Reviewerly/exHarmony huggingface dataset:
Electronic information educational environment and modern student

Introduction. The modern system of domestic higher education is designed to flexibly respond to the challenges of the modern world, one of which is the possibility of obtaining knowledge through distance learning. The use of electronic services in this process, acting as an online and offline learning tool, actualizes the urgent need to develop in a digital environment a high-quality content of academic disciplines that meets the requirements of educational standards in various areas of training of future subject teachers, primary school teachers, preschool educational institution teachers, psychologists, speech therapists, defectologists. At the same time, it is important to assess all the risks that can devalue the physical, moral and material costs of organizing the educational process in the e-learning system from both teachers and support service specialists and the university as a whole. Materials and Methods. The paper uses a descriptive method based on the study and systematization of survey data of pedagogical university students of different directions and training profiles for several years. The respondents were full-time and part-time students. The data collection tool was the e-learning service of Minin University. Results. The analysis of respondents' answers to questions about the pros and cons of distance learning in the system of electronic information educational environment of the pedagogical university allowed us to draw some conclusions about the existence of both positive and negative consequences of the implementation of distance learning, which threaten the psychological safety of participants in the educational process. Discussion and Conclusions. Taking into account the fact that the creation of an e-learning system at Minin University is a large-scale project of modernization of the higher education system, during the implementation of which, since 2012, some experience has already been accumulated that requires reflection, today we can talk about the possibility of considering this electronic information educational environment as minimizing the risks of distance learning through electronic services. Further work in the direction of optimizing the work of the e-learning system of the pedagogical university, including working out the strategy of the "individual educational route" of each student, including assistance to students with special educational needs, should certainly be carried out from the position of involving students in assessing the opportunities and difficulties of working in an electronic educational environment.
OPTIMIZED DEEP NEURAL NETWORKS ARCHITECTURE MODEL FOR BREAST CANCER DIAGNOSIS

Breast cancer has increasingly claimed the lives of women. Oncologists use digital mammograms as a viable source to detect breast cancer and classify it into benign and malignant based on the severity. The performance of the traditional methods on breast cancer detection could not be improved beyond a certain point due to the limitations and scope of computing. Moreover, the constrained scope of image processing techniques in developing automated breast cancer detection systems has motivated the researchers to shift their focus towards Artificial Intelligence based models. The Neural Networks (NN) have exhibited greater scope for the development of automated medical image analysis systems with the highest degree of accuracy. As NN model enables the automated system to understand the feature of problem-solving without being explicitly programmed. The optimization for NN offers an additional payoff on accuracy, computational complexity, and time. As the scope and suitability of optimization methods are data-dependent, the choice of selection of an appropriate optimization method itself is emerging as a prominent domain of research. In this paper, Deep Neural Networks (DNN) with different optimizers and Learning rates were designed for the prediction of breast cancer and its classification. Comparative performance analysis of five distinct first-order gradient-based optimization techniques, namely, Adaptive Gradient (Adagrad), Root Mean Square Propagation (RMSProp), Adaptive Delta (Adadelta), Adaptive Moment Estimation (Adam), and Stochastic Gradient Descent (SGD), is carried out to make predictions on the classification of breast cancer masses. For this purpose, the Mammographic Mass dataset was chosen for experimentation. The parameters determined for experiments were chosen on the number of hidden layers and learning rate along with hyperparameter tuning. The impacts of those optimizers were tested on the NN with One Hidden Layer (NN1HL), DNN with Three Hidden Layers (DNN4HL), and DNN with Eight Hidden Layers (DNN8HL). The experimental results showed that DNN8HL-Adam (DNN8HL-AM) had produced the highest accuracy of 91% among its counterparts. This research endorsed that the incorporation of optimizers in DNN contributes to an increased accuracy and optimized architecture for automated system development using neural networks.
Online Education and Wireless Network Coordination of Electronic Music Creation and Performance under Artificial Intelligence

