Reviewerly/exHarmony huggingface dataset:
Extended reality (XR) virtual practical and educational eGaming to provide effective immersive environments for learning and teaching in forensic science

Online virtual learning resources have been available for learning and teaching in forensic science for some years now, but the recent global COVID-19 related periods of irregular lockdown have necessitated the rapid development of these for teaching, learning and CPD activities. However, these resources do need to be carefully constructed and grounded in pedagogic theory to be effective. This article details eXtended Reality (XR) learning and teaching environments to facilitate effective online teaching and learning for forensic geoscientists. The first two case studies discussed in this article make use of Thinglink software to produce virtual learning and teaching XR resources through an internet system, which was delivered to undergraduate students in 2021. Case one details a range of XR virtual laboratory-based equipment resources, providing a consistent, reliable and asynchronous learning and teaching experience, whilst the second case study presents an XR virtual learning applied geophysics resource developed for a 12-week CPD training programme. This programme involves recorded equipment video resources, accompanying datasets and worksheets for users to work through. Both case studies were positively received by learners, but there were issues encountered by learners with poor internet connections or computer skills, or who do not engage well with online learning. A third case study showcases an XR educational forensic geoscience eGame that was developed to take the user through a cold case search investigation, from desktop study through to field reconnaissance and multi-staged site investigations. Pedagogic research was undertaken with user questionnaires and interviews, providing evidence that the eGame was an effective learning and teaching tool. eGame users highly rated the eGame and reported that they raised awareness and understanding of the use of geophysics equipment and best practice of forensic geoscience search phased investigations. These types of XR virtual learning digital resources, whilst costly to produce in terms of development time and staff resource, provide a complementary virtual learning experience to in-situ practical sessions, and allow learners to asynchronously familiarise themselves with equipment, environments and techniques resulting in more efficient use of in situ time. The XR resources also allow learners to reinforce learning post in-situ sessions. Finally, XR resources can provide a more inclusive and authentic experience for learners who cannot attend or complete work synchronously.
Online product sentiment analysis using random evolutionary whale optimization algorithm and deep belief network

• Image and text normalization techniques are used to improve the data quality. • Used MLBP , SURF, TF-IDF and LSA techniques for feature extraction. • Used RE-WOA to reduce the dimension of the extracted feature vectors. • Used DBN classifier to classify the user's sentiments of the online products. In the recent decades, the online product sentiment analysis is an emerging research topic that assists the customers to take better decisions on purchasing the products and to achieve better sales of the products. Recently, several machine learning techniques are experimented on many datasets for analyzing the customer's sentiments through online portals. Still, the customers are struggling to obtain the aspect sentiments expressed by other customers, particularly in the amazon websites. Therefore, a novel automated model is proposed in this manuscript for an effective online product sentiment analysis. After collecting the multimodal data from the amazon websites, the image and data normalization techniques are employed for better understanding of the collected data. Further, the feature extraction is performed by utilizing Latent Semantic Analysis (LSA), Term Frequency- Inverse Document Frequency (TF-IDF), Modified Local Binary Pattern (MLBP), and Speeded Up Robust Features (SURF) descriptors for extracting the textual and visual feature vectors from the preprocessed data. Finally, the Random Evolutionary Whale Optimization Algorithm (REWOA) and Deep Belief Network (DBN) classifier are integrated for feature vector optimization and sentiment classification. By using feature optimization, the system complexity and running time of the classifier is improved. The experimental investigation states that the developed REWOA-DBN model achieved 96.86% of classification accuracy, which is better compared to other optimizers and classifiers
The Investigation of Mobile Virtual Reality Application Instructional Content in Geography Education: Academic Achievement, Presence, and Student Interaction

In this study, we created an interactive Virtual Reality (VR) instructional application for geography education. The sample consisted of 9th grade students as 20 students in the control group and 20 students in the experimental group. Expository Instruction Methods, one of the traditional teaching methods, were applied to the control group, and the teacher on the slide explained the "Shape and Movements of the Earth" as the subject. The students in the experimental group used the VR-based interactive teaching environment we designed for the same subject under the supervision of a teacher. We explored the relationship between students' level of presence and their academic achievement and received students' opinions. The students' academic scores in the experimental group were higher with a statistically significant difference than the control group, and we found that the motivation was higher in the experimental group. The research aims to show the effect of using VR interactive teaching materials in geography education, specifically for "Shape and Movements of the Earth".
Recommendation System for Research Studies Based on GCR

