Reviewerly/exHarmony huggingface dataset:
Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention

Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demonstrated how deep learning methods can be combined to improve the segmentation and perception performance of road scene understanding systems. This paper proposes a novel segmentation system that uses fully connected networks, attention mechanisms, and multiple-input data stream fusion to improve segmentation performance. Results show comparable performance compared to previous works, with a mean intersection over union of 87.4% on the Cityscapes dataset.
PEMBELAJARAN MENULIS ESAI MELALUI TEKNIK MIND MAPPING RETENTION

Writing Through Mind Mapping Retention Technique Innovation. The purpose of this research is to provide a reference for teaching writing literacy, especially writing essays in limited face-to-face learning. This research is experimental, with a research design including a pretest, treatment, and posttest. The sample for this study consisted of students of the Indonesian Language Education Study Program FKIP Muhammadiyah University of Lampung, consisting of as many as 28 students in one class. Research data were analyzed using descriptive and inferential statistics. The results of this study indicate that the posttest results have increased, so that the Mind Mapping Retention technique can be used in learning to write essays.
Single-label machine learning classification revealed some hidden but inter-related causes of five psychotic disorder diseases

Psychotic disorder diseases (PDD) or mental illnesses are group of illnesses that affect the minds and impair the cognitive ability, retard emotional ability and obstruct the process of communication and relationship with others and are characterized by delusions, hallucinations and disoriented or disordered pattern of thinking. Prognosis of PDD is not sufficient because of the nature of the diseases and as such adequate form of diagnosis is required to detect, manage and treat the illness. This paper applied the single-label classification (SLC) machine learning approach in mining of electronic health records of people with PDD in Nigeria using eleven independent (demographic) variables and five PDD as target variables. The five PDDs are Insomnia, Schizophrenia, Minimal Brain dysfunction (MBD), which is also known as Attention-Deficit/Hyperactivity Disorder (ADHD), Vascular Dementia (VD) and Bipolar Disorder (BD). The aim of using SLC is that it would be easier to detect some PDDs that are related to each other without the loss of information, which is a plus over multi-label classification (MLC). ReliefF algorithm was used at each experiment to precipitate the order of importance of the independent variables and redundant variables were excluded from the analysis. The order of the variables in feature selection was matched with feature importance after the classifications and quantified using the Spearman rank correlation coefficient. The data was divided into: 70% for training and 30% for testing. Four new performance metrics adapted from the root mean square (RMSE) were proposed and used to measure the differences between the performance results of the 10 Machine learning models in terms of the training and testing and secondly, feature and without feature selection. The new metrics are close to zero which is an indication that the use of feature selection and cross validation may not greatly affects the accuracy of the SLC. When the PDDs are included as predictors for classifying others, there was a tremendous improvement as revealed by the four new metrics for classification accuracy (CA), precision and recall. Analysis of variance showed the four different metrics differs significantly for classification accuracy (CA) and precision. However, there were no significant difference between the CA and precision when the duo are compared together across the four evaluation metrics at p value less than 0.05.
TRANSFORMING STRATEGIES IN THE DIGITAL ERA: THE ROLE OF SOCIAL MEDIA IN CUSTOMER VALUE ANALYSIS AND CRISIS MANAGEMENT FOR TOURISM FIRMS

Social media is gaining popularity nowadays and is increasingly being used by many small and large organisations. Organisations are adopting new social platforms and technologies to achieve their key and effective management strategies. However, there are still opportunities to explore the role of new technologies in developing useful strategies. In current research, the utilisation of technological tools especially social media was examined to improve the customer value analysis in the organisations. Besides, the research of social media use for crisis management is also increasing and the relevant strategies are still being-investigated. To overcome this gap, this research aims to evaluate the impact of the use of social media on customer value analysis and crisis management. To attain this, a detailed questionnaire was adapted from several different studies. Data were collected from a diverse targeted sample of tourism-related firms from all over Malaysia, including hotels, resorts, travel agencies and transportation companies. The model was tested using Smart PLS software and the results were generalised. Overall, this research will add a noteworthy contribution to the literature by highlighting the significance of social media and recognising its urgency during crisis for businesses. It will also help in answering questions regarding the role of social media usage towards customer value analysis and crisis management of organisations in the Malaysian tourism sector. Moreover, the practitioners will use the findings to make strategies for crisis management and build customer value chain.
An Evaluation of Bitcoin From Shariah Perspective

