Reviewerly/exHarmony huggingface dataset:
Comparing 360° Virtual Reality Learning Configurations for Construction Education

Abstract Virtual reality is rapidly becoming an effective tool for training in construction. Hazardous environments can be simulated and used for training purposes without endangering students. Furthermore, these virtual environments can be saved and re-used, on-demand, allowing students to observe temporal changes to an environment that would normally require multiple visits to a real project site to achieve. While the virtual construction site visit should not be considered a replacement for the real thing, some site visits get passed over because they are costly and logistically difficult to coordinate. Having options would be desirable. Virtual reality construction site visits using 360° photographs are a novel approach toward substituting for the real thing – yet they can be ineffective. Therefore, considering this media as a replacement for the real experience is not advisable – without some additional upgrades. This research documents a between-groups experiment that compared student’s self-reported learning performance when using 360° photographs that were annotated versus 360° photographs that were not annotated. The annotations included pop-ups, visual cues, audio clips, video clips, and quizzes to direct the learning when viewing the 360° photographs. Results indicated increased performance with the students that used the annotated 360° photographs. This study anticipated a difference in performance between the two groups and therefore used eye tracking technology with the students that used the non-annotated 360° photographs to obtain a better understanding of why their achievement differed.
Sentiment Analysis of Customer for Ecommerce by Applying AI

Conventional and manual systems are being upgraded to autonomous systems using AI techniques for better decision making and improved efficiency. Artificial Intelligence is getting more evolved and have numerous numbers of applications in the field of Health care, IOT, Security, Smart Systems, Decision makings, Intelligent computing, Digital marketing, User experience etc. In the era of digital marketing organizations face various issues regarding promotion, marketing and getting feedback from customers concerning to their products and its difficult for them to retain customer feedback in order to improve their products. In terms of estimating how much a client want to purchase this product, the offered approach gives a solution for prediction of consumer insights on the particular product. The methodology consists of two main models, the first consists of a customized Recurrent Neural Network (RNN) for user-specific perception analysis on social media and the second consists of a machine study model where classification algorithms, such as Random Forest, Na&#x00EF;ve Bayes and BayesNet have been utilized to predict customer willingness to purchase a particular product. Furthermore, the model does not only conduct a customer sentiment analysis in relation to the product&#x2019;s social media marketing in the online industry, but it is also capable of predicting if a consumer would buy it or not. The RNN model has a training accuracy of 97.15&#x0025; and validation precision of 94.25&#x0025; whereas the Random Forest method offers a training accuracy of 97&#x0025; and 94.97&#x0025; respectively.
TUNING OF TWO-DEGREE-OF-FREEDOM IMC BASED ON A STRANGE NONCHAOTIC OPTIMIZATION APPROACH FOR LARGE TIME-DELAY PROCESSES

The internal model control (IMC) has a good control effect for large time-delay systems, but the parameter tuning of the controller is a challenge for researchers. As a typical fractal (strange) attractor, a strange nonchaotic attractor has the dynamic characteristics of randomness and ergodicity. Based on these characteristics, an optimization method is proposed to tune the parameters of an internal model controller for a large time-delay process, which is named a strange nonchaotic optimization algorithm (SNOA). SNOA has the advantages of easy implementation, short execution time and having robust mechanisms to avoid local optimization. It can find the optimal parameters of the internal model controller in large time-delay systems. The results show that SNOA can effectively improve the dynamic stability of the large time-delay systems. It has also proved to be superior to chaotic optimization (CO) algorithm and particle swarm optimization (PSO) algorithm.
Diffractive deep neural network adjoint assist or (DNA)<sup>2</sup>: a fast and efficient nonlinear diffractive neural network implementation

The recent advent of diffractive deep neural networks or D2NNs has opened new avenues for the design and optimization of multi-functional optical materials; despite the effectiveness of the D2NN approach, there is a need for making these networks as well as the design algorithms more general and computationally efficient. The work demonstrated in this paper brings significant improvements to both these areas by introducing an algorithm that performs inverse design on fully nonlinear diffractive deep neural network - assisted by an adjoint sensitivity analysis which we term (DNA)2. As implied by the name, the procedure optimizes the parameters associated with the diffractive elements including both linear and nonlinear amplitude and phase contributions as well as the spacing between planes via adjoint sensitivity analysis. The computation of all gradients can be obtained in a single GPU compatible step. We demonstrate the capability of this approach by designing several types of three layered D2NN to classify 8800 handwritten digits taken from the MNIST database. In all cases, the D2NN was able to achieve a minimum 94.64% classification accuracy with 192 minutes or less of training.
Decoherence-Induced Sudden Death of Entanglement and Bell Nonlocality

