Reviewerly/exHarmony huggingface dataset:
Evidence-Based Software Engineering: A Checklist-Based Approach to Assess the Abstracts of Reviews Self-Identifying as Systematic Reviews

A systematic review allows synthesizing the state of knowledge related to a clearly formulated research question as well as understanding the correlations between exposures and outcomes. A systematic review usually leverages explicit, reproducible, and systematic methods that allow reducing the potential bias that may arise when conducting a review. When properly conducted, a systematic review yields reliable findings from which conclusions and decisions can be made. Systematic reviews are increasingly popular and have several stakeholders to whom they allow making recommendations on how to act based on the review findings. They also help support future research prioritization. A systematic review usually has several components. The abstract is one of the most important parts of a review because it usually reflects the content of the review. It may be the only part of the review read by most readers when forming an opinion on a given topic. It may help more motivated readers decide whether the review is worth reading or not. But abstracts are sometimes poorly written and may, therefore, give a misleading and even harmful picture of the review’s contents. To assess the extent to which a review’s abstract is well constructed, we used a checklist-based approach to propose a measure that allows quantifying the systematicity of review abstracts i.e., the extent to which they exhibit good reporting quality. Experiments conducted on 151 reviews published in the software engineering field showed that the abstracts of these reviews had suboptimal systematicity.
Few-Shot Aircraft Detection in Satellite Videos Based on Feature Scale Selection Pyramid and Proposal Contrastive Learning

To date, few-shot object detection methods have received extensive attention in the field of remote sensing, and no relevant research has been conducted using satellite videos. It is difficult to identify foreground objects in satellite videos duo to their small size and low contrast and the domain differences between base and novel classes under few-shot conditions. In this paper, we propose a few-shot aircraft detection method with a feature scale selection pyramid and proposal contrastive learning for satellite videos. Specifically, a feature scale selection pyramid network (FSSPN) is constructed to replace the traditional feature pyramid network (FPN), which alleviates the limitation of the inconsistencies in gradient computation between different layers for small-scale objects. In addition, we add proposal contrastive learning items to the loss function to achieve more robust representations of objects. Moreover, we expand the freezing parameters of the network in the fine-tuning stage to reduce the interference of visual differences between the base and novel classes. An evaluation of large-scale experimental data showed that the proposed method makes full use of the advantages of the two-stage fine-tuning strategy and the characteristics of satellite video to enhance the few-shot detection performance.
A Customer Behavior Recognition Method for Flexibly Adapting to Target Changes in Retail Stores

To provide analytic materials for business management for smart retail solutions, it is essential to recognize various customer behaviors (CB) from video footage acquired by in-store cameras. Along with frequent changes in needs and environments, such as promotion plans, product categories, in-store layouts, etc., the targets of customer behavior recognition (CBR) also change frequently. Therefore, one of the requirements of the CBR method is the flexibility to adapt to changes in recognition targets. However, existing approaches, mostly based on machine learning, usually take a great deal of time to re-collect training data and train new models when faced with changing target CBs, reflecting their lack of flexibility. In this paper, we propose a CBR method to achieve flexibility by considering CB in combination with primitives. A primitive is a unit that describes an object's motion or multiple objects' relationships. The combination of different primitives can characterize a particular CB. Since primitives can be reused to define a wide range of different CBs, our proposed method is capable of flexibly adapting to target CB changes in retail stores. In experiments undertaken, we utilized both our collected laboratory dataset and the public MERL dataset. We changed the combination of primitives to cope with the changes in target CBs between different datasets. As a result, our proposed method achieved good flexibility with acceptable recognition accuracy.
AI Based Metaverse Technologies Advancement Impact on Higher Education Learners

Avatars of real people inhabit the Metaverse. Some industry players have called it the ""next big bang"" for the Indian EdTech market, which is expected to reach $30 billion by 2032. Technology dominates 21st- century education, whether it’s university-based programmers, real-world technical training, or abstract concepts taught in schools and universities. In India’s education sector, researchers are still studying Augmented Reality. There are few Augmented Reality studies in education. This research evaluates and explains the impact of an Augmented Reality smartphone app on the learning passion of high school/college students. The study examines how augmented reality affects classroom motivation. Core motivation theory boosts classroom motivation. The attention, relevance, confidence, and satisfaction (ARCS) model affected how Augmented Reality was perceived and how the material was updated. This study evaluated Augmented reality smartphone apps using SEM model analysis. The study used the ARCS model to analyses Augmented Reality Education Apps, their effect on higher education, and their relationship with respect to Attention, relevance, satisfaction, and confidence are motivational variables with significant findings. The study found that using an augmented reality smartphone application would help students learn and be more motivated.
Online teaching during COVID-19 pandemic in Fiji: challenges, facilitators and improvement strategies

