Reviewerly/exHarmony huggingface dataset:
3D reconstruction quality assessment using 2D reprojection with dynamic partitioning

ABSTRACTIn 3D reconstruction the stereo technique is one of the most used, generating point clouds of acceptable quality. One way to improve its quality is by fusing it with active systems such as lasers. For this fusion, a registration process can be used. It is important to evaluate the quality of the reconstruction in terms of spatial structure accuracy and visual appearance. A method of evaluating the quality of an active 3D stereo reconstruction is proposed, which takes a stereo camera image and compares it with a reprojected image of the registered cloud. The typical way of comparing two images calculates the point-to-point error, but does not take into account other aspects. Several quality metrics are studied to select the one that contributes the most in a given 3D reconstruction context. To improve the computational cost, a process of partitioning the images by regions is performed. A Quadtree partition was chosen with a homogeneity criterion that is selected among several criteria. The experiments and results for selecting the quality metric and the homogeneity criterion for the Quadtree partition are presented.KEYWORDS: 3D reconstructionstereo visionactive visionimage quality assessment (IQA)Quadtree partition Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsCamilo Chamorro-RiveraCamilo Chamorro-Rivera obtained the title of Engineer in Instrumentation and Control from the Colombian Polytechnic J.I.C. in 2002, where he has been a professor since 2008. He received a M.Sc. in Automation from U.P.B. of Medellín in 2009. His research areas include Process Automation and Computer Vision. He is currently a doctoral student in Electronics and Computing at University of Antioquia.Augusto Salazar-JimenezAugusto Salazar-Jimenez earned a Ph.D. in Automation in 2015. In 2007, he received his M.Sc. in Automation and a Bachelor in Electronics in 2004. All the degrees were received from National University of Colombia. His research interests cover Image Processing, Computer Vision and Pattern Recognition. His research is focused on the developing of tools using Deep Learning. Since 2014, he is professor at the Electronics Engineering of Universidad de Antioquia.
Quantum Image Edge Extraction Algorithm for Noisy Image

AbstractImage preprocessing is important for noise exclusion in order to improve the accuracy of contour detection and image edge extraction. In this paper, a dual quantum spatial filter is proposed in order to reduce statistical noise, image blurring, impulsive noise, and retained details. Non-Maximum-Suppression and instead of manually operating threshold, adaptive thresholding operation is used in the quantum edge detection algorithm to detect more edge information with sharp edges. The circuit of the algorithm is realized through a series of operatives. We have compared PSNR, SSIM, precision rate, accuracy rate, recall rate, error rate, and computational complexity of the algorithm with operator based some existing classical and quantum edge extraction algorithms. The algorithm has the potential to get improved edge continuity, greater sharpness, and extract edge information having less proportion of noise.KEYWORDS: Edge detectionImproved sobel operatorQuantum dual filterQuantum image processing ACKNOWLEDGEMENTThe authors are grateful to Mr S. M. B. Baruah, Department of ECE, Tezpur University for his help in developing Matlab code.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRajib ChetiaRajib Chetia received the Mtech degree in electronics design and technology from Tezpur University, Assam, India, in 2001. He has recently completed the PhD degree in engineering from Tezpur University, India. He is currently working as an assistant professor with the Department of Electronics and Communication Engineering, Central Institute of Technology (CIT) Kokrajhar, India. He has published three papers in peer-reviewed international journals and two international conference proceeding papers. His research interests include quantum computation, quantum information processing, and quantum image processing. Corresponding author. Email: rajibofcit@gmail.comPartha Pratim SahuPartha Pratim Sahu received Mtech degree from Indian Institute of Technology, Delhi and PhD degree in engineering from Jadavpur University. In 1991, he joined Haryana State Electronics Development Corporation Limited. There, he has been engaged in R&D works related to optical fiber components and telecommunication instruments. In 1996, he joined North Eastern Regional Institute of Science and Technology as a faculty member. He is currently working as a faculty at the Department of Electronics and Communication Engg., Tezpur University, India. His field of interest is integrated optics and electronic circuits, wireless and optical communication, clinical instrumentation, quantum communication, etc. He has published more than 98 papers in peer-reviewed international journals, 8 books, and 64 international conference proceeding papers. He has three patents in his credit. He is distinguish fellow of the Optical Society of India and a life member of Indian Society for Technical Education, Sr. member of IEEE and he was awarded with prestigious INSA teacher award by India National Science Academy. Email: ppstezu@gmail.com
Fundamental limits for sensor-based robot control

