Reviewerly/exHarmony huggingface dataset:
Transformations II

Abstract This chapter examines some formal and theoretical considerations involving transformation graphs and networks, as well as transformational relationships among serial structures. The path consistency condition is presented as a property of some transformation graphs, not (as in Lewin) a requirement for all of them, and other related consistency properties are studied. Several kinds of isomorphism and isography of transformation graphs and networks are defined. The theory of Klumpenhouwer networks is outlined, along with the attendant controversy. Some relationships among twelve-tone rows may be modeled by UTTs and similar transformations, as illustrated by examples from the serial music of Webern; UTTs may also play the role of coordinate transformations relating different schemes for labeling the forms of a row. The duality between pitch-class numbers and order numbers is examined using techniques from group theory and permutation theory; representing a row via a 12-by-12 array aids in visualizing this duality.
An Automated Scoring System for Photoshop Course in Secondary Vocational Colleges

With the rapid development of information technology and the growing demand for professional skills by professionals, improving students’ practical software operation level has become an important part of students’ skill training. Photoshop, as one of the most commonly used software in the design field, is getting more and more attention. In secondary vocational and technical schools, the Photoshop course has become a major course for information technology majors. Combined with the practical application background, this paper designs a system for automatic scoring of Photoshop images. The system can comprehensively compare and score the similarity of the edges, colors and textures of the two images. We collect the Mean Opinion Score (MOS) of teachers in specialized classes on images and compare our proposed method with other classical methods according to MOS. Experimental results show that our designed Photoshop image scoring system is completely feasible and outperforms other classical methods.
FCPSO: Evaluation of Feature Clustering Using Particle Swarm Optimization for Health Data

Analysis of feature from health data to diagnose disease is a challenging task. Generally, the datasets contain the irrelevant features with high volume. In this context, feature clustering by using particle swarm optimization is suggested. The particles are initialized efficiently. Afterward, cluster is formed based on the Euclidean distance over the generated particle. To measure the quality of each particle, Dunn Index (DI) function is computed. Also, an extensive simulation is done in terms of sensitivity, specificity, accuracy, and F-score using various machine learning classifiers like multilayer perceptron (MLP) and support vector machine (SVM). Furthermore, comparative analysis is performed with differential evolution algorithm (DE).
<i>Physics with Elliot</i>

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dan MacIsaac; Physics with Elliot. The Physics Teacher 1 March 2023; 61 (3): 239. https://doi.org/10.1119/10.0017368 Download citation file: Ris (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 ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
An Empirical Analysis of Running a Bitcoin Minimal Wallet on an IoT Device

Integrating blockchain with IoT technology is a hot topic in recent years. Some outstanding approaches propose to design new blockchain platforms from scratch to adapt them to the special needs of the resource-constrained devices of the IoT. On the other hand, integrating existing blockchain systems, like Bitcoin or Ethereum, would open the door to extend and use a plethora of already successful applications running on these systems. In this paper, we show the feasibility to interact with the Bitcoin blockchain with an IoT device. To this end, we implement a minimal SPV wallet that we deploy on a microcontroller unit from the STM32F4 family. Then, we empirically study the performance of this minimal wallet analyzing its key functionalities in terms of execution time, memory usage, and network traffic. Beyond demonstrating the feasibility of integrating the most popular blockchain network with the IoT, the results of this experiment show the most demanding operations, which is a necessary first step to construct a wallet optimized for the IoT.
Drunken Dragon Cyber Dance: Application of Augmented Reality Technology in Drunken Dragon Cultural Heritage Education

The Drunken Dragon Festival is a traditional folk sacrificial activity that originated in Xiangshan, Guangdong Province, China. After foreign migrant residents moved to Macao, they brought customs such as the drunken dragon dance in the Drunken Dragon Festival into Macao. Under the influence of subsequent foreign culture and urbanization development, the traditional culture gradually became silent. This research plans to use augmented reality technology to protect the drunken dragon dance and build it into an android app with interaction design enhancement. Then, children can interactively understand the Drunken Dragon Festival anytime, anywhere. The research design is divided into three major parts: field investigation, 3d modeling, and AR interaction design. In the beginning, we obtained the basic gestures of drunken dragon dance from professional performers through field investigation. They were then transferred into an action sequence and 3D model. Finally, user interaction design and augmented reality were used to generate an android application. The generated AR application is used for children’s cultural heritage education.
Ultra-Flow: An Ultra-fast and High-quality Optical Flow Accelerator with Deep Feature Matching on FPGA

