Reviewerly/exHarmony huggingface dataset:
DAFNet: Generating Diverse Actions for Furniture Interaction by Learning Conditional Pose Distribution

Abstract We present DAFNet, a novel data‐driven framework capable of generating various actions for indoor environment interactions. By taking desired root and upper‐body poses as control inputs, DAFNet generates whole‐body poses suitable for furniture of various shapes and combinations. To enable the generation of diverse actions, we introduce an action predictor that automatically infers the probabilities of individual action types based on the control input and environment. The action predictor is learned in an unsupervised manner by training Gaussian Mixture Variational Autoencoder (GMVAE). Additionally, we propose a two‐part normalizing flow‐based pose generator that sequentially generates upper and lower body poses. This two‐part model improves motion quality and the accuracy of satisfying conditions over a single model generating the whole body. Our experiments show that DAFNet can create continuous character motion for indoor scene scenarios, and both qualitative and quantitative evaluations demonstrate the effectiveness of our framework. We propose DAFNet, a novel data‐driven framework that can generate various actions for indoor environment interactions. Given the desired root and upper‐body pose as control inputs, DAFNet generates whole‐body poses for a character appropriate for furniture of various shapes and combinations. image
Kernel Geometric Mean Metric Learning

Geometric mean metric learning (GMML) algorithm is a novel metric learning approach proposed recently. It has many advantages such as unconstrained convex objective function, closed form solution, faster computational speed, and interpretability over other existing metric learning technologies. However, addressing the nonlinear problem is not effective enough. The kernel method is an effective method to solve nonlinear problems. Therefore, a kernel geometric mean metric learning (KGMML) algorithm is proposed. The basic idea is to transform the input space into a high-dimensional feature space through nonlinear transformation, and use the integral representation of the weighted geometric mean and the Woodbury matrix identity in new feature space to generalize the analytical solution obtained in the GMML algorithm as a form represented by a kernel matrix, and then the KGMML algorithm is obtained through operations. Experimental results on 15 datasets show that the proposed algorithm can effectively improve the accuracy of the GMML algorithm and other metric algorithms.
Constructing the Bounds for Neural Network Training Using Grammatical Evolution

Artificial neural networks are widely established models of computational intelligence that have been tested for their effectiveness in a variety of real-world applications. These models require a set of parameters to be fitted through the use of an optimization technique. However, an issue that researchers often face is finding an efficient range of values for the parameters of the artificial neural network. This paper proposes an innovative technique for generating a promising range of values for the parameters of the artificial neural network. Finding the value field is conducted by a series of rules for partitioning the original set of values or expanding it, the rules of which are generated using grammatical evolution. After finding a promising interval of values, any optimization technique such as a genetic algorithm can be used to train the artificial neural network on that interval of values. The new technique was tested on a wide range of problems from the relevant literature and the results were extremely promising.
Alleviating data insufficiency for Chinese sign language recognition

Abstract Continuous Chinese sign language recognition (CCSLR) methods have shown their strong ability to learn excellent model architectures from datasets. However, due to data insufficiency, it is difficult to complete the CCSLR task. In this work, we focus on a simple but important solution to alleviate data insufficiency: how to refine the model architecture of a CCSLR network to improve the robustness of feature processing by using some better-quality non-Chinese sign language datasets. To this end, a simple empirical study was first conducted to verify the feasibility of knowledge transfer in the CCSLR task. Surprisingly, just by pre-training of our recognition model on a foreign sign language dataset, we can refine the model architecture and improve its robustness significantly. To make it more practical, the key issue of how to fine-tune the existing feature processing models for effective guidance should be carefully investigated. Then, we propose a novel scheme for fine-tuning of pre-trained models named FTP, which updates the spatial feature extractor initialized by a pre-trained backbone and freezes the temporal feature extractor implemented by a better shareable transformer encoder. Compared with the baseline method, our FTP method can achieve significant performance improvement on the public dataset USTC-CCSL.
Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs

Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly detection in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address three main challenges associated with this problem: i) how to compare graph snapshots across time, ii) how to capture temporal dependencies, and iii) how to combine different views of a temporal graph. To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot. LAD explicitly models short term and long term dependencies by applying two sliding windows. Next, we propose MultiLAD, a simple and effective generalization of LAD to multi-view graphs. MultiLAD provides the first change point detection method for multi-view dynamic graphs. It aggregates the singular values of the normalized graph Laplacian from different views through the scalar power mean operation. Through extensive synthetic experiments, we show that i) LAD and MultiLAD are accurate and outperforms state-of-the-art baselines and their multi-view extensions by a large margin, ii) MultiLAD’s advantage over contenders significantly increases when additional views are available, and iii) MultiLAD is highly robust to noise from individual views. In five real world dynamic graphs, we demonstrate that LAD and MultiLAD identify significant events as top anomalies such as the implementation of government COVID-19 interventions which impacted the population mobility in multi-view traffic networks.
Perceived ICT Competencies of Female Student Teachers In Relation to Selected Variables

