Reviewerly/exHarmony huggingface dataset:
Measuring the effectiveness of virtual training: A systematic review

The amount of research on virtual reality learning tools increases with time. Despite the diverse environments and theoretical foundations, enough data have been accumulated in recent years to provide a systematic review of the methods used. We pose ten questions concerning the methodological aspects of these studies. We performed a search in three databases according to the PRISMA guidelines and evaluated several characteristics, with particular emphasis on researchers' methodological decisions. We found an increase over time in the number of studies on the effectiveness of VR-based learning. We also identified shortcomings related to how the duration and number of training sessions are reported. We believe that these two factors could affect the effectiveness of VR-based training. Furthermore, when using the Kirkpatrick model, a significant imbalance can be observed in favor of outcomes from the 'Reaction' and 'Learning' levels compared to the 'Behavior' and 'Results' levels. The last of these was not used in any of the 330 reviewed studies. These results highlight the importance of research on the effectiveness of VR training. Taking into account the identified methodological shortcomings will allow for more significant research on this topic in the future.
FPGA Implementation of PRESENT Block Cypher with Optimised Substitution Box

Conventional cryptographic techniques such as Advanced Encryption Standard (AES) being resource intensive are not feasible for low-end Internet of Things (IoT) devices. As such, several lightweight crypto primitives have been designed to offer an optimum level of security along with reduced resource utilisation. Also, because of the trade-offs between different metrics, lightweight cryptography often targets a specific parameter, making it a good fit for a particular field of IoT application. This paper aims to reduce the hardware footprint of the PRESENT block cypher with the area-efficient hardware design of Substitution-Box, which is the most resource-consuming part of the PRESENT cypher. The proposed hardware design for S-Box is implemented in the state-of-the-art architectures reported in the literature. The designs are implemented on the FPGAs to assess resource consumption and performance. The original designs and their implementation with the proposed hardware for S-Box have been compared in terms of resource consumption, maximum achievable throughput, and throughput per slice. The results indicate a 13.67% improvement in resource consumption by adopting the proposed S-Box in the architecture. Moreover, throughput has been increased for certain PRESENT architectures, thus enhancing their overall performance.
Accuracy Prediction on Detection of Breast Cancer Using Machine Learning Classifiers

Breast Cancer becomes a curse to women, and it increases day by day. So, there is a need for a more reliable and precise method for its detection. In this technical era, the medical field has become more advanced with its integration with computer technology. Its detection accuracy is proven more than humans. As it detects the feature that may remain unnoticed by humans. So, in this proposed work it is studied to detect the type of cancers how it can be detected using machine learning algorithm which algorithm is best for detection of these type of dataset i.e. (malignant and benign) these are further introduced in this work. The used dataset has 569 instances with 30 attributes. Therefore, several machine learning techniques i.e., naïve Bayes, logistics Regression, decision tree, k nearest neighbor (KNN), adaptive boosting (Adaboost) are used to make a comparative analysis of these algorithms on basis of their accuracy. Logistic regression outperforms the other classifiers in terms of confusion matrix parameter.
Insufficiency-Driven DNN Error Detection in the Context of SOTIF on Traffic Sign Recognition Use Case

Deep Neural Networks (DNNs) are used in various domains and industry fields with great success due to their ability to learn complex tasks from high-dimensional data. However, the data-driven approach within deep learning results in various DNN-specific insufficiencies (e.g., robustness limitations, overconfidence, lack of interpretability), which makes the usage in safety-critical applications, like automated driving, challenging. An important safety strategy to address these limitations is the detection of DNN errors (e.g., false positives) during runtime. In this work, we present a general error detection approach for DNNs, which combines diverse monitoring methods to address different safety-related DNN insufficiencies simultaneously. To ensure consistency with the automotive safety domain, we take into account established concepts of the automotive safety standard ISO 21448 (SOTIF). We apply our error detection method on the safety-related use case of traffic sign recognition by using self-created 3D driving scenarios. In doing so, we consider different types of DNN errors related to in distribution, out of distribution, and adversarial data. We demonstrate that our approach is able to handle all these error types. Furthermore, we show the performance benefit of our method compared to a baseline DNN and to state of the art DNN monitoring methods.
Rain Removal From Light Field Images With 4D Convolution and Multi-Scale Gaussian Process

