Reviewerly/exHarmony huggingface dataset:
Learning graph representation with Randomized Neural Network for dynamic texture classification

Dynamic textures (DTs) are pseudo periodic data on a space × time support, that can represent many natural phenomena captured from video footages. Their modeling and recognition are useful in many applications of computer vision. This paper presents an approach for DT analysis combining a graph-based description from the Complex Network framework, and a learned representation from the Randomized Neural Network (RNN) model. First, a directed space × time graph modeling with only one parameter (radius) is used to represent both the motion and the appearance of the DT. Then, instead of using classical graph measures as features, the DT descriptor is learned using a RNN, that is trained to predict the gray level of pixels from local topological measures of the graph. The weight vector of the output layer of the RNN forms the descriptor. Several structures are experimented for the RNNs, resulting in networks with final characteristics of a single hidden layer of 4, 24, or 29 neurons, and input layers of sizes 4 or 10, meaning 6 different RNNs. Experimental results on DT recognition conducted on Dyntex++ and UCLA datasets show a high discriminatory power of our descriptor, providing an accuracy of 99.92%, 98.19%, 98.94% and 95.03% on the UCLA-50, UCLA-9, UCLA-8 and Dyntex++ databases, respectively. These results outperform various literature approaches, particularly for UCLA-50. More significantly, our method is competitive in terms of computational efficiency and descriptor size. It is therefore a good option for real-time dynamic texture segmentation, as illustrated by experiments conducted on videos acquired from a moving boat.
Dilated and soft attention‐guided convolutional neural network for breast cancer histology images classification

Breast cancer is one of the most common types of cancer in women, and histopathological imaging is considered the gold standard for its diagnosis. However, the great complexity of histopathological images and the considerable workload make this work extremely time-consuming, and the results may be affected by the subjectivity of the pathologist. Therefore, the development of an accurate, automated method for analysis of histopathological images is critical to this field. In this article, we propose a deep learning method guided by the attention mechanism for fast and effective classification of haematoxylin and eosin-stained breast biopsy images. First, this method takes advantage of DenseNet and uses the feature map's information. Second, we introduce dilated convolution to produce a larger receptive field. Finally, spatial attention and channel attention are used to guide the extraction of the most useful visual features. With the use of fivefold cross-validation, the best model obtained an accuracy of 96.47% on the BACH2018 dataset. We also evaluated our method on other datasets, and the experimental results demonstrated that our model has reliable performance. This study indicates that our histopathological image classifier with a soft attention-guided deep learning model for breast cancer shows significantly better results than the latest methods. It has great potential as an effective tool for automatic evaluation of digital histopathological microscopic images for computer-aided diagnosis.
The Use of Stemming in the Arabic Text and Its Impact on the Accuracy of Classification

The ongoing growth in the vast amount of digital documents and other data in the Arabic language available online has increased the need for classification methods that can deal with the complex nature of such data. The classification of Arabic plays a large and important role in many modern applications and interferes with other sciences, which start from search engines and do not end with the Internet of Things. However, addressing the Arab classification errors with high performance is largely insufficient to deal with the huge quantities to reveal the classification of Arab documents; while some work was tackled out on the classification of the Arabic text, most of the research has focused on English text. The methods proposed for English are not suitable for Arabic as the morphology of the two languages differs substantially. Moreover, morphologically, the preprocessing of Arabic text is a particularly challenging task. In this study, three commonly used classification algorithms, namely, the K-nearest neighbor, Naïve Bayes, and decision tree, were implemented for Arabic text in order to assess their effectiveness with and without the use of a light stemmer in the preprocessing phase. In the experiment, a dataset from Agency France Persse (AFP) Arabic Newswire 2001 consisting of four categories and 800 files was classified using the three classifiers. The result showed that the decision tree with light stemmer had the best accuracy rate for classification algorithm with 93%.
Relationship between financial innovation, financial depth, and economic growth

