Reviewerly/exHarmony huggingface dataset:
The study of the formation of the ecological imperative among students of higher educational institutions

Relevance. Education is a key factor in shaping society's readiness to respond to the challenges of the modern world. Among the global factors that will determine the trends of world development in the near future, the greening of all aspects of human life plays an important role. The aggravation of environmental problems poses a question to the education system about the compliance of the level of environmental education with the needs of society and the education system. The purpose of the work is to assess the level of formation of ecological culture and ecological imperative of students based on the analysis of research data and compare it with the tasks facing modern society. Methodology. To assess the level of ecological knowledge, the formation of ecological culture and ecological imperative, the author's questionnaire was used, aimed at identifying the features of individual consciousness concerning the awareness of the relationship between man, society and nature. The study was conducted in two stages, using Google Forms and focus group methods. The study involved 215 students of technical areas of all bachelor's and specialty courses of the Moscow Aviation Institute (MAI). Results and discussion. The analysis of the results of the study showed an insufficient level of formation of ecological culture and ecological imperative of students. The majority of students – 71% – have an approximate idea of the goals and objectives of modern ecology, and only 14% of respondents demonstrated a good knowledge of the conceptual apparatus. The respondents' low level of knowledge was noted in the field of legal ecology. At the same time, 79.6% are interested in environmental problems and their solution by searching for additional information from various sources. 93% of respondents consider the discipline "Ecology" necessary. The results of the study confirm the importance of environmental education for solving the problems of sustainable development. Conclusion. Trends in the development of society require the introduction of a new paradigm of education that ensures the formation of a person with a humanistic and ecological worldview, the basis of which can be the discipline "Ecology". However, at this stage there is a contradiction between the need and the process of obtaining environmental knowledge in the modern education system. It is necessary to strengthen the environmental component, primarily at the level of higher professional education. The results of the study have theoretical and practical significance in the development of educational and methodological complexes on ecology and programs aimed at the formation of new environmentally valuable worldview attitudes and behavioral skills of high school students.
INVESTIGATING MARITIME CADETS’ DIFFICULTIES IN ENGLISH SPEAKING AT NUSANTARA MARITIME ACADEMY BANJARMASIN

This study examined the English-speaking barriers faced by marine cadets at Nusantara Maritime Academy in Banjarmasin. This study employed a qualitative, descriptive-analytic approach. In this study, there were 30 participants or cadets; 15 were level 1 cadets in their first semester, and 15 were level 2 cadets in their third semester. Interviews and observation were the tools employed in this study. The statistics showed that the Nusantara Nautical Academy in Banjarmasin's maritime cadets had difficulty speaking English. The issues mentioned were solely the linguistic component of speaking ability, specifically a lack of vocabulary, poor pronunciation, and improper grammar. It was discovered that grammar problems were widespread problem among nautical cadets. Lack of vocabulary was the second challenge maritime cadets encountered. The most recent issue was improper pronunciation.&#x0D;
Online Translation

The chapter concerns key aspects of online translation, focusing on the relationship between translators and the web, with a review of the latest trends in this area. A wide range of Internet-based resources, tools and services for translators are presented, highlighting their key features and discussing their pros and cons.
From sports prowess to sintered printing: a lifetime's journey

MPR consulting editor Joe Capus spoke exclusively to Dr Animesh Bose, FAPMI, FASM, VP of Special Projects at Desktop Metal, Inc.
Fast Discriminant Analysis With Adaptive Reconstruction Structure Preserving

Neighborhood reconstruction methods have been widely applied to feature engineering. Existing reconstruction-based discriminant analysis methods normally project high-dimensional data into a low-dimensional space while preserving the reconstruction relationships among samples. However, there are three limitations: 1) the reconstruction coefficients are learned based on the collaborative representation of all sample pairs, which requires the training time to be the cube of the number of samples; 2) these coefficients are learned in the original space, ignoring the interference of the noise and redundant features; and 3) there is a reconstruction relationship between heterogeneous samples; this will enlarge the similarity of heterogeneous samples in the subspace. In this article, we propose a fast and adaptive discriminant neighborhood projection model to tackle the above drawbacks. First, the local manifold structure is captured by bipartite graphs in which each sample is reconstructed by anchor points derived from the same class as that sample; this can avoid the reconstruction between heterogeneous samples. Second, the number of anchor points is far less than the number of samples; this strategy can reduce the time complexity substantially. Third, anchor points and reconstruction coefficients of bipartite graphs are updated adaptively in the process of dimensionality reduction, which can enhance the quality of bipartite graphs and extract discriminative features simultaneously. An iterative algorithm is designed to solve this model. Extensive results on toy data and benchmark datasets show the effectiveness and superiority of our model.
ATTITUDES OF PRIMARY SCHOOL TEACHERS ABOUT THE IMPORTANCE, PLACE AND ROLE OF MODERN TECHNOLOGY AND MATHEMATICS IN STEAM EDUCATION

