Reviewerly/exHarmony huggingface dataset:
AODiMP‐TIR: Anti‐occlusion thermal infrared targets tracker based on SuperDiMP

Authors:
raw_author_name: Shaoyang Ma
raw_affiliation_string: School of Aerospace Engineering Xi'an Jiaotong University  Xi'an China
raw_author_name: Yao Yang
raw_affiliation_string: Unmanned System Research Institute Northwestern Polytechnical University  Xi'an China
raw_author_name: Gang Chen
raw_affiliation_string: School of Aerospace Engineering Xi'an Jiaotong University  Xi'an China

Abstract: Abstract To address the issue of tracking drift and failures in thermal infrared (TIR) tracking tasks caused by target occlusion, this study proposes an anti‐occlusion TIR target tracker named AODiMP‐TIR. This approach involves an anti‐occlusion strategy that relies on target occlusion status determination and trajectory prediction. This enables the prediction of the target's current position when it is identified as occluded, ensuring swift recapture upon reappearance. A criterion is introduced for occlusion status determination based on the classification response map of SuperDiMP. Additionally, a trajectory mapping module designed to decouple target motion from camera motion is presented, enhancing the precision of trajectory prediction. Comparative experiments with other state‐of‐the‐art trackers are conducted on the large‐scale high‐diversity thermal infrared object tracking benchmark (LSOTB‐TIR), LSOTB‐TIR100, and thermal infrared pedestrian tracking benchmark (PTB‐TIR) datasets. The results indicate that the AODiMP‐TIR performs well across all three datasets, particularly exhibiting outstanding performance in occlusion sequences. Furthermore, ablation study experiments confirm the effectiveness of the anti‐occlusion strategy, occlusion determination criterion and trajectory mapping module.
Mutual learning with reliable pseudo label for semi-supervised medical image segmentation

Authors:
raw_author_name: Zhiming Luo
raw_affiliation_string: The Department of Artificial Intelligence, Xiamen University, Fujian, China. Electronic address: zhiming.luo@xmu.edu.cn.
raw_author_name: Sheng Lian
raw_affiliation_string: The College of Computer and Data Science, Fuzhou University, Fujian, China.
raw_author_name: Dazhen Lin
raw_affiliation_string: The Department of Artificial Intelligence, Xiamen University, Fujian, China.
raw_author_name: Shaozi Li
raw_affiliation_string: The Department of Artificial Intelligence, Xiamen University, Fujian, China.

Abstract: Semi-supervised learning has garnered significant interest as a method to alleviate the burden of data annotation. Recently, semi-supervised medical image segmentation has garnered significant interest that can alleviate the burden of densely annotated data. Substantial advancements have been achieved by integrating consistency-regularization and pseudo-labeling techniques. The quality of the pseudo-labels is crucial in this regard. Unreliable pseudo-labeling can result in the introduction of noise, leading the model to converge to suboptimal solutions. To address this issue, we propose learning from reliable pseudo-labels. In this paper, we tackle two critical questions in learning from reliable pseudo-labels: which pseudo-labels are reliable and how reliable are they? Specifically, we conduct a comparative analysis of two subnetworks to address both challenges. Initially, we compare the prediction confidence of the two subnetworks. A higher confidence score indicates a more reliable pseudo-label. Subsequently, we utilize intra-class similarity to assess the reliability of the pseudo-labels to address the second challenge. The greater the intra-class similarity of the predicted classes, the more reliable the pseudo-label. The subnetwork selectively incorporates knowledge imparted by the other subnetwork model, contingent on the reliability of the pseudo labels. By reducing the introduction of noise from unreliable pseudo-labels, we are able to improve the performance of segmentation. To demonstrate the superiority of our approach, we conducted an extensive set of experiments on three datasets: Left Atrium, Pancreas-CT and Brats-2019. The experimental results demonstrate that our approach achieves state-of-the-art performance. Code is available at: https://github.com/Jiawei0o0/mutual-learning-with-reliable-pseudo-labels.
The c-Differential-Linear Connectivity Table of Vectorial Boolean Functions

Authors:
raw_author_name: Said Eddahmani
raw_affiliation_string: Department of Mathematics, University of Paris VIII, F-93526 Paris, France; Laboratory Geometry, Analysis and Applications (LAGA), University Sorbonne Paris Nord, CNRS, UMR 7539, F-93430 Villetaneuse, France
raw_author_name: Sihem Mesnager
raw_affiliation_string: Department of Mathematics, University of Paris VIII, F-93526 Paris, France; Laboratory Geometry, Analysis and Applications (LAGA), University Sorbonne Paris Nord, CNRS, UMR 7539, F-93430 Villetaneuse, France; Telecom Paris, Polytechnic Institute, F-91120 Palaiseau, France

