Reviewerly/exHarmony huggingface dataset:
An empirical study to investigate the efficacy of collaborative immersive virtual reality systems for designing information architecture of software systems

The ability of Immersive Virtual Reality (IVR) systems to mimic the real world has made it possible to use this technology to create environments for remote collaborative work. This study aimed to understand the feasibility of immersive virtual reality when conducting a collaborative Information Architecture (IA) design task-card sorting, with geographically dispersed participants. Using a between-subjects experimental design, thirty groups of two individuals each completed a card sorting activity using conventional in-person, video screen-sharing method or immersive virtual reality methods. The dependent measures included total time, percentage match with master card set, usability, presence and perceived workload. Overall usability was found to be significantly higher for the immersive virtual reality condition when compared to conventional in-person card sorting. In addition, the new immersive virtual reality technology performed as well as the other two conditions for other dependent variables. Qualitative data from the participants also indicated a positive reaction to the use of immersive virtual reality for this task. Overall, the participants felt they were productive and enjoyed the IVR condition, indicating the potential of IVR-based approaches as an alternative to conventional approaches for IA design.
Data by Proxy — Material Traces as Autographic Visualizations

Information visualization limits itself, per definition, to the domain of symbolic information. This paper discusses arguments why the field should also consider forms of data that are not symbolically encoded, including physical traces and material indicators. Continuing a provocation presented by Pat Hanrahan in his 2004 IEEE Vis capstone address, this paper compares physical traces to visualizations and describes the techniques and visual practices for producing, revealing, and interpreting them. By contrasting information visualization with a speculative counter model of autographic visualization, this paper examines the design principles for material data. Autographic visualization addresses limitations of information visualization, such as the inability to directly reflect the material circumstances of data generation. The comparison between the two models allows probing the epistemic assumptions behind information visualization and uncovers linkages with the rich history of scientific visualization and trace reading. The paper begins by discussing the gap between data visualizations and their corresponding phenomena and proceeds by investigating how material visualizations can bridge this gap. It contextualizes autographic visualization with paradigms such as data physicalization and indexical visualization and grounds it in the broader theoretical literature of semiotics, science and technology studies (STS), and the history of scientific representation. The main section of the paper proposes a foundational design vocabulary for autographic visualization and offers examples of how citizen scientists already use autographic principles in their displays, which seem to violate the canonical principles of information visualization but succeed at fulfilling other rhetorical purposes in evidence construction. The paper concludes with a discussion of the limitations of autographic visualization, a roadmap for the empirical investigation of trace perception, and thoughts about how information visualization and autographic visualization techniques can contribute to each other.
Chasing Ghosts: Instruction Following as Bayesian State Tracking

A visually-grounded navigation instruction can be interpreted as a sequence of expected observations and actions an agent following the correct trajectory would encounter and perform. Based on this intuition, we formulate the problem of finding the goal location in Vision-and-Language Navigation (VLN) within the framework of Bayesian state tracking - learning observation and motion models conditioned on these expectable events. Together with a mapper that constructs a semantic spatial map on-the-fly during navigation, we formulate an end-to-end differentiable Bayes filter and train it to identify the goal by predicting the most likely trajectory through the map according to the instructions. The resulting navigation policy constitutes a new approach to instruction following that explicitly models a probability distribution over states, encoding strong geometric and algorithmic priors while enabling greater explainability. Our experiments show that our approach outperforms a strong LingUNet baseline when predicting the goal location on the map. On the full VLN task, i.e. navigating to the goal location, our approach achieves promising results with less reliance on navigation constraints.
DeepV2D: Video to Depth with Differentiable Structure from Motion

We propose DeepV2D, an end-to-end deep learning architecture for predicting depth from video. DeepV2D combines the representation ability of neural networks with the geometric principles governing image formation. We compose a collection of classical geometric algorithms, which are converted into trainable modules and combined into an end-to-end differentiable architecture. DeepV2D interleaves two stages: motion estimation and depth estimation. During inference, motion and depth estimation are alternated and converge to accurate depth.
On the relation between felt trust and actual trust: Examining pathways to and implications of leader trust meta-accuracy.

