Reviewerly/exHarmony huggingface dataset:
Identifying Information and Communication Technology Use Capacity Needs of Extension Networks

Information and Communication Technology (ICT) has the potential to address critical considerations within rural advisory service (RAS) networks, specifically, getting the right information to audiences in a timely and appropriate manner. ICTs are technologies that facilitate knowledge and information sharing. For RAS networks, ICTs can be an important tool to ensure that both explicit and tacit information is shared with network members with the anticipated benefit of increased capacity of the network. Although the importance of ICTs are well documented within the literature, there are limited guidelines for what specific network capacities in ICT use are needed to better provide support for RAS providers to fulfill their responsibilities. Using the Delphi process, a panel of 31 experts from 24 countries arrived at consensus on 51 specific ICT use capacities associated with effective RAS networks. The results of the research provide a practical framework for RAS providers and networks to develop ICT use related capacity building and tactical planning activities. Keywords: Information Communication Technology (ICT); ICT use; Delphi; evaluation; capacity assessment
Retinex-Based Image Enhancement with Particle Swarm Optimization and Multi-Objective Function

Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.
A class of models for Bayesian predictive inference

In a Bayesian framework, to make predictions on a sequence $X_{1},X_{2},\ldots $ of random observations, the inferrer needs to assign the predictive distributions $\sigma _{n}(\cdot )=P(X_{n+1}\in \cdot \mid X_{1},\ldots,X_{n})$. In this paper, we propose to assign $\sigma _{n}$ directly, without passing through the usual prior/posterior scheme. One main advantage is that no prior probability has to be assessed. The data sequence $(X_{n})$ is assumed to be conditionally identically distributed (c.i.d.) in the sense of (Ann. Probab. 32 (2004) 2029–2052). To realize this programme, a class $\Sigma $ of predictive distributions is introduced and investigated. Such a $\Sigma $ is rich enough to model various real situations and $(X_{n})$ is actually c.i.d. if $\sigma _{n}$ belongs to $\Sigma $. Furthermore, when a new observation $X_{n+1}$ becomes available, $\sigma _{n+1}$ can be obtained by a simple recursive update of $\sigma _{n}$. If $\mu $ is the a.s. weak limit of $\sigma _{n}$, conditions for $\mu $ to be a.s. discrete are provided as well.
RELLIS-3D Dataset: Data, Benchmarks and Analysis

Semantic scene understanding is crucial for robust and safe autonomous navigation, particularly so in off-road environments. Recent deep learning advances for 3D semantic segmentation rely heavily on large sets of training data, however existing autonomy datasets either represent urban environments or lack multimodal off-road data. We fill this gap with RELLIS-3D, a multimodal dataset collected in an off-road environment, which contains annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A\&M University and presents challenges to existing algorithms related to class imbalance and environmental topography. Additionally, we evaluate the current state-of-the-art deep learning semantic segmentation models on this dataset. Experimental results show that RELLIS-3D presents challenges for algorithms designed for segmentation in urban environments. This novel dataset provides the resources needed by researchers to continue to develop more advanced algorithms and investigate new research directions to enhance autonomous navigation in off-road environments. RELLIS-3D is available at https://github.com/unmannedlab/RELLIS-3D
Efficient fusion of photonic W-states with nonunitary partial-swap gates

Abstract We introduce a nonunitary partial-swap gate for fusing arbitrary small-sized photonic W -states into a large-scale entangled network of W -state efficiently without ancillary photons. A partial-swap gate is designed in an optical architecture based on linear optics elements. By introducing auxiliary degree of freedom, this gate provides a higher success probability with less cost. Our implementation can create a larger target state with a simpler set-up than previous proposals for W -state fusion. Also all ‘garbage’ states are recyclable, i.e., there is no complete failure output in our scheme in principle.
Training in Digital Skills in Early Childhood Education Teachers: The Case of the University of La Laguna

&lt;p class="0abstract"&gt;&lt;span lang="EN-US"&gt;Information and Communication Technologies (ICT) have become essential tools for the development of quality educational practices. The special situation of COVID-19 has evidenced the problem of the teacher training. This work investigates the perception of the future teachers of Early Childhood Education about the use of ICT and the policies developed by the Government of the Canary Islands regarding the provision of resources, materials and training. The study had a sample of 110 teachers in training from the University of La Laguna, the results showed agreement regarding the quality and quantity of training received, however, despite not showing interest in continuous training in ICT, they consider fundamentally mastering this competence to improve the quality of the educational response in the Canary Islands.&lt;/span&gt;&lt;/p&gt;
Comparative Studies of Self-organizing Algorithms for Forecasting Economic Parameters

