Reviewerly/exHarmony huggingface dataset:
A Tuning Approach Using Genetic Algorithms for Emergency Incidents Classification in Social Media in Ecuador

The social media are an excellent opportunity to extract data of almost any topic and perform different kind of analysis and design of artificial intelligence models, nevertheless, researchers need to consider different kind of relevant issues such as low quality data or the hyperparameter tuning complexity, that can affect the performance of an classification model. Twitter is a very popular social network that allows to researchers to get historical and real time data of any topic. In this paper, we proposed an approach based on a genetic algorithm to perform the hyperparameter tuning for classification models. The CRISP-DM methodology was used with six stages: problem understanding, data collection, data understanding, modeling, testing and deploy. Results show a good overall performance when a genetic algorithm was used to fit the best hyperparameters combination for a certain number of classification models, scoring more than 0.90 for all the tested models. The LSVC (Linear Support Vector Classifier) was the model that performed the best for the extracted data (+170k tweets), with a Matthews correlational coefficient (MCC) of 0.97 and 0.96 for multi-class and binary classification, respectively. From the obtained results, we can conclude that using a genetic algorithm for hyperparameter tuning of classification models was a successful alternative, and with this, a classifier model was implemented for emergency event classification for Spanish tweets from Ecuador.
Axiomatic Method for Constructing a Generalized Solution of a Mixed Problem for a Telegraph Equation

The paper presents an algorithm for constructing a rapidly converging series representing a generalized solution of a mixed problem for a telegraphic equation considered in a half-band. Reviewed the case of an essentially non-self-adjoint operator in a spatial variable. The system of root functions of a differential operator, in addition to its eigenfunctions, contains an infinite number of associated functions. The constructed series can be considered as a generalized d’Alembert formula. A new axiomatic A.P. Khromov’s method is applied to construct the solution. The proposed approach superseds the traditional method of separating variables for solving mixed problems, which usually results in to slowly converging series. For the problem under consideration, in general, the method of separating variables is not applicable, since the coefficient of the equation depends both on the spatial variable and on time.
Finding Significant Project Issues with Machine Learning

We investigated the research question: Can random forest machine learning (ML) identify the most significant issues linked with project failure using declassified data from the military industry? Our literature review developed a feature typology diagram based on decision-making goals and artificial learning capability, which contrasted widely used machine learning techniques. In over 7000 projects, we employed machine learning to identify main failure factors. The random forest ML model properly predicted 81% of project successes and failures, with a training accuracy of 0.784 and a validation accuracy of 0.810 based on the confusion matrix. The average area under the receiver operating characteristic was 0.849, which was a favorable evaluation score compared to similar ML studies. We concluded that random forest machine learning first aided in identifying critical factors in vast data. Other statistical techniques would be necessary to fine-tune the predictive learning model as a future step.
Using CNN Technique and Webcam to Identify Face Mask Violation

In the whole present globe suffering from COVID-19. The continuous COVID-19 upsurge and emerging variants present unprecedented challenges in many health systems. Many regulatory authorities have instituted the mandatory use of face masks, especially in public places where massive contact of people is frequent and inevitable, particularly inside public transport facilities, sports arenas, shopping malls, and workplaces. However, compliance and adherence to proper wearing of face masks have been complex due to various reasons, including diversified mask types, different degrees of obstructions, multiple variations, balancing various model detection accuracy or errors and deployment requirements, angle of view, deployment of detection model on computers with limited processing power, low-resolution images, facial expression, and lack of real-world dataset. Based on the abovementioned problems, we implemented the CNN technique and webcam (webcam, CCTV camera) to identify face detection; it will be one of the fascinating modalities for biometric models.
Video to Text Generation Using Sentence Vector and Skip Connections

Nowadays, video data is increasing rapidly and the need of robust algorithms to process the interpretation of the video. A textual alternative will be more effective and save time. We aim to produce the caption for the video. The most famous architecture used for this is the encoder-decoder (E-D) model. Recent attempts have focused on improving performance by including 3D-CNN, transformers, or structural changes in the basic LSTM units used in E-D. Sentence vectors are used in this work, improving the E-D model’s performance. From the video file, a sentence vector is generated and used by the decoder to generate an accurate description by using previously generated words. Skip connection in the encoder part avoids the vanishing gradients problem. All of our studies use the MSVD and CHARADES datasets. Four famous metrics, BLEU@4, METEOR, ROUGE, and CIDER, are used for performance evaluation. We have compared the performance of BERT, ELMo, and GloVe word embeddings. On experimental analysis, BERT embedding outperformed the ELMo and GloVe embeddings. For feature extraction, pretrained CNNs, NASNet-Large, VGG-16, Inception-v4, and Resnet152 are used, and NASNet-Large outperformed other models.
Sentiment Analysis of Political Tweets for Israel Using Machine Learning

