Reviewerly/exHarmony huggingface dataset:
COMBATANT@TamilNLP-ACL2022: Fine-grained Categorization of Abusive Comments using Logistic Regression

With the widespread usage of social media and effortless internet access, millions of posts and comments are generated every minute.Unfortunately, with this substantial rise, the usage of abusive language has increased significantly in these mediums.This proliferation leads to many hazards such as cyber-bullying, vulgarity, online harassment and abuse.Therefore, it becomes a crucial issue to detect and mitigate the usage of abusive language.This work presents our system developed as part of the shared task to detect the abusive language in Tamil.We employed three machine learning (LR, DT, SVM), two deep learning (CNN+BiLSTM, CNN+BiLSTM with Fast-Text) and a transformer-based model (Indic-BERT).The experimental results show that Logistic regression (LR) and CNN+BiLSTM models outperformed the others.Both Logistic Regression (LR) and CNN+BiLSTM with Fast-Text achieved the weighted F 1 -score of 0.39.However, LR obtained a higher recall value (0.44) than CNN+BiLSTM (0.36).This leads us to stand the 2 nd rank in the shared task competition.
An Experiment on Speech-to-Speech Translation of Hindi to English: A Deep Learning Approach

The purpose of this paper is to design and develop a complete Hindi-to-English speech-to-speech translation system. We employ three modules, i.e. automatic speech recognition (ASR), neural machine translation (NMT) and text-to-speech (TTS). The ASR recognizes speech signal in source language A (i.e. Hindi) and gives the text version of it. The NMT converts the source language’s text into the target language’s text. The TTS module converts the text of the target language into speech (i.e. English). We carry out the experiment of the end-to-end architecture despite the complexity of the speech translation system with reasonable performance.
A side-channel-attack countermeasure for elliptic curve point multiplication based on dynamic power compensation

Elliptic curve point multiplication (ECPM) is the most crucial operation in elliptic curve cryptography (ECC) and is vulnerable to side-channel attack (SCA). Based on power hiding technology, this paper proposes an SCA countermeasure for mutual power compensation of ECC logic units, which eliminates the difference between correct and incorrect keys by constructing a power-mapping model. The correlation power analysis (CPA) experiment for the second-highest key shows that this countermeasure increases the measurement to disclosure (MTD) by more than 1064 times with no performance overhead and loss of 19.9% energy and 6.0% LUT, 2.1% FF.
Trade Between Population Size and Mutation Rate for GAAM (Genetic Algorithm with Aggressive Mutation) for Feature Selection

The “curse of dimensions” is a term that describes the many difficulties that arise in machine learning tasks as the number of features in the dataset increases. One way to solve this problem is to reduce the number of features to be provided to the model during the learning phase. This reduction in the number of dimensions can be done in two ways, either by merging dimensions together or by selecting a subset of dimensions. There are many methods to select the dimensions to be kept. One technique is to use a genetic algorithm to find a subset of dimensions that will maximize the accuracy of the classifier. A genetic algorithm specially created for this purpose is called genetic algorithm with aggressive mutation. This very efficient algorithm has several particularities compared to classical genetic algorithms. The main one is that its population is composed of a small number of individuals that are aggressively mutated. Our contribution consists in a modification of the algorithm. Indeed we propose a different version of the algorithm in which the number of mutated individuals is reduced in favor of a larger population. We have compared our method to the original one on 17 datasets, which allowed us to conclude that our method provides better results than the original algorithm while reducing the computation time.
CNN-LSTM-Based Deep Learning Model for Early Detection of Breast Cancer

Breast cancer has been considered as one of the diseases that puts the existence of women at risk worldwide. Diagnosing breast cancer in advance time is very important as it improves the treatment options and also improves the survival chance of patients. Mammography is identified as an ideal tool for breast cancer screening and is very effective and reliable. However, manually identifying abnormalities in breast and classifying them using mammographic images is a not an easy task for radiologists. Many times, it will not be accurate predictions. Hence, it is very important to automate the prediction using advanced techniques. The paper presents the deep learning approach to detect the abnormalities that are present in breast. The approach employed convolutional neural network (CNN) to classify the breast cancer cells into benign and malignant. Digital database for screening mammography (DDSM) dataset is used for training the CNN classifier. The CNN has achieved promising results by giving better performance and higher accuracy.
Adoption Of Cloud Computing By IT Based Small And Medium Scale Enterprises In Northwestern Nigeria

