Reviewerly/exHarmony huggingface dataset:
Semantic knowledge modeling and evaluation of argument Theory to develop dialogue based patient education systems for chronic disease Self-Management

To improve understanding as well as uptake of health educational material, it should be tailored to informational needs, offer intuitive modes of interaction, and present credible evidence for health claims. Dialogue systems go some way in meeting these requirements, as they emulate interactive and intuitive person-to-person communication. However, most works do not offer a formal model nor modelling process to structure dialogue content, and do not focus on ensuring credibility.We propose an Extended Model of Argument (EMA) dialogue model and modelling process to support educational dialogue systems. In this dialogue model, computerized arguments directly offer evidence for health claims. EMA further offers "dialogue by design", where argument structures and interrelations are dynamically leveraged to offer dialogues, instead of relying on predefining discourse flows. We implemented an EMA-based dialogue education system for Juvenile Idiopathic Arthritis (JIA). We performed a qualitative evaluation with JIA health experts involving a Cognitive Walkthrough and Semi-Structured Interview. We applied Directed Content Analysis using categories from the O'Grady framework, and coded sub-themes within those categories using Grounded Theory.We identified 6 sub-themes within the participant feedback pertaining to Quality, Credibility, and Utility. Participants attached strong importance to credibility and found the dialogue system to be a flexible educational tool. Some participants suggested sorting educational items by importance, and presenting only salient knowledge associations to reduce dialogue complexity.Overall, our qualitative evaluation confirmed the following: the ability of EMA to offer credible and appropriate dialogues; and, in general, the utility of dialogue systems to educate JIA patients and their families. In future work, we will revise the system based on evaluation feedback, and perform a more extensive evaluation with patients and caregivers.
QNet: A Scalable and Noise-Resilient Quantum Neural Network Architecture for Noisy Intermediate-Scale Quantum Computers

Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks. Existing QML models that use deep parametric quantum circuits (PQC) suffer from a large accumulation of gate errors and decoherence. To circumvent this issue, we propose a new QML architecture called QNet. QNet consists of several small quantum neural networks (QNN). Each of these smaller QNN’s can be executed on small quantum computers that dominate the NISQ-era machines. By carefully choosing the size of these QNN’s, QNet can exploit arbitrary size quantum computers to solve supervised ML tasks of any scale. It also enables heterogeneous technology integration in a single QML application. Through empirical studies, we show the trainability and generalization of QNet and the impact of various configurable variables on its performance. We compare QNet performance against existing models and discuss potential issues and design considerations. In our study, we show 43% better accuracy on average over the existing models on noisy quantum hardware emulators. More importantly, QNet provides a blueprint to build noise-resilient QML models with a collection of small quantum neural networks with near-term noisy quantum devices.
Online Graph Learning under Smoothness Priors

The growing success of graph signal processing (GSP) approaches relies heavily on prior identification of a graph over which network data admit certain regularity. However, adaptation to increasingly dynamic environments as well as demands for real-time processing of streaming data pose major challenges to this end. In this context, we develop novel algorithms for online network topology inference given streaming observations assumed to be smooth on the sought graph. Unlike existing batch algorithms, our goal is to track the (possibly) time-varying network topology while maintaining the memory and computational costs in check by processing graph signals sequentially-in-time. To recover the graph in an online fashion, we leverage proximal gradient (PG) methods to solve a judicious smoothness-regularized, time-varying optimization problem. Under mild technical conditions, we establish that the online graph learning algorithm converges to within a neighborhood of (i.e., it tracks) the optimal time-varying batch solution. Computer simulations using both synthetic and real financial market data illustrate the effectiveness of the proposed algorithm in adapting to streaming signals to track slowly-varying network connectivity.
Obstacle type recognition in visual images via dilated convolutional neural network for unmanned surface vehicles

Abstract Recognition of obstacle type based on visual sensors is important for navigation by unmanned surface vehicles (USV), including path planning, obstacle avoidance, and reactive control. Conventional detection techniques may fail to distinguish obstacles that are similar in visual appearance in a cluttered environment. This work proposes a novel obstacle type recognition approach that combines a dilated operator with the deep-level features map of ResNet50 for autonomous navigation. First, visual images are collected and annotated from various different scenarios for USV test navigation. Second, the deep learning model, based on a dilated convolutional neural network, is set and trained. Dilated convolution allows the whole network to learn deep features with increased receptive field and further improves the performance of obstacle type recognition. Third, a series of evaluation parameters are utilised to evaluate the obtained model, such as the mean average precision (mAP), missing rate and detection speed. Finally, some experiments are designed to verify the accuracy of the proposed approach using visual images in a cluttered environment. Experimental results demonstrate that the dilated convolutional neural network obtains better recognition performance than the other methods, with an mAP of 88%.
Evaluating Attribution Methods in Machine Learning Interpretability

