Reviewerly/exHarmony huggingface dataset:
NEO-PGA: Nonvolatile Electro-Optically Programmable Gate Array

Large-scale, electronically reconfigurable photonic integrated circuits can enable programmable gate array with applications in optical information technology. Phase change materials with large nonvolatile change in the refractive index, can potentially transform these gates, providing one order of magnitude reduction in the device size, zero static energy, and minimal crosstalk.
Hybrid Deep Learning Model for Handwritten Bangla and English Digit Recognition

The majority of the works on character recognition are based on manual feature extraction and then transferring them into respective classifiers or using CNN solely. To overcome the limitations of manual feature extraction, CNN can be used as an automated feature extractor. On the other hand, many noteworthy machine learning algorithms are capable of beating CNN in the classification methods. So, to resolve the problem in both cases three hybrid model systems titled CNN-KNN, CNN-SVM, and CNN-Decision Tree respectively are proposed in this work to recognize handwritten Bangla and English Digit recognition. A CNN model is also proposed which solely works as a feature extractor and a classifier to see the difference in results with proposed hybrid models. BanglaLekha-Isolated dataset for handwritten Bangla digit and MNIST dataset for handwritten English digit is used to train the proposed models. The accuracy rate of the proposed CNN model is 96.4 % for Bangla and 98.5% for English digit recognition. The accuracy rate of the proposed hybrid CNN-KNN, CNN-SVM, and CNNDecision Tree models are 97.2%, 96.7% & 92.7% for Bangla, and 98.7%, 98.7%, and 96.3% for English digit recognition respectively.
Vehicle Re-Identification Based on Multi-View Fusion of Positions

Vehicle re-identification encounters two main challenges, including significant appearance variations of vehicles from different viewpoints and the mismatches between extracted features and true features on illumination, occlusion, and low resolution. To address these challenges, we propose a network based on multi-position viewpoint fusion. Firstly, the ConvNeXt network is utilized as the backbone, including response normalization modules to learn all salient local features within each viewpoint adaptively. Secondly, an embedding layer based on multi-position viewpoint information is proposed to extract more details and invariant features from the images. To accelerate the convergence speed of the model, batch normalization is utilized to constrain the features and minimize differences in optimization across various loss functions. We conduct experiments on two public datasets, VeRi-776 and VehicleID, and the results demonstrate the effectiveness of our proposed method in the vehicle re-identification task.
BAC TTS Corpus: Rich Arabic Database for Speech Synthesis

Recorded Speech Corpus (RSC) is one of the key elements of corpus-based synthesis. Sentence length and corpus size are additional factors that affect the Speech Synthesis (SS) sound quality, along with the quality and coverage of the Speech Corpus (SC) in each unit and its representations. This study suggests a method for creating SS systems based on Arabic corpora. This fact covers the investigation of the standards used to create a corpus-based SS. More than 5 million words have been gathered in an initial corpus in order to accomplish our goal. To this end, a variety of sources are gathered, including newspapers and the Shamela Arab Library. To design the sentences for the SC, the initial corpus was analyzed using phonemes and the occurrence of words to identify all high frequency phonemes and words. A 202-sentence supervised phonetically balanced SC with 6174 phonemes has been created. The findings demonstrate that the unit coverage in the corpus has an actual influence on the perceived SS quality.
Temporal Refinement Graph Convolutional Network for Skeleton-based Action Recognition

Human skeleton data, which has served in the aspect of human activity recognition, ought to be the most representative biometric characteristics due to its intuitivity and visuality. The state-of-the-art approaches mainly focus on improving modeling spatial correlations within graph topologies. However, the interframes motional representations are also of vital importance, and we argue that they are worth paying attention to and exploring. Therefore, a temporal refinement module with contrastive learning mechanism is proposed, fusing as a complementary to the conventional spatial graph convolution layer. In addition, in order to further exploiting the inter-frame variances and generalizing GCN operation to temporal dimension, a temporal-correlation matrix is introduced to effectively capture dynamic dependencies within frame-pairs, enhancing semantic feature representation. Moreover, since GCN is a typical local operator which lacks of capability to fully model the long-term relations along spatial and temporal variation, to move beyond the limitation, a spatialtemporal cascaded aggregation module is designed to enlarge the receptive filter scale. The overall recognition framework consists of three above novelties, which is capable of achieving remarkable performance by evaluating on benchmark datasets(i.e., NTU RGB+D 60, NTU RGB+D 120, PKU-MMD and Kinetics Skeleton 400). Extensive experiments demonstrate the effectiveness of the proposed framework, e.g., performing recognition accuracy rate of 90.9% and 96.8% on NTU RGB+D 60, 87.9% and 88.9% on NTU RGB+D 120.
Analysis of Students' Approach to the Study of Informatics During Pandemic Quarantine