Research has long emphasized that being trusted is a central concern for leaders (Dirks & Ferrin, 2002), but an interesting and important question left unexplored is whether leaders feel trusted by each employee, and whether their felt trust is accurate. Across 2 field studies, we examined the factors that shape the accuracy of leaders' felt trust-or, their trust meta-accuracy-and the implications of trust meta-accuracy for the degree of relationship conflict between leaders and their employees. By integrating research on trust and interpersonal perception, we developed and tested hypotheses based on 2 theoretical mechanisms-an external signaling mechanism and an internal presumed reciprocity mechanism-that theory suggests shape leaders' trust meta-accuracy. In contrast to the existing literature on felt trust, our results reveal that leader trust meta-accuracy is shaped by an internal mechanism and the presumed reciprocity of trust relationships. We further find that whether trust meta-accuracy is associated with positive relational outcomes for leaders depends upon the level of an employee's actual trust in the leader. Our research contributes to burgeoning interest in felt trust by elucidating the mechanisms underlying trust meta-accuracy and suggesting practical directions for leaders who seek to accurately understand how much their employees trust them. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Model-free vision-based shaping of deformable plastic materials

We address the problem of shaping deformable plastic materials using non-prehensile actions. Shaping plastic objects is challenging, because they are difficult to model and to track visually. We study this problem, by using kinetic sand, a plastic toy material that mimics the physical properties of wet sand. Inspired by a pilot study where humans shape kinetic sand, we define two types of actions: pushing the material from the sides and tapping from above. The chosen actions are executed with a robotic arm using image-based visual servoing. From the current and desired view of the material, we define states based on visual features such as the outer contour shape and the pixel luminosity values. These are mapped to actions, which are repeated iteratively to reduce the image error until convergence is reached. For pushing, we propose three methods for mapping the visual state to an action. These include heuristic methods and a neural network, trained from human actions. We show that it is possible to obtain simple shapes with the kinetic sand, without explicitly modeling the material. Our approach is limited in the types of shapes it can achieve. A richer set of action types and multi-step reasoning is needed to achieve more sophisticated shapes.
NDNet: Narrow While Deep Network for Real-Time Semantic Segmentation

The rapid development of autonomous driving in recent years presents many challenges for scene understanding. As an essential step towards scene understanding, semantic segmentation has received increased attention in the past few years. Although deep learning based approaches have achieved great success in improving the segmentation accuracy, most of them suffer from an inefficiency problem and can hardly be applied to real-time applications. In this paper, we analyze the computational cost of Convolutional Neural Network (CNN) and find that the inefficiency of CNNs is mainly caused by their wide structure rather than deep structure. In addition, the success of pruning based model compression methods proves that there are many redundant channels in CNNs. Thus, we design a narrow while deep backbone network to improve the efficiency of semantic segmentation. By casting our network to fully convolutional network (FCN32) segmentation architecture, the basic structure of most segmentation methods, we achieve 61.5% mIoU on Cityscapes validation dataset with only 4.2G floating-point operations (FLOPs) on 1024×2048 inputs, which already outperforms one of the earliest real-time deep learning based segmentation methods: ENet (58.3% mIoU, 3.8G FLOPs on 640×360 inputs). By further refining the output resolution of our network to the 1/8 of the input resolution with a simple encoder-decoder structure, we achieve 65.3% mIoU on Cityscapes test set with 14.0G FLOPs and 39.9 frames per second (FPS) on Titan X card. We have made our model publicly available at https://github.com/zgyang-hnu/NDNet.
TicTac: Accelerating Distributed Deep Learning with Communication Scheduling

State-of-the-art deep learning systems rely on iterative distributed training to tackle the increasing complexity of models and input data. The iteration time in these communication-heavy systems depends on the computation time, communication time and the extent of overlap of computation and communication. In this work, we identify a shortcoming in systems with graph representation for computation, such as TensorFlow and PyTorch, that result in high variance in iteration time --- random order of received parameters across workers. We develop a system, TicTac, to improve the iteration time by fixing this issue in distributed deep learning with Parameter Servers while guaranteeing near-optimal overlap of communication and computation. TicTac identifies and enforces an order of network transfers which improves the iteration time using prioritization. Our system is implemented over TensorFlow and requires no changes to the model or developer inputs. TicTac improves the throughput by up to $37.7\%$ in inference and $19.2\%$ in training, while also reducing straggler effect by up to $2.3\times$. Our code is publicly available.
Probing quantum walks through coherent control of high-dimensionally entangled photons

