Reviewerly/exHarmony huggingface dataset:
Attention-Based Monocular Depth Estimation Considering Global and Local Information in Remote Sensing Images

Authors:
raw_author_name: Junwei Lv
raw_affiliation_string: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
raw_author_name: Yueting Zhang
raw_affiliation_string: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
raw_author_name: Jiayi Guo
raw_affiliation_string: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
raw_author_name: Xin Zhao
raw_affiliation_string: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
raw_author_name: Bin Lei
raw_affiliation_string: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Abstract: Monocular depth estimation using a single remote sensing image has emerged as a focal point in both remote sensing and computer vision research, proving crucial in tasks such as 3D reconstruction and target instance segmentation. Monocular depth estimation does not require multiple views as references, leading to significant improvements in both time and efficiency. Due to the complexity, occlusion, and uneven depth distribution of remote sensing images, there are currently few monocular depth estimation methods for remote sensing images. This paper proposes an approach to remote sensing monocular depth estimation that integrates an attention mechanism while considering global and local feature information. Leveraging a single remote sensing image as input, the method outputs end-to-end depth estimation for the corresponding area. In the encoder, the proposed method employs a dense neural network (DenseNet) feature extraction module with efficient channel attention (ECA), enhancing the capture of local information and details in remote sensing images. In the decoder stage, this paper proposes a dense atrous spatial pyramid pooling (DenseASPP) module with channel and spatial attention modules, effectively mitigating information loss and strengthening the relationship between the target’s position and the background in the image. Additionally, weighted global guidance plane modules are introduced to fuse comprehensive features from different scales and receptive fields, finally predicting monocular depth for remote sensing images. Extensive experiments on the publicly available WHU-OMVS dataset demonstrate that our method yields better depth results in both qualitative and quantitative metrics.
Spatial quantum error correction threshold

Authors:
raw_author_name: Ari Mizel
raw_affiliation_string: Laboratory for Physical Sciences, College Park, Maryland 20740, USA

Abstract: We consider a spatial analogue of the quantum error correction threshold. Given individual time-independent subsystems in which quantum information is coherent over sufficiently long lengths, we show how the information can be kept coherent for arbitrarily long lengths by forming time-independent composite systems. The subsystem coherence range exhibits threshold behavior. When it exceeds a range ${\ensuremath{\xi}}_{\mathrm{th}}$, meaningful information can be extracted from the ground state of the composite system. Otherwise, the information is garbled. The threshold transition implies that the parent Hamiltonian of the ground state has gone from gapped to gapless. Ramifications of the construction for projected entangled pair states and for adiabatic quantum computation are considered.
No-signalling constrains quantum computation with indefinite causal structure

Authors:
raw_author_name: Alessandro Bisio
raw_affiliation_string: Dipartimento di Fisica, Università di Pavia, via Bassi 6, 27100 Pavia, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Pavia, Italy
raw_author_name: Paolo Perinotti
raw_affiliation_string: Dipartimento di Fisica, Università di Pavia, via Bassi 6, 27100 Pavia, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Pavia, Italy

Abstract: Quantum processes with indefinite causal structure emerge when we wonder which are the most general evolutions, allowed by quantum theory, of a set of local systems which are not assumed to be in any particular causal order. These processes can be described within the framework of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mo>&amp;#x2212;</mml:mo><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi></mml:math> quantum theory which, starting from considering maps from quantum transformations to quantum transformations, recursively constructs a hierarchy of quantum maps of increasingly higher order. In this work, we develop a formalism for quantum computation with indefinite causal structures; namely, we characterize the computational structure of higher order quantum maps. Taking an axiomatic approach, the rules of this computation are identified as the most general compositions of higher order maps which are compatible with the mathematical structure of quantum theory. We provide a mathematical characterization of the admissible composition for arbitrary higher order quantum maps. We prove that these rules, which have a computational and information-theoretic nature, are determined by the more physical notion of the signalling relations between the quantum systems of the higher order quantum maps.
Cultural Itineraries Generated by Smart Data on the Web