Existing deraining methods mainly focus on a single input image. However, with just a single input image, it is extremely difficult to accurately detect and remove rain streaks, in order to restore a rain-free image. In contrast, a light field image (LFI) embeds abundant 3D structure and texture information of the target scene by recording the direction and position of each incident ray via a plenoptic camera, which has emerged as a popular device in the computer vision and graphics research communities. However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem. In this paper, we propose a novel network, 4D-MGP-SRRNet, for rain streak removal from LFIs. Our method takes as input all sub-views of a rainy LFI. In order to make full use of the LFI, we adopt 4D convolutional layers to build the proposed rain steak removal network to simultaneously process all sub-views of the LFI. In the proposed network, the rain detection model, MGPDNet, with a novel Multi-scale Self-guided Gaussian Process (MSGP) module is proposed to detect high-resolution rain streaks from all sub-views of the input LFI at multi-scales. Semi-supervised learning is introduced for MSGP to accurately detect rain streaks by training on both virtual-world rainy LFIs and real-world rainy LFIs at multi-scales via calculating pseudo ground truths for real-world rain streaks. We then feed all sub-views subtracting the predicted rain streaks into a 4D convolution-based Depth Estimation Residual Network (DERNet) to estimate the depth maps, which are later converted into fog maps. Finally, all sub-views concatenated with the corresponding rain streaks and fog maps are fed into a powerful rainy LFI restoring model based on the adversarial recurrent neural network to progressively eliminate rain streaks and recover the rain-free LFI. Extensive quantitative and qualitative evaluations conducted on both synthetic LFIs and real-world LFIs demonstrate the effectiveness of our proposed method.
Steformer: Efficient Stereo Image Super-Resolution With Transformer

With the rapid development of stereoscopic vision applications, stereo image processing techniques have attracted increasing attention in both academic and industrial communities. In this paper, we study the fundamental stereo image super-resolution (SR) problem, which aims to recover high-resolution stereo images from low-resolution (LR) stereo images. Since disparities between stereo images vary significantly, convolutional network-based stereo image SR methods show a limitation in capturing long-range dependencies. To address this problem, this paper proposes to leverage the capability of self-attention in Transformers to efficiently capture reliable stereo correspondence and incorporate cross-view information for stereo image SR. Our model, named Steformer, consists of three parts: cross attentive feature extraction, cross-to-intra information integration and high-quality image reconstruction. In particular, the cross attentive feature extraction module employs residual cross Steformer blocks (RCSB) for long-range cross-view information extraction. Then, the cross-to-intra information integration module exploits cross-view and intra-view information using cross-to-intra attention mechanism (C2IAM). Finally, residual Steformer blocks (RSB) are designed for feature pre-processing in high-quality image reconstruction. Extensive experiments show that Steformer achieves significant improvements over state-of-the-art approaches on both quantitative and qualitative evaluations, while the total number of parameters can be reduced by up to 40.71%.
Bidirectional steering, entanglement and coherence of accelerated qubit–qutrit system with a stochastic noise

The influence of classical stochastic noise on three quantum correlation measurements of an accelerated qubit–qutrit system is examined. Bidirectional steerability, entanglement, and quantum coherence are investigated. The classical stochastic noises are investigated using either the Gaussian noise, the power-law noise, or a mixed Gaussian-power-law noise. Our results demonstrate that all quantum correlations decrease when the interaction time between the system and the noise channels advances or the acceleration parameter increases. The bidirectional steerability from qubit to qutrit and reversal is explored. The decoherence imposed by the Unruh effect of the observer is more impressive for qutrit than for the qubit subsystem. In a comparative study of bidirectional steerability, we conclude that the steering from qubit to qutrit is better than the steerability from qutrit to qubit. By mixing the various noises, no matter the order of subsystems, we can control the degradation of entanglement and failure in steerability among subsystems.
Artificial Intelligence and Human Rights Activism

