Reviewerly/exHarmony huggingface dataset:
Differences in Student Satisfaction in Online Learning and Remote Teaching Courses during the COVID-19 Adaptation Stage

The COVID-19 pandemic has profoundly changed the way engineering courses are taught worldwide. Consequently, universities have developed strategies that involve social isolation, thus modifying their operations during this period to transform to non-classroom courses. Two types of courses have been developed due to the need to embrace these methods quickly: the Online Learning (OL) course, which was created before the pandemic began, and Remote Teaching (RT) courses, created during this period. This research measures differences in the levels of academic acceptance and recommendations among students regarding both types of non-classroom courses. We evaluated 477 OL classes and 1,597 RT classes conducted in the School of Engineering at Universidad Privada del Norte-Peru. Results show significant differences (p < 0.05) in satisfaction between both types of courses. Our analysis shows that RT courses have a higher level of acceptance among students.
Binary multicriteria collaborative filtering

Collaborative filtering is specialized in suggesting appropriate products and services to the users concerning personal characteristics and past preferences without requiring any effort of users. It might be more efficient to collect preferences of users based on multiple subcriteria of products and services. For this purpose, researchers propose multicriteria recommender systems that are convenient for more accurate and useful evaluation of items. Insuchsystems, it might be preferable tocollect binary ratings instead of numerical ones due to the large number of subcriteria. However, there is a gap in the literature to satisfy a binary preferences-based multicriteria recommender system. In this study, the applicability of multicriteria recommender systems based on binary ratings is investigated. Firstly, recommendations for users on the overall criterion are produced by employing naïve Bayes classifier. In order to improve the quality of recommendations, user- and item-based similarity models are proposed enabling the formation of more successful neighborhoods. Such models are further improved by integrating a concordance measure between overall preference and subcriteria ratings, which helps to provide more personalized and meaningful similarities among users. Finally, a hybrid model is proposed employing user- and item-based models together and real data-based experimental outcomes demonstrate that the quality of estimated binary referrals is improved statistically significantly.
Two‐way constraint network for RGB‐Infrared person re‐identification

RGB-Infrared person re-identification (RGB-IR Re-ID) is a task aiming to retrieve and match person images between RGB images and IR images. Since most surveillance cameras capture RGB images during the day and IR images at night, RGB-IR Re-ID is helpful when checking day and night surveillance for criminal investigations. Previous related work often only extracts sharable and identity-related features in images for identification. Few researches specifically extract and make use of features that do not have the ability to distinguish identity, e.g. identity-unrelated features derived from background and modality. In this Letter, we propose a novel and concise RGB-IR Re-ID network named two-way constraint network (TWCN). Compared with traditional Re-ID networks, TWCN not only extracts and utilises identity-related features but also novelly makes full use of identity-unrelated features to improve the accuracy of the experiment. TWCN uses a reverse-triplet loss to extract identity-unrelated features, and proposes an orthogonal constraint to remove identity-unrelated information from identity-related features, which improves the purity of identity-related features. In addition, a correlation coefficient synergy and central clustering (CCSCC) loss is introduced into TWCN to extract identity-related features effectively. Extensive experiments have been conducted to prove our method is effective.
Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks

Neural relation extraction (NRE) models are the backbone of various machine learning tasks, including knowledge base enrichment, information extraction, and document summarization. Despite the vast popularity of these models, their vulnerabilities remain unknown; this is of high concern given their growing use in security-sensitive applications such as question answering and machine translation in the aspects of sustainability. In this study, we demonstrate that NRE models are inherently vulnerable to adversarially crafted text that contains imperceptible modifications of the original but can mislead the target NRE model. Specifically, we propose a novel sustainable term frequency-inverse document frequency (TFIDF) based black-box adversarial attack to evaluate the robustness of state-of-the-art CNN, CGN, LSTM, and BERT-based models on two benchmark RE datasets. Compared with white-box adversarial attacks, black-box attacks impose further constraints on the query budget; thus, efficient black-box attacks remain an open problem. By applying TFIDF to the correctly classified sentences of each class label in the test set, the proposed query-efficient method achieves a reduction of up to 70% in the number of queries to the target model for identifying important text items. Based on these items, we design both character- and word-level perturbations to generate adversarial examples. The proposed attack successfully reduces the accuracy of six representative models from an average F1 score of 80% to below 20%. The generated adversarial examples were evaluated by humans and are considered semantically similar. Moreover, we discuss defense strategies that mitigate such attacks, and the potential countermeasures that could be deployed in order to improve sustainability of the proposed scheme.
Adapted human pose: monocular 3D human pose estimation with zero real 3D pose data

