Reviewerly/exHarmony huggingface dataset:
Dynamic shape measurement for objects with patterns by Fourier fringe projection profilometry based on variational decomposition and multi-scale Retinex

In practical measurement, we often need to measure the shape of objects with patterns or letters. As far as we know, no paper has ever reported the shape measurement for objects with patterns or letters by Fourier fringe projection profilometry (FPP). In this paper, we propose a method based on the variational decomposition TV-Hilbert-L2 model and multi-scale Retinex (MSR) to measure the shape of objects with patterns and letters by Fourier FPP. In this method, we first use the TV-Hilbert-L2 model to obtain the fringe part, then perform MSR enhancement on the fringe part, and finally decompose the enhanced fringe part with TV-Hilbert-L2 again. We evaluate the performance of this method via application to one computer-simulated noisy fringe projection pattern and two experimental fringe projection patterns with different types of patterns or letters, and comparison with the Fourier transform method, the variational image decomposition TV-Hilbert-L2 model. Furthermore, we apply the proposed method to the dynamic three-dimensional shape measurement of hand posture with pattern. The experimental results show that our method can effectively measure the dynamic shape of objects with patterns or letters from a single-frame fringe projection pattern.
Effective COVID Monitoring Process to Observe Patients Status using Blockchain

Collecting important information helps physicians, specialists, and health care providers to simplify the care of their patients. However, because patients do not explicitly retain their data, autonomous possession and the right to protect personal data develop. This paper discovers the block chain’s potential to improve health care by placing the patient at the center of the system and improving health data protection and collaboration. The main goal of this research work is to discover the vast power of BitCoin technology that can be applied to patient records and health record protection management. As a dispensed era, Blockchain can be very beneficial, giving sufferers control over their statistics and impartial identification. With the COVID-19 pandemic, existing health information exchange systems are being put through the ringer. There is an increase in patient information sharing, as well as the need to respond to medical data requests more efficiently. There are some limitations in the current health information technologies, including the inability to remotely share medical data beyond their protected, local data stores. As a result, a secure and user-centric approach to accessing and controlling sensitive medical data is provided that is based on Blockchain immutability and decentralization. An innovative peer-to-peer system underpins the framework. Information is distributed through smart contracts that are connected to a blockchain-based protocol to ensure data integrity and traceability. Implementing the framework over a pilot study demonstrates its effectiveness.
Robust Homography Estimation from Local Affine Maps

The corresponding point coordinates determined by classic image matching approaches define local zero-order approximations of the global mapping between two images.But the patches around keypoints typically contain more information, which may be exploited to obtain a firstorder approximation of the mapping, incorporating local affine maps between corresponding keypoints.Several methods have been proposed in the literature to compute this first-order approximation.In this paper we present several modifications of the RANSAC (RANdom SAmple Consensus) algorithm, that uses affine approximations and a-contrario procedures to improve the homography estimation between a pair of images.The a-contrario methodology provides a definition of the soundness of an estimation and allows for adaptive thresholds for inlier/outlier discrimination.These approaches outperform the state-of-the-art for different choices of image descriptors and image datasets, and permit to increase the probability of success in identifying image pairs in challenging matching databases.
Spoken Term Detection of Zero-Resource Language Using Posteriorgram of Multiple Languages

We propose in this paper a query-by-example spoken term detection (QbE-STD) method for keyword detection from zero-resource language speech databases. The proposed method employs the phonetic posteriorgram (PPG) trained with multiple resource-rich languages and combines multilingual PPGs for speech representation. The keywords are detected using the dynamic time warping method. We examined three types of combination of multiple languages such as concatenation of PPG (PPG_CONC), a combination of language resources to calculate multilingual PPG (PPG_ALL), and multi-task training of PPG using multiple languages (PPG_DIV). We carried out an experiment of the QbE-STD from Kaqchikel speech. As a result, the use of PPG showed better detection performance than the method based on the conventional speech feature (MFCC), and the use of multiple languages gave a further improvement of detection.
A ConvNext-Based and Feature Enhancement Anchor-Free Siamese Network for Visual Tracking

