Reviewerly/exHarmony huggingface dataset:
On-Premise or Cloud Computing: An Integrated Novel Approach to Study the Adoption of Software Product’s Deployment Model with Different Scopes

Organizations adopt software products for their betterment through on premises and cloud computing depending on their requirement. On a broad level, cloud computing itself is available in three types, namely infrastructure as a service, software as a service, and platform as a service. While each deployment model and its use are well defined, complexities and challenges are there for organizations in deciding among the various models. This conceptual paper is an attempt to represent different scopes for the adoption of appropriate model of software product. There are various scientific models available to study the adoption of technological innovation, but the process of adoptions depends on the different scopes. The use of only one scientific model to study the adoption process will not justify in studying the different scopes of adoption process. Therefore, we have used an integrated approach with three scientific models, namely DEMATEL framework, technology-organization-environment framework (TOE), and diffusion of innovation (DOI).
Research and Analysis on Voice-Based System with Machine Learning

This Chatbots in banking are primarily used to improve the client experience. They do, however, benefit the personnel and help to avoid uncomfortable circumstances that can emerge from direct contact with clients. Customers might benefit from artificial intelligence in a variety of ways. The most prevalent use scenario for chatbots in bank customer service is the automation of mundane repetitive chores. These activities want quick and easy answers that necessitate self-learning. Banking chatbots can be implemented as standalone mobile banking applications or integrated banks’ websites. Chatbots can be custom-made or ready-made in terms of their origin. Custom AI assistants are generally more effective and secure because they are built to exact requirements and under the close supervision of the financial institution where the bot will be incorporated. Ready-made solutions are generic bots designed to be as versatile as possible while yet allowing for some customization.
Nuclei Segmentation Using UNet with EfficientNetV2 as Encoder

Nucleus segmentation of H &E-stained (hematoxylin and eosin) histopathology images is a crucial step in developing a computer-aided diagnostic (CAD) system for cancer prediction and diagnosis. Nucleus segmentation technology has made it possible to subjectively and quantitatively analyze thousands and thousands of nuclei in H &E-stained histopathology images. The segmentation of variable touching nuclei, on the other hand, is a substantial issue during nuclei segmentation. This paper proposes a deep learning model for automatic nuclei segmentation. Since UNet performs well in medical image segmentation areas, a modified version of UNet architecture is utilized for the segmentation of nuclei. Generally, UNet architecture has a contracting path and an expansion path. In this paper, the contracting path or encoder of UNet is replaced by EfficientNetV2-L architecture. By making this change, the proposed model achieved the Jaccard index of value 0.85 along with a dice coefficient of 0.91 on the MoNuSeg dataset. After nuclei segmentation with the proposed model, a post-processing method is utilized to filter out noise in predicted masks images by using the watershed method.
Credit Card Fraud Detection using Machine Learning Algorithms

Credit Card (CC) fraud has been an essential target to be addressed nowadays. E-commerce and many other online sites have increased the number of payment options available online, raising the risk of online fraud. Due to the rise in fraud rates, researchers began employing various Machine Learning (ML) approaches to detect and analyze online transaction fraud. The paper’s major goal is to build and create a novel fraud detection approach for Transaction data streams, with the goal of analyzing customer’s historical transaction details and extracting behavioral patterns. The effects of algorithms are manifested in agreement with established patterns, followed by correctness, accuracy, recall, and F1-score. The F1-score resides as many highly good ideas, particularly ancient ones, and the Naive Bayes, Bernoulli, and Random Forest are displayed with precision, accuracy, and recall.
A Study and Analysis of Algorithms on Intelligent Systems to Recognize Hand Written Digits and Characters

The article includes a vast survey on the algorithms applied for hand written digit and character recognition and analysis of the best effective algorithm that can be used, and the effectiveness here is measured with accuracy. The major uses of recognition of handwritten characters and digits are interpreting postal addresses, check amounts of bank, tax documents, and filled forms. In case of reading the bank check amount, the accuracy of the recognition should be high or should be 100% as it is a very sensitive information and any mistakes in recognizing that will lead to complications within the bank system. So, recognizing the handwritten digit and character accurately plays a very important role in the real-world application, and this paper includes a survey and analysis on the most effective algorithm that can be used. The algorithms considered in this study are random forest, multilayer perceptron, random tree, support vector machine, Naïve Bayes, Bayes Net, J48, CNN, hybrid CNN-SVM, KNN, NN, RFC, single layer network with PCA, multilayer network -LeNet-5 CCN architecture. Analyzing the algorithms that can be used to recognize the handwritten digit and character, it is observed that CNN algorithm produces more accuracy in recognizing the hand written digits and characters.
Automated Surveillance Video Analysis with Textual Descriptions

