Reviewerly/exHarmony huggingface dataset:
Mobile Augmented Reality Apps in Education: Exploring the User Experience Through Large-Scale Public Reviews

Augmented reality (AR) is considered one of the top technologies that will revolutionize the future of education. Real-time interaction, different formats of visualization, and the merge of the real and digital world may open up new opportunities for teaching and learning. Although AR is easily accessible via mobile phones, the extent to which this technology will be adopted greatly depends on the user experience. The user reviews of mobile applications or so-called “apps” are a potential source of information for designers, software developers, and scholars interested in understanding the user experience. This study investigates the current state of the user experience of augmented reality apps by extracting and classifying the information from reviews published in the Google Play Store. A set of 116 educational mobile AR apps were mined from the Google Play Store, and a total of 1,752 user reviews were retrieved and classified. Results suggest developers of educational mobile AR apps need to solve technical problems, improve certain features, and provide more explicit instructions to users. Regardless of these needs, users recognize that these apps have great potential as educational tools. Future developments should focus on tackling these shortcomings, expanding the use of AR apps to more fields of education, and targeting specific audiences to extend the technology adoption.
Optical information processing: A historical overview

Optical information processing lies at the intersection of optics and signal processing. It involves the processing of optical information as well as the use of optical means to process information, the later being the main emphasis of this work. A historical review of various forms of optical signal processing and holography, optoelectronic and digital optical computing, and optical interconnections is given.
Sparse Online Gaussian Process Impedance Learning for Multi-DoF Robotic Arms

Robot interaction control with fixed impedance parameters usually fails to achieve the desired interaction behavior, motivating the introduction of variable impedance or admittance control. A gradient-descent (GD) impedance learning approach with a limited iteration number can make robots more compliant by minimizing an objective function. However, due to the nature of GD optimization, impedance parameters can not be adjusted in time by this approach, resulting in degraded robot compliance. This paper combines GD optimization with sparse online Gaussian process (SOGP) to develop a GD-based SOGP (GD-SOGP) impedance learning approach for variable admittance control. A high-fidelity mathematical model of a 7-DoF collaborative robot called Panda is applied for simulation studies. It is shown that the proposed GD-SOGP impedance learning can make the robot more compliant and outperforms the GD impedance learning in terms of impedance convergence.
STSRNet: Self-Texture Transfer Super-Resolution and Refocusing Network

Biomedical microscopy images with high-resolution (HR) and axial information can help analysis and diagnosis. However, obtaining such images usually takes more time and economic costs, which makes it impractical in most scenarios. In this paper, we first propose a novel Self-texture Transfer Super-resolution and Refocusing Network (STSRNet) to reconstruct HR multi-focal plane (MFP) images from a single 2D low-resolution (LR) wide field image without relying on scanning or any special devices. The proposed STSRNet consists of three parts: the backbone module for extracting features, the self-texture transfer module for transferring and fusing features, and the flexible reconstruction module for SR and refocusing. Specifically, the self-texture transfer module is designed for images with self-similarity such as cytological images and it searches for similar textures within the image and transfers to help MFP reconstruction. As for reconstruction module, it is composed of multiple pluggable components, each of which is responsible for a specific focal plane, so as to performs SR and refocusing all focal planes at one time to reduce computation. We conduct extensive experiments on cytological images and the experiments show that MFP images reconstructed by STSRNet have richer details in the axial and horizontal directions than input images. At the same time, the reconstructed MFP images also perform better than single 2D wide field images on high-level tasks. The proposed method provides relatively high-quality MFP images when real MFP images cannot be obtained, which greatly expands the application potential of LR wide-field images. To further promote the development of this field, we released our cytology dataset named RSDC for more researchers to use.
Outlier detection in animal multivariate trajectories

Outlier trajectory detection systems have received growing attention over the last decades. These systems have highly improved their efficiency through the rise of the Internet of Things (IoT) and the emergence of affordable wireless sensor networks. IoT-based technologies adoption have led to a faster capture of data from several independent information sources with higher quality and greater volume. Besides, data mining and machine learning techniques allow the combination of these data sources in order to boost decision-making processes. In this paper, Outlier Detection in Animal Multivariate Trajectories (ODAMT) is proposed as a new method that combines spatial information with additional independent data sources for detection of anomalies in animal multivariate trajectories. ODAMT has been applied for the detection of cattle reproductive events in a real study achieving promising results.
Application of a time-series deep learning model to predict cardiac dysrhythmias in electronic health records

