Reviewerly/exHarmony huggingface dataset:
Review of the use of human senses and capabilities in cryptography

Cryptography is a key element in establishing trust and enabling services in the digital world. Currently, cryptography is realized with mathematical operations and represented in ways that are not readily accessible to human users. Thus, humans are left out of the loop when establishing trust and security in the digital world. In many areas the interaction between users and machines is being made more and more seamless and user-friendly, but cryptography has not really enjoyed such development. In this paper, we review the previous research on utilizing human senses and capabilities in cryptography. We present the most relevant existing methods and summarize the current state of the art. In addition, we propose several topics and problems that need to be solved in order to build cryptography that is more accessible to humans. These range from practical implementations of existing methods and utilizing a wider range of human senses all the way to building the theoretical foundations for this new form of cryptography.
New Media and Space: An Empirical Study of Learning and Enjoyment Through Museum Hybrid Space

A museum hybrid space combines physical artifacts co-located with virtual and augmented reality displays. Although the technology exists to provide museums with hybrid space, there are no empirical studies on effectiveness of the museum hybrid space in terms of learning and enjoyment. This article takes an experimental approach and measures the enjoyment and learning (dependent variables) of participants in response to selected environments (independent variables) including a traditional environment (based on photos and labels), a video-enhanced environment (based on projected video clips), and a VR-enhanced environment (based on video game). The main outcome of this article is demonstrating that the use of VR technology and the resulting hybrid space (i.e., VR-enhanced environment) results in novel museum experiences that provide greater impacts on audience in terms of learning and enjoyment.
Stance Analysis of Twitter Users: The Case of the Vaccination Topic in Italy

People’s opinion around social and political issues is currently witnessed by messages ordinarily posted on social media. With reference to a specific case study, namely the vaccination topic in Italy, this article discusses a crucial aspect in structuring the data processing pipeline in intelligent systems aimed at monitoring the public opinion through Twitter messages: A plain analysis of tweet contents is not sufficient to grasp the diversity of behavior across users. To get a sharper picture of the public opinion as expressed on social media, user-related information must be incorporated in the analysis. Relying on a dataset of tweets about vaccination and on an established text classification system, we present the results of a stance monitoring campaign with advanced analysis on temporal and spatial scales. The overall methodological workflow provides a sound solution for public opinion assessment from Twitter data.
A status report on a section-based stratigraphic and palaeontological database – the Geobiodiversity Database

Abstract. Big data are significant for quantitative analysis and contribute to data-driven scientific research and discoveries. Here a brief introduction is given to the Geobiodiversity Database (GBDB), a comprehensive stratigraphic and palaeontological database, and its data. The GBDB includes abundant geological records from China and has supported a series of scientific studies on the Paleozoic palaeogeography and tectonic and biodiversity evolution of China. The data that the GBDB has including those that are newly collected are described in detail; the statistical results and structure of the data are given. A comparison between the GBDB; the largest palaeobiological database, the Paleobiology Database (PBDB); and the geological rock database Macrostrat is drawn. The GBDB and other databases are complementary in palaeontological and stratigraphic research. The GBDB will continually provide users access to detailed palaeontological and stratigraphic data based on publications. Non-structured data of palaeontology and stratigraphy will also be included in the GBDB, and they will be organically correlated with the existing data of the GBDB, making the GBDB more widely used for both researchers and anyone who is interested in fossils and strata. The GBDB fossil and stratum dataset (Xu, 2020) is freely downloadable from https://doi.org/10.5281/zenodo.4245604.
Earthquake Early Detection as an IEEE1451 Transducer Network Trigger for Urban Infrastructure Monitoring and Protection

The occurrence of an earthquake has immediate consequences, such as the destruction of buildings. However, the impact can be much more severe and extend over time. The arrival of an earthquake's destructive wave can subsequently: produce a rupture in water distribution pipes, leading to water leakage, when water is even more valuable; break gas pipelines that will continue to lose gas and cause explosions leading to fires; cause the electrical power grid to come into contact with conductive structures, putting humans at risk of electrocution; or cause electrical discharges, which, in the presence of flammable materials, can give rise to fires. On the other hand, the storage of information collected by sensors that monitor the forces and deformations to which the building structure is subjected during the earthquake, allows engineers to know its state of health, right after the occurrence of the earthquake, making it possible to draw conclusions about the safety level of the building, not only for the immediate rescue, but also to execute the recovery and stabilization work [1], [2].
Efficient compression algorithm using learning networks for remote sensing images