Decoherence due to the unwanted interaction between a quantum system and environment leads to the degradation of quantum coherence. In particular, for an entangled state, decoherence makes a loss of entanglement and Bell nonlocality known as entanglement sudden death (ESD), and Bell nonlocality sudden death (BNSD). Here, we theoretically investigate the entanglement and Bell nonlocality of a bipartite entangled state under three types of decoherence, amplitude damping, phase damping, and depolarizing. Our result provides the bound of decoherence strength that does not lose the entanglement and Bell nonlocality. In addition, we find two interesting features. One is that the entanglement can survive even though one of the entangled qubits is affected by a large strength of decoherence if the other qubit is affected by a small enough strength of decoherence except for the depolarizing. The second one is that when a specific form of entangled state is under amplitude damping, the Bell nonlocality shows an asymmetric behavior respect to the decoherence strengths on each qubit. Our work provides comprehensive information on ESD and BNSD for the bipartite entangled state which will be useful to implement quantum information processing in the presence of decoherence.
Teaching Intuitionistic and Classical Propositional Logic Using Isabelle

We describe a natural deduction formalization of intuitionistic and classical propositional logic in the Isabelle/Pure framework. In contrast to earlier work, where we explored the pedagogical benefits of using a deep embedding approach to logical modelling, here we employ a logical framework modelling. This gives rise to simple and natural teaching examples and we report on the role it played in teaching our Automated Reasoning course in 2020 and 2021.
Do Developers Really Know How to Use Git Commands? A Large-scale Study Using Stack Overflow

Git, a cross-platform and open source distributed version control tool, provides strong support for non-linear development and is capable of handling everything from small to large projects with speed and efficiency. It has become an indispensable tool for millions of software developers and is the de facto standard of version control in software development nowadays. However, despite its widespread use, developers still frequently face difficulties when using various Git commands to manage projects and collaborate. To better help developers use Git, it is necessary to understand the issues and difficulties that they may encounter when using Git. Unfortunately, this problem has not yet been comprehensively studied. To fill this knowledge gap, in this article, we conduct a large-scale study on Stack Overflow, a popular Q&amp;A forum for developers. We extracted and analyzed 80,370 relevant questions from Stack Overflow, and reported the increasing popularity of the Git command questions. By analyzing the questions, we identified the Git commands that are frequently asked and those that are associated with difficult questions on Stack Overflow to help understand the difficulties developers may encounter when using Git commands. In addition, we conducted a survey to understand how developers learn Git commands in practice, showing that self-learning is the primary learning approach. These findings provide a range of actionable implications for researchers, educators, and developers.
Authentication of LINE Chat History Files by Information Hiding

With the prevalence of smartphones, message exchanges via mobile chatting programs like LINE have become popular. The messages in the form of chat records in a LINE chat history, after being downloaded for legal uses, might be tampered with illicitly. A novel method for authenticating the chat history against such attacks is proposed. The signal used for authenticating each chat-record segment is created by concatenating the ID label of the segment and a digest yielded by hashing the segment content. The signal is then encoded by three types of spacing code, namely half space, full space, and tab, and embedded into the blank-space areas created by the tab codes in the chat records of the segment. Authentication of a history file is accomplished by extracting the authentication signals embedded in the file and comparing them with the original signals computed directly from the file. The embedded signals are invisible, arousing no suspicion from the hacker. The signals are fragile, because any modification of the records can be detected by the authentication process. Experiments for testing four types of tampering with text files of four languages have been conducted, yielding correct authentication results that show the feasibility of the proposed method.
Patterns for a Hybrid Campus

This chapter discusses good practices to create more hybrid learning opportunities on campus by designing and connecting available spaces appropriately. First, we explore the frameworks of hybrid pedagogy, blended spaces, and seamless learning to identify key dimensions for hybrid spaces. We consider how to dissolve dichotomies within spaces. We also discuss the interplay of different spaces, such as the physical, digital, informational, conceptual and social space. Second, we explore how existing spaces can be transformed into hybrid spaces. For this, we use the identified dimensions as design variables for different scenarios and space planning. We present a case study that shows how the use of spatial dimensions can help to plan and implement hybrid spaces on university campuses for design thinking. Third, we distill the lessons learnt and generalize about existing solutions. We describe the results as a design pattern that balances pedagogical, technological, and organizational forces.
Cross-lingual Named Entity Recognition via FastAlign: a Case Study

