Reviewerly/exHarmony huggingface dataset:
Perspective Chapter: Rapid Measurement of Potentially Toxic Elements (PTEs) in Petroleum Hydrocarbons Polluted Soils by X-Ray Fluorescence (XRF) Spectroscopy

Potentially toxic elements (PTEs) contamination in soils threats human wellbeing and ecological health because of their toxicity and bioaccumulation. This research presents a portable Olympus Delta Premium 6000 Series XRF Analyser (Olympus, USA) as a rapid measurement tool (RMT) for PTEs: Cr, Cu, Fe, Pb, Mn, and Zn in contaminated soils in the Niger Delta, Nigeria. A total of 45 crude oil-contaminated soils were collected from three genuinely oil spill sites. The range of measured PTEs concentrations (mg/kg) in the study sites are as follows: Site 1: chromium (Cr) 54–75, copper (Cu) 5.4–16.6, iron (Fe) 14,841–23,404, lead (Pb) 13.5–21.4, manganese (Mn) 158–555, and zinc (Zn) 32.6–47.2; Site 2: (35–66), (5–16.1), (10166–20,967), (12–17.8), (209–440), (17.6–33.6); and Site 3: (32–115), (6.5–20.8), (7538–22,800), (12–135), (98–338), (19.9–177). The trend of PTEs across the three sites follows the same order: Fe &gt; Mn &gt; Cr &gt; Zn &gt; Pb &gt; Cu. The average concentration values of PTEs in all the 3 sites were higher than background concentration values. Thus, crude oil spill spiked the PTEs concentrations. XRF spectroscopy is recommended as a cost-effective and RMT for PTEs in soils.
Smart Garment Design for an Augmented Reality Body Mapping Experience

Based on previous work, we created wide handmade belts as support for a live augmented reality (AR) event. We describe how the garments were made to best meet the constraints of the AR design and how they were used as part of a fashion show and book promotion event. We determined the kind of features the AR software can detect and then created prototypes based on those characteristics. The belt design followed several steps: 1. Computer image preparation; 2. Thread preparation; 3. Thread dyeing; 4. Weaving; 5. Assembly; 6. Photographing the result and retouching the image. The lack of adjustability to deformation was a concern when dealing with clothing and fabric. We resolved this issue by stiffening the fabric. We discuss belts performances well during the live event; other textile techniques as substitutes (e.g. patchwork) and future research prospects.
Applications of Mixed Reality for Smart Aviation Industry: Opportunities and Challenges

Nowadays, mixed reality has improved operational efficiency and enhanced passenger experience in the aviation industry. Integrated with advanced machine learning and artificial intelligence techniques, mixed reality can easily deal with tons of aviation data to support decision-making processes in this industry. The chapter presents the state-of-the-art applications of mixed reality in smart aviation industry. Opportunities and challenges of integrating mixed reality with advanced machine learning and artificial intelligence techniques into the aviation industry are introduced. This chapter focuses on how the integrated mixed reality can improve the quality and reliability of maintenance, operation, piloting, training, and product design in smart aerospace engineering. It also describes autonomous, self-service, and data visualization systems in smart airports to enhance passenger experience. Finally, this chapter discusses airline’s digital-based responses to the COVID-19 crisis.
Simple Additive Weighting sebagai Metode Pendukung Keputusan terhadap Sistem Customer Satisfaction

Nazwa drugstore is one of the stores that still use manual methods to recognize complaints about service for customers who come to the store, so it is felt to be less effective. Therefore, it is necessary to have a system to measure customer satisfaction in the store quickly and accurately. To build this system, we apply the Simple Additive Weighting (SAW) method to determine user satisfaction. The model used to build this system is the System Development Life Cycle (SDLC) with stages of analysis, design, implementation, and trial. At the analysis stage, we used interview and observation methods to collect data. Our design phase consists of a use case, a system interface, and a diagram class. Testing this system uses a black box to see if all the components of the system are working properly. Our findings are to produce a decision support system for customer satisfaction using the SAW method. In addition, this system also has a user-friendly and responsible interface, so this system is easy to use. The results of the black box test show that all components in this system are already functioning properly.
Computationally efficient adaptive decompression for whole slide image processing

