Reviewerly/exHarmony huggingface dataset:
A Survey: Security, Transparency, and Scalability Issues of NFT’s and Its Marketplaces

Non-fungible Tokens (NFTs) are ownership records stored on a blockchain, and they are typically digital items such as photos and videos. In many ways, an NFT is like a conventional proof-of-purchase document, such as a paper invoice or an electronic receipt. NFTs are attractive among other things because of verifiability; each sale is recorded as a blockchain transaction, allowing ownership to be tracked. Also, NFTs can be used to transfer digital assets between two mutually distrusting parties, since both the crypto payment and the asset transfer take place in one transaction. With NFTs, all marketplaces can freely trade with the help of decentralized applications (DApps). It is currently estimated that there are over 245 NFT marketplaces (NFTM) listed with over 1000 blockchains as of August 2022 with 68 million blockchain wallet users. With the expansion of markets, they must face challenges and issues. The objective of this review is to study the market dynamics of NFT ecosystems. It also focuses on technical components that enable NFTs and their marketplace. The review provides a deeper understanding of its components, how they are related, and why they are important. The paper analyses the challenges faced by NFTs and marketplaces in terms of security, transparency, scalability, and the consequences leading to these issues and how they will address them, as well as future opportunities.
SAPENet: Self-Attention based Prototype Enhancement Network for Few-shot Learning

Few-shot learning considers the problem of learning unseen categories given only a few labeled samples. As one of the most popular few-shot learning approaches, Prototypical Networks have received considerable attention owing to their simplicity and efficiency. However, a class prototype is typically obtained by averaging a few labeled samples belonging to the same class, which treats the samples as equally important and is thus prone to learning redundant features. Herein, we propose a self-attention based prototype enhancement network (SAPENet) to obtain a more representative prototype for each class. SAPENet utilizes multi-head self-attention mechanisms to selectively augment discriminative features in each sample feature map, and generates channel attention maps between intra-class sample features to attentively retain informative channel features for that class. The augmented feature maps and attention maps are finally fused to obtain representative class prototypes. Thereafter, a local descriptor-based metric module is employed to fully exploit the channel information of the prototypes by searching k similar local descriptors of the prototype for each local descriptor in the unlabeled samples for classification. We performed experiments on multiple benchmark datasets: miniImageNet, tieredImageNet, and CUB-200-2011. The experimental results on these datasets show that SAPENet achieves a considerable improvement compared to Prototypical Networks and also outperforms related state-of-the-art methods.
A Survey of Data Marketplaces and Their Business Models

Data is becoming an indispensable production factor for the modern economy, matching or exceeding in importance traditional factors such as land, infrastructure, labor and capital. As part of this, a wide range of applications in different sectors require huge amounts of information to feed machine learning models and algorithms responsible for critical roles in production chains and business processes. A variety of data trading entities including, but not limited to data marketplaces, have thus appeared in order to satisfy and match the offer with the demand for data. In this paper, we present the results and conclusions from a comprehensive survey covering 190 commercial data trading entities, the types of data that their trade, as well as their business models and the technologies that they rely upon. We also point to promising open research questions in the areas of data marketplace federation, pricing, and data ownership protection that could benefit the growing ecosystem of data trading entities that we have surveyed.
AOBERT: All-modalities-in-One BERT for multimodal sentiment analysis

Multimodal sentiment analysis utilizes various modalities such as Text, Vision and Speech to predict sentiment. As these modalities have unique characteristics, methods have been developed for fusing features. However, the overall modality characteristics are not guaranteed, because traditional fusion methods have some loss of intra-modality and inter-modality. To solve this problem, we introduce a single-stream transformer, All-modalities-in-One BERT (AOBERT). The model is pre-trained on two tasks simultaneously: Multimodal Masked Language Modeling (MMLM) and Alignment Prediction (AP). The dependency and relationship between modalities can be determined using two pre-training tasks. AOBERT achieved state-of-the-art results on the CMU-MOSI, CMU-MOSEI, and UR-FUNNY datasets. Furthermore, ablation studies that validated combinations of modalities, effects of MMLM and AP and fusion methods confirmed the effectiveness of the proposed model.
LACN: A lightweight attention-guided ConvNeXt network for low-light image enhancement

