Reviewerly/exHarmony huggingface dataset:
AN EVOLUTIONARY APPROACH TO THE DESIGN OF CONVOLUTIONAL NEURAL NETWORKS FOR HUMAN ACTIVITY RECOGNITION

Automated human activity recognition has a number of applications such as in elderly healthcare monitoring, fitness tracking and in various smart home systems that can adapt to the inhabitants' behavior.Deep learning using Convolutional Neural Networks (CNNs) is increasingly being used for recognition of human activities.However, the CNNs performance is highly dependent on the network architecture and usually the hyper-parameters are manually selected.Various approaches have been used to automate the design of CNN architectures.The paper proposes an evolutionary based approach for optimizing the architecture of one dimensional CNNs for human activity recognition.The suggested approach is tested on three accelerometric data sets.The experimental results show that the CNNs designed using the evolutionary based approach have comparable accuracy on the WISDM Actitracker data set and significantly better performance on the Smartphone-Based Recognition of Human Activities and Postural Transitions data set compared to other deep CNNs.
The unexplored potential of virtual reality for cultural learning

&lt;p&gt;Educational technology tools that improve learning and foster engagement are constantly sought by teachers and researchers. In the domain of Computer-Assisted Language Learning a variety of tools, for instance blogs and podcasts, have been used to promote language and cultural learning (Shih, 2015). More recently, virtual reality has been identified as a technology with great potential for the creation of meaningful and contextualized learning experiences. Despite the learning affordances of virtual reality, in language education most of the literature has focused on the low-immersive version, whereas research investigating highly immersive virtual environments has only emerged in recent years (e.g., Berti, 2019; Blyth, 2018). In other fields, the use of highly immersive virtual reality has been compared to traditional pedagogical resources and demonstrated that students’ learning improved with the use of virtual environments as compared to two-dimensional video and textbook learning conditions (Allcoat &amp;amp; von Mühlenen, 2018). Considering the potential learning benefits of this technology, this paper argues that longitudinal empirical research in language education is strongly needed to investigate its potential unexplored impact on language and cultural learning.&lt;/p&gt;
SGPNet: A Three-Dimensional Multitask Residual Framework for Segmentation and IDH Genotype Prediction of Gliomas

Glioma is the main type of malignant brain tumor in adults, and the status of isocitrate dehydrogenase (IDH) mutation highly affects the diagnosis, treatment, and prognosis of gliomas. Radiographic medical imaging provides a noninvasive platform for sampling both inter and intralesion heterogeneity of gliomas, and previous research has shown that the IDH genotype can be predicted from the fusion of multimodality radiology images. The features of medical images and IDH genotype are vital for medical treatment; however, it still lacks a multitask framework for the segmentation of the lesion areas of gliomas and the prediction of IDH genotype. In this paper, we propose a novel three-dimensional (3D) multitask deep learning model for segmentation and genotype prediction (SGPNet). The residual units are also introduced into the SGPNet that allows the output blocks to extract hierarchical features for different tasks and facilitate the information propagation. Our model reduces 26.6% classification error rates comparing with previous models on the datasets of Multimodal Brain Tumor Segmentation Challenge (BRATS) 2020 and The Cancer Genome Atlas (TCGA) gliomas’ databases. Furthermore, we first practically investigate the influence of lesion areas on the performance of IDH genotype prediction by setting different groups of learning targets. The experimental results indicate that the information of lesion areas is more important for the IDH genotype prediction. Our framework is effective and generalizable, which can serve as a highly automated tool to be applied in clinical decision making.
SAUDI ARABIAN UNDERGRADUATE STUDENTS' PERCEPTIONS OF E-LEARNING QUALITY DURING COVID19 PANDEMIC

The quality of the E-learning education in Saudi Arabia has been a major concern by many academicians, especially, and people in general as this platform has not been a priority for education Not until recently, the world has been impacted by the Covid-19 pandemic, which makes every education institution shifted to the online platform to continue the education for the students Thus, many studies on the perceptions on the online learning have been carried out, and though many are focusing on the perceptions by the education institutions' faculty and administration, there is a lack in the amount of study performed to analyse the students' perceptions of online learning during the pandemic time The current study is conducted by utilising qualitative methods in order to collect information and investigate the students' perception regarding online learning during the pandemic Covid-19, based on their individual experiences A number of fifteen (15) students were selected as respondents for the study, in which structured interviews were conducted by using a convenient sampling technique for data collection Through the discussion, all of the positive and negative perceptions of online learning, as well as the factors contributing to those perceptions were identified The results of the study found that the positive perceptions were contributed based on the flexibility, cost-effectiveness, availability of the electronic research databases, and well-designed online classroom interfaces For the negative perceptions from using online learning platforms, the respondents informed that they were contributed by the lecturer's delayed feedback, lack of technical support by lecturers, low in self-esteem and self-motivation, feel isolated, one-way of educational methods, and poorly-designed class materials Through the findings, the school's administration and lecturers would be able to know the struggles experienced by the students, and eventually come out with better solutions to improve their teaching methods
An Integrated Framework for Traceability and Impact Analysis in Requirements Verification of Cyber–Physical Systems