Dense and accurate optical flow estimation is an important requirement for dynamic scene perception in autonomous systems. However, most of the existing FPGA accelerators are based on classic methods, which cannot deal with large displacements of moving objects in ultra-fast scenes. In this paper, we present Ultra-Flow, an ultra-fast pipelined architecture for efficient optical flow estimation and refinement. Ultra-Flow utilizes binary neural networks to generate the robust feature map, on which hierarchical matching is directly performed. Therefore, multiple usages of neural networks at hierarchical levels can be avoided to achieve hardware efficiency in Ultra-Flow. Optimizations, including local flow regularization and enhanced matching, are further used to improve the throughput and refine the optical flow to obtain higher accuracy. Evaluation results show that, compared to state-of-the-art FPGA accelerators, Ultra-Flow achieves leading accuracy in the Middlebury sequences at ultra-fast processing speed up to 687.92 frames/s for 640 × 480 pixel images.
Action recognition based on multimode fusion for VR online platform

The current popular online communication platforms can convey information only in the form of text, voice, pictures, and other electronic means. The richness and reliability of information is not comparable to traditional face-to-face communication. The use of virtual reality (VR) technology for online communication is a viable alternative to face-to-face communication. In the current VR online communication platform, users are in a virtual world in the form of avatars, which can achieve “face-to-face” communication to a certain extent. However, the actions of the avatar do not follow the user, which makes the communication process less realistic. Decision-makers need to make decisions based on the behavior of VR users, but there are no effective methods for action data collection in VR environments. In our work, three modalities of nine actions from VR users are collected using a virtual reality head-mounted display (VR HMD) built-in sensors, RGB cameras and human pose estimation. Using these data and advanced multimodal fusion action recognition networks, we obtained a high accuracy action recognition model. In addition, we take advantage of the VR HMD to collect 3D position data and design a 2D key point augmentation scheme for VR users. Using the augmented 2D key point data and VR HMD sensor data, we can train action recognition models with high accuracy and strong stability. In data collection and experimental work, we focus our research on classroom scenes, and the results can be extended to other scenes.
Blockchain Technology: A Framework for Endless Applications

Over the last decade, Blockchain Technology has evolved from being a peer-to-peer implementation of digital currency into a framework for numerous applications. That is, blockchain is widely recognized as the technology behind cryptocurrencies such as Bitcoin, but recent research indicates that it can be exploited way more than that. Among the most notable characteristics of the Blockchain Technology are system decentralization, data immutability, transaction transparency, and user privacy. Thus, it becomes apparent that it can significantly benefit many consumer applications, where trust and security are crucial aspects of the implementation. In this paper, we review and present, in a tutorial manner, blockchain's fundamental postulates and characteristics, and we introduce a novel point of view, where we visualize a layered representation of the blockchain infrastructure, so as to analyze it as a framework. Thinking of Blockchain Technology as a framework may assist consumers to better understand its capabilities and limitations. Additionally, we review emerging blockchain-based applications that span across many different scientific fields. We also discuss the relative merits and restrictions. Namely, we have identified applications in finance, smart contracts, internet of things, e-voting systems, storage and data protection, reputation systems, healthcare, and transportation, indicating that the Blockchain Technology is becoming a framework for endless applications.
Using an Interpretable Machine Learning Model to Predict Corifollitropin Alfa Protocol

