Reviewerly/exHarmony huggingface dataset:
An Ontology-based Approach to Facilitate Semantic Interoperability of Context-Aware Systems

Semantic interoperability is one of the most critical challenges for software developers while integrating two or more context-aware systems. In such circumstances, it is essential to understand the meaning and interpretation of various contexts in different business domains and to align context ontologies together. Our investigations reveal that existing works poorly address these requirements. To fill this gap, this paper proposes the Ontology as a Service (OaaS) approach to facilitate the semantic interoperability of context-aware systems. In the proposed solution, the complexities of semantic interoperability are resolved and handled by a standalone ontology service, which can be easily reused and consumed by different ontology service consumers and brokers. The proposed ontology service includes several ontology repositories, a web service that positions a context concept in the existing ontologies and another web service that maps the relationship between existing context concepts. We evaluated our approach with a case study that resolves three semantic differences between two IoT applications originating from heterogeneous domains of smart home and health environments.
Henry V: A linear time algorithm for solving the N-Queens Problem using only 5 patterns

It has been known that the time complexity of solving the N-Queens problem, a classic problem with many applications in computer science is Nondeterministic Polynomial (NP). Many different approaches have been proposed for solving the problem since its first presentation in 1848 including genetic algorithms, brute force search and propositional logic statements. In this paper, a novel idea called Layouts have been proposed to solve this problem in θ(n) time complexity. Layouts are patterns for placing queens on the chessboard. Correctness of the proposed approach has been proven for all natural numbers using proof by contradiction and exhaustion. It is shown that by using only 5 layouts, N-Queens with any size can be solved in linear time. The proposed approach has been verified by being applied to 60 different sizes of the N-Queens problem where the size has been chosen by randomly selecting a number between 1000 and 10000.
Gesture-Related Two-Factor Authentication for Wearable Devices via PPG Sensors

Verifying the user identity of wearable devices is crucial for system security, especially for sensitive operations like making financial payments. A photoplethysmography (PPG)-based two-factor authentication can be a promising solution with widely deployed PPG sensors within wearable devices. Our observations find PPG readings reveal a significant relevance to the user’s hand motions, that is, gestures, while the user’s heartbeat characteristics and wearing habits are also implicitly related, which can be utilized for user authentication. In this article, we design G-PPG, a gesture-related PPG-based two-factor authentication mechanism that can nonintrusively validate the user’s identity. In G-PPG, a gesture detection and segmentation module and a specific feature set are designed for accurate gesture-related PPG characteristic extraction. Moreover, a user-defined security level and an adaptive update scheme are proposed for the high accuracy of long-term authentication. The experimental results among 15 participants demonstrate that G-PPG can achieve over 90% accuracy in different scenarios on average. With a carefully designed authentication mechanism, the pass rate for legitimate users can reach up to 96.67%, while the value is only 0.62% for attackers.
Image-Like Situational Difference Mapping (ILSDM)-Based Method for Detecting Anomalies in Electromagnetic Environment

Electromagnetic environment anomaly radiation source detection (EMEARD) can provide a basis for electromagnetic (EM) equipment behavior cognition and battlefield threat assessment. Existing methods mainly focus on generating EM space situations but ignore the research on anomaly signal detection. In this article, the connections between information geometry and performance of sensor networks for anomaly detection are explored. Nonlinear estimation methods based on information geometry can be directly used for anomaly detection. However, Riemann distance as a metric function faces the disadvantages of extensive computation and high complexity. To tackle this issue here, we propose a method based on image-like situation difference mapping, namely, ILSDM, which takes the data-driven strategy to obtain the analytic expression of situation difference mapping distance function (DMDF), and anomaly detection is equivalent to the mapping reconstruction of information geometry. First, the EM space situation is acquired through the distributed sensor network. In addition, to solve the perceptual blind area problem, the sensors’ data are estimated by a spatial autocovariance optimal interpolation algorithm (kriging). Second, we apply the Gaussian mixture model (GMM) to fit the statistical features of each sensing node into probability density function (pdf), and adjacent sensors with similar pdf are fused by the neighbor average fusion method (NAF). Then, based on the information geometry, the fused pdfs are mapped to the Riemannian manifold space as a series of coordinates, and we further propose to use an image-like situation (ILS) to represent the EM space situation. Finally, DMDF measures the information distance between different ILSs to achieve anomaly detection. Numerical simulations and real experiments were conducted to prove the superiority and feasibility of our method for anomaly detection in EM space.
SVDnet: Singular Value Control and Distance Alignment Network for 3D Object Detection

