Reviewerly/exHarmony huggingface dataset:
Black Birdanity

Research Article| October 01 2021 Black Birdanity James Gordon Williams James Gordon Williams JAMES GORDON WILLIAMS is a transdisciplinary scholar, pianist, improviser, and composer. His book Crossing Bar Lines: The Politics and Practices of Black Musical Space has been published by the University Press of Mississippi (March 2021). He has collaborated with poets, dancers, experimental filmmakers, and video artists. He has performed with poet Fred Moten at the University of California, Los Angeles Hammer Museum, the Village Vanguard, Birdland, as well as jazz festivals in the United States and across Europe. Williams is assistant professor of music in the Syracuse University Department of African American Studies. Search for other works by this author on: This Site Google Jazz and Culture (2021) 4 (2): 85–104. https://doi.org/10.5406/jazzculture.4.2.0085 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation James Gordon Williams; Black Birdanity. Jazz and Culture 1 January 2021; 4 (2): 85–104. doi: https://doi.org/10.5406/jazzculture.4.2.0085 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJazz and Culture Search Advanced Search The text of this article is only available as a PDF. Copyright 2021 by the Board of Trustees of the University of Illinois2021 Article PDF first page preview Close Modal You do not currently have access to this content.
Metacognitive illusions

Accurate metacognition – cognition about cognition – is critical for effective self-regulation and good performance on various cognitive tasks. For this reason, there is a real danger that metacognitive illusions undermine cognitive performance. In this chapter, we present an overview of metacognitive illusions in the domains of learning, knowledge assessment, and thinking. On a theoretical level, the existence of metacognitive illusions indicates that people cannot directly access their cognition, but infer the state of their mind from cues and heuristics. The cues that produce metacognitive illusions are categorized into faulty beliefs about cognition and invalid non-analytic cues. This chapter discusses real-life consequences of metacognitive illusions and provides evidence-based recommendations for the mending of specific metacognitive illusions and for creating conditions that foster accurate metacognition.
Sonic Visualiser: Visualisation, Analysis, and Annotation of Music Audio Recordings

Review| December 01 2021 Sonic Visualiser: Visualisation, Analysis, and Annotation of Music Audio Recordings Sonic Visualiser: Visualisation, Analysis, and Annotation of Music Audio Recordings. Centre for Digital Music at Queen Mary (University of London) and the AHRC Researcher Centre for the History and Analysis of Recorded Music (CHARM). URL: https://www.sonicvisualiser.org/ Marc R. Thompson Marc R. Thompson MARC R. THOMPSON is Senior Researcher in Musicology at the Department of Music, Arts and Culture Studies, University of Jyväskylä (Finland), and a Docent of Interdisciplinary Music Research at the Sibelius Academy. His research focuses on the computational analysis of music and movement, and his published work explores the body’s role within various musical activities such as piano performance and African dance. His current interests include dyadic entrainment in music performance, and music technology pedagogy. Search for other works by this author on: This Site PubMed Google Scholar Journal of the American Musicological Society (2021) 74 (3): 701–714. https://doi.org/10.1525/jams.2021.74.3.701 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Marc R. Thompson; Sonic Visualiser: Visualisation, Analysis, and Annotation of Music Audio Recordings. Journal of the American Musicological Society 1 December 2021; 74 (3): 701–714. doi: https://doi.org/10.1525/jams.2021.74.3.701 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentJournal of the American Musicological Society Search Music recordings have become increasingly important tools for music analysis and many forms of musicological research.1 Compared to music analysis using notation, a recording allows the researcher to investigate music from the performer’s perspective. Much can be learned by exploring the expressive choices a musician makes to elevate the written score. Additionally, recordings are advantageous when analyzing musical styles that might not lend themselves comfortably to standard notational systems (such as some forms of electronic, pop/rock, or folk music). The analysis of music via recording need not be a highly sophisticated process. Insightful investigation can be carried out using a pencil, paper, and stopwatch to track temporal characteristics such as tempo changes and loudness dynamics. However, the process is greatly enriched by using software that displays audio waveform and spectrum visualizations. Waveforms allow one to observe temporal characteristics at a glance, while spectrum displays the music’s frequency components. Significantly,... You do not currently have access to this content.
Autonomous Application in Requirements Analysis of Information System Development for Producing a Design Model