In the field of model-based design of Cyber–Physical Systems (CPS), seamless traceability of the process, from requirements to models to simulation results, is becoming increasingly important. It can be used to support several activities such as variant handling, impact analysis, component reuse, software maintenance and evolution, verification, and validation. Despite the fact that the relevance of traceability in the model-based design of CPSs is well known, current tools that support traceability management are inadequate in practice. The lack of comprehensive whole-lifecycle systems engineering support in a single tool is one of the main causes of such ineffective traceability management, where traceability relationships between artifacts are still manually generated and maintained. This paper aims at presenting an approach and a prototype for automatically generating and maintaining the appropriate traceability links between heterogeneous artifacts ranging from requirement models, through design models, down to simulation and verification results throughout the product life cycle in model-based design of CPSs. A use case study is presented to validate and illustrate the proposed method and prototype.
An Approach Based on Knowledge-Defined Networking for Identifying Video Streaming Flows in 5G Networks

5G aims to provide a complete wireless communication system with various applications, network services and technologies. In terms of 5G network management, Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)are expected to control and manage network resources. Network Softwarization provides better management of network traffic. However, it does not guarantee network performance will not degradation when the traffic rises. Flow identification has been raised as a solution for keeping the network performance, and it has become a hot topic in both, academy and industry. In particular, there is a high interest in identifying video streaming flows since thanks to 5G and its benefits that improve the streaming media industry, the video streaming traffic is expected to increase dramatically due to the massive connection of 5G compatible devices. Motivated by this, we presented a novel approach for identifying video streaming services. Our approach includes three modules: video stream acquisition module, video stream analyzer module, and application module. In the video stream acquisition module, we capture video streaming packets and organize them in to flow records. In the video streaming analyzer module, we analyze the flow records using supervised machine learning algorithms to find the appropriate algorithm that performs better. In the application module, we provide a brief explanation of the applications of our approach. Additionally, we provide an analysis of the overall workload generated by our approach. The results of the evaluation by module corroborate the usefulness and feasibility of our approach for identifying video streaming services.
"Do you speak Corona?": Hashtags and Neologisms since the COVID-19 Pandemic Outbreak

With the current outbreak of COVID-19, our everyday lifestyle has changes thoroughly. Not only the economy, industry and health services witnessed major alterations, but even our ways of communication. This can be found in the linguistic changes that have occurred to the English language since the pandemic started. The main phenomenon witnessed is the appearance of new terms and expressions, which are analysed and discussed in this paper. The main aim is to identify these developments, followed by their classification using Krishnamurthy's framework of Neologism (2010). The dataset consists of over 5 million tweets collected between January and June, 2020, which is analysed both qualitatively and quantitatively. The result analysis indicated that these changes functioned as a tools of coping with the major changes that happened to people's lifestyles globally. It was observed that social media in general, and the hashtags in particular, had a significant impact on its spreading.
Causal Autoregressive Flows

Two apparently unrelated fields -- normalizing flows and causality -- have recently received considerable attention in the machine learning community. In this work, we highlight an intrinsic correspondence between a simple family of autoregressive normalizing flows and identifiable causal models. We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging from causal discovery to making interventional and counterfactual predictions. First, we show that causal models derived from both affine and additive autoregressive flows with fixed orderings over variables are identifiable, i.e. the true direction of causal influence can be recovered. This provides a generalization of the additive noise model well-known in causal discovery. Second, we derive a bivariate measure of causal direction based on likelihood ratios, leveraging the fact that flow models can estimate normalized log-densities of data. Third, we demonstrate that flows naturally allow for direct evaluation of both interventional and counterfactual queries, the latter case being possible due to the invertible nature of flows. Finally, throughout a series of experiments on synthetic and real data, the proposed method is shown to outperform current approaches for causal discovery as well as making accurate interventional and counterfactual predictions.
Persistent Fault Analysis Against SM4 Implementations in Libraries Crypto++ and GMSSL

