Reviewerly/exHarmony huggingface dataset:
Image Colorization with Fast Fourier Convolution

We present a spectrum-based framework, utilizing low and high spectral latent features, to colorize the grayscale image. Current image colorization algorithms, despite significant achievements, constantly struggle with color bleeding and low color saturation. One of the main reasons causing such phenomena is vanilla convolutions in modern neural networks focus on local and fixed scales. To alleviate this issue, we propose a novel approach called Low and High Spectral Colorization(LoHiSC). LoHiSC is based on a new colorization network that uses fast fourier convolution(FFC), which is capable of efficiently connecting two distant locations and has a global receptive field. Particularly, our proposed LoHiSC improves FFC by splitting it into two main branches, one is cascaded with low pass spectral filter, and another with high pass spectral filter, for the reason we experimently find that colors are mainly concentrated in the low frequency part, while contours and edges in the high frequency part. Moreover, we propose a frequency domain loss which is beneficial to restore challenging artifacts of grayscale image colorization. Experiments on both synthetic dataset and real-world old photos demonstrate our method improves the stat-of-the-art and achieves compelling colorizing results.
Diagnosing psychiatric disorders from history of present illness using a large‐scale linguistic model

Recent advances in natural language processing models are expected to provide diagnostic assistance in psychiatry from the history of present illness (HPI). However, existing studies have been limited, with the target diseases including only major diseases, small sample sizes, or no comparison with diagnoses made by psychiatrists to ensure accuracy. Therefore, we formulated an accurate diagnostic model that covers all psychiatric disorders.HPIs and diagnoses were extracted from discharge summaries of 2,642 cases at the Nara Medical University Hospital, Japan, from 21 May 2007, to 31 May 31 2021. The diagnoses were classified into 11 classes according to the code from ICD-10 Chapter V. Using UTH-BERT pre-trained on the electronic medical records of the University of Tokyo Hospital, Japan, we predicted the main diagnoses at discharge based on HPIs and compared the concordance rate with the results of psychiatrists. The psychiatrists were divided into two groups: semi-Designated with 3-4 years of experience and Residents with only 2 months of experience.The model's match rate was 74.3%, compared to 71.5% for the semi-Designated psychiatrists and 69.4% for the Residents. If the cases were limited to those correctly answered by the semi-Designated group, the model and the Residents performed at 84.9% and 83.3%, respectively.We demonstrated that the model matched the diagnosis predicted from the HPI with a high probability to the principal diagnosis at discharge. Hence, the model can provide diagnostic suggestions in actual clinical practice.
Perpetual Futures in NFTs

This study discusses a pricing framework for perpetual futures in collection-based Non-Fungible Tokens (NFTs) which represent unique digital assets in a low liquidity and high price volatility environment. The collection could be viewed as the broad family to which an NFT belongs and the NFTs within the collection are interconnected where they exhibit systematic co-movements. For the perpetual futures pricing we leverage that collection-based NFTs have substantial exposure to the collection they belong to. We discuss both pricing of perpetual and power perpetual futures where we discuss two different model approaches. One approach uses the conventional pricing methodology on NFT indices and another approach leverages on defining the closed-form NFT price process.
Gauging Preference for Democracy in Absence of Free Speech

Whether people prefer a democratic system is difficult to judge when speaking freely carries personal dangers. We introduce an incentivized experimental task to reveal implicit preference for democracy without referencing politically-sensitive terms. We validate the task with data from émigrés from Greater China living in North America, demonstrating our experimental tool’s ability to gauge favorability toward democracy when participants come from backgrounds where eliciting such views is challenging. We corroborate the task’s accuracy and its ability to uncover patterns in democratic sentiment with data from a representative US sample and from a diverse set of participants in China.
English handwriting image edge detection based on fuzzy logic

This paper mainly applies fuzzy logic to the edge detection of handwritten images in English. Image edge detection is an important part of image processing. Through image edge detection, the amount of data can be greatly reduced, and the information that can be considered irrelevant can be eliminated, and the important structural attributes of the image are retained. It uses six traditional operators to detect the edge of English handwritten images: canny, log, sobel, sprewitt, zerocross, and roberts. By comparing the edge detection results of the two kinds of experiments, we can find that the canny operator has the best edge detection effect among the traditional operators. In the fuzzy logic experiment, with the growth of 𝐒<sub>𝐱</sub> and 𝐒<sub>𝐲</sub>, the edge detection effect gradually weakened. In this paper, the experiment with 𝐒<sub>𝐱</sub> = 𝟎. 𝟎𝟓 and 𝐒<sub>𝐲</sub> = 𝟎. 𝟎𝟓 has the best edge detection results. In conclusion, the edge detection effect of English handwritten images based on fuzzy logic is better than that of English handwritten images based on traditional methods.
A novel anti-occlusion and scale adaptation re-tracking method with correlation filtering tracker algorithm

