Reviewerly/exHarmony huggingface dataset:
Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

Authors:
raw_author_name: Siraj Khan
raw_affiliation_string: Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, Peshawar, 25120, Pakistan
raw_author_name: Muhammad Sajjad
raw_affiliation_string: Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, Peshawar, 25120, Pakistan
raw_author_name: Naveed Abbas
raw_affiliation_string: Digital Image Processing Laboratory (DIP Lab), Department of Computer Science, Islamia College University, Peshawar, 25120, Pakistan
raw_author_name: José Escorcia-Gutierrez
raw_affiliation_string: Department of Computational Science and Electronics, Universidad de la Costa, CUC, Barranquilla, 080002, Colombia
raw_author_name: Margarita Gamarra
raw_affiliation_string: Department of System Engineering, Universidad del Norte, Puerto Colombia, 081007, Colombia
raw_author_name: Khan Muhammad
raw_affiliation_string: Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, 03063, South Korea

Abstract: Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aimed of this study to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.
PENGARUH MEDIA PEMBELAJARAN WORDWALL TERHADAP MOTIVASI BELAJAR MATEMATIKA PERKALIAN BILANGAN BULAT

Authors:
raw_author_name: Mochammad Agri Triansyah
raw_affiliation_string: Universitas Jenderal Soedirman

Abstract: This research aims to determine the effect of using word wall based educational games in mathematics learning on student learning motivation in grade VI at SDN Bojonggenteng Elementary School. This research is quantitative research using survey methods. The subjects of this research were 87 Class VI students, where the sample was 37 Class VI students. The data collection technique in this research is by using a questionnaire. Information obtained from respondents using this questionnaire was used to determine the effect of using wordwall-based educational games on learning motivation. This research uses partial testing (t test) to determine the hypothesis. The hypothesis results obtained based on the partial test (t test) are 3.552 (tcount) &gt; 2.045 (ttable) with a significance of 0.000 &lt; 0.05. These results show that H₁ is accepted and H₀ is rejected, which means that there is a positive and significant influence between wordwall-based learning media on learning motivation in mathematics learning for class V1 students at SDN Bojonggenteng.
A 5-D memristive hyperchaotic system with extreme multistability and its application in image encryption

Authors:
raw_author_name: Qianqian Dong
raw_affiliation_string: College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou, People's Republic of China
raw_author_name: Yulong Bai
raw_affiliation_string: College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou, People's Republic of China

Abstract: Abstract By coupling memristors to nonlinear circuits, more complex dynamical behaviors can be induced. However, to date, there has been insufficient attention given to high-dimensional chaotic systems based on memristors. In this paper, a magnetic-controlled memristor is combined with a three-dimensional chaotic system, resulting in a five-dimensional memristive chaotic system. Through dynamic analysis and numerical simulations, the chaotic nature of the system is elucidated based on fundamental system behaviors, including Lyapunov dimension, dissipativity, stability of equilibrium points, 0–1 test, and Poincaré mapping. During the complex dynamical analysis of this system, unique dynamical behaviors are discovered, including intermittent chaos, transient chaos, extreme multistability, and offset-boosting. Moreover, the consistency between numerical calculations and the physical implementation of the actual system is verified through equivalent circuit design. Finally, this system is applied to image encryption, leading to the design of an efficient and secure hyper-chaotic image encryption algorithm, whose effectiveness is confirmed through several security tests.
Benefits of adding radial phase dimples on scalar coronagraph phase masks

Authors:
raw_author_name: Eugene Serabyn
raw_affiliation_string: Caltech (United States); Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, United States

Abstract: Current scalar coronagraph focal plane mask designs are performance-limited by chromaticity. We investigate the effects of adding central Roddier and dual zone phase dimples to scalar masks to improve broadband performance by suppressing the chromatic stellar leakage. We present hybrid designs with radial phase dimples integrated with the sawtooth vortex, wrapped vortex, and cosine phase mask. We show that, using these dimples, it is possible to substantially improve the broadband contrast performance of scalar phase masks. We also show that, although adding a phase dimple does not increase the sensitivity to low-order aberrations, suppressing the central leakage of scalar vortex coronagraphs does not restore the aberration sensitivities to their notional state.
Adaptive wavelet transform defogging scheme for real-time video restoration with field programmable gate array implementation

Authors:
raw_author_name: Yueming Deng
raw_affiliation_string: Hunan Normal University, College of Information Science and Engineering, Changsha, China
raw_author_name: Runmin Wang
raw_affiliation_string: Hunan Normal University, College of Information Science and Engineering, Changsha, China

