Reviewerly/exHarmony huggingface dataset:
TrustAL: Trustworthy Active Learning Using Knowledge Distillation

Active learning can be defined as iterations of data labeling, model training, and data acquisition, until sufficient labels are acquired. A traditional view of data acquisition is that, through iterations, knowledge from human labels and models is implicitly distilled to monotonically increase the accuracy and label consistency. Under this assumption, the most recently trained model is a good surrogate for the current labeled data, from which data acquisition is requested based on uncertainty/diversity. Our contribution is debunking this myth and proposing a new objective for distillation. First, we found example forgetting, which indicates the loss of knowledge learned across iterations. Second, for this reason, the last model is no longer the best teacher-- For mitigating such forgotten knowledge, we select one of its predecessor models as a teacher, by our proposed notion of "consistency". We show that this novel distillation is distinctive in the following three aspects; First, consistency ensures to avoid forgetting labels. Second, consistency improves both uncertainty/diversity of labeled data. Lastly, consistency redeems defective labels produced by human annotators.
Solar Radiation Prediction using Ant Colony Optimization and Artificial Neural Network

This paper proposes a Solar Radiation Prediction Model employing Ant Colony Optimization (ACO) and Artificial Neural Network (ANN), named as SRPM. SRPM aims to incorporate the Feature Selection (FS) technique in the ANN training with the guidance of hybridizing ACO. The main reason behind using FS technique is, it can provide an improved solution from a particular problem by identifying the most salient features from the available feature set. In SRPM, the hybridizing ACO search technique utilizes the information gathered from the correlation among the features and the result of ANN training. To assist the ACO search, pheromone updating technique and measurement of heuristic information have been performed by two particular sets of rules. Thus, SRPM utilizes the benefits of using the wrapper and filter approaches in selecting the salient features during SRP task. The combination eventually creates an equipoise between exploration and exploitation of ants in the way of searching as well as strengthening the capability of global search of ACO for obtaining well qualified solution in SRPM. To evaluate the executive efficiency of SRPM, data samples were collected from BMD and NASA-SSE department. Exploratory outcomes show that SRP can select six utmost salient features by providing 99.74% and 99.78% averaged testing accuracies for BMD and NASA-SSE data samples that are composed of 12 and 15 original features, respectively. Furthermore, the proposed model successfully obtained 0.26% and 0.22% MAPE for BMD and NASA-SSE data samples, respectively with a high correlation of about 99.97% within the actual and predicted data.
Web 3.0 and its Reflections on the Future of E-Learning

Abstract: There is a large number of diversity in Web 3.0 technologies accessible for incorporation in the educational environment, but recognize how to use these and can be fascinating to the instructor. Web 3.0 is also known as technologically advanced medium which enables the user to Read/Write/Execute and also enables the machines to carry out some of the thinking so that it can be accessible for the human beings. The acceptance of Web 3.0 technologies is quite easy and it contains more than the Internet and basic word processing techniques. An analysis on Web 3.0 applications, which are easy to use, reasonable, and require minimum technology skills. Web 3.0 items that can be easily executed by learners or by the instructors include podcasting, weblogs, etc. Web 3.0, always uses the Internet as its transfer technique which can be an effective method of making a dynamic learning and teaching experience. Keywords: Web 3.0 technologies, Semantic Web, e-Learning, educational environment.
Varieties of Relevant S5

In classically based modal logic, there are three common conceptions of necessity, the universal conception, the equivalence relation conception, and the axiomatic conception. They provide distinct presentations of the modal logic S5, all of which coincide in the basic modal language. We explore these different conceptions in the context of the relevant logic R, demonstrating where they come apart. This reveals that there are many options for being an S5-ish extension of R. It further reveals a divide between the universal conception of necessity on the one hand, and the axiomatic conception on the other: The latter is consistent with motivations for relevant logics while the former is not. For the committed relevant logician, necessity cannot be the truth in all possible worlds.
The Efficacy of REV-OPOLY Augmented Reality Board Game in Higher Education

Augmented Reality (AR) in the classroom is becoming a new trend, which incorporates technology in the teaching of topics in a particular course. An instance of the technology used in the classroom is an AR board game which is designed to aid in learning through games. This study is a continuation of a preliminary work, which will concentrate in the designing, developing, and testing phases of REV-OPOLY, a board game with AR intervention in the area of the emerging technology revolution. This study employs a quantitative method of quasi-experimental design using pre-test and post-test, and test assessment. It was conducted within one semester on 100 undergraduate students who enrolled in the Computer Application in Management subject in Universiti Utara Malaysia. Findings show significant improvements in students' scores in all tests preceding the use of REV-OPOLY.
The HoPE Model Architecture: a Novel Approach to Pregnancy Information Retrieval Based on Conversational Agents

