Reviewerly/exHarmony huggingface dataset:
A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems

Industry 5.0 aims at maximizing the collaboration between humans and machines. Machines are capable of automating repetitive jobs, while humans handle creative tasks. As a critical component of Industrial Internet of Things (IIoT) systems for service delivery, network data stream analytics often encounter concept drift issues due to dynamic IIoT environments, causing performance degradation and automation difficulties. In this paper, we propose a novel Multi-Stage Automated Network Analytics (MSANA) framework for concept drift adaptation in IIoT systems, consisting of dynamic data pre-processing, the proposed Drift-based Dynamic Feature Selection (DD-FS) method, dynamic model learning & selection, and the proposed Window-based Performance Weighted Probability Averaging Ensemble (W-PWPAE) model. It is a complete automated data stream analytics framework that enables automatic, effective, and efficient data analytics for IIoT systems in Industry 5.0. Experimental results on two public IoT datasets demonstrate that the proposed framework outperforms state-of-the-art methods for IIoT data stream analytics.
Synthetic Data Supervised Salient Object Detection

Although deep salient object detection (SOD) has achieved remarkable progress, deep SOD models are extremely data-hungry, requiring large-scale pixel-wise annotations to deliver such promising results. In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few labeled data, and these synthesized pairs can replace the human-labeled DUTS-TR to train any off-the-shelf SOD model. Its contribution is three-fold. 1) Our proposed diffusion embedding network can address the manifold mismatch and is tractable for the latent code generation, better matching with the ImageNet latent space. 2) For the first time, our proposed few-shot saliency mask generator can synthesize infinite accurate image synchronized saliency masks with a few labeled data. 3) Our proposed quality-aware discriminator can select highquality synthesized image-mask pairs from noisy synthetic data pool, improving the quality of synthetic data. For the first time, our SODGAN tackles SOD with synthetic data directly generated from the generative model, which opens up a new research paradigm for SOD. Extensive experimental results show that the saliency model trained on synthetic data can achieve $98.4\%$ F-measure of the saliency model trained on the DUTS-TR. Moreover, our approach achieves a new SOTA performance in semi/weakly-supervised methods, and even outperforms several fully-supervised SOTA methods. Code is available at https://github.com/wuzhenyubuaa/SODGAN
Modeling electronic health record data using an end-to-end knowledge-graph-informed topic model

Abstract The rapid growth of electronic health record (EHR) datasets opens up promising opportunities to understand human diseases in a systematic way. However, effective extraction of clinical knowledge from EHR data has been hindered by the sparse and noisy information. We present Graph ATtention-Embedded Topic Model (GAT-ETM), an end-to-end taxonomy-knowledge-graph-based multimodal embedded topic model. GAT-ETM distills latent disease topics from EHR data by learning the embedding from a constructed medical knowledge graph. We applied GAT-ETM to a large-scale EHR dataset consisting of over 1 million patients. We evaluated its performance based on topic quality, drug imputation, and disease diagnosis prediction. GAT-ETM demonstrated superior performance over the alternative methods on all tasks. Moreover, GAT-ETM learned clinically meaningful graph-informed embedding of the EHR codes and discovered interpretable and accurate patient representations for patient stratification and drug recommendations. GAT-ETM code is available at https://github.com/li-lab-mcgill/GAT-ETM .
An integrated LSTM-HeteroRGNN model for interpretable opioid overdose risk prediction

Opioid overdose (OD) has become a leading cause of accidental death in the United States, and overdose deaths reached a record high during the COVID-19 pandemic. Combating the opioid crisis requires targeting high-need populations by identifying individuals at risk of OD. While deep learning emerges as a powerful method for building predictive models using large scale electronic health records (EHR), it is challenged by the complex intrinsic relationships among EHR data. Further, its utility is limited by the lack of clinically meaningful explainability, which is necessary for making informed clinical or policy decisions using such models. In this paper, we present LIGHTED, an integrated deep learning model combining long short term memory (LSTM) and graph neural networks (GNN) to predict patients' OD risk. The LIGHTED model can incorporate the temporal effects of disease progression and the knowledge learned from interactions among clinical features. We evaluated the model using Cerner's Health Facts database with over 5 million patients. Our experiments demonstrated that the model outperforms traditional machine learning methods and other deep learning models. We also proposed a novel interpretability method by exploiting embeddings provided by GNNs to cluster patients and EHR features respectively, and conducted qualitative feature cluster analysis for clinical interpretations. Our study shows that LIGHTED can take advantage of longitudinal EHR data and the intrinsic graph structure of EHRs among patients to provide effective and interpretable OD risk predictions that may potentially improve clinical decision support.
Adaptive Feature Fusion for Small Object Detection

