Reviewerly/exHarmony huggingface dataset:
Optimization of variational-quantum-eigensolver measurement by partitioning Pauli operators using multiqubit Clifford gates on noisy intermediate-scale quantum hardware

Measuring the expectation value of Pauli operators on prepared quantum states is a fundamental task in a multitude of quantum algorithms. Simultaneously measuring sets of Pauli operators allows for fewer measurements and an overall speedup of the measurement process. Using results from coloring random graphs, we give evidence for an upper bound on the expected minimum number of simultaneously measurable parts necessary to partition a set of Pauli operators. By expanding our gate set to allow multiqubit Clifford gates before measurement, we were able to cut down the number of measurements considerably compared to previous approaches. We conjecture that this improvement is, in general, linear with respect to the lengths of the Pauli operators. We conclude by demonstrating the feasibility of this approach by implementing the variational quantum eigensolver on the Aspen-4-4Q-A quantum processor unit. In doing so, we calculate the ground-state energies of ${\mathrm{H}}_{2}$ using fewer measurements than is possible with single-qubit gates with no statistically significant difference in accuracy.
Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging

Thus far, there have been no reported specific rules for systematically determining the appropriate augmented sample size to optimize model performance when conducting data augmentation. In this paper, we report on the feasibility of synthetic data augmentation using generative adversarial networks (GAN) by proposing an automation pipeline to find the optimal multiple of data augmentation to achieve the best deep learning-based diagnostic performance in a limited dataset. We used Waters' view radiographs for patients diagnosed with chronic sinusitis to demonstrate the method developed herein. We demonstrate that our approach produces significantly better diagnostic performance parameters than models trained using conventional data augmentation. The deep learning method proposed in this study could be implemented to assist radiologists in improving their diagnosis. Researchers and industry workers could overcome the lack of training data by employing our proposed automation pipeline approach in GAN-based synthetic data augmentation. This is anticipated to provide new means to overcome the shortage of graphic data for algorithm training.
Digitalization of Education: Commodification Hidden in Terms of Empowerment?

Abstract This article contributes to discussions about the gap between formal educational politics and reforms, and concrete educational inequalities. With our empirical case, we bring into this discussion the contemporary reforms of formal education that lean on an imaginary idea of digitalization as an equalizing power without recognizing the criticism directed towards it. Within this case, the article covers postdigitalism in education where digitalization is not enough to make a difference. We see the postdigital paradigm as something that analyzes the present-day consequences of the digital age. Our empirical data consists of perceptions of Serbian educational authorities collected via a qualitative questionnaire and an interview. In our data collected from authorities, imaginaries of harmonization and equalization can be recognized where educational equality and empowerment are believed to be achievable via digital structures and patterns of schooling. However, selected examples from the concrete educational stratification of the Serbian population show that digitalization per se cannot be a solution: for example, poverty hierarchies within the population are tough and should be solved first. We see that emphasis on digitalization can sometimes strengthen the prevailing educational gaps between different socio-cultural population groups if digitalism is not paying attention to human and societal conditions.
Bidirectional LSTM-Based Sentiment Analysis of Context-Sensitive Lexicon for Imbalanced Text

The authors plan to use a context-based lexicon as a source of energy to investigate totally unbalanced text sentimental analysis in this article. This strategy addresses two important issues in text sentiment classification: scenario interdependence and data corpus imbalance. To begin, it identifies subjective words in different contexts and computes weight rankings for vague generalisations and full review. As a result, RNNs have recently been used to solve real-world problems, specifically natural language processing tasks. The authors employ a context-based lexicon as well as a bidirectional LSTM to interact with text sentiment classification. Second, it interacts with unbalanced data by generating modern and unconventional text test results using a text-based post-processing technique. The experimental results show that using a sentiment lexicon similar to the framework and combining bidirectional LSTM with text-based sampling is beneficial in unbalanced text sentiment classification and yields state-of-the-art results when compared to deep neural learning model benchmarks.
A certificateless ring signature scheme based on lattice

Summary For the problems that ring signature needs key escrow, has some security risks, and cannot avoid malicious key generation center (KGC) in the post‐quantum era, we design a certificateless ring signature scheme based on lattice (L‐CRSS). In our scheme, the bimodal Gaussian distribution is used to improve the security and efficiency. Compared with the previous ring signature scheme based on lattice, our design does not need key escrow, does not disclose the signer's identity, can avoid malicious KGC, achieves the constant signature size, and has higher security and efficiency in the post‐quantum era. Finally, under random oracle model (ROM), we prove that our scheme is anonymous against the full‐key exposure, and existentially unforgeable against adaptive chosen message attacks (EUF‐CMA).
The distribution of fake Australian vaccine digital certificates on an alt-tech platform

