Reviewerly/exHarmony huggingface dataset:
Nested Virtualization, an Implementation Scenario for Higher Education Institutions, Solution, and Performance Issues

In the current scenario the requirements of higher education institutions are exponentially increasing concerning the IT infrastructure to provide the out-an-out environment for students to practice and comprehend pragmatic concepts. Especially engineering students of information technology have significant requirements concerning IT infrastructure that most of the higher education institutions are facing difficulty in meeting such requirements. It is also very important to clearly understand here that it is not just the procurement of IT infrastructure (Hardware and Software) however it is the perfect deployment and design of a facility keeping in view the specific requirements of students for enhanced student experience and teaching excellence. This research study is introducing an innovative learning environment especially for students of computer engineering and IT so that they can comprehensively practice and deploy their models to get perfect pragmatic knowledge of various critical subjects and domain areas. This research study is taking into consideration the example of BEng Computer Engineering students at Middle East College in pathways of Cloud Computing, Data Communication and Systems Administration, and information security. The research study is intended to achieve an innovative approach for facilitating the required lab environment for students, for the enhancement of learning experience, and to achieve academic excellence.
Lightweight Online Semisupervised Learning for Ultrasonic Radar-Based Dynamic Hand Gesture Recognition

Ultrasonic dynamic hand gesture recognition (U-DHGR) is a promising approach of human–computer interaction (HCI) for a broad range of emerging applications. Cross-user robustness is a key challenge for its classification algorithms. Due to the highly personalized gesture characteristics and the limited number of training samples, traditional offline supervised learning algorithms usually fail to achieve as high accuracy on new users as on the training participants. In this article, we propose a lightweight online semisupervised learning algorithm for U-DHGR that aims to manipulate the incrementally collected unlabeled user samples to further improve the personalized recognition performance and reduce the data preparation cost. For each of the defined gesture classes, a tree-ensemble-based binary classifier is deployed, whose partial structures are offline trained to predict pseudo-labels for the incremental training of the remaining structures. Experiments show that by combining the predictions of both the offline and incrementally trained parts of the classifier, improvements ranging from 3.8% to 7.9% are achieved in the recognition accuracies for different users. The average accuracy of the eight defined gestures reaches 96.7%, which is 3.1% higher than its nearest offline supervised competitor. The model size of the classifier is only 0.5 MiB after finishing the incremental training. The average inference time and incremental sample process time on a laptop are 11.7 and 37.9 ms, respectively, which shows the potential to be integrated into resource-constrained platforms.
An Efficient Multi-view Facial Expression Classifier Implementing on Edge Device

The robotic technology demands human-robot interaction to implement a real-time facial emotion detector. This system has a role in recognizing the expressions of the user. Therefore, this application is recommended to work quickly to support the robot's capabilities. It helps the robot to analyze the customer's face effectively. However, the previous methods weakly recognize non-frontal faces. It is caused by the facial pose variations only to show partial facial features. This paper proposes a multi-view real-time facial emotion detector based on a lightweight convolutional neural network. It offers a four-stage backbone as an efficient feature extractor that discriminates specific facial components. The convolution with Cross Stage Partial (CSP) approach was employed to reduce computations from convolution operations. The attention module is inserted into the CSP block. These modules also support the detector to work speedily on edge devices. The classification system learns the information about facial features from the KDEF dataset. As a result, facial emotion recognition achieves comparative performance to other methods with an accuracy of 97.10% on the KDEF, 73.95 on the FER-2013, and 84.91% on the RAFDB dataset. The integrated system using a face detector shows that the system obtains a data processing speed of 30 frames per second on the Jetson Nano.
Stages Classification on Endometrial Cell Images Using Roundness Shape Analysis

