Reviewerly/exHarmony huggingface dataset:
Securing Cyber-Physical Systems Against GPS Spoofing Attacks Using Confidence Attribution

This work addresses the use of a machine-learning based confidence attribution scheme to detect GPS spoofing attacks against cyber-physical systems. The confidence attribution scheme assigns a continuous value, between 0% and 100%, to each measurement, expressing how consistent this measurement is with measurements from other sensors in a cyber-physical system. By verifying the consistency of GPS measurements when compared to other sensors available, such as those present in an inertial measurement unit, it is possible to detect GPS spoofing attacks. The solution is evaluated on an experimentally acquired dataset containing flight logs, including sensor measurements and control signals, of an UAV subject to GPS spoofing attacks. The results show the number of anomalies detected by the confidence attribution scheme increases significantly in the presence of a GPS spoofing attack, rendering its detection.
Sequential Monte Carlo with 3σ limits for Bayesian estimation of latent Markov processes

The sequential Monte Carlo, alias the particle filter, is a well known Bayesian method used for estimating latent Markov processes that are nonlinear in nature. The filter, however, is known to suffer from degeneracy — a problem in which most particles are drawn from regions that do not contribute to the posterior probability density function. This degeneracy is overcome by the use of a large number of particles or an additional step, called resampling. The auxiliary particle filter and the recently proposed improved auxiliary particle filter overcome the need to resample all the time by drawing particles from regions of high importance (weight). However the filters are known to perform poorly when the latent Markov process is highly variable and the observation is highly informative. Resampling, on the other hand, involves high communication overhead within the particles. This paper proposes a new particle filtering approach that reduces the need to employ resampling strategies over all the particles and also ensures that they are drawn from regions of high importance. The filtering accuracy and computational complexity of the developed approach is evaluated using simulations.
Component Training of Turbo Autoencoders

Isolated training with Gaussian priors (TGP) of the component autoencoders of turbo-autoencoder architectures enables faster, more consistent training and better generalization to arbitrary decoding iterations than training based on deep unfolding. We propose fitting the components via extrinsic information transfer (EXIT) charts to a desired behavior which enables scaling to larger message lengths $(k\approx$ 1000) while retaining competitive performance. To the best of our knowledge, this is the first autoencoder that performs close to classical codes in this regime. Although the binary cross-entropy (BCE) loss function optimizes the bit error rate (BER) of the components, the design via EXIT charts enables to focus on the block error rate (BLER). In serially concatenated systems the component-wise TGP approach is well known for inner components with a fixed outer binary interface, e.g., a learned inner code or equalizer, with an outer binary error correcting code. In this paper we extend the component training to structures with an inner and outer autoencoder, where we propose a new 1-bit quantization strategy for the encoder outputs based on the underlying communication problem. Finally, we discuss the model complexity of the learned components during design time (training) and inference and show that the number of weights in the encoder can be reduced by 99.96%.
A Comprehensive Survey of PSO-ACO Optimization and Swarm Intelligence in Healthcare: Implications for Medical Image Analysis and Disease Surveillance

Revolutionizing healthcare is a crucial objective for improving diagnostic accuracy and disease surveillance. This review paper aims to explore the potential of PSO-ACO optimization and swarm intelligence in revolutionizing healthcare through enhanced medical image analysis and disease surveillance techniques. The abstract emphasizes the significance of these techniques in transforming healthcare practices. It provides an overview of the PSO-ACO optimization algorithm and swarm intelligence, highlighting their capabilities and potential applications in healthcare. The objective of the review is to investigate the impact of PSO-ACO optimization and swarm intelligence in improving medical image analysis and disease surveillance. The paper extensively examines the existing literature, discussing the benefits and limitations of these approaches, and showcasing their successful integration in healthcare. Furthermore, the abstract highlights the challenges and future directions in this field, emphasizing the need for further research and development. In conclusion, this comprehensive review paper underscores the transformative potential of PSO-ACO optimization and swarm intelligence, illustrating their ability to revolutionize healthcare by advancing medical image analysis and disease surveillance techniques.
Comparative Analysis of LSTM, GRU and Transformer Models for German to English Language Translation

