Reviewerly/exHarmony huggingface dataset:
Fuzzy rule dropout with dynamic compensation for wide learning algorithm of TSK fuzzy classifier

This paper extends our recent work about dropout for the design of Takagi–Sugeno–Kang(TSK) fuzzy classifiers, i.e., fuzzy-knowledge-out, to the generalized concept, i.e., fuzzy rule dropout with dynamic compensation. This extension is motivated by very complicated firing patterns of all pieces of knowledge in human brain, i.e., binary or continuous or both random ways for different situations. Our theoretical analysis indicates that this generalized concept can encapsulate various random dropouts of fuzzy rules with more match of human cognitive behavior, more capabilities of both generalization and co-adaptation avoidance. Based on this concept, we develop a wide learning algorithm of a TSK fuzzy classifier. Experimental results about the thirteen datasets demonstrate that with high interpretability guarantee, TSK fuzzy classifiers designed by means of fuzzy rule dropout with dynamic compensation outperform the comparative methods, especially in the sense of testing accuracy.
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport

Complex objects are usually with multiple labels, and can be represented by multiple modal representations, e.g., the complex articles contain text and image information as well as multiple annotations. Previous methods assume that the homogeneous multi-modal data are consistent, while in real applications, the raw data are disordered, e.g., the article constitutes with variable number of inconsistent text and image instances. Therefore, Multi-modal Multi-instance Multi-label (M3) learning provides a framework for handling such task and has exhibited excellent performance. However, M3 learning is facing two main challenges: 1) how to effectively utilize label correlation and 2) how to take advantage of multi-modal learning to process unlabeled instances. To solve these problems, we first propose a novel Multi-modal Multi-instance Multi-label Deep Network (M3DN), which considers M3 learning in an end-to-end multi-modal deep network and utilizes consistency principle among different modal bag-level predictions. Based on the M3DN, we learn the latent ground label metric with the optimal transport. Moreover, we introduce the extrinsic unlabeled multi-modal multi-instance data, and propose the M3DNS, which considers the instance-level auto-encoder for single modality and modified bag-level optimal transport to strengthen the consistency among modalities. Thereby M3DNS can better predict label and exploit label correlation simultaneously. Experiments on benchmark datasets and real world WKG Game-Hub dataset validate the effectiveness of the proposed methods.
Metaheuristics Optimization with Deep Learning Enabled Automated Image Captioning System

Image captioning is a popular topic in the domains of computer vision and natural language processing (NLP). Recent advancements in deep learning (DL) models have enabled the improvement of the overall performance of the image captioning approach. This study develops a metaheuristic optimization with a deep learning-enabled automated image captioning technique (MODLE-AICT). The proposed MODLE-AICT model focuses on the generation of effective captions to the input images by using two processes involving encoding unit and decoding unit. Initially, at the encoding part, the salp swarm algorithm (SSA), with a HybridNet model, is utilized to generate effectual input image representation using fixed-length vectors, showing the novelty of the work. Moreover, the decoding part includes a bidirectional gated recurrent unit (BiGRU) model used to generate descriptive sentences. The inclusion of an SSA-based hyperparameter optimizer helps in attaining effectual performance. For inspecting the enhanced performance of the MODLE-AICT model, a series of simulations were carried out, and the results are examined under several aspects. The experimental values suggested the betterment of the MODLE-AICT model over recent approaches.
Virtual Tourism Experiences and Mental Restoration

Virtual reality is providing new opportunities for health and well-being, organizational learning, and tourism management. The study reported in this paper aims to examine whether engaging in a virtual reality tourism experience could function as a restorative intervention strategy to enhance mental well-being of employees in the workplace. The study employed a lab-based pre–post experimental design to test the effectiveness of a virtual reality tourism experience, involving a nature-based marine setting, to enhance mental restoration and reduce mental fatigue. The results show that 3 minutes of a virtual tourism experience can lead to enhanced concentration while boosting the mental well-being of employees, while, at the same time, providing destinations with an opportunity to promote “real” experiences.
An Improved Approach for Real-Time Taillight Intention Detection by Intelligent Vehicles

Vehicle taillight intention detection is an important application for perception and decision making by intelligent vehicles. However, effectively improving detection precision with sufficient real-time performance is a critical issue in practical applications. In this study, a vision-based improved lightweight approach focusing on small object detection with a multi-scale strategy is proposed to achieve application-oriented real-time vehicle taillight intention detection. The proposed real-time detection model is designed based on YOLOv4-tiny, and a spatial pyramid pooling fast (SPPF) module is employed to enrich the output layer features. An additional detection scale is added to expand the receptive field corresponding to small objects. Meanwhile, a path aggregation network (PANet) is used to improve the feature resolution of small objects by constructing a feature pyramid with connections between feature layers. An expanded dataset based on the BDD100K dataset is established to verify the performance of the proposed method. Experimental results on the expanded dataset reveal that the proposed method can increase the average precision (AP) of vehicle, brake, left-turn, and right-turn signals by 1.81, 15.16, 40.04, and 41.53%, respectively. The mean average precision (mAP) can be improved by 24.63% (from 62.20% to 86.83%) at over 70 frames per second (FPS), proving that the proposed method can effectively improve detection precision with good real-time performance.
Rapid Efficient Loss Less Color Image Compression Using RCT Technique and Hierarchical Prediction

