Reviewerly/exHarmony huggingface dataset:
Bounce Mark Visualization System for Ball Sports Judgement Using High-speed Drop Location Prediction and Preceding Mirror Control

Automation of line judgment in tennis enhances the fairness of the game. However, conventional judgment assistance system based on three-dimensional trajectories can cause discrepancies with the actual bounce mark of the ball. Therefore, we propose a ball mark visualization system using high-speed drop location prediction and preceding mirror control. High-resolution imaging toward the predicted drop location using rotational mirrors facilitates visualization of the ball mark simply by comparison with the image after bouncing. We have experimentally evaluated an accuracy of the drop location prediction and response performance of the rotational mirrors. We also demonstrated the validity of the ball mark visualization on artificial turf.
Reinforcement Learning consisting of Two Agents to Control Refrigeration System with Power Consumption Reduction

The purpose of the research is to apply reinforcement learning to the temperature control of a refrigeration system as well as reducing the power consumption. The suggested approach has two kinds of agents, and they control the compressor and the expansion valve at the same time. An exact model of a refrigeration system is configured based on the Model-Based Development method, and the corresponding simulator is used for generating various kinds of training data for reinforcement learning. Through some experiments, we verify the efficiency of reinforcement learning approach, and it can achieve maximally a 9.54% reduction in power consumption while having almost the same ability to control the temperature. As future work, the accuracy of temperature control should be improved, and the suggested approach should be applied to a wide variety of control requirements.
Actual-Vehicle Verification of Automatic Parking in Narrow Spaces Considering Precise Collision Avoidance Constraints for Rectangular Objects

Automated parking technology is becoming increasingly popular to assist non-skilled drivers with parking. However, these systems face challenges when utilized in narrow spaces where precise collision avoidance is required. Most conventional studies approximate the controlled vehicles as circles, ellipsoids, or other shapes, which makes generating paths difficult in narrow spaces due to excess margin. To solve this problem, a new approach is proposed that uses non-linear transformations to describe collision avoidance constraints among rectangles on a manifold within the model predictive control (MPC) framework. This approach reduces the computational cost of optimization calculations compared to some conventional shape-aware collision avoidance methods. This study validated the usefulness of the proposed method by conducting tests with an actual vehicle. The results confirmed that our method enables automated parking in very narrow spaces in real-world application.
TFCSG: An Unsupervised Approach for Question-retrieval Over Multi-task Learning

Most Question-answering (QA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose TFCSG, an unsupervised similar question retrieval approach that leverages pre-trained language models and multi-task learning. Firstly, topic keywords in question sentences are extracted sequentially based on a latent topic-filtering algorithm to construct unsupervised training corpus data. Then, the multi-task learning method is used to build the question retrieval model. There are three tasks designed. The first is a short sentence contrastive learning task. The second is the question sentence and its corresponding topic sequence similarity judgment task. The third is using question sentences to generate their corresponding topic sequence task. The three tasks are used to train the language model in parallel. Finally, similar questions are obtained by calculating the cosine similarity between sentence vectors. The comparison experiment on public question datasets that TFCSG outperforms the comparative unsupervised baseline method. And there is no need for manual marking, which greatly saves human resources.
A Segmentation Method of Lung Tumor by using Adaptive Structural Deep Belief Network

Deep Learning has a hierarchical network architecture to represent the complicated feature of input patterns. In the previous research, the adaptive structure learning method of Deep Belief Network (Adaptive DBN) was developed, which can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and can obtain appropriate number of hidden layers in DBN. In this paper, a new segmentation method using the Adaptive DBN was developed to extract lung tumor from 3D CT images. In the experiment, 156 cases collected by an open dataset in NSCLC Radiogenomics was used to evaluate our model. As a result, our model showed 0.884 dice coefficient for the test data, which was higher value than the 3D U-Net implemented on MONAI.
Genetic Algorithm based Obstacle Avoidance for 4-Wheeled Robot

This paper proposes an algorithm that uses for decision-making and control in Autonomous Mobile Robots (AMR) and performs in a virtual environment for simulation. In the decision-making stage, the algorithm uses the Velocity Obstacle (VO) and Artificial Potential Field (APF) methods to select the optimal velocity for avoiding collision and reaching the goal point in real-time. It additionally computes fitness that helps eliminates rapid changes in velocity. In control, the PID controller moves the robot along the velocity determined in the decision-making stage. The PID gain is updated by the gradient descent method to find the optimal gains. Proper brake torque is applied for the robot moves smoothly according to velocity previously determined. This parameter, $\alpha$ affects chance of avoidance obstacles, however it increase the travel time in experiment. The travel time averagely increase 9.54% at every $\alpha$ 0.1. When comparing the smallest and largest periods, the larger period resulted in a 29.9% decrease in control error. The results also showed that using the proposed PID controller resulted in a 13.6% decrease in control error compared to using a fixed PID controller.
The Causal Relationship between FinTech, Financial Inclusion, and Income Inequality in African Economies

