Reviewerly/exHarmony huggingface dataset:
Augmented Reality (AR) as an Enhancement Teaching Tool: Are Educators Ready for It?

The rapid technological development and revolution have transformed the education field, promoting the betterment of learning and teaching quality. Augmented Reality (AR) is becoming increasingly popular for its ability to help educators to create an engaging and creative method of teaching. The increasing use of technology has piqued researchers’ interests in studying its efficiency. Following this observation, this study aims to explore educators’ readiness in embracing AR as an enhancement teaching tool in the future. This study employed a quantitative methodology and collected 223 respondents’ data from five private universities in Malaysia. The data was analysed using the Partial Least Squares – Structural Equation Modelling (PLS-SEM) software. The results demonstrated that there were significant relationships between educators’ innovation towards the intention to adopt AR moderated by perceived usefulness (PU) and perceived ease of use (PEU). This study provides some insightful AR applications in the education industry, which is in line with the Industrial Revolution 4.0 theme. It successfully identifies the importance of motivating educators and students in embracing AR as an enhancement learning tool, providing a valuable discussion for the government, learning institutions, and educators on the implementation of AR in Malaysia.
Digital technology of generating survey data on the characteristics of an innovative product in a distance learning system

The role of distance education has been increasing in recent years. The experience of 2020 has demonstrated the importance of distance learning systems. It is already clear that distance education is, first of all, a computerized system. Work on the creation and improvement of distance learning environments is ongoing. Marketing is one of the most popular areas for distance learning, in particular digital marketing. Achieving competencies in this area requires the ability to conduct consumer research, for example, through surveys using modern digital tools, including working with the processing and interpretation of collected data.
Lidar and radar fusion for real-time road-objects detection and tracking

In this paper, based on the fusion of Lidar and Radar measurement data, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The lidar and radar devices are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is used for their data fusion. Lidars are accurate in determining objects’ positions but significantly less accurate on measuring their velocities. However, Radars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. Therefore, the merits of both sensors are combined using the proposed fusion approach to provide both pose and velocity data for objects moving in roads precisely. The Grid-Based Density-Based Spatial Clustering of Applications with Noise (GB-DBSCAN) clustering algorithm is used to detect objects and estimate their centroids from the lidar and radar raw data. Then, the estimation of the object’s velocity as well as determining its corresponding geometrical shape is performed by the RANdom SAmple Consensus (RANSAC) algorithm. The proposed technique is implemented using the high-performance language C+⁣+ and utilizes highly optimized math and optimization libraries for best real-time performance. The performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians.
‘You can’t arrest a virus’: The freedom of expression crisis within Egypt’s response to COVID-19

The article discusses the freedom of expression crisis that characterized the authoritarian response of the Egyptian government to the COVID-19 pandemic. Through the case study of the expulsion of the foreign correspondent for The Guardian over contested outbreak data, the article argues that authorities in Egypt exploited the pandemic for political ends by silencing critics and by manipulating the public opinion.
Augmented Reality in Experiential Marketing: The Effects on Consumer Utilitarian and Hedonic Perceptions and Behavioural Responses

This research aims to explore the impact of augmented reality (AR), the digital technology that superimposes virtual elements in a real environment, on consumers in the context of experiential marketing. Specifically, this study proposes a research model based on the stimulus-organism-response model, which considers AR media characteristics as external stimuli, consumers’ value perceptions as the organisms, and purchase intentions as the responses. The research model was tested with 248 consumers using structural equation modelling. The results show that informativeness, ease of use, and telepresence have positive effects on consumers’ utilitarian value perception and that telepresence and interactivity have positive effects on hedonic value perception. Overall, this study contributes to the growing body of knowledge on AR and provides actionable insights for managers implementing digital transformation strategies and AR applications in marketing practices.
Histopathological Image Classification Using Efficient Bag-of-Features and Whale Optimization Algorithm

An automated digital histopathological image analysis is a complex procedure because of the accessibility of different morphological structures in the tissue images. For the equivalent, an efficient bag-of-features strategy has been proposed for histopathological image classification. Taken care bag-of- features method, the visual vocabulary is created by K-implies technique which indicates biasedness towards the at first chosen cluster heads. This paper proposed a whale optimization algorithm that utilizes bag of-features method. The proficiency of the proposed efficient bag-of- features whale optimization method has been tried on different standard benchmark functions. By combining both efficient bag-of-features based classification with whale optimization algorithm has been analyzed on blue histology image dataset and compared with different other meta-heuristic based bag-of-features methods. The explanatory result outcomes justify that the proposed technique outperforms the best in state-of-the-art strategy.
Echocardiogram segmentation using active shape model and mean squared eigenvalue error