This paper is aimed at studying the online education and wireless network collaboration on electronic music creation and performance under artificial intelligence (AI). This paper uses a fuzzy clustering algorithm (FCA), designs the sensor network-related equipment, and uses AI to design an electronic music creation system. The analysis of simulation experiments suggests that under the premise of increasing the number of neighbors, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of collaborative filtering and fuzzy <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>C</mi> </math> -means clustering algorithms show a downward trend. However, with the same number of neighbors, the filtering matching algorithm is greater than FCA regarding the mean values of MAE and MSE. Meanwhile, on the electronic music performance system of AI, the digital module is designed and the sound data are imaged on the oscilloscope, and the collaboration of electronic music online education and wireless network is completed. The following conclusion is drawn: modularizing the creative mode of intelligent electronic music has achieved higher computational efficiency. Through the oscilloscope, the sound feature is converted into the image structure, and the corresponding sound and image mode is formed, which realizes the purpose of online electronic music intelligent matching and optimizes the effect of online education. In the AI environment, the matching degree of verification electronic music curriculum resources is better than traditional matching algorithms, and the accuracy is higher.
An Analysis of Hall’s Theory of Cultural Identity and Its Application in Flipped Class

This paper understands Hall's "cultural identity theory" through the cultural phenomenon in the ethnic dense.Through the study of the connotation, definition, characteristics and construction mode of "cultural identity theory", the author understands and digests this theory further, and through the reflection of this theory, the author put it into the actual teaching, combined with cross-cultural teaching, hoping to understand the definition of cross-cultural identity.
Beyond Grammatical Error Correction: Improving L1-influenced research writing in English using pre-trained encoder-decoder models

In this paper, we present a new method for training a writing improvement model adapted to the writer's first language (L1) that goes beyond grammatical error correction (GEC).Without using annotated training data, we rely solely on pre-trained language models finetuned with parallel corpora of reference translation aligned with machine translation.We evaluate our model with corpora of academic papers written in English by L1 Portuguese and L1 Spanish scholars and a reference corpus of expert academic English.We show that our model is able to address specific L1influenced writing and more complex linguistic phenomena than existing methods, outperforming what a state-of-the-art GEC system can achieve in this regard.Our code and data are open to other researchers 1 .
Character Transformations for Non-Autoregressive GEC Tagging

We propose a character-based nonautoregressive GEC approach, with automatically generated character transformations.Recently, per-word classification of correction edits has proven an efficient, parallelizable alternative to current encoder-decoder GEC systems.We show that word replacement edits may be suboptimal and lead to explosion of rules for spelling, diacritization and errors in morphologically rich languages, and propose a method for generating character transformations from GEC corpus.Finally, we train character transformation models for Czech, German and Russian, reaching solid results and dramatic speedup compared to autoregressive systems.The source code is released at https://github.com/ufal/wnut2021_character_transformations_gec.
Research on corner detection algorithm based on the long depth of field binocular camera with wavefront coding

The long depth of field camera with wavefront coding is introduced in Design of Long Depth of Field Array Optical Components Defect Detection System [1]. It can obtain the whole object information at one time within 3–13 m of the depth of field. However, when binocular calibration is used in corner detection, the scalability and asymmetry of the point spread function will lead to images missing and redundancy. This article describes an improvement in the Hessian corner detector to solve this problem. Firstly, the local negative maximum with the largest change in curvature is extracted around the X corner, and the initial corner set is obtained according to their central symmetry. Then, by making use of the distance characteristics between corners of the checkerboard and constraining the angle characteristics of these corners, the false corners are filtered and the correct corner set is obtained. Finally, the corners set is sorted and the starting direction of the sorting is constrained. The corners extracted through this algorithm can be better applied to camera calibration and positioning. Experiments show that the proposed detector is better than OpenCV detector in the accuracy of positioning, calibration and measurement, and has certain robustness.
Automatic Entity State Annotation using the VerbNet Semantic Parser

Tracking entity states is a natural language processing task assumed to require human annotation.In order to reduce the time and expenses associated with annotation, we introduce a new method to automatically extract entity states, including location and existence state of entities, following Dalvi et al. (2018) and Tandon et al. (2020).For this purpose, we rely primarily on the semantic representations generated by the state of the art VerbNet parser (Gung, 2020), and extract the entities (event participants) and their states, based on the semantic predicates of the generated Verb-Net semantic representation, which is in propositional logic format.For evaluation, we used ProPara (Dalvi et al., 2018), a reading comprehension dataset which is annotated with entity states in each sentence, and tracks those states in paragraphs of natural human-authored procedural texts.Given the presented limitations of the method, the peculiarities of the ProPara dataset annotations, and that our system, Lexis, makes no use of task-specific training data and relies solely on VerbNet, the results are promising, showcasing the value of lexical resources.
Surface Form Competition: Why the Highest Probability Answer Isn't Always Right.

