Reviewerly/exHarmony huggingface dataset:
“Predicting the Future: A Web Platform for Time Series Modeling”

This article describes a WEB platform for forecasting time series using the ARIMA and SARIMA models. These models are widely used to forecast time series through manual or automatic processes. The platform is designed to analyze meteorological data in time series. It integrates open-source languages and technologies such as Python and the Django framework for data visualization and decision support. As a case study, meteorological data provided by a center that operates in the area of irrigation, were used. Despite the work being in evaluation, the existing preliminary results point to high interest on the part of the members and clients of the center.
Automated Catalog Generation using Deep Learning

Automated catalog generation plays a crucial role in e-commerce, retail, and inventory management systems.Object detection holds immense significance within the realm of computer vision, particularly in grocery item recognition.Deep learning techniques have shown remarkable advancements in various domains, including computer vision.In this research, we propose an automated catalog generation framework utilizing deep learning methods.Specifically, we integrate RoboFlow for image annotation and YOLOv7 and object detection algorithms for dataset training and testing.We present a comprehensive methodology for dataset preparation, annotation, training, and evaluation.The proposed framework demonstrates promising results on diverse datasets, including a novel self-generated benchmark dataset.Our model achieves significant accuracy in object detection and annotation, showcasing its potential for efficient and accurate catalog generation.The scalability and adaptability of our framework open avenues for real-world applications in inventory management and ecommerce platforms..This research focuses on the application of deep learning techniques to automate the generation of product catalogs in supermarkets.The project involves annotating a diverse dataset of grocery item images collected from various sources using Roboflow.The images are then processed and trained using the YOLOv7 and object detection algorithms.The results showcase the successful implementation of Roboflow for automated image annotation, enabling accurate and efficient object detection.
The past in the present

The article tells about the history of author’s formation as the specialist on bibliography and the influence on this process from the side of many outstanding domestic lecturers and figures of the book culture.
diffReplication - An Energy-Aware Fault Tolerance Model for Silent Error Detection and Mitigation in Heterogeneous Extreme-scale Computing Environment

At extreme scale, the frequency of silent errors &amp;ndash; a class of errors that remain undetected by low-level error detection mechanisms &amp;ndash; increases significantly with the computational complexity of the application and the scale of the computing infrastructure. As hardware and software advances are made to usher in the next scientific era of computing, developing new approaches to mitigate the impact of silent errors remains a challenging problem. In this work, we propose an energy-aware fault-tolerance model, referred to diffReplication to overcome silent errors. In the proposed model, the main process is associated with one replica that executes at the same rate as the main process, and one diffReplica that is executed at a fraction of the main process&amp;#39; execution rate. If the main and its replica reach consensus at the end of a computation phase, the state of the diffReplica is updated and computation is resumed. If the synchronization attempt results in a disagreement, however, the diffReplica increases its execution speed to complete the computation and quickly reach the synchronization barrier. Assuming a single error over any given synchronization interval, a majority voting is used to reach consensus and tolerate silent errors. To further enhance its performance, diffReplication is augmented with speculative execution, whereby the main or its fast replica is selected to continue execution without waiting for the diffReplica. The selection process is based on the previous behaviour of the main and its replica. A performance analysis study is carried out to assess the performance of diffReplication, in terms of the energy saving and time-to-completion reduction achieved by the diffReplication scheme. The experiment shows that speculative execution reduces the time to completion with additional energy, and dynamic decision-making balances the energy consumption and time to completion.&amp;nbsp;
PENINGKATAN KEMAMPUAN MEMBACA DAN MENULIS MELALUI BIMBINGAN BELAJAR GRATIS BAGI SISWA KELAS 1 DAN 2 DI SD NEGERI 040457 BERASTAGI

Community service activities are one of the mandatory subjects contained in the MBKM curriculum at HKBP Nommensen University, Medan and must be taken by every student. In this activity, each participant carries out these activities in an area adjacent to the student's residence. This activity lasts for a month and is carried out after finishing school hours at each school. Free tutoring activities are carried out with the aim of teaching students to be able to overcome the problem of decreased interest in learning and the low ability of students in reading and writing due to the lengthy online learning process due to the Covid-19 pandemic. All activity participants and students carry out the learning process by applying the applicable health protocols. Our team conducts PKM activities at SD Negeri 040457 Berastagi from February 2, 2022 to February 26, 2022, every Tuesday to Saturday for one and a half hours. The learning process is carried out with guidance in small classes and using learning media such as letter lists, animal pictures, fairy tale books, etc. that can attract students' interest. The results of the activity showed an increase in students' interest in learning and reading and writing skills by up to 70%. This proves that students can be more enthusiastic about learning if there is direct assistance and interaction.
Two Dimensional Balanced Resource Utilization Using Vector Heuristics for Multi-Core Aware Virtual Machine Placement Algorithms in a Cloud Environment

