Reviewerly/exHarmony huggingface dataset:
Cybersickness Prediction from Integrated HMD’s Sensors: A Multimodal Deep Fusion Approach using Eye-tracking and Head-tracking Data

Cybersickness prediction is one of the significant research challenges for real-time cybersickness reduction. Researchers have proposed different approaches for predicting cybersickness from bio-physiological data (e.g., heart rate, breathing rate, electroencephalogram). However, collecting bio-physiological data often requires external sensors, limiting locomotion and 3D-object manipulation during the virtual reality (VR) experience. Limited research has been done to predict cybersickness from the data readily available from the integrated sensors in head-mounted displays (HMDs) (e.g., head-tracking, eye-tracking, motion features), allowing free locomotion and 3D-object manipulation. This research proposes a novel deep fusion network to predict cybersickness severity from heterogeneous data readily available from the integrated HMD sensors. We extracted 1755 stereoscopic videos, eye-tracking, and head-tracking data along with the corresponding self-reported cybersickness severity collected from 30 participants during their VR gameplay. We applied several deep fusion approaches with the heterogeneous data collected from the participants. Our results suggest that cybersickness can be predicted with an accuracy of 87.77% and a root-mean-square error of 0.51 when using only eye-tracking and head-tracking data. We concluded that eye-tracking and head-tracking data are well suited for a standalone cybersickness prediction framework.
Induction of decision trees as classification models through metaheuristics

The induction of decision trees is a widely-used approach to build classification models that guarantee high performance and expressiveness. Since a recursive-partitioning strategy guided for some splitting criterion is commonly used to induce these classifiers, overfitting, attribute selection bias, and instability to small training set changes are well-known problems in them. Other approaches, such as incremental induction, classifier ensembles, and the global search in the decision-tree-space, have been implemented to overcome these problems. In particular, metaheuristics such as simulated annealing, genetic algorithms, genetic programming, and ant colony optimization have been used to induce compact and accurate decision trees. This paper presents a state-of-the-art review of the use of single-solution-based metaheuristics and swarm and evolutionary computation algorithms to build decision trees as classification models. We outline the decision-tree-induction process components and detail the existing literature studies on metaheuristic-based approaches to building these classifiers. Several timelines showing the chronological order in which these approaches were introduced in the literature are included. A summary analysis of these studies is also conducted, focusing on their internal components and experimental studies. This work provides a useful reference point for future research in this field.
Mahalanobis distance-based fading cubature Kalman filter with augmented mechanism for hypersonic vehicle INS/CNS autonomous integration

Inertial Navigation System/Celestial Navigation System (INS/CNS) integration, especially for the tightly-coupled mode, provides a promising autonomous tactics for Hypersonic Vehicle (HV) in military demands. However, INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity, non-additive noise and dynamic complexity. This paper presents a novel nonlinear filtering method for INS/CNS integration by adopting the emerging Cubature Kalman Filter (CKF) to handle the strong INS error model nonlinearity caused by HV’s high dynamics. It combines the state-augmentation technique into the nonlinear CKF to decrease the negative effect of non-additive noise in inertial measurements. Subsequently, a technique for the detection of dynamic model uncertainty is developed, and the augmented CKF is modified with fading memory to tackle dynamic model uncertainty by rigorously deriving the fading factor via the theory of Mahalanobis distance without artificial empiricism. Simulation results and comparison analysis prove that the proposed method can effectively curb the adverse impacts of non-additive noise and dynamic model uncertainty for inertial measurements, leading to improved performance for HV navigation with tightly-coupled INS/CNS integration.
Deep Co-Image-Label Hashing for Multi-Label Image Retrieval

Deep supervised hashing has greatly improved retrieval performance with the powerful learning capability of deep neural network. In multi-label image retrieval, existing deep hashing simply indicates whether two images are similar by constructing a similarity matrix. However, it ignores the dependency among multiple labels that has been shown important in multi-label application. To fulfill this gap, this paper proposes Deep Co-Image-Label Hashing (DCILH) to discover label dependency. Specifically, DCILH regards image and label as two views, and maps the two views into a common deep Hamming space. DCILH proposes to learn prototype for each label, and preserve similarity among images, labels, and prototypes. To exploit label dependency, DCILH further employs the label-correlation aware loss on the predicted labels, such that predicted output on positive label is enforced to be larger than that on negative label. Extensive experiments on several multi-label benchmarks demonstrate the proposed DCILH outperforms state-of-the-art deep supervised hashing on large-scale multi-label image retrieval.
Proposal for modeling social robot acceptance by retail customers: CAN model + technophobia

