Reviewerly/exHarmony huggingface dataset:
Improving Healthcare Applications Security Using Blockchain

Nowadays, the Internet of Medical Things (IoMT) technology is growing and leading the revolution in the global healthcare field. Exchanged information through IoMT permits attackers to hack or modify the patient’s data. Hence, it is of critical importance to ensure the security and privacy of this information. The standard privacy techniques are not secured enough, so this paper introduces blockchain technology that is used for securing data. Blockchain is used with the smart contract to secure private patient records. This paper presents how a patient may send his vital signs to the physician through the Internet without meeting with the latter in person. These vital signs are collected from the IoMT system that we developed before. In the proposed method, each medical record is stored in the block and connected to the previous block by a hashing function. In order to secure the new block, the SHA256 algorithm is used. We modified the SHA256 algorithm by using run-length code in compressing data. If any hacker attempts to attack any medical record, he must change all previous blocks. In order to preserve the rights of the doctor and patient, a smart contract is built into the blockchain system. When the transaction begins, the smart contract withdraws the money from the patient’s wallet and stores it in the smart contract. When the physician sends the treatment to the patient, the smart contract transfers the money to the physician. This paper shows that all recent work implements Blockchain 2 into the security system. This paper also shows that our security system can create a new block with O (n + d) time complexity. As a result, our system can create one hundred blocks in two minutes. Additionally, our system can deposit the money from the patient’s wallet into the physician’s wallet promptly. This paper also shows that our method performs better than all subsequent versions of the original blockchain.
Multi-Layer Hybrid (MLH) balancing technique: A combined approach to remove data imbalance

Data is one of the most important elements currently for business decisions as well as for scientific research. However, data imbalance is a critical issue that affects the outcome of business decisions or the performance of a model as the decision would be biased towards the majority class (MaC). Existing data balancing techniques have a major drawback: these create new artificial samples randomly which create outliers and hamper the potentiality of the original dataset. In this paper, we propose a Multi-Layer Hybrid (MLH) Balancing Scheme which combines three oversampling techniques in two layers. By combining the characteristics of ADASYN, SVM-SMOTE, and SMOTE+ENN with our data processing techniques, our scheme gives a distributed, noise-free output. It also creates new data points within the range of the original dataset, which keeps the originality of the new data points. Thus, the generated dataset is much suitable for machine learning models to achieve results with higher accuracy for highly imbalanced data. Experimental results on datasets with an imbalance ratio of up to 59 show that our proposed scheme can effectively generate a balanced dataset. We apply the resultant dataset to Random Forest and Artificial Neural Network algorithms; comparison with existing techniques shows that our scheme gives better results.
Quorum controlled homomorphic re-encryption for privacy preserving computations in the cloud

Cloud computing provides services to store users’ data, so that they can access it anytime, anywhere at their convenience. In order to preserve data privacy, the data is encrypted before being uploaded to the cloud platform. But it is challenging to process and analyse the encrypted data as well as manage its access control. In Information Sciences 2017, Ding et al. have proposed a novel solution to address this challenge, which consists of a homomorphic re-encryption scheme (HRES) and a toolkit of several basic operations over ciphertexts. Ding’s work has good potentials for various cloud-based applications in which privacy preserving computations are required and the processed results are shared among multiple users. However, the HRES scheme relies on some relatively strong assumptions, making it less practical for real-world applications. For example, the system parameters have to be generated by a trusted third party (TTP), the two proxies who execute the decryption and re-encryption processes have to follow the protocol, and they can neither be out-of-service nor collude. In this paper, we relax these assumptions by extending the HRES scheme into a quorum controlled homomorphic re-encryption scheme (QHRES). Both the decryption and re-encryption processes are carried out by multiple proxies in a distributed fashion, and all the desirable security properties are guaranteed if there exists a quorum of honest proxies. Moreover, no TTP is required and every proxy’s behaviour can be publicly verified. Our extension inherits the homomorphic property as in HRES and it also supports the basic operations over ciphertexts. Therefore, it contributes to a more practical privacy preserving data processing system for real-world applications in the cloud.
Future of Artificial Intelligence in Libraries

Commercial and research applications of artificial Intelligence (AI) are becoming increasingly important. AI is used to improve products, predict customer behavior, keep track of inventory, and analyze Big Data. AI agents are also employed to improve the performance of search engines and smartphones. There are a number of ways that AI is being considered for applications in the library, especially data analysis, supporting remote access to library services, and making the library a center for research using Big Data. AI has the potential to perform routine tasks that now require a human being, which will free up librarians to offer the in-depth expertise that is essential for advanced research.
Digital evidence strategies for digital forensic science examinations

