Reviewerly/exHarmony huggingface dataset:
Analysis of music teaching technology based on a data mining model

Abstract As an important middle ground between the theory of teaching and the practice of teaching, the type of teaching can not only teach teaching in the field of theory but also develop practical processes and develop effective and efficient strategies for specific teaching. The author proposes an online teaching tool for music lessons based on ZigBee. Hardware includes transmitter, sound sensor, and manager, DIN rail type online help manual to add to the transmitter to improve the combination of transmitter and other devices; The primary output current of the sensor is 15 mA, the working voltage is DC12-24 V ± 10, the sensor is connected to other devices with two wires, the control capacity memory is 256 GB, and the chip is CC2430. is added to improve system sensitivity. Chip Software Systems is known for its storage, data management, integration, and online solutions. Also, only information technology will be used to teach the “Learning with Technology” music model. Experiments show that the application system can not only complete the online service of the music class but also improve the teaching of the music class.
Secure transmission of grayscale images with triggered error visual sharing

&lt;p&gt;In the digital era, data transfer plays a crucial role in various industries such as banking, healthcare, marketing, and social media. Images are widely used as a means of communication. The presence of cyber attackers poses a significant risk to data integrity and security during transmission. According to the cost of data breach report 2021, the healthcare industry has experienced the highest costs associated with data breaches, highlighting the need for robust security measures. Visual cryptography (VC) is a technique used to secure image data during transmission. It involves encrypting the image and dividing it into shares, which are then communicated to the intended recipients. Each individual share does not reveal any classified information. At the destination, the shares are digitally combined to reconstruct the original image. When implementing VC, several factors need to be considered, including security, computational complexity, and the quality of the reconstructed image. In this paper, a new method called progressive meaningful visual cryptography (PMVC) is proposed for transferring secret images. The PMVC method introduces an error instance that triggers meaningful shares generation. The proposed method ensures the quality of the reconstructed image by achieving a peak signal-to-noise ratio (PSNR) of up to 37 dB.&lt;/p&gt;
DySTreSS: Dynamically Scaled Temperature in Self-Supervised Contrastive Learning

In contemporary self-supervised contrastive algorithms like SimCLR, MoCo, etc., the task of balancing attraction between two semantically similar samples and repulsion between two samples from different classes is primarily affected by the presence of hard negative samples. While the InfoNCE loss has been shown to impose penalties based on hardness, the temperature hyper-parameter is the key to regulating the penalties and the trade-off between uniformity and tolerance. In this work, we focus our attention to improve the performance of InfoNCE loss in SSL by studying the effect of temperature hyper-parameter values. We propose a cosine similarity-dependent temperature scaling function to effectively optimize the distribution of the samples in the feature space. We further analyze the uniformity and tolerance metrics to investigate the optimal regions in the cosine similarity space for better optimization. Additionally, we offer a comprehensive examination of the behavior of local and global structures in the feature space throughout the pre-training phase, as the temperature varies. Experimental evidence shows that the proposed framework outperforms or is at par with the contrastive loss-based SSL algorithms. We believe our work (DySTreSS) on temperature scaling in SSL provides a foundation for future research in contrastive learning.
Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants

How to enable learnability for new classes while keeping the capability well on old classes has been a crucial challenge for class incremental learning. Beyond the normal case, long-tail class incremental learning and few-shot class incremental learning are also proposed to consider the data imbalance and data scarcity, respectively, which are common in real-world implementations and further exacerbate the well-known problem of catastrophic forgetting. Existing methods are specifically proposed for one of the three tasks. In this paper, we offer a unified solution to the misalignment dilemma in the three tasks. Concretely, we propose neural collapse terminus that is a fixed structure with the maximal equiangular inter-class separation for the whole label space. It serves as a consistent target throughout the incremental training to avoid dividing the feature space incrementally. For CIL and LTCIL, we further propose a prototype evolving scheme to drive the backbone features into our neural collapse terminus smoothly. Our method also works for FSCIL with only minor adaptations. Theoretical analysis indicates that our method holds the neural collapse optimality in an incremental fashion regardless of data imbalance or data scarcity. We also design a generalized case where we do not know the total number of classes and whether the data distribution is normal, long-tail, or few-shot for each coming session, to test the generalizability of our method. Extensive experiments with multiple datasets are conducted to demonstrate the effectiveness of our unified solution to all the three tasks and the generalized case.
Curriculum Guided Domain Adaptation in the Dark

