Reviewerly/exHarmony huggingface dataset:
Quantum Correlation Swapping between Two Werner States Undergoing Local and Nonlocal Unitary Operations

In this paper, quantum correlation (QC) swapping between two Werner-like states, which are transformed from Werner states undergoing local and nonlocal unitary operations, are studied. Bell states measures are performed in the middle node to realize the QC swapping and correspondingly final correlated sates are obtained. Two different QC quantifiers, i.e., measurement-induced disturbance (MID) and ameliorated MID, are employed to characterize and quantify all the concerned QCs in the swapping process. All QCs in the concerned states are evaluated analytically and numerically. Correspondingly, their characteristics and properties are exposed in detail. It is exposed that, through the QC swapping process, one can obtain the long-distance QC indeed. Moreover, the similarities of monotony features of MID and AMID between the initial states and final states are exposed and analyzed.
Inter-subtask Consistent Representation Learning for Visual Commonsense Reasoning

Given an image and a related question, Visual Commonsense Reasoning (VCR) requires to select the correct answer (question answering subtask) and the rationale (answer justification subtask). The commonsense semantic hidden between subtasks is essential for complex reasoning for VCR. Most of the previous studies of VCR focus on the leaning of cross-modal semantics and optimize the two subtasks independently, ignoring the hidden semantic correlations between answers and rationales. In this paper, we propose an Inter-subtask Consistent Representation Learning (ICRL) framework to learn the hidden commonsense semantics. Specifically, we design a joint learning framework to establish the connection between the two subtasks. Furthermore, we propose a multi-level contrastive learning network to ensure the semantic consistency of subtasks in the feature space. Experiments on the VCR dataset demonstrate that the proposed ICRL brings significant performance gain over the state-of-the-arts.
Data Augmented Dual-Attention Interactive Image Classification Network

Fine-grained classification requires identifying images that belong to multiple subcategories within the same category. There are only subtle differences between highly similar images. Most existing methods only use baseline networks or a single attention module to extract features from images to discriminate similar images, which will limit the model to finding fine-grained regions hidden in images. This article proposes an effective method to solve this problem. The first is a novel layered training method that enhances the feature extraction capability of baseline models. The second step is to find multiple attention regions based on the features extracted by the baseline model. Here, the improved Long Short-Term Memory (LSTM) and Multi-Head Attention can be used to focus on the key areas of the image, which is conducive to the discovery of fine-grained features. In the third step, the attention features extracted based on the dual network are spatially mapped using a multi-layer perceptron (MLP). Then, the interaction dot product of the corresponding channels is performed on the attention features and the mapping features to guide the classification. Finally, to achieve good performance, we test several standard benchmark datasets, CUB-200-2011, FGVC Aircraft, and Stanford Cars.
Spatial Foreground Bigraph Matching for Generalizable Person Re-identification

Most existing Domain Generalization person re-identification (DG ReID) methods often evaluate similarity using global features. However, the spatial information will be lost after global pooling, which leads to a lack of interpretability and decreased generalization ability. In this paper, to address these issues, we propose a novel interpretable and generalizable ReID framework, named Spatial Foreground Bigraph Match (SFBM), which is plug-and-play and no training is required. Our SFBM consists of two sub-parts, namely Contextual Foreground Region Extraction (CFE) and Feature Bigraph Module (FBM). The former extracts the foreground in the query’s feature map and uses it to filter out the background noise in the matching relationship. The latter then establishes a matching relationship between each local feature in the feature map of query and gallery. Particularly, we present a judicious definition of the distance from query to gallery based on SFBM to enhance the robustness of existing models when the target domain has an occlusion. Extensive experimental results on three subtasks of DG ReID show that our method is model-agnostic and enables to improve the performance of various methods without re-training the models.
Computer Multimedia-Oriented Chinese Language and Literature Distance Teaching System

The traditional Chinese language and literature distance teaching system has the problem of incomplete instruction recognition function, which leads to the slow connection speed of the system. This paper designs a computer multimedia Chinese language and literature distance teaching system. For the hardware part, strain gauge and sensor are combined, connected to VCC through 10 K ohm pull-up resistance, and connected to other components. The software part obtains the expression form of Chinese language and literature; formulates the training target, according to the antimonotone characteristics of the support degree; adopts the association rule algorithm to build the remote teaching system database, combined with the client to parse the signaling message; and designs the software instruction recognition function by using the computer multimedia technology. Experimental Results. The average connection speeds of the Chinese language and literature distance teaching system in this paper and the other two kinds of Chinese language and literature distance teaching system are 8.737 s, 13.140 s, and 13.156 s, respectively, which proves that the performance of the Chinese language and literature distance teaching system combined with computer multimedia technology is more perfect.
A Metric for Questions and Discussions Identifying Concerns in Software Reviews

