Reviewerly/exHarmony huggingface dataset:
Quantum-resistance in blockchain networks

The advent of quantum computing threatens blockchain protocols and networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH, will be no longer secure. Quantum computers will be able to break them within a short period of time. Similarly, Grover's algorithm concedes a quadratic advantage for mining blocks in certain consensus protocols such as proof of work. Today, there are hundreds of billions of dollars denominated in cryptocurrencies and other digital assets that rely on blockchain ledgers as well as thousands of blockchain-based applications storing value in blockchain networks. Cryptocurrencies and blockchain-based applications require solutions that guarantee quantum resistance in order to preserve the integrity of data and assets in these public and immutable ledgers. The quantum threat and some potential solutions are well understood and presented in the literature. However, most proposals are theoretical, require large QKD networks, or propose new quantum-resistant blockchain networks to be built from scratch. Our work, which is presented in this paper, is pioneer in proposing an end-to-end framework for post-quantum blockchain networks that can be applied to existing blockchain to achieve quantum-resistance. We have developed an open-source implementation in an Ethereum-based (i.e., EVM compatible) network that can be extended to other existing blockchains. For the implementation we have (i) used quantum entropy to generate post-quantum key pairs, (ii) established post-quantum TLS connections and X.509 certificates to secure the exchange of information between blockchain nodes over the internet without needing a large QKD network, (iii) introduced a post-quantum second signature in transactions using Falcon-512 post-quantum keys, and (iv) developed the first on-chain verification of post-quantum signatures using three different mechanisms that are compared and analyzed: Solidity smart-contracts run by the validators for each transaction, modified EVM Opcode, and precompiled smart contracts.
A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images

Trends of kidney cancer cases worldwide are expected to increase persistently and this inspires the modification of the traditional diagnosis system to respond to future challenges. Renal Cell Carcinoma (RCC) is the most common kidney cancer and responsible for 80-85% of all renal tumors. This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. The SCR block shares the information between two different layers and operates the shared data separately by providing beneficial supplements to each other. As a part of this study, we also introduced a new dataset for the grading of RCC with five different grades. We obtained 722 Hematoxylin & Eosin (H &E) stained slides of different patients and associated grades from the Department of Pathology, Kasturba Medical College (KMC), Mangalore, India. We performed comparable experiments which include deep learning models trained from scratch as well as transfer learning techniques using pre-trained weights of the ImageNet. To show the proposed model is generalized and independent of the dataset, we experimented with one additional well-established data called BreakHis dataset for eight class-classification. The experimental result shows that proposed RCCGNet is superior in comparison with the eight most recent classification methods on the proposed dataset as well as BreakHis dataset in terms of prediction accuracy and computational complexity.
Blockchain-Based Framework for Interoperable Electronic Health Records for an Improved Healthcare System

The healthcare industry has been transitioning from paper-based medical records to electronic health records (EHRs) in most healthcare facilities. However, the current EHR frameworks face challenges in secure data storage, credibility, and management. Interoperability and user control of personal data are also significant concerns in the healthcare sector. Although block chain technology has emerged as a powerful solution that can offer the properties of immutability, security, and user control on stored records, its potential application in EHR frameworks is not yet fully understood. To address this gap in knowledge, this research aims to provide an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards such as HIPAA and HL7. The research method employed is a systematic literature review to explore the current state of the art in the field of EHRs, including blockchain-based implementations of EHRs. The study defines the interoperability issues in the existing blockchain-based EHR frameworks, reviews various national and international standards of EHR, and further defines the interoperability requirements based on these standards. The proposed framework can offer safer methods to interchange health information for the healthcare sector and can provide the properties of immutability, security, and user control on stored records without the need for centralized storage. The contributions of this work include enhancing the understanding of the potential application of blockchain technology in EHR frameworks and proposing an interoperable blockchain-based EHR framework that can fulfill the requirements defined by various national and international EHR standards. Overall, this study has significant implications for the healthcare sector, as it can enhance the secure sharing and storage of electronic health data while ensuring the confidentiality, privacy, and integrity of medical records.
Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.
Crowdfunding Fraud Prevention using Smart Contracts

Crowdfunding is the method of generating money for a project or company by soliciting contributions from many individuals, typically online. For the people engaged in getting these monies, trust is crucial. Therefore, this research will aid in the analysis of how to integrate smart contracts and Blockchain technology into the prevalent crowdfunding process schemes. Because it makes it simpler for consumers to hold digital assets and data, blockchain is crucial. It is a worldwide distributed digital ledger that, in addition to using trusted and dependable technology, guarantees transparency for all network participants. Smart contracts have a number of benefits, including quicker transaction times, enhanced security, quicker money transfers, and better liquidity. The most significant benefit of the platform is the increased visibility of the project, which helps all stakeholders, including the creators, backers, and administrators of the platform.
SmS: SMOTE-stacked hybrid model for diagnosis of polycystic ovary syndrome using feature selection method