Acceptance of social robots (SRs) in retail is a new field of research in marketing arising from the dilemma between the potential benefits of SRs and the possibility that shoppers will reject them. The main aim of this paper is to advance in the modeling of new technology (NT) acceptance by proposing an extension of the integrative Cognitive-Affective-Normative (CAN) model that improves its explanatory and predictive power by including technophobia (TE) with a view to understanding SR acceptance in retail. The CAN + TE model is tested with a sample of 1069 individuals, resulting in an R2 of 0.73, surpassing the explanatory power of classical models. Emotions – specifically, pleasure – and TE are found to be the biggest drivers of SR acceptance for retail customers, followed by performance expectancy and social influence. Arousal and effort expectancy have no significant effect. These findings have theoretical implications for NT modeling and practical implications for reviving retail – which has been hit hard by the pandemic – opening research lines in both contexts related to the future of SRs.
Stochastic Partial Swap: Enhanced Model Generalization and Interpretability for Fine-grained Recognition

Learning mid-level representation for fine-grained recognition is easily dominated by a limited number of highly discriminative patterns, degrading its robustness and generalization capability. To this end, we propose a novel Stochastic Partial Swap (SPS) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> scheme to address this issue. Our method performs element-wise swapping for partial features between samples to inject noise during training. It equips a regularization effect similar to Dropout, which promotes more neurons to represent the concepts. Furthermore, it also exhibits other advantages: 1) suppressing over-activation to some part patterns to improve feature representativeness, and 2) enriching pattern combination and simulating noisy cases to enhance classifier generalization. We verify the effectiveness of our approach through comprehensive experiments across four network backbones and three fine-grained datasets. Moreover, we demonstrate its ability to complement high-level representations, allowing a simple model to achieve performance comparable to the top-performing technologies in fine-grained recognition, indoor scene recognition, and material recognition while improving model interpretability.
Feature Selection in Machine Learning by Hybrid Sine Cosine Metaheuristics

Feature selection problem from the domain of machine learning refers to selecting only those features from the high dimensional datasets, that have prominent influence on dependent variable(s). In this way, dataset dimensionallity is reduced and only the riches data is kept, training process of machine learning model becomes more efficient and accuracy is increased. This manuscript proposes a new hybridized version of the sine cosine algorithm adjusting for solving feature selection problem. Hybridization is relatively novel approach for combing and improving metaheuristics optimizer. Notwithstanding that the basic sine cosine algorithm establishes good performance for solving NP hard challenges, based on simulation results, it was concluded that there is still space for improvement in its exploitation process. Original sine cosine algorithm and proposed hybridized implementation were tested on a well-known 21 machine learning datasets retrieved from the UCL repository. Comparative analysis between hybrid sine cosine and original one, as well as with 10 other state-of-the-art metaheuristics was conducted. Established results in terms of classification accuracy and fitness prove the robustness and efficiency of proposed method for solving this type of NP hard challenge.
Building Blocks for Blockchain Adoption in Digital Transformation of Sustainable Supply Chains

Global supply chains shift to meet exceeding expectations in supply chains lead to the need for digital technologies in supply chain management. Industry 4.0 has emerged in sustainable supply chain systems, blockchain is prominent, with the potential of immutable transparent data. Blockchains present potential to disrupt supply chains through digital transformation by enabling provenance, visibility, relationships, collaboration, lower costs, and enabling real-time trusted data. Scholars are increasingly investigating blockchain adoption, with an emphasis on technology acceptance modelling. Current research is valuable to understand potentials of blockchain technology in supply chains however, there is limited work on the building blocks for blockchain adoption in digital transformation of supply chains. This study aims to investigate through a systematic literature review and case studies, the building blocks for blockchain adoption in digital transformation of sustainable supply chains. Blockchain adoption in supply chain management is receiving increasing attention, along with highlights of critical factors. This paper offers a building block model that is in three main phases; pre-adoption, adoption, and the post adoption. The model indicates that adoption context, blockchain technology platform offerings, strategic responses, and adoption readiness are some key building blocks, particularly at the pre-adoption phase. Trust and supply chain network, firm resources, and blockchain costs are all considered critical in the considerations of the building blocks. In addition, aligning supply chain objectives with blockchain systems is critical and so is blockchain compatibility. Law and governance are amongst the two prominent challenges in blockchain adoption and should be considered as part of the building blocks for blockchain adoption.
Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks

Covering from photography to depth and spectral estimation, diverse computational imaging (CI) applications benefit from the versatile modulation of coded apertures (CAs). The lightwave fields as space, time, or spectral can be modulated to obtain projected encoded information at the sensor that is then decoded by efficient methods, such as the modern deep learning decoders. Although the CA can be fabricated to produce an analog modulation, a binary CA is preferred since more straightforward calibration, higher speed, and lower storage are achieved. As the performance of the decoder mainly depends on the structure of the CA, several works optimize the CA ensembles by customizing regularizers for a particular application without considering the critical physical constraints of the CAs. This work presents an end-to-end (E2E) deep learning-based optimization of CAs for CI tasks. The CA design method aims to cover a wide range of CI problems, easily changing the loss function of the deep approach. The designed loss function includes regularizers to fulfill the widely used sensing requirements of the CI applications. Mainly, the regularizers can be selected to optimize the transmittance, the compression ratio, and the correlation among measurements. At the same time, a binary CA solution is encouraged, and the performance of the CI task is maximized in applications such as restoration, classification, and semantic segmentation.
Quantitative loosening detection of threaded fasteners using vision-based deep learning and geometric imaging theory

With the development of signal processing and deep learning, vision-based methods for loosening detection have progressed in recent years. However, existing visual inspection methods are mainly used to measure the loosening angle, which requires prior knowledge of the initial tightening states, and is only applicable to hexagon bolts and nuts with clear edges. This paper proposes a method to quantitatively calculate the length of the exposed bolt for detecting loosening using vision-based deep learning and geometric imaging theory. The newly proposed method consists of three modules: the region of interest (RoI) location, keypoint detection, and length calculation modules. The RoI location module applies a faster regional convolutional neural network (Faster-RCNN)-based deep learning algorithm to locate the exposed bolt, whereas the keypoint detection module employs a cascaded pyramid network (CPN)-based deep learning algorithm to detect five keypoints on the exposed bolt. An accurate expression is derived from the geometric imaging theory, and the coordinates of the five keypoints and camera parameters are imported into the expression to calculate the length of the exposed bolt. In the experiment, the resolution of input image was 640 × 640 pixels, and the test speed of the proposed method was approximately 200 milliseconds per image. Experimental results showed an average measurement error of 0.61 mm using our method, outperforming other measurement methods and the state-of-the-art networks of human pose estimation. Moreover, the robustness of the proposed method was validated by varying the shooting conditions, bolt sizes and colours. Finally, some factors influencing detection and measurement accuracies are discussed, and prospects for further research are proposed.
A New Approach for Mining Correlated Frequent Subgraphs

Nowadays graphical datasets are having a vast amount of applications. As a result, graph mining—mining graph datasets to extract frequent subgraphs—has proven to be crucial in numerous aspects. It is important to perform correlation analysis among the subparts (i.e., elements) of the frequent subgraphs generated using graph mining to observe interesting information. However, the majority of existing works focuses on complexities in dealing with graphical structures, and not much work aims to perform correlation analysis. For instance, a previous work realized in this regard, operated with a very naive raw approach to fulfill the objective, but dealt only on a small subset of the problem. Hence, in this article, a new measure is proposed to aid in the analysis for large subgraphs, mined from various types of graph transactions in the dataset. These subgraphs are immense in terms of their structural composition, and thus parallel the entire set of graphs in real-world. A complete framework for discovering the relations among parts of a frequent subgraph is proposed using our new method. Evaluation results show the usefulness and accuracy of the newly defined measure on real-life graphical datasets.
Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation

Multi-view clustering has become an active topic in artificial intelligence. Yet, similar investigation for graph-structured data clustering has been absent so far. To fill this gap, we present a Multi-View Graph embedding Clustering network (MVGC). Specifically, unlike traditional multi-view construction methods, which are only suitable to describe Euclidean structure data, we leverage Euler transform to augment the node attribute, as a new view descriptor, for non-Euclidean structure data. Meanwhile, we impose block diagonal representation constraint, which is measured by the ℓ1,2-norm, on self-expression coefficient matrix to well explore the cluster structure. By doing so, the learned view-consensus coefficient matrix well encodes the discriminative information. Moreover, we make use of the learned clustering labels to guide the learnings of node representation and coefficient matrix, where the latter is used in turn to conduct the subsequent clustering. In this way, clustering and representation learning are seamlessly connected, with the aim to achieve better clustering performance. Extensive experimental results indicate that MVGC is superior to 11 state-of-the-art methods on four benchmark datasets. In particular, MVGC achieves an Accuracy of 96.17% (53.31%) on the ACM (IMDB) dataset, which is an up to 2.85% (1.97%) clustering performance improvement compared with the strongest baseline.
Intra-Hour Photovoltaic Generation Forecasting Based on Multi-Source Data and Deep Learning Methods