Previous studies reported that reviewers ask questions and engage in discussions during software reviews and that the concerns identified by the questions and discussions help detect defects. Although such concerns about potential defects lead to finding defects, review metrics such as the number of defects detected do not always reflect the questions and discussions because concerns which are not applicable to the review material are excluded from the number of defects. This paper proposes a metric, the number of questions and discussions, which identifies concerns in reviews. First, we defined an effective question, which identifies concerns. Then, we defined detailed review processes (identifying, sharing, and recording processes), which capture how concerns identified by effective questions are shared and defects are documented. We conducted a case study with 25 projects in industry to investigate the impact of the number of effective questions, which identified concerns on the number of detected defects in subsequent testing. The results of a multiple regression analysis show that the number of effective questions predicts the number of defects in subsequent testing at the significance level of 0.05.
Game-Based Virtual Reality for Languaculture Learning: An Example of the Forbidden City

In the digital era, learners are closely engaged with various kinds of technologies. Particularly, virtual reality (VR) has revolutionized our conventional ways of teaching and learning. Despite the wide adoptions of VR in education, our knowledge of how VR can facilitate EFL learners’ L2 linguistic and L1 native culture learning is fairly limited. The present work-in-progress paper reports on an on-going study that explores a self-developed VR system, which aims to support Chinese learners in improving their English language knowledge and Chinese culture knowledge via a simulated world. Research design and methods will also be discussed in this short paper.
The Impact of 3D Stereopsis and Hand-Tool Alignment on Effectiveness of a VR-based Simulator for Dental Training

Recent years have seen the proliferation of VR-based dental simulators using a wide variety of different VR configurations. Differences in technologies and setups used result in important differences in degree of realism. These include 3D stereoscopic rendering and visual alignment of the user's hands with the virtual tools. While each new dental simulator typically is associated with some form of evaluation study, only few comparative studies have been carried out to determine the benefits of various simulation aspects. In this paper, we seek to determine the impact of 3D stereo-scopic rendering and of hand-tool alignment on the teaching effectiveness of a VR dental simulator. We developed a bimanual simulator using an HMD and two haptic devices that provides an immersive environment with both 3D stereoscopic rendering and hand-tool alignment. We then systematically and independently controlled for each of the two aspects of the simulation. We trained four groups of students in root canal access opening using the simulator and measured the learning gains by doing pre- and post-testing using realistic plastic teeth. We found that hand-tool alignment has a positive impact on learning gains, while stereoscopic 3D does not. The effect of stereoscopic 3D is surprising and demands further research in settings with small target objects. The results of our study provide valuable information for the future design of dental simulators, as well as simulators for other high-precision psycho-motor tasks.
The Trusted Hierarchical Access Structure-Based Encryption Scheme for Cloud Computing

Cloud computing delivers various resources and services to users over internet. At present many businesses and individual users are adopting cloud services to avail benefits such as, less time, less cost, less management, and maintenance. Still, security is the primary issue that must be tackled and one of the security issues is access control mechanism. The most widely used access control method includes Ciphertext Policy Attribute based Encryption, which is encrypted access control scheme. In order to ensure trustworthy and encrypted access control, a trusted hierarchical access structure based encryption scheme is proposed in this paper. The proposed scheme uses hierarchical access structure to encrypt multiple messages and avoid generation of multiple ciphertexts. The trust evaluation component is also integrated with the proposed access control mechanism. An efficient method to assign trust level for service providers and data users by utilizing their behavioral parameters is proposed in the paper.
Work-in-progress—Avatar vs Teams: Co-Presence and Visual Behavior in a Videoconference Cooperative Task

The need for effective remote communication solutions is increasing, and videoconferencing tools are the top choice for many. Furthermore, the alternative of using avatars instead of video-based conferencing for enriching the communication process is gaining popularity. This work-in-progress presents an avatar-based remote communication system for studying the exerted sense of co-presence and visual behavior of the user compared to a popular business-oriented communication platform. Users reported a higher sense of co-presence in the avatar condition with an increase in whole visual fixations dedicated to the avatars in comparison to their video feed faces during the cooperative task.
A Generic Semi-Supervised and Active Learning Framework for Biomedical Text Classification

