Reviewerly/exHarmony huggingface dataset:
Image Quality Assessment for Computer Vision Based Perception Algorithms Using Edge and Structural Features

Image Quality Assessment (IQA) is a critical task in image processing, computer vision, and other related fields, as it helps in evaluating the effects of various impairments on the Quality of Experience (QoE) of consumers. However, current IQA metrics may have limitations in evaluating image quality accurately for different types of images or under different conditions. To address this, a proposed approach involves calculating an overall image quality score based on linear combination of individual image quality metrics. This method considers multiple image features such as sharpness, contrast, color, and noise, and assigns weights to each feature based on its relative importance in determining overall image quality. By combining and weighting multiple image features, the proposed approach aims to provide a more comprehensive and accurate evaluation of image quality compared to using individual metrics alone. In this study, we focus on the importance of edge features and structure-based metrics in object detection and analyze existing No-Reference (NR) IQA metrics, including Just Noticeable Blur (JNB), Cumulative Probability Blur Detection (CPBD), Visual Quality Assessment (VQA), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and No-Reference Low- Light Image Enhancement Evaluation (NLIEE), and propose a linear combination formula that combines these metrics to evaluate image. We evaluate the performance of the proposed metric on several datasets by calculating the linear combination of two, three, and all the five metrics. The weights assigned to each metric in the linear combination formula are determined through experimental analysis. The combined metric scores and MOS scores are then used to compute the Spearman's rank order correlation coefficient, which measures the monotonic relationship between two variables. A higher SROCC value indicates a stronger correlation between the combined metric and MOS scores and is used to evaluate the performance of the metric. Our results show that the proposed metric, which combines VQA, BRISQUE and JNB, provides the highest correlation with MOS scores, over all the datasets. This suggests that the proposed approach can provide an effective and comprehensive way of evaluating image quality in object detection algorithms. Our findings can have significant implications in improving the overall performance of object detection algorithms and enhancing the QoE of consumers.
Correspondence Between SWIR and MWIR Images Using Augmentation and Preprocessing for Registration

Multispectral sensors are used to ensure visibility in various applications. However, when multiple sensors are used for capturing images, a misalignment may occur between the images taken by each sensor unless special care is taken. To correct such misalignments, image registration based on feature matching is conducted. However, the features captured by each sensor differ, thereby complicating the registration process. In this study, we develop an approach to overcome these challenges and to improve the registration accuracy between short-wave infrared and mid-wave infrared (SWIR and MWIR, respectively) images. First, we compare and validate SiLK, a detector-based feature matching method, and LoFTR, a detector-free feature matching method. The results clearly demonstrate the superior accuracy of LoFTR. Moreover, SWIR and MWIR images exhibit a characteristic color inversion according to Kirchhoff’ s law. Therefore, by inverting the color of a SWIR image and aligning the color tone between image pairs, we can improve the matching accuracy. Furthermore, by diversifying the color tones of the training data through augmentation, we can handle the domain gap between SWIR and MWIR images, thereby further enhancing the matching accuracy.
Detecting Image Manipulation in Lossy Compression: A Multi-modality Deep-Learning Framework

Due to the advancement of photo editing techniques, it has become easier to create fake photos that look incredibly realistic and are edited in a way that leaves no visible signs of manipulation, making them ideal for synthesis. However, Instagram, WeChat, and TikTok are some of the popular social media platforms where the images have been lossy compressed before uploading them. As a result, learning to spot forged images in their compressed form is crucial. As part of this, some forensic detection techniques have made great strides in uncompressed scenarios, but there is still much to learn about the forensics of lossy compressed images. Therefore, this research proposes a hybrid deep learning framework by dissecting compressed and manipulated images at the preprocessing and feature extraction levels. The suggested noise stream progressively prunes the texture information to prevent the model from fitting the compression noise. Hence, a noise stream is employed to extract temporal correlation characteristics to address the potential problem of ignoring temporal consistency in lossy compressed images. Further, residuals from two streams are fed to custom ResNet blocks to enhance the clues of manipulation and pooled to concatenate the enhanced fingerprints. Finally, the proposed method outperforms state-of-the-art techniques in identifying manipulation in lossy compressed images.
Sensor Fusion Using Error-State Kalman Filter to Improve Localization of Autonomous Underwater Vehicle Under DVL Signal Loss

