Reviewerly/exHarmony huggingface dataset:
Physics-Informed Neural Network for Optical Fiber Parameter Estimation From the Nonlinear Schrödinger Equation

For any system that follows rigorous mathematical and physical theories like fiber-optic system, system parameter estimation is crucial for system detection and monitoring. In this paper, a physics-informed neural network (PINN)-based method is proposed for optical fiber parameter estimation by solving the inverse problem of the nonlinear Schrödinger equation (NLSE), i.e., inferring the unknown parameters of NLSE. The proposed method considers both the prior knowledge of physical law and the characteristics of observation data from only the transmitted and received waveforms. In PINN, the NLSE and observation data are set as the loss function, and the typical fiber parameters (attenuation, dispersion, and nonlinear coefficient) are optimized iteratively until they satisfy the inferred NLSE by learning the limited observation data. Three scenarios are validated, including single Gaussian pulse propagation, ultrashort pulse propagation in highly nonlinear fiber, and optical signal transmission in standard single-mode fiber. The launch power of the signal is embedded in PINN as a parametric feature, which makes PINN learn the signal transmissions under different powers simultaneously. The weights of mean square error terms for NLSE and observation data in the loss function are properly adjusted to balance learning difficulties. Moreover, the statistical results of 128 different signals and model performance under different signal-to-noise ratios are analyzed. As an extension, fiber length estimation is also studied, and the error is 0.375% when the launch power is 0 dBm. Our work verifiably shows that the proposed approach performs well and can be further extended to multiple applications in fiber optics.
A Stable Lightweight and Adaptive Feature Enhanced Convolution Neural Network for Efficient Railway Transit Object Detection

Obstacles in front of a train pose a significant threat to traffic safety, and many accidents happen under shunting mode when the speed of a train is below 45 km/h. The existing track object–detection algorithms encounter difficulty in balancing the detection precision and speed in shunting mode. Additionally, their accuracy is insufficient, particularly for small objects in complex environments. To address these problems, we propose a stable lightweight feature extraction and adaptive feature fusion network for real-time detection of obstacles in railway traffic scenarios to ensure driving safety. The proposed network consists of three modules. The stable bottom feature extraction module reduces the computational load and extracts more image information stably. The lightweight feature extraction module improves feature extraction using a simple and effective network. The enhanced adaptive feature fusion module fuses the image and original features, improving the multiscale detection accuracy under complex environments, particularly in the case of small objects. With a default input size of 416 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 416$ </tex-math></inline-formula> pixels (px), the proposed method achieves a detection speed of 81 FPS and a mean average precision of 94.75% for the railway traffic dataset as well as a detection speed of 78 FPS (26 FPS faster and 0.47% higher than those of YOLOv4, respectively) and a mean average precision of 42.5% for MS COCO. This indicates its potential for real-world railway object detection and other multi-target detection tasks. Additionally, the experimental results based on PASCAL VOC2007 and VOC2012 indicate that the proposed approach is considerably better than the state-of-the-art models.
A Typology of Virtual Reality Locomotion Techniques

Researchers have proposed a wide range of categorization schemes in order to characterize the space of VR locomotion techniques. In a previous work, a typology of VR locomotion techniques was proposed, introducing motion-based, roomscale-based, controller-based, and teleportation-based types of VR locomotion. The fact that (i) the proposed typology is used widely and makes a significant research impact in the field and (ii) the VR locomotion field is a considerably active research field, creates the need for this typology to be up-to-date and valid. Therefore, the present study builds on this previous work, and the typology’s consistency is investigated through a systematic literature review. Altogether, 42 articles were included in this literature review, eliciting 80 instances of 10 VR locomotion techniques. The results indicated that current typology cannot cover teleportation-based techniques enabled by motion (e.g., gestures and gazes). Therefore, the typology was updated, and a new type was added: “motion-based teleporting.”
Transformational approach to subcontractor selection using blockchain-enabled smart contract as trust-enhancing technology

