Reviewerly/exHarmony huggingface dataset:
Stable Autonomous Spiral Stair Climbing of Tracked Vehicles Using Wall Reaction Force

In this letter, an autonomous spiral stair climbing method for tracked vehicles using the reaction force from side walls has been proposed. Spiral stairs are one of the most difficult terrains for tracked vehicles because of their asymmetrical ground shape and small turning radius. Tracked vehicles are expected to be used in industrial plant inspection tasks, where robots should navigate on multiple floors by ascending the stairs. Spiral or curved stairs are installed as part of inspection passages for cylindrical facilities, such as boilers, chimneys, or large tanks. Previously, the authors have experimentally demonstrated that the wall-following motion is effective for stabilizing and accelerating spiral stair climbing. However, the complete automation of climbing motion or the analysis of why the same motion is generated even if a disturbance exists in the initial entry angle to the wall should be investigated. In this study, the authors developed an autonomous spiral stair climbing method using the wall reaction force and clarified the applicable limitations of this method using a geometrical model. Autonomous spiral stair climbing is realized by attaching passive wheels on its collision point and automating the motions of main tracks and sub-tracks. The geometrical model shows the expected trajectory of the robot on the spiral stairs, which suggests that the robot' s rotation radius converges to a specific value; this is experimentally confirmed by measuring the robot's motion. The wall-following motion of robots is equivalent to human inspectors grasping handrails while climbing stairs. Through collisions with surrounding objects, motion is stabilized and certainty is guaranteed.
Promoting Fairness in Learned Models by Learning to Active Learn under Parity Constraints

Machine learning models can have consequential effects when used to automate decisions, and disparities between groups of people in the error rates of those decisions can lead to harms suffered more by some groups than others. Past algorithmic approaches aim to enforce parity across groups given a fixed set of training data; instead, we ask: what if we can gather more data to mitigate disparities? We develop a meta-learning algorithm for parity-constrained active learning that learns a policy to decide which labels to query so as to maximize accuracy subject to parity constraints. To optimize the active learning policy, our proposed algorithm formulates the parity-constrained active learning task as a bi-level optimization problem. The inner level corresponds to training a classifier on a subset of labeled examples. The outer level corresponds to updating the selection policy choosing this subset to achieve a desired fairness and accuracy behavior on the trained classifier. To solve this constrained bi-level optimization problem, we employ the Forward-Backward Splitting optimization method. Empirically, across several parity metrics and classification tasks, our approach outperforms alternatives by a large margin.
A recommender system based on a new similarity metric and upgraded crow search algorithm

In the current era of big data, the recommender system aspires to provide users with a tailored set of personalized items from a pool of a large population. The most popular collaborative filtering system performs this information filtering process by computing similarity among users or items. This paper proposes a similarity metric that comprises of weights and values. Values are calculated by considering the matching set of users for which similarity is to be computed. The optimal values of weights are decided using an upgraded form of the Crow Search Algorithm (CSA). The exploration and exploitation stability of CSA is improvised by making use of Levy flight diffusion, adaptive operator adjustment, and event factor. The performance of the implemented metaheuristic approach is validated on Jester, MovieLens 100K, and MovieLens 1M dataset. Comparative analysis of proposed model against several other traditional metaheuristic based personalization systems reveal that our model is less delicate to the dimension of datasets and it also presents exceptional refinement in terms of prediction complexity and accuracy.
A Study of the Cloud Computing Adoption Issues and Challenges

