Reviewerly/exHarmony huggingface dataset:
‘Pre-training+Fine-tuning’ Model-based 1/2 Folded Image Restoration

The 1/2 folded image refers to folding the original image at the halfway point along the horizontal or vertical direction, creating an image with partial overlap. The purpose of restoring 1/2 folded images is to repair folded, damaged, or partially hidden images in order to restore image integrity, recover data, analyze and process images, and perform reverse hiding and steganography analysis. In response to the restoration problem of 1/2 folded images, a model called ‘pre-training + fine-tuning’ was applied to restore folded images using unsupervised pre-training and supervised fine-tuning modes. The WGAN-GP model with gradient penalty is used for the pre-training model. A four-layer non-linear fully connected network is selected for the fine-tuning model. The experimental process consists of two stages. Firstly, the WGAN-GP network is subjected to unsupervised pre-training to obtain a generator capable of generating clear images. Then, in the fine-tuning stage, the output of the generator is used as the input for a fully connected network, and the target results of the fully connected network model are fixed for fine-tuning, aiming to restore the folded images to be close to the original ones. The applied method is validated on the MNIST and Fashion-MNIST datasets in this paper. The experimental result shows that the “pre-training + fine-tuning” mode achieves good restoration performance on 1/2 folded handwritten digit images and Fashion-MNIST images, with the maximum similarity score between the restored images and the original images is around 0.98. Moreover, this technique can enhance image quality and information integrity, expand the application areas of image processing and computer vision.
Evaluation of the Quality of Compression of Acoustic Signals in the Low-Frequency and Mid-Frequency Sound Range Using a Neural Network Codec

This article compares the compression algorithm using a neural network codec with modern audio compression algorithms such as MP3, AAC, WMA, ALAC, FLAC. The results of the compression quality evaluation taking into account various parameters of signals before and after compression are presented, as well as a comparative characteristic of the algorithm execution speed on AMD Ryzen 7 5800X, Intel(R) Core(TM) i3-5005U, AMD Ryzen 5 3600, AMD Ryzen 5 2500U, Intel(R) Core(TM) i5 processors-10400F. Experiments have proved that the use of a neural network codec is possible in real-time systems on a single processor core with low performance, but the speed of the neural network codec is many times lower than that of classical algorithms. Also, it was revealed that the compression quality of the neural network codec can compete with lossless compression algorithms.
Object Detection Algorithm for UAV Images Based on Lightweight YOLO

In the application of YOLOv7 network to the object detection scene of high-resolution UAV aerial images, the problem of slow detection speed has been exposed.Based on the YOLOv7 network, this paper proposes a lightweight improved model by designing a new module called GSConv and Depthwise Separable Conv(DSC) Combined Module(GSDSCCM) which based on DSC and GSConv to replace the E-ELAN module in backbone and integrating the VoVGSCSP module into the head of the network. Experimental results show that the improved network reduces the number of network parameters (Params) and floating point operations (GFLOPs) by 32.8% and 38.6% respectively and leads to precision loss of 3.9% in mAP@0.5 compared to the base model, achieving a significant improvement in detection speed at a lower cost of precision loss.
Modeling Smart Multi Module 2-Axis Inclinometer with Machine Learning and Simulink

Extensive research has been conducted on 2-axis inclinometer, covering research of Micro-electromechanical systems (MEMS) accelerometer, calibration studies, performance analysis, data transmission using RS 485, and the investigation of temperature effects on measurement errors. In this research, the focus is on developing a smart inclinometer model using Machine Learning. By utilizing acceleration data (ax, ay, and az), valuable information can be obtained regarding the inclination angle, displacement distance of each inclinometer, total displacement distance, direction of displacement axis, which soil layer is shifting, and triggering danger alarms. Obtaining such information would be challenging and complex if using shallow computations. Modeling was performed using random forest machine learning after experimentation with other machine learning techniques. The parameters used for optimal results were set with max_depth set to 0, min_samples_leaf set to 5, and n_estimators set to 200. Two types of machine learning, namely regressor and classifier, were applied. With a test_ size set to 0.1, the MAE in the first method is 0.9 and the Mean Absolute Error (MAE) in the second method is 0.4. The usage of MATLAB Simulink for simulating a Python program was conducted to observe the characteristics of the created model. The validation model is performed by sending accelerometer data from a mobile phone to the MATLAB Cloud using the MATLAB application on the mobile phone, and comparing it with simulated data.
Detection of Fake Data in Medical News Using Text Classification Methods