The unethical practices of bid shopping and peddling during the subcontractor procurement process can reduce trust between the general contractor (GC) and subcontractors (Subs) and lead to low-quality work, claims and disputes, schedule delays, and cost overruns. This paper contributes to the body of knowledge by developing a framework based on a blockchain-enabled smart contract system to address these unethical practices, thus establishing the subcontracting process grounded on system-based trust. The proposed framework is demonstrated through a pilot test, and a survey with construction professionals is conducted to validate its feasibility and effectiveness. The validation results show that the framework can prevent the aforementioned unethical practices and enable Subs to fairly compete for bid awards with proper budgets. Moreover, the validation demonstrates that the framework can enhance trust between the GC and Subs. Future studies should explore the implementation of this framework in the procurement process of real-world construction projects.
Enhancing Non-Fungible Tokens for the Evolution of Blockchain Technology

Non-fungible tokens (NFTs) are a new sort of blockchain-based token that is unique and indivisible. They were first announced in late 2017. NFTs are a type of blockchain-based virtual asset that has sparked a lot of investor interest in a very short amount of time. Since early 2021, the phenomena and its marketplaces have risen dramatically. The blockchain revolution is rolling forth a slew of changes that the art world may take advantage of. The purpose of this paper is to provide extensive information on the NFT, including its application, method of operation, buying, creating, and selling procedures, as well as its use. The NFT when paired with Metaverse, represents a significant advancement and revolution in the realm of virtual reality and blockchain, giving artists a new avenue to express their unique and valuable work. This study fills in the conceptual and practical knowledge gaps by demonstrating the usefulness of NFTs in the realms of substantial development and revolution in new private and public blockchain application.
A comprehensive bibliometric analysis and visualization of smart home research

The smart home penetration rate has grown considerably across the globe. This study presents a comprehensive bibliometric analysis of the trends and patterns in the general smart home research corpus using data from 5167 publications published in the Web of Science indexed sources over 2001–2021. The results show that the volume and impact of studies on smart homes have grown amazingly. Across multiple disciplines, at least 82 % of the research work in this set has been cited at least once. Additionally, China has emerged as a global leader in smart home research publications and influence. The USA has also made a considerable contribution to smart home scholarship. Most of the analyzed research publications are collaborative types. The scholars affiliated with China-based institutions have a close association with those from the USA, Australia, England, and Taiwan in the global network of smart home research papers. The Institute of Electrical and Electronics Engineers (IEEE) is identified as the leading publisher in this research field. The citation and usage count are found to be positively associated with each other. Intelligent sensors, wireless fidelity, load modeling, real-time systems, and computational modeling are identified as emerging knowledge domains in the field of smart homes.
Personalized Complementary Product Recommendation

Complementary product recommendation aims at providing product suggestions that are often bought together to serve a joint demand. Existing work mainly focuses on modeling product relationships at a population level, but does not consider personalized preferences of different customers. In this paper, we propose a framework for personalized complementary product recommendation capable of recommending products that fit the demand and preferences of the customers. Specifically, we model product relations and user preferences with a graph attention network and a sequential behavior transformer, respectively. Two networks are cast together through personalized re-ranking and contrastive learning, in which the user and product embedding are learned jointly in an end-to-end fashion. The system recognizes different customer interests by learning from their purchase history and the correlations among customers and products. Experimental results demonstrate that our model benefits from learning personalized information and outperforms non-personalized methods on real production data.
PPsNet: An improved deep learning model for microsatellite instability high prediction in colorectal cancer from whole slide images

Recent studies have shown that colorectal cancer (CRC) patients with microsatellite instability high (MSI-H) are more likely to benefit from immunotherapy. However, current MSI testing methods are not available for all patients due to the lack of available equipment and trained personnel, as well as the high cost of the assay. Here, we developed an improved deep learning model to predict MSI-H in CRC from whole slide images (WSIs).We established the MSI-H prediction model based on two stages: tumor detection and MSI classification. Previous works applied fine-tuning strategy directly for tumor detection, but ignoring the challenge of vanishing gradient due to the large number of convolutional layers. We added auxiliary classifiers to intermediate layers of pre-trained models to help propagate gradients back through in an effective manner. To predict MSI status, we constructed a pair-wise learning model with a synergic network, named parameter partial sharing network (PPsNet), where partial parameters are shared among two deep convolutional neural networks (DCNNs). The proposed PPsNet contained fewer parameters and reduced the problem of intra-class variation and inter-class similarity. We validated the proposed model on a holdout test set and two external test sets.144 H&E-stained WSIs from 144 CRC patients (81 cases with MSI-H and 63 cases with MSI-L/MSS) were collected retrospectively from three hospitals. The experimental results indicate that deep supervision based fine-tuning almost outperforms training from scratch and utilizing fine-tuning directly. The proposed PPsNet always achieves better accuracy and area under the receiver operating characteristic curve (AUC) than other solutions with four different neural network architectures on validation. The proposed method finally achieves obvious improvements than other state-of-the-art methods on the validation dataset with an accuracy of 87.28% and AUC of 94.29%.The proposed method can obviously increase model performance and our model yields better performance than other methods. Additionally, this work also demonstrates the feasibility of MSI-H prediction using digital pathology images based on deep learning in the Asian population. It is hoped that this model could serve as an auxiliary tool to identify CRC patients with MSI-H more time-saving and efficiently.
Jointly optimized ensemble deep random vector functional link network for semi-supervised classification