The intrinsic property of modern economic development is financial deepening in the light of incremental spearheading financial innovation opportunities. The paper deals with the relationship between financial depth, financial innovation, and economic growth among 22 OECD economies over 2007–2018 by applying pooled OLS and fixed effect panel data regression analysis. The purpose of the paper is to empirically test whether the economic growth depends on financial depth, financial innovation, and institutional environment (Worldwide Governance Indicators). The findings shed light on the recent discussion on the pros and cons of financial innovation. The estimation results show that while financial depth is a strong predictor of economic growth across high- and upper-middle-income economies, financial innovation is a slightly weaker predictor. Despite the identified positive impact of financial innovation on economic growth, it is asserted that the negative effect of financial depth may indicate oversaturated financial market in developed countries. Сonsistent with the general notion that the institutional framework promotes the capacity of the ﬁnancial sector for financial innovations implementation, this paper states that financial depth and financial innovations are better prerequisites of economic growth than institutional development. AcknowledgmentThe paper was funded as a part of the “Relationship between financial depth and economic growth in Ukraine” research project (No. 0121U110766), conducted in the State Institution “Institute for Economics and Forecasting of the NAS of Ukraine”.
A Visibility Graph approach for path planning and real-time collision avoidance on maritime unmanned systems

In high navigation traffic areas, path planning and collision avoidance is a crucial element of safe navigation, mostly in view of possible future applications in hybrid scenarios where both manned and Autonomous Surface Vehicles (ASV) share the environment.This paper deals with a novel procedure to generate optimal paths in presence of static and moving obstacles. The proposed path planning approach deals with an optimization problem based on the so-called Essential Visibility Graph (EVG) [1], an extension of the standard Visibility Graph (VG) [2], in order to find the minimum cost piecewise linear path between two points in a scenario with several obstacles. Such approach can be used also in presence of multiple ASV or movable obstacles, by using a re-planning procedure to update the EVG over a selected prediction time interval. To make the solution compliant with the current regulation and make ASV behaviour predictable by human pilots on manned vehicles, EVG was extended by implementing a cut procedure based on Collision Regulations (COLREGS) [3]. Finally, the use of Dubins curves provides smooth paths, compliant with physics constraints such as the minimum turn radius.A campaign of numerical simulations was carried out to test the effectiveness of the proposed technique in different operational scenarios. Results show that the algorithm is always able to identify COLREGS-compliant trajectories, in order to avoid collisions and assure minimum safety distance as well. Furthermore, the low computational burden suggests that the proposed procedure can be considered a promising approach for real-time applications.
DeepQR: Neural-Based Quality Ratings for Learnersourced Multiple-Choice Questions

Automated question quality rating (AQQR) aims to evaluate question quality through computational means, thereby addressing emerging challenges in online learnersourced question repositories. Existing methods for AQQR rely solely on explicitly-defined criteria such as readability and word count, while not fully utilising the power of state-of-the-art deep-learning techniques. We propose DeepQR, a novel neural-network model for AQQR that is trained using multiple-choice-question (MCQ) datasets collected from PeerWise, a widely-used learnersourcing platform. Along with designing DeepQR, we investigate models based on explicitly-defined features, or semantic features, or both. We also introduce a self-attention mechanism to capture semantic correlations between MCQ components, and a contrastive-learning approach to acquire question representations using quality ratings. Extensive experiments on datasets collected from eight university-level courses illustrate that DeepQR has superior performance over six comparative models.
Control of an Omnidirectional UAV for Transportation and Manipulation Tasks

This paper presents a motion control scheme for a new concept of omnidirectional aerial vehicle for transportation and manipulation tasks. The considered aerial platform is a novel quadrotor with the capability of providing multi-directional thrust by adding an actuated gimbal mechanism in charge of modifying the orientation of the frame on which the four rotors are mounted. The above mechanical design, differently from other omnidirectional unmanned aerial vehicles (UAVs) with tilted propellers, avoids internal forces and energy dissipation due to non-parallel propellers’ axes. The proposed motion controller is based on a hierarchical two-loop scheme. The external loop computes the force to be applied to the vehicle and the reference values for the additional joints, while the inner loop computes the joint torques and the moment to be applied to the multirotor. In order to make the system robust with respect to the external loads, a compensation of contact forces is introduced by exploiting the estimate provided by a momentum based observer. The stability of the motion control scheme is proven via Lyapunov arguments. Finally, two simulation case studies prove the capability of the omnidirectional UAV platform to track a 6-DoFs trajectory both in free motion and during a task involving grasping and transportation of an unknown object.
Well-Designed Teaching Examples Influence the Outcome of Technology Acceptance: The Example of Next-Generation Art Process Learning