Abstract: In the context of real-time video defogging, accurately estimating the transmittance of sky regions and deep fault regions remains a challenging task due to severe color distortion and halo effects. To address this issue, we propose an innovative adaptive wavelet transform defogging (AWTD) scheme aimed at precisely modifying the transmittance of the sky and deep fault regions. Additionally, we present a dedicated field programmable gate array defogging architecture capable of direct application for real-time synchronous defogging of Full HD (1920×1080) videos. Through extensive experiments and comparisons, we demonstrate the effectiveness and practicality of the proposed AWTD approach in mitigating color distortion, halo effects, and other fog-induced artifacts. The results show that the dehazing algorithm attains 60 frames per second for the image resolution of 1920×1080, which is suitable of real time applications.
Monocular vision-based dynamic calibration method for determining the sensitivities of low-frequency tri-axial vibration sensors

Authors:
raw_author_name: Wenfeng Liu
raw_author_name: CHENGUANG CAI
raw_author_name: Zhihua Liu
raw_author_name: Ming Yang
raw_author_name: Shengnan Zuo

Abstract: Low-frequency vibrations exist widely in the natural environment and in human activities. Low-frequency tri-axial vibration sensors are enormously applied in the fields of seismic monitoring, building structure health monitoring, aerospace navigating, etc. Their sensitivity calibration accuracy directly determines whether their applications can work reliably. Although the laser interferometry recommended by the International Standardization Organization (ISO) is commonly used to achieve the vibration calibration, it suffers from the shortages of low-frequency range, high cost, low efficiency, and limited applicable environment. In this study, a novel monocular vision-based dynamic calibration method is proposed, which determines the whole sensitivities of tri-axial sensors by the monocular vision method to accurately measure the spatial input excitation. This method improves the calibration performance by eliminating the installation error and enhancing calibration efficiency via decreasing reinstallations. The experimental results compared with the laser interferometry demonstrate that the investigated method can obtain similar calibration accuracy in the range of 0.16-2 Hz with more efficiency. The corresponding maximum relative deviations of X-, Y-, and Z-axial sensitivities were approximately 2.5%, 1.8%, and 0.4%. In addition, the maximum relative standard deviation of the investigated method was only about 0.3% in this range.
Implementing distributed collaboration and applying the YOLO algorithm to robots

Authors:
raw_author_name: Mohammed Tamali

Abstract: Recently, the world has witnessed the development of artificial intelligence and robot programming, which have enabled robots to work together to perform specific assigned tasks while overcoming obstacles in the environment. Robots can now operate independently of each other. In this research, four-wheeled robots were created in webots and placed in different environments in Gazebo. These robots are associated with LIDAR and Kinect cameras. Due to the distributed collaboration between robots, a robot cannot traverse a path that it has previously traversed. The three robots are given the same goal, and the first robot to reach the goal signals the end of the mission to the remaining robots and learns about the surrounding objects on the way to the goal. Therefore, the YOLO algorithm was used (You Only Look Once). This is one of the best algorithms for detecting objects in their environment. Regarding the results obtained in the simulation, the robot performed all the assigned tasks.
Predicting visually induced motion sickness with lifestyle factors: An online survey study

Authors:
raw_author_name: Narmada Umatheva

Abstract: &lt;p&gt;Visually induced motion sickness (VIMS), a type of motion sickness (MS), is a common side-effect of visual devices and Virtual Reality (VR) usage. Due to its complexity, individual lifestyle factors may be possible predictors of experiencing VIMS and MS. This study aimed to (1) explore the association between lifestyle factors of video game use, fitness, diet, and substance use and self-reported VIMS and MS, as well as, to (2) determine the overall prevalence of VIMS. Responses to an online survey from 325 adults, between the ages of 18-49, were collected and analysed. Significant sex differences were observed for both VIMS and MS susceptibility. With regards to VIMS susceptibility, lifestyle factors including video game usage, physical activity as well as the consumption of fruits and vegetables, legumes, and carbohydrates were identified as relevant predictors. Lifestyle factors were noted to be not relevant for MS susceptibility.&lt;/p&gt;
A Survey on Computer Vision and Its Applications

Authors:
raw_author_name: GOUSIA HAZRA ANJUM KHAN

Abstract: Computer vision is an artificial intelligence field that teaches computers to interpret and comprehend images. Using digital images from cameras, movies, and deep learning models, machines can precisely identify and locate objects, then react to what they "see." As computer vision progressed, programming methods were developed to address specific problems. With practice, machines improved their vision recognition abilities. Deep learning techniques and technology have advanced dramatically over the years. We can now programme supercomputers to self-train, self-improve over time, and deliver capabilities to enterprises as web applications. Keywords— Solid-state physics, Neurobiology, Signal processing, Robotic Navigation, Distinctions
Research on logistics supply chain optimization strategy based on machine learning