Bitcoin is one of the most successful cases of blockchain use. The objective of this paper is to examine the characteristics of cryptocurrencies, with a specific emphasis on bitcoin. The fundamental attributes, features of bitcoin and how they operate on blockchain are evaluated from the Shariah perspective to determine if they are consistent with Shariah principles. To accomplish the objective, the authors adopted a qualitative secondary analysis (QSA) approach by utilizing the existing data.
IMPLEMENTASI APLIKASI WEB SEBAGAI SISTEM INFORMASI GUDANG DI CAFÉ XYZ

Currently the role of technology is growing rapidly in all fields of business. But not all businesses run businesses with technology, such as café nauli. Currently, café nauli still uses a system of collecting the supply of tub materials and stock manually with books. So that in the process of work takes quite a long time. Other risks are data loss and differences in expenditure and income of goods. Departing from the above problems, for this reason, the author implements an application for raw material inventory and stock to help facilitate data collection of goods in warehouses and minimize the risk of losing goods in cafes. The author uses the prototype system design method. This system is designed using PHP programming language and MySQL database. This research resulted in a web warehouse product for café Nauli in real time and up to date.
Research on Fast Retrieval Algorithm for Sports Training Target Images

Current traditional image retrieval methods achieve image retrieval by matching a single feature of an image, which leads to poor retrieval results due to the lack of pre-processing of the image. In this regard, the work proposes a study of a fast retrieval algorithm for sports training target images. By extracting visual features of sports training target images and generating corresponding visual vocabulary, denoising and visually enhancing the images, fast image retrieval is achieved by calculating image similarity measures. In the experiments, the proposed retrieval method is verified for retrieval performance. The experimental analysis shows that the proposed method has high retrieval speed and accuracy in retrieving images, and has a good overall retrieval performance.
Functions of Teachers’ Paralanguage in Online Classroom Interaction (Zoom Cloud Meetings)

Abstract. This paper aims to investigate the use of paralanguage, as a part of non-verbal communication. It is believed that paralanguage has fruitful contribution to the successful teaching and learning process and therefore it is necessary to explore deeply about the functions of teachers’ paralanguage in the classroom interaction. Under the design of qualitative research, this study focused on observing the functions of teachers’ paralanguage in online classes conducted via Zoom Cloud Meetings. This study relied on the use of observation and recording to obtain data. The subject is one English class with one teacher and 31 students in one Senior High School in South Sulawesi, Indonesia, which applied Zoom Cloud Meetings as the medium of interaction. To analyze data, steps of analysis included data collection, data reduction, data display and conclusion (drawing or verifying) . The result of data analysis showed that the use of teacher’s paralanguage in teaching has several functions. Some of those functions were (1) to accent, (2) to complement, (3) to contradict, (4) to regulate, and (5) to repeat. Findings from this study can become a reference for teachers in creating effective classroom interaction both in face-to-face interaction and online interaction. The results of this study are expected to give a contribution to pedagogical knowledge as an important input about non-verbal communication, especially the use of paralanguage in EFL classroom interaction. Keywords: Communication; Non-Verbal Communication; Paralanguage; EFL Classroom Interaction; Online Classes
A Poisson-Based Approximation Algorithm for Stochastic Bin Packing of Bernoulli Items

A cloud scheduler packs tasks onto machines with contradictory goals of (1) using the machines as efficiently as possible while (2) avoiding overloading that might result in CPU throttling or out-of-memory errors. We take a stochastic approach that models the uncertainty of tasks’ resource requirements by random variables. We focus on a little-explored case of items, each having a Bernoulli distribution that corresponds to tasks that are either idle or need a certain CPU share. RPAP, our online approximation algorithm, upper-bounds a subset of items by Poisson distributions. Unlike existing algorithms for Bernoulli items that prove the approximation ratio only up to a multiplicative constant, we provide a closed-form expression. We derive RPAPC, a combined approach having the same theoretical guarantees as RPAP. In simulations, RPAPC’s results are close to FFR, a greedy heuristic with no worst-case guarantees; RPAPC slightly outperforms FFR on datasets with small items.
STUDY OF PENDANTS MADE OF RESIN PRODUCED BY THE ART FOLK VILLAGE INSTITUTION