Global issues pertaining to climate change have necessitated the rapid deployment of new energy sources, such as photovoltaic (PV) generation. In smart grids, accurate forecasting is essential to ensure the reliability and economy of the power system. However, PV generation is severely affected by meteorological factors, which hinders accurate forecasting. Various types of data, such as local measurement data, numerical weather prediction, and satellite images, can reflect meteorological dynamics over different time scales. This paper proposes a novel data-driven forecasting framework based on deep learning, which integrates an advanced U-net and an encoder-decoder architecture to cooperatively process multi-source (time series recording and satellite image) data. The adaption of the neural networks to the data sources and the collaborative learning of both spatial and temporal features boost the model accuracy. The experimental results for 50 real-world PV power stations indicate that the proposed framework features a higher accuracy than that of other baseline models.
The asymmetric effect of internet access on economic growth in sub-Saharan Africa

This article investigates the asymmetric effect of internet access (index of the internet) on economic growth in 42 sub-Saharan African (SSA) countries over the period 2008–2018. The estimation procedure is obtained following a dynamic panel threshold regression technique via 1000 bootstrap replications and the 400 grids search developed by Hansen (1996, 1999, 2000). The investigation first explores the presence of inflection points in the relationship between internet access and economic growth through the application of Hansen's threshold models. The finding from the nonlinearity threshold model revealed a significant internet threshold-effect of 3.55 percent for growth. The article also examines the linear short-run effect of internet access on economic growth while controlling for the effects of private sector credit, trade openness, government regulation, and tariff regimes. The marginal effect of internet access is evaluated at the minimum, and the maximum levels of government regulation and tariffs regime are positive. On the other hand, the minimum and maximum levels of private sector credit and trade openness are negative via the interaction terms. The article advances the literature by its nonlinear transformation of the relevance of internet access on economic growth by exploring interactive mechanisms of internet access versus financial resource, internet access versus trade, internet access versus government regulation, and internet access versus the tariff regimes from end-user subscriptions. In policy terms, the statistical significance of the joint impact of government regulations and tariff regimes is relevant in the operation of the telecommunication industry in SSA countries.
Definition of a novel federated learning approach to reduce communication costs

Contemporary Machine Learning approaches (e.g., Deep Learning) need huge volumes of data to build accurate and robust statistical models. Nowadays, very often, such data are collected by distinct and geographically distributed entities and successively transmitted to and stored by centralized nodes that implement the learning process. This practice, however, exposes data to security and privacy risks that may be even unacceptable in those environments regulated by the General Data Protection Regulation (GDPR). This paper defines a novel Federated Learning approach that avoids the transmission of sensitive data over the network and improves over the classic federated learning schemes by reducing the communication costs. This approach has been validated concerning a healthcare case study that aimed at building a Time-Series based predictive model to identify the level of risk for patients suffering from hypertension. Experimental validation has shown that the proposed approach achieves excellent results both in terms of classification accuracy, superior to the state-of-the-art models with an improvement ranging from 3.01% to 11.09%, and in terms of communication costs with a reduction of about 34%. The analysis of the proposed approach shows promising results in terms of performance and communication cost.
Blockchain on Security and Forensics Management in Edge Computing for IoT: A Comprehensive Survey

Security and forensics represent two key components for network management, especially to guarantee the trusted operation of massive access networks such as the Internet of Things (IoT). As a core technology to provide low latency and high communication for IoT, Mobile Edge Computing (MEC) pulls computing resources from remote cloud centers to devices. The process of MEC service involves three types of entities: devices, data generated by devices and digital evidence generated after the data interaction. These entities are fully distributed and difficult to protect through traditional, highly centralized security and authentication mechanisms. As a decentralized shared ledger and database, the emerging blockchain is considered to provide cooperative trust and collaborative action among multiple subjects while ensuring the integrity and confidentiality of data. Because of its anonymity, non-tampering and traceability, the blockchain arouses research on the combination of blockchain and edge computing for device security, data security and forensics in IoT. This survey analyzes the application of blockchain in MEC-IoT systems and mainly focuses on approaches and technologies to manage the security and forensics issues for IoT. Finally, we present open issues and prospects for future work and research directions.