The goal of this work is the research and comparative analysis of the algorithms for text data classification in the context of solving the problem of validating medical news. The goal is achieved by considering different classification methods and identifying their advantages in solving this problem. The study considers the approaches to evaluating medical news using machine learning methods using different metrics. We describe the algorithm of partitioning data into collections with subsequent preprocessing and investigate a method for vectorizing the text to evaluate the significance of a word in a text document based on the data from the entire document dataset. In an experiment, we established the most efficient classification algorithm and made conclusions based on experimental results.
Dynamic Access Control Scheme for Secure Cloud Data with Efficient Key Management

While the need of cloud storage service grows rapidly, organizations have to consider how to use sensitive data more securely, enhancing the necessity of implementing dynamic encryption cloud storage service. Some existing schemes require a large amount of key encryption operations when revocation actions happen in access control models, resulting in significant time overhead. The existing scheme improving key management performance needs to introduce continuous online third-party. In this paper, we proposed DAC-EKM (Dynamic Access Control Scheme for Secure Cloud Data with Efficient Key Management), a dynamic access control scheme using efficient key management method based on Chinese Remainder Theorem (CRT). In DACEKM, users assigned with the same role (or roles assigned with the same file) are treated as members in the same group, and the secret keys of the role (or the file) are encrypted for all of them only once. The scheme does not need continuous online third-party. Our experiment shows this strategy results in the significant decrease of time cost compared with existing schemes.
Development a Low-cost Smart Eyewear System for Eye &amp; Head Movement Measurement and Analysis

Mental health is a growing concern globally. Early detection and diagnosis of mental abnormalities are crucial to mitigating adverse conditions. Mobile health (m-health) has emerged as a possible solution for enhancing the promotion of early detection, particularly in home or elderly care settings. Recent research has shown that eye and head movement reflect mental status and cognitive conditions. The movement are reliable biomarkers for the early diagnosis of mental illness and dementia. However, the equipment required to monitor these movement is often expensive, thus impeding the widespread use of m-health for the daily detection and monitoring of mental health. To address this issue, a low-cost smart eyewear system for the measurement and analysis of eye and head movement is developed and evaluated. The proposed wearable system gives caretakers visualization of health data and enables them to take early and proactive action if the person being monitored has an abnormality.
Improving the Road Scene Object Detection Algorithm of YOLOv5s

This paper proposes an improved detection algorithm for YOLOv5s in order to address the problem of low detection accuracy in road scene object detection caused by factors such as complex backgrounds, diverse scales, and occlusions. The algorithm incorporates the following techniques: Introducing the Cross Stage Partial Network with 3 Convolutions - Switchable Atrous Convolution (C3_SAC) module to adaptively adjust the receptive field, thereby enhancing the detection performance for objects of varying scales; Utilizing the Reparameterized Generalized Feature Pyramid Network (RepGFPN) to enhance feature fusion capabilities in complex backgrounds; Adopting the Decoupled Head to replace the original detection head in the model, thereby improving recognition accuracy and convergence speed. Experiments conducted on the publicly available datasets, BDD100K and KITTI, demonstrate that the improved model achieves mAP@0.5 scores of 48.5% and 95%, respectively. This represents a 3.6% and 1.6% improvement over the original YOLOv5s model, effectively enhancing the detection capability for road scene objects.
Using Cluster of Algorithms for Biometric Authentication

The article presents the theoretical and practical aspects of using a multilayer perceptron. The choice of algorithms for solving the problems of biometric authentication by voice is carried out, their advantages and disadvantages are considered. It is proposed to combine a multilayer perceptron with selected algorithms into a cluster of algorithms. The advantages and disadvantages of the method of clustering algorithms are considered. A cluster of algorithms is being designed to carry out the authentication procedure. A biometric image of the user is offered. Models are trained based on each algorithm and the user's biometric image. An experiment is conducted with seven users and the results are analyzed.
Remote Sensing Target Recognition Model Based on Small Sample Image Generation

In recent years, remote sensing object recognition methods based on deep learning have received extensive attention. Deep learning methods need to use a large number of labeled data to carry out network training. However, the cost of labeling remote sensing image data is very high. Therefore, how to use a small number of labeled target images to train the remote sensing target classifier with high performance is an urgent problem to be solved. In view of the above problems, this paper has carried out the following work. Firstly, based on WGAN-GP, this paper optimizes the constraint conditions of neural network and proposes a depth generation model, namely CCWGAN-GP, for remote sensing image generation. Secondly, this paper proposes a remote sensing target classification model SCLNet, which makes full use of the original one-hot label and the generated soft classification label to train the neural network. Experimental results show that the proposed model can generate highly similar images to real images, and can significantly improve the performance of the classifier under training conditions with only a few labels.
YOLOv7-based Visual Servoing on 2-DOF Manipulator Robot