The ultimate goal for an inference model is to be robust and functional in real life applications. However, training vs. test data domain gaps often negatively affect model performance. This issue is especially critical for the monocular 3D human pose estimation problem, in which 3D human data is often collected in a controlled lab setting. In this paper, we focus on alleviating the negative effect of domain shift in both appearance and pose space for 3D human pose estimation by presenting our adapted human pose (AHuP) approach. AHuP is built upon two key components: (1) semantically aware adaptation (SAA) for the cross-domain feature space adaptation, and (2) skeletal pose adaptation (SPA) for the pose space adaptation which takes only limited information from the target domain. By using zero real 3D human pose data, one of our adapted synthetic models shows comparable performance with the SOTA pose estimation models trained with large scale real 3D human datasets. The proposed SPA can be also employed independently as a light-weighted head to improve existing SOTA models in a novel context. A new 3D scan-based synthetic human dataset called ScanAva+ is also going to be publicly released with this work.
The impacts of the Online Education Program and Learning Satisfaction on Transfer of Training : Focusing on Mediated Moderation Effect of Internet Self-efficacy

The objectives of this study is to provide the curriculum manager of cultural tourism commentator with meaningful implications by empirically investigating moderating effect of internet self-efficacy on the relationship between online curriculum quality and learning satisfaction, and mediating effect of learning satisfaction on the relationship between online curriculum quality and the transfer of training. To achieve the goals, literatures on ‘quality of online curriculum’, ‘curriculum quality, learning satisfaction, and transfer of training’, ‘internet self-efficacy’, and the present condition of study regarding transfer of training were reviewed, and the empirical researches on perceptions of domestic cultural tourism commentators who took part in the 2020 online training were performed. The findings of the study are as follows. Firstly, the online curriculum quality of culture tourism commentators had a significant impact on learning satisfaction, and the internet self-efficacy of curriculum participants moderated the effect relationship. Secondly, the online curriculum quality had a significant impact on transfer of training, and the learning satisfaction had a mediating effect on the effect relationship. Through the above two major findings, lastly, on the relationship that online curriculum quality had an impact on transfer of training being mediated by learning satisfaction, self-efficacy of curriculum participants finally, had a significant impact on transfer of training by moderating the relationship between the online curriculum quality and learning satisfaction.
GA<sup>2</sup>RM: A GA-Based Action Rule Mining Method

Action Mining is a subfield of Data Mining that tries to extract actions from traditional data sets. Action Rule is a type of rule that suggests some changes in its consequent part. Extracting action rules from data has been one of the research interests in recent years. Current state-of-the-art action rule mining methods like DEAR typically take classification rules as their input; Since traditional classification methods have been designed for prediction and not for manipulation, therefore extracting action rules directly from data can result in more valuable action rules. Here, we have proposed a method to generate action rules directly from data. To tackle the problem of huge search space of action rules, a Genetic Algorithm has been devised. Different metrics have been defined for investigating the effectiveness of our proposed method and a large number of experiments have been done on real and synthetic data sets. The results show that our method can find from 20% to 10 times more interesting (in case of support and confidence) action rules in comparison with its competitors.
Green Technologies for Management on Railroad Transportation

Technique and technology development allows us to discuss the possibility of crucial changes into a conservative way of railroad transportation management. One of the options regarding the train control system is aimed at denial of the classic approach being connected with the implementation of a traditional automation kit. The authors of this article are being presented with the conception of a green IT railroad rolling stock control system called Green Interlocking. The above approach helps us rely on effective inexhaustible energy sources and modern know-how of data processing plus important technologies management.
Neighborhood Structure-Based Model for Multilingual Arbitrarily-Oriented Text Localization in Images/Videos