Cardiac dysrhythmias (CD) affect millions of Americans in the United States (US), and are associated with considerable morbidity and mortality. New strategies to combat this growing problem are urgently needed.Predicting CD using electronic health record (EHR) data would allow for earlier diagnosis and treatment of the condition, thus improving overall cardiovascular outcomes. The Guideline Advantage (TGA) is an American Heart Association ambulatory quality clinical data registry of EHR data representing 70 clinics distributed throughout the US, and has been used to monitor outpatient prevention and disease management outcome measures across populations and for longitudinal research on the impact of preventative care.For this study, we represented all time-series cardiovascular health (CVH) measures and the corresponding data collection time points for each patient by numerical embedding vectors. We then employed a deep learning technique-long-short term memory (LSTM) model-to predict CD from the vector of time-series CVH measures by 5-fold cross validation and compared the performance of this model to the results of deep neural networks, logistic regression, random forest, and Naïve Bayes models.We demonstrated that the LSTM model outperformed other traditional machine learning models and achieved the best prediction performance as measured by the average area under the receiver operator curve (AUROC): 0.76 for LSTM, 0.71 for deep neural networks, 0.66 for logistic regression, 0.67 for random forest, and 0.59 for Naïve Bayes. The most influential feature from the LSTM model were blood pressure.These findings may be used to prevent CD in the outpatient setting by encouraging appropriate surveillance and management of CVH.
PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment

We propose a Progressive One-Shot Neural Architecture Search (PONAS) method to achieve a very efficient model searching for various hardware constraints. Given a constraint, most neural architecture search (NAS) methods either sample a set of sub-networks according to a pre-trained accuracy predictor, or adopt the evolutionary algorithm to evolve specialized networks from the supernet. Both approaches are time consuming. Here our key idea for very efficient deployment is, when searching the architecture space, constructing a table that stores the validation accuracy of all candidate blocks at all layers. For a stricter hardware constraint, the architecture of a specialized network can be efficiently determined based on this table by picking the best candidate blocks that yield the least accuracy loss. To accomplish this idea, we propose the PONAS method to combine advantages of progressive NAS and one-shot methods. A two-stage training scheme, including the meta training stage and the fine-tuning stage, is proposed to make the search process efficient and stable. During search, we evaluate candidate blocks in different layers and construct an accuracy table that is to be used in architecture searching. Comprehensive experiments verify that PONAS is extremely flexible, and is able to find architecture of a specialized network in around 10 seconds. In ImageNet classification, 76.29% top-1 accuracy can be obtained, which is comparable with the state of the arts.
The Synthesis of Unpaired Underwater Images for Monocular Underwater Depth Prediction

Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth .
Context-Aware Based Visual-Audio Feature Fusion for Emotion Recognition

Video emotion recognition is a significant branch in the field of emotion computing. However, traditional recognition works mainly focus on human features, ignoring the contextual clues of video scenes and objects. In our work, we propose a context-aware framework for bi-modal video emotion recognition. Unlike existing methods that directly extract features of the entire video frame, we extract key frames and key regions of videos to obtain emotional cues contained in video scenes and objects. Specifically, for visual stream, the hierarchical Bidirectional Long-Short Term Memory (Bi-LSTM) is applied to summarize video scenes and find key frames that mostly contribute to video emotion; Meantime, we introduce the Region Proposal Network (RPN) to extract corresponding features of object regions in video frames and construct the emotional similarity graph. After using the Feedforward Neural Network (FNN) to assign different weight coefficients to different regions, the Graph Convolutional Network (GCN) is used to reason about the connections between key regions. Moreover, the context information of the frame-level Log-Mel spectrum fragments supplement the visual information. Finally, we fuse the visual and acoustics features by adaptive gated multimodal fusion module for video emotion classification. We conduct experiments on Video Emotion-8 and Ekman-6 datasets. The experimental results demonstrate that our model achieves better classification accuracy than several baseline models.
Horizontal Cross-Silo Federated Recommender Systems

