Reviewerly/exHarmony huggingface dataset:
COVID-19 Literature Mining and Analysis Research

By 2019 COVID-19, since the epidemic, the number of relevant documents exponentially level rise. Faced with a large amount of literature, this research provides convenience for exploring the connection between research topics and fields and quickly understanding relevant literature information. We pass on the data set after data cleansing using the LDA(Latent Dirichlet allocation) methods, and Berts and K-means modeling method extracting topic keywords. Use knowledge graph tools to output relevant visual graphics and systematically extract adequate information. Through text mining of biomedical research papers related to COVID-19, the improved model is used to analyze and make recommendations to respond to and prevent the COVID-19 pandemic. This research can support the rapid and in-depth analysis of a large number of relevant documents and can be used in future research to support real-time scientific disease research.
Critical care for the early web: ethical digital methods for archived youth data

Purpose This paper aims to provide a brief overview of the ethical challenges facing researchers engaging with web archival materials and demonstrates a framework and method for conducting research with historical web data created by young people. Design/methodology/approach This paper’s methodology is informed by the conceptual framing of data materials in research on the “right to be forgotten” (Crossen-White, 2015; GDPR, 2018; Tsesis, 2014), data afterlives (Agostinho, 2019; Stevenson and Gehl, 2019; Sutherland, 2017), indigenous data sovereignty and governance (Wemigwans, 2018) and feminist ethics of care (Cifor et al. , 2019; Cowan, 2020; Franzke et al. , 2020; Luka and Millette, 2018). It demonstrates a new method called an archive promenade, which builds on the walkthrough and scroll-back methods (Light et al. , 2018; Robards and Lincoln, 2017). Findings The archive promenades demonstrate how individual attachments to digital traces vary and are often unpredictable, which necessitates further steps to ensure that privacy and data sovereignty are maintained through research with web archives. Originality/value This paper demonstrates how the archive promenade methodological intervention can lead to better practices of care with sensitive web materials and brings together previous work on ethical fabrications (Markham, 2012), speculation (Luka and Millette, 2018) and thick context (Marzullo et al. , 2018), to yield new insights for research on the experiences of growing up online.
Performance Analysis of Cloud-based Health Care Data Privacy System Using Hybrid Techniques

At present, due to the various hacking approaches, the protection for any data transmitted through any channel or mode is one of the important issues. Nowadays, providing data security is satisfactory, developments are extended for obtaining data among the transceivers. Security level depends on the size of a symmetric key which is employed for encryption and decryption using various cryptography systems management and in modern approaches like block and RF codes including AES use a larger size of key simultaneously and there exists security problems due to hacking approaches. To illustrate the protection level and hacking problems, a new ECC is presented as well as by employing scalar duplication, the synchronous key is generated and consists of point doubling and point addition. The created focuses are encrypted before transmission by using ECC-Elgamal-Holomorphic (ECCEH) and transferred through a distant channel and encipher data is failed at the receiver using ECCEH which includes the reverse process. The unique standards of cryptography context have been generated by MATLAB; the defined framework has endeavored to the extent that speed, delay as well as control, and many others are accepted in MATLAB 2017a. The user of the sender, the original information is transformed into integer value by employing Holomorphic and encodes it by utilizing the Elgamal ECC algorithm which employs point doubling and point addition. The encoded information is uploaded into the cloud for storage, here www.thingspeak.com is utilized for storage. When the user presents at the receiver request the cloud to access from it, initially the cloud server authenticates the access control strategies of the requester, and then access is provided by the cloud server. If the user authenticates the strategies, then encoded data can download and the original data is decoded by synchronous key employing ECC- Elgamal algorithm. Using original and decrypted data, various performance factors are calculated in terms of execution time, packet delivery ratio, throughput, latency and compare these results with conventional methods and found to be 12%, 31%, 24%, and 8% progress concerned with packet delivery ratio, latency, outturn and execution time.
A Design of a Global Path Planner for Nonholonomic Vehicle Based on Dynamic Simulations

In this paper, an algorithm for global path planning of nonholonomic unmanned ground vehicle (UGV), which moves through diverse terrain, is presented. The proposed algorithm utilizes a simplified dynamic model of the vehicle to verify the passability of a planned movement from node to node and to calculate the duration of this movement and the actual velocity of the vehicle in these nodes. The algorithm operates on a grid map that represents terrain elevation obtained from a triangular irregular network (TIN) map. This map was received from publicly available aerial laser scan data of a land surface in Central Europe. The final path is optimised from the point of view of travel time and respects the nonholonomic constraints of the UGV. This approach ensures that the obtained path is feasible, not only considering the geometric constraints of the vehicle but also its physical limits, i.e., maximum applied torque, maximum velocity, steering limit, etc.
CFPA-Net: Cross-layer Feature Fusion And Parallel Attention Network For Detection And Classification of Prohibited Items in X-ray Baggage Images

