Reviewerly/exHarmony huggingface dataset:
Discovering entity inheritance relationships in document stores

While declaring schemas is not required in most NoSQL stores, explicit schemas are crucial to provide essential capabilities in the development of database applications such as schema visualization, code generation, and data quality improvement. Therefore, some published works and data modeling tools have recently presented reverse-engineering approaches to extract NoSQL schemas from data or code. None of them have addressed the inference of inheritance relationships in which a parent entity type is specialized in one or more child entity types. In this paper, we present a strategy to discover entity inheritance relationships from the set of existing structural variations of an entity type. The algorithm proposed also identifies the discriminator field, if it exists. A validation process has been defined, which is applied to three real datasets.
ANALISIS PROSES PEMBELAJARAN MATEMATIKA BERBASIS DARING MENGGUNAKAN APLIKASI GOOGLE CLASSROOM PADA MASA PANDEMI COVID-19

The purpose of this research describe the analysis of the learning process of mathematics online based using the google classroom application in IVB grade at elementary school Aisyiyah, Sukabumi City. This study used descriptive qualitative method. Data collection techniques using interviews and documentation. The writer acts as an instrument for collecting data. Data analysis techniques using the analysis model according to Miles and Huberman which consists of data reduction, data display and conclusion drawing/verificication. The subjects of this study were students of class IV B in the academic year 2019/2020. The number of students is 20 people. The results showed that the google classroom application can help and facilitate teachers and students in implementing the distance learning process. Based on the results of the study it can be concluded that online learning using the google classroom application proved effective carried out during the WFH consequence covid-19 pandemic. Utilization of Google classroom is acceptable and further developed even though it still requires revamping the availability of supporting resources.&#x0D; &#x0D; Keywords: learning process, google classroom, math
Antecedents and consequences of college students' satisfaction with online learning

We delved into the antecedents and consequences of college students' satisfaction with online learning. We proposed the antecedents would be interactions, teaching presence, self-management of learning, and academic self-efficacy, and that the consequence would be intention to continue to use online learning. Participants were 236 college students in South Korea who completed an online survey. Our findings suggest that students' satisfaction with online learning was positively related to the interactions between students and instructor, teaching presence, self-management of learning, and academic self-efficacy. We also found that student satisfaction with online learning positively predicted their intention to continue to use online learning. Thus, our findings in this study provide educators with ways to increase student satisfaction, and add to knowledge about the relationship between students' satisfaction and their intention to take online courses.
Formation of Conceptual Bases of Digital Transformation of Education and Science of Ukraine

In the article reasonably the need to develop digital skills among the general population and the role of the education system in this process. Timeliness of project development was noted Concepts of digital transformation of education and science until 2026 prepared by the Ministry of Education and Science of Ukraine. Briefly outline the content of the project Concepts of digital transformation and its expert evaluation was carried out. Certain positive aspects of the Concept were noted and the omissions and weaknesses of the project were systematized. The main disadvantages include: balance between education and science in the Concept project because it contains digital the transformationя Science hardly considered; not taking into account a number of important normative legal documents of Ukraine, including the framework of digital competencies for Ukrainian citizens; insufficient focus of the document on the digital modernization of the entire content of education at all levels and ensuring the formation of basic digital applicants. It was concluded that the project needs significant improvement, recommendations have been developed that will contribute to the revision of the draft Concept to improve its quality and meet modern digitalization trends.
The Automatic Detection of Dataset Names in Scientific Articles

We study the task of recognizing named datasets in scientific articles as a Named Entity Recognition (NER) problem. Noticing that available annotated datasets were not adequate for our goals, we annotated 6000 sentences extracted from four major AI conferences, with roughly half of them containing one or more named datasets. A distinguishing feature of this set is the many sentences using enumerations, conjunctions and ellipses, resulting in long BI+ tag sequences. On all measures, the SciBERT NER tagger performed best and most robustly. Our baseline rule based tagger performed remarkably well and better than several state-of-the-art methods. The gold standard dataset, with links and offsets from each sentence to the (open access available) articles together with the annotation guidelines and all code used in the experiments, is available on GitHub.
Subjective Quality Assessment for Cloud Gaming

