Reviewerly/exHarmony huggingface dataset:
Addressing quantum’s “fine print” with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks

Authors:
raw_author_name: Jessie M. Henderson
raw_affiliation_string: Los Alamos National Laboratory, CCS-3, Los Alamos, NM, 87545, USA
raw_author_name: John K. Golden
raw_affiliation_string: Los Alamos National Laboratory, CCS-3, Los Alamos, NM, 87545, USA
raw_author_name: Daniel O’Malley
raw_affiliation_string: Los Alamos National Laboratory, EES-16, Los Alamos, NM, 87545, USA

Abstract: Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.
Effective Combination of Oral Reading and Retrieval Practice Strategies: The Moderating Effect of Reading Model Development

Authors:
raw_author_name: Xiying Ji
raw_affiliation_string: Northwest Normal University, Lanzhou, China
raw_author_name: Qiyun Zhang
raw_affiliation_string: Northwest Normal University, Lanzhou, China
raw_author_name: Huizhong Ma
raw_affiliation_string: Northwest Normal University, Lanzhou, China
raw_author_name: Xiaofeng Ma
raw_affiliation_string: Northwest Normal University, Lanzhou, China

Abstract: The complementary view of strategy combinations points to the fact that a combination of strategies that promote memory encoding and retrieval practice strategies can be effective in improving memory retention. This study examines the effectiveness of the combination of oral reading and retrieval practice strategies and the effect of the development of reading patterns on their effectiveness. This study designed a mixed experimental design of 3 (grades: Grade three, Grade five, college students) × 2 (reading mode: oral reading and silent reading) × 2 (learning strategies: re-learning and retrieval practice). Among them, reading mode is the within-subject variable, and learning strategy and grade are the between-subjects variables. The dependent variable is the ratio of specific words recalled in the final test. The results show that the oral and retrieval practice is a significant and effective combination of memory strategies for primary school students who are mainly oral reading or in the transition from oral reading to silent reading. It is significantly better than the three strategy combinations of oral reading combined with re-leaning, silent reading combined with re-learning, and silent reading combined with retrieval practice. For adult students who dominated silent reading, silent reading combined with retrieval practice is the best memory strategy.
Do redundant graphical attributes reduce decision-making efficiency?

Authors:
raw_author_name: Tao Jin
raw_affiliation_string: College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, China
raw_author_name: Chunpeng Chen
raw_affiliation_string: College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, China
raw_author_name: Yuting Xia
raw_affiliation_string: College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, China
raw_author_name: Xinyu Liu
raw_affiliation_string: College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, China
raw_author_name: Xiaoxu Liu
raw_affiliation_string: College of Mechanical and Electrical Engineering, China University of Petroleum (East China), Qingdao, China

Abstract: Multiple time-series graphs are commonly used for data visualisation, but few scholars have investigated the impact of graphical attributes on decision-making efficiency. This study explores the effects of graphical attributes of varying redundancy conditions on decision-making efficiency. Two experimental conditions were developed for the experiment: non-redundant (independent graphical attributes: colour, linear and marker) and redundant (combinations of two and more graphical attributes: colour and linear, colour and marker, etc.). A total of 60 people took part in both experiments and performed two tasks: maximisation and discrimination. The experiments revealed that the addition of attributes, such as colour, marker or linear, decreased response time (RT), but the combination of colour & linear & marker increased RT. This is more significant in discrimination tasks. We provide empirical evidence for the design of time-series data visualisations and encourage the combination of two of these graphical attributes, such as colour & linear, colour & marker or linear & marker, when conditions allow, to improve decision-making efficiency.Few scholars have studied the impact of graphical attributes on decision-making efficiency in data visualisation. This study explores the effect of graphical attributes with different redundancy levels on decision-making efficiency through behavioural experiments. It has been found that moderately redundant graphical attributes in difficult tasks can significantly improve decision-making efficiency.
Application of multi-scale feature fusion algorithm based on motion wearable sensors in feature extraction of sports images

Authors:
raw_author_name: Jungang Yang
raw_affiliation_string: College of Art and Sports, Hebei Institute of Communications, Shijiazhuang, Hebei, 050000, China
raw_author_name: Cao Meng
raw_affiliation_string: Department of Physical Education, Hebei University of Water Resources and Electric Engineering, Cangzhou, Heibei, 061000, China
raw_author_name: Li Ling
raw_affiliation_string: College of Art and Sports, Hebei Institute of Communications, Shijiazhuang, Hebei, 050000, China