Income inequality is one of the biggest problems affecting developing economies. Market imperfections and information asymmetry lead to lack of access to the financial system, which will exacerbate income inequality. The growing adoption of FinTech (financial technology) has altered the structure of how financial services are delivered and makes these services accessible to underserved groups. This study explores the causal relationship between FinTech development, financial inclusion, and income inequality in a panel study of 29 African countries. We apply pooled OLS regression and structural equation models to samples from the years 2011, 2014, and 2017. The findings indicate that FinTech has a positive and statistically significant effect on financial inclusion and income inequality in African countries. The study results also demonstrate that financial inclusion plays a pivotal mediation role in the negative effect of FinTech on income inequality in African economies. Further, financial inclusion (the ability to create a bank account and borrow money) negatively and significantly affects income inequality in African countries, whereas saving shows a positive and significant impact on income inequality. Overall, our study results suggest that to reduce income inequality and increase the effectiveness of FinTech investments, policymakers in African countries should design proper policies to enhance financial inclusion and offer more accessible and equitable financial services.
Posture Classification and Writing Behavior Estimation in Individual Learning Using OpenPose

The development of remote learning environments is being promoted to provide equal opportunities for learning. However, some challenges exist with remote learning, such as difficulty in learners maintaining their concentration and the inability to determine how a learner state of being. In this study, the authors propose a new method to monitor learners’efforts in a remote class while avoiding communication loads, such as video images. The authors use Open- Pose detection library to obtain the body skeletal coordinates of an individual learner’s frontal Red-Green-Blue color (RGB) model video, including the learner’s hands, and attempted to classify the learner’s posture by pattern matching, and estimate the learner’s writing behavior using wrist coordinates. Consequently, both posture classification and writing behavior estimation were possible with an accuracy of more than 80%, suggesting the possibility of perceiving a remote individual learner’s poise without using video communication.
Ultrasound Median Nerve Image Instance Segmentation via Nesting Attention and Boundary-guided Segmentation Mechanism

Most existing deep learning approaches, such as instance segmentation, are for natural images only. However, due to the unique characteristics of medical ultrasound images, they may not be suitable for ultrasound image diagnosis. In this study, we introduce Boundmask, an instance segmentation framework specially designed for medical ultrasound median nerve images. In Boundmark, firstly, we propose the nesting attention module (NAM), which combines spatial and channel attention to enhance the feature information so that we can still get rich feature information even with a simple backbone. Secondly, we design a boundary-guided segmentation mechanism (BGSM) that considers the object's unique traits and border information while segmenting. The experiments conducted using clinical data demonstrate that Boundmask has a high practical value. The results show that it achieves 54.2 AP on the ultrasound median nerve image dataset and outperforms most existing instance segmentation models.
Face Recognition Based Attendance Software and Reporting using OpenCV

The face is the identity of a person. The methods to exploit this physical feature have seen a great change since the advent of image processing techniques. The attendance is taken in every school, colleges and libraries. Traditional approach for attendance is professor calls student name and record attendance. It takes some time to record attendance. For each lecture this is a waste of time. To avoid these losses, we're going to use automatic process which is based on image processing. Identification of any people in any organization or colleges for the purpose of attendance marking is one of such software. Authentication is a significant issue in computer-based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications. The use of Attendance Management System is to perform the regular activities of attendance marking and analysis with less human intervention. In this new approach, we are using face detection and face recognition system. This face detection distinguishes faces from non-faces and is essential for accurate attendance. The other strategy is facing recognition for marking the student’s attendance. We have used OpenCV for this system. The camera will be connected using OpenCV module that converts the image into the RGB format which in turn is mapped to the pre-trained neural networks using HOG algorithm to recognize the face pixels. This system offers effective way to manage the attendance system which is time efficient and offers massive scaling for future purposes. The image is processed as follows: first, faces are identified using a Haarcascade classifier, then faces are recognized using the LBPH (Local Binary Pattern Histogram) Algorithm, histogram data is checked against an established dataset, and the device automatically labels attendance. An Excel sheet is developed, and it is updated every hour with the information from the respective class instructor.
A novel object recognition method for photovoltaic (PV) panel occlusion based on deep learning

