Reviewerly/exHarmony huggingface dataset:
Exploring Effects of Colour and Image Quality in Semantic Segmentation by Deep Learning Methods

Recent advances in convolutional neural networks and vision transformers have brought about a revolution in the area of computer vision. Studies have shown that the performance of deep learning-based models is sensitive to image quality. The human visual system is trained to infer semantic information from poor quality images, but deep learning algorithms may find it challenging to perform this task. In this paper, we study the effect of image quality and color parameters on deep learning models trained for the task of semantic segmentation. One of the major challenges in benchmarking robust deep learning-based computer vision models is lack of challenging data covering different quality and colour parameters. In this paper, we have generated data using the subset of the standard benchmark semantic segmentation dataset (ADE20K) with the goal of studying the effect of different quality and colour parameters for the semantic segmentation task. To the best of our knowledge, this is one of the first attempts to benchmark semantic segmentation algorithms under different colour and quality parameters, and this study will motivate further research in this direction.
Improving the Auditive Competence of Foreign Language Teachers in the System of Additional Professional Education

The aim of the study is to substantiate and describe the features of improving the auditive competence of foreign language teachers. The article deals with the linguistic, psychological, content and organizational problems of teaching listening, analyses the psycholinguistic foundations of text comprehension, as well as the process of increasing the level of auditive competence of foreign language teachers. The study presents tasks aimed at developing the auditive competence of foreign language teachers, and also describes the pedagogical conditions necessary for the implementation of this process. The scientific novelty of the study lies in the development of a methodology for improving the auditive competence of foreign language teachers within the framework of additional professional education system. The presented results of the study confirm the effectiveness of the proposed methodology for improving the auditive competence of foreign language teachers.
Technology of Teaching Leaders’ Activity to Students of Pedagogical University by Means of Pedagogical Design

The aim of the paper is to substantiate the effectiveness of pedagogical design technology as a means of teaching leaders’ activity to students of a pedagogical university. The article analyses the logic of studying the discipline “Fundamentals of Leaders’ Activity”, defines the main content blocks of this discipline and the technology of teaching students to interact with a temporary children’s group. The scientific novelty lies in the development of a pedagogical design technology within the framework of practical classes in the discipline “Fundamentals of Leaders’ Activity” in a pedagogical university, which can be used in educational practice. As a result, the role of pedagogical design in organizing a leader’s activity in educational school organizations is revealed; the project “The Choice of a Profession Determines the Future” developed by the teachers and students of Tula State Pedagogical University is presented; its goals and objectives are defined; the logic and content of the main events, the conduct of which is focused on career guidance to popularize professions in the context of the programs of the Russian Movement of Schoolchildren, are revealed.
New Didactics: Academic and Post-Academic Discourse

The paper aims to compare academic and post-academic discourses in modern didactics - to search for their points of contact and ways to implement the results of didactic research into practice. By analysing postmodern philosophy, the paper explicates the phenomenon of modernisation and renovation of didactic knowledge, ascertains a combination of external negative academisation trends caused by the gnoseological crisis and the predominance of applied research in the field of didactics without its proper understanding at the theoretical and methodological level. The study is novel in that it proposes ways of bringing together academic and post-academic discourses in didactic research based on the results of analysing the activities of the interuniversity research center “New Didactics”. As a result, the researcher has substantiated the need for accumulation of new didactic solutions in practice, their summarisation and didactic analysis by means of fundamental science, consolidation of the efforts of the entire academic and pedagogical community to popularise modern didactic concepts and to test them in practice.
Unsupervised and Supervised Feature Selection for Incomplete Data via L2,1-Norm and Reconstruction Error Minimization

Feature selection has been widely used in machine learning and data mining since it can alleviate the burden of the so-called curse of dimensionality of high-dimensional data. However, in previous works, researchers have designed feature selection methods with the assumption that all the information from a data set can be observed. In this paper, we propose unsupervised and supervised feature selection methods for use with incomplete data, further introducing an L2,1 norm and a reconstruction error minimization method. Specifically, the proposed feature selection objective functions take advantage of an indicator matrix reflecting unobserved information in incomplete data sets, and we present pairwise constraints, minimizing the L2,1-norm-robust loss functionand performing error reconstruction simultaneously. Furthermore, we derive two alternative iterative algorithms to effectively optimize the proposed objective functions and the convergence of the proposed algorithms is proven theoretically. Extensive experimental studies were performed on both real and synthetic incomplete data sets to demonstrate the performance of the proposed methods.
Auto‐encode the synthesis pseudo features for generalized zero‐shot learning