The main technology of traditional information security is firewall, intrusion detection and anti-virus software, which is used in the first anti-outer defence, the first anti-service terminal defence terminal passive defence ideas, the complexity and complexity of these security technologies not only increase the complexity of the autonomous system, reduce the efficiency of the system, but also cannot solve the security problem of the information system, and cannot satisfy the security demand of the information system. After a significant stretch of innovative work, individuals utilize the secret word innovation, network security innovation, set forward the idea “confided in figuring” in view of the equipment security module support, Trusted processing from changing the customary protection thoughts, center around the safety efforts taken from the terminal to forestall framework assaults, from the foundation of the stage, the acknowledgment of the security of data frameworks. Believed figuring is chiefly worried about the security of the framework terminal, utilizing a progression of safety efforts to ensure the protection of clients to work on the security of independent frameworks. Its principle plan thought is implanted in a typical machine to oppose altering the equipment gadget - confided in stage module as the base of the trust, the utilization of equipment and programming innovation to join the trust of the base of trust through the trust bind level to the entire independent framework, joined with the security of information stockpiling insurance, client validation and stage respectability of the three significant safety efforts guarantee that the terminal framework security and unwavering quality, to guarantee that the terminal framework is consistently in a condition of conduct anticipated.
Research on the Impact of Content Fragmentation Propagation in Short Videos

In the era of 5G and the rapid development of the Internet, the pace of people's life is getting faster and faster.Now it is the era of "attention economy", and it is necessary to disseminate fragmented information.This way can catch the audience's attention in the shortest time and let people receive a lot of short, fast and shallow information.Among them, Douyin is a typical product of fragmentation propagation.Since its launch, Douyin has quickly topped the app's download charts.From teenagers to middle-aged and old people, Douyin is regarded as daily spiritual food for entertainment.It not only makes profits for itself, but also provides a social and interactive platform for many content creators.But at the same time, this mode of communication also makes people's aesthetic, emotion, thinking and so on have been greatly affected.This paper studies the influence of people in the era of information fragmentation (with Douyin as the background) from the aspects of introduction, impact analysis and practices through focus group.
Opinion Mining of Pandemic Using Machine Learning

COVID-19 or popularly known as coronavirus is an infectious disease originated in Wuhan, China in 2019, and it has been spread all parts of the world. In India, the first case is found in the early 2020. Soon after it, the lockdown was imposed to control the situation. By now, India has become 2nd most affected country by the virus. In this paper, the sentiments of the people on the social media platform during this current pandemic is determined that is opinion mining of the tweets during this pandemic is done, and also, it is tried to find that which machine learning algorithm will fit best for analyzing the sentiments. About 30,000 tweets from Twitter have been analyzed to determine the positivity, negativity or neutrality of people.
Application of Grey Correlation Method in Electronic Information Equipment Evaluation

The experiment and evaluation of the performance of electronic information equipment require a reasonable quantitative evaluation method. In this paper, the theory of the gray correlation method is described and applied to the performance evaluation of electronic information equipment. This paper screens seven performance indicators of electronic information equipment, quantify the subjective assessments on the indicators by the AHP method, and assigns weights to the indicators. The gray correlation method is adopted to calculate and evaluate four sets of electronic information equipment performance data and derive the performance evaluation results of the equipment, proving the feasibility of the method in the field of electronic information equipment performance experiments and evaluations.
Uncovering the Truth of Electromechanical Association Artifact With Limb Lead Electrodes

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FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification

Unmanned aerial vehicles (UAVs) are now widely applied to data acquisition due to its low cost and fast mobility. With the increasing volume of aerial videos, the demand for automatically parsing these videos is surging. To achieve this, current research mainly focuses on extracting a holistic feature with convolutions along both spatial and temporal dimensions. However, these methods are limited by small temporal receptive fields and cannot adequately capture long-term temporal dependencies that are important for describing complicated dynamics. In this article, we propose a novel deep neural network, termed Fusing Temporal relations and Holistic features for aerial video classification (FuTH-Net), to model not only holistic features but also temporal relations for aerial video classification. Furthermore, the holistic features are refined by the multiscale temporal relations in a novel fusion module for yielding more discriminative video representations. More specially, FuTH-Net employs a two-pathway architecture: 1) a holistic representation pathway to learn a general feature of both frame appearances and short-term temporal variations and 2) a temporal relation pathway to capture multiscale temporal relations across arbitrary frames, providing long-term temporal dependencies. Afterward, a novel fusion module is proposed to spatiotemporally integrate the two features learned from the two pathways. Our model is evaluated on two aerial video classification datasets, ERA and Drone-Action, and achieves the state-of-the-art results. This demonstrates its effectiveness and good generalization capacity across different recognition tasks (event classification and human action recognition). To facilitate further research, we release the code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://gitlab.lrz.de/ai4eo/reasoning/futh-net</uri> .
An Evaluation of Computational Learning-based Methods for the Segmentation of Nuclei in Cervical Cancer Cells from Microscopic Images

