Reviewerly/exHarmony huggingface dataset:
Vague-Segment Technique: Automatic Computation of Tumor Stroma Ratio for Breast Cancer on Whole Slides

The calculation of Tumor Stroma Ratio (TSR) is a challenging medical issue that could improve predictions of neoadjuvant chemotherapy benefits and patient prognoses. Although several studies on breast cancer and deep learning methods have achieved promising results, the drawbacks that pixel-level semantic segmentation processes could not extract core tumor regions containing both tumor pixels and stroma pixels make it difficult to accurately calculate TSR. In this paper, we propose a Vague-Segment Technique (VST) consisting of a designed SwinV2UNet module and a modified Suzuki algorithm. Specifically, the SwinV2UNet identifies tumor pixels and generate pixel-level classification results, based on which the modified Suzuki algorithm extracts the contour of core tumor regions in terms of cosine angle. Through this way, VST obtains vaguely segmentation results of core tumor regions containing both tumor pixels and stroma pixels, where the TSR could be calculated by the formula of Intersection over Union (IOU). For the training and evaluation, we utilize the well-known The Cancer Genome Atlas (TCGA) database to create an annotated dataset, while 150 images with TSR annotations from real cases are also collected. The experimental results illustrate that the proposed VST could generate better tumor identification results compared with state-of-the-art methods, where the extracted core tumor regions lead to more consistencies of calculated TSR with senior experts compared to junior pathologists. The experimental results demonstrate the superiority of our proposed pipeline, which has promise for future clinical application.
Visualization of students’ cognitive knowledge in digital concept mapping

&lt;span lang="EN-US"&gt;Meaningful learning through the creation a digital concept map is the easiest way to gain cognitive knowledge, new information, and possibility how to expand among learners. Only a few empirical studies have been conducted in the field of nursing regarding learning through concept mapping to achieve higher levels of knowledge and digital skills. The study evaluated the student’s ability to visualize cognitive knowledge by creating a digital concept map. The objectives were to identify the perception using digital technologies and conceptual mapping, differences and relationships between gender, age, years of study, and branches. We used the ContextMinds program map creation. There were 92 Slovak students of nursing, public health and physiotherapy participated in the study. Data were collected and analyzed using IBM SPSS version 25.0. Perception of learning using digital concept mapping strategy was assessed using descriptive statistics, where paired t-test, analysis of variance (ANOVA) and Chi-square were used. The study found a positive perception of creating the digital concept map, but a low level of digital skills. First-year students took one or more weeks to submit the final version of the concept map in comparison to third-year students who took one or more hours. For second-year students, a creation of a concept map was less problematic than for first-year students. Students prefer standard teaching and learning methods. Healthcare absolvents are required to be proficient in digital literacy. Applying cognitive knowledge to create a digital concept map is a challenging strategy procedure.&lt;/span&gt;
Investigating pre-service teachers’ perceptions on readability tools: a case study in Indonesia

&lt;span lang="EN-US"&gt;The readability tools allow us to measure the readability of a text. The previous studies have elaborated the use of readability tools to analyze reading materials, but it is relatively sparse regarding the teaching of readability tools especially involving the second language teaching education (SLTE) program as a context. Therefore, this study aimed to explore the acceptance of pre-service English as a foreign language (EFL) teacher upon readability text tools. It covers their acceptance towards the tools, the extent to how the pre-service teachers accept the Web-FX and Readability Analyzer as readability tools and seeks for participants’ concerns using the tools. The study was conducted at one English Language Education Study Program at in East Java, Indonesia, involving 33 third-year undergraduate students taking pre-teaching program. Having incorporated a qualitative data analysis approach on the students’ text analysis reports and interviews, the study revealed that the participants have positive acceptance of the tools for several reasons. Some final concerns from pre-service teachers after readability tools teachings are presented, including suggestions for future research related to readability tools.&lt;/span&gt;
TransCODNet: Underwater Transparently Camouflaged Object Detection via RGB and Event Frames Collaboration

Underwater transparently camouflaged organisms can be perfectly “invisible” in the ocean to avoid the capture of predators. Due to the blurry contour boundaries of their bodies, obtaining their boundary features and determining their specific positions are challenging for detection tasks. To address this issue, first, we propose a large-scale underwater transparently camouflaged object dataset, termed Aqua-Eye, which is obtained from event data and contains five types of underwater transparent organisms, with a total of 6,497 annotated images. Second, to evaluate the effectiveness of this dataset, we propose a simple and effective detection network termed underwater Transparently Camouflaged Object Detection Network (TransCODNet), which can obtain local features and specific locations of targets, providing a better detection method for underwater transparently camouflaged organisms. In this letter, we performed ablation study and nine representative deep learning algorithms were evaluated based on the dataset. Finally, experiments show that the detection accuracy of this algorithm is 84.7%, which is superior to mainstream object detection algorithms, proving the effectiveness of the proposed method. The dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lunaWU628/Aqua-Eye-Dataset.</uri>
Philosophy design of single-trait based multi-feature biometric system

