Reviewerly/exHarmony huggingface dataset:
Autonomous hierarchy creation for computationally feasible near-optimal path planning in large environments

Authors:
raw_author_name: Jelena Gregorić
raw_affiliation_string: University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Control and Computer Engineering, Laboratory for Autonomous Systems and Mobile Robotics (LAMOR), Unska 3, Zagreb, HR-10000, Croatia
raw_author_name: Marija Seder
raw_affiliation_string: University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Control and Computer Engineering, Laboratory for Autonomous Systems and Mobile Robotics (LAMOR), Unska 3, Zagreb, HR-10000, Croatia
raw_author_name: Ivan Petrović
raw_affiliation_string: University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Control and Computer Engineering, Laboratory for Autonomous Systems and Mobile Robotics (LAMOR), Unska 3, Zagreb, HR-10000, Croatia

Abstract: Mobile robots have increasingly been used in warehouses, factories, open spaces, on roads, and urban areas to transport goods and people. Path planning in complex and large environments using classical graph-based search is computationally too intensive. One of the main challenges of mobile robotics is describing the large robot's workspaces with a reduction of graph complexity and improvement of path planning efficiency. A representation by a hierarchical graph (H-graph) facilitates graph creation and reduces the complexity of path planning. In this paper, we present an algorithm for autonomously generating a hierarchical graph of the environment from floor plans. The hierarchical abstraction depicts the environment in levels, from the most detailed to the most abstract representation of the environment, where pre-computed partial paths at the most detailed level are graph edges at a higher level. We use the E* algorithm to find partial paths in the most detailed abstraction level, and we propose the extraction of higher levels automatically from lower levels. We verified the proposed H-graph creation on three cases: our University premises as an example of a multi-building environment, a large warehouse with a lot of shelves, and an outdoor urban environment which is represented by a city map. All cases result in five abstract levels. The result shows that the path planning algorithm searches about 50 to 800 times fewer nodes when using the H-graph as the environment representation than when using a plain graph representing the entire environment as an occupancy grid map.
Evaluation method for the availability of solar energy resources in road areas before route corridor planning

Authors:
raw_author_name: Wei Jiang
raw_affiliation_string: Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an, Shaanxi, 710064, PR China
raw_author_name: Shuo Zhang
raw_affiliation_string: Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an, Shaanxi, 710064, PR China
raw_author_name: Teng Wang
raw_affiliation_string: Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an, Shaanxi, 710064, PR China
raw_author_name: Yufei Zhang
raw_affiliation_string: Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an, Shaanxi, 710064, PR China
raw_author_name: Aimin Sha
raw_affiliation_string: Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an, Shaanxi, 710064, PR China
raw_author_name: Jingjing Xiao
raw_affiliation_string: School of Civil Engineering, Chang'an University, Xi'an, Shaanxi 710064, PR China
raw_author_name: Dongdong Yuan
raw_affiliation_string: Key Laboratory for Special Area Highway Engineering of Ministry of Education, Chang'an University, Xi'an, Shaanxi, 710064, PR China

Abstract: The full utilization of solar energy resources along the road is an effective method to solve the energy shortage in transportation. The key to this is an accurate evaluation of solar energy resources, which provides the rationale for the optimal location of road photovoltaic (PV) projects. However, determining the availability of solar energy resources in road areas before route corridor planning remains difficult. To address the issue, this paper developed a standardized method. Firstly, this paper analyzed the critical factors affecting the availability of solar energy resources. They were meteorology, topography, land use type, geology, and location. On that basis, we developed a four-level evaluation indicator system. Then, it proposed a multi-indicator evaluation method based on the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). It used AHP to calculate the weights of each indicator, with solar radiation being the most important. It used GIS to analyze and overlay the spatial distribution of multiple indicators to obtain the map of suitability grades, which could be used to route corridor selection for road PV projects. It also used a case study to demonstrate how the method is applied. The findings show that the method can accurately evaluate the availability of solar energy resources. Furthermore, it studied the appropriate division of evaluation units and it is advised that they shouldn't be larger than 30 m × 30 m. These results provide guidance as a reference for the application of road PV projects and site selection for route corridors worldwide. That will promote the integration of transportation and energy in the future.
Exploiting fuzzy rough entropy to detect anomalies

Authors:
raw_author_name: Zhong Yuan
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610065, China
raw_author_name: Chuan Luo
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu 610065, China
raw_author_name: Hongmei Chen
raw_affiliation_string: School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China
raw_author_name: Dezhong Peng
raw_affiliation_string: Chengdu Ruibei Yingte Information Technology Co., Ltd, Chengdu 610054, China; College of Computer Science, Sichuan University, Chengdu 610065, China