The text matter in an image or a video provides more important clue and semantic information of the particular event in the actual situation.Text localization task stands an interesting and challenging research-oriented process in the zone of image processing due to irregular alignments, brightness, degradation, and complexbackground.The multilingual textual information has different types of geometrical shapes and it makes further complex to locate the text information.In this work, an effective model is presented to locate the multilingual arbitrary oriented text.The proposed method developed a neighborhood structure model to locate the text region.Initially, the maxmin cluster is applied along with 3X3 sliding window to sharpen the text region.The neighborhood structure creates the boundary for every component using normal deviation calculated from the sharpened image.Finally, the double stroke structure model is employed to locate the accurate text region.The presented model is analyzed on five standard datasets such as NUS, arbitrarily oriented text, Hua's, MRRC and real-time video dataset with performance metrics such as recall, precision, and f-measure.
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical Projections

This paper studies Principal Component Analysis (PCA) for data lying in hyperbolic spaces. Given directions, PCA relies on: (1) a parameterization of subspaces spanned by these directions, (2) a method of projection onto subspaces that preserves information in these directions, and (3) an objective to optimize, namely the variance explained by projections. We generalize each of these concepts to the hyperbolic space and propose HoroPCA, a method for hyperbolic dimensionality reduction. By focusing on the core problem of extracting principal directions, HoroPCA theoretically better preserves information in the original data such as distances, compared to previous generalizations of PCA. Empirically, we validate that HoroPCA outperforms existing dimensionality reduction methods, significantly reducing error in distance preservation. As a data whitening method, it improves downstream classification by up to 3.9% compared to methods that don't use whitening. Finally, we show that HoroPCA can be used to visualize hyperbolic data in two dimensions.
Gender Bias in Natural Language Processing Across Human Languages

Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations about our future world.Gender bias in NLP has been well studied in English, but has been less studied in other languages.In this paper, a team including speakers of 9 languages -Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolofreports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages.We develop extensions to professionlevel and corpus-level gender bias metric calculations originally designed for English and apply them to 8 other languages, including languages that have grammatically gendered nouns including different feminine, masculine, and neuter profession words.We discuss future work that would benefit immensely from a computational linguistics perspective.
High-Performance Large-Scale Image Recognition Without Normalization

Batch normalization is a key component of most image classification models, but it has many undesirable properties stemming from its dependence on the batch size and interactions between examples. Although recent work has succeeded in training deep ResNets without normalization layers, these models do not match the test accuracies of the best batch-normalized networks, and are often unstable for large learning rates or strong data augmentations. In this work, we develop an adaptive gradient clipping technique which overcomes these instabilities, and design a significantly improved class of Normalizer-Free ResNets. Our smaller models match the test accuracy of an EfficientNet-B7 on ImageNet while being up to 8.7x faster to train, and our largest models attain a new state-of-the-art top-1 accuracy of 86.5%. In addition, Normalizer-Free models attain significantly better performance than their batch-normalized counterparts when finetuning on ImageNet after large-scale pre-training on a dataset of 300 million labeled images, with our best models obtaining an accuracy of 89.2%. Our code is available at this https URL deepmind-research/tree/master/nfnets
Grey Relevancy Degree and Improved Eight-Direction Sobel Operator Edge Detection

Edge detection is an important aspect to improve image edge quality in image processing.The purpose of edge detection is to identify the points in digital images with great brightness variation.However, the accuracy of traditional edge detection methods in edge extraction is low.For the actual image, the grey edge is sometimes not very clear, the image also contains noise.The detection result of the traditional Sobel operator is relatively accurate, but the detection result is rough and sensitive to noise.To solve the above problems, this paper proposes an improved eight-direction Sobel operator based on grey relevancy degree, which combines 5 × 5 Sobel operator with a grey relational degree and a new eight-direction grey relevancy method.The results show that this method can detect the useful information of edge more accurately and improve the anti-noise performance.However, the drawback is that the algorithm is not automatic.
Selecting Data Augmentation for Simulating Interventions

Machine learning models trained with purely observational data and the principle of empirical risk minimization \citep{vapnik_principles_1992} can fail to generalize to unseen domains. In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels. We find that many domain generalization methods do not explicitly take this spurious correlation into account. Instead, especially in more application-oriented research areas like medical imaging or robotics, data augmentation techniques that are based on heuristics are used to learn domain invariant features. To bridge the gap between theory and practice, we develop a causal perspective on the problem of domain generalization. We argue that causal concepts can be used to explain the success of data augmentation by describing how they can weaken the spurious correlation between the observed domains and the task labels. We demonstrate that data augmentation can serve as a tool for simulating interventional data. We use these theoretical insights to derive a simple algorithm that is able to select data augmentation techniques that will lead to better domain generalization.
Handwritten Tamil Character Recognition Using Convolution Neural Network by Adam Optimizer