Conversational agents are used to communicating with humans in a friendly manner. To achieve the highest level of performance, agents need to respond assertively and fastly. Transformer architectures are shown to produce excellent performances on recent tasks; however, for tasks involving conversational agents, they may have a lower speed performance. The main goal of this study is to evaluate and propose a HoPE (Healthcare Obstetric in PrEgnancy) model that is tailored to pregnancy data. We carried out a dataset extraction and construction process based on collections of health documents related to breastfeeding, childcare, pregnant care, nutrition, risks, vaccines, exams, and physical exercises. We evaluated two pre-trained models in the Portuguese language for the conversational agent architecture proposal and chose the one with the best performance to compose the HoPE architecture. The BERTimbau model, which has been trained on data augmentation strategies, proves to be able to retrieve information quickly and most accurately than others. For the fine-tuning process, we achieved a Spearman correlation of 95.55 on BERTimbau augmented with a few pairs (1.500 pairs). The HoPE model architecture achieved an F1-Score of 0.89, outperforming other combinations tested in this study. We will evaluate this approach for clinical studies in future studies.
Toward More Effective Human Evaluation for Machine Translation

Improvements in text generation technologies such as machine translation have necessitated more costly and time-consuming human evaluation procedures to ensure an accurate signal.We investigate a simple way to reduce cost by reducing the number of text segments that must be annotated in order to accurately predict a score for a complete test set.Using a sampling approach, we demonstrate that information from document membership and automatic metrics can help improve estimates compared to a pure random sampling baseline.We achieve gains of up to 20% in average absolute error by leveraging stratified sampling and control variates.Our techniques can improve estimates made from a fixed annotation budget, are easy to implement, and can be applied to any problem with structure similar to the one we study.
Intelligence Sensors and Sensing Spaces for Smart Home and Environment

Practitioners in the domains of architecture, engineering, and construction have conducted considerable research on smart homes and smart environments [...].
Correction: Anastopoulos et al. Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events. Int. J. Environ. Res. Public Health 2021, 18, 2600

In the original publication [...]
Multidimensional Information Network Big Data Mining Algorithm Relying on Finite Element Analysis

In recent years, with the rapid development of the Internet, online social networks have been continuously integrated with traditional interpersonal networks and research on information dissemination in social networks has gradually increased. This article studies and analyzes the multidimensional information network big data mining algorithm based on the finite element analysis method. This paper firstly introduces the finite element analysis and calculation process, a finite element data mining simulation application software management system will integrate current data, calculation, and background data into one, then analyzes the data mining clustering algorithm, and conducts an experimental exploration of the influential node mining algorithm in complex networks. The experimental results show that the LIC algorithm is better than the CC algorithm, the DC algorithm, and the BC algorithm; its overall performance is improved by 30%, and the effect is better. The LIC algorithm can effectively and quickly determine the influential nodes, which is helpful for social network analysis.
Robust registration of SAR and optical images based on deep learning and improved Harris algorithm

Abstract Traditional algorithms can achieve good results when registering homologous images, but it cannot reach satisfying results for registration between synthetic aperture radar (SAR) and optical images. The difficulty is that the image texture information and structures of different modalities is very different which leads to poor registration results. To solve this problem, we present a robust matching framework for registration between SAR and optical images. First, a novel deep learning network is utilized to generate high quality pseudo-optical images from SAR images. Next, feature points are detected and extracted using the multi-scale Harris algorithm. Then the feature points are constructed through the gradient position orientation histogram method. Finally, the actual position of the feature points will be reconstructed through a feedback mechanism for matching. Experimental results demonstrate its superior matching performance with respect to the state-of-the-art methods.
Grounding and Propositional Identity: A Solution to Wilhelm’s Inconsistencies

By following a recent result of [Wilhelm, 2021], it can easily be shown that standard conditions for immediate partial grounding and relevant identity conditions for propositions are inconsistent with one another. This is an unfortunate situation for all grounding enthusiasts; however, by adopting the approach presented by Poggiolesi [2016a,b], which displays a more-fined grained use of negations, it can also be shown that consistency can be restored back.
Development of a Cost-Effective Intelligent Clinical Decision Support System for Breast Cancer Early Diagnosis and Triage