Uterine cancer, also known as endometrial cancer, is a form of cancer that affects the female reproductive system. Women over the age of 50 are more likely to get uterine cancer. At this current time, there are two methods that the physician or health care provider tends to use to diagnose cancer, which are ultrasound technique and endometrial biopsy. If the patient is suspected of having cancer, ultrasound is first used to measure the length of the endometrial lining. Then, if the lining is greater than 4mm, an endometrial biopsy is performed. At this stage, small tubular instruments are used to extract the cells from the human body. The cells are then sent to a pathologist to determine the cell type. Using the pathological image analysis system, time is taken, where human factors are the main issues in using the system. In cancer treatment, both aspects of time and human error are important because both issues can lead to death. Also, the cell nucleus is key since the damaged cells do not tend to produce the circular cell nucleus. Therefore, the pathologist usually requires more time to identify whether it is a Low-Grade Squamous Intraepithelial Lesion (LSIL) or a High-Grade Squamous Intraepithelial Lesion (HSIL). Based on the limitations that occur, the paper aims to automate classification techniques based on the nucleus roundness value. Image processing via machine learning is an alternative method that can provide high accuracy with a short processing time. The system used multiple image algorithms to determine the circularity of the cell nucleus and provide additional data to determine the cell type. Based on calculations, detection accuracy was achieved up to 100%.
PyTracer: Automatically Profiling Numerical Instabilities in Python

Numerical stability is a crucial requirement of reliable scientific computing. However, despite the pervasiveness of Python in data science, analyzing large Python programs remains challenging due to the lack of scalable numerical analysis tools available for this language. To fill this gap, we developed PyTracer, a profiler to quantify numerical instability in Python applications. PyTracertransparently instruments Python code to produce numerical traces and visualize them interactively in a Plotly dashboard. We designed PyTracerto be agnostic to numerical noise model, allowing for numerical profiling through Monte-Carlo Arithmetic, random rounding, random data perturbation, or structured noise for a particular application. We illustrate PyTracer's capabilities by testing the numerical stability of key functions in both SciPy and Scikit-learn, two dominant Python libraries for mathematical modeling. Through these evaluations, we demonstrate PyTraceras a scalable, automated, and generic framework for numerical profiling in Python.
A Deep Convolutional Autoencoder Architecture for Automatic Image Colorization

The inherent complexity of image colorization has motivated computer scientists towards the development of algorithms capable of simplifying the image colorization process. Despite the numerous advancements yielded by these efforts, there are still some limitations regarding the resulting image quality and its similarity to the ground truth counterpart. This paper proposes and implements a deep convolutional autoencoder architecture that maximizes the image colorization performance on two different datasets, the Fruit-360 and Flickr-Faces-HQ. To this end, a modification of the VGG16 model and a custom deep CNN model were assembled to predict and portray colors on grayscale images. We obtained mean absolute and square error results under the 0.01% on both datasets, demonstrating the substantial similarity between the output image and its ground truth counterpart. Additionally, the peak signal-to-noise ratio results of 27.72 (Fruits-360) and 26.86 (Flickr-Faces-HQ) indicate that the image colorization process introduces a relatively low drop in image quality.
SimCL:Simple Contrastive Learning for Image Classification

Contrastive learning, a self-supervised learning method, has become one of the main techniques for visual representation learning. It builds contrastive views through data augmentation, maximizing the mutual information between views with the same semantic information. Currently, the main methods used for data augmentation are random cropping, resizing, rotating, and recoloring images. However, due to the diversity and randomness of data augmentation, it is difficult to guarantee the same semantic information between pairs of positive examples during data augmentation. This limits the efficiency and applicability of contrastive learning. To circumvent this problem, in this paper, we propose a Simple Contrastive Learning(SimCL) method for image classification, which abandons data augmentation and constructs contrastive views by adding uniform noise in the representation space for contrastive learning. Specifically, the original image is passed through a convolutional neural network to obtain a representation of the image, and a contrastive view is constructed by adding different noises to the representation. Despite its apparent simplicity, comprehensive experiments on 11 benchmark datasets show that the method can match or even outperform data augmentation-based contrastive learning methods.
Multimodal Motion Prediction Based on Adaptive and Swarm Sampling Loss Functions for Reactive Mobile Robots

Making accurate predictions about the dynamic environment is crucial for the trajectory planning of mobile robots. Predictions are by nature uncertain, and for motion prediction multiple futures are possible for the same historic behavior. In this work, the objective is to predict possible future positions of the target object for the collision avoidance purpose for mobile robots by considering different uncertainty by combining a sampling-based idea with data-driven methods. More specifically, we propose a major improvement on a loss function for multiple hypotheses and test it with convolutional neural networks on motion prediction problems. We implement post-processing heuristics that produce multiple Gaussian distribution estimations, and show that the result is suitable for trajectory planning for mobile robots. The method is also evaluated with the Stanford Drone Dataset.
A Novel Method for Text Summarization and Clustering of Documents

