Reviewerly/exHarmony huggingface dataset:
ROI Segmentation for Breast Cancer Classification : Deep Learning Perspective

Breast cancer mammogram images are types of unstructured data that need to be analyzed in order to classify and detect abnormalities at an early stage of cancer. In this research, ROI segmentation for the breast cancer classification CAD model is proposed. The proposed model is trained and tested on the MIAS dataset, evaluating the performance of CNN (ResNet-18 and ResNet-50) classifiers along with transfer learning. The medical imaging dataset consists of noise and low-quality images, so to overcome this problem, medium filtration for denoising and segmentation with the region of interest process are applied. Region-based segmentation is used to segment the images based on local parameters. For training the adopted model, ResNet-18 (two layers) and ResNet-50 (three layers) were used, achieving the best result at an 80:20 data split ratio in both cases (without and with transfer learning) of 81.6% and 96.6% accuracy by using ResNet-18, and ResNet-50 achieving 89 % and 98.45 % accuracy (without and with transfer learning). Such promising outcomes will provide a great opportunity to use the ResNet classifier with transfer learning as an early detection model in hospitals or in the Healthcare sector.
Estimation of Time Base Distortion of High Speed Sampling Oscilloscope Based on Trust-Region Reflection Least Square Method

Based on the concept and mechanism of time base distortion, this paper focuses on a trust-region reflection least squares algorithm for estimating time base distortion. The method is to estimate the time base distortion of oscilloscope by using multi-frequency and multi-phase signals. In this paper, the third harmonic model and the average method of 20 groups of 4 waveforms are used, and the experimental verification is carried out. The results show that the trust-region reflection least square method is correct and effective to estimate the time base distortion.
Improved Rate Fault-Tolerant Preparation of Q<sub>1</sub> Code-States

A fault tolerant way to prepare logical code-states of $\mathcal{Q}_{1}$ codes, i.e., quantum polar codes encoding one qubit, has been recently proposed. The fault tolerance is guaranteed with the help of an error detection gadget, where if an error is detected, one declares a preparation failure and discards entirely the preparation. This makes the preparation probabilistic, whose success rate is referred to as the preparation rate. In this work, we consider a factory preparation procedure, where one attempts to prepare several copies of $\mathcal{Q}_{1}$ code-states in parallel. With the help of an extra scheduling step, we can avoid discarding the preparation entirely, every time an error is detected, hence, we may achieve an increased preparation rate. Our numerical simulations, for a circuit-level depolarizing model, suggest that the preparation rate increases significantly, especially for large code-lengths N. For example, for N=256, it increases from 2% to 26% for a practically interesting physical error rate of p=$10^{-3}$. Remarkably, a $\mathcal{Q}_{1}$ code of length N=256 achieves logical error rates around $10^{-10}$ and $10^{-15}$, for physical error rates of p=$10^{-3}$ and p=3×$10^{-4}$, respectively, thus showing that factory preparation may allow to access the error protection provided by the larger $\mathcal{Q}_{1}$ code-states.
Investigation of Various Machine Learning Techniques for Banknote Authentication

Banknote authentication is a critical aspect of the financial industry, playing a vital role in ensuring the integrity of transactions and safeguarding against counterfeiting and fraudulent activities. The prevention of counterfeit banknotes is of utmost importance due to the significant economic and national security risks associated with such activities. To address this challenge, advanced techniques utilizing machine learning algorithms trained on extensive datasets are employed to identify distinctive features and patterns characteristic of authentic banknotes. This work focuses on comparing and evaluating the performance of various machine learning models in the task of banknote authentication. The models under consideration include Logistic Regression With LBFGS, Logistic Regression With SGD, SVM With SGD, Random Forest, XgBoost, and Neural Network. By analyzing various parameters, the study aims to identify the most accurate and efficient approach for verifying banknote authenticity, thereby ensuring the integrity and security of financial transactions. The impact of counterfeit banknotes extends beyond the financial sector, affecting the economy and public trust in the currency's value. Thus, an effective and accurate banknote authentication system is crucial for maintaining a stable economy. The findings from this study will contribute to improved accuracy and efficiency in detecting counterfeit banknotes, mitigating financial losses, and preserving the economy's security and stability.
HCT-Det: a hybrid CNN-transformer architecture for 3D object detection from point clouds

