Reviewerly/exHarmony huggingface dataset:
Digital Contact Tracing, Privacy, and Public Health

Abstract Digital contact tracing, in combination with widespread testing, has been a focal point for many plans to “reopen” economies while containing the spread of Covid‐19. Most digital contact tracing projects in the United States and Europe have prioritized privacy protections in the form of local storage of data on smartphones and the deidentification of information. However, in the prioritization of privacy in this narrow form, there is not sufficient attention given to weighing ethical trade‐offs within the context of a public health pandemic or to the need to evaluate safety and effectiveness of software‐based technology applied to public health .
Role-Based Graph Embeddings

Random walks are at the heart of many existing node embedding and network representation learning methods. However, such methods have many limitations that arise from the use of traditional random walks, e.g., the embeddings resulting from these methods capture proximity (communities) among the vertices as opposed to structural similarity (roles). Furthermore, the embeddings are unable to transfer to new nodes and graphs as they are tied to node identity. To overcome these limitations, we introduce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Role2Vec</i> framework based on the proposed notion of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">attributed random walks</i> to learn structural role-based embeddings. Notably, the framework serves as a basis for generalizing any walk-based method. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Role2Vec</i> framework enables these methods to be more widely applicable by learning inductive functions that capture the structural roles in the graph. Furthermore, the original methods are recovered as a special case of the framework when each vertex is mapped to its own function that uniquely identifies it. Finally, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Role2Vec</i> framework is shown to be effective with an average AUC improvement of 17.8 percent for link prediction while requiring on average 853x less space than existing methods on a variety of graphs from different domains.
Deep clustering with a Dynamic Autoencoder: From reconstruction towards centroids construction

In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective function remains static. The absence of concrete supervision suggests that smooth dynamics should be integrated during the training process. Compared to classical static cost functions, dynamic objective functions allow to better make use of the gradual and uncertain knowledge acquired through pseudo-supervision. In this paper, we propose Dynamic Autoencoder (DynAE), a novel model for deep clustering that addresses a clustering-reconstruction trade-off, by gradually and smoothly eliminating the reconstruction objective function in favor of a construction one. Experimental evaluations on benchmark datasets show that our approach achieves state-of-the-art results compared to the most relevant deep clustering methods.
Robust Bi-stochastic Graph Regularized Matrix Factorization for Data Clustering

Data clustering, which is to partition the given data into different groups, has attracted much attention. Recently various effective algorithms have been developed to tackle the task. Among these methods, non-negative matrix factorization (NMF) has been demonstrated to be a powerful tool. However, there are still some problems. First, the standard NMF is sensitive to noises and outliers. Although l2,1 norm based NMF improves the robustness, it is still affected easily by large noises. Second, for most graph regularized NMF, the performance highly depends on the initial similarity graph. Third, many graph-based NMF models perform the graph construction and matrix factorization in two separated steps. Thus the learned graph structure may not be optimal. To overcome the above drawbacks, we propose a robust bi-stochastic graph regularized matrix factorization (RBSMF) framework for data clustering. Specifically, we present a general loss function, which is more robust than the commonly used L2 and L1 functions. Besides, instead of keeping the graph fixed, we learn an adaptive similarity graph. Furthermore, the graph updating and matrix factorization are processed simultaneously, which can make the learned graph more appropriate for clustering. Extensive experiments have shown the proposed RBSMF outperforms other state-of-the-art methods.
NLCA-Net: a non-local context attention network for stereo matching

