Reviewerly/exHarmony huggingface dataset:
A novel encoder-decoder network with guided transmission map for single image dehazing

A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) for single image dehazing scheme is proposed in this paper. The proposed EDN-GTM takes conventional RGB hazy image in conjunction with its transmission map estimated by adopting dark channel prior as the inputs of the network. The proposed EDN-GTM utilizes U-Net for image segmentation as the core network and utilizes various modifications including spatial pyramid pooling module and Swish activation to achieve state-of-the-art dehazing performance. Experiments on benchmark datasets show that the proposed EDN-GTM outperforms most of traditional and deep learning-based image dehazing schemes in terms of PSNR and SSIM metrics. The proposed EDN-GTM furthermore proves its applicability to object detection problems. Specifically, when applied to an image preprocessing tool for driving object detection, the proposed EDN-GTM can efficiently remove haze and significantly improve detection accuracy by 4.73% in terms of mAP measure.
FusionFedBlock: Fusion of blockchain and federated learning to preserve privacy in industry 5.0

• Fusion of Blockchain and Federated Learning to Preserve Privacy in Industry 5.0. • Design federated learning network for privacy preserved data flow in an Industrial Environment. • Provide decentralized secure storage by the Distributed Hash Table (DHT) at the cloud. • Evaluate the proposed scheme with Blockchain and Federated Learning. • Comparison of our novel work with existing research as accuracy. Nowadays, Industries are experiencing rapid changes in the digital environment, referred to as Industry 5.0. The Internet of Things (IoT) and advanced technologies are essential in the industrial environment. Technological advancements can collect, transfer, and analyze vast amounts of data in the industry via promising technologies. Still, IoT has various issues when applied to industrial infrastructures, such as centralization, privacy preservation, latency, and security. This article proposes a scheme as FusionFedBlock: Fusion of Blockchain and Federated Learning to Preserve Privacy in Industry 5.0 to address the aforementioned issues. At the federated layer, the industry's departments (Production, Quality Control, Distribution) allow local learning updates with network automation and communicate to the global model, which miners verify in the Blockchain networks. Federated-Learning offers privacy preservation between various mentioned departments in industries. Decentralized secure storage is provided by the Distributed Hash Table (DHT) at the cloud layer. The validation outcomes of the proposed scheme demonstrate excellent performance as the accuracy of 93.5% in a 50% active node for Industry 5.0 compared to existing frameworks.
Machine Learning based Automatic Hate Speech Recognition System

Social media and knowledge sharing have had a positive impact on humanity. However, this has also led to a number of issues, such as the dissemination and dissemination of hate speech. This new problem of hate speech on social media has been addressed by recent studies that utilized a number of feature engineering techniques and machine learning algorithms. It&#x0027;s not clear if there is a study that compares different methods for generating features and machine learning algorithms in order to determine which one is better for a standard publicly available dataset. With the support vector machine technique, the testing findings showed that bigram features performed best with 79 percent overall accuracy when utilized with the bigram feature set. Detecting automated hate speech messages can be made easier with the findings of our investigation. It will also be used as a benchmark for future research into existing automatic text classification algorithms, based on the results of the various comparisons. The use of natural language processing to classify text and hate speech are all examples of machine learning
Augmented reality-based guidance in product assembly and maintenance/repair perspective: A state of the art review on challenges and opportunities

Manufacturing industries are currently experiencing the fourth industrial revolution with the rapid advancements in immersive technologies for human–machine interaction (HMI) and flexible manufacturing systems (FMS). Product variance is limited due to barriers in the knowledge transfer between the stakeholders that existed in the pre and post-manufacturing phases. Augmented reality (AR) is a promising technology that can offer a high degree of adaptability and independence to support knowledge transfer at the most crucial manufacturing stages such as assembly, repair & maintenance. This article is focused on presenting the state of the art literature on AR technologies in product assembly and disassembly from the Maintenance/Repair perspective. The working of various modules in AR technology on facilitating a user-friendly guiding platform and its applications are discussed with suitable illustrations. The critical difficulties, such as tracking and rendering techniques for estimating human movement and environment experiences are observed to extend the adaptability of the technology. Future research potential, such as enhancing the virtual interface for reality, identifying worker behaviours, and enabling sharing and collaboration between multiple streams in an industrial context are analyzed.
Someone out there? A study on the social presence of anthropomorphized chatbots

