Reviewerly/exHarmony huggingface dataset:
Investigation of assumptions and approximations in the virtual fields method for a viscoplastic material model

Abstract The Virtual Fields Method (VFM) is an inverse technique used for parameter estimation and calibration of constitutive models. Many assumptions and approximations—such as plane stress, incompressible plasticity, and spatial and temporal derivative calculations—are required to use VFM with full‐field deformation data, for example, from Digital Image Correlation (DIC). This work presents a comprehensive discussion of the effects of these assumptions and approximations on parameters identified by VFM for a viscoplastic material model for 304L stainless steel. We generated synthetic data from a Finite‐Element Analysis (FEA) in order to have a reference solution with a known material model and known model parameters, and we investigated four cases in which successively more assumptions and approximations were included in the data. We found that VFM is tolerant to small deviations from the plane stress condition in a small region of the sample, and that the incompressible plasticity assumption can be used to estimate thickness changes with little error. A local polynomial fit to the displacement data was successfully employed to compute the spatial displacement gradients. The choice of temporal derivative approximation (i.e., backwards difference versus central difference) was found to have a significant influence on the computed rate of deformation and on the VFM results for the rate‐dependent model used in this work. Finally, the noise introduced into the displacement data from a stereo‐DIC simulator was found to have negligible influence on the VFM results. Evaluating the effects of assumptions and approximations using synthetic data is a critical first step for verifying and validating VFM for specific applications. The results of this work provide the foundation for confidently using VFM for experimental data.
Improving Oral Communicative Competence in English Using Project-Based Learning Activities

The quasi-experimental study investigated the effectiveness of using project-based learning (PjBL) activities as a teaching strategy in improving the oral communicative competence of Malaysian English language learners. The participants included 44 diploma students enrolled in a Communicative English course at a technical college in the Peninsular Malaysia, who were purposely selected for the study. The intervention comprised a 12-week lessons taught using PjBL teaching strategy and centred on eight PjBL activities. Data were collected using a speaking test and a listening test, which were administered as pre-tests and post-tests, and a student questionnaire which was administered at the end of the study. Data analysis involved the procedure of MANOVA, as well as descriptive statistics such as mean, standard deviation and percentage. The findings revealed a significant improvement in the learners&amp;rsquo; overall oral communicative competence and a high perception of PjBL by the learners. It is concluded that PjBL teaching strategy is effective in improving the oral communicative competence of the English language learners. The study recommends the use of PjBL as a suitable English language teaching strategy for technical students who are generally low proficient English language learners.
AccAnn: A New Subjective Assessment Methodology for Measuring Acceptability and Annoyance of Quality of Experience

User expectations have a crucial impact on the levels of quality of experience (QoE) that they consider acceptable or satisfying. Measuring acceptability and annoyance has mainly been performed in separate or multi-step experiments without any control over participants' expectations. This paper introduces a simple methodology to obtain the information about both of the entities in a single step and compares several data processing strategies useful for results interpretation. A specifically designed subjective experiment, conducted on compressed videos, has shown that the multi-step procedures could be replaced by our proposed single-step approach, regardless of the viewing conditions, while the novel approach is significantly preferred by observers for its low time requirements and higher intuitiveness. The test has simultaneously proven that user expectations can be altered by the instructions and it is, therefore, possible to simulate different user profiles regardless of the participants' real habits. The acceptability/annoyance experimental results are also used to benchmark the state-of-the-art objective video quality metrics in predicting acceptability/annoyance of QoE. A case study on the determination of the threshold of acceptability/annoyance for objective quality metrics is conducted, which can be served as a guideline for video streaming service providers.
Privacy-preserving model and generalization correlation attacks for 1:M data with multiple sensitive attributes

Preserved privacy and enhanced utility are two competing requirements in data publishing. For maintaining a trade-off between the two; a plethora of research work exist in 1:1 scenario (each individual has a single record) with a single sensitive attribute (SA). However, some practical scenarios i.e., data having 1:M records (an individual can have multiple records) with multiple sensitive attributes (MSAs), have been relatively understudied. In our current interconnected and digitalized society, the capability to deal with such scenarios is increasingly important due to the ever-increasing sources of data that could be drawn together and infer one's information (e.g. profile and lifestyle) and consequently, compromising one's privacy. In this paper, we present a new type of attack on 1:M records with MSAs, coined as MSAs generalization correlation attacks and perform formal modeling and analysis of these attacks. Then, we propose a privacy-preserving technique "(p, l)-Angelization" for 1:M–MSAs data publication. Extensive experiments over real-world datasets advocate the outperformance of our technique over its counterparts.
Accomplishing Transparency within the General Data Protection Regulation

