Reviewerly/exHarmony huggingface dataset:
A Secure Electronic Health Record Storage System Based on Hyperledger Fabric, IPFS, and Secret Sharing Scheme

Electronic health records (EHR) systems are now becoming widely used as a system for storing and managing patients’ health records. Patients should be able to access their health records as and when required. Traditional EHR systems have challenges with data security, integrity, interoperability, and management. The typical client–server system is vulnerable to single-point-of-failure since it is centralised. Medical data that is dispersed across different EHR systems is frequently difficult to access. Due to its positive features such as security, privacy, secrecy, and decentralized, blockchain technology has the potential to significantly enhance the healthcare sector, despite all of the existing problems. In this paper, we present a health data storage system based on the permissioned Hyperledger blockchain, an InterPlanetary File System (IPFS), and a multi-image secret sharing approach. Hyperledger Fabric is a peer-to-peer tamper-proof private-permissioned blockchain network that allows an organisation to simultaneously participate in multiple, separate blockchain networks via channels. And further, multi-image secret sharing is applied to provide security to sensitive medical image data during transmission against illegal access or alteration. Our system is integrated with IPFS for EHR storage. The proposed framework provides a secure, decentralised, and distributed model of EHR management system. Hyperledger Fabric is a tamper-proof private-permissioned blockchain network that allows an organisation to participate in many blockchain networks at the same time using channels. Furthermore, multi-image secret sharing is used to protect sensitive medical picture data from unauthorised access or manipulation during transmission. Using which our system is free from single-point-of-failure. For EHR storage, our system is connected with IPFS. The proposed framework provides an EHR administration system that is secure, decentralised, and distributed.
Estimation of Different Transformation Parameters Based on Optimised Scale Invariant Feature Transform for Image Registration

Image registration uses attributes or similarity measures to align images taken at various times or from different points of view. In this work, we provide an image registration parameter estimation approach based on structural features. The scale invariant feature transform (SIFT) retrieves structural features such as edge, corner, and so on. To choose the most intrinsic element from the retrieved feature vector, the arithmetic optimisation algorithm (AOA) is applied. On image databases that are publicly accessible such as standard benchmark images, synthetic aperture radar (SAR) and medical images, the proposed technique’s effectiveness is compared to different earlier state-of-the-art methodologies. According to the results of this experiment, this strategy can reduce the mean square error of inaccurate image registration parameter predictions.
Intuitionistic Fuzzy Stream Cipher for Privacy Preservation of Biometric Data in IoMT

The biometric data is inevitably used in Medical Things (IoMT) like fingerprints, retina scans, X-ray images, MRIs, and many more. In the medical system, it is generally collected by IoT devices like a scanner or by special machines like MRI scanners which are then transferred to a medical database using the Internet or local networking. Thus, security is must during the transmission of this data. An intuitionistic fuzzy system or set is defined with an unspecific boundary, it may have a random output for a specific input or it can be defined by the probability rather than yes/one or no/zero. Intuitionistic fuzzy system is a set theory in which the assessment of the member elements with two functions: for calculating the membership and also for calculating non-membership. In this paper, we proposed an intuitionistic fuzzy-based stream cipher for Privacy Preservation of Biometric Data in IoMT. Here, the linearity of LFSR is made nonlinear by the use of intuitionistic fuzzy system. We use two 64-bit LFSRs, which are initialized first by 128-bit session key / secret key, now, in each clock cycle, both produce 1-bit output, which is fed to the IF system, and finally, we apply the membership or non-membership function to get 1-bit output which is xored with 1-bit plaintext to produce 1-bit ciphertext. We also established that the stream of bits generated, say, 128-bit, is pseudorandom.
MsMED-Net: An Optimized Multi-scale Mirror Connected Encoder-Decoder Network for Multilingual Natural Scene Text Recognition

End-to-end multi-script text recognition from natural scene text images is a difficult task as compared to document text recognition due to the complex background semantics, distinct font style of multilingual data, and fancy font style with low-quality images. Multilingual text recognition in the natural scene can be divided into three distinct tasks: (a) text localization, (b) script identification, and (c) text recognition. The proposed text localization part involves fine-tuning using a multi-scale mirror connection-based encoder-decoder network (MsMED-Net) by replacing the Adam optimizer with the experimentally selected diffGrad optimizer to segment each text pixel correctly. The MsMED-Net outperforms the other popular deep semantic segmentation algorithms for the same task. The detected regions contain three different languages. Local and global feature learning by InceptionNet-V3 with MISH activation function model is applied to identify the script of the detected portions. Finally, Tesseract OCR version-4 is applied to the identified scripts to recognize them. The text recognition part involves fine-tuning Tesseract 4 by applying pre-processing steps to the input data and training the Tesseract with our new camera captured data set. The proposed method is tested on a new camera captured multilingual natural scene text data set entitle JUDVLP-MNSITextdb.v1 (multilingual natural scene Indic text data set) text data set. The data set contains 700 multilingual (Bengali, English, Hindi) text images with their ground truth showing encouraging results.
Breast Cancer Detection Using Transfer Learning Techniques in Convolutional Neural Networks

