Reviewerly/exHarmony huggingface dataset:
Pretrained back propagation based adaptive resonance theory network for adaptive learning

The deep convolutional neural network performs well in current computer vision tasks. However, most of these models are trained on an aforehand complete dataset. New application scenario data sets should be added to the original training data set for model retraining when application scenarios change significantly. When the scenario changes only slightly, the transfer learning can be used for network training by a small data set of new scenarios to adapt it to the new scenario. In actual application, we hope that our model has bio-like intelligence and can adaptively learn new knowledge. This paper proposes a pretrained adaptive resonance network (PAN) based on the CNN and an intra-node back propagation ART network, which can adaptively learn new knowledge using prior information. The PAN network explores the difference between the new data and the stored information and learns this difference to realize the adaptive growth of the network. The model is testified on the MNIST and Omniglot data set, which show the effectiveness of PAN in adaptive incremental learning and its competitive classification accuracy.
Evaluating the Performance of Hand-Based Probabilistic Text Input Methods on a Mid-Air Virtual Qwerty Keyboard

Integrated hand-tracking on modern virtual reality (VR) headsets can be readily exploited to deliver mid-air virtual input surfaces for text entry. These virtual input surfaces can closely replicate the experience of typing on a Qwerty keyboard on a physical touchscreen, thereby allowing users to leverage their pre-existing typing skills. However, the lack of passive haptic feedback, unconstrained user motion, and potential tracking inaccuracies or observability issues encountered in this interaction setting typically degrades the accuracy of user articulations. We present a comprehensive exploration of error-tolerant probabilistic hand-based input methods to support effective text input on a mid-air virtual Qwerty keyboard. Over three user studies we examine the performance potential of hand-based text input under both gesture and touch typing paradigms. We demonstrate typical entry rates in the range of 20 to 30 wpm and average peak entry rates of 40 to 45 wpm.
Enhance Low Visibility Image Using Haze-Removal Framework

We proposed a novel image enhancement framework to raise the visibility of the image’s content. Our primary concern is eliminating haze-like effects and simultaneously increasing images’ brightness. Dehazing and luminance enhancement algorithms are considered standard techniques to overcome these issues. However, natural environments usually involve several unfavorable conditions simultaneously, such as insufficient illumination, blur caused by the haze, and color cast resulting from the sun or scattering; this makes dehazing algorithms challenging to overcome environmental issues. Besides, dehazing algorithms sometimes result in artifacts. The proposed framework solves these issues simultaneously by implementing a double-side-enhancement in contrast and brightness based on a new dehazing algorithm. We compare the new dehazing algorithm with others using full-reference benchmarks to ensure performance stability. Afterward, to show the advantage of using the new dehazing algorithm, we evaluate the compatibility between the proposed framework and all dehazing algorithms using non-reference benchmarks. At last, we pair dehazing and luminance enhancement algorithms and compare the combinations with the proposed framework. Eventually, experimental results prove that the new dehazing algorithm outperforms others and is better compatible with the proposed framework. Meanwhile, the proposed framework is superior in contrast and brightness enhancements and outperforms whether single dehazing algorithm or the combinations.
A Meta-Heuristic Method for Reassemble Bifragmented Intertwined JPEG Image Files in Digital Forensic Investigation

Traditional image recovery from corrupted file systems plays a significant role in Digital Forensics investigation. The images are mainly considered objective court evidence. Since the JPEG image format is less structured than other image formats (e.g., BMP, PNG, GIF, and TIFF), its recovery is more challenging. The paper’s main objective is to apply a metaheuristic optimization algorithm with similarity metric to recover Bifragmented intertwined JPEG images. We propose a new method known as Meta-Heuristic Reassemble Images (MHRI) for forensic recovery of Bifragmented intertwined JPEG images in the scan area, where all the fragments are in a linear order. The contribution of this paper lies in the following key components composing the MHRI method: (i) a Restart marker to remove some strange blocks from the scan area; (ii) a Coherence of Euclidean Distance metric (CoED <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> ) to detect the fragmentation point of intertwined JPEG image; and (iii) a Genetic Algorithm with cost function to predict if every two blocks are sequenced in the same image that searches for the best reassembly solution among the existing solutions. The MHRI method is implemented using MATLAB and tested using a combined dataset of 31 JPEG images. The MHRI process can fully recover all the Bifragmented intertwined JPEG images and 48.4% of all the JPEG images, which comparably performs better than similar methods.
An End-to-End Air Writing Recognition Method Based on Transformer

