Reviewerly/exHarmony huggingface dataset:
A Mapping-based Dynamic Semi-Online Task Scheduling Method for Minimizing Energy in Edge Computing

Semi-online task scheduling in the edge computing platform refers to a scheduling scenario where there exists unknown performance computing nodes in the system. Existing online and offline task scheduling methods may lead to long makespan or transmission time due to the influence of unknown nodes, and existing semi-online methods are not fully applicable to the scenario discussed in this paper, which aggravates the problem of high energy consumption in edge computing plat-form. To solve this problem, this paper proposes a Mapping-based Dynamic Semi-online Scheduling(MDSS) strategy for edge computing platform. Firstly, we consider three main factors that affect the energy consumption in edge computing platform, that is, the energy consumption of task execution, task transmission and idle state. Then an energy consumption optimization problem is established from the perspectives of the processing speed, routing delay and queuing delay of the edge nodes, which correspond to those three factors. Secondly, for the unknown node, this paper introduces a mapping mechanism, which maps the performance of unknown nodes to a certain given node. Then the mapping relations are dynamically adjusted by continuously checking the task queue lengths of the mapped two-sides. Therefore, the prior knowledge of known nodes can be fully used. Thirdly, an im-proved Power-of- D algorithm based on the mapping mechanism is proposed to solving the formulated problem. It utilizes the prior knowledge of known node performance, which reduces task processing time and transmission time, so that the energy consumption can be reduced. Finally, some experiments are conducted to evaluate MDSS. Results show that the dynamic mapping mechanism is effective in the semi-online scenario and MDSS can greatly reduce the energy consumption in the edge computing platform.
Implementasi Data Mining Untuk Klasifikasi Penyakit Liver Dengan C4.5 Adaboost

With the development of the times, it is undeniable that science and technology as well as the ease of development of the internet make it easier for people to identify liver disease, and become strong supporters of diseases with special needs. The initiative of computer experts with artificial intelligence or artificial intelligence, bioinformatics is defined as the application of computational and analytical tools to capture and view biological data. The amount of data on liver disease that continues to increase requires several methods to process and draw conclusions and information from the data, which is expected to be able to improve the quality of data or information as well as the efficiency and effectiveness of data&#x0D; processing. ultimately facilitate or assist in policy making, especially in tackling the problem of liver disease. To support this, data mining techniques can be used to find valuable information from a collection of information or historical data of the liver. Research on liver using data mining classifications has been done, both comparisons of several classifications of data mining models or improvements to data mining classifications. Research on liver has been carried out and research has been carried out. To conduct this research, it is necessary to have a study of previous related research. In order to know what methods were used, what kind of data was studied, and what kind of model was produced. The dataset used does not contain missing values, so there is no need to preprocess the data. In this study, the C4.5, Support Vector Machine (SVM) and C4.5 Adaboost classification methods were used. The results of this study indicate that the C4.5 Adaboost method is 77.12% and the sensitivity value is 76.40%, where the value is greater than the two other methods of analysis used
Plat Nomor Kendaraan dengan Convolution Neural Network

The development of Deep Learning technology is very good at detecting Objects. One of them is detection on the vehicle number plate. This method can be applied to Computer Vision to process images using DensetNet121, NasNetLarge, VGG16 and VGG19 methods. The most basic difference between Machine Learning and Deep Learning is the inclusion of a Hidden Layer and what distinguishes the Deep Learning process using neurons as a process from input, process to output. Feature extraction is done directly with the Deep Learning process. In terms of time, training models with Deep Learning are very long, when compared to Machine Learning. The dataset comes from Kaggle, then training is carried out with four Deep Learning models, resulting in a model. There are differences in conducting the training process. Before carrying out the Training process, a pre-paration process from the Image Dataset is carried out. The dataset is divided into two parts, the Training Dataset and the Testing Dataset. After the training model is completed, it is continued with the Testing process and measuring the performance of the model's accuracy. The accuracy of the four models resulting from Deep Learning training is also presented
Children learning to sketch: sketching to learn

