Reviewerly/exHarmony huggingface dataset:
DCNAS-Net: deformation convolution and neural architecture search detection network for bone marrow oedema

Abstract Background Lumbago is a global disease that affects more than 500 million people worldwide. Bone marrow oedema is one of the main causes of the condition and clinical diagnosis is mainly made by radiologists manually reviewing MRI images to determine whether oedema is present. However, the number of patients with Lumbago has risen dramatically in recent years, which has brought a huge workload to radiologists. In order to improve the efficiency of diagnosis, this paper is devoted to developing and evaluating a neural network for detecting bone marrow edema in MRI images. Related work Inspired by the development of deep learning and image processing techniques, we design a deep learning detection algorithm specifically for the detection of bone marrow oedema from lumbar MRI images. We introduce deformable convolution, feature pyramid networks and neural architecture search modules, and redesign the existing neural networks. We explain in detail the construction of the network and illustrate the setting of the network hyperparameters. Results and discussion The detection accuracy of our algorithm is excellent. And its accuracy of detecting bone marrow oedema reached up to 90.6 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , an improvement of 5.7 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> compared to the original. The recall of our neural network is 95.1 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> , and the F1-measure also reaches 92.8 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> . And our algorithm is fast in detecting it, taking only 0.144 s per image. Conclusion Extensive experiments have demonstrated that deformable convolution and aggregated feature pyramid structures are conducive for the detection of bone marrow oedema. Our algorithm has better detection accuracy and good detection speed compared to other algorithms.
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI

For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning. Inspired by the effectiveness of meta-learning for optimizing a model across multiple tasks, here we propose a framework to adapt it to learn across multiple sites. We tested our meta-learning model for classifying ASD versus typically developing controls in 2,201 T1-weighted (T1-w) MRI scans collected from 38 imaging sites as part of Autism Brain Imaging Data Exchange (ABIDE) [age: 5.2-64.0 years]. The method was trained to find a good initialization state for our model that can quickly adapt to data from new unseen sites by fine-tuning on the limited data that is available. The proposed method achieved an ROC-AUC=0.857 on 370 scans from 7 unseen sites in ABIDE using a few-shot setting of 2-way 20-shot i.e., 20 training samples per site. Our results outperformed a transfer learning baseline by generalizing across a wider range of sites as well as other related prior work. We also tested our model in a zero-shot setting on an independent test site without any additional fine-tuning. Our experiments show the promise of the proposed site-agnostic meta-learning framework for challenging neuroimaging tasks involving multi-site heterogeneity with limited availability of training data.
Pointcheval-Sanders Signature-Based Synchronized Aggregate Signature

Synchronized aggregate signature is a special type of signature that all signers have a synchronized time period and allows aggregating signatures which are generated in the same period. This signature has a wide range of applications for systems that have a natural reporting period such as log and sensor data, or blockchain protocol. In CT-RSA 2016, Pointcheval and Sanders proposed the new randomizable signature scheme. Since this signature scheme is based on type-3 pairing, this signature achieves a short signature size and efficient signature verification. In this paper, we design the Pointchcval-Sanders signature-based synchronized aggregate signature scheme and prove its security under the generalized Pointcheval-Sanders assumption in the random oracle model. Our scheme offers the most efficient aggregate signature verification among synchronized aggregate signature schemes based on bilinear groups.
Heterogeneous Signcryption Scheme with Equality Test from CLC to PKI for Internet of Vehicles

In recent years, a lot of research has been carried out on the privacy protection of heterogeneous Internet of Vehicles (IoV). However, cloud servers in the IoV store a large amount of ciphertext data, so how to efficiently search ciphertext data encrypted by different public keys is also an urgent problem to be solved. This paper proposes a heterogeneous signcryption scheme with equality test from CLC to PKI (CPHS-ET) for the complex IoV environment. It allows roadside units to encrypt sensitive data in the CLC cryptosystem, which is then sent to the vehicle in the PKI cryptosystem and uploaded to the cloud server at the same time. Further, the cloud server can perform equality tests on different ciphertexts. In the random oracle model, our designed CPHS-ET is proved to satisfy confidentiality, unforgeability, one-way. Performance analysis shows that our CPHS-ET scheme is more efficient.
Image Style Transfer based on DeepLabV3+ Semantic Segmentation