The air-writing recognition task entails the computer’s ability to directly recognize and interpret user input generated by finger movements in the air. This form of interaction between humans and computers is considered natural, cost-effective, and immersive within the domain of human-computer interaction (HCI). While conventional air-writing recognition has primarily focused on recognizing individual characters, a recent advancement in 2022 introduced the concept of writing in the air (WiTA) to address continuous air-writing tasks. In this context, we assert that the Transformer-based approach can offer improved performance for the WiTA task. To solve the WiTA task, this study formulated an end-to-end air-writing recognition method called TR-AWR, which leverages the Transformer model. Our proposed method adopts a holistic approach by utilizing video frame sequences as input and generating letter sequences as outputs. To enhance the performance of the WiTA task, our method combines the vision transformer model with the traditional transformer model, while introducing data augmentation techniques for the first time. Our approach achieves a character error rate (CER) of 29.86% and a decoding frames per second (D-fps) value of 194.67 fps. Notably, our method outperforms the baseline models in terms of recognition accuracy while maintaining a certain level of real-time performance. The contributions of this paper are as follows: Firstly, this study is the first to incorporate the Transformer method into continuous air-writing recognition research, thereby reducing overall complexity and attaining improved results. Additionally, we adopt an end-to-end approach that streamlines the entire recognition process. Lastly, we propose specific data augmentation guidelines tailored explicitly for the WiTA task. In summary, our study presents a promising direction for effectively addressing the WiTA task and holds potential for further advancements in this domain.
Accelerated and Improved Stabilization for High Order Moments of Racah Polynomials

Discrete Racah polynomials (DRPs) are highly efficient orthogonal polynomials and used in various scientific fields for signal representation. They find applications in disciplines like image processing and computer vision. Racah polynomials were originally introduced by Wilson and later modified by Zhu to be orthogonal on a discrete set of samples. However, when the degree of the polynomial is high, it encounters numerical instability issues. In this paper, we propose a novel algorithm called Improved Stabilization (ImSt) for computing DRP coefficients. The algorithm partitions the DRP plane into asymmetric parts based on the polynomial size and DRP parameters. We have optimized the use of stabilizing conditions in these partitions. To compute the initial values, we employ the logarithmic gamma function along with a new formula. This combination enables us to compute the initial values efficiently for a wide range of DRP parameter values and large polynomial sizes. Additionally, we have derived a symmetry relation for the case when the Racah polynomial parameters are zero (a=0, α=0, β=0). This symmetry makes the Racah polynomials symmetric about the main diagonal, and we present a different algorithm for this specific scenario. We have demonstrated that the ImSt algorithm works for a broader range of parameters and higher degrees compared to existing algorithms. A comprehensive comparison between ImSt and the existing algorithms has been conducted, considering the maximum polynomial degree, computation time, restriction error analysis, and reconstruction error. The results of the comparison indicate that ImSt outperforms the existing algorithms for various values of Racah polynomial parameters.
The Metaverse: Applications, Concerns, Technical Challenges, Future Directions and Recommendations

The Metaverse is all about expanding connectivity amongst users and objects and seamlessly delivering information and services to the right user at the right time. Its potential advantages are virtually limitless, and its applications are progressively changing the way we live, and are opening new opportunities for innovation and growth. It is crystal clear that the Metaverse can enable fully immersive experience, elements of fantasy, and new degrees of freedom. However, it is still considered controversial since it will also open up opportunities for misconduct and crime. Furthermore, the industry lacks the capacity to carry out a comprehensive study of the potential risks that will come along. This paper highlights the current and envisioned Metaverse applications along with the main concerns and challenges faced by the Metaverse stakeholders. Furthermore, it examines the strengths, weakness, opportunities and threats of the Metaverse technology. Finally, the paper presents the future directions and highlights the most important recommendations for developing the Metaverse systems.
Self-Supervised Multi-Object Tracking for Autonomous Driving From Consistency Across Timescales

Self-supervised multi-object trackers have tremendous potential as they enable learning from raw domain-specific data. However, their re-identification accuracy still falls short compared to their supervised counterparts. We hypothesize that this drawback results from formulating self-supervised objectives that are limited to single frames or frame pairs. Such formulations do not capture sufficient visual appearance variations to facilitate learning consistent re-identification features for autonomous driving when the frame rate is low, or object dynamics are high. In this work, we propose a training objective that enables self-supervised learning of re-identification features from multiple sequential frames by enforcing consistent association scores across short and long timescales. We perform extensive evaluations that demonstrate that re-identification features trained from longer sequences significantly reduce ID switches on standard autonomous driving datasets compared to existing self-supervised learning methods, which are limited to training on frame pairs. Using our proposed SubCo loss function, we set the new state of the art among self-supervised methods and even perform on par with fully supervised learning methods.
An Underwater SINS/DVL Integrated System Outlier Interference Suppression Method Based on LSTM-EEWKF