As objects in the baggage are often heavily overlapped and cluttered, the X-ray baggage inspection is an inherently challenging task. In this paper, we propose a cross-layer feature fusion and parallel attention network named CFPA-Net to detect and classify the prohibited items in X-ray baggage images. The CFPA-Net is based on RetinaNet with three modules: cross-layer feature extraction fusion module (CEF-Module), paralleled attention module (PA-Module) and FreeAnchor. In CEF-Module, an improved feature pyramid network is proposed by adding multi-directional lateral connections for cross-layer feature extraction and fusion. It can help detect objects of various scales and supplement deficient semantic and localization information for low layer and high layer features respectively. PA-Module is presented to learn the feature relationship and fully utilize the extracted features by introducing two paralleled attention subnets Squeeze-and-Excitation module and Non-local module. PA-Module can help improve the performance of detecting and classification by emphasizing useful features, suppressing useless features selectively and capturing long-range dependencies in images. FreeAnchor is adopted to deal with the restriction of hand-crafted anchor assignment according to Intersection-over-Unit. It can help find the best anchor for each object by learning, and improve the performance of detecting slender objects and the ones in crowded scenes. On the public dataset OPIXray, CFPA-Net achieves 85.82% detection mean Average Precision. Moreover, achieving 81.61% classification mean Average Precision on the SIXray10 dataset. The experimental results show that our proposed CFPA-Net is more accurate and robust for the X-ray baggage inspection with densely occluded objects and complicated backgrounds.
Efficient Speed-Up of the Smallest Enclosing Circle Algorithm

The smallest enclosing circle is a well-known problem. In this paper, we propose modifications to speed-up the existing Weltzl’s algorithm. We perform the preprocessing to reduce as many input points as possible. The reduction step has lower computational complexity than the Weltzl’s algorithm and thus speed-ups its computation. Next, we propose some changes to Weltzl’s algorithm. In the end are summarized results, that show the speed-up for ${10^{6}}$ input points up to 100 times compared to the original Weltzl’s algorithm. Even more, the proposed algorithm is capable to process significantly larger data sets than the standard Weltzl’s algorithm.
Mobile Learning as Tool for Implementation of Blended Learning in Nursing Education

This research focuses on the development of mobile learning applications in nursing education in Indonesia. Using the BeeDa application, which is a learning application for nursing students as a complement to blended learning. Blended learning is an integration between classroom learning, online learning and mobile learning. BeeDa facilitates students in accessing learning resources easily, anytime and anywhere. This study describes the design and implementation stages related to the development of BeeDa. Experimental data were collected using a questionnaire to nursing students. The evaluation results show that the BeeDa application is easy to use and useful in supporting learning, so it has the potential to improve learning achievement.
Polarized Subtyping

Abstract Polarization of types in call-by-push-value naturally leads to the separation of inductively defined observable values (classified by positive types), and coinductively defined computations (classified by negative types), with adjoint modalities mediating between them. Taking this separation as a starting point, we develop a semantic characterization of typing with step indexing to capture observation depth of recursive computations. This semantics justifies a rich set of subtyping rules for an equirecursive variant of call-by-push-value, including variant and lazy records. We further present a bidirectional syntactic typing system for both values and computations that elegantly and pragmatically circumvents difficulties of type inference in the presence of width and depth subtyping for variant and lazy records. We demonstrate the flexibility of our system by systematically deriving related systems of subtyping for (a) isorecursive types, (b) call-by-name, and (c) call-by-value, all using a structural rather than a nominal interpretation of types.
Use of modern technologies and expert systems in the educational process

The use of modern technology in the educational environment brings new elements to the process of acquiring knowledge. Mobile applications make it possible to take an innovative approach to solve various set tasks. The interaction between the teacher and the student is maintained during the pedagogical process. A virtual component in the form of a mobile application is added to the process. The addition of another element in the form of a mobile application developed based on the principle of an expert system represents a modern teaching environment. In terms of the use of mobile technologies in the educational environment, there are both advantages and negatives. However, expanding the ability to represent the knowledge offered by mobile technologies contributes to a more effective perception of information. Expert systems group a summary of knowledge in different industries. This knowledge is gained from experts in a specific field. Subsequently, they serve as support tools in the decision-making process. However, in certain cases, they can replace specialists in a given issue. This article presents a summary of the characteristics of the principles of using mobile technologies in the learning environment. Lists aspects of the use of mobile technologies. It also provides a detailed description of the structure and principle of operation of the expert system. The implementation of this knowledge is represented by the design of a mobile application in the form of an expert system. The mobile application will serve as a teaching aid in the identification of herbal drugs based on the recognition of diacritical marks.
Creating Diverse Ensembles for Classification with Genetic Programming and Neuro-MAP-Elites

