Reviewerly/exHarmony huggingface dataset:
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series Prediction

Chaotic time series prediction, a challenging research topic in dynamic system modeling, has drawn great attention from researchers around the world. In recent years extensive researches have been done on developing chaotic time series prediction methods, and various models have been proposed. Among them, recurrent fuzzy neural networks (RFNNs) have shown significant potential in this area. Most of the proposed RFNNs learn a single function, but when dealing with chaotic time series, different outputs may be generated for a specific input based on the system’s state. So, a network is required that can learn multiple functions simultaneously. Based on this concept, a novel multi-functional recurrent fuzzy neural network (MFRFNN) is proposed in this paper. MFRFNN consists of two fuzzy neural networks with Takagi-Sugeno-Kang fuzzy rules, one is used to produce the output, and the other to determine the system’s state. There is a feedback loop between these two networks, which makes MFRFNN capable of learning and memorizing historical information of past observations. Employing the states allows the proposed network to learn multiple functions simultaneously. Moreover, a new learning algorithm, which employs the particle swarm optimization algorithm, is developed to train the networks’ weights. The effectiveness of MFRFNN is validated using the Lorenz and Rossler chaotic time series and four real-world datasets, including Box–Jenkins gas furnace, wind speed prediction, Google stock price prediction, and air quality index prediction. Based on the root mean square error, the proposed method shows a decrease of 35.12%,13.95%, and 49.62% from the second best methods in the Lorenz time series, Box–Jenkins gas furnace, and wind speed prediction dataset, respectively.
How Virtual Reality Technology Has Changed Our Lives: An Overview of the Current and Potential Applications and Limitations

Despite virtual reality (VR) being initially marketed toward gaming, there are many potential and existing VR applications in various sectors and fields, including education, training, simulations, and even in exercise and healthcare. Unfortunately, there is still a lack of general understanding of the strengths and limitations of VR as a technology in various application domains. Therefore, the aim of this literature review is to contribute to the library of literature concerning VR technology, its applications in everyday use, and some of its existing drawbacks. Key VR applications were discussed in terms of how they are currently utilized or can be utilized in the future, spanning fields such as medicine, engineering, education, and entertainment. The main benefits of VR are expressed through the text, followed by a discussion of some of the main limitations of current VR technologies and how they can be mitigated or improved. Overall, this literature review shows how virtual reality technology has the potential to be a greatly beneficial tool in a multitude of applications and a wide variety of fields. VR as a technology is still in its early stages, but more people are becoming interested in it and are optimistic about seeing what kind of changes VR can make in their everyday lives. With how rapidly modern society has adapted to personal computers and smartphones, VR has the opportunity to become the next big technological turning point that will eventually become commonplace in most households.
A blockchain-based hybrid platform for multimedia data processing in IoT-Healthcare

Blockchain technology must have sparked widespread interest, applications associated with data monitoring, banking sectors, computer security, the Internet of Things, and food chemistry to the healthcare sector and cognitive science. The use of multimedia in the healthcare architecture also allows for the storage, processing and transmission of patient information in a wide range of formats such as images, text and audio over the Internet using various smart particles. However, managing large amounts of data, including findings and images of each individual, increases human effort and increases protection risks. In this paper, to address these problems by using IoT in healthcare improves the performance of patient care while lowering costs by efficiently distributing healthcare resources. Nevertheless, various attackers can cause a variety of risks in IoT devices. To avoid these problems, Blockchain technology has been identified as the most effective method for maintaining the secrecy and security of control systems in real-time. This should provide a security architecture for healthcare multimedia content using blockchain technology by producing the hash of every information so that any transition or modification in information, as well as any breaches of medicines, would be evidenced across the whole blockchain platform.
Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective

Financial management is a critical aspect of firms, and entails the strategic planning, direction, and control of financial endeavors. Risk assessment, fraud detection, wealth management, online transactions, customized bond scheme, customer retention, virtual assistant and so on, are a few of the critical areas where Industry 4.0 technologies intervention are highly required for managing firms' finance. It has been identified from the previous studies that they are limited studies that have addressed the significance and application of integrating of Industry 4.0 technologies such as Internet of Things (IoT), cloud computing, big data, robotic process automation (RPA), artificial intelligence (AI), Blockchain, Digital twin, and Metaverse. With the motivation from the above aspects, this study aims to discuss the role of these technologies in the area of financial management of a firm. Based up on the analysis, it has been concluded that these technologies assist to credit risk management based on real-time data; financial data analytics of risk assessment, digital finance, digital auditing, fraud detection, and AI- and IoT- based virtual assistants. This study recommended that digital technologies be deeply integrated into the financial sector to improve service quality and accessibility, as well as the creation of innovative rules that allow for healthy competition among market participants.
OpenFHE

