Reviewerly/exHarmony huggingface dataset:
Ensemble CAD System for Acute Lymphoblastic Leukaemia Classification

Acute Lymphocytic leukaemia is a type of blood cancer which affects white blood cells (WBC) and can cause damage to bone marrow. This disease is diagnosed with a complete blood count (CBC) and a bone marrow aspiration, and it can be lethal if not caught early. In general, manual procedures such as microscopic inspection of stained sample slides are employed to diagnose leukemia. However, manual methods are time-consuming, less accurate, and might result in errors due to numerous human variables such as stress and exhaustion. So, this study discusses the methodology of computer-aided diagnosis systems, including enhancement, augmentation, ensemble, feature extraction, and the usage of CNN-based methods. In proposed method, two classes (Healthy/infected) are discretized using the ALL- IDB-2 dataset, which provided segmented microscopic images. Classification models like VGG16, VGG19, XCEPTION, and Mobile-Net are used with an accuracy of 96 % for vgg16, 94 % for VGG19, 92 5 for XCEPTION, and 96 % for Mobile-Net. In this study, Mobile-Net performed better than the other three models. Finally, all the models are utilized by using ensemble method with highest accuracy of 98%.
Speckle projection profilometry used for detecting magnetic circuit drop height of relay with high reflective surface

Aiming at the problem of camera pixel saturation caused by the high reflective area on the metal surface of a relay, we adopt speckle projection profilometry to detect the magnetic circuit drop height of the relay. The main problem is solved through a detailed analysis of characteristics according to different speckle gray values, speckle sizes, and shapes to effectively distinguish the confusion between the speckle reflection spots and background light reflection spots. The enhanced normalized cross-correlation sub-pixel algorithm is used to get a dense matching pair of points of the right and left images, and three-dimensional reconstruction is based on the triangulation parallax principle. The random sampling consensus algorithm and European clustering algorithm are used to segment the point cloud to obtain the core and yoke iron planes and measure the distance between the two planes. We measured two standard blocks with height difference of 1 mm, and the error is within 2% by the proposed method. The correctness is verified through real-time measurement for a magnetic circuit drop height of the relay assembly line.
Implement strategies for successfully navigating compliance responsibilities

LAS VEGAS — There's so much to keep up with in the arena of athletics compliance, and the work often comes with high expectations, requires quick responses, and easily spills over into after‐work hours. But many compliance professionals have little to no staff assistance and are expected to manage multiple areas of the athletics department on top of their already heavy workload.
Computational Semantics

This book analyzes the application of computer science and artificial intelligence (AI) techniques in the semantics’ analysis for linguistics, classical studies, and philosophy. Similar techniques can be implemented to incorporate the fields of education, psychology, humanities, law, maritime, data science and business intelligence. The book is suitable for the broader audience interested in the emerging scientific field of formal and Natural Language Processing (NLP). The significance of incorporating all aspects of logic design right at the beginning of the creation of a new NLP system is emphasized and analyzed throughout the book. NLP and AI systems offer an unprecedented set of virtues to society. However, the principles of ethical logic design and operation of primitive to deep learning NLP products must be considered in the future, even via the preparation of legislation if needed. As law applications are already taking advantage of the techniques mentioned, the manufacturers should apply the laws and the possible knowledge development of the NLP products could even be monitored after sales. This will minimize the drawbacks of implementing such intelligent technological solutions. NLP systems are a digital representation of ourselves and may even interact with each other in the future. Learning from them is also a way to improve ourselves.
ciDATGAN: Conditional Inputs for Tabular GANs

Conditionality has become a core component for Generative Adversarial Networks (GANs) for generating synthetic images. GANs are usually using latent conditionality to control the generation process. However, tabular data only contains manifest variables. Thus, latent conditionality either restricts the generated data or does not produce sufficiently good results. Therefore, we propose a new methodology to include conditionality in tabular GANs inspired by image completion methods. This article presents ciDATGAN, an evolution of the Directed Acyclic Tabular GAN (DATGAN) that has already been shown to outperform state-of-the-art tabular GAN models. First, we show that the addition of conditional inputs does hinder the model's performance compared to its predecessor. Then, we demonstrate that ciDATGAN can be used to unbias datasets with the help of well-chosen conditional inputs. Finally, it shows that ciDATGAN can learn the logic behind the data and, thus, be used to complete large synthetic datasets using data from a smaller feeder dataset.
Affection: Learning Affective Explanations for Real-World Visual Data