Companies are increasingly employing text-based chatbots as a time and cost-efficient way to interact with customers. While companies begin to explore anthropomorphic chatbot designs by imbuing chatbots with human-like characteristics, the effectiveness of chatbot anthropomorphism remains unclear. We conducted three experiments to assess the effectiveness of chatbot anthropomorphism in customer–chatbot interactions. By equipping chatbots with human-like linguistic cues, we evoke different levels of chatbot anthropomorphism. Our results show significant positive effects of chatbot anthropomorphism on trust, purchase intention, word of mouth, and satisfaction with the shopping experience. More importantly, we identify social presence as the underlying mediating mechanism of these effects. These effects are robust and not contingent on different shopping contexts distinguished by hedonic versus utilitarian shopping motivations or the disclosure of (non-)sensitive information by customers. The present research derives managerial implications for companies that seek to effectively employ chatbots in customer interactions. Further, this study advances research on customers’ reactions towards anthropomorphized chatbots and demonstrates that social presence is a critical driver of successful customer-chatbot interactions.
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval

Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development of video-text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast. However, cross-grained contrast, which is the contrast between coarse-grained representations and fine-grained representations, has rarely been explored in prior research. Compared with fine-grained or coarse-grained contrasts, cross-grained contrast calculate the correlation between coarse-grained features and each fine-grained feature, and is able to filter out the unnecessary fine-grained features guided by the coarse-grained feature during similarity calculation, thus improving the accuracy of retrieval. To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP, for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine-grained and cross-grained similarity matrices to instance-level similarity. To address this challenge, we propose the Attention Over Similarity Matrix (AOSM) module to make the model focus on the contrast between essential frames and words, thus lowering the impact of unnecessary frames and words on retrieval results. With multi-grained contrast and the proposed AOSM module, X-CLIP achieves outstanding performance on five widely-used video-text retrieval datasets, including MSR-VTT (49.3 [email protected]), MSVD (50.4 [email protected]), LSMDC (26.1 [email protected]), DiDeMo (47.8 [email protected]) and ActivityNet (46.2 [email protected]).
Advances in Metaverse Investigation: Streams of Research and Future Agenda

The metaverse has increasingly attracted the attention of academics and practitioners, who attempt to better understand its theoretical foundations and business application areas. This paper provides an overarching picture of what has already been studied and investigated in metaverse academic investigation. It adopts a systematic literature review and a bibliometric analysis. The study designs a thematic map of the metaverse research. It proposes four streams of research (metaverse technologies, metaverse areas of application, marketing and consumer behaviour and sustainability) for future investigation, which academics and practitioners should explore. It also contributes towards a systematic advancement of knowledge in the field, provides some preliminary theoretical contributions by shedding light on future research avenues, and offers insights for business.
Extensible Metaverse Implication for a Smart Tourism City

The metaverse is an innovation that has created the recent phenomenon of new tourism experiences from a virtual reality of a smart tourism destination. However, the existing metaverse platform demonstrated that the technology is still difficult to develop, as the service provider did not disclose the internal mechanisms to developers, and it was a closed system, which could not use or share the user’s data across platforms. The aim of this paper was to design and develop an open metaverse platform called the “extensible metaverse”, which would allow new developers to independently develop the capabilities of the metaverse system. The acquisition of this new technology was conducted through requirements analysis, then the analysis and design of the new system architecture, followed by the implementation, and the evaluation of the system by the users. The results found that the extended metaverse was divided into three tiers that created labels, characters, and virtual objects. Furthermore, the linking tier combined the 3D elements, and the deployment tier compiled the results of the link to use all three parts by using the Blender program, Godot Engine, and PHP + WebGL as their respective key mechanisms. This system was tested in Suphan Buri province, Thailand, which was evaluated by 428 users. The results of the metaverse satisfaction, created tourism experience, and overall satisfaction of the variation of the satisfaction of using the metaverse were 86.0%, 79.7%, and 92.9%, respectively. The relative Chi-square (χ2/df) of 1.253 indicated that the model was suitable. The comparative fit index (CFI) was 0.984, the goodness-of-fit index (GFI) was 0.998, and the model based on the research hypothesis was consistent with the empirical data. The root mean square error of approximation (RMSEA) was 0.024. In conclusion, the extended metaverse is more flexible than other platforms and also creates the user’s satisfaction and tourism experience in the smart destination to support sustainable tourism.
A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