Background: To demonstrate an interpretable machine learning (ML) model for a clinical prediction of corifollitropin alfa protocol. Methods: The retrospective study involved 1,221 cycles from 1,180 patients undergoing corifollitropin alfa protocol with oocyte retrieval events from a single in vitro fertilization (IVF) center. The ML models were assigned to the following tasks, which are the dosage of corifollitropin alfa, trigger type, the dosage of recombinant FSH (rFSH), the dosage of recombinant LH (rLH), the duration between the follow-up visit (FUV), and oocyte retrieval. Interpretable SHapley Additive exPlanations (SHAP) were selected to analyze the input features. The ranking of the prediction powers from each input feature reveals the level of contribution to the model. Result(s): Two series of interpretable ML models were developed to predict classification tasks and regression tasks. The areas under ROC (AUC) for predicting the dosage of corifollitropin alfa and trigger type were 0.933 ([Formula: see text] CI 0.907–0.958) and 0.891 ([Formula: see text] CI 0.864–0.918), while accuracies were 0.944 and 0.904. The mean absolute errors (MAEs) that predict the dosage of rFSH, the dosage of rLH, and the duration between the FUV and oocyte retrieval were 97.08 IU (rFSH), 105.61 IU (rLH), and 0.45 days. Conclusions: The study demonstrates a set of interpretable ML models predicting tasks involved in corifollitropin alfa protocol. The potential for the clinical application is to provide consistency in corifollitropin protocol adjustments.
Computer vision and machine learning approaches for metadata enrichment to improve searchability of historical newspaper collections

Purpose Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue. Design/methodology/approach In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data. Findings The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles. Originality/value The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).
A Comprehensive Review of the Integration of Blockchain Technology in Supply Chain Management

This paper reviews the concept of Supply Chain Management (SCM) and provides an introduction to the use of blockchain technology in SCM. SCM involves the coordination and control of operations related to the production and delivery of goods and services to clients. The traditional SCM model has been facing challenges, including a lack of transparency, inefficiency, and increased costs. Blockchain technology offers a promising solution to these challenges by providing a secure, decentralized, and transparent platform for the management of supply chain operations. This paper highlights the key features of blockchain technology and how it can be applied in the SCM business model. The use of blockchain can improve the efficiency of supply chain processes, reduce costs, increase transparency, and enhance trust among supply chain partners. The paper also discusses the challenges and limitations of implementing blockchain in SCM and provides recommendations for future research.
Image super-resolution using multi-scale non-local attention

Recent convolutional neural network (CNN)-based super-resolution (SR) studies incorporated non-local attention (NLA), to consider the long-range feature correlations and achieve considerable performance improvement. Here, we proposed an innovative NLA scheme called multi-scale NLA (MS-NLA) that computes NLAs at multiple scales and fuses them. To effectively fuse NLAs, we also proposed two learning-based methods and analyzed their performance on a recurrent SR network. Furthermore, the effect of weight sharing in the fusion methods is analyzed as well. In 2 × and 4 × SR experiments on benchmark datasets, our method had higher PSNR values of 0.295 and 0.148 dB on average than those using single-scale NLA and cross-scale NLA, respectively, and produced visually more pleasing SR results. The weight sharing had a limited but positive effect, depending on datasets. The source code is accessible at https://github.com/Dae12-Han/MSNLN.
A novel dual-fusion algorithm of single image dehazing based on anisotropic diffusion and Gaussian filter

Dark channel prior (DCP) is a widely used method in single image dehazing technology. Here, we propose a novel dual-fusion algorithm of single image dehazing based on anisotropic diffusion and Gaussian filter to suppress the halo effect or colour distortion in traditional DCP algorithms. Anisotropic diffusion is used to edge-preserving smooth images and a Gaussian filter is to smooth the local white objects. A dual-fusion strategy is conducted to optimise the atmospheric veil. Besides, the fast explicit diffusion (FED) scheme is used to accelerate the numerical solution of the anisotropic diffusion to reduce time consumption. The subjective and objective evaluation of the experiment shows that the proposed algorithm can effectively suppress the halo effect and colour distortion, and has good dehazing performance on evaluation metrics. The proposed algorithm also reduces the time consumption by 54.2% than DCP with guided filter. This study provides an effective solution for single image dehazing.
Evaluation of Performance of AI System during Voice Recognition in Social Conversation

Ai technology (Cai) refers to either a machine's capacity for thought and learning (AI). It's also a field of research that tries to “intelligentize” machines. The language of a particular speakers may be recognised, distinguished from these other speech, and authenticated using specialist software and systems that use voice control. The world has progressed a lot thanks to the combination combining natural language processing and AI. Dialog AI is expected to transform the sector. Another advantage of technology is that it may be used to assist law enforcement in catching individuals depending on their language. Huge social events may in the future be utilised to aid scientists in a range of subjects. It could also benefit the health care and medical fields, in addition to the public curriculum.
Moment vs local magnitude scaling of small-to-moderate earthquakes from seismic moment estimation of 10 years (2009-2018) of Italian seismicity

