Reviewerly/exHarmony huggingface dataset:
Robustness to Transformations Across Categories: Is Robustness Driven by Invariant Neural Representations?

Abstract Deep convolutional neural networks (DCNNs) have demonstrated impressive robustness to recognize objects under transformations (e.g., blur or noise) when these transformations are included in the training set. A hypothesis to explain such robustness is that DCNNs develop invariant neural representations that remain unaltered when the image is transformed. However, to what extent this hypothesis holds true is an outstanding question, as robustness to transformations could be achieved with properties different from invariance; for example, parts of the network could be specialized to recognize either transformed or nontransformed images. This article investigates the conditions under which invariant neural representations emerge by leveraging that they facilitate robustness to transformations beyond the training distribution. Concretely, we analyze a training paradigm in which only some object categories are seen transformed during training and evaluate whether the DCNN is robust to transformations across categories not seen transformed. Our results with state-of-the-art DCNNs indicate that invariant neural representations do not always drive robustness to transformations, as networks show robustness for categories seen transformed during training even in the absence of invariant neural representations. Invariance emerges only as the number of transformed categories in the training set is increased. This phenomenon is much more prominent with local transformations such as blurring and high-pass filtering than geometric transformations such as rotation and thinning, which entail changes in the spatial arrangement of the object. Our results contribute to a better understanding of invariant neural representations in deep learning and the conditions under which it spontaneously emerges.
American literature news narration based on computer web technology

Driven by internet technology, online has become the main way of news dissemination, but redundant information such as navigation bars and advertisements affects people's access to news content. The research aims to enable users to obtain pure news content from redundant web information. Firstly, based on the narrative characteristics of literary news, the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm is employed to extract pure news content from the analyzed web pages. The algorithm uses keyword matching, text analysis, and semantic processing to determine news content's boundaries and key information. Secondly, the news text classification algorithm (support vector machine, K-nearest neighbor, AdaBoost algorithm) is selected through comparative experiments. The news extraction system based on keyword feature and extended Document Object Model (DOM) tree is constructed. DOM technology analyzes web page structure and extracts key elements and information. Finally, the research can get their narrative characteristics by studying the narrative sequence and structure of 15 American literary news reports. The results reveal that the most used narrative sequence in American literary news is sequence and flashback. The narrative duration is dominated by the victory rate and outline, supplemented by scenes and pauses. In addition, 53.3% of the narrative structures used in literary news are time-connected. This narrative structure can help reporters have a clear conceptual structure when writing, help readers quickly grasp and understand the context of the event and the life course of the protagonists in the report, and increase the report's readability. This research on the narrative characteristics of American literature news can provide media practitioners with a reference on news narrative techniques and strategies.
Improvement of the system of training of technical specialists in a higher education institution

A comparison of the main forms of education in higher education institutions of Ukraine was made; the proposed structure of the electronic textbook, which is used in interactive and distance online learning; substantiated concept of dual form of education and given examples of its implementation. A well-founded system of monitoring the process of training specialists in a higher education institution.&#x0D; Nowadays, it is possible to distinguish five main forms of education: passive, active, and interactive models, as well as distance online learning and a dual form of education. To date, passive, active and interactive learning models have been well developed in terms of methodology. Distance online learning has positively proven itself to ensure the safety of students and teachers during a pandemic and martial law. In Ukraine, the project of the Ministry of Education and Science of Ukraine to introduce a dual form of education in twenty-seven institutions of higher education is currently being implemented. The basic principles of the dual form of education are: increasing the volume of training, which is carried out directly at the workplace; participation of employers' employees in the development and coordination of the educational program of higher education institutions; training on the basis of relevant contracts between the higher education institution, the employer and the student. The practical orientation of dual training plays a special role in the formation of the research competence of the applicant and the training of junior specialists of the specialist qualification (working profession).
Comprehensive Principles for Enhancing the Adhesive Bound Book Performances

In addition to satisfying a book binding criterion, the paperback should provide content moments. Its binding style must be adjusted to book purpose. In publishing longruns production, the perfect (adhesive) binding style with flexible hotmelt provides optimal binding durability with reasonable book price. Paperback should appeal to everyone through its visual-tactile senses. Taking the required actions in binding quality realization keeps track of comprehensive knowledge on various standardized paper substates (EN643:G3) that need to be wisely joined to realize perfect binding technique practices effectively. Moreover, designer technological awareness gained by experience provides potentials to understanding binding ability principles. The conducted research gives specific answers regarding paper cohesive capacity impact on consistency changes of adhesive bound book. It was concluded that higher paper drape capacity leads to increase of single sheet paper tension. Inappropriate bound book constructing was achieved although the flexible adhesive was used
Exploring teaching and learning methods using artificial intelligence (AI) in the mathematics classroom: Focusing on the development of middle school statistic scenarios