Abstract: Anomaly detection has been used in a wide range of fields. However, most of the current detection methods are only applicable to certain data, ignoring uncertain information such as fuzziness in the data. Fuzzy rough set theory, as an essential mathematical model for granular computing, provides an effective method for processing uncertain data such as fuzziness. Fuzzy rough entropy has been proposed in fuzzy rough set theory and has been employed successfully in data analysis tasks such as feature selection. However, it mainly uses the intersection operation, which may not effectively reflect the similarity between high-dimensional objects. In response to the two challenges mentioned above, distance-based fuzzy rough entropy and its correlation measures are proposed. Further, the proposed fuzzy rough entropy is used to construct the anomaly detection model and the Fuzzy Rough Entropy-based Anomaly Detection (FREAD) algorithm is designed. Finally, the FREAD algorithm is compared and analyzed with some mainstream anomaly detection algorithms (including COF, DIS, INFLO, LDOF, LoOP, MIX, ODIN, SRO, and VarE algorithms) on some publicly available datasets. Experimental results indicate that the FREAD algorithm significantly outperforms other algorithms in terms of performance and flexibility. The code is publicly available online at https://github.com/optimusprimeyy/FREAD.
Efficient ray sampling for radiance fields reconstruction

Authors:
raw_author_name: Shilei Sun
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Ming Liu
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Zhongyi Fan
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Qingliang Jiao
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Yuxue Liu
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Liquan Dong
raw_affiliation_string: Beijing Institute of Technology, Beijing, China
raw_author_name: Lingqin Kong
raw_affiliation_string: Beijing Institute of Technology, Beijing, China

Abstract: Accelerating the training process of neural radiance field holds substantial practical value. The ray sampling strategy profoundly influences the convergence of this neural network. Therefore, more efficient ray sampling can directly augment the training efficiency of existing NeRF models. We propose a novel ray sampling approach for neural radiance field that improves training efficiency while retaining photorealistic rendering results. First, we analyze the relationship between the pixel loss distribution of sampled rays and rendering quality. This reveals redundancy in the original NeRF's uniform ray sampling. Guided by this finding, we develop a sampling method leveraging pixel regions and depth boundaries. Our main idea is to sample fewer rays in training views, yet with each ray more informative for scene fitting. Sampling probability increases in pixel areas exhibiting significant color and depth variation, greatly reducing wasteful rays from other regions without sacrificing precision. Through this method, not only can the convergence of the network be accelerated, but the spatial geometry of a scene can also be perceived more accurately. Rendering outputs are enhanced, especially for texture-complex regions. Experiments demonstrate that our method significantly outperforms state-of-the-art techniques on public benchmark datasets.
An active semi-supervised transfer learning method for robot pose error prediction and compensation

Authors:
raw_author_name: Teng Zhang
raw_affiliation_string: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
raw_author_name: Fangyu Peng
raw_affiliation_string: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
raw_author_name: Xiaowei Tang
raw_affiliation_string: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
raw_author_name: Rong Yan
raw_affiliation_string: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
raw_author_name: Chi Zhang
raw_affiliation_string: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

Abstract: Robots are widely employed in industrial settings owing to their efficiency, flexibility, and extensive operational ranges. However, their application in high-precision scenarios is limited owing to their low absolute accuracies. Existing methods suffer from high measurement costs, and limited applicability and accuracy. To address these issues, an active semi supervised transfer learning method (ASTL) is introduced. The pose error prediction problem was modelled as a transfer learning paradigm for the first time. It leverages the proposed multi-stage greedy sampling (MGS) for informed sample labelling combined with coarse calibration and semi supervised transfer learning (STL) to embed theoretical knowledge for globally accurate predictions. The proposed method is compared with other prediction and compensation approaches for four robotic motion tasks. It significantly reduces the time consumption by approximately 89.3% compared with direct measurements and achieves a maximum reduction of approximately 90% in robot pose errors. This substantial enhancement in pose accuracy promotes high-quality applications of robots in the industrial sector.
DODFMiner: An automated tool for Named Entity Recognition from Official Gazettes

Authors:
raw_author_name: Felipe X.B. da Silva
raw_affiliation_string: Department of Computer Science, University of Brasília, Brasilia, Brazil
raw_author_name: Andrei L. Queiroz
raw_affiliation_string: Department of Computer Science, University of Brasília, Brasilia, Brazil
raw_author_name: Ricardo M. Marcacini
raw_affiliation_string: Institute of Mathematics and Computer Sciences, University of São Paulo, São Paulo, Brazil
raw_author_name: Thiago P. Faleiros
raw_affiliation_string: Department of Computer Science, University of Brasília, Brasilia, Brazil
raw_author_name: Vinicius R.P. Borges
raw_affiliation_string: Department of Computer Science, University of Brasília, Brasilia, Brazil
raw_author_name: Luís P.F. Garcia
raw_affiliation_string: Department of Computer Science, University of Brasília, Brasilia, Brazil