The present study intends to determine the perceived level of Information and Communication Technology (ICT) competencies of female student teachers enrolled in a Bachelor of Education (B.Ed.) teacher education programme at six private colleges of education in the Salem district of Tamil Nadu State, India. A convenient sample of 301 female student teachers participated in this study. The researchers used the “Information and Communication Technology Competency Scale (ICTCS)” developed based on existing literature to collect data from female student teachers. The data were collected using a Google form and analysed using descriptive and inferential statistics. The results indicate that female student teachers had competency in most ICT parameters. The study identified several significant factors that influenced the ICT competencies of female student teachers, including locality of residence, education level, possession of a personal computer or laptop, completion of a computer training course, and the number of years of computer usage. However, the major subject of female student teachers did not affect their level of ICT competencies. Policymakers and teacher educators can use these findings to design targeted interventions to enhance the ICT competencies among student teachers in teacher education programmes.
Improvements of birefringence imaging techniques to observe stress-induced ferroelectricity in SrTiO <sub>3</sub> based on <i>K</i> -means clustering with circular statistics

Optical birefringence imaging techniques have enabled quantitative evaluation of macroscopic structures, e.g. domains and grain boundaries. With inhomogeneous samples, the selection of regions for analysis can significantly affect the conclusions; thus, arbitrary selection can lead to inaccurate findings. Thus, in this study, we present a method to cluster all birefringence imaging data using -means multivariate clustering on a pixel-by-pixel basis to eliminate arbitrariness in the region selection process. Linear statistics cannot be applied to the polarization states of light described by angles and their periodicity; thus, circular statistics are used for clustering. By applying this approach to a 42,280-pixel image comprising 12 explanatory variables of stress-induced ferroelectricity in SrTiO3, we were able to select a region of locally developed spontaneous polarization. This region covers only 1.9% of the total area, where the stress and/or strain is concentrated, thereby resulting in a higher ferroelectric phase transition temperature and larger spontaneous polarization than in the other regions. The -means multivariate clustering with circular statistics is shown to be a powerful tool to eliminate arbitrariness. The proposed method is a significant analysis technique that can be applied to images using the polarization of light, azimuthal angle of crystals, scattering angle.
Fertility Indicators for Porphyry-Cu-Au+Pd±Pt Deposits: Evidence from Skouries, Chalkidiki Peninsula, Greece, and Comparison with Worldwide Mineralizations

The research interest for many authors has been focused on the origin, recovery, and exploration of critical metals, including platinum-group elements (PGEs), with the aim of finding new potential sources. Many giant porphyry Cu deposits are well known around the Pacific Rim, in the Balkan–Carpathian system, Himalayas, China, and Malaysia. However, only certain porphyry Cu-Au deposits are characterized by the presence of significant Pd and Pt contents (up to 20 ppm). This contribution provides new analytical data on porphyry-Cu-Au±Pd±Pt deposits from the Chalkidiki Peninsula and an overview of the existing geochemical characteristics of selected porphyry-Cu deposits worldwide in order to define significant differences between PGE-fertile and PGE-poor porphyry-Cu intrusions. The larger Mg, Cr, Ni, Co, and Re contents and smaller LILE elements (Ba and Sr) in fertile porphyry-Cu-Au-(PGE) reflect the larger contribution from the mantle to the parent magmas. In contrast, the smaller Mg, Cr, Ni, Co, and Re contents and larger Ba and Sr in PGE-poor porphyry-Cu-Mo deposits from the Chalkidiki Peninsula (Vathi, Pontokerasia, and Gerakario) and Russia–Mongolia suggest the presence of parent magmas with a more crustal contribution. Although there is an overlap in the plots of those elements, probably due to the evolution of the ore-forming system, consideration of the maximum contents of Mg, Cr, Ni, and Co is proposed. Magnetite which separated from the mineralized Skouries porphyry of Greece showed small negative Eu anomalies (Eu/Eu* ≥ 0.55), reflecting a relatively high oxidation state during the cooling of the ore-forming system. The relatively high, up to 6 ppm (Pd+Pt), and low Cr content towards the transition from the porphyry to epithermal environment, coupled with the occurrence of Pd, Te, and Se minerals (merenskyite, clausthalite), and tetrahedrite–tennantite in fertile porphyry Cu deposits (Elatsite deposit, Bulgaria), reflect a highly fractionated ore-forming system. Thus, in addition to the crustal and mantle recycling, metasomatism, high oxidation state, and abundant magmatic water, other factors required for the origin of fertile porphyry-Cu deposits are the critical degree of mantle melting to release Pt and Pd in the ore-forming fluids and the degree of fractionation, as reflected in the mineral chemistry and geochemical data.
Pairing Hamiltonians of Nearest-Neighbor Interacting Superconducting Qubits on an IBM Quantum Computer