Behrouz Boochani, the Kurdish-Iranian author of No Friends But the Mountains, has been using social media platforms such as Twitter to speak up against social injustice and human rights abuse against immigrants across the globe. This study proposes an artificial intelligence lifecycle for opinion mining of the dominant sentiments, topics, and emotions in Boochani's social media activism. Sentiment analysis (Vader and Textblob), topic modelling (LDA and NMF), and emotion detection are performed to extract hidden sentiments, topics, and emotions from the data that is collected from his tweets from 2017-2021. The results show Vader performs better than Textblob. LDA is considered the best algorithm. It extracted seven main topics as suicide, translator of book, publication of book, human rights, political, immigration, and detention. Finally, the main emotion detected from the tweets is sadness.
Artificial Intelligence Framework for Opinion Mining of Netizen Readers' Reviews of Arundhati Roy's The God of Small Things

In recent years, South-Asian literature in English has experienced a surge of newfound love and popularity both in the local and the global market. In this regard, Arundhati Roy's The God of Small Things (1997) has garnered an astounding mix of positive and negative reactions from readers across the globe. This chapter adopts an artificial intelligence approach to analyse netizen readers' feedback on the novel as documented in the book cataloguing website Goodreads. To this end, an opinion mining framework is proposed based on artificial intelligence techniques such as topic modelling and sentiment analysis. Latent semantic analysis (LSA) and latent Dirichlet allocation (LDA) are applied and compared to find the abstract “topics” that occur in a collection of reviews. Furthermore, lexicon-based sentiment analysis approaches such as Vader and Textblob algorithms are used and compared to find the review sentiment polarities.
Introduction to Artificial Intelligence for the Analytics of Literary Works and Social Media

Advancement of artificial intelligence has opened new horizons for the analytics of literary texts and social media. However, the current studies are very limited, and there is still need for further scholarships that use AI applications to analyze literary works and social media texts. After presenting a working definition of certain key terms in the background section of the study, this chapter offers a detailed review of related studies that have employed artificial intelligence in the analysis of texts. Next, the chapter delineates AI-narrative architecture as a proposed nine-phase method to demonstrate the steps for the analytics of literary texts and social media. Finally, the discussion focuses on the new applications and findings, which are presented in 13 different studies that are included in this volume.
Decomposed adversarial domain generalization

We tackle the problem of generalizing a predictor trained on a set of source domains to an unseen target domain, where the source and target domains are different but related to one another, i.e., the domain generalization problem. Prior adversarial methods rely on solving the minimax problems to align in the neural network embedding space the components of the domains (i.e., a set of marginal distributions, a set of marginal distributions and multiple sets of class-conditional distributions). However, these methods introduce additional parameters (for each set of distributions) to the network predictor and are difficult to train. In this work, we propose to directly align the domains themselves via solving a minimax problem that can be decomposed and converted into a min one. Particularly, we analytically solve the max problem with respect to (w.r.t.) the domain discriminators, and convert the minimax problem into a min one w.r.t. the embedding function. This is more advantageous since in the end our approach introduces no additional network parameters and simplifies the training procedure. We evaluate our approach on several multi-domain datasets and testify its superiority over the relevant methods. The source code is available at https://github.com/sentaochen/Decomposed-Adversarial-Domain-Generalization.
An Artificial Intelligence Application of Theme and Space in Life Writings of Middle Eastern Women

Recently, the revolutionary transformations in social and political landscapes as well as the remarkable developments in artificial intelligence reinforced the importance of geography and spatial analyses in literary and cultural studies. This chapter proposes an analytical framework of topic modelling and sentiment analysis for exploring the connection between theme, place, and sentiment in 36 autobiographical narratives by or about women from the Middle East. In the proposed framework, a latent Dirichlet allocation and latent semantic analysis algorithm from topic modelling, TextBlob library for sentiment analysis are employed to detect the place names that come together and to point out the associated themes and emotions throughout the data source. The model gives a scoring of each topical clusters and reveals that the diasporic authors are more likely to write about their hometown than their current host land. The authors hope that the merging of topic modelling and sentiment analysis would be beneficial to literary critics in the analysis of long texts.
Spatial–temporal graph attention network for video anomaly detection