This paper presents new techniques for designing a simple and reliable multifeatured biometric system based on a single trait source. First, a one-to-one relationship between the feature’s edge and its associated angle is utilized after extracting the contrast feature using the gray-level co-occurring matrix (GLCM) method. Secondly, the classifying stage is modified to process one-dimensional vectors rather than the whole feature’s template. That means whatever the template size is, the matching operation is always processing a one-dimensional vector called a mean-feature vector which requires low storage and less computation complexity. Finally, for comparison purposes, the performances of the three biometric systems are calculated for recognizing 170 subjects taken from four facial databases. These comparisons are made using three error distance measurements. The recognition rates of the angle-based feature were very competitive to the regular edge-based results; however, the overall recognition accuracy is highly improved after fusing the decision of the two unibiometric systems using the Logic-OR operator. The fused system performance was satisfactory and it shows that the decision fusion of the single source trait based multifeatured system has promising performance represented by accuracy improvement, low storage, and low matching time.
Real-time vehicle counting using custom YOLOv8n and DeepSORT for resource-limited edge devices

Recently, there has been a significant increase in the use of deep learning and low-computing edge devices for analysis of video-based systems, particularly in the field of intelligent transportation systems (ITS). One promising application of computer vision techniques in ITS is in the development of low-computing and accurate vehicle counting systems that can be used to eliminate dependence on external cloud computing resources. This paper proposes a compact, reliable and real-time vehicle counting solution which can be deployed on low-computational requirement edge computing devices. The system makes use of a custom-built vehicle detection algorithm based on the you only look once version 8 nano (YOLOv8n), combined with a deep association metric (DeepSORT) object tracking algorithm and an efficient vehicle counting method for accurate counting of vehicles in highway scenes. The system is trained to detect, track and count four distinct vehicle classeses, namely: car, motorcycle, bus, and truck. The proposed system was able to achieve an average vehicle detection mean average precision (mAP) score of 97.5%, a vehicle counting accuracy score of 96.8% and an average speed of 19.4 frames per second (FPS), all while being deployed on a compact Nvidia Jetson Nano edge-computing device. The proposed system outperforms other previously proposed tools in terms of both accuracy and speed.
Improvement of Moroccan Dialect Sentiment Analysis Using Arabic BERT-Based Models

This study addresses the crucial task of sentiment analysis in natural language processing, with a particular focus on Arabic, especially dialectal Arabic, which has been relatively understudied due to inherent challenges. Our approach centers on sentiment analysis in Moroccan Arabic, leveraging BERT models that are pre-trained in the Arabic language, namely AraBERT, QARIB, ALBERT, AraELECTRA, and CAMeLBERT. These models are integrated alongside deep learning and machine learning algorithms, including SVM and CNN, with additional fine-tuning of the pre-trained model. Furthermore, we examine the impact of data imbalance by evaluating the models on three distinct datasets: An unbalanced set, a balanced set obtained through under-sampling, and a balanced set created by combining the initial dataset with another unbalanced one. Notably, our proposed approach demonstrates impressive accuracy, achieving a notable 96% when employing the QARIB model even on imbalanced data. The novelty of this research lies in the integration of pre-trained Arabic BERT models for Moroccan sentiment analysis, as well as the exploration of their combined use with CNN and SVM algorithms. Furthermore, our findings reveal that employing BERT-based models yields superior results compared to their application in conjunction with CNN or SVM, marking a significant advancement in sentiment analysis for Moroccan Arabic. Our method&#039;s effectiveness is highlighted through a comparative analysis with state-of-the-art approaches, providing valuable insights that contribute to the advancement of sentiment analysis in Arabic dialects.
Machine Learning Oceanographic Data for Prediction of the Potential of Marine Resources

