Reviewerly/exHarmony huggingface dataset:
EFNC-Exp: An evolving fuzzy neural classifier integrating expert rules and uncertainty

Data stream classification processes with neuro-fuzzy approaches may involve situations where uncertainties arise, which may directly interfere with the quality of the results of the evolving models. Another factor that can help improve the performance of neuro-fuzzy evolving models is using a priori knowledge about a topic and incorporating it into the model's training procedure. The definition of fuzzy rules with a high degree of representativeness for certain classes can help models increase the significance of the representation of these labels and thus boost their predictive performance for these classes. This article proposes the integration of uncertainty in experts' feedback on the class labels and the integration of expert rules into the classifier architecture and the evolving, adaptive learning engine. This uncertainty integration occurs by combining it in defining neurons' weights in the first layer of the model and incorporating these weight values in the Gaussian neurons in the model's first layer; furthermore, uncertainty is integrated into an incremental feature weighting concept (inducing a weighted version of it) for the curse of dimensionality reduction. The proof of the new concepts will be carried out through tests on binary pattern classification problems in real-world data streams and a comparison between our approach and several related state-of-the-art works in evolving (neuro-) fuzzy modeling. The results obtained by the model showed that, by explicitly respecting the uncertainty of the class labels in the process of updating the evolving neuro-fuzzy classifier, the accuracy trend lines showed a robust behavior as the degree of distortion existing in the class labels of the samples due to uncertainty could be partially compensated.
Diagnosing fashion outfit compatibility with deep learning techniques

Fashion image understanding is a popular research field with many different machine learning applications. There have been many studies regarding outfit prediction and outfit composition in the field of fashion. However, there are few works that explain the prediction. This paper addresses a method of diagnosing outfit compatibility through clothing images. The proposed system not only predicts compatibility, but also diagnoses incompatible clothing items in outfits. First, a new dataset named ModAI, which has clothing images and compatibility comments from different users was created. After this, a common compatibility comment was created according to user comments for each clothing image. Lastly, image captioning techniques were used to generate compatibility suggestion texts from clothing images. Different segmentation techniques were also used to improve captioning capabilities. The model achieves a 0.62 BLEU-4 score. Experiments show that image captioning techniques can also be used to diagnose outfit compatibility.
Seeing is believing: Towards interactive visual exploration of data privacy in federated learning

Federated learning (FL), as a popular distributed machine learning paradigm, has driven the integration of knowledge in ubiquitous data owners under one roof. Although designed for privacy-preservation by nature, the supposed well-sanitized parameters still convey sensitive information (e.g., reconstruction attack), while existing technical countermeasures provide weak explainability for privacy understanding and protection practices of general users. This work investigates these privacy concerns with an exploratory study and elaborates on data owners’ expectations in FL. Based on the analysis, we design the first interactive visualization system for FL privacy that supports intelligible privacy inspection and adjustment for data owners. Specifically, our proposal facilitates sample recommendation for joint privacy–performance training at cold start. Then it provides visual interpretation and attention rendering of privacy risks in view of multiple attacking channels and a holistic view. Further it supports interactive privacy enhancement involving both user initiative and differential privacy technique, and iterative trade-off with real-time inference accuracy estimation. We evaluate the effectiveness of the system and collect qualitative feedbacks from users. The results demonstrate that 96.7% of users acknowledge the benefits to privacy inspection and adjustment and 90.3% are willing to use our system. More importantly, 87.1% increase the willingness of contributing data for FL.
Motivating explanations in Bayesian networks using MAP-independence

In decision support systems the motivation and justification of the system's diagnosis or classification is crucial for the acceptance of the system by the human user. In Bayesian networks a diagnosis or classification is typically formalized as the computation of the most probable joint value assignment to the hypothesis variables, given the observed values of the evidence variables (generally known as the MAP problem). While solving the MAP problem gives the most probable explanation of the evidence, the computation is a black box as far as the human user is concerned and it does not give additional insights that allow the user to appreciate and accept the decision. For example, a user might want to know to whether an unobserved variable could potentially (upon observation) impact the explanation, or whether it is irrelevant in this aspect. In this paper we introduce a new concept, MAP-independence, which tries to capture this notion of relevance, and explore its role towards a potential justification of an inference to the best explanation. We formalize several computational problems based on this concept and assess their computational complexity.
Benefits or harms? The effect of online review manipulation on sales