The new technique of fast efficient loss less color image compression algorithm using Reversible Color Transform (RCT) technique and then the hierarchical prediction coding techniques are presented in this paper. For the encoding processes the conversion of gray scale image compression is based on a hierarchical scheme. This technique enables the use of all the four levels (upper, lower, right side and left) of the pixel prediction. The predictable prediction methods are used to measure the upper left pixel values. In our paper we have proposed techniques of fast efficient loss less color image compression algorithm using Reversible Color Transform (RCT) technique and the hierarchical prediction coding techniques, to obtain high measurable values of Signal to Noise ratio and compression ratio compared with existing algorithm JPEG 2000. The simulation tools used for these research modules are MATLAB.
Blockchain-based student certificate management and system sharing using hyperledger fabric platform

One of the major capabilities of blockchain technology is the sharing of data in verifiable ways without losing control of information possession.Issuing and verifying student certifications for higher study applications or job recruitment require many steps that take days to complete and are considered time-consuming.Most universities around the world use centralized systems to control the entire procedure when a graduate applies for a job or postgraduate studies.Applying blockchain technology to certificate verification protocols through a comprehensive architecture provides authenticity and reduces time significantly.In this paper, a framework has been proposed to issue student certifications locally in addition to sharing them across the internet while maintaining control and ownership of the certifications.This framework leverages the advantages of blockchain technology to electronic certification sharing and verification.Applying the proposed blockchainbased certification system in universities will provide low latency for issuing, sharing, and verification of these certifications.The paper presents the proposed blockchain-based framework for e-certification sharing and an evaluation of the framework, which consists of measuring the average time to issue a certificate and transaction latency time.
A high-precision detection method for coated fuel particles based on improved faster region-based convolutional neural network

The thickness of the coating layers on coated fuel particles is critical to the safe operation of a high-temperature gas-cooled reactor, while how to measure it precisely, efficiently, and automatically remains challenging. The improvement of measurement accuracy and reliability depends on the high-precision detection of coated fuel particles in slice images. However, the coated fuel particles in these images are diverse in shape, tightly arranged, small in size, and inconsistent in scale, making it difficult to detect them with high precision. This paper proposes a high-precision detection method for coated fuel particles based on an improved faster region-based convolutional neural network (Faster-RCNN). First, a backbone network that incorporated a feature pyramid structure is designed to effectively extract the features of coated fuel particles. Next, an anchor generation network that is capable of generating suitable anchors based on a feature map is designed to improve the detection precision. Finally, the region of interest pooling (roi pooling) operation is replaced by the region of interest align (roi align) operation to further improve the detection precision. The test results indicate that the average precision of the proposed method for recognition and location of coated fuel particles in images reached 99.0 %.
Style transfer in conditional GANs for cross-modality synthesis of brain magnetic resonance images

Magnetic resonance imaging (MRI) has become one of the most standardized and widely used neuroimaging protocols in the detection and diagnosis of neurodegenerative diseases. In clinical scenarios, multi-modality MR images can provide more comprehensive information than single modality images. However, high-quality multi-modality MR images can be difficult to obtain in the actual diagnostic process due to various uncertainties. Efficient methods of modality complement and synthesis have aroused increasing attention in the research community. In this article, style transfer is introduced into conditional generative adversarial networks (cGAN) architecture. A cGAN model with hierarchical feature mapping and fusion (ST-cGAN) is proposed to address the cross-modality synthesis of MR images. In order to surmount the sole focus on the pixel-wise similarity as most cGAN-based methods do, the proposed ST-cGAN takes advantage of the style information and applies it to the synthetic image's content structure. Taking images of two modalities as conditional input, ST-cGAN extracts different levels of style features and integrates them with the content features to form the style-enhanced synthetic image. Furthermore, the proposed model is made robust to random noise by adding noise input to the generator. A comprehensive analysis is performed by comparing the proposed ST-cGAN with other state-of-the-art baselines based on four representative evaluation metrics. The experimental results on the IXI (Information eXtraction from Images) dataset verify the validity of the ST-cGAN from different evaluation perspectives.
Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms

The development of Management Information Systems (MIS) is impossible without the use of machine learning (ML). It's a type of Artificial Intelligence (AI) that makes predictions using statistical models. When it comes to financial analysis, there are numerous risk-related concerns to contend with today (FI). In the financial sector, machine learning algorithms are used to detect fraud, automate trading, and provide financial advice to investors. To better serve its customers, the financial sector can now save borrower data according to specific criteria thanks to MIS. In fact, there is a large amount of data about debtors, making load management a difficult task. ML can examine millions of data sets in a short period of time without being explicitly programmed to improve the results. This type of algorithm can aid financial institutions in making grant selections for their clients. For the objective of classifying FI in terms of fraud or not, the Intelligent Information System for Financial Institutions (IISFI) relying on Supervised ML (SML) Algorithms has been created in this work. Bayesian Belief Network, Neural Network, Decision trees, Naïve Bayes, and Nearest Neighbor has been compared for the purpose of classifying FI risks using the performance measures asfalse positive rate, true positive rate, true negative rate, false negative rate, accuracy, F-Measure, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Med AE, Receiver Operating Characteristic (ROC) area,Precision Recall Characteristic (PRC) area, and measures of PC.
Improving zero-shot retrieval using dense external expansion

Pseudo-relevance feedback (PRF) is a classical technique to improve search engine retrieval effectiveness, by closing the vocabulary gap between users' query formulations and the relevant documents. While PRF is typically applied on the same target corpus as the final retrieval, in the past, external expansion techniques have sometimes been applied to obtain a high-quality pseudo-relevant feedback set using the external corpus. However, such external expansion approaches have only been studied for sparse (BoW) retrieval methods, and its effectiveness for recent dense retrieval methods remains under-investigated. Indeed, dense retrieval approaches such as ANCE and ColBERT, which conduct similarity search based on encoded contextualised query and document embeddings, are of increasing importance. Moreover, pseudo-relevance feedback mechanisms have been proposed to further enhance dense retrieval effectiveness. In particular, in this work, we examine the application of dense external expansion to improve zero-shot retrieval effectiveness, i.e. evaluation on corpora without further training. Zero-shot retrieval experiments with six datasets, including two TREC datasets and four BEIR datasets, when applying the MSMARCO passage collection as external corpus, indicate that obtaining external feedback documents using ColBERT can significantly improve [email protected] for the sparse retrieval (by upto 28%) and the dense retrieval (by upto 12%). In addition, using ANCE on the external corpus brings upto 30% [email protected] improvements for the sparse retrieval and upto 29% for the dense retrieval.
Assessment and Optimization of Explainable Machine Learning Models Applied to Transcriptomic Data

Explainable artificial intelligence aims to interpret how machine learning models make decisions, and many model explainers have been developed in the computer vision field. However, understanding of the applicability of these model explainers to biological data is still lacking. In this study, we comprehensively evaluated multiple explainers by interpreting pre-trained models for predicting tissue types from transcriptomic data and by identifying the top contributing genes from each sample with the greatest impacts on model prediction. To improve the reproducibility and interpretability of results generated by model explainers, we proposed a series of optimization strategies for each explainer on two different model architectures of multilayer perceptron (MLP) and convolutional neural network (CNN). We observed three groups of explainer and model architecture combinations with high reproducibility. Group II, which contains three model explainers on aggregated MLP models, identified top contributing genes in different tissues that exhibited tissue-specific manifestation and were potential cancer biomarkers. In summary, our work provides novel insights and guidance for exploring biological mechanisms using explainable machine learning models.
Stable matching-based two-way selection in multi-label active learning with imbalanced data

Multi-label active learning (MLAL) reduces the cost of manual annotation for multi-label problems by selecting high-quality unlabeled data. Existing MLAL methods usually perform a one-way selection by considering the examples' informativeness, representativeness, or diversity. These methods acknowledge only the importance of the examples to the labels, but not vice versa. Due to the imbalanced nature of multi-label data, the selected dataset might also be highly imbalanced, causing negative effects on the learning performance. In this paper, we treat the selection of example-label pairs in MLAL as a two-way matching problem instead of a one-way selection problem. First, the label's preference for example, defined as the informativeness of the example regarding the label, as well as the example's preference for the label, which is defined as the probability of the example belonging to a positive class, are both considered. Then, a simple and effective stable matching (STM) model is adopted to realize the two-way selection. In addition, to provide reasonable candidates for the STM model, the roulette algorithm is utilized to allocate the annotation number for sub-classifiers. Comprehensive experiments demonstrate the competitiveness of the proposed approach and its effectiveness in selecting a relatively balanced dataset.
Comparative Analysis of Aes and Rsa Algorithms for Data Security in Cloud Computing