Abstract: The utilization of moving image feature extraction in sports teaching has garnered increasing attention. However, traditional feature extraction algorithms often struggle to meet the diverse and complex demands of moving images. To address this challenge, this paper proposes a multi-scale feature fusion algorithm aimed at improving feature extraction in moving images. The algorithm begins by decomposing the moving image into multiple scales, followed by extracting features from each scale using a feature extraction network. To obtain a more comprehensive and accurate feature representation, feature fusion technology is employed to merge the features from different scales. The proposed algorithm, based on multi-scale feature fusion, exhibits a significant improvement in both accuracy and stability when compared to traditional feature extraction algorithms. Byaccurately extracting and representing the crucial features within moving images, the algorithm contributes to an improved understanding of athletes' movements, enabling instructors to provide more targeted and insightful feedback. This algorithm effectively captures key features within the moving images, providing robust support for tasks such as movement analysis and skill evaluation in sports teaching.
PAC: A monitoring framework for performance analysis of compression algorithms in Spark

Authors:
raw_author_name: Changpeng Zhu
raw_affiliation_string: Department of Data Science and Big Data, Chongqing University of Technology, China
raw_author_name: Bo Han
raw_affiliation_string: School of Cyber Science and Engineering, Xi’an Jiaotong University, China

Abstract: In Spark, a massive amount of immediate data inevitably leads to excessive I/O overhead. To mitigate this issue, Spark incorporates four compression algorithms to reduce the size of the data for better performance. However, compression and decompression only constitute a portion of the overall logical flows of Spark applications. This indicates a potential considerable interaction between compression algorithms and Spark applications regarding performance. Consequently, identifying factors that significantly impact the performance of compression algorithms in Spark, and subsequently, determining the actual performance benefits these algorithms provide to Spark applications, remains a significant challenge. To address the challenge, this paper presents a monitoring framework, named PAC, for conducting in-depth and systematic performance analysis of compression algorithms in Spark. As the pioneer of such monitoring frameworks, PAC is built on top of Spark core and collaborates with multiple monitors to collect various types of performance metrics of compressors, correlates and integrates them into structured tuples by the data transformer in PAC. This makes it easier to diagnosis of factors that have a significant influence on the performance of compression algorithms in Spark. Upon utilizing PAC, our experiments reveal that new determinants include the input/output data sizes and types of compression/decompression invocations, CPU consumption for compressing a massive amount of data, and hardware utilization, besides traditional determinants. Moreover, these experiments demonstrate that ZSTD is more susceptible to performance issues when compressing and decompressing small data, despite the overall input and output data being huge. In terms of performance, LZ4 serves as a viable alternative to ZSTD. These findings not only benefit researchers and developers in making more informed decisions in terms of configuring and tuning Spark execution environments but also sustainably boost the optimization of compression algorithms for Spark.
Level evaluation of concrete dam fractures based on game theory combination weighting-normal cloud model

Authors:
raw_author_name: Wei-Wei Li
raw_affiliation_string: School of Architecture, Nanyang Institute of Technology, Nanyang, China
raw_author_name: Chao Yang
raw_affiliation_string: School of Civil Engineering, Sichuan University of Science and Engineering, Zigong, China
raw_author_name: Chao Zhao
raw_affiliation_string: School of Civil Engineering, Sichuan University of Science and Engineering, Zigong, China

Abstract: Hazard evaluation of concrete dam fractures is vital for safe operations. The width (S 1 ), length (S 2 ), and depth (S 3 ) of fractures are adopted as the assessment index, and the game theory combination weighting-normal cloud model is introduced. The normal cloud model of certain dam fractures is subsequently established. The weight coefficients of each index are calculated using game theory combination weighting, and the certainty degree of each index is determined using the cloud model. Finally, the hazard levels of the concrete dam fractures are judged. The proposed model solves the fuzziness and randomness of different indexes; the conclusions demonstrate that the model is feasible for the hazard assessment of concrete dam fractures, and its accuracy is very high; therefore, a new approach can be provided for future hazard-level assessments of concrete dam fractures.
SSA‐UNet: Whole brain segmentation by U‐Net with squeeze‐and‐excitation block and self‐attention block from the 2.5D slice image

