Reviewerly/exHarmony huggingface dataset:
Pyramid Texture Filtering

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation---the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement. Code is available at https://rewindl.github.io/pyramid_texture_filtering/.
Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar

Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.
<i>LoCoPalettes</i>: Local Control for Palette‐based Image Editing

Abstract Palette‐based image editing takes advantage of the fact that color palettes are intuitive abstractions of images. They allow users to make global edits to an image by adjusting a small set of colors. Many algorithms have been proposed to compute color palettes and corresponding mixing weights. However, in many cases, especially in complex scenes, a single global palette may not adequately represent all potential objects of interest. Edits made using a single palette cannot be localized to specific semantic regions. We introduce an adaptive solution to the usability problem based on optimizing RGB palette colors to achieve arbitrary image‐space constraints and automatically splitting the image into semantic sub‐regions with more representative local palettes when the constraints cannot be satisfied. Our algorithm automatically decomposes a given image into a semantic hierarchy of soft segments. Difficult‐to‐achieve edits become straightforward with our method. Our results show the flexibility, control, and generality of our method.
Integration of Internet of Things and blockchain technology in healthcare domain: A systematic literature review

Summary Healthcare is a vitally important field in the industry and evolving day by day in the aspect of technology, services, computing, and management. Its potential significance can be increased by incorporating Internet of Things (IoT) technology to make it smart in the aspect of automating activities, which is then further reformed in the healthcare domain with the help of blockchain technology. Blockchain technology provides many features to IoT‐based healthcare domain applications such as restructuring by securing traditional practices, data management, data sharing, patient remote monitoring, and drug analysis. In this study, a systematic literature review has been carried out in which a total of 52 studies were selected to conduct systematic literature review from databases PubMed, IEEE Access, and Scopus; the study includes IoT technology and blockchain integration in healthcare domain‐related application areas. This study also includes taxonomy that mentions the aspects and areas in healthcare domain incorporating the traditional system with the integration of IoT and blockchain to provide transparency, security, privacy, and immutability. This study also includes the incorporation of related sensors, platforms of blockchain, the objective focus of selected studies, and future directions by incorporating IoT and blockchain in healthcare domain. This study will help researchers who want to work with IoT and blockchain technology integration in healthcare domain.
The Use of iPad in Enhancing Creative Writing: An Innovative Approach for Young ESL Learners

Digital technology is essential to today's education development.Research has shown that the use of digital technology such as Computer-Assisted Language Learning (CALL) and Mobile-assisted Language Learning (MALL) positively impacts language learning.This study investigates the language learners' perception of using iPad to enhance creative writing and the potential of using iPad to improve creative writing.An online survey adapted from Technology Acceptance Model was distributed to 50 primary school learners using iPad for developing creative writing across Malaysia.The analysis of the responses demonstrated that language learners have a positive perception towards using iPad to enhance creative writing.On this basis, it is recommended that language teachers and learners use iPad or any similar devices to enhance language learning.
A Color Image Encryption Method Based on Dynamic Selection Chaotic System and Singular Value Decomposition

As the basis for guiding business process decisions, flowcharts contain sensitive information pertaining to process-related concepts. Therefore, it is necessary to encrypt them to protect the privacy or security of stakeholders. Using the principles of image singular value decomposition, chaotic system randomness, and neural network camouflage, a business flow chart encryption method based on dynamic selection chaotic system and singular value decomposition is proposed. Specifically, a dynamic selected chaotic system is constructed based on the nonlinear combination of one-dimensional chaotic system Logistics and Sine, and its randomness is verified. Next, using the neural network, the process image is merged into a gray matrix. The double-bit unitary matrix scrambling based on singular value decomposition is then proposed. Subsequently, using the dynamic selected chaotic system, a new sub-division diffusion method is proposed, which combines, diffuses, and performs weighted superposition to generate a matrix after diffusion and compression. Finally, the asymmetric encryption method encrypts the color image and reduces its dimensionality into a single grayscale ciphertext, and the decryption process is not the reverse of the encryption process. Simulation results and performance analysis show that the proposed image encryption scheme has good encryption performance.
Identification of Language-Induced Mental Load from Eye Behaviors in Virtual Reality