Cardiac function assessment using echocardiography is an essential step in daily cardiology. For this purpose, segmentation of images and, in particular, ventricle boundary segmentation poses several challenges due to shadows and speckle noise. Currently, the segmentation is done manually which is time-consuming and subjective. Therefore, providing an efficient and autonomous segmentation method could improve the evaluation efficiency and outcome. The focus of this paper is on active shape models (ASMs) to handle speckle noise, shadows, and dropouts in echocardiograms effectively, without the need for large training data. In this paper, an echocardiogram segmentation method using ASM, Nakagami and mean squared eigenvalues error (MSEE) is proposed. We propose the use of Nakagami distribution in providing a preprocess classification and hence improving the system performance. Additionally, we depart from the current practice in using (and manually entering) a percentage of eigenvalues in an appearance model of ASM. We show that the higher the percentage does not necessarily mean the better the performance. By integrating a mean squared error (MSE) evaluation method, we automate the choice of that percentage to warrant the minimum MSE. The simulation results are provided to show the effectiveness of the approach. In particular, we illustrate that using Nakagami would reduce the segmentation error. Also mean squared eigenvalues error (MSEE) will reduce the required total variance per landmark, and hence, the memory requirement will be decreased. Also, the proposed technique will provide the optimal total variance for each landmark, resulting in improved accuracy. The final results from segmentation ASM with Nakagami and MSEE illustrate a considerable reduction of the MSE error from 14.521 ± 7.36 in original ASM to 9.21 ± 3.85 pixels, and produced an average of 0.93 ± 0.02 DSI with insignificant additional processing time.
A Multi-instance Multi-label Dual Learning Approach for Video Captioning

Video captioning is a challenging task in the field of multimedia processing, which aims to generate informative natural language descriptions/captions to describe video contents. Previous video captioning approaches mainly focused on capturing visual information in videos using an encoder-decoder structure to generate video captions. Recently, a new encoder-decoder-reconstructor structure was proposed for video captioning, which captured the information in both videos and captions. Based on this, this article proposes a novel multi-instance multi-label dual learning approach (MIMLDL) to generate video captions based on the encoder-decoder-reconstructor structure. Specifically, MIMLDL contains two modules: caption generation and video reconstruction modules. The caption generation module utilizes a lexical fully convolutional neural network (Lexical FCN) with a weakly supervised multi-instance multi-label learning mechanism to learn a translatable mapping between video regions and lexical labels to generate video captions. Then the video reconstruction module synthesizes visual sequences to reproduce raw videos using the outputs of the caption generation module. A dual learning mechanism fine-tunes the two modules according to the gap between the raw and the reproduced videos. Thus, our approach can minimize the semantic gap between raw videos and the generated captions by minimizing the differences between the reproduced and the raw visual sequences. Experimental results on a benchmark dataset demonstrate that MIMLDL can improve the accuracy of video captioning.
GTAE: Graph Transformer–Based Auto-Encoders for Linguistic-Constrained Text Style Transfer

Non-parallel text style transfer has attracted increasing research interests in recent years. Despite successes in transferring the style based on the encoder-decoder framework, current approaches still lack the ability to preserve the content and even logic of original sentences, mainly due to the large unconstrained model space or too simplified assumptions on latent embedding space. Since language itself is an intelligent product of humans with certain grammars and has a limited rule-based model space by its nature, relieving this problem requires reconciling the model capacity of deep neural networks with the intrinsic model constraints from human linguistic rules. To this end, we propose a method called Graph Transformer–based Auto-Encoder, which models a sentence as a linguistic graph and performs feature extraction and style transfer at the graph level, to maximally retain the content and the linguistic structure of original sentences. Quantitative experiment results on three non-parallel text style transfer tasks show that our model outperforms state-of-the-art methods in content preservation, while achieving comparable performance on transfer accuracy and sentence naturalness.
Simultaneous Localization and Mapping [SLAM] of Robotic Operating System for Mobile Robots

