Reviewerly/exHarmony huggingface dataset:
Rancang Bangun Alat Penyiraman Dan Pembasmi Hama Otomatis Pada Tanaman Bayam Dengan Monitoring Berbasis Website

Spinach is one type of plant whose leaves can be consumed, because it contains iron which is useful for the body, people often use it for their daily vegetable needs. Spinach plants cannot grow properly if the water content in the soil does not meet the needs of the plants. In addition, spinach plants are susceptible to pest attacks that can damage the leaves. From these two problems, we need a system of automatic watering and pest control devices that can monitor plants. To make it easier to plant spinach, a monitoring system is needed using a website, because its use which can be accessed anywhere for 24 hours can make it easier to monitor the condition of spinach plants. The website will display soil moisture and PIR sensor data readings sent via the NodeMCU ESP8266 microcontroller to a database in the form of soil percentage, soil status, and plant conditions.
A route-creating algorithm for Saildrones by comparing a pseudo straight-line route and wind direction

Control algorithms for drones are commonly focused on transporting the robot from point A to point B, this type of straight path becomes sufficient in the case of aerial drones, but since it is a Saildrone (marine drone propelled in a partially or totally by a sail, commonly rigid), straight navigation between 2 points is less than optimal. That is why navigation strategies are used to control vehicles with sails, consisting of the variation of the angle of attack of the sail and the route that the vehicle will follow. This last parameter of the strategy is the one that is sought to be obtained using an algorithm based on obtaining the coordinates in real time of the drone and the destination point, together with the direction of the wind and the inclination of the Saildrone concerning magnetic North, uniting all this data through analytical geometry equations; to be implemented in an autonomous drone controlled by a Raspberry Pi 4 SBC. In this way, it is sought that the generated routes are similar to those commonly used in sailboat navigation, with the ability to update the route in case it varies from unexpectedly the initial parameters, whether they are the location of the boat or the direction of the wind, to obtain an energy saving greater than 50% to following a route in a straight line. The purpose of this project is to build a route generator with low processing costs for low-resource drones. In this way, it is sought that the generated routes are similar to those commonly used in sailboat navigation, with the ability to update the route in case the initial parameters change unexpectedly, whether they are the location of the vessel or the direction of the wind. , to obtain energy saving greater than 50% by following a route in a straight line. The purpose of this project is to build a route generator with low processing costs for lowresource drones. In this way, it is sought that the generated routes are similar to those commonly used in sailboat navigation, with the ability to update the route in case the initial parameters change unexpectedly, whether they are the location of the vessel or the direction of the wind. , to obtain energy saving greater than 50% concerning following a route in a straight line. The purpose of this project is to build a route generator with low processing costs for low-resource drones.
Impact of COVID-19 on the use of ICT resources among university professors in Peru

This paper conducts a descriptive quantitative research on the impact of digital migration derived from the COVID-19 pandemic on the frequency of use of ICT resources in the teaching activity of university professors in Peru. The main objective is to identify differences in the aforementioned impact according to the area of knowledge of the professors. A questionnaire designed by the authors was used to measure the frequency of ICT use before and after the COVID-19 pandemic and the responses were statistically analyzed. As a result, it was found that the pandemic has led to a significant increase in the use of ICT in all dimensions of teaching activity, especially in those in which they were less commonly used. Moreover, this increase was greater among professors in humanistic-social areas than among those in scientific-technical areas.
Application of a radio link system in the 400MHz band for remote supervision and control of SEDAPAL service stations in Peru

Within the program for the improvement and modernization of drinking water and sewerage service stations in Latin America, there are projects to expand and improve the service due to its projection of population growth and modernization of the service, since in most cases there is a lack of updating of automation designs which hinders the supply of drinking water. In addition, in some cases in Latin American cities, the monitoring and control of the stations (reservoirs, wells, inlet chambers, re-pumping chambers, etc.) has been carried out manually. For this reason, in this investigation, the interconnection of the SEDAPAL stations is considered, which will be carried out wirelessly, interconnecting the stations through radio links, through a study of different factors such as geographical profiles, height of the antennas, calculation of fresnel zones, antenna gain, azimuth calculation, transmission power, effective radiated power, losses generated by free space, among others.
Use of the online platform and its relationship with academic stress in Jaen high school students (Cajamarca, Peru)

