Reviewerly/exHarmony huggingface dataset:
Quantum Key Distribution Secured Optical Networks: A Survey

Increasing incidents of cyber attacks and evolution of quantum computing poses challenges to secure existing information and communication technologies infrastructure. In recent years, quantum key distribution (QKD) is being extensively researched, and is widely accepted as a promising technology to realize secure networks. Optical fiber networks carry a huge amount of information, and are widely deployed around the world in the backbone terrestrial, submarine, metro, and access networks. Thus, instead of using separate dark fibers for quantum communication, integration of QKD with the existing classical optical networks has been proposed as a cost-efficient solution, however, this integration introduces new research challenges. In this paper, we do a comprehensive survey of the state-of-the-art QKD secured optical networks, which is going to shape communication networks in the coming decades. We elucidate the methods and protocols used in QKD secured optical networks, and describe the process of key establishment. Various methods proposed in the literature to address the networking challenges in QKD secured optical networks, specifically, routing, wavelength and time-slot allocation (RWTA), resiliency, trusted repeater node (TRN) placement, QKD for multicast service, and quantum key recycling are described and compared in detail. This survey begins with the introduction to QKD and its advantages over conventional encryption methods. Thereafter, an overview of QKD is given including quantum bits, basic QKD system, QKD schemes and protocol families along with the detailed description of QKD process based on the Bennett and Brassard-84 (BB84) protocol as it is the most widely used QKD protocol in the literature. QKD system are also prone to some specific types of attacks, hence, we describe the types of quantum hacking attacks on the QKD system along with the methods used to prevent them. Subsequently, the process of point-to-point mechanism of QKD over an optical fiber link is described in detail using the BB84 protocol. Different architectures of QKD secured optical networks are described next. Finally, major findings from this comprehensive survey are summarized with highlighting open issues and challenges in QKD secured optical networks.
When blockchain meets smart grids: A comprehensive survey

Recent years have witnessed an increasing interest in the blockchain technology, and many blockchain-based applications have been developed to take advantage of its decentralization, transparency, fault tolerance, and strong security. In the field of smart grids, a plethora of proposals have emerged to utilize blockchain for augmenting intelligent energy management, energy trading, security and privacy protection, microgrid management, and energy vehicles. Compared with traditional centralized approaches, blockchain-based solutions are able to exploit the advantages of blockchain to realize better functionality in smart grids. However, the blockchain technology itself has its disadvantages in low processing throughput and weak privacy protection. Therefore, it is of paramount importance to study how to integrate blockchain with smart grids in a more effective way so that the advantages of blockchain can be maximized and its disadvantages can be avoided. This article surveys the state-of-the-art solutions aiming to integrate the emergent blockchain technology with smart grids. The goal of this survey is to discuss the necessity of applying blockchain in different components of smart grids, identify the challenges encountered by current solutions, and highlight the frameworks and techniques used to integrate blockchain with smart grids. We also present thorough comparison studies among blockchain-based solutions for smart grids from different perspectives, with the aim to provide insights on integrating blockchain with smart grids for different smart grid management tasks. Finally, we list the current projects and initiatives demonstrating the current effort from the practice side. Additionally, we draw attention to open problems that have not yet been tackled by existing solutions, and point out possible future research directions.
Convergence between Blockchain and the Internet of Things

The objective of this study was to determine the effectiveness of a particular convergencemodel for IoT and blockchain. Multiple regression model was selected to determine the effectiveof a specific convergence model. Three convergence models were selected for this study, includingthe hybrid approach, the IoT-blockchain approach, and the IoT-IoT approach. The findingsindicated that there are issues with convergence between two different technologies. The otherfinding was that the hybrid model provided the best convergence platform for integrating IoT withblockchain. Regarding the IoT and blockchain, convergence challenges included the limitedcapacity of IoT devices to handle the nature of distributed ledgers. The recommendation is that theaspects of traditional blockchain should be redesigned because of new requirements of IoT,including smart contracts, consensus protocol, data privacy, and security.
Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review

