Reviewerly/exHarmony huggingface dataset:
The impact of augmented reality on cognitive load and performance: A systematic review

Abstract Background Previous studies on augmented reality‐enriched learning and training indicated conflicting results regarding the cognitive load involved: some authors report that AR can reduce cognitive load, others have shown that AR is perceived as cognitively demanding and can lead to poorer performance. Objectives The aim of this study is to systematically analyse previous research on AR and cognitive load, including performance, and thus to be able to contribute to answering the question of the impact of AR on cognitive load when used in learning environments. Methods This study applied the systematic review method. A total of 58 studies were identified and analysed using rigorously defined inclusion and exclusion criteria. The results are now reported as a synthesis. Results and Conclusions Compared to other technologies, AR seems to be less cognitively demanding and also leads to higher performance. However, these results are based on media comparison studies that have been criticized for years. The spatial AR type is better compared to see‐through AR. However, the latter can be improved by visual cues and the addition of learning activities, such as value‐added studies have revealed. Major takeaways The essential findings of this study are that the technology used, for example, AR glasses, can unnecessarily increase cognitive load and that still many studies focus on the comparison of AR with more traditional media. Less studies applied alternative research designs, for example, value‐added comparisons. However, such designs are better suited to investigate design principles for AR‐enriched learning environments, which can then in turn reduce cognitive load as well as positively affect performance.
Nanoscale neural network using non-linear spin-wave interference

We demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic-field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.
Advances in Adversarial Attacks and Defenses in Computer Vision: A Survey

Deep Learning is the most widely used tool in the contemporary field of computer vision. Its ability to accurately solve complex problems is employed in vision research to learn deep neural models for a variety of tasks, including security critical applications. However, it is now known that deep learning is vulnerable to adversarial attacks that can manipulate its predictions by introducing visually imperceptible perturbations in images and videos. Since the discovery of this phenomenon in 2013, it has attracted significant attention of researchers from multiple sub-fields of machine intelligence. In 2018, we published the first-ever review of the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses). Many of those contributions have inspired new directions in this area, which has matured significantly since witnessing the first generation methods. Hence, as a legacy sequel of our first literature survey, this review article focuses on the advances in this area since 2018. We thoroughly discuss the first generation attacks and comprehensively cover the modern attacks and their defenses appearing in the prestigious sources of computer vision and machine learning research. Besides offering the most comprehensive literature review of adversarial attacks and defenses to date, the article also provides concise definitions of technical terminologies for the non-experts. Finally, it discusses challenges and future outlook of this direction based on the literature since the advent of this research direction.
Counterfactuals and causability in explainable artificial intelligence: Theory, algorithms, and applications

Deep learning models have achieved high performance across different domains, such as medical decision-making, autonomous vehicles, decision support systems, among many others. However, despite this success, the inner mechanisms of these models are opaque because their internal representations are too complex for a human to understand. This opacity makes it hard to understand the how or the why of the predictions of deep learning models. There has been a growing interest in model-agnostic methods that make deep learning models more transparent and explainable to humans. Some researchers recently argued that for a machine to achieve human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing literature on counterfactuals and causability for explainable artificial intelligence (AI). We performed a Latent Dirichlet topic modelling analysis (LDA) under a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to find the most relevant literature articles. This analysis yielded a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications to real-world data. Our research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Furthermore, our findings suggest that the explanations derived from popular algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous, or even biased explanations. Thus, this paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable AI.
Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak

The cryptocurrency markets are perceived as being dominated by Bitcoin leading the overall system dynamics. Although the previous empirical evidence points towards strong connections among selected cryptocurrencies or, from the other side, weak dependence between Bitcoin and traditional financial assets, a focused study on the dynamics of return and volatility connectedness among a wider range of cryptocurrencies is lacking, and more so, one directed towards the very first actual critical period of the global economy coinciding with relevant crypto-markets. Using data for the 10 most capitalized cryptocurrencies between 1st October 2017 and 5th January 2021, we examine how cryptocurrencies interact and whether they have a clear leader, with a special focus on differences with respect to investment horizons and how the relationship structure evolves in time. We uncover a structural change in the connectedness evolving in 2020 as the market restructures in reaction to the unprecedented monetary injections as a counter to the COVID-19-induced economic standstill. The structural change is shown not only for cryptocurrencies considered separately but also when we jointly examine them with traditional assets.
Tasmanian Devil Optimization: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm

In this paper, a new bio-inspired metaheuristic algorithm called Tasmanian Devil Optimization (TDO) is designed that mimics Tasmanian devil behavior in nature. The fundamental inspiration used in TDO is simulation of the feeding behavior of the Tasmanian devil, who has two strategies: attacking live prey or feeding on carrions of dead animals. The proposed TDO is described, then its mathematical modeling is presented. TDO performance in optimization is tested on a set of twenty-three standard objective functions. Unimodal benchmark functions have analyzed the TDO exploitation capability, while high-dimensional multimodal and fixed-exploitation multimodal benchmark functions have challenged the TDO exploration capability. The optimization results indicate the high ability of the proposed TDO in exploration and exploitation and create a proper balance between these two indicators to effectively solve optimization problems. Eight well-known metaheuristic algorithms are employed to analyze the quality of the obtained results from TDO. The simulation results show that the proposed TDO, with its strong performance, has a higher capability than the eight competitor algorithms and is much more competitive. For further analysis, TDO is tested in optimizing four engineering design problems. Implementation results show that TDO has an effective performance in solving real-world applications.
Dynamic View Synthesis from Dynamic Monocular Video

We present an algorithm for generating novel views at arbitrary viewpoints and any input time step given a monocular video of a dynamic scene. Our work builds upon recent advances in neural implicit representation and uses continuous and differentiable functions for modeling the time-varying structure and the appearance of the scene. We jointly train a time-invariant static NeRF and a time-varying dynamic NeRF, and learn how to blend the results in an unsupervised manner. However, learning this implicit function from a single video is highly ill-posed (with infinitely many solutions that match the input video). To resolve the ambiguity, we introduce regularization losses to encourage a more physically plausible solution. We show extensive quantitative and qualitative results of dynamic view synthesis from casually captured videos.
A reinforcement learning level-based particle swarm optimization algorithm for large-scale optimization

Large-scale optimization problems (LSOPs) have drawn researchers’ increasing attention since their resemblance to real-world problems. However, due to the complex search space and massive local optima, it is challenging to simultaneously guarantee the diversity and convergence of the algorithms. As a widely used evolutionary algorithm with fast convergence, particle swarm optimization (PSO) shows competitive performances on some LSOPs. Nevertheless, it can easily get trapped into local optima. Overcoming the complexity of LSOPs and improving search efficiency have become vital issues. The reinforcement learning method has proven to be an effective technique in self-adaptive adjustment, which can help search for better results in large-scale solution space more effectively. In this paper, we propose a large-scale optimization algorithm called reinforcement learning level-based particle swarm optimization algorithm (RLLPSO). In RLLPSO, a level-based population structure is constructed to improve population diversity. A reinforcement learning strategy for level number control is employed to help improve the search efficiency of RLLPSO. To further enhance the convergence ability of RLLPSO, a level competition mechanism is introduced. The experimental results from two large-scale benchmark test suites demonstrate that, compared with five state-of-the-art large-scale optimization algorithms, RLLPSO shows superiority in most cases.
Robust Structural Sparse Tracking

Sparse representations have been applied to visual tracking by finding the best candidate region with minimal reconstruction error based on a set of target templates. However, most existing sparse trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidate regions, thereby making them less effective when similar objects appear at close proximity or under occlusion. In this paper, we propose a novel structural sparse representation, which not only exploits the intrinsic relationships among target candidate regions and local patches to learn their representations jointly, but also preserves the spatial structure among the local patches inside each target candidate region. For robust visual tracking, we take outliers resulting from occlusion and noise into account when searching for the best target region. Constructed within a Bayesian filtering framework, we show that the proposed algorithm accommodates most existing sparse trackers with respective merits. The formulated problem can be efficiently solved using an accelerated proximal gradient method that yields a sequence of closed form updates. Qualitative and quantitative evaluations on challenging benchmark datasets demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
Renewable Energy-Based Multi-Indexed Job Classification and Container Management Scheme for Sustainability of Cloud Data Centers