Randomized neural networks have become more and more attractive recently since they use closed-form solutions for parameter training instead of gradient-based approaches. Among them, the random vector functional link network (RVFL) and its deeper version ensemble deep random vector functional link network (edRVFL) show great performance on both classification and regression tasks. However, the previous research on these two models mainly focuses on the supervised learning area. Although there have been efforts to extend the RVFL network to solve semi-supervised learning problems, the potential of the edRVFL network has not been fully investigated. Therefore, we propose a jointly optimized learning strategy for the edRVFL network (JOSedRVFL) for semi-supervised learning tasks in this paper. The JOSedRVFL network uses an iterative procedure to compute the output weights and consequently predicts the class labels of the unlabeled training data during the training process. In addition, we propose another semi-supervised edRVFL network (SS-edRVFL) using manifold regularization in this work. We then do a brief comparison between these two methods to illustrate their similarities and differences. In the experimental part, we conduct the first set of experiments using the UCI datasets to compare the performance of our proposed semi-supervised algorithms against 11 other classifiers to demonstrate the superior performance of the SS-edRVFL and JOSedRVFL networks. JOSedRVFL achieves the highest accuracy on all 4 datasets while SS-edRVFL takes the second place 3 times which is only worse than JOSedRVFL. Moreover, we apply the proposed methods to real-world applications using the electroencephalography-based emotion recognition dataset to compare the performance of RVFL-based methods (RVFL, SS-RVFL, and JOSRVFL) and their edRVFL counterparts (edRVFL, SS-edRVFL, and JOSedRVFL). Results from this test revealed that the edRVFL-based models (edRVFL, SS-edRVFL, and JOSedRVFL) can obtain higher accuracy than the RVFL-based versions (RVFL, SS-RVFL, and JOSRVFL) with the same learning framework on 45 real-world semi-supervised benchmarks. We then perform the Wilcoxon signed-rank test to show that JOSedRVFL is significantly better than 5 other competitors, which supports our claim that JOSedRVFL can be treated as a superior classifier for semi-supervised classification on both benchmark datasets and real-world applications.
Combining Markov and Semi-Markov Modelling for Assessing Availability and Cybersecurity of Cloud and IoT Systems

This paper suggests a strategy (C5) for assessing cloud and IoT system (CIS) dependability, availability, and cybersecurity based on the continuous collection, comparison, choice, and combination of Markov and semi-Markov models (MMs and SMMs). It proposes the systematic building of an adequate and accurate model to evaluate CISs considering (1) continuous evolution of the model(s) together with systems induced by changes in the CIS or physical and cyber environment parameters; (2) the necessity of collecting data on faults, failures, vulnerabilities, cyber-attacks, privacy violations, and patches to obtain actual data for assessment; (3) renewing the model set based on analysis of CIS operation; (4) the possibility of choice and utilizing “off-the-shelf” models with understandable techniques for their development to assure improved accuracy of assessment; (5) renewing the models during application of CIS by time, component or mixed combining, taking into consideration different operation and maintenance events. The results obtained were algorithms for data collection and analysis, choice, and combining appropriate MM and SMMs and their different types, such as multi-fragmental and multiphase models, considering changing failure rates, cyber-attack parameters, periodical maintenance, etc. To provide and verify the approach, several private and public clouds and IoT systems were researched and discussed in the context of C5 and proposed algorithms.
A Comprehensive Collection and Analysis Model for the Drone Forensics Field

Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain.
LinkClimate: An interoperable knowledge graph platform for climate data