Existing anchor-based Siamese trackers rely on the anchor’s design to predict the scale and aspect ratio of the target. However, these methods introduce many hyperparameters, leading to computational redundancy. In this paper, to achieve outstanding network efficiency, we propose a ConvNext-based anchor-free Siamese tracking network (CAFSN), which employs an anchor-free design to increase network flexibility and versatility. In CAFSN, to obtain an appropriate backbone network, the state-of-the-art ConvNext network is applied to the visual tracking field for the first time by improving the network stride and receptive field. Moreover, A central confidence branch based on Euclidean distance is offered to suppress low-quality prediction frames in the classification prediction network of CAFSN for robust visual tracking. In particular, we discuss that the Siamese network cannot establish a complete identification model for the tracking target and similar objects, which negatively impacts network performance. We build a Fusion network consisting of crop and 3Dmaxpooling to better distinguish the targets and similar objects’ abilities. This module uses 3DMaxpooling to select the highest activation value to improve the difference between it and other similar objects. Crop unifies the dimensions of different features and reduces the amount of computation. Ablation experiments demonstrate that this module increased success rates by 1.7% and precision by 0.5%. We evaluate CAFSN on challenging benchmarks such as OTB100, UAV123, and GOT-10K, validating advanced performance in noise immunity and similar target identification with 58.44 FPS in real time.
Investigating the Impact of Bank Housing Credit Risk Control Strategy by Blockchain Technology on the Household Consumption Plan

It is essential to solve the problem of information asymmetry in bank housing credit management, thus reducing bank operating costs and credit risk. Therefore, the present work constructs a bank housing credit blockchain alliance system based on the technical characteristics of blockchain to strengthen bank housing credit risk control. First, according to blockchain's elements and system architecture, the application of blockchain to bank housing credit business is analyzed. On this basis, the bank housing credit risk control strategy is proposed. Finally, the mathematical model is used to investigate residents' household consumption to explore the impact of housing credit on the household consumption plan of residents. The results show that when the educational level of the head of household is illiterate or semi-illiterate, the coefficients of participation credit (PC), nonparticipation credit (NPC), and total samples (TSs) of total household consumption are -0.064, 0.067, and 0.174, respectively; when the educational level of the head of household is primary school or junior middle school, the coefficients of PC, NPC, and TS of total household consumption are -0.026, 0.017, and 0.105, respectively; when the educational level of the head of household is high school or above, the coefficients of PC, NPC, and TS of total household consumption are 0.084, 0.073, and 0.064, respectively. The results indicate that the education level of the head of household plays a crucial role in the family consumption plan. Those with a high educational level will compromise on the best consumption decision after constantly weighing the consumption motivation and consumption thinking after obtaining the credit funds. The content reported here is vitally significant in guiding households to clarify the proportion of credit fund distribution and understand their consumption tendency.
Comparative Analysis of State and Parameter Estimation Techniques for Power System Frequency Dynamics

Dynamic state and parameter estimation in current and future power systems are critical for advanced monitoring, control, and protection. There are numerous methods to perform dynamic state and parameter estimation; this paper compares the accuracy and computational time of four methods (i.e., Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), and moving horizon estimation (MHE)) designed to estimate the states and parameters for frequency dynamics of a power system. A simulation study was conducted using Matlab/Simulink by introducing Gaussian and non-Gaussian noise in the measurements. Results under Gaussian noise showed similar accuracy performance for all filters. EKF and UKF presented convergence or numerical instability issues due to incorrect initial guesses of parameters. MHE did not present convergence issues, however, required comparatively higher computation time. Nonetheless, the MHE could still be implemented in real-time for state and parameter estimation of power system. The impact of non-Gaussian noise on the methods was inconclusive and will require further study.
Dance Action Generation Model Based on Recurrent Neural Network

In recent years, modeling methods based on Deep Learning and Recurrent Neural Network (RNN) have achieved rapid development in various tasks in the field of computer vision, and video generation technology has also achieved remarkable results on this basis, with boosting effect. This paper analyzes the requirements and system design of the dance generation system based on cyclic neural network and clearly puts forward the specific functional requirements and nonfunctional requirements. Design, test each function of the system, and show the graphs generated by our application during training and testing, and visualize the specific value of the loss function during training. Combining the latest image generation technology and the most effective open source gesture detection project, a dance generation algorithm is completed, which can generate a video of the target person completing the specified dance action.
Text Summarization on Amazon Food Reviews using TextRank

Text summarization is a technique to create a summary by shortening the length of text but keep the key information. There are two main approaches to summarize the text which are abstractive summarization and extractive summarization. This study is aimed to extract the most important top 5 reviews which can summarize the overall reviews of certain product in Amazon fine food reviews. TextRank algorithm which is one of the extractive summarization approaches is used to perform text summarization automatically. GloVe pre-trained word embedding model with 100 dimensions is used to map each word from the reviews to vector representation. Besides, PageRank algorithm is applied to compute the sentence rankings scores to determine how important and relevant of the sentences can be the representatives of summary. Top 5 reviews with the highest sentence ranking scores are extracted to be the summary and further discussed the customer perception on the product based on the summary generated. The final summary shows that Amazon customer reviews tend to positive for certain food brand. Keywords: Text Summarization, Extractive Summarization, TextRank
Distilling Constraints in Zero-Knowledge Protocols

