Reviewerly/exHarmony huggingface dataset:
Subject Matter Expert (SME) Management Strategy for Multi-Criteria Decision Making (MCDM): A Case Study of Hierarchical Decision Model (HDM)

Subject Matter Experts (SMEs) have been filling the void of the knowledge gap since ancient times through their skills and expertise in one or more fields. Multicriteria Decision Making (MCDM) is a research tool that relies on expert judgment and allows one to take a decision when the final decision depends on various criteria and sub-criteria, many of which could be at odds. The ultimate success of the MCDM tool is contingent to a large extent on the management of SMEs that may involve preparing for communication with research subjects, finding SMEs, taking consent for participation, selecting and organizing panels, interacting and collaborating for validation, disagreement, inconsistency, developing desirability curves, evaluating technologies in terms of desirability curves, and finally, triangulation. The research discusses the process of managing SMEs considering the case of the Hierarchical Decision Model (HDM) used for assessing the Market Diffusion Potential (MDP) of Energy-Efficient (EE) technologies. Each section describes the process of managing SMEs from a theoretical perspective and discusses how it is implemented in practice considering the case of HDM for MDP assessment. Throughout the process, experts are consulted in every single step to make the outcome credible, valid, reliable, and authentic. It is a one-stop reference and guide for managing SMEs in MCDM.
Scrum Customization to Manage Product Design Modifications in a White Line Manufacturing Company

Investing in project management tools has become a strategic concern for companies in dynamic markets such as domestic appliances in white goods. Agile methodologies, particularly Scrum, have gained popularity due to their ability to reduce bureaucracy, help customer collaboration, and empower employees. However, the lack of clarity in the definition of agility and the challenges of adopting this management method can hinder its effective implementation. The aim of this study is to propose a customized version of the Scrum method and its implementation for managing product design changes in a white-line manufacturing company. To achieve this goal, a comprehensive literature review was conducted to explore the operational and conceptual aspects of traditional project management (PMBOK) and agile management methods (Scrum) and the methods for implementing them. To customize Scrum to the company’s specific needs, data collection was conducted together with those involved in the sector activities and these data to identify were exhibited through a flowchart. The representation was used to identify the work packages that needed to be managed and considered in the Scrum customization. The proposed Scrum configuration was then evaluated in the field to evaluate its performance in managing project changes. By customizing Scrum to the company’s unique requirements, this study provides practical insights into the implementation of agile project management in a white-line industry context. The proposed methodology was applied to enhance project modification management and improve the outcomes of these procedures.
A Framework for Systematic Scientific Research Management

many organizations and research groups have adopted methods, models, and standards to improve their research. However, despite these efforts, they can still find it difficult to put clear planning framework for the research that can be used for both researchers and supervisors. This paper aims to provide a theoretical framework for the researchers who are about to write their research proposal. This paper proposes a systematic framework for planning scientific research. The proposed framework extends is based on the multi-method approach for research developed by Nunamaker et al. The Framework is developed and applied based on a systematic review of existing literature and is complemented with the authors’ practical experience. Finally, the framework validation is presented through five real cases studies.
Study on Suitable Management organizations to Promote Usage of Measurement Instruments Produced by University Research

There are the following three steps to increase users of cutting-edge measurement instruments produced by university research: (1) providing information on their use at scientific conferences, (2) conducting collaborative research between the developers and users of the instruments, and (3) providing a contract measurement service to researchers wanting to use the instruments as needed. This research study aims to determine the criteria for selecting suitable operating organization handling contract measurement service above when it is located within or outside the university. As model cases, we investigated the following two types of businesses that operate university-owned instruments to provide measurement results: shared-use facilities for industry-academia collaborations operated by universities (university organizations) and university-launched companies (companies) performing outsourced services under an instrument-use agreement with the universities. We interviewed three university organizations and three companies about their challenges and success points in managing their organizations. A comparison of the interviews revealed that “maintenance of the organization,” “hiring human resources,” and “expanding customer base” are the criteria for selecting the two types of businesses.
Application of ELM Algorithm Incorporating AE Principles in Wireless Communication Signal Detection