These days' mobile robots have seen a great proliferation in real world scenarios they impact lives as they tend to interfere in environments that we live. Mobile can become really handy in situations where there is need for rapid movements. The areas are open new avenues to varied business fortunes the mobile robot market is research in these areas are vital to capture a big market in the world, robot's intelligence is vital in movements of the robot such as motion and navigation, a robot set to gain knowledge about its environment by means of sophisticated technologies like Simultaneous Localization and Mapping [1]. The robot tends to obtain knowledge upon the system by various sensor data and it has various algorithms like SLAM this helps to map the surrounding and it has overview about the indoor environment the input to the SLAM is a laser scan data that is obtained from LIDAR the existing system (TURTLEBOT) tends to perform similar objective but the computation system that is the laptop is on the mobile robot which makes it difficult to operate hence it has operational cost and operational difficulties the proposed model tends to work on a considerably low cost and high interoperability as the system uses ROS based model to get and transfer data. SLAM is better solution in computational world as it helps robots achieve mapping and localizing at the same time this helps better navigation of system [2]. This feature of SLAM helps robots to navigate effectively this data can be utilized to draft out decisions which helps autonomous manoeuvre of domestic robots. This helps to achieve better path planning and navigation & mapping, We are now entering a new era of robust vision, in which working with a higher accepting computer process according to hearing aids and resources, and work-driven understanding is needed. This is available with a Probabilistic road map with this algorithm with the help of pose graph optimization and a loop closure of the help mode. Use the full autonomy of the Robotic system that helps navigating and planning organizations to achieve a higher view of nature and increase the performance of mobile systems distributed in the real world. Thus the robotic navigation skills are guaranteed.
Corpus translation system based on Web Services and its application

In order to construct an efficient translation system, this paper constructs a corpus translation system based on Web Services. Moreover, this paper builds a network term detection system based on machine learning algorithms, expands the corpus data with the support of the crawler system, and uses WEB retrieval translation technology. At the same time, in response to the problem of sentence length changes in English abstracts, this paper proposes a method to obtain standard sentence length changes based on edit distance and SVM sorting. Based on requirements, this paper designs the architecture and data integration process of the data integration system. In addition, this paper outlines the detailed design and implementation process of each module of the system, and proposes a system performance optimization plan, and combines translation requirements to construct a corpus translation system based on Web Services. Finally, this paper designs experiments to verify the performance of the model. The research results show that the system constructed in this paper has a good application effect.
SQLR: Short-Term Memory Q-Learning for Elastic Provisioning

As a growing number of service and application providers choose cloud networks to deliver their services on a software-as-a-service (SaaS) basis, cloud providers need to make their provisioning systems agile enough to meet service level agreements (SLAs).At the same time, they should guard against over-provisioning, which limits their capacity to accommodate more tenants.To this end, we propose Short-term memory Q-Learning pRovisioning (SQLR, pronounced as "scaler"), a system employing a customized variant of the model-free reinforcement learning algorithm.It can reuse contextual knowledge learned from one workload to optimize the number of virtual machines (resources) allocated to serve other workload patterns.With minimal overhead, SQLR achieves comparable results to systems where resources are unconstrained.Our experiments show that we can reduce the amount of provisioned resources by about 20% with less than 1% overall service unavailability (due to blocking), while delivering similar response times to those of an over-provisioned system.
Creating Domain based Dictionary and its Evaluation using Classification Accuracy

This paper creates a domain-based dictionary for different categories in the news domain. It extracts terms - unigrams and collocations (specifically bigrams and trigrams) to construct the dictionary. The created dictionary is then used to classify unseen news data. The paper studies how variation in two parameters - (i) occurrence frequency of extracted terms and (ii) window size of extracted bigrams impact the dictionary size and the classification accuracy. Subsequently, dictionary size and classification accuracy are analyzed by creating dictionaries comprising of just unigrams, bigrams, trigrams, or their combinations. A reasonably sized and accurate dictionary can be created using just bigrams. The inclusion of trigrams to the dictionary accounts for a slight accuracy gain. Including both unigrams and bigrams in the dictionary can achieve a high accuracy score with a significantly smaller dictionary size than adding just unigrams or bigrams to the dictionary. Also, the trio combination adding unigrams, bigrams, and trigrams to the dictionary does not lead to a significant increase in accuracy. These observations will help in creating a meaningful and compact dictionary.
Embodiment in Virtual Reality

Virtual reality (VR) has been a prominent idea for exploring new worlds beyond the physical, and in recent decades, it has evolved in many aspects. The notion of immersion and the sense of presence in VR gained new definitions as technological advances took place. However, even today, we can question whether the degrees of immersion achieved through this technology are profound and felt. A fundamental aspect is the sense of embodiment in the virtual space. To what extent do we feel embodied in virtual environments? In this publication, the authors present works that challenge and question the embodiment sensation in VR, specifically in the artistic aspect. They present initial reflections about embodiment in virtuality and analyze the technologies adopted in creating interactive artworks prepared for galleries and theater stage, questioning the sensations caused by the visual embodiment in virtual reality under the perspective of both the audience and the performer.
Classification by Attention: Scene Graph Classification with Prior Knowledge.