Authors:
raw_author_name: Shaofeng Jiang
raw_affiliation_string: Department of Biomedical Engineering, Nanchang Hangkong University, Jiangxi, China
raw_author_name: Xingyan Chen
raw_affiliation_string: Department of Biomedical Engineering, Nanchang Hangkong University, Jiangxi, China

Abstract: Abstract Whole brain segmentation from magnetic resonance images (MRI) is crucial in diagnosing brain diseases and analyzing neuroimaging data. Despite advances through deep learning, challenges such as uneven gray distribution and the presence of artifacts still present hurdles in medical image processing. These limitations are often a result of insufficient spatial contextual information and lack of attention to important regions within existing models. To address these issues, this paper presents SSA‐UNet (Squeeze‐and‐Excitation and Self‐Attention UNet), a uniquely designed deep convolutional neural network that integrates spatial constraints by converting three consecutive 2D MRI slices into a single 2.5D image. This facilitates capturing inter‐slice dependencies effectively. Additionally, the newly formulated SSA block, which sequentially incorporates channel attention and Self‐Attention mechanisms, is placed before the decoders in the conventional U‐Net architecture. This enables the network to automatically weight different feature maps and focus more effectively on regions requiring precise segmentation. Rigorous evaluations on LPBA40 and IBSR18 datasets substantiate the remarkable improvements in accuracy and stability achieved by SSA‐UNet. Results indicate Dice coefficients of 98.38% and 97.47%, specificity of 99.69% and 99.57%, and sensitivity of 98.5% and 97.98% for the respective datasets. Compared to other existing models, SSA‐UNet shows significant improvements on both the LPBA40 and IBSR18 datasets. On the LPBA40 dataset, SSA‐UNet's Dice coefficient improved by 0.33% compared to the sub‐optimal model, while on the IBSR18 dataset, the improvement reached 1.78%. These empirical findings demonstrate SSA‐UNet's heightened capability in addressing the long‐standing challenges in MRI‐based whole‐brain segmentation.
Diverse branch feature refinement network for efficient multi‐scale super‐resolution

Authors:
raw_author_name: Dacheng Zhang
raw_affiliation_string: School of Computer Science and Engineering, Northeastern University, Shenyang, China
raw_author_name: Weimin Lei
raw_affiliation_string: School of Computer Science and Engineering, Northeastern University, Shenyang, China
raw_author_name: Xinyi Chen
raw_affiliation_string: School of Computer Science and Engineering, Northeastern University, Shenyang, China

Abstract: Abstract Despite the existence of various super‐resolution (SR) methods, most of them focus on designing models for specific upscaling factors rather than fully exploiting inter‐scale correlation to improve efficiency. In contrast, multi‐scale SR methods can effectively reduce the redundancy of network parameters by aggregating the feature extraction processes corresponding to multiple scales into a unified process. The aim of this study is to enhance the compactness and efficiency of the SR model. Thus, an efficient multi‐scale SR method called the diverse branch feature refinement network (DBFRN) is proposed. By decoupling the training process and inference process based on the idea of structural re‐parameterization, multi‐branch topology is adopted to enrich multi‐scale learning and merge branches to achieve efficient inference with equivalent effects. Specifically, two re‐parameterization strategies are designed and two corresponding feature refinement blocks for different feature levels in multi‐scale SR network. Extensive experiments demonstrate that the proposed multi‐scale SR method is effective and efficient, and it can outperform advanced single‐scale methods in terms of quantity and quality.
Blockchain and agricultural sustainability in South America: a systematic review

Authors:
raw_author_name: Juan Carlos Corrales
raw_affiliation_string: Telematics Engineering Group (GIT), Telematics Engineering, University of Cauca, Popayán, Colombia

