Reviewerly/exHarmony huggingface dataset:
Research on a Garbage Distribution Bucket Based on Intelligent Voice

With the development of economy and the improvement of people’s living standards, the garbage problem has become increasingly prominent, and people’s concept of environmental protection has been continuously enhanced. In response to the popularization of garbage classification and environmental pollution of garbage, the industry has seen innovative designs for traditional garbage bins. The intelligent waste distribution bin studied in this paper adopts micro-control chip, infrared detection device, mechanical transmission device and linkage mechanism, which can solve the difficulty in separation from the source, cultivate the public’s awareness of waste separation and reduce the cost of waste disposal, It protects user privacy and improves security through blind processing, and thus has practical research significance.
A Generative Learning Architecture Based on CycleGAN for Steganalysis with Unpaired Training Images

Steganalysis based on deep learning has made noticeable progress over the past few years where the training is all based on paired images. However, scenes without paired training data exist. We present an architecture for learning to generate corresponding pseudo stego image from a cover-image in the absence of paired training images. We seek a mapping G that can generate pseudo stego images indistinguishable from the real but unpaired stego images using an adversarial loss. Because this mapping is highly under-constrained, we designed a CycleGAN and introduce spectrum of stego images to reinforce the adversarial loss. Qualitative comparisons demonstrate the superiority of our approach.
EDUCATIONAL CHATBOTS FOR COLLABORATIVE LEARNING: RESULTS OF A DESIGN EXPERIMENT IN A MIDDLE SCHOOL

Educational chatbots promise many benefits for teaching and learning.Although chatbot use cases in this research field are rapidly growing, most studies focus on individual users rather than on collaborative group settings.To address this issue, this paper investigates how chatbot-mediated learning can be designed to foster middle school students in team-based assignments.Using an educational design research approach, quality indicators of educational chatbots were derived from the literature, which served as a guideline for the development of the chatbot Tubo (meaning tutoring bot).Tubo is part of a web-based team learning environment in which students can chat with each other and collaboratively work on their group assignments.As a team member and tutor of each group, Tubo guides the students through the learning journey by different scaffolding elements and helps with content-related questions the students have.As part of a first design cycle, the chatbot application was tested with a school class of a technical vocational school in Switzerland.The received feedback suggests that the approach of team-based learning with chatbots has a lot of potential from the students' and teachers' point of view.However, the role distribution of the individual group members may have to be further specified to address the different needs of autonomous as well as more control-oriented students.
A System of Interaction and Structure III: The Complexity of BV and Pomset Logic

Pomset logic and BV are both logics that extend multiplicative linear logic (with Mix) with a third connective that is self-dual and non-commutative. Whereas pomset logic originates from the study of coherence spaces and proof nets, BV originates from the study of series-parallel orders, cographs, and proof systems. Both logics enjoy a cut-admissibility result, but for neither logic can this be done in the sequent calculus. Provability in pomset logic can be checked via a proof net correctness criterion and in BV via a deep inference proof system. It has long been conjectured that these two logics are the same. In this paper we show that this conjecture is false. We also investigate the complexity of the two logics, exhibiting a huge gap between the two. Whereas provability in BV is NP-complete, provability in pomset logic is $\Sigma_2^p$-complete. We also make some observations with respect to possible sequent systems for the two logics.
AUGMENTED REALITY IN PRIMARY SCHOOL GEOGRAPHY: COGNITIVE GOALS AND USABILITY

The purpose of this study was to examine the application of Augmented Reality (AR) Application in the Geography lesson of Primary School.The application covered the Earth's atmosphere and was evaluated by 71 students in the sixth grade of primary school who completed questionnaires before and after the educational intervention.The results were positive, since most cognitive goals were achieved, and application proved to be interesting and easy to use for students.The significance of this study is twofold.A new ready-to-use digital educational tool was created, while at the same time it is one of the few studies that have investigated the efficiency of AR in terms of cognitive and usability goals with a large sample of students.
TEACHING MEDICAL STUDENTS THE CONCEPT OF ARTIFICIAL INTELLIGENCE THROUGH INTERACTIVE VISUALIZATION

Medical students may find that artificial intelligence will be integrated in future workplace, but they may perceive technology concepts too abstract or complex.The objective is to adopt interactive visualization to teach medical students the concept of artificial intelligence.In the learning activity, students may interact with a visualization tool, act as a data science without programming.It is expected that interactive visualization may facilitate students' understanding.
Formation of ecological culture in the context of multicultural interaction