Biomedical text classification requires having training examples labeled by clinical specialists, a process that can be costly. To address this problem, active learning incrementally selects a subset of the most informative unlabeled examples, samples that are then labeled and used to train a given classifier, seeking to reduce the number of labeled samples. Nonetheless, the other unlabeled examples are not used by active learning, but incorporating semi-supervised techniques that use unlabeled samples could improve the representativeness of the data and the discriminatory power of the classifiers. This work proposes a generic semi-supervised learning framework for improving active learning and reducing the number of labeled training examples in biomedical text classification. The proposed framework combines manually annotated training examples selected by active learning and pseudo-labels obtained from a trained classifier. To evaluate the proposed framework, three biomedical datasets with textual information on obesity and smoking habit were used across different classification algorithms. The classification results show that the proposed framework can reduce the number of training examples that are manually labeled by clinical specialists by a 10% without affecting the performance of the classifiers. This performance is attributable to the ability of the classifiers to correctly select and label the training examples. Clinical relevance- We demonstrate the effectiveness of the proposed semi-supervised learning framework to reduce manual labeling efforts of biomedical texts by clinical specialists for the training of classifiers.
Teaching Virtual Reality in Virtual Reality

Virtual Reality (VR) technologies have the potential to improve online education for students. This paper reports on a course on the topic of virtual reality using VR technologies and Mozilla Hubs. Findings from student questionnaire responses suggest that students appreciated the unique educational experience of learning about VR in VR and felt more present in VR when compared to other online learning experiences. However, it was noted that using a VR headset made it difficult to take notes and became uncomfortable for some students after long periods of time.
Contextualizing Educational Robotics Programming with Augmented Reality

The development of technology in recent years has led to the research of innovative tools in education. Technologies, such as Augmented, Virtual, and Mixed Reality can be utilized in immersive environments for educational purposes. Educational Robotics has also been prevalent in K-12 and STEM education. In this paper, we describe a mobile augmented reality learning environment to support robotics programming. An augmented reality application was developed and evaluated by two experts. Two experiments were conducted, including the non-AR and the AR condition. The participants were primary school students. Both observation and interviews were conducted. Our results showed that augmented reality attracts students’ interest in robotics programming, prompts them to participate in the learning process and is easy to use. Limitations and advantages of the AR approach in educational robotics were pointed out. The results indicate that contextualizing robotics with augmented reality is promising and should be further studied.
Work-in-Progress—Stress and Flow Assessment during a Virtual Reality Fire Extinguishing Training

Firefighters must be trained to deal with stress during firefighting and applying procedures. Aiming to alleviate the limitations in practice opportunities, a virtual reality (VR) application for training on fire extinguishing was developed, allowing for practice in a safe learning environment, while remaining aligned with the real-life training and the corresponding procedures. This work-in-progress paper investigates whether recruits experience stress and flow during the training by means of self-reported and physiological data. The physiological data measured (heart and respiration rate) significantly increased during the VR training, thus confirming arousal. The participants reported experiencing flow but not stress during the training.
Work-in-Progress—Extended Reality Pilots for Hybrid Learning: a New NUFlex for Northeastern University

The COVID-19 pandemic forced Northeastern University (NU) to pivot to virtual in Spring 2020 and spurred innovations in hybrid learning called NUFlex that were implemented starting in Fall 2020. However, for many courses and topics taught at NU standard hybrid learning is insufficient to create the ideal pedagogical experience for both students and instructors. Extended reality technologies such as augmented, mixed, and virtual reality can be leveraged to make the NUFlex experience more dynamic and immersive. We are developing best practices and pilots that blend XR technologies with course activities through iterative improvement while collecting student reflections, work samples, and survey data, that we present in this Work-in-Progress. We hope that these technologies can be scaled into other courses in the future.
Work-In-Progress—Developing Materials Science Experiments Using Augmented Reality: How Much Reality is Needed?

