Reviewerly/exHarmony huggingface dataset:
Pupils<b>' </b>attitudes to formative assessment in sciences class

This paper discusses formative assessment in Sciences class, which integrates the knowledge of natural and social sciences. In formative assessment there are two different assessment processes – assessment for learning and assessment as learning. Both approaches value the idea of students’ individual progress, and they have a pedocentric orientation. The paper focuses on assessment techniques used in class, which is operationalised in the empirical section of the paper. We established a hierarchical structure of offered techniques according to the criterium of usefulness. The students expressed their views on the process of self-evaluation and peer-evaluation in four dimensions (liking, difficulty, help with learning and discomfort). The approach to research is quantitative. The results show that the techniques we can find at the bottom of the hierarchy are the techniques which require a more complex cognitive deliberation. There is a positive attitude towards self-evaluation and peer-evaluation. In establishing the difference between attitudes in the dimension of help with learning there is a lack of recognition of the potential of peer-evaluation. This paper confirms the importance of formative assessment and points to the need for further research.
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool

Automatic speech recognition (ASR) has evolved from a pipeline architecture with pronunciation dictionaries, phonetic features and language models to the end-to-end systems performing a direct translation from a raw waveform into a word sequence.With the increase in accuracy and the availability of pre-trained models, the ASR systems are now omnipresent in our daily applications.On the other hand, the models' interpretability and their computational cost have become more challenging, particularly when dealing with less-common languages or identifying regional variations of speakers.This research proposal will follow a four-stage process: 1) Proving an overview of acoustic features and feature extraction algorithms; 2) Exploring current ASR models, tools, and performance assessment techniques; 3) Aligning features with interpretable phonetic transcripts; and 4) Designing a prototype AR-POCA to increase awareness of regional language variation and improve models feedback by developing a semi-automatic acoustic features extraction using PRAAT in conjunction with phonetic transcription.
Mining Logical Event Schemas From Pre-Trained Language Models

We present NESL (the Neuro-Episodic Schema Learner), an event schema learning system that combines large language models, FrameNet parsing, a powerful logical representation of language, and a set of simple behavioral schemas meant to bootstrap the learning process.In lieu of a pre-made corpus of stories, our dataset is a continuous feed of "situation samples" from a pre-trained language model, which are then parsed into FrameNet frames, mapped into simple behavioral schemas, and combined and generalized into complex, hierarchical schemas for a variety of everyday scenarios.We show that careful sampling from the language model can help emphasize stereotypical properties of situations and de-emphasize irrelevant details, and that the resulting schemas specify situations more comprehensively than those learned by other systems.
Deep Neural Network Incorporating Domain and Resolution Transformations Model for Histopathological Image Classification

Microscopic image examination is assumed to be a primary measuring technique to assess the presence of cancer from breast lesions. These images can be obtained from breast tissue biopsy slides stained with hematoxylin and eosin. To accurately diagnose the presence and malignancy status of breast cancer, experts including pathologists are required to complete the subjective measure, which is preferable because it can provide a satisfactory examination. However, this measure is inconvenient, time-consuming, and high cost. Therefore, a deep neural network may offer the computer-based observer to diagnose the breast cancer category as benign or malignant. In this study, the binary classification of breast cancer from histopathological images is proposed using multistage transfer learning incorporating domain and resolution transformations. We verified our models with a publicly available BreaKHis dataset. Additionally, we modified and fine-tuned the well-known architectures, for instance, VGG19, ResNet (50 and 101), InceptionV3, Xception, Inception-ResNetV2, and NASNet-Large, to classify [[EQUATION]] magnification histopathology images as benign or malignant, then transferred the pre-trained weights for 100 ×, 200 ×, and 400 × magnification classifications. Our classifiers achieved a remarkable performance by demonstrating a test accuracy of 93.97% - 100% and an F1-score of 0.91 - 1.00. Thus, it may offer an effective approach for breast cancer classification in the clinical environment.
Research on Curriculum Assessment Model Construction of Preschool Education Specialty Based on Ability Cultivation

The undergraduate preschool education major has its own complex characteristics, and the graduates it trains must have solid theoretical knowledge and excellent practical ability.There are many ways to achieve this goal, among which curriculum assessment and process assessment are important components.
Survey on Natural Language-Based Person Search

