Reviewerly/exHarmony huggingface dataset:
Evaluating Efficient Performance Estimators of Neural Architectures

Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS). To reduce the architecture training costs in NAS, one-shot estimators (OSEs) amortize the architecture training costs by sharing the parameters of one "supernet" between all architectures. Recently, zero-shot estimators (ZSEs) that involve no training are proposed to further reduce the architecture evaluation cost. Despite the high efficiency of these estimators, the quality of such estimations has not been thoroughly studied. In this paper, we conduct an extensive and organized assessment of OSEs and ZSEs on five NAS benchmarks: NAS-Bench-101/201/301, and NDS ResNet/ResNeXt-A. Specifically, we employ a set of NAS-oriented criteria to study the behavior of OSEs and ZSEs and reveal that they have certain biases and variances. After analyzing how and why the OSE estimations are unsatisfying, we explore how to mitigate the correlation gap of OSEs from several perspectives. Through our analysis, we give out suggestions for future application and development of efficient architecture performance estimators. Furthermore, the analysis framework proposed in our work could be utilized in future research to give a more comprehensive understanding of newly designed architecture performance estimators. All codes are available at https://github.com/walkerning/aw_nas.
Invariants for EA- and CCZ-equivalence of APN and AB functions

Abstract An ( n , m )-function is a mapping from ${\mathbb {F}_{2}^{n}}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msubsup></mml:math> to ${\mathbb {F}_{2}^{m}}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msubsup></mml:math> . Such functions have numerous applications across mathematics and computer science, and in particular are used as building blocks of block ciphers in symmetric cryptography. The classes of APN and AB functions have been identified as cryptographically optimal with respect to the resistance against two of the most powerful known cryptanalytic attacks, namely differential and linear cryptanalysis. The classes of APN and AB functions are directly related to optimal objects in many other branches of mathematics, and have been a subject of intense study since at least the early 90’s. Finding new constructions of these functions is hard; one of the most significant practical issues is that any tentatively new function must be proven inequivalent to all the known ones. Testing equivalence can be significantly simplified by computing invariants, i.e. properties that are preserved by the respective equivalence relation. In this paper, we survey the known invariants for CCZ- and EA-equivalence, with a particular focus on their utility in distinguishing between inequivalent instances of APN and AB functions. We evaluate each invariant with respect to how easy it is to implement in practice, how efficiently it can be calculated on a computer, and how well it can distinguish between distinct EA- and CCZ-equivalence classes.
Deplatforming, demotion and folk theories of Big Tech persecution

This article examines the moderation of conspiracy narratives surrounding COVID-19 through digital methods analysis of deplatformed or demoted videos. Building upon the literature on moderation, it performs a comparison of the types of content moderated by YouTube during the early stages of the pandemic. It seeks to determine the extent to which YouTube's own moderation actions are brought in as part of the conspiratorial narratives surrounding COVID-19, while investigating how it is that moderation becomes entangled with questions of truth and visibility.
Effectiveness Of Quizizz in Interactive Teaching and Learning Malay Grammar

Abstract: For teaching and learning Malay language grammar, there are many types of online assessment tools and applications have been developed in Malaysia. One such application is Quizizz, a game-based online quiz that facilitates teaching and learning. The purpose of this research is to measure the effectiveness of Quizizz application to teach and learn Malay grammar. We have used descriptive questionnaire research design, and chosen 130 students from Tamil schools by following the simple random sampling strategy. We have explored the effectiveness of Quizizz application as a formative quizzing tool for teaching and learning Malay grammar in year-four Tamil school students from 15 Tamil schools in Manjung district. The results showed that it was imperative to use Quizizz as a formative assessment tool to learn and teach Malay grammar to Tamil students. Students also responded positively to the use of Quizizz in the classroom through survey questionnaire results. At the end, we conclude that it is important for teachers to consider using fun and entertainment technology quizzing tools to teach Malay grammar.&#x0D; &#x0D; Keywords: Malay grammar, Quizizz, Self-assessment, Teaching and learning
Development of the external restructuring method to increase the efficiency of information resource data encoding

