Reviewerly/exHarmony huggingface dataset:
Space from Line: What can Metaverse Support in Education/Learning Activity?

Today, Metaverse is one of big topics in IT as a framework with the challengeable mechanism which can design and construct the new social activity in the virtual world, different from our real world. As one of applications, the education/ learning field is hopefully nominated. Although some prototype systems, or trial systems were developed until today, the commerce systems or the computer-game systems, exemplified as leading systems, provide the activities, authorized with ownerships/concessions or promoted by battles/matches; and the avatars are introduced as the important actors in the virtual world, corresponding to the identical persons in the real world. This framework makes up the pseudo experience actually: in particular, more realistic if VR devices were available. In this situation, the identical person is different from the corresponded avatar. In many activities, currently developed as applications, the effects are the visualized/observed results or the explicit/ stepwise processes, but the effects are not always true in the education/learning field. In this paper, we discuss this viewpoint on the basis of the features of education/learning activity.
Design of Metaverse-Based Physical Fitness Service for the Enhancement of Exercise Capability for Youth

This study explains the case study to develop a metaverse-based training system for the management of the physical fitness of youth. The study targeted developing a smart mirror-based AR physical fitness system and a bone age-based physical fitness management service to enhance exercise capability in youth. Accordingly, the data on youth’s bone age, physical strength, and physique were collected. Based on the collected data, the stage of the human development in youth was categorized, and the physical fitness management system providing the exercise program customized for the phases of physical development was implemented. Furthermore, a solution was devised so that youth with less physical exercise experience could practice physical fitness training without a coach using the smart mirror-based AR physical fitness system. This system uses an RGB-D camera and a three-dimensional posture estimation algorithm to recognize a user’s movement, determine movement accuracy, and provide feedback for the improved exercise effect. The XR Physical Fitness, using a smart mirror implemented in this article, features two features of metaverse’s augmented reality and lifelogging, enabling the youth to enjoy more immersive and enjoyable exercise experiences.
Adaptive Hierarchical Similarity Metric Learning With Noisy Labels

Deep Metric Learning (DML) plays a critical role in various machine learning tasks. However, most existing deep metric learning methods with binary similarity are sensitive to noisy labels, which are widely present in real-world data. Since these noisy labels often cause a severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML. In this paper, we propose an Adaptive Hierarchical Similarity Metric Learning method. It considers two noise-insensitive information, i.e., class-wise divergence and sample-wise consistency. Specifically, class-wise divergence can effectively excavate richer similarity information beyond binary in modeling by taking advantage of Hyperbolic metric learning, while sample-wise consistency can further improve the generalization ability of the model using contrastive augmentation. More importantly, we design an adaptive strategy to integrate this information in a unified view. It is noteworthy that the new method can be extended to any pair-based metric loss. Extensive experimental results on benchmark datasets demonstrate that our method achieves state-of-the-art performance compared with current deep metric learning approaches.
Personality trait estimation in group discussions using multimodal analysis and speaker embedding

Abstract The automatic estimation of personality traits is essential for many human–computer interface (HCI) applications. This paper focused on improving Big Five personality trait estimation in group discussions via multimodal analysis and transfer learning with the state-of-the-art speaker individuality feature, namely, the identity vector (i-vector) speaker embedding. The experiments were carried out by investigating the effective and robust multimodal features for estimation with two group discussion datasets, i.e., the Multimodal Task-Oriented Group Discussion (MATRICS) (in Japanese) and Emergent Leadership (ELEA) (in European languages) corpora. Subsequently, the evaluation was conducted by using leave-one-person-out cross-validation (LOPCV) and ablation tests to compare the effectiveness of each modality. The overall results showed that the speaker-dependent features, e.g., the i-vector, effectively improved the prediction accuracy of Big Five personality trait estimation. In addition, the experimental results showed that audio-related features were the most prominent features in both corpora.
Persona-Based Conversational AI: State of the Art and Challenges

Conversational AI has become an increasingly prominent and practical application of machine learning. How-ever, existing conversational AI techniques still suffer from var-ious limitations. One such limitation is a lack of well-developed methods for incorporating auxiliary information that could help a model understand conversational context better. In this paper, we explore how persona-based information could help improve the quality of response generation in conversations. First, we provide a literature review focusing on the current state-of-the-art methods that utilize persona information. We evaluate two strong baseline methods, the Ranking Profile Memory Network and the Poly-Encoder, on the NeurIPS ConvAI2 benchmark dataset. Our analysis elucidates the importance of incorporating persona information into conversational systems. Additionally, our study highlights several limitations with current state-of-the-art meth-ods and outlines challenges and future research directions for advancing personalized conversational AI technology.
Identity Verification and Document Traceability in Digital Identity Systems using Non-Transferable Non-Fungible Tokens