Recommender systems are becoming increasingly popular on the internet in recent years. The research has advanced by developing numerous iterations of personalized recommendation systems to increase suggestion effectiveness, use, and accessibility. Intelligence graph-based suggestions had already increasingly gained traction in industry and academia due to their ability to focusing on the following scarcity and performance problems. In this study, we present a novel approach that is based on a ranking-oriented personalized recommendation framework that autonomously suggests items of possible interest to viewers. To enhance predictions, the proposed method makes use of comparable author affiliations across articles. With an author-based search pattern, the system suggests papers to individual and special events to all scholars to help individuals gain more competence in their subject of interest. The approach does this by combining articles featuring comparable author affiliations and the author who appears the much more frequently. We demonstrate that the suggested algorithm performs better previous examine in the realm of research article recommendation.
Few-shot Aspect Category Sentiment Analysis via Meta-learning

Existing aspect-based/category sentiment analysis methods have shown great success in detecting sentiment polarity toward a given aspect in a sentence with supervised learning, where the training and inference stages share the same pre-defined set of aspects. However, in practice, the aspect categories are changing rather than keeping fixed over time. Dealing with unseen aspect categories is under-explored in existing methods. In this article, we formulate a new few-shot aspect category sentiment analysis (FSACSA) task, which aims to effectively predict the sentiment polarity of previously unseen aspect categories. To this end, we propose a novel Aspect-Focused Meta-Learning (AFML) framework that constructs aspect-aware and aspect-contrastive representations from external knowledge to match the target aspect with aspects in the training set. Concretely, we first construct two auxiliary contrastive sentences for a given sentence with the incorporation of external knowledge, enabling the learning of sentence representations with a better generalization. Then, we devise an aspect-focused induction network to leverage the contextual sentiment toward a given aspect to refine the label vectors. Furthermore, we employ the episode-based meta-learning algorithm to train the whole network, so as to learn to generalize to novel aspects. Extensive experiments on multiple real-life datasets show that our proposed AFML framework achieves the state-of-the-art results for the FSACSA task.
Text Analytics Model to Identify the Connection Between Theme and Sentiment in Literary Works

The rapid advancements in data science techniques and approaches have influenced disciplines, such as literary studies, that are particularly engaged in qualitative text analysis. This chapter aims to apply natural language preprocessing (NLP) to identify the connection between theme and sentiment in a corpus of six life writings by or about Iraqi people. To do so, the study uses Latent Dirichlet Allocation (LDA) from topic modeling and the two models of Gensim and Mallet. It also implements TextBlob dictionary to calculate the polarity and subjectivity scores to measure the sentiment for detected themes. Nine topics are extracted from both models. The extracted themes point to the prevalence of traumatic events that the authors have personally endured. Gensim works better than Mallet as it has high coherence score and relevant terms. In sentiment analysis, most of the themes appeared as positive. The application of LDA using Gensim also revealed that the selected life writings are shaped and influenced by the authors' personal feelings. It is hoped that the analytical models can encourage future studies to improve existing qualitative methods in literary studies.
Optimized implementation of an improved KNN classification algorithm using Intel FPGA platform: Covid-19 case study

The improved k-nearest neighbor (KNN) algorithm based on class contribution and feature weighting (DCT-KNN) is a highly accurate approach. However, it requires complex computational steps which consumes much time for the classification process. A field programmable gate array (FPGA) can be used to solve this drawback. However, using traditional hardware description language (HDL) to implement FPGA-based accelerators requires a high design time. Fortunately, the open computing language (OpenCL) high level parallel programming tool allows rapid and effective design on FPGA-based hardware accelerators. In this study, OpenCL has been used to examine speeding up the DCT-KNN algorithm on the FPGA parallel computing platform through applying numerous parallelization and optimization techniques. The optimized approach of the improved KNN could be used in various engineering problems that require a high speed of classification process. Classification of the COVID-19 disease is the case study used to examine this work. The experimental results show that implementing the DCT-KNN algorithm on the FPGA platform (Intel De5a-net Arria-10 device was used) gives an extremely high performance when compared to the traditional single-core-CPU based implementation. The execution time for our optimized design on the FPGA accelerator is 44 times faster than the conventional design implemented on the regular CPU-based computational platform.
Novel hybrid ensemble credit scoring model with stacking-based noise detection and weight assignment