Remote sensing image compression plays a vital role in the high-resolution imaging of an on-orbit optical camera. The post-transform-based compression method is of particular importance for remote sensing on-orbit images because it can remove remaining redundancies among high-amplitude coefficients in the wavelet transform, specifically in high-frequency areas. However, current post-transforms are inefficient because the post-transform has to access a large-scale wavelet domain. In this paper, we propose a low-dimensional visual representation convolution neural network (LVR-CNN) for efficient post-transform-based image compression. The LVR-CNN is used to transform the wavelet domain from a large-scale representation to a new wavelet version with a small-scale. We obtain the optimized small-scale wavelet representation by minimization between the original and reconstructed wavelet representations through LVR-CNN. The multi-basis dictionary post-transform is applied to the optimized wavelet representation to achieve high compression performance and calculation efficiency. We experimentally confirm the proposed method and results with test remote sensing images. The experimental results indicate that the LVR-CNN post-transform-based compression method yields high compression performance and low post-transform resource utilization. Compared with conventional methods, the proposed method can increase the peak-signal-noise-ratio (PSNR) by 1.2 dB∼2.7 dB. These merits indicate the proposed compression method is efficient for remote sensing images.
Deep-Learning for Hand-Gesture Recognition with Simultaneous Thermal and Radar Sensors

In this paper, we present a framework for integrating two different types of sensors for hand-gesture recognition using deep-learning. The two sensors utilize completely different approaches for detecting the signal, namely; an ultra-wideband (UWB) impulse radar sensor and a thermal sensor. For robust gesture classification two parallel paths are utilized, each employs a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network on both the radar signal and the thermal signal. The classification results from the two paths are then fused to improve the overall detection probability. The two sensors compliment the capability of each other; while the UWB radar is accurate for radial movement and less accurate for lateral movement, the thermal sensor is vice-versa. Thus, we find that combining both sensors produces near perfect classification accuracy of 99 % for 14 different hand-gestures.
Automated annotation of learner English

Abstract This paper explores the use of natural language processing (NLP) tools and their utility for learner language analyses through a comparison of automatic linguistic annotation against a gold standard produced by humans. While there are a number of automated annotation tools for English currently available, little research is available on the accuracy of these tools when annotating learner data. We compare the performance of three linguistic annotation tools (a tagger and two parsers) on academic writing in English produced by learners (both L1 and L2 English speakers). We focus on lexico-grammatical patterns, including both phrasal and clausal features, since these are frequently investigated in applied linguistics studies. Our results report both precision and recall of annotation output for argumentative texts in English across four L1s: Arabic, Chinese, English, and Korean. We close with a discussion of the benefits and drawbacks of using automatic tools to annotate learner language.
FAN: Feature Adaptation Network for Surveillance Face Recognition and Normalization

This paper studies face recognition (FR) and normalization in surveillance imagery. Surveillance FR is a challenging problem that has great values in law enforcement. Despite recent progress in conventional FR, less effort has been devoted to surveillance FR. To bridge this gap, we propose a Feature Adaptation Network (FAN) to jointly perform surveillance FR and normalization. Our face normalization mainly acts on the aspect of image resolution, closely related to face super-resolution. However, previous face super-resolution methods require paired training data with pixel-to-pixel correspondence, which is typically unavailable between real-world low-resolution and high-resolution faces. FAN can leverage both paired and unpaired data as we disentangle the features into identity and non-identity components and adapt the distribution of the identity features, which breaks the limit of current face super-resolution methods. We further propose a random scale augmentation scheme to learn resolution robust identity features, with advantages over previous fixed scale augmentation. Extensive experiments on LFW, WIDER FACE, QUML-SurvFace and SCface datasets have shown the effectiveness of our method on surveillance FR and normalization.
Strict positivity and <i>D</i>-majorization

