Reviewerly/exHarmony huggingface dataset:
Classification of Leukocytes Using Meta-Learning and Color Constancy Methods

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, reported models do not leverage the knowledge produced by the classification of leukocytes to solve similar tasks. For example, the knowledge can be reused to classify images collected with different types of microscopes and image-processing techniques. Therefore, we propose a meta-learning methodology for the classification of leukocyte images using different color constancy methods involving previous knowledge. Our methodology is trained with a specific task at the meta-level, and the knowledge produced is used to solve a different task at the base-level. For the meta-level, we implemented meta-models based on Xception, and for the base-level, we used support vector machine classifiers. Besides, we analyzed the Shades of Gray color constancy method commonly used in skin lesion diagnosis and now implemented for leukocyte images. Our methodology, at the meta-level, achieved 89.28% for precision, 95.65% for sensitivity, 91.78% for F1-score, and 94.40% for accuracy. These scores are competitive regarding the reported state-of-the-art models, especially the sensitivity which is very important for imbalanced datasets, and our meta-model outperforms previous works by +2.25%. Additionally, for the basophil images that were acquired from a chronic myeloid leukemia-positive sample, our meta-model obtained 100% for sensitivity. Moreover, we present an algorithm that generates a new conditioned output at the base-level obtaining highly competitive scores of 91.56% for sensitivity and F1 scores, 95.61% for precision, and 96.47% for accuracy. The findings indicate that our proposed meta-learning methodology can be applied to other medical image classification tasks and achieve high performances by reusing knowledge and reducing the training time for new similar tasks.
Fast and Accurate Voice Biometrics with Deep Learning Algorithm of CNN Depthwise Separable Convolution Model and Fusion of DWT-MFCC Methods

Theft of private data became a threat of crime in cyberspace. This issue was in line with rapid development of data technology, especially online transactions. To attenuate this problem, voice biometrics was developed as an answer to keep up security identity. This paper develops the voice biometric framework based on Convolutional Neural Network Depthwise Separable Convolution (DSC) model and the fusion of Discrete Wavelet Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC). Such a scheme has targeted to increase the high accuracy, to reduce the burden of high computational costs and to speed up the performance of classification process time. We conduct three testing performance, i.e. voice Biometric Training Performance, speaker Recognition Performance (”Who is speaking?”), and Speech Recognition performance (”What keyword is uttered?”). For each of the testing, the results are compared with CNN Standard performance. The training results has shown that CNN DSC model has reduced the amount of training parameters to 364,506, leading to accelerate the performance of training process time to 5.12 minutes. The results of speaker recognition performance has attained the best performance with an accuracy 99.25%, precision 97.14%, recall 98.17% and F1-score 97.28%. The results of speech recognition performance has been able to improve the best performance with accuracy 100%. It can be concluded that CNN DSC with the fusion of DWT- MFCC has outperformed the CNN Standard. The framework can be applied for the identification and verification of user voices accurately, quickly and efficiently for any applications requiring better security performance.
Topological data analysis reveals differences between simulated galaxies and dark matter haloes

We use topological summaries based on Betti curves to characterize the large-scale spatial distribution of simulated dark matter haloes and galaxies. Using the IllustrisTNG and CAMELS-SAM simulations, we show that the topology of the galaxy distribution is significantly different from the topology of the dark matter halo distribution. Further, there are significant differences between the distributions of star-forming and quiescent galaxies. These topological differences are broadly consistent across all simulations, while at the same time there are noticeable differences when comparing between different models. Finally, using the CAMELS-SAM simulations, we show that the topology of the quiescent galaxies in particular depends strongly on the amount of supernova feedback. These results suggest that topological summary statistics could be used to help better understand the processes of galaxy formation and evolution.
The unreasonable effectiveness of few-shot learning for machine translation