Using subjective testing, we study the effect of the network parameters, delay and packet loss ratio, on the QoE of cloud gaming. We studied three different games, selected based on genre, popularity, content complexity and pace, and tested them in a controlled network environment, using a novel emulator to create realistic lognormal delay distributions instead of relying on a static mean delay, as used previously; we also used Parsec as a good representative of the state of the art. We captured user ratings on an ordinal Absolute Category Rating scale for three quality dimensions: Video QoE, Game-Playability QoE, and Overall QoE. We show that Mean Opinion Scores (MOS) for the game with the highest levels of content complexity and pace are most severely affected by network impairments. We also show that the QoE of interactive cloud applications rely more on the game playability than the video quality of the game. Unlike earlier studies, the differences in MOS are validated using the distributions of the underlying dimensions. A Wilcoxon Signed-Rank test showed that the distributions of Video QoE and Game Playability QoE are not significantly different.
Blog Backlinks Malicious Domain Name Detection via Supervised Learning

Web spam is the unwanted request on websites, low-quality backlinks, emails, and reviews which is generated by an automated program. It is the big threat for website owners; because of it, they can lose their top keywords ranking from search engines, which will result in huge financial loss to the business. Over the years, researchers have tried to identify malicious domains based on specific features. However, lighthouse plugin, Ahrefs tool, and social media platforms features are ignored. In this paper, the authors are focused on detection of the spam domain name from a mixture of legit and spam domain name dataset. The dataset is taken from Google webmaster tools. Machine learning models are applied on individual, distributed, and hybrid features, which significantly improved the performance of existing malicious domain machine learning techniques. Better accuracy is achieved for support vector machine (SVM) classifier, as compared to Naïve Bayes, C4.5, AdaBoost, LogitBoost.
Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens

Standard pretrained language models operate on sequences of subword tokens without direct access to the characters that compose each token's string representation.We probe the embedding layer of pretrained language models and show that models learn the internal character composition of whole word and subword tokens to a surprising extent, without ever seeing the characters coupled with the tokens.Our results show that the embedding layers of RoBERTa and GPT2 each hold enough information to accurately spell up to a third of the vocabulary and reach high character ngram overlap across all token types.We further test whether enriching subword models with character information can improve language modeling, and observe that this method has a near-identical learning curve as training without spelling-based enrichment.Overall, our results suggest that language modeling objectives incentivize the model to implicitly learn some notion of spelling, and that explicitly teaching the model how to spell does not appear to enhance its performance on such tasks. 1
New research on People Counting and Human Detection

People counting methods and human detection methods can be considered in determining the number of people in a room. Lately, computer vision in artificial intelligence (AI) is gaining an increasingly important place by interpreting infrared images according to certain characteristics, namely groups of connected pixels in an image that share some common property. This paper starts from three existing systems, two counting the number of people using two PIR sensors, and two ultrasonic sensors and a human detection method, using the Grid-EYE camera. As the Grid-EYE camera resolution is only 8 x 8 pixels, it is not accurate enough and a better solution is being sought. The Lepton infrared camera is chosen, with a resolution of 80 x 60 pixels. Measurements are made in parallel with the two cameras and the results are compared and analyzed. Finally, conclusions are drawn, and applications are exemplified in which it is useful to determine the number of people in a room.
A Bayesian Dynamical Approach for Human Action Recognition

We introduce a generative Bayesian switching dynamical model for action recognition in 3D skeletal data. Our model encodes highly correlated skeletal data into a few sets of low-dimensional switching temporal processes and from there decodes to the motion data and their associated action labels. We parameterize these temporal processes with regard to a switching deep autoregressive prior to accommodate both multimodal and higher-order nonlinear inter-dependencies. This results in a dynamical deep generative latent model that parses meaningful intrinsic states in skeletal dynamics and enables action recognition. These sequences of states provide visual and quantitative interpretations about motion primitives that gave rise to each action class, which have not been explored previously. In contrast to previous works, which often overlook temporal dynamics, our method explicitly model temporal transitions and is generative. Our experiments on two large-scale 3D skeletal datasets substantiate the superior performance of our model in comparison with the state-of-the-art methods. Specifically, our method achieved 6.3% higher action classification accuracy (by incorporating a dynamical generative framework), and 3.5% better predictive error (by employing a nonlinear second-order dynamical transition model) when compared with the best-performing competitors.
ProoFVer: Natural Logic Theorem Proving for Fact Verification

