Reviewerly/exHarmony huggingface dataset:
Sweating Avatars Decrease Perceived Exertion and Increase Perceived Endurance while Cycling in Virtual Reality

Avatars are used to represent users in virtual reality (VR) and create embodied experiences. Previous work showed that avatars' stereotypical appearance can affect users' physical performance and perceived exertion while exercising in VR. Although sweating is a natural human response to physical effort, surprisingly little is known about the effects of sweating avatars on users. Therefore, we conducted a study with 24 participants to explore the effects of sweating avatars while cycling in VR. We found that visualizing sweat decreases the perceived exertion and increases perceived endurance. Thus, users feel less exerted while embodying sweating avatars. We conclude that sweating avatars contribute to more effective exergames and fitness applications.
Spherical search algorithm with adaptive population control for global continuous optimization problems

Spherical search algorithm (SSA) calculates the spherical boundary and generates new solutions on it by two sub-populations jointly. Many researches have shown that SSA is a promising algorithm, but in some cases, the fixed sub-population size causes it to be prone to search inadequately and easily falling into local optimum. In this paper, a new improved algorithm, named SSAP, is proposed to alleviate these problems. In SSAP, we propose a novel population control strategy to efficiently balance exploration and exploitation. This strategy adaptively adjusts the number of individuals in both sub-populations to improve the search performance of the algorithm. It is realized by adjusting the frequency of search patterns through a cumulative index. Comparative experiments conducted on a large number of benchmark functions show that SSAP significantly outperforms other state-of-the-art algorithms. Additionally, SSAP is used to solve real-world problems to further verify its validity. Finally, the search characteristics and population diversity of SSAP are analyzed.
Lightweight network learning with Zero-Shot Neural Architecture Search for UAV images

Lightweight Network Architecture is essential for autonomous and intelligent monitoring of Unmanned Aerial Vehicles (UAVs), such as in object detection, image segmentation, and crowd counting applications. The state-of-the-art lightweight network learning based on Neural Architecture Search (NAS) usually costs enormous computation resources. Alternatively, low-performance embedded platforms and high-resolution drone images pose a challenge for lightweight network learning. To alleviate this problem, this paper proposes a new lightweight object detection model, called GhostShuffleNet (GSNet), for UAV images, which is built based on Zero-Shot Neural Architecture Search. This paper also introduces the new components which compose GSNet, namely GhostShuffle units (loosely based on ShuffleNetV2) and the backbone GSmodel-L. Firstly, a lightweight search space is constructed with the GhostShuffle (GS) units to reduce the parameters and floating-point operations (FLOPs). Secondly, the parameters, FLOPs, layers, and memory access cost (MAC) as constraints add to search strategy on a Zero-Shot Neural structure search algorithm, which then searches for an optimal network GSmodel-L. Finally, the optimal GSmodel-L is used as the backbone network and a Ghost-PAN feature fusion module and detection heads are added to complete the design of the lightweight object detection network (GSNet). Extensive experiments are conducted on the VisDrone2019 (14.92%mAP) dataset and the our UAV-OUC-DET (8.38%mAP) dataset demonstrating the efficiency and effectiveness of GSNet. The completed code is available at: https://github.com/yfq-yy/GSNet.
Multi-objective Pigeon-inspired Optimized feature enhancement soft-sensing model of Wastewater Treatment Process

Under the increasingly severe fresh water supply pressure, wastewater treatment is considered to be the optimal strategy to satisfy the current and future water demand, thus being highly valued by most countries. As a complicated process, there are some hard-to-measure effluent indicators in wastewater treatment such as 5-day Biological Oxygen Demand (BOD5), which brings significant difficulties to the monitoring of key indicators in sewage disposal process, thus imposing massive constraints on evaluation of effluent quality. In order to realize the real time supervision of the water quality, data-driven artificial intelligence soft-sensing models have been widely considered as an active research field. However, the over-parameterization of artificial intelligence methods and the complication of sewage treatment environment lead to obvious non-Gaussian characteristics in the wastewater data, which makes it difficult to artificially set parameters to maintain the optimal values to satisfy the accuracy requirement in wastewater treatment process. In view of the aforementioned problems, a soft-sensing model for super-parameter intelligent setting of Broad Learning System based on Overcomplete Independent Component Analysis (OICA) is proposed in this paper. A two-stage multi-objective optimization algorithm is adopted in the model to set superparameters intelligently, which reduces human intervention and improves the accuracy of the model. Additionally, the adaptability of the proposed model to data is significantly enhanced through improvement of the feature extraction ability of the Broad Learning System (BLS) and the capture of peculiar non-Gaussianity in wastewater data with statistical methods. Comparative experiments are conducted on the sewage simulation platform BSM1 with state-of-the-art artificial intelligence soft measurement models, and the results show that the advantage of the presented model lies both in accuracy and in modeling speed, demonstrating the effectiveness of the proposed method.
The Craft and Coordination of Data Curation: Complicating Workflow Views of Data Science

