Reviewerly/exHarmony huggingface dataset:
Domain-Specific Cues for the Usability of Marker-Controlled Watershed Algorithm and U-Net for Medical Image Segmentation

In medical image analysis, segmentation of the region-of-interest is the crucial phase for proper diagnosis. However, this task is very challenging due to missing or diffuse organ/tissue boundaries. However, deep learning frameworks based on the U-Net backbone have gained immense popularity since those can deal with image inconsistencies. This study compared U-Net’s performance with a marker-controlled watershed algorithm, a traditional segmentation algorithm. We tried to find which method is more efficient for polyp and overlapping cell segmentation. These two segmentation tasks were chosen since they are the most challenging task in medical history. The study of cell morphology is of great importance since it helps to understand the structure and conditions of cells, which helps us understand our underlying health conditions. A necessary before understanding cell morphology is cell segmentation. Polyps are protrusions from the inner lining of the colon or rectum which turns into colorectal cancer if left untreated. Thus, accurate segmentation of the polyp region is essential to help the physician properly diagnose the disease. In this work, we have used the KVASIR dataset to report the performance of the U-Net and watershed algorithm for polyp segmentation. A private dataset cultured on HeLa cell line was used for cell segmentation. Watershed algorithm obtained higher accuracy of 91.34% over U-Net which obtained accuracy 80.46% for cell segmentation. On the contrary, U-Net performed better for polyp segmentation and obtained accuracy 91.29% compared to watershed with reported accuracy of 89.19%.
Performance Evaluation of EfficientNet Model Towards Malaria Parasite Detection in Segmented Blood Cells from Thin-Blood Smear Images

Malaria is a potentially fatal disease caused by infected Anopheles mosquitoes. Traditional diagnosis of malaria involves examination of thin blood smear slides under a microscope by trained microscopists to detect infected blood cells. This process is expensive, and results depend both on the quality of smear and on the expertise of the microscopist. Thus, work done in this field is focussed on automating this detection of infected cells. Early work for this task included using hand-engineered features and machine learning algorithms. This approach was taken over by the advent of CNNs which provided an end-to-end solution, right from feature extraction to classification. Work done in this field thus shifted towards using state-of-the-art CNNs. The authors of this paper found that most of the existing models that had been used for this problem had good classification accuracy but had big sizes that could not be run on a normal computing device. They identified that a new set of state-of-the-art CNNs, called EfficientNets which promised similar performance at much smaller sizes, had not been used for this task. So, they worked to evaluate the performance of EfficientNet models for this medical image classification task. The authors trained the EfficientNet-B0 model on the malaria images dataset taken from the National Library of Medicine (NLM) (Dataset used: https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html ). The authors then computed the key performance parameters of the model and compared them against the existing models for this problem. The work done showed that using Efficient-Net models for this task achieved similar accuracy to existing models and significantly reduced the number of parameters.
Channel-Based Similarity Learning Using 2D Channel-Based Convolutional Neural Network

Object identification is one of the major aspects of computer vision. In recent years, the development the computing as well as the storage capacity has increased drastically. These breakthroughs in the technology have blessed us with various data storage technologies and computational engines. Because of the breakthrough in recent years, we are generating humongous amounts of data of which 80% of data is unstructured data and only 20% of data is structured. Unstructured data are mainly composed of images, video and as well as the natural language, i.e. text. These 80% unstructured data consist of the enormous information, but it is difficult to unravel the information contained in these data. Convolution neural network (CNN) is backbone of computer vision and deals with extracting information from the image and video, before the invention of recurrent neural network (RNN), CNN was also employed for natural language processing (NLP) task such as classification and text generation, but the specialty of CNN lies where the dataset consists of sound signals, images or sequence of frames. On Internet, we can find 60% of the unstructured dataset consists of images or sequence of image or text. Basically, image consists of the features which is the orientation of the pixels in a well-defined pattern which can be extracted by using kernel’s known as the feature maps and Maxpooling layers to extract the underlying feature present in the image to train the neural network. CNN is one of the parts of supervised learning techniques which uses labelled data, but it is difficult to label huge number of images. The similarity-based learning enables us to control the similarity percentage as well as it has minimum labelling procedure, i.e. labelling of the dataset is to be labelled 0 or 1. Similarity learning is used to compute the percentage of the features which are similar in the target image with respect to the input image. Image consists of three channels, i.e. red, green and blue channels, which is basically a 2D vector with pixel values in range of 0–255. These individual channels contribute to the features present in the images, and if we can calculate the similarity between input image and the query image, then we can be able to present the unstructured images in relation to the similarity with respect to the input image by using the channels in channel-based CNN tower.
Neoplastic—Nuclei Cell Labelling Using Mask R-CNN

