Reviewerly/exHarmony huggingface dataset:
Comparative Study of the Copyright Laws for Artificial Intelligence in the UK and Korea

In the new paradigm in which artificial intelligence plays a critical role in operating a social and technological system, copyright laws have a lot of legal issues. In this paper, the copyright laws of two countries (the UK and Korea) are compared to distinguish the characteristics of major developed and emerging countries. In general, whereas major developed countries such as the US and the UK are favourable to the protection of new technology, emerging and developing countries have tried to lower the level of regulation of new technology in order to promote the business related to the new paradigm. In the case of artificial intelligence, the UK has a legal system that can cover work created by means of artificial intelligence, in contrast, Korea has no copyright law on artificial intelligence, including computer-generated work. Thus, the emerging countries have mitigated the regulations on copyright in order to support the catch-up strategies of their companies. The copyright law on artificial intelligence in emerging and developing countries needs to be designed for fair competition and fair trade. In addition, the duration of protecting work created by means of artificial intelligence should be shortened, because artificial intelligence can produce a lot of work in a very short time. This paper compared several case laws to compare the cases of copyrights related to artificial intelligence or computer-generated works.
A New Cover Signals a New Decade for JMLD

It is my pleasure to introduce the new Journal of Motor Learning and Development (JMLD) cover and branding (Figure 1).As JMLD enters its second decade, the cover redesign provides a powerful visual statement of the Journal's ever growing commitment to advance the understanding of movement skill acquisition and expression across the lifespan.JMLD's mission will be implemented through editorial policy and practice aimed at providing improved support for and wider engagement with the global community of motor learning and development scholars.Researchers publishing in JMLD will now benefit from a higher journal profile.From 2023, JMLD will receive a Journal Impact Factor from Clarivate Analytics.This represents a significant development in the Journal's growth.JMLD's Impact Factor will increase author preference and provide better recognition of the scientific communications it publishes.JMLD is committed to supporting researchers from all countries and encouraging them to publish their work and data in the Journal.JMLD will act to address inequalities with respect to the scientists and data sets that are published.Underrepresentation of researchers or data due to geoeconomic factors is inconsistent with JMLD's global mission.JMLD will enact editorial practices that will increase representation of the Global South (i.e., Majority World countries).The Journal prides itself in providing a constructive and supportive peerreview experience, which will benefit trainee or early-career researchers in particular.For all researchers, the Journal will provide faster editorial decisions while still maintaining a high standard of rigor in peer-review quality.We will continue to develop and leverage parallel social media-based scientific communication channels to showcase the high-quality research published in JMLD.At the outset of JMLD's second decade, the Journal will increase engagement with the global community of motor learning and development scholars.JMLD's Editorial Board will increase in size to provide more research leadership opportunities and better represent and celebrate the diversity of the motor learning and development scholarly community.A larger Editorial Board will provide a more inclusive voice in editorial decisions related to global recognition and representation, open and reproducible science, and journal impact and influence, for example.Expansion of the Editorial Board with a focus on improving diversity will allow for increased outreach Immink
Channel Estimation in the Interplanetary Internet Using Deep Learning and Federated Learning

Intelligent signal processing holds great importance for the future of resilient, adaptable communications networks. The unique qualities of deep space require an interplanetary Internet to be highly autonomous, efficient, and adaptable to varying Quality of Service (QoS). Deep learning has shown great promise in the field of signal processing for being computationally efficient, capable of handling errors from nonlinear effects (e.g., hardware impairments), and handling low signal-to-noise ratios. A recent survey by Pham et al. notes that none of the papers studied the improvements in classification in the high-order modulation regime. Additionally, these papers did not explore performance of their models in resource limited environments. A hierarchical interplanetary Internet that imposes a variety of constraints on its nodes offers a unique opportunity to explore realistic tradeoffs in model performance. This paper seeks to leverage the processing, storage, and data transmission capabilities of each level of the interplanetary Internet through federated learning. This will reduce data redundancy between nodes and minimize overhead transmission costs on the network. The goals of this project are the following: (i) detail possible insights into future channel estimation techniques applied to noisy, nonlinear models; (ii) explore application of deep learning models for high-order modulation schemes; (iii) quantify the resource-demand reduction resulting from the use of a deep neural network for intelligent signal processing; and (iv) analyze the adaptability of an interdependent system of deep neural networks in the context of a centralized/decentralized federated learning network.
An Improved Repository Structure to Identify, Select and Integrate Components in Component-based Development