Data curation is the process of making a dataset fit-for-use and archivable. It is critical to data-intensive science because it makes complex data pipelines possible, studies reproducible, and data reusable. Yet the complexities of the hands-on, technical, and intellectual work of data curation is frequently overlooked or downplayed. Obscuring the work of data curation not only renders the labor and contributions of data curators invisible but also hides the impact that curators' work has on the later usability, reliability, and reproducibility of data. To better understand the work and impact of data curation, we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium for Political and Social Research (ICPSR). We asked: What does curatorial work entail at ICPSR, and what work is more or less visible to different stakeholders and in different contexts? And, how is that curatorial work coordinated across the organization? We triangulated accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. While we identified numerous curatorial actions performed by ICPSR curators, we also found that curators rely on a number of craft practices to perform their jobs. The reality of their work practices defies the rote sequence of events implied by many life cycle or workflow models. Further, we show that craft practices are needed to enact data curation best practices and standards. The craft that goes into data curation is often invisible to end users, but it is well recognized by ICPSR curators and their supervisors. Explicitly acknowledging and supporting data curators as craftspeople is important in creating sustainable and successful curatorial infrastructures.
Strategic factors in implementing blockchain technology in Indonesia's value-added tax system

This study examines the strategic factors to implement blockchain technology in Indonesia's Value-Added Tax (VAT) system. Blockchain technology is a transparent technology in which each party who is a member of the chain know the data or information in the blockchain network. Therefore, blockchain technology can be used only for transparent data. The research topic is limited to the scope of VAT because there is data in Indonesia's VAT which is transparent and safe to know by other parties and public. The approach used in this study is qualitative with the aim of analyzing strategic factors for implementing blockchain in Indonesia's VAT system. Based on the objectives, this study was categorized as descriptive research. The data collection techniques used in this study included field studies, in-depth interviews, and literature review related to the topic. The results of this study indicate that the factors that need to be considered in implementing blockchain in Indonesia's VAT system are organizational factors, which consist of organizational readiness, support from parties with higher authority, technological readiness, innovation, governance, and internal regulation processes; environmental factors which consist of external government support and partner support; and technological factors which consist of benefits, data security, smart contract coding, architecture, permissions, and shared infrastructure.
Fine-Grained Performance and Cost Modeling and Optimization for FaaS Applications

Function-as-a-Service (FaaS) has become a mainstream cloud computing paradigm for developers to build cloud-native applications in recent years. By taking advantage of serverless architecture, FaaS applications bring many desirable benefits, including built-in scalability, high availability, and improved cost-effectiveness. However, predictability and trade-off of performance and cost are still key pitfalls for FaaS applications due to poor infrastructure transparency and lack of performance and cost models that fit the new paradigm. In this study, we therefore fill this gap by proposing formal performance and cost modeling and optimization algorithms, which enable accurate prediction and fine-grained control over the performance and cost of FaaS applications. The proposed model and algorithms provide better predictability and trade-off of performance and cost for FaaS applications, which help developers to make informed decisions on cost reduction, performance improvement, and configuration optimization. We validate the proposed model and algorithms via extensive experiments on AWS. We show that the modeling algorithms can accurately estimate critical metrics, including response time, cost, exit status, and their distributions, regardless of the complexity and scale of the application workflow. Also, the depth-first bottleneck alleviation algorithm for trade-off analysis can effectively solve two optimization problems with fine-grained constraints.
The forecast of power consumption and freshwater generation in a solar-assisted seawater greenhouse system using a multi-layer perceptron neural network

