Reviewerly/exHarmony huggingface dataset:
Development and Evaluation of a Mask-type Display Transforming the Wearer's Impression

The facial expression that arises from our emotion takes an important role in face-to-face communication. Some might not be good at making an appropriate facial expression according to the situation. In this paper, we propose a mask-type display that can transform the wearer's visual impression. The display shows a computer-generated mouth. This virtual mouth is always synchronized with the shape of the wearer's actual mouth in real-time. The wearer can reflect four emotions on the virtual mouth no matter what shape of the mouth the wearer makes. The experimental results suggested that the virtual mouth with joy or anger had some effect on the wearer's impression in face-to-face communication.
Under Water to Outer Space: Augmented Reality for Astronauts and Beyond

Augmented reality (AR) has the potential to help astronauts execute procedures in a quicker, more intuitive, and safer way. A key part of realizing these benefits has been the use of an undersea research facility-the Aquarius-that acts as an analog to the International Space Station to a certain extent. In a June 2019 mission, the Aquarius crew successfully executed a complex procedure taking place across four different task areas by using an AR application called ProtoSpace developed at the Jet Propulsion Laboratory. In this article, we share the detailed results of the study, lessons learned, and future work needed to further enable the enhancement of procedure execution through augmented reality.
Continuous-variable quantum teleportation with vacuum-entangled Rindler modes

We consider a continuous-variable quantum teleportation protocol between a uniformly accelerated sender in the right Rindler wedge, a conformal receiver restricted to the future light cone, and an inertial observer in the Minkowski vacuum. Using a nonperturbative quantum circuit model, the accelerated observer interacts unitarily with the Rindler modes of the field, thereby accessing entanglement of the vacuum as a resource. We find that a Rindler-displaced Minkowski vacuum state prepared and teleported by the accelerated observer appears mixed according to the inertial observer, despite a reduction of the quadrature variances below classical limits. This is a surprising result, since the same state transmitted directly from the accelerated observer appears as a pure coherent state to the inertial observer. The decoherence of the state is caused by an interplay of opposing effects as the acceleration increases: the reduction of vacuum noise in the output state for a stronger entanglement resource, constrained by the amplification of thermal noise due to the presence of Unruh radiation.
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks

Real world data often exhibit low-dimensional geometric structures, and canbe viewed as samples near a low-dimensional manifold. This paper studiesnonparametric regression of H\older functions on low-dimensional manifoldsusing deep ReLU networks. Suppose $n$ training data are sampled from a H\olderfunction in $\mathcal{H}^{s,\alpha}$ supported on a $d$-dimensional Riemannianmanifold isometrically embedded in $\mathbb{R}^D$, with sub-gaussian noise. Adeep ReLU network architecture is designed to estimate the underlying functionfrom the training data. The mean squared error of the empirical estimator isproved to converge in the order of $n^{-\frac{2(s+\alpha)}{2(s+\alpha) +d}}\log^3 n$. This result shows that deep ReLU networks give rise to a fastconvergence rate depending on the data intrinsic dimension $d$, which isusually much smaller than the ambient dimension $D$. It therefore demonstratesthe adaptivity of deep ReLU networks to low-dimensional geometric structures ofdata, and partially explains the power of deep ReLU networks in tacklinghigh-dimensional data with low-dimensional geometric structures.
Using Knowledge Base Semantics in Context-Aware Entity Linking

Entity linking is a core task in textual document processing, which consists in identifying the entities of a knowledge base (KB) that are mentioned in a text. Approaches in the literature consider either independent linking of individual mentions or collective linking of all mentions. Regardless of this distinction, most approaches rely on the Wikipedia encyclopedic KB in order to improve the linking quality, by exploiting its entity descriptions (web pages) or its entity interconnections (hyperlink graph of web pages). In this paper, we devise a novel collective linking technique which departs from most approaches in the literature by relying on a structured RDF KB. This allows exploiting the semantics of the interrelationships that candidate entities may have at disambiguation time rather than relying on raw structural approximation based on Wikipedia's hyperlink graph. The few approaches that also use an RDF KB simply rely on the existence of a relation between the candidate entities to which mentions may be linked. Instead, we weight such relations based on the RDF KB structure and propose an efficient decoding strategy for collective linking. Experiments on standard benchmarks show significant improvement over the state of the art.
On Design of Single-Layer and Multilayer Code-Based Linkable Ring Signatures