This paper provides the first exploration of the online distribution of fake Australian COVID-19 vaccine certificates. Drawing on a collection of 2589 posts between five distributors and their community members on the alt-tech platform Gab, this study gathers key insights into the mechanics of illicit vaccine certificate distribution. The qualitative findings in this research demonstrate the various motivations and binding ideologies that underpinned this illicit distribution (e.g. anti-vaccine and anti-surveillance motivations); the unique cybercultural aspects of this online illicit network (e.g. 'crowdsourcing' the creation of fake vaccine passes); and how the online community was used to share information on the risks of engaging in this illicit service, setting the appropriate contexts of using fake vaccine passes, and the evasion of guardians in offline settings. Implications for future research in cybercrime, illicit networks, and organised crime in digital spaces are discussed.
Learning with smartphones: the acceptance of m-learning in higher education

Purpose In parallel with technological developments, mobile devices have become an important part of our daily lives. Nowadays, people, particularly generation Z, actively engage with the Internet and mobile technologies, including smartphones. The new technologies have also made electronic learning (e-learning) and mobile learning (m-learning) tempting for learners in higher education. Despite the growing use of m-learning, particularly in developed countries, its utilization in higher education is still at its early stage and far from its potential. This study investigates the university students' acceptance of using m-learning with smartphones in a developing country context. Design/methodology/approach Quantitative data collected through conducting questionnaires with 405 higher education students in Turkey were analyzed with structural equation modelling. Findings The findings show that the acceptance of using m-learning is affected by several factors, including perceived enjoyment, complexity and facilitating conditions. The findings are particularly relevant in the current context, where the COVID-19 pandemic has pushed many higher education institutions to adapt m-learning systems to enhance student learning experiences. Originality/value This study's findings offer fresh and important insights that can be used by m-learning developers and educators for designing m-learning systems and using m-learning applications in enhancing students' experience and performance with m-learning. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0516 .
A Novel Approach for Implementation of Software Requirement Specifications Using the Humpback Whale Optimization Model

The requirements elicitation process is the foundation of every software system, and it is focused with software requirement specification procedures. In terms of limitations and rules, constituents are the prime source of these requires. Software requirement prioritizing is regarded as among the most important approaches in the requirements elicitation since its being used to prioritize the order of execution of specifications based on the perspectives of participants. Whales’ optimization model, on either side, has lately been employed in optimization methods because it resembles humpback whale feeding behaviour by utilizing the bubbles net scavenging approach. The whale optimizer (WOA) has been used in the article to prioritize system specifications by presuming the specifications in the problem space and implementing scavenging behaviour to prioritize these specifications. The proposed method is assessed in terms of execution times with analytical hierarchy process (AHP) to prioritize various sizes requirement’s pairs, and the findings are summarized with 40% efficiency.
Data-Driven Approach for Spellchecking and Autocorrection

This article presents an approach for spellchecking and autocorrection using web data for morphologically complex languages (in the case of Kazakh language), which can be considered an end-to-end approach that does not require any manually annotated word–error pairs. A sizable web of noisy data is crawled and used as a base to infer the knowledge of misspellings with their correct forms. Using the extracted corpus, a sub-string error model with a context model for morphologically complex languages are trained separately, then these two models are integrated with a regularization parameter. A sub-string alignment model is applied to extract symmetric and non-symmetric patterns in two sequences of word–error pairs. The model calculates the probability for symmetric and non-symmetric patterns of a given misspelling and its candidates to obtain a suggestion list. Based on the proposed method, a Kazakh Spellchecking and Autocorrection system is developed, which we refer to as QazSpell. Several experiments are conducted to evaluate the proposed approach from different angles. The results show that the proposed approach achieves a good outcome when only using the error model, and the performance is boosted after integrating the context model. In addition, the developed system, QazSpell, outperforms the commercial analogs in terms of overall accuracy.
An efficient computation of discrete orthogonal moments for bio-signals reconstruction