This research is a simulation of the solar seawater greenhouse framework by using a hybrid model of multilayer perceptron neural network and different optimization algorithms. Four key factors, roof transparency, the front evaporator height, greenhouse length, and width, are considered decision-making variables to investigate power consumption and water production. In this regard, six metaheuristic approaches are employed to hyper-tune the learning process of the neural network. The accuracy of the proposed models is estimated via statistical indicators. The obtained results show that biogeography-based optimization and genetic algorithm had the minimum RMSE value for both power consumption and water generation. Based on these criteria, the ACO method has the worst rank. It should be mentioned that some data are training set and some others are employed for the testing procedure. According to the results, less testing error is related to BBO and GA methods. Water consumption assessment represents that the least error is related to ACO strategy. It is found that by change of width and transparency, more water production is related to ES and GA methods with almost 120 m3/day and 105 m3/day, respectively. Moreover, ES, ACO, and GA have less power consumption. Finally, it is found that when transparency was 0.4, the water production value could be 115 m3/day while for 0.6 of transparency this amount was 95.
NOx emission predicting for coal-fired boilers based on ensemble learning methods and optimized base learners

Generally, because the actual operating data tends to be concentrated in local regions due to the habits of operators and control system design, single models can only train a prediction model from a certain data feature, but they can not fully mine and utilize the valuable information hidden in the data. In this paper, new ensemble learning methods are proposed to predict NOx concentration at the SCR inlet in coal-fired boilers. Firstly, to reduce redundant data and data dimension, mutual information(MI) feature selection method is used to select input variables. Then, all input variables are normalized and divided to prepare the data for modeling. Secondly, considering the perspective of diversity and difference, three kinds of single models, which are gate recurrent unit(GRU), convolutional neural networks(CNN) and multivariate linear regression(MLR), are constructed to serve as the base learners. To make each base learner reach their optimal state, the model structures and parameters are optimized respectively. After that, the novel ensemble learning methods are proposed and used to construct NOx emission predicting model based on the three optimized base learners, respectively. To verify the generalization of the presented ensemble models, another dataset from a different thermal power plant is selected for verification. The experimental results indicate that the presented ensemble models have excellent prediction accuracy and generalization. The RMSEs of the proposed Stacking/Blending model are only 14.115/14.017mg/Nm3 (for dataset A) and 6.842/6.447mg/Nm3 (for dataset B) respectively, which are much lower than other compared models. Additionally, the application test once again proves that the presented methods can be used in engineering practice and are able to effectively reduce the NOx emission of coal-fired boiler.
Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving the multiclass classification problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to the softmax activation function with the subsequent minimization of the cross entropy loss via optimizing the parameters of the quantum circuit. Our conceptional improvements here include a new model for a quantum perceptron and an optimized structure of the quantum circuit. We use the proposed approach to solve a 4-class classification problem for the case of the MNIST dataset using eight qubits for data encoding and four ancilla qubits; previous results have been obtained for 3-class classification problems. Our results show that accuracies of our solution are similar to classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our finding provide a new step towards the use of quantum neural networks for solving relevant problems in the NISQ era and beyond.
SMGCL: Semi-supervised Multi-view Graph Contrastive Learning

Graph contrastive learning (GCL), aiming to generate supervision information by transforming the graph data itself, is increasingly becoming a focus of graph research. It has shown promising performance in graph representation learning by extracting global-level abstract features of graphs. Nonetheless, most GCL methods are performed in a completely unsupervised manner and would get unappealing results in balancing the multi-view information of graphs. To alleviate this, we propose a Semi-supervised Multi-view Graph Contrastive Learning (SMGCL) framework for graph classification. The framework can capture the comparative relations between label-independent and label-dependent node (or graph) pairs across different views. In particular, we devise a graph neural network (GNN)-based label augmentation module to exploit the label information and guarantee the discrimination of the learned representations. In addition, a shared decoder module is complemented to extract the underlying determinative relationship between learned representations and graph topology. Experimental results on graph classification tasks demonstrate the superiority of the proposed framework.
Critical Analysis of the Evaluation Methods of Extended Reality (XR) Experiences for Construction Safety

The construction industry has high accident rates. The sector is exploring various tools to improve safety management, training, and awareness to achieve zero accidents. This work focuses on extended reality (XR), which encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. Several authors have developed training experiences for construction safety in XR environments with positive conclusions about their effectiveness. However, there is no standardization regarding the evaluation methods used in the sector, and many experiences do not use any method. This lack is critical, as whenever the aim is to evaluate the degree of awareness of security issues, the implementation of evaluation systems is indispensable to make known the methods used in the literature to evaluate the effectiveness of the experiences and represent support for future research. This research identifies developments in XR experiences and analyzes the validation methods through a systematic review using the PRISMA methodology. It identifies two evaluation methods, objective and subjective, which are each broken down into four categories. The results show the types of evaluation, safety-related purposes, and safety application objectives used by the database classification.
Unsupervised neural networks as a support tool for pathology diagnosis in MALDI-MSI experiments: A case study on thyroid biopsies