Named Entity Recognition is an essential task in natural language processing to detect entities and classify them into predetermined categories.An entity is a meaningful word, or phrase that refers to proper nouns.Named Entities play an important role in different NLP tasks such as Information Extraction, Question Answering and Machine Translation.In Machine Translation, named entities often cause translation failures regardless of local context, affecting the output quality of translation.Annotating named entities is a time-consuming and expensive process especially for low-resource languages.One solution for this problem is to use word alignment methods in bilingual parallel corpora in which just one side has been annotated.The goal is to extract named entities in the target language by using the annotated corpus of the source language.In this paper, we compare the performance of two alignment methods, Grow-diag-final-and and Intersect Symmetrisation heuristics, to exploit the annotation projection of English-Brazilian Portuguese bilingual corpus to detect named entities in Brazilian Portuguese.A NER model that is trained on annotated data extracted from the alignment methods, is used to evaluate the performance of aligners.Experimental results show the Intersect Symmetrisation is able to achieve superior performance scores compared to the Grow-diag-final-and heuristic in Brazilian Portuguese.
Private Liquidity Matching Using MPC

Many central banks, as well as blockchain systems, are looking into distributed versions of interbank payment systems, in particular the netting procedure. When executed in a distributed manner this presents a number of privacy problems. This paper studies a privacy-preserving netting protocol to solve the gridlock resolution problem in such Real Time Gross Settlement systems. Our solution utilizes Multi-party Computation and is implemented in the SCALE MAMBA system, using Shamir secret sharing scheme over three parties in an actively secure manner. Our experiments show that, even for large throughput systems, such a privacy-preserving operation is often feasible.
An improved approach based on dynamic mixed sampling and transfer learning for topic recognition: a case study on online patient reviews

Purpose This study aimed to adapt existing text-mining techniques and propose a novel topic recognition approach for textual patient reviews. Design/methodology/approach The authors first transformed multilabel samples for adapting model training forms. Then, an improved method was proposed based on dynamic mixed sampling and transfer learning to improve the learning problem caused by imbalanced samples. Specifically, the training of our model was based on the framework of a convolutional neural network and self-trained Word2Vector on large-scale corpora. Findings Compared with the SVM and other CNN-based models, the CNN+ DMS + TL model proposed in this study has made significant improvement in F1 score. Originality/value The improved methods based on dynamic mixed sampling and transfer learning can adequately manage the learning problem caused by the skewed distribution of samples and achieve the effective and automatic topic recognition of textual patient reviews. Peer review The peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2021-0059 .
Delegated Proof of Secret Sharing: A Privacy-Preserving Consensus Protocol Based on Secure Multiparty Computation for IoT Environment

With the rapid advancement and wide application of blockchain technology, blockchain consensus protocols, which are the core part of blockchain systems, along with the privacy issues, have drawn much attention from researchers. A key aspect of privacy in the blockchain is the sensitive content of transactions in the permissionless blockchain. Meanwhile, some blockchain applications, such as cryptocurrencies, are based on low-efficiency and high-cost consensus protocols, which may not be practical and feasible for other blockchain applications. In this paper, we propose an efficient and privacy-preserving consensus protocol, called Delegated Proof of Secret Sharing (DPoSS), which is inspired by secure multiparty computation. Specifically, DPoSS first uses polynomial interpolation to select a dealer group from many nodes to maintain the consensus of the blockchain system, in which the dealers in the dealer group take turns to pack the new block. In addition, since the content of transactions is sensitive, our proposed design utilizes verifiable secret sharing to protect the privacy of transmission and defend against the malicious attacks. Extensive experiments show that the proposed consensus protocol achieves fairness during the process of reaching consensus.
A lightweight segmentation method based on residual U-Net for MR images

