Reviewerly/exHarmony huggingface dataset:
Difference analysis of research topics in a specific domain based on different content levels

[Purpose/Significance] This paper aims to explore whether there are differences in the title and abstract, citation content, and full-text content on research topic, and analyze whether the topic content in the title and ab stract can reveal the research content of the full text and the effect of the citation content on the content of the citing literature, so as to provide theoretical support for analyzing the research content of the full text based on the title and abstract of the literature. [Method/Process] This paper conducts an empirical study using Chinese journal papers in the field of COVID-19, extracts feature words from the titles and abstracts, citations and full-text contents of the literature, uses the clustering algorithm to cluster the feature words, and then uses manual interpretation to identify the research topics, and conducts a comparative study to analyze the topic differences among the three parts. [Results/Conclusions] The results show that: the research topics are different in the title and abstract, citation content and full-text content of the literature;compared with the title and abstract, the full text contains more topic content, but the difference in the topic content is small, so the topic content in the title and abstract can be used to represent the research content of the full text;the content of the citation is related to the topic of the citing literature, and they can complement each other. © 2021 Agricultural Information Institute, Chinese Academy of Agricultural Sciences. All Rights Reserved.
Covid19 Pandemics, Distance Teaching and development of Critical Thinking through Moodle Platform

Time, when mankind awoke at a time of covid19 pandemics expresses two dramatically different attitudes. People and their interests were divided into two parts; some of them were those, who could not stand isolation, with aggravation of their depressive background and the others – were able to adjust the reality to their interests and capabilities, they became modern and read the universe in a new way. They have discovered the new vision, they went deeper into implications and managed to adequately perceive the reality, they were able to adapt naturally. The education systems made attempts to launch the distance teaching and this turned out to be more or less creative, cognition oriented process. Georgian education system has dealt with this challenge by practical introduction of various programs and platforms. We would like to focus on the features and capacities of education platform Moodle as it can be a tool for development of critical thinking in distance teaching within the scopes of one specific course. This course is “Principles of Contemporary Thinking” and teaching of Georgian language to the foreign students.
Assessing the Value of Library Resources for Renewal Negotiations

This paper details a number of methods used by staff at the University of Tennessee, Knoxville Libraries throughout 2019 to assess the value of library resources and inform renewal negotiations for electronic resources. This paper discusses quantitative data from usage reports, access issue reports, citation analysis; input gathered from subject librarians; default annual cost increase caps; data analysis using PowerBI; and collaborative decision-making committees. Combining these evaluative methods provided a multi-faceted approach to the data to make evidence-based decisions on resource value.
Safety Protocols for Forensic Inspections in the Time of COVID-19: An Approach to Protect Practitioners

The global pandemic of COVID-19 has impacted the entire world in ways that were unimaginable months ago.It has changed how professionals work and interact with one another.To ensure the safety of experts and attorneys, also forensic inspection procedures must evolve, while still providing a reliable foundation for an expert testimony, relevant to the task at hand.Traditional field inspections require attendees to be physically present and generally in close proximity.Inspecting artifacts may also require that individual components be serially examined by multiple experts, which increases the risk of transmitting the disease.The authors propose a new approach to forensic inspections aimed at protecting consultants from the biological risk due to COVID-19.This approach takes into account the anticontagion safety protocols established in affected countries around the world.
Evolutionary Convolutional Neural Network: a case study

Most of the state-of-the-art Convolutional Neural Network (CNN) architectures are manually crafted by experts, usually with background knowledge from extent working experience in this research field. Therefore, this manner of designing CNNs is highly limited and many approaches have been developed to try to make this procedure more automatic. This paper presents a case study in tackling the architecture search problem by using a Genetic Algorithm (GA) to optimize an existing CNN Architecture. The proposed methodology uses VGG-16 convolutional blocks as its building blocks and each individual from the GA corresponds to a possible model built from these blocks with varying filter sizes, keeping fixed the original network architecture connections. The selection of the fittest individuals are done according to their weighted F1-Score when training from scratch on the available data. To evaluate the best individual found from the proposed methodology, the performance is compared to a VGG-16 model trained from scratch on the same data.
Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids

The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring. Even though there is a natural correspondence of power flow to message-passing in GNNs, their performance on power grids is not well-understood. We argue that there is a gap between GNN research driven by benchmarks which contain graphs that differ from power grids in several important aspects. Additionally, inductive learning of GNNs across multiple power grid topologies has not been explored with real-world data. We address this gap by means of (i) defining power grid graph datasets in inductive settings, (ii) an exploratory analysis of graph properties, and (iii) an empirical study of the concrete learning task of state estimation on real-world power grids. Our results show that GNNs are more robust to noise with up to 400% lower error compared to baselines. Furthermore, due to the unique properties of electrical grids, we do not observe the well known over-smoothing phenomenon of GNNs and find the best performing models to be exceptionally deep with up to 13 layers. This is in stark contrast to existing benchmark datasets where the consensus is that 2--3 layer GNNs perform best. Our results demonstrate that a key challenge in this domain is to effectively handle long-range dependence.
The Potential Blockchain Technology in Higher Education Learning Innovations in Era 4.0

In the world of education, the development of technology systems is increasingly rapid. The implementation of Blockchain continues to be used in various sectors. This study analyzed the use of Blockchain in higher education. The research discussion results lead to the use of blockchain technology, which is a new trend in higher education. Blockchain implementation in higher education creates problems in the use of blockchain technology. Based on the use of Blockchain in research, we are exploring the application of related technologies in higher education. The problem with technology that functions to record small transactions and data used for many sectors such as recording transactions, documentation, and even voting, which is also used in Higher Education, is unfortunately not well implemented. This supports research on the use of blockchain technology that can record transactions in many sectors. Documenting the process of transactions, documentation, and voting can even be used in Higher Education. To improve the blockchain technology system's performance in higher education, it is becoming more effective, efficient, and more accessible in recording transaction processes. It can become a new innovation for Higher Education.
Improving CBR Retrieval Process Through Multilabel Text Categorization for Health Care of Childhood Traumatic Brain Injuries in Road Accident

According to the world health organization, traumatic brain injury (TBI) is in fact the main cause of death and disability worldwide. In Oran Hospital in Algeria and according to pediatric doctors in the critical care service, the majority of TBI cases recorded has been caused by road accidents. It is, therefore, crucial that procedures for early diagnosis, treatment orientation and physical gestures for the child be provided in a timely and efficient manner. In medicine, case-based reasoning (CBR) approach has become a successful model using previous specific patient cases when diagnosing and treating new ones. An important step in the CBR process is the retrieval phase, which deals with looking for similar and useful cases. However, its efficiency is subject to degradation since the quality of cases retrieved and search time may increase as the search space in the case base increases. To overcome this restriction, we proposed in this study a CBR system. A filter step is proposed to reduce the case base before starting the process of searching for similar cases. This filtering will remove from the search space all cases having lesions different from those of the new case. Then a multilabel text categorization tool is used to identify the lesions of the new case. Therefore, this identification will allow selecting from previous cases those with the same lesions as the new case. These cases constitute the important semantic and useful cases in the CBR search space.
Partition of Unity Networks: Deep HP-Approximation.

Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials. Motivated by this, we propose partition of unity networks (POUnets) which incorporate these elements directly into the architecture. Classification architectures of the type used to learn probability measures are used to build a meshfree partition of space, while polynomial spaces with learnable coefficients are associated to each partition. The resulting hp-element-like approximation allows use of a fast least-squares optimizer, and the resulting architecture size need not scale exponentially with spatial dimension, breaking the curse of dimensionality. An abstract approximation result establishes desirable properties to guide network design. Numerical results for two choices of architecture demonstrate that POUnets yield hp-convergence for smooth functions and consistently outperform MLPs for piecewise polynomial functions with large numbers of discontinuities.
Deep Metric Similarity Clustering

Effective data similarity measures are essential in data clustering. This paper proposes a novel deep metric clustering method with simultaneous non-linear similarity learning and clustering. Unlike pre-defined similarity measures, this deep metric enables more effective data clustering on high-dimensional data with various non-linear similarities. In the proposed method, a similarity function is firstly approximated by a deep metric network. The graph Laplacian matrix is introduced to make data cluster assignments. A stochastic optimization is then proposed to efficiently construct the optimal deep metric network and calculate data similarity and cluster assignment on large-scale data set. For N data samples, the proposed optimization effectively reduces the computation of N2 pairs of data to \(M^2 (M\!\!\ll \!\!N)\) pairs at each step of the approximation. A co-training method is further introduced to optimize the deep metric network on a portion of semi-supervised data for clustering with targeted purposes. Finally, this paper shows theoretical connections between the proposed method and spectral clustering in subspace learning. This method is able to achieve ∼20% higher accuracies than the best existing multi-view and subspace clustering methods on Caltech and MSRCV1 object recognition data sets. Further results on benchmark and real-world visual data show competitive performance of the proposed method over deep subspace clustering network and many related and state-of-the-art methods.
The algorithm versus the chimps: On the minima of classifier performance metrics