This manuscript presents the economic research results based on their input-output characteristics and functional description with inductive modeling methods and tools.There are a wide plethora of methods to be used for solving this type of problem, including various neural network models, linear and nonlinear regressions, reference vectors' methods, fuzzy models, etc.The main disadvantage of these methods is that the obtained models cannot always interpret and obtain a model of optimal complexity.Unlike the mentioned methods and tools, the group method of data handling (GMDH) allows building models directly from a data sample without the attraction of additional a priori information.This algorithm admits finding internal dependencies in the data and determining optimal model complexity.There is a broad range of iterative GMDH algorithms that have been developed and studied.Oversampling algorithms are applicable for solving the structural identification problems for a limited number of arguments.Iteration algorithms are suitable for solving tasks with many arguments, but they do not guarantee proper structure development.Multi-row GMDH iteration algorithms are the most popular ones.However, they have several sufficient defects, such as informative argument loss or non-informative argument inclusion, as well as a polynomial degree of exponential growth.In this context, the applicability of the GMDH-based iterative and combined architectures for solving the model's interrelation problems between a volume of capital investments and GDP by activity types in the transport branch is considered.The determination coefficient is utilized for the estimation of the obtained models based on a complicated evaluation procedure.The Kolmogorov-Smirnov criterion estimates the model's adequacy.The F-criterion Fisher assesses the significance of polynomial models.The demonstrated results proved that the combined iterative and combinatorial algorithms turned out to be the most effective solution for all evaluation criteria.
Quality evaluation of English pronunciation based on artificial emotion recognition and gaussian mixture model

At present, the posterior probability measure widely used in English speech recognition has the situation that the posterior probability measure of different phonemes cannot be consistent to measure the pronunciation quality of the phoneme and the acoustic modeling method of voice recognition is inconsistent with the evaluation target. Therefore, in order to improve the evaluation effect of English pronunciation quality in colleges and universities, this article is based on artificial emotion recognition and high-speed hybrid model to analyze and filter various clutters that affect speech quality to improve students’ English speech recognition. Moreover, this article uses the characteristics of the clutter and the target in the data to conform to different distributions and based on the clutter distribution characteristics obtained by statistics, this article realizes the suppression of the clutter to improve the target detection performance. In addition, the method proposed in this paper solves the limitations of the clutter suppression technology in the traditional voice detection system and improves the target detection performance. In order to study the pronunciation quality evaluation effect of this model and its effect in English teaching, this paper designs a controlled experiment to analyze the model’s performance. The research results show that the model constructed in this paper has good performance.
Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems

Graph processing has become an important part of various areas of computing, including machine learning, medical applications, social network analysis, computational sciences, and others. A growing amount of the associated graph processing workloads are dynamic, with millions of edges added or removed per second. Graph streaming frameworks are specifically crafted to enable the processing of such highly dynamic workloads. Recent years have seen the development of many such frameworks. However, they differ in their general architectures (with key details such as the support for the concurrent execution of graph updates and queries, or the incorporated graph data organization), the types of updates and workloads allowed, and many others. To facilitate the understanding of this growing field, we provide the first analysis and taxonomy of dynamic and streaming graph processing. We focus on identifying the fundamental system designs and on understanding their support for concurrency, and for different graph updates as well as analytics workloads. We also crystallize the meaning of different concepts associated with streaming graph processing, such as dynamic, temporal, online, and time-evolving graphs, edge-centric processing, models for the maintenance of updates, and graph databases. Moreover, we provide a bridge with the very rich landscape of graph streaming theory by giving a broad overview of recent theoretical related advances, and by discussing which graph streaming models and settings could be helpful in developing more powerful streaming frameworks and designs. We also outline graph streaming workloads and research challenges.
Coronavirus infection study: bibliometric analysis of publications on COVID-19 using PubMed and Dimensions databases

Information about the disease COVID-19 today dominates over any other and is accompanied by many publications.Bibliometrics is a modern tool for the analysis of publication activity.This article describes the results of bibliometric analysis and visualization of an array of publications [1] related to the new type and previous types of coronavirus.Some patterns have been identified.Analysis of publications in the PubMed database made it possible to isolate and cluster terms related to research on coronavirus infections.The most common keywords in PubMed are "coronavirus" and "COVID-19".Analysis of citation of publication activity during 2019-2020 on the topic of "coronavirus" based on the Dimensions database showed that although the authors of the most cited publications are Chinese doctors and researchers, these articles are published in journals in the UK and USA.A temporal analysis of citation showed that prior to the pandemic, journals published in the Netherlands and the United Kingdom were widely cited, while journals published in the United States were most actively cited during the COVID-19 pandemic.The most cited authors include specialists in the field of virology, immunology, medicine, working in Europe, Hong Kong, USA.
Vilio: State-of-the-art Visio-Linguistic Models applied to Hateful Memes

This work presents Vilio, an implementation of state-of-the-art visio-linguistic models and their application to the Hateful Memes Dataset. The implemented models have been fitted into a uniform code-base and altered to yield better performance. The goal of Vilio is to provide a user-friendly starting point for any visio-linguistic problem. An ensemble of 5 different V+L models implemented in Vilio achieves 2nd place in the Hateful Memes Challenge out of 3,300 participants. The code is available at https://github.com/Muennighoff/vilio.
Faster Depth-Adaptive Transformers