Introduction. The formation of the ecological culture of the individual in the modern world is being updated due to the need to prepare a modern young person for adequate behavior in an environmental crisis and an increase in involvement in the global world. The goal is to reveal the features of the ecological culture formation in school students, taking into account the ethno-cultural component in the conditions of multi-ethnic education. Theoretical analysis. The concept of “ecological culture” is a cumulative unity of ecology and culture; in order to form an ecological culture, it is necessary to seek support in traditional ethical norms and values regarding relationships in the human-nature system. The modern school requires the introduction of new educational paradigms, taking into account ethno-regional characteristics and ethno-cultural values of the educational environment. In the schools of the Volga region, an educational strategy called multi-ethnic education is being successfully implemented, during which children are introduced to ethnic culture and the ability to live in a multinational country. Conclusions. The study of the ethno-cultural component contributes to the formation of a vitally demanded knowledge system for each student and the development of skills to solve regional environmental problems based on the traditional values of environmental management. As a result of educational work, a school graduate shows civic responsibility, respects national traditions, shows tolerance, and has a high ecological culture.
2022 Acknowledgment of <i>mBio</i> Reviewers

On behalf of the editors of mBio, I gratefully acknowledge the following members of our inaugural Junior Editorial Board who served as reviewers for the journal in 2022. The Junior Editorial Board began receiving scholarly publishing and peer review training in January, and formally began reviewing for mBio in September. These dedicated and well-qualified early-career researchers are a welcome addition to our community of reviewers. Cynthia Adinortey Daniel Aridgides Lori Banks Joseph Boll Clotilde Bongrand Jiandong Chen Ousmane Cisse Sebastien Crepin Sophie Darch Nicole De Nisco Stephen Dolan Elizabeth Draganova Melissa Ellermann Alexandre Gouzy Melinda Grosser Andrzej Harris Tania Henríquez Rebeccah Lijek Rogério Lourenço Bruno Martorelli Di Genova Angela Mitchell Roberto Molina-Quiroz Irina Novikova Francisca Nwaokorie Ceren Ozkul Atmika Paudel Peter Ramirez Lennart Schada von Borzyskowski Anna Serquina Liku Tezera Zulema Udaondo Dominguez Rachel Van Duyne Daniel Velez-Ramirez Julia Willett Jason Yang Zhengzhong Zou
Consensus Automatic Speech Recognition (CASR) in Cognitive Testing

Abstract Background Scoring verbal cognitive tests with automatic speech recognition (ASR) engines increases the efficiency of scoring and provides word timestamps that enable detailed temporal analyses of spoken responses. Here, we describe consensus ASR (CASR) procedures that incorporate multiple ASR engines to increase transcription and timing accuracy and generate CASR transcript confidence scores. Method Seven ASR engines produced automatic transcriptions of both speech database samples (GMU Speech Accent Archive and NUS Auditory English Lexicon Project) and verbal test responses of 41 subjects from the California Cognitive Assessment Battery (CCAB). A novel Recognizer Output Voting Error Reduction (ROVER) algorithm was used to mutually align the transcripts, and a Bayesian weighted voting algorithm produced the best CASR transcript, mean word timestamps, and consensus scores. Word error rates (WER) gauged CASR accuracy against either predetermined or manually corrected transcripts. Result Database sentence WERs from 1767 subjects ranged from a mean of 22% (Windows10 UWP) to 6% (Rev.ai) with CASR producing 5%, with no significant gender or age effects but better performance for native english speakers (Figure 1). In CCAB test responses, for limited word response tests CASR WERs ranged from 3% to less than 1% (Figure 2); for expansive word response tests CASR WERs ranged from 8% to 2% (Figure 3); and for discursive speech, CASR WERs ranged from 6% to 5%. Word start time ASR estimates for 594 database words in lists ranged in mean deviations from true times from 250ms std.dev. (Google) to 17ms std.dev. (Amazon) with CASR obtaining 14ms errors (Figure 4). Finally, consensus confidence scores from CCAB test responses, ranging from 0 to 1 (1 = complete agreement across ASR engines), show that CASR words with consensus scores above 0.8 and 0.9 are correct &gt;99% and &gt;99.8% of the time, respectively (Figure 5). Conclusion CASR produces transcripts for verbal test responses accurate enough for estimating scores in most limited word response tests. In large vocabulary response tests, CASR transcripts facilitate quick manual correction, and confidence values can identify transcript words needing manual correction. Patterns in CASR errors also indicate future substantial reductions to CASR WER on a per test basis.
Directly Including Data with Alzheimer’s Publications Through Augmented Reality and Web Technologies – Schol‐AR

Abstract Background Alzheimer’s disease research is multi‐disciplinary by nature and based on many forms of data, from MRI scans to microscopic image stacks. Much of this information is difficult to communicate through standard scientific articles and figures, often resulting in incomplete data reporting and suboptimal comprehension for many readers. Method We introduce a community‐accessible data layering approach for directly including modern digital information on top of scientific figures, enabling entire datasets to be included with scientific articles. Through this approach readers can easily view data associated with publications through mobile devices or web browsers, averting the need to simplify complex data into 2D static figures. Result Data layering facilitates communication and comprehension of Alzheimer’s research by enabling accessible viewing of complex information directly with scientific articles, posters, and other formats. Conclusion Using augmented reality and web technologies we can modernize communication of Alzheimer’s research, bridging the gap between the digital basis of modern science and the natural limitations of journal articles.
Weights analysis for CNN models in MRI classification

