Reviewerly/exHarmony huggingface dataset:
Rotation-inspired circuit cut optimization

Recent works have demonstrated that large quantum circuits can be cut and decomposed into smaller clusters of quantum circuits with fewer qubits that can be executed independently on a small quantum computer. Classical post-processing then combines the results from each cluster to reconstruct the output of the original quantum circuit. However, the runtime for such hybrid quantum-classical algorithms is exponential in the number of cuts on a circuit. We propose Rotation-Inspired Circuit Cut Optimization (RICCO), an alternative method which reduces the post-processing overhead of circuit cutting, at the cost of having to solve an optimization problem. RICCO introduces unitary rotations at cut locations to rotate the quantum state such that expectation values with respect to one set of observables are maximized and others are set to zero. We demonstrate practical application of RICCO to VQE by classically simulating a small instance of VQE and comparing it to one of the existing circuitcutting methods.
Information sharing and deferral option in cybersecurity investment

This study investigates the effect of information sharing and deferral option on a firm's information security investment strategies by considering strategic interactions between a firm and an attacker. We find that 1) information sharing decreases a firm's security investment rate. 2) If a deferral decision is possible, the firm will decrease its immediate investment, and avoid non-investment. 3) After information sharing, the probability of a firm's deferral decision increases for low-benefit information (SL) but decreases for high-benefit information (SH). 4) When information sharing accuracy is low, a firm only defers decisions in a fraction of SL; when information sharing accuracy is high, the firm defers its decisions in all SL and a fraction of SH. 5) Information sharing can improve the effect of deferral decision when accuracy is low but weaken it when accuracy is high. These results contradict the literature, wherein information sharing reduces a firm's uncertainty on cybersecurity investment and decreases deferment options associated with investment.
A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations

Self-attention mechanism-based algorithms are attractive in digital pathology due to their interpretability, but suffer from computation complexity. This paper presents a novel, lightweight Attention-based Multiple Instance Mutation Learning (AMIML) model to allow small-scale attention operations for predicting gene mutations. Compared to the standard self-attention model, AMIML reduces the number of model parameters by approximately 70%. Using data for 24 clinically relevant genes from four cancer cohorts in TCGA studies (UCEC, BRCA, GBM, and KIRC), we compare AMIML with a standard self-attention model, five other deep learning models, and four traditional machine learning models. The results show that AMIML has excellent robustness and outperforms all the baseline algorithms in the vast majority of the tested genes. Conversely, the performance of the reference deep learning and machine learning models vary across different genes, and produce suboptimal prediction for certain genes. Furthermore, with the flexible and interpretable attention-based pooling mechanism, AMIML can further zero in and detect predictive image patches.
Satellite cloud image segmentation based on lightweight convolutional neural network

More than 50% of the images captured by optical satellites are covered by clouds, which reduces the available information in the images and seriously affects the subsequent applications of satellite images. Therefore, the identification and segmentation of cloud regions come to be one of the most important problems in current satellite image processing. Due to the complexity and variability of satellite images, especially when the ground is covered with snow, the boundary information of cloud regions is difficult to be accurately identified. The fast and accurate segmentation of cloud regions is a difficult point in the current research. We propose a lightweight convolutional neural network. Firstly, channel attention is used to optimize the effective information in the feature maps as a way to improve the network's ability to extract semantic information at each scale. Then, we fuse high and low-dimensional feature maps to enhance the network's ability to obtain small-scale semantic information. In addition, the feature aggregation module automatically adjusts the input multi-level feature weights to highlight the details of different features. Finally, we design the fully connected conditional random field to solve the problem that some noise in the input image and local minima during training is passed to the output layer resulting in the loss of edge features. Experimental results show that the proposed method achieves 0.9695 and 0.8218 for overall accuracy and recall, respectively, which has higher segmentation accuracy with the shortest time consumption compared with other state-of-the-art methods.
Future Scenarios for High School Digital Technology in New Zealand

Digital Technologies (DT) achievement standards including computer science and programming were introduced as elective subjects in New Zealand’s high schools more than 10 years ago. However, many challenges still exist with regards to student participation and the teaching of these subjects. This motivated us to seek local and international experts’ opinions regarding the future of high school DT education in New Zealand, and possible solutions to improve the present situation. In this paper we present results from a three round Delphi study with a panel of 32 experts, exploring their perspectives on likely and desirable future scenarios. After three rounds of survey we can provide the following major findings: First, most experts indicated that either Little Change or Slow Improvements are more likely to occur than Significant Improvements within the next ten years. Second, more professional development for teachers and free availability of approved resources were regarded as the most important solutions to be implemented, amongst many others, to achieve a desirable future scenario.
Machine Learning at the Service of Survival Analysis: Predictions Using Time-to-Event Decomposition and Classification Applied to a Decrease of Blood Antibodies against COVID-19