Currently, identity and document verification use either physical documentation or centralized digital databases. A central figure is responsible for verifying these documents and this is thought to prevent document tampering. However, this does not eliminate threats of external hacking or internal bad actors and individuals to commit acts of forgery. in addition, bureaucracy and delays often occur with central entities validating documents that causes wasted time for users. Blockchain technology has brought a positive impact to document traceability and transparency across institutions and industries. This research will analyse and use non-transferable non-fungible tokens (NFTs) with smart contract functions which is a new concept for projecting digital and physical goods on a blockchain, it’s expected to represent complicated operations in an effective manner for document traceability systems, enabling a decentralized approach while preventing fraud, corruption, tampering, and counterfeiting.
MultiAspectEmo: Multilingual and Language-Agnostic Aspect-Based Sentiment Analysis

The paper addresses the important problem of multilingual and language-agnostic approaches to the aspect-based sentiment analysis (ABSA) task, using modern approaches based on transformer models. We propose a new dataset based on automatic translation of the Polish AspectEmo dataset together with cross-lingual transfer of tags describing aspect polarity. The result is a MultiAspectEmo dataset translated into five other languages: English, Czech, Spanish, French and Dutch. In this paper, we also present the original Tr Asp (Transformer-based Aspect Extraction and Classification) method, which is significantly better than methods from the literature in the ABSA task. In addition, we present multilingual and language-agnostic variants of this method, evaluated on the MultiAspectEmo and also the SemEval2016 datasets. We also test various language models for the ABSA task, including compressed models that give promising results while significantly reducing inference time and memory usage.
ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis

In recent years, neural network based methods for multispeaker text-to-speech synthesis (TTS) have made significant progress. However, the current speaker encoder models used in these methods still cannot capture enough speaker information. In this paper, we focus on accurate speaker encoder modeling and propose an end-to-end method that can generate better similarity for both seen and unseen speakers. The proposed architecture consists of three separately trained components: a speaker encoder based on the state-of-the-art ECAPA-TDNN model which is derived from speaker verification task, a FastSpeech2 based synthesizer, and a HiFi-GAN vocoder. The comparison among different speaker encoder models shows our proposed method can achieve better speaker similarity. To efficiently evaluate our synthesized speech, we are the first to adopt and evaluate different deep learning based automatic MOS evaluation methods to assess our results, and these methods show great potential in automatic speech quality assessment.
Multi-speaker Multi-style Text-to-speech Synthesis with Single-speaker Single-style Training Data Scenarios

In the existing cross-speaker style transfer task, a source speaker with multi-style recordings is necessary to provide the style for a target speaker. However, it is hard for one speaker to express all expected styles. In this paper, a more general task, which is to produce expressive speech by combining any styles and timbres from a multi-speaker corpus in which each speaker has a unique style, is proposed. To realize this task, a novel method is proposed. This method is a Tacotron2-based framework but with a fine-grained text-based prosody predicting module and a speaker identity controller. Experiments demonstrate that the proposed method can successfully express a style of one speaker with the timber of another speaker bypassing the dependency on a single speaker’s multi-style corpus. Moreover, the explicit prosody features used in the prosody predicting module can increase the diversity of synthetic speech by adjusting the value of prosody features.
The Volcanic Hazard Maps Database: an initiative of the IAVCEI Commission on Volcanic Hazards and Risk

Abstract In this work we present the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI) Commission on Volcanic Hazards and Risk (CVHR) Volcanic Hazard Maps Database and the accompanying volcanichazardmaps.org website. Using input from a series of IAVCEI CVHR Working Group on Hazard Mapping workshops, we developed a classification scheme and terminology framework for categorizing, discussing, naming, and searching for hazard maps. ≥ The database and website aim to serve as a resource for the volcanology community to explore how different aspects of hazard map development and design have been addressed in different countries, for different hazard processes, and for different intended purposes and audiences. Additionally, they act as a tool for presenting hazard map options to stakeholder groups and serve as a learning resource that can be incorporated into educational materials and training courses. In this work, we present the database and website, discuss the classification scheme, explore the enormous diversity of hazard maps, and suggest ways that the database and website can be used by the volcanic hazard mapping community.
An Improved Gradient-Based Optimization Algorithm for Solving Complex Optimization Problems