Given the size and complexity of many digital forensic science device examinations, there is a need for practitioners to formally and strategically determine a course of conduct which allows them to undertake the most robust and efficient examination possible. This work outlines both the need for practitioners to have a digital evidence strategy (DES) when tackling any given examination scenario, how to construct one and the concerns which exist when no formal DES is in place. Approaches to DES development are examined and the context to which they should be deployed are analysed, with focus being on the use of DESs at the examination/processing stage of the investigative workflow. Finally, a 'DES skeleton' is offered to guide practitioners as they seek to create their own DES.
Dynamic fine‐tuning layer selection using <scp>Kullback</scp>–<scp>Leibler</scp> divergence

Abstract The selection of layers in the transfer learning fine‐tuning process ensures a pre‐trained model's accuracy and adaptation in a new target domain. However, the selection process is still manual and without clearly defined criteria. If the wrong layers in a neural network are selected and used, it could lead to poor accuracy and model generalization in the target domain. This paper introduces the use of Kullback–Leibler divergence on the weight correlations of the model's convolutional neural network layers. The approach identifies the positive and negative weights in the ImageNet initial weights selecting the best‐suited layers of the network depending on the correlation divergence. We experiment on four publicly available datasets and six ImageNet pre‐trained models used in past studies for results comparisons. This proposed approach method yields better accuracies than the standard fine‐tuning baselines with a margin accuracy rate of 10.8%–24%, thereby leading to better model adaptation for target transfer learning tasks.
Fair evaluation of classifier predictive performance based on binary confusion matrix

Abstract Evaluating the ability of a classifier to make predictions on unseen data and increasing it by tweaking the learning algorithm are two of the main reasons motivating the evaluation of classifier predictive performance. In this study the behavior of Balanced $$AC_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:msub> <mml:mi>C</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> </mml:math> — a novel classifier accuracy measure — is investigated under different class imbalance conditions via a Monte Carlo simulation. The behavior of Balanced $$AC_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:msub> <mml:mi>C</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> </mml:math> is compared against that of several well-known performance measures based on binary confusion matrix. Study results reveal the suitability of Balanced $$AC_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:msub> <mml:mi>C</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:mrow> </mml:math> with both balanced and imbalanced data sets. A real example of the effects of class imbalance on the behavior of the investigated classifier performance measures is provided by comparing the performance of several machine learning algorithms in a churn prediction problem.
How gullible are web measurement tools?

Automated browsers are widely used to study the web at scale. Their premise is that they measure what regular browsers would encounter on the web. In practice, deviations due to detection of automation have been found. To what extent automated browsers can be improved to reduce such deviations has so far not been investigated in detail. In this paper, we investigate this for a specific web automation framework: OpenWPM, a popular research framework specifically designed to study web privacy. We analyse (1) detectability of OpenWPM, (2) resilience of OpenWPM's data recording, and (3) prevalence of OpenWPM detection.
Models for Digitally Contact-Traced Epidemics

Contacts between people are the absolute drivers of contagious respiratory infections. For this reason, limiting and tracking contacts is a key strategy for the control of the COVID-19 epidemic. Digital contact tracing has been proposed as an automated solution to scale up traditional contact tracing. However, the required penetration of contact tracing apps within a population to achieve a desired target in the control of the epidemic is currently under discussion within the research community. In order to understand the effects of digital contact tracing, several mathematical models have been proposed. In this article, we survey the main ones and we propose a compartmental SEIR model with which it is possible, differently from the models in the related literature, to derive closed-form conditions regarding the control of the epidemic as a function of the contact tracing apps penetration and the testing efficiency. Closed-form conditions are crucial for the understandability of models, and thus for decision makers (including digital contact tracing designers) to correctly assess the dependencies within the epidemic. With our model, we find that digital contact tracing alone can rarely tame an epidemic: for unrestrained COVID-19, this would require a testing turnaround of around 1 day and app uptake above 80% of the population, which are very difficult to achieve in practice. However, digital contact tracing can still be effective if complemented with other mitigation strategies, such as social distancing and mask-wearing.
Few-shot learning for brain tumor segmentation from MRI images