Optical Character Recognition is a widely used electronic method for recognition of handwritten images. Tamil handwritten character recognition is complex to recognize. Hence considerable research efforts have been taken in this field. The complexities of writers and the characters, structure over looping and unwanted character portions are the major challenges faced in Tamil characters. RGB to grayscale conversion, image complementation and structure morphing are enclosed in the preprocessing phase. The processed images are subject to recognition with optimized CNN. The connected layers are enhanced using ADAM optimizer for improvement of the standard. The accuracy and performance of the proposed work is compared with other models with certain performance measures.
Latent Programmer: Discrete Latent Codes for Program Synthesis

In many sequence learning tasks, such as program synthesis and document summarization, a key problem is searching over a large space of possible output sequences. We propose to learn representations of the outputs that is specifically meant for search: rich enough to specify the desired output but compact enough to make search more efficient. An appealing realization of such representation are discrete latent codes, as this naturally allows sophisticated combinatorial search strategies. The latent codes are learned using a self-supervised learning principle, in which first a discrete autoencoder is trained on the output sequences, and then the resulting latent codes are used as intermediate targets for the end-to-end sequence prediction task. Based on these insights, we introduce the Latent Programmer, a program synthesis method that first predicts a discrete latent codes from input/output examples, and then generates the program in the target language. We evaluate the Latent Programmer on two domains: synthesis of string transformation programs, and generation of programs from natural language descriptions. We demonstrate that the discrete latent representation significantly improves synthesis accuracy.
Mapping of potential rare earth deposits in the Schiel alkaline complex using sentinel-2B multispectral sensor

In this study, the images of Sentinel-2B multispectral sensor are used to map minerals associated with rare earth elements (REE) in an attempt to discover new potential targets for REE deposits in the Schiel Alkaline Complex of South Africa. Spectral Information Divergence (SID) classification was applied to Sentinel-2B images to map areas containing potential monazite and hematite. Generally, the accuracy of SID classification is warranted by selecting appropriate threshold values, which are normally user-defined. In this study, a simple thresholding approach is proposed; this approach requires at least one area, with a confirmed existence of a mineral being sought, to be used as a reference point for selecting optimum threshold value. The aim of this approach is to determine the lowest threshold value at which mineral classification in a reference point starts. The smaller the threshold value, the more likely the classified pixels represent minerals in question in the ground. Following this approach, a value of 0.02 and 0.03 was used as the maximum acceptable threshold value for mapping REE-bearing mineral (monazite) and iron-rich mineral (hematite), respectively. The proposed approach was validated through field sampling and geochemical analysis of samples collected at potential REE deposit targets, including one rock sample (S3) collected in the area void of minerals of interest, based on SID classification. The analytical results show that the samples collected at anomalous mineral clusters have relatively high REE contents that are well beyond the average crustal abundance and S3, thus suggesting the viability of the proposed approach.
Opinion Mining On Web-Based Communities Using Optimised Clustering Algorithms

People like to consume content by downloading it from the internet, together with contribution and creation of new content, thanks to the growth of social networks and the Web. People also turn out to be more anxious to communicate and impart their insights on the web with respect to everyday exercises or worldwide issues. Sentiment analysis and opinion mining are rapidly evolving as a result of the rapid growth of web-based platforms such as Facebook, Reddit, Twitter, and others. This is from the world of data mining and NLP, especially in the mining of web data and the mining of texts. Why has sentiment analysis been more popular in recent years, which is also known as opinion mining? Whenever we attempt to choose to buy an item, we are probably going to hear the thoughts of companions or family members and do a few overviews before we buy the item. Opinions are now undoubtedly the main drivers of our behavior. The positive, negative, or neutral polarities are consistently seen in the opinions. Opinion mining can be used to discover opinion polarity classification, subjectivity identification, spam detection, and opinion summarization using emotional classification.