Demonstrating continuously tunable photonic quantum walks in the frequency domain with high-dimensionally entangled photon pairs.
Large-Scale Adversarial Training for Vision-and-Language Representation Learning

We present VILLA, the first known effort on large-scale adversarial training for vision-and-language (V+L) representation learning. VILLA consists of two training stages: (i) task-agnostic adversarial pre-training; followed by (ii) task-specific adversarial finetuning. Instead of adding adversarial perturbations on image pixels and textual tokens, we propose to perform adversarial training in the embedding space of each modality. To enable large-scale training, we adopt the “free” adversarial training strategy and combine it with KL-divergence-based regularization to promote higher invariance in the embedding space. We apply VILLA to current best-performing V+L models, and achieve a new state of the art on a wide range of tasks, including Visual Question Answering, Visual Commonsense Reasoning, Image-Text Retrieval, Referring Expression Comprehension, Visual Entailment, and NLVR.
Experimental Implementation of a Quantum Autoencoder via Quantum Adders

Abstract Quantum autoencoders allow for reducing the amount of resources in a quantum computation by mapping the original Hilbert space onto a reduced space with the relevant information. Recently, it is proposed to employ approximate quantum adders to implement quantum autoencoders in quantum technologies. Here, the experimental implementation of this proposal in the Rigetti cloud quantum computer is carried out employing up to three qubits. The experimental fidelities are in good agreement with the theoretical prediction, thus proving the feasibility to realize quantum autoencoders via quantum adders in state‐of‐the‐art superconducting quantum technologies.
A framework for constructing and evaluating the role of MR as a holographic virtual guide in museums

Mixed reality (MR) is a cutting-edge technology at the forefront of many new applications in the tourism and cultural heritage sector. This study aims to reshape the museum experience by creating a highly engaging and immersive museum experience for visitors combing real-time visual, audio information and computer-generated images with museum artefacts and customer displays. This research introduces a theoretical framework that assesses the potential of MR guidance system in usefulness, ease of use, enjoyment, interactivity, touring and future applications. The evaluation introduces the MuseumEye MR application in the Egyptian Museum, Cairo using mixed method surveys and a sample of 171 participants. The results of the questionnaire highlighted the importance of the mediating the role of the tour guide in enhancing the relationship between perceived usefulness, ease of use, multimedia, UI design, interactivity and the intention of use. Furthermore, the results of this study revealed the potential future use of MR in museums and ensured sustainability and engagement past the traditional visitor museum experience, which heightens the economic state of museums and cultural heritage sectors.
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks

When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights. Adaptation can be useful in cases when training data is scarce, when a single learner needs to perform multiple tasks, or when one wishes to encode priors in the network. The most commonly employed approaches for network adaptation are fine-tuning and using the pre-trained network as a fixed feature extractor, among others. In this paper, we propose a straightforward alternative: side-tuning. Side-tuning adapts a pre-trained network by training a lightweight “side" network that is fused with the (unchanged) pre-trained network via summation. This simple method works as well as or better than existing solutions and it resolves some of the basic issues with fine-tuning, fixed features, and other common approaches. In particular, side-tuning is less prone to overfitting, is asymptotically consistent, and does not suffer from catastrophic forgetting in incremental learning. We demonstrate the performance of side-tuning under a diverse set of scenarios, including incremental learning (iCIFAR, iTaskonomy), reinforcement learning, imitation learning (visual navigation in Habitat), NLP question-answering (SQuAD v2), and single-task transfer learning (Taskonomy), with consistently promising results.
What do we know about cryptocurrency? Past, present, future

