Reviewerly/exHarmony huggingface dataset:
Methodical Work as a Means of Developing Professional Reflection of Teachers

The article highlights the analysis of theoretical and methodological work on methodological work as a result of the authors' reflection. The main aspects of methodological work are investigated. In the context of researching the problem of professional reflection of a college teacher, we consider methodical work as a means of instrumental and technological support of the process of teaching academic disciplines. Methodical work has a potentially reflective character, during which the college teacher carries out self-analysis of his own attitude to professional activity, assimilates its means and methods. The analysis of experience of methodical work of pedagogical colleges has revealed, that its purpose is mainly identified for the purpose of functioning of methodical offices of these establishments and professional activity of their heads. Comprehension of the information received allowed us to conclude: if development of professional reflection of teachers of colleges is not specifically and purposefully carried out in the process of methodical activity, then spontaneous formation of the main components of professional reflection (motivational, cognitive, activity, personal) is ineffective and slow. Consequently, this process should be guided and improved. The method of analysis and synthesis, exploratory and explanatory methods are applied for the purpose of effectiveness of the study.
Assessing Effectiveness of Animated Instructional Media on Academic Performance and Retention of Genetics Concepts

The present study was carried out to assess the effectiveness of animated instructional media on academic performance and retention of genetic concepts among senior secondary school on biology students in Sokoto State. Adopted through Quasi Experimental design involving pre-test, post-test and post-posttest on two equivalent groups as experimental and control, the population on the study was 10148 SS III Biology students. In this regard, four intact classes were chosen with sample size of 238 SSIII students. Genetic Concept Performance and Retention Test (GCPRT) was the instrument used for data collection. Animated Instruction Media was used as instructional material to teach the experimental group while demonstration method was used to teach the control group. Independent sample t-test was used to test the four (4) hypotheses formulated in the study. The esults obtained in this line of research revealed that there was a significant difference in the academic performance and retention ability between students taught using Animated Instructional Material and those taught using demonstration method. It was therefore recommended for other senior secondary school teachers to harness the considerable benefits of technology, particularly animations, in teaching genetics and other related concepts.Keywords: animated, media, academic performance, retention.
Education for Sustainable Development in Training of Future Biology Teachers for Research Activity: An Applied Aspect

The article presents the experience of creating an innovative format of extracurricular training of applicants as a means of implementation of education for sustainable development – the experimental group “Cytoecologist”. The functioning of this format is based on the implementation of selected principles of STEM-education and open education of applicants. To implement these principles in the training of specialists, the group leader uses a number of formats of students’ activity that are not inherent in traditional education. Among them are dynamic temporary microgroups for experimental search; format “information activities”, Internet surfing, WIKI format, educational coworking or collaboration in space. The pedagogical experiment consisted of two parts. The first part is to find out the state of implementation of research skills by biology teachers in practice, which included determination of the level of their self-realization and interest in the organization of research activities of students. The second part is to find out the effectiveness of the presented experience of creation of a scientific student group “Cytoecologist”, within which the implementation of education for sustainable development by means of research activities of future biology teachers is carried out. A survey of graduates of the group shows that the basic principles of the group provide the formation of a stereotype of a specialist-researcher, and the vast majority of respondents (95%) use research skills in their professional activities, one of the directions of which is education for sustainable development. The experience of creating a research group of students presented in the article is the result of many years of scientific research of authors (teachers-practitioners of leading universities of Ukraine). This acquisition is original and is being published for the first time, and the obtained positive results give grounds to recommend it for implementation in the practice of training biology teachers in Western Europe.
A New Micro-Studio Mode in Vocational Skills Teaching

