Reviewerly/exHarmony huggingface dataset:
Predominant Cognitive Learning Particle Swarm Optimization for Global Numerical Optimization

Particle swarm optimization (PSO) has witnessed giant success in problem optimization. Nevertheless, its optimization performance seriously degrades when coping with optimization problems with a lot of local optima. To alleviate this issue, this paper designs a predominant cognitive learning particle swarm optimization (PCLPSO) method to effectively tackle complicated optimization problems. Specifically, for each particle, a new promising exemplar is constructed by letting its personal best position cognitively learn from a better personal experience randomly selected from those of others based on a novel predominant cognitive learning strategy. As a result, different particles preserve different guiding exemplars. In this way, the learning effectiveness and the learning diversity of particles are expectedly improved. To eliminate the dilemma that PCLPSO is sensitive to the involved parameters, we propose dynamic adjustment strategies, so that different particles preserve different parameter settings, which is further beneficial to promote the learning diversity of particles. With the above techniques, the proposed PCLPSO could expectedly compromise the search intensification and diversification in a good way to search the complex solution space properly to achieve satisfactory performance. Comprehensive experiments are conducted on the commonly adopted CEC 2017 benchmark function set to testify the effectiveness of the devised PCLPSO. Experimental results show that PCLPSO obtains considerably competitive or even much more promising performance than several representative and state-of-the-art peer methods.
TradeChain: Decoupling Traceability and Identity in Blockchain enabled Supply Chains

Blockchain technology can provide immutability, provenance and traceability in supply chains. To utilize Blockchain's full potential, it is important to link supply chain events to the relevant entities for traceability and accountability purposes. Authorized participation is realised through consortium of various organisations. Transactions are verified by peer nodes pertaining to the consortium. Hence, privacy preservation of trade sensitive information such as trade flows and locations of production, storage and retail sites cannot be ascertained. In this work, we propose a privacy-preservation framework, TradeChain, which decouples the trade events of participants using decentralised identities. TradeChain adopts the Self-Sovereign Identity (SSI) principles and makes the following novel contributions: a) it incorporates two separate ledgers: a public permissioned blockchain for maintaining identities and the permissioned blockchain for recording trade flows, b) it uses Zero Knowledge Proofs (ZKPs) on traders' private credentials to prove multiple identities on trade ledger and c) allows data owners to define dynamic access rules for verifying traceability information from the trade ledger using access tokens and Ciphertext Policy Attribute-Based Encryption (CP-ABE). A proof of concept implementation of TradeChain is presented on Hyperledger Indy and Fabric and an extensive evaluation of execution time, latency and throughput reveals minimal overheads.
Reconfigurable continuously-coupled 3D photonic circuit for Boson Sampling experiments

Boson Sampling is a computational paradigm representing one of the most viable and pursued approaches to demonstrate the regime of quantum advantage. Recent results have demonstrated significant technological leaps in single-photon generation and detection, leading to progressively larger experimental instances of Boson Sampling experiments in different photonic systems. However, a crucial requirement for a fully-fledged platform solving this problem is the capability of implementing large-scale interferometers, that must simultaneously exhibit low losses, high degree of reconfigurability and the realization of arbitrary transformations. In this work, we move a step forward in this direction by demonstrating the adoption of a compact and reconfigurable 3D-integrated platform for photonic Boson Sampling. We perform 3- and 4-photon experiments by using such platform, showing the possibility of programming the circuit to implement a large number of unitary transformations. These results show that such compact and highly-reconfigurable layout can be scaled up to experiments with larger number of photons and modes, and can provide a viable direction for hybrid computing with photonic processors.
Quantum blockchain based on asymmetric quantum encryption and a stake vote consensus algorithm

Abstract As emerging next-generation information technologies, blockchains have unique advantages in information transparency and transaction security. They have attracted great attentions in social and financial fields. However, the rapid development of quantum computation and the impending realization of quantum supremacy have had significant impacts on the advantages of traditional blockchain based on traditional cryptography. Here, we propose a blockchain algorithm based on asymmetric quantum encryption and a stake vote consensus algorithm. The algorithm combines a consensus algorithm based on the delegated proof of stake with node behaviour and Borda count (DPoSB) and quantum digital signature technology based on quantum state computational distinguishability with a fully flipped permutation ( $${\text{QSC}}{\text{D}}_{\text{ff}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mtext>QSCD</mml:mtext> <mml:mtext>ff</mml:mtext> </mml:msub> </mml:math> ) problem. DPoSB is used to generate blocks by voting, while the quantum signature applies quantum one-way functions to guarantee the security of transactions. The analysis shows that this combination offers better protection than other existing quantum-resistant blockchains. The combination can effectively resist the threat of quantum computation on blockchain technology and provide a new platform to ensure the security of blockchain.
Deep learning with RGB and thermal images onboard a drone for monitoring operations