Automated brain tumor segmentation from magnetic resonance imaging (MRI) scans is of great significant for brain diagnosis. However, how to segment tumor regions accurately with very limited labeled MRI images is still an appealing challenge. To tackle this issue, the present paper proposes to use few-shot under meta-learning setup. The idea is to exploit knowledge derived from a handful annotated support images during episodic learning to guide the segmentation of query images. Specifically, for each episode, the encoder extract feature maps for the both support and query images. Then, a masked average pooling is performed with the support mask to get the guidance features by only considering the target categories belonging to the support image. We use convolution operation to construct the relationship between the guidance features and query feature maps. With an aim to achieve better generalization on few-shot semantic segmentation, decoder based U-Net architecture is used. The proposed method is applied on the benchmark BraTS2021 dataset for brain tumor segmentation. The experimental results in terms of quantitative and qualitative are satisfactory in generating brain tumor segments. The presented segmentation method might be useful to help doctors perform a subtle diagnosis so that the life expectancy of patients becomes longer.
LV-YOLOv5: A light-weight object detector of Vit on Drone-captured Scenarios

Object detection has a wide range of applications in drone-captured scenarios. However, implementing these cumbersome object detection models on resource-constrained devices, such as cell phones and drones, is challenging not only because of the high computational complexity but also the large amount of storage required. The spatial induction bias in CNN models enable the models to learn representational information with smaller parameters. However, CNN networks are spatially localized and ignore the global information of the image, which is a serious miss for dense object scenarios. With the emergence of self-attention mechanisms, the importance of global information is back in focus. Self-attention-based vision transformers (Vits) are representative network models of pure attention mechanism models, but the number of parameters is huge and difficult to train. Light-weighting is an important task in object detection tasks, and to implement a more light-weight self-attention detection network, we propose the LG-YOLOv5 detection framework. We have conducted experiments on the VisDrone dataset and the results show that the performance in terms of accuracy is 0.4% and 8.2% higher than yolov5l and CornetNet, respectively. More importantly, our model size is only 36% of the yolov5l model and the inference speed is 46% faster compared to YOLOv5l.
Dynamic representation-based tracker for long-term pedestrian tracking with occlusion

This paper presents a dynamic representation-based tracker (DRT) to handle occlusions in the long-term pedestrian tracking of a single target. In our DRT, an adaptive representation network (ARN) is first constructed to extract multiple features, including classical features such as appearance and pose as well as some vector-format deep features. These features are then stacked to form a dynamic representation so as to convert the target tracking into a matching problem between the target features and candidate features, where the Euclidean distance (ED) and locality-constrained linear coding (LLC) are used as measurements in the decision-making. Next, the target state is determined through a voting procedure according to the feature matching error. Finally, a pose supervised module (PSM) and an IOU filtering module (IFM) are applied, respectively, to refine the target state and to filter out some invalid candidate targets that have been detected. Experimental results on public benchmark datasets show that our DRT is quite robust to complex environments with long-term pedestrian occlusions, and outperforms several existing state-of-the-arts trackers as it produces the best performance on both the pedestrian tracking dataset with occlusion (PTDO) and the pedestrian tracking dataset with occlusion plus (PTDO Plus).
On Some Families of Codes Related to the Even Linear Codes Meeting the Grey–Rankin Bound

Bounds for the parameters of codes are very important in coding theory. The Grey–Rankin bound refers to the cardinality of a self-complementary binary code. Codes meeting this bound are associated with families of two-weight codes and other combinatorial structures. We study the relations among six infinite families of binary linear codes with two and three nonzero weights that are closely connected to the self-complementary linear codes meeting the Grey–Rankin bound. We give a construction method and partial classification results for such codes. The properties of the codes in the studied families and their relations help us in constructing codes of a higher dimension from codes with a given dimension.
TwoViewDensityNet: Two-View Mammographic Breast Density Classification Based on Deep Convolutional Neural Network

Dense breast tissue is a significant factor that increases the risk of breast cancer. Current mammographic density classification approaches are unable to provide enough classification accuracy. However, it remains a difficult problem to classify breast density. This paper proposes TwoViewDensityNet, an end-to-end deep learning-based method for mammographic breast density classification. The craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammography provide two different views of each breast. As the two views are complementary, and dual-view-based methods have proven efficient, we use two views for breast classification. The loss function plays a key role in training a deep model; we employ the focal loss function because it focuses on learning hard cases. The method was thoroughly evaluated on two public datasets using 5-fold cross-validation, and it achieved an overall performance (F-score of 98.63%, AUC of 99.51%, accuracy of 95.83%) on DDSM and (F-score of 97.14%, AUC of 97.44%, accuracy of 96%) on the INbreast. The comparison shows that the TwoViewDensityNet outperforms the state-of-the-art methods for classifying breast density into BI-RADS class. It aids healthcare providers in providing patients with more accurate information and will help improve the diagnostic accuracy and reliability of mammographic breast density evaluation in clinical care.
An Anisotropic Diffusion Adaptive Filter for Image Denoising and Restoration Applied on Satellite Remote Sensing Images

