Reviewerly/exHarmony huggingface dataset:
Leveraging Knowledge-Based Features With Multilevel Attention Mechanisms for Short Arabic Text Classification

With the wide spread of short texts through social media platforms, there has become a growing need for effective methods for short-text classification. However, short-text classification has always been challenging due to the ambiguity and the data sparsity of the short text. A common solution is to enrich the short text with additional semantic features extracted from external knowledge, such as Wikipedia, to help the classifier better decide on the correct class. Most existing works, however, focused on text written in English and benefited from the existence of entity-linking tools based on English-based knowledge bases. When it comes to the Arabic language, the exploitation of external knowledge to support the classification of Arabic short text has not been widely explored. This work presents an approach for the classification of short Arabic text that exploits both the Wikipedia-based features and the attention mechanism for effective classification. First, Wikipedia entities mentioned in the short text are identified. Then, Wikipedia categories associated with the identified entities are retrieved and filtered to retain only the most relevant categories. A deep learning model with multiple attention mechanisms is then used to encode the short text and the associated category set. Finally, the short text and category representations are combined together to be fed into the classification layer. The use of the attentive model with category filtering leads to highlighting the most important features while reducing the effect of improper features. Finally, the proposed model is evaluated by comparing it with several deep learning models.
Structural interpretation of the allocated complexes based on remote sensing materials of the Earth and using in geological researches (Case of Southern Nurata Mountains)

The study's purpose is to create a new kind of cosmos geological maps - remote foundations and distinguishable structurally-decipherable complexes of the study area based on remote sensing of the Earth (RSE), use of satellite imagery using modern technologies. The article presents the results of studies on the use remote sensing of the Earth materials to highlight geological structures based on structurally deciphered complexes. Modern digital materials of satellite images of the Earth's surface, obtained using various satellite systems, are used. Remote sensing of the Earth materials in the form of aerial photographs have been widely used for a long time in regional geological surveys, structural and geological research, mineral exploration, hydrogeology and engineering geology, and many other areas of fundamental and applied geology. On their basis, thematic methods of geological interpretation of remote sensing materials were developed.
Improving the Ambient Intelligence Living Using Deep Learning Classifier

Over the last decade, there is a surge of attention in establishing ambient assisted living (AAL) solutions to assist individuals live independently. With a social and economic perspective, the demographic shift toward an elderly population has brought new challenges to today's society. AAL can offer a variety of solutions for increasing people's quality of life, allowing them to live healthier and more independently for longer. In this paper, we have proposed a novel AAL solution using a hybrid bidirectional long-term and short-term memory networks (BiLSTM) and convolutional neural network (CNN) classifier. We first pre-processed the signal data, then used time-frequency features such as signal energy, signal variance, signal frequency, empirical mode, and empirical mode decomposition. The convolutional neural network-bidirectional long-term and short-term memory (CNN-biLSTM) classifier with dimensional reduction isomap algorithm was then used to select ideal features. We assessed the performance of our proposed system on the publicly accessible human gait database (HuGaDB) benchmark dataset and achieved an accuracy rates of 93.95 percent, respectively. Experiments reveal that hybrid method gives more accuracy than single classifier in AAL model. The suggested system can assists persons with impairments, assisting carers and medical personnel.
Learning Semantic-Aware Local Features for Long Term Visual Localization

Extracting robust and discriminative local features from images plays a vital role for long term visual localization, whose challenges are mainly caused by the severe appearance differences between matching images due to the day-night illuminations, seasonal changes, and human activities. Existing solutions resort to jointly learning both keypoints and their descriptors in an end-to-end manner, leveraged on large number of annotations of point correspondence which are harvested from the structure from motion and depth estimation algorithms. While these methods show improved performance over non-deep methods or those two-stage deep methods, i.e., detection and then description, they are still struggled to conquer the problems encountered in long term visual localization. Since the intrinsic semantics are invariant to the local appearance changes, this paper proposes to learn semantic-aware local features in order to improve robustness of local feature matching for long term localization. Based on a state of the art CNN architecture for local feature learning, i.e., ASLFeat, this paper leverages on the semantic information from an off-the-shelf semantic segmentation network to learn semantic-aware feature maps. The learned correspondence-aware feature descriptors and semantic features are then merged to form the final feature descriptors, for which the improved feature matching ability has been observed in experiments. In addition, the learned semantics embedded in the features can be further used to filter out noisy keypoints, leading to additional accuracy improvement and faster matching speed. Experiments on two popular long term visual localization benchmarks (Aachen Day and Night v1.1, Robotcar Seasons) and one challenging indoor benchmark (InLoc) demonstrate encouraging improvements of the localization accuracy over its counterpart and other competitive methods.
Image Translation Based Synthetic Data Generation for Industrial Object Detection and Pose Estimation

