Reviewerly/exHarmony huggingface dataset:
Evolutionary Neural Architecture Search with Semi-supervised Accuracy Predictor

Neural architecture search (NAS) has low efficiency in evaluating a large number of candidate architectures. As an efficient evaluation method, accuracy predictor-based NAS algorithms have become popular because the performance (accuracy) can be evaluated without training the candidate architectures. However, accuracy predictors still need some evaluated architectures that are difficult to train for achieving promising performance. In order to break this bottleneck, we investigate a semi-supervised accuracy predictor-based evolutionary NAS method (MSNAS) which requires only a small number of evaluated neural architectures. The accuracy predictor obtains high prediction performance by extracting the evaluated architectures, strong regressors and truncation mechanism. To find truly high-accuracy candidate architectures more easily, the multi-objective optimization method is presented to trade-off the prediction accuracy and confidence of candidate architectures. The MSNAS variants from different strong regressors are employed to validate the competitive performance of the MSNAS on NAS-Bench 201.
Suspending Contact without Stopping Learning: Technological Response of the South African Higher Education to the COVID-19 Pandemic Challenges

The COVID-19 Pandemic is an emergency that requires higher education administrators to design responses that mitigate the challenges of saving lives, simultaneously saving their academic years through non-disruption of educational programs.This study aims to examine how one university and one TVET college in South Africa responded to the teaching and learning challenges imposed by COVID-19 during the 2021-2021 academic cycle.The study followed an exploratory sequential mixed-method approach, viewed through the lens of the Diffusion of Innovation Theory.The methodology involved gathering quantitative data using a survey questionnaire, conducting a qualitative inquiry through interviews, followed by a comparative analysis of how the university and TVET college use the technology to mitigate the COVID-19 challenges.Analysis performed using SPSS Version 27 software and thematic content analysis brought out descriptive data regarding coping mechanisms of the two institutions.Results revealed that the university was ready for a migration to online and remote pedagogy, and a TVET college was still struggling to adjust to the requirements of the digitally driven pedagogy, despite both being under the same higher education system in the country.Wide gaps in accessibility to resources, digital efficacy levels, and supportive ICT infrastructure exist between the two institutions.These findings should provide insight to higher education administrators and policy makers on the need for appropriate interventions that support effective technological adaptations and implementation of remote teaching and learning across all higher education institutions, to fit them in the digitally driven educational environments of the COVID-19 era and beyond.The study recommends digital upskilling of the TVET staff, establishment of partnerships with Internet service providers and reviewing funding models for higher education ICT infrastructure development.This study contributes to the existing literature on higher education administration by identifying challenges hampering TVET institutions' progress toward technologically driven teaching and learning environments of the 21st century and beyond.
Development and Application of Convergence Education about a Support Vector Machines for Elementary Learners

This paper proposes an artificial intelligence (AI) convergence education program that teaches the concept and principles of support vector machines (SVM).It was applied to elementary school students to check the effectiveness of education.The developed program describes the decision boundaries and margins of the SVM through the distance between the vertical and parallel lines of the elementary mathematics curriculum.The SVM education program was piloted to 18 3rd graders and 16 5th graders.Students' reactions were observed during classes, and learning output and self-evaluation results were analyzed after the classes.As a result, most students intuitively inferred the location of the trails, which represents the decision boundary.At this time, the overall activity performance accuracy of fifth-grade learners was higher, and the rate of reasonable inference on the principle of setting was also higher.The completion of the fourth-grade math curriculum also affected the students' understanding.However, in the self-evaluation of the understanding of learning content, contrary to the actual comprehension, the average value was higher in the third grade.This was because middle-grade learners had a greater tendency to feel satisfaction and achievement when they newly learned about the unfamiliar AI principle.However, higher-grade learners presented more meaningful post-class.The novelty of this paper can be found in that it attempted to educate the SVM for elementary learners.In the future, it is necessary to apply the program to a larger number of learners, supplement and develop the program and contribute to the AI principles education for elementary school students.
Classification of computer games in terms of algorithms