Addressing the rising concerns of privacy and security, domain adaptation in the dark aims to adapt a black-box source trained model to an unlabeled target domain without access to any source data or source model parameters. The need for domain adaptation of black-box predictors becomes even more pronounced to protect intellectual property as deep learning based solutions are becoming increasingly commercialized. Current methods distill noisy predictions on the target data obtained from the source model to the target model, and/or separate clean/noisy target samples before adapting using traditional noisy label learning algorithms. However, these methods do not utilize the easy-to-hard learning nature of the clean/noisy data splits. Also, none of the existing methods are end-to-end, and require a separate fine-tuning stage and an initial warmup stage. In this work, we present Curriculum Adaptation for Black-Box (CABB) which provides a curriculum guided adaptation approach to gradually train the target model, first on target data with high confidence (clean) labels, and later on target data with noisy labels. CABB utilizes Jensen-Shannon divergence as a better criterion for clean-noisy sample separation, compared to the traditional criterion of cross entropy loss. Our method utilizes co-training of a dual-branch network to suppress error accumulation resulting from confirmation bias. The proposed approach is end-to-end trainable and does not require any extra finetuning stage, unlike existing methods. Empirical results on standard domain adaptation datasets show that CABB outperforms existing state-of-the-art black-box DA models and is comparable to white-box domain adaptation models.
TDMD: A Database for Dynamic Color Mesh Subjective and Objective Quality Explorations

Dynamic colored meshes (DCM) are widely used in various applications; however, these meshes may undergo different processes, such as compression or transmission, which can distort them and degrade their quality. To facilitate the development of objective metrics for DCMs and study the influence of typical distortions on their perception, we create the Tencent - dynamic colored mesh database (TDMD) containing eight reference DCM objects with six typical distortions. Using processed video sequences (PVS) derived from the DCM, we have conducted a large-scale subjective experiment that resulted in 303 distorted DCM samples with mean opinion scores, making the TDMD the largest available DCM database to our knowledge. This database enabled us to study the impact of different types of distortion on human perception and offer recommendations for DCM compression and related tasks. Additionally, we have evaluated three types of state-of-the-art objective metrics on the TDMD, including image-based, point-based, and video-based metrics, on the TDMD. Our experimental results highlight the strengths and weaknesses of each metric, and we provide suggestions about the selection of metrics in practical DCM applications. The TDMD will be made publicly available at the following location: https://multimedia.tencent.com/resources/tdmd.
Synthetic Instance Segmentation from Semantic Image Segmentation Masks

In recent years, the development of instance segmentation has garnered significant attention in a wide range of applications. However, the training of a fully-supervised instance segmentation model requires costly both instance-level and pixel-level annotations. In contrast, weakly-supervised instance segmentation methods (i.e., with image-level class labels or point labels) struggle to satisfy the accuracy and recall requirements of practical scenarios. In this paper, we propose a novel paradigm called synthetic instance segmentation (SISeg), which achieves Instance Segmentation results from image masks predicted using off-the-shelf semantic segmentation models. SISeg does not require training a semantic or/and instance segmentation model and avoids the need for instance-level image annotations. Therefore, it is highly efficient. Specifically, we first obtain a semantic segmentation mask of the input image via a trained semantic segmentation model. Then, we calculate a displacement field vector for each pixel based on the segmentation mask, which can indicate representations belonging to the same class but different instances, i.e., obtaining the instance-level object information. Finally, instance segmentation results are obtained after being refined by a learnable category-agnostic object boundary branch. Extensive experimental results on two challenging datasets and representative semantic segmentation baselines (including CNNs and Transformers) demonstrate that SISeg can achieve competitive results compared to the state-of-the-art fully-supervised instance segmentation methods without the need for additional human resources or increased computational costs. The code is available at: SISeg
Evaluation of the Improved Extreme Learning Machine for Machine Failure Multiclass Classification

The recent advancements in sensor, big data, and artificial intelligence (AI) have introduced digital transformation in the manufacturing industry. Machine maintenance has been one of the central subjects in digital transformation in the manufacturing industry. Predictive maintenance is the latest maintenance strategy that relies on data and artificial intelligence techniques to predict machine failure and remaining life assessment. However, the imbalanced nature of machine data can result in inaccurate machine failure predictions. This research will use techniques and algorithms centered on Extreme Learning Machine (ELM) and their development to find a suitable algorithm to overcome imbalanced machine datasets. The dataset used in this research is Microsoft Azure for Predictive Maintenance, which has significantly imbalanced failure classes. Four improved ELM methods are evaluated in this paper, i.e., extreme machine learning with under-sampling/over-sampling, weighted-ELM, and weighted-ELM with radial basis function (RBF) kernel and particle swarm optimization (PSO). Our simulation results show that the combination of ELM with under-sampling gained the highest performance result, in which the average F1-score reached 0.9541 for binary classification and 0.9555 for multiclass classification.
Moving Image Feature Extraction Method Based on Wavelet Transform