Deep learning techniques, in particular generative models, have taken on great importance in medical image analysis. This paper surveys fundamental deep learning concepts related to medical image generation. It provides concise overviews of studies which use some of the latest state-of-the-art models from last years applied to medical images of different injured body areas or organs that have a disease associated with (e.g., brain tumor and COVID-19 lungs pneumonia). The motivation for this study is to offer a comprehensive overview of artificial neural networks (NNs) and deep generative models in medical imaging, so more groups and authors that are not familiar with deep learning take into consideration its use in medicine works. We review the use of generative models, such as generative adversarial networks and variational autoencoders, as techniques to achieve semantic segmentation, data augmentation, and better classification algorithms, among other purposes. In addition, a collection of widely used public medical datasets containing magnetic resonance (MR) images, computed tomography (CT) scans, and common pictures is presented. Finally, we feature a summary of the current state of generative models in medical image including key features, current challenges, and future research paths.
Secure Intelligent Fuzzy Blockchain Framework: Effective Threat Detection in IoT Networks

Integrating blockchain into the Internet of Things (IoT) for security is a new development in computational communication systems. While security threats are changing their strategies and constructing new threats on blockchain-based IoT systems. Also, in combining blockchain with IoT networks, malicious transactions and active attacks deliver more vulnerabilities, privacy issues, and security threats. The concept of blockchain-based IoT attacks is a hot topic in both IoT and blockchain disciplines. Network attacks are a type of security and privacy threat and cover the exact scope of threats related to the combination of IoT and blockchain. Even though blockchain has potential security benefits, new cyberattacks have emerged that make blockchain alone insufficient to deal with threats and attacks in IoT networks since vagueness and ambiguity issues are unavoidable in IoT data. The heterogeneous nature of IoT sources has made uncertainty a critical issue in IoT networks. Deep Learning (DL) models have difficulty dealing with uncertainty issues and cannot manage them efficiently as an essential tool in security techniques. Thus, we need better security, privacy, and practical approaches, such as efficient threat detection against network attacks in blockchain-based IoT environments. Also helpful to consider fuzzy logic to tackle deterministic issues when DL models face uncertainty. This paper designs and implements a secure, intelligent fuzzy blockchain framework. This framework utilizes a novel fuzzy DL model, optimized adaptive neuro-fuzzy inference system (ANFIS)-based attack detection, fuzzy matching (FM), and fuzzy control system (FCS) for detection of network attacks. The proposed fuzzy DL applies the fuzzy Choquet integral to have a powerful nonlinear aggregation function in the detection. We use metaheuristic algorithms to optimize the attack detection error function in ANFIS. We also validate transactions via FM to tackle fraud detection and efficiency in the blockchain layer. This framework is the first secure, intelligent fuzzy blockchain framework that identifies and detects security threats while considering uncertainty issues in IoT networks and having more flexibility in decision-making and accepting transactions in the blockchain layer. Evaluation results verify the efficiency of the blockchain layer in throughput and latency metrics and the intelligent fuzzy layer in performance metrics (Accuracy, Precision, Recall, and F1-Score) in threat detection on both blockchain and IoT network sides. Additionally, FCS demonstrates that we obtain an effective system (stable model) for threat detection in blockchain-based IoT networks.
Novel image encryption scheme based on chaotic signals with finite-precision error

This paper presents a novel framework for generating new chaotic signals for image encryption that is based on the finite precision of computers. First, we select a system from a number of chaotic systems. Next, we obtain a trajectory of this system. Then, another trajectory is obtained by perturbing the initial values of the same chaotic system. Finally, we calculate the evolution error of two trajectories to obtain a new chaotic signal. Our method is successfully applied to simulate three new chaotic signals for image encryption. A novel image cryptography algorithm based on new chaotic signals and true random numbers is developed. These circular diffusion and local versus global scrambling methods are designed. The results show that the proposed method is effective and exhibits a higher level of security than other sophisticated methods.
NeRFReN: Neural Radiance Fields with Reflections

Neural Radiance Fields (NeRF) has achieved unprece-dented view synthesis quality using coordinate-based neu-ral scene representations. However, NeRF's view depen-dency can only handle simple reflections like highlights but cannot deal with complex reflections such as those from glass and mirrors. In these scenarios, NeRF models the virtual image as real geometries which leads to inaccurate depth estimation, and produces blurry renderings when the multi-view consistency is violated as the reflected objects may only be seen under some of the viewpoints. To over-come these issues, we introduce NeRFReN, which is built upon NeRF to model scenes with reflections. Specifically, we propose to split a scene into transmitted and reflected components, and model the two components with separate neural radiance fields. Considering that this decomposition is highly under-constrained, we exploit geometric priors and apply carefully-designed training strategies to achieve reasonable decomposition results. Experiments on various self-captured scenes show that our method achieves high-quality novel view synthesis and physically sound depth es-timation results while enabling scene editing applications.
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