The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing the time-to-event variable into “time” and “event” components and using the latter as a target variable for various machine-learning classification algorithms, which are almost assumption-free, unlike the Cox model. While the time component is continuous and is used as one of the covariates, i.e., input variables for various classification algorithms such as logistic regression, naïve Bayes classifiers, decision trees, random forests, and artificial neural networks, the event component is binary and thus may be modeled using these classification algorithms. Moreover, we apply the proposed method to predict a decrease or non-decrease of IgG and IgM blood antibodies against COVID-19 (SARS-CoV-2), respectively, below a laboratory cut-off, for a given individual at a given time point. Using train-test splitting of the COVID-19 dataset (n=663 individuals), models for the mentioned algorithms, including the Cox proportional hazard model, are learned and built on the train subsets while tested on the test ones. To increase robustness of the model performance evaluation, models’ predictive accuracies are estimated using 10-fold cross-validation on the split dataset. Even though the time-to-event variable decomposition might ignore the effect of individual data censoring, many algorithms show similar or even higher predictive accuracy compared to the traditional Cox proportional hazard model. In COVID-19 IgG decrease prediction, multivariate logistic regression (of accuracy 0.811), support vector machines (of accuracy 0.845), random forests (of accuracy 0.836), artificial neural networks (of accuracy 0.806) outperform the Cox proportional hazard model (of accuracy 0.796), while in COVID-19 IgM antibody decrease prediction, neither Cox regression nor other algorithms perform well (best accuracy is 0.627 for Cox regression). An accurate prediction of mainly COVID-19 IgG antibody decrease can help the healthcare system manage, with no need for extensive blood testing, to identify individuals, for instance, who could postpone boosting vaccination if new COVID-19 variant incomes or should be flagged as high risk due to low COVID-19 antibodies.
Hybrid Particle Swarm Optimization Algorithm Based on the Theory of Reinforcement Learning in Psychology

To more effectively solve the complex optimization problems that exist in nonlinear, high-dimensional, large-sample and complex systems, many intelligent optimization methods have been proposed. Among these algorithms, the particle swarm optimization (PSO) algorithm has attracted scholars’ attention. However, the traditional PSO can easily become an individual optimal solution, leading to the transition of the optimization process from global exploration to local development. To solve this problem, in this paper, we propose a Hybrid Reinforcement Learning Particle Swarm Algorithm (HRLPSO) based on the theory of reinforcement learning in psychology. First, we used the reinforcement learning strategy to optimize the initial population in the population initialization stage; then, chaotic adaptive weights and adaptive learning factors were used to balance the global exploration and local development process, and the individual optimal solution and the global optimal solution were obtained using dimension learning. Finally, the improved reinforcement learning strategy and mutation strategy were applied to the traditional PSO to improve the quality of the individual optimal solution and the global optimal solution. The HRLPSO algorithm was tested by optimizing the solution of 12 benchmarks as well as the CEC2013 test suite, and the results show it can balance the individual learning ability and social learning ability, verifying its effectiveness.
PCEP: Few-Shot Model-Based Source Camera Identification

Source camera identification is an important branch in the field of digital forensics. Most existing works are based on the assumption that the number of training samples is sufficient. However, in practice, it is unrealistic to obtain a large amount of labeled samples. Therefore, in order to solve the problem of low accuracy for existing methods in a few-shot scenario, we propose a novel identification method called prototype construction with ensemble projection (PCEP). In this work, we extract a variety of features from few-shot datasets to obtain rich prior information. Then, we introduce semi-supervised learning to complete the construction of prototype sets. Subsequently, we use the prototype sets to retrain SVM classifiers, and take the posterior probability of each image sample belonging to each class as the final projection vector. Finally, we obtain classification results through ensemble learning voting. The PCEP method combines feature extraction, feature projection, classifier training and ensemble learning into a unified framework, which makes full use of image information of few-shot datasets. We conduct comprehensive experiments on multiple benchmark databases (i.e., Dresden, VISION and SOCRatES), and empirically show that our method achieves satisfactory performance and outperforms many recent methods in a few-shot scenario.
Application of Solar Activity Time Series in Machine Learning Predictive Modeling of Precipitation-Induced Floods