Sentiment analysis is a vital research topic in the field of Computer Science. With the accelerated development of information technology and social networks, a massive amount of data related to comment texts has been generated on web applications or social media platforms like Twitter. Due to this, people have actively started proliferating general information and the information related to political opinions, which becomes an important reason for analyzing public reactions. Most researchers have used social media specifics or contents to analyze and predict public opinion concerning political events. This research proposes an analytical study using Israeli political Twitter data to interpret public opinion toward the Palestinian-Israeli conflict. The attitudes of ethnic groups and opinion leaders in the form of tweets are analyzed using machine learning algorithms like support vector classifier (SVC), decision tree (DT), and Naïve Bayes (NB). Finally, a comparative analysis is done based on experimental results from different models.
A pooling &amp; sharing solution for delivering space data using multiple commercial Satcom providers

We introduce the Pooling &amp; Sharing paradigm that is already a mature solution addressing the needs of government institutional users through a common digital orchestrating platform where Satcom demands and Satcom offers are seamlessly matched in real time. We describe the background and state-of-the art and discuss challenges and possibilities on how this solution could be adapted to address the unique case of Data Relay from Space for space missions on a planetary surface or in near earth orbit. This can result in a cost effective and resilient solution and offer significant benefits to Space Agency and commercial customers that need to download massive amount of mission data or communicate with their scientific instruments. It could also create a new profitable service for commercial satellite operators.
TransCODE: Co-Design of Transformers and Accelerators for Efficient Training and Inference

Automated co-design of machine learning models and evaluation hardware is critical for efficiently deploying such models at scale. Despite the state-of-the-art performance of transformer models, they are not yet ready for execution on resource-constrained hardware platforms. High memory requirements and low parallelizability of the transformer architecture exacerbate this problem. Recently proposed accelerators attempt to optimize the throughput and energy consumption of transformer models. However, such works are either limited to a one-sided search of the model architecture or a restricted set of off-the-shelf devices. Furthermore, previous works only accelerate model inference and not training, which incurs substantially higher memory and compute resources, making the problem even more challenging. To address these limitations, this work proposes a dynamic training framework, called DynaProp, that speeds up the training process and reduces memory consumption. DynaProp is a low-overhead pruning method that prunes activations and gradients at runtime. To effectively execute this method on hardware for a diverse set of transformer architectures, we propose a flexible BERT accelerator, a framework that simulates transformer inference and training on a design space of accelerators. We use this simulator in conjunction with the proposed co-design technique, called TransCODE, to obtain the best-performing models with high accuracy on the given task and minimize latency, energy consumption, and chip area. The obtained transformer–accelerator pair achieves 0.3% higher accuracy than the state-of-the-art pair while incurring <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.2\times $ </tex-math></inline-formula> lower latency and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.0\times $ </tex-math></inline-formula> lower energy consumption.
Motivation for Mathematics of Blockchain

Blockchain is a technology that increasingly attracts the attention of industrials and academics. However, to the best of the author's knowledge, the mathematical characteristics of blockchain have not sufficiently been covered. This is the purpose of the book.
Digital Signature

The Digital Signature Algorithm is a particular asymmetric process, which is applied to the blockchain environment, to sign a transaction. Thus, this section is the straight continuation of the previous one, and explains the mechanism for digital signatures. In particular, the aim is to outline the purpose of unlocking an output for transacting.
Blockchain Contributors: The Network of Users

In this chapter, we focus on the communication between users and nodes through the Network of Users. Each user has connections with some others, and this allows exchange of information. A particular type of information concerns transaction, thus exchange of cash, or transaction lists between all the miners. These exchanges of information can be modeled by means of Graph and Matrix Theories. Papers [26, 27] are giving applications of the below theorization.
Sociological Diaries of Students: Lived Curriculum in the Time of Disaster