Purpose The purpose of this study is to explore online teaching experiences of educators in Fiji National University (FNU). Although they quickly adapted to online teaching, their experiences are yet to be explored and documented, hence the relevance of this study. Design/methodology/approach This study used phenomenological research design to understand the lived experiences of participants' regarding online teaching. A total of 58 in-depth interviews were conducted among educators, at various levels and analysed thematically using an inductive approach. Findings The findings show online teaching challenges including difficulties maintaining students' engagement, teaching practically oriented courses, assessing students and embracing change, limited knowledge impact and output, anxiety and stress, inadequate teaching environment, increased workload and unstable Internet. The facilitators and enablers were flexibility, free Internet, creating conducive environment, Moodle training, skills acquisition and support from faculty members' and information technology team. Recommendations for improvement ranged from modifying course outline, summarising previous lessons, reducing course load, adequate assessment tools, incentives, guidelines enforcement to regular training. Originality/value This is an original work carried out by the researchers in a specific context. The findings suggest the need to address various challenges for effective online teaching. School managers' and relevant key stakeholders need to understand educators’ teaching experiences as well as their recommended improvement strategies, to enhance online teaching.
Experience Matters: Longitudinal Changes in Sensitivity to Rotational Gains in Virtual Reality

Redirected walking techniques use rotational gains to guide users away from physical obstacles as they walk in a virtual world, effectively creating the illusion of a larger virtual space than is physically present. Designers often want to keep users unaware of this manipulation, which is made possible by limitations in human perception that render rotational gains imperceptible below a certain threshold. Many aspects of these thresholds have been studied; however, no research has yet considered whether these thresholds may change over time as users gain more experience with them. To study this, we recruited 20 novice VR users (no more than 1 hour of prior experience with an HMD) and provided them with an Oculus Quest to use for 4 weeks on their own time. They were tasked to complete an activity assessing their sensitivity to rotational gain once each week, in addition to whatever other activities they wanted to perform. No feedback was provided to participants about their performance during each activity, minimizing the possibility of learning effects accounting for any observed changes over time. We observed that participants became significantly more sensitive to rotation gains over time, underscoring the importance of considering prior user experience in applications involving rotational gain, as well as how prior user experience may affect other, broader applications of VR.
Decentralized cross-blockchain asset transfers with transfer confirmation

Abstract Today, several solutions for cross-blockchain asset transfers exist. However, these solutions are either tailored to specific assets or neglect finality guarantees that prevent assets from getting lost in transit. In this paper, we present a cross-blockchain asset transfer protocol that supports arbitrary assets, is adaptable to different means of cross-blockchain communication, and adheres to requirements such as finality. The ability to freely transfer assets between blockchains may increase transaction throughput and provide developers with more flexibility by allowing them to design digital assets that leverage the capacities and capabilities of multiple blockchains. We define the general requirements and specifications for a cross-blockchain asset transfer protocol and provide a proof-of-concept implementation for EVM-based blockchains. Further, we evaluate the protocol concerning costs, transfer duration, and security.
Discrepancy and Gradient-Guided Multi-modal Knowledge Distillation for Pathological Glioma Grading

The fusion of multi-modal data, e.g., pathology slides and genomic profiles, can provide complementary information and benefit glioma grading. However, genomic profiles are difficult to obtain due to the high costs and technical challenges, thus limiting the clinical applications of multi-modal diagnosis. In this work, we address the clinically relevant problem where paired pathology-genomic data are available during training, while only pathology slides are accessible for inference. To improve the performance of pathological grading models, we present a discrepancy and gradient-guided distillation framework to transfer the privileged knowledge from the multi-modal teacher to the pathology student. For the teacher side, to prepare useful knowledge, we propose a Discrepancy-induced Contrastive Distillation (DC-Distill) module that explores reliable contrastive samples with teacher-student discrepancy to regulate the feature distribution of the student. For the student side, as the teacher may include incorrect information, we propose a Gradient-guided Knowledge Refinement (GK-Refine) module that builds a knowledge bank and adaptively absorbs the reliable knowledge according to their agreement in the gradient space. Experiments on the TCGA GBM-LGG dataset show that our proposed distillation framework improves the pathological glioma grading significantly and outperforms other KD methods. Notably, with the sole pathology slides, our method achieves comparable performance with existing multi-modal methods. The code is available at https://github.com/CityU-AIM-Group/MultiModal-learning .
AdaTriplet: Adaptive Gradient Triplet Loss with Automatic Margin Learning for Forensic Medical Image Matching