In this work, we explore the emotional reactions that real-world images tend to induce by using natural language as the medium to express the rationale behind an affective response to a given visual stimulus. To embark on this journey, we introduce and share with the research community a large-scale dataset that contains emotional reactions and free-form textual explanations for 85,007 publicly available images, analyzed by 6,283 annotators who were asked to indicate and explain how and why they felt in a particular way when observing a specific image, producing a total of 526,749 responses. Even though emotional reactions are subjective and sensitive to context (personal mood, social status, past experiences) - we show that there is significant common ground to capture potentially plausible emotional responses with a large support in the subject population. In light of this crucial observation, we ask the following questions: i) Can we develop multi-modal neural networks that provide reasonable affective responses to real-world visual data, explained with language? ii) Can we steer such methods towards producing explanations with varying degrees of pragmatic language or justifying different emotional reactions while adapting to the underlying visual stimulus? Finally, iii) How can we evaluate the performance of such methods for this novel task? With this work, we take the first steps in addressing all of these questions, thus paving the way for richer, more human-centric, and emotionally-aware image analysis systems. Our introduced dataset and all developed methods are available on https://affective-explanations.org
Implication of Soft Computing and Machine Learning Method for Software Quality, Defect and Model Prediction

Software metrics have direct linkage with software quality and defect. Thus, for a software engineer, it becomes very hard to estimate the software quality and provide product assurance to the client. Most of the software becomes failure due to several kinds of defects. The software industry uses different kinds of software models such as SDLC for software product development, and it becomes very difficult to choose the correct software model for software development. The objective of this chapter is to show how we can use machine learning and data mining for software defect, quality and software model prediction. We analyse different kinds of machine learning algorithms for application in software engineering domain. This chapter reviews the various classifications used to predict software defects using software measurements in the literature. In this chapter, we perform a detailed analysis of application of data mining and machine learning approaches used for software quality, defect and quality analysis.
A Novel Tightly Coupled Information System for Research Data Management

Most research projects are data driven. However, many organizations lack proper information systems (IS) for managing data, that is, planning, collecting, analyzing, storing, archiving, and sharing for use and re-use. Many research institutions have disparate and fragmented data that make it difficult to uphold the FAIR (findable, accessible, interoperable, and reusable) data management principles. At the same time, there is minimal practice of open and reproducible science. To solve these challenges, we designed and implemented an IS architecture for research data management. Through it, we have a centralized platform for research data management. The IS has several software components that are configured and unified to communicate and share data. The software components are, namely, common ontology, data management plan, data collectors, and the data warehouse. Results show that the IS components have gained global traction, 56.3% of the total web hits came from news users, and 259 projects had metadata (and 17 of those also had data resources). Moreover, the IS aligned the institution’s scientific data resources to universal standards such as the FAIR principles of data management and at the same time showcased open data, open science, and reproducible science. Ultimately, the architecture can be adopted by other organizations to manage research data.
A Model without Higgs Potential for Quantum Simulation of Radiative Mass-Enhancement in SUSY Breaking

We study a quantum-simulation model of a mass enhancement in the fermionic states, as well as in the bosonic ones, of the supersymmetric quantum mechanics. The bosonic and fermionic states are graded by a qubit. This model is so simple that it may be implemented as a quantum simulation of the mass enhancement taking place when supersymmetry (SUSY) is spontaneously broken. Here, our quantum simulation means the realization of the target quantum phenomenon with some quantum-information devices as a physical reality. The model describes how the quasi-particle consisting of the annihilation and creation of 1-mode scalar bosons eats the spin effect given by the X-gate, and how it acquires the mass enhancement in the fermionic states in the spontaneous SUSY breaking. Our model's interaction does not have any Higgs potential. Instead, the qubit acts as a substitute for the Higgs potential by the 2-level-system approximation of the double-well potential, and then, the spontaneous SUSY breaking takes place and the mass is enhanced.
On the Stability of Low Pass Graph Filter With a Large Number of Edge Rewires

Recently, the stability of graph filters has been studied as one of the key theoretical properties driving the highly successful graph convolutional neural networks (GCNs). The stability of a graph filter characterizes the effect of topology perturbation on the output of a graph filter, a fundamental building block for GCNs. Many existing results have focused on the regime of small perturbation with a small number of edge rewires. However, the number of edge rewires can be large in many applications. To study the latter case, this work departs from the previous analysis and proves a bound on the stability of graph filter relying on the filter's frequency response. Assuming the graph filter is low pass, we show that the stability of the filter depends on perturbation to the community structure. As an application, we show that for stochastic block model graphs, the graph filter distance converges to zero when the number of nodes approaches infinity. Numerical simulations validate our findings.
Obraz otcovstva v metaforike – etnolingvistická sonda

