Reviewerly/exHarmony huggingface dataset:
Pedagogical Agents in Educational VR: An in the Wild Study

Pedagogical agents are theorized to increase humans' effort to understand computerized instructions. Despite the pedagogical promises of VR, the usefulness of pedagogical agents in VR remains uncertain. Based on this gap, and inspired by global efforts to advance remote learning during the COVID-19 pandemic, we conducted an educational VR study in-the-wild (N = 161). With a 2 × 2 + 1 between subjects design, we manipulated the appearance and behavior of a virtual museum guide in an exhibition about viruses. Factual and conceptual learning outcomes as well as subjective learning experience measures were collected. In general, participants reported high enjoyment and had significant knowledge acquisition. We found that the agent's appearance and behavior impacted factual knowledge gain. We also report an interaction effect between behavioral and visual realism for conceptual knowledge gain. Our findings nuance classical multimedia learning theories and provide directions for employing agents in immersive learning environments.
Graph embedding clustering: Graph attention auto-encoder with cluster-specificity distribution

Towards exploring the topological structure of data, numerous graph embedding clustering methods have been developed in recent years, none of them takes into account the cluster-specificity distribution of the nodes representations, resulting in suboptimal clustering performance. Moreover, most existing graph embedding clustering methods execute the nodes representations learning and clustering in two separated steps, which increases the instability of its original performance. Additionally, rare of them simultaneously takes node attributes reconstruction and graph structure reconstruction into account, resulting in degrading the capability of graph learning. In this work, we integrate the nodes representations learning and clustering into a unified framework, and propose a new deep graph attention auto-encoder for nodes clustering that attempts to learn more favorable nodes representations by leveraging self-attention mechanism and node attributes reconstruction. Meanwhile, a cluster-specificity distribution constraint, which is measured by ℓ1,2-norm, is employed to make the nodes representations within the same cluster end up with a common distribution in the dimension space while representations with different clusters have different distributions in the intrinsic dimensions. Extensive experiment results reveal that our proposed method is superior to several state-of-the-art methods in terms of performance.
Content-based Vehicle Selection and Resource Allocation for Federated Learning in IoV

In order to use datasets collected from multiple vehicles to train a machine learning model while ensuring vehicle user privacy, federal learning framework was introduced into the Internet of Vehicles. Federated learning is a distributed learning framework. Under the federated learning framework, the packet error rate and wireless bandwidth have a great influence on the global model training process because the vehicle exchanges model parameters with the central server through the wireless channel. With limited bandwidth, the central server needs to select a more appropriate subset of vehicles candidates to participate in federated learning. In this paper, image classification is taken as a typical application in the Internet of vehicles. The dataset contents of different vehicles are different, and the selection of different subsets of vehicles will affect the accuracy and convergence rate of the global model. Therefore, an algorithm of vehicle selection and wireless resource allocation based on dataset content is proposed. Vehicles selection and wireless resource allocation are designed as an optimization problem by joint considerations of vehicle computing resource, datasets, and wireless resources with the goal of maximizing loss function decay of the global model. Finally, simulation with the CIFAR-10 dataset verifies that the vehicle selection and resource allocation algorithm based on the dataset content is superior to the baseline methods in terms of model accuracy and convergence rate.
ASV-SUBTOOLS: Open Source Toolkit for Automatic Speaker Verification

In this paper, we introduce a new open source toolkit for automatic speaker verification (ASV), named ASV-Subtools. Adopting PyTorch as main deep learning engine and Kaldi toolkit for data processing, ASV-Subtools allows users to develop modern speaker recognizers flexibly and efficiently. The toolkit prioritizes efficiency, modularity, and extensibility with the goal of supporting the state-of-the-art technologies in speaker recognition. In addition to including the commonly used networks, such as the time delay neural networks (TDNN), factorized TDNN (F-TDNN) and ResNet, ASV-Subtools also integrates an upgraded version of SpecAugment data augmentation method, named Inverted SpecAugment, with focus on making it more appropriate for speaker recognition subtasks. Besides, for alleviating the domain mismatch between training and test data, ASV-Subtools provides multiple domain adaptation methods of Probabilistic Linear Discriminant Analysis (PLDA). Experimental results show that state-of-the-art techniques implemented on ASV-Subtools could achieve competitive performance compared to other implementations.
CURL: Neural Curve Layers for Global Image Enhancement

