Reviewerly/exHarmony huggingface dataset:
Investigating high school students’ perceptions and presences under VR learning environment

Recently, Virtual Reality (VR) has developed rapidly. Some studies propose that VR has a great deal of potential as an instructional media for learning, while other studies indicate that VR may be ineffective or even negatively influence learners’ learning experience. Few studies provide reference to educators or teachers based on empirical data. This study investigates 55 10th-grade learners’ perceptions and presences in a VR-based blended learning environment. A research framework based on social presence theory and the Community of Inquiry model (CoI) was adopted in order to study social presence, teaching presence, and cognitive presence of learners in a VR-based practical, blended learning environment. The research findings are as follows: (1) VR enhanced learners’ perception of the teaching equipment and the classroom, social presence, teaching presence. (2) VR-based blended learning can have a positive effect on the interest, attention, and interaction of learners. (3) For transferring well-structured knowledge, there is no significant difference between traditional instructional media and VR. But for transferring ill-structured knowledge, VR is more effective at promoting learning than traditional instructional media. In general, VR-based learning environments can positively affect students’ perceptions and presence.
Privacy-preserving model learning on a blockchain network-of-networks

Abstract Objective To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a “flattened” topology, while real-world research networks may consist of “network-of-networks” which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. Materials and Methods We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. Results HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. Discussion HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. Conclusion We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.
Daily Global Solar Radiation in China Estimated From High‐Density Meteorological Observations: A Random Forest Model Framework

Abstract Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient ( R ) of 0.95, root‐mean‐square error of 2.34 MJ/m 2 , and mean bias of −0.04 MJ/m 2 . The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.
Consensus-oriented cloud manufacturing based on blockchain technology: An exploratory study

In the era of cloud computing and Industry 4.0, significant research efforts on cloud manufacturing have been witnessed in recent years. Nevertheless, challenges, such as issues of trust, safety, payment, remain in this emerging area, which cause less confidence for industry to adopt cloud manufacturing. In this regard, the recent development of blockchain technology provides a potential viable solution thanks to its unique advantages in decentralization and security. As such, we propose a new framework of cloud manufacturing by integrating the blockchain technology. In essence, consensus-oriented mechanisms are employed to generate the operating standards for the blockchain cloud manufacturing model. Moreover, based on the open source Ethereum code, we construct a simulation case study for 3D printing services using the proposed framework. A consortium or federated blockchain is simulated which uses Proof-of-Authority (PoA) as the consensus algorithm of block generation. The simulation involves 939 job requests from 100 users, as well as 10 service providers. The k-nearest neighbors (KNN) algorithm is employed to recommend the service provider for each request. The results show that the provider's score of service evaluation tends to be stabilize, and 934 requests for service are successfully fulfilled by the appropriate providers while the remaining 5 requests fail to be serviced.
Boundaries Extend and Contract in Scene Memory Depending on Image Properties

<h2>Summary</h2> Boundary extension, a memory distortion in which observers consistently recall a scene with visual information beyond its boundaries, is widely accepted across the psychological sciences as a phenomenon revealing fundamental insight into memory representations [1–3], robust across paradigms [1, 4] and age groups [5–7]. This phenomenon has been taken to suggest that the mental representation of a scene consists of an intermingling of sensory information and a schema that extrapolates the views of a presented scene [8], and it has been used to provide evidence for the role of the neocortex [9] and hippocampus [10, 11] in the schematization of scenes during memory. However, the study of boundary extension has typically focused on object-oriented images that are not representative of our visuospatial world. Here, using a broad set of 1,000 images tested on 2,000 participants in a rapid recognition task, we discover "boundary contraction" as an equally robust phenomenon. Further, image composition largely drives whether extension or contraction is observed—although object-oriented images cause more boundary extension, scene-oriented images cause more boundary contraction. Finally, these effects also occur during drawing tasks, including a task with minimal memory load—when participants copy an image during viewing. Collectively, these results show that boundary extension is not a universal phenomenon and put into question the assumption that scene memory automatically combines visual information with additional context derived from internal schema. Instead, our memory for a scene may be largely driven by its visual composition, with a tendency to extend or contract the boundaries equally likely.
Modality Compensation Network: Cross-Modal Adaptation for Action Recognition

With the prevalence of RGB-D cameras, multi-modal video data have become more available for human action recognition. One main challenge for this task lies in how to effectively leverage their complementary information. In this work, we propose a Modality Compensation Network (MCN) to explore the relationships of different modalities, and boost the representations for human action recognition. We regard RGB/optical flow videos as source modalities, skeletons as auxiliary modality. Our goal is to extract more discriminative features from source modalities, with the help of auxiliary modality. Built on deep Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks, our model bridges data from source and auxiliary modalities by a modality adaptation block to achieve adaptive representation learning, that the network learns to compensate for the loss of skeletons at test time and even at training time. We explore multiple adaptation schemes to narrow the distance between source and auxiliary modal distributions from different levels, according to the alignment of source and auxiliary data in training. In addition, skeletons are only required in the training phase. Our model is able to improve the recognition performance with source data when testing. Experimental results reveal that MCN outperforms state-of-the-art approaches on four widely-used action recognition benchmarks.
Multiclass Oblique Random Forests With Dual-Incremental Learning Capacity