The natural connection between mathematics, natural and technical sciences should be present and visible through very intense correlations between the respective school subjects. In many educational systems, the field of STEAM is recognized, which implies a holistic approach and integration of natural sciences (biology, chemistry, physics, physical ge- ography), technical and engineering sciences (electrical engineering, mechanical engineering, construction, hardware and software engineering), art and mathematics. The aim of the paper is to examine the views of elementary school teachers (N = 160) about the importance, place and role of modern technology and mathematics in STEAM education. The study discussed the basic theoretical starting points, possibilities and challenges of applying modern technology and mathematics in integrative STEAM teaching. Also, teachers’ attitudes were examined us- ing the research survey technique, using as a research instrument an anonymous questionnaire created in the Google Forms web application as a five-point Likert-type scale. The research results confirm the positive attitudes of teachers about the importance of modern technology and mathematics in STEAM education. Also, the research results confirm that primary school teachers have positive attitudes toward and often apply modern technology and mathematics in STEAM classes for the preparation of materials, research activities and individualization of the teaching process. However, to a lesser extent, they attend professional development seminars focused on the application of modern technology in STEAM integrative teaching; there is a possibility to improve this important segment of education.
Factors Affecting Resilience in University Students in a Single Region

This study aimed to investigate the effects of optimism and self-esteem on resilience in university students. A total of 127 university students were surveyed on optimism, self-esteem, and resilience. The collected data were analyzed using a t-test, one-way ANOVA, correlation analysis, and multilevel multiple regression analysis. The results of this study are as follows: First, university students displayed a statistically significant positive correlation between optimism, self-esteem, and resilience. Second, the factors that most significantly affected resilience in college student were self-esteem (β = .59, p &lt; .001) and optimism (β = .25, p = .001). and these factors explained 62.5% of resilience. Through this study, we looked for approaches to enhance optimism and self-esteem and ultimately improve resilience in university students. It is hoped that this study will be used I meaningfully in follow-up research.
Internet firms lack sway over internet shutdowns

Headline INT: Internet firms lack sway over internet shutdowns
17 Human-Interpretable Learning-Based Automated Driving Systems

The potential benefits of fully automated driving have inspired tremendous research efforts on the part of industry and academia, but widespread deployment of fully automated driving systems (ADS) in vehicles has still not been achieved, and thus the fully automated driving still remains to be a challenging task. One critical, open problem is the integration of automated vehicles with humans both as passengers and as fellow traffic participants; therefore this chapter focuses on ADS development from this perspective. We argue that the learning-based approaches are essential for incorporating the uncertainty of human behavior into these systems. First, we investigate the algorithmic design aspect of this challenge by proposing two novel approaches: combining end-toend deep reinforcement learning with external planners and combining mid-to-mid driving models with supervised learning. We argue that the interpretable cues obtained from these algorithmic choices can contribute to the development of a better human-machine harmonization experience. We also discuss the implementation details and benchmarking practices for building the driving datasets necessary to train these algorithms as well as challenges encountered during this process. Proof-of-concept experiments confirms that these hybrid systems perform better than conventional end-to-end and mid-to-mid approaches.
Being, resistance and post-truth

In this chapter I am confronting the idea of resistance as it has been elaborated by Eero Tarasti and myself within existential semiotics with the same notion in Umberto Eco (supported by Heidegger, Dilthey and Peirce) when the Italian thinker reflects on the semiotic foundation of being. If we agree with Eco that Being is what can be talked of in many ways, then it should be necessarily also what resists in many ways to our discursive attempts, aimed at its fixation in writing. Follows an articulation of three different typologies of resistance, depending on whether the aim of discourse is the physical world, language/ socially constructed reality or the mind. Such articulation prevents from certain epistemological misunderstandings and helps us understand important distortions in the perceptions of truth in the age of social media and digital culture in general. The consequences of such distortions are illustrated with examples from how the COVID-19 crisis is handled by policy makers and public opinion and how the scientific discourse is overshadowed by alternative discourses, far less resistant to subjective opinions, commercial interests and contingencies.
DSGEM: Dual scene graph enhancement module‐based visual question answering

