Reviewerly/exHarmony huggingface dataset:
A Framework for Design of Conversational Agents to Support Health Self-Care for Older Adults

We examined the potential of conversational agents (CAs) to support older adults' self-care related to chronic illness in light of lessons learned from decades of pedagogical agent research, which investigates the impact and efficacy of CAs for a wide range of learners.The role of CAs in education (i.e., pedagogical agents) has been long studied, but their potential for supporting self-care has received less attention, especially for older adults.We reviewed work on pedagogical agents and considered how it informs the design of CAs for older adults. We propose a framework for designing CAs to support older adult self-care, which organizes a review of work in this area and integration with the pedagogical agent literature.Our review of the pedagogical agent literature revealed an evolution from teaching machines to interactive, social systems that influence student motivational as well as learning outcomes. To integrate this review with work on CAs and self-care, we developed a framework that specifies how self-care goals evolve with stages of an illness, communication goals that support self-care at each stage, patient needs, and requirements for CAs to support these needs. The review identified an agenda for future research on CA functions and features that help older adults accept need for self-care, establish self-care, and sustain self-care over time.Integrating insights from the pedagogical agent literature with research on developing CAs for self-care defines an agenda for developing and evaluating CAs to help older adults manage illness.
Double Auction-Based Manufacturing Cloud Service Allocation in an Industrial Park

Industrial parks are regarded as a possible fashion to reduce the manufacturing costs and increase the efficiency by sharing cost-effective infrastructure and communal services. However, the waste of manufacturing resources could impede the development of manufacturing enterprises that locate in an industrial park when market demands fluctuate dynamically. This article aims to use the cloud manufacturing (CMfg) to relieve the temporary shortage of manufacturing resources and capabilities by sharing them among these enterprises in an industrial park. This article focuses on manufacturing cloud service allocation (MCSA), which is one of the critical processes to implement CMfg. Two multiunit double auction mechanisms are proposed to allocate the manufacturing cloud services (MCSs) with reasonable trade price in bilateral markets, and their properties and bidding strategies are proved. Numerical studies are conducted, and the results show the effectiveness and efficiency of two mechanisms for dealing with MCSA and verify the theoretical proofs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —MCSA in CMfg is a complicated and challenging task. The current industrial applications rely on optimization methods, such as all-in-one methods (e.g., genetic algorithm) and multidiscipline design optimization methods (e.g., analytical target cascading), to allocate MCSs. While these methods can help cloud operators obtain feasible allocation solutions rapidly, there exist several disadvantages: 1) they can only solve problems in one-sided settings (one customer with many providers); 2) there is an assumption that the customer’s demand can be provided by any providers, which means providers’ capacities are infinite; and 3) the problem is optimized based on the fixed prices provided by providers, without considering supply and demand. This article presents two multiunit double auction mechanisms that allow multiple customers to trade with multiple providers. Allocation rules can break the assumption and providers’ capacities are limited. MCSs are also priced dynamically based on supply and demand. We show how cloud operators apply two mechanisms first and demonstrate their properties and bidding strategies of customers and providers when the mechanism is not incentive compatible. Finally, our experiments and analysis fully validate the applicability and efficiency of two mechanisms and illustrate how cloud operators can maximize their utilities.
An innovative bio-inspired flight controller for quad-rotor drones: Quad-rotor drone learning to fly using reinforcement learning

Animals learn to master their capabilities by trial and error, and with out having any knowledge about their dynamics model and mathematical or physical rules. They use their maximum capabilities in an optimized way. This is the result of millions of years of evolution where the best of different possibilities are kept, and makes us rethink How does the nature perform things?, particularly when natural systems outperform our rigid systems. In this study, inspired by the nature, we developed an innovative algorithm by enhancing an existing reinforcement learning algorithm (proximal policy optimization (PPO)). Our algorithm is capable of learning to control a quad-rotor drone in order to fly. This new algorithm called Bio-inspired Flight Controller (BFC) does not use any conventional controller such as PID or MPC to control the quad-rotor drone. The goal of BFC is to completely replace the conventional controller with a controller that acts in a similar way to the animals where they learn to control their movements. It is capable of stabilizing a quad-copter in a desired point, and following way points. We implemented our algorithm in an AscTec Hummingbird quad-copter simulated in Gazebo, and tested it using different scenarios to fully measure its capabilities.
Stereo-rectification and homography-transform-based stereo matching methods for stereo digital image correlation