Abstract: Official gazettes are documents published by governments to publicize their actions, spanning long periods of time and making an important transparency mechanism. These documents have information on laws, contracts, and bidding processes, as well as on civil servants and their careers in public service. Automatic information extraction of these documents may contribute to public transparency, with two tasks being especially useful: the classification of the different segments of these documents, the so called acts; and the Named Entity Recognition (NER) within the acts. The variety of official gazettes and their patterns brings up the necessity of constructing different tools for specific gazettes. In this paper, we propose DODFMiner, a command-line interface tool to classify acts and extract named entities from the Official Gazette of the Federal District. The tool follows a 3-step approach: the pre-processing of the input data; text classification using rule-based systems with regular expressions; and NER with Machine Learning algorithms. It allows users to input JSON files and receive CSV as output, providing information that allows users to track government procurements through years, contracts duration and total amount, among others. We also propose a set of experiments to support the choice of models included in the tool, covering the classification and NER steps. Text classification achieved a mean F1-score of 0.778, while to the NER, we compared 3 different architectures, CRF with a mean F1-score of 0.851, CNN-biLSTM-CRF with 0.787 and CNN-CNN-LSTM with 0.841.
Convolution finite element based digital image correlation for displacement and strain measurements

Authors:
raw_author_name: Ye Lu
raw_affiliation_string: Department of Mechanical Engineering, University of Maryland Baltimore County, Baltimore, USA
raw_author_name: Weidong Zhu
raw_affiliation_string: Department of Mechanical Engineering, University of Maryland Baltimore County, Baltimore, USA

Abstract: This work presents a novel global digital image correlation (DIC) method, based on a newly developed convolution finite element (C-FE) approximation. The convolution approximation can rely on the mesh of linear finite elements and enables arbitrarily high order approximations without adding more degrees of freedom. Therefore, the C-FE based DIC can be more accurate than the usual FE based DIC by providing highly smooth and accurate displacement and strain results with the same element size. The detailed formulation and implementation of the method have been discussed in this work. The controlling parameters in the method include polynomial order, patch size, and dilation. A general choice of the parameters and their potential adaptivity have been discussed. The proposed DIC method has been tested by several representative examples, including the DIC challenge 2.0 benchmark problems, with comparison to the usual FE based DIC. C-FE outperformed FE in all the DIC results for the tested examples. This work demonstrates the potential of C-FE and opens a new avenue to enable highly smooth, accurate, and robust DIC analysis for full-field displacement and strain measurements.
A novel efficient Rank-Revealing QR matrix and Schur decomposition method for big data mining and clustering (RRQR-SDM)

Authors:
raw_author_name: D. Paulraj
raw_affiliation_string: Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India
raw_author_name: T. Sethukarasi
raw_affiliation_string: Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India
raw_author_name: S. Neelakandan
raw_affiliation_string: Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, India
raw_author_name: Adi Alhudhaif
raw_affiliation_string: Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia
raw_author_name: Norah Alnaim
raw_affiliation_string: Department of Computer Science, College of Sciences and Humanities in Jubail, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia

Abstract: Big Data is an emerging technology with enormous potential to develop business and its administration. Due to the enormous volume, efficient data mining and clustering methods are crucial to extracting meaningful insights and patterns from large-scale datasets. Problems may arise from the need to analyze, capture, share, store, and visualize the data. Several methods have already been proposed for mining knowledge from big data. It is practically inefficient or impossible to handle these massive data using the proposed methods in a single machine because big data are frequently acquired from dispersed locations and stored on several machines. Matrix decomposition is one of the critical strategies to retrieve knowledge from diverse, noisy, huge data generated by modern applications and stored in dispersed locations. This study proposes a novel approach called the Rank-Revealing QR Matrix and Schur Decomposition Method (RRQR-SDM) specifically designed for big data mining and clustering tasks. The RRQR-SDM is designed to reveal the rank of the data matrix in a computationally efficient manner by using a modified QR decomposition, eliminating the need for expensive Singular Value Decomposition (SVD) computations. The proposed RRQR-SDM method offers several advantages over existing approaches. Firstly, exploiting the inherent low-rank structure reduces the computational complexity associated with large-scale datasets. By revealing the rank of the input matrix, it enables dimensionality reduction and efficient data compression. Secondly, the Schur decomposition enhances the interpretability of the data by providing a clear separation between the relevant and irrelevant components. This feature makes the RRQR-SDM method particularly suitable for data mining and clustering tasks where identifying the most significant features is essential. To evaluate the performance of the RRQR-SDM method, extensive experiments were conducted on various big data datasets. The results demonstrate that the proposed method outperforms state-of-the-art computational efficiency and clustering accuracy techniques.
Joint group and pairwise localities embedding for feature extraction

