Reviewerly/exHarmony huggingface dataset:
Learning Calibrated Class Centers for Few-Shot Classification by Pair-Wise Similarity

Metric-based methods achieve promising performance on few-shot classification by learning clusters on support samples and generating shared decision boundaries for query samples. However, existing methods ignore the inaccurate class center approximation introduced by the limited number of support samples, which consequently leads to biased inference. Therefore, in this paper, we propose to reduce the approximation error by class center calibration. Specifically, we introduce the so-called Pair-wise Similarity Module (PSM) to generate calibrated class centers adapted to the query sample by capturing the semantic correlations between the support and the query samples, as well as enhancing the discriminative regions on support representation. It is worth noting that the proposed PSM is a simple plug-and-play module and can be inserted into most metric-based few-shot learning models. Through extensive experiments in metric-based models, we demonstrate that the module significantly improves the performance of conventional few-shot classification methods on four few-shot image classification benchmark datasets. Codes are available at: https://github.com/PRIS-CV/Pair-wise-Similarity-module.
Deep Incomplete Multi-View Clustering via Mining Cluster Complementarity

Incomplete multi-view clustering (IMVC) is an important unsupervised approach to group the multi-view data containing missing data in some views. Previous IMVC methods suffer from the following issues: (1) the inaccurate imputation or padding for missing data negatively affects the clustering performance, (2) the quality of features after fusion might be interfered by the low-quality views, especially the inaccurate imputed views. To avoid these issues, this work presents an imputation-free and fusion-free deep IMVC framework. First, the proposed method builds a deep embedding feature learning and clustering model for each view individually. Our method then nonlinearly maps the embedding features of complete data into a high-dimensional space to discover linear separability. Concretely, this paper provides an implementation of the high-dimensional mapping as well as shows the mechanism to mine the multi-view cluster complementarity. This complementary information is then transformed to the supervised information with high confidence, aiming to achieve the multi-view clustering consistency for the complete data and incomplete data. Furthermore, we design an EM-like optimization strategy to alternately promote feature learning and clustering. Extensive experiments on real-world multi-view datasets demonstrate that our method achieves superior clustering performance over state-of-the-art methods.
An Efficient Parallel Reptile Search Algorithm and Snake Optimizer Approach for Feature Selection

Feature Selection (FS) is a major preprocessing stage which aims to improve Machine Learning (ML) models’ performance by choosing salient features, while reducing the computational cost. Several approaches are presented to select the most Optimal Features Subset (OFS) in a given dataset. In this paper, we introduce an FS-based approach named Reptile Search Algorithm–Snake Optimizer (RSA-SO) that employs both RSA and SO methods in a parallel mechanism to determine OFS. This mechanism decreases the chance of the two methods to stuck in local optima and it boosts the capability of both of them to balance exploration and explication. Numerous experiments are performed on ten datasets taken from the UCI repository and two real-world engineering problems to evaluate RSA-SO. The obtained results from the RSA-SO are also compared with seven popular Meta-Heuristic (MH) methods for FS to prove its superiority. The results show that the developed RSA-SO approach has a comparative performance to the tested MH methods and it can provide practical and accurate solutions for engineering optimization problems.
Multi-modal Graph Contrastive Learning for Micro-video Recommendation

Recently micro-videos have become more popular in social media platforms such as TikTok and Instagram. Engagements in these platforms are facilitated by multi-modal recommendation systems. Indeed, such multimedia content can involve diverse modalities, often represented as visual, acoustic, and textual features to the recommender model. Existing works in micro-video recommendation tend to unify the multi-modal channels, thereby treating each modality with equal importance. However, we argue that these approaches are not sufficient to encode item representations with multiple modalities, since the used methods cannot fully disentangle the users' tastes on different modalities. To tackle this problem, we propose a novel learning method named Multi-Modal Graph Contrastive Learning (MMGCL), which aims to explicitly enhance multi-modal representation learning in a self-supervised learning manner. In particular, we devise two augmentation techniques to generate the multiple views of a user/item: modality edge dropout and modality masking. Furthermore, we introduce a novel negative sampling technique that allows to learn the correlation between modalities and ensures the effective contribution of each modality. Extensive experiments conducted on two micro-video datasets demonstrate the superiority of our proposed MMGCL method over existing state-of-the-art approaches in terms of both recommendation performance and training convergence speed.
A novel random multi-subspace based ReliefF for feature selection