In this paper, an improved gradient-based optimizer (IGBO) is proposed with the target of improving the performance and accuracy of the algorithm for solving complex optimization and engineering problems. The proposed IGBO has the added features of adjusting the best solution by adding inertia weight, fast convergence rate with modified parameters, as well as avoiding the local optima using a novel functional operator (G). These features make it feasible for solving the majority of the nonlinear optimization problems which is quite hard to achieve with the original version of GBO. The effectiveness and scalability of IGBO are evaluated using well-known benchmark functions. Moreover, the performance of the proposed algorithm is statistically analyzed using ANOVA analysis, and Holm–Bonferroni test. In addition, IGBO was assessed by solving well-known real-world problems. The results of benchmark functions show that the IGBO is very competitive, and superior compared to its competitors in finding the optimal solutions with high convergence and coverage. The results of the studied real optimization problems prove the superiority of the proposed algorithm in solving real optimization problems with difficult and indefinite search domains.
A Novel Monogenic Sobel Directional Pattern (MSDP) and Enhanced Bat Algorithm-Based Optimization (BAO) with Pearson Mutation (PM) for Facial Emotion Recognition

In this paper, a novel Monogenic Sobel Directional Pattern (MSDP) using fractional order masks is proposed for extracting features. The MSDP uses fractional-order Sobel masks to identify thin edges along with color and texture-based information thereby increasing performance. Other edge-detection methods can identify only thick edges. There are three modules namely feature extraction, dimension reduction via a novel discriminant analysis method, and classification using a Convolutional Neural Network (CNN). The proposed MSDP is insensitive to the rotation and scaling changes existing in the images. The Bat Algorithm-based Optimization (BAO) is used for the selection of the best parameters of MSDP. The best value is modified by the Pearson Mutation (PM) operator in an effort to aid the algorithm in avoiding local optima and achieving a balance between global and local searches. The proposed work uses CNN for classification and achieves higher classification accuracy for six datasets.
MSSF: A Novel Mutual Structure Shift Feature for Removing Incorrect Keypoint Correspondences between Images

Removing incorrect keypoint correspondences between two images is a fundamental yet challenging task in computer vision. A popular pipeline first computes a feature vector for each correspondence and then trains a binary classifier using these features. In this paper, we propose a novel robust feature to better fulfill the above task. The basic observation is that the relative order of neighboring points around a correct match should be consistent from one view to another, while it may change a lot for an incorrect match. To this end, the feature is designed to measure the bidirectional relative ranking difference for the neighbors of a reference correspondence. To reduce the negative effect of incorrect correspondences in the neighborhood when computing the feature, we propose to combine spatially nearest neighbors with geometrically “good” neighbors. We also design an iterative neighbor weighting strategy, which considers both goodness and correctness of a correspondence, to enhance correct correspondences and suppress incorrect correspondences. As the relative order of neighbors encodes structure information between them, we name the proposed feature the Mutual Structure Shift Feature (MSSF). Finally, we use the proposed features to train a random forest classifier in a supervised manner. Extensive experiments on both raw matching quality and downstream tasks are conducted to verify the performance of the proposed method.
Open-Set Signal Recognition Based on Transformer and Wasserstein Distance

Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world scenarios. In the present work, we propose an efficient open-set signal recognition algorithm, which contains three key sub-modules: the signal representation sub-module based on a vision transformer (ViT) structure, a set distance metric sub-module based on Wasserstein distance, and a class space compression sub-module based on reciprocal point separation and central loss. In this algorithm, the representing features of signals are established based on transformer-based neural networks, i.e., ViT, in order to extract global information about time series-related data. The employed reciprocal point is used in modeling the potential unknown space without using the corresponding samples, while the distance metric between different class spaces is mathematically modeled in terms of the Wasserstein distance instead of the classical Euclidean distance. Numerical experiments on different open-set signal recognition tasks show that the proposed algorithm can significantly improve the recognition efficiency in both known and unknown categories.
Integration of Remote Sensing and Field Data in Ophiolite Investigations: A Case Study of Logar Ophiolite Complex, SE Afghanistan

Mafic–ultramafics complexes are crucial for their tectonic implication, upper mantle condition, and for hosting industrial minerals in a region. This study aims to highlight and characterize the mafic–ultramafic rocks of the Logar Ophiolite Complex using the integration of geospatial technology and field data. The spatial distribution of the ophiolitic complex was examined in this study using the mineralogical indices (MI), band ratio (BR), and spectral angle mapper (SAM) methods within the framework of geospatial technology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Additionally, several samples were collected from the identified complexes for validation, petrographic, and mineralogical analyses. Combining geospatial technology and conventional approaches, e.g., field sampling and geological data analysis yields efficient discrimination of mafic–ultramafic rocks with their associated hydrothermal altered minerals. The serpentinization and carbonate processes are predominantly seen along the eastern side of the active fault zone following the detection of ophiolites. Detailed mapping of the ophiolitic complex and associated rocks was achieved using refined mafic index (MI), band ratio 12/14 and 4/8 for rocks and SAM for highlighting the mafic–ultramafic altered minerals, and petrographic analysis of the collected samples. The field works verified the results of the ASTER data. The findings of this study can significantly contribute to detailed tectonic and geologic studies of the detected ophiolites in terms of their emplacement mechanism and ages.
Filter pruning with uniqueness mechanism in the frequency domain for efficient neural networks