Abstract The most widely used Zero-Knowledge (ZK) protocols require provers to prove they know a solution to a computational problem expressed as a Rank-1 Constraint System (R1CS). An R1CS is essentially a system of non-linear arithmetic constraints over a set of signals, whose security level depends on its non-linear part only, as the linear (additive) constraints can be easily solved by an attacker. Distilling the essential constraints from an R1CS by removing the part that does not contribute to its security is important, not only to reduce costs (time and space) of producing the ZK proofs, but also to reveal to cryptographic programmers the real hardness of their proofs. In this paper, we formulate the problem of distilling constraints from an R1CS as the (hard) problem of simplifying constraints in the realm of non-linearity. To the best of our knowledge, it is the first time that constraint-based techniques developed in the context of formal methods are applied to the challenging problem of analysing and optimizing ZK protocols.
Anchor-Free Braille Character Detection Based on Edge Feature in Natural Scene Images

Braille character detection helps communication between normal and visually impaired people. The existing Braille detection methods are all aimed at scanning Braille document images while ignoring natural scene Braille images and CNN shining in the field of pattern recognition is rarely used for Braille detection. Firstly, a natural scene Braille image data set named NSBD was constructed. Then, an anchor-free Braille character detection based on the edge feature was proposed by analyzing that Braille characters in natural scene images that are relatively small in size, and a Braille character is composed of Braille dots that werelocated at the edge region of Braille character. Finally, the performance of the proposed method and other classic methods based on CNN was compared on NSBD. The experimental results show that the proposed method has good performance.
Hong–Ou–Mandel interference linking independent room-temperature quantum memories

To realize a large-scale quantum network, both quantum memory and the interference of retrieved indistinguishable photons are essentially required to perform multi-photon synchronization and quantum-interference-mediated entanglement swapping. Significant progress has been achieved in low-temperature and well-isolated systems. However, linking independent quantum memories at room temperature remain challenging. Here, we present an experimental demonstration of Hong–Ou–Mandel interference between single photons from two independent room-temperature quantum memories. We manage to simultaneously operate two such quantum memories and individually obtain a memory-built-in quantum correlation of Stokes and anti-Stokes photons by a far-off-resonance Duan–Lukin–Cirac–Zoller protocol. We also successfully enhance the Hong–Ou–Mandel interference rate up to about 15 times by increasing each photon rate, which is achieved by coordinating two quantum memories with a repeat-until-success fashion. We observe the visibility of quantum interference up to 75.0% without reduction of any background noise, well exceeding the classical limit of 50%. Our results, together with its straightforward, broadband, and room-temperature features, open up a promising way towards realizing large-scale quantum networks at ambient conditions.
A Spanish dataset for reproducible benchmarked offline handwriting recognition

In this paper, a public dataset for Offline Handwriting Recognition, along with an appropriate evaluation method to provide benchmark indicators at sentence level, is presented. This dataset, called SPA-Sentences, consists of offline handwritten Spanish sentences extracted from 1617 forms produced by the same number of writers. A total of 13,691 sentences comprising around 100,000 word instances out of a vocabulary of 3288 words occur in the collection. Careful attention has been paid to make the baseline experiments both reproducible and competitive. To this end, experiments are based on state-of-the-art recognition techniques combining convolutional blocks with one-dimensional Bidirectional Long Short Term Memory (LSTM) networks using Connectionist Temporal Classification (CTC) decoding. The scripts with the entire experimental setting have been made available. The SPA-Sentences dataset and its baseline evaluation are freely available for research purposes via the institutional University repository. We expect the research community to include this corpus, as is usually done with English IAM and French RIMES datasets, in their battery of experiments when reporting novel handwriting recognition techniques.
Real-Time Metaheuristic Algorithm for Dynamic Fuzzification, De-Fuzzification and Fuzzy Reasoning Processes

This paper presents a systematic approach to designing a dynamic metaheuristic fuzzy logic controller (FLC) to control a piece of non-linear plant. The developed controller is a multiple-input–multiple-output (MIMO) system. However, with the proposed control mechanism is possible to adapt it to single-input–single-output (SISO) systems as well. During real-time operation, the dynamic behavior of the proposed fuzzy controller is influenced by a metaheuristic particle swarm optimization (PSO) mechanism. Nevertheless, to analyze the performance of the developed dynamic metaheuristic FLC as a piece of non-linear plant, a 1 kW four-wheel independent-drive electric rover is controlled under different road constraints. The test results show that the proposed dynamic metaheuristic FLC maintains the wheel slip ratio of all four wheels to less than 0.35 and a top recorded translational speed of 90 km/h is maintained for a fixed orientation.
A delay deviation tolerance IP geolocation method with error estimation