The measurement accuracy and efficiency of stereo digital image correlation (DIC) greatly depend on the stereo matching which is to find the corresponding points (CPs) between the left and right images captured from different perspectives. Hence, it is significant to enhance the accuracy and efficiency of stereo matching in stereo DIC. In this study, a stereo-rectification-based stereo matching (SRSM) method and a homography-transform-based stereo matching (HTSM) method are presented to search the CPs between the left and right images. Although HTSM is only suitable to the planar object, it still has many applications. The presented methods simplify the coarse searching process of stereo matching and alleviate the influence of the shape change caused by the difference of perspectives (DOP). The performances of SRSM, HTSM, and the conventional stereo matching (CSM) method are compared in both simulation and experiment. It is shown that the accuracy and efficiency of HTSM are the highest among the three methods if the object has a planar surface, and the efficiency of SRSM is higher than that of CSM.
Latent Dirichlet Allocation Model Training With Differential Privacy

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for hidden semantic discovery of text data and serves as a fundamental tool for text analysis in various applications. However, the LDA model as well as the training process of LDA may expose the text information in the training data, thus bringing significant privacy concerns. To address the privacy issue in LDA, we systematically investigate the privacy protection of the main-stream LDA training algorithm based on Collapsed Gibbs Sampling (CGS) and propose several differentially private LDA algorithms for typical training scenarios. In particular, we present the first theoretical analysis on the inherent differential privacy guarantee of CGS based LDA training and further propose a centralized privacy-preserving algorithm (HDP-LDA) that can prevent data inference from the intermediate statistics in the CGS training. Also, we propose a locally private LDA training algorithm (LP-LDA) on crowdsourced data to provide local differential privacy for individual data contributors. Furthermore, we extend LP-LDA to an online version as OLP-LDA to achieve LDA training on locally private mini-batches in a streaming setting. Extensive analysis and experiment results validate both the effectiveness and efficiency of our proposed privacy-preserving LDA training algorithms.
A Multiclassification Model of Sentiment for E-Commerce Reviews

Consumer reviews are important information that reflects the quality of E-commerce goods and services and their existing problems after shopping.Due to the possible differences in consumers' experiences with goods and service quality, consumer reviews can involve multiple-aspect expressions of emotions or opinions.This may result in attitudes expressed by a consumer in the same review sometimes having a variety of emotions.We introduce a sentiment multiclassification method based on a directed weighted model.The model represents the sentiment entity vocabulary as the sentiment nodes and represents the relation between nodes as the directed weighted link.The sentiment entity vocabulary is the entity with attributes, which can express sentiment meaning in related reviews.Directed weighted links represent the sentiment similarity between two nodes of entities with attributes and determined by the direct correlation calculation between them.The paths are all connected directed links from one node to another, which are composed of several nodes and links with close sentiment similarity.Then, we can establish a directed weighted model concerning the sentiments.Directed weighted links having similar sentiment relations with each other may constitute a directed weighted path.There are several directed weighted paths from a start node to the end nodes of the sentiment entity vocabulary in the directed weighted model.Each different path is a different sentiment expression, which represents a different sentiment type.The different sentiment classifications can be obtained through the restriction of path length.Experiments and analysis of the results show that the sentiment multiclassification model based on the directed weighted model proposed in this paper can classify the review sentiments according to different limited threshold rules.Comprehensive analysis indicates the classification results have good accuracy and high efficiency.INDEX TERMS E-commerce reviews, sentiment multiclassification, directed weighted model, directed weighted link, directed weighted path.
WITHDRAWN: Experimental investigation of automated system for twitter sentiment analysis to predict the public emotions using machine learning algorithms

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Modeling Two Dimensional Touch Pointing

Modeling touch pointing is essential to touchscreen interface development and research, as pointing is one of the most basic and common touch actions users perform on touchscreen devices. Finger-Fitts Law [4] revised the conventional Fitts? law into a 1D (one-dimensional) pointing model for finger touch by explicitly accounting for the fat finger ambiguity (absolute error) problem which was unaccounted for in the original Fitts? law. We generalize Finger-Fitts law to 2D touch pointing by solving two critical problems. First, we extend two of the most successful 2D Fitts law forms to accommodate finger ambiguity. Second, we discovered that using nominal target width and height is a conceptually simple yet effective approach for defining amplitude and directional constraints for 2D touch pointing across different movement directions. The evaluation shows our derived 2D Finger-Fitts law models can be both principled and powerful. Specifically, they outperformed the existing 2D Fitts? laws, as measured by the regression coefficient and model selection information criteria (e.g., Akaike Information Criterion) considering the number of parameters. Finally, 2D Finger-Fitts laws also advance our understanding of touch pointing and thereby serve as the basis for touch interface designs.
PROMETHEE‐based hybrid feature selection technique for high‐dimensional biomedical data: application to Parkinson's disease classification

