Reviewerly/exHarmony huggingface dataset:
AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer

Artificial intelligence (AI) applications in oncology have been developed rapidly with reported successes in recent years. This work aims to evaluate the performance of deep convolutional neural network (CNN) algorithms for the classification and detection of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) in oral photographic images. A dataset comprising 980 oral photographic images was divided into 365 images of OSCC, 315 images of OPMDs and 300 images of non-pathological images. Multiclass image classification models were created by using DenseNet-169, ResNet-101, SqueezeNet and Swin-S. Multiclass object detection models were fabricated by using faster R-CNN, YOLOv5, RetinaNet and CenterNet2. The AUC of multiclass image classification of the best CNN models, DenseNet-196, was 1.00 and 0.98 on OSCC and OPMDs, respectively. The AUC of the best multiclass CNN-base object detection models, Faster R-CNN, was 0.88 and 0.64 on OSCC and OPMDs, respectively. In comparison, DenseNet-196 yielded the best multiclass image classification performance with AUC of 1.00 and 0.98 on OSCC and OPMD, respectively. These values were inline with the performance of experts and superior to those of general practictioners (GPs). In conclusion, CNN-based models have potential for the identification of OSCC and OPMDs in oral photographic images and are expected to be a diagnostic tool to assist GPs for the early detection of oral cancer.
Trustworthy Federated Learning via Blockchain

The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustworthy AI to guarantee the privacy and security with reliable decisions. As a nascent branch for trustworthy AI, federated learning (FL) has been regarded as a promising privacy preserving framework for training a global AI model over collaborative devices. However, security challenges still exist in the FL framework, e.g., Byzantine attacks from malicious devices, and model tampering attacks from malicious server, which will degrade or destroy the accuracy of trained global AI model. In this article, we shall propose a decentralized blockchain-based FL (B-FL) architecture by using a secure global aggregation algorithm to resist malicious devices, and deploying a practical Byzantine fault tolerance consensus protocol with high effectiveness and low energy consumption among multiple edge servers to prevent model tampering from the malicious server. However, to implement B-FL system at the network edge, multiple rounds of cross-validation in blockchain consensus protocol will induce long training latency. We thus formulate a network optimization problem that jointly considers bandwidth and power allocation for the minimization of long-term average training latency consisting of progressive learning rounds. We further propose to transform the network optimization problem as a Markov decision process and leverage the deep reinforcement learning (DRL)-based algorithm to provide high system performance with low computational complexity. Simulation results demonstrate that B-FL can resist malicious attacks from edge devices and servers, and the training latency of B-FL can be significantly reduced by the DRL-based algorithm compared with the baseline algorithms.
Tiny object detection with context enhancement and feature purification

Tiny object detection is one of the challenges in the field of object detection, which can be applied in a variety of fields. Thanks to the advances in deep learning, significant improvement has been made in image object detection. However, the performance of tiny object detection still needs to be considerably enhanced. In this paper, we proposed a novel feature pyramid composite neural network structure comprising two modules: the context enhancement module (CEM) and feature purification module (FPM). The top-to-bottom input of the feature pyramid network into the multi-scale dilated convolution features in the CEM can augment the context information. When fusing multi-scale features in the FPM, the feature purification procedures for channel and space dimensions are employed to eliminate conflicting information, and tiny objects are more noticeable in contradictory information. Furthermore, a new data-enhancement strategy is introduced to increase the contribution of tiny objects in the loss function, which is named copy-reduce-paste and improves the balance of training samples. Overall, the experiments on the VOC dataset show that the APs score can reach 16.9% and the IOU is 0.5:0.95 for the suggested method. The APs score is more than 3.9% that of YOLOV4, more than 7.7% that of CenterNet, and more than 5.3% that of RefineDet. On TinyPerson dataset, our APs score is more than 0.8% that of YOLOV5, providing a new alternative solution for the tiny-object detection research community.
Using chatbots to scaffold EFL students’ argumentative writing