Depending on the type of computer games can be divided into two types: role-playing and non-role-playing. In a role-playing game on a computer, the player assumes the role of the character he controls, that is, the construction of the game is such that the player acts as a specific or imaginary computer hero. According to the degree of influence on the psychological state of a person, it is role-playing games that have the most powerful effect on the human psyche. Games of this type should predispose a person to enter the role of a computer hero and the atmosphere of the game itself due to the graphic and sound design. The primary importance of role-playing games is not based on the element of excitement, although it is still present in it. In traditional AI research, the goal is to create real intelligence, albeit by artificial means. In projects such as Kismet of the Massachusetts Institute of Technology (MIT), an attempt is being made to create an AI capable of learning and social interaction, the manifestation of emotions. At the time of writing this article, MIT is working on creating an AI that has the level of abilities of a small child, and the results of this work are very promising. From the point of view of games, genuine AI goes far beyond the requirements of an entertainment software project. In games, such power is not needed. Game AI should not be endowed with feelings and self-awareness (to be honest, and it's very good that this is so!), it does not need to learn anything outside the framework of the gameplay. The real purpose of AI in games is to simulate intelligent behavior and to provide the player with a convincing, plausible task.
Implementasi Metode Agile Development Pada Aplikasi Custom Sistem Stok Card Berbasis Website (Studi Kasus: Pt. Rosso Bianco)

System inventory goods is a system that explains how the transaction of receiving goods and transactions.theuse of goods which contains the status of the stock of goods can help increase productivity.companies.PT.Rosso Bianco always keeps records of inventory.for inventory data processing, inventory of goods is still recorded using paper media by the admin (user).For inventory data collection, it is only recorded in the general ledger, so that problems often occur when errors occur in the calculation of goods produced which are inaccurate and inefficient and errors often occur.In addition, the storage is still using Microsoft Excel, so the process of recording causes processing of goods data to take a long time.And every time there is a report on the sale of goods, it requires a long time to process, so that reports are often delayed.This system uses the PHP Native programming language and uses the MySql Database because it is more accessible to users.
Community Input and Engagement for CS2023

An ACM/IEEE-CS/AAAI task force is undertaking a review of the computer science curriculum guidelines. The task force aims to generate an initial draft, known as CS2023, by the end of 2023. It is anticipated that the resulting curriculum guidelines will impact computer science curriculum globally until at least 2034. This review process is undertaken approximately every decade and was last completed in 2013 with the resulting ACM/IEEE CS2013 document CS2013 has served the community well and planned revisions focus on the removal of topics that are no longer relevant, identification of emerging topics, or topics with increased importance or relevance. An integral part of the review process is to solicit input from a wide audience. While reviews can be made on-line, the purpose of this Birds-of-a-Feather (BoF) session is to present the proposed changes to the programming languages knowledge area and to solicit input from the attendees. There are substantial changes being proposed and timely feedback is essential for an informed process. Two of the major changes are the migration to a competency model, similar to that described in CS2020, and the replacement of Tier-1 and Tier-2 hours with a CS-core that all computer science students should know, and a KA-core which students specializing in the knowledge area should know.
Multi-Agent System Perception with Stereovision

Autonomous systems require robots, agents, that can understand and interpret their surroundings. This will allow them to enhance their decision-making processes and potentially minimize risk in downstream tasks. With developments in an individual agent's ability to perceive its surroundings, we can extend to using multiple agents that work together in a system. This multi-agent system (MAS) enables multiple robots to collaborate while exploring the same environment efficiently for full scene understanding and analysis. The deployment of a MAS also allows for the capture of additional information and the potential for exploration of larger environments. Collaborative MASs require correspondence identification that enables each agent to refer to the same objects (i.e., hazards, science targets, etc.) within their own field of view. Yet, this process of correspondence identification has challenges that arise when agents experience non-covisibility of objects, i.e., objects that are not visible in all fields of view. Our approach deploys an object detection model to capture RGB+D images on a stereovision camera and to develop a graph matching algorithm to identify the similarities between objects in different fields of view. This graph matching algorithm will better handle the challenges of correspondence identification, non-covisibility, with higher certainty than previously attempted assignment algorithms. Additionally, compared to the assignment algorithm, the graph matching approach will produce less computationally expensive results, which can be critical for resource-limited agents or tasks. This enhanced correspondence identification develops a world reference frame so that the agents within the system can perform their downstream tasks strategically and efficiently.
A survey of Few-Shot Action Recognition