The methods currently applied in engineering vocational skills teaching are mostly boring for students and even disconnected from the development of enterprises. It is often difficult for students to truly master vocational skills under the traditional teaching mode. Firstly, the concept of agile development mode was introduced to put forward a new studio-based microteaching method according to the characteristics of engineering colleges and the requirements of vocational skills teaching. Under the support of informatization, the procedure of implementing new vocational skills teaching was designed as “project selecting—plan making—activity exploration—achievement exchange—activity evaluation”. Then, the information entropy evaluation method was used to divide the new teaching strategy into four steps: determining the classification of groups, designing the chain mechanism of evaluation, judging the consistency of evaluation results, and identifying the chain relationship and the final ranking. The results show that this new teaching mode can improve students’ motivation of learning vocational skills and promote their mastery of vocational skills. Therefore, it is beneficial to popularize this teaching reform idea in the cultivation of vocational skills among students in engineering colleges.
Multiple Levels of Heuristic Reasoning Processes in Scientific Model Construction

Science historians have recognized the importance of heuristic reasoning strategies for constructing theories, but their extent and degree of organization are still poorly understood. This paper first consolidates a set of important heuristic strategies for constructing scientific models from three books, including studies in the history of genetics and electromagnetism, and an expert think-aloud study in the field of mechanics. The books focus on qualitative reasoning strategies (processes) involved in creative model construction, scientific breakthroughs, and conceptual change. Twenty four processes are examined, most of which are field-general, but all are heuristic in not being guaranteed to work. An organizing framework is then proposed as a four-level hierarchy of nested reasoning processes and subprocesses at different size and time scales, including: Level (L4) Several longer-time-scale Major Modeling Modes , such as Model Evolution and Model Competition; the former mode utilizes: (L3) Modeling Cycle Phases of Model Generation, Evaluation, and Modification under Constraints ; which can utilize: (L2) Thirteen Tactical Heuristic Processes , e.g., Analogy, Infer new model feature (e.g., by running the model), etc.; many of which selectively utilize: (L1) Grounded Imagistic Processes , namely Mental Simulations and Structural Transformations. Incomplete serial ordering in the framework gives it an intermediate degree of organization that is neither anarchistic nor fully algorithmic. Its organizational structure is hypothesized to promote a difficult balance between divergent and convergent processes as it alternates between them in modeling cycles with increasingly constrained modifications. Videotaped think-aloud protocols that include depictive gestures and other imagery indicators indicate that the processes in L1 above can be imagistic. From neurological evidence that imagery uses many of the same brain regions as actual perception and action, it is argued that these expert reasoning processes are grounded in the sense of utilizing the perceptual and motor systems, and interconnections to and possible benefits for reasoning processes at higher levels are examined. The discussion examines whether this grounding and the various forms of organization in the framework may begin to explain how processes that are only sometimes useful and not guaranteed to work can combine successfully to achieve innovative scientific model construction.
Clustering- and Transformer-Based Networks for the Style Analysis of Logo Images

In the design field, designers need to investigate and collect logo materials before designing logos and search a large number of design materials on well-known logo websites to find logos with similar styles as reference images. However, manual work is time-consuming and labor-intensive. To solve this problem, we propose a clustering method that uses K-Means clustering and visual transformer model to group the styles of the logo database. Specifically, we use the visual transformer model as a feature extractor to convert logo images into feature vectors and perform K-Means clustering, use the clustering results as pseudo-labels to further train the feature extractor, and continue to iterate the above process to finally obtain reliable clustering results. We validate our approach by creating the logo image dataset JN Logo, a proposed database for image quality and style attributes, containing 14922 logo design images. Our proposed deep transformer-based cluster (DTCluster) automatic style grouping method is used in JN Logo; the DBI reaches 0.904, and the DI reaches 0.189, which are better than those of other K-Means clustering methods and other clustering algorithms. We perform a subjective analysis of five features of the clustering results to obtain a semantic description of the clusters. Finally, we provide six styles and five semantic descriptions for the logo database.
Design of Cross-Source Education Information Classification Model Based on Cloud Computing Technology

