Reviewerly/exHarmony huggingface dataset:
Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations

Recently, cross-domain recommendation systems have been very helpful in improving the quality of recommendation and solving the problem of cold start and data sparsity. Cross-domain recommender systems allow the transfer of knowledge from one domain with dense ratings to other domain with sparse ratings. Such transfer of knowledge helps in addressing the data sparseness and cold start issues in traditional recommender systems. Although cross-domain recommendations have evolved significantly, yet employment of various factors, such as time, trust, and location remains a challenge. Most of the existing approaches ignore the important fact that at what specific time the user may be interested in the recommended item. Moreover, a person's trust level and sentiments may be influenced by the variation in the location and time, thereby affecting the decision making. In this paper, we propose a cross-domain recommender system that not only takes into account the time at finer granularity levels (e.g., hours, days, weeks, etc.), but also considers a persons location, trust level, and sentiment analysis while computing recommendations. Our proposed model, named as, Trust-Aware Spatial-Temporal Activity based Denoising Autoencoder (TSTDAE), employs autoencoder-based deep-learning models to generate top-N recommendations for a given user and addresses the cold-start problem in the cross-domain scenario of 'User Overlap'. The proposed work is fivefold: i) Filter out the users' biased profiles based on sentiment analysis. ii) Learn the features using autoencoder and then perform clustering among the users of source and target domains to discover the best neighbors. iii) Compute the trust and ratio of preference bias between active user (the user to whom top-N items are recommended) and their neighbors and grade the neighbors based on unbiased preferences iv) Project the best time for recommending the items to an active user and generate the top-N recommendations. We have evaluated the proposed model on a public dataset of e-commerce retail service 'AliExpress' for the evaluation metrics: Precision, Mean Absolute Precision (MAP), Normalized Discounted Cumulative Gain (NDCG), and Hit Ratio (HR). The experimental results showed improved performance of the proposed system over the existing models.
Large-scale machine learning with fast and stable stochastic conjugate gradient

In deterministic optimization, conjugate gradient (CG) type approaches are preferred with a superior convergence rate than the ordinary gradient approaches. The requirement of solving large-scale data, growing exponentially, makes recent works study the effectiveness of the CG-type approaches with stochastic approximation, especially for large-scale machine learning problems. However, it is challenging that how to incorporate the noisy gradients into CG-type approaches. In this paper, we develop a class of fast and robust stochastic conjugate gradient (SCG) type approach via using the stochastic recursive gradient algorithm (SARAH) and the hyper-gradient descent (HD) technique in the mini-batching setting. That the use of the SARAH gradient estimator makes the proposed approaches enjoy the low variance accelerates the convergence rate and saves the gradient complexity of the conventional SCG-type approach. In addition, using HD to determine the learning rate for the SCG-type approach greatly saves the computational burden, comparing with the existing literature that usually works with the line search technique in practice. We rigorously prove that the proposed approach attains a linear convergence rate for strongly convex loss functions and show that its complexity matches modern stochastic optimization approaches. Various experimental results on machine learning problems are provided to demonstrate the property and the effectiveness of the proposed approaches respectively.
Multi-Camera Digital Image Correlation in Deformation Measurement of Civil Components with Large Slenderness Ratio and Large Curvature

To address the limitations of conventional stereo-digital image correlation (DIC) on measuring complex objects, a continuous-view multi-camera DIC (MC-DIC) system and its two forms of camera arrangement are introduced. Multiple cameras with certain overlapping field of view are calibrated simultaneously to form an overall system for measuring the continuous full-surface deformation. The bending experiment of coral aggregate concrete beam and the axial compression experiment of timber column are conducted to verify the capability of continuous-view MC-DIC in deformation measurement of civil components with large slenderness ratio and large curvature, respectively. The obtained deformation data maintain good consistency with the displacement transducer and strain gauge. Results indicate that the continuous-view MC-DIC is a reliable 3D full-field measurement approach in civil measurements.
A parallel programming assessment for stream processing applications on multi-core systems

Multi-core systems are any computing device nowadays and stream processing applications are becoming recurrent workloads, demanding parallelism to achieve the desired quality of service. As soon as data, tasks, or requests arrive, they must be computed, analyzed, or processed. Since building such applications is not a trivial task, the software industry must adopt parallel APIs (Application Programming Interfaces) that simplify the exploitation of parallelism in hardware for accelerating time-to-market. In the last years, research efforts in academia and industry provided a set of parallel APIs, increasing productivity to software developers. However, a few studies are seeking to prove the usability of these interfaces. In this work, we aim to present a parallel programming assessment regarding the usability of parallel API for expressing parallelism on the stream processing application domain and multi-core systems. To this end, we conducted an empirical study with beginners in parallel application development. The study covered three parallel APIs, reporting several quantitative and qualitative indicators involving developers. Our contribution also comprises a parallel programming assessment methodology, which can be replicated in future assessments. This study revealed important insights such as recurrent compile-time and programming logic errors performed by beginners in parallel programming, as well as the programming effort, challenges, and learning curve. Moreover, we collected the participants’ opinions about their experience in this study to understand deeply the results achieved.
ASPD-Net: Self-aligned part mask for improving text-based person re-identification with adversarial representation learning