Depth-adaptive neural networks can dynamically adjust depths according to the hardness of input words, and thus improve efficiency. The main challenge is how to measure such hardness and decide the required depths (i.e., layers) to conduct. Previous works generally build a halting unit to decide whether the computation should continue or stop at each layer. As there is no specific supervision of depth selection, the halting unit may be under-optimized and inaccurate, which results in suboptimal and unstable performance when modeling sentences. In this paper, we get rid of the halting unit and estimate the required depths in advance, which yields a faster depth-adaptive model. Specifically, two approaches are proposed to explicitly measure the hardness of input words and estimate corresponding adaptive depth, namely 1) mutual information (MI) based estimation and 2) reconstruction loss based estimation. We conduct experiments on the text classification task with 24 datasets in various sizes and domains. Results confirm that our approaches can speed up the vanilla Transformer (up to 7x) while preserving high accuracy. Moreover, efficiency and robustness are significantly improved when compared with other depth-adaptive approaches.
Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry

Abstract Background The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt. Method IMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process. Results We report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection. Conclusions IMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts.
A 3D Multiobject Tracking Algorithm of Point Cloud Based on Deep Learning

3D multiobject tracking (MOT) is an important part of road condition detection and hazard warning algorithm in roadside systems and autonomous driving systems. There is a tricky problem in 3D MOT that the identity of occluded object switches after it reappears. Given the good performance of the 2D MOT, this paper proposes a 3D MOT algorithm with deep learning based on the multiobject tracking algorithm. Firstly, a 3D object detector was used to obtain oriented 3D bounding boxes from point clouds. Secondly, a 3D Kalman filter was used for state estimation, and reidentification algorithm was used to match feature similarity. Finally, data association was conducted by combining Hungarian algorithm. Experiments show that the proposed method can still match the original trajectory after the occluded object reappears and run at a rate of 59 FPS, which has achieved advanced results in the existing 3D MOT system.
Iterative guided machine learning-assisted systematic literature reviews: a diabetes case study

Abstract Background Systematic Reviews (SR), studies of studies, use a formal process to evaluate the quality of scientific literature and determine ensuing effectiveness from qualifying articles to establish consensus findings around a hypothesis. Their value is increasing as the conduct and publication of research and evaluation has expanded and the process of identifying key insights becomes more time consuming. Text analytics and machine learning (ML) techniques may help overcome this problem of scale while still maintaining the level of rigor expected of SRs. Methods In this article, we discuss an approach that uses existing examples of SRs to build and test a method for assisting the SR title and abstract pre-screening by reducing the initial pool of potential articles down to articles that meet inclusion criteria. Our approach differs from previous approaches to using ML as a SR tool in that it incorporates ML configurations guided by previously conducted SRs, and human confirmation on ML predictions of relevant articles during multiple iterative reviews on smaller tranches of citations. We applied the tailored method to a new SR review effort to validate performance. Results The case study test of the approach proved a sensitivity (recall) in finding relevant articles during down selection that may rival many traditional processes and show ability to overcome most type II errors. The study achieved a sensitivity of 99.5% (213 out of 214) of total relevant articles while only conducting a human review of 31% of total articles available for review. Conclusions We believe this iterative method can help overcome bias in initial ML model training by having humans reinforce ML models with new and relevant information, and is an applied step towards transfer learning for ML in SR.
BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

Abstract This work proposed a binary variant of the recently-proposed Equilibrium Optimizer (EO) to solve binary problems. A v-shaped transfer function is used to map continuous values created in EO to binary. To improve the exploitation of the Binary Equilibrium Optimizer (BEO), the Simulated Annealing is used as one of the most popular local search methods. The proposed BEO algorithm is applied to 18 UCI datasets and compared to a wide range of algorithms. The results demonstrate the superiority and merits of EO when solving feature selection problems.
FBSEM: A Novel Feature-Based Stacked Ensemble Method for Sentiment Analysis

Sentiment analysis is the process of determining the attitude or the emotional state of a text automatically. Many algorithms are proposed for this task including ensemble methods, which have the potential to decrease error rates of the individual base learners considerably. In many machine learning tasks and especially in sentiment analysis, extracting informative features is as important as developing sophisticated classifiers. In this study, a stacked ensemble method is proposed for sentiment analysis, which systematically combines six feature extraction methods and three classifiers. The proposed method obtains cross-validation accuracies of 89.6%, 90.7% and 67.2% on large movie, Turkish movie and SemEval-2017 datasets, respectively, outperforming the other classifiers. The accuracy improvements are shown to be statistically significant at the 99% confidence level by performing a Z-test.
Analysis of Cyber Bullying on Facebook Using Text Mining

Cyberbullying is a type of cybercrime that involves the use of the internet and other information technology resources to deliberately insult, embarrass, harass, bully, and threaten people online. The ubiquity of internet connectivity has enabled an increase in the volume and pace of cyberbullying activities because the criminals no longer need to be physically present when committing the crime. This work aims to analyze and predict cyberbullying on Facebook using Naïve Bayes algorithm. The score accuracy, classification report, and confusion matrix are also employed to assess the performance of the classifier. The accuracy of the classifier is 0.95(95%) which means the model can predict 95 of every 100 instances correctly. Also, the result of the experimental analysis shows that Naïve Bayes is effective in classifying a word into a bully or non-bully word and can identify the category of the bully word that is being sent online.