BackgroundOver the past decade, the development of molecular high-throughput methods (omics) increased rapidly and provided new insights for cancer research. In parallel, deep learning approaches revealed the enormous potential for medical image analysis, especially in digital pathology. Combining image and omics data with deep learning tools may enable the discovery of new cancer biomarkers and a more precise prediction of patient prognosis. This systematic review addresses different multimodal fusion methods of convolutional neural network-based image analyses with omics data, focussing on the impact of data combination on the classification performance.MethodsPubMed was screened for peer-reviewed articles published in English between January 2015 and June 2021 by two independent researchers. Search terms related to deep learning, digital pathology, omics, and multimodal fusion were combined.ResultsWe identified a total of 11 studies meeting the inclusion criteria, namely studies that used convolutional neural networks for haematoxylin and eosin image analysis of patients with cancer in combination with integrated omics data. Publications were categorised according to their endpoints: 7 studies focused on survival analysis and 4 studies on prediction of cancer subtypes, malignancy or microsatellite instability with spatial analysis.ConclusionsImage-based classifiers already show high performances in prognostic and predictive cancer diagnostics. The integration of omics data led to improved performance in all studies described here. However, these are very early studies that still require external validation to demonstrate their generalisability and robustness. Further and more comprehensive studies with larger sample sizes are needed to evaluate performance and determine clinical benefits.
Digital Quantum Simulation of the Schwinger Model and Symmetry Protection with Trapped Ions

Combining experimental and algorithmic advances in quantum simulation improves the investigation of gauge field theories: pair creation is simulated for longer times using less computational resources.
Ant colony optimization equipped with an ensemble of heuristics through multi-criteria decision making: A case study in ensemble feature selection

Ant Colony Optimization (ACO) is a probabilistic and approximation metaheuristic algorithm to solve complex combinatorial optimization problems. ACO algorithm is inspired by the behavior of a colony of real ants and uses their pheromone trials to find optimal solutions. Since the beginning of the ACO algorithm, many researchers have tried to improve the performance and stability of the algorithm by using various methodologies. Resolving the exploitation/exploration dilemma by an efficient procedure is critical in improving the ACO. One of the critical parameters in ACO is selecting the heuristic that can affect the movements of ants. So far, the use of several heuristics in ACO has not been studied. We believe that using multiple heuristics instead of a single heuristic can improve the ACO algorithm. For this matter, we have proposed an ACO algorithm based on the ensemble of heuristics using a Multi-Criteria Decision-Making (MCDM) procedure. It means that the movement of the ants is defined based on the judgment of multiple experts (criteria). The idea is based on the hypothesis that different heuristics give us more information about the subsequent nodes, and the variety of these methods examines the different aspects to achieve better and optimal solutions in ACO. In this paper, we have applied our proposed method to the ensemble feature selection task to evaluate the performance of the proposed method. Blending several feature selection methods is regular to tackle the feature selection problem, and also efficiently combining feature selection methods is still challenging. Some well-known ensemble feature selection and primary feature selection methods have been compared with Ant-MCDM on twelve datasets to evaluate the performance of the proposed method in the feature selection task.
Towards the industrialisation of quantum key distribution in communication networks: A short survey

Quantum information and communication technology will lead us to the new era of ultra-fast and absolute-secure networks. With the emergence of quantum supremacy on the horizon, the security of various classical encryption systems soon may be deemed obsolete. As a remedy, quantum key distribution (QKD) is proposed as a novel quantum-based secret keys exchange, which is developed to solve the problems of legacy encryption. It is anticipated that QKD will provide stronger security for future communication systems even in the presence of malicious quantum attacks. As the QKD research and development is getting mature, the theoretical use cases of QKD in various industries are proliferating. In this treatise, we summarise the potential applications of QKD for future communication technology while highlighting the ongoing standardisation efforts essential for the sustainability and reliability of the near-future deployment. Additionally, we also present the various challenges faced by both discrete variable and continuous variable QKD schemes hindering their widespread implementation into our future communication networks.
An Evidential Software Risk Evaluation Model

Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.
Communication-Efficient and Cross-Chain Empowered Federated Learning for Artificial Intelligence of Things

