% in 2017-2018 and gradually increased to 4.6% in 2021-2022. Economic growth also began at 3.4% in 2017-2018 and rose to 4.6% during the same period. Rwanda has demonstrated a noteworthy relationship between exchange rate stability and economic growth. The exchange rates remained consistently stable, fluctuating between 4.3% and 4.6%, indicating a favorable environment for international trade and investments. Simultaneously, Rwanda's economic growth rates steadily climbed from 3.4% to 4.6%, reflecting effective economic policies and investments. Rwanda's economic landscape is characterized by a consistent trade deficit in its Balance of Trade (BOT), ranging from -US$1,144.30 million to -US$2,000.40 million. Simultaneously, Rwanda experienced steady economic growth, with growth rates climbing from 3.4% to 4.6%. The research recommends to ensure economic stability in Rwanda, it is crucial to maintain inflation control through prudent fiscal and monetary policies. Additionally, diversifying the export portfolio, especially in agriculture and manufacturing, can reduce the trade deficit. Investment in export infrastructure, such as transportation and logistics, will boost competitiveness globally. Promoting both domestic and foreign investment, particularly in export-oriented sectors, will stimulate economic growth, create jobs, and address the trade deficit issue. Keywords: Foreign Direct Investment, Economic Growth, Rwanda
Real-time Age and Gender Classification using VGG19

Unrestricted real-world facial photographs are arranged into specified age and gender groups using unprocessed face age and gender estimations. This explorer nation has now been prefabbed with earth-shattering enhancements due to its value in speedy real-world applications. However, conventional approaches utilizing unfiltered benchmarks show their incapacity to handle higher levels of variance in such unrestricted photographs. Convolutional Neural Networks (CNNs) enabled approaches have recently been widely used during categorization tasks due to their superior performance in facial psychotherapy. Dimension extraction and categorization are both components of the two-level CNN framework. The article extraction process extracts characteristics such as age and sexual identity, while the classification technique assigns the play photographs to the appropriate age and gender groups. We propose a ground-breaking end-to-end CNN swing in this implementation to achieve better and healthier age units and sexuality categorization of unfiltered real-world faces. We use a bulky person pretreatment approach to prepare and process the unfiltered real-world faces before they are input into the CNN poser in order to handle the significant discrepancies in those faces. When tested for sorting accuracy on the synoptical OIUAudience benchmark, an experimental result reveals that with us assistance achieves state-of-the-art achievement in both age gathering and gender arrangement. Our web is pretrained on an IMDb-WIKI with chanting labels, then fine-tuned on MORPH-II, and eventually on the OIUAudience (first) dataset's training set. In comparison to the best-reported results, the classification of age groups is improved by an excellent percentage (exact accuracy) and a very high percentage (validation accuracy), while the classification of genders is improved by an excellent percentage (exact correctness) and 93.42 percent (validation accuracy).
Using Machine Learning to Classify Information Related to Child Rearing of Infants from Twitter

It is difficult to obtain necessary information accurately from Social Networking Service (SNS) while raising children, and it is thought that there is a certain demand for the development of a system that presents appropriate information to users according to the child's developmental stage. There are still few examples of research on knowledge extraction that focuses on childcare. This research aims to develop a system that extracts and presents useful knowledge for people who are actually raising children, using texts about childcare posted on Twitter. In many systems, numbers in text data are just strings like words and are normalized to zero or simply ignored. In this paper, we created a set of tweet texts and a set of profiles created according to the developmental stages of infants from "0-year-old child" to "6-year-old child". For each set, we used ML algorithms such as NB (Naive Bayes), LR (Logistic Regression), ANN (Approximate Nearest Neighbor algorithms search), XGboost, RF (random forest), decision trees, and SVM (Support Vector Machine) to compare with BERT (Bidirectional Encoder Representations from Transformers), a neural language model, to construct a classification model that predicts numbers from "0" to "6" from sentences. The accuracy rate predicted by the BERT classifier was slightly higher than that of the NB, LR, and ANN, XGboost, and RF, decision trees and SVM classifiers, indicating that the BERT classification method was better.
The MMEQ with Quantum Error Analysis for Advancing Quantum Computing and Quantum Sensing

This paper delves into the profound significance and far-reaching impact of the Modified McGinty Equation (MMEQ) with Quantum Error Analysis in pushing the boundaries of quantum computing and quantum sensing. Quantum error analysis plays a pivotal role in these domains due to the innate vulnerability of quantum systems to errors and decoherence. The Modified McGinty Equation (MMEQ) extends the original equation by introducing the term ΨErrorAnalysis(x,t), which encapsulates the repercussions of error analysis on the quantum field. This extension opens doors to the exploration of error mitigation strategies, elevating the performance of quantum technologies to new heights.
Ecoupon-Chain: Efficient decentralized e-coupon blockchain

E-coupons have gradually gained attention with the development of the e-commerce market. The traditional e-coupon system is based on a centralized organization with problems, such as a single point of failure and e-coupon counterfeiting. Blockchain possesses characteristics such as decentralization that can enhance traditional e-coupon systems. In this study, we designed an e-coupon system based on blockchain called Ecoupon-Chain. First, we defined the mini-block, designed the account, block, and transaction structure, and built the system model. Second, we proposed a simple and efficient consensus protocol called Proof of Activeness to improve the system’s throughput and encourage nodes to perform transactions. Finally, we built a prototype of this e-coupon system and conducted security analysis and experimental evaluation on the prototype. The results show that the system can resist typical attacks, such as fork, double spending, replay, and Sybil attacks. The system throughput can be maintained above 1,000 transactions per second under specific circumstances. The efficiency of the system is significantly improved compared to that of the existing consensus protocol. We present a simplified prototype of Ecoupon-Chain at https://github.com/YangYi215613/Ecoupon-Chain.
Automated Image Captioning with Deep Learning

Abstract: In recent years, deep learning has transformed computer vision, giving rise to automated image captioning systems bridging the gap between visual content and natural language. This paper presents an innovative approach to automated image captioning, combining deep learning models and methodologies. Our system employsConvolutional Neural Networks (CNNs) for robust image feature extraction and Recurrent Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) networks, for generating coherent captions. It is trained on diverse image-caption datasets, learning intricate associations between visual content and textual descriptions.