This paper concerns the development and improvement of the underwater navigation system for Autonomous Underwater Vehicles (AUVs) when faced with velocity-aiding sensor failures. The study addresses this challenge of sensor fusion by applying the Error-State Kalman Filter (ESKF), a form of indirect state filtering. Specifically, the ESKF targets the limitations of velocity measurements encountered during near-bottom operations. The proposed method was applied to the Xplorer-Mini AUV and evaluated using the Gazebo physics engine simulator in ROS 2. To assess its performance in handling the loss of velocity signals, a series of simulation-based experiments were conducted and compared against the traditional Inertial Navigation System (INS) and Extended Kalman Filter (EKF) algorithms. The results of the experiments demonstrate that the ESKF outperforms traditional INS and EKF algorithms, offering valuable insights into state estimation techniques for developing autonomous underwater vehicles in challenging environments.
Tunnel Detection Equipment and Recognition Method Discussion and Prospects

Traffic construction has always been an important prerequisite to achieve China's economic development and social progress. In a country with rich landforms, mountains and hills account for about 67% of the land area, tunnels play a positive role in improving transportation efficiency and alleviating urban traffic pressure, and the diagnosis and treatment of lining diseases is the primary task to ensure the safe operation of tunnels. In this paper, the development status of lining disease identification criteria, identification technology and identification integrated equipment are elaborated and analyzed in detail, and the treatment of lining disease is briefly discussed. The main conclusions of this paper are as follows: 1. All countries have relevant provisions on the judgment and management of tunnel safety, and the safety classification of tunnels has been basically defined. 2. In view of different disease characteristics of tunnels, the integration of multiple detection technologies is the mainstream trend at present. 3. The development of lining disease detection technology and methods has been relatively perfect, and major countries have mature commercial products for tunnel safety hidden danger detection.
A Fault Detection Method for Missing Gate Faults in Reversible Circuits Using Binary to Gray Code Conversion

Reversible computing has a remarkable ability to reduce heat dissipation in computing machinery. It can be broadly applied in various fields, which include Digital signal processing, Cryptography, DNA computing, Network congestion, Database transactions, Quantum computing, etc. Fault detection is a complicated and demanding problem in reversible circuits. Fault detection is an essential process in the field of testing to ensure the reliability and integrity of the circuit. This paper proposes a straightforward approach for Single Missing Gate Fault (SMGF), Multiple Missing Gate Fault (MMGF), Repeated Gate Fault (RGF) and Partial Missing Gate Fault (PMGF) under the Missing Gate Fault (MGF) model. The method includes the process of binary to gray code conversion in order to determine the total number of test vectors to detect the respective MGFs. Experimental results are performed on reversible benchmark circuits to evaluate the number of test vectors required to recognize all the MGFs. The comparative analysis of the proposed work with the existing work is also presented.
Efficient Passenger Counting in Public Transport Based on Machine Learning

Public transportation is a crucial aspect of pas-senger transportation, with buses playing a vital role in the transportation service. Passenger counting is an essential tool for organizing and managing transportation services. However, manual counting is a tedious and time-consuming task, which is why computer vision algorithms are being utilized to make the process more efficient. In this study, different object detection algorithms combining with object tracking are investigated to compare passenger counting performance. The system employs the EfficientDet algorithm, which has demonstrated balanced performance in terms of speed and accuracy. Our results show that the EfficientDet can accurately count passengers in varying conditions with an accuracy of 94 %.
Assessing the Implications of Data Heterogeneity on Privacy-Enhanced Federated Learning: A Comprehensive Examination Using CIFAR-10

In the context of an increasingly digital society with pressing privacy concerns, our research investigates the effectiveness of privacy-preserving artificial intelligence solutions like Federated Learning. This study focuses on three main areas: Federated and Centralized Learning applications, the influence of data heterogeneity on client data accuracy, and the evaluation of contemporary federated algorithms in scenarios of extreme heterogeneity. Federated Learning, using the FedAvg algorithm, demonstrated superior testing accuracy (88.54%) over Centralized Learning (87.98 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) on the non-heterogeneous CIFAR-l0 dataset, indicating its potential as an efficient, privacy-preserving solution for various machine learning applications. Additionally, our findings highlight an inverse relationship between data heterogeneity and Federated Learning model accuracy, underscoring the need for strategies to mitigate this challenge and boost model performance. Upon evaluating several federated learning algorithms under high data heterogeneity (alpha=1.0), SCAFFOLD and FedOpt outperformed FedAvg and FedProx, demonstrating the significance of algorithm design in addressing data heterogeneity. SCAFFOLD and FedOpt showcased greater communication efficiency, attributed to their faster convergence and fewer required communication rounds. This study offers invaluable insights into addressing data heterogeneity, improving communication efficiency, and enhancing federated learning's performance and applicability in real-world scenarios, thereby furthering privacy-preserving artificial intelligence research.
A Machine Learning Approach for Efficient Spam Detection in Short Messaging System (SMS)