Detecting 3D objects from LiDAR points is significant for the environmental perception of robotic systems. Some pillarbased 3D object detectors solely use 2D convolutions as feature encoders, which occupy fewer computation resources but sacrifice model accuracy. To activate the potential performance of pillar-based feature representation manners, we propose HCT-Det, a novel hybrid CNN-Transformer architecture for 3D object detection from point clouds. Motivated by the structure re-parameterization technique and vision transformer (ViT) framework, we redesign the 2D backbone and further introduce the Rep-VGG block and multi-head self-attention (MHSA) mechanism to enrich the scale diversity of feature representation. We perform ablation experiments on the KITTI vision benchmarks to highlight the superiority of our HCT-Det. The evaluation results show that our model outperforms PointPillars baseline, yielding an accuracy of 79.08 moderate AP<sub>3D</sub> on the car category at a speed of 57.46 FPS on the NVIDIA Tesla P40 platform. Without bells and whistles, our HCT-Det can achieve a reasonable trade-off between accuracy and speed.
Airspeed Anomaly Detection of UAV Based on Flight Mode Adaptive with Noise Margin

Unmanned aerial vehicles (UAVs) has been widely used. In order to ensure the completion of the mission, prognostic and health management (PHM) of UAV has been focused on. Airspeed anomaly detection is an important part of UAV PHM. However, most of the existing methods only use single model for different flight modes or phases. This will reduce the accuracy of the model. Therefore, in this paper, a set of airspeed anomaly detection models based on flight mode adaption with noise margin which will further improve the accuracy of precision are raised. This method estimate the airspeed with different Long Short-Term Memory (LSTM) Recurrent Neural Network based the flight mode. In order to detect the faults, PauTa criterion is applied. To verify the effectiveness of the method, the models are deployed on real flight data. The result of the experiment proves that the method is feasible.
Prediction of Heart Disease Diagnosis Using Deep Learning Fusion Algorithm for Patient Monitoring System

Deep learning algorithms have become increasingly popular in the field of medical diagnosis due to their ability to identify complex patterns and correlations in medical data in an efficient manner. In this paper, we present a novel deep learning fusion algorithm for the diagnosis of heart disease. The proposed algorithm combines convolutional neural network (CNN) and long short-term memory (LSTM) neural networks to extract both spatial and temporal features from electrocardiogram (ECG) data. The algorithm is then used to analyse the ECG signals of patients in order to accurately diagnose a heart disease. The performance of the proposed algorithm is evaluated on the MIT-BIH Arrhythmia Database, where it achieved an accuracy of 95.5%. The results suggest that the proposed algorithm could be used for the early and accurate diagnosis of heart disease.
Research on Raft and PBFT algorithms in consortium blockchain application

Blockchain is a distributed system. In order to achieve the consistency of data and operation, consensus algorithm needs to be adopted. In the practical application, there are the log replication consensus algorithm raft and state machine replication consensus algorithm PBFT in the consortium blockchain. This paper makes a comparative study based on the principles and characteristics of the two algorithms, and makes a comparative study on the performance with experiments in FISCO BCOS. The results show that the PBFT consensus algorithm consumes more CPU resources than raft due to the complexity of its interaction, and the memory resources occupied by the network using raft consensus algorithm are more than those occupied by the network using PBFT consensus algorithm.
Effects of Face Image Degradation on Recognition with Vision Transformers: Review and Case Study

Vision Transformers have been recently introduced and achieved great success in the last two years beating convolutional neural networks (CNN) in many computer vision applications including Face Transformer for face recognition. However, one of the rising concerns is how sensitive these models are to various types of image degradations, especially when used for biometric authentication and person identification. This research aims at reviewing related work and presenting a case study to investigate the robustness of a fine-tuned version of Face Transformer under different pose, age, and gender variations for several degradations such as noise, blur, and resolution reduction. Several experiments have been conducted to train and test the model using two benchmark datasets (VGGFace2 and LFW) and benchmark its performance with FaceNet.
Unleashing the Power of Generative AI and Quantum Computing for Mutual Advancements

The paper aims to explore the symbiotic relationship between Generative Artificial Intelligence (AI) and Quantum Computing. This cutting-edge event will delve into the potential synergies between these two transformative technologies and demonstrate how their collaboration can revolutionize various domains. The paper will commence with an insightful overview of Generative AI and Quantum Computing, shedding light on their fundamental concepts and applications, capabilities and challenges. The paper will emphasize how Generative AI can reciprocate and contribute to the advancement of Quantum Computing. It will inspire collaboration, spark new research directions, and pave the way for ground-breaking advancements at the intersection of Generative AI and Quantum Computing.
Classification of word difficulty using M-SVM algorithm