An ultimate goal of software development is to build high quality products. The customers of software industry always demand for high-quality products quickly and cost effectively. The component-based development (CBD) is the most suitable methodology for the software companies to meet the demands of target market. To opt CBD, the software development teams have to customize generic components that are available in the market and it is very difficult for the development teams to choose the suitable components from the millions of third party and commercial off the shelf (COTS) components. On the other hand, the development of in-house repository is tedious and time consuming. In this paper, we propose an easy and understandable repository structure to provide helpful information about stored components like how to identify, select, retrieve and integrate components. The proposed repository will also provide previous assessments of developers and end-users about the selected component. The proposed repository will help the software companies by reducing the customization effort, improving the quality of developed software and preventing integrating unfamiliar components.
Understanding domain-wall encoding theoretically and experimentally

We analyze the method of encoding pairwise interactions of higher-than-binary discrete variables (these models are sometimes referred to as discrete quadratic models) into binary variables based on domain walls on one dimensional Ising chains. We discuss how this is relevant to quantum annealing, but also many gate model algorithms such as VQE and QAOA. We theoretically show that for problems of practical interest for quantum computing and assuming only quadratic interactions are available between the binary variables, it is not possible to have a more efficient general encoding in terms of number of binary variables per discrete variable. We furthermore use a D-Wave Advantage 1.1 flux qubit quantum annealing computer to show that the dynamics effectively freeze later for a domain-wall encoding compared to a traditional one-hot encoding. This second result could help explain the dramatic performance improvement of domain wall over one hot which has been seen in a recent experiment on D-Wave hardware. This is an important result because usually problem encoding and the underlying physics are considered separately, our work suggests that considering them together may be a more useful paradigm. We argue that this experimental result is also likely to carry over to a number of other settings, we discuss how this has implications for gate-model and quantum-inspired algorithms.
Detecting Spam Reviews on Vietnamese E-Commerce Websites

The reviews of customers play an essential role in online shopping. People often refer to reviews or comments of previous customers to decide whether to buy a new product. Catching up with this behavior, some people create untruths and illegitimate reviews to hoax customers about the fake quality of products. These are called spam reviews, confusing consumers on online shopping platforms and negatively affecting online shopping behaviors. We propose the dataset called ViSpamReviews, which has a strict annotation procedure for detecting spam reviews on e-commerce platforms. Our dataset consists of two tasks: the binary classification task for detecting whether a review is spam or not and the multi-class classification task for identifying the type of spam. The PhoBERT obtained the highest results on both tasks, 86.89%, and 72.17%, respectively, by macro average F1 score.
A fractal LTB model cannot explain Dark Energy

We revisited the problem of describing, on average, a fractal distribution of matter using a Lemaitre-Tolman-Bondi (LTB) solution. Here we study the fractal structure of our local universe having a fractal dimension and a scale transition, ensuring an homogeneous bang-time function. We test our model with the latest type Ia supernova data, the Pantheon compilation, and discuss problems and possible improvements for it, concluding that a fractal transition in LTB cosmology cannot be used to explain the effects of dark energy without requiring an inhomogeneous big-bang, but it can be useful to study structures at low scales.
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks

Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader domains and tasks, there is a need to establish richer and more diverse benchmarks to better reflect the reality of the application scenarios. Graph learning is an emerging field of machine learning that urgently needs more and better benchmarks. To accommodate the need, we introduce Graph Learning Indexer (GLI), a benchmark curation platform for graph learning. In comparison to existing graph learning benchmark libraries, GLI highlights two novel design objectives. First, GLI is designed to incentivize \emph{dataset contributors}. In particular, we incorporate various measures to minimize the effort of contributing and maintaining a dataset, increase the usability of the contributed dataset, as well as encourage attributions to different contributors of the dataset. Second, GLI is designed to curate a knowledge base, instead of a plain collection, of benchmark datasets. We use multiple sources of meta information to augment the benchmark datasets with \emph{rich characteristics}, so that they can be easily selected and used in downstream research or development. The source code of GLI is available at \url{https://github.com/Graph-Learning-Benchmarks/gli}.
PALMER: Perception-Action Loop with Memory for Long-Horizon Planning

