Reviewerly/exHarmony huggingface dataset:
Effectiveness of AR Board Game on Computational Thinking and Programming Skills for Elementary School Students

This study integrated the augmented reality (AR) technology into the “Coding Ocean” board game to provide players with real-time simulation of ship paths and learning scaffolds. Combined with Scratch block-based programming, an interactive learning environment is developed to assist elementary school students in learning coding skills from the unplugged board game to enhance their computational thinking concepts. The AR board game is focused on the programming concepts of sequential, and/or and loop. Through the process of treasure hunting, the basic concepts of computational thinking can be developed, i.e., abstraction, problem decomposition, pattern recognition and algorithmic thinking. In order to investigate the learning effectiveness of the AR board game on computational thinking and programming skills, a number of 51 third graders from an elementary school were recruited as research samples. The experimental group (n = 26) used the AR board game and the control group (n = 25) used the traditional board game for game-based learning. The experimental results indicate: (1) the learning effectiveness of computational thinking for the experimental group was significantly higher than that of the control group; (2) the learning achievement of the block-based programming skills for the experimental group was significantly higher than that of the control group; (3) the cognitive load of the experimental group was significantly lower than that of the control group. The AR technology can combine the unplugged board games with plugged learning modules to assist students in game-based learning, which is useful for enhancing computational thinking abilities while reducing the cognitive load.
Linguistic Steganalysis Toward Social Network

With the rapid development of the internet and social media, linguistic steganography can be easily abused in social networks to make considerable damage to varied aspects like personal privacy, network virus and national defense. Currently, considerable linguistic steganalysis methods are proposed to detect harmful steganographic carriers. However, almost all the existing methods fail in real social networks, since they are only devoted to the linguistic features that are extreme insufficient owing to the extreme sparsity and extreme fragmentation challenges of real social networks. In this paper, we attempt to fill the long-standing gap that the datasets and effective methods are absent for hunting steganographic texts in social network scenarios. Concretely, we construct a dataset called Stego-Sandbox to simulate the real social network scenarios, which contains texts and their relation. And we propose an effective linguistic steganalysis framework integrating linguistic features contained in texts and context features represented by these connections. Extensive experimental results demonstrate owing to the captured context features, our proposed framework can effectively compensate for shortcomings of these existing methods and tremendously improve their detection ability in real social network scenarios.
Smart Data Placement Using Storage-as-a-Service Model for Big Data Pipelines

Big data pipelines are developed to process data characterized by one or more of the three big data features, commonly known as the three Vs (volume, velocity, and variety), through a series of steps (e.g., extract, transform, and move), making the ground work for the use of advanced analytics and ML/AI techniques. Computing continuum (i.e., cloud/fog/edge) allows access to virtually infinite amount of resources, where data pipelines could be executed at scale; however, the implementation of data pipelines on the continuum is a complex task that needs to take computing resources, data transmission channels, triggers, data transfer methods, integration of message queues, etc., into account. The task becomes even more challenging when data storage is considered as part of the data pipelines. Local storage is expensive, hard to maintain, and comes with several challenges (e.g., data availability, data security, and backup). The use of cloud storage, i.e., storage-as-a-service (StaaS), instead of local storage has the potential of providing more flexibility in terms of scalability, fault tolerance, and availability. In this article, we propose a generic approach to integrate StaaS with data pipelines, i.e., computation on an on-premise server or on a specific cloud, but integration with StaaS, and develop a ranking method for available storage options based on five key parameters: cost, proximity, network performance, server-side encryption, and user weights/preferences. The evaluation carried out demonstrates the effectiveness of the proposed approach in terms of data transfer performance, utility of the individual parameters, and feasibility of dynamic selection of a storage option based on four primary user scenarios.
Techno–Econo–Enviro Energy Analysis, Ranking and Optimization of Various Building-Integrated Photovoltaic (BIPV) Types in Different Climatic Regions of Iran