A main challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another. Previous works have addressed this by relational reasoning over all objects in an image, or incorporating prior knowledge into classification. Unlike previous works, we do not consider separate models for the perception and prior knowledge. Instead, we take a multi-task learning approach, where the classification is implemented as an attention layer. This allows for the prior knowledge to emerge and propagate within the perception model. By enforcing the model to also represent the prior, we achieve a strong inductive bias. We show that our model can accurately generate commonsense knowledge and that the iterative injection of this knowledge to scene representations leads to a significantly higher classification performance. Additionally, our model can be fine-tuned on external knowledge given as triples. When combined with self-supervised learning, this leads to accurate predictions with 1% of annotated images only.
Calibration Done Right: Noiseless Flush+Flush Attacks

Caches leak information through timing measurements and side-channel attacks. Several attack primitives exist with different requirements and trade-offs. Flush+Flush is a stealthy and fast one that uses the timing of the clflush instruction depending on whether a line is cached. We show that the CPU interconnect plays a bigger role than previously thought in these timings and in Flush+Flush error rate. In this paper, we show that a naive implementation that does not account for the topology of the interconnect yields very high error rates, especially on modern CPUs as the number of cores increases. We therefore reverse-engineer this topology and revisit the calibration phase of Flush+Flush for different attacker models to determine the correct threshold for clflush hits and misses. We show that our method yields close-to-noiseless side-channel attacks by attacking the AES T-tables implementation of OpenSSL, and by building a covert channel. We obtain a maximal capacity of 5.8 Mbit/s with our method, compared to 1.9 Mbit/s with a naive Flush+Flush implementation on an Intel Core i9-9900 CPU.
A Scoping Study on Lightweight Cryptography Reviews in IoT

The efforts in designing and developing lightweight cryptography (LWC) started a decade ago. Many scholarly studies in literature report the enhancement of conventional cryptographic algorithms and the development of new algorithms. This significant number of studies resulted in the rise of many review studies on LWC in IoT. Due to the vast number of review studies on LWC in IoT, it is not known what the studies cover and how extensive the review studies are. Therefore, this article aimed to bridge the gap in the review studies by conducting a systematic scoping study. It analyzed the existing review articles on LWC in IoT to discover the extensiveness of the reviews and the topics covered. The results of the study suggested that many review studies are classified as overview-types of review focusing on generic LWC. Further, the topics of the reviews mainly focused on symmetric block cryptography, while limited reviews were found on asymmetric-key and hash in LWC. The outcomes of this study revealed that the reviews in LWC in IoT are still in their premature stage and researchers are encouraged to explore by conducting review studies in the less-attended areas. An extensive review of studies that cover these two topics is deemed necessary to establish a balance of scholarly works in LWC for IoT and encourage more empirical research in the area.
A Keystroke-based Continuous User Authentication in Virtual Desktop Infrastructure

Demand for remote work has surged as the COVID-19 epidemic has spread around the world. As one of the main implementations of desktop virtualization, Virtual Desktop Infrastructure (VDI) is popular and widely used in corporate remote work. A VDI user can connect to and use a virtual machine in a remote data center by logging in with a username and password using any device anywhere with Internet access. VDI has mobile convenience but is at risk of password leakage and insider threat. Traditional authentication methods, such as password and PIN, cannot withstand these threats. This work presents a keystroke-based continuous user authentication based on the Bidirectional Long Short-Term Memory (Bi-LSTM) network and embedding mechanism in deep learning to defend against such risks. It verifies the current user's identity based on the user's typing behavior continuously and non-invasively. We implement it on SPICE VDI and evaluate its performance and deployment feasibility on a public keystroke dataset - the Clarkson II dataset, which collected in uncontrolled and natural settings. The results show that it achieves state-of-art performance. It detects intruders with 8.28% of EER when only using 30 keystrokes and 0.85% of EER when using 990 keystrokes.