n this paper we seek the minima of performance metrics for binary classification to facilitate comparison between metrics and applications, and to assess the quality of inferential statistics made from non-probability samples. We use these minima to min-max normalize the performance metrics so that they can be interpreted as a percentage of the perfect classifier relative to the proverbial chimps at the zoo†guessing at random. We compare our results with the balanced metrics that have been introduced recently, which are corrected for bias due to class imbalance.
Development of Distance Learning During the Pandemic Period Using the Android Application (e-Sabaky) in Physics Subjects at SMA Negeri 1 Pati

This study aims to develop a learning media for the development of open and distance learning. The learning media developed in the form of an Android-based application during the pandemic. This research method is the development method (RnD), which is developing a model of distance learning facilities and then applying it to the distance learning process during the pandemic. The location of this research is at SMA Negeri Pati then applied in several high schools outside Pati district such as Kudus and Rembang districts. The implementation period is from 2018 to 2020. Media E-SABAKY (Electronic Communicative Active Learning Application System Yuli) is effective and efficient for teaching concrete and fun materials for quantities and units for students which can be installed via student mobile phones. It can also be done cooperatively through SOM application collaboration group activities. Benefits of implementing E-SABAKY in learning Among them are for students to improve learning outcomes in terms of quantities and units. For teachers as a means to improve learning innovation and reference material to conduct similar research. For schools, as a consideration for the addition of supporting media for learning the material of quantity and unit, the results of daily tests will increase. E-SABAKY media is ecofriendly or environmentally friendly because it does not use paper. All learning materials to exams are all in the application. This learning system adapts to the needs of millennials which are identical to Android.
NEAREST NEIGHBOR BIAS IN THE SUBSTITUTION OF MISSING VALUES

We present a simplified illustration of the bias inherent in the general case of the Nearest Neighbor (NN) method used to substitute missing values. This presentation doesn't make any assumptions about the geometry of the sampled subjects. The general examples illustrate that the bias exists mainly at the limits of the data range and not necessarily within the center part of the range. However, the latter is also possible around any significant data gaps. Since the NN data domain stretches across an arbitrary subject characteristic rather than across the physical space, it is possible to reduce the bias by assuring that the domain range of the considered attribute is well-represented within its entire range, especially at its upper and lower limits.
Attention-Privileged Reinforcement Learning

Image-based Reinforcement Learning is known to suffer from poor sample efficiency and generalisation to unseen visuals such as distractors (task-independent aspects of the observation space). Visual domain randomisation encourages transfer by training over visual factors of variation that may be encountered in the target domain. This increases learning complexity, can negatively impact learning rate and performance, and requires knowledge of potential variations during deployment. In this paper, we introduce Attention-Privileged Reinforcement Learning (APRiL) which uses a self-supervised attention mechanism to significantly alleviate these drawbacks: by focusing on task-relevant aspects of the observations, attention provides robustness to distractors as well as significantly increased learning efficiency. APRiL trains two attention-augmented actor-critic agents: one purely based on image observations, available across training and transfer domains; and one with access to privileged information (such as environment states) available only during training. Experience is shared between both agents and their attention mechanisms are aligned. The image-based policy can then be deployed without access to privileged information. We experimentally demonstrate accelerated and more robust learning on a diverse set of domains, leading to improved final performance for environments both within and outside the training distribution.
GEOPHYSICAL SURVEY AS A TOOL TO REVEAL SUBSURFACE STRATIFICATION AT A SMALL AGRICULTURAL HEADWATER CATCHMENT: A CASE STUDY