Compared to the injection of a transient fault, time synchronization and accuracy are not required for the injection process of a persistent fault. However, the known <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">persistent fault analyses</i> (PFAs) do not work on SM4 implementations because the linear transformation layer hides the position where an error occurs during the encryption process. We present the first <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">persistent fault analysis</i> against SM4 implemented with an S-box by combining the inverse linear transformation with differential techniques. In addition, we propose a locating algorithm to figure out not only where an error occurs during the encryption process but also where a fault is inserted in the lookup table. Consequently, the locating algorithm helps break SM4 implemented with a T-table. We validate our PFA on two open-source implementations of SM4 – Crypto++(v8.3) and GMSSL(v1.0.0). The experiments are performed on a PC and the analysis codes are written in C language. The experimental data shows that the probability of successfully recovering the encryption key approximates 1 when the number of normal-and-faulty-ciphertext pairs is 3000 on average. Namely, PFA can break the encryption system of SM4 in practice once valid faults are inserted. Finally, we apply the attack to protected SM4 implementations and prove that the E-and-D mode of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dual modular temporal redundancy</i> (DMTR) can defeat our PFA.
Identification of batik making method from images using convolutional neural network with limited amount of data

This study aims to apply the convolutional neural network (CNN) to classify batik based on its manufacturing method, namely Batik Tulis which are hand drawn, Batik Cap where stamps are used to create the pattern, and Batik Printing which are printed using textile printing machine. We collected 40 images for each type of batik with a total of 120 images. To speed up and simplify the model building process, we implemented transfer learning with 3 basic CNN model architectures, namely ResNet, DenseNet, and VGG with batch normalization. We also experimented with building a new dataset by breaking each image down into 30 smaller images. Image augmentation was also used to prevent overfitting as well as to provide variations in the training data. The experimental results with 5-fold cross validation show that densenet169 gives the best results on the original dataset with an accuracy of 79.17% while vgg13_bn shows the best performance on the modified dataset with an accuracy of 87.61%. All models showed an increase in performance when using the modified dataset, except densenet169 which did not show a significant difference in performance.
Evaluation of objective video quality assessment methods on video sequences with different spatial and temporal activity encoded at different spatial resolutions

With the development of Video on Demand applications due to the availability of high-speed internet access, adaptive streaming algorithms have been developing and improving. The focus is on improving user’s Quality of Experience (QoE) and taking it into account as one of the parameters for the adaptation algorithm. Users often experience changing network conditions, so the goal is to ensure stable video playback with satisfying QoE level. Although subjective Video Quality Assessment (VQA) methods provide more accurate results regarding user’s QoE, objective VQA methods cost less and are less time-consuming. In this article, nine different objective VQA methods are compared on a large set of video sequences with various spatial and temporal activities. VQA methods used in this analysis are: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), MultiScale Structural Similarity Index (MS-SSIM), Video Quality Metric (VQM), Mean Sum of Differences (DELTA), Mean Sum of Absolute Differences (MSAD), Mean Squared Error (MSE), Netflix Video Multimethod Assessment Fusion (Netflix VMAF) and Visual Signal-to-Noise Ratio (VSNR). The video sequences used for testing purposes were encoded according to H.264/AVC with twelve different target coding bitrates, at three different spatial resolutions (resulting in a total of 190 sequences). In addition to objective quality assessment, subjective quality assessment was performed for these sequences. All results acquired by objective VQA methods have been compared with subjective Mean Opinion Score (MOS) results using Pearson Linear Correlation Coefficient (PLCC). Measurement results obtained on a large set of video sequences with different spatial resolutions show that VQA methods like SSIM and VQM correlate better with MOS results compared to PSNR, SSIM, VSNR, DELTA, MSE, VMAF and MSAD. However, the PLCC results for SSIM and VQM are too low (0.7799 and 0.7734, respectively), for the usage of these methods in streaming services instead of subjective testing. These results suggest that more efficient VQA methods should be developed to be used in streaming testing procedures as well as to support the video segmentation process. Furthermore, when comparing results obtained for different spatial resolutions, it can be concluded that the quality of video sequences encoded at lower spatial resolutions in cases of lower target coding bitrate is higher compared to the quality of video sequences encoded at higher spatial resolutions at the same target coding bitrate, particularly when video sequences with higher spatial and temporal information are used.
An Efficient Mechanism to Prevent the Phishing Attacks