We present a novel approach to adjust global image properties such as colour, saturation, and luminance using human-interpretable image enhancement curves, inspired by the Photoshop curves tool.Our method, dubbed neural CURve Layers (CURL), is designed as a multi-colour space neural retouching block trained jointly in three different colour spaces (HSV, CIELab, RGB) guided by a novel multi-colour space loss.The curves are fully differentiable and are trained end-to-end for different computer vision problems including photo enhancement (RGB-to-RGB) and as part of the image signal processing pipeline for image formation (RAW-to-RGB).To demonstrate the effectiveness of CURL we combine this global image transformation block with a pixel-level (local) image multi-scale encoder-decoder backbone network.In an extensive experimental evaluation we show that CURL produces stateof-the-art image quality versus recently proposed deep learning approaches in both objective and perceptual metrics, setting new state-of-the-art performance on multiple public datasets.
Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at Xerox

Modern code review is a common and essential practice employed in both industrial and open-source projects to improve software quality, share knowledge, and ensure conformance with coding standards. During code review, developers may inspect and discuss various changes including refactoring activities before merging code changes in the code base. To date, code review has been extensively studied to explore its general challenges, best practices and outcomes, and socio-technical aspects. However, little is known about how refactoring activities are being reviewed, perceived, and practiced. This study aims to reveal insights into how reviewers develop a decision about accepting or rejecting a submitted refactoring request, and what makes such review challenging. We present an industrial case study with 24 professional developers at Xerox. Particularly, we study the motivations, documentation practices, challenges, verification, and implications of refactoring activities during code review. Our study delivers several important findings. Our results report the lack of a proper procedure to follow by developers when documenting their refactorings for review. Our survey with reviewers has also revealed several difficulties related to understanding the refactoring intent and implications on the functional and non-functional aspects of the software. In light of our findings, we recommended a procedure to properly document refactoring activities, as part of our survey feedback.
A simple graph-based semi-supervised learning approach for imbalanced classification

Graph-based Semi-Supervised Learning (GSSL) methods aim to classify unlabeled data by learning the graph structure and labeled data jointly. In this work, we propose a simple GSSL approach, which can deal with various degrees of class imbalance in given datasets. The key idea is to estimate the class proportion of input data in order to enhance the discriminative power of learned smooth classification function on the graph. Moreover, it has interesting connections to the regularization framework, the Markov stability for graph partition and the group inverse of normalized Laplacain matrix. For classification problems, experimental results demonstrate our approach can achieve promising performance on several datasets with varying class imbalance.
Using Domain-Specific Corpora for Improved Handling of Ambiguity in Requirements

Ambiguity in natural-language requirements is a pervasive issue that has been studied by the requirements engineering community for more than two decades.A fully manual approach for addressing ambiguity in requirements is tedious and time-consuming, and may further overlook unacknowledged ambiguity -the situation where different stakeholders perceive a requirement as unambiguous but, in reality, interpret the requirement differently.In this paper, we propose an automated approach that uses natural language processing for handling ambiguity in requirements.Our approach is based on the automatic generation of a domain-specific corpus from Wikipedia.Integrating domain knowledge, as we show in our evaluation, leads to a significant positive improvement in the accuracy of ambiguity detection and interpretation.We scope our work to coordination ambiguity (CA) and prepositional-phrase attachment ambiguity (PAA) because of the prevalence of these types of ambiguity in natural-language requirements [1].We evaluate our approach on 20 industrial requirements documents.These documents collectively contain more than 5000 requirements from seven distinct application domains.Over this dataset, our approach detects CA and PAA with an average precision of ≈80% and an average recall of ≈89% (≈90% for cases of unacknowledged ambiguity).The automatic interpretations that our approach yields have an average accuracy of ≈85%.Compared to baselines that use generic corpora, our approach, which uses domain-specific corpora, has ≈33% better accuracy in ambiguity detection and ≈16% better accuracy in interpretation.
Deep Supervised Residual Dense Network for Underwater Image Enhancement