In this paper, we present an effective code-based linkable ring signature scheme based on Borromean ring signature. The security of the scheme is based on the hardness of Syndrome Decoding problem, General Syndrome Decoding problem and Codeword Finding problem. We prove that our scheme has all the properties that a linkable ring signature scheme should have: unforgeability, anonymity, linkability and non-slanderability. Efficiency analysis shows that our scheme has a smaller signature size than the existing scheme under the same security conditions. Furthermore, this signature scheme can be easily extended to multiple layers. That is, code-based multilayer linkable ring signature scheme can be obtained and used for transactions with multiple inputs.
Cybersecurity in Educational Networks

The paper discusses the possible impact of digital space on a human, as well as human-related directions in cyber-security analysis in the education: levels of cyber-security, social engineering role in cyber-security of education, “cognitive vaccination”. “A Human” is considered in general meaning, mainly as a learner. The analysis is provided on the basis of experience of hybrid war in Ukraine that have demonstrated the change of the target of military operations from military personnel and critical infrastructure to a human in general. Young people are the vulnerable group that can be the main goal of cognitive operations in long-term perspective, and they are the weakest link of the System.
Evaluating the Trade-offs in the Hardware Design of the LEDAcrypt Encryption Functions

The confidentiality provided by widely employed asymmetric cryptosystems relying on the hardness of factoring large integers or computing discrete logarithms in a cyclic group will be jeopardized by the availability of quantum computers.As a consequence, the design of quantum-computing resistant cryptographic primitives has gained traction lately, especially thanks to the U.S. National Institute of Standards and Technology initiative, which aims at selecting a portfolio of primitives for standardization.A prime position in the set of asymmetric encryption primitives is occupied by the ones relying on decoding random linear error correction codes as their trapdoor.Among these primitives, the LEDAcrypt cryptosystem was recently announced to be admitted to the second round of the standardization initiative, where the investigation of the implementation performance is the main focus.In this paper we analyze the possible hardware designs to implement fast polynomial multiplications tailored to the encryption functions of the LEDAcrypt key encapsulation mechanism, and public key encryption primitives.In particular, we describe two designs for binary polynomial multiplications and analyze the advantages provided by exploiting the sparse nature of one of the operands in LEDAcrypt multiplications, validating our designs on a Xilinx Artix 100.
Distant Viewing Toolkit: A Python Package for the Analysis of Visual Culture

The Distant Viewing Toolkit is a Python package for the computational analysis of visual culture.It addresses the challenges of working with moving images through the automated extraction and visualization of metadata summarizing the content (e.g., people/actors, dialogue, scenes, objects) and style (e.g., shot angle, shot length, lighting, framing, sound) of time-based media.This toolkit is optimized for two purposes: (1) scholarly inquiry of visual culture from the humanities and social sciences, and (2) search and discovery of collections within libraries, archives, and museums.Many open-source projects provide implementations of state-of-the-art computer vision algorithms.However, there are limited options for users looking to quickly build end-to-end pipelines that link together common visual annotations.Those tools that do exist focus on specific subtasks, such as as FaceNet's recognition of faces (Schroff, Kalenichenko, & Philbin, 2015), visual embedding with PixPlot (Duhaime, 2019), and FilmColors' analysis of color (Flueckiger & Halter, 2018).Different algorithms require varying dependencies, different input formats, and produce outputs using different schemas.Because of the rapid pace of scholarship across the many sub-fields of computer vision, it can be difficult to determine which algorithms to use and a significant amount of work to manually test every option.These challenges are exacerbated when working with moving images because most available computer vision libraries take still images as inputs.
EPA’s proposed transparency rule: Factors to consider, many; planets to live on, one

> “In God we trust. All others bring data.”So reads a T-shirt sold by the American Statistical Association. This pithy quip encapsulates a fundamental principle of science: Scientists rely on evidence rather than authority. Indeed, “doubt has been considered essential to science since long before the scientific method was established in the 17th century” (1). Scientists seek reasons for why something should be believed as true, and those reasons involve data, the methods used to collect those data, and the logic connecting those data to conclusions. All other things being equal, absent the opportunity to fully inspect the data, methods, and logical connections of a study, scientists are less able to judge the validity of conclusions or the truth of propositions drawn from a study. As many of us heard from our middle school mathematics teachers, it is important to “show all your work.” Generating and evaluating the scientific evidence to form conclusions about the truth of a proposition is fundamental to the work of science.David B. Allison. Image courtesy of the University of Alabama at Birmingham.Notably, the Environmental Protection Agency (EPA) is not a scientist; it is a regulatory agency. EPA employs scientists and uses science to aid in its mission, but its primary mission is regulation and the protection of the environment and the public health, rather than simply drawing scientific conclusions. Regulatory decisions can, are, and should be informed by science. But science alone is not dispositive of regulatory decisions, and one should not conflate the role of scientists qua scientists with the role of scientists working in a regulatory process. Scientists working in a regulatory process must utilize the best information available to fulfill their charge of making decisions that benefit society, often under conditions of uncertainty.EPA proposed a rule in 2018 that … [↵][1]1To whom correspondence may be addressed. Email: allison{at}iu.edu. [1]: #xref-corresp-1-1
Variational autoencoder based bipartite network embedding by integrating local and global structure