Purpose This study aims to understand children’s sketching behavior while they engage in interest-driven design activities. Particularly, the authors examine their information sharing practices and the learning opportunities that may occur when they engage in a sketching activity. Design/methodology/approach The data collection is based on a participatory design approach, cooperative inquiry. For analysis, the authors used the ethnographic case study approach, which allowed us to consider the particularity and complexity of sketching and its affordances within each distinct design activity. Findings The authors found children share information about their expectations, experiences, beliefs and knowledge via their sketches. Additionally, through sketching activities, they were engaged in multiple learning opportunities including how to label sketches, build on ideas, sketch in collaboration and innovate on ideas. Research limitations/implications The findings demonstrate sketching can be used to gather information about the broader contexts of children’s lives which can be leveraged to identify their needs and improve the design of future technologies for children. Additionally, participating in sketching gives children opportunities to develop their sketching skills, a useful multimodal skillset for both design and personal expression. Originality/value This empirical research is original in its context of focusing on children sketching experiences in an interest-driven design environment occurring virtually in the informal setting of a library.
Assessing the Alignment between the Information Needs of Developers and the Documentation of Programming Languages: A Case Study on Rust

Programming language documentation refers to the set of technical documents that provide application developers with a description of the high-level concepts of a language (e.g., manuals, tutorials, and API references). Such documentation is essential to support application developers in effectively using a programming language. One of the challenges faced by documenters (i.e., personnel that design and produce documentation for a programming language) is to ensure that documentation has relevant information that aligns with the concrete needs of developers, defined as the missing knowledge that developers acquire via voluntary search. In this article, we present an automated approach to support documenters in evaluating the differences and similarities between the concrete information need of developers and the current state of documentation (a problem that we refer to as the topical alignment of a programming language documentation). Our approach leverages semi-supervised topic modelling that uses domain knowledge to guide the derivation of topics. We initially train a baseline topic model from a set of Rust -related Q&amp;A posts. We then use this baseline model to determine the distribution of topic probabilities of each document of the official Rust documentation. Afterwards, we assess the similarities and differences between the topics of the Q&amp;A posts and the official documentation. Our results show a relatively high level of topical alignment in Rust documentation. Still, information about specific topics is scarce in both the Q&amp;A websites and the documentation, particularly related topics with programming niches such as network, game, and database development. For other topics (e.g., related topics with language features such as structs, patterns and matchings, and foreign function interface), information is only available on Q&amp;A websites while lacking in the official documentation. Finally, we discuss implications for programming language documenters, particularly how to leverage our approach to prioritize topics that should be added to the documentation.
A dataset of word segmentation technology evaluation for minority languages (MLWS2021)

According to the morphological rules and characteristics of Mongolian, Tibetan and Uyghur vocabulary, we formulated the evaluation standards for Mongolian, Tibetan and Uyghur word segmentation suitable for computer information processing, established the word segmentation and tagging corpus of Mongolian, Tibetan and Uyghur, and formed a standard evaluation corpus (MLWS2021), so as to solve the problems of automatic word segmentation, part of speech tagging, information retrieval. MLWS2021 contains 25,000 Tibetan sentences, 65,000 Mongolian sentences and 65,000 Uighur sentences. The evaluation dataset will provide free evaluation services available to the public, gradually build an authoritative minority language evaluation platform, and promote the development of minority language information processing technology.
Analyzing Data Privacy for Edge Systems

Internet-of-Things (IoT)-based streaming applications are all around us. Currently, we are transitioning from IoT processing being performed on the cloud to the edge. While moving to the edge provides significant networking efficiency benefits, IoT edge computing creates significant data privacy concerns. We propose a methodology that can successfully privacy protect the continual data streams generated by sensors on the edge device. We implement local differential privacy on streaming data and incorporate Bayesian inference and Gaussian process to evaluate the privacy policy. We demonstrate our methodology on a real-world smart meter testbed and identify the optimal privacy protection settings.
A Fully Restored Quantum Progressive Visual Secret Sharing Scheme

