Reviewerly/exHarmony huggingface dataset:
Experimental investigation on the concrete fracture process zone using electronic speckle pattern interferometry

Abstract In order to advance the understanding of fracture failure mechanisms in concrete, a series of three‐point bend tests of pre‐notched specimens were conducted to study the characteristics of the fracture process zone. Electronic speckle pattern interferometry (ESPI) was used to determine the location and size of the cracks. The experimental results have shown that crack in the pre‐notched concrete beams will initiate when the load increases to 40% of the peak load ( P max ) and expands unstably after reaching the peak load. When the load drops to about 25% P max in the load drop section, a complete fracture process zone (FPZ) was developed, and the corresponding crack tip opening displacement is about 7.1 times to the critical crack tip opening displacement. The analysis of surface strain of concrete specimen showed that microcracks will initiate when the surface tensile strain of concrete reaches about 2 × 10 −4 ; when the surface tensile strain value of concrete reaches 4 × 10 −2 , the FPZ will move forward; the length of the FPZ of concrete essentially remains the same in the entire fracture process, which is about 71% of the ligament height. The analysis of experimental data also shows that the size of the FPZ is not significantly affected by the cubic compressive strength of concrete. In addition, the tension‐softening curves of the specimens were finally determined by using the incremental displacement collocation method, and it is concluded that the critical width of the FPZ is related to the cubic compressive strength of the concrete.
Machine Learning Model for Credit Card Fraud Detection- A Comparative Analysis

In today's world, we are on an express train to a cashless society which has led to a tremendous escalation in the use of credit card transactions. But the flipside of this is that fraudulent activities are on the increase; therefore, implementation of a methodical fraud detection system is indispensable to cardholders as well as the card-issuing banks. In this paper, we are going to use different machine learning algorithms like random forest, logistic regression, Support Vector Machine (SVM), and Neural Networks to train a machine learning model based on the given dataset and create a comparative study on the accuracy and different measures of the models being achieved using each of these algorithms. Using the comparative analysis on the F_1 score, we will be able to predict which algorithm is best suited to serve our purpose for the same. Our study concluded that Artificial Neural Network (ANN) performed best with an F_1 score of 0.91.
MLife: a lite framework for machine learning lifecycle initialization

Machine learning (ML) lifecycle is a cyclic process to build an efficient ML system. Though a lot of commercial and community (non-commercial) frameworks have been proposed to streamline the major stages in the ML lifecycle, they are normally overqualified and insufficient for an ML system in its nascent phase. Driven by real-world experience in building and maintaining ML systems, we find that it is more efficient to initialize the major stages of ML lifecycle first for trial and error, followed by the extension of specific stages to acclimatize towards more complex scenarios. For this, we introduce a simple yet flexible framework, MLife, for fast ML lifecycle initialization. This is built on the fact that data flow in MLife is in a closed loop driven by bad cases, especially those which impact ML model performance the most but also provide the most value for further ML model development—a key factor towards enabling enterprises to fast track their ML capabilities. Better yet, MLife is also flexible enough to be easily extensible to more complex scenarios for future maintenance. For this, we introduce two real-world use cases to demonstrate that MLife is particularly suitable for ML systems in their early phases.
Mapping soil susceptibility to crude oil pollution in the region of Delta, South-South Nigeria: A proportional study of environmetrics, health, ecological risks, and geospatial evaluation

Today, the threat of crude oil pollution globally, has been a resounding epoch of ecosystem degradation. This affects the lives of flora and fauna in the ecosystem as well as influence the human's economic needs. The aim of this study is to assess the heavy metals and petroleum hydrocarbons concentrations (crude oil pollutants) in agricultural soils of selected communities in Niger-Delta region. Models were used to assess the crude oil pollution loads and its source apportionment. The results from the principal components analysis (PCA) for the heavy metals (HMs) and total petroleum hydrocarbons (TPHs) in soils showed high loading factors > 0.32. The source allotment were both of crustal and human origin. The cluster analysis for the physical and chemical parameters of the HMs and TPHs showed that stations 1, 5, 2, 4, 6 and 7 and 2, 6, 7 and 9 were similar respectively. The outcome of the ecological risk status of the sampled soils, showed that the metals in the nine sampling stations were at the slight risk level with Cd being the main donor. The results of the health risk for both non-carcinogenic and carcinogenic pathways for the metals were <1 respectively, indicating no human health risk. The results obtained from the kriging contour mapping of the HMs in the sampled stations, indicated mean variability across the stations with Zn and Pb having the highest values. This might be linked to the high concentration from the crude oil impact on the soil that might possibly elicit ecological and health risks (non-carcinogenic and carcinogenic) when ingested by plants and humans. We recommend steady assessment and monitoring of the soils in order to provide reference point data and information for proper justification and management alternatives.
An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

“Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.
Move The Object or Move The User: The Role of Interaction Techniques on Embodied Learning in VR

To incorporate immersive technologies as part of the educational curriculum, this article is an endeavor to investigate the role of two affordances that are crucial in designing embodied interactive virtual learning environments (VLEs) to enhance students’ learning experience and performance: 1) the sense of presence as a subjective affordance of the VR system, and 2) bodily engagement as an embodied affordance and the associated sense of agency that is created through interaction techniques with three-dimensional learning objects. To investigate the impact of different design choices for interaction, and how they would affect the associated sense of agency, learning experience and performance, we designed two VLEs in the context of penetrative thinking in a critical 3D task in geosciences education: understanding the cross-sections of earthquakes’ depth and geometry in subduction zones around the world. Both VLEs were web-based desktop VR applications containing 3D data that participants ran remotely on their own computers using a normal screen. In the drag and scroll condition, we facilitated bodily engagement with the 3D data through object manipulation, object manipulation. In the first-person condition, we provided the ability for the user to move in space. In other words, we compared moving the objects or moving the user in space as the interaction modalities. We found that students had a better learning experience in the drag and scroll condition, but we could not find a significant difference in the sense of presence between the two conditions. Regarding learning performance, we found a positive correlation between the sense of agency and knowledge gain in both conditions. In terms of students with low prior knowledge of the field, exposure to the VR experience in both conditions significantly improved their knowledge gain. In the matter of individual differences, we investigated the knowledge gain of students with a low penetrative thinking ability. We found that they benefited from the type of bodily engagement in the first-person condition and had a significantly higher knowledge gain than the other condition. Our results encourage in-depth studies of embodied learning in VR to design more effective embodied virtual learning environments.
Adapt Learning Path by Recommending Problems to Struggling Learners

The objective of this work is the creation of a resource recommendation ap-plication in Python integrated into the code of the virtual edX platform, which appears as an additional tab in each course. By selecting this tab, learners will have access at any time to their recommended issues for this course, and so they can adapt their learning path. In this article, we present a recommendation algorithm that will be responsible for proposing these prob-lems according to the scores obtained in the problems already performed by the learner. By calculating the similarity with the rest of the classmates, an estimate of the most practical problems for the learner will be made. We also present the different functions and parameters to implement it.
INTEGRATION OF INTERNET OF THINGS AND BLOCKCHAIN TO INCREASE HUMANITARIAN AID SUPPLY CHAINS PERFORMANCE

This paper provide an overview of the benefits that the integration of IoT and Blockchain can produce on the Humanitarian Aid Supply Chain (HASC). Consdering the large number of partner involved in HASC, traditional centralized information systems become very vulnerable. In the same sense, the lack of mutual knowledge obstruct the needed cooperation. The joint use of IoT and Blockchain can overcome these problems. Main benefit as well as barriers to this implementation is analyzed, considering the diferent phases of the HASC. KeywordsCovid19, Blockchain, Supply Chain Management, Internet of Things, Humanitarian Supply Chain
Modeling Seismic Network Detection Thresholds Using Production Picking Algorithms

Abstract Estimating the detection threshold of a seismic network (the minimum magnitude earthquake that can be reliably located) is a critical part of network design and can drive network maintenance efforts. The ability of a station to detect an earthquake is often estimated by assuming the spectral amplitude for an earthquake of a given size, assuming an attenuation relationship, and comparing the predicted amplitude with the average station background noise level. This approach has significant uncertainty because of unknown regional attenuation and complications in computing small event power spectra, and it fails to account for the specific capabilities of the automatic seismic phase picker used in monitoring. We develop a data-driven approach to determine network detection thresholds using a multiband phase picking algorithm that is currently in use at the U.S. Geological Survey National Earthquake Information Center. We apply this picking algorithm to cataloged earthquakes to determine an empirical relationship of the observability of earthquakes as a function of magnitude and distance. Using this relationship, we produce maps of detection threshold using station spatial configuration and station noise levels. We show that quiet, well-sited stations significantly increase the detection capabilities of a network compared with a network composed of many noisy stations. Because our method is data driven, it has two distinct advantages: (1) it is less dependent on theoretical assumptions of source spectra and models of regional attenuation, and (2) it can easily be applied to any seismic network. This tool allows for an objective approach to the management of stations in regional seismic networks.
Commercial Sentiment Analysis Solutions: A Comparative Study