At present, strapdown inertial navigation system (SINS) and Doppler velocity logs (DVLs) integrated navigation is a common way for autonomous underwater vehicle (AUV) to conduct underwater operations. The complex and changeable Marine environment easily leads to unknown noise and outliers in the measurement information of DVL. When the DVL is disturbed by outliers, the positioning accuracy will be reduced. In order to solve this problem, a long short-term memory extended exponential weighted Kalman filter (LSTM-EEWKF) algorithm assisted by long short-term memory (LSTM) neural network is proposed. When the DVL works normally, the SINS/DVL integrated navigation system is iteratively learned by the LSTM neural network. When the DVL is disturbed by outliers, the trained LSTM neural network is first used to provide correction information for the system, and then, the extended exponential weighted Kalman filter (EEWKF) algorithm is used to make the system continue to work. The EEWKF algorithm realizes weight allocation for different historical moments by introducing an exponential weighting coefficient, which reduces the weight of the estimator when the SINS/DVL integrated system is disturbed by outliers. It can effectively reduce the impact of outliers and historical data on the positioning results of integrated navigation. The simulation and Yangtze River experiment show that the proposed algorithm has higher positioning accuracy than several hybrid algorithms. It can suppress outliers more effectively and make the system more robust.
Multi-aspect attentive text representations for simple question answering over knowledge base

With the deepening of knowledge base research and application, question answering over knowledge base, also called KBQA, has recently received more and more attention from researchers. Most previous KBQA models focus on mapping the input query and the fact in KBs into an embedding format. Then the similarity between the query vector and the fact vector is computed eventually. Based on the similarity, each query can obtain an answer representing a tuple (subject, predicate, object) from the KBs. However, the information about each word in the input question will lose inevitably during the process. To retain as much original information as possible, we introduce an attention-based recurrent neural network model with interactive similarity matrixes. It can extract more comprehensive information from the hierarchical structure of words among queries and tuples stored in the knowledge base. This work makes three main contributions: (1) A neural network-based question-answering model for the knowledge base is proposed to handle single relation questions. (2) A attentive module is designed to obtain information from multiple aspects to represent queries and data, which contributes to avoiding losing potentially valuable information. (3) Similarity matrixes are introduced to obtain the interaction information between queries and data from the knowledge base. Experimental results show that our proposed model performs better on simple questions than state-of-the-art in several effectiveness measures.
WattScope: Non-intrusive application-level power disaggregation in datacenters

Datacenter capacity is growing exponentially to satisfy the increasing demand for many emerging computationally-intensive applications, such as deep learning. This trend has led to concerns over datacenters' increasing energy consumption and carbon footprint. The most basic prerequisite for optimizing a datacenter's energy- and carbon-efficiency is accurately monitoring and attributing energy consumption to specific users and applications. Since datacenter servers tend to be multi-tenant, i.e., they host many applications, server- and rack-level power monitoring alone does not provide insight into the energy usage and carbon emissions of their resident applications. At the same time, current application-level energy monitoring and attribution techniques are intrusive: they require privileged access to servers and necessitate coordinated support in hardware and software, neither of which is always possible in cloud environments. To address the problem, we design WattScope, a system for non-intrusively estimating the power consumption of individual applications using external measurements of a server's aggregate power usage and without requiring direct access to the server's operating system or applications. Our key insight is that, based on an analysis of production traces, the power characteristics of datacenter workloads, e.g., low variability, low magnitude, and high periodicity, are highly amenable to disaggregation of a server's total power consumption into application-specific values. WattScope adapts and extends a machine learning-based technique for disaggregating building power and applies it to server- and rack-level power meter measurements that are already available in data centers. We evaluate WattScope's accuracy on a production workload and show that it yields high accuracy, e.g., often <∼10% normalized mean absolute error, and is thus a potentially useful tool for datacenters in externally monitoring application-level power usage.
Describing Images Using CNN and Object Features with Attention