As a powerful tool for machine learning on the graph, network embedding, which projects nodes into low-dimensional spaces, has a variety of applications on complex networks. Most current methods and models are not suitable for bipartite networks, which have two different types of nodes and there are no links between nodes of the same type. Furthermore, the only existing methods for bipartite network embedding ignore the internal mechanism and highly nonlinear structures of links. Therefore, in this paper, we propose a new deep learning method to learn the node embedding for bipartite networks based on the widely used autoencoder framework. Moreover, we carefully devise a node-level triplet including two types of nodes to assign the embedding by integrating the local and global structures. Meanwhile, we apply the variational autoencoder (VAE), a deep generation model with natural advantages in data generation and reconstruction, to enhance the node embedding for the highly nonlinear relationships between nodes and complex features. Experiments on some widely used datasets show the effectiveness of the proposed model and corresponding algorithm compared with some baseline network (and bipartite) embedding techniques.
Random access prediction structures for light field video coding with MV-HEVC

Computational imaging and light field technology promise to deliver the required six-degrees-of-freedom for natural scenes in virtual reality. Already existing extensions of standardized video coding formats, such as multi-view coding and multi-view plus depth, are the most conventional light field video coding solutions at the moment. The latest multi-view coding format, which is a direct extension of the high efficiency video coding (HEVC) standard, is called multi-view HEVC (or MV-HEVC). MV-HEVC treats each light field view as a separate video sequence, and uses syntax elements similar to standard HEVC for exploiting redundancies between neighboring views. To achieve this, inter-view and temporal prediction schemes are deployed with the aim to find the most optimal trade-off between coding performance and reconstruction quality. The number of possible prediction structures is unlimited and many of them are proposed in the literature. Although some of them are efficient in terms of compression ratio, they complicate random access due to the dependencies on previously decoded pixels or frames. Random access is an important feature in video delivery, and a crucial requirement in multi-view video coding. In this work, we propose and compare different prediction structures for coding light field video using MV-HEVC with a focus on both compression efficiency and random accessibility. Experiments on three different short-baseline light field video sequences show the trade-off between bit-rate and distortion, as well as the average number of decoded views/frames, necessary for displaying any random frame at any time instance. The findings of this work indicate the most appropriate prediction structure depending on the available bandwidth and the required degree of random access.
Fear not, vote truthfully: Secure Multiparty Computation of score based rules

We propose a secure voting protocol for score-based voting rules, where independent talliers perform the tallying procedure. The protocol outputs the winning candidate(s) while preserving the privacy of the voters and the secrecy of the ballots. It offers perfect secrecy, in the sense that apart from the desired output, all other information – the ballots, intermediate values, and the final scores received by each of the candidates – is not disclosed to any party, including the talliers. Such perfect secrecy may increase the voters' confidence and, consequently, encourage them to vote according to their true preferences. The protocol is extremely lightweight, and therefore it can be easily deployed in real life voting scenarios.
Workshop on Privacy in NLP (PrivateNLP 2020)

Privacy-preserving data analysis has become essential in Machine Learning (ML), where access to vast amounts of data can provide large gains the in accuracies of tuned models. A large proportion of user-contributed data comes from natural language e.g., text transcriptions from voice assistants. It is therefore important for curated natural language datasets to preserve the privacy of the users whose data is collected and for the models trained on sensitive data to only retain non-identifying (i.e., generalizable) information. The workshop aims to bring together researchers and practitioners from academia and industry to discuss the challenges and approaches to designing, building, verifying, and testing privacy-preserving systems in the context of Natural Language Processing (NLP).
Evaluating Saliency Map Explanations for Convolutional Neural Networks: A User Study

Convolutional neural networks (CNNs) offer great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research effort has been reported concerning their user evaluation. In this paper, we report on an online between-group user study designed to evaluate the performance of "saliency maps" - a popular explanation algorithm for image classification applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some specific image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the network's output for new images. Drawing on our findings, we highlight implications for design and further research on explainable AI. In particular, we argue the HCI and AI communities should look beyond instance-level explanations.
Photonic dephasing dynamics and the role of initial correlations