Filter pruning has drawn extensive attention due to its advantage in reducing computational costs and memory requirements of deep convolutional neural networks. However, most existing methods only prune filters based on their intrinsic properties or spatial feature maps, ignoring the correlation between filters. In this paper, we suggest the correlation is valuable and consider it from a novel view: the frequency domain. Specifically, we first transfer features to the frequency domain by Discrete Cosine Transform (DCT). Then, for each feature map, we compute a uniqueness score, which measures its probability of being replaced by others. This way allows to prune the filters corresponding to the low-uniqueness maps without significant performance degradation. Compared to the methods focusing on intrinsic properties, our proposed method introduces a more comprehensive criterion to prune filters, further improving the network compactness while preserving good performance. In addition, our method is more robust against noise than the spatial ones since the critical clues for pruning are more concentrated after DCT. Experimental results demonstrate the superiority of our method. To be specific, our method outperforms the baseline ResNet-56 by 0.38% on CIFAR-10 while reducing the floating-point operations (FLOPs) by 47.4%. In addition, a consistent improvement can be observed when pruning the baseline ResNet-110: 0.23% performance increase and up to 71% FLOPs drop. Finally, on ImageNet, our method reduces the FLOPs of the baseline ResNet-50 by 48.7% with only 0.32% accuracy loss.
Cryptanalysis of 2D-SCMCI Hyperchaotic Map Based Image Encryption Algorithm

Chaos-based cryptosystems are considered a secure mode of communication due to their reliability.Chaotic maps are associated with the other domains to construct robust encryption algorithms.There exist numerous encryption schemes in the literature based on chaotic maps.This work aims to propose an attack on a recently proposed hyper-chaotic map-based cryptosystem.The core notion of the original algorithm was based on permutation and diffusion.A bitlevel permutation approach was used to do the permutation row-and column-wise.The diffusion was executed in the forward and backward directions.The statistical strength of the cryptosystem has been demonstrated by extensive testing conducted by the author of the cryptosystem.This cryptanalysis article investigates the robustness of this cryptosystem against a chosen-plaintext attack.The secret keys of the cryptosystem were retrieved by the proposed attack with 258 chosen-plain images.The results in this manuscript suggest that, in addition to standard statistical evaluations, thorough cryptanalysis of each newly suggested cryptosystem is necessary before it can be used in practical application.Moreover, the data retrieved is also passed through some statistical analysis to compare the quality of the original and retrieved data.The results of the performance analysis indicate the exact recovery of the original data.To make the cryptosystem useful for applications requiring secure data exchange, a few further improvement recommendations are also suggested.
Optimization of Quantum Cost for Low Energy Reversible Signed/Unsigned Multiplier Using Urdhva-Tiryakbhyam Sutra

One of the elementary operations in computing systems is multiplication.Therefore, high-speed and low-power multipliers design is mandatory for efficient computing systems.In designing low-energy dissipation circuits, reversible logic is more efficient than irreversible logic circuits but at the cost of higher complexity.This paper introduces an efficient signed/unsigned 4 × 4 reversible Vedic multiplier with minimum quantum cost.The Vedic multiplier is considered fast as it generates all partial product and their sum in one step.This paper proposes two reversible Vedic multipliers with optimized quantum cost and garbage output.First, the unsigned Vedic multiplier is designed based on the Urdhava Tiryakbhyam (UT) Sutra.This multiplier consists of bitwise multiplication and adder compressors.Compared with Vedic multipliers in the literature, the proposed design has a quantum cost of 111 with a reduction of 94% compared to the previous design.It has a garbage output of 30 with optimization of the best-compared design.Second, the proposed unsigned multiplier is expanded to allow the multiplication of signed numbers as well as unsigned numbers.Two signed Vedic multipliers are presented with the aim of obtaining more optimization in performance parameters.DesignI has separate binary two's complement (B2C) and MUX circuits, while DesignII combines binary two's complement and MUX circuits in one circuit.DesignI shows the lowest quantum cost, 231, regarding state-ofthe-art.DesignII has a quantum cost of 199, reducing to 86.14% of DesignI.The functionality of the proposed multiplier is simulated and verified using XILINX ISE 14.2.