Fully Homomorphic Encryption (FHE) is a powerful cryptographic primitive that enables performing computations over encrypted data without having access to the secret key. We introduce OpenFHE, a new open-source FHE software library that incorporates selected design ideas from prior FHE projects, such as PALISADE, HElib, and HEAAN, and includes several new design concepts and ideas. The main new design features can be summarized as follows: (1) we assume from the very beginning that all implemented FHE schemes will support bootstrapping and scheme switching; (2) OpenFHE supports multiple hardware acceleration backends using a standard Hardware Abstraction Layer (HAL); (3) OpenFHE includes both user-friendly modes, where all maintenance operations, such as modulus switching, key switching, and bootstrapping, are automatically invoked by the library, and compiler-friendly modes, where an external compiler makes these decisions. This paper focuses on high-level description of OpenFHE design, and the reader is pointed to external OpenFHE references for a more detailed/technical description of the software library.
Blockchain-based trust mechanism for digital twin empowered Industrial Internet of Things

Emerging technologies such as blockchain and digital twins are essential for the rapid development and employment in Industry 5.0 revolution. With the growing number of industrial IoT nodes, optimizing the network and availing of limited resources to enable secure transmission is difficult. A digital twin is the virtual representation of a physical device that completely depends on sensor data for the critical decision-making simulation process. To this end, we combine a blockchain-based distributed network with a digital twin for the Industrial Internet of Things (IIoT) applications. This paper proposes a blockchain-based Proof of Authority (PoA) trust mechanism to provide high-quality services such as security and data privacy in IIoT. Furthermore, to enhance the authority of decentralized digital twin combined blockchain networks, we introduce a Deterministic Pseudo-Random Generation (DPRG) to generate a genesis block. To evaluate the trustworthiness of the proposed system, we simulate the blockchain network with a digital twin using IIoT sensor nodes. The simulation result shows that the PoA-based authority master nodes can reduce energy consumption and enhance data security.
Uncertainty-Aware Multiview Deep Learning for Internet of Things Applications

As an essential approach in many Internet of Things (IoT) applications, multiview learning synthesizes multiple features to achieve more comprehensive descriptions of data items. Most of the previous studies on multiview learning have been dedicated to increasing the prediction accuracy, while ignoring the reliability of the decision. This would limit their deployment in high-risk IoT and industrial applications such as the automated vehicle. Although a trusted multiview classification model has been proposed recently, it cannot well deal with the highly complementary multiview data. In this work, we present an evidential multiview deep learning (EMDL) method to make reliable decisions. EMDL first seeks view-specific evidence of each category, which could be termed as the amount of support to each category collected from data. It then dynamically fuses different views at the evidence level to construct the multiview common evidence and makes reliable prediction accordingly (strong evidence indicates high prediction confidence). In particular, we establish a degradation layer to learn the mappings from the common evidence (comprehensive information) to view-specific evidences (partial information) for evidence fusion. It aims to explicitly model consistent and complementary relations in multiview data at the evidence level. We apply EMDL on a synthetic toy dataset and five real-world datasets (three datasets are related to industrial scenarios). Experiments show that EMDL outperforms state-of-the-art baseline methods.
End-to-end Generative Pretraining for Multimodal Video Captioning

Recent video and language pretraining frameworks lack the ability to generate sentences. We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining framework for learning from unlabelled videos which can be effectively used for generative tasks such as multimodal video captioning. Unlike recent video-language pretraining frameworks, our framework trains both a multimodal video encoder and a sentence decoder jointly. To overcome the lack of captions in unlabelled videos, we leverage the future utterance as an additional text source and propose a bidirectional generation objective - we generate future utterances given the present mulitmodal context, and also the present utterance given future observations. With this objective, we train an encoder-decoder model end-to-end to generate a caption from raw pixels and transcribed speech directly. Our model achieves state-of the-art performance for multimodal video captioning on four standard benchmarks, as well as for other video understanding tasks such as VideoQA, video retrieval and action classification.
Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model