The manual segmentation of cellular structures on Z-stack microscopic images is time-consuming and often inaccurate, highlighting the need to develop auto-segmentation tools to facilitate this process.This study aimed to compare the performance of three different machine learning architectures, including random forest (RF), AdaBoost, and multi-layer perceptron (MLP), for the autosegmentation of nuclei in proliferating cervical cancer cells on Z-Stack cellular microscopy proliferation images provided by the HCS Pharma. The impact of using post-processing techniques, such as the StarDist plugin and majority voting, was also evaluated.The RF, AdaBoost, and MLP algorithms were used to auto-segment the nuclei of cervical cancer cells on microscopic images at different Z-stack positions. Post-processing techniques were then applied to each algorithm. The performance of all algorithms was compared by an expert to globally generated ground truth by calculating the accuracy detection rate, the Dice coefficient, and the Jaccard index.RF achieved the best accuracy, followed by the AdaBoost and then the MLP. All algorithms achieved good pixel classifications except in regions whereby the nuclei overlapped. The majority voting and StarDist plugin improved the accuracy of the segmentation but did not resolve the nuclei overlap issue. The Z-Stack analysis revealed similar segmentation results to the Z-stack layer used to train the image. However, a worse performance was noted for segmentations performed on different Z-stack positions, which were not used to train the algorithms.All machine learning architectures provided a good segmentation of nuclei in cervical cancer cells but did not resolve the problem of overlapping nuclei and Z-stack segmentation. Further research should therefore evaluate the combined segmentation techniques and deep learning architectures to resolve these issues.
Gender Classification using Central Fibonacci Weighted Neighborhood Pattern Flooding Binary Matrix (CFWNP_FBM) Shape Primitive Features

Gender Classification from facial images is an open research area with wide range of computer vision applications like security, biometrics and human computer interaction applications. In the proposed method the LL band image of facial image is obtained by using wavelet then on this image Fibonacci Weighted Neighborhood Central pixel Flood binary Matrix is computed and then shape features are evaluated. SVM method uses these shape features for gender classification. The proposed approach has been experimented on FG NET database. The experimental results has shown the more accuracy compared to with other existing methods.
Real-Time Anomaly Detection Using Facebook Prophet

With sensors percolating through everyday living, it may be toted that there is an enormous increase in the availability of real-time streaming and time series data. We also see an exponential increase in number of industry applications with sensors driven by IoT and connected with data sources that change over time. This time-series data presents many technical challenges, opportunities, and threats to industries. Thus, streaming analytics to model an unsupervised machine learning system for detecting unusual/anomalous behavior in real-time must be prominently addressed. In this paper, the authors propose a real-time abnormality detection model using a Facebook prophet that addresses issues related to the improper Facebook collection of data, further leading to faulty analysis and wrong results. The proposed unsupervised model detects abnormalities in the data captured through customer order by considering day and date as constraints. The proposed model is found to be even more efficient in RMSE score. The proposed model delivered enhanced performance compared to other traditional approaches.
IDNetwork: A deep illness‐death network based on multi‐state event history process for disease prognostication

Multi-state models can capture the different patterns of disease evolution. In particular, the illness-death model is used to follow disease progression from a healthy state to an intermediate state of the disease and to a death-related final state. We aim to use those models in order to adapt treatment decisions according to the evolution of the disease. In state-of-the art methods, the risks of transition between the states are modeled via (semi-) Markov processes and transition-specific Cox proportional hazard (P.H.) models. The Cox P.H. model assumes that each variable makes a linear contribution to the model, but the relationship between covariates and risks can be more complex in clinical situations. To address this challenge, we propose a neural network architecture called illness-death network (IDNetwork) that relaxes the linear Cox P.H. assumption within an illness-death process. IDNetwork employs a multi-task architecture and uses a set of fully connected subnetworks in order to learn the probabilities of transition. Through simulations, we explore different configurations of the architecture and demonstrate the added value of our model. IDNetwork significantly improves the predictive performance compared to state-of-the-art methods on a simulated data set, on two clinical trials for patients with colon cancer and on a real-world data set in breast cancer.
Multi-Dimensional Dynamic Model Compression for Efficient Image Super-Resolution