Purpose The authors make a fundamental initial effort to conduct a systematic review analysis on “cryptocurrency,” mainly to analyze the way it has been changing the “stereotype” financial transactions, and also identify the probable unexplored research avenues on this innovative investment regime. The study aims to draw the landscape of the current state, prospects, challenges, trends and possible agendas of cryptocurrency in the global market. Design/methodology/approach Using a quali-quantitative approach widely known as meta-literature review, the synthesis analysis on “cryptocurrency” is conducted. Methodologically, the authors review and analyze the most recent and relevant papers preferably published between 2016 and 2020 in leading business and finance journals of ISI Web of Science (ISI WOS) through bibliometric analysis particularly coupled with content analysis. Findings The findings of the meta-analysis summarize the relevant stylized facts of the cryptocurrency market: distinctive features of blockchain technology, decentralized payment method, low-cost facility, ensuring pseudo-anonymity, independence from central authority, double spending attack protection, organic and instantaneous nature, among others. In addition, the analysis identified several future research regimes: pricing model, prospect of investment regime, hedging properties, volatility dynamics, information asymmetry, underlying risk factors and bubble-like nature in global cryptocurrency market. Practical implications This academic novelty significantly contributes to enhance our knowledge on the current state-of-the-art of digital finance, outlines the research agenda and eventually provides important investment implications for financial managers, research analysts, investors, market practitioners, regulatory compliance professionals and policymakers. Therefore, the findings shed the lights on new investment opportunity in the global market. Originality/value Cryptocurrency, virtual currency or digital asset having cryptography for idiosyncratic security features, seems to be a persistent paradigm shift in the digitalized financial system. Despite the continuing growth, the academic research on cryptocurrency is still at nascent stage, particularly because researchers did not deeply draw attention at this financial innovation. In addition, the authors argue that none of the earlier studies yet conducted a meta-analysis on this latest investment regime. Therefore, this review study is the initial attempt to fill up the gap in the finance literature.
Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise

In this paper, we develop convergence analysis of a modified line search method for objective functions whose value is computed with noise and whose gradient estimates are inexact and possibly random. The noise is assumed to be bounded in absolute value without any additional assumptions. We extend the framework based on stochastic methods from [C. Cartis and K. Scheinberg, Math. Program., 169 (2018), pp. 337--375] which was developed to provide analysis of a standard line search method with exact function values and random gradients to the case of noisy functions. We introduce two alternative conditions on the gradient which, when satisfied with some sufficiently large probability at each iteration, guarantees convergence properties of the line search method. We derive expected complexity bounds to reach a near optimal neighborhood for convex, strongly convex and nonconvex functions. The exact dependence of the convergence neighborhood on the noise is specified.
Methodology based on the NIST cybersecurity framework as a proposal for cybersecurity management in government organizations

This research aims to propose the use of the methodology based on the NIST Framework for adequate management of cybersecurity in government organizations within the framework of the delivery of digital services.Many government organizations have been managing cybersecurity without a defined process; this generates that the management is deficient and without indicators.Concerning whether they are implementing the methodology based on the NIST cybersecurity framework", shows that 36.8% of respondents present a level in disagreement, 31.6%(6) an undecided level, 15.8% (3) a level of agreement, 10.5% (2) a level totally in disagreement and 5.3% (1) a level totally in agreement.Meanwhile, the variable "The management of cybersecurity" shows that 36.8% ( 7) of the Ministries surveyed present a level in disagreement; 36.8% ( 7) an undecided level, 15.8% (3) a level of agreement, and 10.5% (2) a level totally in disagreement In conclusion: It has been shown that the use of the methodology based on the NIST cybersecurity framework influences cybersecurity management in government organizations and it is clear that they are currently not using it which causes a relatively poor level of leadership in the implementation of security measures concerning cybersecurity management.
High-efficiency 3D reconstruction with a uniaxial MEMS-based fringe projection profilometry

Micro-Electro-Mechanical System (MEMS) scanning is increasingly popular in 3D surface measurement with the merits of the compact structure and high frame-rate. In this paper, we achieve real-time fringe structured 3D reconstruction by using a uniaxial MEMS-based projector. To overcome the limitations on uniaxial MEMS-based projector of lensless structure and unidirectional fringe projection, a novel isophase plane model is proposed, in which the laser line from MEMS-based projector is regarded as an isophase plane. Our model directly establishes the mapping relationship between phase and spatial 3D coordinates through the intersection point of camera back-projection light ray and isophase plane. Furthermore, a flexible calibration strategy to obtain 3D mapping coefficients is introduced with a specially designed planar target. Experiments demonstrated that our method can achieve high-accuracy and real-time 3D reconstruction.
Liquidity connectedness in cryptocurrency market

Abstract We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57–66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271–296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time–frequency connectedness.