Recently, vision-language pre-training shows great potential in open-vocabulary object detection, where detectors trained on base classes are devised for detecting new classes. The class text embedding is firstly generated by feeding prompts to the text encoder of a pre-trained vision-language model. It is then used as the region classifier to supervise the training of a detector. The key element that leads to the success of this model is the proper prompt, which requires careful words tuning and ingenious design. To avoid laborious prompt engineering, there are some prompt representation learning methods being proposed for the image classification task, which however can only be sub-optimal solutions when applied to the detection task. In this paper, we introduce a novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model. Different from the previous classification-oriented methods, DetPro has two highlights: 1) a background interpretation scheme to include the proposals in image background into the prompt training; 2) a context grading scheme to separate proposals in image foreground for tailored prompt training. We assemble DetPro with ViLD, a recent state-of-the-art openworld object detector, and conduct experiments on the LVIS as well as transfer learning on the Pascal VOC, COCO, Objects365 datasets. Experimental results show that our DetPro outperforms the baseline ViLD [7] in all settings, e.g., +3.4 AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">box</sup> and +3.0 AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mask</sup> improvements on the novel classes of LVIS. Code and models are available at https://github.com/dyabel/detpro.
Label, Verify, Correct: A Simple Few Shot Object Detection Method

The objective of this paper is few-shot object detection (FSOD) - the task of expanding an object detector for a new category given only a few instances for training. We introduce a simple pseudo-labelling method to source high-quality pseudo-annotations from the training set, for each new category, vastly increasing the number of training instances and reducing class imbalance; our method finds previously unlabelled instances. Naively training with model predictions yields sub-optimal performance; we present two novel methods to improve the precision of the pseudo-labelling process: first, we introduce a verification technique to remove candidate detections with incorrect class labels; second, we train a specialised model to correct poor quality bounding boxes. After these two novel steps, we obtain a large set of high-quality pseudo-annotations that allow our final detector to be trained end-to-end. Additionally, we demonstrate our method maintains base class performance, and the utility of simple augmentations in FSOD. While benchmarking on PASCAL VOC and MS-COCO, our method achieves state-of-the-art or second-best performance compared to existing approaches across all number of shots.
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation

Mining precise class-aware attention maps, a.k.a, class activation maps, is essential for weakly supervised semantic segmentation. In this paper, we present L2G, a simple online local-to-global knowledge transfer framework for high-quality object attention mining. We observe that classification models can discover object regions with more details when replacing the input image with its local patches. Taking this into account, we first leverage a local classification network to extract attentions from multiple local patches randomly cropped from the input image. Then, we utilize a global network to learn complementary attention knowledge across multiple local attention maps online. Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks. Experiments show that our method attains 72.1% and 44.2% mIoU scores on the validation set of PASCAL VOC 2012 and MS COCO 2014, respectively, setting new state-of-the-art records. Code is available at https://github.com/PengtaoJiang/L2G.
Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation

Depth estimation is solved as a regression or classification problem in existing learning-based multi-view stereo methods. Although these two representations have recently demonstrated their excellent performance, they still have apparent shortcomings, e.g., regression methods tend to overfit due to the indirect learning cost volume, and classification methods cannot directly infer the exact depth due to its discrete prediction. In this paper, we propose a novel representation, termed Unification, to unify the advantages of regression and classification. It can directly constrain the cost volume like classification methods, but also realize the sub-pixel depth prediction like regression methods. To excavate the potential of unification, we design a new loss function named Unified Focal Loss, which is more uniform and reasonable to combat the challenge of sample imbalance. Combining these two unburdened modules, we present a coarse-to-fine framework, that we call UniMVSNet. The results of ranking first on both DTU and Tanks and Temples benchmarks verify that our model not only performs the best but also has the best generalization ability.
ABC-GSPBFT: PBFT with grouping score mechanism and optimized consensus process for flight operation data-sharing