Marine data and information are very important for human survival, therefore this data and information is attractive to investors because of the potential economic value. This data and information has been difficult to obtain, the solution to overcome this is by analyzing oceanographic data for 2009-2019 collected from the marine database belonging to the Agency for the Study and Application of Technology (BPPT). The data is the result of a collaborative marine survey between Indonesian and foreign researchers from various countries who sailed in various Indonesian waters. Raw oceanographic data is converted and classified into Conductivity, Temperature, and Depth (CTD) data as oceanographic data parameters identified as predictor variables (X) that are correlated with each other. CTD data is processed into numeric data attributes that have been labeled for input and training. The data was modeled using the Machine Learning (ML) type Supervised Learning (SL) method with the Decision Tree (DT), Linear Regression (LR) and Random Forest (RF) algorithms which were interpreted according to the characteristics of the CTD data. ML will learn data models to understand and store. Next, the model is evaluated using accuracy metrics by measuring the difference between the predicted value and the actual value to obtain a good prediction model. The prediction results show a salinity level of 34.0 parts per thousand (ppt), meaning that in this area of marine waters salinity will affect the solubility of Oxygen (O2) and play a major role in the sustainability and growth of the fertility level of biological resources which is supported by sea surface temperature conditions 29.2&deg;C. So the salinity values obtained using ML techniques and marine resource potential can be assumed to have a strong correlation. The research results show that the RF model has the lowest level of prediction error based on the values: Mean Square Error (MSE) = 0.007; Root Mean Squared Error (RMSE) = 0.082; Mean Absolute Error (MAE) = 0.007 compared to DT model: MSE = 0.008; RMSE = 0.088; MAE = 0.012 and LR model: MSE = 1.008; RMSE = 1.004; MAE = 0.281. The equivalent RF and DT models have a Determination Coefficient (R2) = 0.999, meaning that a model is created that is good at predicting, compared to the LR model with a value of R2 = 0.914. The correlation between variables shows that the LR model is very linear with a Correlation Coefficient (r) = 1.000 compared to the DT model (r) = 0.621 and the RF model (r) = 0.379. Therefore the algorithm that has a value of (r) +1 has the best level of accuracy. The use of ML to predict marine resource potential is a relatively new research field, so this research has the potential to contribute data and information as a reference for innovative studies and investment decision material for investors.
Forward Composition Propagation for Explainable Neural Reasoning

This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predictions of feed-forward neural networks operating on structured classification problems. In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron. Composition vectors are initialized using a given input instance and subsequently propagated through the whole network until reaching the output layer. The sign of each composition value indicates whether the corresponding feature excites or inhibits the neuron, while the absolute value quantifies its impact. The FCP algorithm is executed on a post-hoc basis, i.e., once the learning process is completed. Aiming to illustrate the FCP algorithm, this paper develops a case study concerning bias detection in a fairness problem in which the ground truth is known. The simulation results show that the composition values closely align with the expected behavior of protected features. The source code and supplementary material for this paper are available at https://github.com/igraugar/fcp.
Enhancing data publishing privacy: split-and-mould, an algorithm for equivalent specification

&lt;div align="center"&gt;&lt;span&gt;Data sharing and publication have been popular in recent years due to the abundance of options. Evaluating and extracting data from sizable valuable databases i.e., data mining has various challenges which include issues with security, privacy, and data integrity. Anonymized data is used in the majority of privacy preserving data publication approaches, depending on a few utilitarian measures. However, applications that have particular needs for the data they utilize might not be able to use the anonymized data. Practical data anonymization must work to accomplish two opposing objectives: to maintain the data’s usefulness and to satisfy a specific privacy need. &lt;br /&gt; The utility loss when data is anonymized is frequently measured using generic utility metrics, such as the specific values generalized in a specific ontology. As a need for an application, we suggest equivalent specification, a technique that enables a data user to characterize some properties of the anonymized data. We also introduce the “split-and-mould” algorithm, a heuristic anonymization algorithm that applies a generalization method to the user-provided parameters. Our preliminary results indicate that the specification format and procedure can improve significantly the utility of the anonymized data for data mining that develop predictive models, like decision trees (DTs) and Naïve Bayes.&lt;/span&gt;&lt;/div&gt;
Cybersecurity integration in distance learning: an analysis of student awareness and attitudes

&lt;div align="center"&gt;&lt;span&gt;With the rapid growth of distance learning, especially since the COVID-19 pandemic, cybersecurity has become increasingly essential to protect students, instructors, and institutions from cyber threats. This paper examines the role of cybersecurity in enhancing students’ security awareness during distance learning. A literature review covers critical cyber threats in distance learning and strategies to mitigate risks through cybersecurity tools, policies, training, and promoting a culture of cybersecurity. Primary research was conducted by surveying 531 university students engaged in distance learning to assess their cybersecurity awareness, attitudes, and behaviors. Results indicate relatively low awareness and adoption of secure practices. Recommendations include implementing multi-layered cybersecurity defenses, student security awareness training, and nurturing a “human firewall” through a cyber-aware campus culture. Cyber risks can be reduced through proactive partnerships between students, faculty, information technology (IT) staff, and administrators to secure distance learning environments.&lt;/span&gt;&lt;/div&gt;
The effect of technological context on smart home adoption in Jordan

