Reviewerly/exHarmony huggingface dataset:
Bibliometric Analysis of Blockchain in the Healthcare Domain

As an innovation, Blockchain has transformed numerous industries and sparked the interest of the research community due to its abundance of benefits, opening up diverse research routes in the healthcare sector in the last decade. With Health 4.0 becoming ubiquitous in the healthcare industry, end-user transactions are being carried out on a decentralized network, making Blockchain profitable to meet the demands of the modern healthcare sector. Therefore, a detailed analysis of Blockchain is very crucial. This study emphasizes the evolution of science and the preliminary research of Blockchain in healthcare through bibliometric analysis. All the data are extracted from the Scopus database, and the VOSviewer tool is used for analysis. A total of 1152 Scopus articles published between 2018 and 2022 are examined. Results reveal that in 2022, the field of Blockchain experienced a notable increment in the number of publications and a significant growth rate. IEEE Access became well known in this field and had a large number of citations. It is observed that China and India are the leading countries in terms of publications on Blockchain. This study offers a number of recommendations that amateur and professional researchers can use as a benchmark before commencing a Blockchain investigation in the future.
Computational Optimizations in LLMs

Over recent years, the proliferation of microblogging and textual messaging platforms has led to an exponential surge in textual data, necessitating advanced, automated techniques for sentiment elucidation. Contemporary methodologies, despite their efficacy, often demand substantial computational expenditure and manifest pronounced overfitting in scenarios involving non-standard dataset distributions. Parameter-Efficient Transfer Learning (PETL) has emerged as a promising strategy to mitigate the exorbitant computational overheads associated with large-scale model training, albeit not without its computational burden. This research, an extension of extant literature, introduces a pioneering MINIature Orthogonal Network (MINION) approach. It synergistically couples a diminutive sequence-to-sequence recurrent neural network (RNN) with a static transformer model, eschewing the need for backpropagation through the voluminous transformer. Experimental results affirm that MINION ensures a notable reduction in computational requisites in juxtaposition with antecedent implementations and comprehensive model fine-tuning, simultaneously curtailing model over-assurance while preserving commendable accuracy metrics in sentiment categorization tasks.
Unveiling Precision Medicine with Data Mining: Discovering Patient Subgroups and Patterns

Data mining techniques, prominently clustering, assume a pivotal role in fortifying precision medicine by facilitating the revelation of patient subgroups that share common attributes. By harnessing clustering for the analysis of data behavior within the realm of precision medicine, distinctive disease patterns, and progression dynamics are unveiled, thereby contributing to the formulation of precisely tailored treatment strategies. This paper aims to present the outcomes derived from a clustering analysis applied to diverse clinical datasets encompassing critical facets such as vital signs, laboratory exams, medications, sepsis, Glasgow Coma Scale, procedures, interventions, diagnostics, and admission/discharge records. This compilation of datasets pertains to a cohort of seventy patients. The resultant analysis uncovers intrinsic patterns and relationships residing within intricate datasets. Executed following the rigorous CRISP-DM methodology, this discovery study identified three distinct clusters that group similar data characteristics, encompassing both categorical and numerical clinical data, and resulted in three major groups: patients with stable health conditions, recovery stage, and at risk. This pivotal outcome catalyzes future endeavors, including classification tasks aimed at identifying new patients within specific classes, thereby advancing the horizons of precision medicine.
An Adaptive Multiform Evolutionary Algorithm for Global Continuous Optimization

Evolutionary algorithm(EA) with knowledge trans-fer is an emerging research topic that has attracted a lot of attention in the EA community. The multiform evolutionary algorithm is a promising algorithm framework concept falling in this direction, but little work has been done so far to design and discuss this new EA framework. In this paper, we proposed an adaptive multiform evolutionary framework, which integrates the idea of transfer optimization and population-based evolutionary algorithms. The main idea is to utilize multiple equivalent or similar formulations to solve the given problem cooperatively. By adaptively adjusting computational resources of different formulations and transferring knowledge among formulations, the search efficiency and the population diversity can be improved. Furthermore, a new multiform algorithm is implemented based on the proposed framework, which utilizes two different coordinate systems, namely the Cartesian and polar coordinate systems, to model the given problem. To test the effectiveness of the proposed algorithm, we conducted numerical experiments on CEC2013 benchmark test functions, and the experimental results have verified the efficacy of the proposed algorithm.
ECHO: An Approach to Enhance Use Case Quality Exploiting Large Language Models