We demonstrate the potential of few-shot translation systems, trained with unpaired language data, for both high and low-resource language pairs. We show that with only 5 examples of high-quality translation data shown at inference, a transformer decoder-only model trained solely with self-supervised learning, is able to match specialized supervised state-of-the-art models as well as more general commercial translation systems. In particular, we outperform the best performing system on the WMT'21 English - Chinese news translation task by only using five examples of English - Chinese parallel data at inference. Moreover, our approach in building these models does not necessitate joint multilingual training or back-translation, is conceptually simple and shows the potential to extend to the multilingual setting. Furthermore, the resulting models are two orders of magnitude smaller than state-of-the-art language models. We then analyze the factors which impact the performance of few-shot translation systems, and highlight that the quality of the few-shot demonstrations heavily determines the quality of the translations generated by our models. Finally, we show that the few-shot paradigm also provides a way to control certain attributes of the translation -- we show that we are able to control for regional varieties and formality using only a five examples at inference, paving the way towards controllable machine translation systems.
An Augmented Reality Approach to Interact with Visualizations of Industrial Process Tomography

Industrial process tomography (IPT) is a specialized imaging technique widely used in industrial scenarios for process supervision and control. Today, augmented/mixed reality (AR/MR) is increasingly being adopted in many industrial occasions, even though there is still an obvious gap when it comes to IPT. To bridge this gap, we propose the first systematic AR approach using optical see-through (OST) head mounted displays (HMDs) with comparative evaluation for domain users towards IPT visualization analysis. The proof-of-concept was demonstrated by a within-subject user study (n=20) with counterbalancing design. Both qualitative and quantitative measurements were investigated. The results showed that our AR approach outperformed conventional settings for IPT data visualization analysis in bringing higher understandability, reduced task completion time, lower error rates for domain tasks, increased usability with enhanced user experience, and a better recommendation level. We summarize the findings and suggest future research directions for benefiting IPT users with AR/MR.
A new continuous dynamic system for measuring the sphericity error of bearing balls

Abstract The sphericity error is one of the most important tolerances to evaluate the dimensional accuracy of bearing balls. A continuous dynamic measurement system based on an industrial camera and a Z-shaped transparent track is developed to measure the sphericity error of bearing balls. The Z-shaped track, which is considered for the parameter setting of length, width and inclination, is designed to achieve a pure rolling of the ball around three perpendicular axes. During the whole measurement process, 15 images are dynamically captured by an industrial camera and divided into three groups according to the three mutually perpendicular directions. Then, a sub-pixel edge contour of the bearing ball from each image is extracted by an image-processing step, such as image de-noising, contrast enhancement, region-of-interest extraction and edge detection. Finally, the roundness error and sphericity error, as defined by ISO 12181-1 and ISO 3290-1, are acquired from the extracted contour of each image. Three different diameter bearing balls with a precision grade of G1000, representing a sphericity error of approximately 25 μm, are applied as experimental objects. The two times standard deviation roundness error obtained from the contour of each image are all less than 7 μm after six repeated measurements. In addition, the two measurement results respectively acquired from the proposed method and a commercial projection measuring instrument have good consistency.
Comparative Analysis of Ways to Integrate Microsoft Teams, Zoom, Google Meet Into the Educational Process of Higher Education Institutions of Ukraine

The use of online platforms for educational purposes is a topical issue and detailed studies in this field are not sufficient. The purpose of the article is the comparative analysis of ways of integration of the dominant and popular Microsoft Teams, Zoom, Google Meet services into the educational process. The article describes the Kyiv National University of Culture and Arts experience in the use of enumerated platforms in the learning process during the lockdown as well as in terms of the military aggression of the Russian army in 2022. The main scientific methods of the research: analysis, synthesis and empirical (SWOT analysis). The following issues were considered: digital tools used to solve educational tasks, analysis of possibilities and evaluation of the platforms, without specific characteristics of each platform. The results have shown that based on the analysis of the teachers' practice, the Zoom platform was most frequently used, nevertheless Microsoft Teams has a significant potential for the further development, but its security mechanisms and general characteristics are still insufficiently evaluated.
Towards Automatic Property Generation for SoC Security Verification

Formal verification is an emerging technique for validating the soundness of SoC designs and uncover design flaws. However, existing formal solutions heavily rely on verification engineers to manually specify assertion properties, which can be time-consuming but provides no guarantee on the quality or completeness of the property set. In this brief, we move a step towards automatic property generation for SoC security verification. We automatically extract invariant behaviors from functional and security simulation traces and specify them as assertions. We further prove the generated properties to search for property violations to identify design vulnerabilities. Experimental results show the effectiveness of our method in detecting timing channel and stealthy hardware Trojan.
DEEP LEARNING-BASED FACE MASK DETECTION USING YOLOV5 MODEL