Abstract Background In deep neural network learnable parameters like weights and bias of convolution kernel, normalization layer or fully connected layers (FCLs) are the one responsible for giving prediction score. Though one parameter or parameters in a layer has not decisive role, however when whole network prediction is to be done, each one has role to generate prediction value. Hence, they are important for the network performance. Method We modeled a CNN for classification and trained it using 50% of total MRI scans for 3 classes. After training the network for 50 epochs, the network reaches convergences with 100% training accuracy and around 70% validation accuracy. Then, the trained models’ parameters i.e., weights and bias of FCL layers were analyzed class wise for any correlation with its parent class. Result The weights of final FCL are plotted as in Figure 1. The correlation matrix for weights on sample MRIs with trained weights are shown in Tables 1 and 2. Here, two activation functions were used for final test results were ReLU and Leaky ReLU with final test accuracy around 67.3% and 70.9% respectively. Conclusion We attempted to study the weights pattern in FCL layer for a trained CNN model along with its correlation value for each class. [Acknowledgement] This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF‐2021R1I1A3050703). And this research was supported by the BrainKorea21Four Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (4299990114316).
Pengaruh Disiplin Belajar Dan Motivasi Belajar Terhadap Hasil Belajar Dengan Menggunakan Pembelajaran E-Learning Pada Sekolah Minggu Buddha (Smb) Chong De Se-Sumatera Utara

This research was conducted ƭ on ƭ the ƭ factorsƭ thatƭ affectƭ learning ƭ outcomes, ƭ especially ƭ on ƭ the ƭ subject ƭ of ƭ Buddhism at SMB Chong De, North Sumatra. Thisƭ research ƭ wasƭ initially ƭ motivatedƭ by ƭ theƭ fact ƭ thatƭ studentƭ learningƭ outcomesƭ varied and tended to be low. Based on the previous analysis, ƭ it ƭ shows ƭ that ƭ student ƭ learning ƭ outcomes ƭ are ƭ in ƭ line ƭ with ƭ habits ƭ that ƭ do ƭ not show discipline and motivation in learning ƭ when ƭ learning ƭ with ƭ e-learning, ƭ this ƭ is ƭ shown ƭ by ƭ the ƭ tasks ƭ done ƭ by ƭ plagiarizing ƭ from the internet. This research was conductedƭ usingƭ aƭ quantitativeƭ approachƭ withƭ exƭ postƭ factoƭ type.ƭ Theƭ respondentsƭ ofƭ thisƭ study were 336 students and&#x0D; 183 samples used in thisƭ studyƭ wereƭ determinedƭ basedƭ onƭ the ƭ Slovinƭ formula.ƭ Collectingƭ dataƭ usingƭ a ƭ questionnaire with a Likert scale of 4 choices. The validityƭ testƭ wasƭ carried ƭ outƭ with ƭ the ƭ Product ƭ Momentƭ Correlation ƭ andƭ the ƭ reliability was carried out with the Cronbach Alpha formulaƭ whileƭ theƭ homogeneityƭ testƭ wasƭ carriedƭ outƭ withƭ theƭ Bartletƭ test.ƭ Theƭ resultsƭ showed that (1) there was a positive influence of ƭ discipline ƭ with ƭ learning ƭ outcomesƭ indicated ƭ by ƭ the ƭ correlationƭ coefficient ƭ of ƭ 0.895; (2) there is a positive effect of motivation withƭ learningƭ outcomesƭ indicatedƭ byƭ theƭ correlationƭ coefficientƭ ofƭ 0.899;ƭ (3)ƭ thereƭ isƭ an influence of discipline and motivation with learningƭ outcomesƭ together,ƭ indicatedƭ byƭ theƭ correlationƭ coefficientƭ valueƭ ofƭ 0.898
Authentication and Cryptography solutions for Industrial IoT - A Study

The rapid emergence of Internet of Things (IoT) has fueled the wide acceptance of its own extended version Industrial IoT(IIoT). It provides a real-time, robust, and reliable communication in various domains, including robotics, medical sensors, and other software-defined applications. In general, authentication is considered as the prime security control in IIoT systems to ensure that a right user is accessing resources at any specified time. Many research works have been presented in the literature so far with regard to improving the authentication and security aspects of industrial IIoT systems. This paper aims at presenting a comprehensive study on security and authentication aspects in IIoT systems; it highlights the existing cryptographic methods for such IIoT systems. Further, some of the existing research works on the IIoT systems, along with their strengths and weaknesses, are also been reviewed and presented. The work will be useful for researchers or practitioners while applying authentication techniques on IIoT systems.
Design of Immersive Intelligent Translation Software based on Multi-Agent Algorithm