Image style transfer has always been a popular research method in the computer vision community, which aims to learn the style of a given image and the content distribution of other images to generate new images with both the above style and content. Thanks to the rapid development of convolutional neural networks, the accuracy, and visualization of image segmentation and migration have been continuously improved, but there is still the problem of local content distortion. Some recent works introduce the segmentation branch to obtain pixel-level content information in order to obtain a more perfect transfer effect. In this paper, we use the VGG19 convolutional neural network model to extract high-level feature maps representing image content information and perform image style transfer based on the DeepLabV3+ semantic segmentation network, which can move images to the same or similar semantic regions during the entire transmission process. In order to prevent content distortion after image migration, we also introduce an affine function to control the content change of the image during image migration. Extensive experimental results show that the method in this paper can further clarify the segmentation boundary and improve the semantic accuracy of the transferred image. In addition, we also evaluated the expectations of different individuals on the degree of style transfer. By distributing the survey, this paper demonstrates that the method described in this study results in better image quality, more in line with the expectations of the majority of respondents.
A video transformer network for thyroid cancer detection on hyperspectral histologic images

Hyperspectral imaging is a label-free and non-invasive imaging modality that seeks to capture images in different wavelengths. In this study, we used a vision transformer that was pre-trained from video data to detect thyroid cancer on hyperspectral images. We built a dataset of 49 whole slide hyperspectral images (WS-HSI) of thyroid cancer. To improve training, we introduced 5 new data augmentation methods that transform spectra. We achieved an F-1 score of 88.1% and an accuracy of 89.64% on our test dataset. The transformer network and the whole slide hyperspectral imaging technique can have many applications in digital pathology.
CYBER SECURITY

Cybersecurity is a fast-growing field of IT concerned with reducing organizations' risk of hacks or data breaches. According to research from the Enterprise Strategy Group, 46% of organizations say that they have a "problematic shortage" of cybersecurity skills in 2016, up from 28% in 2015. Commercial, government, and non-governmental organizations all employ cybersecurity professionals. The fastest increases in demand for cybersecurity workers are in industries managing increasing volumes of consumer data such as finance, health care, and retail. However, the use of the term "cybersecurity" is more prevalent in government job descriptions. And prevention of cyber security is said here. Today, due to the modern lifestyle, people have joined technology life and use more technology for shopping and financial transactions in their cyberspace. At the same time, the safeguarding of knowledge has become increasingly difficult. In addition, the heavy use and growth of social media, online crime, or a cybercrime has increased. In the present world information security plays an important role. Whenever we think of cyber security, we think of 'cybercrimes,' which expand tremendously every day. Different governments and businesses take various steps to avoid this form of cybercrime. In addition to numerous cyber protection initiatives, many people are also very worried about it. This paper focuses primarily on cyber security concerns related to the new technology. It also concentrates on the new technologies for cyber security, ethics, and developments that impact cyber security. KEYWORDS: Cyber-security, cyber-crime, hacking, android apps.
“How didst thou come beneath the murky darkness?”: sense-making in light of the ancient Greeks and in the spirit of Hegel

Purpose This piece explores the philosophical origins of sense-making as defined in Brenda Dervin’s methodology. Design/methodology/approach This conceptual paper locates the origins of sense-making's rich ontological, epistemological and etymological heritage to the Classical Greece and the Pre-Socratic period. The Greek origins of sense-making‘s philosophical undercurrents surface again in Hegel’s Phenomenology of Spirit before the idea is picked up again in twentieth century philosophy and library science. Findings This is a conceptual paper and no empirical findings are presented. Originality/value This paper makes an original contribution to the study of information seeking and to sense making theory and methodology.
Architecture-Preserving Provable Repair of Deep Neural Networks

Deep neural networks (DNNs) are becoming increasingly important components of software, and are considered the state-of-the-art solution for a number of problems, such as image recognition. However, DNNs are far from infallible, and incorrect behavior of DNNs can have disastrous real-world consequences. This paper addresses the problem of architecture-preserving V-polytope provable repair of DNNs. A V-polytope defines a convex bounded polytope using its vertex representation. V-polytope provable repair guarantees that the repaired DNN satisfies the given specification on the infinite set of points in the given V-polytope. An architecture-preserving repair only modifies the parameters of the DNN, without modifying its architecture. The repair has the flexibility to modify multiple layers of the DNN, and runs in polynomial time. It supports DNNs with activation functions that have some linear pieces, as well as fully-connected, convolutional, pooling and residual layers. To the best our knowledge, this is the first provable repair approach that has all of these features. We implement our approach in a tool called APRNN. Using MNIST, ImageNet, and ACAS Xu DNNs, we show that it has better efficiency, scalability, and generalization compared to PRDNN and REASSURE, prior provable repair methods that are not architecture preserving.
MCANet: Multi-channel attention network with multi-color space encoder for underwater image classification