Abstract This article describes the artificial intelligence (AI) component of a drone for monitoring and patrolling tasks associated with disaster relief missions in specific restricted disaster scenarios, as specified by the Advanced Robotics Foundation in Japan. The AI component uses deep learning models for environment recognition and object detection. For environment recognition, we use semantic segmentation, or pixel‐wise labeling, based on RGB images. Object detection is key for detecting and locating people in need. Since people are relatively small objects from the drone perspective, we use both RGB and thermal images. To train our models, we created a novel multispectral and publicly available data set of people. We used a geo‐location method to locate people on the ground. The semantic segmentation models were extensively tested using different feature extractors. We created two dedicated data sets, which we have made publicly available. Compared with the baseline model, the best‐performing model could increase the mean intersection over union (IoU) by 1.3%. Furthermore, we compared two types of person detection models. The first one is an ensemble model that combines RGB and thermal information via “late fusion”; the second one is a 4‐channel model that combines these two types of information in an “early fusion” manner. The results suggest that the 4‐channel model had a 40.6% increase of average precision for stricter IoU values (0.75) compared with the ensemble model and a 5.8% increase in the average precision compared with the thermal model. All models were deployed and tested on the NVIDIA AGX Xavier platform. To the best of our knowledge, this study was the first to use both RGB and thermal data from the perspective of a drone for monitoring tasks.
Anti-Interference From Noisy Labels: Mean-Teacher-Assisted Confident Learning for Medical Image Segmentation

Manually segmenting medical images is expertise-demanding, time-consuming and laborious. Acquiring massive high-quality labeled data from experts is often infeasible. Unfortunately, without sufficient high-quality pixel-level labels, the usual data-driven learning-based segmentation methods often struggle with deficient training. As a result, we are often forced to collect additional labeled data from multiple sources with varying label qualities. However, directly introducing additional data with low-quality noisy labels may mislead the network training and undesirably offset the efficacy provided by those high-quality labels. To address this issue, we propose a Mean-Teacher-assisted Confident Learning (MTCL) framework constructed by a teacher-student architecture and a label self-denoising process to robustly learn segmentation from a small set of high-quality labeled data and plentiful low-quality noisy labeled data. Particularly, such a synergistic framework is capable of simultaneously and robustly exploiting (i) the additional dark knowledge inside the images of low-quality labeled set via perturbation-based unsupervised consistency, and (ii) the productive information of their low-quality noisy labels via explicit label refinement. Comprehensive experiments on left atrium segmentation with simulated noisy labels and hepatic and retinal vessel segmentation with real-world noisy labels demonstrate the superior segmentation performance of our approach as well as its effectiveness on label denoising.
Lyapunov-Based Optimization of Edge Resources for Energy-Efficient Adaptive Federated Learning

The aim of this paper is to propose a novel dynamic resource allocation strategy for energy-efficient adaptive federated learning at the wireless network edge, with latency and learning performance guarantees. We consider a set of devices collecting local data and uploading processed information to an edge server, which runs stochastic gradient-based algorithms to perform continuous learning and adaptation. Hinging on Lyapunov stochastic optimization tools, we dynamically optimize radio parameters (e.g., set of transmitting devices, transmit powers, bits, and rates) and computation resources (e.g., CPU cycles at devices and at server) in order to strike the best trade-off between power, latency, and performance of the federated learning task. The framework admits both a model-based implementation, where the learning performance metrics are available in closed-form, and a data-driven approach, which works with online estimates of the learning performance of interest. The method is then customized to the case of federated least mean squares (LMS) estimation, and federated training of deep convolutional neural networks. Numerical results illustrate the effectiveness of our strategy to perform energy-efficient, low-latency, adaptive federated learning at the wireless network edge.
Machine Learning-Based Anomaly Detection Using K-Mean Array and Sequential Minimal Optimization