Motivated by quantum thermodynamics we first investigate the notion of strict positivity, that is, linear maps which map positive definite states to something positive definite again. We show that strict positivity is decided by the action on any full-rank state, and that the image of non-strictly positive maps lives inside a lower-dimensional subalgebra. This implies that the distance of such maps to the identity channel is lower bounded by one. The notion of strict positivity comes in handy when generalizing the majorization ordering on real vectors with respect to a positive vector $d$ to majorization on square matrices with respect to a positive definite matrix $D$. For the two-dimensional case we give a characterization of this ordering via finitely many trace norm inequalities and, moreover, investigate some of its order properties. In particular it admits a unique minimal and a maximal element. The latter is unique as well if and only if minimal eigenvalue of $D$ has multiplicity one.
Group Role Assignment with Busyness Degree and Cooperation and Conflict Factors

In collaboration, one of the most important and most challenging problems is role assignment. This paper presents a unique approach to a "group role assignment" (GRA) problem that considers two previously established constraints within the same GRA problem. They are the busyness degree of different agents, and cooperation and conflict factors between agents. The aim of solving a "Group Role Assignment with Busyness degree and Cooperation and Conflict Factors" (GRABCF) problem is to assign agents to specific tasks in order to obtain the most efficient team possible. This paper's contributions include a formalization of the GRABCF problem, a simplified matrix to express cooperation and conflict, a practical solution for this problem, and simulations to prove the benefits of solving the GRABCF problem.
Software fault prediction using Whale algorithm with genetics algorithm

Abstract Software fault prediction became an essential research area in the last few years, there are many prediction and optimization techniques that have been developed for fault prediction. In this paper, an approach is developed by integrating genetics algorithm with support vector machine (SVM) classifier and Whale optimization algorithm for software fault prediction. The developed approach is applied to 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset, and Java open source projects are considered as a small‐scale dataset. Results indicate that integrating Genetics algorithm with SVM and Whale algorithm improves the performance of the software fault prediction process when it is applied to large‐scale and small‐scale datasets and overcome the limitations that appeared in the previous studies.
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Large-scale knowledge graphs containing millions of entities are very common nowadays. Querying knowledge graphs is essential for a wide range of emerging applications, e.g., question answering and semantic search. A star query aims to identify an entity by giving a set of related entities, which is an important query type on knowledge graphs. Answering star queries can be modeled as a graph query problem. Given a query graph Q, the graph query finds subgraphs in a knowledge graph G that match Q. We face two challenges on graph query: (1) existing graph query methods usually find subgraphs that are structurally similar to Q, which cannot measure whether a subgraph match satisfies the semantics of Q (i.e., real query intention), leading to an effectiveness issue, and (2) querying a large-scale knowledge graph is usually time-consuming because of the large search space. In this paper, we propose a Top-k semantic-aware graph query method over knowledge graphs for star queries, which provides semantically similar matches for Q instead of structurally similar matches. The semantic similarity of a match to Q is measured by an online computed bounding match score. By using bounds, we can efficiently prune the unpromising matches with lower semantic similarities without evaluating all matches. Extensive experiments over three real-world knowledge graphs confirm the effectiveness and efficiency of our solution.
EACOFT: An energy-aware correlation filter for visual tracking

Correlation filter based trackers attribute to its calculation in the frequency domain can efficiently locate targets in a relatively fast speed. This characteristic however also limits its generalization in some specific scenarios. The reasons that they still fail to achieve superior performance to state-of-the-art (SOTA) trackers are possibly due to two main aspects. The first is that while tracking the objects whose energy is lower than the background, the tracker may occur drift or even lose the target. The second is that the biased samples may be inevitably selected for model training, which can easily lead to inaccurate tracking. To tackle these shortcomings, a novel energy-aware correlation filter (EACOFT) based tracking method is proposed, in our approach the energy between the foreground and the background is adaptively balanced, which enables the target of interest always having a higher energy than its background. The samples’ qualities are also evaluated in real time, which ensures that the samples used for template training are always helpful with tracking. In addition, we also propose an optimal bottom-up and top-down combined strategy for template training, which plays an important role in improving both the effectiveness and robustness of tracking. As a result, our approach achieves a great improvement on the basis of the baseline tracker, especially under the background clutter and fast motion challenges. Extensive experiments over multiple tracking benchmarks demonstrate the superior performance of our proposed methodology in comparison to a number of the SOTA trackers.
Two graph-regularized fuzzy subspace clustering methods