Experiences of virtual reality (VR) can easily break if the method of evaluating subjective user states is intrusive. Behavioral measures are increasingly used to avoid this problem. One such measure is eye tracking, which recently became more standard in VR and is often used for content-dependent analyses. This research is an endeavor to utilize content-independent eye metrics, such as pupil size and blinks, for identifying mental load in VR users. We generated mental load independently from visuals through auditory stimuli. We also defined and measured a new eye metric, focus offset, which seeks to measure the phenomenon of "staring into the distance" without focusing on a specific surface. In the experiment, VR-experienced participants listened to two native and two foreign language stimuli inside a virtual phone booth. The results show that with increasing mental load, relative pupil size on average increased 0.512 SDs (0.118 mm), with 57% reduced variance. To a lesser extent, mental load led to fewer fixations, less voluntary gazing at distracting content, and a larger focus offset as if looking through surfaces (about 0.343 SDs, 5.10 cm). These results are in agreement with previous studies. Overall, we encourage further research on content-independent eye metrics, and we hope that hardware and algorithms will be developed in the future to further increase tracking stability.
A Customized Deep Sleep Recommender System Using Hybrid Deep Learning

This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user's evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases.
Multi‐modal object detection via transformer network

Abstract According to the fact that single‐modal data usually contain limited information, a great deal of effort has been devoted to making use of the complementary information contained in the multi‐modal data on various patterns. Thus, this paper is concerned with an object detection method that can fully utilize multi‐modal data. First, the method introduces the transformer mechanism to realize the fusion of intra‐modal and inter‐modal features of different modal data. The aim is to take advantage of the complementarity of data between modalities, which helps to improve the performance of multi‐modal object detection. Second, a contrastive loss suitable for contrastive learning is applied. This enables the authors to effectively utilize label information. Extensive experiments are conducted on multiple object detection datasets to demonstrate the effectiveness of our proposed method.
Teaching competency in virtual education: Systematic review

&lt;span lang="EN-US"&gt;This study arises as a need to recognize teaching competencies and their integration to become professionals who respond to current challenges aimed at improving learning achievements and comprehensive training of students. The objective of the research was to analyze and recognize teaching competencies in virtual education in higher education students. The study is important due to the direct relationship with student learning. The study was qualitative research, with a systematic design, followed the methodology proposed by the PRISMA declaration. Inclusion and exclusion criteria were applied to process the articles analyzed. A study was conducted based on 38 articles published in high impact scientific journals between 2019 and 2022. The results showed that teaching competencies in virtual education must be worked in an integrated manner: disciplinary, pedagogical, technological and research competencies that will translate into a real development of teaching-learning processes, better teaching performance and student performance. The studies determine that the disciplinary, pedagogical, technological, and investigative competence must be worked on in an interrelated manner, there is a need to integrate technology effectively, and it is important to promote research in students through experiments that allow building skills.&lt;/span&gt;
Design by Contract Framework for Quantum Software

To realize reliable quantum software, techniques to automatically ensure the quantum software's correctness have recently been investigated. However, they primarily focus on fixed quantum circuits rather than the procedure of building quantum circuits. Despite being a common approach, the correctness of building circuits using different parameters following the same procedure is not guaranteed. To this end, we propose a design-by-contract framework for quantum software. Our framework provides a python-embedded language to write assertions on the input and output states of all quantum circuits built by certain procedures. Additionally, it provides a method to write assertions about the statistical processing of measurement results to ensure the procedure's correctness for obtaining the final result. These assertions are automatically checked using a quantum computer simulator. For evaluation, we implemented our framework and wrote assertions for some widely used quantum algorithms. Consequently, we found that our framework has sufficient expressive power to verify the whole procedure of quantum software.
The Visual Language of Fabrics

We introduce text2fabric, a novel dataset that links free-text descriptions to various fabric materials. The dataset comprises 15,000 natural language descriptions associated to 3,000 corresponding images of fabric materials. Traditionally, material descriptions come in the form of tags/keywords, which limits their expressivity, induces pre-existing knowledge of the appropriate vocabulary, and ultimately leads to a chopped description system. Therefore, we study the use of free-text as a more appropriate way to describe material appearance, taking the use case of fabrics as a common item that non-experts may often deal with. Based on the analysis of the dataset, we identify a compact lexicon, set of attributes and key structure that emerge from the descriptions. This allows us to accurately understand how people describe fabrics and draw directions for generalization to other types of materials. We also show that our dataset enables specializing large vision-language models such as CLIP, creating a meaningful latent space for fabric appearance, and significantly improving applications such as fine-grained material retrieval and automatic captioning.
An Extensible, Data-Oriented Architecture for High-Performance, Many-World Simulation