Quantum progressive visual secret sharing (QPVSS) is an encryption scheme that uses quantum mechanics to share secrets. In this paper, a novel QPVSS scheme is proposed. The secret pixel is encoded as a superposition state of 2n qubits, and the qubits are distributed to n participants. Measurement operation and XOR operation are used in the decryption process. The probability of the secret pixel being restored is proportional to the number of shares. When all participants perform decryption, the probability of the secret pixels being restored is 1. The original secret image can be completely restored. In the proposed scheme, the reconstructed secret image has the same size as the original secret image. There is no pixel expansion. The scheme extends the progressive visual secret sharing (PVSS) to the quantum framework, and the experimental results verify the effectiveness and feasibility of the scheme.
Results of automated scanning electron microscope (SEM) analyses of rock and stream sediment samples from the Taurus porphyry copper deposit area, Tanacross quadrangle, eastern Alaska

First posted July 14, 2022 For additional information, contact: Director, Geology, Geophysics, and Geochemistry Science CenterU.S. Geological SurveyP.O. Box 25046, Mail Stop 973Denver, CO 80225 Numerous porphyry copper-molybdenum-gold and epithermal deposits define a belt that extends from Eastern Alaska to western Yukon, Canada. An orientation study conducted near the Taurus porphyry deposit was designed to test methods that require minimal sample collection, preparation, and analytical time to determine the viability of indicator mineral studies as a reconnaissance exploration method. Bulk stream sediments and altered and mineralized rocks were sieved to the 0.105−0.25 millimeter fraction (+140, −60 mesh) and passed over a shaking table to create a moderate to heavy mineral separate that was mounted in epoxy and subsequently analyzed using automated scanning electron microscope (SEM) techniques. Seven polished thin sections of core were also analyzed. Among the advantages of automated SEM techniques compared to visual mineral identification are that thousands of grains can be rapidly identified in each sample (about 1 hour per sample) and small quantities of indicator minerals that may be missed during traditional visual analyses can be detected. Automated SEM analyses of stream sediment and rock samples show that specific minerals (chalcopyrite, bornite, and jarosite) are indicators of potential mineralized areas. Svanbergite, an aluminum sulfate phosphate mineral, was identified in mineralized rocks and in nearly all stream sediment samples (up to 9 kilometers) downstream from the Taurus and other porphyry occurrences but not epithermal occurrences. It was not identified in areas with no known mineralization and thus it is possibly one of the best indicator minerals for porphyry copper (+/- molybdenum, gold) occurrences.
Nature-Inspired Algorithms Applications to Power System Optimization

Nature has been evolving for several hundred million years, and it has found various ingenious solutions to problem-solving and adaption to ever-changing environments.From Darwinian evolution point of view, survival of the fittest will result in the variations and success of species, which can survive and optimally adapt to environments.Nature-inspired algorithms are still at a very early stage with a relatively short history, it has come up as a new era in comparing with many traditional methods; however, natureinspire algorithms have already shown their great potential, flexibility and efficiency with ever-increasing diverse ranges of applications.Nature is the best tutor and its designs and strengths are extremely massive and strange that it gives inspiration to us to imitate nature to solve hard and complex problems in power system.Different Algorithms are presented for solving power system optimization problems.
The Relevance of Virtual-Assisted Early Childhood Education and Occupational Psychotherapy Based on Emotional Interaction

This paper presents an in-depth study and analysis of the relevance of early childhood education to occupational psychotherapy using a virtual-assisted affective interaction approach. Starting from the educational theory of interactive cognitive psychology, the theoretical basis for parent-child picture book education for interactive learning is explored, as well as the theoretical development after the introduction of AR technology. Firstly, the analysis of young children’s emotions involves massive image processing, and the use of cloud computing architecture leads to high latency, while young children’s safety is a latency-sensitive service. Secondly, face recognition accuracy based on static images is not high due to problems such as inconspicuous facial features of toddlers and low-quality kindergarten surveillance videos. In this paper, a face identity correction model based on location features is proposed and the superiority of the model is demonstrated through experiments. Finally, this paper analyzes and mines the emotional data of young children. The level of kindergarten teachers’ awareness of early childhood mental health education generally showed an upward trend as their titles rose, and there were significant differences in the seven dimensions of early childhood mental health, the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, and effects; significant differences existed between teachers of different kindergarten natures, and there were significant differences in the purpose and meaning of early childhood mental health education, implementers, targets, content, pathways, effects, and mental health education for young children. Therefore, this paper proposes a face identity correction model based on position information, which considers both the correlation between pixel values in the spatial domain and the correlation between frames in the temporal domain. This paper has developed an emotion analysis system for kindergartens and put it into use in kindergartens to meet the needs of monitoring the safety of young children and evaluating early childhood education and has received good feedback from users, demonstrating the effectiveness of the system.
Teaching audiovisual production using gamification and virtual reality