Automatic segmentation of Magnetic Resonance Imaging (MRI), which bases on Residual U-Net (ResU-Net), helps radiologists to quickly assess the condition. However, the ResU-Net structure requires a large number of parameters and storage model space. It is not convenient to apply to mobile MRI device. To solve this problem, Depthwise Separable Convolution and Squeeze-and-Excitation Residual U-Networks (DSRU-Net) is proposed to segment MRI. Squeeze-and-Excitation method is a channel attention mechanism. The proposed method is conducive to simplify ResU-Net model, making ResU-Net more convenient to be applied to mobile MRI device. The fuzzy comprehensive evaluation method, which includes three evaluation factors are that the required parameters of the model, the value of Dice Similarity Coefficient (DSC), and the value of Hausdorff Distance (HD), is used to evaluate the test results of the proposed method on the MICCAI 2012 Prostate MR Image Segmentation (PROMISE12) challenge dataset and Automatic Cardiac Diagnosis Challenge (ACDC) dataset. The fuzzy comprehensive evaluation values obtained by the proposed method in 5 PROMISE12 samples and 15 ACDC samples are 0.9889 and 0.9652, respectively. Combining the average results of the two datasets, the proposed method has the best effect in balancing the accuracy of segmentation and the amount of model parameters.
A blockchain-based and privacy-preserved authentication scheme for inter-constellation collaboration in Space-Ground Integrated Networks

Space-Ground Integrated Networks (SGINs) make it convenient for users to access services anywhere. In the space segment, satellite constellations can cooperate to provide better space services. However, due to the lack of trust among different constellations, communication security and privacy protection are important issues that need to be solved during inter-constellation cooperation. In this work, with recent advances of blockchain, we present a trusted and privacy-preserved authentication scheme for inter-constellation collaboration. To preserve privacy, we design permanent and temporary identities for each satellite. The permanent one is used for inner-constellation communication, and the temporary identity is for inter-constellation cooperation, respectively. Meanwhile, satellites generate temporary identities for themselves to avoid privacy leakage. A consortium blockchain is introduced for sharing information among cooperative satellite constellations. Furthermore, to achieve efficient authentication under limited resources, a replica storage node method is proposed, where well-resourced satellites cache the replicated information shared through the blockchain. The replica storage node selection is formulated as an integer programming problem and solved with a branch and bound algorithm. Security analysis, including formal analysis using BAN Logic and informal verification, shows that the proposed authentication scheme is secure against various potential attacks. A comparative analysis among the proposed scheme and other blockchain-based schemes shows that the proposed scheme achieves efficiency in signaling, computation, and communication overheads with more functionality attributes. In addition, evaluations also demonstrate that the proposed on-board caching scheme achieves low communication latency and storage cost.
Multimedia Forensics Before the Deep Learning Era

Abstract Image manipulation is as old as photography itself, and powerful media editing tools have been around for a long time. Using such conventional signal processing methods, it is possible to modify images and videos obtaining very realistic results. This chapter is devoted to describe the most effective strategies to detect the widespread manipulations that rely on traditional approaches and do not require a deep learning strategy. In particular, we will focus on manipulations like adding, replicating, or removing objects and present the major lines of research in multimedia forensics before the deep learning era and the rise of deepfakes. The most popular approaches look for artifacts related to the in-camera processing chain (camera-based clues) or the out-camera processing history (editing-based clues). We will focus on methods that rely on the extraction of a camera fingerprint and need some prior information on pristine data, for example, through a collection of images taken from the camera of interest. Then we will shift to blind methods that do not require any prior knowledge and reveal inconsistencies with respect to some well-defined hypotheses. We will also briefly review the most interesting features of machine learning- based methods and finally present the major challenges in this area.
Physical publicly verifiable randomness from pulsars

We demonstrate how radio pulsars can be used as random number generators. Specifically, we focus on publicly verifiable randomness (PVR), in which the same sequence of trusted and verifiable random numbers is obtained by multiple parties. PVR is a critical building block for many processes and algorithms (including cryptography, scientific trials, electoral audits and international treaties). However, current approaches (based on number theory) may soon become vulnerable to quantum computers, motivating a growing demand for PVR based on natural physical phenomena. In this context, we explore pulsars as a potential physical PVR source. We first show that bit sequences extracted from the measured flux densities of a bright millisecond pulsar can pass standardised tests for randomness. We then quantify three illustrative methods of bit-extraction from pulsar flux density sequences, using simultaneous observations of a second pulsar carried out with the Parkes telescope in Australia and the Five-hundred-metre Aperture Spherical radio Telescope (FAST) in China, supported by numerical simulations. We demonstrate that the same bit sequence can indeed be obtained at both observatories, but the ubiquitous presence of radiometer noise needs to be accounted for when determining the expected bit error rate between two independent sequences. We discuss our results in the context of an imaginary use-case in which two mutually distrusting parties wish to obtain the same random bit sequence, exploring potential methods to mitigate against a malicious participant.