Credit scoring is used to help financial institutions control default risks and reduce economic losses, and a variety of mainstream machine learning and data mining algorithms have been applied for this purpose. However, real-world datasets are generally noisy, which seriously affects the performance of credit scoring models. Among the mainstream strategies for handling noise, instance filtering may result in information loss, especially for hard-to-access credit datasets, and label noise correction may produce erroneous information in the dataset. In this study, to reduce the adverse impact of noisy data on the performance of classification algorithms, a novel hybrid ensemble credit scoring model with stacking-based noise detection and weight assignment is developed to remove or adapt noisy data in raw datasets and to form noise-detected training data to obtain excellent default risk prediction competence. Furthermore, a new weight assignment approach based on a cloud model is proposed, which is applied to calculate the weight values of the classifiers in the weighted voting ensemble model to improve the prediction accuracy of the proposed model. In this study, five public datasets are adopted using five performance metrics to evaluate the performance of the proposed model. The experimental results demonstrate good model prediction power and robustness.
Image Watermarking Scheme Using LSB and Image Gradient

In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal.
Hyperspectral Image Denoising via Adversarial Learning

Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks. Therefore, HSI denoising has become an essential part of HSI preprocessing. Traditional methods tend to tackle one specific type of noise and remove it iteratively, resulting in drawbacks including inefficiency when dealing with mixed noise. Most recently, deep neural network-based models, especially generative adversarial networks, have demonstrated promising performance in generic image denoising. However, in contrast to generic RGB images, HSIs often possess abundant spectral information; thus, it is non-trivial to design a denoising network to effectively explore both spatial and spectral characteristics simultaneously. To address the above issues, in this paper, we propose an end-to-end HSI denoising model via adversarial learning. More specifically, to capture the subtle noise distribution from both spatial and spectral dimensions, we designed a Residual Spatial-Spectral Module (RSSM) and embed it in an UNet-like structure as the generator to obtain clean images. To distinguish the real image from the generated one, we designed a discriminator based on the Multiscale Feature Fusion Module (MFFM) to further improve the quality of the denoising results. The generator was trained with joint loss functions, including reconstruction loss, structural loss and adversarial loss. Moreover, considering the lack of publicly available training data for the HSI denoising task, we collected an additional benchmark dataset denoted as the Shandong Feicheng Denoising (SFD) dataset. We evaluated five types of mixed noise across several datasets in comparative experiments, and comprehensive experimental results on both simulated and real data demonstrate that the proposed model achieves competitive results against state-of-the-art methods. For ablation studies, we investigated the structure of the generator as well as the training process with joint losses and different amounts of training data, further validating the rationality and effectiveness of the proposed method.
Software engineering for quantum programming: How far are we?

Quantum computing is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially overcome the limits of classical computation. Over the last years, all major companies have provided frameworks and programming languages that allow developers to create their quantum applications. This shift has led to the definition of a new discipline called quantum software engineering, which is demanded to define novel methods for engineering large-scale quantum applications. While the research community is successfully embracing this call, we notice a lack of systematic investigations into the state of the practice of quantum programming. Understanding the challenges that quantum developers face is vital to precisely define the aims of quantum software engineering. Hence, in this paper, we first mine all the GitHub repositories that make use of the most used quantum programming frameworks currently on the market and then conduct coding analysis sessions to produce a taxonomy of the purposes for which quantum technologies are used. In the second place, we conduct a survey study that involves the contributors of the considered repositories, which aims to elicit the developers’ opinions on the current adoption and challenges of quantum programming. On the one hand, the results highlight that the current adoption of quantum programming is still limited. On the other hand, there are many challenges that the software engineering community should carefully consider: these do not strictly pertain to technical concerns but also socio-technical matters.
Eye Tracking-based LSTM for Locomotion Prediction in VR