The accuracy of traditional image feature extraction algorithm for accurate recognition of moving images is not high. To solve such problem, a target recognition algorithm based on edge contour feature segmentation using wavelet transform technology is proposed in this paper. Firstly, through multi-scale moving target edge detection, the process of moving target image acquisition and preprocessing is standardized. Then, using the information redundancy of stationary wavelet transform to remove noise, an image denoising method based on adaptive soft threshold is put forward. Finally, the improved method is tested in the simulation environment. The results show that the root mean square error of pixel feature matching is low, the corner extracted under different thresholds is reliable, and the motion estimation accuracy is better than that of similar algorithms.
Re-examining the quality dimensions of synthetic speech

The aim of this paper is to generate a more comprehensive framework for evaluating synthetic speech.To this end, a line of tests resulting in an exploratory factor analysis (EFA) have been carried out.The proposed dimensions that encapsulate the construct of "synthetic speech quality" are: "human-likeness", "audio quality", "negative emotion", "dominance", "positive emotion", "calmness", "seniority" and "gender", with item-tototal correlations pointing towards "gender" being an orthogonal construct.A subsequent analysis on common acoustic features, found in forensic and phonetic literature, reveals very weak correlations with the proposed scales.Inter-rater and inter-item agreement measures additionally reveal low consistency within the scales.We also make the case that there is a need for a more fine grained approach when investigating the quality of synthetic speech systems, and propose a method that attempts to capture individual quality dimensions in the time domain.
Behavioral Simulation of Biological Neuron Using VHDL

In this proposed work, the piecewise linear approximation technique has been used to get a simpler digitally implemented hardware, i.e., a biological neuron with a nonlinear activation function. This design included the sigmoidal function that has been utilized as an activation function. In this artificial model, the neuron is able to distinguish between the two input attributes in terms of their weights. The suggested digitally implemented neuron hardware could be utilized as a fundamental building component in the creation of a general digital network of neurons or neural networks. The inputs are formatted in a specified way, which is required for the piecewise linear approximation technique. The text of the paper includes an overview of the fundamental component required as well as issues encountered during the design of neuron architecture.
LPS-Net: Lightweight Parallel Strategy Network for Underwater Image Enhancement

Underwater images are frequently subject to color distortion and loss of details. However, previous enhancement methods did not tackle these mixed degradations by dividing them into sub-problems that could be effectively addressed. Moreover, the parameters and computations required for these methods are usually costly for underwater equipment, which has limited power supply, processing capabilities, and memory capacity. To address these challenges, this work proposes a Lightweight Parallel Strategy Network (LPS-Net). Firstly, a Dual-Attention Enhancement Block and a Mirror Large Receptiveness Block are introduced to, respectively, enhance the color and restore details in degraded images. Secondly, we employed these blocks on parallel branches at each stage of LPS-Net, with the goal of achieving effective image color and detail rendering simultaneously. Thirdly, a Gated Fusion Unit is proposed to merge features from different branches at each stage. Finally, the network utilizes four stages of parallel enhancement, achieving a balanced trade-off between performance and parameters. Extensive experiments demonstrated that LPS-Net achieves optimal color enhancement and superior detail restoration in terms of visual quality. Furthermore, it attains state-of-the-art underwater image enhancement performance on the evaluation metrics, while using only 80.12 k parameters.
TOF-assisted binocular vision accuracy improvement method for underwater fish size inspection

To address problems such as the lack of accuracy in acquiring depth maps for dynamic fish 3D measurements by usual binocular vision or a time-of-flight (TOF) depth camera, a TOF-assisted binocular vision depth acquisition algorithm is used to obtain high-quality depth maps. The TOF depth energy function is designed to guide the binocular stereo matching process, which improves the correct matching rate of binocular matching in low-texture regions; the TOF and binocular stereo matching confidence weighting functions are designed to achieve the fusion of the two at pixel level to improve the matching quality of fish in the occluded overlapping regions. The experimental results show that the TOF-assisted binocular vision system improves the accuracy of fish size measurement compared to single binocular vision while reducing the measurement error when the fish body has a significant inclination along the depth axis.
Complications and prognosis of cardiovascular compromise in patients with COVID-19: an integrative literature review