Biomedical data are being collected for fields like cancer diagnosis and prognosis, brain signals, speech signals, genetic engineering to name a few. These data are very high dimensional these days, which makes it difficult to extract knowledge out of it through machine learning algorithms. In this work, the authors proposed a hybrid feature selection method based on a multi-attribute decision-making method PROMETHEE (preference ranking organisation method for enrichment evaluations) and Jaya optimisation algorithm. Their proposed method works in two phases. In the first phase, five filter methods are applied to get the ranking for each feature of the data set. In the second phase, all the five individual ranks are used as input choices for PROMETHEE which gives us a final rank for all the features. Then the top 3% features are selected for training the machine learning model. This technique is applicable for feature reduction in any high-dimensional biomedical data. Here, they have studied Parkinson's disease data set. The result shows that the proposed method improves the classification accuracy by 13.73% and that too in a minimum amount of time with a minimum number of features. Hence, this method can be used as an essential pre-processing step for high-dimensional biomedical data.
Low-Rank Preserving t-Linear Projection for Robust Image Feature Extraction

As the cornerstone for joint dimension reduction and feature extraction, extensive linear projection algorithms were proposed to fit various requirements. When being applied to image data, however, existing methods suffer from representation deficiency since the multi-way structure of the data is (partially) neglected. To solve this problem, we propose a novel Low-Rank Preserving t-Linear Projection (LRP-tP) model that preserves the intrinsic structure of the image data using t-product-based operations. The proposed model advances in four aspects: 1) LRP-tP learns the t-linear projection directly from the tensorial dataset so as to exploit the correlation among the multi-way data structure simultaneously; 2) to cope with the widely spread data errors, e.g., noise and corruptions, the robustness of LRP-tP is enhanced via self-representation learning; 3) LRP-tP is endowed with good discriminative ability by integrating the empirical classification error into the learning procedure; 4) an adaptive graph considering the similarity and locality of the data is jointly learned to precisely portray the data affinity. We devise an efficient algorithm to solve the proposed LRP-tP model using the alternating direction method of multipliers. Extensive experiments on image feature extraction have demonstrated the superiority of LRP-tP compared to the state-of-the-arts.
Face Recognition System using Facenet Algorithm for Employee Presence

This study discusses the appropriate method to be applied in a presence system using faces by comparing two deep learning architectural models, they are FaceNet and Openface. FaceNet is a model developed by Google researchers that has the highest accuracy in face recognition. While Openface is a development from FaceNet that is trained with smaller datasets but has an accuracy that is almost equal to FaceNet. This will start by taking the employee's face into an image dataset. From the dataset, the face preprocessing will be performed by detecting, cropping, and resizing the face. Then extracting facial features into 128 dimensions using the FaceNet and Openface. With the Support Vector Machine (SVM), the classification of facial features will be carried out to obtain accuracy. To validate the model, 5 fold cross-validations are used. FaceNet accuracy results that obtained are higher with perfect accuracy that is 100%, while Openface only 93.33% accuracy. The implementation using the model with the highest accuracy (FaceNet) has the same results as the model testing that is 100% using the introduction threshold probability of 0.25.
A Deep Learning–Based Unsupervised Method to Impute Missing Values in Patient Records for Improved Management of Cardiovascular Patients