This paper tackles the challenge of forensic medical image matching (FMIM) using deep neural networks (DNNs). FMIM is a particular case of content-based image retrieval (CBIR). The main challenge in FMIM compared to the general case of CBIR, is that the subject to whom a query image belongs may be affected by aging and progressive degenerative disorders, making it difficult to match data on a subject level. CBIR with DNNs is generally solved by minimizing a ranking loss, such as Triplet loss (TL), computed on image representations extracted by a DNN from the original data. TL, in particular, operates on triplets: anchor, positive (similar to anchor) and negative (dissimilar to anchor). Although TL has been shown to perform well in many CBIR tasks, it still has limitations, which we identify and analyze in this work. In this paper, we introduce (i) the AdaTriplet loss – an extension of TL whose gradients adapt to different difficulty levels of negative samples, and (ii) the AutoMargin method – a technique to adjust hyperparameters of margin-based losses such as TL and our proposed loss dynamically. Our results are evaluated on two large-scale benchmarks for FMIM based on the Osteoarthritis Initiative and Chest X-ray-14 datasets. The codes allowing replication of this study have been made publicly available at https://github.com/Oulu-IMEDS/AdaTriplet .
Learning large-scale fuzzy cognitive maps under limited resources

Research on the problem of learning large-scale fuzzy cognitive maps (FCMs) with a limited computational budget is outstanding. To learn large-scale FCMs from time series, in most work, this problem is decomposed into learning local connections of each concept, respectively, and then one optimizer is employed to optimize each such sub-problem. Each sub-problem may have different requirements for the computational resource, but the existing methods ignore this issue and allocate the same amounts of computational resources for each sub-problem. In this paper, we propose two strategies to address this problem. We first develop a dynamic resource allocation strategy to maximize the performance of the decomposition-based optimizer under a limited computational budget. Second, we propose a half-thresholding memetic algorithm to improve the performance of the traditional evolutionary algorithm. We term our proposal as a half-thresholding memetic algorithm with a dynamic resource allocation strategy (HTMA-DRA). Finally, the experiments on large-scale synthetic data and DREAM datasets compared with the existing state-of-the-art methods demonstrate the effectiveness of the proposed HTMA-DRA.
Consumer choices under new payment methods

Abstract This study suggests a payment portfolio model that includes new payment methods that have emerged from the development of cryptocurrency markets and central bank digital currencies (CBDCs). Our model analyzes the optimal payment choice for consumers under various macroeconomic conditions. We determine that an individual economic agent chooses payment methods under specific conditions by incorporating policy interest rates on CBDCs and stablecoins used on cryptocurrency exchanges. We analyze the impacts of CBDCs and stablecoins on the choice of whether to use cash or deposits. We also examine how the agent changes her portfolio compositions in response to exogenous macroeconomic policies. If a government replaces cash with a CBDC, the convenience of digital currency would not affect consumer choices. The higher the government’s interest rate on CBDCs, the more consumers will use CBDCs than deposits.
Combining UNet 3+ and Transformer for Left Ventricle Segmentation via Signed Distance and Focal Loss

Left ventricle (LV) segmentation of cardiac magnetic resonance (MR) images is essential for evaluating cardiac function parameters and diagnosing cardiovascular diseases (CVDs). Accurate LV segmentation remains a challenge because of the large differences in cardiac structures in different research subjects. In this work, a network based on an encoder–decoder architecture for automatic LV segmentation of short-axis cardiac MR images is proposed. It combines UNet 3+ and Transformer to jointly predict the segmentation masks and signed distance maps (SDM). UNet 3+ can extract coarse-grained semantics and fine-grained details from full scales, while a Transformer is used to extract global features from cardiac MR images. It solves the problem of low segmentation accuracy caused by blurred LV edge information. Meanwhile, the SDM provides a shape-aware representation for segmentation. The performance of the proposed network is validated on the 2018 MICCAI Left Ventricle Segmentation Challenge dataset. The five-fold cross-validation evaluation was performed on 145 clinical subjects, and the average dice metric, Jaccard coefficient, accuracy, and positive predictive value reached 0.908, 0.834, 0.979, and 0.903, respectively, showing a better performance than that of other mainstream ones.
Extrinsic calibration method for 3D scanning system with four coplanar laser profilers