Authors:
raw_author_name: Cosmo Capodiferro
raw_affiliation_string: National Research Council, Istituto di Analisi dei Sistemi ed Informatica, LabGeoInf, Via dei Taurini, 19, I-00185 Rome, Italy
raw_author_name: Massimo De Maria
raw_affiliation_string: Academy of Engineering, Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
raw_author_name: Mauro Mazzei
raw_affiliation_string: National Research Council, Istituto di Analisi dei Sistemi ed Informatica, LabGeoInf, Via dei Taurini, 19, I-00185 Rome, Italy
raw_author_name: Matteo Spreafico
raw_affiliation_string: National Research Council, Istituto di Analisi dei Sistemi ed Informatica, LabGeoInf, Via dei Taurini, 19, I-00185 Rome, Italy
raw_author_name: Oleg V. Bik
raw_affiliation_string: Academy of Engineering, Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia
raw_author_name: Armando L. Palma
raw_affiliation_string: National Research Council, Istituto di Analisi dei Sistemi ed Informatica, LabGeoInf, Via dei Taurini, 19, I-00185 Rome, Italy
raw_author_name: Anna V. Solovyeva
raw_affiliation_string: Academy of Engineering, Peoples Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya Street, 117198 Moscow, Russia

Abstract: The development of storage standards for databases of different natures and origins makes it possible to aggregate and interact with different data sources in order to obtain and show complex and thematic information to the end user. This article aims to analyze some possibilities opened up by new applications and hypothesize their possible developments. With this work, using the currently available Web technologies, we would like to verify the potential for the use of Linked Open Data in the world of WebGIS and illustrate an application that allows the user to interact with Linked Open Data through their representation on a map. Italy has an artistic and cultural heritage unique in the world and the Italian Ministry of Cultural Heritage and Activities and Tourism has created and made freely available a dataset in Linked Open Data format that represents it. With the aim of enhancing and making this heritage more usable, the National Research Council (CNR) has created an application that presents this heritage via WebGIS on a map. Following criteria definable by the user, such as the duration, the subject of interest and the style of the trip, tourist itineraries are created through the places that host this heritage. New possibilities open up where the tools made available by the Web can be used together, according to pre-established sequences, to create completely new applications. This can be compared to the use of words, all known in themselves, which, according to pre-established sequences, allow us to create ever new texts.
A Universally Composable Linkable Ring Signature Supporting Stealth Addresses

Authors:
raw_author_name: Zhen Liu
raw_affiliation_string: Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; Shanghai Qizhi Institute, Shanghai 200003, China

Abstract: The linkable ring signature supporting stealth addresses (SALRS) is a recently proposed cryptographic primitive, which is designed to comprehensively address the soundness and privacy requirements associated with concealing the identities of both the payer and payee in cryptocurrency transactions. However, concerns regarding the scalability of SALRS have been underexplored. This becomes notably pertinent in intricate blockchain systems where multiple cryptographic primitives operate concurrently. To bridge this gap, our work revisited and formalized the ideal functionality of SALRS within the universal composability (UC) model. This encapsulates all correctness, soundness, and privacy considerations. Moreover, we established that the newly proposed UC-security property for SALRS is equivalent to the concurrent satisfaction of signer-unlinkability, signer-non-slanderability, signer-anonymity, and master-public-key-unlinkability. These properties represent the four crucial game-based security aspects of SALRS. This result ensures the ongoing security of previously presented SALRS constructions within the UC framework. It also underscores their adaptability for seamless integration with other UC-secure primitives in complex blockchain systems.
A Novel Unmanned Surface Vehicle Path-Planning Algorithm Based on A* and Artificial Potential Field in Ocean Currents

Authors:
raw_author_name: Jiacai Pan
raw_affiliation_string: Navigation College, Jimei University, Xiamen 361021, China

Abstract: Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents.
Chernoff Fusion Using Observability Gramian-Centric Weighting

Authors:
raw_author_name: Wangyan Li
raw_affiliation_string: College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China
raw_author_name: Yuru Hu
raw_affiliation_string: College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China
raw_author_name: Lifeng Wu
raw_affiliation_string: College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China
raw_author_name: Guoliang Wei
raw_affiliation_string: Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China
raw_author_name: Fuwen Yang
raw_affiliation_string: Griffith School of Engineering and Built Environment -Electrical and Electronic Engineering, Griffith University, Gold Coast Campus, QLD 4222, Australia