With growing technology, the cloud is becoming a center of sensitive information, making it more open to danger, especially when access to users with malicious plans increases. A huge amount of users use the cloud for various reasons, therefore, data should be safe and protected. In order to provide a safe environment, the aim of this paper is to analyze the well-known symmetric algorithm of the Advanced Encryption Standard (AES) and the Rivest&ndash;Shamir&ndash;Adleman (RSA) asymmetric algorithm based on time complexity, space, resource and power consumption, and suggest a new hybrid encryption process that is a combination of symmetric and asymmetric cryptographic methods. Based on experimental analysis, this paper proposes a AES cryptographic method as a first choice for data encryption processes for cloud applications and data storage.
Dynamic Hypergraph Convolutional Network

Hypergraph Convolutional Network (HCN) has be-come a proper choice for capturing high-order relationships. Existing HCN methods are tailored for static hypergraphs, which are unsuitable for the dynamic evolution in real-world scenarios. In this paper, we explore a dynamic HCN based on the attention mechanism (DyHCN) for time series prediction. It not only effectively exploits the spatial and temporal relationships in the dynamic hypergraph, but also continuously aggregates the temporal evolution cues of time-varying hypergraphs with the global and local embeddings. Specifically, these merits can be attributed to 1) dynamic hypergraph construction (DHC), which captures the feature of historical context content and provides a guideline for dynamic hypergraph construction; 2) spatio-temporal hypergraph convolution module (STHC), responsible for extracting the spatial and temporal relationships among nodes and hyperedges, and 3) collaborative prediction module (CP), for the overall time-varying hypergraphs embedding aggregation. Such modules endeavor to well learn feature embedding from nodes, hyperedges, and hypergraphs, which produces informative representations for downstream tasks. Experiments on three datasets including Tiingo, Stocktwits, and NYC-Taxi demonstrate that the proposed DyHCN achieves sound performance over existing cousins, and both STHC and CP modules play a key role in modeling the dynamic evolution property of hypergraphs.
Efficient Participant Contribution Evaluation for Horizontal and Vertical Federated Learning

Federated Learning (FL) enables multiple partici-pants to collaboratively train a model in a privacy-preserving way. The performance of the FL model heavily depends on the quality of participants' local data, which makes measuring the contributions of participants an essential task for various purposes, e.g., participant selection and reward allocation. The Shapley value is widely adopted by previous work for contribution assessment, which, however, requires repeatedly leave-one-out retraining and thus incurs the prohibitive cost for FL. In this paper, we propose a highly efficient approach, named DIG-FL, to estimate the Shapley value of each participant without any model retraining. It's worth noting that our approach is applicable to both vertical federated learning (VFL) and horizontal federated learning (HFL), and we provide concrete design for VFL and HFL. In addition, we propose a DIG-FL based reweight mechanism to improve the model training in terms of accuracy and convergence speed by dynamically adjusting the weights of participants according to their per-epoch contributions, and theoretically analyze the convergence speed. Our extensive evaluations on 14 public datasets show that the estimated Shapley value is very close to the actual Shapley value with Pearson's correlation coefficient up to 0.987, while the cost is orders of magnitude smaller than state-of-the-art methods. When there are more than 80% participants holding low-quality data, by dynamically adjusting the weights, DIG-FL can effectively accelerate the convergence and improve the model accuracy.
An Adaptive Specific Emitter Identification System for Dynamic Noise Domain

With the sharply increasing number of wireless devices, specific emitter identification (SEI) technology based on radio frequency fingerprint (RFF) is employed to enhance devices’ security for authentication. However, the problems of poor performance in a highly dynamic interference environment, high dependence on quality of data sets, and less consideration of calculation resources are urgent to be resolved in a practical SEI applying domain. Our adaptive SEI system focuses on these problems, proposing a preprocessing algorithm improving synchrosqueezed wavelet transforms by energy regularization (ISWTE) to simplify the process of signal preprocessing while improving the robustness of RFF, an unsupervised neural network noise feature extracting GAN (NEGAN) to reduce the dependence of the data sets quality while obtaining precise clean RFF features from signals with noises and an optimized structure of waveform and classifier to achieve a better performance with less complexity and little cost. To test the system performance, we have generated a real wireless device data sets with ten devices, including a training set and a base test set both with SNR 10 dB, and a dynamic test set with SNR ranges −20 to 10 dB. The proposed adaptive SEI system by our work obtains a test accuracy of 0.96 at SNR 10 dB, 0.85 at SNR 0 dB, and 0.25 at SNR −20 dB only through SNR 10-dB training set. Most of the existing methods through neither of high SNR or enhanced mixed SNRs training set failed to complete devices identification under SNR −10 dB, and test accuracy of them deceased dramatically at SNR 0 dB.