Whole slide image (WSI) analysis is increasingly being adopted as an important tool in modern pathology. Recent deep learning-based methods have achieved state-of-the-art performance on WSI analysis tasks such as WSI classification, segmentation, and retrieval. However, WSI analysis requires a significant amount of computation resources and computation time due to the large dimensions of WSIs. Most of the existing analysis approaches require the complete decompression of the whole image exhaustively, which limits the practical usage of these methods, especially for deep learning-based workflows. In this paper, we present compression domain processing-based computation efficient analysis workflows for WSIs classification that can be applied to state-of-the-art WSI classification models. The approaches leverage the pyramidal magnification structure of WSI files and compression domain features that are available from the raw code stream. The methods assign different decompression depths to the patches of WSIs based on the features directly retained from compressed patches or partially decompressed patches. Patches from the low-magnification level are screened by attention-based clustering, resulting in different decompression depths assigned to the high-magnification level patches at different locations. A finer-grained selection based on compression domain features from the file code stream is applied to select further a subset of the high-magnification patches that undergo a full decompression. The resulting patches are fed to the downstream attention network for final classification. Computation efficiency is achieved by reducing unnecessary access to the high zoom level and expensive full decompression. With the number of decompressed patches reduced, the time and memory costs of downstream training and inference procedures are also significantly reduced. Our approach achieves a 7.2× overall speedup, and the memory cost is reduced by 1.1 orders of magnitudes, while the resulting model accuracy is comparable to the original workflow.
A systematic mapping study and practitioner insights on the use of software engineering practices to develop MVPs

[Background] The MVP concept has influenced the way in which development teams apply Software Engineering practices. However, the overall understanding of this influence of MVPs on SE practices is still poor. [Objective] Our goal is to characterize the publication landscape on practices that have been used in the context of software MVPs and to gather practitioner insights on the identified practices. [Method] We conducted a systematic mapping study and discussed its results in two focus groups sessions involving twelve industry practitioners that extensively use MVPs in their projects to capture their perceptions on the findings of the mapping study. [Results] We identified 33 papers published between 2013 and 2020 and observed some trends related to MVP ideation and evaluation practices. For instance, regarding ideation, we found six different approaches and mainly informal end-user involvement practices. Regarding evaluation, there is an emphasis on end-user validations based on practices such as usability tests, A/B testing, and usage data analysis. However, there is still limited research related to MVP technical feasibility assessment and effort estimation. Practitioners of the focus group sessions reinforced the confidence in our results regarding ideation and evaluation practices, being aware of most of the identified practices. They also reported how they deal with the technical feasibility assessments and effort estimation in practice. [Conclusion] Our analysis suggests that there are opportunities for solution proposals and evaluation studies to address literature gaps concerning technical feasibility assessment and effort estimation. Overall, more effort needs to be invested into empirically evaluating the existing MVP-related practices.
ECML driven geographical location of utility poles in smart grid: Data analysis and high-definition recognition

The construction of smart grid is inseparable from the appropriate deployment of utility poles. China has many poles; if they are fully utilized, can they bring more power to the smart grid? Fortunately, thanks to advances in technologies such as artificial intelligence and the Internet of Things, can we use Google Earth to recognize utility poles and optimize the deployment of power facilities? Fundamentally, Google Earth generates static images. Faced with many static images, we need further image processing for high-definition recognition of utility poles. In addition to the current chaotic distribution of poles, we also need to optimize the deployment of power facilities. However, due to the resolution of Google Earth and the aerial photography angle, many backlight phenomena are not conducive to the recognition of utility poles. Therefore, this paper proposes a backlight image enhancement algorithm based on convolutional neural networks (CNN) and constructs a novel network architecture that integrates decomposition, restoration, and adjustment to recognize poles in high-definition under backlight. Furthermore, to solve the problems of the overflow of CNN parameters and unclear training effect, particle swarm optimization (PSO), the evolutionary computing-based machine learning (ECML) is used to search CNN parameters automatically and seek the optimal solution to achieve the optimization of the overall model. The experiments prove that the CNN-based image enhancement algorithm effectively recognizes the utility poles under different illumination and backlight. At the same time, the experimental results show that the PSO-based optimization method can optimize CNN parameters obviously, and the classification accuracy is increased.
Implementation and Development of Learning Vector Quantization Supervised Neural Network

Abstract Electricity is vital energy for the sustainability of human activities both as individuals, community groups, and the industrial world. Electricity users are increasing every year, which causes irresponsible users and does not comply with existing rules; the number of users causes the staff to find it challenging to determine whether the power used is appropriate with household needs. This study uses data on 100 electricity users obtained from PT. PLN Rayon Trade is one of the branch offices of PLN in Indonesia. The method used to classify electricity users is the Learning Vector Quantization (LVQ) algorithm using the 4-8-3 architectural model. Several input variables are used, such as the number of bills, the number of hours, the metered rate, and the class. The results obtained an accuracy rate of 72% with a time of 11 minutes 53 seconds. So it can be concluded that the LVQ algorithm with the 4-8-3 architectural model can be used to classify electricity users. However, it is not very good because the accuracy still needs to be improved.
Artificial Neural Network Model for Forecasting Natural Disasters: Polak-Ribiere and Powell-Beale Comparison