With the upgrade of hardware and the consumer experience, the application of high-standard digital art technology to produce finished products is the current trend of digital entertainment visual development, through the application of next-generation art technology, which has become the basis for the application of the 3D digital media art industry. Based on the technology acceptance model, this study measured student acceptance of next-generation art production software by surveying students in two school districts who took a next-generation art course that used blended learning and worked examples. Intention to use was also assessed using variables such as the type of graduation project and the number of credits in the student’s major course. A total of 104 valid questionnaires were obtained from four classes in the fourth year of college. Specifically, students’ perceived usefulness, perceived ease of use, and attitudes toward using next-generation art software were determined, and correlations between students’ acceptance of next-generation art use and their willingness to use next-generation art methods were explored. The worked examples increased the acceptance and willingness of students with poor 3D technical skills to use next-generation art, but also allowed them to overestimate their technical skills.
Distributed resilient state estimation over sensor networks with random nonlinearities, fading measurements, and stochastic gain variations

Abstract The distributed resilient state estimation problem of nonlinear discrete systems in sensor networks is investigated in this article. The system model under consideration involves three phenomena of incomplete information: randomly occurring nonlinearities, fading measurements, and random gain variations. The probabilistic characteristics of the above phenomena are depicted by three sets of independent random variables subject to more general random distribution. Based on the above model, by applying Lyapunov functional approach and random distribution solution method, the asymptotic stability in the mean square sense of the estimation error system with a given attenuation level is proved. Further, the estimator parameters are solved by introducing a novel linearization method. Finally, a numerical simulation is given to illustrate the validity of the theoretical results.
PURA-SCIS Protocol: A Novel Solution for Cloud-Based Information Sharing Protection for Sectoral Organizations

Over recent years, the incidence of data breaches and cyberattacks has increased significantly. This has highlighted the need for sectoral organizations to share information about such events so that lessons can be learned to mitigate the prevalence and severity of cyber incidents against other organizations. Sectoral organizations embody a governance relationship between cross-sector public and private entities, called public-private partnerships (PPPs). However, organizations are hesitant to share such information due to a lack of trust and business-critical confidentially issues. This problem occurs because of the absence of any protocols that guarantee privacy protection and protect sensitive information. To address this issue, this paper proposes a novel protocol, Putra-Ramli Secure Cyber-incident Information Sharing (PURA-SCIS), to secure cyber incident information sharing. PURA-SCIS has been designed to offer exceptional data and privacy protection and run on the cloud services of sectoral organizations. The relationship between organizations in PURA-SCIS is symmetrical, where the entities must collectively maintain the security of classified cyber incident information. Furthermore, the organizations must be legitimate entities in the PURA-SCIS protocol. The Scyther tool was used for protocol verification in PURA-SCIS. The experimental results showed that the proposed PURA-SCIS protocol provided good security properties, including public verifiability for all entities, blockless verification, data privacy preservation, identity privacy preservation and traceability, and private information sharing. PURA-SCIS also provided a high degree of confidentiality to protect the security and integrity of cyber-incident-related information exchanged among sectoral organizations via cloud services.
Question Difficulty Estimation Based on Attention Model for Question Answering

This paper addresses a question difficulty estimation of which goal is to estimate the difficulty level of a given question in question-answering (QA) tasks. Since a question in the tasks is composed of a questionary sentence and a set of information components such as a description and candidate answers, it is important to model the relationship among the information components to estimate the difficulty level of the question. However, existing approaches to this task modeled a simple relationship such as a relationship between a questionary sentence and a description, but such simple relationships are insufficient to predict the difficulty level accurately. Therefore, this paper proposes an attention-based model to consider the complicated relationship among the information components. The proposed model first represents bi-directional relationships between a questionary sentence and each information component using a dual multi-head co-attention, since the questionary sentence is a key factor in the QA questions and it affects and is affected by information components. Then, the proposed model considers inter-information relationship over the bi-directional representations through a self-attention model. The inter-information relationship helps predict the difficulty of the questions accurately which require reasoning over multiple kinds of information components. The experimental results from three well-known and real-world QA data sets prove that the proposed model outperforms the previous state-of-the-art and pre-trained language model baselines. It is also shown that the proposed model is robust against the increase of the number of information components.
Pre-Train and Learn: Preserving Global Information for Graph Neural Networks

Graph neural networks (GNNs) have shown great power in learning on attributed graphs. However, it is still a challenge for GNNs to utilize information faraway from the source node. Moreover, general GNNs require graph attributes as input, so they cannot be appled to plain graphs. In the paper, we propose new models named G-GNNs (Global information for GNNs) to address the above limitations. First, the global structure and attribute features for each node are obtained via unsupervised pre-training, which preserve the global information associated to the node. Then, using the global features and the raw network attributes, we propose a parallel framework of GNNs to learn different aspects from these features. The proposed learning methods can be applied to both plain graphs and attributed graphs. Extensive experiments have shown that G-GNNs can outperform other state-of-the-art models on three standard evaluation graphs. Specially, our methods establish new benchmark records on Cora (84.31\%) and Pubmed (80.95\%) when learning on attributed graphs.
A Deep Learning Based Approach to Predict Lung Cancer from Histopathological Images