Underwater images suffer from multiple quality degradation issues due to the complex underwater environment. Unfortunately, currently available underwater image enhancement methods do not enhance all degradation types of underwater images, so it is important to study underwater image enhancement methods dedicated to specific degradation categories for underwater vision tasks. To tackle the above issues, we propose a Multi-channel Attention Network (MCANet) with Multi-color Space Encoder, which is instructive for designing dedicated classes of underwater image enhancement methods. Specifically, first, we use a multi-color space encoding method to fully integrate the advantages of features in different color spaces. Then, we obtain global and local deeper features of images in multiple dimensions through a multi-channel attention path aggregation strategy. Finally, we form the MCANet network architecture by embedding and stacking multi-channel attention modules to continuously strengthen the perception of image features. The experimental results show that the classification accuracy of MCANet reaches 98.737%.
Predicting onset of disease progression using temporal disease occurrence networks

Early recognition and prevention are crucial for reducing the risk of disease progression. This study aimed to develop a novel technique based on a temporal disease occurrence network to analyze and predict disease progression. This study used a total of 3.9 million patient records. Patient health records were transformed into temporal disease occurrence networks, and a supervised depth first search was used to find frequent disease sequences to predict the onset of disease progression. The diseases represented nodes in the network and paths between nodes represented edges that co-occurred in a patient cohort with temporal order. The node and edge level attributes contained meta-information about patients’ gender, age group, and identity as labels where the disease occurred. The node and edge level attributes guided the depth first search to identify frequent disease occurrences in specific genders and age groups. The patient history was used to match the most frequent disease occurrences and then the obtained sequences were merged together to generate a ranked list of diseases with their conditional probability and relative risk. The study found that the proposed method had improved performance compared to other methods. Specifically, when predicting a single disease, the method achieved an area under the receiver operating characteristic curve (AUC) of 0.65 and an F1-score of 0.11. When predicting a set of diseases relative to ground truth, the method achieved an AUC of 0.68 and an F1-score of 0.13. The ranked list generated by the proposed method, which includes the probability of occurrence and relative risk score, can provide physicians with valuable information about the sequential development of diseases in patients. This information can help physicians to take preventive measures in a timely manner, based on the best available information.
DSGA-Net: Deeply separable gated transformer and attention strategy for medical image segmentation network

To address the problems of under-segmentation and over-segmentation of small organs in medical image segmentation. We present a novel medical image segmentation network model with Depth Separable Gating Transformer and a Three-branch Attention module (DSGA-Net). Firstly, the model adds a Depth Separable Gated Visual Transformer (DSG-ViT) module into its Encoder to enhance (i) the contextual links among global, local, and channels and (ii) the sensitivity to location information. Secondly, a Mixed Three-branch Attention (MTA) module is proposed to increase the number of features in the up-sampling process. Meanwhile, the loss of feature information is reduced when restoring the feature image to the original image size. By validating Synapse, BraTs2020, and ACDC public datasets, the Dice Similarity Coefficient (DSC) of the results of DSGA-Net reached 81.24%,85.82%, and 91.34%, respectively. Moreover, the Hausdorff Score (HD) decreased to 20.91% and 5.27% on the Synapse and BraTs2020. There are 10.78% and 0.69% decreases compared to the Baseline TransUNet. The experimental results indicate that DSGA-Net achieves better segmentation than most advanced methods.
Validation of Remote Digital Pathology based diagnostic reporting of Frozen Sections from home

Despite the promising applications of whole-slide imaging (WSI) for frozen section (FS) diagnosis, its adoption for remote reporting is limited. To assess the feasibility and performance of home-based remote digital consultation for FS diagnosis. Cases accessioned beyond regular working hours (5 pm–10 pm) were reported simultaneously using optical microscopy (OM) and WSI. Validation of WSI for FS diagnosis from a remote site, i.e. home, was performed by 5 pathologists. Cases were scanned using a portable scanner (Grundium Ocus®40) and previewed on consumer-grade computer devices through a web-based browser (http://grundium.net). Clinical data and diagnostic reports were shared through a google spreadsheet. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM, and turnaround time (TAT), were recorded. The overall diagnostic accuracy for OM and WSI (from home) was 98.2% (range 97%–100%) and 97.6% (range 95%–99%), respectively, when compared with the reference standard. Almost perfect inter-observer (k = 0.993) and intra-observer (k = 0.987) agreement for WSI was observed by 4 pathologists. Pathologists used consumer-grade laptops/desktops with an average screen size of 14.58 inches (range = 12.3–17.7 inches) and a network speed of 64 megabits per second (range: 10–90 Mbps). The mean diagnostic assessment time per case for OM and WSI was 1:48 min and 5:54 min, respectively. Mean TAT of 27.27 min per case was observed using WSI from home. Seamless connectivity was observed in approximately 75% of cases. This study validates the role of WSI for remote FS diagnosis for its safe and efficient adoption in clinical use.
Making Video Quality Assessment Models Robust to Bit Depth

