Reviewerly/exHarmony huggingface dataset:
Focusing on cybersickness: pervasiveness, latent trajectories, susceptibility, and effects on the virtual reality experience

Although virtual reality (VR) usage has become widespread in the last decade, its adoption has been hampered by experiences of user discomfort known as cybersickness. The present study, in line with the "2020 cybersickness R&D agenda", sought to provide a broad examination of the cybersickness phenomenon, assessing its pervasiveness, latent trajectories, impacts on the VR experience, and predictor variables. The study was composed of 92 participants living in the Dominican Republic with ages ranging from 18 to 52 years (M = 26.22), who experienced a 10-min VR immersion in two environments designed for psychotherapy. The results indicated that cybersickness was pervasive, with 65.2% of the participants experiencing it, and 23.9% severely. Additionally, the latent trajectories of cybersickness were positive and curvilinear, with large heterogeneity across individuals. Cybersickness also had a substantive negative impact on the user experience and the intentions to adopt the VR technology. Finally, motion sickness susceptibility, cognitive stress, and recent headaches uniquely predicted greater severity of cybersickness, while age was negatively related. These combined results highlight the critical role that cybersickness plays on the VR experience and underscore the importance of finding solutions to the problems, such as technological advancements or special usage protocols for the more susceptible individuals.The online version contains supplementary material available at 10.1007/s10055-022-00636-4.
Feature-Level Attentive ICF for Recommendation

Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender systems. ICF recommends items to a target user based on their similarities to the previously interacted items of the user. Great progresses have been achieved for ICF in recent years by applying advanced machine learning techniques (e.g., deep neural networks) to learn the item similarity from data. The early methods simply treat all the historical items equally and recently proposed methods attempt to distinguish the different importance of historical items when recommending a target item. Despite the progress, we argue that those ICF models neglect the diverse intents of users on adopting items (e.g., watching a movie because of the director, leading actors, or the visual effects). As a result, they fail to estimate the item similarity on a finer-grained level to predict the user’s preference to an item, resulting in sub-optimal recommendation. In this work, we propose a general feature-level attention method for ICF models. The key of our method is to distinguish the importance of different factors when computing the item similarity for a prediction. To demonstrate the effectiveness of our method, we design a light attention neural network to integrate both item-level and feature-level attention for neural ICF models. It is model-agnostic and easy-to-implement. We apply it to two baseline ICF models and evaluate its effectiveness on six public datasets. Extensive experiments show the feature-level attention enhanced models consistently outperform their counterparts, demonstrating the potential of differentiating user intents on the feature-level for ICF recommendation models.
A permissioned blockchain enabled trustworthy and incentivized emission trading system

The emission trading systems (ETSs) have encouraged the more usage of renewable energy, aiming for energy generation. However, the existing ETSs suffer from carbon credit scams, poor monitoring, verification and reporting (MRV), participants’ unwillingness and non-transparent trading. To address these issues, we propose a trustworthy and incentivized emission trading system using hyperledger (a permissioned blockchain) and smart contracts. It assures a transparent trading of carbon credits (CC) with better MRV. It applies three smart contracts viz; CC selling, CC buying and CC trading to achieve reliable and secure transactions of the energy trading with the improved trust. For emission trading, we propose a priority based auction, in which priority to CC sellers is assigned based on their carbon credits, while priority to CC buyers is assigned based on their reputation. This strategy rewards the participants for their efforts towards avoiding the deforestification and the use of renewable energy. In addition, it is incentivized for both CC buyers as well as sellers, and thus, it encourages more number of participants. The proposed system satisfies more than 84.37% bids with minimum transaction latency (0.41 s) and sufficient throughput (102.42 tps). In addition, multi-criteria analysis using different statistical methods is performed to evaluate and compare the effectiveness of the proposed systems.
From digital competences to technology integration: Re-formation of pre-service teachers’ knowledge and understanding

In this study, a training was carried out in order to improve pre-service teachers' digital competences with regards to technology integration based on the DigComp framework. In this regard, participants' progress in digital competence and technological pedagogical content knowledge (TPACK) were analysed. In addition, what kind of digital knowledge and skills they utilize in the technology integration process were investigated. As a result, it was found that while pre-service teachers showed progress in the sub-dimensions of TPACK-deep, they made use of their skills in information and data literacy, communication and collaboration, content creation, safety, and problem-solving. In addition, positive correlations were found between digital competences and TPACK competences. Consequently, the roles of digital competences should be considered for successful technology integration.
Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT

Cervical cell classification has important clinical significance in cervical cancer screening at early stages. However, there are fewer public cervical cancer smear cell datasets, the weights of each classes' samples are unbalanced, the image quality is uneven, and the classification research results based on CNN tend to overfit. To solve the above problems, we propose a cervical cell image generation model based on taming transformers (CCG-taming transformers) to provide high-quality cervical cancer datasets with sufficient samples and balanced weights, we improve the encoder structure by introducing SE-block and MultiRes-block to improve the ability to extract information from cervical cancer cells images; we introduce Layer Normlization to standardize the data, which is convenient for the subsequent non-linear processing of the data by the ReLU activation function in feed forward; we also introduce SMOTE-Tomek Links to balance the source data set and the number of samples and weights of the images we use Tokens-to-Token Vision Transformers (T2T-ViT) combing transfer learning to classify the cervical cancer smear cell image dataset to improve the classification performance. Classification experiments using the model proposed in this paper are performed on three public cervical cancer datasets, the classification accuracy in the liquid-based cytology Pap smear dataset (4-class), SIPAKMeD (5-class), and Herlev (7-class) are 98.79%, 99.58%, and 99.88%, respectively. The quality of the images we generated on these three data sets is very close to the source data set, the final averaged inception score (IS), Fréchet inception distance (FID), Recall and Precision are 3.75, 0.71, 0.32 and 0.65 respectively. Our method improves the accuracy of cervical cancer smear cell classification, provides more cervical cell sample images for cervical cancer-related research, and assists gynecologists to judge and diagnose different types of cervical cancer cells and analyze cervical cancer cells at different stages, which are difficult to distinguish. This paper applies the transformer to the generation and recognition of cervical cancer cell images for the first time.The online version contains supplementary material available at 10.1007/s11042-022-12670-0.
Federated Learning With Taskonomy for Non-IID Data

Classical federated learning approaches incur significant performance degradation in the presence of non-independent and identically distributed (non-IID) client data. A possible direction to address this issue is forming clusters of clients with roughly IID data. Most solutions following this direction are iterative and relatively slow, also prone to convergence issues in discovering underlying cluster formations. We introduce federated learning with taskonomy (FLT) that generalizes this direction by learning the task relatedness between clients for more efficient federated aggregation of heterogeneous data. In a one-off process, the server provides the clients with a pretrained (and fine-tunable) encoder to compress their data into a latent representation and transmit the signature of their data back to the server. The server then learns the task relatedness among clients via manifold learning and performs a generalization of federated averaging. FLT can flexibly handle a generic client relatedness graph, when there are no explicit clusters of clients, as well as efficiently decompose it into (disjoint) clusters for clustered federated learning. We demonstrate that FLT not only outperforms the existing state-of-the-art baselines in non-IID scenarios but also offers improved fairness across clients. Our codebase can be found at: https://github.com/hjraad/FLT/
Interpretable Temporal Attention Network for COVID-19 forecasting

The worldwide outbreak of coronavirus disease 2019 (COVID-19) has triggered an unprecedented global health and economic crisis. Early and accurate forecasts of COVID-19 and evaluation of government interventions are crucial for governments to take appropriate interventions to contain the spread of COVID-19. In this work, we propose the Interpretable Temporal Attention Network (ITANet) for COVID-19 forecasting and inferring the importance of government interventions. The proposed model is with an encoder-decoder architecture and employs long short-term memory (LSTM) for temporal feature extraction and multi-head attention for long-term dependency caption. The model simultaneously takes historical information, a priori known future information, and pseudo future information into consideration, where the pseudo future information is learned with the covariate forecasting network (CFN) and multi-task learning (MTL). In addition, we also propose the degraded teacher forcing (DTF) method to train the model efficiently. Compared with other models, the ITANet is more effective in the forecasting of COVID-19 new confirmed cases. The importance of government interventions against COVID-19 is further inferred by the Temporal Covariate Interpreter (TCI) of the model.
Deep Learning-Based Digital Image Forgery Detection System