This paper presents a new fuzzy subspace clustering (FSC) method which finds some subspaces as clusters such that each point belongs to the nearest subspace with a certain weight or probability. Then we propose two graph-regularized versions for it, in which two points are more likely to be assigned to the same cluster if they are close spatially or with the same labels. In the proposed two graph regularizations, one encodes the weight (or probability) of a point assigned to a cluster and the other encodes the projection coefficients of a point on a subspace. We develop iterative solutions for these methods through constructing a surrogate, which monotonically decrease the cost function with a simple structure. The experimental results, using both synthetic and real-world databases, demonstrate the effectiveness and flexibility of the proposed methods.
A Low-Cost, IMU-Based Real-Time On Device Gesture Recognition Glove

This paper evaluates the possibility of performing fine gesture recognition including finger movements on a low-tech device. In particular, we present a solution with a recognition model that is small enough to fit in the memory of a low- tech device and describe related difficulties associated with this approach. Several different Machine Learning techniques are employed and their individual advantages and drawbacks are explored for the task at hand. Our results indicate an average of 95% accuracy during real-time testing for an eight class decoding task with a custom Recurrent Neural Network approach, that runs on the low-tech device, namely an Arduino Nano 33 BLE. The novelty and strength of this research lies in the fact that we are able to recognize fine hand gestures including finger movements rather than recognizing only coarse hand gestures. The recognition process is conducted on the low-tech device and as a result this solution has all advantages that are typically associated with embedded systems, namely cost-efficiency, battery life efficiency, and a high degree of independence from other devices as well as compatibility with them.
Missing data reconstruction in VHR images based on progressive structure prediction and texture generation

Very high resolution (VHR) satellite and aerial images often suffer from scene occlusion caused by redundant objects. The task of removing these redundant objects can be solved by missing data reconstruction technology. However, when dealing with VHR images with large-scale missing regions, existing spatial-based methods often destroy the structural information of ground objects. To alleviate this problem, this paper proposes a novel missing data reconstruction method based on deep learning. The reconstruction process is divided into two parts: structure prediction and texture generation. First, a progressive edge generation network (PEGN) is designed to predict the edges of objects in missing regions in a progressive manner. Then, the edge map predicted by PEGN is input to a texture generation network (TGN) as structural information to produce the reconstruction results. This is a spatial-based method that can produce realistic and reasonable results without any need for auxiliary spectral or temporal data. Experiments demonstrate that our model can better restore the structure of ground objects in VHR images than other spatial-based methods and outperform them in SSIM and PSNR indices. In addition, our model also has a strong generalization capability by introducing Poisson blending and histogram matching.
An Efficient Elliptic-Curve Point Multiplication Architecture for High-Speed Cryptographic Applications

This work presents an efficient high-speed hardware architecture for point multiplication (PM) computation of Elliptic-curve cryptography using binary fields over GF(2163) and GF(2571). The efficiency is achieved by reducing: (1) the time required for one PM computation and (2) the total number of required clock cycles. The required computational time for one PM computation is reduced by incorporating two modular multipliers (connected in parallel), a serially connected adder after multipliers and two serially connected squarer units (one after the first multiplier and another after the adder). To optimize the total number of required clock cycles, the point addition and point double instructions for PM computation of the Montgomery algorithm are re-structured. The implementation results after place-and-route over GF(2163) and GF(2571) on a Xilinx Virtex-7 FPGA device reveal that the proposed high-speed architecture is well-suited for the network-related applications, where millions of heterogeneous devices want to connect with the unsecured internet to reach an acceptable performance.
Face Recognition Based on Convolution Neural Network (CNN) Applications in Image Processing: A Survey

Face Recognition is a creative innovation since 1960 and the regularly enhancing strategy with different genuine applications. Numerous calculations and methods have been created for enhancing the execution of face acknowledgment. As of late Deep learning has been exceptionally investigated for desktop applications. Convolutional Neural Networks can be utilized with the end goal to extricate significant facial highlights. These highlights are permitted to think about appearances between them in a productive way. The framework can be prepared to perceive an arrangement of individuals. We give different applications that can be produced that make utilization of this Face Recognition innovation. The goal of this paper is to investigate the utility of profound CNN designs for face recognition utilizing images under various impediment conditions, using diverse closeness estimations (Similarity Index).