&lt;P&gt;Background: The cloud computing is the modern trend in high-performance computing. Cloud computing becomes very popular due to its characteristic of available anywhere, elasticity, ease of use, cost-effectiveness, etc. Though the cloud grants various benefits, it has associated issues and challenges to prevent the organizations to adopt the cloud. &lt;/P&gt;&lt;P&gt; Objective: The objective of this paper is to cover the several perspectives of Cloud Computing. This includes a basic definition of cloud, classification of the cloud based on Delivery and Deployment Model. The broad classification of the issues and challenges faced by the organization to adopt the cloud computing model are explored. Examples for the broad classification are Data Related issues in the cloud, Service availability related issues in cloud, etc. The detailed sub-classifications of each of the issues and challenges discussed. The example sub-classification of the Data Related issues in cloud shall be further classified into Data Security issues, Data Integrity issue, Data location issue, Multitenancy issues, etc. This paper also covers the typical problem of vendor lock-in issue. This article analyzed and described the various possible unique insider attacks in the cloud environment. &lt;/P&gt;&lt;P&gt; Results: The guideline and recommendations for the different issues and challenges are discussed. The most importantly the potential research areas in the cloud domain are explored. &lt;/P&gt;&lt;P&gt; Conclusion: This paper discussed the details on cloud computing, classifications and the several issues and challenges faced in adopting the cloud. The guideline and recommendations for issues and challenges are covered. The potential research areas in the cloud domain are captured. This helps the researchers, academicians and industries to focus and address the current challenges faced by the customers.&lt;/P&gt;
Construction of typhoon disaster knowledge graph based on graph database Neo4j

The typhoon knowledge graph can correlate various kinds of information in the typhoon data, conduct overall and related analysis, and finally provide effective assistance for typhoon prevention and post-disaster protection. The data of typhoon landing in China from 2000 to 2015 were selected to build a typhoon knowledge graph based on Neo4j graph database platform. The typhoon knowledge graph can be used to understand the occurrence of historical typhoons and obtain the distribution of typhoon data in time and space.
Improved Feedback in Automated Grading of Data Science Assignments

The paper deals with the automated grading of assignments made up of R commands, their output and comments written in natural language. Compared to other tools presented in the literature, our tool supports both students and teachers, uses static source code analysis for the code-snippets and a supervised classifier based on sentence embeddings for the open-ended answers, and provides a feedback to students and includes the instructor review. After more than one year of use, improvements in terms of the feedback provided to the students are discussed in the paper, that in turn should offer manifold benefits to both students and teachers. Finally, the paper proposes a study finalised to refine and test the effectiveness of the proposal.
Resampling-based noise correction for crowdsourcing

Crowdsourcing services provide an economic and efficient means of acquiring multiple noisy labels for each training instance in supervised learning. Ground truth inference methods, also known as consensus methods, are then used to obtain the integrated labels of training instances. Although consensus methods are effective, there still exists a level of noise in the set of integrated labels. Therefore, it is necessary to handle noise in the integrated labels to improve label and model quality. In this paper, we propose a resampling-based noise correction method (simply RNC). Different from previous label noise correction methods for crowdsourcing, RNC first employs a filter to obtain a clean set and a noisy set and then repeatedly resamples the clean and noisy sets several times according to a certain proportion. Finally, multiple classifiers built on the resampled data sets are used to re-label the training data. Experimental results based on 18 simulated data sets and five real-world data sets demonstrate that RNC rarely degrades the label and model quality compared to other three state-of-the-art noise correction methods and, in many cases, improves quality dramatically.
EDA and Quantum Computing: a symbiotic relationship?

Editor's notes: Quantum computing and quantum information science are collectively viewed as a potential game-changing paradigm of computing that can help accelerate various computationally intractable problems across numerous scientific and engineering domains. This article outlines the interplay between electronic design automation (EDA) and quantum computing. -Partha Pratim Pande, Washington State University.
Fast Uncertainty Estimation for Deep Learning Based Optical Flow

We present a novel approach to reduce the processing time required to derive the estimation uncertainty map in deep learning-based optical flow determination methods. Without uncertainty aware reasoning, the optical flow model, especially when it is used for mission critical fields such as robotics and aerospace, can cause catastrophic failures. Although several approaches such as the ones based on Bayesian neural networks have been proposed to handle this issue, they are computationally expensive. Thus, to speed up the processing time, our approach applies a generative model, which is trained by input images and an uncertainty map derived through a Bayesian approach. By using synthetically generated images of spacecraft, we demonstrate that the trained generative model can produce the uncertainty map 100~700 times faster than the conventional uncertainty estimation method used for training the generative model itself. We also show that the quality of uncertainty map derived by the generative model is close to that of the original uncertainty map. By applying the proposed approach, the deep learning model operated in real-time can avoid disastrous failures by considering the uncertainty as well as achieving better performance removing uncertain portions of the prediction result.
Character Recognition of Tulu Script Using Convolutional Neural Network