Modern single image super-resolution (SR) system based on convolutional neural networks achieves substantial progress. However, most SR deep networks are computationally expensive and require excessively large activation memory footprints, impeding their effective deployment to resource-limited devices. Based on the observation that the activation patterns in SR networks exhibit high input-dependency, we propose Multi-Dimensional Dynamic Model Compression method that can reduce both spatial and channel wise redundancy in an SR deep network for different input images. To reduce the spatial-wise redundancy, we propose to perform convolution on scaled-down feature-maps where the down-scaling factor is made adaptive to different input images. To reduce the channel-wise redundancy, we introduce a low-cost channel saliency predictor for each convolution to dynamically skip the computation of unimportant channels based on the Gumbel-Softmax. To better capture the feature-maps information and facilitate input-adaptive decision, we employ classic image processing metrics, e.g., Spatial Information, to guide the saliency predictors. The proposed method can be readily applied to a variety of SR deep networks and trained end-to-end with standard super-resolution loss, in combination with a sparsity criterion. Experiments on several benchmarks demonstrate that our method can effectively reduce the FLOPs of both lightweight and non-compact SR models with negligible PSNR loss. Moreover, our compressed models achieve competitive PSNR-FLOPs Pareto frontier compared with SOTA NAS-based SR methods.
A No-Reference Edge-Preservation Assessment Index for SAR Image Filters under a Bayesian Framework Based on the Ratio Gradient

Denoising is an essential preprocessing step for most applications using synthetic aperture radar (SAR) images at different processing levels. Besides suppressing the noise, a good filter should also effectively preserve the image edge information. To quantitatively assess the edge-preservation performance of SAR filters, a number of indices have been investigated in the literature; however, most of them do not fully employ the statistical traits of the SAR image. In this paper, we review some of the typical edge-preservation assessment indices. A new referenceless index is then proposed. The ratio gradient is utilized to characterize the difference between two non-overlapping neighborhoods on opposite sides of each pixel in both the speckled and despeckled images. Based on these gradients and the statistical traits of the speckle, the proposed indicator is derived under a Bayesian framework. A series of experiments conducted with both simulated and real SAR datasets reveal that the proposed index shows good performances, in both robustness and consistency. For reproducibility, the source codes of the index and the testing datasets are provided.
Efficient and Secure Use of Cryptography for Watermarked Signal Authentication

This work discusses and applies techniques to efficiently and securely use cryptography for signal authentication in civil (open) satellite navigation signals such as Chips-Message Robust Authentication (“Chimera”). By efficient and secure, we mean maintaining a cryptographic security strength requirement with minimum data bandwidth and watermark signal degradation. We exploit many strategies, including using Timed-Efficient Stream Loss-tolerant Authentication (“TESLA”), sectioned parallelized cryptographic secret distribution, and only one constant, independent spreading code watermark degradation. Our design allows multi-cadence (i.e., slow and fast) distribution even with only a single watermark degradation. Our design maintains standard 128-bit security with a single minimum bandwidth channel while allowing for authentication of many GNSS services.
Preserving Privacy for Blockchain-driven Image Watermarking using Fully Homomorphic Encryption

Blockchain-driven image watermarking enables traceable and transparent watermark embedding for undeniable authenticity or proof of ownership. Nonetheless, blockchain public ledger is not suitable for privacy concern applications such as for personal health records or digital image transactions. This paper investigates the feasibility and impact of using Fully Homomorphic Encryption (FHE) to preserve user privacy during watermark embedding while retaining the watermarking traceability using blockchain smart contracts. We evaluate the FHE feasibility using SVD-based watermarking by breaking down the SVD operations into fully and partial SVD operations. We consider three performance metrics: the noise budget, storage requirement, and computation cost. For quality evaluation, we analyze the effect of fractional encoding on the quality of the watermarked image and the embedded watermark. The results show that only the partial singular value addition (SVA) operation has a feasible performance by having low computation cost and low noise expense. It incurs a negligible degradation of the watermark quality, either on the watermarked image or in the extracted watermark data.
Utilization of K-means Clustering and Color Homography for Automatic Color Calibration in Image Processing

Often camera calibration in terms of lighting has become a challenge in machine learning. Large training datasets are usually required due to various light conditions that affect the colors on the images, making objects difficult to recognize. This paper proposes the utilization of K-means clustering to extract colors on the images to be used in combination of color Homography to correct colors in low light images automatically. This method aims to solve tedious camera calibration in terms of color and reduce the number of datasets.