Cloud users request the resources from the data center in the form of a virtual machines(VM). These virtual machines have to be created inside the physical machines(PM) of a data center. The creation of virtual machines in physical machines is a process of Virtual Machine Placement(VMP)[11]. Most existing VMP algorithms does not consider the physical processor core (pCPU) capacity of a physical machine for placing the virtual machines which leads to physical machine overload and pCPU overload[1], [2]. Also, the CPU and memory resources are not utilized in a balanced manner. This research work has proposed a Multi-Core Aware Balanced Virtual Machine Placement algorithm(MCAB-VMP) considering the pCPU capacity of a physical machine, so that the physical machine overload is reduced. Also, our algorithm (MCAB-VMP) focuses on two dimensional balanced resource utilization using vector heuristics, along with the multi-core aware technology. We used CloudSim simulation tool to simulate our proposed algorithm using GoCJ(Google Cloud Jobs) instances configurations. Our MCAB-VMP algorithm improves resource utilization and reduces physical machine overload by 2% and pCPU overload by 1%.
The Impact of The Digital Revolution on The Efficiency of Auditors in Auditing Computerized Programs

This study aimed to research the impact of the digital revolution and its various fields, secrets, and ramifications on the auditor's efficiency in auditing computerized programs that precede auditing financial statements, and to ensure their integrity to give an impartial technical opinion.To achieve the study objectives, a questionnaire comprising of 16 sections was created and distributed to two groups: auditors in Jordan and computerized program administrators.Each group was allocated 100 questionnaires, with 70 and 80 questionnaires retrieved from the first and second group respectively.Statistical analysis using the SPSS program was conducted to extract the mean and standard deviation, and Chi-square analysis was employed to obtain the study results.The results of the study indicate that there isa statistically significant positive impact of the digital revolution on the efficiency of auditors from the point of view of auditors, while there is no impact of the digital revolution on the efficiency of auditors.A discrepancy is noted in the views of both samples.Therefore, the study recommended boosting research on the efficiency of auditors in auditing information technology systems in light of the digital revolution, in addition to the need for training and qualification courses in information technology auditing.
Saudi Food Industry Value: Profile Disclosure, Profitability, and Sustainable Supply Chain Effect

The purpose of the current research is to examine the impact of industry profile, profitability, and sustainable supply chain on the value of the firm.The firm's success is directly related to the efficiency of its supply chain.This research was conducted on firms that typically trade on the Saudi Stock Exchange as belonging to the food industry.Purposive sampling was used in this study to show the sample.In addition, the data collected in this study from the annual report for 20 firms, for three years (2019)(2020)(2021).This study finds that the Profitability, and Reports of Sustainable supply chain of the firm affect the firm value, and these results are consistent with the perspective of Stakeholder and Agency Theories.However, the firm's value is unaffected by the industry profile.
A STUDY ON SUPERVISED LEARNING MODEL – K-NN CLASSIFICATION

In the world of machine learning, the k-Nearest Neighbors (kNN) algorithm has emerged as a powerful tool for classification tasks.With its simplicity and effectiveness, kNN has gained popularity across various domains.In this article, we will explore the fundamentals of kNN, its applications, and implementation.Whether you are a beginner or an experienced practitioner in machine learning, this article will provide valuable insights into the capabilities of kNN and how it can be leveraged to make accurate predictions.So, let's dive into the world of k-Nearest Neighbors and unlock its potential!
Fair Data Protection Damages: Tension between Troublemakers, Legal Techs and Real Harm

The General Data Protection Regulation (GDPR) provides for a comprehensive right to compensation for damage caused by data protection infringements. This article investigates whether the GDPR’s compensation regime attracts abuse by alleged damaged parties which have themselves caused the conditions for entitlement to compensation, whether damaged parties can expect ‘full and effective’ compensation when enforcing their claim, and how this situation is influenced by legal tech companies. It concludes that attempts to abuse the right to compensation are plausible but can neither be conclusively confirmed nor refuted from a neutral standpoint. Data subjects damaged by a data protection infringement cannot expect full and effective compensation due the difficulties in practice of proving their entitlement to compensation; also, when the claim is monetised through the services of legal tech companies, the compensation cannot be considered full. Finally, changes in the way the right to compensation is handled are proposed, in order simplify the enforcement of justified claims while making abusive claims unattractive. Keywords: GDPR| Compensation | Abuse| Enforcement| Legal Tech
Sistem Pendukung Keputusan Penentuan Siswa Berprestasi Dengan Menggunakan AHP (Studi Kasus : SMA Negeri 1 Bandar Lampung)