The SOTA methods proposed voxelization or pillarization to regularize unordered point clouds, improving computing efficiency for LiDAR-based 3D object detection. However, they usually trade partial accuracy for speed. Thus, we bring up a new problem setting: “Is it possible to keep high detection accuracy while point-cloud quantization is applied?”. To this end, we found that the inconsistent sparsity of the point cloud over the depth distance, which is still an open question, might be the main reason. To address the inconsistency effect, we first proposed a new pillar-based vehicle detection model, named SVDnet, in which novel plug-ins are introduced in its backbone and neck. Specifically, a novel low-rank objective is designed to force the backbone to extract distance/sparsity-aware features and suppress the other feature variations among vehicle samples. Next, we alleviated the remaining feature inconsistency resulting from distance/sparsity in the neck by dynamic feature selection and adaptive feature fusion. Here, feature selection is realized by a position attention network, while feature fusion is achieved by a Distance Alignment Ratio-generation Network (DARN). Later, the selected and fused features, less sensitive to sparsity, are concatenated and fed to an SSD-like detection head. Besides, we also integrate the proposed plug-ins with multiple pillar/voxel-based methods for performance boosting. Our evaluation shows that SVDnet improves the average precision of the distant cases by 8.11% with only 0.23 milliseconds speed drop compared with PointPillars. Furthermore, the extensional results validate that our plug-ins can help SOTA pillar/voxel-based methods to gain noticeable improvement, especially for far-range objects.
A Taxonomy of Testable HTML5 Canvas Issues

The HTML5 < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > is widely used to display high quality graphics in web applications. However, the combination of web, GUI, and visual techniques that are required to build < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > applications, together with the lack of testing and debugging tools, makes developing such applications very challenging. To help direct future research on testing < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > applications, in this paper we present a taxonomy of testable < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > issues. First, we extracted 2,403 < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> >-related issue reports from 123 open source GitHub projects that use the HTML5 < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> >. Second, we constructed our taxonomy by manually classifying a random sample of 332 issue reports. Our manual classification identified five broad categories of testable < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > issues, such as Visual and Performance issues. We found that Visual issues are the most frequent (35%), while Performance issues are relatively infrequent (5%). We also found that many testable < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > issues that present themselves visually on the < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > are actually caused by other components of the web application. Our taxonomy of testable < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > issues can be used to steer future research into < <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">canvas</monospace> > issues and testing.
On Proper and Improper Points in Geometric Algebra for Conics and Conic Fitting Through Given Waypoints

We introduce a new conic fitting algorithm using Geometric Algebra for Conics (GAC) that not only minimises the overall distance to a data set, but also causes the fitted conic to pass through prescribed waypoints. Moreover, the expression for points at infinity (also called improper points) in terms of GAC is derived; hence, the use of the improper waypoints in the conic fitting problem is enabled. Finally, a MATLAB implementation of the fitting algorithm and experimental results based on custom data sets are included.
Design and Development of Maiji Mountain Grottoes Immersive Virtual Reality Learning Environment

This study attempted to take Maiji Mountain Grottoes as an example to design an immersive virtual reality learning environment serving for the field of cultural heritage or archaeology. Cultural heritage needs to be protected and not easy to arrive, which brings difficulties and challenges to learners. Virtual reality learning environment can provide immersive experience for users without worrying about time and space and allow flexibly switch between macro and micro world to learn more intuitively and efficiently. Based on the constructivist learning principles, this study designed a learning-guide model for immersive virtual reality learning environment, which not only dynamically display the 3D knowledge map and guide users to explore interactively in the immersive virtual reality environment, but also intelligently recommend the learning path according to the real-time feedback of learning performance and learning behavior. This paper will focus on the key points of the design of the immersive virtual reality learning environment and put forward the plan of future work.
Gamified Augmented Reality Mobile Application for Tourism in Kuching

