Reviewerly/exHarmony huggingface dataset:
Patient-Centric Token-Based Healthcare Blockchain Implementation Using Secure Internet of Medical Things

Security and privacy are becoming increasingly difficult to preserve in today&#x2019;s fast-growing data world. We use the Internet of Medical Things (IoMT) to make quick diagnoses and get results. IoMT devices measure the human body&#x2019;s functioning in various parameters and collect, process, and store data in cloud servers. There is a public channel used to collect data from humans and send it to the cloud, which is highly unreliable and vulnerable to attacks. Another risk is storing data in a single centralized system, which can be vulnerable to a single-point failure. Blockchain is being used for secure data storage in a decentralized manner to avoid single-point failure. We propose a secure IoMT-based data collection method for patients and storing the data on the blockchain in accordance with general data protection regulation (GDPR). In the proposed system, IoMT devices send data to the cloud via the patient&#x2019;s personal digital assistant (PDA), and the cloud server transacts data on the blockchain. We propose a miner selection algorithm to avoid bias in the blockchain. We simulate various attacks on open channels between IoMT devices and the cloud servers. This scheme is implemented on a custom Python-based blockchain. IoT simulation is performed using the Bevywise IoT simulator and the message queuing telemetry transport (MQTT) simulator. The security protocol is analyzed using Scyther.
FedCLAR: Federated Clustering for Personalized Sensor-Based Human Activity Recognition

Sensor-based Human Activity Recognition (HAR) has been a hot topic in pervasive computing for several years mainly due to its applications in healthcare and well-being. Centralized supervised approaches reach very high recognition rates, but they incur privacy and scalability issues. Federated Learning (FL) has been recently proposed to mitigate these issues. Each subject only shares the weights of a personal model trained locally, instead of sharing data. A cloud server is in charge of aggregating the weights to generate a global model. However, since activity data is non-independently and identically distributed (non-IID), a single model may not be sufficiently accurate for a large number of diverse users. In this work, we propose FedCLAR, a novel federated clustering method for HAR. Based on the similarity of the local model updates, the cloud server in FedCLAR derives groups of users that exhibit similar ways of performing activities. For each group, FedCLAR uses a specialized global model to mitigate the non-IID problem. We evaluated FedCLAR on two well-known public datasets, showing that it outperforms state-of-the-art FL solutions.
A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price

Several machine learning techniques and hybrid architectures for predicting bitcoin price movement have been presented in the past. Our paper proposes a hybrid model encompassing classification and regression models for predicting bitcoin prices. Our analysis found that the automated feature interactions learner (deep cross networks) error performance using a plethora of technical indicators, including crypto-specific technical indicator difficulty ribbon compression and control variables such as Metcalfe’s value of bitcoin, number of unique active addresses, bitcoin network hash rate, and S&P 500 log returns, in a hybrid architecture is better than the single-stage architecture. The hybrid model predicted a 100% directional hit rate and maintained steady volatility in returns for the out-of-sample period. Our paper concludes that in terms of risk (Sharpe ratio 1.03) and profitability (260% and 82%), the hybrid model’s bitcoin futures strategy performed better than the deep cross network regression and buy-and-hold benchmark strategies.
Using the e-learning course “Analytic Geometry” in the process of training students majoring in Computer Science and Information Technology

As a result of literature analysis the expediency of free access of bachelors majoring in Computer Sciences and Information Technologies to modern information educational resources, in particular, e-learning courses in the process of studying mathematical disciplines is substantiated. It was established that the e-learning course is a complex of teaching materials and educational services created for the organization of individual and group training using information and communication technologies. Based on the outlined possibilities of applying the e-learning course, as well as its didactic functions, the structure of the certified e-learning course “Analytic Geometry” based on the Moodle platform was developed and described. Features of application of cloud-oriented resources are considered: Desmos, Geogebra, Wolfram|Alpha, Sage in the study of the discipline “Analytic Geometry”.The results of the pedagogical experiment on the basis of Borys Grinchenko Kyiv University and A. S. Makarenko Sumy State Pedagogical University are presented. The experiment was conducted to verify the effectiveness of the implementation of the e-learning course “Analytic Geometry”. Using the Pearson criterion it is proved that there are significant differences in the level of mathematical preparation of experimental and control group of students.The prospect of further scientific research is outlined through the effectiveness of the use of e-learning courses for the improvement of additional professional competences of students majoring in Computer Sciences and Information Technologies (specialization “Programming”, “Internet of Things”).
Meta-HGT: Metapath-aware HyperGraph Transformer for heterogeneous information network embedding