Based on the background of rapid transmission of novel coronavirus and various pneumonia, wearing masks becomes the best solution to effectively reduce the probability of transmission. For a series of problems arising from crowded public places and collective units, where face recognition is difficult to increase target density, a deep convolutional neural network is used for real-time mask detection and recognition.This paper presents the method based on YOLOv5 model for deep learning and mask detection in image recognition as well as a live camera to label the pedestrians without masks in time. This experiment will use LabelImg software to preprocess 5003 images and make lightweight improvements based on the original YOLOv5 model to generate the final face mask recognition model. The Mosaic method is added to merge the images effectively and process the images in batch, and secondly, the GIoU loss function is selected to calculate the bounding box regression loss by comparison, which improves the localization accuracy even more. According to the experimental detection results, analogized with the original model YOLOv5, the recall and accuracy are effectively improved. In this paper, YOLOv3, SSD, Fast-R-CNN detection algorithms are used for comparison, the detection results of this model have a high mAP value which is equal to 92.9, which are higher than the detection results of other models. Real-time target recognition based on this model combined with practical applications can be applied in hospitals and crowd-gathering places to achieve effective reduction of epidemic transmission probability in a short period of time.
Large $N$ von Neumann algebras and the renormalization of Newton's constant

I derive a family of Ryu--Takayanagi formulae that are valid in the large $N$ limit of holographic quantum error-correcting codes, and parameterized by a choice of UV cutoff in the bulk. The bulk entropy terms are matched with a family of von Neumann factors nested inside the large $N$ von Neumann algebra describing the bulk effective field theory. These factors are mapped onto one another by a family of conditional expectations, which are interpreted as a renormalization group flow for the code subspace. Under this flow, I show that the renormalizations of the area term and the bulk entropy term exactly compensate each other. This result provides a concrete realization of the ER=EPR paradigm, as well as an explicit proof of a conjecture due to Susskind and Uglum.
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks

Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that proposed activating the dropout layers only during inference for evaluating uncertainty. This approach, which we call dropout injection, provides clear benefits over its traditional counterpart (which we call embedded dropout) since it allows one to obtain a post hoc uncertainty measure for any existing network previously trained without dropout, avoiding an additional, time-consuming training process. Unfortunately, no previous work compared injected and embedded dropout; therefore, we provide the first thorough investigation, focusing on regression problems. The main contribution of our work is to provide guidelines on the effective use of injected dropout so that it can be a practical alternative to the current use of embedded dropout. In particular, we show that its effectiveness strongly relies on a suitable scaling of the corresponding uncertainty measure, and we discuss the trade-off between negative log-likelihood and calibration error as a function of the scale factor. Experimental results on UCI data sets and crowd counting benchmarks support our claim that dropout injection can effectively behave as a competitive post hoc uncertainty quantification technique.
Multispectral Image Matching Using SIFT and SURF Algorithm: A Review

SIFT and SURF image matching were used in many industries such as survey and mapping, geology, medical and automotive. Multispectral sensors offered today become new challenge for researchers to study the performances of SIFT and SURF algorithms on multispectral image. Basically, multispectral image consists of more than three bands. As a result, the differences between those bands leads to nonlinear intensity between images. Both algorithm detectors using ‘blob detector’ that extracting the feature points as a key point for image matching later on. Hence, the less visibility of the feature on the multispectral images was one of the issues need to be solved. Many researchers investigate and propose a new strategy to extract and match the feature point using SIFT and SURF on multispectral image. The image fusions, combinations of different descriptors and revised or alteration of the algorithm themselves were among the approached taken by researchers in order to achieved good results.
Implementing Agile Scrum Methodology in The Development of SICITRA Mobile Application

Software development management system is important in application development. A proper software development management system will create a team that can adapt to system requirements and changes during application development. Various software development management systems are developed and widely implemented in software development, one of which is Agile Scrum. This study aims to implement well-documented Scrum for end-to-end application development, including the development of servers and mobile applications that we develop. We developed a bus application called SICITRA, with the main feature of being able to help passengers share their travel information with those closest to them. Scrum is used because it has agility which can make application development faster and more organized, and there is a close relationship between everyone involved in the project. The results of this study are that by using well-documented Scrum, we can make it easier to track progress, become a guide during system development, become history and evaluate Scrum implementation during development.
Ancillary Gaussian modes activate the potential to witness non-Markovianity

