Reviewerly/exHarmony huggingface dataset:
Population response to earthquakes and earthquake early warnings in Mexico

Human responses to various natural hazards have been studied for more than a few decades. However, the effects of early warning interventions that alert the population to allow them time to take protective actions are not well known, at least to an extent where it could be compared within a socio-culturally similar region under two different scenarios: events without early warning versus those where early warning is transmitted. To understand to what extent the behaviors of populations and their emotional reactions differ under these two scenarios, we compared individual responses to an earthquake with and without an earthquake early warning (EEW) alert of the populations' (N = 430) from the states of Mexico, Guerrero, and Mexico City in Mexico. We considered the warnings from the Mexican Seismic Early Warning System (SASMEX) and private providers. Our results indicate that EEW potentially has implications for emotional responses and risk perception during the event and that the populations that respond to the EEW that provides a window of opportunity to perform protective actions, may not necessarily use the time adequately for life saving activities. On the other hand, population reactions to seismic events without any warning include evacuation during the event, which may have negative implications to their life safety. The results draw attention to the need of education of the public to events under two different circumstances – those with and without EEW. To further strengthen the understanding of population response, socio-technical aspects should be considered in future studies.
Influence of statistical feature normalisation methods on K-Nearest Neighbours and K-Means in the context of industry 4.0

Normalisation is a preprocessing technique widely employed in Machine Learning (ML)-based solutions for industry to equalise the features’ contribution. However, few researchers have analysed the normalisation effect and its implications on the ML algorithm performance, especially on Euclidean distance-based algorithms, such as the well-known K-Nearest Neighbours and K-means. In this sense, this paper formally analyses the effect of normalisation yielding results significantly far from the state-of-the-art traditional claims. In particular, this paper shows that normalisation does not equalise the contribution of the features, with the consequent impact on the performance of the learning process for a particular problem. More concretely, this demonstration is made on K-Nearest Neighbours and K-means Euclidean distance-based ML algorithms. This paper concludes that normalisation can be viewed as an unsupervised Feature Weighting method. In this context, a new metric (Normalisation weight) for measuring the impact of normalisation on the features is presented. Likewise, an analysis of the normalisation effect on the Euclidean distance is conducted and a new metric referred to as Proportional influence that measures the features influence on the Euclidean distance is proposed. Both metrics enable the automatic selection of the most appropriate normalisation method for a particular engineering problem, which can significantly improve both the computational cost and classification performance of K-Nearest Neighbours and K-means algorithms. The analytical conclusions are validated on well-known datasets from the UCI repository and a real-life application from the refinery industry.
Memory deduplication for serverless computing with Medes

Serverless platforms today impose rigid trade-offs between resource use and user-perceived performance. Limited controls, provided via toggling sandboxes between warm and cold states and keep-alives, force operators to sacrifice significant resources to achieve good performance. We present a serverless framework, Medes, that breaks the rigid trade-off and allows operators to navigate the trade-off space smoothly. Medes leverages the fact that the warm sandboxes running on serverless platforms have a high fraction of duplication in their memory footprints. We exploit these redundant chunks to develop a new sandbox state, called a dedup state, that is more memory-efficient than the warm state and faster to restore from than the cold state. We develop novel mechanisms to identify memory redundancy at minimal overhead while ensuring that the dedup containers' memory footprint is small. Finally, we develop a simple sandbox management policy that exposes a narrow, intuitive interface for operators to trade-off performance for memory by jointly controlling warm and dedup sandboxes. Detailed experiments with a prototype using real-world serverless workloads demonstrate that Medes can provide up to 1×-2.75× improvements in the end-to-end latencies. The benefits of Medes are enhanced in memory pressure situations, where Medes can provide up to 3.8× improvements in end-to-end latencies. Medes achieves this by reducing the number of cold starts incurred by 10--50% against the state-of-the-art baselines.
Towards trusted node selection using blockchain for crowdsourced abnormal data detection