Natural Language Processing (NLP) encompasses a broad range of techniques and methodologies for processing and understanding human language. One of the most important NLP applications that has experienced significant advancements and has gained immense importance over the years is Neural Machine Translation. Research on German-to-English language machine translation has remained a prominent area of research within the field of Natural Language Processing and Deep. This paper presents an in-depth analysis of three significant models that are used for Neural Machine Translation namely Recurrent Neural Network with Long Short-Term Memory, Recurrent Neural Network with Gated Recurrent Unit, and the Transformer. For the implementation of each model, a large data corpus of 221,534 sentence pairs is used. Two evaluation metrics are employed to assess the performance of models i.e., the BLEU Score and the ROUGE Score. BLEU-4 Score of 0.386, 0.402, and 0.482 is obtained for RNN+LSTM, RNN+GRU, and Transformer model respectively. Precision, Recall, and F1 Score of ROUGE Score are studied which points to similar results as that Learning of the BLEU Score. Both the evaluation metrics suggest that the transformer model outperforms both variants of RNN. The study also paves the way for further investigation in this area by offering important information about how each model is implemented and the outcomes it produces.
A Large Visual, Qualitative, and Quantitative Dataset for Web Intelligence Applications

The Web is the communication platform and source of information par excellence. The volume and complexity of its content have grown enormously, with organizing, retrieving, and cleaning Web information becoming a challenge for traditional techniques. Web intelligence is a novel research area to improve Web-based services and applications using artificial intelligence and automatic learning algorithms, for which a large amount of Web-related data are essential. Current datasets are, however, limited and do not combine visual representation and attributes of Web pages. Our work provides a large dataset of 49,438 Web pages, composed of webshots, along with qualitative and quantitative attributes. This dataset covers all the countries in the world and a wide range of topics, such as art, entertainment, economics, business, education, government, news, media, science, and the environment, addressing different cultural characteristics and varied design preferences. We use this dataset to develop three Web Intelligence applications: knowledge extraction on Web design using statistical analysis, recognition of error Web pages using a customized convolutional neural network (CNN) to eliminate invalid pages, and Web categorization based solely on screenshots using a CNN with transfer learning to assist search engines, indexers, and Web directories.
Glomeruli Detection Using Faster R-CNN and CenterNet

Histopathology is a complex discipline that requires the expertise of skilled pathologists to analyze tissue specimens accurately and provide guidance to clinicians in the management of a patient's healthcare. Digital histopathology has revolutionized the field of pathology and has indeed offered a helping hand to pathologists. The integration of Artificial Intelligence(AI) based digital pathology not only streamlines the pathology workflow but also offers a more comprehensive and individualized perspective, empowering pathologists to better manage the progression of intricate diseases. The renal biopsy is an essential diagnostic tool for identifying glomerular diseases. The detection and localization of glomeruli are crucial in the initial stages of diagnosing kidney diseases. Accurate counting of glomeruli in WSIs can aid in research studies aimed at under-standing the pathophysiology of kidney diseases and developing new treatments. This work aims at finding the best possible object detection mechanism to accomplish the automated detection of glomeruli in renal disease diagnosis. The experiment is carried out with the variations of developing Faster R-CNN which takes the input of single glomeruli and, the Faster R-CNN model and CenterNet model which takes WSI patches with multiple glomeruli separately. This study is a comparative analysis among the developed models and the results obtained show that Faster R-CNN is achieving promising results with an average IoU of 64.2% and an mAP of 65.7% with WSI image patches.
Performance Analogy of Autoencoders for Image Denoising

Image Denoising is one of the current day challenges in the field of image processing. Noise in the image is the existence of artifacts that do not start from the original picture content. Autoencoders have made considerable progress in image denoising because of their ability to reconstruct image inputs. Autoencoders for denoising images usually accepts a degraded image as input and is trained to predict the reconstructed image as its output. An effort has been made to analyse the functioning of selected autoencoders for image denoising, such as Basic Autoencoder, Denoising Autoencoder and Convolutional Denoising Autoencoder using the Modified National Institute of Standards and Technology database (MNIST) dataset. The performance of these models is evaluated and compared using Structural Similarity Index Measure, Peak Signal to Noise Ratio evaluation metrics. The strengths and weaknesses of each autoencoder are discussed and the factors that influence their performance are explored. Qualitative and quantitative analysis of the dataset provides an insight of effectiveness and efficiency of each autoencoder in denoising images. Observations show that Convolutional Denoising Autoencoder out-performs other autoencoders in terms of the evaluation metrics and better reconstructed image. This study can act as a base for researchers and practitioners to identify suitable autoencoders for denoising images in real-life applications.
Software and Hardware Co-Design Method for Ship Detection in Remote Sensing Images

The on-board processing of ship target detection in remote sensing images can transmit the detection results directly back to the ground and obtain fast processing speed. However, the increase in the amount of remote sensing image data leads to the decrease in the timeliness of on-board remote sensing image ship detection. In this article, a software and hardware co-design method for target detection in remote sensing images is proposed. In this method, the processing system in Zynq system on chip (SoC) is used for pre-processing as well as integration and output of detection results, and the programmable logic in Zynq SoC is used for accelerating the MobileNetV3 target detection method. The experimental results show the proposed method can accomplish satisfying ship detection of remote sensing images. Meanwhile, the average processing time of a remote sensing image of 64 * 64 size is 2.59 ms, which is a relatively fast processing speed.
A Sports Training System Based on Kinect Intelligent Wearable Device