Breast cancer is the primary cause of death in women, despite significant efforts to avoid it through screening programmes. Given the situation, the number of mammograms gathered has increased at an exponential rate. The correct detection of the tumour is vital in determining the course of treatment and the severity of the illness. Computer-assisted diagnosis has become considerably more efficient nowadays. The use of automatic image processing in this context is important. Deep learning-based techniques are a viable option in the detection of breast cancer. In this research work, a transfer learning-based strategy for classifying breast tissue images into two broad categories, benign or malignant, is proposed. The breast histology images corresponding to the BreakHis data set are taken as input from which after normalization, patches are extracted and fed into Google’s Inception-V3 and ResNet152V2 Convolutional Neural Networks (CNNs) such that they are able to learn domain-specific features necessary for the categorization of histology images into infectious and non-infectious.
RIWT Generative Feedback Residual Network for Secure Clinical Data Communication in Healthcare Unit

At present, data hiding has moved from a traditional approach to deep learning models. Deep learning has the potential to strengthen the security and privacy of sensitive information in medical images. In this paper, a novel RIWT-based generative feedback residual CNN model has been designed for hiding and recovery of secret information. Feedback system helps to re-learn and refine the deep features, while the residual system maintains the integrity and quality of the cover image. The model is trained simultaneously both on data hiding and recovery network with a learning rate of 0.001 and epoch of 1200. The model demonstrates an average PSNR of 42.73 dB and SSIM value of 0.99 for the stego images. Also, in comparison with state-of-the-art learning schemes, the proposed model performs superior for stego images, while its limitations are seen for a recovered secret images.
Healthcare Security of Patient’s Medical Images by PEE-Based RIW Without Location Mapping

Here, we propose a new effective technique for prediction error expansion-based digital reversible watermarking of a color image where embedding the location map (mapping the data of overflow/underflow pixel) into a watermarked image is not required at all. So this technique gives the extra space to embed more secret bits or increase the payload. Basically, we have done a prepossessing of the cover image so that no location map is required but it degrades the quality of the cover image a little bit which is negligible. But it does not affect the embedded 3 bpp color secret/watermarking image. We have done it on MATLAB, and the results are quite good.
Impregnable Healthcare Ecosystem Using Blockchain and Artificial Intelligence Approaches

Blockchain holds the potential to revolutionize health care. The ledger technology facilitates secure access and storage of electronic health records (EHRs). The proposed blockchain architecture coalesced with artificial intelligence (AI) and data obtained from the Internet of Things (IoT) provides a significant platform that can benefit the medical industry exceptionally. The data in blockchain can be viewed in a folder-file structure by the branched blockchain approach. Moreover, analytics dashboards are built on the prediction results of the AI models and the providers can envision the data interpreted in a comprehensive manner. Multiple attack predictions are computed after analyzing the EHR data stored in the blockchain. Hyperglycemia live prediction is implemented using a time-series model and random forest. Pulmonary infection classification is determined using visual geometry group-16 (VGG), convolutional neural network (CNN) model and heart attack prediction using K-nearest neighbor (KNN) binary classification. An accuracy of 93, 84 and 88 are obtained for the prediction of hyperglycemia, pulmonary and heart attack, respectively.
LSEA-IOMT: On the Implementation of Lightweight Symmetric Encryption Algorithm for Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) has made a monumental improvement in patient monitoring, early detection of diseases and treatment methods. This not only enriched the quality of living but also the healthcare cost and errors are minimized. This promising technology has greatly helped the patient and frontline healthcare workers. However, security and privacy breaches are one of the major concerns in IoMT due to wide variety of devices and communication network. The lack of security can affect a patient life since IoMT devices transmit patient data to a cloud-based medical server over insecure channel. Hence, maintaining the privacy and confidentiality becomes an utmost issue worth of further exploration and resolution. Cryptography is one of the primitive technologies which can ensure the confidentiality of a data; however, the complex solutions are not a good choice for resource-constrained IoMT device. Accordingly, we have designed a novel lightweight symmetric encryption algorithm (LSEA) for low-powered IoMT devices. Proposed algorithm uses 8-bit block ciphering technique using two 8-bit secret keys. Physiological parameter such as pulse rate of human body has been considered as one the secret keys while designing the algorithm. Security analysis shows that the algorithm is resilient to known plain text attack and known cipher text attack. LSEA has been tested in Arduino with varying payload. Experimental result depicts that it requires only 1.25 ms time for 16 byte and 7.4 ms for 512 byte data. Proposed algorithm has been compared to AES in a similar experimental environment.
Combatting Front-Running in Smart Contracts: Attack Mining, Benchmark Construction and Vulnerability Detector Evaluation