Catchment drainage area is a basic spatial unit in landscape hydrology within which the authorities estimate a water balance and manage water resources. The catchment drainage area is commonly delineated based on the surface topography, which is determined using a digital elevation model. Therefore, only a flow over the surface is implicitly considered. However, a substantial portion of the rainfall water infiltrates and percolates through the soil profile to the groundwater, where geological structures control the drainage area instead of the topography of the soil surface. The discrepancy between the surface topography-based and bedrock-based drainage area can cause large discrepancies in water balance calculation. It this paper we present an investigation of the subsurface media stratification in a headwater catchment in the central part of the Czech Republic using a geophysical survey method - electrical resistivity tomography (ERT). Results indicate that the complexity of the subsurface geological layers cannot be estimated solely from the land surface topography. Although shallow layers copy the shape of the surface, the deeper layers do not. This finding has a strong implication on the water transport regime since it suggests that the deep drainage may follow different pathways and flow in other directions then the water in shallow soil profile or shallow subsurface structures.
Digital competences and media literacy for an open and online education

We live in a society where physical and virtual reality are increasingly intertwined. The boundaries between the body and the imaginary gain new contours and definitions, affecting important and essential aspects, such as the power of participation and emancipation of the individual in the society in which he/she is inserted. The rapid pace of the technological and digital society makes it essential that both teachers and students know how to use different digital resources to create and develop learning environments that lead not only to the acquisition of knowledge, but also to scenarios that promote the development of different learning skills. Recently, the emphasis given to the relevance of digital competence has resulted in the development of various initiatives in Europe aimed at promoting digital and media competence. In addition, evaluating media in a critical and conscious way, developing the skills necessary to participate in an active and informed way in society, is considered today a fundamental competence for an increasingly mediated society and also for an education that increasingly moves in a fluid way between formal and non-formal spaces. To this end, it is necessary to rethink the objectives and methodologies of education, based on a concept of literacy that is continuously evolving, related to the skills needed to read, understand and communicate.
Mobile Agent Security Using Lagrange Interpolation with Multilayer Perception Neural Network

Mobile agents are an emerging computing area which replaces client server computing model. Mobile agents are small piece of code and data works automatically on the behalf of owner. Processed different type of activity during the life cycle of mobile agent and execute code on another host computer. Mobile agents execute their assigned function in malicious heterogeneous environment. Mobile agent is using in many applications like E-commerce, parallel computing, and Network management etc. To provide protection for mobile agents is one of the prime issues in broadens of the mobile agent computing. Today, the greatest challenge to the mobile agent technology is security. The numerous advantages accompanying its usage have been fettered by the security concerns/threats. In this article, propose a new framework for securing the secret key which is based on Shamir’s Secret Share and Error Back propagation ANN. Shamir’s SS has been employed for share generation and BP-ANN is used for secret retrieval. Some keystone features of this scheme are: the shareholders need not disclose their identities at any stage (anonymous), no imposition of threshold limit, liberty to use public communication channel, etc. Furthermore, the ongoing research and developments in ANN technology broadens the scope for improvements in the scheme.
Review and Enlightenment of Sponging Renovation Design of Old Communities Based on Smoke-free City Reconstruction and Ecological Restoration

Objectives: The COVID-19 epidemic crisis has attracted the attention of people who are experiencing climate change and resources shortage to the human environment and ecology. In the meantime, COVID-19 and cigarette overlay have become one of the greatest global public menace, which aggravates people’s concern of well-being and urgency about the risk of spreading smoke and poses new challenge to public sanitation, urban management in every nation. City is the key of the revival from COVID-19 crisis and worldwide economy. Community is the basic unit in city. Material circumstance is the vital carrier of healthy ecology in community. Faced with issues concerning healthy ecological chain circle in communities involving frequent abnormal climate, constant urban waterlogging, scarcity of infrastructure in old communities and so on, it is imperative to incorporate relevant mechanism and ways of sponge reconstruction design on old communities based on smoke-free city reconstruction and ecological reparation from visual exploration in all domains. For the sake of that, the study probe for the accordance of smoke-free city reconstruction and ecological restoration via originating from researches and practices concerning smoke-free city, domestic and foreign reconstruction of old communities, rain and flood management/sponge city, green infrastructure and so on, so as to prompt big strategy of health and ecology and construct resilient community and city with resistant ability.
Bi-GRU model based on pooling and attention for text classification

Aiming at the problems that most of the text classification models based on neural network are easy to overfit and ignore key words in sentences in the training process, an improved text classifica...
Hough Transform Processing in 3D Object Detection and Visualization

We present the detection of partially occluded spherical objects by means of the Circular Hough Transform of stereo pair of the scene. Refocusing after filtering allows then to reconstruct the object at its own depth.