Images captured under low-light conditions usually have poor visual quality, and hence greatly reduce the accuracy of subsequent tasks such as image segmentation and detection. In the low-light image enhancement task, noises in the dark areas are generally amplified while the images’ brightness is enhanced. It should be pointed out that many deep learning methods cannot effectively suppress the noise at this stage and capture important feature information. To address the above problem, this paper proposes a Lightweight Attention-guided ConvNeXt Network (LACN) for low-light image enhancement. A novel Attention ConvNeXt Module (ACM) is first proposed by introducing a parameter-free attention module (i.e. SimAM) into the ConvNeXt backbone network. Then, a nontrivial lightweight network LACN based on a multi-attention mechanism is established through stacking two ACMs and fusing their features. In what follows, an improved hybrid attention mechanism, Selective Kernel Attention Module (SKAM), is adopted to effectively extract both global and local information. Such a module realizes the evaluation of lighting conditions for the whole image and the adaptive adjustment of the receptive field. Finally, through the feature fusion module, the features of different stages are aggregated to improve the ability of network to retain color information. Numerous experiments on low-light image enhancement are implemented via comparison with other state-of-the-art methods. Experiments show that the proposed method significantly improves the brightness and contrast of low-illumination images, preserves color information, and suppresses the generation of noises after image brightening.
Unsupervised graph-level representation learning with hierarchical contrasts

Unsupervised graph-level representation learning has recently shown great potential in a variety of domains, ranging from bioinformatics to social networks. Plenty of graph contrastive learning methods have been proposed to generate discriminative graph-level representations recently. They typically design multiple types of graph augmentations and enforce a graph to have consistent representations under different views. However, these techniques mostly neglect the intrinsic hierarchical structure of the graph, resulting in a limited exploration of semantic information for graph representation. Moreover, they often rely on a large number of negative samples to prevent collapsing into trivial solutions, while a great need for negative samples may lead to memory issues during optimization in graph domains. To address the two issues, this paper develops an unsupervised graph-level representation learning framework named Hierarchical Graph Contrastive Learning (HGCL), which investigates the hierarchical structural semantics of a graph at both node and graph levels. Specifically, our HGCL consists of three parts, i.e., node-level contrastive learning, graph-level contrastive learning, and mutual contrastive learning to capture graph semantics hierarchically. Furthermore, the Siamese network and momentum update are further involved to release the demand for excessive negative samples. Finally, the experimental results on both benchmark datasets for graph classification and large-scale OGB datasets for transfer learning demonstrate that our proposed HGCL significantly outperforms a broad range of state-of-the-art baselines.
Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing

In this paper, we study a new latency optimization problem for blockchain-based federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile devices (MDs) communicate with a set of edge servers (ESs) to handle both machine learning (ML) model training and block mining simultaneously. To assist the ML model training for resource-constrained MDs, we develop an offloading strategy that enables MDs to transmit their data to one of the associated ESs. We then propose a new decentralized ML model aggregation solution at the edge layer based on a consensus mechanism to build a global ML model via peer-to-peer (P2P)-based blockchain communications. Blockchain builds trust among MDs and ESs to facilitate reliable ML model sharing and cooperative consensus formation, and enables rapid elimination of manipulated models caused by poisoning attacks. We formulate latency-aware BFL as an optimization aiming to minimize the system latency via joint consideration of the data offloading decisions, MDs’ transmit power, channel bandwidth allocation for MDs’ data offloading, MDs’ computational allocation, and hash power allocation. Given the mixed action space of discrete offloading and continuous allocation variables, we propose a novel deep reinforcement learning scheme with a parameterized advantage actor critic algorithm. We theoretically characterize the convergence properties of BFL in terms of the aggregation delay, mini-batch size, and number of P2P communication rounds. Our numerical evaluation demonstrates the superiority of our proposed scheme over baselines in terms of model training efficiency, convergence rate, system latency, and robustness against model poisoning attacks.
Enhancing differential evolution algorithm using leader-adjoint populations