Research suggests that English as a foreign language (EFL) students’ argumentative writing benefits from interactions with peers. However, finding an ideal partner to interact with can be difficult in practice, and chatbots have been suggested as a potential solution to this problem. Chatbots are a form of artificial intelligence (AI) designed to engage in turn-by-turn conversations with human users. In this paper, we propose a chatbot-assisted approach to the teaching and learning of argumentative writing. We describe Argumate, a novel chatbot system that we developed to scaffold students’ argument construction, and we discuss the advantages and limitations of using this system to assist students in producing high-quality argumentative writing. • Integration of artificial intelligence (AI) chatbots into writing classrooms. • Proposal of a chatbot-assisted approach to argumentative writing. • Design and development of a chatbot system named Argumate. • Argumate can help with students’ idea generation and counterargument integration.
Privacy-preserving federated learning for residential short-term load forecasting

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting.
An Optimized Learning Model Augment Analyst Decisions for Seismic Source Discrimination

Efficient handling and planning for the urban regions’ sustainable development require a vast range of up-to-date and thematic information. Besides, obtaining an uncontaminated catalog of seismic activity is desirable to study the earthquake (EQ) clusters’ spatial allocation, which is a key role in mitigating seismic hazards and alleviating EQ losses by enhancing the assessment of seismic hazards. This article considers the northeastern part of Egypt where the seismicity catalog is contaminated by quarry blasts (QBs) operated throughout the mapped area. Consequently, it is desirable to discriminate these QBs from the EQs for genuine seismicity and hazard analysis. Accordingly, we provide an efficient machine learning (ML) model for decontaminating the seismicity database so that EQ clusters can be properly delineated by relying on 870 events (EQs and QBs) observed by only one seismic station called “GLL,” a member of the Egyptian National Seismic Network (ENSN). The model focuses on magnitudes < 3 that have high uncertainty of being EQs or QBs and take a long time for analysis. The approach examines several linear and nonlinear ML models and, finally, selects the best model with only two features leading to the optimal classification between the EQs and QBs. The optimization process is accomplished throughout two stages. The obtained results prove that the proposed scheme achieves 100% discrimination between the EQs and QBs relying on the extreme gradient boosting (XGB) model.
A Perception-Aware Decomposition and Fusion Framework for Underwater Image Enhancement

This paper presents a perception-aware decomposition and fusion framework for underwater image enhancement (UIE). Specifically, a general structural patch decomposition and fusion (SPDF) approach is introduced. SPDF is built upon the fusion of two complementary pre-processed inputs in a perception-aware and conceptually independent image space. First, a raw underwater image is pre-processed to produce two complementary versions including a contrast-corrected image and a detail-sharpened image. Then, each of them is decomposed into three conceptually independent components, i.e., mean intensity, contrast, and structure, via structural patch decomposition (SPD). Afterwards, the corresponding components are fused using tailored strategies. The three components after fusion are finally integrated via inverting the decomposition to reconstruct a final enhanced underwater image. The main advantage of SPDF is that two complementary pre-processed images are fused in a perception-aware and conceptually independent image space and the fusions of different components can be performed separately without any interactions and information loss. Comprehensive comparisons on two benchmark datasets demonstrate that SPDF outperforms several state-of-the-art UIE algorithms qualitatively and quantitatively. Moreover, the effectiveness of SPDF is also verified on another two relevant tasks, i.e., low-light image enhancement and single image dehazing. The code will be made available soon.
Understanding public opinions on social media for financial sentiment analysis using AI-based techniques

The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the market is driven by public opinions, and social media today provides an encouraging platform to share ideas and views; organizations and policy-makers could use the natural language processing (NLP) technology of AI to analyze public sentiments. Recently, a new and moderately unconventional instrument known as non-fungible tokens (NFTs) is emerging as an upcoming business market. Unlike the stock market, no precise quantitative parameters exist for the price determination of NFTs. Instead, NFT markets are driven more by public opinion, expectations, the perception of buyers, and the goodwill of creators. This study evaluates human emotions on the social media platforms Twitter posted by the public relating to NFTs. Additionally, this study conducts secondary market analysis to determine the reasons for the growing acceptance of NFTs through sentiment and emotion analysis. We segregate tweets using Pearson Product-Moment Correlation Coefficient (PPMCC) and study 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) along with Positive and Negative sentiments. Tweets majorly contained positive sentiment (∼ 72%), and positive emotions like anticipation and trust were found to be predominant all over the world. This is the first of its kind financial and emotional analysis of tweets pertaining to NFTs to the best of our understanding.
Gradient-based optimizer improved by Slime Mould Algorithm for global optimization and feature selection for diverse computation problems