Cloud computing has emerged as one of the most popular technologies of the modern era for providing on-demand services to the end users. Most of the computing tasks in cloud data centers are performed by geodistributed data centers which may consume a hefty amount of energy for their operations. However, the usage of renewable energy resources with appropriate server selection and consolidation can mitigate the energy related issues in cloud environment. Hence, in this paper, we propose a renewable energy-aware multi-indexed job classification and scheduling scheme using container as-a-service for data centers sustainability. In the proposed scheme, incoming workloads from different devices are transferred to the data center which has sufficient amount of renewable energy available with it. For this purpose, a renewable energy-based host selection and container consolidation scheme is also designed. The proposed scheme has been evaluated using Google workload traces. The results obtained prove 15%, 28%, and 10.55% higher energy savings in comparison to the existing schemes of its category.
Deep learning algorithm for data-driven simulation of noisy dynamical system

We present a deep learning model, DE-LSTM, for the simulation of a stochastic process with an underlying nonlinear dynamics. The deep learning model aims to approximate the probability density function of a stochastic process via numerical discretization and the underlying nonlinear dynamics is modeled by the Long Short-Term Memory (LSTM) network. It is shown that, when the numerical discretization is used, the function estimation problem can be solved by a multi-label classification problem. A penalized maximum log likelihood method is proposed to impose a smoothness condition in the prediction of the probability distribution. We show that the time evolution of the probability distribution can be computed by a high-dimensional integration of the transition probability of the LSTM internal states. A Monte Carlo algorithm to approximate the high-dimensional integration is outlined. The behavior of DE-LSTM is thoroughly investigated by using the Ornstein-Uhlenbeck process and noisy observations of nonlinear dynamical systems; Mackey-Glass time series and forced Van der Pol oscillator. It is shown that DE-LSTM makes a good prediction of the probability distribution without assuming any distributional properties of the stochastic process. For a multiple-step forecast of the Mackey-Glass time series, the prediction uncertainty, denoted by the 95\% confidence interval, first grows, then dynamically adjusts following the evolution of the system, while in the simulation of the forced Van der Pol oscillator, the prediction uncertainty does not grow in time even for a 3,000-step forecast.
New Framework of Reversible Data Hiding in Encrypted JPEG Bitstreams

This paper proposes a novel framework of reversible data hiding in encrypted JPEG bitstream. We first provide a JPEG encryption algorithm to encipher a JPEG image to a smaller size and keep the format compliant to JPEG decoders. After an image owner uploads the encrypted JPEG bitstreams to cloud storage, the server embeds additional messages into the ciphertext to construct a marked encrypted JPEG bitstream. During data hiding, we propose a combined embedding algorithm including two stages, the Huffman code mapping and the ordered histogram shifting. The embedding procedure is reversible. When an authorized user requires a downloading operation, the server extracts additional messages from the marked encrypted JPEG bitstream and recovers the original encrypted bitstream losslessly. After downloading, the user obtains the original JPEG bitstream by a direct decryption. The proposed framework outperforms previous works on reversible data hiding in encrypted images. First, since the tasks of data embedding/extraction and bitstream recovery are all accomplished by the server, the image owner and the authorized user are required to implement no extra operations except JPEG encryption or decryption. Second, the embedding payload is larger than state-of-the-art works.
A meta-analytic examination of attrition in virtual reality exposure therapy for anxiety disorders