To achieve autonomy in a priori unknown real-world scenarios, agents should be able to: i) act from high-dimensional sensory observations (e.g., images), ii) learn from past experience to adapt and improve, and iii) be capable of long horizon planning. Classical planning algorithms (e.g. PRM, RRT) are proficient at handling long-horizon planning. Deep learning based methods in turn can provide the necessary representations to address the others, by modeling statistical contingencies between observations. In this direction, we introduce a general-purpose planning algorithm called PALMER that combines classical sampling-based planning algorithms with learning-based perceptual representations. For training these perceptual representations, we combine Q-learning with contrastive representation learning to create a latent space where the distance between the embeddings of two states captures how easily an optimal policy can traverse between them. For planning with these perceptual representations, we re-purpose classical sampling-based planning algorithms to retrieve previously observed trajectory segments from a replay buffer and restitch them into approximately optimal paths that connect any given pair of start and goal states. This creates a tight feedback loop between representation learning, memory, reinforcement learning, and sampling-based planning. The end result is an experiential framework for long-horizon planning that is significantly more robust and sample efficient compared to existing methods.
Contrastive View Design Strategies to Enhance Robustness to Domain Shifts in Downstream Object Detection

Contrastive learning has emerged as a competitive pretraining method for object detection. Despite this progress, there has been minimal investigation into the robustness of contrastively pretrained detectors when faced with domain shifts. To address this gap, we conduct an empirical study of contrastive learning and out-of-domain object detection, studying how contrastive view design affects robustness. In particular, we perform a case study of the detection-focused pretext task Instance Localization (InsLoc) and propose strategies to augment views and enhance robustness in appearance-shifted and context-shifted scenarios. Amongst these strategies, we propose changes to cropping such as altering the percentage used, adding IoU constraints, and integrating saliency based object priors. We also explore the addition of shortcut-reducing augmentations such as Poisson blending, texture flattening, and elastic deformation. We benchmark these strategies on abstract, weather, and context domain shifts and illustrate robust ways to combine them, in both pretraining on single-object and multi-object image datasets. Overall, our results and insights show how to ensure robustness through the choice of views in contrastive learning.
A systematic literature review on Virtual Reality and Augmented Reality in terms of privacy, authorization and data-leaks

In recent years, VR and AR has exploded into a multimillionaire market. As this emerging technology has spread to a variety of businesses and is rapidly increasing among users. It is critical to address potential privacy and security concerns that these technologies might pose. In this study, we discuss the current status of privacy and security in VR and AR. We analyse possible problems and risks. Besides, we will look in detail at a few of the major concerns issues and related security solutions for AR and VR. Additionally, as VR and AR authentication is the most thoroughly studied aspect of the problem, we concentrate on the research that has already been done in this area.
ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model's capacity in shadow removal via progressively refining the desired output with both degradation prior and diffusive generative prior, which by nature can serve as a new strong baseline for image restoration. Furthermore, ShadowDiffusion progressively refines the estimated shadow mask as an auxiliary task of the diffusion generator, which leads to more accurate and robust shadow-free image generation. We conduct extensive experiments on three popular public datasets, including ISTD, ISTD+, and SRD, to validate our method's effectiveness. Compared to the state-of-the-art methods, our model achieves a significant improvement in terms of PSNR, increasing from 31.69dB to 34.73dB over SRD dataset.
Language-driven Diversified Image Retargeting

Content-aware image resizing could automatically retarget an image to different aspect ratios while preserving visually salient contents. However, it is difficult for users to interact with the retargeting process and control the results. In this paper, we propose a language-driven diversified image retargeting (LDIR) method that allows the users to control the retargeting process by providing additional textual descriptions. Taking the original image and user-provided texts as inputs, LDIR retargets the image into the desired resolution while preserving the content indicated by texts. Following a self-play reinforcement learning pipeline, a multimodel reward function is proposed by considering both the visual quality and language guidance. Preliminary experiments manifest that LDIR can achieve diversified image retargeting guided by texts.
Near-Optimal Differentially Private Reinforcement Learning