This research is devoted to the determination of hidden dependencies between the flow of particles that come from the Sun and precipitation-induced floods in the United Kingdom (UK). The analysis covers 20 flood events during the period from October 2001 to December 2019. The parameters of solar activity were used as model input data, while precipitations data in the period 10 days before and during each flood event were used as model output. The time lag of 0–9 days was taken into account in the research. Correlation analysis was conducted to determine the degree of randomness for the time series of input and output parameters. For establishing a potential causative link, machine learning classification predictive modeling was applied. Two approaches, the decision tree, and the random forest were used. We analyzed the accuracy of classification models forecast from 0 to 9 days in advance. It was found that the most important factors for flood forecasting are proton density with a time lag of 9, differential proton flux in the range of 310–580 keV, and ion temperature. Research in this paper has shown that the decision tree model is more accurate and adequate in predicting the appearance of precipitation-induced floods up to 9 days ahead with an accuracy of 91%. The results of this study confirmed that by increasing technical capabilities, using improved machine learning techniques and large data sets, it is possible to improve the understanding of the physical link between the solar wind and tropospheric weather and help improve severe weather forecasting.
Deep-Learning-Based Carrier Frequency Offset Estimation and Its Cross-Evaluation in Multiple-Channel Models

The most widely used Wi-Fi wireless communication system, which is based on OFDM, is currently developing quickly. The receiver must, however, accurately estimate the carrier frequency offset between the transmitter and the receiver due to the characteristics of the OFDM system that make it sensitive to carrier frequency offset. The autocorrelation of training symbols is typically used by the conventional algorithm to estimate the carrier frequency offset. Although this method is simple to use and low in complexity, it has poor estimation performance at low signal-to-noise ratios, which has a significant negative impact on the performance of the wireless communication system. Meanwhile, the design of the communication physical layer using deep-learning-based (DL-based) methods is receiving more and more attention but is rarely used in carrier frequency offset estimation. In this paper, we propose a DL-based carrier frequency offset (CFO) model architecture for 802.11n standard OFDM systems. With regard to multipath channel models with varied degrees of multipath fadding, the estimation error of the proposed model is 70.54% lower on average than that of the conventional method under 802.11n standard channel models, and the DL-based method can outperform the estimation range of conventional methods. Besides, the model trained in one channel environment and tested in another was cross-evaluated to determine which models could be used for deployment in the real world. The cross-evaluation demonstrates that the DL-based model can perform well over a large class of channels without extra training when trained under the worst-case (most severe) multipath channel model.
EverAnalyzer: A Self-Adjustable Big Data Management Platform Exploiting the Hadoop Ecosystem

Big Data is a phenomenon that affects today’s world, with new data being generated every second. Today’s enterprises face major challenges from the increasingly diverse data, as well as from indexing, searching, and analyzing such enormous amounts of data. In this context, several frameworks and libraries for processing and analyzing Big Data exist. Among those frameworks Hadoop MapReduce, Mahout, Spark, and MLlib appear to be the most popular, although it is unclear which of them best suits and performs in various data processing and analysis scenarios. This paper proposes EverAnalyzer, a self-adjustable Big Data management platform built to fill this gap by exploiting all of these frameworks. The platform is able to collect data both in a streaming and in a batch manner, utilizing the metadata obtained from its users’ processing and analytical processes applied to the collected data. Based on this metadata, the platform recommends the optimum framework for the data processing/analytical activities that the users aim to execute. To verify the platform’s efficiency, numerous experiments were carried out using 30 diverse datasets related to various diseases. The results revealed that EverAnalyzer correctly suggested the optimum framework in 80% of the cases, indicating that the platform made the best selections in the majority of the experiments.
A Novel Methodology to Develop STEAM Projects According to National Curricula