Feature selection is an important preprocessing technology for dimensionality reduction, which reduces the dimension of the dataset by acquiring a subset of features with the largest amount of information, and improves the classification accuracy to the greatest extent at the same time. Although different types of feature selection algorithms have achieved remarkable success, most of them lack the ability to mine information in different subspaces, and ignore the useful information contained in the abundant samples. In this research, a novel random multi-subspace based ReliefF (RBEFF) is proposed for feature selection. In this method, firstly, multiple feature partitions containing a large number of random subspaces with the same size are generated. Secondly, the ReliefF algorithm is used in each random subspace to obtain the local weight of the feature. The local weight vectors of random subspaces in each feature partition are combined to obtain the full weight vector. Finally, the full weight vectors of multiple feature partitions are integrated into the final weight vector, which contains the final weight of each feature in the original feature space feature. The feature selection is carried out dynamically according to the final weight vector. We evaluated the performance of the RBEFF on 28 UCI datasets with different sizes and compare RBEFF with 6 feature selection algorithms using KNN and DT classifiers’ three evaluation indicators. The comparisons and experimental results demonstrate the effectiveness, competitiveness, and superiority of RBEFF in solving feature selection problems.
Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data

Rooftop solar photovoltaics (PV) play increasing role in the global sustainable energy transition. This raises the challenge of accurate and high-resolution geospatial assessment of PV technical potential in policymaking and power system planning. To address the challenge, we propose a general framework that combines multi-resource satellite images and deep learning models to provide estimates of rooftop PV power generation. We apply deep learning based inversion model to estimate hourly solar radiation based on geostationary satellite images, and automatic segmentation model to extract building footprint from high-resolution satellite images. The framework enables precise survey of available rooftop resources and detailed simulation of power generation on an hourly basis with a spatial resolution of 100 m. The case study in Jiangsu Province demonstrates that the framework is applicable for large areas and scalable between precise locations and arbitrary regions across multiple temporal scales. Our estimates show that rooftop resources across the province have a potential installed capacity of 245.17 GW, corresponding to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs.
Towards Public Verifiable and Forward-Privacy Encrypted Search by Using Blockchain

Dynamic Searchable Symmetric Encryption (DSSE) is a practical cryptographic primitive that assists servers to provide search and update functionalities in the ciphertext domain. Recent work on DSSE schemes has focused on the direction of forward-privacy, requiring that newly added files cannot be linked to previously query results. However, due to the complexity of forward-privacy updates, existing schemes can only address an honest-but-curious server. It is difficult to verify updated results while preserving forward-privacy. In this paper, we explore how blockchain techniques can help us achieve a verifiable and forward-privacy DSSE scheme. Our scheme resorts to the emerging smart contract as a trusted platform to store digests for public result verification, and carefully crafts dynamic query protocols to enable encrypted search with forward-privacy. In our design, indexes are collocated with encrypted files and stored at storage-servers, which makes the blockchain light-weighted and search operations more efficient. Moreover, we propose a hybrid index design to support efficient files deletion. By using our blockchain-assisted primitive, the property collision between dynamic result verification and forward-privacy can be solved. We formally analyze the security strengths and provide the prototype implementation on Ethereum. Experiment results demonstrate the feasibility and usability of our blockchain-assisted DSSE scheme.
A Framework to Make Voting System Transparent Using Blockchain Technology

A widespread mistrust towards the traditional voting system has made democratic voting in any country very critical. People have seen their fundamental rights being violated. Other digital voting systems have been challenged due to a lack of transparency. Most voting systems are not transparent enough; this makes it very difficult for the government to gain voters&#x2019; trust. The reason behind the failure of the traditional and current digital voting system is that it can be easily exploited. The primary objective is to resolve problems of the traditional and digital voting system, which include any kind of mishap or injustice during the process of voting. Blockchain technology can be used in the voting system to have a fair election and reduce injustice. The physical voting systems have many flaws in it as well as the digital voting systems are not perfect enough to be implemented on large scale. This appraises the need for a solution to secure the democratic rights of the people. This article presents a platform based on modern technology blockchain that provides maximum transparency and reliability of the system to build a trustful relationship between voters and election authorities. The proposed platform provides a framework that can be implemented to conduct voting activity digitally through blockchain without involving any physical polling stations. Our proposed framework supports a scalable blockchain, by using flexible consensus algorithms. The Chain Security Algorithm applied in the voting system makes the voting transaction more secure. Smart contracts provide a secure connection between the user and the network while executing a transaction in the chain. The security of the blockchain based voting system has also been discussed. Additionally, encryption of transactions using cryptographic hash and prevention of attack 51% on the blockchain has also been elaborated. Furthermore, the methodology for carrying out blockchain transactions during the process of voting has been elaborated using Blockchain Finally, the performance evaluation of the proposed system shows that the system can be implemented in a large-scale population.
Video Captioning Using Global-Local Representation