In recent years, there has been an increase in online review manipulation on the platforms of electronic commerce. Previous studies clarify the benefits and harms of online review manipulation for firms, and they provide mixed conclusions on the influence of online review manipulation. However, the effect of online review manipulation on product sales has not yet been thoroughly studied due to the covert nature of review manipulation. To fill this research gap, this paper examines the different effects of review manipulation in three dimensions: quantity manipulation, quality manipulation, and relation manipulation. Drawing on the Information Manipulation Theory, which reveals the manipulation behaviors of different dimensions, it is proposed that the influence of online review manipulation differs significantly among different information manipulation dimensions. The results of the empirical experiments show that the effect of review quantity manipulation on product sales exhibits an inverted U-shape. In addition, review quality manipulation positively affects product sales, but review relation manipulation exerts a negative effect. Moreover, the magnitude of the effect of review manipulation is contingent upon review manipulation duration. The findings shed light on the heterogeneous effect of review manipulation dimensions on product sales from an information manipulation perspective and suggest a need for improvement in online fraudulent review detection in the early stage of review manipulation.
Security, Privacy, and Trust Issues in Intelligent Transportation System

Introduction to security, privacy, and trust in intelligent transportation systems (ITS), security, privacy, trust requirements in intelligent transportation systems, and cryptographic approaches in ITS are discussed in this chapter. In addition, the chapter discusses biometric-based intelligent transportation systems, blockchain-based intelligent transportation systems, and peer-to-peer ride-sharing via blockchain-based intelligent transportation systems. Aside from that, the chapter discusses the automotive intelligent transportation system and the various services available in an intelligent transportation system. Traditional cryptography is ineffective in a communication environment with limited space or time. A deep reinforcement learning algorithm is used to improve the performance of each subsystem as well as the overall mix of cars and trucks. Some argue that the blockchain is a public database that no one owns. A distributed data storage system with point-to-point transmissions and consensus mechanisms. Blockchain blocks store time stamps and digital signs in addition to using encryption algorithms, smart contracts, and other computer tools. Data is stored in multiple locations, making it easier to share data. The data records’ digital signatures can also verify the data. Because hash points connect the blocks, hackers cannot alter the data. People don’t have to trust a central authority or anyone else to agree on what’s going on with the ledger. Using the blockchain, people can share data securely, dependable, and traceable manner. There are several ways to improve transportation using blockchain technology. The blockchain has the potential to transform the concept of car sharing. All information is kept in one place on the blockchain, making it easy to find and track.
An effective spatial relational reasoning networks for visual question answering

Visual Question Answering (VQA) is a method of answering questions in natural language based on the content of images and has been widely concerned by researchers. The existing research on the visual question answering model mainly focuses on the point of view of attention mechanism and multi-modal fusion. It only pays attention to the visual semantic features of the image in the process of image modeling, ignoring the importance of modeling the spatial relationship of visual objects. We are aiming at the existing problems of the existing VQA model research. An effective spatial relationship reasoning network model is proposed, which can combine visual object semantic reasoning and spatial relationship reasoning at the same time to realize fine-grained multi-modal reasoning and fusion. A sparse attention encoder is designed to capture contextual information effectively in the semantic reasoning module. In the spatial relationship reasoning module, the graph neural network attention mechanism is used to model the spatial relationship of visual objects, which can correctly answer complex spatial relationship reasoning questions. Finally, a practical compact self-attention (CSA) mechanism is designed to reduce the redundancy of self-attention in linear transformation and the number of model parameters and effectively improve the model's overall performance. Quantitative and qualitative experiments are conducted on the benchmark datasets of VQA 2.0 and GQA. The experimental results demonstrate that the proposed method performs favorably against the state-of-the-art approaches. Our best single model has an overall accuracy of 71.18% on the VQA 2.0 dataset and 57.59% on the GQA dataset.
Extraction of microRNA–target interaction sentences from biomedical literature by deep learning approach