Traditional classification algorithms can be well applied to limited data sets, but the application effect of uncertain data sets was poor. Therefore, this paper proposed a cross-source education information classification model based on cloud computing technology, which aimed to provide support for education information services in the cloud computing environment. Firstly, based on the analysis of the structure and function of the cloud computing platform, this paper expounded the cloud computing service mode and its deployment mode and gave a multisource information processing method based on the cloud computing center combined with the characteristics of information distribution in the cloud computing environment. Secondly, by analyzing the types of educational information resources, this paper summarized the feature extraction of educational information using data mining technology and gave the classification method of educational information based on text features. Finally, a cross-source education information classification model in the cloud computing environment was designed. The experimental comparison showed that the method proposed in this paper can effectively classify the multisource education information under the cloud computing platform. Compared with other traditional classification models, this model not only had higher classification accuracy but also can achieve better classification efficiency. The classification model proposed in this paper can provide a reference for the classification of other information resources in the cloud computing environment.
Extracting the Secrets of OpenSSL with RAMBleed

Concomitant with the increasing density of semiconductors, various attacks that threaten the integrity and security of dynamic random access memory (DRAM) have been devised. Among these, a side-channel attack called RAMBleed is a prolific one that utilizes a general user-level account without special rights to read secret information. Studies have reported that it can be used to obtain OpenSSH secret keys. However, a technique for deriving the Rivest-Shamir-Adleman (RSA) secret keys used in OpenSSL under realistic parameters and environments has not been reported. We propose a method that uses RAMBleed to obtain OpenSSL secret keys and demonstrate its efficacy using the example of an Apache server. The proposed method exploits the fact that, in the operation of an Apache server that uses OpenSSL, the RSA private keys are deployed on DRAM at a set time. Although the result of reading this secret information contains a few errors, error-free secret information is obtainable when it is used with RSA cryptanalysis techniques. We performed a series of attacks incorporating RAMBleed and eventually retrieved the OpenSSL RSA private key, indicating that secret information is obtainable with high probability. The proposed method can easily and externally be executed without administrator privileges on a server using DRAM that is vulnerable to RAMBleed, showing that RAMBleed is also a major threat to OpenSSL.
Pengembangan Media Interaktif Berbasis Android pada Mata Pelajaran Ekonomi

This research was conducted because teaching aids in the form of media used by teachers during learning activities were still relatively few. The purpose of this research is to assess the level of feasibility, effectiveness (learning outcomes) and practicality (response). In this development research, the test subjects were 20 students of class X IPS SMAN 1 Taman. The results of the research on the development of android-based interactive media were declared feasible with an average score of 79.9%, the effectiveness level of interactive media obtained an N-Gain score of 0.724 with a very effective category and a practicality level of 85% with very practical criteria.
Basic Theory and Practice Teaching Method Based on the Cerebellar Model Articulation Controller Learning Algorithm

With the continuous in-depth development of science and technology in my country, the field of artificial intelligence has also made considerable progress. For example, the comprehensiveness and operability of technology have been enhanced. Artificial intelligence, as a comprehensive and intersecting research field in the fields of computer science, information science, and mathematics, has also made a certain contribution to the progress of international students in colleges and universities. This article is aimed at studying the problems based on basic theories and time teaching methods, mainly through a comprehensive data analysis of artificial intelligence technology and fusion differential evolution algorithm, and then improving the learning ability of students and comprehensive quality. This paper proposes a swarm intelligence algorithm, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means clustering algorithm, and vector machine algorithm and summarizes the process and application fields of the swarm intelligence algorithm. In addition, the CMAC learning algorithm is proposed, which shows that robots under the background of industrial intelligence and machine learning can organize themselves to form groups to perform complex tasks. The experimental results of this paper show that the communication range is between 0 and 1, which can enable more robots to communicate in order to improve the individual’s social learning opportunities, thereby enhancing the individual’s learning ability to improve the performance of learning behavior and improving the efficiency of practical teaching and research, making the efficiency reach 0.9.
A Data Cartography based MixUp for Pre-trained Language Models

MixUp is a data augmentation strategy where additional samples are generated during training by combining random pairs of training samples and their labels.However, selecting random pairs is not potentially an optimal choice.In this work, we propose TD-MixUp, a novel MixUp strategy that leverages Training Dynamics and allows more informative samples to be combined for generating new data samples.Our proposed TD-MixUp first measures confidence, variability, (Swayamdipta et al., 2020), and Area Under the Margin (AUM) (Pleiss et al., 2020) to identify the characteristics of training samples (e.g., as easy-to-learn or ambiguous samples), and then interpolates these characterized samples.We empirically validate that our method not only achieves competitive performance using a smaller subset of the training data compared with strong baselines, but also yields lower expected calibration error on the pre-trained language model, BERT, on both in-domain and out-of-domain settings in a wide range of NLP tasks.We publicly release our code. 1
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations

Adversarial perturbations are critical for certifying the robustness of deep learning models. A ``universal adversarial perturbation'' (UAP) can simultaneously attack multiple images, and thus offers a more unified threat model, obviating an image-wise attack algorithm. However, the existing UAP generator is underdeveloped when images are drawn from different image sources (e.g., with different image resolutions). Towards an authentic universality across image sources, we take a novel view of UAP generation as a customized instance of ``few-shot learning'', which leverages bilevel optimization and learning-to-optimize (L2O) techniques for UAP generation with improved attack success rate (ASR). We begin by considering the popular model agnostic meta-learning (MAML) framework to meta-learn a UAP generator. However, we see that the MAML framework does not directly offer the universal attack across image sources, requiring us to integrate it with another meta-learning framework of L2O. The resulting scheme for meta-learning a UAP generator (i) has better performance (50% higher ASR) than baselines such as Projected Gradient Descent, (ii) has better performance (37% faster) than the vanilla L2O and MAML frameworks (when applicable), and (iii) is able to simultaneously handle UAP generation for different victim models and data sources.
Classifying Emotional traits from Speech file using Machine Learning

With the proliferated usage of speech as a voice command over various devices in existing times, it offers hands-free as well as comfortable experience for operating the current trends of devices. However, the devices are operated just on the basis of recognition of the speech commands and not its context. It is now well known that context factor plays a contributory role in mechanism an artificial intelligence in such devices where a comprehensive analytical modeling can be carried out. In the direction of extraction contextual information from speech, it is first necessary to recognize the speech in it logical form which can be disrupted because of various external causes. Apart from this, even if the recognition is carried out well than the next challenging part will be to extract the emotional factor within the speech. The existing review shows that research-based approaches are not sufficient enough to extract meaning full information which causes a greater degree of impediment in performing classification. Therefore, the proposed system introduces a novel and cost effective framework where machine learning is utilized in a very unique manner for performing this task. The paper discusses about an analytica approach which perform feature extraction followed by applying machine learning to show that proposed system offers faster response time with higher matching found during the testing operation phase.
Knowledge-Aware Meta-learning for Low-Resource Text Classification

Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task.However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks.This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases.Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph.The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings.
Optimized Features of SIFT Transform Function for Digital Image Watermarking using Hybrid Swarm Intelligence and Neural Network

Authentication of digital multi-media is a challenging task in the current scenario of internet technology for the authentication of digital multi-media data used digital watermarking techniques. The feature-based watermarking techniques provide the robustness of digital watermarking methods. In this paper proposed optimized features based digital watermarking techniques using a neural network. For the process of features, optimization used hybrid swarm intelligence algorithms. The hybrid algorithms are a combination of PSO and ACO. The PSO used as feature optimizer and ACO used for the selection of features point for the processing of neural network models. Two neural networks models used BP and RBF. The property of both models is different for the processing of data and desired output for the enhancement of the proposed model used cascaded neural network models using BP and RBF. The cascaded models generate dynamic patterns for the embedding of a digital watermark. The dynamic patterns provide the randomness of the pixel value and decrease the value of attack predication. The proposed algorithms have been tested on an extensive database of 300 images. The analysis of the proposed algorithm is satisfactory against different types of attacks and enhance the strength of robustness.
NTRUEncrypt – A Quantum Proof Replacement to RSA Cryptosystem

The purpose of encryption is to provide a secure environment for communication and to keep information safe from unauthorized.There are two types of cryptosystem symmetric cryptosystem and asymmetric cryptosystem.Asymmetric cryptosystem is also called as Public Key Encryption (PKE).In this paper, we analyze two asymmetric cryptosystem -RSA and NTRU.NTRU have reasonably short and easily created keys, high speed and low memory requirements.As, NTRU is relatively new and cryptosystem of future, we talk more about it in the paper.