Interpretability is a key feature to broaden a conscious adoption of machine learning models in domains involving safety, security, and fairness. To achieve the interpretability of complex machine learning models, one approach consists in explaining the outcome of machine learning models through input features attribution. Attribution consists in scoring the features of an input instance by establishing how important is each feature value in a fixed instance to obtain a specific classification outcome from the machine learning model. In literature, several attribution methods are defined for specific machine learning models (e.g., neural networks) or more general ones that are model agnostic (i.e., can interpret any machine learning models). Attribution is particularly appreciated for its easy understanding of the interpretation, which is the attribution. In domains involving safety, security, and fairness, properties of the explanation such as precision and generality are crucial to establish human trust in machine learning interpretability and then on the machine learning model itself. However, even if precision and generality are clearly defined in rule-based interpretation models, they are not defined or measure on attribution models. In this work, we propose a general methodology to estimate the degree of precision and generality in attribution methods. In addition, we propose a way to measured consistency in attribution between two attribution methods. Our experiments focus on the two most popular model agnostic attribution methods, SHAP and LIME, and we evaluate them to two real applications in the field of attack detection. Our proposed methodology shows in these experiments that both SHAP and LIME lack precision, generality, and consistency and that still more investigation in the attribution research field is required.
An Ensemble Latent Factor Model for Highly Accurate Web Service QoS Prediction

How to accurately predict quality of service (QoS) data is a great challenge in Web service selection or recommen-dation. To date, a latent factor (LF)-based QoS predictor is one of the most successful and popular approaches to address this chal-lenge as its high efficiency and scalability. However, current LF -based QoS predictors are mostly developed on inner product space with an L2 norm-oriented loss function only, thereby they cannot comprehensively represent target QoS data's characteris-tics to make accurate prediction as inner product space and L2 norm have their respective limitations. To address this issue, this study proposes an ensemble LF (ELF) model. It has three-fold ideas: 1) two kinds of LF models are developed as QoS predictors on inner product space and distance space, respectively, 2) both of these two QoS predictors adopt an Ll-and-L2-norm-oriented loss function, and 3) building an ensemble of these two QoS predictors by a weighting strategy. By doing so, ELF integrates multi-merits originating from inner product space, distance space, L1 norm, and L2 norm, making it achieve highly accurate and robust QoS prediction. Experiments on a real-world QoS dataset demonstrate that the proposed ELF model outperforms state-of-the-art QoS predictors in predicting the missing QoS data.
Detection of moving objects using thermal imaging sensors for occupancy estimation

Thermal imaging sensors have been increasingly integrated in a wide range of smart building and Internet of Things systems. Low-resolution thermal imaging sensors are especially suitable for applications that require non-intrusive monitoring with proper privacy protection. In this paper, we present an in-depth investigation of a low-resolution thermal imaging sensor (i.e., Melexis MLX90640) focusing on algorithm design issues and solutions when detecting moving objects. This type of sensors are designed to operate with a two-subpage chessboard reading pattern, which gives rise to blob displacements across two subpages when target objects are in motion. We have conducted systematic characterization of the sensor and demonstrated issues through experimental measurements and analysis. We have also proposed a subpage bilinear interpolation method and an enhanced sensor data preprocessing method for occupancy estimation with moving objects. The performance of the proposed method is analyzed by training and testing classification algorithms using two datasets collected with objects of different moving speeds. Our performance results indicate that the proposed method could be used for occupancy estimation in various smart building and Internet of Things applications.
Anytime 3D Object Reconstruction Using Multi-Modal Variational Autoencoder