Recently, artificial intelligence (AI) techniques have been used to describe the characteristics of information, as they help in the process of data mining (DM) to analyze data and reveal rules and patterns. In DM, anomaly detection is an important area that helps discover hidden behavior within the data that is most vulnerable to attack. It also helps detect network intrusion. Algorithms such as hybrid K-mean array and sequential minimal optimization (SMO) rating can be used to improve the accuracy of the anomaly detection rate. This paper presents an anomaly detection model based on the machine learning (ML) technique. ML improves the detection rate, reduces the false-positive alarm rate, and is capable of enhancing the accuracy of intrusion classification. This study used a dataset known as network security-knowledge and data discovery (NSL-KDD) lab to evaluate a proposed hybrid ML technology. K-mean cluster and SMO were used for classification. In the study, the performance of the proposed anomaly detection was tested, and results showed that the use of K-mean and SMO enhances the rate of positive detection besides reducing the rate of false alarms and achieving a high accuracy at the same time. Moreover, the proposed algorithm outperformed recent and close work related to using similar variables and the environment by 14.48% and decreased false alarm probability (FAP) by (12%) in addition to giving a higher accuracy by 97.4%. These outcomes are attributed to the common algorithm providing an appropriate number of detectors to be generated with an acceptable accurate detection and a trivial false alarm probability (FAP). The proposed hybrid algorithm could be considered for anomaly detection in future data mining systems, where processing in real-time is highly likely to be reduced dramatically. The justification is that the hybrid algorithm can provide appropriate detectors numbers that can be generated with an acceptable detection accuracy and trivial FAP. Given to the low FAP, it is highly expected to reduce the time of the preprocessing and processing compared with the other algorithms.
Representing Videos as Discriminative Sub-graphs for Action Recognition

Human actions are typically of combinatorial structures or patterns, i.e., subjects, objects, plus spatio-temporal interactions in between. Discovering such structures is therefore a rewarding way to reason about the dynamics of interactions and recognize the actions. In this paper, we introduce a new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos. Specifically, we present MUlti-scale Sub-graph LEarning (MUSLE) framework that novelly builds space-time graphs and clusters the graphs into compact sub-graphs on each scale with respect to the number of nodes. Technically, MUSLE produces 3D bounding boxes, i.e., tubelets, in each video clip, as graph nodes and takes dense connectivity as graph edges between tubelets. For each action category, we execute online clustering to decompose the graph into sub-graphs on each scale through learning Gaussian Mixture Layer and select the discriminative sub-graphs as action prototypes for recognition. Extensive experiments are conducted on both Something-Something V1 & V2 and Kinetics-400 datasets, and superior results are reported when comparing to state-of-the-art methods. More remarkably, our MUSLE achieves to-date the best reported accuracy of 65.0% on Something-Something V2 validation set.
NeRF-SR: High Quality Neural Radiance Fields using Supersampling

We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly low-resolution (LR) inputs. Our method is built upon Neural Radiance Fields (NeRF) that predicts per-point density and color with a multi-layer perceptron. While producing images at arbitrary scales, NeRF struggles with resolutions that go beyond observed images. Our key insight is that NeRF benefits from 3D consistency, which means an observed pixel absorbs information from nearby views. We first exploit it by a super-sampling strategy that shoots multiple rays at each image pixel, which further enforces multi-view constraint at a sub-pixel level. Then, we show that NeRF-SR can further boost the performance of super-sampling by a refinement network that leverages the estimated depth at hand to hallucinate details from related patches on only one HR reference image. Experiment results demonstrate that NeRF-SR generates high-quality results for novel view synthesis at HR on both synthetic and real-world datasets without any external information. Project page: https://cwchenwang.github.io/NeRF-SR
Privacy-Preserving Continuous Authentication for Mobile and IoT Systems Using Warmup-Based Federated Learning

Continuous authentication for mobile devices acknowledges users by studying their behavioral interactions with their devices. It provides an extended protection mechanism that supplies an additional layer of security for smartphones and Internet of Things (IoT) devices and locks out intruders in cases of stolen credentials or hijacked sessions. Most of the continuous authentication efforts in the literature consist of collecting behavioral, sensory data from users, and extracting statistical patterns through adopting various Machine Learning (ML) techniques. The main drawback of these approaches is their heavy reliance on acquiring the users' personal data, which exposes the latter's privacy. To address this limitation, we introduce a novel Federated Learning (FL) based continuous authentication mechanism for mobile and IoT devices. Our approach preserves the users' privacy by allowing each individual to locally train an ML model that captures his/her behavior and then shares the model weights with the server for global aggregation. An extended scheme with a warmup FL approach for continuous authentication is proposed. Performance evaluation is done with a unique non-IID dataset built from three well-known datasets: MNIST, CIFAR-10, and FEMNIST. The extensive experimental results show a major accuracy increase in user authentication.
From the experience of organizing artistic and productive activities of older preschool children by means of distance education in the conditions of quarantine measures for the spread of COVID-19