Few-shot learning (FSL) is an important and topical problem in computer vision that has motivated extensive research into numerous methods spanning from sophisticated metalearning methods to simple transfer learning baselines. We seek to push the limits of a simple-but-effective pipeline for real-worldfew-shot image classification in practice. To this end, we explore few-shot learning from the perspective of neural architecture, as well as a three stage pipeline of pre-training on external data, meta-training with labelled few-shot tasks, and task-specific fine-tuning on unseen tasks. We investigate questions such as: ① How pre-training on external data benefits FSL? ② How state of the art transformer architectures can be exploited? and ③ How to best exploit finetuning? Ultimately, we show that a simple transformer-based pipeline yields surprisingly good performance on standard benchmarks such as Mini-ImageNet, CIFAR-FS, CDFSL and Meta-Dataset. Our code is available at https://hushell.github.io/pmf.
X-Pool: Cross-Modal Language-Video Attention for Text-Video Retrieval

In text-video retrieval, the objective is to learn a cross-modal similarity function between a text and a video that ranks relevant text-video pairs higher than irrelevant pairs. However, videos inherently express a much wider gamut of information than texts. Instead, texts often capture sub-regions of entire videos and are most semantically similar to certain frames within videos. Therefore, for a given text, a retrieval model should focus on the text's most semantically similar video sub-regions to make a more relevant comparison. Yet, most existing works aggregate entire videos with-out directly considering text. Common text-agnostic ag-gregations schemes include mean-pooling or self-attention over the frames, but these are likely to encode misleading vi-sual information not described in the given text. To address this, we propose a cross-modal attention model called X-Pool that reasons between a text and the frames of a video. Our core mechanism is a scaled dot product attention for a text to attend to its most semantically similar frames. We then generate an aggregated video representation conditioned on the text's attention weights over the frames. We evaluate our method on three benchmark datasets of MSR-VTT, MSVD and LSMDC, achieving new state-of-the-art re-sults by up to 12% in relative improvement in Recall@ 1. Our findings thereby highlight the importance of joint text-video reasoning to extract important visual cues according to text. Full code and demo can be found at: layer6ai-labs.github.iolxpooll.
Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer

Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel DualStyleGAN with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an intrinsic style path and a new extrinsic style path, respectively. The del-icately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible stylecontrol. Code is available at https://github.com/williamyang1991/DualStyleGAN.
CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation

We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based framework for panoptic segmentation designed around clustering. It rethinks the existing transformer architectures used in segmentation and detection; CMT-DeepLab considers the object queries as cluster centers, which fill the role of grouping the pixels when applied to segmentation. The clustering is computed with an alternating procedure, by first assigning pixels to the clusters by their feature affinity, and then updating the cluster centers and pixel features. Together, these operations comprise the Clustering Mask Transformer (CMT) layer, which produces cross-attention that is denser and more consistent with the final segmentation task. CMT-DeepLab improves the performance over prior art significantly by 4.4% PQ, achieving a new state-of-the-art of 55.7% PQ on the COCO test-dev set.
Learn from Others and Be Yourself in Heterogeneous Federated Learning

Federated learning has emerged as an important distributed learning paradigm, which normally involves collaborative updating with others and local updating on private data. However, heterogeneity problem and catastrophic forgetting bring distinctive challenges. First, due to non-i.i.d (identically and independently distributed) data and heterogeneous architectures, models suffer performance degradation on other domains and communication barrier with participants models. Second, in local updating, model is separately optimized on private data, which is prone to overfit current data distribution and forgets previously acquired knowledge, resulting in catastrophic forgetting. In this work, we propose FCCL (Federated CrossCorrelation and Continual Learning). For heterogeneity problem, FCCL leverages unlabeled public data for communication and construct cross-correlation matrix to learn a generalizable representation under domain shift. Mean- while, for catastrophic forgetting, FCCL utilizes knowledge distillation in local updating, providing inter and intra domain information without leaking privacy. Empirical results on various image classification tasks demonstrate the effectiveness of our method and the efficiency of modules.