This study introduces a novel method to generate informative descriptions for images by leveraging Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The proposed method extracts feature from images using object detection techniques and CNN and then utilizes an LSTM network to generate captions for the images. This research proposes a novel Encoder Decoder architecture with an attention mechanism. The architecture utilizes convolutional features obtained from the Xception model, which is pre-trained on ImageNet. Additionally, it incorporates object features extracted from the SSD model, pretrained on the MS COCO dataset. Much work has been done on image captioning, but still, it needs way more improvement to caption an image like a human does. In our model, we tried to improve the score and make the generated captions more coherent with the image with a single-shot detector (SSD) object detection model. Our novel approach to feature extraction leads to a significant improvement of 29.75% in the METEOR score. The results indicate that the proposed approach outperforms existing methods, suggesting that it has the potential to be a valuable tool for automated image captioning in various applications.
A Mixed Collaborative Recommender System Using Singular Value Decomposition and Item Similarity

Nowadays, Recommendation system plays a vital role in industries like e-commerce, music apps or newsgroup, retailers, etc. Broadly, recommender system techniques are categorized into collaborative filtering and content based. In contrast, most recommendation models adopt collaborative filtering techniques such as matrix factorization (MF) and cosine similarity. However, the above model only deploys single techniques, which leads to poor recommendations to the individuals. Therefore we propose a mixed collaborative filtering-based recommender system (RS), a novel approach to improve the performance of the RS and mitigate the drawback of a single technique-based collaborative model. The proposed model is composed of two techniques such as singular value decomposition and cosine similarity techniques. Further, we examined our model's performance on real-world datasets and found that the proposed approach significantly outperformed the baseline models.
Enhancing Website Design: The Implementation of Sequential Monadic Concept Testing on User Interface and User Experience Design

Websites have become crucial in today's digital landscape as they transform traditional systems into computerized platforms. User Interface (UI) and User Experience (UX) design are vital in creating user-friendly websites that meet functional and non-functional requirements. Concept testing, a research strategy commonly used in marketing, can be applied to UX research to enhance a website design. This study aimed to improve the financial management website of BUMDes Putri Nyale Kuta by implementing sequential monadic concept testing on UI and UX design. By employing this method, the study overcame limitations in the number of respondents and obtained valuable feedback. Additionally, we chose to use sequential monadic with a qualitative approach to gain rich insights from the process results, considering various empathic aspects in line with the website design development method using design thinking from previous research [1]. The website design achieved an overall concept testing score of 4.6 out of 5 for positive reactions, usefulness, trust, liking, and needs, indicating successful improvements. The findings demonstrate the effectiveness of concept testing in enhancing website design and emphasize the importance of considering user expectations in the early stages of development. This study contributes to existing research on UI and UX design in the context of website development, highlighting the potential of concept testing as a valuable tool for enhancing human and user-centric solutions.
Design and research of music teaching system based on virtual reality system in the context of education informatization

Virtual Reality (VR) technology uses computers to simulate the real world comprehensively. VR has been widely used in college teaching and has a huge application prospect. To better apply computer-aided instruction technology in music teaching, a music teaching system based on VR technology is proposed. First, a virtual piano is developed using the HTC Vive kit and the Leap Motion sensor fixed on the helmet as the hardware platform, and using Unity3D, related SteamVR plug-ins, and Leap Motion plug-ins as software platforms. Then, a gesture recognition algorithm is proposed and implemented. Specifically, the Dual Channel Convolutional Neural Network (DCCNN) is adopted to collect the user's gesture command data. The dual-size convolution kernel is applied to extract the feature information in the image and the gesture command in the video, and then the DCCNN recognizes it. After the spatial and temporal information is extracted, Red-Green-Blue (RGB) color pattern images and optical flow images are input into the DCCNN. The prediction results are merged to obtain the final recognition result. The experimental results reveal that the recognition accuracy of DCCNN for the Curwen gesture is as high as 96%, and the recognition accuracy varies with different convolution kernels. By comparison, it is found that the recognition effect of DCCNN is affected by the size of the convolution kernel. Combining convolution kernels of size 5×5 and 7×7 can improve the recognition accuracy to 98%. The research results of this study can be used for music teaching piano and other VR products, with extensive popularization and application value.
Legal Aspects of Virtual Assets Regulation in Ukraine

Abstract Virtual assets are a concept that has made its way into the modern financial and legal lexicon in recent years. Markets are formed around them, transactions are conducted with them, they are the object of increased interest in the field of investment activities. The need for legal regulation of new types of social relations transiting to the virtual plane stimulates lawmakers to take active measures concerning the formation of a legislative basis in this area. This study intends to consider the challenges facing the Ukrainian legislator in the light of the adoption of this law based on existing foreign practices. The study suggests that such obstacles now include not only terminological differences, but also issues of legal qualification of illegal actions with cryptoassets, issues of theoretical and legal understanding of the new legal relations.