The COVID-19 pandemic and the subsequent quarantine measures to stop its spread in particular states have significantly affected the educational process at all levels of the educational system. Almost instantly, teachers and students were forced to change their common practices in education, access to information and its sharing, including our educational institution. Although the Learning Management System (LMS) Moodle was previously actively used in the educational process of our course, in order to streamline studies in state of emergency, it was necessary to change the approach in its environment. It was required to implement and actively use multiple tools of LMS which have not been used before and to change the rules of implementation and evaluation of the students. Our course is dedicated to the informatics for the students of the economic studies. In Slovakia, still persists the general opinion that information and communication technologies (ICT) should be the domain of men, because of its technical background, although there is a gradual improvement in the view of women in the field of ICT. Given the above facts, it was interesting to investigate, how this awareness of the need for active use of ICT and its knowledge will reflect in students’ activity during the studying of the subject. Therefore, during the pandemic quarantine, the activity of our students was monitored and their opinions and feelings were compared between the genders. The paper will show the description of the implementation and adjustment of the educational process during the pandemic quarantine and the subsequent evaluation of the data obtained from different perspectives, especially focusing on the different approaches of both genders in the study.
Fake Instagram Profile Identification and Classification Using Machine Learning

Fake Instagram profiles are a growing problem, as they can be used to spread false information, deceive people, and harass and threaten others. The project aims to develop a robust and scalable system for identifying and classifying fake Instagram profiles using machine learning. This system will collect a large and diverse dataset of Instagram profiles, both real and fake. The data will be preprocessed and cleaned, and then relevant features will be extracted. Various machine learning algorithms will be evaluated to select the best model for identifying and classifying fake Instagram profiles. The trained model will be evaluated on a hold-out test set to ensure that it is able to generalize to new data. Once the model is evaluated and deemed satisfactory, it will be deployed in a production environment to identify and classify fake Instagram profiles in real time. The system is expected to have a significant impact on the fight against fake Instagram profiles. By identifying and classifying fake Instagram profiles, the system can help make the social media platform safer and more secure for everyone.. Key Words: Profile identification, Data preprocessing, Online security, User authentication, Model training, Machine learning.
Slime Mold Optimization with Relational Graph Convolutional Network for Big Data Classification on Apache Spark Environment

Lately, Big Data (BD) classification has become an active research area in different fields namely finance, healthcare, e-commerce, and so on. Feature Selection (FS) is a crucial task for text classification challenges. Text FS aims to characterize documents using the most relevant feature. This method might reduce the dataset size and maximize the efficiency of the machine learning method. Various researcher workers focus on elaborating effective FS techniques. But most of the presented techniques are assessed for smaller datasets and validated by a single machine. As textual data dimensionality becomes high, conventional FS methodologies should be parallelized and improved to manage textual big datasets. This article develops a Slime Mold Optimization based FS with Optimal Relational Graph Convolutional Network (SMOFS-ORGCN) for BD Classification in Apache Spark Environment. The presented SMOFS-ORGCN model mainly focuses on the classification of BD accurately and rapidly. To handle BD, the SMOFS-ORGCN model uses an Apache Spark environment. In the SMOFS-ORGCN model, the SMOFS technique gets executed for reducing the profanity of dimensionality and to improve classification accuracy. In this article, the RGCN technique is employed for BD classification. In addition, Grey Wolf Optimizer (GWO) technique is utilized as a hyperparameter optimizer of the RGCN technique to enhance the classification achievement. To exhibit the better achievement of the SMOFS-ORGCN technique, a far-reaching experiments were conducted. The comparison results reported enhanced outputs of the SMOFS-ORGCN technique over current models.
Multimodal Software Defect Severity Prediction Based on Sentiment Probability

Software systems generate a large number of software bugs during their life cycle, and timely detection and repair of these bugs is a key issue in improving software quality and maintaining software security. Therefore, this paper proposes a severity prediction on affective probabilistic multimodel software bugs. First, this paper uses RoBERTa as a sentiment analysis model and proposes a model training method for defective knowledge enhancement. We use Stack Overflow to construct a manually annotated sentiment probability dataset. Evaluating consistency between sentiment annotators by calculating Fleiss’ kappa values. Next, the difference in the impact of defects of different severity on users is reflected by the probability of sentiment. Using sentiment traits for the next stage of prediction. Finally, these include robust data processing of heterogeneous bug data, a complementary integrated learning framework that incorporates large linguistic and traditional tabular models, and a powerful model integration strategy based on a novel combination of multi-layer stacking and cyclic k-fold integrated bagging. Our comprehensive empirical assessment shows that SPM is more accurate and reliable than the popular defect severity prediction methods.
Fast and Accurate Detection of Hematoma Boundaries in Transcranial Ultrasound Brain Imaging Using Non-Convex Total Variation Regularization and Frequency Component Layer Separation