With the development of society, the requirements for communication are getting higher and higher, but the detection performance of wireless communication signals is insufficient. To solve this problem, this paper combines the principle of Auto-encoder (AE) and the feature classification function of Extreme Learning Machine (ELM) algorithm to obtain AE-ELM algorithm, and builds a new wireless communication signal detection model based on this algorithm, hoping to improve the detection performance of wireless communication signals. The performance comparison experiment of the proposed AE-ELM algorithm shows that the bit error rate and average relative time complexity of the algorithm are 0.0004 and 0.5292s respectively, which is better than the comparison algorithm. Additionally, the effectiveness of the fusion algorithm-based signal detection model for wireless communication is investigated, and the findings demonstrate that this model outperforms the others with the lowest detection error rate of 0.0031. The above results demonstrate the high application potential of the novel wireless communication signal detection model and its ability to significantly improve the performance of wireless communication signal detection. Its application to wireless communication signal detection can greatly promote the development of the signal detection field in China.
Ensemble Synthesized Minority Oversampling-Based Generative Adversarial Networks and Random Forest Algorithm for Credit Card Fraud Detection

The recent increase in credit card fraud is rapidly has caused huge monetary losses for individuals and financial institutions.Most credit card frauds are conducted online by illegally obtaining payment credentials through data breaches, phishing, or scamming.Many solutions have been suggested to address the credit card fraud problem for online transactions.However, the high class imbalance is the major challenge that faces the existing solutions to construct an effective detection model.Most of the existing techniques used for class imbalance overestimate the distribution of the minority class, resulting in highly overlapped or noisy and unrepresentative features, which cause either overfitting or imprecise learning.In this study, a credit card fraud detection model (CCFDM) is proposed based on ensemble learning and a generative adversarial network (GAN) assisted by Ensemble Synthesized Minority Oversampling techniques (ESMOTE-GAN).Multiple subsets were extracted using under-sampling and SMOTE was applied to generate less skewed sets to prevent the GAN from modeling the noise.These subsets were used to train diverse sets of GAN models to generate the synthesized subsets.A set of Random Forest classifiers was then trained based on the proposed ESMOTE-GAN technique.The probabilistic outputs of the trained classifiers were combined using a weighted voting scheme for decision-making.The results show that the proposed model achieved 1.9%, and 3.2% improvements in overall performance and the detection rate, respectively, with a 0% false alarm rate.Due to the massive number of transactions, even a tiny false positive rate can overwhelm the analysis team.Thus, the proposed model has improved the detection performance and reduced the cost needed for manual analysis.
Sagitta: An Energy-Efficient Sparse 3D-CNN Accelerator for Real-Time 3-D Understanding

Three-dimensional (3D) understanding or inference has received increasing attention, where 3D convolutional neural networks (3D-CNNs) have demonstrated superior performance compared to two-dimensional CNNs (2D-CNNs), since 3D-CNNs learn features from all three dimensions. However, 3D-CNNs suffer from intensive computation and data movement. In this paper, Sagitta, an energy-efficient low-latency on-chip 3D-CNN accelerator, is proposed for edge devices. Locality and small differential value dropout are leveraged to increase the sparsity of activations. A full-zero-skipping convolutional microarchitecture is proposed to fully utilize the sparsity of weights and activations. A hierarchical load-balancing scheme is also introduced to increase the hardware utilization. Specialized architecture and computation flow are proposed to enhance the effectiveness of the proposed techniques. Fabricated in a 55-nm CMOS technology, Sagitta achieves 3.8 TOPS/W for C3D at a latency of 0.1 s and 4.5 TOPS/W for 3D U-Net at a latency of 0.9 s at 100 MHz and 0.91 V supply voltage. Compared to the state-of-the-art 3D-CNN and 2D-CNN accelerators, Sagitta enhances the energy efficiency by up to 379.6× and 11×, respectively.
Decentralized Blockchain Governance and Transaction Costs in Digital Transformation: The Case of the DAO Revisited

The aim of this study is to examine the efficiency of decentralized autonomous organizations (DAOs) derived from differences in corporate governance structures and transaction costs compared to traditional organizations. Core assumptions on these differences from academic and grey literature are integrated into a model of the governance layers of the innovative socio-technical arrangement that is a DAO. We test this model against the case of “The DAO”, an early experiment in decentralized autonomous organization, which was forced to be stopped after a hacker stole around 50 Million USD in ETH tokens in 2016. The case history and its effects on actors in and outside “The DAO” highlight important aspects to be considered in judging the efficiency of governance structures of DAOs and related transaction costs. Results indicate that especially in situations of crisis, transaction costs rise again and that people are not as far at the margins of the corporate governance as the concept of a DAO proposes.
Digital Transformation in Secondary Schools: A Systematic Literature Review