In the presented study, our goal was to examine the stereotypical image of fatherhood / father in the Slovak cultural-linguistic area. Using the methodology of cognitive ethnolinguistics, the author focuses on the analysis of metaphorical concepts in which the image of fatherhood and father with various axioms was established. Examining metaphorical concepts, the author works with the Slovak National Corpus. Based on the peculiarities of the acquired concepts, the author was able to define metaphorical groups in which fatherhood acts primarily as an inanimate object and a living being. In this way, this study contributes to the precise meaning identification and definition of the cognitive concept of fatherhood from an ethnolinguistic point of view, and therefore contributes to a more detailed knowledge of the concept and its specifics, present in the Slovak language and in the Slovak cultural symbolism.
A Magnitude Estimation Approach and Application to the Yunnan Earthquake Early Warning Network

Abstract Estimating the magnitude based on P waves is important to issue early messages to the general public for earthquake early warning (EEW) purposes. First, we reviewed part of the magnitude estimation results of the current EEW system deployed in Yunnan and found that the magnitude of the mainshock of the Yangbi earthquake sequence (Ms 6.4, as formally determined by the Yunnan Seismic Network) is severe underestimated by 0.9, whereas a number of small earthquakes are overestimated. Then, we determined the reason for this phenomenon and improved the magnitude estimation precision in Yunnan by introducing the fixed effect regression technique, adopting a flexible time window and deriving the magnitude estimation equation strictly. Finally, we examined the deficiencies of this magnitude estimation approach and demonstrated that it is possible to provide instant and reliable parameters of an earthquake through numerous inexpensive accelerometers on the ground.
Digital ECG Signal Watermarking and Compression

Data privacy on the Internet seems to be becoming a thing of the past. It is going to be a struggle for individuals to manage personal medical information on the Internet. ECG data is considered as the most vulnerable dataset. To safeguard and protect the personal ECG data, the use of security schemes like watermarking of ECG data is the focus of the present Chapter.
REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer

Human Video Motion Transfer (HVMT) aims to, given an image of a source person, generate his/her video that imitates the motion of the driving person. Existing methods for HVMT mainly exploit Generative Adversarial Networks (GANs) to perform the warping operation based on the flow estimated from the source person image and each driving video frame. However, these methods always generate obvious artifacts due to the dramatic differences in poses, scales, and shifts between the source person and the driving person. To overcome these challenges, this paper presents a novel REgion-to-whole human MOtion Transfer (REMOT) framework based on GANs. To generate realistic motions, the REMOT adopts a progressive generation paradigm: it first generates each body part in the driving pose without flow-based warping, then composites all parts into a complete person of the driving motion. Moreover, to preserve the natural global appearance, we design a Global Alignment Module to align the scale and position of the source person with those of the driving person based on their layouts. Furthermore, we propose a Texture Alignment Module to keep each part of the person aligned according to the similarity of the texture. Finally, through extensive quantitative and qualitative experiments, our REMOT achieves state-of-the-art results on two public benchmarks.
CLG Authorship Analytics: a library for authorship verification

The task of authorship verification consists in detecting whether two texts have been written by the same person. This paper describes the CLG Authorship Analytics software, which implements several individual methods as well as a stacked generalization system for authorship verification. The approach relies primarily on ensemble learning methods, i.e. repeatedly sampling the data in order to capture the invariant stylistic patterns. The approach is tested through a series of experiments designed to test the ability of the system to generalize, depending on various parameters. The code and results of the experiments are publicly available https://github.com/erwanm/clg-authorship-experiments .
An Analysis of Research Trends in English Textbooks in Korea

The aims of this study are to examine the research trends of journal articles on elementary, middle, and high school English textbooks based on the 2015 revised national curriculum and to explore implications for future related research. To this end, 110 academic papers on English textbooks were collected using KCI(Korea Citation Index), and they were classified by school level, year of issue, language skills, research topics, and textbook components. According to the research results, English textbooks in elementary schools were studied the most, and the articles on English textbooks were published the most from 2019 to 2021, during which the 2015 national curriculum came into full force. Second, reading, vocabulary, culture, and integrated language skills were the most focused language skills in the articles on English textbooks. Thirdly, among seven research topics on English textbooks, most of the articles were found to be focused on content analysis. Thus, research on textbook selection, teaching methods, and teachers’ perception of textbooks needs to be conducted in the future. Lastly, the textbook components mainly studied were teaching and learning activities, continuity and systematicity, culture, vocabulary, and functions, so content topics, language forms, authenticity, and evaluation also need to be addressed in future research. Finally, the limitations of the study were discussed.
Cache-locality Based Adaptive Warp Scheduling for Neural Network Acceleration on GPGPUs