In order to alleviate the situation that small objects are prone to missed detection and false detection in natural scenes, this paper proposed a small object detection algorithm for adaptive feature fusion, referred to as MMF-YOLO. First, aiming at the problem that small object pixels are easy to lose, a multi-branch cross-scale feature fusion module with fusion factor was proposed, where each fusion path has an adaptive fusion factor, which can allow the network to independently adjust the importance of features according to the learned weights. Then, aiming at the problem that small objects are similar to background information and small objects overlap in complex scenes, the M-CBAM attention mechanism was proposed, which was added to the feature reinforcement extraction module to reduce feature redundancy. Finally, in light of the problem of small object size and large size span, the size of the object detection head was modified to adapt to the small object size. Experiments on the VisDrone2019 dataset showed that the mAP of the proposed algorithm could reach 42.23%, and the parameter quantity was only 29.33 MB, which is 9.13% ± 0.07% higher than the benchmark network mAP, and the network model was reduced by 5.22 MB.
Blockchain’s Grand Promise for the Real Estate Sector: A Systematic Review

In recent years, blockchain technology has received increasing attention in the real estate literature. Moreover, recent evidence suggests that blockchains could provide some added benefits for the real estate sector even in the current hybrid settings, where blockchain is usually just an add-on to existing systems. This research provides an up-to-date and systematic understanding of blockchain’s theoretical potential, pros, and cons for the real estate sector. The research aims to understand why and where to apply blockchain in real estate by conducting a systematic review that identified 296 relevant documents and performed a thematic content analysis. The identified blockchain adoption proposals were classified using standardized real estate vocabulary. Most of the literature focused on blockchain possibilities within land administration, followed by property transactions, real estate investment, leasing and renting, and real estate administration. However, real estate development and real estate maintenance did not appear as attractive real estate subsectors for blockchain. This paper provides a detailed analysis of blockchain technology’s possibilities for each real estate subsector, i.e., blockchain merits, and discusses the pros and cons. Last, this review provides suggestions for future research directions.
Hyperspectral Video Target Tracking Based on Deep Features with Spectral Matching Reduction and Adaptive Scale 3D Hog Features

Hyperspectral video target tracking is generally challenging when the scale of the target varies. In this paper, a novel algorithm is proposed to address the challenges prevalent in the existing hyperspectral video target tracking approaches. The proposed approach employs deep features along with spectral matching reduction and adaptive-scale 3D hog features to track the objects even when the scale is varying. Spectral matching reduction is adopted to estimate the spectral curve of the selected target region using a weighted combination of the global and local spectral curves. In addition to the deep features, adaptive-scale 3D hog features are extracted using cube-level features at three different scales. The four weak response maps thus obtained are then combined using adaptive weights to yield a strong response map. Finally, the region proposal module is utilized to estimate the target box. The proposed strategies make the approach robust against scale variations of the target. A comparative study on different hyperspectral video sequences illustrate the superior performance of the proposed algorithm as compared to the state-of-the-art approaches.
An Online Diagnosis Method for Sensor Intermittent Fault Based on Data-Driven Model

The intermittent fault (IF) is usually overlooked in power electronic applications. In this letter, an intelligent diagnosis method based on a data-driven model is proposed for sensor IFs. First, the manifestation of IF in the time domain is discussed to explore its distinctive characteristics. Then, a signal predictor is constructed in a data-driven way by utilizing the nonlinear autoregressive exogenous structure with the extreme learning machine algorithm. In addition, the residual is generated online by comparing the output of the devised data-driven predictor and that of the real sensor. The fault diagnosis decision-making scheme is finally designed based on the residual evaluation to identify the sensor IF and permanent fault simultaneously. The feasibility and effectiveness of the proposed method are demonstrated by offline tests and real-time experimental tests.
Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel Attention