Physicians increasingly depend on electronic health records (EHRs) to manage their patients. However, many patient records have substantial missing values that pose a fundamental challenge to their clinical use. To address this prevailing challenge, we propose an unsupervised deep learning-based method that can facilitate physicians' use of EHRs to improve their management of cardiovascular patients. By building on the deep autoencoder framework, we develop a novel method to impute missing values in patient records. To demonstrate its clinical applicability and values, we use data from cardiovascular patients and evaluate the proposed method's imputation effectiveness and predictive efficacy, in comparison with six prevalent benchmark techniques. The proposed method can impute missing values and predict important patient outcomes more effectively than all the benchmark techniques. This study reinforces the importance of adequately addressing missing values in patient records. It further illustrates how effective imputations can enable greater predictive efficacy with regard to important patient outcomes, which are crucial to the use of EHRs and health analytics for improved patient management. Supported by the complete data imputed by the proposed method, physicians can make timely patient outcome estimations (predictions) and therapeutic treatment assessments.
An Enhanced Comprehensive Learning Particle Swarm Optimizer with the Elite-Based Dominance Scheme

In recent years, swarm-based stochastic optimizers have achieved remarkable results in tackling real-life problems in engineering and data science. When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. However, when the single modal function is processed, the convergence speed of the algorithm is too slow to converge quickly to the optimum during optimization. In this paper, the elite-based dominance scheme of another well-established method, grey wolf optimizer (GWO), is introduced into the CLPSO, and the grey wolf local enhanced comprehensive learning PSO algorithm (GCLPSO) is proposed. Thanks to the exploitative trends of the GWO, the algorithm improves the local search capacity of the CLPSO. The new variant is compared with 15 representative and advanced algorithms on IEEE CEC2017 benchmarks. Experimental outcomes have shown that the improved algorithm outperforms other comparison competitors when coping with four different kinds of functions. Moreover, the algorithm is favorably utilized in feature selection and three constrained engineering construction problems. Simulations have shown that the GCLPSO is capable of effectively dealing with constrained problems and solves the problems encountered in actual production.
Maintaining Trust in VANETs using Blockchain

Vehicular ad-hoc networks are networks formed by fast moving vehicles which come in contact momentarily and exchange information. Since it's an ad-hoc network, it becomes difficult to maintain trust, security and authenticity of information being exchanged in the network. In this paper, we leverage the concepts of blockchain to maintain trust in the network. Since blockchain provides a tamperproof, decentralized mechanism to store data, we use it to store information related to events such as collision, accident, SOS etc. The information stored on blockchain can be used to validate it at later points of time so as to minimize the false benefit cases by use of Proof of Location certificates. The proposed system has the potential to increase the trust of end users in VANETs. It can also be integrated into the design of future vehicles because of its ease of implementation. The paper also discusses the benefits and constraints of the proposed model along with the related future work.
Structural Relationship Representation Learning with Graph Embedding for Personalized Product Search

To provide more accurate personalized product search (PPS) results, it is compulsory to go beyond modeling user-query-item interaction. Graph embedding techniques open the potential to integrate node information and topological structure information. Existing graph embedding enhanced PPS methods are mostly based on entity-relation-entity graph learning. In this work, we propose to consider structural relationship in users' product search scenario with graph embedding by latent representation learning. We argue that explicitly modeling the structural relationship in graph embedding is essential for more accurate PPS results. We propose a novel method, Graph embedding based Structural Relationship Representation Learning (GraphSRRL), which explicitly models the structural relationship in users-queries-products interaction. It combines three key conjunctive graph patterns to learn graph embedding for better PPS. In addition, GraphSRRL facilitates the learning of affinities between users (resp. queries or products) in the designed geometric operation in low-dimensional latent space. We conduct extensive experiments on four datasets to evaluate GraphSRRL for PPS. Experimental results show that GraphSRRL outperforms the state-of-the-art algorithm on real-world search datasets by at least 50.7% in term of [email protected] and 48.7% in terms of [email protected]
Bearings-Only Filtering Using Uncorrelated Conversion Based Filters

Bearings-only filtering (BOF) is important in many practical applications. It is also a challenging nonlinear filtering problem due to limited information contained in highly nonlinear measurements. Researchers have proposed various nonlinear filters for BOF problems. We propose a new approach to nonlinear filtering using pseudomeasurement construction based uncorrelated conversion for BOF. This approach can more effectively utilize the measurement information than the original linear minimum mean square error estimator. The constructed pseudomeasurement is uncorrelated with the original measurement. Based on the recently proposed uncorrelated conversion based filter (UCF), we propose a UCF using pseudorange construction (UCF-PRC) for the BOF problem. An improved filter, the optimized UCF-PRC, is also proposed to minimize the mean square error. The effectiveness of the new filters is demonstrated by simulation results. Specifically, compared with the particle filter, the UCF-PRC has better estimation accuracy with nearly the same computational cost.