Handwriting classification and identification is one of the most interesting issues in the current research because of its variety of applications. It has leveraged its potential in reducing the manual work of converting the documents containing handwritten characters to machine-readable texts. The deep convolutional neural networks (DCNNs) are successfully implemented for the recognition of characters in various languages. This paper proposes a DCNN-based architecture for the classification of Tulu language characters. Tulu is one of the five Dravidian groups of languages used by around 50 Lakh people in the states of Karnataka and Kerala. This model is mainly developed to assist the character recognition of Tulu documents. A total of 90,000 characters including both vowels and consonants have been included in the dataset. This architecture is showing a satisfactory test accuracy of 92.41% for the classification of 45 handwritten characters.
Learning instance-aware object detection using determinantal point processes

Recent object detectors localize instances and classify candidate regions simultaneously. The number of candidate regions is typically larger than the number of objects and each region is evaluated independently. To assign a single detection bounding box for each object, heuristic algorithms, such as non-maximum suppression (NMS), have been used widely. While simple heuristic algorithms are effective for stand-alone objects, they often fail to detect overlapped objects. In this paper, we address this issue by training a network to distinguish different objects using the relationship between candidate boxes. We propose an instance-aware detection network (IDNet), which can learn to extract features from candidate regions and measure their similarities. Based on pairwise similarities and detection qualities, the IDNet selects a subset of candidate bounding boxes using instance-aware determinantal point process inference (IDPP). Extensive experiments demonstrate that the proposed algorithm achieves significant improvements for detecting overlapped objects compared to existing state-of-the-art detection methods on CrowdHuman, Pascal VOC, and MS COCO datasets.
Survey of Multi-Agent Strategy Based on Reinforcement Learning

There are many multi-agent systems in life, such as driving vehicles, playing football games, and even bees building their hives. These systems are cooperative or competitive among multiple agents to fufill a task. Compared with single agent reinforcement learning, multi-agent has a larger search space, perception of other agents, and system robustness. The main purpose of this paper is to provide a clear overview of current multi-agent reinforcement learning strategy training methods, and to review the latest progress in multi-agent reinforcement learning. Finally, intorduced the application prospects and development trends of multi-agent reinforcement learning, summarized the technology of collaboration or competition. At present, multi-agent reinforcement learning has gradually been applied in many fields, such as robot systems, human-machine games, and autonomous driving. In the future, it will be widely used in resource management, transportation systems, medical care, finance and other fields.
Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

Summary This article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low‐dose X‐ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low‐dose X‐ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low‐level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and visual quality in mammograms compared with other state‐of‐the‐art methods.
Active Learning for Enumerating Local Minima Based on Gaussian Process Derivatives

We study active learning (AL) based on gaussian processes (GPs) for efficiently enumerating all of the local minimum solutions of a black-box function. This problem is challenging because local solutions are characterized by their zero gradient and positive-definite Hessian properties, but those derivatives cannot be directly observed. We propose a new AL method in which the input points are sequentially selected such that the confidence intervals of the GP derivatives are effectively updated for enumerating local minimum solutions. We theoretically analyze the proposed method and demonstrate its usefulness through numerical experiments.
Video frame interpolation using deep cascaded network structure

Video frame interpolation is a technology that generates high frame rate videos from low frame rate videos by using the correlation between consecutive frames. Presently, convolutional neural networks (CNN) exhibit outstanding performance in image processing and computer vision. Many variant methods of CNN have been proposed for video frame interpolation by estimating either dense motion flows or kernels for moving objects. However, most methods focus on estimating accurate motion. In this study, we exhaustively analyze the advantages of both motion estimation schemes and propose a cascaded system to maximize the advantages of both the schemes. The proposed cascaded network consists of three autoencoder networks, that process the initial frame interpolation and its refinement. The quantitative and qualitative evaluations demonstrate that the proposed cascaded structure exhibits a promising performance compared to currently existing state-of-the-art-methods.
An improved genetic algorithm with Lagrange and density method for clustering