Short Message Service (SMS) is a generally used communication method due to its convenience and affordability. SMS spam message is an unauthorized text message that contains a variety of content types such as advertisements, fraudulent texts, and promotions. These messages can pose a serious threat to mobile phone users as they may contain security threats, malicious activities, and other concerning issues. These can lead to identity theft, financial loss, and other types of fraud. To deal with the problem of spamming, various machine-learning models are applied to develop an optimized model that effectively, reliably, and precisely identifies and filter out spam or junk message from a genuine SMS text. The dataset used is a combination of self-acquired data and internet collected dataset with 60–40 ham to spam partitions. With regards to the accuracy of the model, the Bernoulli Naive Bayes achieved the highest performance with 96.63% accuracy upon optimization.
Design and Implementation of a Book Borrowing Management System Based on Intelligent Recommendation Algorithm

A library is an institution that collects and organizes book materials for people to read and reference. The design of this system is aimed at information-based management of the library's borrowing function, enabling it to achieve the practical goal of improving the work efficiency of administrators. The book borrowing management system in this article adopts a B/S architecture, PHP language, and MySQL database. The administrator of this system can manage the library's book information, reader information, and borrowing and returning information; Readers and users can query book information, modify personal information, and report the loss of their own documents. Unlike general book borrowing management systems, this system uses intelligent recommendation algorithms to recommend books to readers based on their borrowing records. The article provides a detailed introduction to the technical architecture used in the development of the system, as well as a description of various functions of the system. It also studies the functional requirements, usage process, architecture, and database table design of the book borrowing management system.
Improving the Yolov5 Detection Accuracy Through Enhancing The K-means Algorithm

As technology advances, various cutting-edge innovations have brought greater convenience to human life. One such influential advancement is autonomous driving technology, which revolutionizes the automotive industry. By harnessing image recognition techniques, autonomous vehicles now possess unprecedented perception and environmental interpretation capabilities. However, over time, the proliferation of autonomous driving has given rise to several challenges. The increasing traffic volume, complex road conditions, intersecting pedestrian pathways, and an array of traffic signs have intensified the issues that autonomous driving technology must address. Consequently, a growing number of individuals have also become actively engaged in the pursuit of enhancing autonomous driving technology.
Path Planning for Unmanned Delivery Vehicles Based on Machine Vision

With the continuous development of e-commerce, the demand for express delivery services is constantly increasing. The emergence of unmanned express vehicles has perfectly solved the problem of end logistics. This article takes the living area of Hangzhou University of Electronic Science and Technology as an example. Based on the existing unmanned vehicle parking nodes, the greedy algorithm is first used to solve the optimal original path between the starting point and each node of the unmanned vehicle delivery route. Then, combined with machine vision theory, the real-time road conditions are measured through Hough transform and object detection, and the path planning is re carried out. Finally, based on Flexsim, an unmanned express vehicle delivery model for the living area of Hangzhou University of Electronic Science and Technology is established, Compare the efficiency of express delivery before and after machine vision path optimization to determine the degree of optimization in path planning.
MFF-CNER: A Multi-feature Fusion Model for Chinese Named Entity Recognition in Finance Securities

The objective of Chinese financial securities named entity recognition is to extract relevant entities from unstructured Chinese text, such as news, announcements, and research reports, that impact security prices. Recognizing entities in this field is challenging due to the abundance of specialized terms, diverse expressions, and the limited feature extraction capabilities of traditional models. To address this, we propose MFF-CNER, a multi-feature fusion model, to improve the effectiveness of Chinese financial securities named entity recognition. MFF-CNER encompasses several key steps. Firstly, it leverages a BERT pre-training model to capture semantic features at the character level. Secondly, a BiLSTM network is utilized to capture contextual features specific to financial securities text. Additionally, we introduce an Iterated Dilated Convolutional Neural Network (IDCNN) to blend, and extract local features, incorporating an Attention mechanism for weighted feature integration. Finally, the predicted sequences are optimized, and decoded using the Conditional Random Field (CRF). To validate the state-of-the-art performance of MFF-CNER in this domain, we compare it with five popular methods on a Chinese financial securities dataset annotated with the BIO labeling scheme. Notably, MFF-CNER demonstrates superior performance while maintaining compatibility among its components. Furthermore, we evaluate the applicability of MFF-CNER in the Chinese financial securities domain by utilizing public datasets from diverse domains, including social media (WEIBO), and news (MSRA). This research holds practical significance for downstream applications, such as constructing financial securities knowledge graphs, and analyzing factors that influence security prices.
Enhancing Breast Masses Detection and Segmentation: A Novel U-Net-Based Approach