Zero-shot learning (ZSL) is to identify target categories without labeled data, in which semantic information is used to transfer knowledge from some seen categories. In the existing Generalized Zero-Shot Learning (GZSL) methods, domains shift problem always appeared during generating feature stage. In order to solve this problem, a new method to Auto-Encode the Synthesis Pseudo Features for the GZSL task (AESPF-GZSL) is proposed in this manuscript. Specifically, the AESPF-GZSL method trains the generated features under the semantic auto-encoder framework and exploits attention mechanism to train the generated features again. Then, the generated features are input to the classifier. The proposed method is performed on three benchmark data sets referred as to AWA, CUB and SUN. The experimental results show that the proposed method achieves the state-of-the art classifier accuracy both in ZSL and GZSL settings. In ZSL setting, the classification accuracy of our method is superior to the compared algorithms, improved by 0.40% in AWA and 0.30% in SUN, respectively. And in GZSL setting, the classification accuracy of the method is superior to the comparison algorithm 0.41% in Harmonic mean on AWA, and 1.01%, 0.62%, and 1.05% in training data set, testing data set, and harmonic average on SUN.
Digital Technology in Professional training Of Students

The article considers the use of digital technologies in the educational process and their impact on increasing digital literacy among future specialists.The results of the study of students' readiness for the use of digital technologies in the educational process has been presented.The measures to improve the digital educational environment (DEE) of the university has been proposed.
Suspicious tweet identification using machine learning approaches for improving social media marketing analysis

Social media acts as one of the eminent platforms for communication. Twitter is one of the leading social media microblogging platforms, where users can post and interact. #Hashtags specify the tweeter trends on a certain topic. Currently, the hashtag value or trend ranking for a particular hashtag has been calculated based on the cumulative number of tweets. This type of cumulative amount of hashtag ranking may result in an anonymous intervention of irrelevant tweets, which affects social media marketing. The proposed approach uses the relevance of tweets and #hashtags to improve and identify the suspicious or irrelevant tweets of media marketing. The proposed research work uses the linear regression algorithm, which is one of the familiar machine learning approaches to explain the spam tweet generation and the method to identify. The test results found the proposed system has 84% of significance when compared to the market analysis algorithms.
Single image deraining using multi‐stage and multi‐scale joint channel coordinate attention fusion network

Rain streaks can seriously degrade the visual quality of an image and are detrimental to subsequent algorithms such as object detection and semantic segmentation. Therefore, removing rain streaks is a very important task. The deraining task has two main limitations: the first is to encode information about rain streaks in different densities and directions, the second is to keep the background details of the image while removing the rain streak. To address these limitations, we propose an effective algorithm, called multi-stage and multi-scale joint channel coordinate attention fusion network (MMAFN). We mainly propose a two-stage network structure, both of which use an encoder-decoder network to extract features. The first-stage network extracts coarse features and the second-stage network integrates the features of the former to further refine features. We design the joint channel coordinate attention block to encode features of rain streaks in different directions and densities. In addition, to better fuse features of different scales and enhance the generalization performance of the network, the inception attention branch block and the multi-level feature fusion block are designed. Extensive experiments substantiate the superiority of the proposed network and prove that our method outperforms the recent state-of-the-art method. The average PSNR of the five test sets is improved by 0.2dB. On the Test100 test set, the PSNR is increased by 0.93dB at most.
Transforming the Bard through Digital Media: Modern Design Approaches for Shakespeare Education

Original Shakespeare texts can be difficult to teach to the current generation of students in secondary and tertiary education. Although the universal themes in Shakespeare still resonate with young audiences of today, the antiquated language complicates access. A possible way forward is to design engaging texts to reinvigorate traditional curriculum with multimodal forms of mediation that increase the accessibility of the Bard’s classical canon. Therefore, the authors are investigating a model to adapt the Bard’s texts for fully immersive digital media: ShakesVR “mashes up” three major Shakespeare texts—Macbeth, The Tempest and A Midsummer Night’s Dream—for an animated virtual reality (VR) experience. This experience allows the user to discover three different story-worlds on their own. While the animated adaptation needs to employ abridged and altered versions of the plays, it uses only unaltered Shakespeare dialogue. Its lyrical beauty remains intact, yet becomes more accessible to contemporary audiences through its modern mode of delivery. The adaptation for VR posed the challenge to direct the user’s attention towards the story, requiring inventive approaches in storytelling, design and animation. The design process was used to iteratively test the efficacy of style and suitability for the requirements in visual storytelling. Designs were developed according to the script, evaluated and redeveloped in response to feedback from the directors. In summary, the authors will offer insight into their creative design process and propose a designerly approach to Shakespeare education.
The University Strategic Plan to Face Disruptive Classes During the Covid-19 Pandemic