In recent years, with the development of network technology, countless videos are produced every day.Many achievements have also been made in the field of action recognition in computer vision.Training action recognition models requires a large number of labeled samples, but in reality, the amount of data is scarce, and it is extremely difficult to obtain a large amount of data due to costs and other reasons.The few-shot learning aims to solve the problem of using several samples to learn new categories.This paper combs the relevant research in recent years of few-shot action recognition technology.According to the classification of training process, this paper summarizes the research progress and typical models of few-shot action recognition from the perspectives of data processing, feature embedding, feature augmentation, and metric learning; finally points out the challenges faced by current research and the future development directions.
Secure and efficient communications in quantum networks

Quantum technologies have the potential to improve in an unprecedented way the security and efficiency of communications in network infrastructures. We discuss photonic implementations, using encoding in discrete or continuous properties of light, of central quantum network protocols, enabling secret key distribution, flipping coins in the presence of bias, and fingerprinting with real data sets, featuring a provable quantum advantage in security or use of communication resources with respect to their classical counterparts. We also describe our efforts towards the miniaturization of the developed systems and their integration into telecommunication network infrastructures, including with satellite links. These advances enrich the resources and applications of the emerging quantum networks that will play a central role in the context of future global-scale quantum-safe communications.
An update on the UK Quantum Communications Hub

The Quantum Communications Hub is a major collaboration of academic and industry partners, and national laboratories. As part of the wider UK National Quantum Technologies Programme (UKNQTP), our work focuses on research and development on all aspects of quantum communications. The grand vision of our Hub during the second phase of the UKNQTP (2019-2024) is quantum communications at all distance scales. Starting with a brief summary of our work during the first phase of the UKNQTP (2014-19) for context, I’ll give an overview of our second phase work, including progress to date and objectives for the remainder.
Efficient deterministic generation of photonic graph states

Photonic graph states constitute important resources in quantum information processing. They are used in measurement-based and fusion-based quantum computing and in all-photonic repeaters for quantum networks. However, generating such highly entangled states is challenging due to the fact that photons do not interact with each other. We propose a protocol for generating arbitrary graph states using matter qubits as quantum emitters. Our approach guarantees the minimal number of emitters and provides in polynomial time the quantum circuit for the graph state generation (which includes the non-unitary process of photon emission). We find that the quantum circuit depth for general graphs also scale polynomially with system size.
A Neutrosophic Decision-Making Model for Determining Young People’s Active Engagement

Young people’s skills and attitudes must be observed, studied and evaluated in order to create appropriate models that would serve an educational purpose. In this paper, we propose a decision-making model with the aim to detect certain attitudinal and behavioral patterns of actively engaged young people. The data used in this research resulted from a questionnaire drawn up by a group of researchers from six European countries with the aim to investigate the youth’s awareness about the Sustainable Development Goals and their engagement as active agents of development and change at regional level. For the purpose of this study, we selected the regional results obtained from administering this questionnaire in Dolj County (Romania) and La Rioja (Spain). We developed a neutrosophic model that determines the Mindchanger profile of the respondents based on a minimal set of questions, which is dependent on the inclusion or exclusion of the nationality-specific traits. The resulting decisions were then compared with the respondents’ self-evaluations, yielding high precisions (more than 0.83) for all the investigated evaluation scenarios. Our results were significantly better than the ones provided by several machine learning models applied on the same set of data. A direct impact of our model is that it can be applied to questionnaires which include linguistic responses that express, among others, unclear or vague thoughts. Additionally, it offers the possibility to identify the minimal set of questions that impact the respondent’s answer choice to a target question.
Tourism Practitioner’s Language Politeness Model in Kota Tua Jakarta