Transparency is a user-centric principle proposed to empower users to hold data processors accountable for the usage and the processing of the user’s personal data. Accomplishing transparency may come with some resistance because it requires significant architectural changes, but it is mandatory by law under the recently approved General Data Protection Regulation. To help the transition, we systematically review what Transparency Enhancing Technologies can help to accomplish transparency in agreement with technical requirements that we elicited from the Regulation’s articles. We discuss our findings in the domain of medical data systems, where accomplishing transparency looks particularly controversial due to sensitivity of the personal medical data.
Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning.

3D face reconstruction from a single 2D image is a challenging problem with broad applications. Recent methods typically aim to learn a CNN-based 3D face model that regresses coefficients of 3D Morphable Model (3DMM) from 2D images to render 3D face reconstruction or dense face alignment. However, the shortage of training data with 3D annotations considerably limits performance of those methods. To alleviate this issue, we propose a novel 2D-assisted self-supervised learning (2DASL) method that can effectively use in-the-wild 2D face images with noisy landmark information to substantially improve 3D face model learning. Specifically, taking the sparse 2D facial landmarks as additional information, 2DSAL introduces four novel self-supervision schemes that view the 2D landmark and 3D landmark prediction as a self-mapping process, including the 2D and 3D landmark self-prediction consistency, cycle-consistency over the 2D landmark prediction and self-critic over the predicted 3DMM coefficients based on landmark predictions. Using these four self-supervision schemes, the 2DASL method significantly relieves demands on the the conventional paired 2D-to-3D annotations and gives much higher-quality 3D face models without requiring any additional 3D annotations. Experiments on multiple challenging datasets show that our method outperforms state-of-the-arts for both 3D face reconstruction and dense face alignment by a large margin.
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere

Among the various architectures of Recurrent Neural Networks, Echo State Networks (ESNs) emerged due to their simplified and inexpensive training procedure. These networks are known to be sensitive to the setting of hyper-parameters, which critically affect their behavior. Results show that their performance is usually maximized in a narrow region of hyper-parameter space called edge of criticality. Finding such a region requires searching in hyper-parameter space in a sensible way: hyper-parameter configurations marginally outside such a region might yield networks exhibiting fully developed chaos, hence producing unreliable computations. The performance gain due to optimizing hyper-parameters can be studied by considering the memory-nonlinearity trade-off, i.e., the fact that increasing the nonlinear behavior of the network degrades its ability to remember past inputs, and vice-versa. In this paper, we propose a model of ESNs that eliminates critical dependence on hyper-parameters, resulting in networks that provably cannot enter a chaotic regime and, at the same time, denotes nonlinear behavior in phase space characterized by a large memory of past inputs, comparable to the one of linear networks. Our contribution is supported by experiments corroborating our theoretical findings, showing that the proposed model displays dynamics that are rich-enough to approximate many common nonlinear systems used for benchmarking.
Weighted samples based semi-supervised classification

Graph-based semi-supervised classification (GSSC) takes labeled and unlabeled samples as vertices in a graph, and edge weights as the similarity between samples. Most GSSC methods handle each labeled sample as equally important in the graph, and they mainly focus on optimizing the graph to improve the performance. In fact, samples are not always evenly distributed. Labeled samples close to the decision boundary of different classes are generally more important than labeled samples far away from the boundary. To account for the different importances, we propose an approach called Weighted Samples based Semi-Supervised Classification (WS3C for short). WS3C firstly executes multiple clusterings on the dataset to explore the structure of samples and summarizes these clustering results. Second, it quantifies the hard-to-cluster index of each labeled sample with respect to other samples based on the summarized results and employs the index to weight that sample. Next, it constructs a graph whose edge weights are equal to the frequency of two samples grouped into the same clusters in multiple clusterings. After that, it performs semi-supervised classification based on the constructed graph and weighted samples. Empirical study on synthesized and real datasets demonstrates that assigning labeled samples with different weights significantly improves the accuracy than equally treating labeled samples. WS3C not only has better performance than other related comparing methods, but also is robust to the input parameters.
Entity Linking via Symmetrical Attention-Based Neural Network and Entity Structural Features