Firstly, the multi-agent network is divided into several communities based on the hierarchical clustering algorithm; secondly, a node importance evaluation algorithm is proposed. Chinese sentences are automatically segmented and imported into the database, and finally the construction of a large-scale English-Chinese bilingual parallel corpus is completed. A new combinatorial optimization method, the combinatorial optimization multi-agent evolutionary algorithm, is proposed. This method fixes the agents on the grid, and each agent competes with its neighbors to increase its energy. The bottleneck in the development of machine translation, computer-aided translation and other technologies is expounded, and the ideas for solving the problem are given and an empirical analysis is carried out.
Selection Method of Fuzzy Semantics in Machine Translation and the Integration of LBP Algorithm

This paper studies the accuracy and rationality of machine English translation based on the LBP algorithm, and proposes a machine English translation method based on the selection of the optimal solution of fuzzy semantics. Construct an information extraction model for machine English translation, establish a fuzzy semantic topic word attribute table for machine English translation, and use phrases as the basic granularity to produce paraphrase results that are semantically consistent with the translation hypothesis set. Extract phrase paraphrase resources by using massively parallel corpus. Experimental test results show that using this method for machine English translation improves the recall performance of semantic information by 6.7%, and the feature matching degree of topic words is higher.
Machine Learning based Analysis of Histopathological Images of Breast Cancer Classification using Decision Tree Classifier

Cancer is a significant public health problem that is experienced by people all around the world. This disease has already taken the lives of a significant number of people, and it will continue to do so in the years to come. Breast cancer has already surpassed cervical cancer as the largest frequent form of cancer detected in females in both developed and developing countries, making it the second leading cause of cancer death among women worldwide. This disease claims the lives of a significant number of women each and every year. If detected at an earlier stage, breast cancer is substantially easier to treat. In this study, a decision tree-based categorization of breast cancer in histological images is presented for the first time. Both benign and malignant breast growths can eventually develop into breast cancers. Researchers use classification as a tool to assess and classify the medical data they collect. Segmentation is a key factor in the identification of breast cancer. In order to train the model, the cancer specimens that can be found in the Kaggle archive are employed. The classification used by Decision Tree has an overall accuracy of 87.28 percent. These results provide evidence to support the utilization of the suggested machine learning-based Decision Tree classifier in the pre-evaluation of patients for the purposes of triage and decision-making prior to the provision of data.
Simulation of the Laser Stripe Reflection in Sub-Pixel Estimation with Autoconvolution Algorithm

The accuracy of the profile scanners depends, among other factors, mostly on algorithms for image processing of the reflected laser stripe and its sub-pixel position. Different signal processing techniques can be easily tested and analyzed with correctly simulated physical effects of the system. This study explains a method that simulates laser stripe reflection in a dynamic environment, especially in the case when the scanner images are saturated with the flattened top of the reflection. The expected shape of the laser stripe reflection follows the Gaussian profile which can easily be distorted by ambient light, improper camera exposure time, or irradiated laser power. In this case, some of the pre-processing techniques, such as autoconvolution, can be used for mitigating those effects. Implemented as a multiplierless filter, this paper analyzes the usage of autoconvolution for filtering techniques on Field Programmable Gate Array (FPGA) through quantization of the pixel word length.
Multi-Center Backup Framework based on Audio and Video Complex Information Extraction Algorithm

This paper first introduces the principle, development and application of expert system, and proposes a robust agricultural science and education video text extraction method based on complex background. The algorithm mainly realizes video text extraction through video decoding, MSER text positioning, projection segmentation and Tesseract text recognition. An English-assisted teaching module based on expert system theory, which is aimed at teachers and users. The module classifies and summarizes English knowledge points using a combination of frame-based and production-based knowledge expressions. BP neural network is used to establish students’ autonomous learning and the system module analyzes the test situation of the students, enables the students to self-diagnose, and combines the memory forgetting curve through the students’ repeated practice.
Computer-Aided Modeling and Empirical Analysis in Big Data Environment

Based on full reference to the existing theoretical research and empirical analysis methods at home and abroad, this paper uses the random coefficient model and computer-aided modeling based on the relevant panel data of different provinces in China. Under the Granger causality test, the financial There is a two-way causal relationship between development and economic growth in the short, medium, long and full cycles; under the cross-spectrum analysis, financial development lags behind economic growth in the short and long cycles, and slightly leads economic growth in the medium and full cycles Interpretation of the relationship between inflation, finance and economic growth through the use of three-dimensional analysis methods, empirically test the impact and role of the financial development on economic growth from multiple aspects and layers, and explore the two iteraction rules, influence paths, and function provision among other aspects.