It is now more crucial than ever to offer enhanced methods for accurately and effectively identifying and presenting textual material due to the rapid growth of internet information. Information overload is an issue that has arisen as a result of the vast amount of information that is currently available. A document summary is one potential remedy for the information overload issue. The process of collecting information from a textual document and giving the user the most important content in a condensed form that is responsive to their needs or those of an application is referred as document summarizing. In this study, we provide an autonomous summarising approach that generates a summary for each input document. We also discuss how to achieve discourse coherence in summaries and how to write co-herent, understandable summaries. We also extract the keyword from the many documents that have been provided. Then, using clustering algorithm, we generate clusters based on keyword similarity. Experimentaion results shows that our method can provide efficient clusters of document summeries.
A Situation-aware Wearable Computing System for Human Activity Recognition

Wearable devices support the detection of human activities without the expensive and obstructive presence of environmental sensors like cameras, and without the need to constrain the users to remain in controlled environments. Human activity recognition supports many applications in several domains like safety, security, surveillance, health monitoring. To concretely and effectively support such applications, however, the simple recognition of the activities alone is not enough. What is really needed is a deep understanding of the situations in which the activities are performed to estimate the potential risks and opportunities for the application goals. In such a context, built atop a reference architecture for situation-aware wearable computing systems, this work proposes a situation-aware human activity recognition system based on hidden Markov models and the Endsley’s model of situation awareness. The proposed system, implemented as a set of microservices, is flexible and adaptive as it is able to exploit the number of sensors that are available, without the need to reconfigure the system itself, thus providing the users with a best-effort approach to estimate the activities and the situations using all the currently available data.
STARDUST: A Novel Process Mining Approach to Discover Evolving Models From Trace Streams

In this article we introduce <inline-formula><tex-math notation="LaTeX">${{\sf STARDUST}}$</tex-math></inline-formula> (event STream Analysis for pRocess Discovery Using Sampling sTragies), a process discovery approach that analyses a trace stream, in order to discover a process model that may change over time. The basic idea is to adopt a sampling technique to select the most representative trace variants to be considered for the process discovery, then to alert a concept drift as the trace variants to be sampled change over time and, finally, to trigger the discovery of a new process model as a drift is alerted. We formulate the proposed approach under the assumption that the trace distribution commonly follows the Pareto's principle (i.e., a few trace variants covers the majority of cases) which is commonly satisfied in several business processes. Experimental results on various benchmark event logs handled as streams show the effectiveness of the proposed approach also compared to a state-of-the- art concept drift detection approach.
Android Application For Analysis Review On Google Playstore Using Support Vector Machine Method

Nowadays, Google Play Store have more than 2.77 million of mobile applications. Though there are several other app stores available for Android, none of them compare to the Google Play Store. Many people who use the application would leave their review after using the application at Google Play Store. Google Play Store also be used to do research from their review. Information that related to advantages, features and other functions may not necessarily be explained in detail by the application developer. Reviews from the users can be used as illustration of the experience from using an application for potential users who will download the application. This study aims to provide information to users regarding the results of sentiment analysis of user reviews from applications on the Google Play Store and sort applications in certain categories based on the best results of the sentiment analysis. This study used 500 data review from each application based on the most relevant filter. The classification results will be evaluated using a confusion matrix. The analysis results are displayed in an android application that build using the JavaScript programming language with react native library. From the test results, the best kernel for performing calculations is the RBF kernel with a combination of 80% training data and 20% test data. The accuracy value is 73.97%, the precision value is 76.53%, the recall value is 71.57% and the F1 score is 73.35%.
Cross-Modal Knowledge Transfer Without Task-Relevant Source Data

Cost-effective depth and infrared sensors as alternatives to usual RGB sensors are now a reality, and have some advantages over RGB in domains like autonomous navigation and remote sensing. As such, building computer vision and deep learning systems for depth and infrared data are crucial. However, large labeled datasets for these modalities are still lacking. In such cases, transferring knowledge from a neural network trained on a well-labeled large dataset in the source modality (RGB) to a neural network that works on a target modality (depth, infrared, etc.) is of great value. For reasons like memory and privacy, it may not be possible to access the source data, and knowledge transfer needs to work with only the source models. We describe an effective solution, SOCKET: SOurce-free Cross-modal KnowledgE Transfer for this challenging task of transferring knowledge from one source modality to a different target modality without access to task-relevant source data. The framework reduces the modality gap using paired task-irrelevant data, as well as by matching the mean and variance of the target features with the batch-norm statistics that are present in the source models. We show through extensive experiments that our method significantly outperforms existing source-free methods for classification tasks which do not account for the modality gap.
MetaGNN-Based Medical Records Unstructured Specialized Vocabulary Few-Shot Representation Learning