Accurate disparity prediction is a hot spot in computer vision, and how to efficiently exploit contextual information is the key to improve the performance. In this paper, we propose a simple yet effective non-local context attention network to exploit the global context information by using attention mechanisms and semantic information for stereo matching. First, we develop a 2D geometry feature learning module to get a more discriminative representation by taking advantage of multi-scale features and form them into the variance-based cost volume. Then, we construct a non-local attention matching module by using the non-local block and hierarchical 3D convolutions, which can effectively regularize the cost volume and capture the global contextual information. Finally, we adopt a geometry refinement module to refine the disparity map to further improve the performance. Moreover, we add the warping loss function to help the model learn the matching rule of the non-occluded region. Our experiments show that (1) our approach achieves competitive results on KITTI and SceneFlow datasets in the end-point error and the fraction of erroneous pixels ; (2) our proposed method particularly has superior performance in the reflective regions and occluded areas.
Applying DevOps Practices of Continuous Automation for Machine Learning

This paper proposes DevOps practices for machine learning application, integrating both the development and operation environment seamlessly. The machine learning processes of development and deployment during the experimentation phase may seem easy. However, if not carefully designed, deploying and using such models may lead to a complex, time-consuming approaches which may require significant and costly efforts for maintenance, improvement, and monitoring. This paper presents how to apply continuous integration (CI) and continuous delivery (CD) principles, practices, and tools so as to minimize waste, support rapid feedback loops, explore the hidden technical debt, improve value delivery and maintenance, and improve operational functions for real-world machine learning applications.
A Survey on Neural Trojans

Neural networks have become increasingly prevalent in many real-world applications including security critical ones. Due to the high hardware requirement and time consumption to train high-performance neural network models, users often outsource training to a machine-learning-as-a-service (MLaaS) provider. This puts the integrity of the trained model at risk. In 2017, Liu et al. found that, by mixing the training data with a few malicious samples of a certain trigger pattern, hidden functionality can be embedded in the trained network which can be evoked by the trigger pattern [33]. We refer to this kind of hidden malicious functionality as neural Trojans. In this paper, we survey a myriad of neural Trojan attack and defense techniques that have been proposed over the last few years. In a neural Trojan insertion attack, the attacker can be the MLaaS provider itself or a third party capable of adding or tampering with training data. In most research on attacks, the attacker selects the Trojan's functionality and a set of input patterns that will trigger the Trojan. Training data poisoning is the most common way to make the neural network acquire the Trojan functionality. Trojan embedding methods that modify the training algorithm or directly interfere with the neural network's execution at the binary level have also been studied. Defense techniques include detecting neural Trojans in the model and/or Trojan trigger patterns, erasing the Trojan's functionality from the neural network model, and bypassing the Trojan. It was also shown that carefully crafted neural Trojans can be used to mitigate other types of attacks. We systematize the above attack and defense approaches in this paper.
A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling

Tool condition monitoring (TCM) in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. TCM based on multiple sensors can provide more information related to tool condition and is a topic of great interest. A novel TCM method for milling processes based on a two-layer angle kernel extreme learning machine (TAKELM) and binary differential evolution (BDE) was proposed. The TAKELM could enhance inherent feature extraction in microarray data and don’t require manual presetting of the kernel function or optimization of its hyperparameter. The BDE algorithm was applied to search the optimal feature parameter combinations in multi-domain alternative feature parameters to achieve the fewest feature parameters satisfying a very small prediction error. Finally, the performance of the proposed TAKELM- BDE method was verified on two milling TCM experiments (one open-access benchmark TCM data set and one TCM experiment) by comparison to the KELM- BDE, PCC- TAKELM, and mRMR- TAKELM methods. The results indicated that the mean absolute error, mean absolute percentage error, and root mean square error of the proposed method are less than 0.005 on two milling TCM experiments, and outperforms the other three methods.
Conversational Recommendation: Formulation, Methods, and Evaluation

Recommender systems have demonstrated great success in information seeking. However, traditional recommender systems work in a static way, estimating user preferences on items from past interaction history. This prevents recommender systems from capturing dynamic and fine-grained preferences of users. Conversational recommender systems bring a revolution to existing recommender systems. They are able to communicate with users through natural languages during which they can explicitly ask whether a user likes an attribute or not. With the preferred attributes, a recommender system can conduct more accurate and personalized recommendations.
Mobile health technology combats COVID-19 in China

