Reviewerly/exHarmony huggingface dataset:
The influence of COVID-19 on the learning and developing processes of practical skills in military educational institutions

Authors:
raw_author_name: Maria Cristina Bularca
raw_affiliation_string: Faculty of Sociology and Communication, Transilvania University of Brasov, Brașov, Romania
raw_author_name: Ghita Barsan
raw_affiliation_string: “Nicolae Bălcescu” Land Forces Academy from Sibiu, Sibiu, Romania
raw_author_name: Claudiu Coman
raw_affiliation_string: Faculty of Sociology and Communication, Transilvania University of Brasov, Brașov, Romania

Abstract: 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

Authors:
raw_author_name: Maria-Eugenia Sánchez-Morales
raw_affiliation_string: Departamento de Ciencias Tecnológicas, Centro Universitario de la Ciénega, Universidad de Guadalajara, Av. Universidad No. 1115, Lindavista, Ocotlán 47810, Jalisco, Mexico
raw_author_name: José-Trinidad Guillen-Bonilla
raw_affiliation_string: Departamento de Electro-Fotónica, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. M. García Barragán 1421, Guadalajara 44410, Jalisco, Mexico
raw_author_name: Héctor Guillen-Bonilla
raw_affiliation_string: Departamento de Ingeniería de Proyectos, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd-M. García Barragán 1421, Guadalajara 44410, Jalisco, Mexico
raw_author_name: Jorge Aguilar-Santiago
raw_affiliation_string: Departamento de Ciencias Básicas, Centro Universitario de la Ciénega, Universidad de Guadalajara, Av. Universidad No. 1115, Lindavista, Ocotlán 47810, Jalisco, Mexico
raw_author_name: Maricela Jiménez-Rodríguez
raw_affiliation_string: Departamento de Ciencias Básicas, Centro Universitario de la Ciénega, Universidad de Guadalajara, Av. Universidad No. 1115, Lindavista, Ocotlán 47810, Jalisco, Mexico

Abstract: 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

Authors:
raw_author_name: Nilton Curi
raw_affiliation_string: Departamento de Ciência do Solo, Universidade Federal de Lavras, Lavras 37203-202, MG, Brazil
raw_author_name: Junior Cesar Avanzi
raw_affiliation_string: Departamento de Ciência do Solo, Universidade Federal de Lavras, Lavras 37203-202, MG, Brazil

Abstract: 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

Authors:
raw_author_name: Shahid Mohammad Ganie
raw_affiliation_string: AI Research Centre, School of Business , Woxsen University, India

Abstract: 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

Authors:
raw_author_name: A. Albert
raw_author_name: M. Anguita
raw_author_name: F. Bretaudeau
raw_author_name: D. Calvo
raw_author_name: P. Castaldi
raw_author_name: A. Chen
raw_author_name: A. Hekalo
raw_author_name: P. de Jong
raw_author_name: O. Kalekin
raw_author_name: R. Lahmann
raw_author_name: J. Majumdar
raw_author_name: L. Martin
raw_author_name: A. Moussa
raw_author_name: V. Popa
raw_author_name: R. Randriatoamanana
raw_author_name: A. Sharma
raw_author_name: F. Šimkovic
raw_author_name: F. Vazquez de Sola

Abstract: 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

Authors:
raw_author_name: Mingyoung Jeng
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
raw_author_name: Alvir Nobel
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
raw_author_name: Vinayak Jha
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
raw_author_name: David Levy
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
raw_author_name: Dylan Kneidel
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
raw_author_name: Manu Chaudhary
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
raw_author_name: Ishraq Islam
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
raw_author_name: Manish Singh
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
raw_author_name: Esam El-Araby
raw_affiliation_string: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA

Abstract: 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

Authors:
raw_author_name: Xiangling Ding
raw_affiliation_string: Hunan University of Science and Technology, Xiangtan, China
raw_author_name: Pu Huang
raw_affiliation_string: Changsha University of Science and Technology, Changsha, China
raw_author_name: Dengyong Zhang
raw_affiliation_string: Changsha University of Science and Technology, Changsha, China
raw_author_name: Wei Liang
raw_affiliation_string: Hunan University of Science and Technology, Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, China
raw_author_name: Feng Li
raw_affiliation_string: Changsha University of Science and Technology, Changsha, China
raw_author_name: Gaobo Yang
raw_affiliation_string: Hunan University, Changsha, China
raw_author_name: Xin Liao
raw_affiliation_string: Hunan University, Changsha, China

Abstract: 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

Authors:
raw_author_name: Leonardo Zambrano
raw_affiliation_string: ICFO - Institut de Ciencies Fotoniques, Av. Carl Friedrich Gauss, 3, Castelldefels, Barcelona, 08860, SPAIN
raw_author_name: Andrés Damián Muñoz-Moller
raw_affiliation_string: Instituto Milenio de Investigación en Óptica, Esteban Iturra S/N, Concepción, 4030000, CHILE
raw_author_name: Mario Muñoz
raw_affiliation_string: Ingeniería Matemática, Universidad de Concepción, Obispo Hipólito Salas 1541, depto C 53, Concepción, Biobío, 4030000, CHILE
raw_author_name: Luciano Pereira Valenzuela
raw_affiliation_string: Instituto De Fisica Fundamental, Calle Serrano 113b, Madrid, 28006, SPAIN
raw_author_name: Aldo Delgado
raw_affiliation_string: Departamento de Física, Universidad de Concepción, Esteban Iturra S/N, Concepcion, 4030000, CHILE