Optimization algorithms have shown significant advantages in solving diverse several real-world problems, especially where are limitations in computations and hardware requirements. However, they might get trapped in local optima or fast convergence. To solve these challenges, the exploration ability of an optimization algorithm should be evaluated and improved. This paper proposes a new approach for solving global optimization and feature selection problems to overcome these drawbacks and balance exploitation and exploration abilities. We propose a hybrid approach combining both, Gradient-Based Optimizer (GBO) using Slime Mould Algorithm (SMA), which exploits the strength factors of Gradient-Based Optimizer and Slime Mould Algorithm. The SMA is applied as a local search for the GBO to enhance its ability to explore more regions in the search space. This strategy is employed to achieve a balance between both exploration and exploitation. Two main experiments are applied to evaluate the performance of the GBOSMA. The first one solves a well-known global optimization problem (CEC2017), whereas the second one solves feature selection problems using several benchmark datasets. The GBOSMA is compared to the standard GBO, SMA, and the most recent optimization algorithms in both experiments. The results also show that the GBOSMA approach obtains promising results in both experiments outperforming other algorithms. Comparing with eight well-known optimization algorithms, the results (for both global optimization and feature selection problems) demonstrate that the GBOSMA overcomes others in terms of performance, speed and stability.
An overview on cross-chain: Mechanism, platforms, challenges and advances

After years of in-depth development of blockchain, various blockchains with different characteristics and suitable for different application scenarios coexist in large numbers. Due to the isolation of blockchains and the high degree of heterogeneity between chains, value transfer and data communication between existing blockchains are facing unprecedented challenges, and the phenomenon of value isolated island is gradually emerging. The cross-chain technology of blockchain is an important technical means to realize the interconnection of blockchains and improve the interoperability and scalability of blockchains. In this paper, the development and application of blockchain cross-chain technology are studied, the background and significance of cross-chain technology are described, the research status of cross-chain technology is expounded, the current mainstream cross-chain technologies and cross-chain projects are introduced, the mentioned cross-chain technologies and cross-chain projects are analyzed and compared. In addition, this paper also summarizes the difficulties existing in the current cross-chain technology and provides solutions for reference, so as to lead to the discussion of the development trend of cross-chain technology, and finally complete the summary of the research content of the full text and the prospect of cross-chain technology. It is hoped that the relevant summary results can help relevant researchers and practitioners quickly grasp the research progress in the field of blockchain interoperability, and obtain relevant knowledge and application methods in this field.
Fusing topology contexts and logical rules in language models for knowledge graph completion

Knowledge graph completion (KGC) aims to infer missing facts based on the observed ones, which is significant for many downstream applications. Given the success of deep learning and pre-trained language models (LMs), some LM-based methods are proposed for the KGC task. However, most of them focus on modeling the text of fact triples and ignore the deeper semantic information (e.g., topology contexts and logical rules) that is significant for KG modeling. For such a reason, we propose a unified framework FTL-LM to Fuse Topology contexts and Logical rules in Language Models for KGC, which mainly contains a novel path-based method for topology contexts learning and a variational expectation–maximization (EM) algorithm for soft logical rule distilling. The former utilizes a heterogeneous random-walk to generate topology paths and further reasoning paths that can represent topology contexts implicitly and can be modeled by a LM explicitly. The strategies of mask language modeling and contrastive path learning are introduced to model these topology contexts. The latter implicitly fuses logical rules by a variational EM algorithm with two LMs. Specifically, in the E-step, the triple LM is updated under the supervision of observed triples and valid hidden triples verified by the fixed rule LM. And in the M-step, we fix the triple LM and fine-tune the rule LM to update logical rules. Experiments on three common KGC datasets demonstrate the superiority of the proposed FTL-LM, e.g., it achieves 2.1% and 3.1% [email protected] improvement over the state-of-the-art LM-based model LP-BERT in the WN18RR and FB15k-237, respectively.
Examining the Factors Affecting the Adoption of Blockchain Technology in the Banking Sector: An Extended UTAUT Model