The use of the online platform for educational services is essential for the achievement of academic objectives and goals, so that in the face of connectivity problems and lack of skills, these become a constraint to the teaching and learning process, causing stress, anxiety, anguish and other emotional problems in the student. The objective of this research is to determine the relationship between the use of the online platform and academic stress in secondary school students in Jaen (Cajamarca, Peru). The research has a quantitative approach, correlational level, nonexperimental and cross-sectional design. The population consisted of a total of 120 high school students. Two questionnaires were used: one measuring the use of the online platform (in its three dimensions: use of didactic materials, classroom tutoring system and technological environment) and the other measuring academic stress (in its three dimensions: stressors, symptoms of stress and coping strategies). As result, we report that the use of online platform is high in30.00% of the sample (n=120), medium in 60.83% and low in the remaining 9.17%.Similarly, academic stress is high in 30.00% of the sample, medium in 57.50% and low in the remaining 12.50%.Finally, there is significant relationship (positive and moderate) between the use of the online platform and academic stress (r=0.678).
Towards Designing an Intelligent Recommender System using Adaptive Collaborative Filtering Technique

In recent years, intelligent recommendation systems using adaptive collaborative filtering have received great attention due to their ability to provide personalized and relevant recommendations. The innovation of this study lies in proposing an adaptive collaborative intelligent recommendation model (ACIR), which combines adaptive collaborative filtering with context, user behavior, and movie feature information. It utilizes automatic encoders and deep learning algorithms to capture the characteristic relationships of the context, explore the internal connections between the context and users, and improve the accuracy and interpretability of the recommendation system to meet the personalized needs of users. The aim of this study is to predict user preferences by considering the characteristics of users, projects, and contexts, and to evaluate the potential changes in user preferences over time. In the experimental stage, LDOS CoMoDa was used to test and train the dataset, and the model was validated. Choose traditional collaborative recommendation, k-means algorithm, CAMF, and CAMF_ CM algorithm for comparison. After experimental verification, this article proposes that compared with other traditional recommendation algorithms, ACIR has higher accuracy and stronger application value
Towards a New Seismic Short-Term Prediction Methodology for Critical Service Operators and Manufacturing Companies Against Earthquake

This paper is concerned with the novel short-term and operational-term seismic hazard assessment approach within the critical service operators and the manufacturing industry.The Cosmetecor earthquake prediction methodology has been tested and validated in the recent two decades.A prototype, Kuznetsov method, for exploring the Earth's interior has been used to create global monitoring network, which automatically detects spatial-temporal clusters and identifies electric potential anomalies.Research team developed the mathematical modelling of proton migration in terms of the fundamental Vlasov-Maxwell equation to convert original time series into visualization of electromagnetic wave.A 2-layer neural network model is used to fine-grained classification.Further, the statistical and scaling laws of seismicity have been exploited to present case of earthquake seasonality, i.e., a dataset of abnormal seismic scenarios for machine learning task.Finally, authors evaluated results in terms of reliability and accuracy of earthquake warnings at M5.2 threshold in Kamchatka: 17% of all warning represent missed alerts, and 83% represent correct alerts where events occurred in a 10-year time horizon.Common outcome in almost every non-random sample of the Italian companies assessed new benefits of methodology during survey.The stakeholders confirmed that they will be able to activate business continuity plan to mitigate earthquake consequences in a specific time frame.It is anticipated the emergence of new risk management practices on the Cosmetecor-based high technology of the 21st century, and the replacement of the long-term, one-in-a-hundred-year return period, assessment with a short-term, seasonal, seismic risk assessment.
Distributed Energy Storage Data Sharing Based on Privacy Protection using Secure Multiparty Computation

Background:: Data sharing can improve the utilization rate of distributed energy storage and solve the problem of data silos, but there are privacy and data security issues in distributed energy storage data sharing. Objective:: To address the privacy and security issues in distributed energy storage data sharing. Method:: In this paper, a distributed privacy-preserving data sharing scheme based on secure multi-party computing is proposed. Firstly, the requirements for distributed energy storage data sharing are analyzed, and the concept of distributed energy storage data sharing is defined. Secondly, the additive secret sharing technology is adopted to realize the safe transmission and storage of energy storage data between energy storage nodes. Finally, combined with the relevant forgetting transmission extension protocol, privacy-preserving data computing is realized, so as to solve the problem of secure sharing of distributed energy storage data. Results:: The simulation results show that the privacy protection method does not affect the convergence round of specific communication. However, in order to achieve the effect of privacy protection, the number of communication times has become twice that of conventional communication, and the privacy protection communication time accounts for about 0.8 of the total communication time. Conclusion:: Compared with the traditional data-sharing method, the proposed method realizes privacy-preserving data sharing without affecting algorithm convergence. For distributed scenarios, a flexible P2P sharing architecture is proposed, which has strong scalability and regret, and has good application prospects. conclusion: Experimental results show that compared with traditional data sharing methods, the proposed method has lower computational cost, achieves privacy-protecting data sharing, has strong scalability and deplorability, and good application prospects. other: No attention
Sentiment Analysis Framework of Social Media Text by Feature Extraction and Machine Learning Model