Conventional machine learning approaches aggregate all training data in a central server, which causes massive communication overhead of data transmission and is also vulnerable to privacy leakage. Thereby, blockchain-based federated learning has emerged to protect Artificial Intelligence of Things (AIoT) devices from exposing their private data by the Federated Learning (FL) framework, and also enables decentralized model training without the vulnerability of a central server. However, the existing blockchain-based FL systems still suffer from (i) limited scalability of the single blockchain framework; and (ii) large communication costs incurred by iterative large-size model update transmission. To this end, we first design an efficient cross-chain framework for scalable and flexible model training management, in which multiple blockchains are customized for specific FL tasks and individually perform learning tasks for privacy protection. A cross-chain scheme is proposed to enable secure blockchain collaboration and interactions for efficient model training, model trading, and payment. We then propose a flexible model update compression scheme to save communication costs almost without compromising accuracy. Moreover, for model trading markets, we design a dynamic pricing scheme using machine learning-based auction for model trading. Numerical results demonstrate that the proposed framework and schemes achieve scalable, flexible, and communication-efficient decentralized FL systems in AIoT.
ANAF-IoMT: A Novel Architectural Framework for IoMT-Enabled Smart Healthcare System by Enhancing Security Based on RECC-VC

The Internet of Medical Things (IoMT) is an arising trend that provides a significant amount of efficient and effective services for patients as well as healthcare professionals for the treatment of disparate diseases. The IoMT has numerous benefits; however, the security issue still persists as a challenge. The lack of security awareness among novice IoMT users and the risk of several intermediary attacks for accessing health information severely endanger the use of IoMT. In this article, rooted elliptic curve cryptography with Vigenère cipher (RECC-VC) centered security amelioration on the IoMT is proposed for enhancing security. First, this work utilizes the exponential K-anonymity algorithm for privacy preservation. Second, a new improved Elman neural network (IENN) is proposed for analyzing the sensitivity level of data. The Gaussian mutated chimp optimization is employed for weight updating in this IENN. Finally, a novel RECC-VC is proposed for securely uploading the data to the cloud server. Additionally, data are stored in the cloud server using blockchain technology. In experimental analysis, the proposed methodologies attain better results than the prevailing methods. The proposed IENN model achieves an accuracy of 96% and is validated against state-of-the-art methods. Also, the proposed RECC-VC attains 98% of the security level.
FRNet: Feature Reconstruction Network for RGB-D Indoor Scene Parsing

We recently demonstrated the remarkable performance of scene parsing, and one of its aspects was shown to be relevant to performance, namely, generation of multilevel feature representations. However, most existing scene parsing methods obtain multilevel feature representations with weak distinctions and large spans. Therefore, despite using complex mechanisms, the effects on the feature representations are minimal. To address this, we leverage the inherent multilevel cross-modal data and back propagation to develop a novel feature reconstruction network (FRNet) for RGB-D indoor scene parsing. Specifically, a feature construction encoder is proposed to obtain the features layerwise in a top-down manner, where the feature nodes in the higher layer flow to the adjacent low layer by dynamically changing their structure. In addition, we propose a cross-level enriching module in the encoder to selectively refine and weight the features in each layer in the RGB and depth modalities as well as a cross-modality awareness module to generate the feature nodes containing the modality data. Finally, we integrate the multilevel feature representations simply via dilated convolutions at different rates. Extensive quantitative and qualitative experiments were conducted, and the results demonstrate that the proposed FRNet is comparable to state-of-the-art RGB-D indoor scene parsing methods on two public indoor datasets.
Hybrid Contrastive Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) aims to learn discriminative features without human-annotated labels. Recently, contrastive learning has provided a new prospect for unsupervised person Re-ID, and existing methods primarily constrain the feature similarity among easy sample pairs. However, the feature similarity among hard sample pairs is neglected, which yields suboptimal performance in unsupervised person Re-ID. In this paper, we propose a novel Hybrid Contrastive Model (HCM) to perform the identity-level contrastive learning and the image-level contrastive learning for unsupervised person Re-ID, which adequately explores feature similarities among hard sample pairs. Specifically, for the identity-level contrastive learning, an identity-based memory is constructed to store pedestrian features. Accordingly, we define the dynamic contrast loss to identify identity information with dynamic factor for distinguishing hard/easy samples. As for the image-level contrastive learning, an image-based memory is established to store each image feature. We design the sample constraint loss to explore the similarity relationship between hard positive and negative sample pairs. Furthermore, we optimize the two contrastive learning processes in one unified framework to make use of their own advantages as so to constrain the feature distribution for extracting potential information. Extensive experiments demonstrate that the proposed HCM distinctly outperforms existing methods.
The Effect of Knowledge Management on the Sustainability of Technology-Driven Businesses in Emerging Markets: The Mediating Role of Social Media