Recommender systems (RSs) completely rely on the knowledge of training information to generate recommendations. However, due to privacy, ownership, and protection of users' information, such training information is not easily accessible or shared with an RS. Moreover, with recent regulations in privacy laws (e.g, GDPR), collecting user preferences and perform centralized training may not be feasible. Federated Learning (FL) is a form of machine learning technique where the goal is to learn a high-quality recommendation model without never directly accessing raw training data. In this work, we specifically focus on situations where multiple stakeholders (referred to as corporate companies like e-commerce business partners, hospitals, banks, news media publishers) participate in federated learning to build a shared recommendation model. We performed offline experiments by simulating a real federated learning setup and investigated the benefits federated learning brings to stakeholders in terms of ranking compared to an RS model trained without participating in federated learning. Our experimental results reveal that stakeholders can significantly benefit from federated learning to generate accurate recommendations. Moreover, we also study the use and benefits of federated learning in situations when there are not enough preferences available for users.
Building a Reputation Attack Detector for Effective Trust Evaluation in a Cloud Services Environment

Cloud computing is a widely used technology that has changed the way people and organizations store and access information. This technology is versatile, and extensive amounts of data can be stored in the cloud. Businesses can access various services over the cloud without having to install applications. However, cloud computing services are provided over a public domain, which means that both trusted and non-trusted users can access the services. Although there are a number of advantages to cloud computing services, especially for business owners, various challenges are posed in terms of the privacy and security of information and online services. A threat that is widely faced in the cloud environment is the on/off attack, in which entities exhibit proper behavior for a given time period to develop a positive reputation and gather trust, after which they exhibit deception. Another threat often faced by trust management services is a collusion attack, which is also known as collusive malicious feedback behavior. This is carried out when a group of people work together to make false recommendations with the intention of damaging the reputation of another party, which is referred to as a slandering attack, or to enhance their own reputation, which is referred to as a self-promoting attack. In this paper, a viable solution is provided with the given trust model for preventing these attacks. This method works by providing effective security to cloud services by identifying malicious and inappropriate behaviors through the application of trust algorithms that can identify on/off attacks and collusion attacks by applying different security criteria. Finally, the results show that the proposed trust model system can provide high security by decreasing security risk and improving the quality of decisions of data owners and cloud operators.
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes

Abstract Skew-Gaussian Processes (SkewGPs) extend the multivariate Unified Skew-Normal distributions over finite dimensional vectors to distribution over functions. SkewGPs are more general and flexible than Gaussian processes, as SkewGPs may also represent asymmetric distributions. In a recent contribution, we showed that SkewGP and probit likelihood are conjugate, which allows us to compute the exact posterior for non-parametric binary classification and preference learning. In this paper, we generalize previous results and we prove that SkewGP is conjugate with both the normal and affine probit likelihood, and more in general, with their product. This allows us to (i) handle classification, preference, numeric and ordinal regression, and mixed problems in a unified framework; (ii) derive closed-form expression for the corresponding posterior distributions. We show empirically that the proposed framework based on SkewGP provides better performance than Gaussian processes in active learning and Bayesian (constrained) optimization. These two tasks are fundamental for design of experiments and in Data Science.
Quantum algorithms for group convolution, cross-correlation, and equivariant transformations

Group convolutions and cross-correlations, which are equivariant to the actions of group elements, are commonly used to analyze or take advantage of symmetries inherent in a given problem setting. Here, we provide efficient quantum algorithms for performing linear group convolutions and cross-correlations on data stored as quantum states. Runtimes for our algorithms are poly-logarithmic in the dimension of the group and the desired error of the operation. Motivated by the rich literature on quantum algorithms for solving algebraic problems, our theoretical framework opens a path for quantizing many algorithms in machine learning and numerical methods that employ group operations.
Firm productivity, innovation, and financial development