Model diversity is essential for ensemble classifiers, which make predictions by combining predictions from multiple simpler models. While ensemble classifiers often outperform single-model classifiers, their success crucially depends on the ensemble's construction. Genetic programming (GP) is a powerful evolutionary algorithm that can evolve populations of simple classifiers; however, standard GP algorithms produce populations of models with correlated predictions. Recent work in the broader evolutionary computing community has begun focusing on methods for evolving diverse populations, such as MAP-Elites [24], which can evolve populations that are diverse in a low dimensional behavior space. In this work, we demonstrate a novel technique for using MAP-Elites to create diverse GP populations, which can be used as ensemble classifiers. We demonstrate the utility of our framework, which we call Neuro-MAP-Elites, by comparing it with other classification algorithms across a diverse set of classification datasets.
Time to Assess Bias in Machine Learning Models for Credit Decisions

Focus on fair lending has become more intensified recently as bank and non-bank lenders apply artificial-intelligence (AI)-based credit determination approaches. The data analytics technique behind AI and machine learning (ML) has proven to be powerful in many application areas. However, ML can be less transparent and explainable than traditional regression models, which may raise unique questions about its compliance with fair lending laws. ML may also reduce potential for discrimination, by reducing discretionary and judgmental decisions. As financial institutions continue to explore ML applications in loan underwriting and pricing, the fair lending assessments typically led by compliance and legal functions will likely continue to evolve. In this paper, the author discusses unique considerations around ML in the existing fair lending risk assessment practice for underwriting and pricing models and proposes consideration of additional evaluations to be added in the present practice.
Collaborative Filtering for Recommendation in Geometric Algebra

At present, recommender system plays an important role in many practical applications. Many recommendation models are based on representation learning, in which users and items are embedded into a low-dimensional vector space, and then historical interactions are used to train the models. We find that almost all of these methods model users and items in real-valued embedding space, which neglect the potential value of other non-real spaces. In this paper, we propose a Geometric Algebra-based Collaborative Filtering (GACF) model for recommendation. Specifically, GACF firstly uses multivectors to represent users and items. Then GACF uses geometric product and inner product to model the historical interaction between users and items. By using geometric product, the model prediction can obtain inter-dependencies between components of multivectors, which enable complex interactions between users and items to be captured. Extensive experiments on two real datasets demonstrate the effectiveness of GACF.
Unbiased Spatiotemporal Representation With Uncertainty Control for Person Reidentification

For person reidentification (re-id), most current research aims to encode the spatial and temporal information by using convolutional neural networks (CNNs) to extract spatial features and recurrent neural networks (RNNs) or their variations to discover the time dependencies. However, it ignores the effect of the complex background, which leads to a biased spatial representation. Furthermore, it often uses the backpropagation through time (BPTT) to train RNNs. Unfortunately, it is hard to learn the long-term dependency via BPTT due to the gradient vanishing or exploding. The significance of a frame should not be biased by its position in a given sequence. In this article, a new method is proposed to learn an unbiased semantic representation for video-based person re-id. To handle the background clutter and occlusion, a two-branch CNN model is used to obtain the enriched representation from both the foreground person and original pedestrian images. Then, an unbiased bidirectional CNN architecture is developed to learn the unbiased spatial and temporal representation. The experimental results on three public data sets demonstrate the effectiveness of the proposed method.
Towards Dynamic Consistency Checking in Goal-Directed Predicate Answer Set Programming

Goal-directed evaluation of Answer Set Programs is gaining traction thanks to its amenability to create AI systems that can, due to the evaluation mechanism used, generate explanations and justifications. s(CASP) is one of these systems and has been already used to write reasoning systems in several fields. It provides enhanced expressiveness w.r.t. other ASP systems due to its ability to use constraints, data structures, and unbound variables natively. However, the performance of existing s(CASP) implementations is not on par with other ASP systems: model consistency is checked once models have been generated, in keeping with the generate-and-test paradigm. In this work, we present a variation of the top-down evaluation strategy, termed Dynamic Consistency Checking, which interleaves model generation and consistency checking. This makes it possible to determine when a literal is not compatible with the denials associated to the global constraints in the program, prune the current execution branch, and choose a different alternative. This strategy is specially (but not exclusively) relevant in problems with a high combinatorial component. We have experimentally observed speedups of up to 90 $$\times $$ w.r.t. the standard versions of s(CASP).
ETBTRank: Ranking Biterms in Paper Titles for Emerging Topic Discovery