Deep learning-based methods have shown excellent potential on object detection and pose estimation with vast amounts of training data to achieve good performance. Obtaining enough comprehensive manual labeling training data is a time-consuming and error-prone task in industrial scenes, and most current time-saving synthetic data generation methods require detailed high-quality CAD models. Instead, we introduce an image-to-image translation based synthetic data generation approach, which requires only untextured CAD models and a small number of real images. The proposed approach starts with edge maps extracted by mesh model projection. And each edge map is provided with realistic appearance learned from real images through image-to-image translation to achieve patch-level realism, and then superimposed on randomly selected backgrounds with a simple Cut-and-Paste strategy. Experiments on our custom dataset and T-LESS dataset show that our synthetic data is competitive with uncomprehensive real data and gives a considerable improvement when compared to the same degree of model rendering images. In addition, the proposed approach allows for extensibility to accommodate new parts with similar texture and structure. And it shows the potential to train image translation model on only one single object and synthesize data for multiples in one take.
Human Posture Classification Using Interpretable 3-D Fuzzy Body Voxel Features and Hierarchical Fuzzy Classifiers

This article proposes a human posture classification system to classify the four postures of standing, bending, sitting, and lying using three-dimensional (3-D) fuzzy body voxel features obtained from two cameras and hierarchical fuzzy classifiers (FCs). The system consists of four parts: human body segmentation, 3-D body voxel model construction, feature extraction, and classification. A region-dependent Gaussian mixture model with shadow removal is proposed for human body segmentation. The silhouette volume intersection method with computation speedup is then used to construct the 3-D body model using the segmented images from two cameras. After that, fuzzy 3-D body voxel features for posture classification are extracted from a minimum enclosed cube of the 3-D model. For classification, this article proposes an FC with soft margin minimization (FC-SMM) and its hierarchical structure to improve the classification performance. The consequent and antecedent parameters are optimized through a linear batch support vector machine and margin selective gradient descent algorithm, respectively. The FC-SMM also shows the advantage of model interpretability through visualization of the fuzzy rules. Experimental results of the comparison of different feature extraction methods and classifiers in different videos show the superiority of the proposed classification method.
Single Convolutional Neural Network With Three Layers Model for Crowd Density Estimation

Crowd density estimation is an important topic in computer vision due to its widespread applications in surveillance, urban planning, and intelligence gathering. Resulting from extensive analysis, crowd density estimation reflects many aspects such as similarity of appearance between people, background components, and inter-blocking in intense crowds. In this paper, we are interested to apply machine learning for crowd management in order to monitor populated area and prevent congestion situations. We propose a Single-Convolutional Neural Network with Three Layers (S-CNN3) model to count the number of people in a scene and conclude about the crowd estimation. Then, a comparative study for density counting establishes the performance of the proposed model against the convolutional neural networks with four layers (single-CNN4) and Switched Convolutional neural networks (SCNN). ShanghaiTech dataset, considered as the largest data base for crowd counting, is used in this work. The proposed model proves high effectiveness and efficiency for crowd density estimation with 99.88% of average test accuracy and 0.02 of average validation loss. These results achieve better performance than the existing state-of-the-art models.
FVP-EOC: Fair, Verifiable, and Privacy-Preserving Edge Outsourcing Computing in 5G-Enabled IIoT

The 5G-enabled Industrial Internet of Things tilts the data processing model from the cloud to the edge. Users are more inclined to get feedback and data analysis of outsourcing computing results timely from the edge. However, the existing solutions undermine the fairness of multitask outsourcing in edge environment and cannot guarantee the correctness of results. To tackle these challenges, in this article, we propose a fair, verifiable, and privacy-preserving edge outsourcing computing scheme based on blockchain (FVP-EOC). Initially, we propose a task bidding method in the same round of task outsourcing, which improves the utilization of resources and the fairness of the FVP-EOC by dividing tasks into blocks. Furthermore, we design a result verification algorithm and a consensus algorithm to ensure the correctness of the results without a trusted third party. Finally, theoretical analysis and ample simulations indicate that the FVP-EOC is secure and verifiable and ensure the benefits of all the participants in edge outsourcing computing.
A Degraded Reconstruction Enhancement-Based Method for Tiny Ship Detection in Remote Sensing Images With a New Large-Scale Dataset

