Reviewerly/exHarmony huggingface dataset:
Classification of Typical Static Objects in Road Scenes Based on LO-Net

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]

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.
An Approach to Optimize Conversion Rate using Behavioral Economics

Purpose: This paper explores the integration of behavioral economics into the digital marketing landscape, emphasizing its potential to enhance conversion rates.&#x0D; Methodology: It highlights the significance of understanding human decision-making, incorporating behavioral elements into user profiles, and implementing a comprehensive conversion rate optimization framework.&#x0D; Findings: However, ethical considerations are crucial in this context, necessitating transparency, user autonomy, and responsible data handling to build enduring trust.&#x0D; Unique contributor to theory, policy and practice: As technology evolves, particularly with the rise of AI, establishing and reinforcing ethical guidelines becomes imperative for organizations to navigate the digital landscape with integrity and responsible behavioral economics practices.
Validation of widefield aberrometer for global peripheral aberration scanning

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

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

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.
The Comparative Socio-Educational Effect of the Use of E-Learning Vs Face-To-Face Education as a Proposal within the Teaching-Learning Processes in Higher Education

In the contextual framework of Information and Communication Technologies (ICT) in the educational field, this research was carried out on E-learning education and face-to-face education. Education is currently developed within a framework of innovation and transformation, so ICTs have been incorporated to promote access and quality of teaching-learning processes. Face-to-face education E-learning is a modality that supports flexible teaching and learning methods. Even so, at the same time, they present advantages and disadvantages that facilitate or hinder the tools for educational tasks. This research article’s main objective is to know the opinion of the Delfín 2019 Centro Universitario de la Costa program students about e-learning and face-to-face education to compare both modalities later. To develop this work, an instrument was created as a questionnaire from which the quantitative analysis and conclusions were obtained. An answer was given to each of the research questions, and the main question presented qualitative moments; due to the above, it was mixed.
Bitcoin halving and the integration of cryptocurrency and forex markets: An analysis of the higher-order moment spillovers

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

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

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
The influence of COVID-19 on the learning and developing processes of practical skills in military educational institutions

During the COVID-19 pandemic, the educational process in military higher education institutions underwent changes, especially in terms of carrying out practical activities. The purpose of the research was to analyze how the COVID-19 pandemic influenced the educational process and the leadership activities carried out in a Romanian military academy, in the context in which the practical activities had to be carried out in the online environment. Practical skills play an important role in the life of military students and they can help them within the process of becoming efficient leaders. In the context of the research, practical skills refer to decision-making abilities, conflict resolution skills, communication skills, the ability to delegate, or the capacity to motivate and inspire others. In order to conduct the research, we used a quantitative approach. A questionnaire was applied to 288 students of “Nicolae Bălcescu” Land Forces Academy from Sibiu, Romania. The results of the research revealed that students pointed out the difficulty of interacting with their teachers, and of developing practical activities. While the opinions of the students were not influenced by their gender, they were influenced by the type of studies followed. Compared to bachelor students, master students were more affected by the changes which took place, and they believed to a higher extent that they possess leadership skills and that they have the necessary knowledge to lead a specific military group.
Vectorial Image Representation for Image Classification

This paper proposes the transformation S→C→, where S is a digital gray-level image and C→ is a vector expressed through the textural space. The proposed transformation is denominated Vectorial Image Representation on the Texture Space (VIR-TS), given that the digital image S is represented by the textural vector C→. This vector C→ contains all of the local texture characteristics in the image of interest, and the texture unit T→ entertains a vectorial character, since it is defined through the resolution of a homogeneous equation system. For the application of this transformation, a new classifier for multiple classes is proposed in the texture space, where the vector C→ is employed as a characteristics vector. To verify its efficiency, it was experimentally deployed for the recognition of digital images of tree barks, obtaining an effective performance. In these experiments, the parametric value λ employed to solve the homogeneous equation system does not affect the results of the image classification. The VIR-TS transform possesses potential applications in specific tasks, such as locating missing persons, and the analysis and classification of diagnostic and medical images.
Evaluation of Synthetic-Temporal Imagery as an Environmental Covariate for Digital Soil Mapping: A Case Study in Soils under Tropical Pastures

Digital soil maps are paramount for supporting environmental process analysis, planning for the conservation of ecosystems, and sustainable agriculture. The availability of dense time series of surface reflectance data provides valuable information for digital soil mapping (DSM). A detailed soil survey, along with a stack of Landsat 8 SR data and a rainfall time series, were analyzed to evaluate the influence of soil on the temporal patterns of vegetation greenness, assessed using the normalized difference vegetation index (NDVI). Based on these relationships, imagery depicting land surface phenology (LSP) metrics and other soil-forming factors proxies were evaluated as environmental covariates for DSM. The random forest algorithm was applied as a predictive model to relate soils and environmental covariates. The study focused on four soils typical of tropical conditions under pasture cover. Soil parent material and topography covariates were found to be similarly important to LSP metrics, especially those LSP images related to the seasonal availability of water to plants, registering significant contributions to the random forest model. Stronger effects of rainfall seasonality on LSP were observed for the Red Latosol (Ferralsol). The results of this study demonstrate that the addition of temporal variability of vegetation greenness can be used to assess soil subsurface processes and assist in DSM.
Deep Learning Techniques for Alzheimer's Disease Detection