Although the manipulating of the unmanned aerial manipulator (UAM) has been widely studied, vision-based UAM approaching, which is crucial to the subsequent manipulating, generally lacks effective design. The key to the visual UAM approaching lies in object tracking, while current UAM tracking typically relies on costly model-based methods. Besides, UAM approaching often confronts more severe object scale variation issues, which makes it inappro-priate to directly employ state-of-the-art model-free Siamese-based methods from the object tracking field. To address the above problems, this work proposes a novel Siamese network with pairwise scale-channel attention (SiamSA) for vision-based UAM approaching. Specifically, SiamSA consists of a pairwise scale-channel attention network (PSAN) and a scale-aware anchor proposal network (SA-APN). PSAN acquires valuable scale information for feature processing, while SA-APN mainly attaches scale awareness to anchor proposing. Moreover, a new tracking benchmark for UAM approaching, namely UAMT100, is recorded with 35K frames on a flying UAM platform for evaluation. Exhaustive experiments on the benchmarks and real-world tests validate the efficiency and practicality of SiamSA with a promising speed. Both the code and UAMT100 benchmark are now available at https://github.com/vision4robotics/SiamSA.
CLIP Meets Video Captioning: Concept-Aware Representation Learning Does Matter

For video captioning, “pre-training and fine-tuning” has become a de facto paradigm, where ImageNet Pre-training (INP) is usually used to encode the video content, then a task-oriented network is fine-tuned from scratch to cope with caption generation. This paper first investigates the impact of the recently proposed CLIP (Contrastive Language-Image Pre-training) on video captioning. Through the empirical study on INP vs. CLIP, we identify the potential deficiencies of INP and explore the key factors for accurate description generation. The results show that the INP-based model is tricky to capture concepts’ semantics and sensitive to irrelevant background information. By contrast, the CLIP-based model significantly improves the caption quality and highlights the importance of concept-aware representation learning. With these findings, we propose Dual Concept Detection (DCD) further to inject concept knowledge into the model during training. DCD is an auxiliary task that requires a caption model to learn the correspondence between video content and concepts and the co-occurrence relations between concepts. Experiments on MSR-VTT and VATEX demonstrate the effectiveness of DCD, and the visualization results further reveal the necessity of learning concept-aware representations.
PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images

The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations. To achieve that, we make the following four contributions: (i) in pursuit of generalisation, we propose a two-stage open-vocabulary object detector, where the class-agnostic object proposals are classified with a text encoder from pre-trained visual-language model; (ii) To pair the visual latent space (of RPN box proposals) with that of the pre-trained text encoder, we propose the idea of regional prompt learning to align the textual embedding space with regional visual object features; (iii) To scale up the learning procedure towards detecting a wider spectrum of objects, we exploit the available online resource via a novel self-training framework, which allows to train the proposed detector on a large corpus of noisy uncurated web images. Lastly, (iv) to evaluate our proposed detector, termed as PromptDet, we conduct extensive experiments on the challenging LVIS and MS-COCO dataset. PromptDet shows superior performance over existing approaches with fewer additional training images and zero manual annotations whatsoever. Project page with code: https://fcjian.github.io/promptdet .
Big Data Sentiment Analysis of Twitter Data

The term "big data" is becoming increasingly common these days. The amount of data generated is directly proportional to the amount of time spent on social media each day. The majority of users consider Twitter to be one of the most popular social networking platforms. The rise of social media has sparked an incredible amount of curiosity among those who use the internet nowadays. The information collected from these social networking sites may be put to a variety of uses, including forecasting, marketing, and the study of user sentiment. Twitter is a social media platform that is commonly used for making remarks in the form of brief status updates. A sentiment analysis may be performed on some or all of the millions of tweets that are received each year. Managing such a massive volume of unstructured data, on the other hand, is a laborious effort to do. To effectively manage large amounts of data, the analytics tools and models that are now on the market are insufficiently equipped and positioned. For this reason, it is essential to make use of a cloud storage solution for the applications of this kind. As a result, we have used Hadoop for the intelligent analysis as well as the storing of large amounts of data. In this article, we offer a system that does sentiment analysis on tweets using the Cloud.
E-CIR: Event-Enhanced Continuous Intensity Recovery

A camera begins to sense light the moment we press the shutter button. During the exposure interval, relative motion between the scene and the camera causes motion blur, a common undesirable visual artifact. This paper presents E-CIR, which converts a blurry image into a sharp video represented as a parametric function from time to intensity. E-CIR leverages events as an auxiliary input. We discuss how to exploit the temporal event structure to construct the parametric bases. We demonstrate how to train a deep learning model to predict the function coefficients. To improve the appearance consistency, we further introduce a refinement module to propagate visual features among consecutive frames. Compared to state-of-the-art event-enhanced de-blurring approaches, E-CIR generates smoother and more realistic results. The implementation of E-CIR is available at https://github.com/chensong1995/E-CIR.
An Improved Method for Ship Target Detection Based on YOLOv4