A quantum simulation experiment pairing Hamiltonians of nearest-neighbor interacting superconducting qubits was performed with a complete set of algorithms on an IBM Quantum Computer-IBMq Lima. The experiment revealed that the fidelity is a function of iteration using Suzuki–Trotter decomposition for four different types of nearest-neighbor Heisenberg, XY, transverse, and longitudinal Ising superconducting qubit couplings of Hamiltonians. The experiment displayed the models of how the experimental density matrices shift from the theoretical density matrices and how their behavior changes with different numbers of iterations. It also demonstrated the reconstruction of quantum states and how the states change as a function of iteration with the IBM Quantum Computer-IBMq Lima. The time evolutions of the states for different models were also shown to predict the dominance of each state.
An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images

Denoising computed tomography (CT) medical images is crucial in preserving information and restoring images contaminated with noise. Standard filters have extensively been used for noise removal and fine details’ preservation. During the transmission of medical images, noise degrades the visibility of anatomical structures and subtle abnormalities, making it difficult for radiologists to accurately diagnose and interpret medical conditions. In recent studies, an optimum denoising filter using the wavelet threshold and deep-CNN was used to eliminate Gaussian noise in CT images using the image quality index (IQI) and peak signal-to-noise ratio (PSNR). Although the results were better than those with traditional techniques, the performance resulted in a loss of clarity and fine details’ preservation that rendered the CT images unsuitable. To address these challenges, this paper focuses on eliminating noise in CT scan images corrupted with additive Gaussian blur noise (AGBN) using an ensemble approach that integrates anisotropic Gaussian filter (AGF) and wavelet transform with a deep learning denoising convolutional neural network (DnCNN). First, the noisy image is denoised by AGF and Haar wavelet transform as preprocessing operations to eliminate AGBN. The DnCNN is then combined with AGF and wavelet for post-processing operation to eliminate the rest of the noises. Specifically, we used AGF due to its adaptability to edge orientation and directional information, which prevents blurring along edges for non-uniform noise distribution. Denoised images are evaluated using PSNR, mean squared error (MSE), and the structural similarity index measure (SSIM). Results revealed that the average PSNR value of the proposed ensemble approach is 28.28, and the average computational time is 0.01666 s. The implication is that the MSE between the original and reconstructed images is very low, implying that the image is restored correctly. Since the SSIM values are between 0 and 1.0, 1.0 perfectly matches the reconstructed image with the original image. In addition, the SSIM values at 1.0 or near 1.0 implicitly reveal a remarkable structural similarity between the denoised CT image and the original image. Compared to other techniques, the proposed ensemble approach has demonstrated exceptional performance in maintaining the quality of the image and fine details’ preservation.
Quantum multiplication algorithm based on the convolution theorem

The problem of efficient multiplication of large numbers has been a long-standing challenge in classical computation and has been extensively studied for centuries. It appears that the existing classical algorithms are close to their theoretical limit and offer little room for further enhancement. However, with the advent of quantum computers and the need for quantum algorithms that can perform multiplication on quantum hardware, a new paradigm emerges. In this paper, inspired by the convolution theorem and quantum amplitude amplification paradigm we propose a quantum algorithm for integer multiplication with time complexity of $O(\sqrt{n}{log}^{2}n)$ which outperforms the best-known classical algorithm, the Harvey algorithm with time complexity of $O(nlogn)$. Unlike the Harvey algorithm, our algorithm does not have the restriction of being applicable solely to extremely large numbers, making it a versatile choice for a wide range of integer multiplication tasks. The paper also reviews the history and development of classical multiplication algorithms and motivates us to explore how quantum resources can provide new perspectives and possibilities for this fundamental problem.
Analytical approach to higher-order correlation functions in U(1) symmetric systems

We derive a compact analytical solution of the $n\mathrm{th}$-order equal-time correlation functions by using scattering matrix ($S$ matrix) under a weak coherent state input. Our solution applies to any dissipative quantum system that respects the U(1) symmetry. We further extend our analytical solution into two categories depending on whether the input and output channels are identical. The first category provides a different path for studying cross-correlation and multiple-drive cases, while the second category is instrumental in studying waveguide quantum electrodynamics systems. Our analytical solution allows for easy investigation of the statistical properties of multiple photons even in complex systems. Furthermore, we have developed a user-friendly open-source library in Python known as the quantum correlation solver, and this tool provides a convenient means to study various dissipative quantum systems that satisfy the above-mentioned criteria. Our study enables using $S$ matrix to study the photonic correlation and advance the possibilities for exploring complex systems.
QuickAware: a virtual reality tool for quick clay landslide hazard awareness

