Reviewerly/exHarmony huggingface dataset:
DII-GCN: Dropedge Based Deep Graph Convolutional Networks

Graph neural networks (GNNs) have gradually become an important research branch in graph learning since 2005, and the most active one is unquestionably graph convolutional neural networks (GCNs). Although convolutional neural networks have successfully learned for images, voices, and texts, over-smoothing remains a significant obstacle for non-grid graphs. In particular, because of the over-smoothing problem, most existing GCNs are only effective below four layers. This work proposes a novel GCN named DII-GCN that originally integrates Dropedge, Initial residual, and Identity mapping methods into traditional GCNs for mitigating over-smoothing. In the first step of the DII-GCN, the Dropedge increases the diversity of learning sample data and slows down the network’s learning speed to improve learning accuracy and reduce over-fitting. The initial residual is embedded into the convolutional learning units under the identity mapping in the second step, which extends the learning path and thus weakens the over-smoothing issue in the learning process. The experimental results show that the proposed DII-GCN achieves the purpose of constructing deep GCNs and obtains better accuracy than existing shallow networks. DII-GCN model has the highest 84.6% accuracy at 128 layers of the Cora dataset, highest 72.5% accuracy at 32 layers of the Citeseer dataset, highest 79.7% accuracy at 32 layers of the Pubmed dataset.
Solving Data De-Duplication Issues on Cloud using Hashing and MD5 Techniques

De-duplication is a method that removes duplicates of data to save storage space; it is commonly used in cloud storage. Cloud computing is considered as the basis of this research study. De-duplication addresses the issue of removing many duplicate files on the cloud. To maintain data security, it was recommended that data be encrypted and decrypted such that it could not be viewed by anyone. The existing systems lack accuracy and security. The proposed approach varies from traditional de-duplication systems, where each client has their own rights to the data that they have supplied. In the proposed hybrid cloud architecture, the S-CSP is hosted in the public cloud. According to a security study, the proposed research plan is reliable in terms of data protection.
A Federated Learning based Approach for Heart Disease Prediction

Cardiovascular diseases are the number one cause of death globally. Seventeen million deaths are recorded annually due to cardiovascular diseases. It is also estimated that four out of five cardiovascular deaths are due to heart failure. The existing machine learning methods to detect heart failure require data to be present in a centralized location. Due to data security and privacy, hospitals cannot share their patient&#x2019;s data. Hence it is very difficult to store all data centrally. In this work, Federated learning is leveraged to train a shared model. Unlike the traditional machine learning approaches, the details regarding the patients are distributed in local databases. A shared model is computed by aggregating local updates from distributed clients. Logistic Regression and Support Vector Machine (SVM) algorithms are applied to train the model in client devices and only the updates are sent back to the centralized server. Experimental results have shown an accuracy of around 89% on the UCI benchmark dataset. The results of this experiment prove that federated learning could be applied where data privacy is a major concern. Federated Learning could be applied to process electronic health records (EHR) to build a shared model while keeping the data distributed across many geographic locations.
Survey of Security Issues in Cloud

Cloud computing’s exponential development is drastically affecting how people use current network services. Cloud-based service offerings and enabling flexible adoptions of on-demand requirements have a variety of applications. While there are several advantages of using plenty of valuable cloud technology services, it also has a range of security risks and challenges. Security issues are challenges for the researchers that need to be adequately addressed to prevent security breaches and attacks that are threats for service providers and service users. This paper comprises some generally accepted security threats and potential countermeasures for cloud computing environments.
The Relationship between Professional Environmental Factors and Teacher Professional Development in Israeli Schools

(1) Background: This paper examines the elements essential to effective teacher professional development (TPD) and the relationship between various professional environmental factors (professional learning community perceptions, self-efficacy, professional identity, principal’s transformation leadership patterns), TDP in primary education schools, and TDP prediction. (2) Methods: Data were collected during the 2020 school year from 412 teachers in primary education schools in five Israeli districts. The professional learning community, self-efficacy, professional identity, transformation leadership patterns, and TPD scales were used to collect data. Descriptive statistics, Pearson moment-product correlation, and multiple regression analysis were used for data analysis. (3) Results: Overall, participants’ TPD was high (4.12 ± 0.83). Statistically significant correlations were observed between TPD and four independent variables (0.41–0.64; p &lt; 0.0001). No significant associations were seen between TPD and work-related characteristics. TPD differences were observed in participants with different educational levels (F = 4.63; p = 0.003). Higher TPD levels were predicted by perceptions of the principal’s transformation leadership patterns, self-efficacy, the professional learning community, and education (F-ratio = 57.85; adjusted R2 = 0.50; p &lt; 0.001). Conclusions: The present study attests to the importance of the school principal’s leadership patterns for TPD, alongside the contribution of self-efficacy, professional learning community, and professional identity.
Implementing Multiclass Classification to find the Optimal Machine Learning Model for Forecasting Malicious URLs