The resolution of remote sensing images has increased with the maturation of satellite technology. Ship detection technology based on remote sensing images makes it possible to monitor a large range and far sea area, which can greatly enrich the monitoring means of maritime departments. In this paper, we conducted research on small target detection and resistance to complex background interference. First, a ship dataset with four types of targets (aircraft carriers, warships, merchant ships and submarines) is constructed, and experiments are conducted on the dataset using the object detection algorithm YOLOv4. The Kmeans++ clustering algorithm is used for a priori frame selection, and the migration learning method is used to enhance the detection effect of the YOLOv4. Second, the model is improved to address the problems of missed detection of small ships and difficulty in resisting background interference: the RFB_s (Receptive Field Block) with dilated convolution is introduced instead of the SPP (Spatial Pyramid Pooling) to enlarge the receptive field and improve the detection of small targets; the attention mechanism CBAM (Convolutional Block Attention Module) is added to adjust the weights of different features to highlight salient features useful for ship detection task, which improve the detection performance of small ships and improve the model’s ability to resist complex background. Compared to YOLOv4, our proposed model achieved a large improvement in mAP (mean Average Precision) from 77.66% to 91.40%.
A Proposed Meta-Reality Immersive Development Pipeline: Generative AI Models and Extended Reality (XR) Content for the Metaverse

The realization of an interoperable and scalable virtual platform, currently known as the "metaverse," is inevitable, but many technological challenges need to be overcome first.With the metaverse still in a nascent phase, research currently indicates that building a new 3D social environment capable of interoperable avatars and digital transactions will represent most of the initial investment in time and capital.The return on investment, however, is worth the financial risk for firms like Meta, Google, and Apple.While the current virtual space of the metaverse is worth $6.30 billion, that is expected to grow to $84.09 billion by the end of 2028.But the creation of an entire alternate virtual universe of 3D avatars, objects, and otherworldly cityscapes calls for a new development pipeline and workflow.Existing 3D modeling and digital twin processes, already well-established in industry and gaming, will be ported to support the need to architect and furnish this new digital world.The current development pipeline, however, is cumbersome, expensive and limited in output capacity.This paper proposes a new and innovative immersive development pipeline leveraging the recent advances in artificial intelligence (AI) for 3D model creation and optimization.The previous reliance on 3D modeling software to create assets and then import into a game engine can be replaced with nearly instantaneous content creation with AI.While AI art generators like DALL-E 2 and DeepAI have been used for 2D asset creation, when combined with game engine technology, such as Unreal Engine 5 and virtualized geometry systems like Nanite, a new process for creating nearly unlimited content for immersive reality is possible.
Digital financial inclusion and economic growth in Sub-Saharan Africa: the role of institutions and governance

Purpose This study examines the role of institutions and governance on the digital financial inclusion and economic growth nexus in Sub-Saharan Africa (SSA) from 2014 to 2020. Design/methodology/approach This study adopts the generalised method of moments technique which controls for endogeneity. The authors employed four main variables namely, index of digital financial inclusion, gross domestic product per capita growth, institutions and governance. Findings The results suggest a significant positive effect of institutional quality and governance on the digital financial inclusion-economic growth nexus in SSA. Furthermore, the authors find that effect of trade and population growth on economic growth was significantly positive while inflation reduces economic growth in the region. Research limitations/implications This study also ignored the effect of digital financial inclusion on environmental quality. Future researches should focus on addressing these drawbacks and replicating the study in Africa as a whole and other developing countries across the world that are experiencing digital financial inclusion and economic growth challenges. The results from the study imply that a positive relationship between digital financial inclusion and economic growth. It is important to note that the study was carried out on the premise that institutions play a pivotal role in enhancing economic growth in SSA. Practical implications The results confirm the significance of policies that enhances institutional quality and governance which are other avenues the authorities can pursue to enhance economic growth in SSA. Social implications The paper documents the importance of institutions in boosting economic growth which impacts on social life rather than digital financial inclusion only. Originality/value The paper makes a contribution through analysing the role of institutions and governance on the digital financial inclusion-economic growth nexus rather than the traditional financial inclusion–economic growth nexus which is common to the majority of the available empirical studies.
Face Recognition using Raspberry Pi

Modern day security related concerns require modern solutions. With the advancement in technologies and proper knowledge, it has become easier to gain access to one’s id, password and passcodes by snooping, fishing, hacking, eavesdropping, and stealing which remains the same and forms base foundation to do malpractices. Hence, there is a constant threat to security, be it personal (if required), organizational, institutional, and so on. We need an approach that will address these security related concerns of systems that require passwords, passcodes, and RFID cards which can be tricked. This can be achieved with the help of technology called facial recognition.