Neoplastic cells are tumorous cells that damage the cells around them and are the prologue for cancer development in organs. However, identifying these cells poses a bottle-neck in the research of cancer cure as it is an extremely tedious job to manually isolate these from the rest of the cells in the tissue. Hence, the automation of this process using deep learning (DL)-based object detection and segmentation techniques such as Mask R-CNN will allow researchers and pathologists to save valuable time otherwise consumed in manually identifying these nuclei. The main objective of this research paper is to provide an instance segmentation technique to label and segment neoplastic cell nuclei from multiple instances of whole-slide images (WSI). For this process, a contemporary neural network architecture called the mask region-based convolutional neural network (Mask R-CNN) was used. This proposed technique generates a pixel-wise binary mask. These masks are capable of segmenting these instances and facilitating the advancement of intelligent systems in medical imaging and computational pathology. This time can instead be devoted to developing better cures by conducting more research. The paper also highlights the best techniques and practices that can be employed while training a model for a task of such complexity. The results of these techniques provide a mean average precision (mAP) score of 0.756 and a binary panoptic quality (bPQ) score of 0.675.
The Improvement Effect of Online Teaching Based on Intelligent Speech Recognition Technology on the Teaching Management Mode in Colleges and Universities

In order to improve the advanced nature of teaching management mode in colleges and universities, this paper analyzes the teaching management process in colleges and universities combined with intelligent speech recognition technology. For the dataset of linear teaching speech information distribution, in the case of overlapping of various datasets, this paper finds the overlapping points of various datasets through an algorithm. Moreover, this paper makes full use of the known information to find a vector that can equivalently represent the linear teaching speech information where each data point is located. After that, this paper takes it as the feature vector of each data point and then performs clustering through spectral clustering to obtain the clustering result. The experimental research results show that the model in this paper has a good effect on speech recognition in college teaching, and the model in this paper has a good role in promoting teaching management in colleges and universities.
Enhanced ensemble learning for aspect-based sentiment analysis on multiple application oriented datasets

The main goal of this article is to develop and propose a novel ABSA method using enhanced ensemble learning (EEL) with optimal feature selection. Initially, the data from multiple applications is gathered and subjected to the preprocessing by ‘stop word removal and punctuation removal, lower case conversion and stemming’. Then, the aspect extraction is done by separating ‘noun and adjective and verb and adverb combination’. From this, the ‘Vader sentiment intensity analyzer’ is used to capture the weighted polarity feature, and then, the word2vector and ‘term frequency-inverse document frequency’ are extracted as features. The optimal feature selection using best and worst fitness-based galactic swarm optimization (BWF-GSO) is used for selecting the most significant features. With these features, ensemble learning with different classifiers like ‘recurrent neural network, support vector machine and deep belief network’ performs for handling the sentiment analysis with parameter optimization. The suggested models are helpful and generate better than the existing outcomes, according to experimental data. Through the performance analysis, the accuracy of BWF-GSO-EEL was 1.16%, 1.58%, 2.01% and 1.37% better than FF-MVO-EEL, FF-EEL, MVO-EEL and PSO-EEL, respectively. Thus, the promising performance has been observed while comparing with other algorithms.
Adaptively Secure KP-ABE For Circuits With Fan-In n And Fan-Out 1

Abstract The attribute-based encryption (ABE) scheme is suitable for access control of ciphertext in cloud computing. Kowalczyk and Wee proposed an adaptively secure attribute encryption scheme that supports $NC^1$ circuits. However, the circuit depth increases because this scheme supports only circuits with fan-in 2, which increases the key length and computational complexity of the key generation and decryption algorithms. To improve efficiency, we designed a secret sharing scheme for circuits with fan-in $n$, and we proposed a key-policy ABE scheme that supports circuits with fan-in $n$. We also designed pebbling rules for secret sharing in circuits with fan-in $n$, improving the compactness of the security reduction. Finally, we proved the adaptive security of the scheme by using a piecewise guessing framework and dual-system encryption. Compared with existing schemes, our scheme reduces the key size, improves the efficiency of the key generation algorithm and the decryption algorithm and provides tighter security reduction.
Reimplementing the Wheel: Teaching Compilers with a Small Self-Contained One

