Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Streaming Environment by Evolving Neural Network with Interim Decision

Algorithms in a streaming environment recognise a concept in real time since patterns are presented continuously; yet, in the presence of anomalies, algorithms fail to recognise the underlying concept, making anomaly identification a critical task. Neural networks detect anomalies efficiently; however, to use neural networks in a streaming environment, the architecture must be adaptive to learn ideas that vary over time. The proposed architecture places each node of the neural network in individual sites, and each node is made up of a perceptron. These perceptrons have two activation functions: one is used for architecture training, while the other is utilised for classification. Each layer has a decision-making node that takes decisions from the last layer nodes and decides the anomalous nature by majority voting. These layer-wise decisions help to select the training phase and appropriate architecture based on the desired performance. To demonstrate its usefulness, the proposed structure is tested on real-world data sets and compared to conventional and alternative neural networks.
Laplace Transformation of Eigen Maps of Locally Preserving Projection (LE-LPP) Technique and Time Complexity

K Nearest Neighbour (k-NN) is one of the most common machine learning algorithms; however, it frequently fails to operate well due to an incorrect distance measure or the existence of a large number of irrelevant pieces of information. To improve k-NN classification, linear and non-linear feature transformation approaches were used to extract class-relevant information. In this paper, we describe the combination of Laplace transformation of Eigen maps and Gradient conjugate iterative approach to sort out the non-linear data or irrelevant data in which a non-linear feature mapping is sought through Laplacian Eigen maps or kernel mixtures to remove it whereas the Locally preservation projection (LPP) are applied to save the original values during the reconstruction of linear data and create the large-margin distant in the hybrid k-NN model. The algorithm offers a computationally efficient solution to nonlinear dimensionality reduction with locality-preserving qualities, and a linear transformation matrix is subsequently trained to fulfil the goal of a large margin distance framework.
Toward a Balanced Feature Space for the Deep Imbalanced Regression

Regression with imbalanced data has been regarded as a more realistic scenario due to the difficulty of data acquisition and label annotations. However, it has not been extensively studied compared to the imbalanced classification. In imbalanced regression scenario, the classical regression approach may lead to regression bias toward high-frequency target regions. In this study, we present a novel framework for effectively handling imbalanced data in regression tasks. We introduce a density-based stochastic mask that perturbs the mini-batch distribution by assigning probabilities based on the data distribution statistics. The mask assigns a higher probability to more frequent samples on the Bernoulli distribution. Next, we employ consistency-based learning to encourage the encoder to produce similar representations for perturbed versions of the same input, drawing inspiration from modern consistency-based learning approaches. By jointly training with the two proposed learning objectives, we achieved state-of-the-art performance on AgeDB-DIR and IMDB-WIKI-DIR, which are representative imbalanced age estimation datasets. Furthermore, we evaluated the generalization performance using UTKFace. Through extensive experiments, we confirmed that our method demonstrates efficacy in dealing with imbalanced regression data. The forthcoming task involves extending the suggested approach to different uses, such as predicting the progress of diseases in medical diagnoses and estimating monocular depth in self-driving technology.
Implementation and Validation of Multiple Target Tracking Technique for Indoor Applications

Multiple target tracking (MTT) in the presence of false alarms (effect of clutter) and miss detections has always been a hot topic for research in a radar system. However, there is a shortage of literature describing the complex working and components of the MTT algorithm, especially in an indoor environment. Hence, this paper proposes a unique implementation of the MTT algorithm. The various features that make the working of the MTT algorithm possible are compiled in one place and thoroughly discussed. The developed MTT algorithm is validated for all potential challenges in an indoor scene, making the developed MTT algorithm more appealing to be used in practice. Further, extensive computer simulations with real and simulated measurements validate the working of the designed MTT algorithm. Lastly, the developed MTT algorithm is also used for people counting in an indoor environment, which has a shortage in the existing literature.
Zero-Shot Learning For Text Classification: Extending Classifiability Beyond Conventional Techniques