The purpose of this study is to explore the teaching and learning method using artificial intelligence (AI) in the mathematics classroom. To this end, to predict the direction of mathematics education using AI in the mathematics classroom, this study investigates the fields where AI is applied to education, and discuss issues to consider when introducing AI through scenario development using AI in middle school statistics. This study is meaningful in that it specifically considered how artificial intelligence can be grafted into the mathematics classroom through the development of scenarios that integrate and apply artificial intelligence that has been developed and used segmentally in the current middle school statistics. Afterwards, based on the contents of this study, implications for using AI in the math classroom were derived.
An Information Education Management and Teaching Information Management System Construction of Universities in Guangdong Province

Information education management is a complex and tedious work, especially with the continuous expansion of the scale of colleges and universities, the number of students has risen sharply, the relevant student information has doubled and doubled, to bring more pressure to the information management. The construction of information-based educational administration and teaching information management system is an inevitable choice for the development of contemporary education, which drives the modernization of education through informationization and realizes the leapfrog development of education. Given department in colleges and universities set up more less tedious, management staff , all kinds of students to the current situation of increasing year by year, the new system can design students throughout the school each department and each link of the students to meet the needs of the school information management, information management of students, teachers and other information digitization, automation, scientifi c management, to improve the teaching quality and management level, Promote the informatization reform of education.
Melocoton: A Program Logic for Verified Interoperability Between OCaml and C

In recent years, there has been tremendous progress on developing program logics for verifying the correctness of programs in a rich and diverse array of languages. Thus far, however, such logics have assumed that programs are written entirely in a single programming language. In practice, this assumption rarely holds since programs are often composed of components written in different programming languages, which interact with one another via some kind of foreign function interface (FFI). In this paper, we take the first steps towards the goal of developing program logics for multi-language verification. Specifically, we present Melocoton, a multi-language program verification system for reasoning about OCaml, C, and their interactions through the OCaml FFI. Melocoton consists of the first formal semantics of (a large subset of) the OCaml FFI—previously only described in prose in the OCaml manual—as well as the first program logic to reason about the interactions of program components written in OCaml and C. Melocoton is fully mechanized in Coq on top of the Iris separation logic framework.
Study on optimization of mine ventilation network characteristic map based on improved GA algorithm

The feature graph (Q-H graph) is the best way to intuitively and quantitatively reflect all features of the ventilation network. In this paper, an optimized adaptive genetic algorithm is proposed to solve the problem that rectangular blocks are cut in the process of drawing a Q-H diagram of a three-dimensional ventilation network. The algorithm adopts binary coding based on node sorting and mixed genetic coding based on integer coding. The formulas for calculating the adaptive crossover rate and mutation rate are designed, which can effectively generate new individuals and get rid of the search for local optimal values, ensuring the global optimal solution. Matlab was used to test the optimization effect of the Q-H graph; the results show that, for a complex ventilation network, the improved adaptive genetic algorithm can make the Q-H graph significantly reduce the number of rectangular pieces, which is divided, and make the Q-H graph have better effect to draw clearly and intuitively.
Analytical Study on Group Effects on the Pre-Knowledge Level of “Music Education for Young Children Based on Musical Concepts”