This paper proposes an operating approach based on the anisotropic diffusion method to restore and denoise Satellite Remote Sensing Images (SRSIs). The contents of the approach are the motion by mean curvature to detect the noise direction for each degraded pixel and preserve the original edges of the image, and the gradient in the Gaussian kernel which restores the degraded pixel locally, assuring the estimation of its original value and saving the contrast of the image. The algorithm, concluded by our proposed system, treats noised SRSIs regardless of noise type, so better restoration is achieved. Experiments of the proposed system and of other approaches were conducted in MATLAB in order to demonstrate the efficiency of the proposed approach and its performance was confirmed through evaluation with PSNR and SSIM.
Estimation of Human Body Height Using Consumer-Level UAVs

Consumer-level UAVs are often employed for surveillance, especially in urban areas. Within this context, human recognition via estimation of biometric traits, like body height, is of pivotal relevance. Previous studies confirmed that the pinhole model could be used for this purpose, but only if the accurate distance between the aerial camera and the target is known. Unfortunately, low positional accuracy of the drones and the difficulties of retrieving the coordinates of a moving target like a human may prevent reaching the required level of accuracy. This paper proposes a novel solution that may overcome this issue. It foresees calculating the relative altitude of the drone from the target by knowing only the ground distance between two points visible in the image. This relative altitude can be then used to calculate the target-to-camera distance without using the coordinates of the drone or the target. The procedure was verified with real data collected with a quadcopter, first considering a controlled environment with a wooden pole of known height and then a person in a more realistic scenario. The verification confirmed that a high level of accuracy can be reached, even with regular market drones.
XtoE: A Novel Constructive and Camouflaged Adaptive Data Hiding and Image Encryption Scheme for High Dynamic Range Images

High dynamic range (HDR) image data hiding and encryption has attracted much interest in recent years due the benefits of providing high quality realistic images and versatile applications, such as copyright protection, data integrity, and covert communication. In this paper, we propose a novel constructive and camouflaged adaptive data hiding and image encryption scheme for HDR images. Our algorithm disguises hidden messages when converting an original OpenEXR format to the RGBE encoding, which contains the Red, Green, and Blue color channels and an exponent E channel. During the conversion process, we determine an optimal base for each pixel by considering the user’s demands and the exponent E channel information to achieve adaptive message concealment. To prevent inappropriate access to the stego image, we perform the bit-level permutation and confusion using a 2D Sine Logistic modulation map with hyperchaotic behavior and a random permutation scheme with the time complexity of ON. To the best of our knowledge, our algorithm is the first in HDR data hiding literature able to predict the image distortion and satisfy a user’s request for the embedding capacity. Our algorithm offers 18% to 32% larger embedding rate than that provided by the current state-of-the-art works without degrading the quality of the stego image. Experimental results confirm that our scheme provides high security superior to the competitors.
A designated tester-based certificateless public key encryption with conjunctive keyword search for cloud-based MIoT in dynamic multi-user environment

One of the essential technologies in the medical industry for achieving high performance and accuracy is the Medical Internet of Things (MIoT). The MIoT technology processes a large amount of patient information in real-time. And therefore, it poses a high risk of leaking personal information. Furthermore, as data is sent over public networks, security is critical in MIoT. To overcome the above issues, we developed a designated tester-based Certificateless Public-key Encryption with Conjunctive Keyword search for cloud-based MIoT in dynamic multi-user environment (dCL-PECK). This study explores three features: (1). A secure channel is not necessary to transfer the data from the data-user (i.e., data owner or data receiver) to the designated tester (i.e., medical cloud server) and allows only the designated tester to execute the test algorithm. (2). Data is encrypted only once for multiple data users and supports conjunctive keyword search. (3). Users are added and revoked dynamically. Furthermore, the proposed scheme is provably secure under ciphertext indistinguishable against inside and outside adversaries and trapdoor indistinguishable against outside adversaries. Finally, theoretical and experimental analyses have been performed, demonstrating that the proposed system is better than similar Certificateless Keyword Search (CLKS) systems. As a result, we feel that it is suitable for cloud-based MIoT.