Objectives: This research paper aims to analyze sentiment and opinions in online resources like discussion forums, review sites, and blogs.It also compares the effectiveness of three feature extraction techniques (TF-IDF, Word2Vec, and WAM) and evaluates three machine learning algorithms (Naïve Bayes, SVM, and ANN) for sentiment classification to determine the most accurate algorithm.Methods: The study utilizes sentiment-rich datasets from IMDB movie reviews, Yelp reviews, and tweets.Three feature extraction techniques are applied to extract relevant features and patterns from the text.Three machine learning algorithms are implemented to classify sentiments into positive, negative, and neutral categories.Accuracy, precision, recall, and F-measure are used to assess algorithm performance.The model is updated and refined three times to ensure reliability.Findings: The Artificial Neural Network (ANN) algorithm outperforms Naïve Bayes and Support Vector Machines, achieving an impressive accuracy rate of 99.74% for sentiment classification.Precision, recall, and F-measure exceed 98.5% after model refinement, demonstrating the approach's robustness.The study highlights the potential of sentiment analysis in online resources and emphasizes the ANN's superior accuracy, providing valuable insights for future sentiment analysis studies.Novelty: This research combines three popular feature extraction techniques in sentiment analysis, compares three machine learning algorithms on multiple datasets, and achieves a remarkable accuracy rate of 99.74% with the ANN.The study demonstrates the robustness of the approach through model refinement and contributes insights into sentiment analysis in online resources.
Upaya Peningkatan Motivasi Siswa dalam Melanjutkan Studi ke Jenjang Perguruan Tinggi di Desa Langkaplancar Kabupaten Pangandaran

The role of education in higher education is very important to prepare students to become individuals who have the ability to be able to develop themselves further. Based on the results of a field survey, data on students who continue their tertiary education in Langkaplancar Village are still lacking, and their motivation to continue on to tertiary education is still low. The reality is that currently in Langkaplancar Village there are many high school graduates/equivalents who decide not to continue their education at tertiary institutions, some choose to work or even become unemployed, due to economic factors and lack of motivation. One of the efforts made to solve this problem is holding an Education Motivation Training Seminar. The purpose of this activity is to increase student motivation to continue their studies at tertiary institutions and to raise awareness of all parties, especially parents, in providing support for children to continue on to tertiary institutions. The results of this community service activity are (1) Socialization and coordination with related elements (2) Implementation of activities and (3) Responses of the Village Government, School Principals, Teachers and Parents to activities.
PELATIHAN PEMBUATAN SITASI DAN DAFTAR PUSTAKA KARYA TULIS ILMIAH MAHASIWA FEBI IAIN TERNATE MENGGUNAKAN MENDELAY

Writing scientific papers is very important in the academic world to improve quality and quality for students in making citations and bibliography. Community service in the form of training on making citations and bibliography of scientific papers is carried out to provide knowledge and use of the Mendeley application as a software or reference manager to make references and reference lists easier, practical and accurate. This activity is carried out using the training method with lecture techniques, providing training and direct guidance to students. Participants in the training activities in this PkM were students of the Sharia Banking Study Program, the Sharia Management Study Program, the Shariah Accounting Study Program and the Islamic Economics Study Program, Faculty of Islamic Economics and Business, IAIN Ternate, with a total of 30 participants. The purpose of this activity is that students are expected to be able to independently manage references well. The survey results show that this training is very beneficial for students. This is evidenced by the information provided by students through questionnaires distributed. Based on the results of the questionnaire, it was found that students' knowledge and skills in utilizing the Mendeley application increased.Keywords: Training; Mendeley Application; Students.
Research Progress of Water Surface Object Detection

Numerous models with outstanding performance for detecting typical water surface targets emerged in the first “Ship-Sea Data Intelligence Application Innovation Competition”. By analyzing the differences between these models and their baseline models, a series of improvement strategies were summarized and applied, including improved non-maximum suppression processing, data augmentation methods, and model training processes. Furthermore, experimental analysis results showed a significant increase in the average detection accuracy of the improved models, providing technical support for intelligent and unmanned situation awareness needs on ocean platforms. The team also constructed the first large-scale high-definition ship dataset in China, which includes three types of scenes including dock, lake and ocean-going as well as finely annotated data for various water surface environments such as occlusion, rain, fog, and blur.
Multimedia task scheduling based on improved PSO in cloud environment