The performance of differential evolution (DE) significantly depends on the settings of mutation strategies and control parameters. Inappropriate settings may cause an imbalance between exploration and exploitation of the algorithm, thus resulting in two extremes: premature convergence and failure to converge. In this paper, we propose a Differential Evolution using Leader-Adjoint populations (LADE), which simultaneously integrates four mutation strategies to meet the needs of exploration and exploitation at different evolutionary stages. In LADE, the population in each generation is divided into leader population and adjoint population by using a leader-adjoint model. The leader population adopts two mutation strategies with strong exploration ability to maintain the diversity and avoid premature convergence, while the adjoint population uses the other two mutation strategies with strong exploitation ability to promote convergence and avoid stagnation. In addition, the interaction and collaboration between both populations are achieved through the sharing between their individuals, thus achieving a good trade-off between exploration and exploitation. The performance of LADE is evaluated on single-objective benchmark problems of the 2017 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation (IEEE CEC2017). Experimental results indicate that LADE shows competitive convergence performance, and outperforms various state-of-the-art DE variants and two well-known metaheuristics.
Blockchain applications for secured and resilient supply chains: A systematic literature review and future research agenda

Firms are using blockchain technology to prevent fraudulent activities through consensus mechanisms and help identify data tampering through its underlying characteristics, such as immutability, encryption transparency, and operational resilience. However, a holistic review of blockchain application in building a cyber-secure supply chain remains elusive, which can impede the advancement of the field when competing arguments are made, and pure replications are initiated. Therefore, the present study carries a systematic literature review to assess the state of literature review on the significance of blockchain in developing a cyber-secure and resilient supply chain. A systematic review of 122 peer-reviewed papers published between 2012 and 2022 has been conducted using a structured methodological approach. Further, the Theory, Context and Methodology (TCM) framework has been applied to suggest avenues for future research. Our analysis reveals six dominant themes related to blockchain and its adoption, blockchain application for resilience, blockchain application for cyber-security, blockchain and its role in intermediation, disintermediation and reintermediation, challenges of blockchain adoption and benefits of blockchain applications in the supply chain. Although the role of blockchain application in the cyber secure and resilient supply chain has been recognised, very little attention has been given to contextual factors affecting its adoption and consequences on key performance parameters. The present study contributes to the literature by synthesising the prime findings of reviewed papers and the managerial implications, thereby giving a holistic view of the existing research and presenting an array of future opportunities for firms to adopt blockchain technology in the supply chain.
Host load prediction in cloud computing with Discrete Wavelet Transformation (DWT) and Bidirectional Gated Recurrent Unit (BiGRU) network

Providing pay-as-you-go storage and computing services have contributed to the widespread adoption of cloud computing. Using virtualization technology, cloud service providers can execute several instances on a single physical server, maximizing resource utilization. A challenging issue in cloud data centers is that available resources are rarely fully utilized. The server utilization rate is poor and often below 30%. An accurate host workload prediction enhances resource allocation resulting in more efficient resource utilization. Recently, numerous methods based on deep learning for predicting cloud computing workload have been developed. An efficient strategy must predict long-term dependencies on nonstationary host workload data and be quick enough to respond to incoming requests. This study employs a Bidirectional Gated-Recurrent Unit (BiGRU), Discrete Wavelet Transformation (DWT), and an attention mechanism to improve the host load prediction accuracy. DWT is used to decompose input data into sub-bands with different frequencies and to extract patterns from nonlinear and nonstationary data in order to improve prediction accuracy. The extracted features are fed into BiGRu to predict future workload. The attention mechanism is used in order to extract the temporal correlation features. This hybrid model was evaluated with cluster data sets from Google and Alibaba. Experimental results reveal that our method improves prediction accuracy by 3% to 56% compared to a variety of state-of-the-art methods.
The interaction effect of emoji and social media content on consumer engagement: A mixed approach on peer-to-peer accommodation brands