This study utilizes firm-level data from the World Bank’s Enterprise Survey Indicator Database, conducted between 2009 and 2018 for 32 countries in Africa, to examine the causal relationship between firm productivity, innovation, and financial development. We show evidence that firm innovation significantly and positively affects firm productivity. We also show the mediating role of well-developed financial markets on productivity. In a well-developed financial market, the impact of firm innovation is significant through the facilitation and financing of innovation activities; and innovative firms to boost productivity and lower production costs. These findings are significant for countries in Africa (and other less-developed countries) who spend less on R&D but can adopt or imitate existing innovative ideas from technology-rich countries for accelerated economic growth and increased productivity.
Textbook Question Answering with Multi-type Question Learning and Contextualized Diagram Representation

Textbook question answering (TQA) is a multi-modal task that requires complex parsing and reasoning over scientific diagrams and long text to answer various types of questions, including true/false questions, reading comprehension, and diagram questions, making TQA a superset of question answering (QA) and visual question answering (VQA). In this paper, we introduce a Multi-Head TQA architecture (MHTQA) for solving the TQA task. To overcome the long text issue, we apply the open-source search engine Solr to select sentences from lesson essays. In order to answer questions that have different input formats and share knowledge, we build a bottom-shared model with a transformer and three QA networks. For diagram questions, previous approaches did not incorporate the textual context to produce diagram representation, resulting in insufficient utilize of diagram semantic information. To address this issue, we learn a contextualized diagram representation through the novel Contextualized Iterative Dual Fusion network (CIDF) using the visual and semantic features of the diagram image and the lesson essays. We jointly train different types of questions in a multi-task learning manner for knowledge sharing by an efficient sampling strategy of Multi-type Question Learning (MQL). The experimental results show that our model outperforms the existing single model on all question types by a margin of 4.6%, 1.7%, 1%, 1.9% accuracy on Text T/F, Text MC, Diagram, and overall accuracy.
Self-Supervised Deep Correlational Multi-View Clustering

In conventional unsupervised multi-view clustering (MVC), learning of representations from heterogeneous multiview data and its subsequent clustering are often separately optimized. The disparate optimization would lead to suboptimal performance because multi-view representation learning is not goal-directed. In this paper, we unify unsupervised multi-view learning and deep clustering in a novel discriminative Self-supervised Deep Correlational Multi-view Clustering (SDC-MVC) network. A new unified loss function is proposed to incorporate consensus information into discriminative representations, in which, the former is learnt by maximizing the canonical correlation among multi-view representations projected by neural networks, and the later is achieved through using confident clustering assignments as supervision. Further, multi-view representations are harnessed by our proposed Deep Serial Feature-level (DSF) Fusion layer. Experiments on three public datasets demonstrated that our method outperforms six state-of-the-art correlation-based MVC algorithms in terms of three evaluation metrics.
Improving Human-Centric Software Defect Evaluation, Reporting, and Fixing

Defect reporting customarily exists in most applications and web sites to support issue reporting by end users and for developers to receive actionable feedback. However, the impact of issues - such as age, gender, language, culture, physical and mental challenges, and socio-economic status - is often overlooked in the development process and during product inception and defect reporting. Most defect reporting tools lack necessary human-centric features to enable a challenged user to adequately navigate and report defects i.e. do not take into account the human differences between end users that cause defects for them in their software and make reporting of such defects difficult for them. Most defect reporting tools also lack sufficient defect report structuring, reporting guidance, and do not emphasize the possible perceived severity of the defect to developers from end users who are very different to them. If users are unable to report defects due to usability issues, this makes those same defect reports difficult to understand by the developer. In this paper, we aim to improve human-centric defect reporting by employing cognitive walkthrough with diverse end user personas, develop a prototype of an improved defect reporting tool, and evaluate the prototype’s defect reporting process from end user and developer perspectives. Our findings offer a foundation for improved human-centric defect evaluation, reporting and fixing.
The Roles and Challenges of Computing Terminology in Non-Computing Disciplines

Aspects of computing are used in all professions. Regardless of academic discipline, graduates require ever-improving computing competencies – applied in their own disciplinary contexts – to become proficient experts in their chosen careers, as well as informed members of society. However, teaching and learning contextually relevant computing competencies is challenging. One such challenge is the effective use of computing terminology. Terminology is fundamental to most human activities and is critical in making or breaking the interfaces where disciplines meet.