Reviewerly/exHarmony huggingface dataset:
Automated identification of vehicles in very high-resolution UAV orthomosaics using YOLOv7 deep learning model

Authors:
raw_author_name: Taşkın KAVZOĞLU

Abstract: The utilization of remote sensing products for vehicle detection through deep learning has gained immense popularity, especially due to the advancement of unmanned aerial vehicles (UAVs). UAVs offer millimeter-level spatial resolution at low flight altitudes, which surpasses traditional airborne platforms. Detecting vehicles from very high-resolution UAV data is crucial in numerous applications, including parking lot and highway management, traffic monitoring, search and rescue missions, and military operations. Obtaining UAV data at desired periods allows the detection and tracking of target objects even several times during a day. Despite challenges such as diverse vehicle characteristics, traffic congestion, and hardware limitations, the detection task must be executed swiftly and accurately. This study successfully achieved automated detection and instance segmentation of parked and moving vehicles across a large university campus by employing the robust learning capabilities of the You Only Look Once version 7 (YOLOv7) deep learning algorithm. The generation of an ultrahigh-resolution orthomosaic of the university campus was accomplished through photogrammetric processing, employing 20-megapixel aerial images obtained from RGB UAV flights with polygonal nadir-view and bundle-grid oblique-view imaging geometries. The vehicle dataset was created by cropping image patches containing vehicle objects from the orthomosaic and manually labeling the boundaries of the vehicle targets using the LabelMe annotation tool. After expanding the dataset by applying data augmentation, the YOLOv7 algorithm was trained and tested using the transfer learning approach. The accuracy metric of precision, recall, and mAP@0.50 scores for the bounding boxes and masks of vehicles were estimated as 99.79, 97.54, and 99.46%, respectively. In addition, the robustness of the trained algorithm was also tested on a short video and (>80%) prediction scores were achieved.
Fusion of standard and ordinal dropout techniques to regularise deep models

Authors:
raw_author_name: César Hervás-Martínez
raw_affiliation_string: Maimonides Biomedical Research Institute of Córdoba, IMIBIC, University of Córdoba, Córdoba, 14071, Spain
raw_author_name: Pedro A. Gutiérrez
raw_affiliation_string: Maimonides Biomedical Research Institute of Córdoba, IMIBIC, University of Córdoba, Córdoba, 14071, Spain

Abstract: Dropout is a popular regularisation tool for deep neural classifiers, but it is applied regardless of the nature of the classification task: nominal or ordinal. Consequently, the order relation between the class labels of ordinal problems is ignored. In this paper, we propose the fusion of standard dropout and a new dropout methodology for ordinal classification regularising deep neural networks to avoid overfitting and improve generalisation, but taking into account the extra information of the ordinal task, which is exploited to improve performance. The correlation between the outputs of every neuron and the target labels is used to guide the dropout process: the higher the neuron is correlated with the expected labels, the lower its probability of being dropped. Given that randomness also plays a crucial role in the regularisation process, a balancing factor (β) is also added to the training process to determine the influence of the ordinality with respect to a constant probability, providing a hybrid ordinal regularisation method. An extensive battery of experiments shows that the new hybrid ordinal dropout methodology perform better than standard dropout, obtaining improved results in most evaluation metrics, including not only ordinal metrics but also nominal ones.
Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism

Authors:
raw_author_name: Daniel Kifer
raw_affiliation_string: Pennsylvania State University
raw_author_name: Danfeng Zhang
raw_affiliation_string: Pennsylvania State University
raw_author_name: Brian Karrer
raw_affiliation_string: Meta

Abstract: Sparse histogram methods can be useful for returning differentially private counts of items in large or infinite histograms, large group-by queries, and more generally, releasing a set of statistics with sufficient item counts. We consider the Gaussian version of the sparse histogram mechanism and study the exact epsilon, delta differential privacy guarantees satisfied by this mechanism. We compare these exact epsilon, delta parameters to the simpler overestimates used in prior work to quantify the impact of their looser privacy bounds.
Real-time disruption prediction in multi-dimensional spaces leveraging diagnostic information not available at execution time