Oblique random forests (ObRFs) have attracted increasing attention recently. Their popularity is mainly driven by learning oblique hyperplanes instead of expensively searching for axis-aligned hyperplanes in the standard random forest. However, most existing methods are trained in an off-line mode, which assumes that the training data are given as a batch. Efficient dual-incremental learning (DIL) strategies for ObRF have rarely been explored when new inputs from the existing classes or unseen classes come. The goal of this article is to provide an ObRF with DIL capacity to perform classification on-the-fly. First, we propose a batch multiclass ObRF (ObRF-BM) algorithm by using a broad learning system and a multi-to-binary method to obtain an optimal oblique hyperplane in a higher dimensional space and then separate the samples into two supervised clusters at each node, which provides the basis for the following incremental learning strategy. Then, the DIL strategy for ObRF-BM, termed ObRF-DIL, is developed by analytically updating the parameters of all nodes on the classification route of the increment of input samples and the increment of input classes so that the ObRF-BM model can be effectively updated without laborious retraining from scratch. Experimental results using several public data sets demonstrate the superiority of the proposed approach in comparison with several state-of-the-art methods.
A Fast CP-ABE System for Cyber-Physical Security and Privacy in Mobile Healthcare Network

A healthcare network as a new context-aware cyber-physical system (CPS) includes embedded sensors, Internet of Things technology, and cloud computing. With the intellectualization of mobile terminal devices, mobile healthcare networks are automatically generated and can provide convenient healthcare services and real-time communication. However, two problems in the CPS need to resolved. A solution to the first problem is to mitigate the computational and storage cost of a mobile terminal. A solution to the second problem is to provide the cyber-physical privacy and security for healthcare data. Ciphertext-policy attribute-based encryption (CP-ABE) can provide data privacy and secure sharing in an open environment. In this article, a fast CP-ABE scheme is provided in order to satisfy the use of mobile terminal devices with low computational and storage power. In the proposed scheme, the work of a system can deliver expensive computational tasks to semitrusted third parties, ensuring a constant number of simple local computations. The scheme can also verify the correctness of the decryption result by using a Boneh–Lynn–Shacham short signature scheme. The CP-ABE scheme not only ensures the cyber-physical security of healthcare data, but also reduces the local load from the efficiency analysis and experimental results.
Challenges in End-to-End Neural Scientific Table Recognition

In recent years, end-to-end trained neural models have been applied successfully to various optical character recognition (OCR) tasks. However, the same level of success has not yet been achieved in end-to-end neural scientific table recognition, which involves multi-row/multi-column structures and math formulas in cells. In this paper, we take a step forward to full end-to-end scientific table recognition by constructing a large dataset consisting of 450K table images paired with corresponding LaTeX sources. We apply a popular attentional encoder-decoder model to this dataset and show the potential of end-to-end trained neural systems, as well as associated challenges.
Tag recommendation by text classification with attention-based capsule network

Tag recommendation has been attracting much attention with the growth of digital resources. The goal of a tag recommendation system is to provide a set of tags for a piece of text to ease the tagging process done manually by a user. These tags have been shown to enhance the capabilities of search engines for navigating, organizing and searching content. However, tag text manually is time-consuming and labor-intensive. In this paper, we introduce a tag recommendation by text classification. We explore the capsule network with dynamic routing for the tag recommendation task. The capsule network encodes the intrinsic spatial relationship between a part and a whole constituting viewpoint invariant knowledge that automatically generalizes to novel viewpoints. In addition, an attention mechanism is incorporated into the capsule network to distill important information from the input documents. We conduct extensive experiments on large publication datasets to evaluate the effectiveness of our model. The experimental results demonstrate that our model substantially outperforms the compared baseline methods and achieves the state-of-the-art results.
Neural Path Planning: Fixed Time, Near-Optimal Path Generation via Oracle Imitation

Fast and efficient path generation is critical for robots operating in complex environments. This motion planning problem is often performed in a robot's actuation or configuration space, where popular pathfinding methods such as A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , RRT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> , get exponentially more computationally expensive to execute as the dimensionality increases or the spaces become more cluttered and complex. On the other hand, if one were to save the entire set of paths connecting all pair of locations in the configuration space a priori, one would run out of memory very quickly. In this work, we introduce a novel way of producing fast and optimal motion plans for static environments by using a stepping neural network approach, called OracleNet. OracleNet uses Recurrent Neural Networks to determine end-to-end trajectories in an iterative manner that implicitly generates optimal motion plans with minimal loss in performance in a compact form. The algorithm is straightforward in implementation while consistently generating near-optimal paths in a single, iterative, end-to-end roll-out. In practice, OracleNet generally has fixed-time execution regardless of the configuration space complexity while outperforming popular pathfinding algorithms in complex environments and higher dimensions <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Text Line Segmentation in Historical Document Images Using an Adaptive U-Net Architecture