In recent years, Kernel Correlation Filter (KCF) has been a highlight topic due to its excellent performance on visual tracking tasks in real-scenes. However, fixed search boxes which are used to delineate candidate objects are widely used in the current KCF based algorithms, and its tracking ability degrades when the scale of the object changes. Besides, occlusion always make the tracked object lost in real scene as the tracker highly relies on the response maximum. To improve robustness and reliability, we propose a scale-adaptive target tracking algorithm based on KCF to meet the challenge of the scale variation. At the same time, we propose a new strategy for template update with the average peak correlation energy to reduce the drift problem. When both the average peak correlation energy and the maximum response peak of the current frame are lower than the certain thresholds, the proposed re-detection model will be activated to detect the corresponding object through the particle resampling strategy and regain the position. Experiments on public data sets OTB2013 are used to verify the behaviors of the proposed method, and the results indicate that the proposed algorithm holds the superiority to the current correlation filtering algorithm in the evaluation index of success rate and range accuracy, and is more stable in the case of large scale variation of target and occlusion.
Transformer-based multi-scale gradient feature fusion for low-light image enhancement

In deep learning-based low-light image enhancement (LLIE) methods, the more effective use of image features plays a crucial role in enhancing the quality of images. In the paper, a Transformer-based multi-scale gradient feature fusion network (TMGFFN) is proposed to improve the overall image quality by making full use of the multi-scale, non-local gradient features of an image. Specifically, to exploit the non-local features of an image, an asymmetric Transformer structure is designed to enhance the image as a whole by learning the non-local mapping of the image in different directions. In addition, to extract richer features, a multi-scale structure with an asymmetric Transformer as a benchmark is designed to accomplish accurate information injection by progressive fusion of features from different sensory fields and by exploiting the contextual features of the image. Finally, a comparison with several classical LLIE methods is made. Experimental results show that our method can significantly improve the quality and sharpness of low-light images while retaining more detailed information.
Dense pedestrian detection based on multiple link feature pyramid networks

Pedestrian detection is widely employed in a variety of industries, including security, autonomous driving, and pedestrian flow statistics. However, because most pedestrians in real-world scenes are densely distributed with occlusion issues, scale variation and pose variability, majority pedestrian detection algorithms have low detection accuracy and a high miss detection rate. In this paper, we propose a new FPN neck module, Multiple Link Feature Pyramid Networks (MLFPN) for dense pedestrian detection, which innovative information transfer pathways and incorporation of Poly-Scale Convolution (PSconv) capable of long-range interaction into feature extraction tasks to reduce feature information loss and enhanced representation of effective pedestrian features, achieving 0.6MR<sup>-2</sup>, 0.35AP, and 0.21JI improvements on CrowdHuman dataset, improving detection accuracy and reducing missed detection rates.
Rotated vehicle object detection and tracking on drone-captured scenarios

The vehicle object detection and tracking on drone-captured scenarios will play a significant role in the intelligent transportation in future. In order to provide accurate orientation and scale information, the angle vector module is introduced to improve the model to realize the rotated detection. For the small and densely distributed objects, the C3_TR structure is proposed to improve the detection accuracy. The image weight and the category weight are used in training to improve accuracy on the self-built vehicle dataset with the category imbalance. Finally, the mAP of the improved YOLOv5 detector is 82.4% higher than the baseline model by 8.1 percentage points. The angle parameter is taken into account and the rotated IOU matching is used instead of the horizontal IOU matching to realize the tracking with rotated bounding boxes by DeepSORT. In tracking evaluation results on the self-built dataset, the HOTA, MOTA, MOTP and ID_SW are 76.86%, 92.8%, 81.3% and 2, respectively.
Interactive image segmentation method based on asymmetric quadratic metric

In this paper, we propose an interactive image segmentation method based on the geodesic voting model. Basically, the segmentation is implemented by thresholding a feature map generated by a geodesic voting strategy. In contrast to the classical geodesic voting method which considers isotropic metric, we exploit state-of-the-art asymmetric quadratic metric for geodesic computation. The main contribution of this work lies at an obstacle-based geodesic computation way, in order to encourage the computed geodesic paths to pass through image features of interest. The use of asymmetric quadratic metric for computing geodesic paths can take advantages from the anisotropy and asymmetry geometric features, thus overcoming the shortcuts problem suffered in the isotropic case. Experiments on real images demonstrate that the proposed method indeed can obtain promising results.
Research on stereo matching algorithm of underwater binocular detection