Augmented Reality (AR) for tourism utilizing mobile application mostly features scanning prefixed image marker by tourist in order to retrieve additional multimedia content for learning. This paper proposes the use of similar AR application for tourism that extends beyond learning to include gamification for the enhanced tourist’s experience. Majority of tourism AR uses marker-based method like scanning QR code to load virtual objects on the screen. The current study will explore marker-less method using spatial anchor. Furthermore, functionality for site operator, an aspect often neglected in literature, like the ability to dynamically add content without relying on developer, will be categorically examined. Platforms, frameworks and services used include Unity, AR Core or AR Foundation and Azure cloud services. This would culminate in implementation and evaluation of an AR mobile application prototype that benefits both tourists and site operators towards proliferation of gamified AR tourism in Kuching city of Sarawak state in Malaysia.
Image Generation Method Based on the Recoverable Byte Sequence using the Neural Networks

Due to the rapid development of information technologies, the tasks of ensuring information integrity, safety and confidentiality, as well as the possibility of guaranteed confirmation of its source, are becoming more relevant than ever. One of the possible solutions to this problem could be the steganographic methods, which allow both hiding the fact of information transfer and imperceptibly adding the useful data. Scientific literature describes a large number of steganographic algorithms. However, only insignificant number of works is devoted to the data hiding methods using the neural networks, and even less are devoted to generating containers for them. A method for generating images based on the hidden information is proposed, which guarantees possibility of both hiding and subsequent extraction of information eliminating the need to select an appropriate container. As part of the method, an algorithm was developed that included description of the stages of input data preprocessing, transformation into the container image and extraction of the hidden information. Examples of the proposed method operation are provided. The method could serve both as a steganographic algorithm for hiding information and as the algorithm for adding information in the form of watermarks
Rethinking Matching-Based Few-Shot Action Recognition

Few-shot action recognition, i.e. recognizing new action classes given only a few examples, benefits from incorporating temporal information. Prior work either encodes such information in the representation itself and learns classifiers at test time, or obtains frame-level features and performs pairwise temporal matching. We first evaluate a number of matching-based approaches using features from spatio-temporal backbones, a comparison missing from the literature, and show that the gap in performance between simple baselines and more complicated methods is significantly reduced. Inspired by this, we propose Chamfer++, a non-temporal matching function that achieves state-of-the-art results in few-shot action recognition. We show that, when starting from temporal features, our parameter-free and interpretable approach can outperform all other matching-based and classifier methods for one-shot action recognition on three common datasets without using temporal information in the matching stage. Project page: https://jbertrand89.github.io/matching-based-fsar
BrackishMOT: The Brackish Multi-Object Tracking Dataset

There exist no publicly available annotated underwater multi-object tracking (MOT) datasets captured in turbid environments. To remedy this we propose the BrackishMOT dataset with focus on tracking schools of small fish, which is a notoriously difficult MOT task. BrackishMOT consists of 98 sequences captured in the wild. Alongside the novel dataset, we present baseline results by training a state-of-the-art tracker. Additionally, we propose a framework for creating synthetic sequences in order to expand the dataset. The framework consists of animated fish models and realistic underwater environments. We analyse the effects of including synthetic data during training and show that a combination of real and synthetic underwater training data can enhance tracking performance. Links to code and data can be found at https://www.vap.aau.dk/brackishmot .
Spatio-temporal Attention Graph Convolutions for Skeleton-based Action Recognition

In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the spatial correlations between human joints and often overlook the temporal relationships. In an action sequence, the consecutive frames in a neighborhood contain similar poses and using only temporal convolutions for extracting local features limits the flow of useful information into the calculations. In many cases, the discriminative features can present in long-range time steps and it is important to also consider them in the calculations to create stronger representations. We propose an attentional graph convolutional network, which adapts self-attention mechanisms to respectively model the correlations between human joints and between every time steps for skeleton-based action recognition. On two common datasets, the NTU-RGB+D60 and the NTU-RGB+D120, the proposed method achieved competitive classification results compared to state-of-the-art methods. The project’s GitHub page: STA-GCN .
Depth-Aware Image Compositing Model for Parallax Camera Motion Blur

