Reviewerly/exHarmony huggingface dataset:
Noise robust Zernike phase retrieval via learning based algorithm only with 2-step phase shift measurements

We present a noise robust deep learning based aberration analysis method using 2-step phase shift measurement data. We first propose a realistic aberration pattern generation method to synthesize a sufficient amount of real-world-like aberration patterns for training a deep neural network by exploiting the asymptotic statistical distribution parameters of the real-world Zernike coefficients extracted from a finite number of experimentally measured real-world aberration patterns. As a result, we generate a real-world-like synthetic dataset of 200,000 different aberrations from 15 sets of real-world aberration patterns obtained by a Michelson interferometer under a variety of measurement conditions using the 4-step derivative fitting method together with the exploitation of the Gaussian density estimation. We then train the deep neural network with the real-world-like synthetic dataset, using two types of network architectures, GoogLeNet and ResNet101. By applying the proposed learning based 2-step aberration analysis method to the analysis of numerically generated aberrations formed under 100 different conditions, we verify that the proposed 2-step method can clearly outperform the existing 4-step iterative methods based on 4-step measurements, including the derivative fitting, transport of intensity equation (TIE), and robust TIE methods, in terms of noise robustness, root mean square error (RMSE), and inference time. By applying the proposed 2-step method to the analysis of the real-world aberrations experimentally obtained under a variety of measurement conditions, we also verify that the proposed 2-step method achieves compatible performance in terms of the RMSE between the reconstructed and measured aberration patterns, and also exhibits qualitative superiority in terms of reconstructing more realistic fringe patterns and phase distributions compared to the existing 4-step iterative methods. Since the proposed 2-step method can be extended to an even more general analysis of aberrations of any higher order, we expect that it will be able to provide a practical way for comprehensive aberration analysis and that further studies will extend its usefulness and improve its operational performance in terms of algorithm compactness, noise robustness, and computational speed.
Myocardial Revascularization in Women, An Epidemiological Analysis

Introduction: Due to its high mortality rate, it is necessary to discuss and understand the aspects surrounding atherosclerotic diseases, including the current role of revascularization in a large number of cases. Additionally, it is necessary to discuss the aging process in women and the effects of hormonal changes. Methodology: This is a literature review with a search in databases, using 60 scientific articles as a basis and being filtered based on scientific impact and approach.
Adaptable and Efficient Digit Recognition System for Challenging Datasets: A Case Study on Pump Flowmeter Digits

Machine digit recognition from various multi-digit displays is a complex task due to the sheer number of unique digit forms, each varying significantly in shape, size, and orientation.Traditional digit recognition libraries may not perform well for all cases, especially when dealing with digital screens that can be highly variable in terms of style, fonts, colour, contrast, intensity, pixel resolution, digit aspect ratio, and spacing.To address these challenges, we present a digit recognition algorithm that is designed to be fast, easy to use, and highly adaptable.Unlike a single fit-all solution, our system can be easily modified to fit different use cases and applications, incorporating additional layers of flexibility and adaptability.This is desirable since different types of displayed digits may have unique features or characteristics that traditional digit recognition libraries do not capture well.To further demonstrate the efficacy of the proposed system, we tested it on a unique pump-flowmeter digits format, which poses significant challenges for digit recognition algorithms due to the complicated shape and layout of the digits.This paper provides a detailed step-by-step account of our system's development and its performance on this challenging dataset.The presented system achieved an accuracy of 80% on test data, is simple and can be used by researchers, developers, and practitioners working in fields such as handwriting recognition, computer vision, machine learning, image processing, pattern recognition, and neural networks.
Relation-Aware Distribution Representation Network for Person Clustering With Multiple Modalities

Person clustering with multi-modal clues, including faces, bodies, and voices, is critical for various tasks, such as movie parsing and identity-based movie editing. Related methods such as multi-view clustering mainly project multi-modal features into a joint feature space. However, multi-modal clue features are usually rather weakly correlated due to the semantic gap from the modality-specific uniqueness. As a result, these methods are not suitable for person clustering. In this paper, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> elation- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> istribution representation Network (RAD-Net) to generate a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distribution representation</i> for multi-modal clues. The distribution representation of a clue is a vector consisting of the relation between this clue and all other clues from all modalities, thus being <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">modality agnostic</i> and good for person clustering. Accordingly, we introduce a graph-based method to construct distribution representation and employ a cyclic update policy to refine distribution representation progressively. Our method achieves substantial improvements of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+6%</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+8.2%</b> in F-score on the Video Person-Clustering Dataset (VPCD) and VoxCeleb2 multi-view clustering dataset, respectively. Codes will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/bonaldli/RADNet</uri> .
Utilization of Spatio-Temporal and Social Information for POI Group Recommendation