This study examines how the interaction between emoji (emotional vs semantic) and social media content (aesthetic experience vs promotion) influences consumer engagement in tourism brands' digital communication. Based on real Twitter data and an online experiment, our results show that, for aesthetic experience content, emotional emoji elicits more consumer engagement than semantic emoji does. Moreover, emotional emoji increases consumer engagement by eliciting a higher level of emotional responses for aesthetic experience content, whereas semantic emoji enhances consumer engagement by generating greater credibility for promotion content. This study contributes to the textual paralanguage literature in tourism by offering theoretical explanations on how and why the matching effects of emoji and content type on consumer engagement occur in tourism brands’ digital communication. This study also provides practical implications for tourism social media marketers on how to increase consumer engagement via the appropriate use of emoji.
Perceiving distance in virtual reality: theoretical insights from contemporary technologies

Decades of research have shown that absolute egocentric distance is underestimated in virtual environments (VEs) when compared with the real world. This finding has implications on the use of VEs for applications that require an accurate sense of absolute scale. Fortunately, this underperception of scale can be attenuated by several factors, making perception more similar to (but still not the same as) that of the real world. Here, we examine these factors as two categories: (i) experience inherent to the observer, and (ii) characteristics inherent to the display technology. We analyse how these factors influence the sources of information for absolute distance perception with the goal of understanding how the scale of virtual spaces is calibrated. We identify six types of cues that change with these approaches, contributing both to a theoretical understanding of depth perception in VEs and a call for future research that can benefit from changing technologies. This article is part of the theme issue 'New approaches to 3D vision'.
A Survey on Deep-Learning-Based LiDAR 3D Object Detection for Autonomous Driving

LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast decision-making when driving. The sensor is used in the perception system, especially object detection, to understand the driving environment. Although 2D object detection has succeeded during the deep-learning era, the lack of depth information limits understanding of the driving environment and object location. Three-dimensional sensors, such as LiDAR, give 3D information about the surrounding environment, which is essential for a 3D perception system. Despite the attention of the computer vision community to 3D object detection due to multiple applications in robotics and autonomous driving, there are challenges, such as scale change, sparsity, uneven distribution of LiDAR data, and occlusions. Different representations of LiDAR data and methods to minimize the effect of the sparsity of LiDAR data have been proposed. This survey presents the LiDAR-based 3D object detection and feature-extraction techniques for LiDAR data. The 3D coordinate systems differ in camera and LiDAR-based datasets and methods. Therefore, the commonly used 3D coordinate systems are summarized. Then, state-of-the-art LiDAR-based 3D object-detection methods are reviewed with a selected comparison among methods.
Smart contract-based security architecture for collaborative services in municipal smart cities

The Internet of Things (IoT) can provide intelligent and effective solutions to various applications with higher accuracy that requires less or no human intervention. Smart Cities are one of the significant applications of the IoT comprising a collection of various services such as intelligent transportation, waste management, smart homes, etc. These heterogeneous services offer a wide range of collaborative applications in smart cities. A smart municipality in a smart city is a concept in which a digital municipal corporation is developed to provide comprehensive local government collaboration services based on digitization and automation aiming towards raising the living standards of citizens. Interoperability between heterogeneous services for collaborative tasks creates challenges for data security and privacy. Ensuring integrity and confidentiality of information is critical, and reliable data is essential to both the government and its citizens. In this paper, we proposed a service security architecture based on authentication and authorization for constrained environments during collaborative tasks for Software Defined Networking (SDN) and smart contract-enabled municipal smart cities. The proposed collaborative service security framework is being tested on the Multichain Blockchain networks. We present a novel method for using smart contracts in multichain blockchains for data security during collaborative tasks in smart city municipal architecture. The proposed security solution is based on the dynamism of smart contracts to govern and control all interactions and transactions securely between different heterogeneous IoT networks. We implemented a supportive use case for collaborative services in an SDN-enabled IoT architecture to evaluate the feasibility of the proposed service security architecture.
Clinical Validation of Artificial Intelligence–Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection

Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking.To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance.Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads.Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase.This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.