Video captioning is a challenging task as it needs to accurately transform visual understanding into natural language description. To date, state-of-the-art methods inadequately model global-local vision representation for sentence generation, leaving plenty of room for improvement. In this work, we approach the video captioning task from a new perspective and propose a GLR framework, namely a global-local representation granularity. Our GLR demonstrates three advantages over the prior efforts. First, we propose a simple solution, which exploits extensive vision representations from different video ranges to improve linguistic expression. Second, we devise a novel global-local encoder, which encodes different video representations including long-range, short-range and local-keyframe, to produce rich semantic vocabulary for obtaining a descriptive granularity of video contents across frames. Finally, we introduce the progressive training strategy which can effectively organize feature learning to incur optimal captioning behavior. Evaluated on the MSR-VTT and MSVD dataset, we outperform recent state-of-the-art methods including a well-tuned SA-LSTM baseline by a significant margin, with shorter training schedules. Because of its simplicity and efficacy, we hope that our GLR could serve as a strong baseline for many video understanding tasks besides video captioning. Code will be available.
PFLF: Privacy-Preserving Federated Learning Framework for Edge Computing

Federated learning (FL) can protect clients’ privacy from leakage in distributed machine learning. Applying federated learning to edge computing can protect the privacy of edge clients and benefit edge computing. Nevertheless, eavesdroppers can analyze the parameter information to specify clients’ private information and model features. And it is difficult to achieve a high privacy level, convergence, and low communication overhead during the entire process in the FL framework. In this paper, we propose a novel privacy-preserving federated learning framework for edge computing (PFLF). In PFLF, each client and the application server add noise before sending the data. To protect the privacy of clients, we design a flexible arrangement mechanism to count the optimal training times for clients. We prove that PFLF guarantees the privacy of clients and servers during the entire training process. Then, we theoretically prove that PFLF has three main properties: 1) For a given privacy level and model aggregation times, there is an optimal number of participating times for clients; 2) There is an upper and lower bound of convergence; 3) PFLF achieves low communication overhead by designing a flexible participation training mechanism. Simulation experiments confirm the correctness of our theoretical analysis. Therefore, PFLF helps design a framework to balance privacy levels and convergence and achieve low communication overhead when there is a part of clients dropping out of training.
CNN models for readability of Chinese texts

&lt;p style='text-indent:20px;'&gt;Readability of Chinese texts considered in this paper is a multi-class classification problem with &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ 12 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; grade classes corresponding to &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ 6 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; grades in primary schools, &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ 3 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; grades in middle schools, and &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ 3 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; grades in high schools. A special property of this problem is the strong ambiguity in determining the grades. To overcome the difficulty, a measurement of readability assessment methods used empirically in practice is adjacent accuracy in addition to exact accuracy. In this paper we give mathematical definitions of these concepts in a learning theory framework and compare these two quantities in terms of the ambiguity level of texts. A deep learning algorithm is proposed for readability of Chinese texts, based on convolutional neural networks and a pre-trained BERT model for vector representations of Chinese characters. The proposed CNN model can extract sentence and text features by convolutions of sentence representations with filters and is efficient for readability assessment, which is demonstrated with some numerical experiments.&lt;/p&gt;
MRGAT: Multi-Relational Graph Attention Network for knowledge graph completion

One of the most effective ways to solve the problem of knowledge graph completion is embedding-based models. Graph neural networks (GNNs) are popular and promising embedding models which can exploit and use the structural information of neighbors in knowledge graphs. The current GNN-based knowledge graph completion methods assume that all neighbors of a node have equal importance. This assumption which cannot assign different weights to neighbors is pointed out in our study to be unreasonable. In addition, since the knowledge graph is a kind of heterogeneous graph with multiple relations, multiple complex interactions between nodes and neighbors can bring challenges to the effective message passing of GNNs. We then design a multi-relational graph attention network (MRGAT) which can adapt to different cases of heterogeneous multi-relational connections and then calculate the importance of different neighboring nodes through a self-attention layer. The incorporation of self-attention mechanism into the network with different node weights optimizes the network structure, and therefore, significantly results in a promotion of performance. We experimentally validate the rationality of our models on multiple benchmark knowledge graphs, where MRGAT achieves the best performance on various evaluation metrics including MRR score, Hits@ score compared with other state-of-the-art baseline models.
Efficient Bag of Deep Visual Words Based features to classify CRC Images for Colorectal Tumor Diagnosis