The rapid detection of ships within the wide sea area is essential for intelligence acquisition. Most modern deep learning-based ship detection methods focus on locating ships in high-resolution (HR) remote sensing (RS) images. Seldom efforts have been made on ship detection in medium-resolution (MR) RS images. An MR image covers a much wider area than an HR one of the same size, thus facilitating quick ship detection. To this end, we propose a tiny ship detection method namely, Degraded Reconstruction Enhancement Network (DRENet), for MR RS images. Different from previous methods that mainly focus on feature fusion strategies to improve the expression ability of the detector, we design an additional network branch, i.e., degraded reconstruction enhancer, to learn to regress an object-aware blurred version of the input image in the training phase. Our intuition is that the proposed reconstruction branch may guide the backbone to focus more on tiny ship targets instead of the vast background. Moreover, we incorporate a CRoss-stage Multi-head Attention module in the detector to further improve the feature discrimination by leveraging the self-attention mechanism. To fill the gap of lacking a large-scale MR ship detection dataset, we introduce Levir-Ship, which contains 3876 GF-1/GF-6 multi-spectral images and over 3K tiny ship instances. Experiments on Levir-Ship validate the effectiveness and efficiency of the proposed method. Our method achieves 82.4 AP with 85 FPS, which outperforms many state-of-the-art ship detection methods. Our code and dataset will be made public.
The power behind the screen: Educating competent technology users in the age of digitized inequality

Digital technologies are deeply embedded in social, economic, and political hegemonies both past and present. Understanding the power dynamics, inequalities, and oppressions at work in and through digital technologies stands as a precondition to educating fully literate, fully competent digital citizens and technology users. This article is situated within an area of overlap between digital literacy and digital competence; that is, it is situated at the overlap of functional and cognitive skills, pedagogy and policymaking. We argue that it is crucial to introduce students to the language and theoretical frameworks examining what power is and how it functions in order to empower students to critically engage with the tangled ethics and power structures attendant with digital technologies and their data.
Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes.The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources.The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users.Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues.Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations.This paper proposes a comprehensive review of security services and threats.Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms.A threat model has been applied to validate the proposed authentication frameworks with different security threats.The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.
Edge Computing-Based SAT-Video Coding for Remote Sensing

This paper proposes an edge computing-based video coding implementation on an Earth observation satellite (SAT-video coding), which can encode video using limited resources and the power of mini/microsatellites. SAT-video coding proposes a hardware-related quantization (Q) function, hardware reduction of the motion estimation (ME) method, and simplified entropy coding (EC), which reduces the computation complexity. The hardware-related Q reduces hardware resource and power consumption by 72% and 55%, respectively, compared with traditional Q implementation. The hardware reduction of ME reduces resource use compared with regular ME implementation (59 % of lookup tables [LUTs] and 79% of Registers). The total number of LUTs used for the simplified EC function is also much lower than other EC hardware implementations. The SAT-video encoder IP uses fewer hardware resources, and the power consumption is estimated at 0.0894 W at a high working frequency (125 MHz). The SAT-video encoding speed is 18.95 frames per second for 2560×2560 video. Therefore, the proposed SAT-video coding is an edge computation suitable for micro/minisatellites. The coding efficiency records the highest compression ratio at 33.8, with a peak signal-to-noise ratio of 34.46 dB. With the important task of designing edge computing based on satellite video encoding, these are adequate values for remote sensing video.
Influential Gene Selection From High-Dimensional Genomic Data Using a Bio-Inspired Algorithm Wrapped Broad Learning System

The classification of high dimensional gene expression/ microarray data always plays an important role in various disease diagnoses and drug discovery. To avoid the curse of high dimensionality, the selection of the most influential genes remains a challenging task for the researchers in the machine learning field. As the extraction of influential features by a bio-inspired algorithm is a non-deterministic polynomial-time (NP-Hard) task, the possibility of applying new algorithm is always there. In this suggested work, a recently developed bio-inspired algorithm, Monarch Butterfly Optimization (MBO), is wrapped with the Broad Learning System (BLS), called MBO-BLS, to choose the most influential features and classify the microarray data simultaneously. In the first stage, a pre-selection method (Relief) is used to select a feature subset. Then, this selected feature subset undergoes further execution with the MBO-BLS model. To estimate the efficacy of the presented model, six cancerous microarray datasets are taken. Here, sensitivity, specificity, precision, F-score, Kappa, and MCC measures are used for an impartial comparison. Further, to prove the supremacy of the presented method, the basic BLS, Genetic Algorithm wrapped BLS (GA-BLS), Particle Swarm Optimization wrapped BLS (PSO-BLS), and the existing ten models are taken for comparison. Moreover, to examine the designed model statistically, Analysis of variance (ANOVA) test is also performed here. From the above qualitative and quantitative analysis, it is concluded that the proposed MBO-BLS model outclasses other considering models.
$L_{1}$-Regularized Reconstruction Model for Edge-Preserving Filtering