The emergence of under-skull transcranial ultrasound (TUS) imaging holds promise for revolutionizing quantitative ultrasound diagnostics, providing a cost-effective alternative to conventional modalities such as CT scans and MRI. Medical ultrasound (US) imaging stands as a remarkable technological advancement, showcasing merits including portability, affordability, safety, fast imaging capabilities, and consistent diagnostic quality [1], [2], [5], [6]. TRUBI is a 3D transcranial ultrasound brain imaging system developed by Tessonics <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> [7], [9], [10], [12]. It aims to overcome the limitations of traditional transcranial imaging, specifically the distortions caused by the human skull. Beyond the hardware and interface software aspects, the design and analysis of robust and consistent signal and image processing units for the system present a notable challenge. Within TRUBI, one of its processing units deals with the exploration of hematoma boundary detection, enhancing the segmentation execution. While the precision of the extracted boundaries remains pivotal, the system’s ability to perform in near real-time demands due attention. In this context, we propose a new method to efficiently extract desired features with the aim of detecting hematoma boundaries during transcranial ultrasound brain imaging. Recent studies have demonstrated that non-convex total variation penalty can be defined in terms of the generalized Moreau envelope of a convex function, rendering the total cost function to be minimized in a convex form. Although the primary purpose of total variation is to estimate piecewise constant signals affected by noise, we employ this concept for the separation of signal components. The approach significantly mitigates the limitations observed in prior methods, enhancing accuracy while addressing computational complexity.
Dual-biprism-based single-camera high-speed 3D-digital image correlation for deformation measurement on sandwich structures under low velocity impact

Abstract Herein, a single-camera stereo digital image correlation (DIC) method is developed for dynamic deformation measurement, and a dual-biprism high-speed stereo camera system is proposed to capture surface images. Then, full-field displacement and strain distributions are obtained after correlation and differential calculation using the stereo-DIC algorithm. After a typical experiment is performed to verify the accuracy of the proposed system, full-field displacement and strain distributions of sandwich panels subjected to low-velocity impacts are determined. These results show that the proposed system is an efficient and convenient tool for dynamic deformation measurement.
Speech Signal Imaging and Emotion Recognition Based on Symmetric-Diagonal Matrix Model

Speech emotion recognition has a huge contribution in human-computer interaction. Feature extraction is one of the focuses of speech emotion recognition. Influenced by the success of computer vision, this research proposes a new feature extraction method. This method uses Gram's Angles Field (GAF), converts the time series to images. Based on the property of symmetric matrix, extracted the feature values with the imaging data. Four algorithms are used to pre-processing the speech signal. The result displays, compared with VGG and ResNet network models, the new method has better classification effect. It illustrates that the method has great potential for identify speech emotion in the future.
Analysis of Data Balancing Techniques in Fraudulent Transactions Datasets

In the preprocessing stage that prepares data to be used in training a machine learning model, the available datasets are usually highly unbalanced. This paper analyzes and compares three methods for solving the issue of imbalanced datasets, using data from electronic payment transactions, where fraudulent operations represent the minority class. The Python implementation of the methods proves that the SMOTE with Tomek Links technique provides the best results, based on analyzing F1-scores, Precision-Recall and ROC curves.
Towards Automated Classification of Code Review Feedback to Support Analytics

Background: As improving code review (CR) effectiveness is a priority for many software development organizations, projects have deployed CR analytics platforms to identify potential improvement areas. The number of issues identified, which is a crucial metric to measure CR effectiveness, can be misleading if all issues are placed in the same bin. Therefore, a finer-grained classification of issues identified during CRs can provide actionable insights to improve CR effectiveness. Although a recent work by Fregnan et al. proposed automated models to classify CR-induced changes, we have noticed two potential improvement areas – i) classifying comments that do not induce changes and ii) using deep neural networks (DNN) in conjunction with code context to improve performances. Aims: This study aims to develop an automated CR comment classifier that leverages DNN models to achieve a more reliable performance than Fregnan et al. Method: Using a manually labeled dataset of 1,828 CR comments, we trained and evaluated supervised learning-based DNN models leveraging code context, comment text, and a set of code metrics to classify CR comments into one of the five high-level categories proposed by Turzo and Bosu. Results: Based on our 10-fold cross-validation-based evaluations of multiple combinations of tokenization approaches, we found a model using CodeBERT achieving the best accuracy of 59.3%. Our approach outperforms Fregnan et al.'s approach by achieving 18.7% higher accuracy. Conclusion: In addition to facilitating improved CR analytics, our proposed model can be useful for developers in prioritizing code review feedback and selecting reviewers.
Answer features extraction from StackOverflow - An Analysis of StackExchange Questions and Answers