Abstract We study how the number of employed modes impacts the ability to witness non-Markovian evolutions via correlation backflows in continuous-variable quantum dynamics. We first prove the existence of non-Markovian Gaussian evolutions that do not show any revivals in the correlations between the mode evolving through the dynamics and a single ancillary mode. We then demonstrate how this scenario radically changes when two ancillary modes are considered. Indeed, we show that the same evolutions can show correlation backflows along a specific bipartition when three-mode states are employed, and where only one mode is subjected to the evolution. These results can be interpreted as a form of activation phenomenon in non-Markovianity detection and are proven for two types of correlations, entanglement and steering, and two classes of Gaussian evolutions, a classical noise model and the quantum Brownian motion model.
Changes of the air jet motion, tone spectra and perceived organ pipe sound caused by voicing adjustments

The modifications of a flue organ pipe made in the voicing process can alter its perceived sound and are used for this purpose in the general practice of the organ building crafts. The contribution observes the effects of select adjustment parameters (the cut-up height and air pressure) on the stable tone labial air stream and the perceived sound. The adjustments are studied for the stable tone on an adjustable and transparent rectangular flue pipe and the air motion is observed using laser particle image velocimetry (PIV). Maps of the computed particle velocities for different temporal phases are presented in the results alongside spectra of the sound and its subjective description from a listening test. The results indicate clear relations among the air stream features (temporal and spatial) and the sound (spectral amplitudes and perceived attributes). Several causal mechanisms for the interactions are suggested in the conclusion.
Hue-specific Color Correction for Raw-RGB Images

Some natural scenes show a reduced set of colors. These scenes are often encountered in space imaging, for instance for ocean observation which deals with hues of blue and for Mars exploration which deals with hues of &#x201C;yellowish-brown&#x201D;. In this context the interest of performing hue-specific (or scene-specific) color corrections for the reconstruction of these images is tested. The study is performed on the Next Generation Target (Avian Rochester, LLC - 130 color patches) for both the color correction matrix computation and efficiency testing. The results show that such hue-specific corrections are efficient on the hues of interest, and evaluate the impact on subsidiary hues.
Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data

During the last few years, supervised deep convolutional neural networks have become the state-of-the-art for image recognition tasks. Nevertheless, their performance is severely linked to the amount and quality of the training data. Acquiring and labeling data is a major challenge that limits their expansion to new applications, especially with limited data. Recognition of Lego bricks is a clear example of a real-world deep learning application that has been limited by the difficulties associated with data gathering and training. In this work, photo-realistic image synthesis and few-shot fine-tuning are proposed to overcome limited data in the context of Lego bricks recognition. Using synthetic images and a limited set of 20 real-world images from a controlled environment, the proposed system is evaluated on controlled and uncontrolled real-world testing datasets. Results show the good performance of the synthetically generated data and how limited data from a controlled domain can be successfully used for the few-shot fine-tuning of the synthetic training without a perceptible narrowing of its domain. Obtained results reach an AP50 value of 91.33% for uncontrolled scenarios and 98.7% for controlled ones.
Academic Text Quality Improvement By English Department Students of University of Mataram : A Study on Pre-Editing of Google Neural Machine Translation

This study aims to determine the quality of students in pre-editing academic text input to GNMT. The participants in this study were students of the English Language Education Study Program, Faculty of Teacher Training and Education, the University of Mataram, who took the "Translation and Interpreting" course in semester five of the 2021/2022 Academic Year. This data was collected from assignments completed by 20 students. Supporting information is collected through observation by sitting while learning takes place. Data were analyzed using content analysis procedures such as identifying, categorizing, describing, and explaining. The results of this study indicate that almost all students have good quality results in pre-editing, but it could be better, and some students fail to pre-edit text. The pre-edited output looks like a revised version of the text. Pre-editing shows how the source text changes, especially in language structure, word choice, and punctuation. The good or bad quality of the GNMT translated text represents the student's ability in pre-editing the source text. Thus, the more effort is put into pre-editing the text context, the more likely it is to produce text with better translation quality by GNMT.
Assistive Robots for Healthcare and Human–Robot Interaction

Assistive robots are still mostly prototypes that only remotely recall human interactive dynamics [...].