We report on a one-semester compiler construction course based on the idea of implementing a small self-contained compiler for a small model language from scratch, not using other compiler construction frameworks.The course is built around an evolving family of languages with increasing expressiveness and complexity, which finally is crowned by a language with first-class functions, S-expressions, pattern matching, and garbage collection.The code generation technique is based on the idea of symbolic interpreters, which allows to implement a robust albeit not a very efficient native code generator.We give the motivation for the course, describe its structure, and report some results of teaching based on students' post-course surveys.
On Fairness in Face Albedo Estimation

Digital avatars will be crucial components for immersive telecommunication, gaming, and the coming metaverse. Unfortunately, current methods for estimating the facial appearance (albedo) are biased to estimate light skin tones. This talk raises awareness of the problem with an analysis of (1) dataset biases and (2) the light/albedo ambiguity. We show how these problems can be ameliorated by recent advances, improving fairness in albedo estimation.
ABBA Voyage: High Volume Facial Likeness and Performance Pipeline

For the ABBA: Voyage concert experience, Industrial Light & Magic (ILM) was tasked with digitally time traveling the iconic band′s members Agnetha, Anni-Frida, Björn and Benny back to their prime time appearances. For the duration of this fully computer graphics generated concert four continuous photo-real digital human facial performances had to be synthesised driven by their original current day counterparts and stand-in young actors. This talk will dive into the extensive research and development that was undertaken to cater for high volume facial capture and processing, true-to-likeness face-retargeting and additional techniques for breaking through the uncanny valley.
From Procedural Panda-monium to Fast Vectorized Execution using PCF Crowd Primitives

In animation and VFX, crowds are too often considered an “edge case”, to be handled by specialized pipeline outside the main workflows. Requirements of scale and traditional reliance on history based simulation have been obstacles to properly building crowd systems into the core functionality of digital content creation software. Pixar’s crowds team has worked to reverse this trend, developing a fast vectorized crowd system directly within the execution engine of our proprietary animation software, Presto. Dubbed Pcf, for Presto Crowds Framework, this system uses aggregate models, called crowd primitives, to provide artists directly manipulable crowds while maintaining proceduralism for mass edits. Like traditional models, they contain rigs (a graph of operators) which run parallelized through Presto’s execution engine [Watt et al. 2014], but rather than posing points, they set joint angles and blendshape weights to pose entire crowds. The core operations of crowd artists: placement, casting, clip sequencing, transitions, look-ats, and curve following, are all well expressed as rigging operators (known as “actions” in Presto parlance) in Pcf. They provide interactive control of entire crowds in context using the same animation tool as our layout artists, animators, simulation TDs, etc. The first film to use Pcf, Turning Red, reaped massive benefits by building a stadium’s worth of characters in a fraction of the time of previous films’ efforts. However, because Pcf is tightly integrated into Presto, the benefits extended beyond efficiency for the crowds team. By providing our layout department Pcf rigging controls, they were able to shoot inside the crowd and use procedural operators to clear room for the camera and maintain crowd density only where needed. Similarly, the principal animation team could animate main characters in context of the crowd they were acting in, providing the proper context which all too often is absent in crowd shots. Taken together Pcf, is a huge step forward in bringing crowds out of the margins and into the core of animation workflows at Pixar, demonstrating that fast vectorized crowds can be an integral part of digital content creation software.
Equivalence relations and inference for sparse Markov models

Equivalence relations can be useful for statistical inference. This is demonstrated for modeling and statistical inference using sparse Markov models (SMMs). SMMs arise as a mapping of higher-order Markov models, based on the equivalence relation of conditioning histories having equal conditional probability distributions. After discussing advantages of SMMs for statistical modeling, we give two algorithms for their fitting, and highlight their use in modeling and classification applications. We also show through an application to alignment of DNA sequences that equivalence relations can greatly reduce the number of states of an auxiliary Markov chain used for computing the distribution of a pattern statistic, an important inference task for categorical time series.
Development of a small-scale testing machine for use with interferometric monitoring methods