Camera motion introduces spatially varying blur due to the depth changes in the 3D world. This work investigates scene configurations where such blur is produced under parallax camera motion. We present a simple, yet accurate, Image Compositing Blur (ICB) model for depth-dependent spatially varying blur. The (forward) model produces realistic motion blur from a single image, depth map, and camera trajectory. Furthermore, we utilize the ICB model, combined with a coordinate-based MLP, to learn a sharp neural representation from the blurred input. Experimental results are reported for synthetic and real examples. The results verify that the ICB forward model is computationally efficient and produces realistic blur, despite the lack of occlusion information. Additionally, our method for restoring a sharp representation proves to be a competitive approach for the deblurring task.
Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-Motion

Self-supervised monocular depth estimation approaches suffer not only from scale ambiguity but also infer temporally inconsistent depth maps w.r.t. scale. While disambiguating scale during training is not possible without some kind of ground truth supervision, having scale consistent depth predictions would make it possible to calculate scale once during inference as a post-processing step and use it over-time. With this as a goal, a set of temporal consistency losses that minimize pose inconsistencies over time are introduced. Evaluations show that introducing these constraints not only reduces depth inconsistencies but also improves the baseline performance of depth and ego-motion prediction.
Some Remarks on Super-Gram Operators for General Bipartite Quantum States

The Gramian matrices approach to study certain aspects of quantum entanglement contained in the bipartite pure quantum states is being extended to the level of a general quantum bipartite states. The corresponding Gram matrices, called here super-gram matrices are being constructed over the Hilbert-Schmidt structure build on the Hilbert space of pure states. The main result is the extension of the widely known realignment criterion to the level of super-operators.
Fostering Digital Literacy and Learner Autonomy Readiness Among Pre-service Teachers in Online Education

This chapter aims to investigate the effects of online education on digital literacy and autonomy readiness in a Turkish pre-service teacher education program. The study also examines the relationship between digital literacy and learner autonomy readiness and explores the perceptions of pre-service teachers (PTs) about digital literacy and autonomy. The participants were 49 PTs enrolled in the freshmen program of the Department of English Language Teaching (ELT) at a private university in Istanbul. Data were collected through pre- and post-test results of the Digital Literacy Scale (DLS) and Learner Autonomy Readiness Questionnaire (LARQ), and semi-structured interviews. The results reported an increase in digital literacy and learner autonomy readiness after online education. However, there was a non-significant correlation between digital literacy and learner autonomy readiness. The interviews supported the findings that revealed positive attitudes toward technology and autonomous learning. The results revealed pedagogical implications and suggestions for promoting digital literacy and autonomy among PTs in online education.
The Sharp-Tongued Loudmouth: Incivility and Debasement in Brazil's President Jair Bolsonaro's YouTube Livestreams

This chapter focuses on debasement language in contemporary Brazilian political communication, analyzing Jair Bolsonaro's YouTube livestreams from 2019 to 2020. We understand debasement language as a discourse aiming to demean, degrade, or discredit a specific target, such as individuals, social groups, and personnel within political institutions. To assess and measure debasement language in Bolsonaro's livestreams, we apply content analysis that codes its elements. Findings indicate that debasement discourse is essential to his messages throughout 2019 and 2020. Bolsonaro's derogatory language addresses two main targets: left-wingers and the media. Using name-calling and insulting language, Bolsonaro attacks political parties, politicians, outlet groups, and journalists, among other subjects. Bolsonaro's interactions (often with the Brazilian press and mainstream media) are also subject to misinformation. While the topics of his interactions with the media vary, he usually attacks media groups and journalists when he disagrees with something they published or said, especially when he is the target of criticism.
Study the Effectiveness of ISO 27001 to Mitigate the Cyber Security Threats in the Egyptian Downstream Oil and Gas Industry

As Egyptian oil and gas downstream information technology has grown digitally over the past decade, security breaches against these digitally connected systems have also increased.These cyber security threats could have devastating effects on the operations and reputation of these companies.Preventing such cyberattacks is crucial.Especially, with the significance of the Egyptian oil and gas downstream sector to the local economy and the fact that many of these connected systems are sometimes managed remotely.This paper examines the value of the ISO 27001 standard in mitigating the effect of cyber threat and seeks to inspire decision-makers to the importance of the proactive measures to strengthen their organization's cybersecurity posture and protect information critical assets.The study stresses the importance of improving the local educational system to bridge the gap between supply and demand for cybersecurity specialists by implementing a structured approach that emphasizes behavior modification to get a high return on investment in cybersecurity awareness.