Node selection plays an important role to design and implement the crowdsourced abnormal data detection system with the purpose of completing complex tasks to meet the requirements of computing performance. Even though the blockchain-based trusted node selection approaches improve the reliability of the crowdsourcing task results, they still need to consider the crowdsourcing cost during the node selection process so as to embody a tradeoff between reliability and cost. In this paper, we propose to study the node selection problem for the crowdsourced abnormal data detection under both reliability and cost requirements. First, the working node selection is modeled as an inverse 0−1 knapsack problem in order to minimize the crowdsourcing cost under the budget constraints of the trustworthiness of the selected working nodes, where blockchain is used to calculate the trustworthiness of the working nodes. Then, a trusted working node selection (TWNS) algorithm is developed to select trusted working nodes with the minimum crowdsourcing cost for crowdsourced abnormal data detection, where the branch and bound method is utilized to efficiently solve the inverse 0−1 knapsack problem. Finally, extensive simulations are conducted based on three groups of real-world datasets. The results show that the trust value evaluation is accurate by using blockchain and the TWNS algorithm can ensure the reliability of the detection result. The crowdsourcing cost is minimized in trusted working node selection process. Compared to existing approaches without considering the cost, the TWNS algorithm reduces the crowdsourcing cost by 64.6%.
Student Approaches and Performance in Element Sequencing Tasks Using 2D and Augmented Reality Formats

In civil and construction engineering education research, a focus has been on using 3D models to support students’ design comprehension. Despite this trend, the predominant mode of design communication in the industry relies on 2D plans and specifications, which typically supersede other modes of communication. Rather than focusing on the presentation of less common 3D content as an input to support students’ understanding of a design, this paper explores more common 2D inputs, but compares different visualization formats of student output in two educational interventions. In the first intervention, students document a construction sequence for wood-framed elements in a 2D worksheet format. In the second, students work with the same wood-framed design, but document their sequence through an augmented reality (AR) format where their physical interactions move full-scale virtual elements as if they were physically constructing the wood frame. Student approaches and performance were analyzed using qualitative attribute coding of video, audio, and written documentation of the student experience. Overall, results showed that the 2D worksheet format was simple to implement and was not mentally demanding to complete, but often corresponded with a lack of critical checks and a lack of mistake recognition from the students. The AR approach challenged students more in terms of cognitive load and completion rates but showed the potential for facilitating mistake recognition and self-remediation through visualization. These results suggest that when students are tasked with conceptualizing construction sequences from 2D documentation, the cognitive challenges associated with documenting a sequence in AR may support their recognition of their own mistakes in ways that may not be effectively supported through 2D documentation as an output for documenting and planning a construction sequence. The results presented in this paper provide insights on student tendencies, behaviors, and perceptions related to defining construction sequences from 2D documentation in order for educators to make informed decisions regarding the use of similar learning activities to prepare their students for understanding the 2D design documents used in industry.
Experimental assessment of noise robustness of the forward-additive, symmetric-additive and the inverse-compositional Gauss-Newton algorithm in digital image correlation

Digital Image Correlation (DIC) is a well-known optical experimental method based on the assumption that pixel intensity does not change with motion. This assumption is never exactly satisfied because various sources of noise exist (read-, shot-, dark-noise of the Charge Coupled Device (CCD), thermal dilatation of lens, non-constant illumination,...); moreover, to accurately track displacements, pixel intensity has to be interpolated at non-integer locations, thus introducing both phase and intensity errors due to the use of theoretically incorrect interpolating kernels. DIC can be implemented using various formulations, the most used being the Forward-Additive Gauss-Newton (FA-GN) and the Inverse-Compositional Gauss-Newton formulation (IC-GN). Even though both formulations give the same results at the first order, their speed, converging characteristics, and noise robustness differ considerably in particular with regard to the noise bias. Indeed, Shao et al. [1], using a theoretical analysis of a mono-dimensional signal and a numerical simulation on FFT-shifted images, recently showed that the latter formulation is not affected by noise bias, providing the correct differential operator is used. In this work, a fully experimental assessment of the noise-bias sensitivity of both algorithms is performed, confirming the theoretical findings and the numerical simulations available in the literature.
MALL and EFL Learners’ Speaking: Impacts of Duolingo and WhatsApp Applications on Speaking Accuracy and Fluency