Sustainability is a fundamental principle for human resource management worldwide. This study aimed to examine the effect of knowledge management components on the sustainability of technology-driven businesses mediated by social media in emerging markets. This descriptive-correlational study was conducted in April 2022. The statistical population comprised 537 businesses, and the sample size (estimated using Cochran’s formula) was 224 research and development employees. A 25-item questionnaire scored on a 5-point Likert scale was administered to collect data, which were analyzed in SmartPLS 3. The findings showed that for technology-driven businesses to be in a sustainable environment, knowledge management components must be applied in all parts of the company. The application of knowledge management creates value and a sustainable competitive advantage in a dynamic environment. Furthermore, effective participation in the organizational social network can activate knowledge management and create value. Thus, knowledge acquisition through social media optimizes learning and ideation, and for technology-driven companies that lack resources, this acquisition enables development and sustainability in a dynamic environment.
BIoMT: A State-of-the-Art Consortium Serverless Network Architecture for Healthcare System Using Blockchain Smart Contracts

Owing to the sensitive nature of healthcare data, the aforementioned approach to transferring patient data to central servers creates serious security and privacy issues. In addition, blockchain distributed ledger technology has introduced immutable storage and decentralized data management capability, which handles a large number of distributed nodes of E-Healthcare transactions via a serverless network, but in a limited manner because of blockchain-enabled resources. In this scenario, the medical industries are concerned about constituting an innovation in health information preservation and exchanging service delivery protocols without the connectivity of an untrusted third-party infrastructure. In this study, we proposed a blockchain hyperledger fabric-enabled consortium architecture called BIoMT, which provides security, integrity, transparency, and provenance to health-related transactions and exchanges sensitive clinical information in a serverless peer-to-peer (P2P) secure network environment. A consensus is designed and created to reduce the rate of blockchain resource constraints on the Internet of Medical Things (IoMT). The privacy of individual health transactions before sharing is protected using the NuCypher Re-Encryption mechanism, which increases security and provides medical ledger integrity and transparency. Smart contracts are created and deployed to automate device registration, exchange transactions, and ledger preservation in immutable storage (filecoin) after cross verification and validation. The experimental results show that the proposed BIoMT reduces the computational cost by 26.13%, and the robust medical node generation increases to 60.37%. Thus, only 31.79% and 74.21% of IoMT-related information and serverless P2P network usage are maintained and saved, respectively.
FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients

Federated learning (FL) is vulnerable to model poisoning attacks, in which malicious clients corrupt the global model via sending manipulated model updates to the server. Existing defenses mainly rely on Byzantine-robust or provably robust FL methods, which aim to learn an accurate global model even if some clients are malicious. However, they can only resist a small number of malicious clients. It is still an open challenge how to defend against model poisoning attacks with a large number of malicious clients. Our FLDetector addresses this challenge via detecting malicious clients. FLDetector aims to detect and remove the majority of the malicious clients such that a Byzantine-robust or provably robust FL method can learn an accurate global model using the remaining clients. Our key observation is that, in model poisoning attacks,the model updates from a client in multiple iterations are inconsistent. Therefore, FLDetector detects malicious clients via checking their model-updates consistency. Roughly speaking, the server predicts a client's model update in each iteration based on historical model updates and flags a client as malicious if the received model update from the client and the predicted model update are inconsistent in multiple iterations. Our extensive experiments on three benchmark datasets show that FLDetector can accurately detect malicious clients in multiple state-of-the-art model poisoning attacks and adaptive attacks tailored to FLDetector. After removing the detected malicious clients, existing Byzantine-robust FL methods can learn accurate global models.
Mitiq: A software package for error mitigation on noisy quantum computers

We introduce Mitiq, a Python package for error mitigation on noisy quantum computers. Error mitigation techniques can reduce the impact of noise on near-term quantum computers with minimal overhead in quantum resources by relying on a mixture of quantum sampling and classical post-processing techniques. Mitiq is an extensible toolkit of different error mitigation methods, including zero-noise extrapolation, probabilistic error cancellation, and Clifford data regression. The library is designed to be compatible with generic backends and interfaces with different quantum software frameworks. We describe Mitiq using code snippets to demonstrate usage and discuss features and contribution guidelines. We present several examples demonstrating error mitigation on IBM and Rigetti superconducting quantum processors as well as on noisy simulators.