Breast cancer is a frequent form of cancer for women, and globally it is a major cause of death. Early detection is crucial for effective diagnosis and treatment, and can also help to lower mortality rates. In recent years, Convolutional Neural Networks have been applied to mammography to assist radiologists improve their efficiency and precision. U-Net is a fully convolutional neural network and a popular model that uses the encoder-decoder architecture, it is known for its capacity to effectively segment biomedical images. This work aims to present a new model for breast cancer detection and segmentation. Our first purpose was to discover a reliable segmentation model for breast masses in mammography images by combining the ResNet architecture and U-Net. Subsequently, we trained several standard models for classification in normal and abnormal images, such as ResNet101, ResNet152, DenseNet169, MobileNet, InceptionV3, and NasNet. Furthermore, based on the segmentation model that yielded good experimental results, we created our classification model by modifying the output layers in order to generate a prediction label (Normal or Abnormal) instead of generating a mask. Our study contributes to the field of breast cancer detection by employing a novel approach that performs detection and segmentation directly on the entire mammography image, unlike traditional methods that operate on image patches. The main advantage of the proposed approach is to simplify the workflow of breast masses segmentation by eliminating intermediate steps which can introduce errors and increase the complexity of the system. Furthermore, our approach focuses on reducing false positives, which are a common challenge in breast cancer detection. Our classification model reached better results compared to other experimented classification models. We used the INbreast dataset of mammography images to train and evaluate our models. In addition to that we employed GradCAM in order to visualize the prediction results and to have an interpretation of the results.
Data Leakage Prevention Approach Based On Insider Trust Calculation

Nowadays, in the era of technological evolution that the world is currently experiencing, data protection is becoming increasingly important, and keeps coming to the forefront. In this research work, we propose a new layer of detection based on insider’s behavior as well as insider’s trust. The proposed system is based on multi-agent paradigm, which is ideal to threat detection as it can solve the issue of behavioral logs in terms of self-sufficiency and performance. ETL techniques are also employed for data transformation and cleansing. In addition, our method adds a new dimension of analysis based on behavior, taking account of the insider who is the internal employee and his various interactions within the information system.
Enhanced SH-CNN: Gender Prediction Based on Convolutional Neural Network

Gender detection and age estimation have emerged as active and vital research areas, finding widespread applications in diverse fields such as biometrics, social networks, targeted advertising, access control, human-computer interaction, and electronic customer services. As the demand for enhanced recognition and classification rates continues to grow, researchers strive to improve the performance of existing methods. In our previous work, we introduced an approach called SH-CNN, which leverages the Discrete Shearlet Transform (DST) as the initial feature extraction layer and the Deep Convolutional Neural Network (DCNN) as the subsequent automatic feature extraction and classification step. The underlying idea was to extract multiple features, obtained through DST’s various decompositions and orientations, from an image. These extracted features served as inputs to the DCNN, enriching the training process and consequently improving the recognition rate. The experimental results demonstrated that the SH-CNN approach indeed led to a substantial enhancement in performance. In this paper, driven by the desire to further boost the recognition rate of our approach, we have proposed an enhanced version called ESH-CNN (Enhanced SH-CNN). ESH-CNN eliminates the constraint of texture differences by feeding only the coefficients generated by the Shearlet transform into the CNN, without including the corresponding face photo. Preliminary results have shown promising outcomes, with a significant improvement in the recognition rate observed when compared to previous approaches.
New Algorithm of Weak Password Detection

This article presents a program to detect weak passwords by leveraging combinations of user-provided information such as name, surname, and birth year. The program incorporates a dictionary of passwords susceptible to brute-force attacks, which is dynamically expanded with new combinations as new users provide their information. The program identifies and alerts users about weak passwords using this dictionary, promoting more robust security practices. This article explores the program’s motivations and implementation in a front-end application and discusses the significance of password strength in safeguarding personal information. Additionally, it delves into existing research on password security, drawing on various articles to highlight the importance of addressing weak passwords as a crucial aspect of data protection.