Authors:
raw_author_name: Jesus Vega
raw_affiliation_string: Fusion, Centro de Investigaciones Energeticas Medioambientales y Tecnologicas, Avda Complutense 40, 28040 Madrid, Madrid, SPAIN
raw_author_name: Sebastian Dormido-Canto
raw_affiliation_string: Departmento Informatica y Automatica, Universidad Nacional de Educacion a Distancia, Madrid, Madrid, SPAIN
raw_author_name: Rodrigo Castro
raw_affiliation_string: Fusion, Centro de Investigaciones Energeticas Medioambientales y Tecnologicas, Madrid, SPAIN
raw_author_name: Andrea Murari
raw_affiliation_string: Consorzio RFX, Padova, 35127, ITALY

Abstract: Abstract This article describes the use of privileged information to train supervised classifiers, applied for the first time to the prediction of disruptions in tokamaks. The objective consists of making predictions with real-time signals during the discharges (as usual) but after training the predictor also with any kind of data at training time that is not available during discharge execution. The latter kind of data is known as privileged information. Taking into account the limited number of foreseen real time signals for disruption prediction at the beginning of operation in JT-60SA, a predictor with a line integrated density signal and the mode lock signal as privileged information has been developed and tested with 1437 JET discharges. The success rate with positive warning time has been improved from 45.24% to 90.48% and the tardy detection rate has diminished from 50% to 8.33%. The use of privileged information in an adaptive way also provides a remarkable reduction of false alarms from 11.53% to 1.15%. The potential of the methodology, exemplified with data relevant to the beginning of JT-60SA operation, is absolutely general and can be applied to any combination of diagnostic signals
Ensemble WGAN (EWG): Advancing Image Synthesis and Deepfake Detection with Heterogeneous Discriminator Approach

Authors:
raw_author_name: Preeti Sharma
raw_author_name: Manoj Kumar
raw_author_name: Hitesh Kumar Sharma

Abstract: In the present time, deepfakes pose a big threat to the security of our society. Concerns regarding these fake images being used for malevolent reasons on social networking sites have increased. As a solution it, this paper proposed a new model called EWG (Ensemble WGAN) which helps to detect deepfake using its unique ensemble architecture. The EWG model is an expansion of the WGAN architecture that improves deepfake detection and GAN training issues. It employs a voting ensemble of three unique discriminators and a single generator. The approach works with generator weights updated by the best discriminator on each epoch. The model dynamically selects the best discriminator based on a unique diverse loss function that combines adversarial loss and the SSIM metric, boosting diversified performance. Leveraging the “Indian Actor Images Dataset” and “5-Celebrity Faces,” the EWG model achieves remarkable deepfake detection accuracy of 98.480% and 96.417%, with computation times of 1813.251 and 2197.011 seconds. Furthermore, it mitigates GAN training challenges like mode collapses, gradient penalties, and convergence and provides superior image quality, surpassing basic WGAN and other state-of-the-art methods. The EWG model demonstrates its dependability and potential for countering deepfakes and improving GAN capabilities.
Governance of Permissionless Blockchain Networks

Authors:
raw_author_name: Cy Watsky
raw_affiliation_string: Board of Governors of the Federal Reserve System (U.S.)

Abstract: A permissionless blockchain network is a system of physically distributed computers running a copy of a shared ledger and using the same software rules that enable all network participants to “read, submit, and validate transactions” (Beck, Müller-Bloch, and King, 2018, p. 1022). A permissionless system’s accessibility stands in contrast to that of permissioned systems, in which a central authority pre-selects validators and potentially restricts viewing and submission rights (Krause, Natarajan, and Gradstein, 2017; Beck, Müller-Bloch, and King, 2018).
The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Authors:
raw_author_name: Bowen Xing
raw_affiliation_string: College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
raw_author_name: Xiao Wang
raw_affiliation_string: College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
raw_author_name: Zhenchong Liu
raw_affiliation_string: Shanghai Zhongchuan NERC-SDT Co., Ltd., Shanghai 201114, China

Abstract: The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.
Classification of Typical Static Objects in Road Scenes Based on LO-Net

Authors:
raw_author_name: Yongqiang Li
raw_affiliation_string: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
raw_author_name: Jiale Wu
raw_affiliation_string: School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
raw_author_name: Zhihua Xu
raw_affiliation_string: College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
raw_author_name: Jian Zhang
raw_affiliation_string: Henan Provincial Surveying and Mapping Institute, Zhengzhou 450000, China

Abstract: Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the “Unite_module” for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%.
Textile-Based Inkjet-Printed RFIDs: Exploring wearable antennas in the real world [Bioelectromagnetics]