Training AI agents to perform complex tasks in simulated worlds requires millions to billions of steps of experience. To achieve high performance, today's fastest simulators for training AI agents adopt the idea of batch simulation: using a single simulation engine to simultaneously step many environments in parallel. We introduce a framework for productively authoring novel training environments (including custom logic for environment generation, environment time stepping, and generating agent observations and rewards) that execute as high-performance, GPU-accelerated batched simulators. Our key observation is that the entity-component-system (ECS) design pattern, popular for expressing CPU-side game logic today, is also well-suited for providing the structure needed for high-performance batched simulators. We contribute the first fully-GPU accelerated ECS implementation that natively supports batch environment simulation. We demonstrate how ECS abstractions impose structure on a training environment's logic and state that allows the system to efficiently manage state, amortize work, and identify GPU-friendly coherent parallel computations within and across different environments. We implement several learning environments in this framework, and demonstrate GPU speedups of two to three orders of magnitude over open source CPU baselines and 5-33× over strong baselines running on a 32-thread CPU. An implementation of the OpenAI hide and seek 3D environment written in our framework, which performs rigid body physics and ray tracing in each simulator step, achieves over 1.9 million environment steps per second on a single GPU.
A user-centred evaluation of DisCERN: Discovering counterfactuals for code vulnerability detection and correction

Counterfactual explanations highlight actionable knowledge which helps to understand how a machine learning model outcome could be altered to a more favourable outcome. Understanding actionable corrections in source code analysis can be critical to proactively mitigate security attacks that are caused by known vulnerabilities. In this paper, we present the DisCERN explainer for discovering counterfactuals for code vulnerability correction. Given a vulnerable code segment, DisCERN finds counterfactual (i.e. non-vulnerable) code segments and recommends actionable corrections. DisCERN uses feature attribution knowledge to identify potentially vulnerable code statements. Subsequently, it applies a substitution-focused correction, suggesting suitable fixes by analysing the nearest-unlike neighbour. Overall, DisCERN aims to identify vulnerabilities and correct them while preserving both the code syntax and the original functionality of the code. A user study evaluated the utility of counterfactuals for vulnerability detection and correction compared to more commonly used feature attribution explainers. The study revealed that counterfactuals foster positive shifts in mental models, effectively guiding users towards making vulnerability corrections. Furthermore, counterfactuals significantly reduced the cognitive load when detecting and correcting vulnerabilities in complex code segments. Despite these benefits, the user study showed that feature attribution explanations are still more widely accepted than counterfactuals, possibly due to the greater familiarity with the former and the novelty of the latter. These findings encourage further research and development into counterfactual explanations, as they demonstrate the potential for acceptability over time among developers as a reliable resource for both coding and training.
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport

Graph alignment, which aims at identifying corresponding entities across multiple networks, has been widely applied in various domains. As the graphs to be aligned are usually constructed from different sources, the inconsistency issues of structures and features between two graphs are ubiquitous in real-world applications. Most existing methods follow the "embed-then-cross-compare" paradigm which computes node embeddings in each graph and then processes node correspondences based on cross-graph embedding comparison. However, we find these methods are unstable and sub-optimal when structure or feature inconsistency appears. To this end, we propose SLOTAlign, an unsupervised graph alignment framework that jointly performs Structure Learning and Optimal Transport Alignment. We convert graph alignment to an optimal transport problem between two intra-graph matrices without the requirement of cross-graph comparison. We further incorporate multi-view structure learning to enhance graph representation power and reduce the effect of structure and feature inconsistency inherited across graphs. Moreover, an alternating scheme based algorithm has been developed to address the joint optimization problem in SLOTAlign and the provable convergence result are also established. Finally, we conduct extensive experiments on six unsupervised graph alignment datasets and the DBP15K knowledge graph (KG) alignment benchmark dataset. The proposed SLOTAlign shows superior performance and strongest robustness over seven unsupervised graph alignment methods and five specialized KG alignment methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
A Nonsupervised Learning Approach for Automatic Characterization of Short-Distance Boxing Training

Human activity recognition (HAR) for boxing training typically requires specialized hardware or large labeled datasets to identify different types of punches and analyze their basic properties. However, measuring the unpredictability of boxers, a key characteristic to deceive opponents during combats, has remained an unexplored challenge. We tackled this challenge by combining a series of novel techniques and ideas. Unpredictability was computed as the entropy of the trajectories of the Markov chain characterizing the training session, estimated from the combinations (sequences of punches) thrown by the boxer to the training bag. Punch combinations were obtained by detecting punches as outliers in the raw acceleration sensor datastream coming from the bag, and separating them by type with an ensemble of PCA+GMM clusters. Punches were then divided into combinations using statistical analysis of separation intervals. The detection procedure works even with noisy data, in contrast to force-threshold methods commonly reported in the literature. As distinctive features of our system, it uses only unlabeled data from the current training session, and sensor positions and orientations inside the bag are unconstrained. However, punch clustering achieves up to 94% accuracy, comparable to state-of-the-art supervised approaches. These features are currently unseen in the literature and demonstrate the capacity for unsupervised learning techniques to address challenging problems in sports. Multiple validation experiments were consistent with boxers’ level and session performance. Overall, our approach provides significant contributions to the field of HAR and has the potential to improve the way boxing and other sports are trained and evaluated.