Iran is one of the most energy-consuming countries, especially in the construction sector, and more than 40% of its energy consumption is in the construction sector. Therefore, considering the very high potential of Iran in the field of solar energy, the need to pay attention to providing part of the energy required by buildings by solar energy seems necessary. The study of the effect of climate on the performance of a BIPV has not been done in Iran so far. Also, the use of ranking methods using the weighting of parameters affecting the performance of BIPV has not been done so far. The purpose of this study is to investigate the power supply of BIPV connected to the grid in the eight climates of Iran. Technical–economic–environmental energy analyses were performed by HOMER 2.81 software. In order to study different types of BIPV, four angles of 0°, 30°, 60°, and 90° were considered for the installation of solar cells. The effective output parameters of HOMER software were weighted by the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on the opinion of experts, and it was observed that payback time (year) has the highest weight among the studied criteria. Then, different cities were ranked using the evaluation based on distance from the average solution (EDAS) method. The results showed that Jask is the most suitable and Ramsar is the most unsuitable city. Also, the results of the EDAS method were confirmed by Additive Ratio Assessment (ARAS), Weighted Aggregates Sum Product Assessment (WASPAS), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods.
Environmental, social, governance risk versus cooperation models between financial institutions and businesses. Sectoral approach and ESG risk analysis

Introduction: ESG risk and the business sector are essential in the process of adapting business models towards sustainability. The article aims to analyze ESG risk, taking into account these business sectors that are most affected by ESG risk, and to identify the relationship between ESG risk and cooperation models of financial institutions and companies (Anglo-Saxon/German-Japanese). The original research approach was based on including the financial system model in the analysis of ESG risk impact and the companies’ methods of achieving sustainable business models with external funding. Methods: The study is based on a two-stage analysis. First, fuzzy cognitive maps are used to evaluate the strength and direction of the relations between factors included in the companies’ sustainable business models according to the business sectors. At the second stage, a correspondence analysis was carried out to distinguish four groups of companies’ industries characterized by homogenous features related to the business sector, sustainable business model (SBM), and the way to transform to sustainability. Results: Methods of achieving sustainability in companies’ business models differ depending on business sectors. The “financial institution—company” cooperation model and recommendations were proposed. Discussion: Financial institutions play a crucial role in financing the transition into sustainable business models. However, their impact differs depending on the business sector and the model of the financial system (bank or market oriented). The study assumes that the financial system model determines the form of cooperation between financial institutions and companies in the process of financing costs towards sustainability.
Development of Digital Teaching Competence: Pilot Experience and Validation through Expert Judgment

The development of digital teaching competence is one of the fundamental requirements of what is known as the “knowledge society”. With the aim of evaluating, from an expert point of view, the design of a training itinerary oriented to the training development of non-university teachers under the t-MOOC architecture for the improvement of digital teaching competence (DTC), the following quantitative research is presented. For this purpose, a quantitative validation design was established using the expert judgement technique. To measure the level of the expert coefficient, the expert competence (K) index was calculated for a random sample of 292 subjects participating in the study: teachers belonging to preschool and primary schools in the Andalusian community. The responses of those experts who scored ≥0.8 on the external competence index were then selected. The results demonstrate the validity of the tool produced (T-MOOC) as well as the uniformity of the criteria of the experts participating in the evaluation. Consequently, the necessary structuring of personalised training plans supported by reference models is discussed.
Teleportation of unknown states of a qubit and a single-mode field in strong coupling regime without Bell-state measurement

Abstract In this paper, we develop the teleportation scheme in [Zheng in Phys Rev A 69 , 064302, 2004], in the sense that, we work in the strong atom-field coupling regime wherein the rotating wave approximation (RWA) is no longer valid. To achieve the purpose, a scheme consisting of a qubit interacting with a single-mode quantized field is described via the Rabi model (counter rotation terms are taken into account). Our first aim is to teleport an unknown atomic state of a qubit (which interacts with the quantized field in a cavity) to a second qubit (exists in another distant cavity field), beyond the RWA and without the Bell-state measurement method. In the continuation, in a similar way, we teleport an unknown state of a single-mode field too. In fact, it is shown that, in this regime, after applying some particular conditions, containing the interaction time of atom-field in the cavities, adjusting the involved frequencies, as well as the atom-field coupling in the model, if a proper measurement is performed on the state of the first qubit (the related field in the cavity), the unknown states of the qubit (field) can be teleported from the first qubit (cavity field) to the second qubit (cavity field), appropriately. We show that in both considered cases, the teleportation protocol is successfully performed with the maximum possible fidelity, 1, and the acceptable success probability, 0.25.
Entangling remote qubits using the single-photon protocol: an in-depth theoretical and experimental study