Our goal is to develop theory and algorithms for establishing fundamental limits on performance imposed by a robot’s sensors for a given task. In order to achieve this, we define a quantity that captures the amount of task-relevant information provided by a sensor. Using a novel version of the generalized Fano's inequality from information theory, we demonstrate that this quantity provides an upper bound on the highest achievable expected reward for one-step decision-making tasks. We then extend this bound to multi-step problems via a dynamic programming approach. We present algorithms for numerically computing the resulting bounds, and demonstrate our approach on three examples: (i) the lava problem from the literature on partially observable Markov decision processes, (ii) an example with continuous state and observation spaces corresponding to a robot catching a freely-falling object, and (iii) obstacle avoidance using a depth sensor with non-Gaussian noise. We demonstrate the ability of our approach to establish strong limits on achievable performance for these problems by comparing our upper bounds with achievable lower bounds (computed by synthesizing or learning concrete control policies).
A Fast and Accurate Prediction of BPG Compression Parameters in Optimal Operation Point Neighbourhood for Three-channel Noisy Images

This chapter considers the lossy compression of three-channel images corrupted by additive white Gaussian noise. The better portable graphics (BPG) encoder that has three different operation modes is studied. As for other encoders, a noise filtering effect is observed and it influences the encoder performance. For certain conditions (image complexity and noise intensity), the so-called optimal operation point (OOP) may exist. The main property of OOP is that the quality of a compressed image (calculated with respect to the true image) is better than the quality of the corresponding noisy image according to a given metric (either conventional such as mean square error or some visual quality metric). Knowledge concerning the OOP existence and compression parameters in it for a given noisy image can be useful. In the case of OOP existence, it is reasonable to compress a given image in OOP or its neighborhood. If OOP does not exist, a more “careful” lossy compression is expedient. The problem is that in practice the true image is not available, and, thus, it is impossible to calculate full-reference metrics for compressed and noise-free images. Meanwhile, we show in this paper that it is possible to predict compression parameters for a given noisy image in advance. Moreover, this prediction is fast and accurate enough for a decision undertaking on what coder parameter to set to reach OOP. In addition, in this paper, we demonstrate that it is possible to predict the compression parameters not only in OOP but also in its neighborhood. This allows for undertaking more reliable decisions. Prediction opportunities are shown for two visual quality metrics and three typical modes of the BPG coder operation: 4:4:4, 4:2:2, and 4:2:0. One novel way of prediction accuracy improvement based on the joint use of two input parameters is shown.
Automatic refactoring of conditions and substitutions for B state transition models

Summary The automation of programming, which lies at the intersection of software engineering and artificial intelligence, enables machines to automatically generate programs that satisfy given requirements. In the context of B formal design modeling, one of the challenges is the refactoring of substitutions in design specifications, which often uses state transitions to describe how program or system statuses change during execution. This paper proposes a condition and substitution refactoring algorithm for the B formal specification language. The aim of the work is to automatically derive B operational predicates based on given transitions. The work has been extremely useful to machine‐driven formal design model repair as well as automated design specification generation. Given a set of state transitions, common relations of their state variables can be discovered and clustered into a number of classes. These relations can be further used to synthesize substitutions that derive new states from existing states. To restrict application domains of the synthesized substitutions, conditions that guard these substitutions are generated using first‐order logic. We have implemented the proposed algorithm as an extension to the ProB model checker. Experiments were conducted based on the B model public dataset. The evaluation results demonstrated that our solution is able to synthesize conditions and substitutions for various sets of state transitions in a wide range of B models.
An Improved <scp>D2GAN</scp>‐based oversampling algorithm for imbalanced data classification

Abstract To address the problems of pattern collapse, uncontrollable data generation and high overlap rate when generative adversarial network (GAN) oversamples imbalanced data, we propose an imbalanced data oversampling algorithm based on improved dual discriminator generative adversarial nets (D2GAN). First, we integrate the positive class attribute information into the generator and the discriminator to ensure that the generator only generates the samples for positive class samples, which overcomes the problem of uncontrollable data generation by the generator. Second, we introduce a classifier into D2GAN for discriminating the generated samples and the original data, which avoids the overlap among the generated samples and the negative class samples, and ensures the diversity of the generated samples, the problem of pattern collapse is solved. Finally, the performance of the proposed algorithm is evaluated on 9 datasets by using SVM and neural network classification algorithm for oversampling experiments, the results show that the proposed algorithm effectively improve the classification performance of imbalanced data.
PooRaa‐Agri KG: An agricultural knowledge graph‐based simplified multilingual query system