Authors:
raw_author_name: Fang Zhang
raw_affiliation_string: Liaoning Technical University (China)

Abstract: With the process of globalization and the continuous development of the economy, the role of the logistics supply chain in corporate operations has become more and more prominent, and its optimization has become a core topic of modern management. This research aims to explore the optimization strategy of logistics supply chain through machine learning technology. First, based on an in-depth analysis of machine learning theory and supply chain processes, a specific machine learning model adapted to this scenario was selected, and empirical research was conducted to explore the problems and challenges that may be encountered in practical applications, and for this purpose, specific suggestions were put forward. sexual optimization plan. It is expected that through the application of machine learning, efficient, accurate and flexible management of the logistics supply chain can be achieved, bringing substantial benefit improvements to modern enterprises.
Analysis of the destruction resistance of multi-mode freight network in Beijing-Tianjin-Hebei urban agglomeration

Authors:
raw_author_name: Zhijun Liu
raw_affiliation_string: Shanghai Maritime University (China)

Abstract: Based on the county-level unit cities in the Beijing-Tianjin-Hebei urban agglomeration as nodes, the multi-mode freight network topology model between urban agglomerations is constructed, and Python is used to realize random attack simulation and Intentional attack simulation of multi-mode freight network and single-mode freight network of urban agglomeration, and the multi-mode freight network and single-mode freight network of urban agglomeration are evaluated by maximum connectivity index and network efficiency index. This paper selects the Beijing-Tianjin-Hebei urban agglomeration as an example object, mainly measures the destruction resistance of the road-railway multi-mode freight network of the urban agglomeration, hoping to provide decision-making support for the freight safety and improve the destruction resistance of the freight network to natural and man-made disasters in the Beijing-Tianjin-Hebei region.
Unmanned surface vehicles path planning based on improved PRM algorithm

Authors:
raw_author_name: Hongwei Bian
raw_affiliation_string: Dalian Maritime University (China)
raw_author_name: Weifeng Li
raw_affiliation_string: Dalian Maritime University (China)
raw_author_name: Mingzhe Qi
raw_affiliation_string: Dalian Maritime University (China)
raw_author_name: Shicai Chen
raw_affiliation_string: Dalian Maritime University (China)

Abstract: Unmanned Surface Vehicles(USV) path planning has important application value in the field of autonomous navigation. In order to plan a path with simple calculation, less time consumption and low complexity, an improved Probabilistic Roadmap(PRM) algorithm based on USV path planning is proposed. The randomly generated sampling points in the traditional PRM algorithm results in high computational complexity and insufficient flexibility and smoothness in the planned path. In order to optimize the generation strategy of sampling points and improve the smoothness of the path, the boundary box analysis technology and Catmull-Rom Spline interpolation of the path are introduced into the PRM algorithm to obtain I-NPRM algorithm. Using three different path planning schemes for simple, complex and special water environments and comparing them with the planning effectiveness of I-NPRM algorithm, NPRM algorithm, PRM algorithm and A* algorithm under three indicators of runtime, path length and number of inflection points. The experimental results indicate that the I-NPRM algorithm can optimize the sampling point generation strategy while reducing runtime and path length, as well as reducing the number of inflection points. The planned path has high smoothness, which is more in line with the motion constraints and maneuverability of the USV and improves the safety and stability of the autonomous navigation system
Development of a suspended intelligent detection system for pipe culverts

Authors:
raw_author_name: Xiaobing Wang
raw_affiliation_string: Northwest Engineering Corporation Limited (China)
raw_author_name: Weidong Chen
raw_affiliation_string: Northwest Engineering Corporation Limited (China)

Abstract: To solve the problems of low detection efficiency, high safety risk and production cost of urban pipe culverts and drainage outlets, a suspended intelligent detection system has been developed. The system adopts self-developed RGBD camera and amphibious suspended carrier, and develops 3D reconstruction technology of underground space, which can realize the output of panoramic 3D models. Test results show that the robot has strong adaptability to the underground environment, and the modeling error and positioning accuracy can meet the engineering requirements. The system overcomes the limitation that detectors installed on traditional carriers cannot fully adapt to the complex environment inside the pipe culvert, and has been applied in several water environment and utility tunnel projects.
Object detection for traffic management based on YOLO