MicroRNA (miRNA)-target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29.
EnsFace: An Ensemble Method of Deep Convolutional Neural Networks with Novel Effective Loss Functions for Face Recognition

In recent years, designing new effective Deep Convolutional Neural Network (DCNN) architectures and loss functions are two crucial trends in improving face recognition (FR) accuracy. However, building an optimal DCNN and ameliorating FR performance are still the main challenges for researchers. Thus, we first investigate and analyzes the effect of several novel effective loss functions based on softmax on DCNN with the ResNet architecture. We then propose an ensemble learning, namely EnsFace, by taking advantage of recent novel FR methods based on CosFace, ArcFace, and MagFace. EnsFace elaborates on the voting mechanism that utilizes non-optimal pre-trained models to obtain better discriminative ability for FR. To prove the performance of both the speed and accuracy of EnsFace, we carry out rigorous experiments using several popular benchmarks, including LFW, CFP-FP, and AgeDB-30, as well as two renovations of LFW: CALFW and CPLFW. The results of our experiments achieve state-of-the-art figures, which show the proposed method’s massive potential to improve FR performance. Ablation studies and overall benchmarks undeniably prove the effectiveness of our EnsFace.
Leveraging Deep Learning Image Classifiers for Visual Similarity-based Phishing Website Detection

Phishing is a major cybersecurity threat that is increasingly dangerous and complicated, especially during a global pandemic when there is a great need for remote work and communication between Internet users. Moreover, the challenge is even greater when the crime of using high technology increase with the speed of development of science and technology. Machine learning-based approaches for phishing detection have been explored and applied, which have gained many significant results. Though, the problem with using visual similarity-based phishing detection techniques is the complexity of the visual feature extraction process. In this work, we propose a method that uses the transfer learning technique with pre-trained image classification models to extract features as input to machine learning algorithms for phishing website detection. Our proposed method takes advantage of the research results in the field of computer vision, opening up prospects for application in the field of information security. In the experiments, we tested five deep learning image classification models combined with eleven machine learning algorithms in the task of training to classify phishing websites based on visual similarity. Experimental results show that VGG16 is well compatible with several ML-based algorithms in the task of classifying phishing sites. Our proposed method offers promise for the problem of detecting phishing websites based on visual similarity when combining CNN image classification models and machine learning algorithms.
A Privacy-Preserving Approach For Building Learning Models in Smart Healthcare using Blockchain and Federated Learning

Nowadays, the amount of data generated from Internet of Things (IoT) devices is increasing, paving the way for the development of artificial intelligence (AI) applications. However, with the traditional AI approach, users sharing their raw data causes many concerns in terms of privacy leakage. There have been reports that there are privacy violations on private data of users. In the medical field, the creation of devices to help automatically diagnose the user's disease is gradually becoming a trend in the future. These devices help users monitor their own health, thereby reducing pressure on medical facilities that are often overloaded. However, the healthcare data of individuals will often be very sensitive and rarely shared by users. In addition, the profits generated from machine learning (ML) models mostly belong to the owners of that model. It also becomes an obstacle in encouraging users to share their data. In this article, we propose PriFL-Chain, a privacy-preservation framework to take advantage of data resources of data owners for training ML-based models, while ensuring the privacy of data owners. Specifically, we apply Differential privacy (DP) to federated learning (FL) to train ML models. Users just share the ML model trained on their data instead of sharing the raw data. Furthermore, the contribution activities of users in the system are recorded to the Blockchain to ensure transparency. We also leverage Mobile Edge Computing (MEC) and InterPlanetary File System (IPFS) to reduce the pressure on the central server and reduce data communication costs, making the system more flexible. Experimental results have demonstrated that the combined strategy of FL, Blockchain, IPFS, and MEC can help reduce the cost of training ML models, effectively protect privacy, and utilize data sources of diversity from the community.
Improving text recognition by combining visual and linguistic features of text