This work-in-progress paper presents two Augmented Reality applications for teaching fractography. The first application has little connection with reality, whereas the second application has a strong anchoring and dependency with reality: students need to have prepared physical lab samples to trigger the display of virtual elements on their phone. Student feedback reveals a tension with the teacher’s view on how much reality is needed. Where the teacher sees the use of physical samples to assess laboratory skills, many students view Augmented Reality as a learning complement on the theoretical rather than the experimental aspects of fractography.
Advancing Extended Reality Teaching and Learning Opportunities Across the Disciplines in Higher Education

The emergence of Extended Reality (XR) technologies in diverse fields, along with research demonstrating opportunities for innovation in teaching and learning, warrants further exploration. However, investigation into the supports required by instructors for experimentation, design, and deployment of XR technologies is needed. This study explores the benefits and challenges of XR technologies for teaching and learning, and reviews XR pedagogical initiatives at three universities. The research aims to discern strategies to promote and support XR initiatives in higher education, develop best practices for using and integrating XR technologies into university courses, and guide future research into XR technologies in higher education. To develop an empirical understanding of the opportunities and challenges faced by instructors using XR technologies, we performed a thematic analysis of sixteen semi-structured interviews to capture insights from instructors’ experiences. We found that instructors were cognizant of the many benefits associated with XR use in terms of experiential learning, making learning content more accessible, and the potential for its application. However, the findings also highlight the lack of guidance and support available to instructors in the implementation of these technologies in educational contexts. Our results provide recommendations for future institutional supports that could advance XR and expand the current teaching and learning opportunities available to instructors and students.
A Deep Learning Based Predictive Model for Healthcare Analytics

Healthcare informatics is an interdisciplinary area where computer science, data science, cognitive science, informatics principles, and information technology meet to address problems and support healthcare, medicine, public health, and/or everyday wellness. In many medical and healthcare applications, having models that can learn from historical healthcare data or instances to make predictions on future instances is helpful. However, partially due to privacy issues, the availability of healthcare data to be learned may be limited. Hence, in this paper, we present a deep learning based predictive model for healthcare analytics. In particular, our model consists of an autoencoder (comprising an encoder and a decoder) and a predictor to make accurate predictions. It can learn from a few shots of historical healthcare data to make either binary or multi-label predictions. Evaluation results on real-life datasets demonstrates the effectiveness of our deep learning-based predictive model in supporting healthcare analytics.
Identification and Classification of Benign and Malignant Masses based on Subtraction of Temporally Sequential Digital Mammograms

Breast cancer remains the leading cause of cancer deaths and the second highest cause of death, in general, among women worldwide. Fortunately, over the last few decades, with the introduction of mammography, the mortality rate of breast cancer has significantly decreased. However, accurate classification of breast masses in mammograms is especially challenging. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. In this study, classification of benign and malignant masses, based on the subtraction of temporally sequential digital mammograms and machine learning, is proposed. The performance of the algorithm was evaluated on a dataset created for the purposes of this study. In total, 196 images from 49 patients, with precisely annotated mass locations and biopsy confirmed malignant cases, were included. Ninety-six features were extracted and five feature selection algorithms were employed to identify the most important features. Ten classifiers were tested using leave-one-patient-out and 7-fold cross-validation. Neural Networks, achieved the highest classification performance with 90.85% accuracy and 0.91 AUC, an improvement compared to the state-of-the-art. These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the classification of breast masses as benign or malignant.
Identifying Obviously Artificial Medical Images Produced by a Generative Adversarial Network

Synthetic medical images have an important role to play in developing data-driven medical image processing systems. Using a relatively small amount of patient data to train generative models that can produce an abundance of additional samples could bridge the gap towards big-data in niche medical domains. These generative models are evaluated in terms of the synthetic data they generate using the Visual Turing Test (VTT), Fréchet Inception Distance (FID), and other metrics. However, these are generally interpreted at the group level, and do not measure the artificiality of individual synthetic images. The present study attempts to address the challenge of automatically identifying artificial images that are obviously-artificial-looking, which may be necessary for filtering out poorly constructed synthetic images that might otherwise deteriorate the performance of assimilating systems. Synthetic computed tomography (CT) images from a progressively-grown generative adversarial network (PGGAN) were evaluated with a VTT and their image embeddings were analyzed for correlation with artificiality. Images categorized as obviously-artificial (≥0. 7 probability of being rated as fake) were classified using a battery of algorithms. The top-performing classifier, a support vector machine, exhibited accuracy of 75.5%, sensitivity of 0.743, and specificity of 0.769. This is an encouraging result that suggests a potential approach for validating synthetic medical image datasets. Clinical Relevance - Next-generation medical AI systems for image processing will utilize synthetic images produced by generative models. This paper presents an approach towards verifying artificial image legibility for quality-control before being deployed for these purposes.