Abstract The current work proposes PooRaa‐Agri KG, an agricultural knowledge graph‐based simplified multilingual query system that works in real time to provide concise answers for agriculture‐based queries. The proposed approach accommodates real‐time and low‐resource queries in English and Hindi with a novel multi‐stage solution consisting of data pre‐processing, sentence simplification, triplet extraction, knowledge graph generation, sentence reconstruction, query‐to‐reconstructed sentence matching, and machine translation as its sub‐modules. In this work, a novel combination of rule‐based sentence simplification and triplet extraction is carried out resulting in a triplet similarity score of 86.56% for the extracted triplets. This method is superior to the existing triplet extraction method whose triplet similarity score was found to be 60.65%. Further, the proposed work makes use of heuristic rules to reconstruct sentences which when evaluated by human evaluators for meaningfulness and grammar resulted in a score of 3.09/4 and 2.95/4 respectively. To complete end‐to‐end communication in the proposed system, a similarity‐based query answer system is proposed in this work.
rpkiller: Threat Analysis of the BGP Resource Public Key Infrastructure

The Resource Public Key Infrastucture (RPKI) has been created to solve security shortcomings of the Border Gateway Protocol (BGP). This creates an infrastructure where resource holders (autonomous systems) can make attestations about their resources (IP-subnets). RPKI Certificate Authorities make these attestations available at Publication Points. Relying Party software retrieves and processes the RPKI-related data from all publication points, validates the data and makes it available to routers so they can make secure routing decisions. We contribute to this work by doing a threat analysis for Relying Party software, where an attacker controls a Certificate Authority and Publication Point. We implement a prototype testbed to analyse how current Relying Party software implementations react to scenarios originating from that threat model. Our results show that all current Relying Party software was susceptible to at least one of the identified threats. In addition to this, we also identified threats stemming from choices made in the protocol itself. Taken together, these threats potentially allowed an attacker to fully disrupt all RPKI Relying Party software on a global scale. We elaborate on our process, and we discuss the types of responses we received from other parties. We performed a Coordinated Vulnerability Disclosure to the implementers.
Entanglement witness measurement of time-bin two-qubit states using fiber-based Franson interferometers

Entanglement, that is, quantum correlations that do not have a classical counterpart, is a precondition to establishing communication protocols beyond the existing classical protocols, such as quantum key distribution, that achieves a higher level of security without computational assumptions. In this work, we present a proof of demonstration of detecting various entangled states, prepared by time-bin encoding with photons that are natural resources for long-distance quantum communication. We generate a maximally entangled state in time-bin qubits and verify the state in two ways. We first consider measurements that realize entanglement witnesses for the verification of entanglement. We then perform a quantum state tomography for the full characterization. Experimental resources are also discussed.
Photon-varied quantum states: Unified characterization

This paper introduces photon-varied quantum states (PVQSs), which generalize the nonclassical states obtained via photon addition or subtraction operations. We provide a unified characterization of PVQSs in terms of characteristic function, quasiprobability distribution, Fock representation, and Mandel $Q$ parameter. In the special case of photon-varied Gaussian states (PVGSs), the characteristic functions and the quasiprobability distributions are found to be in a simple canonical product structure. Necessary and sufficient conditions for the negativity of the quasiprobability distributions are also obtained for PVGSs. The unified characterization enables the design and analysis of quantum systems that exploit the non-Gaussian properties of PVQSs.
Source Camera Identification with a Robust Device Fingerprint: Evolution from Image-Based to Video-Based Approaches