Abstract: Vectorial Boolean functions and codes are closely related and interconnected. On the one hand, various requirements of binary linear codes are needed for their theoretical interests but, more importantly, for their practical applications (such as few-weight codes or minimal codes for secret sharing, locally recoverable codes for storage, etc.). On the other hand, various criteria and tables have been introduced to analyse the security of S-boxes that are related to vectorial Boolean functions, such as the Differential Distribution Table (DDT), the Boomerang Connectivity Table (BCT), and the Differential-Linear Connectivity Table (DLCT). In previous years, two new tables have been proposed for which the literature was pretty abundant: the c-DDT to extend the DDT and the c-BCT to extend the BCT. In the same vein, we propose extended concepts to study further the security of vectorial Boolean functions, especially the c-Walsh transform, the c-autocorrelation, and the c-differential-linear uniformity and its accompanying table, the c-Differential-Linear Connectivity Table (c-DLCT). We study the properties of these novel functions at their optimal level concerning these concepts and describe the c-DLCT of the crucial inverse vectorial (Boolean) function case. Finally, we draw new ideas for future research toward linear code designs.
Impact of Employee Engagement On Service Quality in Nigerian Public Agencies

Authors:
raw_author_name: Oluseye Taiwo Adepoju
raw_author_name: Esther Yimi Bagobiri

Abstract: This study investigates the connection between employee engagement and service quality in the Asset Management Company of Nigeria (AMCON), which is a key player in Nigeria's financial sector. This study examines three key dimensions of employee engagement – absorption, vigor, and dedication – and their effects on service quality, specifically focusing on the responsiveness dimension of the SERVQUAL model. This study is important because there is a lack of representation of AMCON and similar institutions in the existing literature, particularly within the context of Nigeria's unique financial landscape. This study utilized a mixed-methods approach that included quantitative surveys and qualitative interviews with AMCON employees. The analysis employed the SERVQUAL model to evaluate service quality and multiple regression analysis to explore the influence of each engagement dimension. The findings of this study are expected to contribute to a theoretical understanding of the impact of employee engagement on service quality within the asset management sector. Additionally, this study aims to provide practical insights for AMCON, potentially improving service delivery strategies and effectiveness in fulfilling its mandate. The broader implications of this study extend to similar asset management entities in emerging economies, providing a comparative perspective, and understanding of these dynamics in diverse economic and regulatory environments.
Cloud-based Metabase GIS Data Analysis Platform Quality Management According to ISO 9126 Indicators

Authors:
raw_author_name: Rani Purbaningtyas
raw_author_name: Moh Munih Dian Widianta
raw_author_name: Mochammad Rifki Ulil Albaab

Abstract: Platform metabase GIS data analysis based on the cloud that has been successfully developed is an alternative solution for spatial-text data analysis. The output of this cloud-based platform not only provides accurate textual information but also precise location representation of the objects. This research examines the quality of the developed platform based on ISO 9126, which consists of six main indicators: functionality, reliability, feasibility, efficiency, maintainability, and portability. Each indicator has different sub-indicators, totaling 22 sub-indicators. The quality assessment results indicate that the platform for metabase GIS data analysis based on the cloud exhibits excellent quality, with an average test result based on the ISO 9126 indicators reaching 93%.
Deep Learning-Based Semantic Segmentation in Autonomous Driving

Authors:
raw_author_name: Hrag-Harout Jebamikyous

Abstract: &lt;p&gt;Perception is a fundamental task of autonomous driving systems, which gathers all the necessary information about the surrounding environment of the moving vehicle. Then a decision-making system takes the perception data as input and provides the optimum decision given a scenario, which maximizes the safety of the passengers. In this project, we have developed variants of the U-Net model to perform semantic segmentation on urban scene images to understand the surroundings of an autonomous vehicle. The U-Net model and its variants are adopted for semantic segmentation in this project to account for the power of the U-Net in handling large and small datasets. We have also compared the best-performing variant with other commonly used semantic segmentation models. The comparative analysis was performed using three well-known models, including FCN-16, FCN-8, and SegNet. After conducting sensitivity and comparative analysis, it is concluded that the U-Net variants performed the best in terms of the Intersection over Union (IoU) evaluation metric and other quality metrics.&lt;/p&gt;
Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText

Authors:
raw_author_name: Ahmad Rofiqul Muslikh
raw_author_name: Ismail Akbar
raw_author_name: De Rosal Ignatius Moses Setiadi
raw_author_name: Hussain Md Mehedul Islam