SMAN 1 Bandar Lampung is one of the senior high schools located on Jl. Gen. Sudirman No.41, Rw. Sea, District. Tj. Karang Tim., Bandar Lampung City. The process that has been running so far in determining the outstanding students of SMAN 1 Bandar Lampung is by looking at the daily grades at school given by the teacher. where it is deemed less effective because there is a high possibility of errors in the calculation process in determining outstanding students, to assist the school in determining outstanding students, a system is needed to support decisions using the Analytical Hierarchy Process (AHP) method in determining outstanding students so that the decision results become more effective and efficient. The purpose of the research is to create and implement a decision support system that can assist in identifying students who excel as well as build a system that determines students who excel. While the benefit of this research is a decision support system based on AHP is anticipated to make it easier to identify outstanding students.
Rancang Bangun Aplikasi Sistem Informasi Jasa Service Sepeda Motor Berbasis Android

With the increase of smartphone users and motor users, it can be designed a service provider application that is devoted to motorcyclists. With the large number of motor vehicles, there are also many service services or maintenance services and motor vehicle repairs to help motorists in getting comfortable motor vehicles by doing regular motor vehicle maintenance. Manual recording is very difficult for the workshop when it comes to providing information to customers about service schedules. In addition to difficulties from the workshop side, difficulties are experienced by the customer where in the intended workshop. The quota provided by the party has sometimes exceeded the maximum limit provided and customers must find another workshop or have to reschedule the next day.
Perancangan Sistem Informasi Geografis Pemetaan Kantor Pelayanan JNE Berbasis Android

AbstractThe development of information technology has been growing rapidly, this can be seen from the number of intelligent computers that can be used to help human movement space as well as in solving various problems. The city of Bandar Lampung at this time continues to improve and develop services in all fields, especially in the field of delivery services. To meet the needs needed in geographic information system research, the search for JNE service offices will later be able to facilitate the public in finding the location and information of the desired delivery service. After going through several stages of analysis of the geographic information system design, the mapping of the JNE service office can run according to the initial objectives of the research. This information system can make it easier for users to find JNE offices and JNE representatives, especially in the Bandar Lampung area, able to search for the closest route to the JNE office that you want to visit and make it easier to find JNE offices near the user because it is displayed in map form on google maps.
Perancangan Sistem Informasi Pengolahan Data Pelayanan Ibu Dan Anak Pada Posyandu Berbasis Web

Currently information systems are developing very quickly and rapidly, including in the health sector. One of the uses of information systems in the health sector is the Integrated Service Post (Posyandu). In general, Posyandu is a form of Community-Based Health Efforts (UKBM) which is managed from, by, for and with the community. Based on this, the researchers proposed a Mother and Child Data Processing system at Posyandu. The presence of Posyandu can indirectly provide convenience to the community, but raises new problems, namely the large amount of data on mothers and children participating in the Posyandu program, causing reports and archives of Posyandu activities to accumulate at the coordinator. In addition, problems in finding data are due to the large amount of data which causes a lack of control over children. Therefore, this research produces a web-based data processing system that can process data storage more securely, assist in data retrieval and minimize errors when inputting patient data at Posyandu. With the presence of this data processing system, it can help Posyandu cadres work more quickly and precisely in helping the community in terms of health, especially pregnant women and toddlers (children).
Dynamic Graph Construction Framework for Multimodal Named Entity Recognition in Social Media

Multimodal named entity recognition (MNER) aims to detect named entities and identify the entity types based on texts and attached images, which also generates inputs for other comprehensive tasks, such as multimodal machine translation, visual dialog, and multimodal sentiment analysis. Existing studies have limitations in text-image matching and multimodal semantic disparity reduction. For one thing, current methods fail to resolve both overall and local text-image matching issues in a self-guided way. For another, the static graphs constructed in MNER models are challenging in bridging the semantic gap between different modalities. In this work, a dynamic graph construction framework (DGCF) is proposed to solve the above-mentioned limitations. A similarity vector-based text-image matching inferring strategy is designed to obtain the overall and local matching relation between text and image while the overall matching determines the retained proportion of visual information. Then, a multimodal dynamic graph interaction module is developed. Within each layer of the module, the local matching relations and part of speech (POS)-based multihead attention are integrated to construct a dynamic cross-modal graph and a semantic graph. Lastly, a CRF layer is used to predict entity label. Extensive experiments are performed on two benchmark datasets. The experimental results reveal that our model is a competitive alternative and achieves state-of-the-art performance.
Effectiveness of Online Nutrition Education Modalities: A Narrative Review