This research has taken a Quantitative, interpretive and cross-sectional designs in the form of a self-administered questionnaire through survey. The aim is to investigate the adoption of Cloud Computing by IT based small and medium scales enterprises in Northwestern Nigeria. About one hundred and fifty (150) questionnaires were distributed among seven states in the North-Western Nigeria and average of one hundred and nine (109) was responded. The result of the survey shown that most of the IT professional in these SMEs are cloud provider’s end users, continue to use cloud provider in the future, is part of their strategic effort, recommended Cloud provider to others and they are very satisfied with the cloud providers. Challenges that are preventing them from getting the maximum value out of cloud providers are lack of encouragement, poor training, application is missing and lack of executive sponsorship.
Engineering for Sustainable Development: A Smart Pedagogical Framework for Developing IoT Projects Applied to Climate Action – Practices and Challenges

To promote UNESCO’s Sustainable Development Global Goals (SDGs) in Higher Education and document a set of good practices emerging from the construction of Arduino Internet of Things (IoT) projects, particularly as contributions to climate action (SDG 13), authors followed the three-stage design-based methodology. Thus, a conceptual framework was designed and aligned with an educational scenario. The framework was orchestrated with the principles of smart pedagogy aiming at smart learning. Accordingly, an e-lab was developed, incorporating technological platforms/tools, and was delivered as an e-course lab to 67 tertiary education student engineers. Finally, data were collected and analyzed by a Strengths-Weaknesses-Opportunities-Threats (SWOT) data analysis. From the findings (Strengths), authors concluded to a set of strategies that could be used as good practices, emerged from the construction of IoT projects when they are aligned with smart pedagogical frameworks’ guidelines. However, Weaknesses, Opportunities and Threats were considered as challenges and thus an impetus for refinement and redesign.
Designing an Inclusive and Accessible Mathematical Learning Environment Based on a Theorem Prover

A novel approach to design an inclusive and accessible mathematical learning environment is presented: The technology of theorem proving shall be employed to support a student in solving mathematical problems by giving hints to him/her based on formal proofs of each step in a calculation. The system shall be made accessible by making use of the built-in accessibility coming with VSCode, a standard editor used as front-end for the theorem prover Isabelle.
Machine Learning Methods for Automatic Gender Detection

Automatic gender detection has attracted the attention of many research fields such as forensic linguistics or marketing. Within these areas, gender detection has been approached as a classification problem and, for this reason, supervised Machine Learning algorithms such as Naïve Bayes, Logistic Regression and Support Vector Machines, among others, have been employed. The latter algorithm has exhibited a better performance on gender detection. In recent years, with the development of Deep Learning methods, various neural networks structures such as Convolutional Neural Networks have been designed for gender detection. However, Deep Learning methods have led to a loss in the interpretability of the models. In this article, we review the AI techniques applied on gender detection.
Ontology Metrics and Evolution in the GF Framework for Ontology-Based Data Access

There exists a need of producing high-quality ontologies for Semantic Web applications that access legacy, relational and non-relational data sources. We present GF, a tool for materialization of ontologies from relational and non-relational data sources including H2 databases, CSV files and Excel spreadsheets. We evaluate a sample case generated by GF with a third-party ontology evaluation tool called OntoMetrics. We also introduce an scripting language for performing Ontology-Based Data Access allowing a semi-naive user to automate ontology generation and document ontologies by adding annotations. The results obtained show that the ontologies generated with GF are reasonably good for being used in Semantic Web applications because they are validated correctly and pass all of the filters for the OWL2 main profiles, thus making them suitable for processing with lightweight reasoners. The metrics originally indicated that our application was lacking quality in the annotation area regarding the documentation of the classes and properties generated by the application and that the functionality introduced by the scripting language allows to generate correctly annotated ontologies. An executable standalone application along with the data used in this paper are uploaded to a GitHub repository for reproducibility of the results presented here.
Optimization Algorithms Surpassing Metaphor

Achieving novel systems with optimal performance has been converted into a critical preoccupation amongst engineers and scientists across a wide range of fields, whereby optimization has played a crucial role in current research. Indeed, optimizers find the collection of variables to reach the optimal amount of cost functions by considering the possible domains of the variables restricted. algorithms, of Meta‐heuristic and evolutionary, inspired by natural behaviour, mathematical foundations, and physics, are considered optimization techniques commonly hired to determine optimal solutions. Notwithstanding the traditional optimization methods, comprised of linear, nonlinear, integer, and dynamic programming, algorithms, of meta‐heuristic and evolutionary, provide reliable modeling results in various engineering subject matters like real‐world and complex obstacles. In addition, although the number of algorithms based on natural behaviours has increased, the majority of them deal with some ordeals such as being stuck in local optimal results. Hence, these ordeals pave the way for the advent of new mathematical, population-based techniques. In this chapter, three powerful metaphor-free, population-based optimization approaches are introduced, being categorized into gradient-based optimizer (GBO), Runge Kutta optimizer (RUN), and differential evolution (DE). Each technique is evaluated in terms of the basic concept; the algorithms are then described based on mathematical statements. In the last part, the pseudo-code of the methods and the guidelines for coding are presented. This chapter is intended for those who have an interest in engineering optimization.
Online Route Scheduling for a Team of Service Robots with MOEAs and mTSP Model