Abstract: Studying the Qur'an is a pivotal act of worship in Islam, which necessitates a structured understanding of its verses to facilitate learning and referencing. Reflecting this complexity, each Quranic verse is rich with unique thematic elements and can be classified into a range of distinct categories. This study explores the enhancement of a multi-label classification model through the integration of FastText. Employing a CNN+Bi-LSTM architecture, the research undertakes the classification of Quranic translations across categories such as Tauhid, Ibadah, Akhlak, and Sejarah. Based on model evaluation using F1-Score, it shows significant differences between the CNN+Bi-LSTM model without FastText, with the highest result being 68.70% in the 80:20 testing configuration. Conversely, the CNN+Bi-LSTM+FastText model, combining embedding size and epoch parameters, achieves a result of 73.30% with an embedding size of 200, epoch of 100, and a 90:10 testing configuration. These findings underscore the significant impact of FastText on model optimization, with an enhancement margin of 4.6% over the base model.
Neuroscience as a basis for the improvement of educational ICTs

Authors:
raw_author_name: Valeria Carolina Mendoza Chavarria
raw_affiliation_string: Universidad Regional Amazónica Ikiam
raw_author_name: Daniel Alejandro Robles Ortega
raw_affiliation_string: Universidad Regional Amazónica Ikiam
raw_author_name: Yamileth Arteaga-Alcívar
raw_affiliation_string: Instituto Superior Tecnológico Internacional

Abstract: Neuroscience, with its in-depth study of the human brain, has been identified as a vital tool for improving Information and Communication Technologies (ICT) in education. In order to provide more efficient and personalized didactic tools, the integration of neuroscientific findings into ICT design has been essential. Using qualitative methodologies involving documentary analysis, case studies, and systematic reviews, the importance of adapting ICT to the ways in which the brain naturally learns has been identified. Results indicate that adapting ICT based on neuroscientific principles leads to increased engagement, retention, and knowledge transfer among students. In addition, the use of digital tools aligned with neurodidactic strategies has proven to be more effective in creating inclusive and diversified learning environments, addressing the individual needs of each learner and promoting holistic learning.
Exploring Convolutional Neural Networks for the Classification of Acute Lymphoblast Leukemia Blood Cell Images

Authors:
raw_author_name: Dmitry Savelyev
raw_affiliation_string: Samara National Research University, Russia
Image Processing Systems Institute, NRC Kurchatov Instinute, Samara, Russia

Abstract: This paper introduces a novel approach to blood cell classification using convolutional neural networks (CNNs). Our emphasis lies in methodological insights derived from comparative analyses across various CNN architectures, implemented in Python with PyTorch such as ResNet152V2, Xception, EfficientNetB5, and EfficientNetV2M. In exploring model capabilities and limitations, we observe that the simple and shallow architectures is not sufficient to learn patterns compared to more complex networks. During research we have discovered that EfficientNetV2M demonstrate stable results with mean F 1 =0.891 score, which is higher compare to other models. Saliency maps are applied to reveal significant regions within or near cells, offering nuanced insights into morphological influences like cell shape. Extensive data augmentation allows one effectively mitigate overfitting, aligning train learning curves with validation and test splits. Our study is methodologically rich, comprising a meticulous data-specific overview, a robustness analysis through multiple model training, a systematic architecture comparison, and an in-depth examination using saliency maps.
Sequential Polar Decoding with Cost Metric Threshold

Authors:
raw_author_name: Fedor Ivanov
raw_affiliation_string: Higher School of Economics, National Research University, 101000 Moscow, Russia

Abstract: Polar codes have established themselves as a cornerstone in modern error correction coding due to their capacity-achieving properties and practical implementation advantages. However, decoding polar codes remains a computationally intensive task. In this paper, we introduce a novel approach to improve the decoding efficiency of polar codes by integrating the threshold-based SC-Creeper decoding algorithm, originally designed for convolutional codes. Our proposed decoder with an additional cost function seamlessly merges two established decoding paradigms, namely the stack and Fano approaches. The core idea is to leverage the strengths of both decoding techniques to strike a balance between computational efficiency and performance, with an additional method of controlling movement along a code tree. Simulations demonstrate the superiority of the proposed improved SC-Creeper decoder with tuned parameters. The improved SC-Creeper decoder achieves the performance of the CA-SCL-8 decoder in terms of high code rates and overcomes it in terms of the N=1024 code length, while simultaneously surpassing the efficiency of the traditional Fano decoding algorithm.
Pre-Segmented Down-Sampling Accelerates Graph Neural Network-Based 3D Object Detection in Autonomous Driving

Authors:
raw_author_name: Zhenming Liang
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
raw_author_name: Yingping Huang
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
raw_author_name: Yanbiao Bai
raw_affiliation_string: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China