For effective human-robot teaming, it is important for the robots to be able to share their visual perception with the human operators. In a harsh remote collaboration setting, data compression techniques such as autoencoder can be utilized to obtain and transmit the data in terms of latent variables in a compact form. In addition, to ensure real-time runtime performance even under unstable environments, an anytime estimation approach is desired that can reconstruct the full contents from incomplete information. In this context, we propose a method for imputation of latent variables whose elements are partially lost. To achieve the anytime property with only a few dimensions of variables, exploiting prior information of the category-level is essential. A prior distribution used in variational autoencoders is simply assumed to be isotropic Gaussian regardless of the labels of each training datapoint. This type of flattened prior makes it difficult to perform imputation from the category-level distributions. We overcome this limitation by exploiting a category-specific multi-modal prior distribution in the latent space. The missing elements of the partially transferred data can be sampled, by finding a specific modal according to the remaining elements. Since the method is designed to use partial elements for anytime estimation, it can also be applied for data over-compression. Based on the experiments on the ModelNet and Pascal3D datasets, the proposed approach shows consistently superior performance over autoencoder and variational autoencoder up to 70% data loss. The software is open source and is available from our repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
A Sovereign PKI for IoT Devices Based on the Blockchain Technology

In the Internet of Things (IoT) environment, public key distribution and device authentication remain the most significant security challenges. To validate the authenticity of the identity of IoT devices, existing solutions depend on Public Key Infrastructure (PKI) backed by Certificate Authorities (CA). CA-based PKI has flaws in terms of a single point of failure and certificate transparency. While some blockchain-based PKI solutions exist, they either have a high storage overhead or require a lot of cryptographic computations in the smart contract, which can exceed the transaction size limit on the blockchain network. Hence, we propose a sovereign PKI for IoT devices based on blockchain technology, in which individual controls and maintains the public and private keys for the IoT devices he or she owns. Public keys are kept in a decentralized key store database (DKSB). The blockchain serves as the ground proof for authenticating identities (public keys) on the DKSB. Cryptographic operations like identity authentication are done off-chain without incurring transaction fees.
A Constrained Parametric Approach for Modeling Uncertain Data

Data obtained from the real-world tends to be uncertain: Measurement inaccuracies, variability in opinions, and human errors are just some of the reasons that make the information collection process noisy. In recent years, fuzzy sets have been used to capture the uncertainty in data and then build automatic reasoning systems. In some contexts, data on a given subject is gathered from multiple sources and each instance modeled through a fuzzy set. A typical example of this scenario is represented by surveys, in which many participants express their opinions on the same topics. The fuzzy sets representing individual instances can be combined in a new (type-1 or type-2) fuzzy set in order to capture expert or measurement variation. In this article, we propose a novel approach which combines uncertain data modeled through parametric fuzzy sets in an intuitive manner, using the recently introduced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constrained interval type-2 (IT2) fuzzy sets</i> . By intuitive, we mean that each resultant constrained IT2 fuzzy set preserves the shape used to represent a single data instance, while making use of the footprint of uncertainty to represent uncertainty around its parameters. This novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constrained parametric approach</i> is applied to interval-valued data gathered from real surveys and compared to the other algorithms in the literature, showing how it differs from them, with discussion of the contexts in which it represents a valuable alternative. Finally, it is shown how this novel approach can be used to model not just intervals but data in which individual instances can be modeled through any parametric fuzzy sets (e.g., triangular).
Adaptive Private Distributed Matrix Multiplication

We consider the problem of designing codes with flexible rate (referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rateless</i> codes), for private distributed matrix-matrix multiplication. A master server owns two private matrices <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {A}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbf {B}$ </tex-math></inline-formula> and hires worker nodes to help computing their multiplication. The matrices should remain information-theoretically private from the workers. Codes with fixed rate require the master to assign tasks to the workers and then wait for a predetermined number of workers to finish their assigned tasks. The size of the tasks, hence the rate of the scheme, depends on the number of workers that the master waits for. We design a rateless private matrix-matrix multiplication scheme, called RPM3. In contrast to fixed-rate schemes, our scheme fixes the size of the tasks and allows the master to send multiple tasks to the workers. The master keeps sending tasks and receiving results until it can decode the multiplication; rendering the scheme flexible and adaptive to heterogeneous environments. Despite resulting in a smaller rate than known straggler-tolerant schemes, RPM3 provides a smaller mean waiting time of the master by leveraging the heterogeneity of the workers. The waiting time is studied under two different models for the workers’ service time. We provide upper and lower bounds for the mean waiting time under both models. In addition, we provide lower bounds on the mean waiting time under the worker-dependent fixed service time model.
Using Image Feature Extraction to Identification of Ancient Ceramics Based on Partial Differential Equation