Abstract Bio-signals are extensively used in diagnosing many diseases in wearable devices. In signal processing, signal reconstruction is one of the essential applications. Discrete orthogonal moments (DOMs) are effective analysis tools for signals that can extract digital information without redundancy. The propagation of numerical errors is a significant challenge for the computation of DOMs at high orders. This problem damages the orthogonality property of these moments, which restricts the ability to recover the signal's distinct and unique components with no redundant information. This paper proposes a stable computation of DOMs based on QR decomposition methods: the Gram–Schmidt, Householder, and Given Rotations methods. It also presents a comparative study on the performance of the types of moments: Tchebichef, Krawtchouk, Charlier, Hahn, and Meixner moments. The proposed algorithm's evaluation is done using the MIT-BIH arrhythmia dataset in terms of mean square error and peak signal to noise ratio. The results demonstrate the superiority of the proposed method in computing DOMs, especially at high moment orders. Moreover, the results indicate that the Householder method outperforms Gram–Schmidt and Given Rotations methods in execution time and reconstruction quality. The comparative results show that Tchebichef, Krawtchouk, and Charlier moments have superior reconstruction quality than Hahn and Meixner moments, and Tchebichef generally has the highest performance in signal reconstruction.
Effectiveness Evaluation of Optical Illusion Deceleration Markings for a V-Shaped Undersea Tunnel Based on the Set Pair Analysis Method and the Technique for Order Preference by Similarity to Ideal Solution Theory

To improve traffic safety in undersea tunnels, the speed control effect of optical illusion deceleration markings (OIDMs) in V-shaped undersea tunnels and their applicability to drivers were explored. In this study, UC-Win/Road software was used to build a simulation scenario, and the V-shaped Jiaozhou Bay undersea tunnel in Qingdao, China, was selected as the simulated road section. Four kinds of OIDMs were selected as the research objects. According to the physiological and psychological response characteristics of drivers to the visual recognition process of OIDMs and vehicle driving parameters, three primary and six secondary evaluation indices were identified. The analytic hierarchy process-entropy weight method was applied to assign weights to each evaluation index in combination, and a comprehensive evaluation model based on the set pair analysis method and the technique for order preference by similarity to ideal solution was constructed to comprehensively evaluate the effectiveness of the OIDMs based on the ternary coefficient and the comprehensive evaluation index. To verify the accuracy of the model, the data of the three secondary evaluation indicators with larger weight coefficients were statistically analyzed, and the results showed that the results of the statistical analysis of the data were consistent with the results of the model test. In the entrance section of the tunnel, the combination of white inverted fishbone deceleration markings and red warning pavement has the highest comprehensive evaluation index of γ A1 = 0.9611. In the downhill section of the tunnel, the white variable-spacing inverted fishbone deceleration markings have the highest comprehensive evaluation index, γ B1 = 0.9644.
Understanding Relevance Judgments in Legal Case Retrieval

Legal case retrieval, which aims to retrieve relevant cases given a query case, has drawn increasing research attention in recent years. While much research has worked on developing automatic retrieval models, how to characterize relevance in this specialized information retrieval (IR) task is still an open question. Towards an in-depth understanding of relevance judgments, we conduct a laboratory user study that involves 72 participants of different domain expertise. In the user study, we collect the relevance score along with detailed explanations for the relevance judgment and various measures of the judgment process. From the collected data, we observe that both the subjective (e.g., domain expertise) and objective (e.g., query/case property) factors influence the relevance judgment process. By investigating the collected user explanations, we identify task-specific patterns of user attention distribution and re-think the criteria for relevance judgments. Moreover, we investigate the similarity in attention distribution between models and users. Further, we propose a two-stage framework that utilizes user attention to improve relevance estimation for legal case retrieval. Our study sheds light on understanding relevance judgments in legal case retrieval and provides implications for improving the design of corresponding retrieval systems.
Joint optimization of linear and nonlinear models for sequential regression

We investigate nonlinear regression and introduce a novel approach based on the joint optimization of linear and nonlinear models. In order to capture both the nonlinear and linear characteristics in sequential data, we model the underlying data as a combination of linear and nonlinear models, where we optimize the models jointly to minimize the final regression error. As the nonlinear model, we employ a differentiable version of the boosted decision trees. As the linear model, we use the well-known SARIMAX model. Our approach is generic so that any differentiable nonlinear or linear model can be readily employed provided that they are differentiable. By this joint optimization, we alleviate the well-known underfitting and overfitting problems in modeling sequential data. Through our experiments on synthetic and real-life data, we demonstrate significant improvements over individual components as well as the combination/mixture methods in the literature.
Dimension-Free Mixing for High-Dimensional Bayesian Variable Selection

Abstract Yang et al. proved that the symmetric random walk Metropolis–Hastings algorithm for Bayesian variable selection is rapidly mixing under mild high-dimensional assumptions. We propose a novel Markov chain Monte Carlo (MCMC) sampler using an informed proposal scheme, which we prove achieves a much faster mixing time that is independent of the number of covariates, under the assumptions of Yang et al. To the best of our knowledge, this is the first high-dimensional result which rigorously shows that the mixing rate of informed MCMC methods can be fast enough to offset the computational cost of local posterior evaluation. Motivated by the theoretical analysis of our sampler, we further propose a new approach called ‘two-stage drift condition’ to studying convergence rates of Markov chains on general state spaces, which can be useful for obtaining tight complexity bounds in high-dimensional settings. The practical advantages of our algorithm are illustrated by both simulation studies and real data analysis.
Shortwave Radiation on Horizontal and Incline Surfaces—One Year of Solar Radiation Measurements at Athalassa, an Inland Location in Cyprus