Abstract: In this work, an observability Gramian (OG)-based Chernoff fusion (CF) rule is investigated for dealing with unknown correlated probability density functions (PDFs). Specifically, we introduce a generalised uniform observability (GUO) condition, which ensures that error covariances and estimate errors are bounded under nonlinear settings within the extended Kalman filter (EKF) framework. Leveraging the GUO condition, we develop an OG-centric weighting selection method that optimises fusion weights while guaranteeing non-divergent performance using an approximated Chernoff fusion (ACF) algorithm. The resulting OG-centric weights are then embedded to develop an OG-based approximated Chernoff fusion (OGBACF) algorithm that can compute fusion weights and error covariances in parallel. Finally, we conduct simulations to demonstrate the efficacy of our proposed fusion methodology.
Applying Statistical Analysis and Economics Models to Unscramble the Depositional Signals from Chemical Proxies in Black Shales

Authors:
raw_author_name: Karin Goldberg
raw_affiliation_string: Department of Geology, Kansas State University, 108 Thompson Hall, Manhattan, KS 66506, USA

Abstract: The complex controls on the accumulation of organic-rich rocks remain elusive, despite their economic importance as source rocks and unconventional reservoirs, partially due to the multitude of factors that may impact production and preservation of organic matter in sediments. The complexity of Earth systems is comparable to the intricacies of Economics, and application of statistical and econometrics methods and models to analyze geological data may assist interpretation of the processes controlling organic burial. Chemical indices calculated for mudrock datasets from modern sediments and the Woodford Formation were used as proxies for detrital input, primary productivity, redox conditions, and upwelling, and a series of statistical analyses were run to test whether these methods were useful to discriminate different depositional conditions and establish the controls on total organic carbon (TOC) in the sediments. Model results showed that chemical proxies reliably predict not only TOC but also indicate correlations between indices. Our results suggest that detrital input, primary productivity and bottom-water anoxia are relevant drivers of organic content in the sediments, but the first two appear to have a more significant role in organic burial, illustrating the usefulness of these methods to assess depositional parameters in organic-rich rocks.
Coverage Path Planning of UAV Based on Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization

Authors:
raw_author_name: Yan Jiang
raw_affiliation_string: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
raw_author_name: Tingting Bai
raw_affiliation_string: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
raw_author_name: Daobo Wang
raw_affiliation_string: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China
raw_author_name: Yin Wang
raw_affiliation_string: College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China

Abstract: In contrast to rotorcraft, fixed-wing unmanned aerial vehicles (UAVs) encounter a unique challenge in path planning due to the necessity of accounting for the turning radius constraint. This research focuses on coverage path planning, aiming to determine optimal trajectories for fixed-wing UAVs to thoroughly explore designated areas of interest. To address this challenge, the Linear Programming—Fuzzy C-Means with Pigeon-Inspired Optimization algorithm (LP-FCMPIO) is proposed. Initially considering the turning radius constraint, a linear-programming-based model for fixed-wing UAV coverage path planning is established. Subsequently, to partition multiple areas effectively, an improved fuzzy clustering algorithm is introduced. Employing the pigeon-inspired optimization algorithm as the final step, an approximately optimal solution is sought. Simulation experiments demonstrate that the LP-FCMPIO, when compared to traditional FCM, achieves a more balanced clustering effect. Additionally, in contrast to traditional PIO, the planned flight paths display improved coverage of task areas, with an approximately 27.5% reduction in the number of large maneuvers. The experimental results provide validation for the effectiveness of the proposed algorithm.
Data management method for building internet of things based on blockchain sharding and DAG

Authors:
raw_author_name: Wenhu Zheng
raw_affiliation_string: Qingdao University of Technology, Qingdao, Shandong, China
raw_author_name: Xu Wang
raw_affiliation_string: Qingdao University of Technology, Qingdao, Shandong, China
raw_author_name: Yixin Li
raw_affiliation_string: Qingdao University of Technology, Qingdao, Shandong, China
raw_author_name: Xiaoyun Ye
raw_affiliation_string: Qingdao University of Technology, Qingdao, Shandong, China
raw_author_name: Jinlong Wang
raw_affiliation_string: Qingdao University of Technology, Qingdao, Shandong, China
raw_author_name: Xiaoyun Xiong
raw_affiliation_string: Qingdao University of Technology, Qingdao, Shandong, China