With the increasing prevalence of digital multimedia content, the need for reliable and accurate source camera identification has become crucial in applications such as digital forensics. While effective techniques exist for identifying the source camera of images, video-based source identification presents unique challenges due to disruptive effects introduced during video processing, such as compression artifacts and pixel misalignment caused by techniques like video coding and stabilization. These effects render existing approaches, which rely on high-frequency camera fingerprints like Photo Response Non-Uniformity (PRNU), inadequate for video-based identification. To address this challenge, we propose a novel approach that builds upon the image-based source identification technique. Leveraging a global stochastic fingerprint residing in the low- and mid-frequency bands, we exploit its resilience to disruptive effects in the high-frequency bands, envisioning its potential for video-based source identification. Through comprehensive evaluation on recent smartphones dataset, we establish new benchmarks for source camera model and individual device identification, surpassing state-of-the-art techniques. While conventional image-based methods struggle in video contexts, our approach unifies image and video source identification through a single framework powered by the novel non-PRNU device-specific fingerprint. This contribution expands the existing body of knowledge in the field of multimedia forensics.
Survey of Methods Applied in Cooperative Motion Planning of Multiple Robots

Recent advances in robotics, autonomous systems, and artificial intelligence (AI) enable robots to perform complex tasks such as delivery, surveillance, inspection, rescue, and others. However, they are unable to complete certain tasks independently due to specific restrictions. In the last few years, researchers are keenly interested in deploying multi-robots for such tasks due to their scalability, robustness, and efficiency. Multiple robots; mobile robots, unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and unmanned underwater vehicles (UUVs); are gaining much momentum and versatility in their operations, whereas cooperative motion planning is a crucial aspect of incorporating these robots into boundless applications. The purpose of this review chapter is to present an insightful look into the problem of cooperative motion planning with its solution and a comprehensive assessment of various path-planning techniques, task-based motion planning techniques, and obstacle avoidance protocols. It further explores the role of AI in the motion planning of multi-robots. Moreover, it highlights various applications and existing issues in these applications that require future consideration. This review chapter implies that researchers, industries, and academia should aspire to cooperative motion planning for robotic expansions.
Digital Inequities: Promoting Digital Justice during the COVID-19 Pandemic

This qualitative study examines the actions taken by 12 US content teachers during the pandemic to provide their emergent bilingual learners (EBLs) with a digitally just education. When the pandemic forced schools to close their doors and transition to a virtual setting, teachers were faced with the reality that EBLs lacked the digital resources, skills, and literacies to successfully participate in virtual schooling. Based on the theory of digital justice, which seeks to promote digital equity for minoritized populations, this investigation was carried out to better understand how content teachers supported their EBLs during the pandemic. Through a series of semi-structured focus group interviews, data were collected and analyzed through open and axial coding. The findings from this study revealed how teachers provided technology for their EBLs, which strategies they took to support EBLs’ digital literacy development, and how virtual instruction impacted educational expectations. The findings also highlighted the issue of who was responsible for providing digital justice which has large implications on providing socially just educational experiences. This work emphasizes the importance of not only closing the digital divide, but also proactively developing EBLs’ digital skills and literacies necessary for 21st-century careers.
Green Power Consumption Digital Identification System Based on Blockchain Technology

In China, the promulgation of a green power identification system has gradually shifted from electricity power generation to consumption. However, there is no mature digital identification system for green power consumption enterprises. Exploiting the open, transparent, and immutable characteristics of blockchains, this study establishes a digital identification system for green power consumption based on blockchain technology. This system uses expert scoring and the entropy weight method as the evaluation algorithm for the green power consumption chain, issues non-fungible tokens as digital identification for consumption enterprises, and realizes the automatic generation of identification through smart contracts. These functions guarantee the credibility of the certification process and the uniqueness of the generated identification. The results of the experiments show that, in the case of multi-user concurrent requests, the number of concurrent users that the system could handle at optimal processing efficiency was 500, and block generation was stable. The proposed system has high practicability and stability.
A Blockchain Solution for Remote Sensing Data Management Model

A large number of raw data collected by satellites are processed by the production chain to obtain a large number of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data provide a critical foundation for data analysis and decision-making. Traditional centralized cloud computing systems are vulnerable to attack and, once the central server is successfully attacked, all data will be lost. Distributed ledger technology (DLT) is an innovative computer technology that can ensure information security and traceability, is tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data have the characteristics of a large data volume, a spatiotemporal nature, global scale, and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain technology. The novelty of this paper is the proposal of a multi-level blockchain architecture in which the system collects remote sensing data and stores them in the Interplanetary File System (IPFS) network; after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. The distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management.