The sense of knowledge base (KB) of the website contains an enormous amount of information. High complexity questions like scenes based on reality, object classes can be easily handled by a human. It needs to be trained by the Question Answering (QA) system. Several QA systems have been built over KBs to make these data available; both structured and unstructured data need query information which is an important aspect. It is challenging to construct a QA system to solve many different problems on KBs. QA programmers usually include NLP, data acquisition, machine intelligence, and the Semantic Web to solve these challenges. The need to query information content in various formats, including structured and unstructured data (text in natural language, semi-structured Web documents, structured RDF data in the semantic Web, etc.), has become increasingly important. Thus, Question Answering Systems (QAS) are essential to satisfy this need. In this paper, we surveyed various QAS with some statistics and analyzed their result. It will improve clarity and help researchers to choose the appropriate solution to their issue. Researchers can adapt or reuse QAS techniques for specific research issues. The survey conducted for the following paper had different approaches for the person’s search using the interactive question response process.
A First Attempt at Cryptanalyzing a (Toy) Block Cipher by Means of QAOA

The discovery of quantum algorithms that may have an impact on cryptography is one of the main reasons of the rise of quantum computing. Currently, all quantum cryptanalysis techniques are purely theoretical and none of them can be executed on existing or near-term quantum devices. So, this paper investigates the capability of already existing quantum computers to attack a toy block cipher (namely the Heys cipher) using the Quantum Approximate Optimization Algorithm (QAOA). Starting from a known-plaintext key recovery problem, we transform it into an instance of the MAX-SAT problem. Then, we propose two ways to implement it in a QAOA circuit and we try to solve it using publicly available IBM Q Experience quantum computers. The results suggest that the limited number of qubits requires the use of exponential algorithms to achieve the transformation of our problem into a MAX-SAT instance and, despite encouraging simulation results, that the corresponding quantum circuit is too deep to work on nowadays (too-)noisy gate-based quantum computers.
Information Security Enhancement by Increasing Randomness of Stream Ciphers in GSM

Information security is a crucial issue and needs to be addressed efficiently.Encryption of the original information is used to ensure privacy during exchange of information.In GSM (Global System for Mobile) standard, once the voice traffic initiates after signaling, encryption comes into the picture to ensure privacy during the call, after authentication.Here in this process, the plaintext is encrypted in to cipher-text using stream ciphers.For stronger security, strong ciphers with strong randomness are required.Linear Feedback Shift Registers (LFSRs) based A5 algorithm family is used for encryption in GSM.There are many shortcomings of this cipher and with these, privacy can't be assured.Some ways are proposed in this paper to ensure better security by enhancing the randomness of the generated bit stream being used for encryption.These are incorporation of user's current location, reuse of already generated 32 bit SRES during authentication process and conversion of linear FSRs into nonlinear FSRs.Statistical Test Suite NIST is used to test the various properties of random bit stream and an attempt has been made to achieve better randomness, hence more security.
SSN_MLRG1@DravidianLangTech-ACL2022: Troll Meme Classification in Tamil using Transformer Models

The ACL shared task of DravidianLangTech-2022 for Troll Meme classification is a binary classification task that involves identifying Tamil memes as troll or not-troll.Classification of memes is a challenging task since memes express humour and sarcasm in an implicit way.Team SSN_MLRG1 tested and compared results obtained by using three models namely BERT, ALBERT and XLNet.The XL-Net model outperformed the other two models in terms of various performance metrics.The proposed XLNet model obtained the 3rd rank in the shared task with a weighted F1-score of 0.558.
Handwritten digit recognition by three-layer diffractive neural network

Optical diffractive neural network (ODNN) uses light wave as a computing medium to perform the inference and prediction function of neural network, which has the advantages of high speed, low power consumption, and parallel processing. In this work, an ODNN with only three layers of phase modulation is designed, and a method to improve the recognition performance of ODNN based on the first-order spectral distribution of targets is proposed. Using this method, the parameters of a three-layer ODNN are effectively optimized and the optimal pixel size, diffraction distance, and method for image preprocessing are obtained. The three-layer ODNN designed in this work has a recognition accuracy rate of 95.3% for MNIST (handwritten digit set), compared with the 91.75% accuracy achieved by the five-layer ODNN in the reference (Lin X, Rivenson Y, Yardimci N T, Veli M, Luo Y, Jarrahi M, Ozcan A &lt;ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://doi.org/10.1126/science.aat8084"&gt;2018 &lt;i&gt;Science&lt;/i&gt; &lt;b&gt;361&lt;/b&gt; 1004&lt;/ext-link&gt;). In addition, the system volume is greatly reduced and the system structure is simplified. Combined with the advantages of high speed and low power consumption, it has huge potential applications in the fields such as edge computing in the future.
BPHC@DravidianLangTech-ACL2022-A comparative analysis of classical and pre-trained models for troll meme classification in Tamil