Abstract: 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.
Real-time continuous gesture recognition system based on PSO-PNN

Authors:
raw_author_name: Zhiqiang Gao
raw_affiliation_string: School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, People's Republic of China
raw_author_name: Yuhan Li
raw_affiliation_string: School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, People's Republic of China
raw_author_name: Jie Li
raw_affiliation_string: School of Materials Science and Engineering, North University of China, Taiyuan 030051, People's Republic of China

Abstract: Abstract In the field of natural human–machine interaction, wearable gesture interaction technologies have received considerable attention, particularly continuous gesture (CG) recognition. However, CG faces several challenges, including the impact of motion characteristics on gesture recognition and performance that is not sufficiently robust. Traditional algorithms are highly dependent on samples, thus meeting the requirements of low sample volume and high accuracy simultaneously is challenging. To address these challenges, we propose a real-time CG recognition system based on particle swarm optimization (PSO) and probabilistic neural network (PNN). This system employs principal component analysis for signal dimensionality reduction to alleviate computational burden and uses K-means clustering and Pearson correlation coefficient to extract optimal features for gesture classification. In offline gesture recognition experiments involving six CGs, the algorithm achieved an accuracy rate of 97% with a training set of 300 samples and a runtime of just 31.25 ms. Compared to other five algorithms, the proposed algorithm improved accuracy by at least 9% and reduced the runtime by 40.475 ms. Moreover, gesture recognition experiments were conducted using different datasets, with the PSO-PNN algorithm achieving an average recognition rate of 90.17%, at least 9.84% higher than other algorithms. Finally, in experiments on online CG control for robots in complex environments, the PSO-PNN demonstrated real-time performance of 28.56 ms and a task completion rate of 90.67%, validating the feasibility of PSO-PNN. This research provides a substantial theoretical and technical foundation for the ongoing enhancement and application of CG recognition technology.
A New Dataset and a Distractor-Aware Architecture for Transparent Object Tracking

Authors:
raw_author_name: Alan Lukežič
raw_affiliation_string: Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000, Ljubljana, Slovenia
raw_author_name: Jiří Matas
raw_affiliation_string: Center for Machine Perception, Czech Technical University in Prague, Karlovo namesti 13, 12135, Prague, Czech Republic
raw_author_name: Matej Kristan
raw_affiliation_string: Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000, Ljubljana, Slovenia

Abstract: Abstract Performance of modern trackers degrades substantially on transparent objects compared to opaque objects. This is largely due to two distinct reasons. Transparent objects are unique in that their appearance is directly affected by the background. Furthermore, transparent object scenes often contain many visually similar objects (distractors), which often lead to tracking failure. However, development of modern tracking architectures requires large training sets, which do not exist in transparent object tracking. We present two contributions addressing the aforementioned issues. We propose the first transparent object tracking training dataset Trans2k that consists of over 2k sequences with 104,343 images overall, annotated by bounding boxes and segmentation masks. Standard trackers trained on this dataset consistently improve by up to 16%. Our second contribution is a new distractor-aware transparent object tracker (DiTra) that treats localization accuracy and target identification as separate tasks and implements them by a novel architecture. DiTra sets a new state-of-the-art in transparent object tracking and generalizes well to opaque objects.
DefenseFea: An Input Transformation Feature Searching Algorithm Based Latent Space for Adversarial Defense

Authors:
raw_author_name: Zhang Pan
raw_affiliation_string: 1 School of Cyber Science and Engineering , Zhengzhou University , Zhengzhou , China
raw_author_name: Cao Yangjie
raw_affiliation_string: 1 School of Cyber Science and Engineering , Zhengzhou University , Zhengzhou , China
raw_author_name: Zhu Chenxi
raw_affiliation_string: 1 School of Cyber Science and Engineering , Zhengzhou University , Zhengzhou , China
raw_author_name: Zhuang Yan
raw_affiliation_string: 1 School of Cyber Science and Engineering , Zhengzhou University , Zhengzhou , China
raw_author_name: Wang Haobo
raw_affiliation_string: 1 School of Cyber Science and Engineering , Zhengzhou University , Zhengzhou , China
raw_author_name: Li Jie
raw_affiliation_string: 2 Department of Computer Science and Engineering , Shanghai Jiaotong University , China

Abstract: Abstract Deep neural networks based image classification systems could suffer from adversarial attack algorithms, which generate input examples by adding deliberately crafted yet imperceptible noise to original input images. These crafted examples can fool systems and further threaten their security. In this paper, we propose to use latent space protect image classification. Specifically, we train a feature searching network to make up the difference between adversarial examples and clean examples with label guided loss function. We name it DefenseFea(input transformation based defense with label guided loss function), experimental result shows that DefenseFea can improve the rate of adversarial examples that achieved a success rate of about 99% on a specific set of 5000 images from ILSVRC 2012. This study plays a positive role in the further investigation of the relationship between adversarial examples and clean examples.