Photons are useful quantum systems for investigating the fundamental and practical aspects of dephasing, decoherence, and other types of open quantum dynamics. However, descriptions of open dynamics of photonic systems are usually in terms of decoherence functions and in many cases an explicit master-equation-based description is not available. This is particularly true when considering multipartite photonic open systems having possibly correlated environments. We obtain generic master equations for the reduced dephasing dynamics of a two-photon polarization state coupled to respective frequency degrees of freedom when the frequency degrees of freedom that form the environment are initially correlated. Our results show the explicit dependence of the operator form of the master equation and the decay rates on these correlations as well as on various types of frequency distributions. Furthermore, we use the recently developed bath-positive decomposition method to treat an initially correlated polarization-frequency state of a photon and demonstrate how this allows us to gain new insight and detailed information on how the contributions of different origins influence the photonic dephasing.
Research on Dynamic Path Planning of Wheeled Robot Based on Deep Reinforcement Learning on the Slope Ground

The existing dynamic path planning algorithm cannot properly solve the problem of the path planning of wheeled robot on the slope ground with dynamic moving obstacles. To solve the problem of slow convergence rate in the training phase of DDQN, the dynamic path planning algorithm based on Tree-Double Deep Q Network (TDDQN) is proposed. The algorithm discards detected incomplete and over-detected paths by optimizing the tree structure, and combines the DDQN method with the tree structure method. Firstly, DDQN algorithm is used to select the best action in the current state after performing fewer actions, so as to obtain the candidate path that meets the conditions. And then, according to the obtained state, the above process is repeatedly executed to form multiple paths of the tree structure. Finally, the non-maximum suppression method is used to select the best path from the plurality of eligible candidate paths. ROS simulation and experiment verify that the wheeled robot can reach the target effectively on the slope ground with moving obstacles. The results show that compared with DDQN algorithm, TDDQN has the advantages of fast convergence and low loss function.
Lens Application: Mobile Application Using Augmented Reality

Smartphones and Mobile App are drawing closer to an acceptable learning tool, especially related to history that are viewed as a tool which control the present and build the future, and the study of history is the practical application of the Qur'an and the Sunnah, in which the stories of the sermon are frequently repeated. This paper we aim to add more entertainment and creative ways to teach history by using Augmented Reality in teaching process that will help both student to more interact and visualize the historical stories, and teachers by providing them a more helpful tool in teaching process at classrooms, through our goal we will rely on the 2030 vision by implementing the goal of” National Character Enrichment” vision program. In this project we are focusing on the book of social and national education (first history subject level) that contains units of Islamic history for the fourth grade students of elementary schools especially
Satisfaction and willingness to consume immersive journalism

Immersive journalism has been touted to revolutionize journalism due to its ability to afford a multi-modal engrossing experience. However, hardly any experiments have been conducted whether consumers' satisfaction and consequent intentions to use immersive journalistic media may differ from traditional forms of journalistic content. Therefore, in this study, we investigate the differences in satisfaction and continued use intentions between article, 360 video and VR-based interaction with content. The data was collected via a randomized controlled laboratory experiment with between-subjects design (N = 87). Participants were randomly assigned to reading a written article based on the video (article) and watching the video on a computer screen (2D 360) or in mobile VR (VR 360). The collected data consisted of demographics (age and gender) and reported satisfaction and intention to continue use. Results suggest that those who were assigned to VR 360 had higher intentions to continue use, but not greater satisfaction than those in the other two conditions. However, the intention was predicted to an extent by satisfaction as suggested by previous literature. Finally, age and gender did not predict continued use. These findings imply that users prefer the new media technology for consuming journalism content and support previous findings of the relationship between satisfaction and intention to continue use. Finally, avenues for further research are presented.
Application of Weak Signal Denoising Based on Improved Wavelet Threshold

Abstract In this Study, the application of wavelet threshold denoising in weak signal detection of underwater targets under complex sea conditions is discussed. Firstly, the basic principle and steps of wavelet threshold denoising method based on Mallat decomposition are analyzed. Then a new threshold function is proposed to overcome the shortcomings of traditional Donoho hard and soft thresholds. The denoising effects of wavelet and wavelet packet threshold denoising methods under hard threshold, soft threshold and new threshold functions are simulated, and the signal-to-noise ratio (SNR) and mean square error (MSE) after denoising are taken as evaluation indexes. The results show that the improved threshold denoising method outperforms the traditional threshold denoising methods.