In this chapter, we reflect on the concept of a lived curriculum from our students’ perspectives and ourselves as university teachers. The general aim of the paper is to examine the unprecedented condition surrounding our lives during the university lockdown in the Covid-19 Spring 2020. Furthermore, drawing from our observations and our students’ reflective testimonies (personal experience narratives), we contrast the formal strategies and visions of the university’s performance- and competence-based curriculum thinking to our daily practices as teachers and sociology students. We ponder our lived experience and coexistence, our lives as teachers and students during the corona outbreak in Spring 2020.
On Improving the Merkle Trees: The n-Trees

In the context of Bitcoin Simplified Payment Verification (SPV) and Solvency, there are tree configurations where the path to the root is shorter than the one given by the usual binary tree, for the same computational memory – which is an essential strength. The n-trees have such properties, for n ≥ 2 (binary trees are for n = 2). We show that, for some values of number of transactions N ≥ 3, the system could be much faster, plus gain computational power, memory, and significantly reduce the computational time.
Halving and Cycles Theorem

This chapter is studying a pattern that has been identified in the Bitcoin halving process. As spurious as it might appear, the division by 2 of the block reward actually reveals some interesting characteristics in terms of available bitcoins. Collecting the 21 million bitcoins might appear to be impossible due to the protocol imposing the irreducible value of bitcoin, i.e., the Satoshi precision. The question of a change in the protocol through a soft fork might appear legitimate in order to allow the obtention of all the bitcoins. The reason is that minting new bitcoins is going to be less and less likely; this possibly could imply an inflation effect, thus motivating crypto-exchanges to possess more bitcoins than allowed by the current protocol.
Detection and Classification of Tumor Tissues in Colorectal Cancer Using Pathology Images

Gastrointestinal cancers are dominated by both genetic and food environmental factors. Among gastrointestinal cancers, the most common cancer that is highly treatable is GI cancer. The biopsy tissue sample analysis is the confirmation cancer diagnosis test among other cancer diagnosis tests. This analysis test is reported by pathologists as pathology/histological images and categorizes complex phenotypes such as tumor stage. These images are the essential features to identify tissue structures and the manipulation of cells. Automation in the detection and classification of colorectal cancer using histological images is achieved using image processing and AI models. Computer-aided diagnosis techniques such as image enhancement, stain normalization, and feature extraction are applied to extract insights from histological images. The patch images were classified using transfer learning techniques in the field of artificial intelligence. Implementation of various artificial intelligence algorithms such as convolution neural networks, random forests, and support vector machines is applied, and a comparative analysis of results is discussed, alongside possible future research approaches. The performance of the RF, SVM, and ANN algorithms is 83%, 83%, and 80%, respectively.
Performance Comparison of YOLO Variants for Object Detection in Drone-Based Imagery

With the advances and development in technology, drones mounted with cameras are being used in various real-life applications like surveillance, vehicle tracking, agriculture, disaster management, pedestrian monitoring, search and rescue operations, rapid delivery services and aerial photography. Hence, there is a high demand for understanding the visual data collected through these drones for their efficient use in these areas. Computer vision is an obvious solution for the detection and tracking of objects in drone-captured images and sequences. However, the already developed techniques and algorithms cannot be directly applied to the drone platforms because of the inherent challenges in this field and the requirement of balance between accuracy and speed due to constraints of limited onboard computation power and memory of the embedded devices being used in drone systems. Thus, there is a requirement of lightweight real-time object detectors for drone systems. Out of the various real-time detectors, YOLO series models have emerged as the fastest object detection algorithm, which makes them a potential contender for drone-based object detectors. In this paper, we have compared the performance of the various YOLO algorithms, namely, YOLOv3, YOLOv4 and YOLOv5 for multiclass object detection in drone-based images. These algorithms have been evaluated and compared using the VisDrone2019-Det benchmark dataset.
Handwritten Digit Recognition Using Neural Network with Gabor Filter for Information Fusion

Because of several aspects such as variability in individual’s personal writing style, handwriting recognition is the most difficult area to master. Despite the massive amount of research and development that has gone into it, it has yet to tackle all of the commercially important and conceptually intriguing concerns. Many document processing and evaluation systems that use digital image processing techniques need handwritten digit recognition. In many pattern recognition applications, document processing and document analysis are becoming more ubiquitous. The aim of this paper is to propose a system which will take a digital image as an input and will automatically detect and display the text present in it. This will save the time and efforts and will reduce the chances of involvement of human error in the system. This system can be further used in many applications like reading postal addresses, bank check amount, forms, etc. The paper used a Gabor filter bank for extracting features from a digit image. This reduction in preprocessing overhead brings up the performance of the system in prediction of digits. The prediction of this system is almost real time with very high accuracy.
Training Generative Adversarial Networks (GANs) Over Parameter Server and Worker Node Architecture