<h2>Abstract</h2> China empowers mobile health technologies to fight against COVID-19 pandemic. The success of mobile health here may be a useful reference for other parts of the world. We explore China's application of mobile health technologies to replenishing traditional public-health and social approaches for mitigating and suppressing COVID-19, and found that Internet hospitals alleviate the unavailability, inaccessibility, and inequity of health services during the outbreak; the fact-check and information-release platforms reduce the spread of misinformation; and the infection risk scoring systems facilitate restoring the order of production and life.
Enhancing firefly algorithm with courtship learning

The firefly algorithm (FA) is a nature-inspired heuristic optimization algorithm based on the luminescence and attraction behavior of fireflies. Although the FA can effectively solve complex optimization problems, it suffers from premature convergence because of its simple full attraction model. However, in nature, fireflies exhibit luminous behavior to attract mates. The attractiveness between fireflies of opposite sexes depends not only on the light intensity they emit, but also on their individual size, location, and other factors. In the original FA, all fireflies are assumed to be the same, i.e., they have no gender-based difference, which is not true biologically. Therefore, in this paper, we proposed a novel courtship learning (CL) framework to enhance the performance of the FA. In the proposed framework, the population is divided into female and male subpopulations. The female archiving mechanism is adopted to select excellent fireflies, which are assumed to be female fireflies. When the selected male firefly emits light that is less bright than that of the current firefly, a female individual will be selected from the female archive to guide the movement of the selected male firefly. Comprehensive experiments are conducted on the CEC 2013 benchmark set and the proposed CL framework is integrated with other advanced FA variants to verify its effects. Experimental results confirm that the proposed framework significantly enhances the performance of the original FA and advanced FA variants.
AEnet: Automatic Picking of P-Wave First Arrivals Using Deep Learning

First arrival time picking is one of the critical processing steps of acoustic emission (AE)/microseismic (MS) monitoring for studying rock fracture processes. Because of massive monitoring data, the automatic arrival time picking technique is particularly desired. Inspired by recent successful applications of machine learning (ML) in earthquake phase identification, we propose a deep learning (DL)-based P-wave first arrival time picking method named AE Network (AEnet) for laboratory AE monitoring data. Our approach consists of two steps: classification and picking. The convolutional neural network (CNN) is used to classify each sample point of acoustic waveforms into either noise or signal. Different from prior DL-based phase picking studies using raw waveforms, we combine the waveform and high-order statistics as the input to enrich the input data features and accelerate the CNN model learning process. Our approach is examined using the laboratory AE monitoring data and the performance of each component of AEnet is also analyzed. The results show that the CNN model can classify the sample points accurately for the picking procedure. With this classification result, we pick the first arrival time of each trace using the curve fitting method and an unsupervised clustering algorithm. To evaluate the performance of AEnet, we apply Akaike Information Criterion-Short Term Averaging/Long Term Averaging Method (AIC-STA/LTA), one of the most popular and traditional picking methods, on the same waveforms and use the manual picks as the reference. Error analysis results show that AEnet outperforms AIC-STA/LTA.
Energy Transaction for Multi-Microgrids and Internal Microgrid Based on Blockchain

With the decreasing reserves of fossil energy and the increasing capacity of renewable energy generation, the scale of microgrid based on distributed generations is expanding. However, more operation data and transaction information of microgrids will also bring several problems: the sufficient capacity of the server in the central management needed, the crises of trust among members, the transparency of transaction information and the confidentiality of data storage. In this paper, blockchain technology is used to deal with these problems as distributed data storage technology. A double-layer framework of energy transactions based on blockchain in multi-microgrids is proposed to provide decentralized trading, information transparency and mutual trust system of each node in the trading market. The central node within the microgrid collects the demand information of the trading market in lower layer and sends them to the trading market of multi-microgrids in higher layer to seek the energy transaction. The continuous double auction mechanism is used in the trading market to guarantee free and fair transactions among nodes. The proposed transaction framework effectively reduces the transaction volume with the main grid which improves energy utilization. Comprehensive simulation results are presented to prove the feasibility of the proposed transaction framework.
Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis

Breast cancer diagnosis based on medical imaging necessitates both fine-grained lesion segmentation and disease grading. Although deep learning (DL) offers an emerging and powerful paradigm of feature learning for these two tasks, it is hampered from popularizing in practical application due to the lack of interpretability, generalization ability, and large labeled training sets. In this article, we propose a hierarchical fused model based on DL and fuzzy learning to overcome the drawbacks for pixelwise segmentation and disease grading of mammography breast images. The proposed system consists of a segmentation model (ResU-segNet) and a hierarchical fuzzy classifier (HFC) that is a fusion of interval type-2 possibilistic fuzzy c-means and fuzzy neural network. The ResU-segNet segments the masks of mass regions from the images through convolutional neural networks, while the HFC encodes the features from mass images and masks to obtain the disease grading through fuzzy representation and rule-based learning. Through the integration of feature extraction aided by domain knowledge and fuzzy learning, the system achieves favorable performance in a few-shot learning manner, and the deterioration of cross-dataset generalization ability is alleviated. In addition, the interpretability is further enhanced. The effectiveness of the proposed system is analyzed on the publicly available mammogram database of INbreast and a private database through cross-validation. Thorough comparative experiments are also conducted and demonstrated.
Conceptual Metaphors Impact Perceptions of Human-AI Collaboration

With the emergence of conversational artificial intelligence (AI) agents, it is important to understand the mechanisms that influence users' experiences of these agents. In this paper, we study one of the most common tools in the designer's toolkit: conceptual metaphors. Metaphors can present an agent as akin to a wry teenager, a toddler, or an experienced butler. How might a choice of metaphor influence our experience of the AI agent? Sampling a set of metaphors along the dimensions of warmth and competence---defined by psychological theories as the primary axes of variation for human social perception---we perform a study $(N=260)$ where we manipulate the metaphor, but not the behavior, of a Wizard-of-Oz conversational agent. Following the experience, participants are surveyed about their intention to use the agent, their desire to cooperate with the agent, and the agent's usability. Contrary to the current tendency of designers to use high competence metaphors to describe AI products, we find that metaphors that signal low competence lead to better evaluations of the agent than metaphors that signal high competence. This effect persists despite both high and low competence agents featuring identical, human-level performance and the wizards being blind to condition. A second study confirms that intention to adopt decreases rapidly as competence projected by the metaphor increases. In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth. These results suggest that projecting competence may help attract new users, but those users may discard the agent unless it can quickly correct with a lower competence metaphor. We close with a retrospective analysis that finds similar patterns between metaphors and user attitudes towards past conversational agents such as Xiaoice, Replika, Woebot, Mitsuku, and Tay.
Complex image processing with less data—Document image binarization by integrating multiple pre-trained U-Net modules

Artificial neural networks have been shown significant performance in various image-to-image conversion tasks. However, complex conversions often require a large number of images for model training. Therefore, we propose a convolutional model for image-to-image conversions using a pipeline of simpler image processing modules. To verify our proposed approach, we use a document image binarization as the task. Document image binarization is an important process that affects the accuracy of document analysis and recognition. In this paper, we propose a novel document binarization method called Cascading Modular U-Nets (CMU-Nets). CMU-Nets consist of pre-trained modular modules useful for overcoming the problem of a shortage of training images. We also propose a novel cascading scheme for improving overall cascading model performance. We verify the proposed model on all available Document Image Binarization Competition (DIBCO) and the Handwritten-DIBCO (H-DIBCO) datasets.
Recommendation System Using Autoencoders

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.
Subjective QoE of 360-Degree Virtual Reality Videos and Machine Learning Predictions

360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360-degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user's familiarity with VR and user's interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.