Empirical insights into high-promising commercial sentiment analysis solutions that go beyond their vendors’ claims are rare. Moreover, due to ongoing advances in the field, earlier studies are far from reflecting the current situation due to the constant evolution of the field. The present research aims to evaluate and compare current solutions. Based on tweets on the airline service quality, we test the solutions of six vendors with different market power, such as Amazon, Google, IBM, Microsoft, and Lexalytics, and MeaningCloud, and report their measures of accuracy, precision, recall, (macro) F1, time performance, and service level agreements (SLA). For positive and neutral classifications, none of the solutions showed precision of over 70%. For negative classifications, all of them demonstrate high precision of around 90%, however, only IBM Watson NLU and Google Cloud Natural Language achieve recall of over 70% and thus can be seen as worth considering for application scenarios w here negative text detection is a major concern. Overall, our study shows that an independent, critical experimental analysis of sentiment analysis services can provide interesting insights into their general reliability and particular classification accuracy beyond marketing claims to critically compare solutions based on real-world data and analyze potential weaknesses and margins of error before making an investment.
Experimental Investigation of the Dynamics of Coupled Oscillators as Ising Machines

Coupled electronic oscillator networks, under second harmonic injection, have recently been shown to behave as Ising machines capable of solving computationally hard combinatorial optimization problems. In this work, we experimentally investigate the dynamical properties of a reconfigurable network of up to 30 oscillators ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\equiv $ </tex-math></inline-formula> spins) configured as an Ising machine. Specifically, we analyze the characteristics of the solutions to the Ising model produced by the oscillators and show that as the system evolves towards the ground state through the high-dimensional phase space, it gets trapped in local minima resulting in sub-optimal solutions. Moreover, the exact local minima where the system gets trapped also changes implying that the trajectory of evolution of the system also changes with each trial. Finally, we illustrate experimentally how an appropriately designed annealing scheme can help the coupled oscillators escape a local minimum and attain a lower energy state.
The effect of augmented reality and mobile application supported instruction related to different variables in 7th grade science lesson

The purpose of this study is to investigate the effect of augmented reality and mobile application supported instruction on students' academic achievement, attitude towards astronomy, anxiety and motivation towards learning science in the "Solar System and Beyond/Earth and Universe" units in seventh grade Science lesson.In the study, pre-test-post-test control group quasi-experimental design was used.The study group consisted of 56 students (29 students in the experimental and 27 students in the control group) selected with the convinience sampling method, who were studying in 7th grades of a secondary school in Hatay province, Turkey.Data was collected through the "Solar System and Beyond Success Test" (SSBST) developed by the researcher; "Science Learning Anxiety Scale" (SLAS) developed by Yıldırım (2015) "Astronomy Attitude Scale" (AAS) developed by Zeilik, Schau and Mattern (1999) and adapted into Turkish by Bilici, Armağan, Çakır and Yürük (2012) and "Students' Motivation Scale for Science Learning" (SMSS) developed by Tuan, Chin and Shieh (2005) and adapted to Turkish by Yılmaz and Çavaş (2007) was used.The data were analyzed using descriptive statistics as the mean, standard deviation, frequency percentage values, and the inferential statistics as dependent and independent t-tests.As a result, it was found that mobile application and augmented reality supported instruction had a positive impact on academic success, did not have any effect on anxiety and motivation towards the lesson, and negatively affected attitude towards the content according to the methods suggested by the current program.
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation

Precise information of word boundary can alleviate the problem of lexical ambiguity to improve the performance of natural language processing (NLP) tasks.Thus, Chinese word segmentation (CWS) is a fundamental task in NLP.Due to the development of pre-trained language models (PLM), pre-trained knowledge can help neural methods solve the main problems of the CWS in significant measure.Existing methods have already achieved high performance on several benchmarks (e.g., Bakeoff-2005).However, recent outstanding studies are limited by the small-scale annotated corpus.To further improve the performance of CWS methods based on fine-tuning the PLMs, we propose a novel neural framework, LBGCN, which incorporates a lexiconbased graph convolutional network into the Transformer encoder.Experimental results on five benchmarks and four cross-domain datasets show the LBGCN successfully captures the information of candidate words and helps to improve performance on the benchmarks (Bakeoff-2005 and CTB6) and the cross-domain datasets (SIGHAN-2010).Further experiments and analyses demonstrate that our proposed framework effectively models the lexicon to enhance the ability of basic neural frameworks and strengthens the robustness in the cross-domain scenario.
Data-Driven Learning of Geometric Scattering Modules for GNNs