Abstract Visual Question Answering (VQA) aims to appropriately answer a text question by understanding the image content. Attention‐based VQA models mine the implicit relationships between objects according to the feature similarity, which neglects the explicit relationships between objects, for example, the relative position. Most Visual Scene Graph‐based VQA models exploit the relative positions or visual relationships between objects to construct the visual scene graph, while they suffer from the semantic insufficiency of visual edge relations. Besides, the scene graph of text modality is often ignored in these works. In this article, a novel Dual Scene Graph Enhancement Module (DSGEM) is proposed that exploits the relevant external knowledge to simultaneously construct two interpretable scene graph structures of image and text modalities, which makes the reasoning process more logical and precise. Specifically, the authors respectively build the visual and textual scene graphs with the help of commonsense knowledge and syntactic structure, which explicitly endows the specific semantics to each edge relation. Then, two scene graph enhancement modules are proposed to propagate the involved external and structural knowledge to explicitly guide the feature interaction between objects (nodes). Finally, the authors embed such two scene graph enhancement modules to existing VQA models to introduce the explicit relation reasoning ability. Experimental results on both VQA V2 and OK‐VQA datasets show that the proposed DSGEM is effective and compatible to various VQA architectures.
Chinese named entity recognition based on adaptive transformer

In recent years, lexical enhancement methods have been the focus of research in NER tasks. In Chinese NER research,the SoftLexion model has achieved the best results on several Chinese datasets using lexical augmentation methods. In this paper,SoftLexion is investigated, and it is found that the model does not encode the input text characters but directly uses the corresponding embedding vectors found from the pre-trained character embedding matrix. Secondly, Bi-LSTM is used in the sequence modeling layer,but this method is inferior to Transformer in terms of contextual feature extraction capability, long-range feature capture capability, and parallel computing capability, so the research work in this paper adopts the latest Adaptive Transformer to encode the input text of the model and then fuse it with lexical information. The character representation of the fused lexical information is then sequence modeled by the adaptive Transformer and finally decoded by the tag decoding layer. In this paper, experiments are conducted on three Chinese datasets and the study shows that the model performs better with the addition of a character encoding layer and sequence modeling by Adaptive Transformer.
Identification of Complaint Microblogs based on Multimodal and Bi-layer Fusion Framework

Monitoring and responding to complaints generated by consumers are critical for enterprises and governments. In this paper we propose a multimodal and bi-layer fusion framework to identify microblogs posted on SinaWeibo, the largest social media platform in China. We design an effective and simple method for feature fusion. Based bag-of words model, we represent text and images of a post of microblogs as a unified representation. Using multimodal features, we propose a bi-layer fusion framework. Experiments on two datasets of 5000 microblogs demonstrate our method performs better than text-based methods. The weighted F-measure reaches 0.904, and F-measure of complaint class is 37.07% higher than the baseline.
Aspect-based Sentiment Classification with Dual Cooperative Graph Attention Networks

Aspect-based sentiment analysis is a fine-grained sentiment classification task. The task analysis determines the corresponding sentiment polarity based on the aspect words. Recent approaches perform aspect-based sentiment analysis outstandingly by using graph convolution networks over dependency trees, but the approach relies heavily on the accuracy of dependency analysis results and is limited by the complexity of informal online comment expression. To overcome these challenges, in this paper, we explore two kinds of sentence information to enrich the expression of the sentences, i.e., syntactic information obtained from the dependency trees and semantic information obtained by the attention mechanism. On this basis, we propose a dual cooperative graph attention networks(D-CoGAT) model for cooperatively learning sentence information. We utilize the attention mechanism for the dependency matrix generated by the dependency tree. The attention mechanism is also introduced for the embedding of sentences. Furthermore, we propose adaptive modules to adaptively scale the features from grammar and semantics, and design pyramid modules to enhance the fitting and extraction of both features. We conducted experiments on three publicly available datasets, and extensive experimental results demonstrated the effectiveness of our method.
Chinese vascular ultrasound report Generation based on LSTM-RNN-Attention model