Abstract The prediction problem is an interesting topic to be discussed today. The many predictive methods used to solve problems have become an obstacle for researchers and academics alike. This study aimed to analyze the ability of the ANN prediction method using the Polak-Ribiere and Powell-Beale conjugate gradients. The dataset used for the analysis is disaster times-series data in Indonesia for the last ten years (2011-2020). Data obtained from the Indonesian Disaster Geoportal sourced from the National Disaster Management Agency can be seen on the infographic menu on the website https://gis.bnpb.go.id/. The results obtained based on the analysis that has been carried out, that the 4-10-1 architectural model with the Powell-Beale Conjugate gradient method can produce lower MSE Testing/Performance than the Polak-Ribiere Conjugate gradient method, another advantage is faster time. And fewer iterations. So it can be concluded that based on comparing these two methods, the Conjugate gradient Powell-Beale method with the architectural model 4-10-1 can be used for forecasting/predicting natural disasters because it is a better method.
Convolutional Neural Network and Deep Learning Approach for Image Detection and Identification

Abstract There are many different varieties of clouds, each with a unique set of properties. As a result of this variability, it is difficult to discern these sorts of clouds. A database’s objects must be categorized using data categorization in order to be organized into multiple categories. This study made use of the Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which falls under the low cloud category and includes photos of Cumulus (182 images), and Cumulonimbus (242 photographs), and Stratus (242 images) (202 images). A fast R-CNN detector with feature extraction = Resnet50 was used to create a system for classifying cloud kinds. A significant amount of training time is saved by the quicker R-CNN due to its lack of a selective search algorithm. Training loss values for cloud images had an average of 0.9030 from the first epoch through the last one. Using the Faster R-CNN object detection method with the Resnet50 architecture, cloud photos were added and the accuracy was 94.12 and the average precision was 0.76. - Faster R-advantages CNN affect the architecture utilized and are marginally influenced by the algorithm choice, however CNN with Resnet50 is superior overall where these advantages are held.
Brain Image Decomposition on Image Alteration

Abstract Image transformation is essential to explore and find out specific information that does not exist and has not been previously known from an image, such as pixels, geometry, size or colour. Therefore, this paper aims to analyze the image transformation by generating the value of the thresholding method from low to high in image segmentation. The segmentation process works based on two-colour models, namely HSV and RGB colours. Image segmentation problems occur when an object has more than two colours. There is lighting that affects the condition of the thing, so it is proposed to change the threshold value from low to high to compare the segmentation results of each given value to determine the optimal threshold value. The image data used is a brain MRI image from the website www.hipwee.com. The Matlab 2019a application is used to assist the analysis process. Based on the analysis results, in this case, the smaller the HSV and RGB threshold values, the segmentation of the detected image results is not detailed and the less precise, and vice versa. The best Brain MRI Image Segmentation results when using a threshold value of HSV=0.5 and RGB=5.
Backpropagation Artificial Neural Network Enhancement using Beale-Powell Approach Technique

Abstract Machine learning algorithms can study existing data to perform specific tasks. One of the well-known machine learning algorithms is the backpropagation algorithm, but this algorithm often provides poor convergence speed in the training process and a long training time. The purpose of this study is to optimize the standard backpropagation algorithm using the Beale-Powell conjugate gradient algorithm so that the training time needed to achieve convergence is not too long, which later can be used as a reference and information for solving predictive problems. The Beale-Powell conjugate gradient algorithm can solve unlimited optimization problems and is much more efficient than gradient descent-based algorithms such as standard backpropagation. The research data used for the analysis were formal education participation data in Indonesia. To be trained and tested using the 7-10-1 architecture. The results showed that the Beale-Powell Conjugate Gradient algorithm could more quickly perform the training and convergence process. However, the MSE value of testing and performance is still superior to the backpropagation algorithm. So it can be concluded that for the prediction case of Formal Education Participation in Indonesia, the Conjugate Gradient Beale-Powell algorithm is good enough to optimize the performance of backpropagation standards seen from the convergence speed and training performance.
An algebraic analysis of the bimodality of the generalized von Mises distribution

Bimodal probability distributions for planar directions are relevant to many scientific fields. Besides mixtures of two unimodal circular distributions, there are relatively few other circular distributions that allow for bimodality. One of these is the generalized von Mises distribution, which can be symmetric or asymmetric, bimodal or unimodal. Moreover, the GvM model possesses many important theoretical properties that are not available with competing models. This article provides an algebraic analysis of the bimodality of the GvM distribution. The problem of bimodality is reduced to study the nature of the roots of a quartic. Four real roots over the interval [−1,1] of the quartic correspond to bimodality. Situations with complex roots, multiple real roots and real roots outside [−1,1] are analyzed. The set of all parameters of the GvM distribution that yields bimodality is identified.
Intention-guided deep semi-supervised document clustering via metric learning