본 연구는 ‘음악 개념에 기초한 유아음악교육’ 수업의 효과가 음악 개념에 대한 사전지식 수준에 따라 집단별로 어떻게 나타나는지 분석하는 것을 목적으로 하였다. 연구대상은 수도권 소재 4년제 대학에서 ‘유아음악교육’을 수강한 예비유아교사 97명이었고, ‘예비유아교사의 음악 개념 이해 측정 도구’를 활용하여 음악 개념 이해 수준을 진단하였다. 수업을 실행하기 전 사전검사를 실시하였고, 15주 동안 수업을 실행한 후, 사전검사와 동일한 도구로 사후검사를 실시하였다. 자료분석을 위하여 사전검사 결과를 기준으로 정답 문항 수에 따라 상, 중, 하집단을 구성하였고, OxEdit 9.04를 활용하여 음악 개념에 대한 집단별 숙달 수준을 확인하였으며, 사전, 사후검사에서의 숙달 확률 차이에 대한 통계적 유의성을 확인하기 위하여 대응표본 t검정을 실시하였다. 연구결과, 상, 중, 하집단 모두에서 사전검사와 사후검사의 숙달 확률이 변화하였으나 통계적으로 유의한 변화는 일부 개념에서만 나타났고, 집단에 따라 변화가 상이하였다. 사전검사와 사후검사의 숙달 수준을 비교한 결과, 상집단은 사전검사와 사후검사 모두에서 8개 음악 개념을 숙달하고 있었고, 중집단은 사전검사에서 4개 개념을 숙달하지 못하였으나 사후검사에서는 8개 개념 모두 숙달한 것으로 나타났다. 하집단은 사전검사에서 모든 개념을 숙달하지 못한 상태였으나 사후검사에서는 2개 개념만을 숙달한 것으로 분석되었다. 이러한 결과는 본 연구에서 실행한 수업의 효과가 집단별로 차이가 있음을 의미하며, 교육의 효과를 높이기 위해서는 학생들의 사전지식 수준을 정확하게 진단한 결과를 토대로 집단을 분류하고, 교육이 요구되는 내용을 파악하여 그에 맞는 교육을 실행할 필요가 있음을 시사한다.The purpose of this study is to analyze how the effects of “Music Education for Young Children Based on Musical Concepts” vary with each group according to their level of prior knowledge of musical concepts. The subjects of this study were 97 preservice early childhood teachers who were trained in “Music Education for Young Children” from a four-year university located in the metropolitan area. To measure the level of pre-service early childhood teachers' understanding of musical concepts, the “The Test for Preservice Early Childhood Teachers” was used. In order to analyze the data received from the test results, the average and standard deviations were calculated by classifying preservice early childhood teachers groups according to the number of correct answers and the knowledge level of the groups on the music concepts. This procedure was confirmed with OxEdit 9.04 and the paired sample t-test was conducted to verify the statistical significance of the difference in mastery probabilities from the findings of the pre-test and post-test. The study showed that the approximate values of the knowledge-level found from the pre-test and post-test were changed in all of the upper, middle, and lower groups, but statistically significant changes were found only in some music concepts, and that change was not uniform among the groups. After comparing the mastery probabilities of the datasets received from pre-test and the post-test, the upper group was found to have mastered eight musical concepts in both the pre-test and the post-test, and the middle group mastered four concepts in the pre-test but mastered all eight concepts in the post-test. The lower group did not master all the concepts in the pre-test, but in the post-test, they mastered two concepts. This result shows that the effect of the music class implemented in this study is different for each group. To increase the effectivity of education, the study suggests that accurately understanding of each student’s prior knowledge-level and improvement areas along with the application of student-specific education are necessary.
Benchmarking of ResNet models for breast cancer diagnosis using mammographic images

Breast cancer is one of the cancer types with a high mortality rate worldwide. Early diagnosis is of great importance to reduce this mortality rate. Computer-aided early diagnosis systems enable doctors to make more precise and faster decisions. The Mammographic Image Analysis Society (MIAS) dataset was used in this study. The breast area was selected by masking in mammography images. The number of images was increased using data augmentation techniques. Mammography images were classified as normal, benign and malignant using four different ResNet models. The highest classification accuracy was achieved by using ResNet18 model with 93.83%. The accuracies obtained with ResNet50, ResNet101 and ResNet152 were 87.24%, 87.44% and 91.25% respectively.
An analysis of the integration of sustainability concepts into blockchain technology

The acceleration of data production and consumption due to the transition to an information society and industrial revolutions has had a significant impact on the expansion of the global economy. The emergence of Industry 4.0 has led to the adoption of various technologies, including blockchain, which is known for its potential to transform different domains through its solutions. This is particularly relevant in the context of data governance. Thus, blockchain technology has the potential to enhance the sustainability of diverse industries. Sustainability is a crucial concept that refers to the capacity to meet the requirements of the current generation without compromising the ability of future generations to do so. The integration of blockchain technology across diverse industries holds the potential to greatly improve sustainability efforts. The objective of this study is to assess the relationship between blockchain technology and sustainability through a descriptive review of literature utilizing the latent semantic analysis topic modeling and clustering method, which is a social spider optimization technique. This study focuses on analyzing the impact of blockchain technologies on the sustainability sector. A corpus of 1069 papers has been sourced from the Scopus database. The results underscore the significance of cybersecurity, supply chain management, and the circular economy in the extant academic literature. The broad recognition of the supply chain domain's importance is evident in its application of blockchain technology and adherence to the sustainability principle. The present research focuses on the analysis and assessment of topics pertaining to traceability, cyber security, circular economy, energy, and transparency.
Arabic and English Relative Clauses and Machine Translation Challenges