Authors:
raw_author_name: Wenjun Hu
raw_affiliation_string: School of Information Engineering, Huzhou University, Second Ring East Road, Huzhou, 313000, Zhejiang, China; Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Second Ring East Road, Huzhou, 313000, Zhejiang, China
raw_author_name: Ke Zhang
raw_affiliation_string: School of Information Engineering, Huzhou University, Second Ring East Road, Huzhou, 313000, Zhejiang, China; Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Second Ring East Road, Huzhou, 313000, Zhejiang, China
raw_author_name: Shitong Wang
raw_affiliation_string: School of Artificial Intelligence and Computer Science, Jiangnan University, Lihu Avenue, Wuxi, 214122, Jiangsu, China
raw_author_name: Yong Zhang
raw_affiliation_string: School of Computer and Information Technology, Liaoning Normal University, Liushu South Street, Dalian, 116081, Liaoning, China; School of Information Engineering, Huzhou University, Second Ring East Road, Huzhou, 313000, Zhejiang, China; Zhejiang Province Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Second Ring East Road, Huzhou, 313000, Zhejiang, China

Abstract: Many practical applications generate high-dimensional data, which poses challenges in terms of computational time and storage. To address this issue, feature extraction has become a popular research topic. Learning by way of graph embedding is useful for discovering potential intrinsic low-dimensional structures and has thus gained wide attention. However, traditional embedding models typically utilize only one graph or one type of multiple graphs to capture local relationships, which may be insufficient for high-dimensional data that may exhibit different kinds of local relationships. To overcome this drawback, we developed a joint embedding framework that incorporates multiple types of graphs. Under this framework, a novel joint group and pairwise locality embedding model (GPE) is proposed. The GPE model has the following distinctive merits: (1) it simultaneously incorporates simple graphs, hypergraphs, and probabilistic hypergraphs, enabling the use of not only one type of multiple graphs but also multiple types of multiple graphs; (2) it can leverage both group relationships and pairwise graphs to discover local information during feature extraction; and (3) its objective function can be solved using an alternating optimization strategy, which involves solving eigenvalue problems and quadratic programming problems, resulting in very fast convergence. Finally, we arranged classification tasks and clustering tasks on several high-dimensional real-world datasets, and the experimental results prove that the identification capability of GPE is encouraging.
Preserving data privacy in machine learning systems

Authors:
raw_author_name: Soumia Zohra El Mestari
raw_affiliation_string: SnT, University of Luxembourg, 2 Av. de l'Universite, Esch-sur-Alzette, L-4365, Luxembourg
raw_author_name: Gabriele Lenzini
raw_affiliation_string: SnT, University of Luxembourg, 2 Av. de l'Universite, Esch-sur-Alzette, L-4365, Luxembourg

Abstract: The wide adoption of Machine Learning to solve a large set of real-life problems came with the need to collect and process large volumes of data, some of which are considered personal and sensitive, raising serious concerns about data protection. Privacy-enhancing technologies (PETs) are often indicated as a solution to protect personal data and to achieve a general trustworthiness as required by current EU regulations on data protection and AI. However, an off-the-shelf application of PETs is insufficient to ensure a high-quality of data protection, which one needs to understand. This work systematically discusses the risks against data protection in modern Machine Learning systems taking the original perspective of the data owners, who are those who hold the various data sets, data models, or both, throughout the machine learning life cycle and considering the different Machine Learning architectures. It argues that the origin of the threats, the risks against the data, and the level of protection offered by PETs depend on the data processing phase, the role of the parties involved, and the architecture where the machine learning systems are deployed. By offering a framework in which to discuss privacy and confidentiality risks for data owners and by identifying and assessing privacy-preserving countermeasures for machine learning, this work could facilitate the discussion about compliance with EU regulations and directives. We discuss current challenges and research questions that are still unsolved in the field. In this respect, this paper provides researchers and developers working on machine learning with a comprehensive body of knowledge to let them advance in the science of data protection in machine learning field as well as in closely related fields such as Artificial Intelligence.