Polycystic Ovary syndrome (PCOS) is the most common serious health problem in women worldwide. It affects the women’s fertility and may lead to various health problems like type 2 diabetes, cardiovascular diseases, etc. Early diagnosis of PCOS is recommended for proper treatment. In this paper, we propose SmS: SMOTE-Stacked hybrid model, for the early diagnosis of PCOS. In the proposed model, we combine the ensemble technique ‘stacking’ with oversampling technique ‘SMOTE’ i.e., Synthetic minority oversampling technique. In the Stacking ensemble technique, training is performed at two levels: base level and meta level. We use six classifiers Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and AdaBoost (AdaB) as base leaners at the base level and consider each of these classifiers separately at the meta-level to implement SmS hybrid models: Stack-LR, Stack-SVM, Stack-DT, Stack-RF, Stack-NB and Stack-AdaB. Standardization, SMOTE and Backward Feature Elimination (BFE) are performed before training the classifiers. We evaluate the performance of the individual classifiers and hybrid models on PCOS dataset (Imbalanced) taken form the Kaggle Repository and observe that Stack-AdaB shows the most promising results.
BERT- and TF-IDF-based feature extraction for long-lived bug prediction in FLOSS: A comparative study

The correct prediction of long-lived bugs could help maintenance teams to build their plan and to fix more bugs that often adversely affect software quality and disturb the user experience across versions in Free/Libre Open-Source Software (FLOSS). Machine Learning and Text Mining methods have been applied to solve many real-world prediction problems, including bug report handling. Our research aims to compare the accuracy of ML classifiers on long-lived bug prediction in FLOSS using Bidirectional Encoder Representations from Transformers (BERT)- and Term Frequency - Inverse Document Frequency (TF-IDF)-based feature extraction. Besides that, we aim to investigate BERT variants on the same task. We collected bug reports from six popular FLOSS and used the Machine Learning classifiers to predict long-lived bugs. Furthermore, we compare different feature extractors, based on BERT and TF-IDF methods, in long-lived bug prediction. We found that long-lived bug prediction using BERT-based feature extraction systematically outperformed the TF-IDF. The SVM and Random Forest outperformed other classifiers in almost all datasets using BERT. Furthermore, smaller BERT architectures show themselves as competitive. Our results demonstrated a promising avenue to predict long-lived bugs based on BERT contextual embedding features and fine-tuning procedures.
Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models

Artificial intelligence applications have shown success in different medical and health care domains, and cardiac imaging is no exception. However, some machine learning models, especially deep learning, are considered black box as they do not provide an explanation or rationale for model outcomes. Complexity and vagueness in these models necessitate a transition to explainable artificial intelligence (XAI) methods to ensure that model results are both transparent and understandable to end users. In cardiac imaging studies, there are a limited number of papers that use XAI methodologies. This article provides a comprehensive literature review of state-of-the-art works using XAI methods for cardiac imaging. Moreover, it provides simple and comprehensive guidelines on XAI. Finally, open issues and directions for XAI in cardiac imaging are discussed.
Content-based medical image retrieval with opponent class adaptive margin loss

The increasing utilization of medical imaging technology with digital storage capabilities has facilitated the compilation of large-scale data repositories. Fast access to image samples with similar appearance to suspected cases in these repositories can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on quantitative assessment of image similarity based on image features in a latent space. Since conventional methods based on hand-crafted features typically show poor generalization performance, learning-based CBIR methods have received attention recently. A common framework in this domain involves classifier-guided models that are trained to detect different image classes. Similarity assessments are then performed on the features captured by the intermediate stages of the trained models. While classifier-guided methods are powerful in inter-class discrimination, they are suboptimally sensitive to within-class differences in image features. An alternative framework instead performs task-agnostic training to learn an embedding space that enforces the representational discriminability of images. Within this representational-learning framework, a powerful method is triplet-wise learning that addresses the deficiencies of point-wise and pair-wise learning in characterizing the similarity relationships between image classes. However, the traditional triplet loss enforces separation between only a subset of image samples within the triplet via a manually-set constant margin value, so it can lead to suboptimal segregation of opponent classes and limited generalization performance. To address these limitations, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. To maintain optimally discriminative representations, OCAM considers relationships among all image pairs within the triplet and utilizes an adaptive margin value that is automatically selected per dataset and during the course of training iterations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). On average, OCAM shows an mAP performance of 86.30% in the KVASIR dataset, 70.30% in the ISIC 2019 dataset, and 85.57% in the X-RAY dataset. Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against competing triplet-wise methods at 1.52%, classifier-guided methods at 2.29%, and non-triplet representational-learning methods at 4.56%.
Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence

Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.83 versus 0.76 in an external validation cohort, p = 0.0005) and can identify a subset of patients with excellent prognoses who may not need further genomic testing.
The Return of the SDitH