Technology innovation has dramatically transformed banks over time. Digital innovation in the banking sector began with the introduction of money to replace barter systems, and then gradually replaced wax seals with digital signatures. One such disruptive innovation that is transforming the banking sector around the world is blockchain technology (BCT). The banking sector in India has also started adopting blockchain technology in various financial transactions. However, they are encountering some difficulties in adapting to and implementing this new technology. The successful and speedy adoption of blockchain in banking largely depends on the users’ intention to use the services. Therefore, this study extended “the unified theory of acceptance and use of technology” (UTAUT) to understand the significant predictors of the bankers’ intention to use blockchain technology. The data was collected from leading banking institutions and FinTech firms in the country to empirically test and validate the extended model. The results found that facilitating conditions, performance expectancy, and initial trust, are the significant antecedents to predicting the bankers’ intention to use blockchain in banking transactions. The study also established the significant mediating role of initial trust in predicting usage intention to use blockchain. This study’s results would help government authorities, decision-makers, and technocrats to improve banking instructions for the speedy and smooth adoption of blockchain technology. The study suggested an extended UTAUT model that incorporates contextual factors based on the scope and usage of blockchain in Indian banking activities. The study helped to identify the key factors influencing blockchain adoption among Indian bankers. The proposed model and the findings make more sense in promoting the adoption of blockchain in the Indian banking sector.
A Survey on Deep Transfer Learning and Beyond

Deep transfer learning (DTL), which incorporates new ideas from deep neural networks into transfer learning (TL), has achieved excellent success in computer vision, text classification, behavior recognition, and natural language processing. As a branch of machine learning, DTL applies end-to-end learning to overcome the drawback of traditional machine learning that regards each dataset individually. Although some valuable and impressive general surveys exist on TL, special attention and recent advances in DTL are lacking. In this survey, we first review more than 50 representative approaches of DTL in the last decade and systematically summarize them into four categories. In particular, we further divide each category into subcategories according to models, functions, and operation objects. In addition, we discuss recent advances in TL in other fields and unsupervised TL. Finally, we provide some possible and exciting future research directions.
Cooperated Spectral Low-Rankness Prior and Deep Spatial Prior for HSI Unsupervised Denoising

Model-driven methods and data-driven methods have been widely developed for hyperspectral image (HSI) denoising. However, there are pros and cons in both model-driven and data-driven methods. To address this issue, we develop a self-supervised HSI denoising method via integrating model-driven with data-driven strategy. The proposed framework simultaneously cooperates the spectral low-rankness prior and deep spatial prior (SLRP-DSP) for HSI self-supervised denoising. SLRP-DSP introduces the Tucker factorization via orthogonal basis and reduced factor, to capture the global spectral low-rankness prior in HSI. Besides, SLRP-DSP adopts a self-supervised way to learn the deep spatial prior. The proposed method doesn't need a large number of clean HSIs as the label samples. Through the self-supervised learning, SLRP-DSP can adaptively adjust the deep spatial prior from self-spatial information for reduced spatial factor denoising. An alternating iterative optimization framework is developed to exploit the internal low-rankness prior of third-order tensors and the spatial feature extraction capacity of convolutional neural network. Compared with both existing model-driven methods and data-driven methods, experimental results manifest that the proposed SLRP-DSP outperforms on mixed noise removal in different noisy HSIs.
A Survey of Blockchain Applications in the FinTech Sector

FinTech has proven its true potential in traditional financial offerings by delivering digital financial services to individuals worldwide.The pandemic has accelerated how people interact with financial services and has resulted in long-term changes to societies and economies.FinTech has expanded access to financial services and has made such changes possible.FinTech or Financial Technology refers to using new technologies for financial services.Artificial Intelligence, Blockchain, and cloud computing are a few technologies currently being applied to FinTech.In this paper, we consider FinTech, which partly uses blockchain technology.Blockchain technology plays a vital role in the financial sector as it ultimately lifts trust and the need for third-party verification by using consensus-based verification.This survey provides a comprehensive summary of the most relevant blockchain-based FinTech implementations and an overview of FinTech sectors and segments.For each segment, we provide a critique and a discussion on how each blockchain implementation contributes to solving the majority of problems faced by FinTech companies and researchers.This research aims to direct the future of financial solutions by providing an outline of the applications of blockchain technology and distributed ledger technology (DLT) for FinTech.We discuss various implementations, limitations, and challenges of blockchain-based FinTech applications.We conclude this work by exploring possible strengths, weaknesses, opportunities, and threats (SWOT) analysis and future research directions.