Heterogeneous information network embedding aims to learn low-dimensional node vectors in heterogeneous information networks (HINs), concerning not only structural information but also heterogeneity of diverse node and relation types. Most existing HIN embedding models mainly rely on metapath to define composite relations between node pairs and thus extract substructures from the original HIN. However, due to the pairwise structure of metapath, these models fail to capture the high-order relations (such as "Multiple authors co-authoring a paper") implicitly contained in HINs. To tackle the limitation, this paper proposes a Metapath-aware HyperGraph Transformer (Meta-HGT) for node embedding in HINs. Meta-HGT first extends metapath to guide the high-order relation extraction from original HIN and constructs multiple metapath based hypergraphs with diverse composite semantics. Then, Meta-HGT learns the latent node and hyperedge embeddings in each metapath based hypergraph through Meta-HGT layers. Each layer consists of two types of components, i.e., intra-hyperedge aggregation and inter-hyperedge aggregation, in which a novel type-dependent attention mechanism is proposed for node and hyperedge feature aggregation. Finally, it fuses multiple node embeddings learned from different metapath based hypergraphs via a semantic attention layer and generates the final node embeddings. Extensive experiments have been conducted on three HIN benchmarks for node classification. The results demonstrate that Meta-HGT achieves state-of-the-art performance on all three datasets.
Interval-Type 3 Fuzzy Differential Evolution for Designing an Interval-Type 3 Fuzzy Controller of a Unicycle Mobile Robot

Recently, interval-type 3 fuzzy systems have begun to appear in different research areas. This article outlines a methodology for the parameterization of interval type-3 membership functions using vertical cuts applied to the dynamic parameter adaptation of the differential evolution algorithm and implemented in an interval-type 3 Sugeno controller. This methodology was applied to the dynamic adaptation of the F (mutation) parameter in differential evolution to improve the performance of this method as the generations occur. To test the type-3 fuzzy differential evolution algorithm, the optimal design of a type-3 Sugeno controller was considered. In this case, the parameterization of the type-3 membership functions of this Sugeno fuzzy controller was performed. The experimentation is based on the application of three different noise levels for validation of the efficacy of the method and performing a comparison study with respect to other articles in the literature. The main idea is to implement the parameterization of interval type-3 membership functions to enhance the ability of differential evolution in designing an optimal interval type-3 system to control a unicycle mobile robot.
Offline-Online Associated Camera-Aware Proxies for Unsupervised Person Re-Identification

Recently, unsupervised person re-identification (Re-ID) has received increasing research attention due to its potential for label-free applications. A promising way to address unsupervised Re-ID is clustering-based, which generates pseudo labels by clustering and uses the pseudo labels to train a Re-ID model iteratively. However, most clustering-based methods take each cluster as a pseudo identity class, neglecting the intra-cluster variance mainly caused by the change of cameras. To address this issue, we propose to split each single cluster into multiple proxies according to camera views. The camera-aware proxies explicitly capture local structures within clusters, by which the intra-ID variance and inter-ID similarity can be better tackled. Assisted with the camera-aware proxies, we design two proxy-level contrastive learning losses that are, respectively, based on offline and online association results. The offline association directly associates proxies according to the clustering and splitting results, while the online strategy dynamically associates proxies in terms of up-to-date features to reduce the noise caused by the delayed update of pseudo labels. The combination of two losses enables us to train a desirable Re-ID model. Extensive experiments on three person Re-ID datasets and one vehicle Re-ID dataset show that our proposed approach demonstrates competitive performance with state-of-the-art methods. Code will be available at: https://github.com/Terminator8758/O2CAP.
Deep Convolution Neural Network sharing for the multi-label images classification