The objective of this work is the elaboration of a methodology to develop STEAM projects (Science (S), Technology (T), Engineering (E), Art (A) and Mathematics (M)). The methodology proposed in this article is part of the results of the project ERASMUS+DART4City (2020-1-ES01-KA227-SCH-095545) “Empowering Arts and creativity for the cities of tomorrow”, whose objective is to design a methodology in order to develop STEAM projects from European curricula. The proposed methodology emerges after analysing, among others, the curricula from Spain, Italy, Cyprus, France, Finland and Germany, taking into account the international perspective of STEAM education, their priorities and problems. The proposal has two variants: “forward” and “backward”. Both variants begin with the analysis of the curriculum in which the contents are grouped by similarity, classified according to STEAM disciplines so that the thematic areas can be obtained. Subsequently, in the “forward” variant, the thematic areas with most connections are selected as areas of opportunity; for the development of the STEAM project, so from the “forward” methodology, the teacher will be able to select an area of opportunity and develop the project around it, so, the concept or project idea comes from an area of oportunity. The “backward” variant starts from a concept considered interesting for society or just for the teacher and which will become the main theme of the STEAM project and, from that concept, we will select the thematic areas of the curriculum that can be included in the project. The main difference bewteen both methodologies is that in the “forward” variant, the STEAM project concept comes from an area of oportunity detected in the curriculum meanwhile in the “backward” variant the concept of the STEAM project comes from the teacher, without taking into acount, initially, the curriculum. This article shows an example of application of each variant. From the “forward” variant, the STEAM project “Sustainable City” is shown for years 4, 5 and 6 of Primary Education, which has been carried out from the opportunity area “Sustainability”. From the “backward” variant, we develop the STEAM project, whose main theme is cooking, in which, based on this theme, 10 tests are developed, each of them related to different thematic areas of the Spanish curriculum.
Model of Threats to the Integrity and Availability of Information Processed in Cyberspace

Depending on their motivation, offenders have different goals, and disclosure of information is not always such a goal. It often happens that the purpose of the offender is to disrupt the normal operation of the system. This can be achieved both by acting directly on the information and by acting on the elements of the system. Actions of this kind lead to a violation of integrity and availability, but not confidentiality. It follows that the process of forming a threat model for the integrity and availability of information differs from a similar process for confidentiality threats. The purpose of this study is to develop an information integrity threat model that focuses on threats disrupting the normal operation of the system. The research methodology is based on the methods of system analysis, graph theory, discrete mathematics, and automata theory. As a result of the research, we proposed a model of threats to the integrity and availability of information. The proposed threat model differs from analogues by a high level of abstraction without reference to the subject area and identification of threats to the availability of information as a subset of threats to the integrity of the information transmission channel.
Feature-enhanced text-inception model for Chinese long text classification

To solve the problem regarding unbalanced distribution of multi-category Chinese long texts and improve the classification accuracy thereof, a data enhancement method was proposed. Combined with this method, a feature-enhanced text-inception model for Chinese long text classification was proposed. First, the model used a novel text-inception module to extract important shallow features of the text. Meanwhile, the bidirectional gated recurrent unit (Bi-GRU) and the capsule neural network were employed to form a deep feature extraction module to understand the semantic information in the text; K-MaxPooling was then used to reduce the dimension of its shallow and deep features and enhance the overall features. Finally, the Softmax function was used for classification. By comparing the classification effects with a variety of models, the results show that the model can significantly improve the accuracy of long Chinese text classification and has a strong ability to recognize long Chinese text features. The accuracy of the model is 93.97% when applied to an experimental dataset.
Reference-free lossless compression of nanopore sequencing reads using an approximate assembly approach

The amount of data produced by genome sequencing experiments has been growing rapidly over the past several years, making compression important for efficient storage, transfer and analysis of the data. In recent years, nanopore sequencing technologies have seen increasing adoption since they are portable, real-time and provide long reads. However, there has been limited progress on compression of nanopore sequencing reads obtained in FASTQ files since most existing tools are either general-purpose or specialized for short read data. We present NanoSpring, a reference-free compressor for nanopore sequencing reads, relying on an approximate assembly approach. We evaluate NanoSpring on a variety of datasets including bacterial, metagenomic, plant, animal, and human whole genome data. For recently basecalled high quality nanopore datasets, NanoSpring, which focuses only on the base sequences in the FASTQ file, uses just 0.35-0.65 bits per base which is 3-6[Formula: see text] lower than general purpose compressors like gzip. NanoSpring is competitive in compression ratio and compression resource usage with the state-of-the-art tool CoLoRd while being significantly faster at decompression when using multiple threads (> 4[Formula: see text] faster decompression with 20 threads). NanoSpring is available on GitHub at https://github.com/qm2/NanoSpring .