Large language models have shown promising results in zero-shot settings. For example, they can perform multiple choice tasks simply by conditioning on a question and selecting the answer with the highest probability. However, ranking by string probability can be problematic due to surface form competition—wherein different surface forms compete for probability mass, even if they represent the same underlying concept in a given context, e.g. “computer” and “PC.” Since probability mass is finite, this lowers the probability of the correct answer, due to competition from other strings that are valid answers (but not one of the multiple choice options). We introduce Domain Conditional Pointwise Mutual Information, an alternative scoring function that directly compensates for surface form competition by simply reweighing each option according to its a priori likelihood within the context of a specific task. It achieves consistent gains in zero-shot performance over both calibrated and uncalibrated scoring functions on all GPT-2 and GPT-3 models on a variety of multiple choice datasets.
Tumor Classification of Gene Expression Data by Fuzzy Hybrid Twin SVM

A new classification model, the fuzzy hybrid twin support vector machine (FHTWSVM), is proposed by combining the fuzzy TWSVM and the hypersphere SVM. The hypersphere SVM is utilized for generating the hyperspheres for the positive and negative class with the smallest possible radius, so that the hyperspheres can contain as many samples as possible. The samples which the hyperspheres cover form a new sample set. Furthermore a distance-based fuzzy function is utilized to calculate the fuzzy factors for the samples. Finally FHTWSVM is used to train all samples with the parameters optimized by grid search. This method can maximize intra-class clustering for noise removal and reduce the influence of outliers. To demonstrate the superiority of the performance of FHTWSVM over other classifiers, e.g., KNN, RF, Bayesian, TWSVM, AdaBoost and XGBoost, a series of experiments is conducted using eight gene expression datasets. The evaluation results show that the proposed approach can improve the classification performance as well as reduce prediction errors for the datasets.
Visual Sequential Search Test Analysis: An Algorithmic Approach

In this work we present an algorithmic approach to the analysis of the Visual Sequential Search Test (VSST) based on the episode matching method. The data set included two groups of patients, one with Parkinson’s disease, and another with chronic pain syndrome, along with a control group. The VSST is an eye-tracking modified version of the Trail Making Test (TMT) which evaluates high order cognitive functions. The episode matching method is traditionally used in bioinformatics applications. Here it is used in a different context which helps us to assign a score to a set of patients, under a specific VSST task to perform. Experimental results provide statistical evidence of the different behaviour among different classes of patients, according to different pathologies.
Digital geological map and geochronological database of the Cenozoic cover of the southern Mesa Central province, Mexico

The digital geological map of the southern sector of the Mesa Central province is presented, covering an area of ~41 000 km2 in central Mexico. This first interactive map is a compilation of the geological maps available in the literature and the unpublished maps done by our work team. The map focuses on the Cenozoic stratigraphy, supported by a geochronological database of 261 isotopic ages derived from K-Ar, U-Pb, Ar-Ar, and fission tracks methods. The interactive map displays the lithostratigraphic and chronostratigraphic units and the major and second-order Cenozoic faults. Map construction considered lithostratigraphy and geochronological correlation criteria and the main unconformities. Integrating these data in a single digital map allows a regional vision of the southern Mesa Central, making the map a valuable work tool to better understand the Cenozoic geologic evolution of central Mexico.
Cloud as platform for monetizing complementary data for AI-driven services: A two-sided cooperative game

In this paper, we design a strategic game that aims to deliver complementary data services among multiple data providers over a cloud intermediary platform. More specifically, we formalize the problem as an extended two-sided market model by courting on one side some influential data providers in order to attract other data providers on the same side to form a bundling of data services.
The True Cost of Field Education is a Barrier to Diversifying Geosciences

Fieldwork is considered critical to developing technical skills in geoscience education, and typical undergraduate degrees require >30 days in the field. Tuition costs of enrolling in field camp are acknowledged as a barrier to participation in geosciences; however, the cost of participation in field activities may also include the cost of personal field gear (hiking boots, backpacks, etc.), travel, lost wages, and dependent care. To neutralize impacts of systemic bias on the future geoscience workforce, it is imperative that we (a) examine how the cost of field work presents barriers to participation, and (b) intentionally direct financial resources towards dismantling these barriers. We show that the financial burden associated with a week-long field endeavor, excluding potential tuition costs and including personal field gear, domestic air travel, lodging, dependent care and lost wages range from 1271 to 2,007 U.S. dollars (USD), and can be as large as 6,262 USD. This sum is likely to be out of reach for individuals from low-income groups, and represents a fundamental barrier to diversifying participation in our field. Budgets for inclusive field research and education must account for and accommodate these financial challenges to broader participation.
Logarithmic Observation of Feature Depth for Image-Based Visual Servoing