Abstract: Sharding technology can address the throughput and scalability limitations that arise when single-chain blockchain are applied in the Internet of Things (IoT). However, existing sharding solutions focus on addressing issues like malicious nodes clustering and cross-shard transactions. Existing sharding solutions cannot adapt to the performance disparities of edge nodes and the characteristic of three-dimensional data queries in building IoT. This leads to problems such as shard overheating and inefficient data query efficiency. This paper proposes a dual-layer architecture called S-DAG, which combines sharded blockchain and DAG blockchain. The sharded blockchain processes transactions within the building IoT, while the DAG blockchain stores block headers from the sharded network. By designing an Adaptive Balancing Load Algorithm (ABLA) for periodic network sharding, nodes are divided based on their load performance values to prevent the aggregation of low-load performance nodes and the resulting issue of shard overheating. By combining the characteristics of the KD tree and Merkle tree, a block structure known as 3D-Merkle tree is designed to support three-dimensional data queries, enhancing the efficiency of three-dimensional data queries in building IoT. By deploying and conducting simulation experiments on various physical devices, we have verified the effectiveness of the solution proposed in this paper. The results indicate that, compared to other solutions, the proposed solution is better suited for building IoT data management. ABLA is effective in preventing shard overheating issue, and the 3D-Merkle tree significantly enhances data query efficiency.
Sparse least-squares Universum twin bounded support vector machine with adaptive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e21955" altimg="si10.svg"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math>-norms and feature selection

Authors:
raw_author_name: Hossein Moosaei
raw_affiliation_string: Department of Applied Mathematics, School of Computer Science, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; Department of Informatics, Faculty of Science, Jan Evangelista Purkyně University, Ústí nad Labem, Czech Republic
raw_author_name: Fatemeh Bazikar
raw_affiliation_string: Department of Applied Mathematics, Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran
raw_author_name: Milan Hladík
raw_affiliation_string: Department of Applied Mathematics, School of Computer Science, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic
raw_author_name: Panos M. Pardalos
raw_affiliation_string: Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, Gainesville, 32611, USA; LATNA, Higher School of Economics, Russia

Abstract: In data analysis, when attempting to solve classification problems, we may encounter a large number of features. However, not all features are relevant for the current classification, and including irrelevant features can occasionally degrade learning performance. As a result, selecting the most relevant features is critical, especially for high-dimensional data sets in classification problems. Feature selection is an effective method for resolving this issue. It attempts to represent the original data by extracting relevant features containing useful information. In this research, our aim is to propose a p-norm least-squares Universum twin bounded support vector machine (LSp-UTBSVM) to perform classification and feature selection at the same time. Indeed, the proposed method, which outperforms the traditional least-squares Universum twin bounded support vector machine, can achieve good classification accuracy in a reasonable amount of time while also providing a sparse solution. The model we propose is an adaptive learning procedure with p-norm (0<p<1), where the parameter p can be automatically selected by the data set. The algorithm we use to find the approximate solution of this model involves solving systems of linear equations. Furthermore, we obtain new bounds for the absolute values of non-zero components of a local optimal solution. These bounds allow us to remove the zero components from an arbitrary numerical solution. Setting the parameter p, LSp-UTBSVM improves classification accuracy and selects the relevant features. Numerical experiments on a handwritten digit recognition, University of California Irvine (UCI) benchmark, Normally Distributed Clusters (NDC) and high dimensional data sets confirm the superiority of the proposed method in the accuracy of classification and the selection of relevant features in comparison with some popular methods.
Research on Unmanned Aerial Vehicle Path Planning

Authors:
raw_author_name: Junhai Luo
raw_affiliation_string: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Abstract: As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aiming to examine their performance thoroughly. Additionally, this paper provides insights into future research directions for UAVs, intending to assist researchers in future explorations.