During the long-term operation of the photovoltaic (PV) system, occlusion will reduce the solar radiation energy received by the PV module, as well as the photoelectric conversion efficiency and economy. However, the occlusion detection of the PV power station has the defects of low efficiency, poor accuracy, and untimely detection, which will cause unknown system losses. Based on the deep learning algorithm, this paper conducts research on PV module occlusion detection. In order to accurately obtain the occlusion area and position information of the PV panel, a PV module occlusion detection model based on the Segment-You Only Look Once (Seg-YOLO) algorithm is established. Based on the YOLOv5 algorithm, the loss function is modified, the Segment Head detection module is introduced, and the convolutional block attention module (CBAM) attention mechanism is added to achieve the accurate detection of small targets by the algorithm model and the fast detection of the PV module occlusion area identify. The model performance research is carried out on three types of occlusion datasets: leaf, bird dropping, and shadow. According to the experimental results, the proposed model has better recognition accuracy and speed than SSD, Faster-Rcnn, YOLOv4, and U-Net. The precision rate, recall rate, and recognition speed can reach 90.52%, 92.41%, and 92.3 FPS, respectively. This model can lay a theoretical foundation for the intelligent operation and maintenance of PV systems.
Hybrid trust-based optimized virtual machine migration for dynamic load balancing and replica management in heterogeneous cloud

Cloud computing is an upcoming technology that has garnered interest from academic as well as commercial domains. Cloud offers the advantage of providing huge computing capability as well as resources that are positioned at multiple locations irrespective of time or location of the user. Cloud utilizes the concept of virtualization to dispatch the multiple tasks encountered simultaneously to the server. However, allocation of tasks to the heterogeneous servers requires that the load is balanced among the servers. To address this issue, a trust based dynamic load balancing algorithm in distributed file system is proposed. Load balancing is performed by predicting the loads in the physical machine with the help of the Rider optimization algorithm-based Neural Network (RideNN). Further, load balancing is carried out using the proposed Fractional Social Deer Optimization (FSDO) algorithm, where the virtual machine migration is performed based on the load condition in the physical machine. Later, replica management is accomplished for managing the replica in distributed file system with the help of the devised FSDO algorithm. Moreover, the proposed FSDO based dynamic load balancing algorithm is evaluated for its performance based on parameters, like predicted load, prediction error, trust, cost and energy consumption with values 0.051, 0.723, 0.390 and 0.431J correspondingly.
Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language

Despite a few attempts to automatically crawl Ewe text from online news portals and magazines, the African Ewe language remains underdeveloped despite its rich morphology and complex "unique" structure. This is due to the poor quality, unbalanced, and religious-based nature of the crawled Ewe texts, thus making it challenging to preprocess and perform any NLP task with current transformer-based language models. In this study, we present a well-preprocessed Ewe dataset for low-resource text classification to the research community. Additionally, we have developed an Ewe-based word embedding to leverage the low-resource semantic representation. Finally, we have fine-tuned seven transformer-based models, namely BERT-based (cased and uncased), DistilBERT-based (cased and uncased), RoBERTa, DistilRoBERTa, and DeBERTa, using the preprocessed Ewe dataset that we have proposed. Extensive experiments indicate that the fine-tuned BERT-base-cased model outperforms all baseline models with an accuracy of 0.972, precision of 0.969, recall of 0.970, loss score of 0.021, and an F1-score of 0.970. This performance demonstrates the model’s ability to comprehend the low-resourced Ewe semantic representation compared to all other models, thus setting the fine-tuned BERT-based model as the benchmark for the proposed Ewe dataset.
An Underwater Image Restoration Deep Learning Network Combining Attention Mechanism and Brightness Adjustment

This study proposes Combining Attention and Brightness Adjustment Network (CABA-Net), a deep learning network for underwater image restoration, to address the issues of underwater image color-cast, low brightness, and low contrast. The proposed approach achieves a multi-branch ambient light estimation by extracting the features of different levels of underwater images to achieve accurate estimates of the ambient light. Additionally, an encoder-decoder transmission map estimation module is designed to combine spatial attention structures that can extract the different layers of underwater images’ spatial features to achieve accurate transmission map estimates. Then, the transmission map and precisely predicted ambient light were included in the underwater image formation model to achieve a preliminary restoration of underwater images. HSV brightness adjustment was conducted by combining the channel and spatial attention to the initial underwater image to complete the final underwater image restoration. Experimental results on the Underwater Image Enhancement Benchmark (UIEB) and Real-world Underwater Image Enhancement (RUIE) datasets show excellent performance of the proposed method in subjective comparisons and objective assessments. Furthermore, several ablation studies are conducted to understand the effect of each network component and prove the effectiveness of the suggested approach.
A Robust Preprocessing Method for Measuring Image Visual Quality Using Log-Polar FFT Features

