Reviewerly/exHarmony huggingface dataset:
Academic Women Co-designing Education Futures in a Postdigital World

Abstract This paper draws on the collective knowledge-building of nine women from diverse disciplines, roles, cultures, and institutions in Australasian women in leadership programme. Brought together during Covid-19 through a shared interest and purpose concerning current and future developments in digital education, we offer knowledge and insight from our perspective as women leaders in academia, on co-designing futures in a postdigital world. Drawing on a duoethnographic research design, we reflected on our experiences as academic leaders and practitioners to systematically explore people, situations, and contexts through co-construction and dialogue. Our joint exploration uncovered themes of visibility, gravitas, and relationships. We provide evidence of the role co-design plays in our own practices, in our classrooms, and how our research design was strengthened through co-design. Finally, we offer an evolving model of co-design for leadership in higher education with communities of practice at its core.
Artistic Energy of the Performers in the Mirror of Their Repertoire Preferences

"The problem field of this article consists of learning the essence of the phenomenon of artistic energy and understanding the performing repertoire as one of the ways of its objectification. Each component of the specified subject of study has its own problem, which is also subject to development. The choice of the proposed subject of scientific cognition is motivated by the desire to understand the phenomenon of the subject of performing interpretative activity. An important factor in its study is the disclosure of the artistic properties of the pianist’s personality, which contributes to the establishment of contact between the concert performer and the listening audience. A little-studied aspect of the analytical consideration of artistic energy remains the question of methods of its objectification, which is the subject of the proposed article. It is stipulated by the perspective of the direction of modern musicology related to the scientific development of the performing repertoire. The location of the selected research subject at the intersection of the named scientific contexts is designed to understand the various properties of the subject of the performing activity as a component of a single system. Therefore, the purpose of this article is to substantiate the idea of the performers’ repertoire preferences as a reflection of their artistic energy. Keywords: pianist-performer, artistic energy, piano repertoire, interpretation, performing style."
Network traffic prediction model based on linear and nonlinear model combination

Abstract We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.
Accessing User Satisfaction of Makerspace in Academic Libraries

Makerspaces have developed from a trend to a core service in higher education libraries. Many academic libraries have been actively expanding the “makerspace” within the physical library and revitalizing the library as a center of learning and innovation. This case study investigates the application of makerspace technologies in a major comprehensive library in Hong Kong, which has designed a specific makerspace to encourage innovation and creativity. Few studies have focused on in-depth studies of makerspaces in East Asian academic libraries and how patrons perceive makerspace services and innovative spaces. A survey instrument was developed using the 5E instructional model (engage, explore, explain, elaborate, and evaluate) to evaluate makers' experiences systematically. The finding revealed that respondents applauded the importance of innovative spaces and the perceived outcomes from makerspaces, including nurturing creativity and critical thinking. However, they did not have sufficient skills to use emerging technologies, resulting in low usage.
Real-Time Terrain Correction of Satellite Imagery-Based Solar Irradiance Maps Using Precomputed Data and Memory Optimization

Satellite imagery-based solar irradiance mapping studies are essential for large-scale solar energy assessments but are limited in spatial resolution and accuracy. Despite efforts to increase map resolution by correcting inaccuracies caused by shadows on the terrain, the computational time of these models and the massive volume of generated data still pose challenges. Particularly, forecasting generates large amounts of time series data, and the data production rate is faster than the computational speed of traditional terrain correction. Moreover, while previous research has been conducted to expedite computations, a novel and innovative technology in terrain correction is still required. Therefore, we propose a new correction method that can bypass complex calculations and process enormous data within seconds. This model extends the lookup table concept, optimizes the results of many shadow operations, and stores them in memory for use. The model enabled 90 m scale computations across Korea within seconds on a local desktop computer. Optimization was performed based on domain knowledge to reduce the required memory to a realistic level. A quantitative analysis of computation time was also conducted, revealing a previously overlooked computational bottleneck. In conclusion, the developed model enables real-time terrain correction and subsequent processing of massive amounts of data.
On-Board Multi-Class Geospatial Object Detection Based on Convolutional Neural Network for High Resolution Remote Sensing Images

Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for Object Detection in Aerial Images-Cloud Detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method.
Reinforcement Learning Derived High-Alpha Aerobatic Manoeuvres for Fixed Wing Operation in Confined Spaces

Reinforcement learning has been used on a variety of control tasks for drones, including, in previous work at the University of Bristol, on perching manoeuvres with sweep-wing aircraft. In this paper, a new aircraft model is presented representing flight up to very high angles of attack where the aerodynamic models are highly nonlinear. The model is employed to develop high-alpha manoeuvres, using reinforcement learning to exploit the nonlinearities at the edge of the flight envelope, enabling fixed-wing operations in tightly confined spaces. Training networks for multiple manoeuvres is also demonstrated. The approach is shown to generate controllers that take full advantage of the aircraft capability. It is suggested that a combination of these neural network-based controllers, together with classical model predictive control, could be used to operate efficiently within the low alpha flight regime and, yet, respond rapidly in confined spaces where high alpha, agile manoeuvres are required.
Ideal dynamic threshold Multi-secret data sharing in smart environments for sustainable cities

Nowadays, with continuous integration of big data, artificial intelligence and cloud computing technologies, there are increasing demands and specific requirements for data sharing in sustainable smart cities: (1) practical data sharing should be implemented in the non-interactive fashion without a trusted third party to be involved; (2) dynamic thresholds are preferred since the participants may join or leave at any time; (3) multi-secret sharing is desirable to increase the packing capacity. To fulfil these requirements, we propose a general construction of ideal threshold changeable multi-secret sharing scheme (TCMSS) with information-theoretic security, in which polynomials are employed to achieve dealer-free and non-interactive in the secret reconstruction phase. The TCMSS scheme can be built on any existing linear secret sharing scheme, and it is simpler and more efficient than the existing TCSS schemes in the literature. The main difference between TCMSS and Shamir's SS is that univariate polynomial is used in Shamir's SS to generate the shares for all shareholders; while in TCMSS, each shareholder can recover her own univariate polynomial using her share. This article demonstrates that with this novel modification, the classic polynomial-based SS can be transformed into an ideal TCMSS. Moreover, the TCMSS scheme is lightweight and it can resist both internal and external attacks. It does not require pairwise key distribution and its secret reconstruction phase is improved with enhanced properties. Therefore, the designed proposal is fairly suitable and attractive to be deployed in sustainable cities.
An efficient randomized QLP algorithm for approximating the singular value decomposition

The rank-revealing pivoted QLP decomposition approximates the computationally prohibitive singular value decomposition (SVD) via two consecutive column-pivoted QR (CPQR) decomposition. It furnishes information on all four fundamental subspaces of the matrix. In this paper, we introduce a randomized version of the QLP decomposition called Rand-QLP. Operating on a matrix A, Rand-QLP gives A=QLPT, where Q and P are orthonormal, and L is lower-triangular. Under the assumption that the rank of the input matrix is k, we derive several error bounds for Rand-QLP: bounds for the first k approximate singular values and for the trailing block of the middle factor L, which show that the decomposition is rank-revealing; bounds for the distance between approximate subspaces and the exact ones for all four fundamental subspaces of a given matrix; and bounds for the errors of low-rank approximations constructed by the columns of Q and P. Rand-QLP is able to effectively leverage modern computational architectures, due to the utilization of random sampling and the unpivoted QR decomposition, thus addressing a serious bottleneck associated with classical algorithms such as the SVD, CPQR and most recent matrix decomposition algorithms. We utilize a modern computing architecture to assess the performance behavior of different algorithms. In comparison to CPQR and the SVD, Rand-QLP respectively achieves a speedup of up to 5 times and 6.6 times on the CPU and up to 3.8 times and 4.4 times with the hybrid GPU architecture. In terms of quality of approximation, our results on synthetic and real data show that the approximations by Rand-QLP are comparable to those of pivoted QLP and the optimal SVD, and in most cases are considerably better than those of CPQR.
Finite key performance of satellite quantum key distribution under practical constraints