Surveillance video analysis plays an important role in the modern security systems. Since the manual analysis becomes tedious, automatic analytics of the surveillance videos are preferred, hence, it finds its own place in the research area of video processing. This paper presents a meaningful text generation algorithm by detecting the most salient or relevant area/objects in the frames and considering the spatial, temporal and semantic relationship between the objects present in the video. First, the input video is split up into segments and each segment is fragmented into individual frames. Each of these frames is undergone object detection to extract the details contained in the frame as well as the spatial relationship between various objects in it. These details together with the temporal features of the video segments are utilized for generating textual representations of the segment using the transformer model in natural language processing (NLP). The developed algorithm is applied for the analysis of three surveillance video data sets: BOSS, AVENUE and VISIOCITY, for proving its effectiveness by generating good textual descriptions that highlights the semantics in the video. It also scores a good BLEU4 and CIDEr value of 40.8 and 48.1, respectively, for MSR-VTT data set.
Improving Accuracy of Ataxic Gait Monitoring Using SVM and ANN

Ataxic gait monitoring and assessment of neurological disorders play significant roles in multidisciplinary regions that are supported by computerized signal handling techniques and Artificial Intelligence (AI) devices. This paper focuses on offering a chance of utilizing accelerometric information to identify the disorders more accurately using algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM). The trial dataset incorporates 860 sign fragments for about 16 patients who are ataxic and 19 patients with the control set for a timestamp of 39.6 and 38.6 years individually. The proposed model deals with investigating the recurrence parts of accelerometric flags that are continuously recorded from the human body in several directions using testing recurrence. The profound learning framework involves the recurrence parts in the ataxic gait. The characteristics offered by algorithms like SVM and ANN are discussed along with the functionality which gives a clear understanding of these algorithms.
Fall Detection of Elderly Using YOLO

Unintentional injury caused by falls is the second leading cause of death in the elderly. To detect falls, several strategies are used, but they are controlled in number by the algorithm’s computing complexity. The captured images are routed through the system to a server at the back end for the processor. As the IOT fills in prevalence, this engineering will confront difficulties, for example, rising transmission capacity expenses and server packing. New strategies for reducing server stress are being developed by shifting specific computing worth loads from cloud server to edge figuring stages. To that purpose, our research developed a revolutionary technique in light of neuromorphic processing equipment that smoothes out and moves the brain network modal from the back end PC to the edge figuring stage. A neural network model with int8-bit8 bit accuracy is built using the edge figuring stage to change over the camera’s item pictures into human movement ascribes, and a help vector machine is then utilized for order. The overall system was found to have 96% accuracy, a detection speed of 11.5 fps, and power consumption of 0.3 W after testing. This innovation can follow the falls of more seasoned people continuously and throughout an extensive period. To compute all of the data, an edge computing platform was used. A computer system that can automatically and effectively identify and classify falls would be valuable for monitoring the elderly population and speeding up the assistance process, reducing the risk of long-term harm and mortality. A YOLO Version 3-based fall detection has been included in the project thanks to the help of the darknet framework. A low loss function is used in the training procedure. This project intends to create an automated image-based fall detection system that can be used to monitor geriatric behavior using the YOLOv3 algorithm. Fall events will be identified and alerted as soon as they are discovered. In order to meet low cost, high accuracy, and real-time computing needs, our project proposed combining the YOLOv3 object detection algorithm with the IFADS fall detection method.
Detection of Grocery Items in Store Using Deep Learning in Retail Business

Deep convolutional networks were used to classify the ImageNet dataset with remarkable accuracy. This research uses a special dataset that was created from scratch to produce a deep learning strategy with a unique architecture for categorizing. We provide a network of classifiers architecture based on the most trying to cut feedforward network design available. Our system consists of a senior classifier who sends the photo to three of the ten classifiers for classification. Our system consists of a master predictor who sends the data to three of the ten classifiers for classification. We used 1920 images of well-known supermarket products and goods to train the algorithm over 131 different classes. We had a top-1 mistake rate of a few %, a top-3 false positive rate of 25.36%, and a top-5 margin of error of 20.28% during our testing. This study uses terminology like product identification, image processing, CNN, deep learning, Inception v3, retraining, and transfer learning.
Multi‐agent architecture approach for self‐healing systems: <scp>Run‐time</scp> recovery with <scp>case‐based</scp> reasoning