Combined with the research of somatosensory technology and human motion recognition, this paper proposes an intelligent motion training system based on somatosensory recognition. Firstly, this paper filters the bone data collected by Kinect through jitter elimination and double exponential smoothing, and then extracts the feature vector of bone data by using space vector method to build a finite state machine model. The DTW algorithm combined with the weighted Euclidean distance is used to match the motion template to achieve the accurate and low delay motion recognition target. This paper first analyzes the design requirements of the system, designs the hardware architecture and software architecture of the system according to the requirements, and makes a detailed description of the specific implementation of motion recognition in the software. The experimental results show that after 198 tests, the motion recognition rate of the pull-up is 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and the accuracy rate is 99%. After 200 tests, the motion recognition rate of the squat is 99%, and the accuracy rate is 97.5%. After 594 tests, the motion recognition rate of the standing long jump is 97.8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , and the accuracy rate is 87.9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> . It is proved that the recognition rate of the system for motion is very high and the motion recognition function is very reliable, but the accuracy rate can reach the level of consumer grade products except for standing long jump. Result finding that although the system still has some drawbacks in the uninterrupted long jump performance, the success of the overall bow motion recognition algorithm indicates good motion recognition performance without rapid movement, can accurately identify the position and motion-decomposing motions.
Research on Measurement and Evaluation Technology of Thread Pitch Diameter Based on Extended Virtual Three Ball Method

The thread pitch diameter is an important parameter to measure the thread manufacturing accuracy. In order to effectively measure and evaluate the thread pitch value which satisfies the actual thread fit effect by using machine vision, this paper presents an extended virtual three ball method combined with standard thread profile to measure thread pitch diameter. In this method, the middle diameter model of the extended virtual three ball method is constructed on the shaft section, and then the standard thread tooth profile matched with the actual thread profile is obtained. the extended virtual three ball method is applied to the standard tooth profile to obtain the center diameter value which is closer to the actual effect. Finally, the average relative error and standard deviation are used as evaluation indexes to evaluate the center diameter values of different experimental equipment. The experimental results show that the relative error between the middle diameter value measured by the image measuring instrument and the middle diameter value obtained by the comprehensive thread measuring instrument is stable at 3.3 μm, which provides an efficient and reliable measuring method for machine vision to measure the middle diameter of screw thread.
Graph Neural Networks for Natural Language Processing in Human-Robot Interaction

Human-robot interaction (HRI) has become a promising field that focuses on developing intelligent systems that are capable of understanding human language. Natural Language Processing (NLP) plays a huge role in enabling robots to interpret and generate natural language, making it easier to effectively communicate. However, traditional NLP approaches sometimes struggle to capture the right structural dependencies and contextual information.To overcome these limitations, Graph Neural Networks (GNNs) have emerged as a powerful model for NLP tasks in the context of human-robot interaction. GNNs extend traditional neural network architectures to effectively model and reason about structured data, such as graphs. In the context of NLP, these graphs can represent semantic relationships between words or sentences.In this study, we model human-robot conversations as graphs. The aim of the research is to conduct a test using GNNs to predict personality traits based on conversations between human and robot. Presented results, despite a very small training set, show that GNNs can be effective in capturing the dynamics and context of conversations. GNNs lead then to improved performance in tasks such as predicting personality and estimating engagement levels. These findings suggest that GNNs have the potential to enhance the quality of HRI and improve the overall user experience.
Local Color Space Correction for Color Difference in Machine Vision Imaging Based on Polynomial Regression

For correcting the color difference in machine vision imaging, all colors in the color space are usually used to train the correction model in traditional methods, which will lead to a complex training process of the model, long training time, and can not achieve high correction precision for each color. In order to solve these problems, a local color space correction scheme is proposed in this paper. In the scheme, the total color space is divided into smaller sub-color space based on color difference threshold and space distance respectively, and the colors in the sub-color space is corrected by polynomial regression method. The experimental results show that local color space correction has higher accuracy and more stable performance than total color space correction.
SW-YOLO: Improved YOLOv5s Algorithm for Blood Cell Detection