Text classification plays a crucial role in organizing and understanding huge amounts of text data. However, traditional text classification methods often face challenges when dealing with unseen or novel classes. Zero-shot learning (ZSL) offers a promising solution to this problem by enabling the classification of text instances into classes that have not been encountered during training. There is a plethora of potential benefits of ZSL in several applications, emphasizing its ability to handle new classes and adapt to evolving domains. In this paper, we have used the AG news dataset which is a commonly used benchmark dataset for text classification tasks. It consists of news articles from the AG's corpus, collected from four different categories: World, Sports, Business, and Science/Technology. Each article is assigned a label corresponding to one of these categories. We applied state-of-the-art deep learning algorithms such as Convolutional Neural Networks and Recurrent Neural Networks to compare the performance with Zero Shot Learning (ZSL). ZSL proved to be robust and performed better compared to the other algorithms in terms of accuracy and F1 Score.
SETAR: Stacking Ensemble Learning for Thai Sentiment Analysis Using RoBERTa and Hybrid Feature Representation

Sentiment classification of social media posts is among the most challenging and time-consuming tasks for analysts. This is particularly true when applied to languages that employ scriptio continua, such as the Thai language, in which there are no spaces between written words and where there is no end of sentence punctuation. Thai is considered a scarce-resource language as few datasets are available to researchers. Although machine-learning (ML) and deep-learning (DL) algorithms can identify sentiment classification polarity, the performance of the existing classification models are still inadequate. This study proposes a novel stacking ensemble learning technique for identifying sentiment classification polarity in the Thai language, SETAR. Our stacking ensemble strategy utilized the pre-trained Thai language model (WangChanBERTa), based on a Robustly Optimized BERT Pretraining Approach (RoBERTa) architecture to form a feature vector. This feature was combined with three distinct feature vectors obtained from three well-known categories, namely Word2Vec, TF-IDF, and bag-of-words, as a new hybrid sentence representation. The base learners were trained using seven chosen complex heterogeneous ML algorithms, including support vector machine (SVM), random forest (RF), extremely randomized trees (ET), light gradient boosting machine (LGBM), multi-layer perceptron (MLP), partial least squares (PLS), and logistic regression (LR) to enable the development of the final meta-learners. The results revealed that our proposed stacking ensemble model outperformed the baseline models of all classification metrics among the training and test sets, as was determined by extensive benchmarking, carried out on the four datasets, which included our developed sentiment corpus that domain experts annotated.
Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML) methodologies, with a unique perspective on their applications for wireless communications, is presented. The paper begins by delving into the fundamental principles of quantum computing, and then goes through different operations and techniques that are involved in QML deployments. Subsequently, it provides an in-depth look at various methods peculiar to quantum computing, such as quantum search algorithms, and discusses their potentials towards maximizing the performance of wireless systems. The integration of quantum-based learning models into the existing machine learning methodologies, such as within the frameworks of unsupervised learning and reinforcement learning, are then examined. Taking the viewpoint of wireless communications, diverse studies in the literature that employ QML-based optimization methods are also highlighted. Finally, to ensure the applicability and feasibility of QML for optimizing wireless systems, potential solutions for deployment challenges are addressed.
A Study on Different Fuzzy Image Enhancement Techniques

Modern medical science has seen a revolution in medical image processing. We would all be able to diagnose and treat patients without side effects. Medical imaging allows doctors to see patients without opening them. Medical imaging allows us to learn more about human neurobiology and human behavior. Brain imaging is used to study why some people become addicted to cocaine over time. Medical imaging combines biology, chemistry, and physics. The technology created can be used in many other fields. This article explains how medical imaging can be improved in the frequency and time domains. Contrast enhancement is performed using the local transform histogram method. The images are then enhanced using Fuzzy-Neural techniques. Fuzzy logic and fuzzy set are very good at dealing with multiple uncertainties. Recent research has focused on the ability of fuzzy theory to enhance low-contrast images and fuzzy technique and better approach for new research.
Statistical Dialog Tracking and Management for Task-Oriented Conversational Systems

Conversational interfaces offer users a natural way to interact with a range of applications and devices. Human-machine interaction using these systems involves different components that mimic the mechanisms used by humans when using language and speech interaction. In this paper, we are interested in automatically developing the dialog state tracking component for task-oriented conversational systems to automatically decide the best system response from a set of predefined responses. To do this, we have evaluated several statistical methodologies to develop this component for a conversational system that provides technical program information during conferences. Gradient boosting trees have been selected as the best performing method for this specific domain.
Generative Conversational AI And Academic Integrity: A Mixed Method Investigation To Understand The Ethical Use of LLM Chatbots In Higher Education