&lt;p&gt;&lt;span lang="EN-MY"&gt;This research examines the use of internet of things (IoT) smart home technologies in Jordan, using the technologies-organization-environment (TOE) framework and social exchange theory (SET). This study investigates the influence of security and privacy considerations on the behavioral intention to embrace IoT smart home solutions. The research examines the function of trust (TR) in service providers as a mediator and investigates how information technology (IT) knowledge acts as a moderator between technological elements and behavioral intention. Analyzed using smart partial least squares (PLS), data from 315 responses of executives in both listed and non-listed businesses were examined. The results highlight the need of giving priority to technological factors and building TR in service providers to ensure the effective implementation of IoT smart home technology in Jordan.&lt;/span&gt;&lt;/p&gt;
Class imbalance aware drift identification model for detecting diverse attack in streaming environment

&lt;div align="center"&gt;&lt;span&gt;Detecting fraudulent transactions in a streaming environment presents several challenges including the large volume of data, the need for real-time detection, and the potential for data drift. To address these challenges a robust model is needed that utilizes machine learning techniques to classify transactions in real-time. Hence, this paper proposes a model for detecting fraudulent transactions in a streaming environment using xtream gradient boost (XGBoost), cross-validation and class imbalance aware drift identification (CIADI) model. The performance of the proposed method is evaluated using datasets named credit card and Network Security Laboratory (NSL-KDD) dataset. The results demonstrate that the model can effectively detect fraudulent transactions with high accuracy, recall, and F-measure. The results show that the proposed CIADI model attained 95.63% for the credit card dataset which is higher accuracy in comparison to the generative-adversarial networks (GAN), network-anomaly-detection scheme-based on feature-representation and data-augmentation (NADS-RA) and feature-aware XGBoost (FA-XGB). Further the proposed CIADI model attained 98.5% for the NSL-KDD dataset which is higher accuracy in comparison to the NADS-RA, stacked-nonsymmetric deep-autoencoder (sNDAE) and convolutional neural-network (CNN). This study suggests that the proposed method can be an effective model for detecting fraudulent transactions in streaming environments.&lt;/span&gt;&lt;/div&gt;
Method comparison of blood film differential performed by digital image and traditional microscope morphology using RCPAQAP datasets

Introduction: Historically, the RCPAQAP has issued a glass slide case for the Blood Film Differential Program. However, in 2022, the program was moved to a fully virtual EQA program due to increased difficulty in acquiring donor cases. To ensure statistical acceptability and facilitate the transition to a digital program for manual blood film differential, three historical glass slide cases were re-issued as digital images. A study was conducted to determine differences between the manual differential on glass slides and the re-issued digital images.
Joint remote state preparation protocol for non-maximally entangled W-states and fidelity analysis in noisy environments

In this paper, we discuss a protocol for preparing a non-maximally entangled W-state at a distant location where there are two parties intending such creation each of whom possesses only partial information about the state. These two parties along with a third party Controller, participate in a single protocol in order to create the intended state in the hands of a fourth party. The role of the controller is to fabricate an 8-qubit entangled state and distribute the respective qubits involved in the entangled state to the relevant parties and thereby creating a noisy quantum channel. The protocol is first ideally discussed in the noise-free case which is then a perfect joint remote state preparation protocol with fidelity 1. Then, we discuss the performance of the protocol in the presence of noise in which the fidelity is diminished. We consider three different types of noises and analyze the variation of fidelity of the process.
An extreme random tree model combining EDA and PCA for predicting PV module temperature method

The variation in temperature of PV modules can affect the efficiency of power generation. Therefore, many studies have been conducted to predict the temperature of PV modules. In this paper, the extreme random tree algorithm is utilized with EDA and PCA analysis to accurately predict the temperature of PV modules in the next hour by filtering and structuring available features. EDA and PCA analysis enhance the model's understanding of the degree of addition, subtraction, and similarity between different features. They also help filter out and construct new key features. Compared to the random forest commonly used in previous studies, the extreme random tree algorithm is more random in the choice of bifurcation in tree building, enabling it to jump out of the vicious circle of local optima and learn the data adequately. After training, the model is tested using actual operating wind turbine data for validation, and the results indicate that the method is highly accurate, noise-resistant, targeted, and practical.