In the process of knowledge graph construction, entity linking is a pivotal step, which maps mentions in text to a knowledge base. Existing models only utilize individual information to represent their latent features and ignore the correlation between entities and their mentions. Besides, in the process of entity feature extraction, only partial latent features, i.e., context features, are leveraged to extract latent features, and the pivotal entity structural features are ignored. In this paper, we propose SA-ESF, which leverages the symmetrical Bi-LSTM neural network with the double attention mechanism to calculate the correlation between mentions and entities in two aspects: (1) entity embeddings and mention context features; (2) mention embeddings and entity description features; furthermore, the context features, structural features, and entity ID feature are integrated to represent entity embeddings jointly. Finally, we leverage (1) the similarity score between each mention and its candidate entities and (2) the prior probability to calculate the final ranking results. The experimental results on nine benchmark dataset validate the performance of SA-ESF where the average F1 score is up to 0.866.
Learning When Not to Answer: a Ternary Reward Structure for Reinforcement Learning Based Question Answering

Fréderic Godin, Anjishnu Kumar, Arpit Mittal. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Blockchain Technology Application in the Sharing Economy: A Proposed Model of Effects on Trust and Intermediation

Combining blockchain technology and smart contracts has the potential to facilitate disintermediation and realize true peer-to-peer transactions provided that sufficient trust is build.An advanced research model of blockchain-mediated trust is conceptualized by incorporating extant trust building concepts.The conceptualized model helps to advance future empirical trust research in connection with blockchain technology in the sharing economy by suggesting moderating and mediating effects on trust in online settings.
Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems

Photovoltaic (PV) output power inherently exhibits an intermittent property depending on the variation of weather conditions. Since PV power producers may be charged to large penalties in forthcoming energy markets due to the uncertainty of PV power generation, they need a more accurate PV power prediction scheme in energy market operation. In this paper, we characterize the effect of PV power prediction errors on energy storage system (ESS)-based PV power trading in energy markets. First, we analyze the prediction accuracy of two machine learning (ML) schemes for the PV output power and estimate their error distributions. We propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviations between the day-ahead (DA) and real-time (RT) dispatch in energy markets. In addition, we estimate the capacity of ESSs, which can absorb the prediction errors and then compare the PV power producer’s profit according to ML-based prediction schemes with/without ESS. In case of ML-based prediction schemes with ESS, the ANN and SVM schemes yield a decrease in the deviation penalty by up to 87% and 74%, respectively, compared with the profit of those schemes without ESS.
AHNG: Representation learning on attributed heterogeneous network

Network embedding aims to encode nodes into a low-dimensional space with the structure and inherent properties of the networks preserved. It is an upstream technique for network analyses such as link prediction and node clustering. Most existing efforts are devoted to homogeneous or heterogeneous plain networks. However, networks in real-world scenarios are usually heterogeneous and not plain, i.e., they contain multi-type nodes/links and diverse node attributes. We refer such kind of networks with both heterogeneities and attributes as attributed heterogeneous networks (AHNs). Embedding AHNs faces two challenges: (1) how to fuse heterogeneous information sources including network structures, semantic information and node attributes; (2) how to capture uncertainty of node embeddings caused by diverse attributes. To tackle these challenges, we propose a unified embedding model which represents each node in an AHN with a Gaussian distribution (AHNG). AHNG fuses multi-type nodes/links and diverse attributes through a two-layer neural network and captures the uncertainty by embedding nodes as Gaussian distributions. Furthermore, the incorporation of node attributes makes AHNG inductive, embedding previously unseen nodes or isolated nodes without additional training. Extensive experiments on a large real-world dataset validate the effectiveness and efficiency of the proposed model.
Hybrid Multiverse Optimization Algorithm With Gravitational Search Algorithm for Multithreshold Color Image Segmentation

Multithreshold segmentation of color images is an important part of modern image processing. Multithreshold segmentation techniques that use the ideas of optimization in mathematics to process the information contained in the three channels of RGB images have received much attention in recent years. In this paper, a hybrid algorithm of multiverse optimization algorithm with a gravitational search algorithm (GSMVO) is proposed. On the one hand, the gravitational mechanism among individuals in the universe enables particles to share information and move toward particles with large mass (the optimal solution), thus optimizing local optimization. On the other hand, the problem of a slow interpopulation update is improved. On the premise of improving the optimization precision, the convergence speed of the whole system is accelerated and the robustness can be improved. The time complexity of a proposed hybrid algorithm is analyzed. The quality of a segmented image is evaluated by using peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and Wilcoxon test. The experimental results illustrate that the proposed method has obvious advantages in objective function value, image quality measurement, convergence performance, and robustness.
A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines

Data classification is the process of organizing data by relevant categories. In this way, the data can be understood and used more efficiently by scientists. Numerous studies have been proposed in the literature for the problem of data classification. However, with recently introduced metaheuristics, it has continued to be riveting to revisit this classical problem and investigate the efficiency of new techniques. Teaching-learning-based optimization (TLBO) is a recent metaheuristic that has been reported to be very effective for combinatorial optimization problems. In this study, we propose a novel hybrid TLBO algorithm with extreme learning machines (ELM) for the solution of data classification problems. The proposed algorithm (TLBO-ELM) is tested on a set of UCI benchmark datasets. The performance of TLBO-ELM is observed to be competitive for both binary and multiclass data classification problems compared with state-of-the-art algorithms.
College Students’ Usage of and Preferences for Print and Electronic Textbooks

Although print textbooks have been central for schooling for more than two centuries, electronic textbooks (e-textbooks) are steadily growing in popu-larity among students at all levels of education. This sharp growth has esca-lated the need for further research to enable a better understanding of the changing patterns of students’ usage of print and electronic media. The study explores college students’ usage of and preference for print and elec-tronic textbooks in Bahrain Teachers’ College, in Bahrain. The sample of the study consisted of 271 undergraduate students selected from courses that utilized both formats of textbook as clearly indicated in their syllabi. De-scriptive analyses were used to analyze data collected. Results indicate that regardless of textbook format, the highest percentage of students use their textbooks only when their instructors require them to do so. For print text-books, results show that the highest percentage of students spend between 1 to 3 hours a week on reading. However, for e-textbooks, the highest per-centage of students spend only less than 1 hour per week on reading. An in-teresting finding for this study is that compared to print textbooks, e-textbooks gain a higher percentage of students’ usage when it comes to spending higher numbers of hours of reading per week. The study affirms that students are in general positive to the use of electronic format but still show a preference for print format as the best medium for academic study. Lastly, the study uncovers reasonable findings as to why students prefer one textbook format over the other.
Virtual reality in the hotel industry: assessing the acceptance of immersive hotel presentation

In the hotel industry, it is crucial to reduce the inherent information asymmetry with regard to the goods offered. This asymmetry can be minimised through the use of smartphone-based virtual reality applications (SBVRs), which allow virtual simulation of real experiences and thus enable more efficient information retrieval. The aim of the study is to determine for the first time the user acceptance of these immersive hotel presentations for assessing the performance of a travel accommodation. For this purpose, the Technology Acceptance Model (TAM) was used to explain the acceptance behaviour for this new technology. A virtual reality application was specially developed, in which the participants could explore a hotel virtually. A total of 569 participants took part in the study. The structural equation model and the hypotheses were tested using a Partial Least Squares (PLS) analysis. The results illustrate that the immersive product experience leads to more efficient information gathering. The perceived usefulness significantly affects the attitude towards using the technology as well as the intention to use it. In contrast to the traditional TAM, the perceived ease of use of SBVRs has no effect on the perceived usefulness or attitude towards using the technology.
Students’ Perception at the Use of Padlet in Linguistics Class

This study aims at investigating students’ perceptions of the use of Padlet in the class of Introduction to Functional Linguistics. The population of this study was a class of 2014 batch student of English Language Education program at Sultan Agung Islamic University (UNISSULA) Semarang with a sample of 28 people. This research is in the form of qualitative descriptive design. The data collection method uses observations and questionnaire. The data is then analyzed using interpretive descriptive analysis. The specific target to be achieved in this study is that by knowing students' perceptions of the use of Padlet, then this result can be used as a reference for the Faculty of Language and Communication Science and other faculties in the UNISSULA environment to improve the performance and quality of service to students especially in the learning and teaching process. In addition, the results of this study can also be a reference for both the Study Program and the Faculty to review the curriculum that has been implemented so far. From the data obtained through questionnaires distributed to 28 students of the English Language Education Study Program at the Faculty of Languages and Communication Science of Sultan Agung Islamic University Semarang in the class of 2014/2015, researchers can conclude that these students have very diverse perceptions in the use of Padlet media in learning Introduction to Functional Linguistics. To be sure, most students really positively appreciate the use of this media in linguistic learning in general.