We computed moment (Mw) and local magnitude (ML) of about 250,000 earthquakes occurred in Italy from 2009 to 2018 and recorded at seismic stations of the Italian National Network managed by INGV.For moment magnitude computation, we start from raw velocity waveforms and invert the displacement spectra of more than 2,000,000 S-waves manually picked. We use the probabilistic method of Supino et al. [2019] to estimate the a-posteriori joint probability density function of the source parameters: seismic moment M0, corner frequency fc and high-frequency decay &amp;#947;. Mw is obtained from M0 using the Kanamori [1977] equation.We start from the same waveforms to compute local magnitude using two designed on purpose codes, PyAmp and PyML [Di Stefano et al., 2023], and an attenuation law specific for the Italian region, Di Bona et al. [2016], obtaining ML values characterized by quality and homogeneity.Both magnitude catalogs can be reproduced due to the availability in open databases of all the input and output parameters used for processing.We observe a self-similar scaling between fc and M0 for Mw larger than ~2.0. For smaller magnitudes, S-wave spectra show an almost constant corner frequency (~10 Hz), which does not scale with the earthquake source (seismic moment). We interpret this as the constant cut-off frequency of the anelastic attenuation, which acts as a low-pass filter and produces an apparent corner frequency. The latter is lower than expected, and corresponds to an apparent larger source duration.Because of the conservation of total displacement integral after a low-pass filtering, signals must exhibit a maximum amplitude lower than expected to &amp;#8220;compensate&amp;#8221; the apparent larger source duration. ML values are therefore expected to be underestimated while moment magnitudes, by definition, are not affected by this as they are proportional to the displacement integral.Coherently, the comparison of our Mw and ML estimates shows the systematic underestimation of ML with respect to Mw for small magnitude events. The deviation from a 1:1 scaling relationship between ML and Mw overlaps the magnitude range where the constant apparent corner frequency arises in the M0-fc scaling (ML &lt;~ 2).Regarding the upcoming of a new generation of earthquake catalogs characterized by very low completeness magnitudes (MC &lt;&lt; 2), our results suggest that a robust analysis of the statistical features of these catalogs (e.g., event size distribution) should consider the use of a precise magnitude estimate such as Mw instead of ML.
Detection and characterization of seismic sequences in the normal fault system of the Irpinia region, Southern Italy

Understanding mechanical processes occurring on faults and catching the preparation phase of large magnitude events require a detailed characterization of the microseismicity, which can be enhanced today using advanced techniques for earthquake detection. These techniques decrease the detection threshold of seismic networks and provide augmented catalogs, which enable improved statistical analysis associated with event occurrence and size. However, seismic events recorded at the level of the noise typically emerge only at a few stations, making earthquake characterization challenging. This issue is further complicated in areas where seismicity occurs deep in the crust, as happens in the normal fault system of the Irpinia region, Southern Italy, where earthquakes occur at depths between 8 and 15 km.In this work we focus on the detection and characterization of seismic sequences occurring in the Irpinia region featuring low magnitude mainshocks (Ml&amp;#8764;3), using data from the Irpinia Near Fault Observatory.Event detection for the sequences is performed through the integration of a machine learning based detector (EQTransformer, Mousavi et al., 2020) and a template matching technique (Chamberlain et al., 2018), with the former providing a wider set of templates for the similarity search. This strategy outperforms auto-similarity techniques based on fingerprints (FAST, Yoon et al., 2015) and template matching grounded in manual catalogs. On average, the final catalog of the analyzed sequences increases the manually revised network bulletin by a factor 7. We compared P- and S- arrival time estimates, grounded in the machine learning phase picking and cross-correlation for template matching, using manual identifications to assess the reliability of automatic picks; the mean residual between manual and automatic values is ~0 for both P- and S-waves, with a larger residuals standard deviation for the latter.We apply a double-difference location technique using both catalog and cross-correlation differential travel times for locating the events, with the goal of resolving and highlighting fault structures where seismicity takes place. We finally track the spatio-temporal evolution of the seismicity, and apply a mechanical model based on static stress, to discriminate whether sequences in the area are mainly triggered by static stress change, dynamic stressing, or aseismic mechanisms such as fluid diffusion.