Authors:
raw_author_name: Xuebin Hong
raw_affiliation_string: Shantou Polytechnic (China)
raw_author_name: Jubin Huang
raw_affiliation_string: Shantou Polytechnic (China)
raw_author_name: Weiwei Zhao
raw_affiliation_string: Shantou Polytechnic (China)
raw_author_name: Huiwen Zou
raw_affiliation_string: Shantou Polytechnic (China)
raw_author_name: Zhe Lin
raw_affiliation_string: Shantou Polytechnic (China)

Abstract: This paper presents an object detection method for traffic management based on the YOLOV7 model, using the bdd100k dataset for experimentation. The results show that the proposed method has good detection performance in traffic scenes. The main contribution of this paper is to improve the accuracy and efficiency of object detection in traffic scenes by applying the YOLOV7 model to the field of traffic management. The research results of this paper are of great significance for the optimization and improvement of traffic management systems. Future research can explore YOLOV7's performance on other target categories and consider algorithm optimizations to improve accuracy on new datasets.
Multi-scale object detection based on attention mechanism

Authors:
raw_author_name: Shenshen Sun
raw_affiliation_string: Shenyang University (China)
raw_author_name: Xue Bao
raw_affiliation_string: Shenyang University (China)
raw_author_name: Xirui Wang
raw_affiliation_string: Shenyang University (China)

Abstract: In order to solve the problem of missing target detection in traffic road target detection task due to excessive change of target scale in complex environment. An improved object detection method based on YOLOv5 is proposed, which introduces Coordinate Attention (CA), it fuses the coordinate attention mechanism into the last layer of the backbone network C3 module and the Bottleneck module in the C3 module to enhance the feature extraction ability of the target. The experimental results show that the vehicle detection accuracy of the fusion model reaches 96.5% on KITTI data set. Compared with other models, it can effectively improve the problem that the target scale of traffic road changes greatly.
Students' Interest in Learning English at Iain Syekh Nurjati Cirebon: Study of Instrumental and Integrative Motivation

Authors:
raw_author_name: Huriyah Huriyah

Abstract: Motivation is vital for learning and mastering a language. Motivation related to second language learning is integrative and instrumental motivation. This research aims to describe students’ interest in learning English from the terms instrumental and integrative motivation. The research design is quantitative descriptive. The information obtained is interpreted as tables, diagrams and sentences. The research location is at the IAIN Syekh Nurjati Cirebon campus for the odd academic year 2023-2024. The sample size was 100 students studying English using simple random sampling techniques. Data collection tools are questionnaires related to instrumental and integrative motivation, with 20 statements. The results show that integrative motivation is higher than instrumental motivation for various reasons. The instrumental motivation of students' desire to study English leads to the conclusion that their motivation is tied to their employment, future profession, and education. Regarding integration motivation, they recognized that English is a crucial language for worldwide communication. They are also eager to visit English-speaking countries, engage with native speakers, and utilize English with English-speaking friends or acquaintances. The majority of students learn English because of integrative motivation.
Crossed-IoT Device Portability of Electromagnetic Side Channel Analysis: Challenges and Dataset

Authors:
raw_author_name: Asanka Sayakkara
raw_affiliation_string: School of Computing, University of Colombo, Colombo, Sri Lanka
raw_author_name: Nhien-An Le-Khac
raw_affiliation_string: School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland

Abstract: Internet of Things (IoT) refers to the network of interconnected physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity, enabling them to collect and exchange data. IoT forensics aim to investigate cybercrimes, security breaches, and other malicious activities that may have taken place on these connected devices. In particular, Electromagnetic Side-Channel Analysis (EM-SCA) has become an essential tool for IoT forensics due to its ability to reveal confidential information about the internal functions of IoT devices without interfering with these devices or wiretapping their networks. However, the accuracy and reliability of EM-SCA results can be limited by device variability, environmental factors, and data collection and processing methods. In fact, very few studies have explored potential solutions to address the significant impact of these limitations on the accuracy of EM-SCA approaches applied to crossed IoT devices. Therefore, this paper examines the impact of device variability on the accuracy and reliability of machine learning (ML)-based approaches for EM-SCA. We first present the background, basic concepts and techniques used to evaluate the limitations of current EM-SCA approaches and datasets. Our study then addresses one important limitation, which is caused by the multi-core architecture of the processors (aka. System-On-Chip). We present an approach to collect the EM-SCA datasets and demonstrate the feasibility of using transfer learning to obtain more meaningful and reliable results from EM-SCA in IoT forensics of crossed-IoT devices. Moreover, this study contributes a new dataset for using deep learning (DL) models in analyzing Electromagnetic Side-Channel data with regard to the cross-device portability matter.