The existing methods of data restructuring, implemented in modern algorithms for coding data of information resources, are investigated. The problematic aspects of using methods of external data restructuring are analyzed from the standpoint of ensuring the appropriate level of reliability. The main ones among them are the possibility of losing key information during the reconstruction of the original message, which can lead to the adoption of erroneous and untimely decisions by the relevant security sector authorities to overcome crisis situations. Requirements for information resources used in the interests of security sector bodies in the context of the need for a prompt response to overcoming crisis situations are being formed. In order to increase the efficiency of data coding of an information resource from the position of a compact representation in conditions of ensuring an appropriate level of quality, a method of external restructuring is being developed, which additionally allows to eliminate psycho-visual, statistical and structural redundancy of the message. External restructuring of data means the formation of a new message alphabet (color palette for an image). A new approach to restructuring these information resources is proposed, the essence of which is to determine the significance of individual elements in the initial message by a quantitative indicator, and then adjust the power of the original message, which allows creating more favorable conditions for further coding.
Linear Regression Analysis and SVR in Predicting Motor Vehicle Theft

Computer-based systems that apply machine learning methods can help analyze crime patterns and predict crime rates. Machine learning methods can function to analyze data automatically and can display visual analysis results. We propose analyzing the Linear Regression algorithm and SVM Regression to predict motor vehicle theft in the East Java Regional Police. The goal is to create a motor vehicle theft prediction system to assist the police in monitoring regional security and reducing crime. Initial data obtained from the East Java Regional Police consisted of the police column, date, month, year, city, location of the theft, mode, number of wheels, brand, and thefts. The data consists of 7264 lines; however, the data is processed first before the prediction is carried out. The processing result dataset used is 60 lines, with the month's input variables, the location of the theft, and the output variable, the number of thefts. The total data of 60 lines is divided into 52 training data and six testing data. The method of Linear Regression has MAE 34.26. The SVR method has MAE 134.7. This study concludes that Linear Regression has a more petite MAE, so it is the best.
The cost of cybercrime in the US healthcare sector

The US healthcare system is burdened with the highest costs in the world, and they are still rising as a result of the system itself, its own inefficiencies and government politics. However, there is yet another factor coming more and more into play, adding additional costs and risks to stakeholders in the healthcare system, affecting patients, employees, governments and the doctors themselves. While digitisation and technology provide vast benefits for the quality and efficiency of the work of medical professionals and care of patients, cybercrime in the healthcare sector has risen as well.
The effect of the media comic education on science learning outcome

The purpose of this study is to determine the effect of implementing educational comic media on student learning outcomes for science content of fifth-grade students at SDN 2 Pengenjek. This type of research is an experimental research design with a quasi-experimental type of non-equivalent control group design. The data collection technique used is an observation of the learning implementation applying educational comic media and tests to collect data on learning outcomes using multiple-choice tests. The research population was 44 respondents, namely the fifth-grade students of SDN 2 Pengenjek, determined by the saturated sampling technique. The data were analyzed using descriptive statistics and inferential statistical analysis (t-test). The results showed the average value in the experimental class was 69.09 while the class was 44.85. The results of the t-test analysis on studentsÃ¢â¬â¢ science learning outcomes obtained a significance value of 0.000 at a significance level of 5%. Based on these results, it can be concluded that there is an impact of using educational comic media on the learning outcomes of fifth-grade students at SDN 2 Pengenjek.
Gaze-driven placement of items for proactive visual exploration

Recent advances in digital signage technology have improved the ability to visually select specific items within a group. Although this is due to the ability to dynamically update the display of items, the corresponding layout schemes remain a subject of research. This paper explores the sophisticated layout of items by respecting the underlying context of searching for favorite items. Our study begins by formulating the static placement of items as an optimization problem that incorporates aesthetic layout criteria as constraints. This is further extended to accommodate the dynamic placement of items for more proactive visual exploration based on the ongoing search context. Our animated layout is driven by analyzing the distribution of eye gaze through an eye-tracking device, by which we infer how the most attractive items lead to the finally wanted ones. We create a planar layout of items as a context map to establish association rules to dynamically replace existing items with new ones. For this purpose, we extract the set of important topics from a set of annotated texts associated with the items using matrix factorization. We also conduct user studies to evaluate the validity of the design criteria incorporated into both static and dynamic placement of items. After discussing the pros and cons of the proposed approach and possible themes for future research, we conclude this paper.
Multimodal Art Pose Recognition and Interaction With Human Intelligence Enhancement