Material testing procedures are increasingly using image-based methods for strain field measurements, with extensive usage of Digital Image Correlation in particular. Nonetheless, there is interest in extending this use to other image-based interferometric methods with increased resolution, such as Electronic Speckle Pattern Interferometry (ESPI) and Shearography. In particular, ESPI requires very stable conditions for measurement, usually achieved by assembly on an optical table. Therefore, this work reports the development of a portable testing machine that can be assembled on INEGI’s optical table and is compatible with our previously developed 3D ESPI setup. The machine is an inexpensive custom design for static or quasi-static loading, with the main objective of withstanding a maximum load and velocity of 20 kN and 240 mm/min, respectively. The machine was built around a brushless dc motor and controller, both from Trinamic, and is currently being successfully used with ESPI methodology.
Growth Strategy of The International Journal of Industrial Distribution &amp; Business

Purpose -The purpose of this study is to analyze the current status of various fusion research journals and its operational strategies, comparing them to those of IJIDB.Research design, data, and methodology -This study focused on the content analysis and strategies of operations for other convergence journals, comparing them to IJIDB in terms of the number of publication journals, publications, academic scope, and the impact factor.Results -IJIDB is relatively new and in its developmental stages compared to other convergence journals.But, IJIDB also shows much promise in that it came second in the field of economic publications of 54 journals in 2017.In order to To elevate IJIDB journal to the top in Korea Citation Index Journals, KODISA should host more international conferences to increase its international recognition levels, develop its managerial functions for increased stability, and open more opportunities for Candidates of Ph.D and Masters degree students to publish for fostering and building potential leaders and innovators.Conclusions -IJIDB is a newly born academic journal.In the future, IJIDB will be able to develop exponentially and be one of the leading journals in the Korea Citation Index by continually learning and developing its operational strategies.
Making and Modalities

The World Economic Forum's 2018 report indicated that students will need to be prepared for a rapidly changing, technology-filled world in which their future jobs likely do not yet exist. Recent education reform initiatives have focused on preparing the workforce for 21st century jobs by improving STEM literacy and acknowledging the importance of teacher preparation. Unfortunately, many teachers, designers, and technologists have not been trained in the same ways as they are expected to prepare students, and training opportunities are often delivered in traditional, business-as-usual formats. To better prepare individuals to prepare students, reimagining traditional educational delivery and modalities, while integrating STEM, making, and play to encourage the development and practice of 21st century skills may prepare those adult learners build toward the future. This chapter will discuss administrative and curricular changes we made geared toward meeting our adult audiences' needs in a teacher education program following their learning preferences.
INFORMATION SPACE OF THE EDUCATIONAL INSTITUTION: STRUCTURAL AND FUNCTIONAL FEATURES OF THE EDUCATIONAL WEBSITE

The article actualizes the digitalization of the educational process as the future of school education, which creates opportunities for distance learning in all its&nbsp;forms. The importance of developing high-quality electronic educational resources and forming on their basis the information educational space of the educational institution with the creation of special information platforms, which is now an important condition for providing quality educational services. The main theoretical approaches to the definition of "information educational space" and "information educational environment" are considered and the relationship between them is investigated. It is characterized that the information educational space is the result of constructive educational activities in the context of informatization, while the information and educational environment is a set of subsystems that provide pedagogical interaction of participants in the educational process through modern information and technical and educational tools. It is noted that the creation of a&nbsp;common&nbsp;information and educational space is possible through the introduction of&nbsp;IC&nbsp;and interactive information technologies in the educational process. The peculiarities of the website functioning in the organization of the information educational space of the educational institution as an important step towards the openness of the educational process of the school to the public, parents and stakeholders in the formation and implementation of educational policy. It has been proven that the school website provides teachers, students and their parents with access to educational information and aims to provide additional assistance and support to all participants in the educational process. The direction of the website to promote the development of scientific research of students and its motivating component for future students is argued. The main functions inherent in the website are highlighted: representative, informational and communication. It is concluded that the information and educational space has a great impact on all participants in the educational process, while the website of the educational institution is aimed at improving the efficiency of educational activities, implementing educational innovations, increasing information openness and transparency of general secondary education.