Web attacks such as spamming, phishing, and malware are common on the Internet. When an unsuspecting user hits the URL, the user becomes a victim of the assaults, which have significant consequences for commercial, finance, and social networking sites. Lexical features, host-based features, content-based features, DNS features, popularity features, and other discriminative features are used to generate a decent feature representation of the URL. URL dataset is collected from ISCX-URL. The goal of this research is to create a multi-class classification model that can categorise URLs as a possible threat to system security by combining several criteria to get the optimal Machine Learning Model.
A Method for Unsupervised Semi-Quantification of Inmunohistochemical Staining with Beta Divergences

In many research laboratories, it is essential to determine the relative expression levels of some proteins of interest in tissue samples. The semi-quantitative scoring of a set of images consists of establishing a scale of scores ranging from zero or one to a maximum number set by the researcher and assigning a score to each image that should represent some predefined characteristic of the IHC staining, such as its intensity. However, manual scoring depends on the judgment of an observer and therefore exposes the assessment to a certain level of bias. In this work, we present a fully automatic and unsupervised method for comparative biomarker quantification in histopathological brightfield images. The method relies on a color separation method that discriminates between two chromogens expressed as brown and blue colors robustly, independent of color variation or biomarker expression level. For this purpose, we have adopted a two-stage stain separation approach in the optical density space. First, a preliminary separation is performed using a deconvolution method in which the color vectors of the stains are determined after an eigendecomposition of the data. Then, we adjust the separation using the non-negative matrix factorization method with beta divergences, initializing the algorithm with the matrices resulting from the previous step. After that, a feature vector of each image based on the intensity of the two chromogens is determined. Finally, the images are annotated using a systematically initialized k-means clustering algorithm with beta divergences. The method clearly defines the initial boundaries of the categories, although some flexibility is added. Experiments for the semi-quantitative scoring of images in five categories have been carried out by comparing the results with the scores of four expert researchers yielding accuracies that range between 76.60% and 94.58%. These results show that the proposed automatic scoring system, which is definable and reproducible, produces consistent results.
Feature Compression for Rate Constrained Object Detection on the Edge

Recent advances in computer vision has led to a growth of interest in deploying visual analytics model on mobile devices. However, most mobile devices have limited computing power, which prohibits them from running large scale visual analytics neural networks. An emerging approach to solve this problem is to offload the computation of these neural networks to computing resources at an edge server. Efficient computation offloading requires optimizing the trade-off between multiple objectives including com-pressed data rate, analytics performance, and computation speed. In this work, we consider a “split computation” system to offload a part of the computation of the YOLO object detection model. We propose a learnable feature compression approach to compress the intermediate YOLO features with light-weight computation. We train the feature compression and decompression module together with the YOLO model to optimize the object detection accuracy under a rate constraint. Compared to baseline methods that apply either standard image compression or learned image compression at the mobile and perform image de-compression and YOLO at the edge, the proposed system achieves higher detection accuracy at the low to medium rate range. Furthermore, the proposed system requires sub-stantially lower computation time on the mobile device with CPU only.
Cultural Familiarity, Foreign Language Speaking Skill, and Foreign Language Anxiety: The Case of Indonesian EFL Learners

Since cultural aspects of language learning are not incorporated in some EFL materials and textbooks, this study can be important to investigate the impacts of using cultural-based instruction on developing the speaking skill of Indonesian EFL learners. In addition, this research tried to examine the effects of using cultural-based instruction on foreign language anxiety of Indonesian EFL learners. To reach these aims, the Oxford Quick Placement Test (OQPT) was given to 83 EFL learners, and 50 of them were selected for the target sample of the current investigation. The selected respondents were then randomly divided into two equal experimental and control groups. Afterward, the Foreign Language Classroom Anxiety Scale (FLCAS) questionnaire was given to both groups. After providing the stated questionnaire, both groups were pretested by a speaking test. After that, the experimental group was instructed by applying a cultural-based instruction, while the control group received a noncultural-based education. After the instruction ended, the FLCAS questionnaire and the posttest of speaking were given to the respondents to determine the effects of the cultural-based instruction on their speaking skills and foreign language anxiety. The collected data were analyzed by using independent samples and paired sample <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math> -tests. The results revealed that the experimental group had better performances than the control group on the speaking and anxiety posttests. Finally, based on the results, some conclusions and implications were suggested.
Translation of Sri Lankan Sign Language to Sinhala Text: A Leap Motion Technology-based Approach