Trolling refers to any user behavior on the internet to intentionally provoke or instigate conflict, predominantly on social media.This paper aims to classify troll meme captions in Tamil-English code-mixed form.Embeddings are obtained for raw code-mixed text, and the translated and transliterated version of the text and their relative performances are compared.Furthermore, this paper compares the performances of 11 different classification algorithms using Accuracy and F1-Score.We conclude that we were able to achieve a weighted F1 score of 0.74 through MuRIL pretrained model.
FEATURES OF THE FORMATION OF INITIAL IDEAS ABOUT ECONOMIC CULTURE IN PRESCHOOL ORGANIZATIONS

The article focuses on the relevance of the formation of initial ideas about economic culture of preschoolers as an important component of the holistic development of personality in modern society. Economic culture is a part of the general human culture that influences economic policy in the state, the efficiency of work in society, and the well-being of each family. The formation of an economic culture among older preschoolers is relevant, modern and necessary.The theoretical context of the economic development of a preschooler’s personality testifies to the importance of the problem under consideration. The theoretical aspects of the essence of the problem of economic literacy of children at the stage of preschool childhood have been revealed in the article. In the process of organizing the experimental part of the study, the authors paid special attention to the attitude of teachers of preschool educational organizations to the problem of the formation of economic culture among preschoolers, since the teacher is the main figure of the pedagogical process and a decisive factor in economic education.
Advanced Web Challenges

The first two chapters focus on traditional Web vulnerabilities. This chapter mainly starts from the language features of PHP and Python, and introduces the common vulnerabilities of these two mainstream Web languages in CTF competition, namely deserialization vulnerabilities and Python security issues. Meanwhile, it introduces Web vulnerabilities and Web logic vulnerabilities related to cryptography, so that readers can have a more comprehensive understanding of the vulnerabilities in the direction of Web.
Emergent Structures and Training Dynamics in Large Language Models

Large language models have achieved success on a number of downstream tasks, particularly in a few and zero-shot manner.As a consequence, researchers have been investigating both the kind of information these networks learn and how such information can be encoded in the parameters of the model.We survey the literature on changes in the network during training, drawing from work outside of NLP when necessary, and on learned representations of linguistic features in large language models.We note in particular the lack of sufficient research on the emergence of functional unitssubsections of the network where related functions are grouped or organized -within large language models, and motivate future work that grounds the study of language models in an analysis of their changing internal structure during training time.
Vocational Training of Workers and Vocational Education of Students in Vocational Schools (1980s): Legal Aspects

The article is the second part of a series devoted to topical legal issues of the development of the training system for workers and vocational education in vocational schools in the Soviet Union. Part two is devoted to the study of the legal foundations of vocational education in the 1980s. The author characterizes the regulatory framework and the organization of vocational training in connection with their changes reflected in the standard framework documents.
Providing Interpretability of Document Classification by Deep Neural Network with Self-attention

Deep learning has been widely used in natural language processing (NLP) such as document classification. For example, self-attention has achieved significant improvement in NLP. However, it has been pointed out that although deep learning accurately classifies documents, it is difficult for users to interpret the basis of the decision. In this paper, we focus on the task of classifying open-data news documents by their theme with a deep neural network with self-attention. We then propose methods for providing the interpretability for these classifications. First, we classify news documents by LSTM with a self-attention mechanism and then show that the network can classify documents highly accurately. Second, we propose five methods for providing the basis of the decision by focusing on various values, e.g., attention, the gradient between input and output values of a neural network, and classification results of a document with one word. Finally, we evaluate the performance of these methods in four evaluating ways and show that these methods can present interpretability suitably. In particular, the methods based on documents with one word can provide interpretability, which is extracting the words that have a strong influence on the classification results.
EAGL: Enhancement Algorithm based on Gamma Correction for Low Visibility Images

Under poor light conditions or improper acquisition settings, the image degrades due to low contrast, poor brightness and suffer poor visual quality of the picture.An enhancement is required to manipulate the scale of pixel intensity for significant improvement in the image.This paper proposed the method of gamma correction with a self-adaptive value in accordance with the intensity scale of the image.After transformation to HSI (hue, saturation and intensity) channel, a multi-scale wavelet transform is implemented on the intensity component of the image.The gamma scale is computed from the combination of reformed scale constant of logarithm function and Minkowski distance measure.Lastly, wavelet based denoising technique is applied to suppress high noise coefficients to improve quality of the image.The proposed method is evaluated in terms of visual appearance, measure of information content, signal to noise ratio, and universal image quality index.It demonstrated that the proposed method showed its efficacy in terms of quality and improved visibility.
The Effect of a Proposed program Based on the Implications of John Hattie's Visible Learning on College Students' English Oral Fluency