Addressing issues related to multi-label classification is relevant in many fields of applications. In this work. We present a multi-label classification architecture based on Multi-Branch Neural Network Model (MBNN) that permits the network to encode data from multiple semi-parallel subnetworks or layers outputs separately. Different types of neural networks can be used in the MBNN, but the proposal is made with Convolutional Neural Networks subnetworks, trained, and joined in classifying the outputs (i.e., labels). The proposed work makes it possible to perform incremental changes on existing Multitask Learning architectures for an adaptation to the multi-label classification. These transformations lead us to define two new architectures (neural network multi-outputs and neural network multi-features) using the feature extractors from the pre-trained neural networks. The empirical and statistical results verify that the proposed multibranch neural network architecture performs better than other simple multi-label classification architectures. Later, the “network with multi-features” obtained the highest classification score than other deep neural networks with 83.31% of the f1-score for the Amazon rainforest dataset. The f1-score values are 88.81% for Pascal VOC 2007 dataset, 87.71% for Nuswide, and 88.64% for Pascal VOC 2012.
Pedagogical Agents

chapter Share on Pedagogical Agents Authors: H. Chad Lane View Profile , Noah L. Schroeder View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, ApplicationOctober 2022 Pages 307–330https://doi.org/10.1145/3563659.3563669Published:03 November 2022Publication History 8citation12DownloadsMetricsTotal Citations8Total Downloads12Last 12 Months12Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Health-Related Applications of Socially Interactive Agents

chapter Share on Health-Related Applications of Socially Interactive Agents Author: Timothy Bickmore View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 2: Interactivity, Platforms, ApplicationOctober 2022 Pages 403–436https://doi.org/10.1145/3563659.3563672Published:03 November 2022Publication History 7citation15DownloadsMetricsTotal Citations7Total Downloads15Last 12 Months15Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Mediating Role of Strategic Planning in the Impact of Project Management Strategy on Firm Performance

This study aims at the role that the project management strategy plays in improving the performance of firms and through the companies' adoption of the concept of strategic planning, which in turn helps in knowing the company's plans in the short and long term. In order to improve the company's performance and the continuity of the company's survival in the market. The purpose of this article is to study the relationship between project management strategy and the performance of SMEs in Jordan and the mediating role of strategic planning. The researcher used the descriptive and analytical approach to analyze and classify the data, and 291 valid questionnaires were used, and the descriptive analytical approach was used to analyze the data. The “probability sampling method” was applied to collect the data. The main results are that the project management strategy affects the performance of companies and strategic planning. Strategic planning as a mediator influences the performance of firms through this study.
Tensile testing of polymers: Integration of digital image correlation, infrared thermography and finite element modelling

Tensile tests are often used as part of material characterisation strategies; however, the observed deformation is often complex, and it can be difficult to distinguish the underlying material behaviour from the structural response of the specimen. The objective of the research in this paper was to investigate whether a more accurate calibration of a material model could be obtained by considering not just the global behaviour of the specimen, but also the local strain-time response calculated from full-field displacement information obtained using digital image correlation. Tensile experiments were performed using ISO standard, flat, dog bone specimens. Optical and infra-red imaging were used to calculate full field displacement and temperature maps, and a finite element model of the experiment was produced. These were combined with compression test data from the same material to calibrate a constitutive model, which was shown to describe well the deformation and temperature rise in the specimen. The research demonstrates that it is insufficient to use force-displacement information from tensile experiments to calibrate, or validate, constitutive models of polymers. Further, it demonstrates a more applicable method, which could be further automated in the future.
Facial expression recognition based on convolutional block attention module and multi-feature fusion

In this paper, we focus on the research of facial expression recognition. A novel convolutional block attention module and multi-feature fusion method are proposed for facial expression recognition. The local feature clustering loss function is proposed, which can reduce the difference between the same classes of images and enlarge the difference between different classes of images in the training process. The convolutional block attention module is adopted to better express facial expressions in local areas with rich expressions. Experimental results show that the proposed method can effectively recognise different expressions on the RAF dataset and CK+ dataset compared with other state-of-the-art methods.
Co-advise: Cross Inductive Bias Distillation