The intention expresses the user’s preference for document structure division. Intention-guided document structure division is an important task in the field of text mining. To achieve this goal, deep semi-supervised document clustering provides a promising solution to personalized document clustering. However, traditional deep semi-supervised clustering models suffer from the problem of the limited number of constraints which is insufficient for intention-guided document clustering. Moreover, documents normally have various emphases on their representations to reflect different structural opinions. In this paper, we proposed an intention-guided deep semi-supervised document clustering model, namely IGSC, to divide document structure based on a small amount of user-provided supervised information. IGSC designs a deep metric learning network to solve the above problems. The deep metric learner explores the user’s global intention and outputs an intention matrix. The intention is explored from the small amount user provided pairwise constraints and is used to guide the representation learning. Moreover, IGSC uses the intention matrix to guide the clustering process, to get the clustering results that best meet the user’s intention. This paper compares IGSC with a number of document clustering models on four real-world text datasets, namely Reu-10k, BBC, ACM, and Abstract. The results show that IGSC evidently improves the clustering performance and outperforms the best result of benchmark models with 7% on average. The comparison with other models and the visualization results can demonstrate that IGSC is effective.
SLF: A passive parallelization of subgraph isomorphism

Subgraph isomorphism is one of the most important graph query operations. Existing subgraph isomorphic parallelization methods suffer from redundant sharing or fine-grained parallelism. SLF, a parallel approach for subgraph isomorphism algorithm based on tree-search, is proposed in this paper. In SLF, threads are prevented from sharing tasks blindly by an “Ask for Sharing” mechanism. The “Low-depth Priority Sharing” rule filters out unnecessary sharing, which reduces the number of sharing. The context of shared tasks is represented by embedding, which reduces the time and memory usage of sharing. We prove that the parallelization overhead of SLF is smaller than that of previous method. The experimental results show that SLF achieves higher speedup and efficiency than state-of-the-art parallelization methods.
Successively focused details: attention-based recurrent multiscale network for sketch-based image retrieval

Sketch-based image retrieval (SBIR) is a cross-domain retrieval problem whose main challenge is that sketches and images differ in many aspects, such as visual content and abstract degree. Existing approaches solve cross-domain gaps using deep neural networks (DNNs) to learn a joint global feature representation for sketches and images. To improve the retrieval effect using local features, some researchers have introduced attention mechanisms and multi-scale inputs. However, these cannot effectively locate regions of detail difference between sketch–image pairs, and the discriminative ability of these models is insufficient. Just as human observational habits range from global to local, this paper constructs a multiscale network with the help of attention modules. The network is stacked in a recurrent way and focuses stepwise on the most discriminative regions from coarse to fine through region-specific attention models. The part-aware attention module is specifically designed for each scale network to make it robust against local spatial misalignment. To address the challenges of SBIR, we extend the network into a three-channel structure that integrates the sketch, edge map, and RGB photo branches to acquire a more discriminative cross-domain feature representation. The proposed network is alternately optimized by weighted regularization triplet loss, classification loss, and pairwise ranking loss. We conduct extensive experiments on several commonly used SBIR datasets, quantitative, and qualitative results demonstrate that our network learns more fine-grained features and outperforms all baselines.
The Role of a Deep Learning-Based Computer-Aided Diagnosis System and Elastography in Reducing Unnecessary Breast Lesion Biopsies

Ultrasound examination has inter-observer and intra-observer variability and a high false-positive rate. The aim of this study was to evaluate the value of the combined use of a deep learning-based computer-aided diagnosis (CAD) system and ultrasound elastography with conventional ultrasound (US) in increasing specificity and reducing unnecessary breast lesions biopsies.Conventional US, CAD system, and strain elastography (SE) were retrospectively performed on 216 breast lesions before biopsy or surgery. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and biopsy rate were compared between conventional US and the combination of conventional US, SE, and CAD system.Of 216 lesions, 54 were malignant and 162 were benign. The addition of CAD system and SE to conventional US increased the AUC from 0.716 to 0.910 and specificity from 46.9% to 85.8% without a loss in sensitivity while 89.2% (66 of 74) of benign lesions in Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions would avoid unnecessary biopsies.The addition of CAD system and SE to conventional US improved specificity and AUC without loss of sensitivity, and reduced unnecessary biopsies.