The recent introduction of large language model-powered chatbots, such as ChatGPT and Google Bard, has raised significant academic integrity concerns in higher education. There have been reports of students misusing these tools to complete academic assignments. However, scant research has empirically investigated the factors that could promote their ethical use. Using a quantitative and qualitative method approach, this research proposes and tests a model of the ethical usage of chatbots. Analysis of data collected from university students in the US reveals several key findings. First, an environment (ethical climate) that prioritizes ethical considerations and holds students accountable for their actions makes them feel responsible for using chatbots ethically. It also heightens their awareness of ethical implications (ethical sensitivity) and boosts their confidence (ethical chatbot self-efficacy) in using chatbots ethically. Second, students who are more aware of the ethical implications of using chatbots and are confident in their ability to make ethical choices are more likely to use them responsibly. Third, risk factors serve as deterrents to unethical behavior. Fourth, despite understanding the importance of addressing unethical conduct, students lean toward persuasion rather than reporting as a means to encourage ethical usage among peers. Implications for educational institutions are discussed.
Genetically Optimized UFLANN for Uncovering Clusters

In this work, we present a novel clustering approach which is inheriting the best characteristics of Unsupervised Functional Link Artificial Neural Network (UFLANN) and Genetic Algorithms (GAs) for uncovering clusters embedded in dataset represented through ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> ) <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Nxd</i></sub> , where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> consists of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> data points with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -dimensions. With an aim to realize natural clusters in a linear space UFLANN mapped the input vectors from lower dimension to higher dimension with a greater hope to achieve linearity in higher dimensional space. As a result, UFLANN introduces the problem of curse of dimensionality in the given datasets. However, it has been realized that the problems like sparse data and distance concentration associated with curse of dimensionality cast this problem to again a very complex problem. Hence to address some of the issues of curse of dimensionality, we have used GAs for selecting optimal number of features in the higher dimension for UFLANN to discover clusters embedded in the dataset. The proposed approach herein after named as GAUFLANN has been experimentally evaluated by using the metrics like (i) Davies-Bouldin Index, ii) Silhouette Score, and iii) Completeness score on different synthetic and real datasets. Our experimental study confirms that GAUFLANN is evidently scoring better in DB-index, Silhouette score, and completeness score than the clustering methods like K-means, Hierarchical-Agglomerative (Average Linkage), and UFLANN across the datasets like Circles, Moons, Iris, and CORD-19.
Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China

The rapid development of Internet money funds (IMFs) may become the main development direction of money funds in the future. For the characteristics of IMFs return time series data with solid nonlinearity and poor smoothness, this study uses long and short-term memory (LSTM) neural network to predict IMFs return. By constructing a 1DCNN (one-dimensional convolutional neural network) and a self-attentive mechanism, the LSTM feature extraction capability is optimized, and an XGBOOST model is built after the output layer to construct a prediction error sequence to compensate for the original prediction sequence to achieve a correction effect. Finally, the trained model is applied to rolling forecast the 43-day return data of the actual trading days in the next two months, and the VaR method is applied to realize the IMF risk measure. The results displayed the following: (1) The 1DCNN-LSTM-SAM-XG has a significant improvement in accuracy compared with models such as LSTM neural network and SVR, and the MAPE values are reduced by 1.372% and 2.887%, respectively, indicating that the model established in this study is characterized by high accuracy and robustness. (2) According to the VaR methodology, the FUND series has the highest risk, the BANK series has the second highest risk, and the THIRD series has the lowest risk.
Glass-Cleaning Robot That Scales Walls

For the purpose of high-rise building maintenance, where human intervention can be dangerous, this project proposes to design a cheap and easy-to-manufacture wall-climbing cleaning robot. The robot is able to cling to vertical surfaces thanks to the propeller thrust mechanism. During the climbing process, the robot's complete body was attached to the wall using a calculated amount of force. The friction coefficient of the rubber wheels on the robot was determined experimentally on a whiteboard. The code used to control the robot is essential to its operation and must be created with precision. The chassis material was chosen based on stress and deformation predictions, and a bespoke cleaning mechanism was developed for this robot. Consideration was also given to the robot's total weight distribution and the configurations of its components to ensure its continued stability and optimal performance. The capabilities and effectiveness of the robot can be improved by additional study and development, despite the fact that the project is still in its infancy.
RDFM: Robust Deep Feature Matching for Multimodal Remote-Sensing Images