This article shows the development of a project based on a methodology focused on a processual and temporalized development of the different developments to be achieved through gamification and design of the prototype. The serious game prototype, through the use of games and gamification concepts, will positively influence the learning process, which we believe will facilitate the teaching of audiovisual production in a practical way. It begins with a mapping of the main theories of Virtual Reality and gamified scenarios; the essential objective is the use of games in education. The student selects an avatar that introduces him/her to the virtual world of a television set, where he/she selects the work to be done, the game allows him/her to choose and advance through levels until achieving an audiovisual product with the correct elements and quality corresponding to a documentary or report as selected. Advancing through all the levels will give you badges. It is scalable because it allows projecting new levels of practice that the student will be able to experience. It is also capable of applying the technique to new learning topics in any area of knowledge, allowing the student to create a practical world of his profession. The application of the methodology has also allowed us to identify the skills and competencies that students have developed in the XXI century and that tools and methodologies such as those applied allow us to strengthen.
Blockchain application in accounting and auditing: a bibliometric and systemic analysis

Blockchain technology or also called "chain of blocks" has positioned itself in the economic scenario as one of the most powerful technological innovations that allow the speeding up of transactions or accreditations that were carried out for payments and that previously took days. Currently adopting this system in accounting and auditing practices, many possibilities have been opened in terms of optimizing resources and time of its processes; additionally, the accuracy and reliability of the registered transactions, which are now externally audited, create greater confidence for investors. Therefore, the objective of this research was to analyze the existing scientific literature on blockchain through a bibliometric and systemic study in the context of accounting and auditing, using the Proknow-C methodology and considering the Scopus database to describe the bibliographic variables. of articles published regarding the blockchain. The results show a list of relevant journals and authors in the field under study. All the authors highlight the disruptive innovation of the blockchain in the transparency and quality of financial and sustainability reports in companies.
Multimedia Computer Network Education in Students’ Wisdom Course Teaching

With the development of multimedia related technologies, smart education has entered a new stage. With the support of multimedia computer network education, education is no longer traditional school education, network education can be carried out through smart devices, and knowledge innovation and dissemination can be achieved through the huge teaching resources of the multimedia and the Internet. By investigating the application of multimedia computer network education in smarter course teaching, this paper analyzes the students’ recognition of the use of multimedia tools for smarter course teaching in smarter course teaching activities, students’ participation in smarter course learning activities under multimedia computer network education, and students’ learning effects to prove that multimedia technology is conducive to the improvement of students’ learning quality.
Development of digital competencies of future teachers during organizing interuniversity events

The main task of ongoing and planned changes in education is the formation of digital competence of participants of the educational process and, first of all, of teachers. It is possible already during the period of study at university to create conditions for understanding the potential of using digital technologies in the educational process, gaining practical experience in their use in situations close to professional activities, and, as a result, the formation of digital competencies of a future teacher. An important task is to identify didactic opportunities for the development of digital competencies of future teachers while involving them in organizing interuniversity events. The article describes the model for organizing an interuniversity event, which implies four interrelated stages: goal setting, organization, activity, and evaluation. For each stage, a digital support environment is defined, as well as digital competencies those should be formed in the student. The implementation of the proposed model is discussed on the example of the project “Organization and conducting of an international computer graphics competition”. It is concluded that the involvement of students in organizing an interuniversity event allows to create conditions for the formation of such digital competencies as digital literacy, communicative literacy, digital content creation, digital security, digital competence.
QCDCL with Cube Learning or Pure Literal Elimination - What is Best?

Quantified conflict-driven clause learning (QCDCL) is one of the main approaches for solving quantified Boolean formulas (QBF). We formalise and investigate several versions of QCDCL that include cube learning and/or pure-literal elimination, and formally compare the resulting solving models via proof complexity techniques. Our results show that almost all of the QCDCL models are exponentially incomparable with respect to proof size (and hence solver running time), pointing towards different orthogonal ways how to practically implement QCDCL.