Underwater binocular detection is a detection method using binocular stereo vision. Binocular stereo vision is increasingly used in robot vision, aerial mapping, reverse engineering, military applications, medical imaging and industrial detection. The AUV (Autonomous Underwate Vehicle) equipped with binocular optical system restores the real underwater three-dimensional world through binocular stereo detection results, and becomes an important means for underwater resource detection and underwater target search. Due to the imaging characteristics of underwater images, there are a large number of weak texture and repeated texture areas, and the current underwater stereo matching algorithm has poor matching effect in these areas. To solve this problem, this paper proposes a multi-cost fusion BP(belief propagation) algorithm based on image segmentation information to improve the matching effect of such regions. Experimental results of Middlebury data and real underwater binocular image data show that the proposed algorithm improves the matching effect of weak texture and non-texture regions.
Design and implementation of an optimization human pose estimation scheme

Human pose estimation is attracting increasing attention from researchers as a fundamental technology for human action recognition, while edge computing applications pose higher challenges. In this paper, we propose an optimization human pose estimation method for edge computing applications. This method uses YOLOv4 to improve the accuracy of AlphaPose at the object detection stage and accelerates human target detection and pose skeleton node inference by optimizing both the object detection model and the pose estimation model. Moreover, the proposed scheme is transplanted onto an embedded development board and compared with current mainstream human pose estimation algorithms. Experimental results show that the proposed algorithm achieves better detection frame rate and accuracy than the compared algorithms on the embedded platform. The algorithm has high real-time performance and accuracy and provides a basis for lightweight human action recognition scheme in edge applications.
The Rate of Use of Digital Tools in Relation to Teachers' Creativity

Despite the fact that digitization is still a hot subject of debate, not many writers have addressed the use of digital tools to foster creativity in the classroom. Our paper focuses on digital resources, both online and offline, that foster students' imagination and innovation and may be thought of as creative applications. The article begins with a discussion of what creativity is, how it works, and why it's so crucial to have. We also provide a compilation of many writers' approaches to inspiring original thought and action. The article's second half focuses on clarifying the value of creative teaching and briefly describes a few useful digital tools. The article's key component is an analysis of a questionnaire survey of Slovak educators done in light of the training in the field of digital tools in the classroom that was put into effect; its objectives were to first familiarize educators with online and offline tools in the classroom, and then to ascertain the current state of the field and educators' interest in further training in this area. In Section 2.2, we give the analysis in both written and visual form. The survey results are presented as the last section of our study.
Comparative Analysis on Medical Image Prediction of Breast Cancer Disease using Various Machine Learning Algorithms

Breast Cancer disease is the utmost characterized heterogeneous illnesses consisting of various types. Apart from lung cancer, Breast cancer is spreading widely everywhere. This research work confines to accurately analyzing the benign cells and the defective malignant cells by data mining technique like Support Vector Machine (SVM). To have the comparative study, a total number of 659 sample are drawn from the UCI Machine learning laboratory. The G power calculation with a confidence interval of 0.8 using maximum level of acceptable error rate of 0.5 is used for this analysis. Support Vector Machine offer better prediction in terms of F1 score, precision and recall as 100%, 92%, 97% for benign cells 94%, 100%, 97% for malignant cells respectively. The significance value is arrived as 0.36 for this proposed system. The SVM appears to have better results in finding the benign and malignant cells diagnosis using Wisconsin Dataset.
Trash Image Classification Using Autoencoder

With the expanding sum of waste produced around the world, it has ended up vital to create effective strategies for waste management. One of the key challenges in waste management is the classification of waste based on its sort, because it is time-consuming and requires critical exertion. In this paper, an autoencoder-based deep learning method for classifying trash images was provided. The proposed strategy includes the use of an autoencoder for highlight extraction and classification. Particularly, an autoencoder on a dataset of TrashNet was prepared to memorize the fundamental highlights and after that the learned highlights were used to classify the waste pictures. The proposed strategy was implemented on a dataset of real-world waste pictures and appear that it accomplishes high exactness in classifying diverse sorts of waste. The proposed method has the potential to be conveyed in waste management frameworks to computerize the method of waste classification, in this way sparing time and exertion. The proposed autoencoder-based deep learning method for classifying trash images using TrashNet dataset achieved high accuracy in identifying various types of waste. This approach has the potential to be deployed in trash management systems to automate the process of trash sorting and reduce the time and effort required for it. Overall, this method could prove to be a valuable tool in addressing the challenges of waste management.
Sentiment Prediction based on Movie Reviews using Machine Learning Techniques

Internet has become a rich source of user-generated his/her general opinion for products, services, social events, company strategies etc. This has created a need for emotion mining using sentiment analysis. There is a wealth of information and data available today because technology is the foundation of every business. As a result, a recommendation system is useful for managing this massive quantity of data and rapidly and in accordance with the user’ s tastes filtering out the relevant data. This paper describes a system for making movie recommendations. The major goal of this study has been to examine several machine learning techniques to classify a text into positive, neutral and negative emotion polarities. An algorithm used to train a Movie review data by support vector machine (SVM), Logistic regression (LR), XGB classifier models.
Handwritten mathematical expression recognition based on ResNet and transformer