Manipulator robot are widely used in industry to handle repetitive tasks such as moving components. To compensate for conventional robot control weakness, visual servoing control technique was developed. Feature extraction in visual servoing must achieve fast response with precise accuracy. Current research in deep learning based object detection algorithm provides satisfactory result and able to detect objects in real time. YOLOv7 set the new state-of-the-art performance, with better accuracy and speed compared to other object detectors. In this work we compare various models of YOLOv7 and YOLOv5 in accuracy and speed. The results shows that YOLOv7tiny achieved 12.3 FPS with Mean-Squared Error of pixel coordinate (u,v) consecutively 0.0092 pixel/width and 0.00739 pixel/height when applied to edge device. We then designed and implemented YOLOv7tiny-based Image Based Visual Servoing (IBVS) in 2 joints of PUMA260 robot Our implementation successfully tracked the designated object, making the center point of the object corresponds to our desired setpoint (160, 100) on less than 30 visual subsystem iterations. This shows than YOLOv7 can be used for manipulator visual servoing applications, and further research can develop 6-DOF (Degree of Freedom) visual servoing control using interaction matrices.
Research on Eye Tracking Control Technology Based on Head Posture

In this paper, the technique of eye tracking control based on head posture is discussed and studied. Firstly, the gaze positioning and tracking scheme is discussed, the pupil-corneal reflex method is proposed for gaze tracking, Canny edge detection and Hough circle is performed on the obtained image to obtain the pupil coordinates, with morphological operation, the fixation point calibration is carried out to achieve accurate positioning of the sight. Secondly, in terms of the sight positioning of the eye tracker, the factor of head pose recognition is introduced to increase the robustness of the system. In addition, for eye tracking control, a dynamic threshold segmentation method is proposed to detect eye movement status. Finally, an eye tracking control experiment is performed, with satisfactory results.
Pose-Independent Digital Anthropometry Based on Perpendicular Two-Dimensional Imagery

Postural body analysis was recently evaluated using distant and non-contact digital anthropometry. The implementation coverage could be enhanced by accessible technology utilization. Using a commercially marketed camera, it can produce an accurate and precise non-contact infant anthropometric postural analysis system with automatic segmentation and pose detection from two perpendicular images. The system can provide an excellent precision level, i.e., between 0.30% and 2.18%, in comparison to manual measurement, which was between 2.58% and 8.62%, as well as a low range of errors, i.e., between 0.32% and 3.5%, which reflect discrepancies from the manual measurements.
Ship Detection in Maritime Surveillance System Based on YOLOX

In this paper, we study the problem of ship target detection in maritime video surveillance systems, with special attention to the detection accuracy and adaptability under bad weather conditions. To this end, we propose an improved ship target detection method, including the addition of additive attention mechanism, and through testing on ship data sets, we verify the effectiveness of the proposed method. To further evaluate the performance of the method in severe weather conditions, we also used a flexible data enhancement strategy to generate a diverse synthetic test datasets, including different conditions such as rain, fog, and low illumination. Based on this datasets, we constructed a test datasets specifically for small vessel target detection, in which we evaluated the proposed approach. Experimental results show that compared with other mainstream competing algorithms such as Faster R-CNN, YOLOv3, YOLOv4, and YOLOX, our method has superior performance in terms of detection accuracy, robustness, and efficiency. In addition, the proposed method proves its good adaptability in ship detection under harsh imaging conditions. Therefore, the improved method proposed in this paper is expected to provide higher accuracy and reliability for ship target detection in Marine video surveillance systems.
An Intelligent Image Compression Method Based on Generative Adversarial Networks for Satellites

This paper analyzes methods to enhance the real-time transmission of remote sensing satellite and ground linkage, pointing out the limitations of the most commonly used on-board remote sensing image compression method, JPEG, and its high bandwidth requirements for satellite-ground transmission. With limited on-board resources, the application of JPEG is constrained, and image compression technology is a feasible and effective approach to addressing this problem. Given the rapid development of deep learning in recent years, this paper summarizes several neural network-based image compression algorithms, considering the balance between image quality and bitrate after decompression and reconstruction, and proposes the use of a lightweight image compression method based on Generative Adversarial Networks. Compared with JPEG, this method significantly reduces the appearance of blocky artifacts, achieves double the bitrate, and reduces computational resource usage by nearly 40% through neural network lightweight processing.
Character Recognition in Images under High Noise Levels