Summary To overcome the shortcomings of K‐means clustering including clustering numbers, sensitivity to clustering center (seeds) and local optimization, this article proposes an improved genetic algorithm (GA) with a novel Lagrange‐based fitness function and an initial population technique(called NicheClust algorithm); the NicheClust can determine the best chromosomes and then feeds these into K‐means as initial seeds to achieve higher‐quality clustering results by allowing the initial seeds to readjust in terms of clustering demands. The GA approach is proposed to search for a global optimally solution. The initial population method is presented to automatically capture the appropriate number of clusters and find the initial seeds. The Lagrange‐based approach is used to prevent the fitness function from prematurely converging and capture global optimization for K‐means clustering results. Experimental results based on six taxi Global Positioning System (GPS) datasets verify the higher performance of NicheClust compared to other clustering methods and validate the effectiveness with statistical analysis method.
Finding reusable structured resources for the integration of environmental research data

Successful data integration requires careful examination of data semantics, a task that has often been approached with the use of ontologies. However, there are some barriers to build ontologies for data integration in complex domains such as the environmental one. A relevant problem is the development of new ontologies disregarding previous knowledge resources such as reference models and vocabularies. This paper addresses this challenge by proposing a systematic approach (dubbed CLeAR) for the identification and selection of reusable artifacts for building ontologies with the purpose of research data integration. CLeAR follows some principles of the systematic literature reviews, supporting the search for structured resources in the scientific literature. We apply CLeAR to the environmental domain. A total of 543 publications were surveyed. The results obtained provide a set of 75 structured resources for the environmental domain, evaluated according domain coverage and some quality attributes (e.g., proper documentation, community acceptance).
Stereo Vision-Based Depth Estimation

This paper presents a lightweight depth estimation method for feature point based on binocular vision technology. First, based on the ORB feature and the brute force matching method, we obtained matching feature point pairs for two frames of image. Secondly, according to the obtained pixel coordinates of the matching point pairs, the motion of the camera can be estimated by the principle of the epipolar constraint. Finally, based on triangulation theory and camera motion, we implemented depth estimation of feature points in the input image. The experiment results indicate that our proposed approach can achieve good real-time performance, high depth estimation accuracy, and has broad application prospects in the case of constrained computing resources.
Modality and Uncertainty in Data Visualizations: A Corpus Approach to the Use of Connecting Lines

In data visualizations, connecting lines may have various semiotic functions, including the semiotic potential of indicating modality and uncertainty. The goal of this article is to find out how this semiotic potential is realized in current best practices of data visualizations and what conventions exist for the visual manifestations of these functions. This issue is addressed by using a corpus-based approach and a two-level analysis method within a social semiotic framework. First, the article offers a theoretical discussion on how the concepts of modality and uncertainty interrelate. Second, a method for investigating how these concepts are visualized at different levels is presented. Third, a corpus analysis including 163 award-winning data visualizations is presented. The results indicate the existence of certain conventions for visual modality markers, and thus offer new insights relevant for both design theory and practice.
Dark Web Traffic Analysis of Cybersecurity Threats Through South African Internet Protocol Address Space

Cybersecurity crimes masterminded at dark web pose social security threats global and open a conundrum for researchers in the field of security informatics. Dark web describes a layer beneath deep web on Internet protocol stack that is designed to be concealed from orthodox search engines. The concealment of orthodox search engines has made it extremely hard for law enforcement agencies to track specific websites that pose great cybersecurity threats. This research was supported financially by the BankSeta, Council on Scientific and Industrial Research and National Research Foundation of South Africa to track the malicious use of dark web through South African Internet protocol address space. The study applies the method of dark web crawling using onion router to track traffic with high tendency for cybersecurity threats. The results of crawling experimental indicate that child pornography, sales of spyware, hacking, sales of drugs, planning of violence and sales of dangerous weapons are the frequent malicious use of dark web in South Africa. The outcome of this study can help in creating an accurate revelation of cybersecurity threats to assist law enforcement agencies to combat cybercriminals in the country.
A Survey of Hierarchical Classification Algorithms with Big-Bang Approach

Hierarchical classification is a technique used to solve problems with hierarchical concepts, which are usually arranged into trees or directed acyclic graphs (DAGs). Research in this field is mostly done in the field of bioinformatics and text classification, because both fields have hierarchical problems. Completion of hierarchy classification can be done with a local approach and a big-bang approach. From several previous studies, it was found that hierarchical classification with the big bang approach got good results, both in terms of predictive accuracy, model size and time needed to build the model. In this paper, we survey previous research on hierarchical classification algorithms using bing-bang approach.