Hearing-impaired and inarticulate people interact with each other through visual languages. In Sri Lanka, the standard visual language is Sri Lankan Sign Language (SSL). Even though SSL is mainly based on British Sign Language (BSL), there are numerous unique signs in the SSL dictionary, as visual languages are evolved on what a person sees and how well he/she can express their ideas in the form of facial expressions and body movements. In SSL, there are signs with multiple meanings and also combined signs which express a piece of particular information that does not have a dedicated sign. Due to the significance of such signs, interpretation of SSL into meaningful text has become a research challenge. At the same time, hand gestures play a major part in expressing information in visual languages. Hence, identifying hand gestures through vision-based or sensor-based approaches is very important in sign language interpretation. However, vision-based approaches are susceptible to conditions such as background color, light variations, etc., and also demand more computing power. Moreover, the sensor-based solutions are better in gesture recognition. In this paper, we, therefore, present a sensor-based combined approach using Leap Motion technology with geometric template matching and Natural Language Processing (NLP) to identify and interpret unique signs, signs with multiple meanings, and combined signs in SSL into Sinhala text. The proposed approach shows very promising results for the interpretation of both static and dynamic signs of SSL.
Integrating augmented reality technologies into architectural education: application to the course of landscape design at Port Said University

Purpose This study examines the benefits and potential applications of integrating augmented reality (AR) technology into landscape design education to create a more rewarding educational environment that provides an interesting learning atmosphere and deepens students' knowledge of the landscape design process. Design/methodology/approach Experimental method was conducted on the fourth-year architecture students at Port Said University. The experiment went through the following steps: a mobile-based AR application was designed, some exercises were developed and the students performed them using the application, and a questionnaire was formulated and distributed to the students to examine their feedback on integrating AR into the course. Findings The success of the experiment was evaluated based on the students' progress, tracked by the instructor, in the exercises and their feedback acquired from the questionnaire. The study found that the integration of AR with traditional teaching methods is perceived as being useful and having a positive impact on landscape design education. Originality/value Lessons learned from this study can help architectural educators in planning to use AR in their curricula to enrich architectural education.
Accuracy Assessment of Structured-Light Based Industrial Optical Scanner

This paper presents accuracy assessment of structured light based industrial optical scanner. Comet LED 5M scanner, as an active stereo system, is calibrated using different approaches, and the impact of the calibration results on the system accuracy is verified in experiments. Besides predefined system accuracy verification, an additional method is proposed, and the obtained results were compared. Experimental results show that the system fulfills highly rigorous requirements, even in not strictly controlled environment, thus making it very suitable for use in, not only industrial but also in high precision and high sensitive applications.
Evaluation System of Foreign Language Teaching Quality Based on Spatiotemporal Feature Fusion

How to evaluate the teaching quality of foreign language teachers objectively and quantitatively is one of the important directions of teaching evaluation institutions and teaching and research personnel. To solve this problem, this article proposes a foreign language teaching quality evaluation system based on the integration of spatiotemporal features. Given the behavior characteristics of multiperson interaction in class, a framework-based spatiotemporal modeling method is presented in this article. Spatiotemporal modeling features are input into generalized graph convolution for feature learning. The interaction information between skeletons is designed to capture the extra interaction information to increase the accuracy of action recognition. The experimental results show that the proposed method has higher accuracy and can be applied to the evaluation of foreign language teaching quality.
Proposing the RecursiVerse Overlay Application for the MetaVerse

In a still uncertain future for the Meta Verse, we dare to envision one of its possible future developments, the RecursiVerse. The RecursiVerse is an overlay application that may be built upon the Meta Verse, amounting to symmetrical virtual-real space where a human being may rely on human digital twins which may move, operate and recursively replicate to collaboratively perform multiple tasks. The RecursiVerse may hence extend what will eventually be possible thanks to the Meta Verse, providing a service to a society that poses increasing cognitive and perceptual challenges due to growing work-life imbalances and increasing cognitive loads.
Research on Internet of Things Context-Aware Information Fusion Technology for Smart Libraries