CRC images are one of the most essential diagnostic techniques for colorectal cancer detection. The bag of deep visual words-based methodology (BoDVW) has proven to be a significant representation of CRC images for its enhanced discriminability when compared to earlier deep learning-based methods. This study presents a new Efficient Bag of Deep Visual Words (EBoDVW) feature that takes advantage of three alternative scales of the VGG16 model's 4th pooling layer's output feature map. Max pooling operation has done with three kernels for EBoDVW-based features. The proposed characteristics are evaluated using the Support Vector Machine (SVM) classification technique on the CRC public dataset, which contains over 1133 CRC pictures. The results of proposed experiments reveal that our strategy generates a consistent and prominent classification accuracy of 98.2 %.
Unsupervised Domain Adaptation Through Dynamically Aligning Both the Feature and Label Spaces

In unsupervised domain adaptation (UDA), a target-domain model is trained by the supervised knowledge from a source domain. Although UDA has recently received much attention, most existing UDA methods have ignored the alignment in label space while mostly concentrating on alignment of feature space. Even worse, they have payed less attention to the dynamic relationship between domain alignment and discrimination, leading to degenerated performance. In this work, we propose a new kind of UDA through aligning in both the feature and label spaces (DAFL), in which a dynamic weight is designed and deployed between domain alignment and discrimination enhancement according to their conditions. Specifically, the cross-domain distribution divergence is reduced by the weighted class-level feature space alignment as well as the compacted and discriminative label space alignment. Furthermore, the balancing weight between adaptation alignment and discrimination enhancement is dynamically adjusted to regularize the adversarial domain adaptation. Then, the generalization ability of the DAFL model is enhanced by adding discrepant classification with theoretical analysis. Finally, extensive experiments validate effectiveness and superiority of the proposed approach.
Joint Detection Threshold Optimization and Illumination Time Allocation Strategy for Cognitive Tracking in a Networked Radar System

In this paper, a joint detection threshold optimization and illumination time allocation (JDTOITA) strategy was developed for multi-target tracking in an asynchronous networked radar system under cluttered background. The basis of this strategy is to facilitate detection and tracking using the prior target information in the tracking recursive cycle. The information reduction factor in the Bayesian detection framework is derived, optimized, and incorporated in the posterior Cramer-Rao lower bound (PCRLB), which is then utilized to serve as the optimization metric. Due to the asynchronous data and cluttered environment, the objective function needs to be recursively deduced and is nonlinear and nonconvex. We propose an efficient solver integrating the convex relaxation with the local search technique for this problem solving. Simulation results demonstrate the superiority of the JDTOITA strategy compared with the benchmarks with no optimization or optimization of either the illumination time allocation (ITA) or detection threshold alone. The results also imply that the target reflectivity and sampling interval of local radars are two important factors that influence the resource optimization.
Metaverse assisted Telesurgery in Healthcare 5.0: An interplay of Blockchain and Explainable AI

Smart healthcare has transitioned towards health-care 5.0, which allows ambient tracking of patients, emotive telemedicine, telesurgery, wellness monitoring, virtual clinics, and personalized care. Thus, the metaverse is a potential tool to leverage digital connectivity via improved healthcare experience in virtual environments. However, despite its potential benefits, patient’s sensitive information is captured, and digital avatars are created that interact with healthcare stakeholders for connected virtual care. As metaverse components are decentralized, blockchain (BC) is a potential solution to induce transparency and immutability in stored transactions on metaverse. For clinical decision support in BC-assisted metaverse enabled, Healthcare 5.0, accurate and interpretable diagnosis is critical. Thus, explainable AI (xAI) forms another critical component that provides trust in the healthcare informatics front. A dual solution of trusted informatics is possible via the interplay of BC and xAI in metaverse-enabled Healthcare 5.0. The article investigates the interplay through a proposed telesurgical scheme between patients, virtual hospitals, and doctors. Next, we discuss the potential challenges and present an experimental use-case of the benefits of our proposed architecture over traditional telesurgery systems.