Service robots aim at helping humans in their tasks at home or office. Some tasks can be performed by a team of robots that need to visit target locations in the environment. This problem can be modeled as an instance of the multiple Traveling Salesman Problem (mTSP). The goal of mTSP is to minimize the total distance traveled. However, when the total distance is minimized, some salesmen tend to travel more than others depending on the distribution of the cities. Therefore, balancing individual routes is important to ensure equal battery consumption for the team to complete the task. We proposed a centralized system supported by Multi Objective Evolutionary Algorithms (MOEAs) that generate routes by minimizing the sum of travel distance and standard deviation simultaneously. Experiments were carried out to find MOEAs capable of generation high quality routes in a reasonable amount of time. Moreover, we introduce a novel robot-server architecture that connects robots with our system by means of threads, ROS nodes, and Websocket.
Implicit Maximum Likelihood Clustering

Clustering is a popular unsupervised machine learning and data mining problem defined as a process of assigning objects to groups so that objects in the same group are similar to each other and differ from objects in other groups. In this paper, a data clustering method is proposed that is based on unsupervised training of a generative neural network using the technique of Implicit Maximum Likelihood Estimation (IMLE). Given a dataset, IMLE is an unsupervised method that trains a neural network that takes random noise as input and produces synthetic data samples whose distribution is close to the original data. We have developed an appropriate adaptation of the IMLE generative approach that also achieves clustering of the dataset. The proposed clustering method has been evaluated on several popular datasets of various types and complexity yielding promising results.
Devulgarization of Polish Texts Using Pre-trained Language Models

We propose a text style transfer method for replacing vulgar expressions in Polish utterances with their non-vulgar equivalents while preserving the meaning of the text. We fine-tune three pre-trained language models on a newly created parallel corpus of vulgar/non-vulgar sentence pairs, then we evaluate style transfer accuracy, content preservation and language quality. To the best of our knowledge, the proposed solution is the first of its kind for Polish.
Neural Additive Models for Explainable Heart Attack Prediction

Heart attack (HA) is a sudden health disorder when the flow of blood to the heart is blocked, causing damage to the heart. According to the World Health Organization (WHO), heart attack is one of the greatest causes of death and disability globally. Early recognition of the various warning signs of a HA can help reduce the severity. Different machine learning (ML) models have been developed to predict the heart attack. However, patients with arterial hypertension (AH) are especially prone to this disorder and have several features that distinguish them from other groups of patients. We apply these features to develop a special model for people suffering from AH. Moreover, we contribute to this field bringing more transparency to the modelling using interpretable machine learning. We also compare the patterns learned by methods with prior information used in heart attack scales and evaluate their efficiency.
Scaling the PageRank Algorithm for Very Large Graphs on the Fugaku Supercomputer

The PageRank algorithm is a widely used linear algebra method with many applications. As graphs with billions or more of nodes become increasingly common, being able to scale this algorithm on modern HPC architectures is of prime importance. While most existing approaches have explored distributed computing to compute an approximation of the PageRank scores, we focus on the numerical computation using the power iteration method. We develop and implement a distributed parallel version of the PageRank. This application is deployed on the supercomputer Fugaku, using up to one thousand compute nodes to assess scalability on random stochastic matrices. These large-scale experiments show that the network-on-chip of the A64FX processor acts as an additional level of computation, in between nodes and cores.
HDR10+ Adaptive Ambient Compensation Using Creative Intent Metadata

The proposes a high dynamic range (HDR) tone mastering system which dynamically corrects the picture quality based on creative intent metadata to preserve content providers’ creative intent in different ambient light levels/ranges. Luminance percentile information based creative intent metadata is used to compensate for imagery degradation caused by ambient light. Multiple sections of tone mapping curves with multiple adjustment points along explicit Bezier curve are modified for more flexible tone mapping curve control than simply single section tone mapping curves. Simulation results show that the proposed method can adapt to both ambient light level and the scene content to keep the creative intent in different ambient conditions.
Classification and Application of Teaching Evaluation Text Based on CNN and Stacked Bidirectional LSTM

To thoroughly mine the vertical information contained in the unstructured data (text evaluation) in the teaching evaluation data and to make sure that the effective content contained in it can be used and the teaching quality will be improved completely, considering that the traditional text sentiment analysis method cannot adapt to the complexity and change. The language context has certain limitations. Taking objective data from the Department of Computing of Qinghai University as an example, this paper proposes two efficient classification methods, Convolutional Neural Network (CNN) and Stacked Bidirectional Long Short Term Memory (LSTM), and performs sentiment value calculation, descriptive analysis, and characteristic analysis based on classification, and further Excavated the essential information contained in the text teaching evaluation. The experimental results show that the average classification accuracy of the proposed method can reach 98%, which effectively solves the problem of text classification and application for teaching evaluation. This method has been applied in the actual teaching improvement link of the Computer Department of Qinghai University, and its effectiveness further demonstrates the advanced nature of the method and provides an important reference for the fundamental improvement of the teaching level.
Anomaly Detection in Crowdsourced Work with Interval-Valued Labels

Crowdsourcing is an emerging paradigm in AI and machine learning. It involves gathering input from human crowds, usually through the Internet, to solve a given task. Due to its open nature, the selected crowd-workers usually come from a variety of social-economic backgrounds and bring with them differing levels of reliability. There is also a threat of people with adversarial intentions launching attacks to derail crowdsourced projects. In this paper, we apply interval-valued labels (IVLs) and worker reliability to detect anomalous behavior in crowd-workers. Three of the four worker reliability measures-confidence, stability, and predictability-do not rely on the correctness of a worker’s label [28]. Therefore, by comparing a worker’s IVLs on gold questions with regular ones, we may detect anomalies within our workers. Doing so in our computational experiments, we have successfully been able to identify adversarial attackers for quality assurance of crowdsourcing.
Benchmarking D-Wave Quantum Annealers: Spectral Gap Scaling of Maximum Cardinality Matching Problems

Quantum computing, in particular Quantum Annealing (QA), provides a theoretically promising alternative to classical methods for solving combinatorially difficult optimization problems. In particular, QA is suitable for problems that can be formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, such as SAT, graph colouring and travelling salesman. With commercially available QA hardware, like that offered by D-Wave Systems (D-Wave), reaching scales capable of tackling real world problems, it is timely to assess and benchmark the performance of this current generation of hardware. This paper empirically investigates the performance of D-Wave’s 2000Q (2048 qubits) and Advantage (5640 qubits) quantum annealers in solving a specific instance of the maximum cardinality matching problem, building on the results of a prior paper that investigated the performance of earlier QA hardware from D-Wave. We find that the Advantage quantum annealer is able to produce optimal solutions to larger problem instances than the 2000Q. We further consider the problem’s structure and its implications for suitability to QA by utilising the Landau-Zener formula to explore the potential scaling of the diabatic transition probability. We propose a method to investigate the behaviour of minimum energy gaps for scalable problems deployed to quantum annealers. We find that the minimum energy gap for our target QA problem does not scale favourably. This behaviour raises questions as to the suitability of this problem for benchmarking QA hardware, as it potentially lacks the nuance required to identify meaningful performance improvements between generations.
Comparison of Guidelines for the Accessible Design of Augmented Reality Applications

Innovative technologies, such as augmented reality, have the potential to disrupt the way people use technology today. Unfortunately, numerous people will not be able to use and derive value from augmented reality applications in their daily lives, if these are not designed with accessibility in mind. Yet, still only few guidelines on the accessible design of augmented reality applications exist to date. This work summarizes and compares the most important guidelines devised at this point. This includes both guidelines by commercial vendors and the World Wide Web Consortium (W3C) as well as international standards. In addition, a catalog of common criteria is derived from these guidelines.
A Model Based on Convolutional Neural Network (CNN) for Vehicle Classification

The convolutional neural network (CNN) is a form of artificial neural network that has become very popular in computer vision. We proposed a convolutional neural network for classifying common types of vehicles in our country in this paper. Vehicle classification is essential in many applications, including surveillance protection systems and traffic control systems. We raised these concerns and set a goal to find a way to eliminate traffic-related road accidents. The most challenging aspect of computer vision is achieving effective outcomes in order to execute a device due to variations of data shapes and colors. We used three learning methods to identify the vehicle: MobileNetV2, DenseNet, and VGG 19, and demonstrated the methods detection accuracy. Convolutional neural networks are capable of performing all three approaches with grace. The system performs impressively on a real-time standard dataset—the Nepal dataset, which contains 4800 photographs of vehicles. DenseNet has a training accuracy of 94.32% and a validation accuracy of 95.37%. Furthermore, the VGG 19 has a training accuracy of 91.94% and a validation accuracy of 92.68%. The MobileNetV2 architecture has the best accuracy, with a training accuracy of 97.01% and validation accuracy of 98.10%.