Upon completion, participants will be more aware of the usefulness and effectiveness of online nutrition education modalities to better help patient and client populations. Massive open online courses (MOOCs) and other online modalities are a growing method of delivering evidence-based nutrition education via the internet. The purpose of this review was to evaluate the effectiveness of online nutrition education modalities for patient and client populations. A search of three databases (CINAHL, MEDLINE, and Google Scholar) was conducted in November 2022. Search keywords included: “online learning or e-learning or distance learning”, “nutrition education or nutrition knowledge or healthy eating education”, “public or community or people”, “nutrition education online and older adults”, and “effectiveness of virtual nutrition education”. MOOCs were found to be a powerful communication platform for disseminating evidence-based nutrition education to diverse populations. MOOCs and other online approaches were typically learner-centric and promoted the development of supportive learning communities. Almost all studies reported high participant satisfaction and improved dietary behaviors with online nutrition education. Additionally, online modalities had higher rates of behavior change retention compared with in-person education. This review suggests that MOOCs and other online modalities have the potential to positively influence behavior change and accessibility to nutrition education. Pursuing online approaches to nutrition education could support learning autonomy, education access, and long-term behavior change.
Blockchain Enterprise Ontologies: TOVE and DEMO

Enterprise ontology for blockchain transactions includes datalogical, infological and essential levels. OntoClean analyzes ontologies based on formal, domain-independent properties (metaproperties), being the first attempt to formalize the notion of ontological analysis for computer systems. The notions are extracted from the philosophical ontology. In the semantic web, a property is a binary relationship, with a subtle distinction between ownership and class. Thus, a metaproperty is a property of a property or a class. The design of ontology can be done when there is a basic understanding of the blockchain analysis.
Exposure notification system activity as a leading indicator for SARS-COV-2 caseload forecasting

Purpose Digital methods to augment traditional contact tracing approaches were developed and deployed globally during the COVID-19 pandemic. These “Exposure Notification (EN)” systems present new opportunities to support public health interventions. To date, there have been attempts to model the impact of such systems, yet no reports have explored the value of real-time system data for predictive epidemiological modeling. Methods We investigated the potential to short-term forecast COVID-19 caseloads using data from California’s implementation of the Google Apple Exposure Notification (GAEN) platform, branded as CA Notify. CA Notify is a digital public health intervention leveraging resident’s smartphones for anonymous EN. We extended a published statistical model that uses prior case counts to investigate the possibility of predicting short-term future case counts and then added EN activity to test for improved forecast performance. Additional predictive value was assessed by comparing the pandemic forecasting models with and without EN activity to the actual reported caseloads from 1–7 days in the future. Results Observation of time series presents noticeable evidence for temporal association of system activity and caseloads. Incorporating earlier ENs in our model improved prediction of the caseload counts. Using Bayesian inference, we found nonzero influence of EN terms with probability one. Furthermore, we found a reduction in both the mean absolute percentage error and the mean squared prediction error, the latter of at least 5% and up to 32% when using ENs over the model without. Conclusions This preliminary investigation suggests smartphone based ENs can significantly improve the accuracy of short-term forecasting. These predictive models can be readily deployed as local early warning systems to triage resources and interventions.
Construction and Intelligent Application of Map Projection Knowledge Graph

The Map Projection module is indispensable in the research process of Intelligent Transportation and Smart City related projects. Aiming at the problems of inconvenient query of Map Projection knowledge, poor intelligent selection effect and less research results of Map Projection knowledge map at present, a method for constructing a Map Projection Knowledge Graph based on domain ontology for multi-source data is proposed. This article has defined the concept of Map Projection Knowledge Graph, explained the basic process and key technologies of this method in detail, and verified the feasibility of this method in practice through experiments. This method has been proved that it is more intuitive and clear than traditional methods in visualization, and as a new method in Knowledge Query and Map Projection Intelligent Selection for this area at the same time by experiment in this article.