Artificial intelligence is getting a foothold in medicine for disease screening and diagnosis. While typical machine learning methods require large labeled datasets for training and validation, their application is limited in clinical fields since ground truth information can hardly be obtained on a sizeable cohort of patients. Unsupervised neural networks – such as Self-Organizing Maps (SOMs) – represent an alternative approach to identifying hidden patterns in biomedical data. Here we investigate the feasibility of SOMs for the identification of malignant and non-malignant regions in liquid biopsies of thyroid nodules, on a patient-specific basis. MALDI-ToF (Matrix Assisted Laser Desorption Ionization - Time of Flight) mass spectrometry-imaging (MSI) was used to measure the spectral profile of bioptic samples. SOMs were then applied for the analysis of MALDI-MSI data of individual patients' samples, also testing various pre-processing and agglomerative clustering methods to investigate their impact on SOMs' discrimination efficacy. The final clustering was compared against the sample's probability to be malignant, hyperplastic or related to Hashimoto thyroiditis as quantified by multinomial regression with LASSO. Our results show that SOMs are effective in separating the areas of a sample containing benign cells from those containing malignant cells. Moreover, they allow to overlap the different areas of cytological glass slides with the corresponding proteomic profile image, and inspect the specific weight of every cellular component in bioptic samples. We envision that this approach could represent an effective means to assist pathologists in diagnostic tasks, avoiding the need to manually annotate cytological images and the effort in creating labeled datasets.
Depth-aware inverted refinement network for RGB-D salient object detection

Recent advances in multi-modal feature fusion boost the development of RGB-D salient object detection (SOD), and many remarkable RGB-D SOD models have been proposed. However, though some existing methods consider fusing the cross-level multi-modal features, they ignore the difference between inter-level having the multi-modal details in convolutional neural networks (CNNs) based RGB-D SOD. Therefore, exploring the correlations and differences of cross-level multi-modal features is a critical issue. In this paper, we present a novel depth-aware inverted refinement network (DAIR) to progressively guide the cross-level multi-modal features through backward propagation, which considerably preserves the different level details with multi-modal cues. Specifically, we innovatively design an end-to-end inverted refinement network to guide cross-level and cross-modal learning for revealing complementary relations of the cross-modal. The inverted refinement network also refines the low-level spatial details by the high-level global contextual cues. In particular, considering the difference of multi-modal and the effect of depth quality, a depth-aware intensified module (DAIM) is proposed with capturing the paired relationship of the pixel-level and inter-channel for the depth map. It promotes the representative capability of the depth details. Extensive experiments on nine challenging RGB-D SOD datasets demonstrate remarkable performance boosting of our proposed model against the fourteen state-of-the-art (SOTA) RGB-D SOD approaches.
SGCSumm: An extractive multi-document summarization method based on pre-trained language model, submodularity, and graph convolutional neural networks

The increase in online text generation by humans and machines needs automatic text summarization systems. Recent research studies commonly use deep learning, besides sentence embedding and feature learning mechanisms, to find a solution for text summarization. But they ignore the fact that while finding the optimal solution for extractive text summarization is NP-hard, how do they ensure the quality of their solution? In our previous work, an extractive summarizer, called DSNSum, was proposed based on deep submodular network (DSN) that uses handcrafted features. It leverages submodularity to guarantee a minimum bound for performance. In this paper, submodular graph convolutional summarizer (SGCSumm), an extractive multi-document summarization method, is presented where DSN is used to guarantee a minimum performance bound. It addresses DSNSum’s shortcomings and makes enhancements that yield a summarizer supporting sentence embedding and graph structure-aware feature learning. It takes a formal approach to show how the enhancements are possible without losing the guarantee of a minimum bound for performance. Finally, to evaluate the performance of the proposed summarization method, the DUC 2004 and DailyMail/CNN datasets are used. The experimental results show that the performance of SGCSumm is comparable to that of state-of-the-art summarization methods in terms of ROUGE scores.