We propose a new graph neural network (GNN) module, based on relaxations of recently proposed geometric scattering transforms, which consist of a cascade of graph wavelet filters. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations. The incorporation of our LEGS-module in GNNs enables the learning of longer-range graph relations compared to many popular GNNs, which often rely on encoding graph structure via smoothness or similarity between neighbors. Further, its wavelet priors result in simplified architectures with significantly fewer learned parameters compared to competing GNNs. We demonstrate the predictive performance of LEGS-based networks on graph classification benchmarks, as well as the descriptive quality of their learned features in biochemical graph data exploration tasks.
A Comparative Study of Different Deep Learning Model for Recognition of Handwriting Digits

With the expansion of Artificial Neural Network (ANN), Deep Learning (DL) has brought interesting turn in the various fields of Artificial Intelligence (AI) by making it smarter and more efficient than what we had even in 10-2 years back. DL has been in use in various fields due to its versatility. Convolutional Neural Network (CNN) is at the major point of advancement that brings together the ANN and innovative DL techniques. In this research paper, we have contrived a multi-layer, fully connected neural network (NN) with 10 and 12 hidden layers for handwritten digits (HD) recognition. The testing is performed on the publicly attainable MNIST handwritten database. We selected 60,000 images from the MNIST database for training, and 10,000 images for testing. Our multi-layers ANN (10), ANN (12) and CNN are able to achieve an overall accuracy of 99.10%, 99. 34% and 99.70% respectively while determining digits using the MNIST handwriting dataset.
A Retail Itemset Placement Framework Based on Premiumness of Slots and Utility Mining

Retailer revenue is significantly impacted by item placement in retail stores. Notably, placement of items in the premium slots (i.e., slots with increased visibility/accessibility) improves the probability of sale w.r.t. item placement in non-premium slots. Moreover, customers often tend to buy sets of items (i.e., itemsets) instead of individual purchases. In this paper, we address the problem of maximizing retailer revenue by determining the placement of itemsets in different types of slots with varied premiumness. Our key contributions are as follows. First, we introduce the notion of premiumness of retail slots and discuss the issue of itemset placement in slots with varied premiumness. Second, we propose two efficient schemes, namely Premiumness and Revenue-based Itemset Placement (PRIP) and Premiumness and Average Revenue-based Itemset Placement (PARIP), for placing itemsets with varying revenue revenue in slots with varied premiumness. Third, we perform a detailed performance analysis using both real and synthetic datasets to showcase the effectiveness of our proposed schemes. We also perform a comprehensive mathematical analysis of our proposed schemes w.r.t. the complexity analysis.
How Length Prediction Influence the Performance of Non-Autoregressive Translation?

Length prediction is a special task in a series of NAT models where target length has to be determined before generation.However, the performance of length prediction and its influence on translation quality has seldom been discussed.In this paper, we present comprehensive analyses on length prediction task of NAT, aiming to find the factors that influence performance, as well as how it associates with translation quality.We mainly perform experiments based on Conditional Masked Language Model (CMLM) (Ghazvininejad et al., 2019), a representative NAT model, and evaluate it on two language pairs, En-De and En-Ro.We draw two conclusions: 1) The performance of length prediction is mainly influenced by properties of language pairs such as alignment pattern, word order or intrinsic length ratio, and is also affected by the usage of knowledge distilled data.2) There is a positive correlation between the performance of the length prediction and the BLEU score.
Blockchain's Impact on Accounting and Auditing: A Use Case on Supply Chain Traceability

ABSTRACT We use the transaction cost theory, originally proposed by Coase (1937), as a theoretical framework to examine the potential impact of the blockchain technology on accounting and auditing processes in terms of information timeliness, information quality, and auditing costs. Since a blockchain enables recording, tracking, and managing business transactions and assets of firms, it offers natural advantages to accounting and auditing processes, and has the potential to lower various transaction costs. A use case on a supply chain of food products is presented to illustrate how a blockchain can be used to trace the movement of goods and record the related transactions. Subsequently, building on this scenario, we use the transaction cost framework to generate propositions related to accounting and auditing that can be empirically tested in future studies as data become available. JEL Classifications: D83; G14; M41; M42; O33.