UML use cases are commonly used in software engineering to specify the functional requirements of a system since they are an effective tool for interacting with stakeholders thanks to the use of natural languages. However, producing high-quality use cases can be challenging due to the lack of precise guidelines and suitable tools. This can lead to problems, e.g. inaccuracy and incompleteness, in the derived software artifacts and the final product. Recent advancements in Natural Language Processing and Large Language Models (LLMs) can provide the premises for developing tools supporting activities based on natural languages. In this paper, we propose ECHO, a novel approach for supporting software engineers in enhancing the quality of UML use cases using LLMs. Our approach consists of a co-prompt engineering approach and an iterative and interactive process with the LLM to improve the quality of use cases, based on practitioners’ feedback. To prove the feasibility of the proposal, we instantiated the approach using ChatGPT and performed a controlled experiment to assess its effectiveness by involving seven software engineering professionals. Three were part of the experimental group and used ECHO to improve the quality of the use cases. Three others were the control group and enhanced the quality of use cases manually. Finally, the last participant acted as an oracle, blind w.r.t. the groups, and evaluated the quality of the enhanced use cases, both qualitatively by means of a questionnaire, and quantitatively, by means of the Use Case Points metric. Results show that ECHO can effectively support software engineers to improve use cases’ quality thanks to the prompts suitably designed to interact with ChatGPT.
Enhancing Data Security in Peer-to-Peer Networks Using Optimized RSA Algorithm

This research introduces a novel RSA encryption methodology tailored to accelerate encryption and decryption processes in peer-to-peer networks. Leveraging the RSA algorithm, the study addresses the critical need for robust data security in dynamic P2P environments. By employing both public and asymmetric key cryptographic techniques, the system ensures secure data communication. The public key, disseminated openly, enables data encryption, while the private key remains concealed for decryption. This approach safeguards data integrity across diverse digital communication channels. Employing advancements in the RSA Encryption Algorithm, the study achieves a notable enhancement in speed and efficiency. Empirical evidence demonstrates encryption and decryption within 42 milliseconds, fortifying data security through the incorporation of the Chinese Remainder Theorem. The integration of four public key pairs within the RSA approach significantly bolsters data security while streamlining encryption and decryption operations. This research makes a substantial contribution to secure data transmission in peer-to-peer networks and broader digital communication contexts.
Facial Parameter Splicing: A Novel Approach to Efficient Talking Face Generation

In recent years, generating talking faces has become a popular research area due to their applications in various fields. However, most current models require high computational demands, which limits their practicality. To address this issue, some researchers have developed phoneme-face indexes to generate talking videos quickly and efficiently. But when the training video is too short, it is not possible to create mappings for all phonemes. To overcome this limitation, we introduced a large-scale phoneme-face dictionary to complete the feature mapping, designed a novel method for fast phoneme-face indexes search and trained a generative adversarial network (GAN) to generate video from phoneme-face sequences. Our proposed method is capable of completing the phoneme-face mapping using less than 10 seconds training video of the target person based on the large-scale dictionary and fast search algorithm and reducing the preprocessing and training time for talking videos generation.
Industry-Led Blockchain Projects for Smart Grids: An In-Depth Inspection

In this paper, we investigate industry-led blockchain projects in the field of smart grids. Our investigation is guided by five research questions related to each industry-led project: (1) is the project active? (2) what smart grid applications does the project target? (3) what technical approach does the project take? (4) what is the maturity level of the project? and (5) what we can learn from the success or failure of the project? Our findings show that only a few projects are still active, and many have been terminated when the funding was exhausted. Nevertheless, the few active projects give us hope that sustainable technical approaches in conjunction with sound business models could lead to long-term blockchain-based projects in smart grids. Most of the active projects are targeting energy trading and using custom tokens to incentivize the production of green energy and energy savings. Furthermore, it appears that layer-2 blockchains are becoming the preferred platform for achieving high throughput with low transaction fees while preserving the security and trust of traditional large public blockchains.
Analisis Kadar Kesadahan Total Pada Air Sumur dan Air PDAM Metode Kompleksometri di Kaliabang Bekasi

Water is a natural resource that is very necessary for the life of many people. Good water is water that meets the requirements of physics, microbiology, chemistry. Chemically there is no water in the form of chemicals Arsenic, Iron, chloride, and hardness in the form of CaCO3. Hardness is a condition with excessive lime content in the water. Hardness with high concentrations consumed in the long term can be detrimental to health. Several factors that interfere with residents' wells are the presence of white sediment in the water which causes public health problems, and the frequent blockage of the pipeline used. The method used is the complexometric method, this kind of research is descriptive in nature. The results of checking the well water obtained Ca levels, namely 100 ppm and Mg, namely 61.44 ppm, while the results of checking tap water with Ca kadae were 150.2 ppm and Mg levels were 75.80 ppm. From the results of this study it can be concluded that well water and pump water can be used for consumption because they have a low hardness value.
Effect of dimensionality change on the bias of word embeddings

Word embedding methods (WEMs) are extensively used for representing text data. The dimensionality of these embeddings varies across various tasks and implementations. The effect of dimensionality change on the accuracy of the downstream task is a well-explored question. However, how the dimensionality change affects the bias of word embeddings needs to be investigated. Using the English Wikipedia corpus, we study this effect for two static (Word2Vec and fastText) and two context-sensitive (ElMo and BERT) WEMs. We have two observations. First, there is a significant variation in the bias of word embeddings with the dimensionality change. Second, there is no uniformity in how the dimensionality change affects the bias of word embeddings. These factors should be considered while selecting the dimensionality of word embeddings.
A Comparison of Product Weight Method and Simple Addition Weight Method in Employee Selection System

Tegar Mandiri Steam Ironing Service is a business operating in the steam ironing service industry, located in the Boyolali area, specifically in Dlingo Village, Mojosongo Subdistrict, Boyolali Regency, Central Java. "Tegar Mandiri" still employs conventional methods in the selection and recruitment of employees without leveraging technology. Although the implementation of conventional employee recruitment has been smooth, several issues have arisen. For instance, prospective applicants must visit in person to find out about job vacancies. Furthermore, the registration process for potential employees is still manually executed, requiring individuals to physically visit the site. Consequently, the administrative staff must record each applicant's details individually, leading to an ineffective data collection process and vulnerability to data loss or damage in physical records. The implementation phase was conducted through coding in PHP, adopting the Simple Additive Weighting (SAW) and Weight Product (WP) methods. Although both methods exhibit minor differences in their comparison, the results tend to favor the SAW method. The accuracy level of the SAW method reaches 96% as it has advantages over the WP method in terms of data normalization. The SAW method can address issues related to scale and unit differences among various criteria. Therefore, the SAW method is more accurate and dominant in determining the priority of employee recruitment
Modifikasi Metode Kalium Bikromat Untuk Uji Kualitatif Alkohol Dalam Urine

Alcohol is a psychoactive substance that can cause dependence. Alcohol as an intoxicant can affect various structures and processes in the central nervous system and increase the risk of intentional and unintentional injury. One of the direct risks due to alcohol consumption is an accident while driving a vehicle and accidents at work. Rate alcohol It is measured in various body fluids including urine. This study aims to determine the sensitivity of the modified potassium dichromate method for qualitative test of alcohol in urine without filter paper and water bath. This research is a laboratory experimental study, the data obtained were analyzed in a descriptive way, namely seeing the color change of the reaction between the alcohol sample in urine and the Potassium bichromate reagent to green or blue with a modified working method. Tested all showed positive results with the modified Potassium bichromate method. A color change from green to blue is read as a positive alcohol result. The conclusion of this study is that the modified Potassium bichromate method is effective for the qualitative alcohol test process and there is an increase in the sensitivity of the test.
Analisis Prediksi Kasus DBD Berdasarkan Faktor Cuaca Dengan Multivariat ARIMA

Indonesia is facing a severe public health issue with dengue hemorrhagic fever (DHF). Anyone, including toddlers and adults, can be affected by DHF. Several weather factors, including humidity, air temperature, air pressure, and wind speed, contribute to frequent rainfall. ARIMA (Autoregressive Integrated Moving Average) is a time series method commonly used in research. To incorporate independent variables, multivariate ARIMA was employed. The analysis results revealed that weather factors, namely humidity, and Precipitation, have a linear relationship with DHF cases. The accuracy evaluation kof the applied model yielded an Mean Absolute Error (MAE) value of 18.12. The prediction estimates that the number of cases will peak in April 2023 and then decline to its lowest point in December 2023. For future development, it is necessary to enhance the quality of the data used to generate more significant results. Consequently, further evaluation is essential to achieve a smaller MAE value.
Website Based UI/UX Application Design of Balinese Traditional Products Using the Design Thinking Method