To register two images, existing methods normally estimate the parameters of an affine transform, and then perform an inverse affine transform to the test image by using the estimated parameters. Any metric can then be applied to the reference image and the normalized image. In this paper we propose a new method for measuring visual image quality which circumvents the need to estimate the parameters of the affine transform. Instead, we use the log-polar transform and the fast Fourier transform (FFT) to extract features that are invariant to translation, rotation, and scaling. We apply the existing structural similarity index measure (SSIM) to the two invariant feature images, where no inverse transform is needed. Experimental results show that our proposed method outperforms the standard metric, the mean SSIM (MSSIM), significantly in terms of visual quality scores.
Monte Carlo graph search for quantum circuit optimization

The authors introduce a search algorithm aimed at optimizing the arrangement of quantum gates, leveraging different sampling techniques to dynamically grow a graph that represents potential quantum circuits. They demonstrate efficacy in reducing the quantum gate count, both theoretically and numerically, albeit with acknowledged computational challenges as the graph expands.
Navit(oo)n: Open Source Mobile Robot Project for Nakanoshima Robot Challenge

Recently, the legislation regarding autonomous mobile robots for outdoor pedestrian areas have been advancing, leading to increased expectations for task automation such as transportation and cleaning. Outdoor environments like parks, where vehicles cannot enter, present many three-dimensional terrains such as stairs and inclined surfaces, causing difficulty in achieving accurate environment recognition and autonomous movement. Furthermore, robots that navigate pedestrian walkways must be smaller and lighter than cars and also have a robust system capable of traversing steps and uneven surfaces and withstanding rainy weather. Currently, robots designed for paved roads are commercially available; however, robots capable of navigating park walkways are still in the research and development stage. Therefore, to accelerate the research and development of outdoor autonomous mobile robots, this study proposes the Navit(oo)n platform, designed for use in outdoor environments. This robot can be manufactured using easily obtainable parts, and all CAD data, circuit design data, and autonomous movement software are provided as open source. This paper introduces an overview of Navit(oo)n that successfully completed the course and achieved all tasks in the recent Nakanoshima Robot Challenge.
La traducción al español de la marca dialectal siciliana presente en la literatura contemporánea Entre la estandarización, la sustitución y otras estrategias (des)acertadas

This paper focuses on the analysis of translation models and strategies selected by translators of some contemporary Italian narrative authors that include in their works dialectal marks, specifically of the Sicilian variety (e.g. Camilleri, Consolo, or Torregrossa). The texts analysed have been selected according to the distinctive characteristics they deploy for the translation process: their dialectal density, the value of the diatopic markers reflected by the author and, of course, the determining factor shaping the function or purpose of the target text; in short, we will analyse the solutions and strategies proposed by the translators.
A novel three-dimensional surface reconstruction method for fabric appearance evaluation

Traditional fabric appearance evaluation method mainly relies on subjective and manual operation, which is not uniform and time-consuming. In this paper, one set of self-developed 3D reconstruction system and method based on photometric stereo is proposed for objective fabric appearance evaluation. A photometric stereo image acquisition system equipped with multi-directional lighting sources is developed for the 3D surface reconstruction. Lighting and camera calibration is conducted to obtain the position of light sources and the intrisic parameters of digital camera. Our experimental results show that 3D fabric surface could be reconstructed effectively based on photometric stereo vision method, it has the potential to be used for fabric appearance evaluation.
Exploring the impact of non‐verbal cues on user experience in immersive virtual reality

Abstract Face‐to‐face communication relies extensively on non‐verbal cues (NVCs) which complement, or at times dominate, the communicative process as they convey emotions with intense salience, thus definitively affecting interpersonal communication. The capture, transference, and subsequent interpretation of NVCs becomes complicated in computer‐mediated communicative processes, particularly in shared virtual worlds, for which there is growing interest both in regard to NVCs technological integration and their affective impact. This paper presents a between‐groups experimental setup which is facilitated in immersive virtual reality (IVR), and examines NVCs effects on user experience, with special emphasis on degree of attention toward each NVC as an isolated controlled variable of a scripted performance by a virtual character (VC). This study aims to evaluate NVCs fidelity based on the capabilities of the motion‐capture technologies utilized to address cue integration developmental challenges and examines NVCs impact on users' perceived realism of the VC, their empathy toward him, and the degree of social presence experienced. To meet the objectives set the affective impact of low‐fidelity automated NVCs and high‐fidelity real‐time captured NVCs were compared. The findings of the evaluation suggest that although NVCs do impact user experience to an extent, their effects are notably more subtle compared to previous studies.