This study aims to describe the politeness model of tourism practitioners in Kota Tua Jakarta. This research method uses a qualitative research design. The source of research data is the speech of tour guides and tourism officers in Kota Tua Jakarta. Data collection techniques were carried out through field observations, interviews, FGDs, and document review. The data analysis technique adopted the qualitative data analysis technique developed by Miles and Huberman, namely the interactive model of analysis. The results showed that from the speech data which amounted to 74 dialogues, it was found that the generosity maxim was 4 utterances or 6%, the tact maxim was 6 utterances or 8%, the approbation maxim was 15 utterances or 21%, the modesty maxim was 1 utterance or 1%, Obligation Speaker to Other maxim is 6 utterances or 8%, Obligation Other to Speaker maxim is 1 utterance or 1%, Agreement maxim is 13 utterances or 18%, Opinion reticence maxim is 14 utterances or 19%, Sympathy maxim is 8 utterances or 11%, The feeling ritincence maxim is 5 utterances or 7%. The dominance of the use of approbation maxim, opinion reticence maxim, and agreement maxim, it can be said that to show politeness in language, tourism practitioners apply the rules of indecision. The use of grammatical and lingual markers in the speech of tourism practitioners such as the use of the word mungkin, terima kasih, iya, bagus, boleh, seperti itu, nah jadi … (maybe, thank you, yes, good, may, like that, well so ...) become a model of politeness in the language of tourism practitioners in the Kota Tua Jakarta.
Separation of Interleaved Pulse Stream Based on Directed Acyclic Graphs

In pulse deinterleaving, the search procedure may fail because of missing pulses and pulse repetition interval (PRI) jitter. This study proposes a search method based on the directed acyclic graph (DAG). In this graph, pulses are vertices and the extracted pulse pairs are arcs. The greater the length of the “path”, the higher is the confidence level of the corresponding pulse sequence. Therefore, this problem is transformed into a problem associated with the extraction of the longest “path” from DAG. Since pulse segments have more confidence than individual pulses, the proposed method may have important implications and broad application prospects.
APPLICATION OF NAIVE BAYES ALGORITHM IN EDUCATIONAL GAMES LEARN TO WRITE AKSARA BALI

Balinese script is a script from the Balinese area that was commonly used by people in ancient times to describe a word, there are many ways to keep this script sustainable and not extinct. One of them is by making a Balinese character recognition game. Which in this application the user will provide input in the form of Balinese characters written on the application, which then the input will go through a preprocessing process and then proceed with diagonal feature extraction, then the results of the feature extraction will go through a classification process using the Naïve Bayes method. The result is a web-based application that can recognize Balinese script writing using the Naïve Bayes classification method with an accuracy rate of 70.3% and get a good response from every respondent who has tested the application.&#x0D; Keyword : Balinese Scription, Diagonal Feature Extraction, Naïve Bayes.
Design of Multimedia Courseware Synchronous Display System for Distance Teaching

In view of the poor effect of courseware display in the current multimedia courseware display system, a multimedia courseware synchronous display system for distance teaching is designed. The component library is constructed by SQL Server 2000, and the navigation interface based on distance teaching is designed to reflect the friendliness of multimedia interactive system. Use the visual card editor to present the visual integration environment. The edited metadata file is transmitted to the multimedia of distance teaching through the editing subsystem. The multi-level virtual memory is used to control the remote access process through the unique identification of multimedia files. Calculate the maximum and minimum values in the information base, and judge whether the information is abnormal point information based on this. Use the information extraction method to edit the distance teaching courseware. Design the sending and receiving process of multimedia courseware information for complete transmission of information. Through the synchronous display function of courseware, it can be broadcast on demand by students. The experimental results show that the number of downloads of relevant theoretical articles, teaching courseware downloads and relevant website clicks of the system users are within the range of actual downloads, with an error of 0, and has a good display effect.
Research on Named Entity Recognition Method Based on Language Pre-Training Model

Aiming at the problem that the existing named entity recognition models have insufficient ability to recognize common unknown words in data, this paper proposes a text vectorization representation method based on language pre-training model.The program can't understand the text directly, and it can only be understood by the program after the text is converted into a numerical value.Firstly, this paper introduces the methods of word vector representation, including discrete representation and distributed representation.The traditional word vector representation method can't deal with the problem of polysemy and can't fully express semantic features.Aiming at the defects of word vector method, this paper proposes a text vectorization method based on language pre-training model.The idea of fine-tune is introduced, and the pre-training model, which completed training on massive data sets, is transferred to the People's Daily data set, and the parameters are optimized.Finally, this paper designs a comparative experiment on the People's Daily data set, compares it with the traditional word embedding methods using CBOW, Skip-gram and GloVe, analyzes the results, and verifies the effectiveness of the proposed method.