This paper provides an in-depth study and analysis of human artistic poses through intelligently enhanced multimodal artistic pose recognition. A complementary network model architecture of multimodal information based on motion energy proposed. The network exploits both the rich information of appearance features provided by RGB data and the depth information provided by depth data as well as the characteristics of robustness to luminance and observation angle. The multimodal fusion is accomplished by the complementary information characteristics of the two modalities. Moreover, to better model the long-range temporal structure while considering action classes with sub-action sharing phenomena, an energy-guided video segmentation method is employed. And in the feature fusion stage, a cross-modal cross-fusion approach is proposed, which enables the convolutional network to share local features of two modalities not only in the shallow layer but also to obtain the fusion of global features in the deep convolutional layer by connecting the feature maps of multiple convolutional layers. Firstly, the Kinect camera is used to acquire the color image data of the human body, the depth image data, and the 3D coordinate data of the skeletal points using the Open pose open-source framework. Then, the action automatically extracted from keyframes based on the distance between the hand and the head, and the relative distance features are extracted from the keyframes to describe the action, the local occupancy pattern features and HSV color space features are extracted to describe the object, and finally, the feature fusion is performed and the complex action recognition task is completed. To solve the consistency problem of virtual-reality fusion, the mapping relationship between hand joint point coordinates and the virtual scene is determined in the augmented reality scene, and the coordinate consistency model of natural hand and virtual model is established; finally, the real-time interaction between hand gesture and virtual model is realized, and the average correct rate of its hand gesture reaches 99.04%, which improves the robustness and real-time interaction of hand gesture recognition.
Highly Efficient Parts of Speech Tagging in Low Resource Languages with Improved Hidden Markov Model and Deep Learning

Over the years, many different algorithms are proposed to improve the accuracy of the automatic parts of speech tagging.High accuracy of parts of speech tagging is very important for any NLP application.Powerful models like The Hidden Markov Model (HMM), used for this purpose require a huge amount of training data and are also less accurate to detect unknown (untrained) words.Most of the languages in this world lack enough resources in the computable form to be used during training such models.NLP applications for such languages also encounter many unknown words during execution.This results in a low accuracy rate.Improving accuracy for such lowresource languages is an open problem.In this paper, one stochastic method and a deep learning model are proposed to improve accuracy for such languages.The proposed languageindependent methods improve unknown word accuracy and overall accuracy with a low amount of training data.At first, bigrams and trigrams of characters that are already part of training samples are used to calculate the maximum likelihood for tagging unknown words using the Viterbi algorithm and HMM.With training datasets below the size of 10K, an improvement of 12% to 14% accuracy has been achieved.Next, a deep neural network model is also proposed to work with a very low amount of training data.It is based on word level, character level, character bigram level, and character trigram level representations to perform parts of speech tagging with less amount of available training data.The model improves the overall accuracy of the tagger along with improving accuracy for unknown words.Results for "English" and a low resource Indian Language "Assamese" are discussed in detail.Performance is better than many state-of-the-art techniques for low resource language.The method is generic and can be used with any language with very less amount of training data.
Applying the Cloud Intelligent Classroom to the Music Curriculum Design of the Mental Health Education

The cloud intelligent classroom, supported by modern technologies, is the main trend of curriculum design in the future. The purpose of this study is to explore the promotion and integration between digital technology and the curriculum design of mental health education in colleges and universities and realize their real value. First, the overall idea and practical value of the study are clarified after the relevant literature is reviewed. Second, the setting, the teaching methods, and the ideas of the cloud classrooms based on digital technology are elaborated in detail. Then, the final effect of mental health education in cloud intelligent classrooms is demonstrated and summarized after the teaching practice, a questionnaire survey, and the expert assessment. Finally, the research conclusions are drawn and the suggestions for constructing the cloud intelligent classrooms of mental health education are proposed based on the practice and surveys. The research is based on the reality of mental health education in colleges and universities, rational thinking, and action. While updating the means and methods of the curriculum design of the mental health education in the high school, it expands the connotation of cloud intelligent classroom and pursues the unity of “form” and “content.” The cloud intelligent classroom helps to improve the teaching quality of mental health education for the music majors in colleges and universities in the short term. Cloud intelligent classrooms can also help to achieve the curriculum design and teaching objectives.
Natural Image Mosaicing based on Redundant Keypoint Elimination Method in SIFT algorithm and Adaptive RANSAC method