Abstract: Graph neural networks (GNNs) have been proven to be an ideal approach to deal with irregular point clouds, but involve massive computations for searching neighboring points in the graph, which limits their application in large-scale LiDAR point cloud processing. Down-sampling is a straightforward and indispensable step in current GNN-based 3D detectors to reduce the computational burden of the model, but the commonly used down-sampling methods cannot distinguish the categories of the LiDAR points, which leads to an inability to effectively improve the computational efficiency of the GNN models without affecting their detection accuracy. In this paper, we propose (1) a LiDAR point cloud pre-segmented down-sampling (PSD) method that can selectively reduce background points while preserving the foreground object points during the process, greatly improving the computational efficiency of the model without affecting its 3D detection accuracy. (2) A lightweight GNN-based 3D detector that can extract point features and detect objects from the raw down-sampled LiDAR point cloud directly without any pre-transformation. We test the proposed model on the KITTI 3D Object Detection Benchmark, and the results demonstrate its effectiveness and efficiency for autonomous driving 3D object detection.
A Study of Online Teaching Strategies During Covid-19 of Secondary School Teachers

Authors:
raw_author_name: Dharmesh Patel
raw_affiliation_string: Researcher, Department of Education, Faculty of Education and Psychology, The Maharaja Sayajirao University of Baroda, Vadodara

Abstract: During the advent of COVID-19, it became inevitable to switch from traditional face-to-face teaching to online teaching mode. Switching to the online teaching process was the only option left for uninterrupted learning. One of the ways to make the online teaching-learning experience was the ability to energize the class environment. Creating a classroom community where students can communicate after lectures and can share their ideas and thoughts. Recording of the lectures must be given to students so that they can see them later for better understanding. Effective teaching strategies implementation was utmost required to improve the online teaching and make online learning a successful experience for both teachers and the students. Descriptive survey method was used for the present study. Objectives were to study the role of teacher in online class during covid -19 pandemic, to find different types of educational strategies for making online classes, to study the role of parents and students in implementation during online classes. Sample was selected from the 7 Secondary Schools affiliated to the State Board of Gujarat in Vadodara city taking as random sampling technique which included 70 teachers. Self made questionnaire was used to achieve the objectives of the study. It has been found in present study that the maximum number of teachers were able to perform their role in helping students according to their needs, perform as a mentor, guide and friend, to inspire the students and the remaining have faced difficulty motivating their students. Able to maintain the proper interpersonal relationships, interactions were also possible between the teacher &amp; students in online mode of teaching, but very few teachers face the difficulty in fulfilling or maintaining the interpersonal relationships. Different types of educational strategies were used in which maximum number of teachers were able to perform their interactive classroom though various activities. All the teachers were able to perform stories as strategies to clear the concept, majority of teachers are able to inspire the students and the remaining have faced difficulty with an inductive deductive approach &amp; ICT based lesson plan in online mode of teaching. Majority of teachers were able to make good rapport with the students &amp; Use teaching aids, various online quiz platforms in online mode of teaching, but very few teachers face the difficulty in fulfilling open forum or discussions &amp; encourage Group feedback for engagement. The maximum number of teachers agreed that parents and students were found able to perform their roles, to perform online cultural programs, competitions &amp; activities, to motivate online morning assemblies, dignity of online assessments, to connect in online parents and teacher meeting during online class.
Seeding Gaussian boson samplers with single photons for enhanced state generation

Authors:
raw_author_name: Valerio Crescimanna
raw_affiliation_string: Department of Physics, University of Ottawa, 25 Templeton Street, Ottawa, Ontario, K1N 6N5 Canada; National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1N 5A2, Canada
raw_author_name: Aaron Z. Goldberg
raw_affiliation_string: Department of Physics, University of Ottawa, 25 Templeton Street, Ottawa, Ontario, K1N 6N5 Canada; National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1N 5A2, Canada
raw_author_name: Khabat Heshami
raw_affiliation_string: Department of Physics, University of Ottawa, 25 Templeton Street, Ottawa, Ontario, K1N 6N5 Canada; Institute for Quantum Science and Technology, Department of Physics and Astronomy, University of Calgary, Alberta T2N 1N4, Canada; National Research Council of Canada, 100 Sussex Drive, Ottawa, Ontario K1N 5A2, Canada

Abstract: Non-Gaussian quantum states are crucial to fault-tolerant quantum computation with continuous-variable systems. Usually, generation of such states involves trade-offs between success probability and quality of the resultant state. For example, injecting squeezed light into a multimode interferometer and postselecting on certain patterns of photon-number outputs in all but one mode, a fundamentally probabilistic task, can herald the creation of cat states, Gottesman-Kitaev-Preskill (GKP) states, and more. We consider the addition of a non-Gaussian resource state, particularly single photons, to this configuration and show how it improves the qualities and generation probabilities of desired states. With only two modes, adding a single-photon source improves GKP-state fidelity from 0.68 to 0.95 and adding a second then increases the success probability eightfold; for cat states with a fixed target fidelity, the probability of success can be improved by factors of up to four by adding single-photon sources. These demonstrate the usefulness of additional commonplace non-Gaussian resources for generating desirable states of light.