Abstract Global-scale quantum communication networks will require efficient long-distance distribution of quantum signals. While optical fibre communications are range-limited due to exponential losses in the absence of quantum memories and repeaters, satellites enable intercontinental quantum communications. However, the design of satellite quantum key distribution (SatQKD) systems has unique challenges over terrestrial networks. The typical approach to modelling SatQKD has been to estimate performances with a fully optimised protocol parameter space and with few payload and platform resource limitations. Here, we analyse how practical constraints affect the performance of SatQKD for the Bennett-Brassard 1984 (BB84) weak coherent pulse decoy state protocol with finite key size effects. We consider engineering limitations and trade-offs in mission design including limited in-orbit tunability, quantum random number generation rates and storage, and source intensity uncertainty. We quantify practical SatQKD performance limits to determine the long-term key generation capacity and provide performance benchmarks to support the design of upcoming missions.
YOLOv5-OCDS: An Improved Garbage Detection Model Based on YOLOv5

As the global population grows and urbanization accelerates, the garbage that is generated continues to increase. This waste causes serious pollution to the ecological environment, affecting the stability of the global environmental balance. Garbage detection technology can quickly and accurately identify, classify, and locate many kinds of garbage to realize the automatic disposal and efficient recycling of waste, and it can also promote the development of a circular economy. However, the existing garbage detection technology has some problems, such as low precision and a poor detection effect in complex environments. Although YOLOv5 has achieved good results in garbage detection, the detection results cannot meet the requirements in complex scenarios, so this paper proposes a garbage detection model, YOLOv5-OCDS, based on an improved YOLOv5. Replacing the partial convolution in the neck with Omni-Dimensional Dynamic Convolution (ODConv) improves the expressiveness of the model. The C3DCN structure is constructed, and parts of the C3 structures in the neck are replaced by C3DCN structures, allowing the model to better adapt to object deformation and target scale change. The decoupled head is used for classification and regression tasks so that the model can learn each class’s characteristics and positioning information more intently, and flexibility and extensibility can be improved. The Soft Non-Maximum Suppression (Soft NMS) algorithm can better retain the target’s information and effectively avoid the problem of repeated detection. The self-built garbage classification dataset is used for related experiments, and the mAP@50 of the YOLOv5-OCDS model is 5.3% higher than that of the YOLOv5s; the value of mAP@50:95 increases by 12.3%. In the experimental environment of this study, the model’s Frames Per Second (FPS) was 61.7 f/s. In practical applications, when we use some old GPU, such as the GTX1060, it can still reach 50.3 f/s, so that real-time detection can be achieved. Thus, the improved model suits garbage detection tasks in complex environments.
Semi-Supervised Graph Neural Networks for Graph Partitioning Problem

The Graph Partitioning Problem (GPP) is a classical combinatorial optimization problem that has been extensively researched. In recent years, many methods for solving the GPP have been proposed, which can be divided into direct partitioning approaches and iterative improvement approaches. Direct partitioning approaches directly give a complete partition of the input graph. Iterative improvement approaches require further adjustment of feasible solutions based on the result of direct partitioning approaches in order to obtain a better performance. In this paper, we summarize the recent trends in algorithms and applications for GPP. In addition, we propose a graph partitioning algorithm based on semi-supervised learning in combination with graph filtering methods.
Consumer preference disaggregation based on online reviews to support new energy automobile purchase decision