Authors:
raw_author_name: Lauren Linkous
raw_affiliation_string: Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA, USA

Abstract: Growing demand in the wearable device industry for noninvasive, user-connected, and real-time activity trackers and body monitors is increasing the need for low-profile wearable antennas on fabric substrates, particularly for health and safety applications. Current data regarding environmental and human factors in this field that affect the real-world use of the devised antennas are incomplete. This article presents data regarding the impact of garment position, especially the distance between the skin and fabric substrate, on antenna performance. A distance of the substrate from the skin in the less than a 5-mm range was found to have a significant influence on antenna performance. The effects of humidity and dampness on the permittivity of five types of fabrics commonly used in commercial and health care-related garments are also investigated. The performance of a textile-based RFID printed directly on a nonconductive interface layer situated on single jersey knit cotton fabric was studied, and sheet resistance results of inkjet printing with two conductive nanoparticle inks from NovaCentrix are presented.
Validation of widefield aberrometer for global peripheral aberration scanning

Authors:
raw_author_name: Yanbo Zhao
raw_affiliation_string: Centre of Micro/Nano Manufacturing Technology (MNMT-Dublin), University College Dublin, Dublin 4, Ireland
raw_author_name: Fengzhou Fang
raw_affiliation_string: Centre of Micro/Nano Manufacturing Technology (MNMT-Dublin), University College Dublin, Dublin 4, Ireland; State Key Laboratory of Precision Measuring Technology and Instruments, Centre of Micro/Nano Manufacturing Technology (MNMT), Tianjin University, Tianjin 300072, China

Abstract: Aberrometers based on Hartmann-Shack wavefront sensing methods have been widely used in the assessment of aberrations in the human eye, a crucial aspect in addressing or adjusting peripheral vision for myopia control, among other applications. Nevertheless, most available aberrometers exhibit limitations such as slow scanning speeds and restricted visual fields when measuring peripheral aberrations. Even the fastest scanning capabilities of commercialized aberrometer are confined to limited visual field and meridians. In this paper, a novel scanning aberrometer for efficient measurement of human eye peripheral aberrations is developed. This instrument allows the measurement of a large visual field angle up to ± 50° and takes all meridian scanning into account, which means it is a widefield aberrometer that can scan the peripheral aberrations globally. Due to the use of the precision motorized rotation stage, both the meridional and visual field angular resolutions of this instrument can be lower to 1°. During the entire measurement process, the subject only needs to focus on a single fixation target. No eye turn or head turn is required. By the instrument installation completed in April 2023, it stands as the only peripheral aberration scanning aberrometer capable of measuring up to a 50° visual field at all meridians using a single fixation target and achieving high angular resolution.
Two entanglement conditions and their connection to negativity

Authors:
raw_author_name: Mark Hillery
raw_affiliation_string: Department of Physics and Astronomy, Hunter College of CUNY, 695 Park Avenue, New York, New York 10065, USA; Physics Program, Graduate Center of CUNY, 365 Fifth Avenue, New York, New York 10016, USA
raw_author_name: Vadim Oganesyan
raw_affiliation_string: Center for Computational Quantum Physics, Flatiron Institute, 162 Fifth Avenue, New York, New York 10010, USA; Department of Physics and Astronomy, College of Staten Island (CUNY), 2800 Victory Boulevard, Staten Island, New York 10314, USA; Physics Program, Graduate Center of CUNY, 365 Fifth Avenue, New York, New York 10016, USA

Abstract: We examine two conditions that can be used to detect bipartite entanglement, and show that they can be used to provide lower bounds on the negativity of states. We begin with two-qubit states, and then show how what was done there can be extended to more general states. The resulting bounds are then studied by means of a number of examples. We also show that if one has some knowledge of the Schmidt vectors of a state, better bounds can be found.
Using data clustering to reveal trainees’ behavior in cybersecurity education

Authors:
raw_author_name: Karolína Dočkalová Burská
raw_affiliation_string: Faculty of Informatics, Masaryk University, Botanická 68a, Brno, 60200, Czech Republic
raw_author_name: Radek Ošlejšek
raw_affiliation_string: Faculty of Informatics, Masaryk University, Botanická 68a, Brno, 60200, Czech Republic