Climate science has become more ambitious in recent years as global awareness about the environment has grown. To better understand climate, historical climate(e.g. archived meteorological variables such as temperature, wind, water, etc.) and climate-related data (e.g. geographical features and human activities) are widely used by today's climate research to derive models for an explainable climate change and its effects. However, such data sources are often dispersed across a multitude of disconnected data silos on the Web. Moreover, there is a lack of advanced climate data platforms to enable multi-source heterogeneous climate data analysis, therefore, researchers must face a stern challenge in collecting and analyzing multi-source data. In this paper, we address this problem by proposing a climate knowledge graph for the integration of multiple climate data and other data sources into one service, leveraging Web technologies (e.g. HTTP) for multi-source climate data analysis. The proposed knowledge graph is primarily composed of data from the National Oceanic and Atmospheric Administration's daily climate summaries, OpenStreetMap, and Wikidata, and it supports joint data queries on these widely used databases. This paper shows, with a use case in Ireland and the United Kingdom, how climate researchers could benefit from this platform as it allows them to easily integrate datasets from different domains and geographical locations.
AMAM: An Attention-based Multimodal Alignment Model for Medical Visual Question Answering

Medical Visual Question Answering (VQA) is a multimodal task to answer clinical questions about medical images. Existing methods have achieved good performance, but most medical VQA models focus on visual contents while ignoring the influence of textual contents. To address this issue, this paper proposes an Attention-based Multimodal Alignment Model (AMAM) for medical VQA, aiming for an alignment of text-based and image-based attention to enrich the textual features. First, we develop an Image-to-Question (I2Q) attention and a Word-to-Question (W2Q) attention to model the relations of both visual and textual contents to the question. Second, we design a composite loss composed of a classification loss and an Image–Question Complementary (IQC) loss. The IQC loss concentrates on aligning the importance of the questions learned from visual and textual features to emphasize meaningful words in questions and improve the quality of predicted answers. Benefiting from the attention mechanisms and the composite loss, AMAM obtains rich semantic textual information and accurate answers. Finally, due to some data errors and missing labels on the VQA-RAD dataset, we further constructed an enhanced dataset, VQA-RADPh, to raise data quality. Experimental results on public datasets show better performance of AMAM compared with the advanced methods. Our source code is available at: https://github.com/shuning-ai/AMAM/tree/master.
AI in Breast Cancer Imaging: A Survey of Different Applications

Breast cancer was the most diagnosed cancer in 2020. Several thousand women continue to die from this disease. A better and earlier diagnosis may be of great importance to improving prognosis, and that is where Artificial Intelligence (AI) could play a major role. This paper surveys different applications of AI in Breast Imaging. First, traditional Machine Learning and Deep Learning methods that can detect the presence of a lesion and classify it into benign/malignant-which could be important to diminish reading time and improve accuracy-are analyzed. Following that, researches in the field of breast cancer risk prediction using mammograms-which may be able to allow screening programs customization both on periodicity and modality-are reviewed. The subsequent section analyzes different applications of augmentation techniques that allow to surpass the lack of labeled data. Finally, still concerning the absence of big datasets with labeled data, the last section studies Self-Supervised learning, where AI models are able to learn a representation of the input by themselves. This review gives a general view of what AI can give in the field of Breast Imaging, discussing not only its potential but also the challenges that still have to be overcome.
Novel Leader–Follower-Based Particle Swarm Optimizer Inspired by Multiagent Systems: Algorithm, Experiments, and Applications

In this article, as inspired by multiagent systems, a novel leader–follower-based particle swarm optimization (LFPSO) algorithm is presented where the particles are classified into leaders and followers according to their respective roles. The leaders are responsible for searching a wide range of the optimal candidate solutions so as to ensure the diversity of the particle population, and the followers are dedicated to seeking the global-best solution in order to guarantee the convergence of particles. A controller parameter is introduced to fine tune the impact of the leaders on the followers. Owing to the leader–follower mechanism, the proposed LFPSO algorithm not only maintains the diversity of the particle population but also improves the possibility of escaping from the locally optimal solution. It is demonstrated via experimental results that the proposed LFPSO algorithm significantly improves the accuracy and convergence rate of conventional particle swarm optimization algorithms. Furthermore, the LFPSO algorithm is successfully applied to denoise real-time signals in oilfield pipeline network and its superiority over existing denoising algorithms is verified as well.