Abstract 3D scanning is a crucial step to ensuring the machining quality of the workpiece and is an essential part of intelligent manufacturing. However, existing scanning systems usually have only one profiler, which must be combined with a dynamic tracking system to achieve a complete scan of a workpiece. This scanning method has low efficiency and complicated path planning for ring-shaped workpieces. Therefore, in this article, an efficient and high-accuracy 3D scanning system composed of a linear translation stage and four uniformly distributed laser profilers is built, and its extrinsic calibration method is studied. At first, based on the working parameters and spatial layout of multiple profilers, a stereoscopic calibrator composed of three non-collinear target balls (TBs) is designed. Then, a multi-profiler data fusion method is proposed, which utilizes a linear encoder to trigger the four profilers synchronously. Finally, by simultaneously using all data from the multiple profilers and the spherical constraint of each TB, all extrinsic parameters are accurately calibrated at the same time. Experimental results show that the average probing size error of the TB with a 38.1 mm diameter is stable at about 0.007 mm, and its extended uncertainty is about 0.100 mm ( k = 2). In addition, standard cylinders and bend tubes are scanned. The results show that the proposed method can meet the high-accuracy calibration requirements of the tube-bending deformation detection system.
Reliable neural networks for regression uncertainty estimation

While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains a challenging task. To address this challenge, we propose and implement a loss function for regression uncertainty estimation based on the Bayesian Validation Metric (BVM) framework while using ensemble learning. The proposed loss reproduces maximum likelihood estimation in the limiting case. A series of experiments on in-distribution data show that the proposed method is competitive with existing state-of-the-art methods. Experiments on out-of-distribution data show that the proposed method is robust to statistical change and exhibits superior predictive capability.
Gravelly soil uniformity identification based on the optimized Mask R-CNN model

The uniformity of gravelly soil has an important influence on compaction quality. The most important task to judge the uniformity of gravelly soil is to segment the gravels from the image. However, gravels are widely and densely distributed, and their particle size varies greatly, increasing segmentation difficulty. Among existing studies, research on rapid and quantitative judgment methods of gravelly soil uniformity remains scarce. To address the abovementioned issue, a gravelly soil uniformity identification based on the optimized Mask R-CNN model is proposed. The original Mask R-CNN only produces one combined mask of multiple overlapping gravels, which hinders postprocessing and uniformity calculation. To address this problem, separate masks for each gravel are generated for better parameter calculation. Then, according to the characteristics of the pixel image of a single mask, the calculation of static moment is deduced and simplified. Finally, the single mask dataset of the optimized Mask R-CNN and static distance theory are used to establish a quantitative evaluation index of gravelly soil uniformity, in which the uniformity coefficient (UC) and area ratio coefficient (ARC) are adopted. In addition, the convergence curves and the Average Precision (AP) of the ResNet101 and the ResNet50 backbones are compared, and the result proves the superiority of ResNet101 in gravel segmentation. Furthermore, three data enhancement methods (namely, rotation, mirroring, and brightness transformation) are adopted to improve the AP performance and result in a 2.32% increase. The application in a real large-scale hydropower project shows that the AP can reach 88.96%, and each calculation and analysis can be controlled within one minute, which shows the effectiveness, convenience and efficiency of the method.
The interaction of normalisation and clustering in sub-domain definition for multi-source transfer learning based time series anomaly detection

This paper examines how data normalisation and clustering interact in the definition of sub-domains within multi-source transfer learning systems for time series anomaly detection. The paper introduces a distinction between (i) clustering as a primary/direct method for anomaly detection, and (ii) clustering as a method for identifying sub-domains within the source or target datasets. Reporting the results of three sets of experiments, we find that normalisation after feature extraction and before clustering results in the best performance for anomaly detection. Interestingly, we find that in the multi-source transfer learning scenario clustering on the target dataset and identifying subdomains in the target data can result in improved model performance, as compared to identifying sub-domains through defining clusters using the multi-source dataset.
A Deep-Discrete Learning Framework for Spherical Surface Registration

Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches. Classically, image registration is performed by optimizing a complex objective similarity function, leading to long run times. This contributes to a convention for aligning all data to a global average reference frame that poorly reflects the underlying cortical heterogeneity. In this paper, we propose a novel unsupervised learning-based framework that converts registration to a multi-label classification problem, where each point in a low-resolution control grid deforms to one of fixed, finite number of endpoints. This is learned using a spherical geometric deep learning architecture, in an end-to-end unsupervised way, with regularization imposed using a deep Conditional Random Field (CRF). Experiments show that our proposed framework performs competitively, in terms of similarity and areal distortion, relative to the most popular classical surface registration algorithms and generates smoother deformations than other learning-based surface registration methods, even in subjects with atypical cortical morphology. The code can be found in https://github.com/mohamedasuliman/DDR/ .