Athalassa is the main actinometric station of Cyprus and is located in the center of the island at a height of about 160 m. The station is equipped with shortwave and longwave radiation instruments. The time step of the measurements is 10 min, and hourly and daily values were derived for the period of June 2020–May 2021. The solar data underwent an extensive quality control process based mainly on the suggested tests of Baseline Surface Radiation Network (BSRN) for both the hourly and daily datasets. More than 98% of the data were within the limits recommended by the BSRN and other radiation networks. A statistical analysis of the shortwave solar radiation components was then performed. Linear and quadratic relationships were established between various radiation components, and their diurnal and monthly variability was assessed. The annual average daily global radiation amount was approximately 19 MJ/m2, whereas the amounts of horizontal beam and diffuse radiation were 12.9 MJ/m2 and 4.7 MJ/m2, respectively. Regarding the modeling of diffuse irradiance, the BRL diffuse fraction model (Boland-Ridley-Lauret) was applied. The results showed that the BRL model can satisfactorily estimate both the diffuse solar irradiance as well as the direct normal irradiance. Furthermore, the levels of the shortwave components were estimated based on the classification of four categories of the clearness index. The annual average of the direct normal beam radiation on clear days was 27.3 MJ/m2, and the direct horizontal radiation was 17.7 MJ/m2. Finally, the total energy received by an inclined surface was estimated based on measurements on the horizontal surfaces. In practice, photovoltaics were installed with an annual permanent slope of 26° with respect to the horizontal surface, and in a southern direction.
A modified adaptive Kalman filtering method for maneuvering target tracking of unmanned surface vehicles

The filtering methods are crucial for an unmanned surface vehicle (USV) to realize target tracking. Due to the poor observation caused by the strong vibration during the navigation of the USV, the target tracking accuracy of the traditional filtering method has been significantly degraded. A modified strong tracking-based expended Sage-Husa adaptive robust Kalman filter (MST-ESHARKF) algorithm is proposed to overcome this problem in this paper. In the proposed algorithm, a modified fading factor for the strong tracking Kalman filter is introduced to eliminate disturbance-induced filter divergence. In addition, the adaptive factor of robust Kalman filtering is designed to balance the predicted and observed states dedicated to improving the robustness of the algorithm. Finally, the biased and unbiased estimators for measurement and process noise covariances are merged, and the measurement noise covariance matrix’s interval is constrained, resulting in a simultaneous evaluation of measurement and process noise covariance matrices with improved dependability of the proposed algorithm. The simulation and experiment results show that the proposed MST-ESHARKF outperforms the existing filters in target tracking.
Uncertainty guided pruning of classification model tree

A model tree is a hybrid learning algorithm that integrates decision trees and embedding models, with simple structure and high interpretability. However, all the existing works on model trees neglect to estimate the reliability of the output. This estimate not only plays an important role in model selection but also provides an effective guide to the optimization of model tree performance. This work first introduces the output uncertainty of the embedding model into the model tree building process. Specifically, we model the tree based on an expanded post-pruning rule which introduces output uncertainty. At the same time, we include an error estimation term for the embedded model, in which the output uncertainty gives reverse guidance to the model performance. Besides, the proposed optimization rules can be extended as a supplementary condition to any existing post-pruning method. The uncertainty guided model tree is introduced and presented in detail by extending two post-pruning methods which are generally expected to have higher accuracy. Experiments on 12 benchmark data sets demonstrate the superiority of the proposed method.
An efficient video watermark method using blockchain

Nowadays, one of the most common approaches for video watermarking is to use singular value decomposition in the discrete wavelet transform domain. In our prior work, we formulated a video watermarking problem to achieve the maximum peak signal to noise ratio as an optimization problem. It chooses wavelet coefficient points in key frames with minimum distortion cost. Yet the existing solution has two drawbacks (1) it requires to store a large number of coordinates depending on the watermark size; (2) it is vulnerable to video frame attacks on the video frame texture area. To avoid them, this paper proposes a blockchain-based approach. Its main concept is to choose wavelet coefficient blocks in key frames and leverage blockchain to connect all the blocks. Our experimental results show that this approach is memory efficient because it only requires to store a single key for a key frame, which is independent of the watermark size; and the overall robustness is greatly improved due to the randomness of the hash function used in the blockchain. Hence, this work had made novel and significant contributions to the field of video watermarking.