In many emerging applications such as convolutional neural networks (CNNs), general purpose graph processing units (GPGPUs) are widely used as computing devices. For GPGPU computing, warp scheduling policy is crucial for the overall performance. We find that a single warp scheduling policy cannot provide optimal performance for all layers in a CNN model. In this paper, we analyze the workload of each layer in typical CNN models. It is observed that multiple layers of a network model have a significant performance gap under different scheduling policies. As a result, we propose a cache tag buffer to evaluate the workload characteristics according to the type of cache locality. Based on that, the warp scheduler adaptively selects the proper scheduling policy among Loosely-Round-Robin (LRR) and Greedy-Then-Oldest (GTO). Evaluation results show that the proposed mechanism is able to select the better warp scheduler between LRR and GTO at runtime, translating to superior performance for the computation of various CNN models on GPGPUs. We also show that the overhead introduced by the proposed scheduling method is small.
Improving Neural Network Time Series Prediction with a GA-BFGS Grown Dynamic Architecture

A dynamic recurrent neural network is seen to be the most efficient nonlinear approach to time series prediction, especially if its architecture is grown to construct the prediction model. To further advance its performance, this paper develops a hybrid evolutionary algorithm to grow and train the network and its dynamic architecture holistically. The training approach is based on the genetic algorithm (GA) and the fastest quasi-Newton algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS). The proposed GA-BFGS method is suitable for training both discrete structural parameters and continuous weighting parameters of the network and is applicable to parallel computation. This approach eliminates the need for setting any hyperparameters of the neural network for the highest possible optimality and accuracy to emerge. The method is tested on the Mackey-Glass time series and its forecasting performance is compared with currently widely adopted deep learning methods such as the long short-term memory (LSTM) and echo state networks (ESN) models. Results show that the proposed method incurs lower losses, requires a smaller network size, and performs faster.
Self‐supervised image clustering from multiple incomplete views via constrastive complementary generation

Incomplete Multi-View Clustering aims to enhance clustering performance by using data from multiple modalities. Despite the fact that several approaches for studying this issue have been proposed, the following drawbacks still persist: 1) It's difficult to learn latent representations that account for complementarity yet consistency without using label information; 2) and thus fails to take full advantage of the hidden information in incomplete data results in suboptimal clustering performance when complete data is scarce. In this paper, we propose Contrastive Incomplete Multi-View Image Clustering with Generative Adversarial Networks (CIMIC-GAN), which uses GAN to fill in incomplete data and uses double contrastive learning to learn consistency on complete and incomplete data. More specifically, considering diversity and complementary information among multiple modalities, we incorporate autoencoding representation of complete and incomplete data into double contrastive learning to achieve learning consistency. Integrating GANs into the autoencoding process can not only take full advantage of new features of incomplete data, but also better generalize the model in the presence of high data missing rates. Experiments conducted on \textcolor{black}{four} extensively-used datasets show that CIMIC-GAN outperforms state-of-the-art incomplete multi-View clustering methods.
Markerless motion evaluation via OpenPose and fuzzy activity evaluator

Human motion evaluation techniques can be categorized as marker-based and markerless-based. The former can be time-consuming and expensive, while the latter has the advantage of being online, fast, and noninvasive. This study presents the development of a markerless-based system by means of OpenPose, Python codes, and fuzzy inference for push-up motion evaluation. The markerless technique generally produced good accuracy in locating human joint centers and formed the skeleton. A fuzzy activity evaluator was introduced to help resolve possible uncertainty occurring at the elbow joint due to possible abduction or adduction. The fuzzy evaluator used degree of truth for each of all three joint angles to consider the overall quality of motion. With the aid of fuzzy inference, the evaluation system produced satisfactory and reliable results by taking both qualitative and quantitative measures into account. The presented technique demonstrated how a combination of hard computing and soft computing can be a powerful tool for developing a reliable motion evaluation. Notably, the developed system used low-cost hardware and open-source software. The technique can also be applied to evaluation of rehabilitation exercise, or industrial safety, such as instantly identifying improper or dangerous motions in production.
Online Trolling of Journalists

Academic studies and news stories alike have been documenting a quite devastating picture of the online trolling of journalists, especially women and minorities, around the world. The scale and the magnitude of trolling have attracted the attention of governments and international organizations, particularly those considering it a threat to human rights. This chapter examines the dimensions and consequences of online trolling of journalists and suggest four lines of action to address the problem. Although journalists across news organizations and news beats are frequent targets of trolling, not all journalists are similarly vulnerable. Trolling is disproportionately directed at two clusters of journalists. Digital harassment is both a relatively new form of anti-press violence and censorship as well as the continuation of authoritarian forces determined to silence critical journalism and other forms of public expression.