Abstract The generation of entanglement between remote matter qubits has developed into a key capability for fundamental investigations as well as for emerging quantum technologies. In the single-photon, protocol entanglement is heralded by generation of qubit-photon entangled states and subsequent detection of a single photon behind a beam splitter. In this work we perform a detailed theoretical and experimental investigation of this protocol and its various sources of infidelity. We develop an extensive theoretical model and subsequently tailor it to our experimental setting, based on nitrogen-vacancy centers in diamond. Experimentally, we verify the model by generating remote states for varying phase and amplitudes of the initial qubit superposition states and varying optical phase difference of the photons arriving at the beam splitter. We show that a static frequency offset between the optical transitions of the qubits leads to an entangled state phase that depends on the photon detection time. We find that the implementation of a Charge-Resonance check on the nitrogen-vacancy center yields transform-limited linewidths. Moreover, we measure the probability of double optical excitation, a significant source of infidelity, as a function of the power of the excitation pulse. Finally, we find that imperfect optical excitation can lead to a detection-arm-dependent entangled state fidelity and rate. The conclusion presented here are not specific to the nitrogen-vacancy centers used to carry out the experiments, and are therefore readily applicable to other qubit platforms.
Deep learning based energy efficient optimal RMC-CNN model for secured data transmission and anomaly detection in industrial IOT

As we know that the generated data from the million sensors in Industrial Internet of Things (IIoT) is tremendously heterogeneous, large, & dynamic. This, in turn, poses a significant demand on the decision making and real-time analysis for detection of an anomaly in IIoT. In this paper, an effective scheme for energy efficient secured data transmission is presented for outlier detection in IIoT. The main intention of this approach is to offer a secure and privacy preserved data transmission scheme in industrial IoT. For this, industrial data like power, loop sensor, and the land sensor is taken as input, and the system network model is created which is then optimized with the use of Multi-scale grasshopper optimization scheme. Then the data is stored in the cloud and is transmitted in a secured manner. For this purpose, the attack detection in the network is made using Robust Multi-cascaded CNN (RMC-CNN) classification approach to distinguish the type of attack. Then the data is encrypted by key generation mechanism using a dynamic honey pot encryption algorithm. Thus, the encrypted information is transmitted securely and is stored in the IoT cloud, which can be decrypted as per user request. The performance analysis is made and is compared with existing techniques in term of accuracy, precision, recall, and F1-score for power, loop sensor and land sensor dataset. And also, the comparison is made for throughput, delay, and detection rate analysis. Finally, the encryption, decryption and execution time is estimated and are compared with existing techniques. The study shows that the proposed system achieves a better outcome for the proposed system than the existing methods.
Screen-monitored stitching deflectometry based on binocular stereo vision

The measurement accuracy of the current phase measuring deflectometry (PMD) methods for in-situ measurement of the surface shape of large-aperture optics, which are placed at different inclination angles, is affected by the varied pose and deformed shape of screen due to manufacturing, external force and temperature variation, etc. This paper proposes a novel PMD method, the screen-monitored stitching deflectometry (SMSD), by combining binocular stereo vision with stitching deflectometry. And a four-camera PMD measurement system is constructed accordingly. The coordinates of the point source are determined using binocular stereo vision technology; based on the acquired coordinates, the stereo-iterative reconstruction and stitching algorithm are used to achieve the surface shape measurement. Simulations and experiments are accomplished to validate the advantages of the proposed method in reducing measurement errors caused by the varied pose and deformed shape of screen.
Multi-view multi-label learning with high-order label correlation

Multi-label learning deals with a kind of problem that the given samples areassociated with multiple labels simultaneously. Recently, multi-label learning has become a populartopic in the literatures of machine learning and has attracted lots of researches. In this paper, we propose a new multi-view multi-label learning method by considering the label correlation, which is called ELSMML. Based on the high-order strategy, we construct a crafted label correlation matrix to describe the relationships among labels. We further utilize multi-view learning and dimension reduction to exploit the high-level latent semantic label information and the latent feature information, so as to build a classifier in the low dimensional space. In addition, we apply manifold regularization terms to make the data samples in the low dimensional space have the same intrinsic structure as the original data. After that, we put forward the accelerated proximal gradient method to optimize the ELSMML model and obtain thepredictive classifier. Besides, we conduct convergence analysis and computational complexity analysis for ELSMML method. In the experiments, the ELSMML method can achieve better performance on the evaluation metrics compared with other baselines.
A novel multi-label feature selection method with association rules and rough set