While being studied for several decades, Optical Character Recognition (OCR) has still been attracting considerable attention from researchers. Previous studies tend to focus on visual features of optical texts, such as texture, shape, and color to build OCR models. However, linguistic features, an important factor for OCR, has not been extensively investigated, especially for Vietnamese-OCR scanned documents. Therefore, we introduce a method to improve the performance of Vietnamese OCR by combining both visual and linguistic features of the optical text. The proposed method consists of (i) a domain-specific dictionary and (ii) a modified natural language processing model termed ABCNet, employed at the training and inference step, to determine the best candidate for the visual appearance of the text. Moreover, our method can easily be integrated with existing OCR methods to further increase their performance. Experimental results on a newly collected dataset show that the proposed method achieves an accuracy of 83.61% and a F1 score of 84.1%.
Analysis Model for Decentralized Lending Protocols

Decentralized Finance (DeFi) – an ecosystem of financial applications built on top of blockchain technology – has kept powering ahead and attracted a large amount of capital in just a few years. As the core part of the DeFi ecosystem, decentralized lending protocols allow users to become lenders or borrowers in a completely decentralized fashion, such that an individual has complete control over their funds at all times. Collecting, analyzing, and visualizing lending data are crucial as these lending protocols are growing steadily. This paper presents an analysis model for decentralized lending protocols. Our main contributions are as follows. (i) We analyze and compare the top lending protocols; (ii) We present a general model to collect and process a large amount of data from multiple lending protocols in an exact and efficient manner; (iii) We design and implement a lending visualization system with various charts divided into three categories, i.e., lending market overview, token analysis, and user analysis.
MoRec: User’s Definition Inspired Analytical Approach for Movie Recommendation

As of 2020, streaming service giant Netflix has 204 million subscribers. Data experts suggest that Netflix users will spend just 60–90 s browsing and reviewing 10 to 20 titles before finally deciding upon watching content or not. In continuation, a robust recommender system is required to produce user definition-inspired analytics. This paper aims to derive analytical conclusions about cinema data and recommend movies to the user based on their specifications. The problem statement at hand is promptly getting a list of movies to watch next from a dataset of 45,000+ movies. The methods used for analyzing the metadata in cinema are implemented through Python libraries. For building the machine learning model that recommends movies, Natural Language Processing and other relevant statistical/programming like Regular Expression, similarity methods, etc., have been used. The key result of the paper is building a “Content-based recommendation system” based on the user’s definition of similarity.
Interpretation of Dynamic Models Based on Neural Networks in the Form of Integral-Power Series

The paper is devoted to the problem of interpretation the dynamic models based on neural network. Proposed approach is conclude in the building of the interpretive model in the form of an analytical integral-power series, saving the dynamic and nonlinear properties of the primary neural network model. The purpose of the paper is development a method for interpreting dynamic models based on neural networks, providing high accuracy of the interpreting models construction. Scientific novelty consists in using of intego-power series in the form of multidimensional weight functions to build analytical models that interpret dynamic neural networks. This method allows to obtain an interpretation of dynamic neural network models while preserving their nonlinear and dynamic properties. Practical usefulness of the developed method consists in providing high accuracy and speed of interpreting models construction, allows to provide modeling of nonlinear dynamic states of objects in both test and functional modes of operation. The proposed method tested on the data of a test nonlinear dynamic object. The results of the experiment demonstrate high accuracy and speed of construction of analytical interpreting models.
Automatic Extractive and Generic Document Summarization Based on NMF

Abstract Nowadays, textual information grows exponentially on the Internet. Text summarization (TS) plays a crucial role in the massive amount of textual content. Manual TS is time-consuming and impractical in some applications with a huge amount of textual information. Automatic text summarization (ATS) is an essential technology to overcome mentioned challenges. Non-negative matrix factorization (NMF) is a useful tool for extracting semantic contents from textual data. Existing NMF approaches only focus on how factorized matrices should be modeled, and neglect the relationships among sentences. These relationships provide better factorization for TS. This paper suggests a novel non-negative matrix factorization for text summarization (NMFTS). The proposed ATS model puts regularizes on pairwise sentences vectors. A new cost function based on the Frobenius norm is designed, and an algorithm is developed to minimize this function by proposing iterative updating rules. The proposed NMFTS extracts semantic content by reducing the size of documents and mapping the same sentences closely together in the latent topic space. Compared with the basic NMF, the convergence time of the proposed method does not grow. The convergence proof of the NMFTS and empirical results on the benchmark data sets show that the suggested updating rules converge fast and achieve superior results compared to other methods.