The inductive bias of vision transformers is more relaxed that cannot work well with insufficient data. Knowledge distillation is thus introduced to assist the training of transformers. Unlike previous works, where merely heavy convolution-based teachers are provided, in this paper, we delve into the influence of models inductive biases in knowledge distillation (e.g., convolution and involution). Our key observation is that the teacher accuracy is not the dominant reason for the student accuracy, but the teacher inductive bias is more important. We demonstrate that lightweight teachers with different architectural inductive biases can be used to co-advise the student transformer with outstanding performances. The rationale behind is that models designed with different inductive biases tend to focus on diverse patterns, and teachers with different inductive biases attain various knowledge despite being trained on the same dataset. The diverse knowledge provides a more precise and comprehensive description of the data and compounds and boosts the performance of the student during distillation. Furthermore, we propose a token inductive bias alignment to align the inductive bias of the token with its target teacher model. With only lightweight teachers provided and using this cross inductive bias distillation method, our vision transformers (termed as CiT) outperform all previous vision transformers (ViT) of the same architecture on ImageNet. Moreover, our small size model CiT-SAK further achieves 82.7% Top-1 accuracy on ImageNet without modifying the attention module of the ViT. Code is available at https://github.com/OliverRensu/co-advise.
Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation

Recommender system usually suffers from severe <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">popularity bias</i> — the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such skewed distribution may result from the users’ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">conformity</i> to the group, which deviates from reflecting users’ true preference. Existing efforts for tackling this issue mainly focus on completely eliminating popularity bias. However, we argue that not all popularity bias is evil. Popularity bias not only results from conformity but also <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">item quality</i> , which is usually ignored by existing methods. Some items exhibit higher popularity as they have intrinsic better property. Blindly removing the popularity bias would lose such important signal, and further deteriorate model performance. To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity. Although important, it is quite challenging as we lack an explicit signal to differentiate the two factors of popularity bias. In this paper, we propose to leverage temporal information as the two factors exhibit quite different patterns along the time: item quality revealing item inherent property is stable and static while conformity that depends on items’ recent clicks is highly time-sensitive. Correspondingly, we further propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ti</b> me-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> is <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> ntangled framework ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TIDE</b> ), where a click is generated from three components namely the static item quality, the dynamic conformity effect, as well as the user-item matching score returned by any recommendation model. Lastly, we conduct interventional inference so that the recommendation can benefit from the benign popularity bias while circumvent the harmful one. Extensive experiments on four real-world datasets demonstrated the effectiveness of TIDE.
MCAFNet: A Multiscale Channel Attention Fusion Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation for urban remote sensing images is one of the most-crucial tasks in the field of remote sensing. Remote sensing images contain rich information on ground objects, such as shape, location, and boundary and can be found in high-resolution remote sensing images. It is exceedingly challenging to identify remote sensing images because of the large intraclass variance and low interclass variance caused by these objects. In this article, we propose a multiscale hierarchical channel attention fusion network model based on a transformer and CNN, which we name the multiscale channel attention fusion network (MCAFNet). MCAFNet uses ResNet-50 and Vit-B/16 to learn the global–local context, and this strengthens the semantic feature representation. Specifically, a global–local transformer block (GLTB) is deployed in the encoder stage. This design handles image details at low resolution and extracts global image features better than previous methods. In the decoder module, a channel attention optimization module and a fusion module are added to better integrate high- and low-dimensional feature maps, which enhances the network’s ability to obtain small-scale semantic information. The proposed method is conducted on the ISPRS Vaihingen and Potsdam datasets. Both quantitative and qualitative evaluations show the competitive performance of MCAFNet in comparison to the performance of the mainstream methods. In addition, we performed extensive ablation experiments on the Vaihingen dataset in order to test the effectiveness of multiple network components.