Recognition of handwritten mathematical expressions (HMER) is a branch of Optical Character Recognition (OCR). In this paper, a seq2seq model with encoder-decoder structure is constructed based on ResNet and Transformer deep neural networks, and end-to-end training is performed using open source datasets. The results of the experiments name that the character error rate of the model proposed in this paper is improved by 11.36% compared with the CNN and LSTM-based HMER model.
Traffic police command gesture recognition technology based on machine vision and two-stream spatio-temporal attention graph convolutional network

For the requirement of automatic recognition of traffic police gestures in complex backgrounds based on vision sensors for driverless cars, we propose a method for traffic police gesture action recognition based on two-stream spatio-temporal attention graph convolutional network (2s-AGCN) with two different dimensional skeletal data. Firstly, detect the commanding traffic policeman in the video, extract the 2D and 3D skeletal data with the pose estimation algorithm to reduce the influence of complex background and joint overlap on action recognition, then, build the spatio-temporal graph model ; After that, we construct a 2s-AGCN network, input 2D and 3D skeletal sequences into the network to learn the spatio-temporal features of gesture actions. Finally, a fusion of the two-stream information is done and then output the final traffic police gesture category. 2s-AGCN uses Non-Local and TopK at the spatial level to focus on all nodes directly, selecting the strongest K neighbors of interaction strength; Temporal attention is used to focus on the frames that have higher contribution. The ablation study is done on the dataset CTPGD, and the results show that the method significantly improves the recognition accuracy of traffic police command gesture actions, especially those with overlapping skeleton points.
Examining Journalistic Practices in Online Newspapers in the Era of Artificial Intelligence

This study aimed to determine the extent to which Jordanian journalists in digital newspapers are aware about artificial intelligence (AI) techniques in journalistic practices and to identify the most important applications, difficulties, and motives for employing AI from the perspectives of 59 journalists in Jordanian digital newspapers. An online questionnaire was used. The results were as follows: the weak reality of employing AI techniques in journalistic practices; few journalists know about the technologies sufficiently; and the most used systems in journalistic practices are photo and video verification techniques, followed by breaking news tracking techniques. The findings revealed that economic factors and the absence of technological infrastructure in press institutions are among the most important factors affecting the adoption of AI techniques in Jordanian digital newspapers. This study had some limitations that should be addressed in future studies.
Development of an Obstacle Avoidance Wall Climbing Robot

This study focuses on the construction of a wall-climbing robot that can recognize and avoid obstacles. The design process begins with the selection of relevant components, such as motors, sensors, and microcontrollers that are compatible with the design criteria, load capacity, and power supply of the robot. The body of the robot is 3-D printed, and it climbs walls using magnetic adhesion technology. The robot features eight servo motors that provide omnidirectional mobility and are controlled by an Arduino UNO, which drives the motors in the desired direction. The integrated sensors provide the data required to steer the motors in the proper direction for movement. The robot is tested to confirm that all components are properly linked and secured, as well as that the magnetic adhesion is steady and performs properly. Based on the test results, any necessary changes to the robot’s design or programming are made. The resulting robot is an efficient and dependable wall-climbing machine that can avoid obstacles, making it a versatile solution for a variety of applications
PASSD: an improved SSD object detection algorithm based on feature fusion with feedback-based loss for small object detection

With the rapid development of deep learning, object detection methods have made significant progress, but small object detection is still a very challenging task. Based on object detector Single Shot Multibox Detector (SSD), we conjecture this is due to two factors: (1) In Convolutional neural networks (CNN), shallow feature maps contain strong spatial position information and insufficient semantic information. Therefore, single-layer feature detectors could not make full use of the limited information of small objects in the images; (2) The loss is not a good measure of the relationship between the prediction box and ground truth, what’s more, it is easy to cause the unbalanced loss attribution, thus impairing the accuracy of small object detection. To solve the above problems, we propose an improved SSD algorithm, named PASSD, including a multi-scale feature fusion module, which provides contextual semantic information and enhances high-resolution spatial information for shallow layers. Furthermore, a feedback-based loss function based on Gaussian Wasserstein Distance is introduced to measure the similarity between boxes in a new way. Experiment results show that the proposed method outperforms the previous method, achieving good speed and comparable accuracy trade-off. For 300×300 input, the proposed method achieves 86.28% mAP on the PASCAL VOC dataset, respectively. Especially APs is improved by 4% on MS COCO dataset compared to the benchmark model SSD.