Abstract: Abstract In cyber security education, hands-on training is a common type of exercise to help raise awareness and competence, and improve students’ cybersecurity skills. To be able to measure the impact of the design of the particular courses, the designers need methods that can reveal hidden patterns in trainee behavior. However, the support of the designers in performing such analytic and evaluation tasks is ad-hoc and insufficient. With unsupervised machine learning methods, we designed a tool for clustering the trainee actions that can exhibit their strategies or help pinpoint flaws in the training design. By using a k-means++ algorithm, we explore clusters of trainees that unveil their specific behavior within the training sessions. The final visualization tool consists of views with scatter plots and radar charts. The former provides a two-dimensional correlation of selected trainee actions and displays their clusters. In contrast, the radar chart displays distinct clusters of trainees based on their more specific strategies or approaches when solving tasks. Through iterative training redesign, the tool can help designers identify improper training parameters and improve the quality of the courses accordingly. To evaluate the tool, we performed a qualitative evaluation of its outcomes with cybersecurity experts. The results confirm the usability of the selected methods in discovering significant trainee behavior. Our insights and recommendations can be beneficial for the design of tools for educators, even beyond cyber security.
Bitcoin halving and the integration of cryptocurrency and forex markets: An analysis of the higher-order moment spillovers

Authors:
raw_author_name: Javier Perote
raw_affiliation_string: Department of Economics and Economic History and IME, Faculty of Economics and Business, University of Salamanca, Campus Miguel de Unamuno (Edif. F.E.S.), 37007, Salamanca, Spain

Abstract: This paper introduces a flexible semi-nonparametric model, the so-called SNP-VSK model, to analyze the higher-order moment interactions among cryptocurrencies and the forex markets, i.e., accounting for conditional volatility, skewness, and kurtosis spillovers. These transmissions among cryptos are identified considering Bitcoin halving as a factor of price uncertainty. To study the degree of integration of cryptocurrency markets in the economy, spillover effects with the forex market are considered, showing that Bitcoin and Ethereum are the prime receivers of positive volatility transmission, a sign of integration with the currency markets, but also potential sources of financial instability.
Influence of digital competencies of personnel on the development of social capital of organizations as a management resource

Authors:
raw_author_name: Yakov A. Margulyan

Abstract: In this article there is analyzed the current state of the digitalization process of Russian society, identified the conceptual characteristics of social capital as a management resource, and revealed its substantive characteristics and structural components. It has been established that social capital is an important resource for management activities, determined by the nature and content of social relations. Within the framework of the resource approach, a classification analysis of digital competencies of personnel is carried out, the characteristic features of their formation are considered, the directions of influence of digital competencies on the development of the social capital of organizations are determined, and a model of resource interaction of digital competencies of personnel within the social capital of the organization is substantiated.
Classification Analysis Of The Eligibility Of Recipients Of Non-Cash Food Assistance And Family Hope Programs In The City Of Sukabumi Using The Naïve Bayes Classifier Algorithm

Authors:
raw_author_name: None Prajoko
raw_affiliation_string: Universitas Muhammadiyah Sukabumi
raw_author_name: Agung Pambudi
raw_affiliation_string: Universitas Muhammadiyah Sukabumi

Abstract: Providing social assistance is the government's effort to improve the welfare of the underprivileged. Non-Cash Food Assistance (BPNT) and the Family Hope Program (PKH) are two social assistance programs provided by the Indonesian government. BPNT is a food assistance program that is provided non-cash through electronic cards, while PKH is a cash social assistance program provided to poor families with certain criteria. Both programs aim to help the poor meet their food and education needs. To evaluate the effectiveness and efficiency of social assistance programs, a method is needed that can process and analyze data quickly and accurately. One method that can be used is the Naïve Bayes Classifier, which is a probabilistic classification method based on Bayes' theorem. This method can be used to classify data into certain categories based on its probability. In this study, researchers used the Naïve Bayes Classifier method to analyze social assistance data obtained from the BPNT and PKH programs. Data from the Sukabumi City Social Service was used to classify the eligibility of beneficiaries using the Naïve Bayes Classifier algorithm. Out of 5,183 data, 31.2% were classified as "Eligible" and 68.8% as "Ineligible". The algorithm showed 98.77% accuracy in eligibility classification. These results indicate the effectiveness of the Naïve Bayes Classifier algorithm in analyzing social data, providing new insights for better decision-making by relevant agencies in the development of more targeted and efficient social assistance policies