Text-based person re-identification aims to retrieve images of the corresponding person from a large visual database according to a natural language description. When it comes to visual local information extraction, most of the state-of-the-art methods adopt either a strict uniform strategy which can be too rough to catch local details properly, or pre-processing with external cues which may suffer from the deviations of the pre-trained model and the large computation consumption. In this paper, we proposed an Adversarial Self-aligned Part Detecting Network (ASPD-Net) model which extracts and combines multi-granular visual and textual features. A novel Self-aligned Part Mask Module was presented to autonomously learn the information of human body parts, and obtain visual local features in a soft-attention manner by using K Self-aligned Part Mask Detectors. Regarding the main model branches as a generator, a discriminator is employed to determine whether the representation vector comes from the visual modality or the textual modality. With Adversarial Loss training, ASPD-Net can learn more robust representations, as long as it successfully tricks the discriminator. Experimental results demonstrate that the proposed ASPD-Net outperforms the previous methods and achieves the state-of-the-art performance on the CUHK-PEDES and RSTPReid datasets.
Small Seismic Events in Oklahoma Detected and Located by Machine Learning–Based Models

ABSTRACT A complete earthquake catalog is essential to understand earthquake nucleation and fault stress. Following the Gutenberg–Richter law, smaller, unseen seismic events dominate the earthquake catalog and are invaluable for revealing the fault state. The published earthquake catalogs, however, typically miss a significant number of small earthquakes. Part of the reason is due to a limitation of conventional algorithms, which can hardly extract small signals from background noise in a reliable and efficient way. To address this challenge, we utilized a machine learning method and developed new models to detect and locate seismic events. These models are efficient in processing a large amount of seismic data and extracting small seismic events. We applied our method to seismic data in Oklahoma, United States, and detected ∼14 times more earthquakes compared with the standard Oklahoma Geological Survey catalog. The rich information contained in the new catalog helps better understand the induced earthquakes in Oklahoma.
QoS-Aware Resource Bargaining for Federated Learning Over Edge Networks in Industrial IoT

This work addresses the problem of optimal resource allocation for federated learning (FL) over wireless edge-networks to maximize the quality of Industrial Internet of Things (IIoT)-based services in terms of delay and revenue. The resource-constrained nature of IIoT devices necessitates the efficient utilization of their computation and communication resources for performing FL. Additionally, the selection of an optimal set of IIoT devices by a service-provider is necessary to perform FL with a required QoS. Furthermore, to ensure the participation of the desired IIoT devices, it is necessary for the service provider to properly incentivize these devices. Although few works aim to address these issues, the possibility of partial contribution of CPU power and dataset by IIoT devices and their corresponding incentivization is overlooked by researchers. Hence, in this work, we propose FedBarg, a game-theoretic scheme, in which we model the aforementioned problem using a Nash bargaining game. We show that the corresponding optimization problem is non-convex. Thereafter, we propose a joint coalition formation game- and utility theory-based scheme to find the optimal solutions. Simulations show that FedBarg improves the incurred delay by 16.7% and revenue of the users and service provider by 14.7% and 11.1%, respectively, compared to existing benchmarks.
Scalable spatio‐temporal smoothing via hierarchical sparse Cholesky decomposition

Abstract We propose an approximation to the forward filter backward sampler (FFBS) algorithm for large‐scale spatio‐temporal smoothing. FFBS is commonly used in Bayesian statistics when working with linear Gaussian state‐space models, but it requires inverting covariance matrices which have the size of the latent state vector. The computational burden associated with this operation effectively prohibits its applications in high‐dimensional settings. We propose a scalable spatio‐temporal FFBS approach based on the hierarchical Vecchia approximation of Gaussian processes, which has been previously successfully used in spatial statistics. On simulated and real data, our approach outperformed a low‐rank FFBS approximation.
A geometric characterization of sensitivity analysis in monomial models