Cancer is the deadliest disease in the world and Lung cancer is one of them. The number of deaths from lung cancer is much higher than the number of deaths from other cancers. In most cases, lung cancer is diagnosed at the last stage. To solve this problem currently, cancer is being diagnosed with the help of computer-assisted diagnostic systems. Cancer early detection and level prediction using a deep learning-based model is one of them. In this research, a deep learning model has been proposed which can perfectly detect and predict lung cancer levels from histopathological information. The model has been trained and validated using 15,000 lung cancer histopathological image data and has got 99.80% prediction accuracy from our model
Fostering Performance in Hands-On Laboratory Work with the Use of Mobile Augmented Reality (AR) Glasses

The learning of laboratory skills is essential in science education, but students often get too little individual guidance in this area. Augmented reality (AR) technologies are a promising tool to tackle this challenge and promote students’ high-level learning and performance in science laboratories. Thus, the purpose of this study was (1) to design an AR-assisted learning environment to support individual knowledge construction, (2) to investigate students’ learning processes and learning outcomes and (3) to examine the usability of the system. Pharmacy students (n = 16) were assigned to experimental (n = 10) and control (n = 6) groups and performed the same laboratory work together with pre- and post-tests. The experimental group worked with AR glasses that provided additional support and timely guidance during the work with additional info-screens, questions related to choosing correct reagents and laboratory tools and think-aloud questions, whereas the control group worked in a traditional laboratory context. The results showed that AR was more effective in fostering performance in the science laboratory compared to traditional laboratory instruction and prevented most of the mistakes. The AR group considered the guidance and feedback provided by AR to be beneficial for their learning. However, no apparent differences were found in tasks measuring students’ understanding of the content knowledge. Thus, an AR environment embedded with supportive tools could partly replace the teacher in science teaching laboratories by providing individual and timely guidance for the students.
Accelerating Hyperparameter Tuning in Machine Learning for Alzheimer’s Disease With High Performance Computing

Driven by massive datasets that comprise biomarkers from both blood and magnetic resonance imaging (MRI), the need for advanced learning algorithms and accelerator architectures, such as GPUs and FPGAs has increased. Machine learning (ML) methods have delivered remarkable prediction for the early diagnosis of Alzheimer’s disease (AD). Although ML has improved accuracy of AD prediction, the requirement for the complexity of algorithms in ML increases, for example, hyperparameters tuning, which in turn, increases its computational complexity. Thus, accelerating high performance ML for AD is an important research challenge facing these fields. This work reports a multicore high performance support vector machine (SVM) hyperparameter tuning workflow with 100 times repeated 5-fold cross-validation for speeding up ML for AD. For demonstration and evaluation purposes, the high performance hyperparameter tuning model was applied to public MRI data for AD and included demographic factors such as age, sex and education. Results showed that computational efficiency increased by 96%, which helped to shed light on future diagnostic AD biomarker applications. The high performance hyperparameter tuning model can also be applied to other ML algorithms such as random forest, logistic regression, xgboost, etc.
Virtual Campus Tour Application through Markerless Augmented Reality Approach

Augmented Reality (AR) technology has been widely used on campus tours by universities all around the world. However, the students that stay very far away do not have a chance to visit around the campus. Also, the information that is available on the official website is static, resulting in the visitors feeling less engaged with the information. Hence, the virtual campus tour application using the markerless AR technology, namely AR-UTHM Tour is proposed to be developed on the Android mobile-based platform to visualize the buildings and facilities that are available in the university, specifically Universiti Tun Hussein Onn Malaysia (UTHM). This approach allows the users to visualize the 3D models by pointing the camera at any flat surface. Then, the feature point will be generated to generate a virtual plane. The information about the facilities was obtained from the UTHM official website and the 3D models of the buildings were referred to the floor plan and the actual images. The user acceptance test has been conducted on 30 students of UTHM using Technology Acceptance Model (TAM). The result shows that more than 50% of the respondents have successfully executed the AR session without any error. Overall results show that the users are satisfied with the AR-UTHM Tour application. In conclusion, this application is suitable to be used as a medium to introduce and promote UTHM virtually. Future improvements in terms of detailing the aesthetic of the 3D model will be taken into consideration.