Video anomaly detection, which is weakly supervised by video-level annotations, is a frequent yet challenging task in computer vision owing to its unexpectedness, equivocality, rarity, irregularity, and diversity. Although previous seminal works successfully leveraged graph convolutions to assist in the detection of anomalies, they failed to subsequently explore the potential of this approach. In this study, we developed a spatial–temporal graph attention network (STGA), which, to the best of our knowledge, is the first effort to combine graph convolutions with a multi-head graph attention mechanism for video anomaly detection. Specifically, a spatial correlation graph and temporal dependence graph were devised to learn distinguishable representations with the complementation of graph attention techniques. Furthermore, STGA was incorporated within the multiple instance learning (MIL) pipeline, and optimized via top-k MIL ranking loss after a sparse continuous sampling strategy was put into effect. We conducted experiments using four multi-scale datasets to validate the efficacy of our model. Quantitatively, our method performs equivalently to the current best result on ShanghaiTech with a frame-level area under the curve (AUC) of 97.21%, obtains the second-best result on UCSD-Ped2 with a frame-level AUC of 97.4%, gains a frame-level AUC of 80.28% on UCF-Crime, and achieves a new state-of-the-art performance on TAD with a frame-level AUC of 91.42%. Additionally, our false alarm rate results outperform those obtained in previous studies on ShanghaiTech and UCF-Crime, which demonstrates the robustness of our approach. All relevant code has been made available at https://github.com/hychen96/STGA-VAD.
Conditional generative data-free knowledge distillation

Knowledge distillation has made remarkable achievements in model compression. However, most existing methods require the original training data, which is usually unavailable due to privacy and security issues. This paper proposes a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without real data. This framework realizes efficient knowledge distillation based on conditional image generation. Specifically, we treat the preset labels as ground truth to train a semi-supervised conditional generator. The trained generator can produce specified classes of training images. During training, we force the student model to extract the hidden knowledge in teacher feature maps, which provide crucial cues to the learning process. Meanwhile, we construct an adversarial training framework to promote distillation performance. The framework will help the student model to explore larger data space. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments on different datasets. Compared with other data-free works, our method obtains state-of-the-art results on CIFAR100, Caltech101, and different versions of ImageNet datasets. The codes will be released.
Generating multiple conceptual models from behavior-driven development scenarios

Researchers have proposed that generating conceptual models automatically from user stories might be useful for agile software development. It is, however, unclear from the state-of-the-art what a consistent and complementary set of models to generate is, how these models can be generated such that relationships and dependencies in a set of related user stories are unveiled, and why these models are useful in agile software development projects. In this paper, we address these questions through a Design Science research study. First, we define four stylized versions of Unified Modeling Language (UML) diagrams (i.e., use case diagram, class diagram, activity diagram, state machine diagram) that will be the target of the model generation. Although these stylized UML diagrams have a reduced abstract syntax, they offer different perspectives on the software system in focus with potential usefulness for requirements and software engineering. Second, we develop an automated model generation approach based on different design artifacts including a Natural Language Processing (NLP) tool that implements our approach. Key to our solution is the use of the Behavior-Driven Development (BDD) scenario template to document user stories. Using an example set of BDD scenarios as source of the model generation, we demonstrate the feasibility of our approach via the NLP tool that implements our approach. Third, we conduct an empirical study with experts in agile software development involving the researcher-guided interactive use of our tool to explore the use of the generated models. This study shows the perceived usefulness of the models that our tool can generate and identifies different uses and benefits of the models for requirements analysis, system design, software implementation, and testing in projects that employ agile methods.
MRE: A translational knowledge graph completion model based on multiple relation embedding

Knowledge graph completion (KGC) has attracted significant research interest in applying knowledge graphs (KGs). Previously, many works have been proposed to solve the KGC problem, such as a series of translational and semantic matching models. However, most previous methods suffer from two limitations. First, current models only consider the single form of relations, thus failing to simultaneously capture the semantics of multiple relations (direct, multi-hop and rule-based). Second, the data-sparse problem of knowledge graphs would make part of relations challenging to embed. This paper proposes a novel translational knowledge graph completion model named multiple relation embedding (MRE) to address the above limitations. We attempt to embed multiple relations to provide more semantic information for representing KGs. To be more specific, we first leverage PTransE and AMIE+ to extract multi-hop and rule-based relations. Then, we propose two specific encoders to encode extracted relations and capture semantic information of multiple relations. We note that our proposed encoders can achieve interactions between relations and connected entities in relation encoding, which is rarely considered in existing methods. Next, we define three energy functions to model KGs based on the translational assumption. At last, a joint training method is adopted to perform KGC. Experimental results illustrate that MRE outperforms other baselines on KGC, demonstrating the effectiveness of embedding multiple relations for advancing knowledge graph completion.