During the spread of COVID-19, education was interrupted in universities worldwide. This new situation created a challenge in the higher education landscape, where universities were obliged to switch to online teaching for completing the syllabus. Moroccan universities have taken the steps needed to change from face-to-face teaching to distance and online teaching, to ensure pedagogical continuity. This chapter discusses strategic plans carried out by Cadi Ayyad University during this time of crisis. The results, which are related to learners’ results, reveal the effectiveness of the solutions taken to guarantee the continuity of education.
Survey on Services and Support in Cloud Computing Environment

Cloud computing serves a large portion of enterprises and intellectuals with the facility of computation as per their demand and proves to be successful. There are multiple services being provided by numerous cloud service providers (CSPs) along with widespread range of framework for software. Also, the client possesses some requirement of application and capabilities of system that are not outsourced. Thus, making it difficult to fit everything under one cloud environment. In this paper, we explain the services and platforms provided by cloud service providers. We also showcase a study, comparative in nature, detailing the services by open-source-based cloud computing environment. The paper gives justifications to support which cloud environment a client is comfortable with according to the requirement and resources of client.
Magnetic Field Crosstalk Suppression Method on Superconducting Quantum Chips

Abstract The arithmetic power of a quantum computer is determined by a combination of the number of quantum bits and the fidelity of a single bit. Superconducting quantum chips can be prepared using a process similar to that of semiconductor chips, and are therefore of interest because of their natural advantages in terms of scalability. However, as the number of quantum bits increases, the z-control lines (magnetic field control lines) on the superconducting quantum chip also increase, leading to increased magnetic field crosstalk between individual control lines, which affects the fidelity of a single bit and further affects the arithmetic power of superconducting quantum computing. To address the above problems, this paper proposes two methods to reduce the magnetic field crosstalk between the control lines by modeling and simulating the superconducting quantum chip using air bridges and inflow structures, and the simulation results show that the magnetic field crosstalk between the control lines is significantly improved after the introduction of air bridges and inflow structures. This work provides a basis for further suppression of magnetic field crosstalk between the z-control lines of superconducting quantum chips.
Applications of Fractional-Order Chaotic Systems in Secure Communications

The rich dynamics and the successful implementation of digital circuits of the fractional-order chaotic system lay the theoretical and hardware foundation for its application. This chapter focuses on the synchronization control of fractional-order chaotic systems, pseudo-random sequence generator based on DSP digital circuit implementation, and image encryption. In addition, the fractional-order chaotic system is applied to a spread spectrum communication system as a spread spectrum code, and the bit error rate (BER) of the system at different signal-to-noise ratios (SNR) is tested by simulation and compared with other spread spectrum codes.
THE EFFECT OF CONCRETE THINGS ON VOCABULARY LEARNING OUTCOMES OF GRADE SEVEN STUDENTS AT SMP NEGERI 13

This research aims at finding out the effect of concrete things on students’ learning outcomes in learning vocabulary of grade seven at SMP Negeri 13 Palu. This research applied quasi experimental design, in which there were two groups, experimental and control group. Seventh grade E as the experimental group, while seventh grade F as the control group. This research involved 50 students of SMP Negeri 3 Palu. The samples of this research were taken from the population of grade seven students by using purposive sampling technique. The researcher used tests as the instrument of collecting data. The tests were divided into two; pretest and posttest that were given to the experimental and control group. The treatment was conducted six times which was given to the experimental group by using concrete things, while the control group using the other technique. The data obtained from the test were analysed statistically. From the analysis, there was a difference between the achievement of experimental (85.5) and control group(54.6). It was based on the hypothesis testing done, by using 0.05 level of significance and 48 degree of freedom (df), the researcher found that t-counted (5.11) is higher than t-table (2.0126). It means that the hypothesis (Ha) is accepted or in the other words the use of concrete things has significant effect on students’ learning outcomes in learning vocabulary.
SOME PSYCHOLOGICAL FEATURES OF STUDENTS IN THE „EARLY ADULTHOOD” CATEGORY IN THE CONTEXT OF EARLY PIANO LEARNING

The practice of the native musical - pedagogical educational institutions shows that at the present time, among the students studying in the general piano class, in fact, there are many of them who do not have any piano playing background. Deprived of the advantages of a receptive childhood, flexibility of the motor-play apparatus, they find it difficult to get used to playing this instrument. The development of a practical methodology based on the achievements of modern theoretical thinking will make it possible to find the most effective methods for teaching adult learners, and, above all, the students that belong to the category of „early adulthood”.
Development and Validation of Learning Module with Integrated Improvised Laboratory Apparatuses (LMIILA) for Remote Learning in Science