Sensitivity analysis in probabilistic discrete graphical models is usually conducted by varying one probability at a time and observing how this affects output probabilities of interest. When one probability is varied, then others are proportionally covaried to respect the sum-to-one condition of probabilities. The choice of proportional covariation is justified by multiple optimality conditions, under which the original and the varied distributions are as close as possible under different measures. For variations of more than one parameter at a time and for the large class of discrete statistical models entertaining a regular monomial parametrisation, we demonstrate the optimality of newly defined proportional multi-way schemes with respect to an optimality criterion based on the I-divergence. We demonstrate that there are varying parameters' choices for which proportional covariation is not optimal and identify the sub-family of distributions where the distance between the original distribution and the one where probabilities are covaried proportionally is minimum. This is shown by adopting a new geometric characterization of sensitivity analysis in monomial models, which include most probabilistic graphical models. We also demonstrate the optimality of proportional covariation for multi-way analyses in Naive Bayes classifiers.
Conditional Feature Learning Based Transformer for Text-Based Person Search

Text-based person search aims at retrieving the target person in an image gallery using a descriptive sentence of that person. The core of this task is to calculate a similarity score between the pedestrian image and description, which requires inferring the complex latent correspondence between image sub-regions and textual phrases at different scales. Transformer is an intuitive way to model the complex alignment by its self-attention mechanism. Most previous Transformer-based methods simply concatenate image region features and text features as input and learn a cross-modal representation in a brute force manner. Such weakly supervised learning approaches fail to explicitly build alignment between image region features and text features, causing an inferior feature distribution. In this paper, we present CFLT, Conditional Feature Learning based Transformer. It maps the sub-regions and phrases into a unified latent space and explicitly aligns them by constructing conditional embeddings where the feature of data from one modality is dynamically adjusted based on the data from the other modality. The output of our CFLT is a set of similarity scores for each sub-region or phrase rather than a cross-modal representation. Furthermore, we propose a simple and effective multi-modal re-ranking method named Re-ranking scheme by Visual Conditional Feature (RVCF). Benefit from the visual conditional feature and better feature distribution in our CFLT, the proposed RVCF achieves significant performance improvement. Experimental results show that our CFLT outperforms the state-of-the-art methods by 7.03% in terms of top-1 accuracy and 5.01% in terms of top-5 accuracy on the text-based person search dataset.
Fuzzy Ontology-based Intelligent Agent for High-School Student Learning in AI-FML Metaverse

This article applies the fuzzy ontology concepts of Heart Sutra to artificial intelligence (AI) for elementary and high-school students learning English and fuzzy markup language (FML) in AI-FML Metaverse. Computational Intelligence (CI) is an imperative branch of AI and it includes fuzzy logic (FL), neural network (NN), and evolutionary computation (EC). Based on the technologies of reinforcement learning (RL), intelligent agent, ontology, and AI-FML human and machine co-learning, we propose a fuzzy ontology-based intelligent agent for young students to learn and experience CI applications, including basic concepts of FL, NN, and EC, on the AI-FML Metaverse platform. Additionally, we combine the core technologies of fuzzy ontology and AI-FML with the tenets of the Heart Sutra for constructing the intelligent agent in the AI-FML Metaverse with computational, perceptive, and cognitive intelligence. Moreover, we also apply the human semantic understanding of the Heart Sutra to explain the concept of the AI-FML Metaverse platform to elementary and high-school students to learn and experience CI. The intelligent agent interacts with the environment of the learning field and AI-FML Metaverse, it can help teachers and students evaluate learners’ interest in each episode’s learning content. Finally, it predicts their learning temperature in thermometers to present learners’ performance in the learning contents, such as English or CI application. After evaluating the implementation of AI-FML Metaverse in the elementary school (grades 5-6), junior high school (grades 7-8), and senior high school (grades 10-12) students in Taiwan, we observe that the intelligent agent can work effectively and students’ learning thermometer has an increasing tendency at the end of the 2021 fall semesters in AI-FML Metaverse and 2022 AI-FML winter camp in Taiwan.
Implementing Defuzzification Operators on Quantum Annealers

Due to the built-in parallelism of quantum computing, there is an unexplored potential for some complex fuzzy logic computations to take the advantage of the future quantum computers. Recently, it has been introduced a novel representation of fuzzy sets and implementations of some basic fuzzy logic operators (union, intersection, alpha-cut and maximum) based on solving a Quadratic Unconstrained Binary Optimization (QUBO) problems, on a type of quantum computers known as quantum annealers. In this paper, this work is extended by presenting an implementation of centroid defuzzification on quantum annealer machines, based on binary quadratic model (BQM) but this time using Ising model. Having the basic operations and defuzzification implemented on quantum computers, this paper paves the way towards the implementation of a whole fuzzy inference engine on enhanced devices, such as quantum annealers.
Comparing User Perception of Explanations Developed with XAI Methods