In the information age, the rapid innovation and development of information technology plays an important supporting role in the dissemination and utilization of information. As an important concentration of literature information and knowledge resources, the library also actively introduces various advanced technologies, optimizes the service model, and transforms to intelligence. This paper expounds the development status of smart library and context-aware technology at home and abroad, analyzes the application of context-aware technology in smart library, constructs a framework of smart library based on context awareness, and puts forward some suggestions for smart library’s context-aware service.
Human performance and mental workload in augmented reality: brain computer interface advantages over gestures

Augmented reality (AR) technologies have been introduced in manufacturing planning functions. Working in augmented environments, users usually select virtual objects with hand gestures that are associated with arm fatigue. In this paper, a Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI) for ‘hologram’ selection in AR is proposed. The BCI and hand gesture performances, workload, and usability have been measured for 4 different tasks. Although the results showed that BCI were on average 2.52 seconds slower than hand gestures, the BCI was more precise than hand gestures, with accuracies close to 100%. The BCI had an overall workload of 38.52, that resulted to be lower than the hand gestures’ one of 52.40. Finally, the BCI’s System Usability Scale of 77.8 overcame the hand gestures one of 11.3 points. These results highlighted the potential of coupling together BCI and AR, demonstrating a possible future application of these technologies in industrial settings.
Using External Video to Attack Behavior-Based Security Mechanisms in Virtual Reality (VR)

As virtual reality (VR) systems become prevalent in domains such as healthcare and education, sensitive data must be protected from attacks. Password-based techniques are circumvented once an attacker gains access to the user&#x0027;s credentials. Behavior-based approaches are susceptible to attacks from malicious users who mimic the actions of a genuine user or gain access to the 3D trajectories. We investigate a novel attack where a malicious user obtains a 2D video of genuine user interacting in VR. We demonstrate that an attacker can extract 2D motion trajectories from the video and match them to 3D enrollment trajectories to defeat behavior-based VR security.
Assessment of tissue toxicity risk in breast radiotherapy using Bayesian networks

The purpose of this analysis is to predict worsening post-treatment normal tissue toxicity in patients undergoing accelerated partial breast irradiation (APBI) therapy and to quantitatively identify which diagnostic, anatomical, and dosimetric features are contributing to these outcomes.A retrospective study of APBI treatments was performed using 32 features pertaining to various stages of the patient's treatment journey. These features were used to inform and construct a Bayesian network (BN) based on both statistical analysis of feature distributions and relative clinical importance. The target feature for prediction was defined as a measurable worsening of telangiectasia, subcutaneous tissue induration, or fibrosis when compared against the observed baseline. Parameter learning for the network was performed using data from the 299 patients included in the ACCEL trial and predictive performance was measured. Feature importance for the BN was quantified using a novel information-theoretic approach.Cross-validated performance of the BN for predicting toxicity was consistently higher when compared against conventional machine learning (ML) techniques. The measured BN receiver operating characteristic area under the curve was 0.960 ±$\,{\pm}\,$ 0.013 against the best ML result of 0.942 ±$\,{\pm}\,$ 0.021 using five-fold cross-validation with separate test data across 100 trials. The volume of the clinical target volume, gross target volume, and baseline toxicity measurements were found to have the highest feature importance and mutual dependence with normal tissue toxicity in the network, representing the strongest contribution to patient outcomes.The BN outperformed conventional ML techniques in predicting tissue toxicity outcomes and provided deeper insight into which features are contributing to these outcomes.
Integration of CQCC and MFCC based Features for Replay Attack Detection

This paper evaluates the performance of integration of CQCC and MFCC based features for automatic speaker verification (ASV) system. The detection of replay attack is challenging. For the detection of spoofing attacks, it is important to focus on front-end processing i.e., feature extraction. This paper discusses feature extraction techniques, LPC, MFCC and CQCC. The performance of baseline <tex>$\text{CQCC}+\text{GMM},\ \text{LPC}+\text{GMM}$</tex>, and <tex>$\text{MFCC}+\text{GMM}$</tex> is evaluated on ASVspoof 2017 version 2 dataset. Further, integration of CQCC and MFCC showed improved performance resulting in EER 10.18&#x0025;.