The flight operation data sharing not only brings huge benefits to all participants, but also puts forward higher requirements for data security. Consortium blockchain provides a new approach to safely sharing flight operation data in an untrusted environment. Practical Byzantine Fault-Tolerant (PBFT) algorithm is one of the most widely used consensus algorithms in consortium blockchain, but it exists some problems, such as static structure, low consensus efficiency and large communication overhead. To solve these problems, a PBFT with grouping score mechanism and artificial bee colony-optimized consensus process, namely ABC-GSPBFT, is designed to construct the consortium blockchain for sharing flight operation data. Firstly, the artificial bee colony algorithm is used to pre-select a collection of reliable nodes to narrow the scope of consensus. Then, the grouping score mechanism is employed to dynamically update the consensus nodes to further narrow the scope of consensus and enhance the reliability of consensus nodes. Finally, the commit stage of the PBFT algorithm is reasonably simplified to improve consensus efficiency. The actual flight operation data is selected to test and verify the effectiveness of the ABC-GSPBFT algorithm. The analysis and experimental results show that the ABC-GSPBFT algorithm can greatly reduce consensus delay and communication overhead, and improve dynamic performance and consensus efficiency of the flight data sharing. It is an effective method for safely, reliably and efficiently sharing flight data.
Edge Intelligence Empowered Vehicle Detection and Image Segmentation for Autonomous Vehicles

Edge intelligence (EI) migrates data and artificial intelligence (AI) to the “edge” of a network, enhancing the high-bandwidth and low-latency of wireless data transmission with the multiplier effect of 5G and AI, greatly improving the edges’ processing speed. Through integrating EI and computer vision technology, video surveillance systems in ITS can improve the processing capability of traffic information, which improves traffic efficiency and ensures traffic safety. Accordingly, first, we propose an edge intelligence-based improved-YOLOv4 vehicle detection algorithm, introducing an efficient channel attention (ECA) mechanism and a high-resolution network (HRNet) to enhance vehicle detection ability. Second, an edge intelligence-based improved DeepLabv3+ image segmentation algorithm is proposed, replacing the original backbone network with MobileNetv2 and using the softpool method, thus reducing the network size while improving the segmentation accuracy. Experimental results show that our proposed model has a higher average precision (AP) and can improve vehicle detection accuracy from 82.03% to 86.22%. The mean intersection over union (mIOU) of the image segmentation model improves from 73.32% to 75.63%.
Ecomindsponge: A Novel Perspective on Human Psychology and Behavior in the Ecosystem

Modern society faces major environmental problems, but there are many difficulties in studying the nature–human relationship from an integral psychosocial perspective. We propose the ecomind sponge conceptual framework, based on the mindsponge theory of information processing. We present a systematic method to examine the nature–human relationship with conceptual frameworks of system boundaries, selective exchange, and adaptive optimization. The theoretical mechanisms were constructed based on principles and new evidence in natural sciences. The core mechanism of ecomindsponge is the subjective sphere of influence, which is the limited mental representation of information received from and processed based on the objective sphere of influence–actual interactions in reality. The subjective sphere is the sum of two sub-spheres: influencing (proactive) and being influenced (reactive). Maladaptation in thinking and behavior of the mind as an information collection-cum-processor results from the deviation of the subjective sphere from reality, which includes two main types: “stupidity” and “delusion”. Using Bayesian Mindsponge Framework (BMF) analytics on a dataset of 535 urban residents, we provide consistent statistical evidence on the proposed properties of subjective spheres. The dynamic framework of ecomindsponge can be used flexibly and practically for environmental research as well as other psychosocial fields.
Identifying performance anomalies in fluctuating cloud environments: A robust correlative-GNN-based explainable approach

Cloud computing provides scalable and elastic resources to customers as a low-cost, on-demand utility service. Multivariate time series anomaly detection is crucial to promise the overall performance of cloud computing systems. However, due to the complexity and high dynamics of cloud environments, anomaly detections caused by irregular fluctuations in data and the robustness of models are challenging. To address these issues, we propose a deep learning-based anomaly detection method for multivariate time series for real-world operational clouds: Correlative-GNN with Multi-Head Self-Attention and Auto-Regression Ensemble Method (CGNN-MHSA-AR). Our method utilizes two parallel graph neural networks (GNN) to learn the time and feature inter-dependencies to achieve fewer false positives. Our approach leverages a multi-head self-attention, GRU, and AR model to capture multiple-dimensional information, leading to better detection robustness. CGNN-MHSA-AR can also provide an abnormal explanation based on the prediction error of its constituent univariate series. We compare the detection performance of CGNN-MHSA-AR with seven baseline methods on seven public datasets. The evaluation shows that the proposed CGNN-MHSA-AR outperforms its competitors with an F1-Score of 0.871 on average and is 19.9% better than state-of-the-art baseline methods. In addition, CGNN-MHSA-AR also offers to correctly identify the root cause of detected anomalies with up to 74.1% accuracy.