Underwater images are important carriers and forms of underwater information, playing a vital role in exploring and utilizing marine resources. However, underwater images have characteristics of low contrast and blurred details because of the absorption and scattering of light. In recent years, deep learning has been widely used in underwater image enhancement and restoration because of its powerful feature learning capabilities, but there are still shortcomings in detailed enhancement. To address the problem, this paper proposes a deep supervised residual dense network (DS_RD_Net), which is used to better learn the mapping relationship between clear in-air images and synthetic underwater degraded images. DS_RD_Net first uses residual dense blocks to extract features to enhance feature utilization; then, it adds residual path blocks between the encoder and decoder to reduce the semantic differences between the low-level features and high-level features; finally, it employs a deep supervision mechanism to guide network training to improve gradient propagation. Experiments results (PSNR was 36.2, SSIM was 96.5%, and UCIQE was 0.53) demonstrated that the proposed method can fully retain the local details of the image while performing color restoration and defogging compared with other image enhancement methods, achieving good qualitative and quantitative effects.
A Residual-Learning-Based Multi-Scale Parallel-Convolutions- Assisted Efficient CAD System for Liver Tumor Detection

Smart multimedia-based medical analytics and decision-making systems are of prime importance in the healthcare sector. Liver cancer is commonly stated to be the sixth most widely diagnosed cancer and requires an early diagnosis to help with treatment planning. Liver tumors have similar intensity levels and contrast as compared to neighboring tissues. Similarly, irregular tumor shapes are another major issue that depends on the cancer stage and tumor type. Generally, liver tumor segmentation comprises two steps: the first one involves liver identification, and the second stage involves tumor segmentation. This research work performed tumor segmentation directly from a CT scan, which tends to be more difficult and important. We propose an efficient algorithm that employs multi-scale parallel convolution blocks (MPCs) and Res blocks based on residual learning. The fundamental idea of utilizing multi-scale parallel convolutions of varying filter sizes in MPCs is to extract multi-scale features for different tumor sizes. Moreover, the utilization of residual connections and residual blocks helps to extract rich features with a reduced number of parameters. Moreover, the proposed work requires no post-processing techniques to refine the segmentation. The proposed work was evaluated using the 3DIRCADb dataset and achieved a Dice score of 77.15% and 93% accuracy.
Deep Learning for Object Detection: A Survey

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people s life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of target detection, a comprehensive literature review of target detection and an overall discussion of the works closely related to it are presented in this paper. This paper various object detection methods, including one-stage and two-stage detectors, are systematically summarized, and the datasets and evaluation criteria used in object detection are introduced. In addition, the development of object detection technology is reviewed. Finally, based on the understanding of the current development of target detection, we discuss the main research directions in the future.
A self-adaptive hybridized differential evolution naked mole-rat algorithm for engineering optimization problems

This paper presents a self-adaptive hybrid variant of differential evolution (DE) algorithm and naked mole-rat algorithm (NMRA), namely SaDN. The algorithm is altogether a new version, designed to overcome the local optima stagnation and poor exploration properties of DE and NMRA respectively. The new algorithm has been designed by incorporating DE into the worker phase of NMRA while keeping all the major parameters of both the algorithms intact. In order to make the algorithm self-adaptive, seven different mutation strategies have been explored for different parameters, and it was found that Lévy based scaling factor and sigmoidal mating factor are the best parameters. Apart from these parameters, adaptive properties have been introduced to all other parameters so that no user-based initialization of parameters is required. For performance evaluation, the proposed SaDN is tested on CEC 2005, CEC 2014 and CEC 2019 benchmark problems and comparison is performed for variable population size and higher dimension sizes. From the experimental results, it has been found that the proposed SaDN performs better with respect to other major state-of-the-art algorithms from the literature. Apart from that, SaDN is subjected to three engineering design problems and compared with other algorithms. Numerical results demonstrate that SaDN shows better performance and is statistically significant in terms of Wilcoxon's rank-sum test, Freidman's test and computational complexity. The source code for the proposed algorithms is available at:https://github.com/rohitsalgotra.
Do Embeddings Actually Capture Knowledge Graph Semantics?