Abstract Disaster emergency management is crucial for safeguarding lives and the environment in the face of natural and human-caused calamities, such as quick clay landslides. Disaster emergency management encompasses hazard identification, prevention, response, and recovery, most of which require knowledge and information acquired through training initiatives. Quick clay landslides pose a substantial hazard in regions like Scandinavia, Canada, Alaska, and Russia. With numerous historical incidents, knowledge-based awareness of quick clay landslide hazards is of paramount importance. In recent years, the area of application of virtual reality (VR) has grown tremendously from the entertainment industry to the military, to mental health, to hazard identification training and our daily lives. VR has been widely employed in hazard identification and prevention, safety training, evacuation, search and rescue, and damage identification of hazards. This study outlines the design, development, and implementation of QuickAware, a VR tool designed to create awareness of quick clay hazards. The development process of the tool started with a co-design approach where stakeholder experts were brought to collaborate in setting up VR scenarios and defining the VR environment contents. The contents were then conceptualized and translated into a VR experience. The novelty of the tool is that it immerses users in a realistic experience, allowing them to engage directly with the causes and consequences of quick clay landslide disasters which would be dangerous in real-life. The usability of the VR tool was examined by surveying 16 participants. The preliminary results of the survey indicated that the tool has a promising value in improving awareness creation for quick clay landslide hazards.
Cybersecurity in the Quantum Age: Threats, Challenges, and Solutions

In an increasingly interconnected digital landscape, cybersecurity has become an indispensable facet of our modern world. This research paper delves into the dynamic realm of cybersecurity, exploring its multifaceted dimensions, challenges, and innovative solutions. As the frequency and sophistication of cyberattacks continue to rise, organizations and individuals face unprecedented risks to their data, privacy, and digital infrastructure. The paper examines the evolving threat landscape, encompassing diverse actors, motivations, and tactics. It underscores the importance of proactive cybersecurity measures as a crucial defence against these threats. The research discusses the strategies and best practices in cybersecurity, emphasizing the significance of threat intelligence, encryption, access control, and incident response. It also highlights the critical role of user education and awareness in building a resilient cybersecurity posture. Challenges in cybersecurity, including the shortage of skilled professionals, the emergence of novel attack vectors, and the ethical considerations surrounding offensive cybersecurity operations, are also explored in depth. To tackle these challenges, the paper introduces emerging cybersecurity technologies and trends, such as artificial intelligence, blockchain, and quantum-resistant encryption. It emphasizes the importance of collaboration between public and private sectors, information sharing, and international cooperation in mitigating global cyber threats. Ultimately, this research underscores the imperative of continuous innovation, vigilance, and adaptability in the ever-evolving landscape of cybersecurity. It serves as a call to action for individuals, organizations, and governments to collectively reinforce their cybersecurity foundations to safeguard the digital future
Connecting to AWS OpenSearch Serverless using Python

I recently wanted to use ElasticSearch (or OpenSearch as Amazon terms it from the fork) in an AWS environment, using Python. When I tried to connect I got a very painful 403 Forbidden error.
Self-referential language counting (I learned another word today bringing my total to fourteen words and twenty letters)

A recent XKCD caused some amusement: “Vocabulary update: I learned another word today, bringing my total to twelve”. We wondered whether there might be possible formulations of this joke that also contained the unique character count, so I wrote the following python script: import num2word import numpy def find_self_reference(): sentence = "I learned another word today bringing my total to {0} words and {1} letters" words = 12 for i
Searching images in a web archive

This article presents the research and development of a large-scale image search system applied to launch a word-wide innovative service that enables searching billions of historical images archived from the web since the 1990s. Contributions of this work were applied to enhance the Arquivo.pt web archive with an image-search service where users submit text queries, through a web user interface or an API, and immediately receive a list of historical web-archived images. However, supporting image search over web archives raised new challenges. The volume of data to be processed was big and heterogeneous, summing over 530TB of historical web data published since the early days of the web. The main contributions of this work are a toolkit of algorithms that extracts textual metadata to describe web-archived images, a system architecture and workflow to index large amounts of web-archived images considering their specific temporal features and a ranking algorithm to order image-search results by relevance. This research was applied to launch an enhanced image-search service that is publicly available since March 2021. All the developed software is fully available as free open-source software.