Front-running attacks have been a major concern on the blockchain. Attackers launch front-running attacks by inserting additional transactions before upcoming victim transactions to manipulate victim transaction executions and make profits. Recent studies have shown that front-running attacks are prevalent on the Ethereum blockchain and have caused millions of US dollars loss. Vulnerable smart contracts, blockchain programs invoked by transactions, are held responsible for front-running attacks. Although techniques to detect front-running vulnerabilities have been proposed, their performance on real-world vulnerable contracts is unclear. There is no large-scale benchmark based on real attacks to evaluate their capabilities. This motivates us to build a benchmark consisting of 513 real-world attacks with vulnerable code labeled in 235 distinct smart contracts. We propose automated techniques to effectively collect real-world attacks and localize the corresponding vulnerable code at scale. Our experiments show that our approaches are effective, achieving higher recall in finding real attacks and higher precision in pinpointing vulnerabilities compared to the existing techniques. The evaluation of seven state-of-the-art vulnerability detection techniques on the benchmark reveals their inadequacy in detecting front-running vulnerabilities, with a low recall of at most 6.04%. Our further analysis identifies four common limitations in existing techniques: lack of support for inter-contract analysis, inefficient constraint solving for cryptographic operations, improper vulnerability patterns, and lack of token support.
Mask RFCT: Recursive Fully Convolutional Tracker for Video Instance Segmentation

Video instance segmentation has recently attracted a lot of attention due to the challenge of simultaneous segmentation and tracking in video clips. In this work, we propose a new video instance segmentation method, called Mask RFCT, which performs tracking via a precise feature vector matching by generating feature vectors from a recursive fully convolutional tracking branch. Our main contribution is the novel recursive fully convolutional tracker (RFCT) that generates corresponding feature vector for each instance within a bounding-box, which will be used for the following instance category assignment as tracking process. Model evaluation is conducted on YOUTUBE-VIS dataset. It shows that our Mask RFCT outperforms the baseline model, MaskTrack RCNN, obtaining an absolute gain of 2.8% for mean AP and 2.65% for mean AR. And we also conduct comparison experiments on different trackers we proposed and the baseline. We hope our work will provide a new method for architecture modification and instance tracking.
Soft-Actor-Attention-Critic Based on Unknown Agent Action Prediction for Multi-Agent Collaborative Confrontation

Reinforcement learning in multi-agent scenarios is very important for practical applications, but the challenges in this scenario are far greater than those in the single-agent environment. In order to solve the multi-agent scene problems, we propose an algorithm called soft-actor-attention-critic. The algorithm adopts centralized training with distributed execution, and adds a centralized critic. The critic selects the relevant information of each agent at each time step through the attention mechanism, and makes decisions based on this information. At the same time, the counterfactual baseline is introduced to the credit allocation problem, and the agents are trained accordingly. In addition, a supervised learning method based on GRU(gated recurrent unit) is proposed to predict the behavior of unknown agents. In the complex multi-agent cooperation environment, this method has higher learning efficiency than the latest method. Our method is not only applicable to collaboration scenarios with shared rewards, but also to confrontation scenarios with personalized rewards, as well as scenarios without global state. Therefore, it has enough flexibility and can be applied to most multi-agent learning problems.
Research on Image Super-resolution Restoration Algorithm Based on High Order Degradation Model

Appropriate training of the design is crucial for real-world super-resolution Low-Quality (LQ) images, which are difficult to obtain matched High-Quality ground truth images (HQ), or realistic degraded LQ observations from synthetic photography. Most of the recent work is concerned with modelling degradation using artificial or estimated degradation parameters, but these metrics cannot model complex types of real-world degradation, leading to limited quality improvement. We have demonstrated excellent performance of the image Super-Resolution (SR) model based on deep convolutional neural networks in restoring basic High resolution (HR) images from Low resolution (LR) images obtained from pre-defined downscaling methods. It is generally believed that when the assumed degradation model departs from the single image sequence (SISR) method in the real image, it performs poorly. Other factors are considered in many degradation models, such as Blur, but they are still insufficient to cover the different degradations of real-world images. In the case of high order degradation, we introduce the synchronous design of the process of obtaining the core of the de-degradation pool and modeling the noise. In order to better simulate the deterioration of the complex real world, high-resolution learning gives a realistic picture.
Research on Pulse Radar Jamming Recognition based on DE-SVDD