The relevance of the article relates to the need for continuing preschool education under quarantine conditions to prevent the spread of COVID-19 by means of distance technologies and preparation of children for STEAM education. Artistic and productive activities are considered to be the resource of STEAM education. The aim of the article is to substantiate educational opportunities of distance education programs for undertaking artistic and productive activities of older preschool children on quarantine. Research methods: theoretical analysis, surveys, generalization of experience, analysis of children’s products, parental feedback. The scientific basis of the study is a set of approaches of different levels. Scientific novelty of the research is two-fold. The possibility of organizing artistic and productive activities of older preschool children by means of remote technologies in terms of preparing parents to interact with educators and organizational, technical, informational assistance to children. The features of the organization of such education in Ukraine under conditions of introducing quarantine measures are revealed. Practical significance is viewed through development and approbation of the content of classes on artistic and productive activities, integrated with the fundamentals of mathematics. There have been developed the questionnaires on digital competence for educators and digital literacy for parents.
Adoption of Federated Learning for Healthcare Informatics: Emerging Applications and Future Directions

The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.e., the Internet to train artificial intelligence (AI) models efficiently and effectively. The open nature of communication channels puts data privacy at high risk and affects the model training of collected data at centralized servers. To overcome this, an emerging concept, i.e., federated learning (FL) is a viable solution. It performs training at client nodes and aggregates their results to train the global model. The concept of local training preserves the privacy, confidentiality, and integrity of the patient&#x2019;s data which contributes effectively to the training process. The applicability of FL in the healthcare domain has various advantages, but it has not been explored to its extent. The existing surveys majorly focused on the role of FL in diverse applications, but there exists no detailed or comprehensive survey on FL in healthcare informatics (HI). We present a relative comparison of recent surveys with the proposed survey. To strengthen healthcare data privacy and increase the QoL of patients, we proposed an FL-based layered healthcare informatics architecture along with the case study on FL-based electronic health records (FL-EHR). We discuss the emerging FL models, and present the statistical and security challenges in FL adoption in medical setups. Thus, the review presents useful insights for both academia and healthcare practitioners to investigate FL application in HI ecosystems.
Quantifying <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>n</mml:mi></mml:math> -Photon Indistinguishability with a Cyclic Integrated Interferometer

We report on a universal method to measure the genuine indistinguishability of n-photons - a crucial parameter that determines the accuracy of optical quantum computing. Our approach relies on a low-depth cyclic multiport interferometer with N = 2n modes, leading to a quantum interference fringe whose visibility is a direct measurement of the genuine n-photon indistinguishability. We experimentally demonstrate this technique for a 8-mode integrated interferometer fabricated using femtosecond laser micromachining and four photons from a quantum dot single-photon source. We measure a four-photon indistinguishability up to 0.81$\pm$0.03. This value decreases as we intentionally alter the photon pairwise indistinguishability. The low-depth and low-loss multiport interferometer design provides an efficient and scalable path to evaluate the genuine indistinguishability of resource states of increasing photon number.
NFTs and Cryptocurrencies—The Metamorphosis of the Economy under the Sign of Blockchain: A Time Series Approach

Although NFTs (non-fungible tokens) and cryptocurrencies are active on the same market, their prices are not so closely related over time. The objective of this paper is to identify the relationship between the two types of assets (NFTs and the cryptocurrencies Ethereum, Crypto Coin, and Bitcoin), using data for the period between September 2020 until February 2022. The conclusions of the study are useful for cryptocurrency and NFT issuers, but also for investors on the financial market who are reconfiguring their portfolios with increasing frequency, and use these new assets for speculative or hedging purposes based on blockchain technology. The results highlighted relationships between NFTs and Ethereum, between Ethereum and Crypto Coin, and between Bitcoin and Ethereum, Ethereum being a bridge between all four. Therefore, NFTs present a relationship with Ethereum, the NFTs price had a causal effect on the price of Ethereum.
The effect of global price movements on the energy sector commodity on bitcoin price movement during the COVID-19 pandemic

This study aims to determine the effect of global price movements for energy sector commodities, especially Crude Oil and Natural Gas Prices, on cryptocurrency price movements. This study focuses more on the Bitcoin cryptocurrency. This study uses quantitative methods, and the data collection used is secondary data with weekly data and the period from January 1, 2020–July 31, 2021. The number of observations used in this study amounted to 79 observations. Secondary data sources are obtained through the website finance.yahoo.com. The data processing technique will be carried out using Stata and SPSS software, the Multiple Linear Regression method, and the Classical Assumption Test. The results of this study show that global prices for energy sector commodities, especially Crude Oil, Natural Gas, have a positive effect on Bitcoin price movements. These results indicate a link between energy and Bitcoin caused by Bitcoin miners who are mining Bitcoin using energy so that when the price of Bitcoin rises, the price of energy will also increase.