As the educational landscape changes rapidly, secondary schools increasingly adopt digital technologies to transform teaching and learning experiences. Recognizing the challenges and potential advantages that arise in the digital era, educators are actively undertaking digital transformation initiatives across various domains, with secondary schools being the key focus of this paper. To clarify the matter, four research questions were formulated in this systematic literature review: 1) What is the state-of-the-art for digital transformation research in secondary schools? 2) What are the essential indicators for conducting digital transformation in secondary schools, as identified by researchers? 3) What are the models or frameworks for digital transformation in secondary schools? 4) How to implement digital transformation in secondary schools? A formal protocol was followed to conduct automatic and manual searches of relevant articles published between 2012 to 2023 using the PSALSAR method. As a result, 65 papers were selected for review. This study highlighted six key factors successfully implemented in digital transformation: leadership, digital competency, professional development, technology access, school evaluation, and school competency. Furthermore, the study discussed current research trends, methods, and strategies to develop standard models and frameworks related to digital transformation in secondary schools. Challenges and potential areas for further development were investigated to facilitate a successful digital transformation.
A Novel Motion State Recognition Method for Blast Furnace Burden Surface in Ironmaking Process

Real-time and accurate motion state recognition of blast furnace burden surface is significant in the timely monitoring of abnormal furnace conditions and guiding top charging operations. However, unlike the general scene, achieving accurate and effective motion state recognition of the burden surface is currently unavailable and challenging due to the harsh environment inside the blast furnace. To address this challenge, we propose a novel image-based burden surface motion state recognition method using the feature-point optical flow clustering in the saliency-driven target region. First, a high-quality burden surface image acquisition system is devised, including image acquisition using developed equipment and image enhancement using the illumination-guided camera response model, and the various burden surface motion states are displayed through the enhanced images. Next, a target region detection model based on bidirectional prioritized random walks is constructed, and the feature-point optical flow in the target region is extracted. Finally, a maximum local density-based Gaussian mixture model of nonparametric estimation is constructed to recognize the motion state of the burden surface. Extensive experiments demonstrate that the proposed method can accurately and efficiently identify the different motion states of the burden surface, which provides furnace conditions data for guiding blast furnace charging operation.
RST Discourse Parsing as Text-to-Text Generation

Previous studies have made great advances in RST discourse parsing through specific neural frameworks or features, but they usually split the parsing process into two subtasks and heavily depended on gold discourse segmentation. In this paper, we introduce an end-to-end method for sentence-level RST discourse parsing via transforming it into a text-to-text generation task, which can also be simply applied to document-level parsing. Our method unifies the traditional two-stage parsing and generates the parsing tree directly from the input text through our constrained decoding and postprocessing algorithms, without requiring a complicated model. Moreover, the discourse segmentation can be simultaneously generated and extracted from the parsing tree. Experimental results on the RST Discourse Treebank demonstrate that our proposed method outperforms existing methods in both the tasks of discourse parsing and segmentation. We further carry out ablation studies and more targeted comparisons with traditional patterns to analyze our method in more detail. Considering the lack of annotated data in RST parsing, we also create high-quality augmented data and implement self-training, which further improves the performance of our method.
A Robust Infrared and Visible Image Registration Method for Dual-Sensor UAV System

Single-modal image registration methods are generally not feasible for visible and infrared images. Besides, multi-modal image registration methods still suffer from uneven distribution of extracted features, low repeatability, and ambiguous features. To address these issues, a coarse-to-fine infrared and visible image registration approach for dual sensor UAV imaging system is proposed, which is resilient to the difference of focal lengths and field of view. First, in the coarse registration step, the infrared image is transformed to the same scale as the visible image by using the similarity transformation. This operation makes the proposed method robust to the variation of field of view. Then, the feature point pairs are initialized using feature detectors in the infrared image’s blocked phase congruency feature map. Next, the feature point pairs are optimized by estimating the offset based on the relationship between the constructed feature descriptors. Finally, using elastic deformation, the pixel-level registered infrared image is obtained. Extensive experiments demonstrate the superior performance of the proposed coarse-to-fine image registration methodology in the real infrared-visible image pairs. The code and dataset are available at https://drive.google.com/drive/folders/1mpUWwHUbKTrBdOrNMNRRnuJclDUAC7nU?usp=sharing.
Robust M-Estimation-Based ICKF for GNSS Outlier Mitigation in GNSS/SINS Navigation Applications