Virtual Reality (VR) allows users to perform natural movements such as hand movements, turning the head and natural walking in virtual environments. While such movements enable seamless natural interaction, they come with the need for a large tracking space, particularly in the case of walking. To optimise use of the available physical space, prediction models for upcoming behavior are helpful. In this study, we examined whether a user&#x2019;s eye movements tracked by current VR hardware can improve such predictions. Eighteen participants walked through a virtual environment while performing different tasks, including walking in curved paths, avoiding or approaching objects, and conducting a search. The recorded position, orientation and eye-tracking features from 2.5 s segments of the data were used to train an LSTM model to predict the user&#x2019;s position 2.5 s into the future. We found that future positions can be predicted with an average error of 65 cm. The benefit of eye movement data depended on the task and environment. In particular, situations with changes in walking speed benefited from the inclusion of eye data. We conclude that a model utilizing eye tracking data can improve VR applications in which path predictions are helpful.
Depth Perception in Augmented Reality: The Effects of Display, Shadow, and Position

Although it is commonly accepted that depth perception in augmented reality (AR) displays is distorted, we have yet to isolate which properties of AR affect people&#x2019;s ability to correctly perceive virtual objects in real spaces. From prior research on depth perception in commercial virtual reality, it is likely that ergonomic properties and graphical limitations impact visual perception in head-mounted displays (HMDs). However, an insufficient amount of research has been conducted in augmented reality HMDs for us to begin isolating pertinent factors in this family of displays. To this end, in the current research, we evaluate absolute measures of distance perception in the Microsoft HoloLens 2, an optical see-through AR display, and the Varjo XR-3, a video see-through AR display. The current work is the first to evaluate either device using absolute distance perception as a measure. For each display, we asked participants to verbally report distance judgments to both grounded and floating targets that were rendered either with or without a cast shadow along the ground. Our findings suggest that currently available video see-through displays may induce more distance underestimation than their optical see-through counterparts. We also find that the vertical position of an object and the presence of a cast shadow influence depth perception.
Deterministic Grover search with a restricted oracle

Grover's quantum search algorithm provides a quadratic quantum advantage over classical algorithms across a broad class of unstructured search problems. The original protocol is probabilistic, returning the desired result with significant probability on each query, but in general, requiring several iterations of the algorithm. We present a modified version to return the correct result with certainty without having user control over the quantum search oracle. Our deterministic, two-parameter "D2p" protocol utilizes generalized phase rotations replacing the phase inversions after a standard oracle query. The D2p protocol achieves a 100% success rate in no more than one additional iteration compared to the optimal number of steps in the original Grover's search enabling the same quadratic speed up. We also provide a visualization using the Bloch sphere for enhanced geometric intuition.
Markerless 3D Skeleton Tracking Algorithm by Merging Multiple Inaccurate Skeleton Data from Multiple RGB-D Sensors

Skeleton data, which is often used in the HCI field, is a data structure that can efficiently express human poses and gestures because it consists of 3D positions of joints. The advancement of RGB-D sensors, such as Kinect sensors, enabled the easy capture of skeleton data from depth or RGB images. However, when tracking a target with a single sensor, there is an occlusion problem causing the quality of invisible joints to be randomly degraded. As a result, multiple sensors should be used to reliably track a target in all directions over a wide range. In this paper, we proposed a new method for combining multiple inaccurate skeleton data sets obtained from multiple sensors that capture a target from different angles into a single accurate skeleton data. The proposed algorithm uses density-based spatial clustering of applications with noise (DBSCAN) to prevent noise-added inaccurate joint candidates from participating in the merging process. After merging with the inlier candidates, we used Kalman filter to denoise the tremble error of the joint's movement. We evaluated the proposed algorithm's performance using the best view as the ground truth. In addition, the results of different sizes for the DBSCAN searching area were analyzed. By applying the proposed algorithm, the joint position accuracy of the merged skeleton improved as the number of sensors increased. Furthermore, highest performance was shown when the searching area of DBSCAN was 10 cm.