The purpose of this study was to investigate the effect of an instructional program based on the implications of John Hattie's visible learning on enhancing college students' oral fluency skills.Thirty 1 st year students enrolled in STEM program at the Faculty of Education, Ain Shams University, participated in the study which took place in the 1 st semester of the academic year 2021-2022.The study utilized a mixed-methods approach.A pre/post oral fluency test was administered to the participants prior to implementing the suggested instructional program.Then, an instructional program based on the implications of visible learning developed by the researchers was taught to the participants.The oral fluency test was post administered and the participants were asked to respond to a satisfaction questionnaire.Quantitative data was collected from the students' scores in the pre/post tests and was analyzed using the convenient statistical techniques.Qualitative data, on the other hand, was collected from the Students' Satisfaction Questionnaire.Findings of the study indicated the effectiveness of the proposed program in developing participants' oral fluency skills as a whole and in separation.Findings of the study are discussed and recommendations for further research are stated.
Discourse-based Opinion Mining of Customer Responses to Telecommunications Services in Saudi Arabia during the COVID-19 Crisis

This study used opinion mining theory and the potentials of artificial intelligence to explore the opinions, sentiments, and attitudes of customers expressed on Twitter regarding the services provided by the Saudi telecommunications companies during the COVID-19 crisis.A corpus of 12,458 Twitter posts was constructed covering the period 2020-2021.For data analysis, the study adopted a discourse-based mining approach, combining vector space classification (VSC) and collocation analysis.The results indicate that most users had negative attitudes and sentiments regarding the performance of the telecommunications companies during the pandemic, as reflected in both the lexical semantic properties and discoursal and thematic features of their Twitter posts.The study of collocates and the discoursal properties of the data was useful in attaining a deeper understanding of the users' responses and attitudes to the performance of the telecommunications companies during the COVID-19 pandemic.It was not possible for text clustering based on the "bag of words" model alone to address the discoursal features in the corpus.Opinion mining applications, especially in Arabic, thus need to integrate discourse approaches to gain a better understanding of people's opinions and attitudes regarding given issues.
Fast Facts

Spam messages in the United StatesGoogle links to WorldCatFree Internet service plansyInternet live statsCOVID-19 impact on higher education
Amanda Nichols Hess. Modular Online Learning Design: A Flexible Approach for Diverse Learning Needs. Chicago, IL: ALA Editions, 2020. 144p. Paper, $65.99 (ISBN: 978-0838948125).

Modular Online Learning Design presents a modular approach to the design of online learning objects. The term modular is typically applied to a product with individually engineered components, such as a house or a smartphone (assembled with Processor X, Screen Y, Camera Z, and so on). Applied to an online class or tutorial, this looks like “constructing broader academic experiences out of smaller learning units” (2). A modular approach lends itself to more easily scaled and modified content. In a pedagogical sense, it also echoes the concept of “chunking” content into smaller, more digestible bites for learners.
Effective Malicious URL Detection by Using Generative Adversarial Networks

Malicious URL, a.k.a. malicious website, pose a great threat to Web security. In particular, concept drift caused by variants of malicious URL degrades the performance of existing detection methods based on the available attack patterns. In this paper, We conduct an extensive measurement study of the realistic URL and find that the hierarchical semantics feature is suitable for identifying malicious URL. Therefore, we propose URLGAN, a deep neural network model equipped with the hierarchical semantics features, to detect distinguish between malicious and normal URL. Firstly, we embed the entire URL into a hierarchical semantics structure. Secondly, hierarchical semantics features are extracted from the hierarchical semantics structure through BERT. Then, the extracted features are combined with features generated by the generator, similar but slightly different, to enable the condition discriminator to extract the essential difference between normal and malicious URL. Notably, with the features generated by the generator, we enhance the robustness of the system to detect malicious URL variants. Extensive experiments on the public dataset and our data collected from specific targets demonstrate that our method achieves superior performance to other methods and protects specific targets from the susceptibility of malicious URL.
Implementing the African Continental Free Trade Area Agreement (AfCFTA) and the Challenges of Corruption in Africa

Since the first quarter of 2020, African nations have been forced to battle to scourge of COVID-19 and its toll on the economies across the African continent.As normalcy resumes, the timing of the implementation of the provisions of the African Continental Free Trade Agreement (AfCFTA) could not have been timelier.While the objectives of the AfCFTA include the economic integration within the continent, which is considered noble and timely, this paper argues that the objectives of the AfCFTA may not be realised if Africa refuses to combat corruption within the continent and among member nations.Following a doctrinal approach, this paper recommends the incorporation of the provisions of the African Union Convention on Preventing and Combating Corruption (AUCPCC) into the AfCFTA to demonstrate political will of African leaders to combat corruption.