Alzheimer's disease (AD) is a common chronic disorder with a high incidence rate that disproportionately affects elderly people. Deep learning (DL) has been increasingly popular in recent years, resulting in notable developments and innovations in medical imaging. Consequently, deep learning has emerged as the preferred approach for analyzing medical visuals, particularly in the realm of Alzheimer's disease detection. In this chapter, the authors performed a comparative analysis of various DL models such as convolutional neural networks, DenseNet, ResNet, EfficientNet, etc., which have shown some groundbreaking results for AD disease detection. Also, they focused on investigating data collection and feature extraction techniques pertinent to AD. In addition, they further discussed briefly the deep learning models for AD detection. This not only increases hope for the advancement of AD research and therapy but also highlights how deep learning can revolutionize the field of medical image analysis and illness identification.
KM3NeT real-time analysis framework

KM3NeT is a deep-sea neutrino observatory under construction at two sites in the Mediterranean Sea. The ARCA telescope (Italy), aims at identifying and studying TeV-PeV astrophysical neutrino sources, while the ORCA telescope (France), aims at studying the intrinsic properties of neutrinos in the few-GeV range. Since they are optimised in complementary energy ranges, both telescopes can be used to do neutrino astronomy from a few MeV to a few PeV, despite of their different primary goals. The KM3NeT observatory takes active part to the real-time multi-messenger searches, which allow to study transient phenomena by combining information from the simultaneous observation of complementary cosmic messengers with different observatories. In this respect, a key component is the real-time distribution of alerts when potentially interesting detections occur, in order to increase the discovery potential of transient sources and refine the localization of poorly localized triggers, such as gravitational waves. The KM3NeT real-time analysis framework is currently reconstructing all ARCA and ORCA events, searching for spatial and temporal coincidences with alerts received from other operating multi-messenger instruments and performing core-collapse supernova analyses. The selection of a sample of interesting events to send alerts to the external multi-messenger community is presently under definition. This contribution deals with the status of the KM3NeT real-time analysis framework and its first results.
Optimizing Multidimensional Pooling for Variational Quantum Algorithms

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role in extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data to 2-D image data to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results.
MSEConv: A Unified Warping Framework for Video Frame Interpolation

Within the context of video frame interpolation, complex motion modeling is the task of capturing, in a video sequence, where the moving objects are located in the interpolated frame, and how to maintain the temporal consistency of motion. Existing video frame interpolation methods typically assign either a fixed size of the motion kernel or a refined optical flow to model complex motions. However, they have the limitation of data redundancy and inaccuracy representation of motion. This paper introduces a unified warping framework, named multi-scale expandable deformable convolution (MSEConv), for simultaneously performing complex motion modeling and frame interpolation. In the proposed framework, a deep fully convolutional neural network with global attention is proposed to estimate multiple small-scale kernel weights with different expansion degrees and adaptive weight allocation for each pixel synthesis. Moreover, most of the kernel-based interpolation methods can be treated as the special case of the proposed MSEConv, thus, MSEConv can be easily transferred to other kernel-based frame interpolation methods for performance improvement. To further improve the robustness of motion occlusions, an operation of mask occlusion is introduced. As a consequence, our proposed MSEConv shows strong performance on par or even better than the state-of-the-art kernel-based frame interpolation works on public datasets. Our source code and visual comparable results are available at https://github.com/Pumpkin123709/MSEConv.
Avoiding barren plateaus in the variational determination of geometric entanglement

Abstract The barren plateau phenomenon is one of the main obstacles to implementing variational quantum algorithms in the current generation of quantum processors. Here, we introduce a method capable of avoiding the barren plateau phenomenon in the variational determination of the geometric measure of entanglement for a large number of qubits. The method is based on measuring compatible two-qubit local functions whose optimization allows for achieving a well-suited initial condition, from which a global function can be further optimized without encountering a barren plateau. We analytically demonstrate that the local functions can be efficiently estimated and optimized. Numerical simulations up to 18-qubit GHZ and W states demonstrate that the method converges to the exact value. In particular, the method allows for escaping from barren plateaus induced by hardware noise or global functions defined on high-dimensional systems. Numerical simulations with noise are in agreement with experiments carried out on IBM’s quantum processors for 7 qubits.