In recent years, new energy automobiles have developed rapidly and become the choice of more and more consumers. At the same time, a vast number of online reviews of automobiles with inferred consumer preferences emerge on platforms. There is an urgent need to mine useful information from online reviews to support consumer purchase decisions. In this study, we propose a multi-criteria product recommendation method that considers consumer' preferences and risk psychology estimated from online reviews. Firstly, a sentiment analysis method is developed to mine product performance under different attributes from text reviews. Secondly, a preference model with unknown preference parameters is predefined based on the multi-attribute value theory to represent consumers' value systems for purchase decision. On this basis, we propose a preference disaggregation analysis method that combines the prospect theory. Consumers' specific preference models are estimated from online reviews, which are then utilized to measure product performance and generate product recommendations. Finally, the validity of the proposed method is demonstrated by a case study of new energy automobile ranking from Autohome.
Legal Policies Failing on Data Breaches?–An Empirical Study of U.S. Information Security Law Implementations

Data breaches are increasingly disruptive and have caused dramatic financial implications for the organizations affected by incidents. Although the government has enacted and amended relevant legal provisions, the number of data breaches is broadly on the rise. The laws concerning data breaches aim to decrease the risk of such incidents, but their true efficacy remains to be quantitively evaluated. In this study, we use panel data on data breaches in the United States from 2005 to 2019 to empirically verify whether laws enacted can reduce the probability of data breaches. First, we attempt to confirm that the law is in force to curb the occurrence of data breaches; second, we further study the effectiveness of the penalty regulations. Our findings will clearly illustrate whether data breach laws can play a deterrent role in reducing incidents and provide new insights into the field of government regulations.
Feature Selection Based on Two-stage Resampling Technique for Imbalanced Dataset

The conventional feature selection methods fail to work on imbalanced datasets due to the overfitting issue caused by the extremely imbalanced positive and negative samples. In addition, the large number of negative samples makes the feature selection operation inefficient. To address these issues, an automatic feature selection method for addressing feature selection issues on extremely imbalanced datasets is proposed. An undersampling operation is performed to generate a small balanced dataset at first. Then, the feature importance scores are estimated based on the occurrence of the feature columns in the feature combinations that have a higher score than the base score. Finally, a repeated feature removal operation and classification model retrain are performed on the new dataset generated with the oversampling technique until the removal of the unimportant feature column does not bring any score improvement. In the proposed approach, the oversampling operation can fully utilize the dataset to train the classification model properly and the importance score is easily interpreted as the frequency of each feature column in the satisfied feature combinations. The experiment shows the superior performance of the proposed feature selection method.
Iterative Privileged Learning for Multi-view Classification

Multi-view data is closely associated with privileged information, presenting opportunities for advancements in current studies. Firstly, they often overlook the dynamic nature of privileged information; Secondly, the current state-of-the-art privileged support vector machine, dSVM+, is primarily designed for single-view learning; Thirdly, some methods only address two-view classification problems; Finally, the performance enhancements achieved by these approaches remain unsatisfactory. To this end, this paper proposes a multi-view privileged support vector machine called MVdSVM+. The iterative learning strategy is put forward to update the privileged information dynamically. To our best knowledge, this is the first attempt of dSVM+ in multi-view learning. Meanwhile, it is designed for multiple views. Experiments show the effectiveness of MVdSVM+ over the benchmark methods.
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge

Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient. The information included in Impression is also often covered in Findings. While Findings and Impression can be deduced by inspecting the image, Clinical Indications often require additional context. The cognitive task of interpreting medical images remains the most critical and often time-consuming step in the radiology workflow. Instead of generating an end-to-end radiology report, in this paper, we focus on generating the Findings from automated interpretation of medical images, specifically chest X-rays (CXRs). Thus, this work focuses on reducing the workload of radiologists who spend most of their time either writing or narrating the Findings. Unlike past research, which addresses radiology report generation as a single-step image captioning task, we have further taken into consideration the complexity of interpreting CXR images and propose a two-step approach: (a) detecting the regions with abnormalities in the image, and (b) generating relevant text for regions with abnormalities by employing a generative large language model (LLM). This two-step approach introduces a layer of interpretability and aligns the framework with the systematic reasoning that radiologists use when reviewing a CXR.