Pandemic circumstances caused approximately 37.000 small and medium-sized enterprises or also known as (MSMEs) faced a decline. Employing technology can assist these businesses in marketing and selling their products beyond local boundaries, extending their reach to the national level. User Interface and User Experience market application for Balinese custom products as well as providing services related to Balinese custom. The Balinese Traditional Products marketplace application can provide products related to Balinese customs such as Hindu religious infrastructure, Balinese snacks, taru premana event organizer services, traditional massage services and others. The user interface and user experience designed using the Design Thinking method which produces a high fidelity prototype that can be tested like the final application. User experience development can be done as well as possible because development focuses on user needs. Through prototype testing using the usability testing method, the test results using System Usability Scale method get a score of 88.375. Based on these scores the designed prototype is included in the acceptable category in the acceptance rating, category B in the class scale and in the adjective ranking is included in the very good category.
Rancang Bangun Alat Penjernih Air Tipe Portable Menggunakan Metode Nigel Cross

The earth's surface consists of 71% water, water itself has unique characteristics, both physical and chemical. The Kampar river basin has a direct influence on the quality of groundwater (wells) for the residents of Kampung Baru Salo Hamlet. This is because this area is directly adjacent to the Kampar river, ground water (wells) in every house of the residents of Kampung Baru Hamlet is used for daily needs such as consumption, bathing and washing. Along with rapid population growth, the need for clean water is also increasing. This is one of the problems that occurs in Kampung Baru Hamlet, namely the lack of groundwater (wells) that is clean and suitable for consumption. The research carried out by the author discusses how to design a portable water purifier that is efficient and uses easily available materials. This design was carried out using the Nigel cross method. Data collection was carried out by giving questionnaires to 25 respondents who experienced difficulties with clean well water (fit for consumption). Based on the results of the research that has been carried out, the researchers concluded that portable water purifiers can overcome the problem of lack of clean water that occurs in the community and also that the materials for making them are easy to obtain so that people can make them at home. Based on the results of consumption standard testing, the water produced from this filtration process meets the standard consumption requirements, namely 7 - 9 pH.
Visual Point Cloud Forecasting enables Scalable Autonomous Driving

In contrast to extensive studies on general vision, pre-training for scalable visual autonomous driving remains seldom explored. Visual autonomous driving applications require features encompassing semantics, 3D geometry, and temporal information simultaneously for joint perception, prediction, and planning, posing dramatic challenges for pre-training. To resolve this, we bring up a new pre-training task termed as visual point cloud forecasting - predicting future point clouds from historical visual input. The key merit of this task captures the synergic learning of semantics, 3D structures, and temporal dynamics. Hence it shows superiority in various downstream tasks. To cope with this new problem, we present ViDAR, a general model to pre-train downstream visual encoders. It first extracts historical embeddings by the encoder. These representations are then transformed to 3D geometric space via a novel Latent Rendering operator for future point cloud prediction. Experiments show significant gain in downstream tasks, e.g., 3.1% NDS on 3D detection, ~10% error reduction on motion forecasting, and ~15% less collision rate on planning.
Robust Quantum Control in Closed and Open Systems: Theory and Practice

Robust control of quantum systems is an increasingly relevant field of study amidst the second quantum revolution, but there remains a gap between taming quantum physics and robust control in its modern analytical form that culminated in fundamental performance bounds. In general, quantum systems are not amenable to linear, time-invariant, measurement-based robust control techniques, and thus novel gap-bridging techniques must be developed. This survey is written for control theorists to highlight parallels between the current state of quantum control and classical robust control. We present issues that arise when applying classical robust control theory to quantum systems, typical methods used by quantum physicists to explore such systems and their robustness, as well as a discussion of open problems to be addressed in the field. We focus on general, practical applications and recent work to enable control researchers to contribute to advancing this burgeoning field.
Earthenware Clay as A Base Material For Ash Glaze

This research explores local clay located at Kota Kinabalu, Sabah using line blend formula for producing glaze. This study aims to document the basic introduction of earthenware clay that is in Sabah as a basic material in producing glaze. The method used is a qualitative method and carried out a test process at the ceramic studio, Universiti Malaysia Sabah. The formula used is the line blend formula. The line blend formula is an approach that applies a combination of one or two natural ingredients to obtain glossy, satin, and matt effects. A total of six research samples that have been carried out in the ceramic studio by applying the technique of combining earthenware clay (EC) and wood ash (WA) in different ratios using firing temperatures ranged from bisque firing (800?, cone 014) to glaze firing (1260?, cone 6). The results of the study found that the six samples showed different effects in terms of the surface and color of the glaze. This study is an indicator that is easy to use to produce glaze. The research findings show that the production of glaze using clay as the basis is the understanding and technical use of earthenware clay as the main material, the preparation of clay and adjustments for glaze using clay found in Sabah is effective in the creation of ceramics. This study hopes to inform and serve as guides to students how to produce a good and consistent ash glaze formula for ceramic creation.