POI group recommendation offers a list of locations to a group of users based on their visiting preferences, which is crucial for Location-Based Social Networks(LBSNs) to improve user experience quality and group satisfaction. Current studies either regard the POI as a general item for group recommendation, or do not make full use of the geospatial information of POIs and users’ social friends’ information to generate the group visiting preference representations. In this paper, a neural network-based POI group recommendation method named STSPGR is proposed. STSPGR first learns the user embedding vector that fuses temporal, spatial, categorical, and social information to represent each user’s visiting preference. Then it utilizes the attention network to dynamically learn the impact degrees of each member in the group decision-making process to aggregate the group visiting preference embedding. Finally, the POI recommender decodes the group’s embedding to the preference scores over all POIs to make the recommendation. Experiments are conducted on three real-world datasets, which show that the proposed STSPGR has better recommendation accuracy than other POI group recommendation methods.
Semantic Segmentation of Liver Magnetic Resonance Images Based on Hybrid Attention Mechanism

Liver segmentation has been a hot research topic in medical image processing technology, and liver segmentation is the basis for liver vascular segmentation, liver tumor segmentation, and subsequent 3D reconstruction. From the viewpoint of data distribution, the background of liver images is complex and variable, and the boundary with surrounding tissues is blurred; from the viewpoint of the segmentation method, the U-Net network structure leads to the absence of important features due to the adoption of the traditional pooling operation. To address the above limitations, this paper proposes a network structure that mixes U-Net with the CBAM attention mechanism to enhance feature extraction in the encoding part, strengthen the acquisition of global contextual information in the bottleneck layer, and promote the fusion of deep and shallow features in the decoding part. After experiments, all segmentation indexes of the improved network are improved compared with the benchmark network U-Net, and it is proved that CBAMU-Net can be well used for the segmentation of MR liver images.
Identification of Knee Prostheses from Lateral Radiographs Using Deep Learning Techniques

The field of medical imaging has seen significant progress in recent years, particularly with the evolution of deep learning techniques.In the context of arthroplasty, the ability to accurately detect and identify specific implant models is crucial for proper patient care and revision surgery.However, manual identification of implants from radiographic images (X-Ray) takes a lot of time and is affected by manual human error.To solve this, this research proposes a deep learning-based novel approach to automate the identification of Knee arthroplasty implants from Lateral (LAT) view X-ray images of the implant.Pre trained convolutional neural networks are used for this purpose.Best results are obtained using VGG16 which produces a higher accuracy of 81.61% and stronger Area Under Curve (AUC) of 0.9547.
An Efficient Reed-Solomon Erasure Code over Cantor-constructed Binary Extension Finite Fields

In this paper, we investigate the properties of the novel polynomial basis proposed by Lin, Chung, and Han over Cantor-constructed binary extension finite fields and propose an improved truncated LCH transform for discrete intervals. Incorporating these results leads us to the development of efficient encoding/decoding algorithms of (n,k) Reed-Solomon erasure codes with time complexity O(nlog(T)) and O (1) space complexity, where T < n. We also propose its performance-tuned variation of the decoding algorithm when only recovery of message symbols is concerned. Our experiment in the production environment indicates a performance gain of ×1 on average and ×2 at most towards the original decoder algorithm.
Maximal Leakage of Masked Implementations Using Mrs. Gerber's Lemma for Min-Entropy

A common countermeasure against side-channel attacks on secret key cryptographic implementations is $d$ thorder masking, which splits each sensitive variable into $d + 1$ random shares. In this paper, maximal leakage bounds on the probability of success of any side-channel attack are derived for any masking order. Maximal leakage (Sibson's information of order infinity) is evaluated between the sensitive variable and the noisy leakage, and is related to the conditional "min-entropy" (Arimoto's entropy of order infinity) of the sensitive variable given the leakage. The latter conditional entropy is then lower-bounded in terms of the conditional entropies for each share using majorization inequalities. This yields a generalization of Mrs. Gerber's lemma for min-entropy in finite Abelian groups.
Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels

Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, i.e., CLIP [22], to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-to-label correspondence. In this paper, we advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior about the label-to-label corre-spondence via a semantic prior prompter. We then present a novel Semantic Correspondence Prompt Network (SCP-Net), which can thoroughly explore the structured semantic prior. A Prior-Enhanced Self-Supervised Learning method is further introduced to enhance the use of the prior. Comprehensive experiments and analyses on several widely used benchmark datasets show that our method significantly out-performs existing methods on all datasets, well demonstrating the effectiveness and the superiority of our method. Our code will be available at https://github.com/jameslahm/SCPNet.
The Ship Detection Method of SAR Image Based on Improved YOLOv5

To solve the issue of ship detection in synthetic aperture radar (SAR) images from mainstream convolutional neural object detection networks due to dense ship distribution, we propose an improved ship detection method for SAR using YOLOv5s. We replace the C3 module with a Swin Transformer in the feature extraction network of YOLOv5s and use a Transformer module to model the global information of the convolutionally extracted object features for detection, enhancing ship detection capability in SAR images. Experiments are conducted on publicly available SAR image ship detection dataset resulting in an average mean accuracy improvement from 94.3% to 96.0%, and network model parameters reduced by 230801, showing the superiority and effectiveness of our proposed method with better detection speed and network complexity than other algorithms.
All-Optical ReLU as a Photonic Neural Activation Function

We experimentally demonstrate an alloptical rectified linear unit (ReLU) activation function for neuromorphic photonics applications enabled by optical frequency coding of signals. Furthermore, a comparison is made with an electro-optic approach building on a directly modulated laser.
Research on Multi-modal Track Association and Fusion Algorithm in Air Defense Weapon System

In the air defense integrated cooperative operation, a multi-modal track association and fusion algorithm is proposed to meet the needs of multi-sensor data fusion of air defense weapon systems. The algorithm constructs a virtual point on a known two-dimensional vector, and then constructs a space triangle with known coordinates of the three vertices based on the point and the vector. Based on the space triangle, calculate the distance from the space point to the straight line, and then obtain the correlation coefficient between any three-dimensional coordinate point in space and two-dimensional vector, and then complete the multi-mode track association fusion processing between three-dimensional air situation and two-dimensional air situation. Simulation experiments show that the algorithm has high track association accuracy, small fusion error, fast processing speed, and advanced technical indicators. It can effectively deal with multi-mode track association and fusion problems in different application scenes, improve the situation awareness and information sharing ability of air defense weapon system, and provide useful reference for the intelligent development of air defense weapon system.
Adaptive optimal exposure selection based on time cost function for 3D reconstruction of high dynamic range surfaces

Abstract High dynamic range surfaces are prone to overexposure and underexposure in the camera imaging process, which poses a challenging issue for three-dimensional (3D) reconstruction. Capturing multiple images of high dynamic range surfaces at different exposure times is widely used for 3D reconstruction, and the exposure sequence can significantly affect reconstruction performance. However, most existing methods select the exposure sequence based on the overall performance of the image or groups of image pixels, without considering individual pixels in the image. To circumvent the problem, this paper presents the adaptive optimal exposure sequence selection based on time cost function for the 3D reconstruction of high dynamic range surfaces. Different from existing methods that use the overall performance, the acceptable exposure time range for each pixel in the image is determined independently. Furthermore, the time cost function is established to evaluate the priority of each exposure time and used to select the optimal exposure sequence iteratively. Experimental results verify both the reconstruction performance and suitability of the proposed method.
Reading Digital Skills Network

Abstract The latest BCS Digital Skills Network event headed to Reading in June, attended by over 100 delegates. The event included speakers from industry, education, training providers, and some apprentices. Georgia Smith reports.
Highlight Generation Method for Game Commentary Using Acoustic Features

Viewers can easily grasp the main points of numerous videos in a short time using a video summary. Considerable research has been conducted on different video contents such as sports, news, and dramas based on genre-specific acoustic and image features. This paper proposes a method that focuses on the acoustic features of game videos intending to generate video summaries, particularly for game commentary videos. The first step of the proposed method is to define the scene to be extracted. Because the video data handled in this paper involve a game commentary video, the target scene to be extracted is the occurrence of an important event in the game. Next, an acoustic analysis of the video recording of the game commentary is conducted. After extracting the sound pressure from the video, the sound pressure level transition is determined to detect particularly exciting scenes. The summary results verify that the obtained scenes are at the target event occurrence and show the efficiency of the proposed method.
Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity

Federated Learning (FL) is a decentralized learning method used to train machine learning algorithms. In FL, a global model iteratively collects the parameters of local models without accessing their local data. However, a significant challenge in FL is handling the heterogeneity of local data distribution, which often results in a drifted global model that is difficult to converge. To address this issue, current methods employ different strategies such as knowledge distillation, weighted model aggregation, and multi-task learning. These approaches are referred to as asynchronous FL, as they align user models either locally or post-hoc, where model drift has already occurred or has been underestimated. In this paper, we propose an active and synchronous correlation approach to address the challenge of user heterogeneity in FL. Specifically, our approach aims to approximate FL as standard deep learning by actively and synchronously scheduling user learning pace in each round with a dynamic multi-phase curriculum. A global curriculum is formed by an auto-regressive auto-encoder that integrates all user curricula on the server. This global curriculum is then divided into multiple phases and broadcast to users to measure and align the domain-agnostic learning pace. Empirical studies demonstrate that our approach outperforms existing asynchronous approaches in terms of generalization performance, even in the presence of severe user heterogeneity.
Moving Object Detection Based on Improved Gaussian Mixture Model and Three-Frame Difference

The proposed algorithm is based on an improved Gaussian Mixture Model (GMM) and a combination of three-frame difference for addressing the issue of low detection accuracy in complex backgrounds such as abnormal closure of doors, suspended objects, and unauthorized personnel intrusion. Firstly, the improved three-frame difference method can rapidly distinguish between background and targets in the video, and it can also address illumination changes. Furthermore, the algorithm incorporates adaptive selection and background updating strategies into the Gaussian Mixture Model (GMM), which significantly improves modeling efficiency and accelerates the elimination of "ghosting" artifacts. Lastly, morphological operations are applied to the results to obtain moving objects, and the performance of the algorithm is validated through comparative experiments with simulations. The experimental results demonstrate that the proposed algorithm can effectively detect moving objects in complex environments with high accuracy.
A constraint programming approach for quantitative frequent pattern mining

Itemset mining is the first pattern mining problem studied in the literature. Most of the itemset mining studies have considered only Boolean datasets, where each transaction can contain or not items. In practical applications, items appear in some transactions with some quantities. In this paper, we propose an extension of the current efficient constraint programming approach for itemset mining, to take into account quantitative items in order to find patterns with their quantities directly on the original quantitative dataset. The contribution is two folds. Firstly, we facilitate the modelling task of mining problems through a new constraint. Secondly, we propose a new filtering algorithm to handle the frequency and closeness constraints. Experiments performed on standard benchmark datasets with numerous mining constraints show that our approach enables to find more informative quantitative patterns, which are better in running time than quantitative approaches based on classical Boolean patterns.
Decoding Cognitive Processes in Arithmetic Tasks: An EEG-Based Convolutional Neural Network Model

In this study, we introduce a novel system, developed in Python, for classifying cognitive processes based on EEG signals.The system employs a Convolutional Neural Network (CNN) trained on a dataset comprising 4-minute EEG recordings from 30 subjects.Each EEG sample processed for CNN input is 0.5 seconds long and is transformed into EEG power levels for each channel.The primary achievement of this research is the successful use of the CNN to classify whether a subject is performing a cognitive task well or poorly.The system's performance has been validated by experts in cognitive neuroscience and psychology, and its results have been benchmarked against state-ofthe-art studies in the field.This work represents a significant contribution to the field of EEG-based cognitive process classification, demonstrating the effective integration of machine learning techniques and neuroscience data.
Emerging trends with social network - design, models and handling

Social networking sites experiencing tremendous growth along with the advancement of web 2.0 in the past few decades. Online social sites offer alluring services for making relationships and for smooth communication among people. It became a fundamental need of human life and used for almost all area of lifelike entertainment, medical, shopping, education, legitimated activities, and business. Unfortunately, these become the target for intruders who attempt unlawful activities and harm social users&apos; prestige and financial health. This study focuses on various concerns associated with social networking. This chapter presents the different types of social networks (SNs) that emerged due to the advancement of the Internet. Several challenges occur in SNs with the increasing demand for their uses. So, here we discussed some research and open challenges associated with SNs. However, SNs have gained popularity over the years, but users do not trust these platforms due to the increasing cyber-crimes. We provided a list of different security threats and promising solutions for securing social media and discussed the various SN security perspectives.