Summary Self‐healing is an approach that maintains the health of the system with proper supervision of its functioning and emerges from any unacceptable state during the execution. The complexity of modern distributed systems and dynamic hike in terms of users' access has led to an increase in maintenance. In this paper, a self‐healing architecture for services that exploit the autonomous capability of agent technology is proposed. The proposed mechanism is a multi‐agent system that comprises different agents with different capabilities and roles. The planning agent, responsible for taking the right decision to revive the system from an unhealthy state to a healthy state, uses a case‐based fault recovery mechanism at runtime. The architecture contains a persistent layer that maintains previously occurred failed cases. To determine the best suitable solution, the similarity between the detected fault and recorded failed cases is calculated. The case, having a maximum similarity index value is considered closest to the failure. Multiple recovery strategies like a replacement, restart, alternative resources are been utilized. Also, to validate the proposed architecture, an SOA‐based application is used and performance‐based evaluation metrics are analyzed.
Disentangling classification and regression in Siamese‐based network for visual tracking

Summary Siamese‐based trackers have made great progress in visual tracking community, however, the shared structure of network between classification and regression tasks limits the ability of the trackers to obtain more robust classification prediction and more accurate regression prediction. In this paper, we propose an effective visual tracking framework (named Siamese Disentangled Tracking‐Head, SiamDTH), which disentangles classification and regression in Siamese‐based network for visual tracking from two aspects: feature decoupling and differentiated tracking‐head. First of all, we gather the features of receptive fields with different scales and ratios, and decouple the correlation features through two different styles of feature fusion mode for classification and regression respectively. Moreover, we design the differentiated tracking‐head structure in the sibling head for discriminately handling the parallel classification and regression tasks on visual tracking. Extensive experiments on visual tracking benchmarks including VOT2018, VOT2019 and OTB100 demonstrate that our proposed SiamDTH achieves state‐of‐the‐art performance with a considerable real‐time speed. Our source code is available at: https://github.com/xl0312/SiamDTH .
Hybrid Pipeline for Building Arabic Tunisian Dialect-standard Arabic Neural Machine Translation Model from Scratch

Deep Learning is one of the most promising technologies compared to other methods in the context of machine translation. It has been proven to achieve impressive results on large amounts of parallel data for well-endowed languages. Nevertheless, for low-resource languages such as the Arabic Dialects, Deep Learning models failed due to the lack of available parallel corpora. In this article, we present a method to create a parallel corpus to build an effective NMT model able to translate into MSA, Tunisian Dialect texts present in social networks. For this, we propose a set of data augmentation methods aiming to increase the size of the state-of-the-art parallel corpus. By evaluating the impact of this step, we noticed that it has effectively boosted both the size and the quality of the corpus. Then, using the resulted corpus, we compare the effectiveness of CNN, RNN and transformers models to translate Tunisian Dialect into MSA. Experiments show that a better translation is achieved by the transformer model with a BLEU score of 60 vs., respectively, 33.36 and 53.98 with RNN and CNN models.
Chinese Grammatical Error Correction Using Pre-trained Models and Pseudo Data

In recent studies, pre-trained models and pseudo data have been key factors in improving the performance of the English grammatical error correction (GEC) task. However, few studies have examined the role of pre-trained models and pseudo data in the Chinese GEC task. Therefore, we develop Chinese GEC models based on three pre-trained models: Chinese BERT, Chinese T5, and Chinese BART, and then incorporate these models with pseudo data to determine the best configuration for the Chinese GEC task. On the natural language processing and Chinese computing (NLPCC) 2018 GEC shared task test set, all our single models outperform the ensemble models developed by the top team of the shared task. Chinese BART achieves an F score of 37.15, which is a state-of-the-art result. We then combine our Chinese GEC models with three kinds of pseudo data: Lang-8 (MaskGEC), Wiki (MaskGEC), and Wiki (Backtranslation). We find that most models can benefit from pseudo data, and BART+Lang-8 (MaskGEC) is the ideal setting in terms of accuracy and training efficiency. The experimental results demonstrate the effectiveness of the pre-trained models and pseudo data on the Chinese GEC task and provide an easily reproducible and adaptable baseline for future works. Finally, we annotate the error types of the development data; the results show that word-level errors dominate all error types, and word selection errors must be addressed even when using pre-trained models and pseudo data. Our codes are available at https://github.com/wang136906578/BERT-encoder-ChineseGEC .
Validating colour representation in synthetic scenes using a virtual colour checker chart