We introduce a novel feature set, which we call HDRMAX features, that when included into Video Quality Assessment (VQA) algorithms designed for Standard Dynamic Range (SDR) videos, sensitizes them to distortions of High Dynamic Range (HDR) videos that are inadequately accounted for by these algorithms. While these features are not specific to HDR, and also augment the equality prediction performances of VQA models on SDR content, they are especially effective on HDR. HDRMAX features modify powerful priors drawn from Natural Video Statistics (NVS) models by enhancing their measurability where they visually impact the brightest and darkest local portions of videos, thereby capturing distortions that are often poorly accounted for by existing VQA models. As a demonstration of the efficacy of our approach, we show that, while current state-of-the-art VQA models perform poorly on 10-bit HDR databases, their performances are greatly improved by the inclusion of HDRMAX features when tested on HDR and 10-bit distorted videos.
Creation of the Theory of Reading: Contribution of Yulia Petrovna Melentyeva

The paper considers contribution of Y.P. Melentyeva to the formation of the general theory of reading and determining the social purpose of library. The author analyses various approaches to the social functions of library, its responsibility to individual and society. In developing the theory of reading, Y.P. Melentyeva proceeds from the understanding of reading as the essential foundation of civilization, and the library as an institution of socialization of individual. Presenting information on the study of reading in the early 20th century and in the second half of the 20th century, the author characterizes publications by Y.P. Melentyeva devoted to this problem. The book “General Theory of Reading” (2015) includes all the necessary elements of scientific theory and bases on the study of reading as a cultural phenomenon. Encyclopaedic dictionary “Reading” (2021) summarizes the results of the study of reading and outlines prospective problems for further analysis. In the publication, reading is considered as an interdisciplinary concept for philosophy, pedagogy, psychology, book studies, library science, philology, physiology and other sciences. Y.P. Melentyeva connects the development of theory of reading with the study of problems of library ethics. Under her leadership, there was prepared and adopted in 1999 the first “Code of Professional Ethics of the Russian Librarian”; it was of great importance as a tool for consolidation of the library community in the dynamic controversial period of the domestic history. Y.P. Melentyeva is the leading specialist in the field of library science; she has made the most significant contribution to the formation of the general theory of reading. Her works are important for the formation of the professional worldview of modern librarians.
Julia language features for processing statistical data

The Julia programming language is a specialized language for scientific computing. It is relatively new, so most of the libraries for it are in the active development stage. In this article, the authors consider the possibilities of the language in the field of mathematical statistics. Special emphasis is placed on the technical component, in particular, the process of installing and configuring the software environment is described in detail. Since users of the Julia language are often not professional programmers, technical issues in setting up the software environment can cause difficulties that prevent them from quickly mastering the basic features of the language. The article also describes some features of Julia that distinguish it from other popular languages used for scientific computing. The third part of the article provides an overview of the two main libraries for mathematical statistics. The emphasis is again on the technical side in order to give the reader an idea of the general possibilities of the language in the field of mathematical statistics.
DATA COLLECTION AND PERFORMANCE EVALUATION OF RUNNING TRAINING SPORT USING DIFFERENT NEURAL NETWORK TECHNIQUES

With the increasing engagement of human beings in the pursuit of healthcare, running as a sport has become a fashionable and healthcare first choice. This research uses artificial intelligence technology to carry out intelligent analysis when conducting running training. Artificial intelligence technology can accurately analyze and predict the application requirements of sports training postures. We proposed an analysis of sports posture and a prediction system, which uses running training data in the form of a heart rate, recorded on a GPS smart sports watch, as well as using the recurrent neural network (RNN), long and short-term memory (LSTM) and the gate recursive unit (GRU). These three types of neural network methods can predict which method is best suited for a road race and can confirm that it will be completed within the scheduled finish time; these models will also perform an intelligent analysis of physical fitness (heart rate, pace) and running technology (cadence, pace). The training and test data are collected from the running training records (running distance, time, heart rate, stride frequency, stride length, pace, calories, altitude and other characteristic values) as input parameters, to test and compare the running completion time trends of the RNN, LSTM and GRU neural network methods in the exercise table, so as to evaluate their predictive abilities. The results show that the GRU method has the best predictive accuracy, and the least accurate is the LSTM method. After the hidden layers are added to the three predictive methods, the RNN is slightly regressive, the LSTM indicates a trend of significant improvement and the GRU exhibits less obvious changes.