Question &amp; Answering (QA) sites (e.g., StakeExchange (SE) and StackOverflow (SO)) provide a platform where users can ask questions on specialized topics and get feedback provided by users who can have knowledge on the subject. With more than 2.9 billion answers submitted in 2017 is a challenge to get the best answer. In this paper we present preliminary findings based on an analysis of data from a Q&amp;A site, StackOverflow StackExchange, concentrating on discovering characteristics present in the answers that have the Python tag. Identify characteristics of the preferred answers can give us a way to classify which answer has a better probability to be chosen as an accepted answer. We used a quantitative approach to analyze the data and discover if some characteristics are true. We analyzed 5 features and their representativeness, such as scope awareness, explained codes, presence of code, length and time after the question been made. Our findings shows what features are relevant in the answers from the extracted patterns. The exploratory study used in this paper could improve the understanding about characteristics of preferred answers.
Structured Fourier contour embedding for arbitrary-shaped slender text detection

Arbitrary-shaped text detection is a challenging task where a text instance representation should be well designed to cover diverse text geometry variances. Most of current methods represent text instances as image mask or contour point sequences, but they might lead to expensive post-processing or restricted capability of shape modeling. Most recently, Zhu et al. proposed Fourier Contour Embedding (FCE) method to represent arbitrary shaped text contours in the Fourier domain, and constructed FCENet to obtain state-of-the-art (SOTA) performance. However, even the FCE signature and FCENet may suffer significant detection performance degradation on slender-shaped text instances like most other text detectors because of the mismatch between the square-shaped receptive fields and the large aspect ratio of arbitrary-shaped slender text}. To tackle this problem, we designed Structured Fourier Contour Embedding (Structured-FCE) to encode both instance-level features and local geometric features of the boundaries. Then, we constructed Structured-FCENet to predict and merge Structured-FCE signatures to construct the boundaries of arbitrary-shaped slender texts. Qualitative and quantitative results illustrate that Structured-FCENet obtain SOTA performance on CTW1500, Total-Text and DAST1500 benchmark datasets, especially for the challenging slender text instances of various shape.
Multiple object tracking using space-time adaptive correlation tracking

&lt;span&gt;In application of tracking and detecting the suspicious activities, multiple object tracking (MOT) has been given fine attention due to its application as it provides the parallel task of identification and tracking of human. MOT ensures the identification and trajectory for each object frame as they interact, despite the changes in its appearance, occlusion and various other tasks involved. Recent adoption of deep learning has given a new perspective but still achieving high metrics remains a major issue to overcome such issues, this research work presents the integrated architecture of deep convolutional covariance networks (DCCNs) and space-time adaptive correlation tracking (STACT) algorithm with similarity map function (SMF). Moreover, in proposed work, DCCNs is utilized for feature extractions through each frame capturing the distinctive information, STACT is tracking approaches that utilizes the SMF for locating and tracking objects. SMFs are updated for any changes in human appearances and motion, also it deals with occlusion. Here the proposed model is evaluated on MOT17 and MOT20 dataset. Performance analysis is carried out through comparing the existing model and Integrated-DCCN achieves higher metrics.&lt;/span&gt;
Implementation of Digital Image Analysis in Assessment of Ki67 Index in Breast Cancer

The clinical utility of the proliferation marker Ki67 in breast cancer treatment and prognosis is an active area of research. Studies have suggested that differences in pre-analytic and analytic factors contribute to low analytical validity of the assay, with scoring methods accounting for a large proportion of this variability. Use of standard scoring methods is limited, in part due to the time intensive nature of such reporting protocols. Therefore, use of digital image analysis tools may help to both standardize reporting and improve workflow. In this study, digital image analysis was utilized to quantify Ki67 indices in 280 breast biopsy and resection specimens during routine clinical practice. The supervised Ki67 indices were then assessed for agreement with a manual count of 500 tumor cells. Agreement was excellent, with an intraclass correlation coefficient of 0.96 for the pathologist-supervised analysis. This study illustrates an example of a rapid, accurate workflow for implementation of digital image analysis in Ki67 scoring in breast cancer.
Perancangan Sistem Informasi Manajemen Penerimaan Pesanan di PT Aulia Jaya Corp Berbasis Java

Abstract— The purpose of this research is to design an Order Acceptance Management Information System, Case Study: PT Aulia Jaya Corp. In addition, the authors hope that users can understand the purpose of this system. The research method used is to use interviews and field observations. After that the authors designed, analyzed, and drew the conclusion that previously the Order Acceptance Management Information System at PT Aulia Jaya Corp was not always developing, so that by making this application program it could be used to facilitate work such as data input, orders and delivery, data search, and making reports to be more accurate.&#x0D; Keywords— Design, Information Systems, Stock, Orders, Delivery