In multi-label learning, each instance is associated with a set of labels. To improve the accuracy and efficiency of multi-label learning tasks, label correlations have been explored. However, existing conventional algorithms obtain label correlation directly from the raw label space, ignoring the significance of the label to the instance. In this study, a novel feature selection method was proposed to select a more relevant and compact feature subset by considering the label distribution and inter-label correlations. First, the concept of label distribution was defined to reflect the significance of each label. Second, a new algorithm for mining association rules was designed to obtain the correlation between labels by improving the existing association rules algorithm. Thus, a new information system was designed by combining the label distribution and correlation between labels. Subsequently, a novel feature selection algorithm was designed in this information system which can effectively remove irrelevant and redundant features in the feature space. Finally, the experimental results demonstrated that the proposed algorithm effectively improves the classification performance and perform better than some state-of-the-art methods.
Fusion of blockchain and IoT in scientific publishing: Taxonomy, tools, and future directions

Scientific publishing systems (SPS) provides platforms to authors, reviewers, and editors to express research for the betterment of the community. Traditionally, the research databases are assigned electronic identifiers, and manuscripts are preserved in electronic form. Owing to the large scale of submissions in the databases, it becomes difficult for the repositories to manage their electronic volumes. The search queries and retrievals are complex, and the publishing process takes a lot of time, which defeats the purpose of the contribution in many cases by the author. Moreover, the process is non-transparent, and is limited due to ineffective article tracking policies. With the advent of the Internet of Things (IoT), the libraries have transitioned towards smart objects that process academic repositories with low-powered computations. In the same way, meta-information passes through lightweight IoT protocols to distributed servers. Coupled with blockchain (BC), a secured and trusted publishing platform is assured in SPS, with transparency among all academic stakeholders. Traditional SPS platforms do not provide any rewarding method for peer review and do not support and store unsuccessful articles. Besides, published works are not verified thoroughly, and this can lead to misconduct in scientific publishing. Motivated by these facts, in this paper, we present a survey on the fusion of BC and IoT for SPS, which serves the dual purpose of low-powered computational tagging of manuscripts as smart objects, and that also supports rewarding and completing the verification of transactions by peers without involving a third party. A case study of a hyperledger driven IoT-enabled scientific publishing system (SPS) is proposed to address the limitations of the traditional SPS. Lastly, we present open issues and challenges concerning the current SPSs and the proposed BC-driven SPS.
Dimensionality reduction techniques in structural and earthquake engineering

Dimensionality reduction (DR) techniques are becoming increasingly important in engineering and scientific applications for feature extraction and data visualization purposes. This problem gets significantly more demanding when encountering class-imbalanced data sets with a disproportionate ratio of observations in each class. The main objective of this paper is to thoroughly evaluate the performance of various dimensionality reduction techniques for analyzing a cognate set of computational and experimental data in the field of structural and earthquake engineering with varying numbers of classes. Notably, a data set is generated using a computer simulation that characterizes risks posed by earthquakes to structures. Depending on the severity of damage in the simulations, data is classified into three, five, and eight classes. This research study also uses three existing multi-class imbalanced data sets that have been developed independently. For each problem, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) are used, to cover a wide range of linear/non-linear and supervised/unsupervised DR techniques. To overcome the class-imbalance problem, the efficacy of the Synthetic Minority Oversampling (SMOTE) technique considering various classifiers, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees (DT), Random Forest (RF), MLPClassifier (MLP), and AdaBoost are investigated. It is observed that combining SMOTE and LDA shows promising results for training classifiers on the reduced data. In light of the findings of this study, it is recommended to train DT classifiers after reducing the dimensionality of the input data. It is through this process that the scientific data is transformed into a two-dimensional space for visualizing decision surfaces, which is helpful for practitioners to understand the performance of classifiers.
Validating the integrity of Convolutional Neural Network predictions based on zero-knowledge proof

Machine Learning as a Service can provide outsourced deep learning model prediction services to clients that do not have computing resources. Meanwhile, the integrity of the prediction services cannot be guaranteed due to model theft attacks from the services provider aspect or fake services from client aspect. Therefore, when clients access such deep learning models, it is urgent to verify the integrity of the model prediction process to ensure trusted interaction. For such, we propose a deep learning integrity verification scheme for integrity verification of the model prediction process without leaking information about key or private parameters of the deep learning model. When conducting integrity verification, the zero-knowledge succinct non-interactive knowledge argument is strategically used to construct a zero-knowledge proof. Specifically, we take the basic structure of the common single-layer Convolutional Neural Networks in deep learning as an example, construct Rank-1 Constraint Satisfaction according to the computational logic of each hidden layer in the model prediction process, so then design the proof circuits. We conduct extensive experiments for different scale CNN model inputs to demonstrate the practicality and feasibility of proposed scheme.