This paper presents an in-depth study and analysis of the image feature extraction technique for ancient ceramic identification using an algorithm of partial differential equations. Image features of ancient ceramics are closely related to specific raw material selection and process technology, and complete acquisition of image features of ancient ceramics is a prerequisite for achieving image feature identification of ancient ceramics, since the quality of extracted area-grown ancient ceramic image feature extraction method is closely related to the background pixels and does not have generalizability. In this paper, we propose a deep learning-based extraction method, using Eased as a deep learning support platform, to extract and validate 5834 images of 272 types of ancient ceramics from kilns, celadon, and Yue kilns after manual labelling and training learning, and the results show that the average complete extraction rate is higher than 99%. The implementation of the deep learning method is summarized and compared with the traditional region growth extraction method, and the results show that the method is robust with the increase of the learning amount and has generalizability, which is a new method to effectively achieve the complete image feature extraction of ancient ceramics. The main content of the finite difference method is to use the ratio of the difference between the function values of two adjacent points and the distance between the two points to approximate the partial derivative of the function with respect to the variable. This idea was used to turn the problem of division into a problem of difference. Recognition of ancient ceramic image features was realized based on the extraction of the overall image features of ancient ceramics, the extraction and recognition of vessel type features, the quantitative recognition of multidimensional feature fusion ornamentation image features, and the implementation of deep learning based on inscription model recognition image feature classification recognition method; three-layer B/S architecture web application system and cross-platform system language called as the architectural support; and database services, deep learning packaging, and digital image processing. The specific implementation method is based on database service, deep learning encapsulation, digital image processing, and third-party invocation, and the service layer fusion and relearning mechanism is proposed to achieve the preliminary intelligent recognition system of ancient ceramic vessel type and ornament image features. The results of the validation test meet the expectation and verify the effectiveness of the ancient ceramic vessel type and ornament image feature recognition system.
A Novel Smart Lightweight Visual Attention Model for Fine-Grained Vehicle Recognition

Vehicle Make and Model Recognition (VMMR) requires fast and accurate recognition of a vehicle’s information. Generally, the vision-based VMMR method recognizes different vehicle models that mainly rely on locating and extracting the discriminative part features of a vehicle. In this paper, we propose a Lightweight Recurrent Attention Unit (LRAU) to enhance the feature extraction ability of the standard Convolutional Neural Network (CNN) architectures for VMMR. The proposed LRAU extracts the discriminative part features by generating attention masks to locate the keypoints of a vehicle (e.g., logo, headlight). The attention mask is generated based on the feature maps received by the LRAU and the preceding attention state generated by the preceding LRAU. By adding LRAUs to receive the multi-scale feature maps generated by the standard CNN architecture, discriminative features of different scales can be efficiently extracted and combined. We conduct comprehensive experiments on three challenging VMMR datasets to evaluate the proposed VMMR models. Experimental results show our models have a stable performance under different environmental conditions. Our models achieve state-of-the-art results with 93.94% accuracy on the Stanford Cars dataset, 98.31% accuracy on the CompCars dataset, and 99.41% accuracy on the NTOU-MMR dataset. Moreover, we demonstrate that our models outperform the traditional machine learning-based VMMR models in terms of recognition accuracy and processing speed. In addition, we construct a one-stage Vehicle Detection and Fine-grained Recognition (VDFR) model by combining our LRAU with the general object detection model. Results show the proposed VDFR model can achieve excellent performance with real-time processing speed.
Benchmarking Seismic-Based Feature Groups to Classify the Cotopaxi Volcanic Activity

We systematically and comprehensively tested almost 100 feature groups in four commonly used automatic event classifiers to find the combinations that maximize the classification performance for long-period and volcano-tectonic seismic events at Cotopaxi volcano, Ecuador. The feature groups tested fall into the following categories: time, fast-Fourier, wavelet transform, intensity-statistics, shape, and texture. An analysis of the relevance of feature groups highlights and finds that each classifier performs similar to one another when using the best combination of features, as determined by the mean of the area under the curve metric, with no statistically significant differences between them. The feed-forward back-propagation neural network and random forest classifiers achieved scores of 0.96 while the naive Bayes and k-nearest neighbor (kNN) classifiers achieved scores of 0.95. The shape and texture feature groups were the most frequently utilized (three times each) among the different classifiers, and thus, the most appropriate for classifying both types of seismic events. The kNN ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k=3$ </tex-math></inline-formula> ) classifier using the shape feature group reached an excellent performance with lower algorithmic complexity. Moreover, nontraditional features like those computed from spectrogram images overcame traditional time, Fourier, and scale features when classifying seismic events.