Abstract: In its fundamental role for food security in South America, sustainable agriculture faces the challenge of addressing the current and future needs of the region while ensuring profitability, environmental health, and social and economic equity. Currently, as support for sustainable agriculture, a significant transformation is observed in the agricultural landscape due to the development of advanced information systems. Technologies such as Artificial Intelligence, Machine Learning, and Blockchain have emerged as crucial tools to document and support sustainable agricultural processes. Blockchain technology has proven to be highly beneficial for sustainable agriculture, effectively addressing a significant issue in the agricultural supply chain by providing solutions for transparent and traceable processes. This technology solves the problem by establishing a permanent and open record of all transactions and activities in the supply chain, allowing consumers and stakeholders to track the origin and quality of agricultural products, thereby fostering trust and fair trade. For this reason, this article conducted a review of the current state of blockchain technology in sustainable agriculture, aimed at researchers and farmers in South America. The advantages and disadvantages of blockchain technology were identified, focusing on technologies developed and tested during the design and pilot phases. The PRISMA methodology was used in this review, and documents were searched in Scopus and Web of Science databases. Six hundred and fifty-six articles were identified and selected (2018–2023 period), but only 104 met the eligibility and inclusion criteria. The findings indicate a 30% increase in the adoption of decentralized applications (DAPs) powered by blockchain in the agribusiness sector compared to the previous year. After a thorough analysis, it has been determined that smart contracts, non-fungible tokens for digital assets, and blockchain oracles will provide promising solutions for sustainable agricultural technology in the future.
Modalities combining two negations

Authors:
raw_author_name: Rodolfo C Ertola-Biraben
raw_affiliation_string: CLE , Cidade Universitária, Sérgio Buarque de Holanda, 251, PO Box 13083-859, Campinas, Brazil

Abstract: Abstract In the context of positive logic expanded with the dual of intuitionistic negation, obtaining intuitionistic negation itself as a consequence, those two connectives allow to introduce modal concepts such as necessity and possibility. We study the resulting modal logic, paying attention to different extensions of it. We provide a syntactic approach and both algebraic and Kripke semantics.
Epidemiological contemplation for a currently pragmatic COVID-19 health passport: a perspective

Authors:
raw_author_name: Julia Marczika
raw_affiliation_string: Research Department, The Self Research Institute, Broken Arrow, OK, United States

Abstract: The coronavirus disease 2019 (COVID-19) has caused a global pandemic that has wreaked havoc on the lives of millions of people around the world. Confinement measures aim to reduce the epidemic's spread and minimize the burden of morbidity and mortality. In response to the challenges caused by the pandemic, digital health passports have been developed exponentially. We highlight the latent epidemiological barriers to health passports to achieve standardized digital care platforms. This review paper not only highlights the epidemiological barriers but also articulates the possible infrastructure required to make the International Standard for a multi-factor authenticated and validated health passport.
Designing ICT integrated lesson activities: an intervention to bolster tutors’ pedagogical use of ICT competences in Tanzania teacher education

Authors:
raw_author_name: Placidius Ndibalema
raw_affiliation_string: Educational Foundations and Continuing Education, College of Education, The University of Dodoma, Dodoma, Tanzania

Abstract: Purpose The study aims to assess the effectiveness of engaging tutors in designing and using ICT integrated lesson activities in strengthening their pedagogical use of ICT competences. Design/methodology/approach Survey data from an intervention group of 70 tutors from two teachers colleges (TCs) were used to compare their level of ICT competences and domains of professional practice before and after the intervention. Document analysis, lesson observations and feedback from the learning management system (LMS) were used to describe tutors’ experiences from the intervention. Findings There was a statistically significant increase in tutors’ level of pedagogical use of ICT competences and domains of professional practice associated with hands-on practice in designing and implementing the intervention. Research limitations/implications The intervention focus on hands-on practice, actual teaching and learning needs, and the use of active learning strategies like flipped classroom and the LMS, were useful means for tutors to make sense of pedagogical use of ICT competences. Practical implications The results offer useful insights to teacher education institutions and policymakers on how to prepare professional learning and supportive policies to enhance teaching and learning with ICT for addressing the learning needs of the subject matter. Originality/value Creating 16 ICT integrated lesson activities helped tutors to learn pedagogical use of ICT competences by doing. Use of such intervention could be a useful strategy in teacher education institutions to reposition ICT competence development from reproducing technological competences toward developing knowledge creators who could innovate their pedagogical practice with support from mentors, digital learning resources and networks.
Structure similarity virtual map generation network for optical and SAR image matching

Authors:
raw_author_name: Shiwei Chen
raw_affiliation_string: Department of Automation, Rocket Force University of Engineering, Xi’an, China
raw_author_name: Liye Mei
raw_affiliation_string: School of Computer Science, Hubei University of Technology, Wuhan, China; The Institute of Technological Sciences, Wuhan University, Wuhan, China

Abstract: Introduction: Optical and SAR image matching is one of the fields within multi-sensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images. Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network. Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice.