Reviewerly/exHarmony huggingface dataset:
A Systematic Literature Review of Text Classification: Datasets and Methods

We study the literature in major journals and conferences on the usage of shallow learning and deep learning methods for text classification. Shallow learning techniques such as Naive Bayes, Support Vector Machine, Random Forests were initially widely used to solve problems in text classification. however, these techniques generally require the presence of a precise feature extraction model, which is often very complex to produce precise accuracy. For this reason, researchers continue to try to find other learning techniques that are more efficient and provide a significant increase in accuracy. So currently deep learning methods such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are more widely used to solve text classification cases. From 2016 up to the present, this literature study aimed to recognize and assess research methods and datasets utilized in text classification studies. Seventy-three text classification research articles posted from January 2016 until July 2021 were retained and chosen to be explored further based on the established inclusion and exclusion criteria. This literature review was conducted in a methodical manner. A systematic literature review is defined as a method for recognizing, evaluating, and interpreting all available study materials for the purpose of answer certain research questions. The following diagram depicts the overall distribution of text classification methods. Furthermore, public datasets were used in 85 percent of the research projects, whereas private datasets were used in 15 percent of the research studies. Twenty different strategies have been used. Eight of the most commonly used approaches in text classification were identified from the twenty methods. Researchers recommended integrating various machine learning methods, employing an increased algorithm, appending feature selection, and applying parameter optimization for some classifiers to improve the accuracy of machine learning classifiers for text classification. The findings of this study also revealed that are frequently mentioned and thus significant in the field of text classification.
The aging effects on face recognition algorithms: the accuracy according to age groups and age gaps

This paper aims to examine the effects of aging on the efficiency of facial recognition algorithms in terms of age groups and age difference intervals. A comparative analysis of the recognition performance of two approaches is conducted for different age groups and different length time intervals between images. The first approach uses a two-dimensional discrete cosine transform (2D-DCT) and a Kernel Fisher Analysis (KFA) as description tools; classification is made using a k-NN classifier based on Euclidean distance. However, the second one is performed in two ways: first, we considered face as a single entity, then we viewed face as an independent component set. This approach makes use of Convolutional Neural Networks (CNN) for description and Support vector machines (SVM) for classification. Achieved results using the publicly accessible FG-NET face database prove that age groups influence the performance of face recognition algorithms. Also, time length lapses between images can significantly reduce the performance of face recognition.
Evaluation and Selection of Autoencoders for Expressive Dimensionality Reduction of Spatial Ensembles

This paper evaluates how autoencoder variants with different architectures and parameter settings affect the quality of 2D projections for spatial ensembles, and proposes a guided selection approach based on partially labeled data. Extracting features with autoencoders prior to applying techniques like UMAP substantially enhances the projection results and better conveys spatial structures and spatio-temporal behavior. Our comprehensive study demonstrates substantial impact of different variants, and shows that it is highly data-dependent which ones yield the best possible projection results. We propose to guide the selection of an autoencoder configuration for a specific ensemble based on projection metrics. These metrics are based on labels, which are however prohibitively time-consuming to obtain for the full ensemble. Addressing this, we demonstrate that a small subset of labeled members suffices for choosing an autoencoder configuration. We discuss results featuring various types of autoencoders applied to two fundamentally different ensembles featuring thousands of members: channel structures in soil from Markov chain Monte Carlo and time-dependent experimental data on droplet-film interaction.
Real-Time Hand Tracking and Gesture Recognizing Communication System for Physically Disabled People

Regardless of its importance, a systematic literature analysis and classification scheme for sign language are lacking. Communication is the most basic form of interaction. Deaf people lack this form of communication, and hence, sign language interaction is of utmost importance for them to be understood. In recent years, emerging technology developments, such as smartphones, have provided consumers with a slew of new features. If such mobile devices can recognize sign languages, sign language users can access a far more comprehensive range of mobile applications. This paper proposes a paradigm for sign language interaction between a consumer and a computer in a complex environment. The interface that is being proposed here can be used in various applications such as image browsers, games, and so on. Even in a complicated setting, the suggested model exhibited good accuracy and effectively recognized motions in low-resolution picture mode. The suggested model was tested using a labeled dataset of hand movements consisting of 99,000 gestural images. In our suggested Inceptionv3 based model, we achieved superior results by extracting features from the hand and identifying hand motions with 91% accuracy. We also compared our model to Resilient Back, VGG-16, and ResNet50, all of which had accuracies of 75–85%. The trained model may be used to recognize static hand pictures as well as dynamic motions captured on video in real-time.
A Robust Service Mapping Scheme for Multi-Tenant Clouds

In a multi-tenant cloud, cloud vendors provide services ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , elastic load-balancing, virtual private networks) on service nodes for tenants. Thus, the mapping of tenants’ traffic and service nodes is an important issue in multi-tenant clouds. In practice, unreliability of service nodes and uncertainty/dynamics of tenants’ traffic are two critical challenges that affect the tenants’ QoS. However, previous works often ignore the impact of these two challenges, leading to poor system robustness when encountering system accidents. To bridge the gap, this paper studies the problem of robust service mapping in multi-tenant clouds (RSMP). Due to traffic dynamics, we take a two-step approach: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">service node assignment</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tenant traffic scheduling</i> . For service node assignment, we prove its NP-Hardness and analyze its problem difficulty. Then, we propose an efficient algorithm with bounded approximation factors based on randomized rounding and knapsack. For tenant traffic scheduling, we design an approximation algorithm based on fully polynomial time approximation scheme (FPTAS). The proposed algorithm achieves the approximation factor of 2+ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> is an arbitrarily small value. Both small-scale experimental results and large-scale simulation results show the superior performance of our proposed algorithms compared with other alternatives.
A Comprehensive Study on Feature Extraction and Classification Techniques for Sign Language Recognition

Sign language is the single means by using which the deaf community can interact with individuals living across the world. However, a small population of normal people can comprehend sign language adequately. As a result, a good Sign Language recognition system is needed to assist bridge the gap between normal people and persons with physical disabilities. This study presents a comprehensive overview of state-of-the-art feature extraction and classification techniques utilized in recent research on the recognition of sign language and hand gestures. Overall, the goal of this research is to give readers a thorough overview of the field of automated gesture and sign language recognition as well as to inspire more research work in the respective domain. The number of existing papers is reviewed based on used techniques and acquired accuracy. As a result, it is analyzed that the most reliable image processing and feature extraction techniques are vision-based techniques, whereas, for classification, the most efficient techniques are machine learning techniques.
A Novel Multi-class Classification Architecture Combining Population-based Sampling and Multi-expert Classifier for Imbalanced Data

Training a classifier based on imbalanced data set is considered a great challenge in classification tasks, as classifiers are often "biased" due to highly skewed data distribution and overlapping borderline between different classes. When the imbalanced data appears in the multi-class classification scenario, the classification difficulty increases exponentially. In this paper, we propose an integrated approach to handle imbalanced multi-class classification by combining the population-based sampling method and a multi-expert classifier. In the implementation, we choose the Ant Colony Optimization to realize the sampling process. As for the classifier, the voting mechanism is applied to intensify the weak classifiers. To test the algorithm’s performance, we choose 10 representative imbalanced multi-class data sets from the UCI Machine Learning Repository. G – mean and mAUC are chosen as the metrics. According to the experimental results, the proposed algorithm dominates in 8 data sets and gets a second place in 1 data set when evaluated by G – mean, and ranks first in 3 data sets and top-3 among the most for mAUC.
Automatically Estimate Clusters in Autoencoder-based Clustering Model for Anomaly Detection

In a previous work, a clustering-based method had been incorporated with the latent feature space of an autoencoder to discover sub-classes of normal data for anomaly detection. However, the work has the limitation in manually setting up the numbers of clusters in the normal training data. Finding a proper number of clusters in datasets is often ambiguous and highly depends on the characteristics of datasets. This paper proposes a novel data-driven empirical approach for automatically identifying the number of normal sub-classes (clusters) without human intervention. This clustering-based method, afterward, is co-trained with an autoencoder to automatically discover the appreciated number of clusters of normal training data in the middle hidden layer of the autoencoder. The resulting clustering centers are then used to identify anomalies in querying data. Our approach is tested on four scenarios from the CTU13 datasets, and the experimental results show that the proposed model often perform better than those of the model in the previous work on almost scenarios.
Comparisons of RRT and MCTS for Safe Assured Path Planning in Urban Air Mobility

Urban air mobility (UAM) using electrical vertical take-off and landing (eVTOL) aircraft is an emerging way of air transportation within metropolitan areas. However, the ability to navigate eVTOL aircraft through congested urban air environment poses a great challenge for path planning. For the successful operations of autonomous aerial vehicles in UAM, it is of great importance to take the collision-free requirement into consideration. It is also necessary to consider the presence of various forms of uncertainty in path planning. To address these issues, in this paper the path planning problem is first formulated as a Markov Decision Process (MDP) and then solved by an online algorithm of Monte Carlo Tree Search (MCTS) incorporating chance constraints of uncertainty. For the sake of illustration, a high-density free flight airspace simulator is created to test the performance of this proposed algorithm. Numerical simulation results demonstrate that this proposed algorithm outperforms the Chance Constrained Rapidly-exploring Random Tree (CCRRT) Algorithm, a path planning algorithm we proposed before, in terms of many aspects.
Combining Semantic Self-Supervision and Self-Training for Domain Adaptation in Semantic Segmentation

This work presents a two-staged, unsupervised domain adaptation process for semantic segmentation models by combining a self-training and self-supervision strategy. Self-training (i. e., training a model on self-inferred pseudo-labels) yields competitive results for domain adaptation in recent research. However, self-training depends on high-quality pseudo-labels. On the other hand, self-supervision trains the model on a surrogate task and improves its performance on the target domain without further prerequisites.Therefore, our approach improves the model’s performance on the target domain with a novel surrogate task. To that, we continuously determine class centroids of the feature representations in the network’s pre-logit layer on the source domain. Our surrogate task clusters the pre-logit feature representations on the target domain regarding these class centroids during both training stages. After the first stage, the resulting model delivers improved pseudo-labels for the additional self-training in the second stage. We evaluate our method on two different domain adaptions, a real-world domain change from Cityscapes to the Berkeley Deep Drive dataset and a synthetic to real-world domain change from GTA5 to the Cityscapes dataset. For the real-world domain change, the evaluation shows a significant improvement of the model from 46% mIoU to 54% mIoU on the target domain. For the synthetic to real-world domain change, we achieve an improvement from 38.8% to 46.42% on the real-world target domain.
Performance Analysis of Various Supervised Binary Classification Algorithms and their Optimized Variants on High-Dimension Limited-Sample-Size Data

In modern times, high-dimension limited-sample-size (HDLSS) data have become a lot prevalent in data mining research areas. Performing analysis on HDLSS data is inherently difficult because of large number of features and low sample size. Dimensionality reduction is a recommended step before working on HDLSS data because of a phenomenon known as the ‘curse of dimensionality’. However in medical and psychiatric areas, these large number of features often prove to be extremely important in cases of life threatening disease detection or identifying biomarkers. The purpose of this study is to perform a comparative performance analysis of various supervised classification algorithms and their optimized variants on HDLSS data without any dimensionality reduction to recreate the aforementioned situations. Various hyper-parameter optimization techniques and AdaBoost meta-estimator classifier are implemented on these algorithms to find their optimized variants. Optimized Random Forest and CatBoost classifiers produced the most optimal results on the used HDLSS datasets. Relevant arguments and findings regarding the used eight different supervised classification algorithms, both base and optimized, are evaluated and summarized in this paper.
Causal Generalization in Autonomous Learning Controllers

Any machine targeted for human-level intelligence must be able to autonomously use its prior experience in novel situations, unforeseen by its designers. Such knowledge transfer capabilities are usually investigated under an assumption that a learner receives training in a source task and is subsequently tested on another similar target task. However, most current AI approaches rely heavily on human programmers, who choose these tasks based on their intuition. Another largely unaddressed approach is to provide an artificial agent with methods for transferring relevant knowledge autonomously. One step towards effective autonomous generalization capabilities builds on (autonomous) causal modeling and inference processes, using task-independent knowledge representations. We describe a controller that enables an agent to intervene on a dynamical task to discover and learn its causal relations cumulatively from experience. Our controller bootstraps its learning from knowledge of correlation, then removes non-direct-cause correlations – correlations that are due to a common (external) cause, be spurious, or invert cause and effect – through strategic causal interventions, while learning the functions relating a task’s causal variables. The effectiveness of knowledge transfer by the proposed controller is tested through simulation experiments.
Cryptocurrencies as a Financial Asset: An Evidence from an Institutional Investors Perspective

The study provides a systematic analysis and review of the relevant empirical literature based on the fundamental topics that have been associated with the cryptocurrencies market. The research was carried out by using the method of description, literature analysis, and research conducted. The study investigates the empirical verification of the efficacy of investment diversification using the main stock exchange indices in the Eurozone countries and Bitcoin. The aim of the research is to examine whether it is justified and to what extent to include Bitcoin in the portfolio of an institutional investor. The analysis includes data on the daily movement of selected action indices as well as the movement of Bitcoin. The methodology involves the analysis of high-frequency data, given that daily trading data were used. The results show that it is justified to include Bitcoin or any other cryptocurrency in the portfolio structure. Also, the results show which share of Bitcoin in the portfolio is justified from the aspect of institutional investors. The data used in the analysis cover the period from 2020. The results of the research show that Bitcoin is a good source of diversification in a portfolio that contains traditional financial instruments, both for an investor who is not prone to risk, and for those investors who have a greater appetite for risk. The conclusion is that the rational behavior of institutional investors requires consideration of investing in Bitcoin using the Markowitz model.
Testing Federated Learning on Health and Wellbeing Data

Nowadays artificial intelligence is used in healthcare applications, too. Based on current research personalized medicine could transform the healthcare domain. Thus, medical data from users must be collected and used for training models. In order to preserve the privacy of data, federated learning represents a good candidate. This paper proposes an extension of the federated learning model that is evaluated for learning over aa distributed dataset. The proposed architecture is a client-server, where the clients are clustered by the server, according to their data similarity (without exposing data to the server). The server stores the clusters models and manages the clients. Different tests were performed on three datasets: CIFAR-10, MNIST and a non-standard one - a sleep dataset. Results show that an increase of the convergence rate was obtained (in case of the MNIST dataset was 50 times faster). Also, the method has the ability to learn patterns from the data, by keeping data locally.
Blockchain’s potential and opportunities for tax administrations: a systematic review

The development of Blockchain applications is still at an early stage, considering that, after 13 years of the Bitcoin introduction, cryptocurrencies remain the single example of a well-established Blockchain system. On the other hand, the digitalization of taxes and its underlying processes is gaining speed worldwide, with not only developed countries adopting it. This trend can be the foundation of a Blockchain integration to the tax system, so that this domain could take advantage of desired inherent features offered by the technology, such as transparency, security, immutability and real-time information. Consequently, tax authorities would achieve higher revenue levels and improve compliance tracking, reducing tax evasion and fraud. At the same time, taxpayers would benefit from reduced tax compliance costs and better experiences through more efficient processes. Therefore, this work aims at investigating the state of the art within the field of Blockchain application on taxation, in order to drive new researches that could close current gaps and to support the development of new applications.
ResCNN: An alternative implementation of Convolutional Neural Networks

Convolutional Neural Networks (CNN) have been used for long for feature extraction from images in deep learning. Here we introduce ResilientCNN or ResCNN for short where we show that when convolution is implemented as an matrix-matrix operation coupled with some image processing techniques like Singular Value Decomposition (SVD) can be used as an better alternative to traditional convolution. We show that our ResCNN learns with bigger batch sizes and at much higher learning rates (7x) without compromising on accuracy compared to traditional convolutional networks by implementing both models on the MNIST Dataset.
Automatic Hate and Offensive speech detection framework from social media: the case of Afaan Oromoo language

The easily accessibility of different online platform allows every individuals people to express their ideas and share experiences easily without any restriction because of freedom of speech. Since social media don't have general framework to identify hate and neutral speech this results anonymity. However, the propagation of hate speech on social media distresses the society in many aspects, such as affecting the mental health of targeted audiences, affects social interaction and distraction of properties. This research proposed the SVM with TF-IDF, N-gram, and W2vec feature extraction to construct dataset which is binary classifier to detect hate speech for Afaan Oromoo language. To construct dataset for this study first we crawl data from Facebook posts and comments by using Face pager and scrap storm API. After we collect we labeled the collected data to two class hate and neutral class. The general objective of this research is to design a framework which classify hate and neutral speech. Furthermore, when we compare the results of different Machine Learning algorithms. The experiment is evaluated based on accuracy, F-score, recall and precision measurements. The framework based on SVM with n-gram combination with TF-IDF achieve 96% in all metrics.