Objective: The aim of this study is to develop a classification model based on word attributes and address the classification problem of word difficulty. Methods: We constructed word indexes for difficulty classification based on structure and usage habits, and then trained the SVM classification model using the constructed database. The optimal penalty coefficient C was determined using Monte Carlo simulation with the dichotomous method to optimize the iterative process. Finally, we evaluated the model objectively using five-fold cross-validation and model comparison. Results: The experimental results showed that the highest classification accuracy of 98.4% and 82% for the training and prediction sets, respectively, was achieved when the optimal penalty coefficients were calculated to be (944, 273, 756, 1064, 671). The average classification accuracy obtained by five-fold cross-validation was 0.892. Comparison with other classification models indicated that this model improved the classification accuracy. In summary, the M-SVM model showed better robustness in solving the multi-classification problem, to some extent avoiding the overfitting phenomenon and demonstrating a better classification effect.
Solar Irradiance Prediction Model Based on LSTM

The most available natural resource on earth is solar energy, and it is the best source to produce electricity. As for the growing needs of humans, there is a huge need for electricity, so this model is a step towards sustainable development, which helps to predict the solar radiation that the earth will receive in the future and with which humans can make efficient use of solar energy. There are so many techniques to store solar energy, but there is no correct estimation of when and where the maximum amount of energy can be received, so with the help of the proposed method, the amount of energy that can be received will be estimated. The proposed model expresses an idea-based, most advanced machine learning technique called LSTM in order to predict solar irradiance. There are models that exist that will estimate the energy with image data, and some other models will take text data and estimate very short-term predictions. The proposed model achieves better prediction rate for solar irradiance using LSTM with less error.
Mixed Precision Quantized Neural Network Accelerator for Remote Sensing Images Classification

The application of convolutional neural network (CNN) for classifying optical remote sensing images holds great significance in areas such as maritime management, environmental protection, and more. However, the significant computational demands of CNN pose obstacles to the efficient deployment at the edge. Quantization techniques, as effective model compression methods, have been widely used to reduce computational demands during model deployment. Current uniform low-precision quantization methods suffer from significant accuracy loss. Mixed-precision quantization compresses models while maintaining accuracy, but it introduces computations with multiple bit widths. Due to the introduction of multiple bit-width computations, general-purpose computing platforms are unable to leverage the computational efficiency of quantized network. In this article, a Zynq SoC-based mixed-precision neural network accelerator design is proposed, which can fully leverage its energy efficiency. Experimental results show that the mixed-precision neural network accelerator achieves 4.96 × improvement in inference speed compared with the baseline.
Learning to Decode Trapping Sets in QLDPC Codes

Quantum low-density parity-check (QLDPC) codes with asymptotically nonzero rates are promising candidates for fault-tolerant quantum computation. Belief propagation (BP) based iterative decoding algorithms, a primary choice for classical LDPC codes, perform poorly for QLDPC codes due to stabilizer-induced trapping sets, resulting in a high error floor. Several decoding algorithms, like post-processing decoders, normalized BP decoders, and neural decoders, have been proposed to increase the performance in the error-floor region. However, this improvement comes at the expense of an increase in the execution time of the decoder. This paper proposes a general framework for error correction for a class of QLDPC codes called lifted-product codes using recurrent neural networks (RNNs). The RNN is employed to learn message-passing rules that can decode quantum-trapping sets. Then the standard message-passing rules are used with the learned rules to improve the error floor. While training the RNN, the quasi-cyclic property of the lifted product codes is exploited to reduce the size of the training set and the number of parameters in the network. This reduction in the number of parameters makes these decoders amenable to hardware implementation. Simulation results show that the proposed decoder performs better than the existing decoders in the literature.
Urban Road Automatic Mapping Method Based On Remote Sensing Image

Roads are the skeleton of cities and important artificial features. As the basic data in GIS, whether the road information can be updated in time directly affects the application in map drawing, path analysis and emergency treatment. With the rapid development of space technology, high-resolution remote sensing image has become an important geographic information data source because of its wide coverage and high precision. The research of urban road automatic mapping method based on remote sensing image is the research of urban road automatic mapping. The main purpose of this project is to develop a new urban road automatic detection and classification system. The proposed system will be able to accurately detect, classify and map all types of roads (such as residential, commercial and industrial roads) using only one satellite image. This method can be used in conjunction with other data obtained from various sources, such as street maps, GPS coordinates (precision), aerial photos or GIS information. In addition, users can get more details.
Real-time traffic sign detection algorithm based on improved YOLOV7