In the era of modern trends such as cloud computing, social media applications, emails, mobile applications, and URLs that lead to increased risks for defrauding authorized users, and then the attackers try to gain illegal access to accounts of users through a malicious attack. The phishing attack is one of the dangerous attacks caused to access of authorized account illegally way. The finances, business, banking, and other sensitive in states are faces by this type of attacks due to the important information they have. In this paper, we propose a secure verification scheme that can overcome the above-mentioned issues. Additionally, the proposed scheme can resist famous cyberattacks such as impersonate attacks, MITM attacks. Moreover, the proposed scheme has security features like strong verification, forward secrecy, user’s identity anomaly. The security analysis and the experimental results proved the strongest of the proposed scheme compared with other related works. Finally, our proposed scheme balanced between the performance and the security merits.
Mixed reality in oral and maxillofacial surgery: a symbiosis of virtual and augmented reality or a pointless technological gadget?

Introduction Mixed reality (MR) represents a new evolution in technological development that combines both virtual reality (VR) and augmented reality (AR) to create a blend of the physical and digital worlds. However, the potential role of MR in preoperative diagnostics in oral and maxillofacial surgery has not been scientifically investigated and remains generally unclear. This article presents a workflow that integrated MR in its scheme. It also evaluates the potential benefit of MR compared with its predecessors, ﻿VR and ﻿AR. Material and methods MR technology was used to plan the surgical treatment of a clinical case with an extensive tumor of the left maxilla. A workflow proposal incorporating both the surgeon and radiation oncologist is presented based on this experience. A total of 10 examiners rated the usability and applicability of MR for daily routines. Results MR showed good results during preoperative planning for a surgically extensive case in terms of displaying 3D structures and enhancing the physical and virtual interactions among the examiners. Previously described drawbacks of other VR/AR applications such as nausea and motion sickness were not observed with MR. However, MR seems to lack intraoperative usability, which is a drawback. Conclusion MR shows great potential in improving the preoperative assessment of 3D DICOM datasets and thus facilitating diagnostic measures. However, further improvements should be made to implement an MR workflow and incorporate it into the clinical treatment planning tree.
HH-CompWordNet: Holistic Handwritten Word Recognition in the Compressed Domain

Holistic word recognition in handwritten documents is an important research topic in the field of Document Image Analysis. For some applications, given strong language models, it can be more robust and computationally less expensive than character segmentation and recognition. This paper presents HH-CompWordNet, a novel approach to applying a Convolutional Neural Network (CNN) to directly to the DCT coefficients of the compressed domain word images. The efficacy of the HH-CompWordNet is demonstrated with the JPEG compressed version of the CMATERdb2.1.2.1 dataset - standard Handwritten Bangla word images. Experiments show the system obtains state-of-the-art accuracy of 86.80%.
An Improved NSGA-II Algorithm for UAV Path Planning Problems

The path planning problem is an important research in the field of UAV application. In practical applications, the path planning problem is usually multi-objective. This paper proposes an improved NSGA-II algorithm to achieve multi-objective optimization path planning. The algorithm introduces an improved directional mutation strategy by adaptively adjusting the crossover probability and the mutation probability, and searches for the optimal path of the UAV under the premise of considering the path length, threat, and concealment. Simulation experiments show that, compared with the NSGA and NSGA-II algorithm, the improved NSGA-II algorithm can reduce the risk of falling into a local optimum, increase the convergence speed, and better realize path planning in an obstacle environment.
Gaussian Process-Based Real-Time Learning for Safety Critical Applications

The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its high computational complexity, Gaussian process regression must be used offline on batches of data, which prevents applications, where a fast adaptation through online learning is necessary to ensure safety. In order to overcome this issue, we propose the LoG-GP. It achieves a logarithmic update and prediction complexity in the numberof training points through the aggregation of locally active Gaussian process models. Under weak assumptions on the aggregation scheme, itinherits safety guarantees from exact Gaussian process regression. These theoretical advantages are exemplarily exploited in the design of a safeand data-efficient, online-learning control policy. The efficiency and performance of the proposed real-time learning approach is demonstrated in a comparison to state-of-the-art methods.
A Survey on Object Detection and Tracking in Soccer Videos

"The players and ball are the most important object in soccer game videos and detected them are a challenging task because of many difficulties, such as shadow and illumination, ball size, several other objects look like a ball, often the ball overlapping with players or merged with lines, as well as the ball may be disappear which be hidden in the stadium or flying on air, and similar appearance of players, etc. The detect ball is the first step for tracking in broadcast soccer video. There are several methods of ball-tracking are based on their problem. In this paper, we have discussed different methods of object detection and tracking in the soccer videos which are available in the literature.