Smoothing images while preserving salient edges is a crucial task in computational photography. Existing edge-preserving filters suffer from various artifacts, such as halos, gradient reversals, and intensity shifts. Observing that various artifacts are strongly related to salient edges with large gradients, we propose a continuous mapping function to process the gradients. The proposed function is literally edge-preserving, i.e., it keeps large gradients intact while attenuating small gradients. We propose an L<sub>1</sub>-regularized reconstruction model based on the processed gradients for edge-preserving image filtering. The L<sub>1</sub>-regularization facilitates the edge-preserving property in the reconstructed results. To solve the proposed L<sub>1</sub>-regularized model, we implement an efficient algorithm based on the alternating direction method of multipliers (ADMM) and Fourier domain optimization. We have conducted qualitative and quantitative experiments to evaluate the proposed filter. The results demonstrate that our filter better handles various artifacts and delivers superior image quality on various applications. The proposed filter is highly efficient, our GPU implementation takes 70ms to process a color image with 1 megapixel on an NVIDIA GTX 1070 GPU.
Quantum Networks for Resilient Power Grids: Theory and Simulated Evaluation

Quantum networks are considered to be the future of secure communication in the coming quantum era. However, significant efforts are lacking on developing practical quantum networks for power grids. This paper establishes a quantum network-based power grid (QNetGrid) framework and develops a real-time, reliable, flexible, programmable, and cost-effective QNetGrid software testbed containing repeater-based quantum communication, quantum routing, real software-defined networking (SDN) switches, and real-time networked microgrids (NMs) operations. It makes the following contributions: 1) a repeater and routing based quantum network simulator (QNSim) is developed, 2) repeaters with and without quantum memories are respectively simulated in QNSim, 3) different routing scenarios in QNetGrid are investigated, 4) a real-time QNetGrid software testbed is built in RTDS incorporating QNSim and real SDN switches, and 5) various test cases are designed, and experimental results produced with the QNetGrid testbed provide valuable insights for building quantum networks in power grids.
From Tools to Complexity?—A Systematic Literature Analysis of Digital Competence Among Pre-service Teachers in Norway

In the last decades, the field of education has rapidly developed along with the development of digital technology. The recent COVID-19 pandemic has affected this development, leading to an educational revolution that involves the extensive use of online learning. This change makes it crucial to understand how teachers' digital competence has developed along with this phenomenon, as well as how teacher students are being prepared to work as future teachers. This article presents a literature review regarding how the term 'digital competence' has been understood and operationalised in the context of Norwegian teacher education in the last two decades, as well as how pre-service teachers' digital competence has been measured when researched. In the earliest findings uncovered by the review, the research is tool oriented, while a greater awareness of the professional complexity of digital competence in education emerges from 2014 to 2017. From then on, a challenge arises regarding added complexity. A somewhat complex understanding of teachers' professional digital competence (PDC) makes measuring PDC a difficult task, and it is challenging to link theoretical foundations with conducted research on the subject. This article addresses these issues and contributes to the discussion regarding the term 'professional digital competence' and how it is understood in a Norwegian educational context.
Computational Analysis of Human Navigation in a VR Spatial Memory Locomotor Assessment Using Density-Based Clustering Algorithm of Applications with Noise DBSCAN

In this study, we explore human navigation as evaluated by the VR Magic Carpet TM (VMC) [1], a variation of the Corsi Block Tapping task (CBT) [2, 3], employing Density-based spatial clustering of applications with noise (DBSCAN) [4]. As a result of the VMC, we acquired raw spatial data in 3D space, which we processed, analyzed, and turned into trajectories before evaluating them from a kinematic standpoint. Our previous research [5] revealed three unique groupings. However, the categorization remained ambiguous, showing clusters with diverse people (patients and healthy). We utilized DBSCAN to compare patients’ navigation behavior to healthy individuals, highlighting the most notable differences and assessing our existing classifiers. Our research aims to produce insights that may help clinicians and neuroscientists adopt machine learning, especially clustering algorithms, to identify cognitive impairments and analyze human navigation behavior.