The advancements of technology in every aspect of the current age are leading to the misuse of data. Researchers, therefore, face the challenging task of identifying these manipulated forms of data and distinguishing the real data from the manipulated. Splicing is one of the most common techniques used for digital image tampering; a selected area copied from the same or another image is pasted in an image. Image forgery detection is considered a reliable way to verify the authenticity of digital images. In this study, we proposed an approach based on the state-of-the-art deep learning architecture of ResNet50v2. The proposed model takes image batches as input and utilizes the weights of a YOLO convolutional neural network (CNN) by using the architecture of ResNet50v2. In this study, we used the CASIA_v1 and CASIA_v2 benchmark datasets, which contain two distinct categories, original and forgery, to detect image splicing. We used 80% of the data for the training and the remaining 20% for testing purposes. We also performed a comparative analysis between existing approaches and our proposed system. We evaluated the performance of our technique with the CASIA_v1 and CASIA_v2 datasets. Since the CASIA_v2 dataset is more comprehensive compared to the CASIA_v1 dataset, we obtained 99.3% accuracy for the fine-tuned model using transfer learning and 81% accuracy without transfer learning with the CASIA_v2 dataset. The results show the superiority of the proposed system.
Data Augmentation by Program Transformation

Data Augmentation has been recognized as one of the main techniques to improve deep learning models’ generalization ability. However, it has not been widely leveraged in big code tasks due to the essential difficulties of manipulating source code to generate new labeled data of high quality. In this paper, we propose a general data augmentation method based on program transformation. The idea is to extend big code datasets by a set of source-to-source transformation rules that preserve not only the semantics but also the syntactic naturalness of programs. Through controlled experiments, we demonstrated that semantic and syntax-naturalness preserving are the expected properties for a transformation rule to be effective in data augmentation. We designed 18 transformation rules that are proved semantic-preserving and tested syntax-naturalness-preserving. We also implemented and open-sourced a partial program transformation tool for Java based on the rules, named SPAT, whose effectiveness for data augmentation is validated in three big code tasks: method naming, code commenting, and code clone detection. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
AI companions for lonely individuals and the role of social presence

Artificial intelligence (AI) companiosns (e.g., social machine agents, social robots) are becoming increasingly available. Considering that AI companions can be beneficial for individuals seeking companionships or relationships, the social and relational aspects of an AI companion are important to investigate. To understand people’s perceptions of an AI companion, this study examines the roles of social presence and warmth of an AI companion through an online experiment. Primary findings indicate that social presence of a disembodied AI companion fosters greater perceived usefulness of the AI companion and willingness to recommend the AI companion for lonely individuals. Collectively, the study highlights the importance of social presence for disembodied AI companions.
LWMEO: An efficient equilibrium optimizer for complex functions and engineering design problems

Optimization algorithm is an important method to solve complex extremum function and engineering design constraint problems, and the improvement of algorithm optimization performance is inseparable from efficient strategies and mechanisms. Equilibrium optimizer (EO) is a novel intelligent optimization algorithm with simple mechanism and easy implementation. It has a good solution effect on some optimization problems. However, when solving complex multimodal problems and engineering design problems, EO has some disadvantages, such as easy to fall into local optimal, low searching accuracy and slow convergence speed. In order to solve complex function extremum and engineering design optimization problems more effectively, improve the optimization performance and application ability of the equilibrium optimizer, and further expand the application field and space of the algorithm, an adaptive equilibrium optimizer (LWMEO) with three efficient optimization strategies is proposed. Based on the basic update strategy of the equilibrium optimizer, the random walk strategy of preferred area based on Lévy flight is introduced to expand the algorithm's search range and enhances its exploration capability; by adding the whale optimization algorithm's spiral encirclement mechanism enables the individuals in the population to perform a spiral search near the best solution and improves the algorithm's exploitation capability; the introduction of the adaptive proportional mutation strategy strengthens the algorithm's ability to jump out of the local optimal solutions, while accelerating its convergence speed. Time complexity analysis proves that the LWMEO algorithm has the same time complexity as the equilibrium optimizer. Using the Markov chain process, it is theoretically proven that the LWMEO has convergence and can converge to the global optimum. LWMEO and 6 representative comparison algorithms with superior performance are used to solve CEC2014 test function suite and several classical multimodal test functions with different optimization characteristics. Each algorithm is run independently for 50 times in the same environment, and the best value, average value and standard deviation obtained by LWMEO were generally superior to the other six comparison algorithms. The convergence curve drops faster and the line body is smoother. These experimental data and convergence curves show that the LWMEO's optimization ability, convergence speed and solution stability are obviously superior to the comparison algorithm. Meanwhile, the p-value of Wilcoxon's rank-sum test are less than 0.05, indicating that LWMEO has significant differences over comparison algorithm. Finally, the LWMEO is applied to 8 different engineering design optimization problems, and the best value, average value and standard deviation obtained from 50 optimization designs are also significantly better than other comparison algorithms, which verifies the applicability, stability and efficiency of LWMEO in solving engineering design constrained optimization problems.
A survey of deep nonnegative matrix factorization

Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for feature extraction in recent years. By decomposing the matrix recurrently on account of the NMF algorithms, we obtain a hierarchical neural network structure as well as exploring more interpretable representations of the data. This paper mainly focuses on some theoretical researches with respect to Deep NMF, where the basic models, optimization methods, properties together with its extensions and generalizations are included systematically. We partition the Deep NMF algorithms into five categories: Deep NMF, Constrained Deep NMF, Generalized Deep NMF, Multi-View Deep Matrix Factorization (MF), the association between deep neural network (DNN) and NMF. Besides, we investigate the clustering performance of Deep NMF Algorithms on some face databases. Then the design principles, major steps, relationships, application domains and evolution of Deep NMF methods are comprehensively analyzed. Moreover, some open problems of Deep NMF are discussed.
A Robust Coverless Steganography Based on Generative Adversarial Networks and Gradient Descent Approximation

Aiming at resolving the problem of the irreversibility in some common neural networks for secret data extraction, a novel image steganography framework is proposed based on the generator of GAN (Generative Adversarial Networks) and gradient descent approximation. During data embedding, the secret data is first mapped into a stego noise vector by a specific mapping rule, and it is input into the generator of a GAN to produce a stego image. The data extraction is accomplished by iteratively updating the noise vector using the gradient descent with the generator. When the error is declined within the allowable error, the output image of the generator is approximate to the stego image, and the updated noise vector will also approach to the stego noise vector. Finally, the secret data is extracted from the updated noise vector. Experiments and analysis with WGAN-GP (Wasserstein GAN-Gradient Penalty) show that it can achieve good performance in extraction accuracy, capacity and robustness. Furthermore, the discussions also illustrate its good generalization with different GAN models and image datasets.
Explicable recommendation based on knowledge graph

Most of the existing researches on recommendation system assemble in how to enhance precision of recommendation, ignoring acceptance and recognition of users. To work out the problem, a model of explainable recommendation on account of knowledge graph as well as many-objective evolutionary algorithm is proposed, which combines recommendation and explanation. In this work, embedding vectors obtained by embedding-based method are used to quantify the explainability, so as to obtain the explainability of paths between users and items. Candidate recommendation list of users is gained from constructed knowledge graph. Many-objective evolutionary algorithm is used to optimize the list of candidate recommendation so as to seek a set of tradeoff solutions to the four objective functions of accuracy, diversity, novelty and explainability. Then, the best path among object user and recommended items is chosen in knowledge graph as the explanation. Finally, the conclusion that can be drawn from various experiments is that the presented model can boost explainability without reducing the precision, diversity as well as novelty.
Efficient Search and Browsing of Large-Scale Video Collections with Vibro

In this paper, we present the newest version of our interactive video browser tool Vibro. For this iteration, we focused on improving the user interface to enable a more accessible temporal search, upgrading the shot-detection algorithm, replacing a keyword-based search with rich text input, and reducing query times by applying a graph-based approximate nearest neighbor search method. With these extensive updates, we feel well-equipped to handle the huge amounts of data coming our way in the next VBS competitions and achieve competitive results in the contest.
Personalized knowledge-aware recommendation with collaborative and attentive graph convolutional networks

Knowledge graphs (KGs) are increasingly used to solve the data sparsity and cold start problems of collaborative filtering. Recently, graph neural networks (GNNs) have been applied to build KG-based recommender systems and achieved competitive performance. However, existing GNN-based methods are either limited in their ability to capture fine-grained semantics in a KG, or insufficient in effectively modeling user-item interactions. To address these issues, we propose a novel framework with collaborative and attentive graph convolutional networks for personalized knowledge-aware recommendation. Particularly, we model the user-item graph and the KG separately and simultaneously with an efficient graph convolutional network and a personalized knowledge graph attention network, where the former aims to extract informative collaborative signals, while the latter is designed to capture fine-grained semantics. Collectively, they are able to learn meaningful node representations for predicting user-item interactions. Extensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method compared with state-of-the-arts.