Emerging topics, which often originate from the collaboration of two scientific subfields, can be represented by biterms (pairs of terms) where each term represents a distinct subfield. However, it is challenging to automatically find such two critical terms to represent an emerging topic exactly. First, existing term weighting models (such as TF-IDF, TextRank, RAKE, KECNW, and YAKE) may be effective for finding critical single-terms but not for critical biterms. Second, a potential biterm that may be suitable to represent the emerging topic has very low occurrences in a text (e.g., a corpus comprised of paper titles). So, even we combine two terms to generate a bag of biterms, the above term weighting models are still invalid, which will filter out these rare potential biterms. This paper proposes a novel Emerging Topic BiTerm Rank (ETBTRank) model to help automatically extract biterms for representing emerging topics, distinguishing emerging-topic biterms from unimportant biterms. In ETBTRank, we separately weigh the two terms in a biterm and find the emerging-topic biterms by a rule: if a biterm itself is rare, but each of the two terms in it has a high weight, then it is an emerging topic biterm. Experimental studies on paper title datasets demonstrate the effectiveness of the proposed model.
RATE: A Reliability-Aware Tester-Based Evaluation Framework of User Simulators

Evaluation of user simulators is needed in order to use them for evaluating Interactive Information Retrieval (IIR) Systems. Previous work has proposed a tester-based approach to evaluate user simulators, but it has not addressed the important question about the reliability of the testers themselves, nor has it studied how to generate a single reliability score for a user simulator based on multiple testers. In this paper, we address these two limitations and propose a novel Reliability-Aware Tester-based Evaluation (RATE) framework for evaluating the reliability of both User Simulators and testers. In this framework, the reliability of Testers and that of Simulators are jointly learned through unsupervised learning using iterative propagation of reliability. We propose and evaluate two algorithms for unsupervised learning of reliabilities. Evaluation results using TREC data sets show that the proposed RATE framework is effective in measuring the reliability of simulators and testers, thus serving as a foundation for potentially establishing a new paradigm for evaluating IIR systems using user simulation.
Toward Autonomous Detection of Anomalous GNSS Data Via Applied Unsupervised Artificial Intelligence

Artificial intelligence applications within the geosciences are becoming increasingly common, yet there are still many challenges involved in adapting established techniques to geoscience data sets. Applications in the realm of volcanic hazards assessment show great promise for addressing such challenges. Here, we describe a Jupyter Notebook we developed that ingests real-time Global Navigation Satellite System (GNSS) data streams from the EarthCube CHORDS (Cloud-Hosted Real-time Data Services for the geosciences) portal TZVOLCANO, applies unsupervised learning algorithms to perform automated data quality control (“noise reduction”), and explores autonomous detection of unusual volcanic activity using a neural network. The TZVOLCANO CHORDS portal streams real-time GNSS positioning data in 1[Formula: see text]s intervals from the TZVOLCANO network, which monitors the active volcano Ol Doinyo Lengai in Tanzania, through UNAVCO’s real-time GNSS data services. UNAVCO’s real-time data services provide near-real-time positions processed by the Trimble Pivot system. The positioning data (latitude, longitude and height) are imported into the Jupyter Notebook presented in this paper in user-defined time spans. The positioning data are then collected in sets by the Jupyter Notebook and processed to extract a useful calculated variable in preparation for the machine learning algorithms, of which we choose the vector magnitude for further processing. Unsupervised K-means and Gaussian Mixture machine learning algorithms are then utilized to locate and remove data points (“filter”) that are likely caused by noise and unrelated to volcanic signals. We find that both the K-means and Gaussian Mixture machine learning algorithms perform well at identifying regions of high noise within tested GNSS data sets. The filtered data are then used to train an artificial intelligence neural network that predicts volcanic deformation. Our Jupyter Notebook has promise to be used for detecting potentially hazardous volcanic activity in the form of rapid vertical or horizontal displacement of the Earth’s surface.
Capsule Embedded ResNet for Image Classification

Various neural network models have been proposed in the past decade. Among them, the residual neural network (ResNet) is one of the most successful models of convolutional neural networks (CNNs), while the capsule neural network (CapsNet) is more robust to the rotation, translation and other transformations of objects in the images. In this paper, a capsule embedded ResNet (CE-ResNet) is proposed to combine the strengths of both. The proposed CE-ResNet utilizes the ResNet blocks based convolutional layers to extract low to medium level features from the images, while two capsule layers are embedded to process the high-level information in order to provide a better performance for image classification. The transformation between convolutional layers and capsules is carefully designed to embed the capsules properly. In the experiments, the proposed model achieves 90.8% and 99.64% accuracies on CIFAR-10 and MNIST datasets respectively, which are higher than the accuracies reported by the vanilla ResNet or CapsNet.