Artificial Intelligence (AI) has gained notable momentum, culminating in the rise of intelligent machines that deliver unprecedented levels of performance in many application sectors across the field. In recent years, the sophistication of these systems has increased to an extent where almost no human intervention is required for their deployment. A crucial feature for the practical deployment of AI-powered systems in critical decision-making processes is the ability to understand how these systems derive their decisions. Accordingly, the AI community is confronted with the barrier of explaining the reasoning behind machine-made decisions. Paradigms underlying this problem fall within the field of eXplainable AI (XAI). Research in this field has introduced various methods to shed light into black box models such as deep neural networks. While local explanation methods explain the reasoning behind an output for a single decision, global explanations aim to describe the general behaviour of a model, i.e. for all decisions. This paper investigates users’ perceptions of local and global explanations generated with popular XAI methods — LIME, SHAP, and PDP — by conducting a survey to find which of the explanations are preferred by different users. Meanwhile, two hypotheses are tested: first, explanations increase users’ trust in a system, and second, AI novices prefer local over global explanations. The results show that explanations from PDP achieved the best user evaluation among the considered XAI methods.
A Survey on Attention Mechanisms for Medical Applications: are we Moving Toward Better Algorithms?

The increasing popularity of attention mechanisms in deep learning algorithms for computer vision and natural language processing made these models attractive to other research domains.In healthcare, there is a strong need for tools that may improve the routines of the clinicians and the patients.Naturally, the use of attention-based algorithms for medical applications occurred smoothly.However, being healthcare a domain that depends on high-stake decisions, the scientific community must ponder if these high-performing algorithms fit the needs of medical applications.With this motto, this paper extensively reviews the use of attention mechanisms in machine learning methods (including Transformers) for several medical applications based on the types of tasks that may integrate several works pipelines of the medical domain.This work distinguishes itself from its predecessors by proposing a critical analysis of the claims and potentialities of attention mechanisms presented in the literature through an experimental case study on medical image classification with three different use cases.These experiments focus on the integrating process of attention mechanisms into established deep learning architectures, the analysis of their predictive power, and a visual assessment of their saliency maps generated by post-hoc explanation methods.This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.
Camera Invariant Feature Learning for Unsupervised Person Re-Identification

Fully unsupervised person re-identification (ReID) methods aim to learn discriminative features without using labeled ReID data. Because these methods are easily affected by camera discrepancies, similar studies have typically designed optimization methods to enable the model to learn camera-invariant features. However, they often ignore the impact of camera discrepancies on clustering results. Specifically, camera discrepancies will reduce the intra-class camera diversity and promote the generation of noise labels. To solve the above problems, we propose a unified unsupervised learning framework: camera invariant feature learning (CIFL) framework. First, we designed a novel DBSCAN-NN algorithm in the CIFL framework that improves the intra-class camera diversity by forcibly merging samples from different cameras. Then, we designed feature ensemble clustering that improves the accuracy of the pseudo-labels by clustering feature ensembles. In addition, we designed an optimization method for camera discrepancies: stochastic pulled loss. With the stochastic pulled loss, the ReID model is forced to learn camera-invariant features. We verified the effectiveness and generalization of CIFL on four ReID datasets (Market-1501, DukeMTMC-reID, MSMT17 and CUHK03-NP). The experimental results show that CIFL not only outperforms the existing fully unsupervised methods but also is superior to the unsupervised domain adaptation methods.
A Novel Ellipsoidal Semisupervised Extreme Learning Machine Algorithm and Its Application in Wind Turbine Blade Icing Fault Detection

The conventional semi-supervised extreme learning machine (SS-ELM) algorithm can provide a solution to the lack of labeled samples in wind turbine blade icing fault detection, but its performance is limited by the irrationality of the spherical nearest neighbor graph (SNNG) calculation strategy. To solve this problem, a novel ellipsoidal semi-supervised extreme learning machine (ESS-ELM) algorithm is proposed in this paper and applied to wind turbine blade icing fault detection. In this study, we creatively propose a novel ellipsoidal nearest neighbor graph (ENNG) calculation strategy that considers the distribution information of the labeled samples to construct the ESS-ELM algorithm. Different from the conventional SNNG, the ENNG can adaptively assign corresponding calculation weights to each feature in the data space according to the Fisher criterion, which allows it to better match the smoothness assumption of the manifold regularization learning framework. In addition, the two adjustable parameters of the enhancement factor and the degradation factor are ingeniously introduced into the ENNG, which further ensure the applicability and security of the proposed ESS-ELM. The superiority of the ESS-ELM algorithm is verified by extensive benchmark industrial fault datasets and the real-world icing dataset of two wind turbines. It is demonstrated that the proposed ESS-ELM algorithm achieves better performance than the ELM, its existing variants, and some state-of-the-art wind turbine blade icing detection methods.