Abstract Fact verification systems typically rely on neural network classifiers for veracity prediction, which lack explainability. This paper proposes ProoFVer, which uses a seq2seq model to generate natural logic-based inferences as proofs. These proofs consist of lexical mutations between spans in the claim and the evidence retrieved, each marked with a natural logic operator. Claim veracity is determined solely based on the sequence of these operators. Hence, these proofs are faithful explanations, and this makes ProoFVer faithful by construction. Currently, ProoFVer has the highest label accuracy and the second best score in the FEVER leaderboard. Furthermore, it improves by 13.21% points over the next best model on a dataset with counterfactual instances, demonstrating its robustness. As explanations, the proofs show better overlap with human rationales than attention-based highlights and the proofs help humans predict model decisions correctly more often than using the evidence directly.1
Blockchain based Trusted Charity Fund-Raising

The proposed system is a decentralized authentic platform that aims to leverage blockchain along with other technologies to design a trusted framework which would enable charity donations to be as accountable, trustworthy and transparent. The paper explores the potential for deploying blockchain within existing organizations to support smooth conduction of charity funds from the donor to the actual needy person using a stable Ethereum based Blockchain oriented platform. In this fast developing world of modernization, some people are becoming too competitive to earn money while others have no clue about getting even a penny. But at the same time, there exist people who wish to contribute to the society out of altruism. There exist many online donation platforms in the world and yet issues concerning extra fees, accountability and processing delay still exist as well as these existing centralized systems for charities are so corrupt that people lose belief in these trustless systems and hence the charities become futile. This paper explores how the blockchain can be leveraged in the philanthropic sector, through charitable donation services via a web- based donor platform.
Recall@k Surrogate Loss with Large Batches and Similarity Mixup

This work focuses on learning deep visual representation models for retrieval by exploring the interplay between a new loss function, the batch size, and a new regularization approach. Direct optimization, by gradient descent, of an evaluation metric, is not possible when it is nondifferentiable, which is the case for recall in retrieval. A differentiable surrogate loss for the recall is proposed in this work. Using an implementation that sidesteps the hardware constraints of the GPU memory, the method trains with a very large batch size, which is essential for metrics computed on the entire retrieval database. It is assisted by an efficient mixup regularization approach that operates on pairwise scalar similarities and virtually increases the batch size further. The suggested method achieves state-of-the-art performance in several image retrieval benchmarks when used for deep metric learning. For instance-level recognition, the method outperforms similar approaches that train using an approximation of average precision.
A Deeply Modulated Scheme for Variable-Rate Video Compression

Rate adaption is one of the decisive factors for the applications of video compression. Previous deep video compression methods are usually optimized for a single fixed rate-distortion (R-D) tradeoff. While they can achieve multiple bitrates by training multiple independent models, the achievable bitrates are limited to several discrete points on the R-D curve and the storage cost increases proportionally to the number of models. We propose a variable-rate scheme for deep video compression, which can achieve continuously variable rate by a single model, i.e., reaching any point on the R-D curve. In our scheme, two deep auto-encoders are used to compress the residual and the motion vector field respectively, which directly generate the final bitstream. The basic rate adaptation can be achieved by using the R-D tradeoff parameter to deeply modulate all the internal feature maps of the auto-encoders. In addition, other modules in our scheme, notably motion estimation and motion compensation, also affect the final bitrate indirectly. We further use the R-D tradeoff parameter to modulate them via a conditional map, thereby effectively improving the compression efficiency. We use a multi-rate-distortion loss function together with a step-by-step training strategy to optimize the entire scheme. The experimental results show the proposed scheme achieves continuously variable rate by a single model with almost the same compression efficiency as multiple fixed-rate models. The additional parameters and computation of our model are negligible when compared with a single fixed-rate model.
Automatic Speech Recognition Features Extraction Techniques: A Multi-criteria Comparison

Features extraction is an important step in Automatic Speech Recognition, which consists of determining the audio signal components that are useful for identifying linguistic content while removing background noise and irrelevant information.The main objective of features extraction is to identify the discriminative and robust features in the acoustic data.The derived feature vector should possess the characteristics of low dimensionality, long-time stability, nonsensitivity to noise, and no correlation with other features, which makes the application of a robust feature extraction technique a significant challenge for Automatic Speech Recognition.Many comparative studies have been carried out to compare different speech recognition feature extraction techniques, but none of them have evaluated the criteria to be considered when applying a feature extraction technique.The objective of this work is to answer some of the questions that may arise when considering which feature extraction techniques to apply, through a multicriteria comparison of different features extraction techniques using the Weighted Scoring Method.
Fair Conformal Predictors for Applications in Medical Imaging