Due to the robustness to robot modeling and camera calibration errors and avoidance of complete target geometry, image-based visual servoing has always been an important topic in the fields such as robotics, computer vision and so forth. When the image information obtained by the camera is mapped to the robotic task space to design the servoing control law, the resulting interaction matrix, which links the spatial velocity of the camera to the temporal variation of the selected image features, depends on the unknown feature depths. The use of inaccurate feature depths may influence the stability and robustness of the controller, and even cause the failure of the task. In this article, based on the perspective camera model, by employing the principle of reduced order observer, a novel logarithmic observer is presented for on-line recovery of feature depth. Compared with the typical observers now available, the presented observer offers several advantages: global convergence, a faster convergence rate of error structure than exponential error structure, a less restricted observability condition and greater robustness against measurements with noise. The comparison results of numerical simulations indicate the superiority of the presented observer, and real experiments with Kinect v2 sensor further validate the effectiveness of the presented observer in practical situation. Note to Practitioners—This article was motivated by the depth problem in the image-based visual servoing scheme, but it can also be used in other situations where the image depth information is needed, such as 3D reconstruction, robot navigation, etc. The existing depth acquisition methods include TOF sensors, stereo vision, depth observers and so on. However, TOF sensors are sensitive to light conditions, and the mounting space of stereo vision is slightly large, and there is contradiction between observation performance and computational complexity in most existing observers. In this article, a novel structure of logarithmic reduced order observer is described in detail, which can be utilized to estimate the depth information of images easily. The simulations and experiments verify the good performance of the observer. The limitations of the given observer are that the estimation accuracy is not very good under weak excitation, and the camera needs to be calibrated in advance. Future work will focus on overcoming these two limitations.
Real Time Traffic Prediction Based On Social Media Text Data Using Deep Learning

Due to urbanization Traffic management is one of the major issues in contemporary civic management, considering this circumstance traffic analysis is turning into the need of the present world. Text data generated by Twitter, Facebook and other social media platforms can be used for traffic management. Big data helps in traffic prediction and traffic analysis of advancing metropolitan zones. Constant traffic investigation requires preparing of information streams that are produced persistently to increase fast experiences. To measures stream information at a fast rate advancements on high figuring limit is required. Social media text data can be processed by using batch processing and stream processing with big data architecture through Spark and Hadoop framework. In this paper big data architecture is proposed for real time traffic text data analysis. In architecture Spark and Kafka are used in combination. Kafka helps in pipelines text data used in conjunction with spark stream processing engine. Big data architecture using Spark, Kafka with ability for processing and preparing huge measure of information, have settled the serious issue of handling and putting away constantly streaming data. The traffic information from Twitter API is streamed. In The proposed model pointed toward ensemble neural network model to reduce the variance in results for better prediction foreseeing traffic stream text data by incorporating Spark and Kafka that will be of an extraordinary incentive to the public authority for traffic management and analysis.
Amharic Text Summarization for News Items Posted on Social Media

This paper introduces Amharic Text Summarization for News Items posted on social media, to summarize the news items posted Amharic texts over a time posted documents from social media on Twitter and Facebook; The main problems of the social media posted texts are that most people would probably read their posted in Amharic texts with duplicate posted documents.However, to find the information the user is looking for she or he will have to find summary posted texts and read important portions of posts as Amharic documents to extract desired information on social media.Summarization is dealing with information overload presenting and posted with a text document for the current time representation of the posted documents to summarize.Our proposed approach has three main components: First, calculate the similarity between each posted document within the two pair of sentences.Second, clustering based on the similarity results of the documents to group them by using Kmeans algorithm.Third, summarizing the clustered posted document individually using TF-IDF algorithms that involve finding statistical ways for the frequent terms to rank the documents.We applied the summarization technique is an extractive summarization approach that is assigned an extract the sentences with highest ranked sentences in the posted documents to form the summaries and the size of the summary can be identified by the user.In the experiment one the highest F-measure score is 87.07%for extraction rate at 30%, in the clustered group of protests posts.The second experiment the highest F-measure score is 84% for extraction rate at 30%, in droughts post groups.In the third experiment the highest F-measure score is 91.37% for extraction rate at 30%, in the sports post groups and also the fourth experiments the highest F-measure score is 93.52% for extraction rate at 30% to generate the summary post texts.If the system to generate the size of summary is increased, the extraction rate also increased in posted texts.For this the evaluation system shown that a very good results to summaries the posted texts on social media.