On most document image transcription, indexing and retrieval systems, text line segmentation remains one of the most important preliminary task. Hence, the research community working in document image analysis is particularly interested in providing reliable text line segmentation methods. Recently, an increasing interest in using deep learning-based methods has been noted for solving various sub-fields and tasks related to the issues surrounding document image analysis. Thanks to the computer hardware and software evolution, several methods based on using deep architectures continue to outperform the pattern recognition issues and particularly those related to historical document image analysis. Thus, in this paper we present a novel deep learning-based method for text line segmentation of historical documents. The proposed method is based on using an adaptive U-Net architecture. Qualitative and numerical experiments are given using a large number of historical document images collected from the Tunisian national archives and different recent benchmarking datasets provided in the context of ICDAR and ICFHR competitions. Moreover, the results achieved are compared with those obtained using the state-of-the-art methods.
Rethinking Semantic Segmentation for Table Structure Recognition in Documents

Based on the recent advancements in the domain of semantic segmentation, Fully-Convolutional Networks (FCN) have been successfully applied for the task of table structure recognition in the past. We analyze the efficacy of semantic segmentation networks for this purpose and simplify the problem by proposing prediction tiling based on the consistency assumption which holds for tabular structures. For an image of dimensions H × W, we predict a single column for the rows (ŷ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">row</sub> ϵ H) and a predict a single row for the columns (ŷ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">row</sub> ϵ W). We use a dual-headed architecture where initial feature maps (from the encoder-decoder model) are shared while the last two layers generate class specific (row/column) predictions. This allows us to generate predictions using a single model for both rows and columns simultaneously, where previous methods relied on two separate models for inference. With the proposed method, we were able to achieve state-of-the-art results on ICDAR-13 image-based table structure recognition dataset with an average F-Measure of 92.39% (91.90% and 92.88% F-Measure for rows and columns respectively). With the proposed method, we were able to achieve state-of-the-art results on ICDAR-13. The obtained results advocate that constraining the problem space in the case of FCN by imposing valid constraints can lead to significant performance gains.
Trivially and Efficiently Composing Masked Gadgets With Probe Isolating Non-Interference

We revisit the analysis and design of masked cryptographic implementations to prevent side-channel attacks. Our starting point is the (known) observation that proving the security of a higher-order masked block cipher exhaustively requires unrealistic computing power. As a result, a natural strategy is to split algorithms in smaller parts (or gadgets), with as main objectives to enable both simple composition (as initiated by Barthe et al. at CCS 2016) and efficient implementations. We argue that existing composition strategies allow either trivial composition with significant overheads or optimized composition with more analysis efforts. As a result, we first introduce a new definition of Probe Isolating Non-Interference (PINI) that allows both trivial composition and efficient implementations. We next prove general composition theorems for PINI gadgets that considerably simplify the analysis of complex masked implementations. We finally design efficient multiplication gadgets that satisfy this definition. As additional results, we exhibit a limitation of existing compositional strategies for the analysis of Multiple-Inputs / Multiple-Outputs (MIMO) gadgets, extend Barthe et al. definition of Strong Non-Interference (SNI) to deal with this context, and describe an optimization method to design efficient MIMO-SNI (sub)circuits. Our results allow proving the security of a recent masked AES implementation by Goudarzi and Rivain (EUROCRYPT 2017). From the implementation viewpoint, PINI implementations reach the level of performance of the best composable masking schemes for the AES Rijndael, and outperform them by significant factors for lightweight ciphers.
Clustering method for production of Z-number based if-then rules

Application of clustering algorithms to extract or summarize data from large data sets is a straightforward and effective approach. Sometimes data sets are not only large in size but also include imprecise and partially reliable pieces of information of probabilistic and possibilistic (fuzzy) nature. Information of this kind is referred to as bimodal information. There is a need to summarize such data into a compact set of human tractable "If-Then" rules, being a result of synergy of imprecision of both types. In this paper, we first present an approach to clustering with the purpose of extraction of probabilistic and fuzzy information (bimodal) categories suitable for formation of human-tractable rules. The suggested clustering algorithm utilizes the FCM objective function and an evolutionary algorithm to produce fuzzy sets of fuzzy clusters. To form Z-clusters and use them as components for the rules we exploit the relationship existing between Type-2 Fuzzy and Z-number concepts. A benchmark problem and a real-world application are considered to demonstrate the usefulness of the proposed approach.
‘Quantizing international relations’: The case for quantum approaches to international theory and security practice

This special issue is conceived out of the proposition that recent developments in quantum theory as well as innovations in quantum technology have profound implications for international relations, especially in the field of international security. Interaction with quantum theory outside the circle of physics has been limited; our goal is to catalyse an informed debate on the virtues of quantum theory for international relations. As new scientific discoveries and technological applications suggest large-scale quantum phenomena, near-simultaneous interconnectivity creates global entanglements, and ubiquitous media produce profound observer-effects, we wish to make of quantum theory a human science. With the arrival of quantum computing, communications and artificial intelligence, we can also expect to see significant transformations in the nature, production and distribution of power and knowledge. This special issue introduces quantum approaches that can help us better understand, anticipate and perhaps even ameliorate the most pressing global issues facing us today and in the near future.