Deep learning has the potential to automate many clinically useful tasks in medical imaging. However translation of deep learning into clinical practice has been hindered by issues such as lack of the transparency and interpretability in these ``black box'' algorithms compared to traditional statistical methods. Specifically, many clinical deep learning models lack rigorous and robust techniques for conveying certainty (or lack thereof) in their predictions -- ultimately limiting their appeal for extensive use in medical decision-making. Furthermore, numerous demonstrations of algorithmic bias have increased hesitancy towards deployment of deep learning for clinical applications. To this end, we explore how conformal predictions can complement existing deep learning approaches by providing an intuitive way of expressing uncertainty while facilitating greater transparency to clinical users. In this paper, we conduct field interviews with radiologists to assess possible use-cases for conformal predictors. Using insights gathered from these interviews, we devise two clinical use-cases and empirically evaluate several methods of conformal predictions on a dermatology photography dataset for skin lesion classification. We show how to modify conformal predictions to be more adaptive to subgroup differences in patient skin tones through equalized coverage. Finally, we compare conformal prediction against measures of epistemic uncertainty.
Ship Target Detection and Recognition Method on Sea Surface Based on Multi-Level Hybrid Network

This paper proposes a method of ship detection and recognition based on a multi-level hybrid network, designing a noise reducing and smoothing image enhancement algorithm based on multi-level two-dimensional template filter and three-layer pyramid structure. This work constructs an adaptive segmentation detection and ultra-lightweight target classification network model combining global and local image gray statistics. With a combination of traditional image processing and deep learning methods, the demand for computing and storage resources is reduced greatly. This method can detect and recognize the ship targets near the sea-sky-level quickly and has been verified by real flight camera data, and the accuracy rate is more than 90%. In comparison to the Tiny YOLOV3 network, the accuracy rate is reduced by 5%, but the calculation efficiency is increased by 50 times, and the parameters are reduced by 550 times.
The Effect of Adaptive Learning Rate on the Accuracy of Neural Networks

Learning rates in gradient descent algorithms have significant effects especially on the accuracy of a Capsule Neural Network (CNN).Choosing an appropriate learning rate is still an issue to date.Many developers still have a problem in selecting a learning rate for CNN leading to low accuracies in classification.This gap motivated this study to assess the effect of learning rate on the accuracy of a developed (CNN).There are no predefined learning rates in CNN and therefore it is hard for researchers to know what learning rate will give good results.This work, therefore, focused on assessing the effect of learning rate on the accuracy of a CNN by using different learning rates and observing the best performance.The contribution of this work is to give an appropriate learning rate for CNNs to improve accuracy during classification.This work has assessed the effect of different learning rates and came up with the most appropriate learning rate for CNN plant leaf disease classification.Part of the images used in this work was from the PlantVillage dataset while others were from the Nepal database.The images were pre-processed then subjected to the original CNN model for classification.When the learning rate was 0.0001, the best performance was 99.4% on testing and 100% on training.When the learning rate was 0.00001, the highest performance was 97% on testing and 99.9% on training.The lowest performance observed was 81% accuracy on testing and 99% on training when the learning rate was 0.001.This work observed that CNN was able to achieve the highest accuracy with a learning rate of 0.0001.The best Convolutional Neural Network accuracy observed was 98% on testing and 100% on training when the learning rate was 0.0001.
Efficient-ROD

Radar signal-based object detection has become a primary and critical issue for autonomous driving recently. Recently advanced radar object detectors indicated that the cross-model supervision-based approach presented a promising performance based on 3D hourglass convolutional networks. However, the trade-off between computational efficiency and performance of radar object detection tasks is rarely investigated. When higher performance is required in the detection tasks, the 3D convolutional backbone network rarely meets the real-time applications. This paper proposes a lightweight, computationally efficient, and effective network architecture to conquer this issue. First, Atrous convolution, as well-known as dilated convolution, is adopted in our backbone network to make a smaller convolutional kernel having a larger receptive field so as a larger convolutional kernel can be eliminated to reduce the number of parameters. Furthermore, a densely connected residual block (DCSB) is proposed to better deliver the gradient flow from the loss function to improve the feature representation ability. Finally, the hourglass network structure is made by stacking several DCSBs with Mish activation function to form our detection network, termed as DCSN. In this manner, we can keep a larger receptive field and reduce the number of parameters significantly, resulting in an efficient radar object detector. Experiments are demonstrated that the proposed DCSN achieves a significant improvement of inference time and computational complexity, with comparable performance for radar object detection. The source code can be found in https://github.com/jesse1029/RADER-DCSN.