With the rapid development of modern electronic warfare, new jamming based on digital radio frequency memory forwarding emerge in an endless stream. In order to solve the problem that only known jamming can be distinguished, this paper proposes a pulsed radar jamming recognition based on DE-SVDD. The MobileNetV3 is used as the backbone feature extraction network. Then the features are sent to the DE-SVDD module based on the differential evolution algorithm to accept available classes and reject unknown classes, realizing the effective recognition of new class jamming under small samples. The simulation results show that the proposed method has an overall recognition rate of up to 98.3% for seven jamming, including three unknown jamming within the SNR of 0~10dB, and the recognition rate of known types reaches 99.79% under 8dB. The jamming recognition rate reached 91.33%, realizing the effective classification and recognition of unknown modulation methods.
Dim-Light Instance Segmentation Based on Soft Thresholding and Improved BCNet

Instance segmentation is a new method in the field of security monitoring. The current models perform well in sufficient light. However, in the actual environment, due to the change of light in the scene, the picture will randomly appear with high noise, low brightness and other problems which seriously affect the segmentation accuracy. Therefore, two S-BCNet based on BCNet are proposed in this paper, which respectively achieve better performance under complex light and constant dim-light conditions. In order to improve the generalization, we propose a new Shrinkage Module based on a soft thresholding function and insert it into BCNet's backbone so that it can learn and filter noise autonomously. Moreover, we propose a dim-light processing method and produce two datasets: COCO-D(dark light) and COCO-M(complex light) based on COCO, which are specifically used to train and test the performance of the mode in various degrees of dim-light environments. Furthermore, we use AdaBN to improve our S-BCNet and use the COCO-D for transfer learning to train the model for dark-light specialization, specifically for use in constant dim-light scenes. Experiments show that our models improve by 10.7% and 17.2% respectively compared with the best mainstream model in the two environments.
An optimization high-resolution network for human pose recognition based on attention mechanism

To solve the problems of high complex calculation and low recognition precision of similar actions in the high-resolution network (HRNet), we proposed an optimized HRNet based on attention mechanism. Firstly, the dilated convolution (DC) module is introduced into cross-channel sampling to obtain global features, which ensures that the feature map can cover all the information of the original image; Secondly, the channel attention Squeeze-and-Excitation (SE) module is employed in the process of cross-channel feature fusion to learn the correlations, improving the recognition precision without changing the parameter quantity and operation complexity; Finally, the experiment results on KTH dataset show that the proposed algorithm has a better performance.
Missing Values Imputation Using Auto-associative Neural Network with Local Neighborhood Information

Missing values of real-world datasets bring challenges to data mining. Popular methods for handling missing values are to replace missing values in a dataset using predicted values, called imputation, which is a fundamental task in many research fields. In this paper, we propose an auto-associative neural network with local neighborhood information for missing values imputation. Specifically, we introduce local neighborhood information into auto-associative neural network to make the spatial relationships among samples work. With the spread of neighbor information, the similarity of samples is used to supplement the missing attributes of incomplete samples, which facilitates network analysis of attributes associations. Besides, we propose a training method to iteratively update estimation by treating missing values as variables, in which the imputation error and the incomplete model inputs are continuously optimized during training. The training method allows incomplete samples to participate in the network training as well, enabling all known attributes to be useful. Experiments based on several public datasets validate the effectiveness of the proposed method.
Dynamic Decision Boundaries for Improved Robustness against Adversarial Attacks

Deep neural networks is vulnerable to adversarial attacks, where an attacker crafts input data to mislead the model. Adversarial training is a common approach to improve the robustness of deep neural networks against adversarial attacks, but existing methods mainly focus on maximizing the inter-class distance and minimizing the intra-class distance of adversarial examples at the feature level, as well as keeping them as far as possible from the decision boundary. However, these methods often rely on the assumption that the model has a determined decision boundary. In this work, we propose a novel method called Uncertainty Defender (UD) and based on uncertainty estimation to improve the robustness of deep neural networks against adversarial attacks. UD models the weight vector of the classifier as a Gaussian distribution and samples from it multiple times to obtain a dynamic decision boundary. Our experimental results on CIFAR10, MNIST and ImageNet show that our method performs well in both white-box and black-box attack scenarios.
High-Speed Tracking of Discriminative Correlation Filters Based on GPU

Most existing high-speed trackers are based on generative models, which cannot distinguish between target and background. In contrast, the discriminative correlation filter (DCF) based on discriminative model treats tracking as a classification of background and target. To address the problem that DCF cannot track at high speed. In this paper, we propose a high-speed GPU-based parallel discriminative correlation filter (PDCF), decompose the DCF into parallel tasks and map them to GPU with different coarse-grained and multi-level memory. The experiments result show that PDCF is 58 times faster than DCF running on a single CPU with the same accuracy, and the speed is 2213 frames per second (FPS) when the patch size is 200*200pixel, which fully satisfies the task of high-speed tracking.