This paper proposes an improved target detection algorithm SW-YOLO based on the YOLOv5s framework to solve the problems of low detection accuracy, wrong detection and missed detection in blood cell detection tasks. To begin with, the end of the backbone network is fused with Swin Transformer to improve network feature extraction. Next, since blood cells are mostly small and medium-sized targets, resulting in poor detection of large cells, the network layer that identifies large cells is removed. In addition, the normal convolution in the PANet network is replaced with depth-separable convolution during the feature fusion process to ensure the accuracy and real-time detection while having better detection results for small targets. At last, the loss function of the prediction layer uses EIOU to solve the positive and negative sample imbalance problem of CIOU. Compared with existing target detection algorithms such as Faster-RCNN, YOLOv4 and YOLOv5s, SW-YOLO improves to 99.5%, 95.3% and 93.3% mAP on the blood cell dataset BCCD for white blood cells, red blood cells and platelets respectively. The experimental results are eximious and the algorithm is highly practical for blood cell detection.
Prediction Accuracy Improvement of Side-channel Information Leakage by Using EM-Circuit Co-simulation of PDN with Filters

Ever since the side-channel attack (SCA) was proposed, there is a danger that the secret key used for encryption can be recovered by analyzing the noise in the power supply system. Therefore, it is important to predict the extent to which electromagnetic interference (EMI) measures can suppress such attack. To predict power noise and SCA, models of IC and a power distribution network (PDN) are required. Previous studies have targeted the case of implementing one decoupling capacitor on the PDN to evaluate the validity of the IC model, as well as the application of an equivalent circuit model of the PDN. In the current work, to improve the prediction accuracy when multiple decoupling capacitors are mounted, we examined the application of an electromagnetic (EM) field and circuit co-simulation. In this co-simulation, ports are placed at the mounting points of the electronic components on the EM field model of PDN. We then obtain a PDN multi-port network, and connect it to the electronic components in a circuit simulator. Finally, the transfer impedance from the IC to the measurement port is predicted by circuit simulation. This improves the prediction accuracy because the couplings between components are included in the analysis. In addition, multiple decoupling conditions can be evaluated at high speed because the capacitor mounting conditions on the circuit model can be changed. We implemented an AES circuit on the FPGA and predicted power noise and SCA. The results show that the prediction accuracy of noise and SCA is improved.
Genetic Algorithm For Convolutional Neural Network Hyperparameter Tuning

Image classification has been applied in various fields, from agriculture to health. In order to perform well, an image classification method needs an appropriate hyperparameter setting. However, tuning hyperparameters in a classification method is not an easy task. Many approaches have been developed to solve the problem, such as combining metaheuristics methods with a convolutional neural network (CNN). An example of metaheuristics method is genetic algorithm. Genetic algorithms have been proven to optimize machine learning and deep learning. This research contributes to the automatic tuning of hyperparameters using genetic algorithms. The proposed method is evaluated using MNIST dataset. The experiments results show that using a genetic algorithm for tuning hyperparameters automatically, the accuracy of validation data is 97.02% and the accuracy of training data is 99.77%. The performance of Genetic Algorithm is compared with Harmony Search. The accuracy of Harmony Search for validation data is 83.96% and for training data is 88.33%. The Genetic algorithm takes 24,97 seconds for training while Harmony Search needs 56.44 seconds.
Reordering-Less FFT: A Novel FFT Processor with Parallel Input/Output in Normal Order

The fast Fourier transform (FFT) plays a vital role in a wide range of applications including image and video processing, data compression, and wireless communication systems. The output samples of the FFT are generated in a bit-reversed order, which necessitates the implementation of a reordering scheme. However, this introduces additional latency, limits the overall throughput, and increases the hardware complexity and cost. In this paper, a novel parallel and flexible architecture for computing FFT with arbitrary lengths is proposed. The key idea is to leverage the in-memory computing (IMC) technique to eliminate the need for a dedicated reordering scheme, while enabling ultra high-throughput and low-latency FFT computation.
Automatic Modulation Recognition Based on Dual-Channel Fusion Hybrid Neural Network

Aiming at the shortcomings of low recognition accuracy, few recognition types, and poor anti-interference ability in existing automatic modulation recognition methods, a new modulation recognition method based on dual-channel fusion hybrid neural network (DCFHNN) was proposed. By utilizing In-phase/Quadrature (I/Q) data and Amplitude/Phase (A/P) data as inputs to the DCFHNN, the complementary information in different domains can enrich the feature dimension. The spatial features of the dual-channel data are extracted by the deep residual shrinkage network (DRSN), then fuse the extracted features and sent to the bidirectional gated recurrent unit (BiGRU) to extract temporal features. Furthermore, adding the attention mechanism helps to improve the effectiveness of feature extraction by enhancing the focus on key features. Experiments are evaluated on open source RadioML2016.10a dataset, the experimental results demonstrate that the proposed method outperforms other deep learning methods. When the signal-to-noise ratio (SNR) is above OdB, the classification accuracy can reach 92.3%, effectively improving automatic modulation recognition's accuracy and robustness.