Regarding the importance of using MALL in learning English, this study aimed to investigate the effects of Duolingo and WhatsApp applications on developing the speaking accuracy and fluency of Iranian EFL learners. To fulfill this objective, three groups of Iranian intermediate EFL learners were selected as the participants of this research. After that, all groups took the speaking pretest and then received the treatment. One group was taught ten conversations from Family and Friends Book 5 using the Duolingo application, and the other group was taught the same conversations using the WhatsApp application. The third group, considered the control group (CG), was instructed on the conversations in a traditional classroom. After teaching the conversations, a speaking posttest was administered to all groups to compare their performances after the treatment. The collected data were analyzed using the one-way ANOVA test. The results revealed that two experimental groups (EGs) had better performances than the CG on the speaking posttest. In addition, the outcomes showed that two EGs performed similarly on their speaking posttests. Eventually, the conclusions and the pedagogical implications of this research were explained.
A novel ramp loss-based multi-task twin support vector machine with multi-parameter safe acceleration

Direct multi-task twin support vector machine (DMTSVM) is an effective algorithm to deal with multi-task classification problems. However, the generated hyperplane may shift to outliers since the hinge loss is used in DMTSVM. Therefore, we propose an improved multi-task model RaMTTSVM based on ramp loss to handle noisy points more effectively. It could limit the maximal loss value distinctly and put definite restrictions on the influences of noises. But RaMTTSVM is non-convex which should be solved by CCCP, then a series of approximate convex problems need to be solved. So, it may be time-consuming. Motivated by the sparse solution of our RaMTTSVM, we further propose a safe acceleration rule MSA to accelerate the solving speed. Based on optimality conditions and convex optimization theory, MSA could delete a lot of inactive samples corresponding to 0 elements in dual solutions before solving the model. Then the computation speed can be accelerated by just solving reduced problems. The rule contains three different parts that correspond to different parameters and different iteration phases of CCCP. It can be used not only for the first approximate convex problem of CCCP but also for the successive problems during the iteration process. More importantly, our MSA is safe in the sense that the reduced problem can derive an identical optimal solution as the original problem, so the prediction accuracy will not be disturbed. Experimental results on one artificial dataset, ten Benchmark datasets, ten Image datasets and one real wine dataset confirm the generalization and acceleration ability of our proposed algorithm.
Gray-Box Shilling Attack: An Adversarial Learning Approach

Recommender systems are essential components of many information services, which aim to find relevant items that match user preferences. Several studies have shown that shilling attacks can significantly weaken the robustness of recommender systems by injecting fake user profiles. Traditional shilling attacks focus on creating hand-engineered fake user profiles, but these profiles can be detected effortlessly by advanced detection methods. Adversarial learning, which has emerged in recent years, can be leveraged to generate powerful and intelligent attack models. To this end, in this article we explore potential risks of recommender systems and shed light on a gray-box shilling attack model based on generative adversarial networks, named GSA-GANs . Specifically, we aim to generate fake user profiles that can achieve two goals: unnoticeable and offensive. Toward these goals, there are several challenges that we need to address: (1) learning complex user behaviors from user-item rating data, and (2) adversely influencing the recommendation results without knowing the underlying recommendation algorithms. To tackle these challenges, two essential GAN modules are respectively designed to make generated fake profiles more similar to real ones and harmful to recommendation results. Experimental results on three public datasets demonstrate that the proposed GSA-GANs framework outperforms baseline models in attack effectiveness, transferability, and camouflage. In the end, we also provide several possible defensive strategies against GSA-GANs. The exploration and analysis in our work will contribute to the defense research of recommender systems.
Data Market Design: A Systematic Literature Review

Data markets are platforms that provide the necessary infrastructure and services to facilitate the exchange of data products between data providers and data consumers from different environments.Over the last decade, many data markets have sprung up, capitalising on the increased appreciation of the value of data and catering to different domains.In this work, we analyse the existing body of scientific literature on data markets to provide the first comprehensive overview of research into the design of data markets, regardless of scientific background or application domain.In doing so, we contribute to the field in several ways: 1) We present an overview of the state of the art in academic research on data markets and compare this with existing market trends to identify potential gaps.2) We identify important application domains and contexts where data markets are being put into practice.3) Finally, we provide taxonomies of both design problems for data markets and the solutions that are being investigated to address them.We conclude our work by identifying common types of data markets and corresponding best practices for designing them.The outcome of this work is intended to serve as a starting point for software architects and engineers looking to design data markets.
An Alternative Digital Image Correlation-Based Experimental Approach to Estimate Fracture Parameters in Fibrous Soft Materials