A proposed advantage of virtual reality exposure therapy for anxiety disorders is that people will be less likely to drop out of treatment prematurely if the treatment involves facing one's fear in a virtual world rather than the real world, but this has yet to be empirically tested. The present meta-analyses assess the odds of dropout from virtual reality exposure therapy compared to in vivo exposure therapy, estimate the overall rate of dropout from virtual reality exposure treatment, and test potential moderating variables. The odds ratio meta-analysis indicated that there was no significant difference in the likelihood of attrition from virtual reality exposure therapy relative to in vivo exposure therapy. The overall attrition rate for virtual reality exposure therapy across 46 studies with a combined sample size of 1057 participants was 16%. This rate is slightly lower than other estimates of dropout from in vivo therapy and from cognitive-behavioral therapy for anxiety disorders. Incorporation of between-session intervention (i.e., homework) was identified as a moderator; specifically, inclusion of between-session interventions in the treatment was associated with better retention. Overall, the findings of the present study indicate that virtual reality exposure and in vivo exposure therapy show similar rates of attrition.
Lightweight and Privacy-Preserving Delegatable Proofs of Storage with Data Dynamics in Cloud Storage

Cloud storage has been in widespread use nowadays, which alleviates users' burden of local data storage. Meanwhile, how to ensure the security and integrity of the outsourced data stored in a cloud storage server has also attracted enormous attention from researchers. Proofs of storage (POS) is the main technique introduced to address this problem. Publicly verifiable POS allowing a third party to verify the data integrity on behalf of the data owner significantly improves the scalability of cloud service. However, most of existing publicly verifiable POS schemes are extremely slow to compute authentication tags for all data blocks due to many expensive group exponentiation operations, even much slower than typical network uploading speed, and thus it becomes the bottleneck of the setup phase of the POS scheme. In this article, we propose a new variant formulation called “Delegatable Proofs of Storage (DPOS)”. Then, we construct a lightweight privacy-preserving DPOS scheme, which on one side is as efficient as private POS schemes, and on the other side can support third party auditor and can switch auditors at anytime, close to the functionalities of publicly verifiable POS schemes. Compared to traditional publicly verifiable POS schemes, we speed up the tag generation process by at least several hundred times, without sacrificing efficiency in any other aspect. In addition, we extend our scheme to support fully dynamic operations with high efficiency, reducing the computation of any data update to O(log n) and simultaneously only requiring constant communication costs. We prove that our scheme is sound and privacy preserving against auditor in the standard model. Experimental results verify the efficient performance of our scheme.
Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data

Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. We identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. We contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. Participants infer which of two possible underlying trends is more likely to have produced a sample of time series data by referencing uncertainty visualizations which depict the two trends with variability due to sampling error. By modeling each participant's accuracy as a function of the level of evidence presented over many repeated judgments, we find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static aggregate uncertainty visualizations as a decision aid. Modeling approaches like ours contribute theoretically grounded and richly descriptive accounts of user perceptions to visualization evaluation.
Deep Binary Reconstruction for Cross-Modal Hashing

To satisfy the huge storage space and organization capacity requirements in addressing big multimodal data, hashing techniques have been widely employed to learn binary representations in cross-modal retrieval tasks. However, optimizing the hashing objective under the necessary binary constraint is truly a difficult problem. A common strategy is to relax the constraint and perform individual binarizations over the learned real-valued representations. In this paper, in contrast to conventional two-stage methods, we propose to directly learn the binary codes, where the model can be easily optimized by a standard gradient descent optimizer. However, before that, we present a theoretical guarantee of the effectiveness of the multimodal network in preserving the inter- and intra-modal consistencies. Based on this guarantee, a novel multimodal deep binary reconstruction model is proposed, which can be trained to simultaneously model the correlation across modalities and learn the binary hashing codes. To generate binary codes and to avoid the tiny gradient problem, a novel activation function first scales the input activations to suitable scopes and, then, feeds them to the tanh function to build the hashing layer. Such a composite function is named adaptive tanh. Both linear and nonlinear scaling methods are proposed and shown to generate efficient codes after training the network. Extensive ablation studies and comparison experiments are conducted for the image2text and text2image retrieval tasks; the method is found to outperform several state-of-the-art deep-learning methods with respect to different evaluation metrics.