Robust feature matching for multimodal remote sensing images remains challenging due to the significant nonlinear radiation difference (NRD) caused by modality variations. In this letter, we present a novel feature-matching method for multimodal remote sensing images, called RDFM, which exploits only deep features extracted by a pre-trained VGG network to achieve competitive performance. It is shown that template matching of these pre-trained features is robust to NRD for various multi-modal remote sensing images, and no additional training is required to improve the matching performance. In order to extract as many correspondences as possible, we use dense template matching to obtain point correspondences and introduce a 4D convolution-based implementation of dense template matching for the sake of computational efficiency. RDFM consists of two main steps. First, enormous coarse correspondences are extracted by applying dense template matching at the deep layer of the pre-trained network, and then a coarse-to-fine hierarchical refinement is performed to obtain high-quality correspondences. To verify the effectiveness of RDFM, six different types of multimodal image datasets are used in our experiments, including day-night, depth-optical, infrared-optical, map-optical, optical-optical, and SAR-optical datasets. The comprehensive experimental results show that RDFM is able to overcome the problem caused by NRD and achieves a better performance than the state-of-the-art methods for multimodal remote sensing image matching. The code of RDFM is publicly available at https://github.com/Fans2017/RDFM.
A Pilot Study in Korea for Implementing Artificial Intelligence Smart Poultry Farm Appropriate Technology Camp in Tanzania

Since the pandemic, the study purposed to derive improvements of the educational program through pilot operation for youth in Korea before local application to disseminate artificial intelligence smart poultry farm in Tanzania. For this purpose, we developed an artificial intelligence smart poultry farm appropriate technology camp program, and 49 high school students participated in this pilot study for two days. After the pilot operation, a questionnaire was conducted to measure students' perception and experience of artificial intelligence through this appropriate technology camp. The questionnaire consisted of 22 questions measuring the perception of artificial intelligence by the Likert scale and 1 question describing the participation experience. Students' perception of artificial intelligence was primarily composed of three areas: value perception (6 questions), efficacy (4 questions), and risk perception (12 questions). As a result, students' value perception (M=4.30, SD=.491) and efficacy (M=4.42, SD=.487) to artificial intelligence were high. However, the risk perception (M=3.42, SD=1.023) was relatively low. In addition, through this appropriate technology camp, students' motivation to learn artificial intelligence improved, their interest in careers and jobs related to artificial intelligence increased, and they had a negative perception and a positive impact on society due to artificial intelligence.
TBFNT3D: Two-Branch Fusion Network With Transformer for Multimodal Indoor 3D Object Detection

Indoor 3D object detection based on point clouds has been widely applied for robotics, augmented reality and virtual reality. The point clouds generated from RGB-D cameras are sparse for distant objects, which affects the detection performance. Multimodal 3D object detection can improve the detection performance by fusing features for point clouds and images. RGB images can be converted to dense 3D features, which can be applied as a complement to 3D object detection using only point clouds. We refer to the 3D data transformed from RGB images as estimated 3D data. Therefore, we propose a two-branch fusion network with a transformer for multimodal indoor 3D object detection named TBFNT3D. In TBFNT3D, voxels converted from the point clouds and images are added together to obtain a consistent voxel representation. The features for the voxel space are enriched, and features from different modalities do not require a complex alignment process. To make better use of estimated 3D data, we need to process noise and remove redundant estimated 3D data. The receptive field for 3D sparse convolution is expanded into the 2D image space, which weakens the effect of noise. A bin-based sampling strategy is applied for near objects and distant objects, removing the redundant estimated 3D data. In addition, to fuse the multimodal features efficiently, we apply a deformable transformer to obtain the detection results. Finally, TBFNT3D is evaluated on the SUN RGB-D dataset and ScanNet dataset, and state-of-the-art results are achieved.
Aleatoric Propositions: Reasoning About Coins

Aleatoric propositions are a generalisation of Boolean propositions, that are intrinsically probabilistic, or determined by the toss of a (biased) coin. Rather than let propositions take a true/false valuation, we assume they act as a biased coin, that will sometimes land heads (true), and sometimes land tails (false). Complex propositions then correspond to a conditional series of tosses of these coins. We extend the syntax and semantics for Aleatoric Logic to include a novel fixed-point operator that is able to represent a weak form of iteration. We examine the expressivity of the of the language, showing a correspondence to classes of rational functions over (0, 1)