Motivated by personalized healthcare and other applications involving sensitive data, we study online exploration in reinforcement learning with differential privacy (DP) constraints. Existing work on this problem established that no-regret learning is possible under joint differential privacy (JDP) and local differential privacy (LDP) but did not provide an algorithm with optimal regret. We close this gap for the JDP case by designing an $\epsilon$-JDP algorithm with a regret of $\widetilde{O}(\sqrt{SAH^2T}+S^2AH^3/\epsilon)$ which matches the information-theoretic lower bound of non-private learning for all choices of $\epsilon> S^{1.5}A^{0.5} H^2/\sqrt{T}$. In the above, $S$, $A$ denote the number of states and actions, $H$ denotes the planning horizon, and $T$ is the number of steps. To the best of our knowledge, this is the first private RL algorithm that achieves \emph{privacy for free} asymptotically as $T\rightarrow \infty$. Our techniques -- which could be of independent interest -- include privately releasing Bernstein-type exploration bonuses and an improved method for releasing visitation statistics. The same techniques also imply a slightly improved regret bound for the LDP case.
Dynamic Test-Time Augmentation via Differentiable Functions

Distribution shifts, which often occur in the real world, degrade the accuracy of deep learning systems, and thus improving robustness is essential for practical applications. To improve robustness, we study an image enhancement method that generates recognition-friendly images without retraining the recognition model. We propose a novel image enhancement method, DynTTA, which is based on differentiable data augmentation techniques and generates a blended image from many augmented images to improve the recognition accuracy under distribution shifts. In addition to standard data augmentations, DynTTA also incorporates deep neural network-based image transformation, which further improves the robustness. Because DynTTA is composed of differentiable functions, it is directly trained with the classification loss of the recognition model. We experiment with widely used image recognition datasets using various classification models, including Vision Transformer and MLP-Mixer. DynTTA improves the robustness with almost no reduction in classification accuracy for clean images, which is a better result than the existing methods. Furthermore, we show that estimating the training time augmentation for distribution-shifted datasets using DynTTA and retraining the recognition model with the estimated augmentation significantly improves robustness.
On the Evolution of Boomerang Uniformity in Cryptographic S-boxes

S-boxes are an important primitive that help cryptographic algorithms to be resilient against various attacks. The resilience against specific attacks can be connected with a certain property of an S-box, and the better the property value, the more secure the algorithm. One example of such a property is called boomerang uniformity, which helps to be resilient against boomerang attacks. How to construct S-boxes with good boomerang uniformity is not always clear. There are algebraic techniques that can result in good boomerang uniformity, but the results are still rare. In this work, we explore the evolution of S-boxes with good values of boomerang uniformity. We consider three different encodings and five S-box sizes. For sizes $4\times 4$ and $5\times 5$, we manage to obtain optimal solutions. For $6\times 6$, we obtain optimal boomerang uniformity for the non-APN function. For larger sizes, the results indicate the problem to be very difficult (even more difficult than evolving differential uniformity, which can be considered a well-researched problem).
Quantum entanglement without superposition

Superposition states are at the origin of many paradoxes in quantum mechanics. By unraveling the von Neumann equation for density matrices, we develop a superposition-free formulation of quantum mechanics. Stochastic quantum jumps are a key feature of this approach, in blatant contrast with the continuity of the deterministic Schr\"odinger equation. We explain how quantum entanglement arises. Our superposition-free formulation results offers a new perspective on quantum mechanics.
Pose Estimation for Human Wearing Loose-Fitting Clothes: Obtaining Ground Truth Posture Using HFR Camera and Blinking LEDs

Human pose estimation, particularly in athletes, can help improve their performance. However, this estimation is difficult using existing methods, such as human annotation, if the subjects wear loose-fitting clothes such as ski/snowboard wears. This study developed a method for obtaining the ground truth data on two-dimensional (2D) poses of a human wearing loose-fitting clothes. This method uses fast-flushing light-emitting diodes (LEDs). The subjects were required to wear loose-fitting clothes and place the LED on the target joints. The LEDs were observed directly using a camera by selecting thin filmy loose-fitting clothes. The proposed method captures the scene at 240 fps by using a high-frame-rate camera and renders two 30 fps image sequences by extracting LED-on and -off frames. The temporal differences between the two video sequences can be ignored, considering the speed of human motion. The LED-on video was used to manually annotate the joints and thus obtain the ground truth data. Additionally, the LED-off video, equivalent to a standard video at 30 fps, confirmed the accuracy of existing machine learning-based methods and manual annotations. Experiments demonstrated that the proposed method can obtain ground truth data for standard RGB videos. Further, it was revealed that neither manual annotation nor the state-of-the-art pose estimator obtains the correct position of target joints.