Reviewerly/exHarmony huggingface dataset:
College Chatbot

Creating a chatbot to be used by the students to get their queries responded easily from the college website. The College Chatbot has the capacity to make friendly conversations; respond the course and faculty details; give the link for the academic calendar; answer the frequently asked questions; calculate the fees based on the student's input; and give the timings, address, contacts, and events information of the departments like Union and Library. To build the chatbot, Microsoft Azure bot service as well as Microsoft cognitive services, namely, Text Analytics, LUIS, and QnA Maker are used. In order to address these problems, the College Enquiry Chatbot extends the implementation of the current chatbots by adding sentiment analysis and active learning
Children’s Accent-Based Preferences and Stereotypes in Media Contexts

&lt;p&gt;Children spend a substantial amount of time consuming screen media—particularly television and mobile apps. Research has shown that non-native and non-standard accents tend to be underrepresented in adult-oriented and children’s media, and that representations of accents tend to be stereotypical in nature. Relatedly, research has shown that children demonstrate social preferences and ascribe traits on the basis of spoken accents. In a series of four experiments, the present research investigated children’s accent-based preferences and stereotypes in two media contexts. Children aged 5-6 and 9-10 years selected characters from a variety of exemplars that spoke in different accents to play different archetypical characters in a television program (Study 1) or to serve as teachers in an educational mobile app (Study 2). Study 1 (Experiments 1 and 2) showed that children generally preferred for television characters to speak with a native, Canadian accent (compared to high-status British and non-native accents). However, 9-10-year-old children held stereotypes about the Russian accent specifically, and preferred for negative television characters (e.g., mad scientist, villain) to speak with a Russian accent. The same pattern was not observed for Chinese or Indian accents. Study 2 (Experiments 3 and 4) revealed that children generally preferred for teaching characters in educational apps to speak with a native, Canadian accent (compared to high-status British and non-native accents), particularly when the subject matter being taught was culturally neutral (e.g., oceans, space). Children aged 9-10 preferred for teaching characters to speak with non-native accents (Russian, Chinese, and Indian) when the subject matter was culturally relevant (e.g., Russian dancing, Chinese pottery, Indian cuisine). Findings of this research contribute to our knowledge about children’s accent-based biases in social perception and accent-based assumptions about cultural expertise, and may help guide the development of more inclusive and less stereotypical media offerings for children.&lt;/p&gt;
Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

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

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

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

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.
A detailed study of speech signal cryptography using simple Put _operation and Get_operation

Protecting digital speech files is an important issue. In this paper's research, a simplified method of speech file cryptography will be provided, and the encryption and decryption functions will require a reduced number of operations. The encryption function will perform a put_operation to reorder the speech sample to get the encrypted file, while the decryption function will perform a get_operation to reorder the encrypted samples to get the decrypted speech file; these operations will be implemented based on the generated secret indices key. The Put and get operations will affect the speech file by recording the samples keeping the histograms of the speech files (source, encrypted, and decrypted) the same and without any changes. The secret key generation phase will be analyzed for efficiency purposes, and two methods will be presented: The first one will use the chaotic logistic map model to generate the secret indices key, while the second method will use a selected secret image to generate the secret indices key, both methods will be tested and examined to give some recommendations for the users. Each of the introduced methods will provide a high level of security, the private keys will provide a huge key space and they will be very sensitive to resist any hacking attacks. The quality, sensitivity, security, and speed of the proposed methods will be examined, the method will be tested and implemented, and the obtained results will be analyzed to prove the achievements provided by the proposed method.
WAF and WMF to improve the performance of average and median filters

Salt and spice noise is considered one of the most common types of noise that affect grayscale and color digital images, as it affects them negatively, and this negative effect increases with an increase in the noise ratio. Many digital filters are used to mitigate the negative effects of salt and pepper noise, and the most widely used of these filters are the average filter and the median filter. The average and median filters work on processing all pixels in the image, whether these pixels are intact from the noise or infected with it, and accordingly, mitigating this noise, especially if it has a high noise ratio, is ineffective. In this research paper, new window average and window median filters will be proposed to enhance the performance of standard average and median filters. The proposed filter will treat the infected pixels, leaving the clean pixels as they were. For a noisy pixel, a special window will be created and the pixel value will equal the mean (or average) of the cleaned pixels in the window (excluding the noisy pixels). To simplify the window processing an index window will be used, this window will point to the noisy and cleaned pixels in the selected pixel window. Several images with various values of noise ratios will be tested; several windows with different sizes will be examined to get the most suitable window size. The selected window size will be used to filter various noisy images, the obtained results will be compared with average and median filter results to show the improvements provided by the proposed method.
Adaptive wavelet transform defogging scheme for real-time video restoration with field programmable gate array implementation

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

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

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

&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

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

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

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

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

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

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

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.