This study introduced the printed learning module with integrated improvised laboratory apparatuses (LMIILA) for remote learning in science, bridging the gap for laboratory experiences during COVID-19 pandemic and for remote learning where stable internet connection is impossible.The LMIILA included electroscope and microscope developed and integrated in self-learning module piloted to seventy-five (75) Grade 10 students in Matabao, Agusan del Norte, Philippines.The study results showed that students enjoyed learning science while using LMIILA as it positively influenced their learning experiences.
Research on a Blended Teaching Mode of Advanced Mathematics

Since the outbreak of the pandemic, the "Internet + education",an emerging teaching mode, has left a huge impact on teaching tools and educational concepts.This paper is thus aimed to discuss the innovativeness of the blended teaching mode, based on the Mosotech-aided advanced mathematics teaching.The advantages and disadvantages of blended teaching were analysed.Attention should be paid to the integration of online and offline teaching, while building up professional discipline teams, infusing ideological and political elements, and strengthening network security education, so as to cultivate innovative talents.
Conservative party's online vote is vulnerable to hackers

Hacking concerns have dogged the Tory leadership contest in the UK. Should it ever have used online voting, asks Matthew Sparkes
RESEARCH ON THE DESIGN AND CONSTRUCTION OF COLLEGE ENGLISH ONLINE AND OFFLINE BLENDED CURRICULUM

During the epidemic period, large-scale online teaching accelerated the deep integration of Internet plus education and teaching, information technology and various educational forms. The innovation of curriculum construction is an important starting point for the construction of new engineering and new liberal arts, and online and offline mixed courses will become an important way and carrier of teaching reform in colleges and universities. Taking College English, an online and offline mixed course in application-oriented universities, as an example, this paper explains how to create a "new normal" of foreign language mixed teaching based on the network platform by reconstructing the traditional teaching time and space, and provides enlightenment for constructing the foreign language teaching ecology of "Internet plus".
Opinion Mining of Online Reviews about Certain Islamabad Hotels: A Business Intelligence Study

Online reviews exert significant influence on the sale and purchase of any product. They not only play a prominent role in the selection of hotels but are also eye openers for the hotel managers because they expose problem areas which need dire attention for the upgradation of quality. Browsing big data from multiple online platforms to find reviews is a time-consuming task. Consequently, Business intelligence is a field which collects and analyses data for customers and managers. This study analyses reviews of 7 hotels in Islamabad through the Social mention tool which gives a visual representation of the strength, sentiment,passion, and reach of each. Multi modal Sentiment Analysis theory has been applied to identify the nature of the reviews. The major findings reveal sentiment analysis of the hotels’ reviews from social media. There search helps individuals in decision making and selection of hotels; and guides the management staff for rectification.
Unlearnable Examples: Protecting Open-Source Software from Unauthorized Neural Code Learning

The vast volume of "free" code maintained on open-source code management systems significantly simplifies the process of producing and sharing open-source software.Recently, we have seen a growing trend in which these open-source software is being used for neural code learning without authorization.Note that open-source software does not necessarily imply "unrestricted usage," e.g., software under the BSD license requires users to retain the copyright notice and credit the software's developers.The unauthorized use of software for (commercial) neural code learning models has raised copyright concerns.This paper, for the first time, provides approaches for protecting opensource software from unauthorized neural code learning via unlearnable examples.Our proposed technique applies a set of lightweight transformations toward a program before it is open-source released.When these transformed programs are used to train models, they mislead the model into learning the unnecessary knowledge of programs, then fail the model to complete original programs.The transformation methods are sophisticatedly designed to ensure that they do not impair the general readability of protected programs, nor do they entail a huge cost.We focus on code autocompletion as a representative downstream task of unauthorized neural code learning.We demonstrate highly encouraging and cost-effective protection against neural code autocompletion.
Zero-Shot Object Detection with Multi-label Context

Zero-shot detection (ZSD) , the problem of object detection when training and test objects are disjoint, i.e. no training examples of the target classes are available.ZSD increasingly gains importance for large scale applications because collecting and labeling sufficient data is extremely hard.In this paper, inspired from human cognitive experience, we propose a simple but effective Multi-label Context (MLC) framework to facilitate the detection ability for both seen and unseen objects by mining contextual cues.We design a multilabel classifier which leverages the holistic image-level context to learn object-level concepts.Then, novel RoI features are generated by exploiting context information beneath both whole images and interested regions.Moreover, background dynamic generator (BDG) can reduce the confusion between background and unseen classes.Our extensive experiments show that MLC outperforms the current state-of-the-art methods on MS-COCO.