The study aims at performing an error analysis as well as providing an evaluation of the quality of neural machine translation (NMT) represented by Google Translate when translating relative clauses. The study uses two test suites are composed of sentences that contain relative clauses. The first test suite composes of 108 pair sentences that are translated from English to Arabic whereas the second composes of 72 Arabic sentences that are translated into English. Errors annotation is performed by 6 professional annotators. The study presents a list of the annotated errors divided into accuracy and fluency errors that occur based on MQM. Manual evaluation is also performed by the six professionals along with a BLEU automatic evaluation using the Tilde Me platform. The results show that fluency errors are more frequent than accuracy errors. They also show that the frequency of errors and MT quality when translating from English into Arabic is lower than the frequency of errors and MT quality when translating from Arabic into English is also presented. Based on the performed error analysis and both manual and automatic evaluation, it is pointed out that the gap between MT and professional human translation is still large.
The Role of Blockchain Technology in Organizational Cyber Security

Distributed ledger technology (DLT), decentralized finance (DeFi), blockchain – these are terms that have been trending especially in technology circles. Today, blockchain has gained more acceptance, but sceptics continue to raise concerns about the technology's scalability, security, and long-term viability. There are also concerns regarding its being associated with crime and the dark web, which might imply it will have negative consequences if adopted. It has its own peculiar cyber security loopholes, but these can be addressed. That notwithstanding, organizations stand to gain from the blockchain in terms of cyber security; especially its qualities of decentralization and immutability. It has been successfully implemented in some sectors with positive results. This chapter seeks to illustrate how the blockchain can be used to boost and optimize cyber security for organizations.
Real-time UAV Position Control based on Visual Servo Control Algorithm

Position control in UAVs has an important function in modern times UAVs. With the development of computer vision, UAV visual servo control became a research topic. In this paper, a visual servo control system based on color characteristic detection is proposed. By introducing the concept of the pixel of error and proportional control, the input of the PID controller can be adjusted to reach the desired sensitivity of the whole system. A simulation experiment is conducted in Simulink, and it shows that the visual servo control has achieved its goal of real-time position control of a UAV with a target object.
SENTIMENT ANALYSIS OF TEXT AND EMOJI DATA FOR TWITTER NETWORK

Twitter is a social media platform where users can post, read, and interact with 'tweets'. Third party like corporate organization can take advantage of this huge information by collecting data about their customers' opinions. The use of emoticons on social media and the emotions expressed through them are the subjects of this research paper. The purpose of this paper is to present a model for analyzing emotional responses to real-life Twitter data. The proposed model is based on supervised machine learning algorithms and data on has been collected through crawler “TWEEPY” for empirical analysis. Collected data is pre-processed, pruned and fed into various supervised models. Each tweet is assigned to sentiment based on the user's emotions, positive, negative, or neutral.
Performance review results: a key moment for employees and managers in the Algerian company

The economic environment in Algeria is constantly changing, presenting businesses with crucial challenges. To maintain &nbsp;their competitiveness, these businesses now recognize the vital importance of enhancing the performance of their human resources. Measuring, assessing, and communicating employee performance outcomes are essential steps to ensure the success and longevity of the company. In this context, a study was conducted within an Algerian leather company to evaluate the impact of transparent communication of assessment results on performance improvement. The initial hypothesis suggests that non-confidential communication of results promotes a qualitative enhancement in performance by highlighting each employee's specific skills and areas for improvement. A methodology was established to collect and interpret data, enabling a deeper understanding of the effects of this transparent communication on employee motivation, engagement, and perception of evaluations. By utilizing questionnaires, we collected essential insights from employees and pertinent managers, enabling us to gain a more comprehensive understanding of how transparently communicating assessment results influences their motivation, engagement, and perception of the evaluation process. Our research discovered that employees who perceive their supervisors as just and impartial tend to experience higher job satisfaction and greater motivation to excel in their roles. This phenomenon is attributed to the fact that managers who uphold fairness and equity foster a positive workplace atmosphere, cultivating a sense of worth and assistance among employees. Additionally, they establish well-defined expectations and objectives, enabling employees to remain focused and attain their utmost potential. The process of performance appraisal holds significant potential for enhancing both employee and organizational performance. &nbsp;JEL Classification: M1, M12, M0.
To probabilistic description of an ensemble of trajectories to a continuous-discrete control system with incomplete information

A linear control system with continuous and discrete times and discrete memory is considered. The model includes an uncertainty in the description of operators implementing control actions. This uncertainty is a consequence of random disturbances under the assumption of their uniform distribution over known intervals. With each implementation a corresponding trajectory arises from random perturbations, and in the aggregate - an ensemble of trajectories, for which a component-by-component probabilistic description is given in the form of a set of probability density functions parametrized by the current time. To construct these functions, the previously obtained representation of the Cauchy operator of the system under consideration is used. The proposed probabilistic description of perturbations for trajectory variables allows one to find their standard characteristics, including expectation and variance, as well as the entire possible range of values. The results are constructive in nature and allow for effective computer implementation. An illustrative example is given.