The task of extracting information in the form of text from scanned or photographed objects using Optical Character Recognition (OCR) is an important and ubiquitous technology for digitizing and indexing physical documents. Existing technologies have shown to work correctly with near-ideal documents, but if the document is visually degraded or contains non-text elements, the quality of OCR can be greatly affected, especially due to false detections. In this article, we present an improved detection network with a masking system to improve the quality of document recognition. By filtering out non-text elements in an image, we can use document-level OCR to include contextual information to improve OCR results. We conduct a single evaluation of a publicly available data set, demonstrating the usefulness and broad applicability of our method. In addition, we present data and calculation results of a categorical cross entropy function specially tuned to improve data discovery results.
A Vehicle Detection Method Based on the Decision-Level Fusion of LiDAR and Camera

Due to the limitations of single-sensor sensing capabilities, most current detection algorithms using unimodal data usually perform poorly when there are situations such as shadows or occlusions in the environment, and there are also cases of false and missed detections. To solve the problems above, we use RGB images and dense depth maps processed by a depth completion algorithm based on a greedy strategy as the input of YOLO-C and YOLO-D, respectively, to obtain their respective detection results as accurately as possible, and propose an adaptive evidence combination rule based on different confidence levels for decision-level fusion, and experiments performed with the KITTI dataset have shown that the suggested approach reduces the probability for false positives and missed detections in complex environments compared to YOLOv5s alone, and improves the average precision while completing the detection task in real-time, which has specific theoretical and application values.
A Domain Confusion Adaptation Method for Semi-supervised Action Recognition in Dark Environments

Action recognition in the dark (ARID) is a significant task, which is widely utilized in self-driving at night, night surveillance, etc. However, current fully supervised networks require additional annotation costs to generate large-scale labeled datasets in dark environments for training, which is unreasonable. A feasible solution is to achieve domain transfer between labeled normal illumination datasets and unlabeled datasets in dark environments. Therefore, we propose a novel two-stream CNN architecture employing a semi-supervised learning strategy to facilitate domain transfer. Specifically, the architecture introduces a brightness enhancement module to enhance feature representations in dark environments and an additional domain confusion loss to jointly align domain-invariant representations between labeled source domains and unlabeled target domains. Our domain adaptation method achieves superior performance on the semi-supervised ARID dataset.
Fusion and Inpainting: A Novel Salient Object Detection Network

Aiming at obtaining an accurate salient image map, a common practice in salient object detection is to fuse multi-level features and incorporate auxiliary context information of the object. However, this often involves a large number of sampling operations, leading to the blurring of object features such as edges in complex regions. To address this issue, we propose incorporating an inpainting module after the multi-level feature fusion process to reconstruct these blurry areas. To this end, we present a novel salient object detection network structure that comprises a feature fusion module and an inpainting module. In the inpainting module, we use recursive gated convolution to capture both the global structure and local detail information of the salient object to repair the faded and unclear areas with high quality. We conduct extensive experiments on four benchmark datasets, demonstrating that the proposed network outperforms most state-of-the-art methods.
Human Target Detection Model in Dense Traffic Scenes Based on Attention Mechanism

In the problem of crowd detection, pedestrians are often difficult to detect because they gather together and block each other. In dense and occluded complex scenes, how to solve the problem of pedestrian occlusion, effectively extract the characteristics of small target population, and improve the detection accuracy is a huge challenge. To solve the above problems, this paper proposes a pedestrian detection algorithm based on deformable Detr. The deformable Detr model combines the best sparse spatial sampling method of deformable convolution with the relational modeling ability of transformer. Through experimental comparison, compared with the original Detr, the detection accuracy in dense population data set is improved by 16.4%. This method can realize pedestrian recognition in highly dense scenes, and has certain practical significance.
A Two-Branch Framework for Monocular Depth Estimation

This paper aims to solve the problem that supervised monocular depth estimation cannot effectively extract complete information. Although existing methods based on convolutional neural networks (CNNs) have made significant progress in depth estimation by gradually exploring high-level contextual and semantic features, the further improvement of performance has encountered a bottleneck due to the local induction bias of CNNs. Recently, Transformer has been widely used to model global information in computer vision. However, there is a lack of spatial sensing bias in Transformer networks, causing the loss of local details. To this end, we propose a new two-branch framework for monocular depth estimation, including a Transformer branch and a CNNs branch. The proposed framework can fully take advantage of both CNNs and Transformer and extract complementary information. Meanwhile, we design a bilateral feature aggregation module (BFAM) to realize the information interaction by computing the key maps and value maps between the two branches, so as to gain fine-grained features. In addition, we propose a bottom-level information extraction module (BIEM) in the CNNs branch to capture texture-related information and enhance details in a statistical manner. Extensive experiments on the outdoor dataset KITTI and the indoor dataset NYU show that the proposed method achieves better results compared to the state-of-the-art monocular depth estimation methods.