The background of the problem in this study is the lack of craftsmen in making pendants made from resin, this becomes a problem because this pendant is quite a in demand to be used as souvenirs at weddings, by doing this research the public can be invited directly to see the process of making this pendant. , as an effort to find people who are interested in becoming a pendant craftsman made of resin. This study aims to describe the process of making pendants made from. The type of research is survey research with a descriptive qualitative data analysis technique. Data collection techniques in the form of observation, interviews, and documentation. Data analysis techniques in the form of data collection, data reduction, data presentation, and data verification. The results showed 1) the pendant manufacturing process consisted of several stages, first: preparation of tools and materials, second: selection of raw materials, third: assembling the main material with additional materials such as resins, catalysts, and dyes, fourth: printing process, fifth: drying process, sixth: finishing includes sanding and brassoing.
The role of emoji in enhancing communication between people through the application of Messenger and WhatsApp

The communication process is not limited to speaking or writing only. It is a complex process that requires influential factors such as language, grammar, and experience. It also includes elements of the non-verbal medium. Sign language and facial expressions can reveal what a person is trying to express through a face-to-face conversation. In the foundations of conversation, we find that the absence of a non-verbal medium causes a lack of understanding and confusion in interpersonal communication. This study focuses on the impact of emoji in electronic communication, especially the (Messenger and WhatsApp) program, where this study relied on the theory of media richness. The method used is the qualitative research method, and the results showed that emoji have an important role in electronic communication between people.
Reducing Networks of Ethnographic Codes Co-occurrence in Anthropology

The use of data and algorithms in the social sciences allows for exciting progress, but also poses epistemological challenges. Operations that appear innocent and purely technical may profoundly influence final results. Researchers working with data can make their process less arbitrary and more accountable by making theoretically grounded methodological choices. We apply this approach to the problem of reducing networks representing ethnographic corpora. Their nodes represent ethnographic codes, and their edges the co-occurrence of codes in a corpus. We introduce and discuss four techniques to reduce such networks and facilitate visual analysis. We show how the mathematical characteristics of each one are aligned with a specific approach in sociology or anthropology: structuralism and post-structuralism; identifying the central concepts in a discourse; and discovering hegemonic and counter-hegemonic clusters of meaning.
Mineral resources of the Gandaki Province of Nepal: present status and prospect

Mineral resources serve as the foundation of prosperity for any country or community. However, the process of prospecting and exploring these resources is time-consuming and involves financial risks. It requires the combined expertise of both geoscientists and mining engineers, encompassing academic knowledge and professional skills. In this research, we provide an overview of the mineral resources within the Gandaki province of Nepal and evaluate their present status. The main objective is to examine the current status and potential of mineral resources in the province. To achieve this, we extensively utilize available reports, journal articles, and online sources. We gather both primary and secondary data on mineralization. Additionally, we systematically list the potential locations of all documented mineral resources within the Gandaki province, along with their GPS coordinates. Based on the current geological and mineralogical investigations, as well as our understanding of the province’s mineral resources, we identify ten minerals or mineral groups with potential economic value: iron, copper, uranium, tourmaline, syenite, thermal springs, limestone, dolomite, slate, and meta-basic rocks like amphibolite. We strive to provide geological evidence and context for supporting the presence of these minerals.&#x0D; In general, iron mineralization is observed as sedimentary deposits, exhibiting a syngenetic nature. Many copper ores are hydrothermally deposited originating from the underlying meta-basic rocks like amphibolite. The region is also known for its abundance of gemstones, including quartz crystals, garnet, kyanite, and tourmaline. These minerals are concentrated in the vicinity of the Main Central Thrust zone and its surrounding areas. Moreover, the province has significant potential for high-quality dimension stones for flooring, roofing, and pavements such as quartzite, slate, phyllite, schist, granite, and gneisses. Additionally, there are ample opportunities for the extraction of construction materials like sand and gravel from the river terraces and natural rock outcrops. Nevertheless, a systematic study focused on prospecting, exploration, and utilization of these potential and possible mineral resources is imperative for an accurate economic evaluation. To achieve this, the provincial government must develop plans and policies to facilitate the development of its own mineral resources. Furthermore, establishing strong collaboration between professionals and academia is crucial to make well-informed investments in the mineral sector. Considering the present status of minerals, the Gandaki province of Nepal holds promising prospects for harnessing its mineral resources for sustainable development.
Prototype and Metric Based Prediction for Data-Efficient Training