The accuracy and robustness of tightly coupled GNSS/SINS integrated navigation parameter estimation are hindered by outliers caused by GNSS measurements under multipath interference and non-line-of-sight (NLOS) conditions. To address the limitations of robust estimation and the mismatch of state posterior PDF approximation, a novel M-estimation-based robust iterated cubature kalman filter (ICKF) is developed to minimize the impact of GNSS outliers while improving the correction effect of high-quality LOS GNSS measurement in this paper. The nonlinear weighted least squares regression model and objective function are established for outlier mitigation, including a sigma-point iterative filtering framework and an M-estimation method of down-weighting GNSS measurements. Specifically, the maximum a posteriori (MAP) estimation idea is used to design the sigma-point iterated filtering framework, which makes the posteriori probability density function (PDF) constantly close to the high likelihood confidence interval to obtain excellent nonlinear approximation effects. Furthermore, to avoid the inherent non-convexity of the traditional M-estimation method, the relaxation compensation scheme based on the adaptive control factor is introduced into the robust loss function to accelerate the convergence of the penalty weight of GNSS outliers. The availability of the proposed method for GNSS outlier mitigation is proved by vehicle field tests under multipath interference and NLOS conditions.
Channel-Weighted Structured Correlation Filters for UAV Tracking

In the visual object tracking via unmanned aerial vehicle (UAV) the correlation filtering (CF) is one of the mainstream methods for the reason that optimizing filters by circulant samples facilitates the calculation. The prevalent discriminative CF (DCF) frameworks credited to a ridge regression model followed by various regularizations focus on regressing to a fixed Gaussian label, which maybe easily induce over-fitting. To these concerns, we integrate the hinge-squared loss (HSL) of structured SVM with the CF model so as to attain a dynamical label which regresses the difference between target and background samples and thus enhances the robustness in tracking. Moreover, we assume weighted channels to augment the discrimination of hinge-squared loss, thus indicating it has as good extensibility as the ridge regression in usual CF frameworks. The proposed method channel-weighted structured correlation filtering (CWSCF) has achieved excellent performance compared with other methods in mainstream UAV tracking benchmarks.
Blind 3D-Synthesized Image Quality Measurement by Analysis of Local and Global Statistical Properties

Depth-Image-Based-Rendering (DIBR) procedure aims to create virtual viewpoint using the adjacent viewpoints and the corresponding depth maps for free-viewpoint video applications. However, this processing inevitably introduces visual artifacts which differ from those of natural images, mainly including holes, stretching, and blurring. To accurately evaluate the quality of 3D-synthesized images, a robust blind metric is developed by analysis of Local and Global Statistical Properties (LGSP). On the one hand, for local features, since the DIBR processing tends to damage the amount of information at the disoccluded regions in 3D-synthesized images, we extract the local two-dimensional entropy features from the spatial domain and log-Gabor domain guided by the corresponding saliency map to measure local information variation. Moreover, local statistical features are extracted from fractal dimension maps to capture local texture variations due to significant local texture degradation engendered by the DIBR procedure. On the other hand, for global features, to measure the DIBR-induced geometric distortion, steerable pyramid decomposition is utilized to extract its cross-scale and cross-orientation joint statistical features. Meanwhile, log-energies features are extracted from multi-scale and multi-orientation wavelet subbands to characterize its global blurring. Finally, the combined local and global quality-aware features are utilized to train the quality prediction model using regression function to establish the mapping between visual features and subjective scores. Extensive experiments over three public 3D-synthesized image benchmark databases show that our metric outpaces the newly-developed 3D-synthesized image metrics and the popular 2D image metrics.
The Metaverse Is Just a Software Layer …

The Metaverse is now just a software layer, and Neal Stephenson has nothing to say about it …
ZernikeNet: a deep learning-based approach for accurate Zernike coefficients calculation in aspheric optical components

This paper presents a new method called ZernikeNet for accurately calculating Zernike coefficients in aspheric optical components. Surface figure error (SFE) measurements obtained using interferometer often include alignment errors and low-order aberrations, such as piston, tip, tilt, and defocus, which need to be removed to effectively analyze high-order aberrations. The traditional method for removing low-order aberrations involves Zernike polynomial fitting to the SFE, but this assumes that the optical component is circular and can be decomposed into an orthogonal basis set of Zernike polynomials. However, for aspheric optical components, the orthogonality of Zernike polynomials may not hold, making it challenging to accurately represent the SFE. To address this challenge, ZernikeNet employs a deep learning-based approach, where interferometer map of the optical component is fed into a multi-layer neural network structure to output a set of 36 Zernike coefficients. The proposed deep learning network is trained using a single-shot metrology approach, where a single input interferometer map is used to generate high-accuracy Zernike coefficients through intentional overfitting. Experimental results using data from aspheric mirror show that ZernikeNet can effectively remove low-order aberrations, leaving only high-order aberrations, resulting in a low residual SFE RMS value. This method offers a significant advantage over traditional Zernike polynomial fitting approaches for optical components with complex shapes, making it a promising tool for the design and analysis of advanced optical systems.