With the continuous breakthroughs in artificial intelligence technology, it has become easier to extract general-purpose knowledge using machine learning, but it is a challenging task to extract and learn small samples of knowledge in medical expertise. On the one hand, it is difficult to represent medical expertise entities, and on the other hand, the training samples of such expertise are small, and deep learning methods often require a large number of samples to complete the learning task. To this end, we proposes a graph network learning method for specialized vocabulary representation. Specifically, a contextual knowledge representation model based on graph meta-learning is proposed, which combines text, phrase, vocabulary, and other information to solve the problem of sparse data of medical electronic medical record entities that cannot be extracted and learned. In this method, a text-independent lexical representation learning method, a context-aware graph neural network, and a combined LSTM language model are used to model information from different perspectives as a way to learn semantic representations of professional discourse entities. The experimental results show that the accuracy of the method outperforms other similar methods and proves its effectiveness.
People Thoughts Prediction Using Machine Learning on Online Shopping in Bangladesh During COVID-19 Pandemic

E-commerce business has become a prominent entity of global retail as online transaction saves time and cost at the same time. COVID-19 pandemic and lockdown accelerated the growth of e-commerce. The new e-business companies are booming rapidly whereas insincerity in customer concern is noticeable. As a result, the purchasers are facing numerous problems while buying online. The main objective of this study is to predict preferences on online shopping of buyers and based on that analysis, the pattern can be observed. While doing the study, we used some popular Supervised machine learning algorithms such as Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naıve Bayes (NB) algorithm. Amongst those, best accuracy was delivered by the Decision Tree algorithm. The output clearly demonstrates that, people are more likely to participate in online shopping if the obstacles could be alleviate which means, buyers are still not satisfied and confident about the online platform. Hopefully, the result of this study can be a great asset for improving the E-commerce sector of Bangladesh if it is optimized wisely.
A Kendall Shape Space Approach to 3D Shape Estimation from 2D Landmarks

3D shapes provide substantially more information than 2D images. However, the acquisition of 3D shapes is sometimes very difficult or even impossible in comparison with acquiring 2D images, making it necessary to derive the 3D shape from 2D images. Although this is, in general, a mathematically ill-posed problem, it might be solved by constraining the problem formulation using prior information. Here, we present a new approach based on Kendall's shape space to reconstruct 3D shapes from single monocular 2D images. The work is motivated by an application to study the feeding behavior of the basking shark, an endangered species whose massive size and mobility render 3D shape data nearly impossible to obtain, hampering understanding of their feeding behaviors and ecology. 2D images of these animals in feeding position, however, are readily available. We compare our approach with state-of-the-art shape-based approaches, both on human stick models and on shark head skeletons. Using a small set of training shapes, we show that the Kendall shape space approach is substantially more robust than previous methods and results in plausible shapes. This is essential for the motivating application in which specimens are rare and therefore only few training shapes are available.
Variational Optimization of the Time Evolution Operator in Correlated Systems

Simulating the time evolution of quantum systems is a classically challenging and often intractable task due to the exponential scaling of its constituent operators. This scaling makes it a promising application for quantum computation, especially for correlated systems. Constructing an approximate time evolution operator can be done using the Trotter-Suzuki decomposition with an error that scales at most polynomially in the target simulation time and can be decreased at the cost of longer circuit depth. Variational approaches to classically optimize this approximation have shown significant improvement in accuracy for large quantum systems in both gate count and accuracy by several orders of magnitude. However, it is unclear if a similar improvement can be obtained for strongly correlated systems. We extend this variational approach to two example systems with strong correlations: a one-dimensional Fermi-Hubbard chain and an extended Fermi-Hubbard chain. The encoding between qubits and fermionic modes in these systems is generally nonlocal and so we propose an encoding scheme for these Hamiltonians with minimal overall operator distance between nearest neighbor connected qubits. We demonstrate that this variational method grants a similarly strong improvement in both gate count and accuracy and examine the performance of these optimized gate sequences using realistic noise models to estimate their performance on noisy quantum hardware. Finally, we discuss an approach to combine the optimized nonlocal gates of small chains to larger systems without the need for additional computational resources.