This study aims to learn about the cardiovascular complications of COVID-19 and the clinical, laboratory and imaging predictors of myocardial injury and dysfunction resulting from SARS-CoV-2 infection, with a view to developing a plan of measures to reduce the risk of death from COVID-19 in people with cardiovascular disease. This is an integrative literature review carried out in electronic databases: PubMed, LILACS, Embase, Scopus, WOS and CINAHL, with the descriptors: Cardiovascular system; risk factors; Comorbidity; Cardiovascular diseases; Heart injury and Covid-19 with the inclusion of original studies in the period 2019-2022. The search resulted in 44 articles, in English, which highlighted the risk factors and/or comorbidities associated with cardiovascular complications potentiated by COVID-19; prognosis and increased mortality from cardiovascular causes induced by COVID-19. It is concluded that cardiovascular impairment can determine the severity and contribute to death in individuals with COVID-19, potentiated by pre-existing cardiovascular disease or cardiometabolic risk factors, affecting the need for ventilatory and hemodynamic support.
Environment Diversification with Multi-head Neural Network for Invariant Learning

Neural networks are often trained with empirical risk minimization; however, it has been shown that a shift between training and testing distributions can cause unpredictable performance degradation. On this issue, a research direction, invariant learning, has been proposed to extract invariant features insensitive to the distributional changes. This work proposes EDNIL, an invariant learning framework containing a multi-head neural network to absorb data biases. We show that this framework does not require prior knowledge about environments or strong assumptions about the pre-trained model. We also reveal that the proposed algorithm has theoretical connections to recent studies discussing properties of variant and invariant features. Finally, we demonstrate that models trained with EDNIL are empirically more robust against distributional shifts.
Princeps Editions from the Collections of the Romanian Academy Library – Cluj-Napoca Office. Debut Volumes

The first editions of books authored by the most valuable writers and important personalities of the Romanian and universal science and culture are included in the category of bibliophile books. The princeps editions incorporate a particular value, both in terms of their bibliophilic aspect as well as for the biography and bibliography of a given author. When it comes to literary history and especially the different types of first edition books, whether it is a true first edition, a believed first, a first thus, a first book club and an advanced reader copy, several features may be identified. The authors are usually more invested and involved in the first printing process because it's the first time their work is born and introduced to the world. Moreover, the volume itself may reveal fundamental details on a literary school, on a literary current or on an entire writing era. Our library holds, according to preliminary research, the debut volumes of C. Conachi, I. Vulcan, V. Cârlova, P. Cerna, C. Pavelescu, G. Topârceanu, Al. Philippide, Camil Petrescu, Z. Stancu. The current article will focus on some of those volumes, such as A Collection of the Poems of Mr. D. Bolintineanu [Colecție din poeziile domnului D. Bolintineanu], Poems [Poesii] (M. Eminescu), Poems [Poesii] (O. Goga), Sentimental Songs for Later [Romanțe pentru mai târziu] (I. Minulescu), Native Lowlands [Șesuri natale], (N. Crainic), Poems of Light [Poemele Luminii] (L. Blaga), Right Words. Verses [Cuvinte potrivite. Stihuri] (T. Arghezi), Dark April [Întunecatul april] (Em. Botta). Considering the value and rarity of first editions, the limited number of collections that have in custody such bibliophilic jewels, the inherent loss of physical integrity that develops with the passing of time, it is, with greater reason, the right moment to respect and to preserve the cultural heritage of future generations.
A Transformer-based Framework For Multi-variate Time Series: A Remaining Useful Life Prediction Use Case

In recent times, Large Language Models (LLMs) have captured a global spotlight and revolutionized the field of Natural Language Processing. One of the factors attributed to the effectiveness of LLMs is the model architecture used for training, transformers. Transformer models excel at capturing contextual features in sequential data since time series data are sequential, transformer models can be leveraged for more efficient time series data prediction. The field of prognostics is vital to system health management and proper maintenance planning. A reliable estimation of the remaining useful life (RUL) of machines holds the potential for substantial cost savings. This includes avoiding abrupt machine failures, maximizing equipment usage, and serving as a decision support system (DSS). This work proposed an encoder-transformer architecture-based framework for multivariate time series prediction for a prognostics use case. We validated the effectiveness of the proposed framework on all four sets of the C-MAPPS benchmark dataset for the remaining useful life prediction task. To effectively transfer the knowledge and application of transformers from the natural language domain to time series, three model-specific experiments were conducted. Also, to enable the model awareness of the initial stages of the machine life and its degradation path, a novel expanding window method was proposed for the first time in this work, it was compared with the sliding window method, and it led to a large improvement in the performance of the encoder transformer model. Finally, the performance of the proposed encoder-transformer model was evaluated on the test dataset and compared with the results from 13 other state-of-the-art (SOTA) models in the literature and it outperformed them all with an average performance increase of 137.65% over the next best model across all the datasets.