IP geolocation is an important basis of location-based network services, while error estimation is an important basis for judging the reliability of results. Most of the existing IP geolocation algorithms cannot estimate the geolocation error. A few can achieve error estimation through high-precision delay measurement, but their performance is also affected by the common delay inflation in the actual network. A new IP target location estimation method is proposed in this manuscript to achieve geolocation with reliable error estimation of IP targets in actual network. Firstly, after the landmark set divided into training set and verification set for path detection, the metropolitan area network (MAN) topology is extracted through train path set. Secondly, the governed landmarks are searched level by level through the MAN, and the minimum covering circles are calculated through the geographical distribution of the landmarks to infer the routers' area center. Then, geolocation errors are counted after simulated geolocation through the verification path set, and the minimum mean square error radius of the error mean and the minimum covering circle radius is calculated as the router area radius. Finally, the path to the IP target is measured and compared with the MAN to get the location estimation result. The experimental results based on 12 cities in China show that compared with the existing typical algorithms, the proposed method not only improves the error estimation accuracy, but also has finer geolocation granularity and lower median error.
Models-Based Analysis of Both User and Attacker Tasks: Application to EEVEHAC

The design and development of security mechanisms, such as authentication, requires analysis techniques that take into account usability along with security. Although techniques that are grounded in the security domain target the identification and mitigation of possible threats, user centered design approaches have been proposed in order to also take into account the user’s perspective and needs. Approaches dealing with both usability and security focus on the extent to which the user can perform the authentication tasks, as well as on the possible types of attacks that may occur and the potential threats on user tasks. However, to some extent, attacker can be considered as user of the system (even if undesirable), and the analysis of attacker tasks provides useful information for the design and development of an authentication mechanism. We propose a models-based approach to analyse both user and attacker tasks. The modeling of attacker tasks enables to go deeper when analysing the threats on an authentication mechanism and the trade-offs between usability and security. We present the results of the application of this models-based approach to the EEVEHAC security mechanism, which enables the setup of a secure communication channel for users of shared public computers.
Snapshot compressive imaging based digital image correlation: temporally super-resolved full-resolution deformation measurement

The limited throughput of a digital image correlation (DIC) system hampers measuring deformations at both high spatial resolution and high temporal resolution. To address this dilemma, in this paper we propose to integrate snapshot compressive imaging (SCI)-a recently proposed computational imaging approach-into DIC for high-speed, high-resolution deformation measurement. Specifically, an SCI-DIC system is established to encode a sequence of fast changing speckle patterns into a snapshot and a high-accuracy speckle decompress SCI (Sp-DeSCI) algorithm is proposed for computational reconstruction of the speckle sequence. To adapt SCI reconstruction to the unique characteristics of speckle patterns, we propose three techniques under SCI reconstruction framework to secure high-precision reconstruction, including the normalized sum squared difference criterion, speckle-adaptive patch search strategy, and adaptive group aggregation. For efficacy validation of the proposed Sp-DeSCI, we conducted extensive simulated experiments and a four-point bending SCI-DIC experiment on real data. Both simulation and real experiments verify that the Sp-DeSCI successfully removes the deviations of reconstructed speckles in DeSCI and provides the highest displacement accuracy among existing algorithms. The SCI-DIC system together with the Sp-DeSCI algorithm can offer temporally super-resolved deformation measurement at full spatial resolution, and can potentially replace conventional high-speed DIC in real measurements.
Embedding Arithmetic of Multimodal Queries for Image Retrieval

Latent text representations exhibit geometric regularities, such as the famous analogy: queen is to king what woman is to man.Such structured semantic relations were not demonstrated on image representations.Recent works aiming at bridging this semantic gap embed images and text into a multimodal space, enabling the transfer of text-defined transformations to the image modality.We introduce the SIMAT dataset to evaluate the task of Image Retrieval with Multimodal queries.SIMAT contains 6k images and 18k textual transformation queries that aim at either replacing scene elements or changing pairwise relationships between scene elements.The goal is to retrieve an image consistent with the (source image, text transformation) query.We use an image/text matching oracle (OSCAR) to assess whether the image transformation is successful.The SIMAT dataset will be publicly available.We use SIMAT to evaluate the geometric properties of multimodal embedding spaces trained with an image/text matching objective, like CLIP.We show that vanilla CLIP embeddings are not very well suited to transform images with delta vectors, but that a simple finetuning on the COCO dataset can bring dramatic improvements.We also study whether it is beneficial to leverage pretrained universal sentence encoders (FastText, LASER and LaBSE).