Aiming at multimedia task scheduling in cloud computing environment, this paper constructs a corresponding model to optimize makespan and average task completion delay, and then proposes an improved algorithm AICLPSO (Adaptive inertia weight and Chaotic learning factor Particle Swarm Optimization) based on PSO to solve the problem. Through experimental simulation, compared with classical RR algorithm and particle swarm optimization algorithm, the algorithm designed in this paper has better optimization effect.
Overview of cloud computing deployment mode and technology development trend

Cloud computing technology has gained widespread recognition and adoption in recent years, yet there remains ambiguity surrounding its precise academic definitions. The concerns of various enterprises and organizations have emerged as significant obstacles to further development of the cloud computing industry. In this paper, we undertake a comprehensive survey of the prevailing forms of cloud computing, examining in detail the most pressing application challenges faced by organizations in the industry. Among these challenges, issues related to security and standardization loom large. Finally, we provide a forecast for the future trajectory of cloud computing technology, informed by our analysis of current trends and emerging innovations.
Evaluating the Impact of Risk Component in Supply Chain Management Using Intuitionistic Fuzzy Cognitive Map

The growing complexity of supply chains is attributed to a range of factors, including globalization, competitive conditions, growing relationships among supply chain partners, and evolving technology. This phenomenon is associated with increased uncertainties and new risks that adversely affect many firms. Therefore, identifying, assessing, and managing these risks is essential for establishing a profitable, competitive, and sustainable supply chain over the long term. Supply chain risk management (SCRM) has emerged as a critical area of inquiry and practice for managing these risks. The primary objective of this study is to develop a model that predicts supply chain risks. To achieve this objective, the study commences with a comprehensive literature review to identify relevant articles related to the topic of interest. Based on the document coding rules using the identified articles, variables were determined. The identified variables were subsequently transformed into linguistic variables and triangular membership functions using a fuzzy set approach. An intuitionistic fuzzy cognitive map was created using the adjacency matrix obtained from the normalization process. Furthermore, scenario analyses were conducted to identify measures. The resulting model provides a comprehensive framework for predicting supply chain risks and identifying measures to mitigate them. This study advances the state of the art in SCRM research and practice.
Study of Multiverse Optimizer Variations with Chaos Theory and Fuzzy Logic Over Benchmark Optimization

In this work, we are presenting multiple variations of the Multiverse Optimizer algorithm incorporating the use of Fuzzy inference systems and chaotic maps (FCMVO) with the purpose of analyzing their performance in a benchmark with some mathematical functions. We implement some of the most used chaotic maps over literature for metaheuristics in order to replace multiple parameters of the original algorithm, and change the original behavior, and by the other hand, we use Fuzzy Logic to adapt dynamically some of the parameters. By adding both Chaos theory and Fuzzy Logic, the resulting algorithm can have the best of both, resulting in a competitive variant. By using Chaos theory, we saw that not all the chaotic maps behave a good behavior in MVO, then we select a set of six variants that we called Elitist FCMVO. In the study, we do a brief comparison between the resulting variants with the benchmark functions, where each variant changes a set of three selected chaotic maps. The main objective is to define the best set of chaotic maps that FCMVO can used, and it could be used in other cases of study.
Acceptance of Professional Service Robots: A Cross-Cultural Study

Service robots are humanoid and non-humanoid machines that communicate and deliver services to customers of an organisation. They are Artificial intelligence (AI) enabled and display human intelligence (Wirtz et al., 2018; Blut et al., 2021). Service robots may undertake cognitive-analytical activities and emotional-social duties. Artificial Intelligence is built-in to service robots, allowing them to interact with the customer as regular hospitality services thrive on providing interpersonal interactions to create customer value. As substitutes for human employees, service robots may posit a psychological and emotional challenge to the traditional view of hospitality services, such as human frontline employees. Professional Service Robots (PRS) have proven to have the potential to drastically change the service industry. The use of PSR is lagging in an African context, necessitating more research on factors that may influence acceptance. This study aims to explore the cultural factors that influence consumers’ acceptance of PSR. The Service Robot Acceptance Model (sRAM) is adopted as a guiding framework for this study. Using an exploratory qualitative research approach data is collected using three focus groups, with 16 participants in total, using the simulation method. Interviews were also conducted with seven participants who were purposively selected based on age, gender, and race. Sexual orientation was found to have a positive influence on acceptance while beliefs and norms were barriers to acceptance, with the Ubuntu philosophy being one of the main reasons for rejection. Language appeared to have a huge role, as forwarded by the sRAM. The results suggest that acceptance of PSR is also dependent on cultural factors, however, its influence is lesser in certain types of service sectors. The research recommends that practitioners, service robot developers, and implementers should consider the culture of the consumers when implementing service robots.