Evaluating the visible signature of operational platforms has long been a focus of military research. Human observations of targets in the field are perceived to be the most accurate way to assess a target’s visible signature, although the results are limited to conditions observed in the field. Synthetic imagery could potentially enhance visible signature analysis by providing a wider range of target images in differing environmental conditions than is feasible to collect in field trials. In order for synthetic images to be effective, the virtual scenes need to replicate reality as much as possible. Simulating a maritime environment presents many difficult challenges in trying to replicate the lighting effects of the oceanic scenes precisely in a virtual setting. Using the colour checker charts widely used in photography we present a detailed methodology on how to create a virtual colour checker chart in synthetic scenes developed in the commercially available Autodesk Maya software. Our initial investigation shows a significant difference between the theoretical sRGB values calculated under the CIE D<sub>65</sub> illuminant and those simulated in Autodesk Maya under the same illuminant. These differences are somewhat expected, and must be accounted for in order for synthetic scenes to be useful in visible signature analysis. The sRGB values measured from a digital photograph taken at a field trial also differed, but this is expected due to possible variations in lighting conditions between the synthetic and real images, the camera’s sRGB output and the spatial resolution of the camera which is currently not modelled in the synthetic scenes.
A scenario-based approach for the evaluation of video object tracking algorithm performance

Describing a useful performance evaluation method for object tracking algorithms is difficult. Algorithms that are very successful w.r.t. general-purpose performance metrics may perform poorly for a specific scenario. Additionally, algorithm developers frequently face an unanswerable question: will it satisfy the needs of that system (which is currently in the design phase)?”. Even when special time and resources can be allocated to collect reasonably representative data for the scenarios of interest, the answer usually remains ambiguous. Many times, during field tests or usage, the user experiences insufficient performance and the algorithm needs to be revised. In this study, we propose an approach to address this problem. Our approach is based on iterative improvement of the evaluation process. The performance requirements are determined by the field experts or the system designers. Standard questions are asked to the user/system developer and the test dataset is determined in cooperation. Each video segment in the dataset is assigned several tags for scenario type, difficulty and importance. For any novel failure case, representative videos are added to the dataset. This way, quantitative results can be organized to be more informative for the user and improvements to the algorithms can be evaluated more systematically.
Incoherent and Online Dictionary Learning Algorithm for Motion Prediction

Accurate model development and efficient representations of multivariate trajectories are crucial to understanding the behavioral patterns of pedestrian motion. Most of the existing algorithms use offline learning approaches to learn such motion behaviors. However, these approaches cannot take advantage of the streams of data that are available after training has concluded, and typically are not generalizable to data that they have not seen before. To solve this problem, this paper proposes two algorithms for learning incoherent dictionaries in an offline and online manner by extending the offline augmented semi-non-negative sparse coding (ASNSC) algorithm. We do this by adding a penalty into the objective function to promote dictionary incoherence. A trajectory-modeling application is studied, where we consider the learned atoms of the dictionary as local motion primitives. We use real-world datasets to show that the dictionaries trained by the proposed algorithms have enhanced representation ability and converge quickly as compared to ASNSC. Moreover, the trained dictionaries are well conditioned. In terms of pedestrian trajectory prediction, the proposed methods are shown to be on par (and often better) with the state-of-the-art algorithms in pedestrian trajectory prediction.
A practical time series action detection algorithm

This paper proposes a time series action detection algorithm. Based on the classical SlowFast algorithm, the input image of the slow channel is determined according to the image stability index to improve the detection accuracy of the slow module. Mixed algorithm to reduce fast-channel computations by determining input video segments for fast-channel based on fast skeletal action detection. By preprocessing, it can greatly reduce the computational load of SlowFast algorithm and improve its accuracy.
Similar Impact, Different Readiness: A Comparative Study of the Impact of COVID-19 on ECTE Practice

COVID-19 lockdowns forced universities to deliver classes wholly online, resulting in various impacts on higher teacher education institutions (TEIs) that were differently prepared for such a change. However, few studies have explored the impact of the pandemic on the shift of early childhood teacher education programs to online delivery, especially from a cross-national comparative perspective. To address this gap, this study compared how early childhood teacher educators (ECTEs) in one Australian and one Chinese TEI viewed and coped with the challenges caused by online teaching during the lockdowns. A total of 14 ECTEs participated in this triangulated qualitative study: six from the Australian TEI and eight from the Chinese TEI. Thematic analysis was used to analyze the research data. The results indicated that the Australian ECTEs were better prepared for online education than their Chinese counterparts regarding proficiency and advance in using online teaching platforms, trying different kinds of teaching styles, and their online teaching skills, literacy, and competence. However, the coded data showed that the participant Australian and Chinese ECTEs shared similar views on the negative impact of the change, such as producing ineffective interaction, broken social-emotional connections, heavier workloads, and drained staff. The findings suggest that TEIs from Australia and China need to develop contextually appropriate strategies and innovative solutions to cope with the lockdown challenges.