One of the main challenges in experimental fracture mechanics is to correctly estimate fracture parameters of a nonhomogeneous and nonlinear material under large deformation. The crack tip detection is strongly affected by fibers at crack tip, leading to inaccurate measures. To overcome this limitation, a novel methodology based on the Digital Image Correlation (DIC) method for crack tip detection of fibrous soft composites is proposed in this work. The unidirectional composite was manufactured using a matrix of polydimethylsiloxane reinforced with a single layer of extensible cotton knit fabric. For two different fiber orientations, the crack growth (da), Crack Tip Opening Displacement (CTOD) and Crack Tip Opening Angle (CTOA) were determined using pure shear specimens under mode I fracture. A consistent estimation of fracture parameters was obtained. The location of the crack tip position during the fracture test using the DIC-based methodology was validated against a visual inspection approach. Results indicated that the DIC-based methodology is easily replicable, precise and robust.
A deep learning method for automatic <scp>SMS</scp> spam classification: Performance of learning algorithms on indigenous dataset

Abstract SMS, one of the most popular and fast‐growing GSM value‐added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F‐measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine‐tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state‐of‐the‐art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F‐measure.
Mix Method Approach of Measuring VR as a Pedagogical Tool to Enhance Experimental Learning: Motivation from Literature Survey of Previous Study

This research has been experimented on our previous literature review. Technological advancement has prevailed in the modern era from the 20th century. Artificial intelligence and virtual worlds have been created for rapid technological development. This paper is aimed at exploring the effect of virtual reality as a pedagogical tool for enhancing experiential learning among undergraduate students. Considering this, it was a mixed-methods study following the design of sequential exploratory–which includes qualitative followed by quantitative part. The targeted population was undergraduate students taking education programs from Public Sector Universities of Sindh. For the qualitative part, the sample of eight undergraduate students was interviewed for exploring their perceptions about virtual reality for enhancing experiential learning. The interviewed data were thematically analyzed, which revealed seven themes. For the quantitative part, emerged seven themes were used as constructs for developing the questionnaire. It was then used for quantitative data collection having excellent reliability. A sample of 80 students was taken, and data were collected by self-administration. The quantitative data were analyzed by descriptive as well as inferential statistics. Hence, the results found a statistically significant effect of virtual reality for enhancing experiential learning. However, user-friendliness ensures the applicability of virtual reality. Thus, it concluded that the implementation of virtual reality influences experiential learning enhancement. However, the implementation of virtual reality still depends upon the user-friendly nature of technology and virtual reality.
Deep Reinforcement Learning-Based DQN Agent Algorithm for Visual Object Tracking in a Virtual Environmental Simulation

The complexity of object tracking models among hardware applications has become a more in-demand task to accomplish with multifunctional algorithm skills in various indeterminable environment tracking conditions. Experimenting with the virtual realistic simulator brings new dependencies and requirements, which may cause problems while experimenting with runtime processing. The goal of this paper is to present an object tracking framework that differs from the most advanced tracking models by experimenting with virtual environment simulation (Aerial Informatics and Robotics Simulation—AirSim, City Environ) using one of the Deep Reinforcement Learning Models named as Deep Q-Learning algorithms. Our proposed network examines the environment using a deep reinforcement learning model to regulate activities in the virtual simulation environment and utilizes sequential pictures from the realistic VCE (Virtual City Environ) model as inputs. Subsequently, the deep reinforcement network model was pretrained using multiple sequential training image sets and fine-tuned for adaptability during runtime tracking. The experimental results were outstanding in terms of speed and accuracy. Moreover, we were unable to identify any results that could be compared to the state-of-the-art methods that use deep network-based trackers in runtime simulation platforms, since this testing experiment was conducted on the two public datasets VisDrone2019 and OTB-100, and achieved better performance among compared conventional methods.