A real-time detection algorithm based on the improved YOLOv7 (You Only Look Once v7) was proposed to address the issues of slow detection speed, low accuracy, and easy omission of small targets in traffic sign detection algorithms for intelligent transportation and autonomous driving. The algorithm adopted K-means++ algorithm to cluster initial anchor box sizes of the traffic sign target and used Ghost Net network to replace the backbone feature extraction network of the original YOLOv7 algorithm, reducing the network's parameter size. Additionally, the Convolutional Block Attention Module (CBAM) was introduced in the feature fusion network to enhance the network's sensitivity to key features of traffic signs. Furthermore, a small target detection layer with a smaller receptive field was added to improve the accuracy of detecting small target traffic signs. The SIoU (Scylla-Intersection over Union) loss function was introduced to accelerate the model's convergence. Experimental results showed that the improved YOLOv7 algorithm achieved an accuracy of 96%, a recall rate of 96%, and an average precision of 97%. Compared to the original model, there was an improvement in all metrics. In comparison to YOLOv7, the parameter size and computational cost were decreased by 39% and 32%, respectively. Detection speed was 112.72 frames per second. It can enable real-time and accurate traffic sign detection.
Evaluating Interactive Detection of Code Smells on Software Development Activities

Traditional code smell detection techniques rely on Non-Interactive Detection (NID), which only allows developers to identify smells in the entire source code upon request. However, studies suggest that NID may lead to fewer correctly identified smells, increasing remaining code smell instances. To address NID’s limitations, Interactive Detection (ID) has emerged, allowing developers to reveal smell instances without explicit requests and promoting early detection and correction. In this study, we aimed to evaluate the impact of ID on detecting code smells during software development by conducting a controlled experiment using Eclipse ConCAD with software developers and students. Our results indicated that ID could decrease up to 40% of remaining smell instances compared to NID during software development activities. Our findings suggest that ID is an effective technique to help developers quickly detect and fix code smell instances, improving overall code quality.
Low-light image enhancement combining dehazing and auxiliary channels

Image enhancement tasks have garnered significant attention in recent years, particularly regarding images captured in low-light conditions. Due to their inherent under-illumination characteristics, these images often suffer from challenges such as low contrast, low brightness, and noise that hinder their quality. With the recent advancements in deep learning, convolutional neural networks (CNNs) have demonstrated a high potential in addressing these challenges. In this paper, we propose a deep CNN-based approach that enhances color components a and b through Lab-space decomposition of images, utilizing a deep unsupervised dehazing network model as a baseline. Our approach considers both algorithmic and image decomposition, splitting the low-light image enhancement task into two parallel subtasks. Extensive experiments demonstrate that our proposed approach outperforms previous methods.
Feature enhancement based detection of traffic signs in foggy environment

Aiming at the problem of missing detection and low accuracy of traffic signs in a foggy environment, the original YOLOv5 target detection algorithm was improved. First, to reduce the loss of traffic signs in the Foggy image in the process of deep convolution network transmission, replace the Backbone end convolution with the Transformer module, established the YOLOv5-Transformer model; Secondly, to enrich the semantic information in the shallow network, by joining the Path Aggregation Networks (PAN) to achieve semantic information fusion with the deep network; Further, the YOLOv5- 4detect model is proposed, which added small size detection head to the original YOLOv5 network model to detect the fused feature maps . The results showed that the YOLOv5-Transformer model and YOLOv5-4 detect model have improved the testing performance of traffic signs by 5.5% and 9.3% in sunny environment; The detection results of traffic signs in foggy environment increased by 9.4% and 15.8%. The experiments illustrate that it is more effective in foggy environments by adding fusion channels than introducing a self-attention mechanism via Transformer. YOLOv5-4detect can significantly improve the performance of YOLOv5 in traffic signs detection.
The Impact of Remote Work on Corporate Security

The increased use of remote working in the corporate world has led to an escalation of cybersecurity threats to corporate networks. Numerous studies have explored the impact of remote work on corporate security, including potential threats and recommended best practices. However, there is a significant research gap in these areas, highlighting the need for more studies to compare key findings and identify the most effective best practices. To address these gaps, the present study systematically analyzes recent research on the impacts of remote working on corporate security. A total of 10 empirical studies were identified based on specific criteria, including the publication date and relevance to the study focus. The research findings shed light on the impact of remote working on corporate security and highlight potentials risks and best practices. These practices include never storing passwords in plain text, regularly changing passwords, using passwords with a minimum length of eight characters, selecting memorable passwords, promptly reporting data compromise or password loss, and reporting violations of company guidelines. Companies can implement these best practices to enhance corporate security. Workforces and information security managers should prioritize efforts to mitigate threats associated with remote working.