The latest technological discovery in the field of artificial intelligence (AI) is the learning and widespread use of different Generative Adversarial Networks (GANs) applications. GANs have made progress in numerous applications like image editing, style transfer, scene generation, so on. However, these types of generative models demand high computation because GANs are made out of two deep neural networks and in light of the fact that it trains on huge datasets. As with other AI models, GANs also face problems of insufficient data while training for some real-world situations. In numerous situations, available databases might be restricted and distributed over various worker nodes (i.e., end users) where the local datasets are intrinsically private and ultimately workers toward the end do not want to share them. In this chapter, we addressed the issue of training GANs in a distributed way so that they can train over datasets that are distributed to various worker nodes. We have developed a training framework for GANs under the setting of the parameter server and worker node. Under this framework, various workers can produce results similar to real data while keeping it completely in a distributed way and also keeping their information confidential. Test results obtained with the CIFAR-10 dataset indicate that our architecture can produce high-quality data samples that look similar to real data and can be used in various real-life applications.
Corporate social responsibility practices of public and private firms: an exploration of education and healthcare initiatives

The purpose of the present study is to compare the key corporate social responsibility (CSR) practices being practiced by selected private and public sector companies with special reference to education and healthcare practices. The findings of the study indicate CSR activities of the selected firms in India are primarily addressing the sustainable development goals (SDGs) relating to health, education, work and economic growth, gender equality, water and sanitation, and life on land. The most desired activity as a part of healthcare according to policymakers of public sector entities were organising health check-up camps. However, it has also been seen that the percentage of involvement of the public sector is more than that of the private sector. The private sector corporates in India need to follow the example of these large public sector companies of India, and there is a need to bridge the gap in resource allocation for CSR activities between the public and the private firms.
The Orders of Order: Curriculum Design and a Hauntonlogy of Efficiency

Curriculum design is commonly understood as the principles and procedures for creating an organized course of study foundational for a good life. But what life is this? And what does a curriculum presume in designing it? Indebted to Derrida’s thinking on hauntology, the chapter approaches curriculum as a haunted house, examining the assumptions that are built into its shifting design principles since the early 20th century. Through discussion of curriculum’s contingent relationships with evolutionary developmentalism, cybernetic processes, and anticipatory algorithms, the chapter examines how curriculum is haunted by principles that seek to design life efficiently, embodying hopes and fears of who or what menaces these aims and must be tamed. These design principles are examined as immanent to the practices that seek to bring forth hidden developmental potentialities, improve mental processes, and anticipate environmental eventualities. They are analyzed in terms of how they generate differences and exclusions. These design principles cultivate, rank, and compare children’s inborn traits, justifying segregating those seen as “less able”; they systematize processes of continual surveillance, evaluation, and feedback on behaviors; and, they create data-driven systems to act preemptively upon risk. Taken together, curriculum design principles are orders of order. By approaching curriculum design not as “how-to” but as “haunted” by the norms and values of life itself, the chapter invites the reader to think otherwise to seemingly natural differences and exclusions that are in fact by design.
Evaluation of a Virtual Reality Introductory Training to Enable New Learning Possibilities

Integrating virtual reality in learning factory training provides new possibilities for learning and can help to overcome some of the existing limitations in physical learning factories. Although one of the main drivers for the technology is the gaming industry, an increasing number of companies and academia alike, start to acknowledge the potential of virtual reality. The most immersive and interactive form to use virtual reality are so-called Head Mounted Devices. Especially when dealing with Head Mounted Devices for the first time, users need a proper introduction to their handling and technical capabilities. Otherwise, there is a high risk, that users won´t be able to exploit the full potential of virtual reality, due to a lack of knowledge. This paper describes the concept of an introductory training, that teaches users (1) basic theoretical knowledge about virtual reality (2) independent handling of virtual reality devices, and (3) prepares them for further training modules and learning experiences in a virtual environment. The training is the base for more advanced trainings, that use virtual reality, in a Learning Factory as well as a Makerspace at Graz University of Technology. In the paper findings of a first evaluation are presented.