Natural Image Mosaicing based on Redundant Keypoint Elimination Method in SIFT algorithm and Adaptive RANSAC method
Study on Blockchain Scalability Solutions

The adoption of blockchain and Distributed Ledger Technology is continuously increasing and offering solutions to various application areas. In the last few decades, much more developments in various features are seen to improve the performance, but still scalability is becoming the bottleneck for the performance enhancement. The scalability problem further leads to severe delays and high costs in a bitcoin network. Nowadays, the real-time transactions in cryptocurrencies need to be scaled up from seven transactions per second to thousands of transactions per second to handle real-life problems in the field of visa, healthcare, flights, etc. In this paper, a detailed study on the scalability issues, Proof of Work, and Practical Byzantine Fault Tolerant in blockchain was given. It also explored and analyzed the recent promising research areas in blockchain scalability such as net neutrality, sharding, side-chain, and off-chain scaling through blockchain distributed networks along with their effects.
Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section

Currently the hospital is a place that is very vulnerable to the transmission of Covid-19, so giving birth in a hospital is very risky. In addition, the hospital currently only accepts cesarean deliveries, while mothers who can give birth vaginally are recommended to give birth in a midwife because the chances of being exposed to Covid-19 are much lower. In general, this study aims to examine the performance of the LDA-SVM method in predicting whether a prospective mother needs to undergo a C-section or simply give birth normally. The aims of this study are: 1) to determine the best parameters for building the detection model; 2) to determine the best accuracy from the model; 3) to compare the accuracies with the other methods. The data used in this study is the dataset of caesarian section. This data consists of the results of 80 pregnant women following C-section with the most important characteristics of labor problems in the clinical field. Based on the results of the experiments that have been carried out, several parameter values that provide the best results for building the detection model are obtained, namely σ (sigma) –5.9 for 70 % training data, σ=4, –6.1 and ‑6.6 for 80 % training data and σ=4 and 16 for 90 % training data. Besides, the results obtained show that the LDA-SVM method is able to classify the C-section method properly with an accuracy of up to 100 %. This research is also able to surpass the methods in previous studies. The results show that LDA-SVM for this case study generates an accuracy of 100.00 %. This method has great potential to be used by doctors used as an early detection to determine whether a mother needs to go through a C-section or simply give birth vaginally. So that mothers can prevent the transmission of Covid-19 in the hospital
Improving and Simplifying Pattern Exploiting Training

Recently, pre-trained language models (LMs) have achieved strong performance when fine-tuned on difficult benchmarks like SuperGLUE. However, performance can suffer when there are very few labeled examples available for fine-tuning. Pattern Exploiting Training (PET) is a recent approach that leverages patterns for few-shot learning. However, PET uses task-specific unlabeled data. In this paper, we focus on few-shot learning without any unlabeled data and introduce ADAPET, which modifies PET's objective to provide denser supervision during fine-tuning. As a result, ADAPET outperforms PET on SuperGLUE without any task-specific unlabeled data. Our code can be found at https://github.com/rrmenon10/ADAPET.
Augmented and Mixed Reality in Language Learning

Abstract The use of Extended Reality technologies in education, and especially in language learning, has attracted the interest of language experts for the last 15 years. However, the recent technological progress as well as the simultaneous dramatic reduction of the cost of the necessary hardware has led to an impressive growth of the XR market, creating, thus, new perspectives concerning the adoption of XR technologies in education. The educational XR market is also growing very fast, not only thanks to the offer of innovative applications, but also due to technological developments in network technologies. Advances in wireless and cellular networks can make XR experiences more immersive and more accessible to local and remote users. This paper aims to present the current developments in the field of utilization of Augmented (AR) and Mixed Reality (MR) technologies in language education and to explore their future perspectives. Towards this end, AR/MR technologies, the theoretical bases of their use in language education, as well as the available for each technology hardware and software solutions are presented in more detail. Examples of AR/MR technologies in language learning applications, as well as the conclusions drawn from the literature review concerning the benefits and limitations AR/MR applications in language learning will also be presented. Finally, market data and future research directions will be discussed, in order to identify the perspectives of these technologies in language learning.