This paper presents a code-based signature scheme based on the well-known syndrome decoding (SD) problem. The scheme builds upon a recent line of research which uses the Multi-Party-Computation-in-the-Head (MPCitH) approach to construct efficient zero-knowledge proofs, such as Syndrome Decoding in the Head (SDitH), and builds signature schemes from them using the Fiat-Shamir transform. At the heart of our proposal is a new approach, Hypercube-MPCitH, to amplify the soundness of any MPC protocol that uses additive secret sharing. An MPCitH protocol with N parties can be repeated D times using parallel composition to reach the same soundness as a protocol run with $$N^D$$ parties. However, the former comes with D times higher communication costs, often mainly contributed by the usage of D ‘auxiliary’ states (which in general have a significantly bigger impact on size than random states). Instead of that, we begin by generating $$N^D$$ shares, arranged into a D-dimensional hypercube of side N containing only one ‘auxiliary’ state. We derive from this hypercube D sharings of size N which are used to run D instances of an N party MPC protocol. Hypercube-MPCitH leads to a protocol with $$1/N^D$$ soundness error, requiring $$N^D$$ offline computation, but with only $$N\cdot D$$ online computation, and only 1 ‘auxiliary’. As the (potentially offline) share generation phase is generally inexpensive, this leads to trade-offs that are superior to just using parallel composition. Our novel method of share generation and aggregation not only improves certain MPCitH protocols in general but also shows in concrete improvements of signature schemes. Specifically, we apply it to the work of Feneuil, Joux, and Rivain (CRYPTO’22) on code-based signatures, and obtain a new signature scheme that achieves a 8.1x improvement in global runtime and a 30x improvement in online runtime for their shortest signatures size (8,481 Bytes). It is also possible to leverage the fact that most computations are offline to define parameter sets leading to smaller signatures: 6,784 Bytes for 26 ms offline and 5,689 Bytes for 320 ms offline. For NIST security level 1, online signature cost is around 3 million cycles (<1 ms on commodity processors), regardless of signature size.
Fused Weighted Federated Deep Extreme Machine Learning Based on Intelligent Lung Cancer Disease Prediction Model for Healthcare 5.0

In the era of advancement in information technology and the smart healthcare industry 5.0, the diagnosis of human diseases is still a challenging task. The accurate prediction of human diseases, especially deadly cancer diseases in the smart healthcare industry 5.0, is of utmost importance for human wellbeing. In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a dizzying pace, from a small wristwatch to a big aircraft. With this advancement in the healthcare industry, there also rises the issue of data privacy. To ensure the privacy of patients’ data and fast data transmission, federated deep extreme learning entangled with the edge computing approach is considered in this proposed intelligent system for the diagnosis of lung disease. Federated deep extreme machine learning is applied for the prediction of lung disease in the proposed intelligent system. Furthermore, to strengthen the proposed model, a fused weighted deep extreme machine learning methodology is adopted for better prediction of lung disease. The MATLAB 2020a tool is used for simulation and results. The proposed fused weighted federated deep extreme machine learning model is used for the validation of the best prediction of cancer disease in the smart healthcare industry 5.0. The result of the proposed fused weighted federated deep extreme machine learning approach achieved 97.2%, which is better than the state-of-the-art published methods.
Democratizing artificial intelligence: How no-code AI can leverage machine learning operations

Organizations are increasingly seeking to generate value and insights from their data by integrating advances in artificial intelligence (AI) such as machine learning (ML) systems into their operations. However, there are several managerial challenges associated with ML operations (MLOps). In this article we outline three key challenges and discuss how an emerging form of AI platforms – 'no-code AI' – may help organizations to address and overcome them. We outline how no-code AI can leverage MLOps by closing the gap between business and technology experts, enabling faster iterations between problems and solutions, and aiding infrastructure management. After outlining important remaining challenges associated with no-code AI and MLOps we propose three managerial recommendations. By doing so, we provide insights into an important novel, emerging phenomenon in AI software and set the stage for further research in the area.
Blockchain-based management of demand response in electric energy grids: A systematic review

The complexities associated with modern energy grids, including high penetration levels of renewable energy sources at the end use customers, the proliferation of electric vehicles (EVs), as well as decentralized energy management systems and energy data portals and services, require secure, reliable and cost-effective new platforms to administer such systems in a transparent manner. Blockchain technology can address such challenging complexities efficiently. This paper presents a thorough literature review of all potential blockchain applications in electric energy systems. First, all potential blockchain application possibilities and constraints in electric energy systems have been identified. Then, the implementations of blockchain technology in power systems are divided into various categories, such as demand response (DR), EVs, decentralized energy management, energy trading and distributed renewable energy. A detailed literature analysis is conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines in a systematic approach using credible and reliable database systems such as IEEE Xplore, ScienceDirect, and Scopus to highlight current advancements in blockchain technology uses in electric energy systems particularly in DR, as well as future potential and existing problems. The paper is divided into three parts. The first part lists the important publications that have delved into the implementation of Blockchain in demand response. Trends of Blockchain applications in other electric energy system areas are detailed in the second section. The final part highlights the evolution and advancement of blockchain-based electric energy systems including favourable attributes of structures and designs, as well as the challenges and future trends.