We propose a prototype- and metric-based prediction method together with several training pipelines suitable for training a network without using any additional data in the few-shot learning tasks with different intra-class variances. Being tested on two datasets commonly used for few-shot learning, our method has shown satisfactory ability to improve data efficiency and prevent overfitting. It even competes with the meta-learning-based method trained with a lot of extra labeled samples on the dataset with low intra-class variance and shows no significant performance gap when it comes to the dataset with a high intra-class variance. We reported 99.0% acc on the Omniglot dataset and 48.0% acc on the mini-ImageNet for 5-way 5-shot tasks.
Perbandingan Metode KNN, Naive Bayes, dan Regresi Logistik Binomial dalam Pengklasifikasian Status Ekonomi Negara

The classification of a country's economic status as developed or developing often involves factors such as life expectancy and its underlying variables. This research aims to compare the performance of three machine learning algorithms, namely KNN (K-Nearest Neighbors), naive Bayes, and binomial logistic regression, in classifying the economic status of countries as developed or developing. The data used in this study is "Life Expectancy (WHO) Fixed," obtained from the Kaggle website. The first statistical analysis conducted was Principal Component Analysis (PCA) using 16 predictor variables. PCA resulted in three principal components capable of explaining 71.41% of the variance, which were subsequently used in the KNN, naive Bayes, and binomial logistic regression methods. The analysis results from the KNN, naive Bayes, and binomial logistic regression methods produced F1-scores of 100\%, 98.19%, and 97.36%, respectively.
Evaluating Service Computing Framework with Scrum through a Case Study

The Service Computing Framework with Scrum (SCFS) is a framework that combines Services Computing Systems Engineering (SCSE) with the Scrum Framework. This framework was created to produce a system with a good service concept by following the SCSE workflow in an agile manner through the application of Scrum principles. This paper will provide an overview of how to evaluate the SCFS framework using SOA and Agile principles through a case study. The research methodology used in this paper is a Design Science Research Methodology (DSRM). This research results in project evaluations using SCFS with good Service Oriented Architecture (SOA) parameter results. The SCFS framework can also be used in Agile development environments with fairly good parameters of velocity, story points, sprint burn down, release burn up, value delivered, and job satisfaction. In case studies conducted, this framework can help accelerate the release of service systems. The first release on the test only took 34 working days, 18 working days to get the second release, and 21 working days to get the third release. If done as a whole in one cycle, the service system will take approximately 73 working days to be used.
Malleable Techniques and Resource Scheduling to Improve Energy Efficiency in Parallel Applications

The high energy consumption of computing platforms has become one of the major problems in high-performance computing (HPC). Computer energy consumption represents a significant percentage of the CO2 emissions that occur each year in the world, therefore, it is crucial to develop energy efficiency techniques in order to reduce the energy consumption in HPC systems. In this work, we present a resource scheduler capable of choosing, using real-time data, the optimal number of processes of running applications. The solution takes advantage of the use the FlexMPI runtime to dynamically reconfigure the application number of processes and DVFS to modify the frequency of cores of the platform. The scheduling algorithms presented in this work include one that minimizes the application energy and another more holistic one that allows the user to balance between energy and execution time minimization. This work presents a description of the methodologies and a experimental evaluation on a real platform.
A Proposal for Regulation of The Spectrum Usage Fee in 5G Private Network

This study evaluates the types of regulation models for The Indonesia Spectrum Usage Fee—the so-called Biaya Hak Pengguna (BHP) Frequency in 5G private network technology that are most suitable for implementation in Indonesia by implementing the Fuzzy Analytical Hierarchy Process (F-AHP) method. This method accommodates the opinions of telecommunications experts from mobile network operators (MNOs), regulators, vertical industries, and telecommunications consultants through a series of scientific steps to produce weights for each type of alternative solution offered. The results obtained show that the proposed model most suitable for implementation in Indonesia, taking into account the given criteria, is the one that uses unlicensed 5G frequencies. This model involves vertical industries not using licensed frequencies established by the government but rather choosing to use unlicensed frequencies to develop 5G technology for their own use. The implementation of this model is expected to encourage the optimization of regulation for Spectrum Usage Fee in 5G private network technology owned by the government, providing opportunities for vertical industries to develop 5G technology on private networks independently without relying on existing MNOs. This can stimulate innovation and technological progress in Indonesia to support Industry 4.0.