Knowledge graph embeddings that generate vector space representations of knowledge graph triples, have gained considerable popularity in past years. Several embedding models have been proposed that achieve state-of-the-art performance for the task of triple completion in knowledge graphs. Relying on the presumed semantic capabilities of the learned embeddings, they have been leveraged for various other tasks such as entity typing, rule mining and conceptual clustering. However, a critical analysis of the utility as well as limitations of these embeddings for semantic representation of the underlying entities and relations has not been performed by previous work.
Convolutional Complex Knowledge Graph Embeddings

We investigate the problem of learning continuous vector representations of knowledge graphs for predicting missing links. Recent results suggest that using a Hermitian inner product on complex-valued embeddings or convolutions on real-valued embeddings can be effective means for predicting missing links. We bring these insights together and propose ConEx—a multiplicative composition of a 2D convolution with a Hermitian inner product on complex-valued embeddings. ConEx utilizes the Hadamard product to compose a 2D convolution followed by an affine transformation with a Hermitian inner product in \(\mathbb {C}\). This combination endows ConEx with the capability of (1) controlling the impact of the convolution on the Hermitian inner product of embeddings, and (2) degenerating into ComplEx if such a degeneration is necessary to further minimize the incurred training loss. We evaluated our approach on five of the most commonly used benchmark datasets. Our experimental results suggest that ConEx outperforms state-of-the-art models on four of the five datasets w.r.t. Hits@1 and MRR even without extensive hyperparameter optimization. Our results also indicate that the generalization performance of state-of-the-art models can be further increased by applying ensemble learning. We provide an open-source implementation of our approach, including training and evaluation scripts as well as pretrained models (github.com/dice-group/Convolutional-Complex-Knowledge-Graph-Embeddings).
A Model-Driven Deep Unfolding Method for JPEG Artifacts Removal

Deep learning-based methods have achieved notable progress in removing blocking artifacts caused by lossy JPEG compression on images. However, most deep learning-based methods handle this task by designing black-box network architectures to directly learn the relationships between the compressed images and their clean versions. These network architectures are always lack of sufficient interpretability, which limits their further improvements in deblocking performance. To address this issue, in this article, we propose a model-driven deep unfolding method for JPEG artifacts removal, with interpretable network structures. First, we build a maximum posterior (MAP) model for deblocking using convolutional dictionary learning and design an iterative optimization algorithm using proximal operators. Second, we unfold this iterative algorithm into a learnable deep network structure, where each module corresponds to a specific operation of the iterative algorithm. In this way, our network inherits the benefits of both the powerful model ability of data-driven deep learning method and the interpretability of traditional model-driven method. By training the proposed network in an end-to-end manner, all learnable modules can be automatically explored to well characterize the representations of both JPEG artifacts and image content. Experiments on synthetic and real-world datasets show that our method is able to generate competitive or even better deblocking results, compared with state-of-the-art methods both quantitatively and qualitatively.
Semantic Supplementary Network With Prior Information for Multi-Label Image Classification

The multi-label image classification problem is one of the most important problems in the field of computer vision, which needs to predict and output all the labels in an image. Multiple labels to be classified in an image increases the difficulty of image classification, and multi-label image classification usually requires additional attention to the positions of the object with different scales and poses. Hence, how to use the dependency relationship between labels to improve the recognition accuracy is an important problem when the object is difficult to directly identify. In this paper, we propose a designed network called the Semantic Supplementary Network with Prior Information (SSNP) to address this problem. The proposed SSNP first generates prior information by using a prior information network with different convolutional layers. Then the semantic supplementary module generates semantic information of the potential labels that is highly relevant to the current information based on the prior information, thereby effectively using the dependency relationship between the labels to improve the classification accuracy. Different from existing methods which pay more attention to the image feature extraction process, we focus on the impact of high-level semantic information generated after feature extraction on the results and tap the potential of high-level semantic information through a semantic supplementary module to strengthen the potential dependence between labels. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves the state-of-the-art performance, especially when predicting some semantically dependent labels.
Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography

Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure that the transmitted data are secure. This paper focuses on the security, speed and load of transmission. To prove security, combined steganographic methods involving cryptographic algorithms are used. The proposed model begins by updating two entries, medical image data and medical report data. Digital imaging and communications in medicine image data hold the medical report data to be encrypted and transmitted over the network channel. Although the proposed work follows the conventional method of data transmission from encryption until transmission, an effort has been made to split up the given data without transmitting them as such. As a public cryptography mechanism, the algorithm is also capable of transmission during decryption. The method of this article is genuine in proving its secure actions during the transmission of medical data and medical images. The proposed method justifies its performance when tested in hiding medical transcription data of different sizes varying across 30, 45, 64, 128 and 256 bytes in sample images with an average PSNR ranging from 55 to 70 dB, an MAE averaging from 0.2 to 0.7, and an SSIM, SC and correlation coefficient averaging to 1. This research is proven to work well in a simulation environment, and the results prove the genuine nature of the proposed technique.