It is a tedious task to summarize the diagnostic impressions from the image description of vascular ultrasound, and it is more possible to make mistakes because it requires rich experience and clear logical thinking ability of ultrasound radiologists. In this paper, we explore the use of a LSTM-RNN-Attention (LRA) model to automatically generate diagnostic impressions based on the description of vascular ultrasound images, and evaluate the performance of the model by ROUGE scores. Also, the paper compares this model with pointer generation network (PGN) and basic encoder-decoder (ED) model. The LRA Model achieved a ROUGE-1 score of 26.353, a ROUGE-2 score of 11.587, a ROUGE-L score of 26.168, each score was almost 8 times higher than it of (PGN). The results showed that LRA model presented better performance than these most commonly used models in the field of chinses radiological text summarization and is useful in clinical practice.
Context Prefix Tree Beam Search Algorithm Based on Domain Classification

Nowadays, the end-to-end (E2E) speech recognition model has achieved good performance, but it performs worse on utterances containing infrequent proper nouns. In this work,we proposed a context prefix tree beam search algorithm based on domain classification to improve its ability to recognize contextual phrases. It is used to search for matching candidate characters before pruning in beam search by constructing the context prefix tree in advance. The contextual phrases can be preserved from being pruned prematurely by adding scores to the matching beam path. At the same time, the pre-enhancement score and length reward method is proposed to address the problem that the biasing effect of short contextual phrases is not obvious. The domain classification method is used to decode twice to avoid this method affecting the performance of general domain recognition. On the test set with rich contextual phrases,the experimental results demonstrate that the character error rate (CER) is decreased by 1.3%–4.8% and the context recall rate is increased by 6%–17%,which has no effect on the performance of the general domain.
Extraction of key information from Chinese text of gas station safety accident cases

Keyword matching-based text structuring of gas station safety accident cases can automatically perform keyword extraction and store them in the database, and then the structured data can be subjected to multi-dimensional statistics, visual analysis, knowledge graph management and law mining. 516 cases of gas station safety accidents since 1981, which belong to unstructured electronic text data, were collected in four ways. Through the establishment of a keyword dictionary, 516 case texts were extracted and stored in a structured way using a progressive model of exact matching, phrase matching and intelligent matching+manual verification. The actual evaluation results show that the success rate of exact matching is 30% - 87%, the cumulative success rate of phrase matching is 70% - 100%, the cumulative matching rate of intelligent matching+manual verification is 91% - 100%, the overall matching effect is good, and the method is practical and useful.
Quantum Image Information Hiding Based on Genetic Algorithm

Information hiding based on quantum images mostly adopts the least significant qubit (LSQb) algorithm, which is constrained by the performance indexes of the quality of vision and capacity of imbedding. To meet the requirements of the quality of vision, capacity of imbedding, and the security of the embedded information, this paper proposes a data hiding method for quantum image based on genetic algorithm. Firstly, an enhanced quantum model of digital images is used to represent the image, and a chaotic method is used to encrypt and disrupt the image to be hidden. By designing quantum operations such as selection, crossover and mutation, the secret image is embedded into the relevant qubit plane of the quantum cover image. Finally, the quantum secret image is extracted through the quantum line inverse operation. Simulation results show that the quantum image information hiding method based on genetic algorithm not only enhances the embedding security, but also improves the PSNR by about 9% compared to the LSQb method with the same embedding capacity. The research results have theoretical significance and practical value for research related to quantum image information security and protection.
Ten Years of Snap! - Where Should We Go Next?

Spring 2023 marks 10 years since \snap was first used with students, and the Snap! Cloud was developed for sharing projects. Since then, nearly 700,000 students and educators have worked on more than 6 million Snap! projects. This BOF serves as an opportunity to meet with the large community of Snap! users that attend SIGCSE. The past three releases have focused on power ideas, such as native array-based data programming, building microworlds, and metaprogramming. We want to have a chance to share success stories, and then discuss: what's next?
Enhancing Feedback Messages for Debugging Runtime Errors in an Introductory Java Programming Course

Learning how to program is an essential skill, but many students struggle with debugging errors in their code due to inadequate compiler error messages. To better understand students' errors while working on programming assignments, we automatically collect snapshots of students' code after a period of inactivity. We ran the snapshot codes to extract all potential errors. We developed a feedback tool to provide customized responses for runtime errors extracted from the snapshots. We used a survey to evaluate the effectiveness of the feedback tool and found that as the semester progressed, more students used the feedback tool. The survey also revealed that around 40% of the respondents found the tool useful.
Teaching Cybersecurity

Cybersecurity is a growing job field--and even for students who don't go on to pursue cybersecurity careers, it is crucial to have some level of security awareness. But until recently, the intrigue and opportunity of cybersecurity was usually not introduced until advanced undergraduate CS courses. However, the real world implications and applied nature of the topic lends itself well to catching the interest of a diverse group of students in CS at a younger age.