Women population screening using mammography has dramatically reduced breast cancer rates worldwide. Nowadays, in many countries, the prevention of breast cancer policy is based on frequent and repeated mammographies, followed by breast ultrasound and if necessary, by histological examination in the biological material of the biopsy. However, evaluating mammography findings is considered as a difficult process which can properly performed only by a highly experienced and well-trained medical staff. Subsequently, the interpretation of those findings could be easily influenced by subjective factors and therefore can be prone to diagnostic errors as evidenced in the present study. Breast MRI, is a diagnostic practice indicated in cases of high breast density and at the same time, a high-cost examination for healthcare systems. Furthermore, genetic testing that is used to diagnose hereditary breast cancer, represents a small proportion of breast cancers and at the same time it is a high-cost specialized test. Even the most widely used biopsy, Fine Needle Aspirate (FNA), in some cases involves risks and provides false negative results. This study presents a novel intelligent Clinical Decision Support System (CDSS), which uses data from common practice, non-invasive and low-cost diagnostic tests, together with medical health record, in order to provide clinicians with a viable, cost-effective and accurate diagnostic solution. After implementing several algorithms, Random Forest classifier showed the highest values of sensitivity 96,2 %, specificity 94,6%, PPV 96,2% and NPV 94,6%, being thus an effective algorithm in the development of our innovative CDSS model aiming to constitute a very useful tool in clinical practice for breast cancer early diagnosis.
Securing Medical Records of COVID-19 Patients Using Elliptic Curve Digital Signature Algorithm (ECDSA) in Blockchain

The rapid and dangerous spread of COVID-19 has forced governments in various countries to provide information on patients’ medical records to the public in the context of prevention. Meanwhile, patients’ medical records are vital and confidential because they contain patients’ privacy. Changing and falsifying a patient’s medical record leads to various dangerous consequences, such as mishandling which results in the patient’s death. From these problems, the research introduces a new model with a combination of blockchain technology and the Elliptic Curve Digital Signature Algorithm (ECDSA) to secure the medical records of COVID-19 patients. This model is an improvement from the model and framework proposed by previous researchers. The proposed model consists of two big parts (front and back end). Then, the simulations are carried out to measure and prove the level of security of blockchain technology in securing patient medical records. The research results show that the ECDSA algorithm can protect patients’ medical records from being opened by unauthorized parties. Then, blockchain technology can prevent changes or manipulation of patient medical records because the information recorded on the blockchain network is impossible to change and will be immutable. The research has successfully introduced a new model in securing patient medical records.
Superconnexivity Reconsidered

We reconsider the idea of superconnexivity, an idea that has not received much attention so far.We inspect more closely the problems with the proposal that are responsible for this disregard.However, we also suggest a slight modification of the idea that has a much better chance of delivering the desired results, which we call super-bot-connexivity.
Combining First-Order Classical and Intuitionistic Logic

This paper studies a first-order expansion of a combination C + J of intuitionistic and classical propositional logic, which was studied by Humberstone (1979) and del Cerro and Herzig (1996), from a proof-theoretic viewpoint.While C + J has both classical and intuitionistic implications, our firstorder expansion adds classical and intuitionistic universal quantifiers and one existential quantifier to C + J.This paper provides a multi-succedent sequent calculus G(FOC + J) for our combination of the first-order intuitionistic and classical logic.Our sequent calculus G(FOC + J) restricts contexts of the right rules for intuitionistic implication and intuitionistic universal quantifier to particular forms of formulas.The cut-elimination theorem is established to ensure the subformula property.As a corollary, G(FOC + J) is conservative over both first-order intuitionistic and classical logic.Strong completeness of G(FOC + J) is proved via a canonical model argument.
The external auditors' policy after the COVID-19 pandemic and the accounting outlook in Tunisia

Research Question: The objective of this article is to help external auditors resume their activities and enhance their foresight strategy. Specifically, it aims to identify variables that contribute to improved auditors activities primarily post-COVID. Motivation: In light of the challenges presented by the coronavirus pandemic to the global economy, a number of specific lessons can be drawn from this situation. Idea: This research examines the audit mission strategy in which the focus is placed on the spirit and prospective reflection of external auditors. Data: This study uses a qualitative method for collecting information based on non-structured interviews. Tools: The method used in this research is based on cognitive mapping, which makes it possible to analyze the prospective strategies after the COVID-19 pandemic. Indeed, the cognitive map helps to clarify the mental orientation of a person and to visualize some ideas and beliefs on the achievement of their objectives. Findings: The obtained results showed that "digitization and Technology" are the most influential variables in the thinking of the external auditors as they represent the evolution during the COVID-19 period while "communication, the quality of audit social mission and the number of clients", which are the most dependent, represent the objectives to be achieved while the variables "teleworking, teleconference and E-learning" are the means to achieve these objectives. Contribution: This research could raise awareness of the positive outcomes of the COVID-19 era and develop a future work strategy.
Analysis of Basketball Technical Movements Based on Human-Computer Interaction with Deep Learning

With the continuous development of computer technology, analysis techniques based on various types of sports data sets are also evolving. One typical representative is image-based motion recognition technology, which enables video action recognition with a certain degree of feasibility. In basketball technical action videos, technical action has obvious characteristics. The athletes in the footage in sports videos are relatively fixed, and the scenes are relatively homogeneous, so technical action analysis of basketball technical action videos has certain advantages. However, there are many challenges in basketball technical action recognition, mainly including the fact that basketball techniques are numerous and complex. To address the above issues, this research proposes a 3D convolutional neural network framework that two different resolution image inputs are performed on the basketball technical action dataset. The experimental results show that the algorithmic process designed in this study is effective for action recognition on the basketball technical action dataset.
Study on Discrete Dynamic Modeling of College Students’ Innovative Employment Security Mechanism under the Environment of Internet of Things

The innovative mechanism of college students' employment security is a series of security measures implemented by the state and society to solve the mismatch between the scale growth of college graduates and the jobs provided by the society. In order to promote the development of emerging Internet of things technologies, users can find interesting and valuable information from a large number of data sets and use this information to meet the needs of users. This article mainly studies the historical development trend and discrete dynamic modeling and analysis technology of college students' innovative employment security mechanism under the environment of Internet of things. Using two different modeling methods of Bayesian network and BP neural network, this article makes discrete dynamic modeling on the influencing factors and employment security mechanism of college students' employment, so as to improve the college biological network innovation employment security mechanism and better help college students' employment.
Placement Prediction Using Multiple Logistic Regression Method

Standing in the early 21st century, the world has experienced various regression analysis such as Simple Linear regression, Multiple Linear regression, Logistic regression, Multiple Logistic regression etc.Multiple Logistic regression (MLR) or we can say multiple regression is one of them.A widely used statistical technique that allow predictions of systems with multiple explanatory(independent) variables.In this paper, we collected the final year placement data of a university.Our main objective is to select the explanatory variables for predicting the placement results.Data that has been used in this research were taken from Kaggle website based on the college placements data compiled over 2 years.Then the data will be analysed by using step by step multiple regression techniques.Here, we used train_test_split and 10_fold_cross_validation in our model.
Survey of using grasshopper algorithm

The metaheuristic optimization algorithm is used to explain a large region solution space. One of these algorithms is a grasshopper which divides the search process into exploitation and exploration. This article focuses on research efforts directed at gaining a clear understanding of the behavior of grasshoppers and it is using optimization algorithms. It is concluded that the benefits have been effective in answering global unrestricted and restricted optimization issues, easy development, high accuracy, and obtaining a good solution. However, the disadvantages of the GOA algorithm are simple to fall into local optimum and slow convergence speed.
Dilated Generative Adversarial Networks for Underwater Image Restoration

Underwater images often come with blurriness, lack of contrast, and low saturation due to the physics of light propagation, absorption, and scattering in seawater. To improve the visual quality of underwater images, many have proposed image processing methods that vary based on different approaches. We use a generative adversarial network (GAN)-based solution and generate high-quality underwater images equivalent to given raw underwater images by training our network to specify the differences between high-quality and raw underwater images. In our proposed method, which is called dilated GAN (DGAN), we add an additional loss function using structural similarity. Moreover, this method can not only determine the realness of the entire image but also functions with classification ability on each constituent pixel in the discriminator. Finally, using two different datasets, we compare the proposed model with other enhancement methods. We conduct several comparisons and demonstrate via full-reference and nonreference metrics that the proposed approach is able to simultaneously improve clarity and correct color and restores the visual quality of the images acquired in typical underwater scenarios.